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Идеи о системе ИИ: Система команд. Часть 1

Всем привет. Меня зовут Алмаз Хуснутдинов. Я делаю материалы, связанные с созданием ИИ. В этой статье я начну рассказывать про идею о некой системе команд, в основе которой лежит идея о детерминированности работы любой системы.

Содержание: Описание задачи из этой статьи, множественная логистическая регрессия, токенизация букв и слов, классификация слов, классификация команд, распознавание и выполнение команд.

Описание задачи

Описание задачи, которую я рассматриваю в этой статье.

Нужно сделать программу, которая сможет выполнять какие-то действия на основе входных данных. В качестве входных данных будем рассматривать текст. В качестве действий будем рассматривать выполнение ветвлений (вызов функции), которые будут выполняться на основе прогноза модели, коротая обрабатывает входной текст.

Программа может получать входные данные через строку ввода. Затем она должна обработать текст и распознать команду, которую ввел пользователь. Данные могут быть как-то искажены, могут не представляться в том, виде, в котором система обучалась их получать.

Для того, чтобы правильно распознавать команды, необходимо как-то их аппроксимировать, кроме того, также необходимо аппроксимировать сами слова. Для этого будет использоваться множественная логистическая регрессия. Под аппроксимацией я понимаю именно то, что это означает, либо обобщение, как это обычно называют.

Схема системы команд

После того, как модель обработает текст она выводит список степени соответствия входных данных каждому возможному действию. Происходит выделение наибольшего из возможных вариантов ответа. Далее на основе этого варианта выполняется соответствующее ветвление и вызов соответствующей функции.

Множественная логистическая регрессия

Я использую логистическую регрессию чисто интуитивно, этот выбор ничем не обоснован.

У меня есть туториал по логистической регрессии, здесь я расскажу только про то, как сделать множественную. Она представляет собой список из обычных моделей логистической регрессии, только каждая из них обучается классифицировать какой-то отдельный класс, а все остальные не классифицировать.

На вход этой модели подается список признаков, которые описывают объект. Каждый признак представляет собой число — количество данного признака в объекте, также признак может быть представлен в бинарном виде — присутствует или отсутствует, но это зависит от конкретной задачи.

На выходе модель должна выводить вероятность того, что данный на вход объект принадлежит к определенному классу. Каждая отдельная модель логистической регрессии делает прогноз того, что объект принадлежит к классу, который она обучена распознавать.

Код класса.

class MultyRegression:
    def __init__(self, in_features, out_features):
        self.logr_list = [LogisticRegression(in_features) for _ in range(out_features)]


    def forward(self, input_features):
        return [logr.forward(input_features) for logr in self.logr_list]


    def fit(self, inputs, targets, epochs=100, lr=0.1):
        for epoch in range(epochs):
            for i in range(len(inputs)):
                outputs = self.forward(inputs[i])

                for j, logr in enumerate(self.logr_list):
                    logr.train(inputs[i], outputs[j], targets[j][i], len(inputs), lr)

В метод init нужно передать число входных и выходных признаков. Внутри создается список с моделями логистической регрессии. Каждая из них создается с одинаковым числом входных признаков. Число моделей равно числу выходных признаков (классов).

В методе forward создается список из ответов каждой модели. Каждой модели на вход передается один и тот же входной вектор.

В метод fit передается список входных векторов и целевых. В каждой итерации обучения нужно вычислить ответ — список ответов от всех моделей. После этого нужно вызвать у каждой модели метод train. В качестве целевого значения передается число, то есть единица или ноль, которое каждая модель должна выводить на данный inputs[i].

Далее пример использования.

inputs = [[0, 1], [0, 0], [0, 0], [0, 0], [1, 0], [1, 1]]  # input data
targets = [[1, 0], [0, 1], [0, 1], [0, 1], [1, 0], [1, 0]]  # target data
targets = transpose_matrix(targets)

model = MultyRegression(2, 2)
model.fit(inputs, targets, epochs=100, lr=0.8)

for i, inp in enumerate(inputs):
    print(model.forward(inp), targets[0][i], targets[1][i])

Это набор данных для задачи «логическое или». На входе два числа, на выходе два класса (0 или 1). Матрицу целевых векторов нужно транспонировать, это из-за особенности реализации — в методе fit в том месте, где вызывается метод train для каждой модели.

Токенизация букв и слов

Для того, чтобы работать с буквами и словами, нужно их представлять в виде векторов признаков. Самый базовый метод сделать это называется «one hot encoding». Вектор представляется в виде нулей и одной единицы в том элементе, который соответствует букве или слову. Так обычно делается для обработки последовательностей букв или слов.

В этом примере будем использовать способ под названием «мешок слов». Токены кодируются так же, только все векторы одного слова или предложения суммируются. То есть слово или предложение представляется в виде одного вектора из нулей, на местах определенных элементов будут числа, которые обозначают количество этой буквы или этого слова в последовательности. Например, последовательность «ппрривет», в ней 2 буквы (токена) «п», 1 буква (токен) «и» и так далее, остальные токены не встречаются в этой последовательности, поэтому на их местах стоят нули.

В векторе указывается число встречаемости каждого токена в данной последовательности

Слова представляются аналогично буквам, только их намного больше, поэтому их представляют в виде сжатых векторов признаков (embeddings), но в этом примере без ембедингов. Я не буду учитывать знаки препинания, если их учитывать, то возможно лучше будет рассматривать их как слова, а не как символы (буквы).

Для токенизации создадим отдельный класс, чтобы делегировать на него всю неудобную работу.

class Tokenizer:
    def __init__(self, token_list):
        self.token_to_idx = {token: i for i, token in enumerate(token_list)}
        self.idx_to_token = {i: token for i, token in enumerate(token_list)}
        self.tokens_num = len(token_list)

В метод init нужно передать список всех уникальных токенов, то есть букв или слов. В нем создаются 2 словаря, которые создают соответствие между индексом токена и его значением, чтобы можно было легко преобразовать токен в индекс и наоборот.

Метод token_to_ohe представляет токен в виде вектора из нулей и одной единицы на месте элемента, который соответствует индексу токена. Таким образом создаем целевой вектор для слова (или буквы), нужно просто указать 1 в соответствующем элементе выходного вектора в индексе данного слова (буквы). Метод vecto_to_token делает обратную операцию.

def token_to_ohe(self, token):
    vector = [0 for _ in range(self.tokens_num)]

    idx = self.token_to_idx[token]
    vector[idx] = 1

    return vector
def vector_to_token(self, vector):
    idx = numpy.argmax(vector)
    token = self.idx_to_token[idx]

    return token

Метод sequence_to_vector представляет последовательность токенов в виде одного вектора «мешок слов». Таким образом можно создавать вектор представления слова. Создаем вектор из нулей и для каждой буквы (или слова) прибавляем 1 в соответствующем элементе вектора по индексу буквы.

def sequence_to_vector(self, sequence):
    vector = [0 for _ in range(self.tokens_num)]

    for item in sequence:
        idx = self.token_to_idx[item]
        vector[idx] += 1

    return vector

Этот класс можно использовать для букв и для слов.

Распознавание слов

На вход могут подаваться искаженные данные, то есть слова с опечатками. На основе этой логистической регрессии можно распознавать слова, если они введены не правильно. Если какие-то слова классифицированы не правильно, то сама команда может быть распознана не правильно (это нужно учитывать, в этом примере я не сделал эту проверку).

Перед тем, как распознавать команду из слов, нужно распознать сами слова. Каждое слово состоит из нескольких букв, буквы рассматриваем как признаки объектов (слов). Всего будет 33 буквы, значит признаков тоже 33, дополнительно можно использовать другие символы, такие как знаки препинания.

На вход модели для распознавания слов будет подаваться вектор, который содержит информацию о введенном слове. Каждая буква имеет свой порядковый номер (индекс) в списке всех признаков. Нужно создать вектор из нулей с числом элементов 33. Для каждой буквы в слове нужно увеличить соответствующий элемент в этом векторе на 1. То есть в векторе указывается количество каждой буквы в слове.

Рассмотрим модель для распознавания слов. Ей нужно передать все буквы и уникальные слова в наборе данных. Для букв и слов создаем токенайзеры, они отвечают за представление букв и слов. Создаем модель множественной логистической регрессии. Обучаться она будет отдельно.

class WordModel:
    def __init__(self, letters, unique_words):
        self.letter_tokenizer = Tokenizer(letters)
        self.words_tokenizer = Tokenizer(unique_words)
        self.model = MultyRegression(len(letters), len(unique_words))


    def fit(self, inputs, targets, epochs=100, lr=0.1):
        self.model.fit(inputs, targets, epochs, lr)


    def get_input(self, word):
        return self.letter_tokenizer.sequence_to_vector(word)

    def get_target(self, word):
        return self.words_tokenizer.token_to_ohe(word)


    def forward(self, inp):
        return self.model.forward(inp)

Метод get_input преобразует строку в входной вектор признаков. Метод get_target преобразует слово в целевой вектор признаков. Эти методы обеспечивают небольшой функционал, с помощью которого можно подготавливать слова к тому, чтобы обработать их моделью.

Функция, чтобы преобразовывать обучающие данные. На вход передается список слов и список меток. Список целевых векторов нужно транспонировать, так как это особенность реализации множественной логистической регрессии.

def make_words_data(word_model, input_words, target_words):
    inputs = [word_model.get_input(word) for word in input_words]
    targets = [word_model.get_target(word) for word in target_words]
    targets = transpose_matrix(targets)

    return inputs, targets

Функция для проверки вывода модели после обучения. Выводится слово, которое подается на вход, выход модели и целевой вектор (целевые векторы нужно сначала транспонировать обратно, чтобы вывести их корректно).

def print_output(model, items_list, inputs, targets):
    print("=========================")
    targets = transpose_matrix(targets)

    for i, inp in enumerate(inputs):
        print(items_list[i], model.forward(inp), targets[i])

Создаем список всех символов. Потом нужно сделать список всех слов, например, загрузить их из файла со словами, для простоты я просто сделал инициализацию прямо в файле. Также необходимо указать метки для каждого слова. Потом создаем список уникальных слов и их число. Так же делаем тестовые данные.

letters = "абвгдеёжзийклмнопрстуфхцчщшъыьэюя"
words_list = ["привет", "пока", "покаа", "как", "каак", "дела", "сделай"]
target_words = ["привет", "пока", "пока", "как", "как", "дела", "сделай"]

test_words = ["првт", "пка", "поа"]
test_target_words = ["привет", "пока", "пока"]
unique_words = list(set(target_words))

Теперь можно создать обучающий и тестовый наборы данных, обучить модель и проверить ее.

inputs, targets = make_words_data(word_model, words_list, target_words)
test_inputs, test_targets = make_words_data(word_model, test_words, test_target_words)

word_model = WordModel(letters, unique_words)
word_model.fit(inputs, targets, epochs=100, lr=0.8)

print_output(word_model, words_list, inputs, targets)
print_output(word_model, test_words, test_inputs, test_targets)

output:
=========================
привет [0.005118926577732819, 0.9914886883246045, 0.0024402194981650084,
0.00696090047348459, 0.0012475341272907671] [0, 1, 0, 0, 0]
…
сделай [0.009077970566943741, 0.010123286193314486, 0.8890793203926629,
0.10124257506319338, 0.00636310358780177] [0, 0, 1, 0, 0]
=========================
првт [0.07809694837775111, 0.9703325366717745, 0.01052546174740245,
0.015548304709811485, 0.005337346636761862] [0, 1, 0, 0, 0]
…
поа [0.9782841249308825, 0.10765944142494452, 0.020116303139164023,
0.037643814697091084, 0.002395138327556411] [1, 0, 0, 0, 0]

Набор данных выглядит примерно так. Сначала выводится список целевых векторов, потом список входных векторов.

print(targets)
print(inputs)

output:
[[0, 0, 0, 1, 1, 0, 0, 0, 0],
…
[0, 0, 0, 0, 0, 0, 0, 0, 1]]
[[0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
…
[1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]

В целевых векторах каждая единица обозначает нужно ли распознать данное слово или не нужно для каждой отдельной логистической регрессии. Матрица целевых векторов транспонирована, поэтому тут каждый вектор представлен для каждой отдельной модели, то есть первый вектор для первой модели, последний — для последней. Всего 9 примеров, для каждого из 9 примеров каждая модель должна распознать как 0 или как 1. Всего тут выведено 5 целевых векторов, так как всего 5 слов, и всего 5 моделей единичных регрессий.

Количество входных векторов тут выведено 9 (число элементов в каждом составляет 33). Во входных векторах представлено число каждой буквы в слове. Например, в первом векторе отображена единица в позиции буквы «в», потом «е» и так далее.

Теперь можно обработать вектор слова моделью распознавания слов и получить ответ модели. Метод predict получает на вход слово, обрабатывает его моделью и выводит распознанное слово. На выходе класс распознанного слова. Также можно посмотреть и вероятность, она в переменной out.

def predict(self, word):
    inp = self.get_input(word)
    out = self.forward(inp)

    word = self.words_tokenizer.vector_to_token(out)
    return word

print(word_model.predict("прввт"))

output:
привет

Можно оценивать влияние каждой буквы в слове на то, как будет распознано слово. Это можно узнать посмотрев на веса после обучения. Например, слова "пка" и "поа" распознаются по-разному, первое распознается на 0.46, а второе на 0.98 как слово «пока». Но это зависит от обучающих данных.

Распознавание команд

Далее делаем то же самое для распознавания последовательности слов. Теперь вместо букв будут слова. Соответственно, нужно сделать список уникальных слов, для того, чтобы представлять каждую команду в виде вектора. Каждое слово в команде рассматриваем как признак, также учитываем количество одинаковых слов, как и букв («мешок слов»).

Команды представлены в виде словаря, ключ словаря — команда, значение — метка команды. Также добавляем новые слова и указываем их метки.

commands = {
    "привет": "привет",
    "как дела": "как дела",
    "пока": "отключение",
    "отключить": "отключение",
    "выйти": "отключение",
    "изменить переменную": "изменить переменную"
}

words_list += ["отключить", "выйти", "изменить", "переменную"]
target_words += ["отключить", "выйти", "изменить", "переменная"]

Класс для распознавания команд сделан немного по-другому. В нем для токенизации слов используется модель для распознавания слов, они одновременной распознаются и токенизируются. В метод init передается список уникальных меток команд и модель для распознавания слов. Токенизатор слов берем из модели для распознавания слов. Для токенизации имен команд делаем отдельный токенизатор.

class CommandModel:
    def __init__(self, unique_commands_names, words_recognizer):
        self.words_recognizer = words_recognizer
        self.words_tokenizer = words_recognizer.words_tokenizer

        unique_words_num = words_recognizer.words_tokenizer.tokens_num

        self.command_name_tokenizer = Tokenizer(unique_commands_names)
        self.model = MultyRegression(unique_words_num, len(unique_commands_names))

В методе get_input происходит преобразование строки, в которой записана команда. Сначала команда разбивается на слова, потом каждое слово классифицируется моделью для распознавания слов. Потом токенизатор команд преобразует последовательность слов в вектор представления команды, который потом передается на вход модели для распознавания команд.

def get_input(self, input_command):
    command_sequence = [self.words_recognizer.predict(word) for word in input_command.split()]
    return self.words_tokenizer.sequence_to_vector(command_sequence)

Остальные методы работают аналогично как у класса WordModel.

Создание набора данных и обучение модели распознавания команд.

unique_words = list(set(target_words))
unique_commands_names = list(set(list(commands.values())))

input_commands = list(commands.keys())
target_commands = list(commands.values())

word_model = WordModel(letters, unique_words)
word_inputs, word_targets = make_words_data(word_model, words_list, target_words)
word_model.fit(word_inputs, word_targets)

command_model = CommandModel(unique_commands_names, word_model)
command_inputs, command_targets = make_commands_data(command_model, input_commands, target_commands)
command_model.fit(command_inputs, command_targets, epochs=100, lr=0.9)

Для работы command_model нужна модель word_model, поэтому сначала нужно создать и обучить ее.

Проверяем работу модели, выводим как модель распознает каждый пример из обучающих данных. Выводим ответ на искаженную команду «какк дделла», модель ее распознала правильно.

print_output(command_model, input_commands, command_inputs, command_targets)

input_command = "какк дделла"
print(command_model.predict(input_command))

output:
=========================
привет [0.07785547864835976, 0.014539640208980364, 0.8951833713017856, 0.028339080113225123] [0, 0, 1, 0]
...
изменить переменную [0.03903072069752127, 0.011182745156889445, 0.01253249822182718, 0.9440049246646178] [0, 0, 0, 1]
как дела

Основная программа

Теперь сделаем основную программу, в которой будет использоваться модель для распознавания команд. Я сделал отдельный метод, который заранее обучает модели и возвращает их, их можно легко получить в другом файле.

word_model, command_model = get_models()

Основной функционал программы выглядит так. Словарь, в котором хранятся некоторые переменные. Функции, которые сопоставляются с командами. И словарь с командами, каждое название команды сопоставляется с ссылкой на функцию.

variables = {"is_run": True}

def bye():
    variables["is_run"] = False

actions = {"отключение": bye}

Основной цикл программы работает, пока не будет изменена переменная is_run. Сначала нужно ввести команду, потом передать введенную строку в модель для распознавания команд, она возвращает метку распознанной команды. Если такая метка есть в ключах словаря actions, то происходит вызов соответствующей функции по ссылке, которая хранится в значении ключа.

while variables["is_run"]:
    input_command = input("enter a command: ")

    command_name = command_model.predict(input_command)

    print(command_name)

    if command_name in actions:
        actions[command_name]()

Вводим команду «ппокка», она распознается как команда «отключение». Далее происходит вызов функции bye, в ней изменяется переменная is_run и цикл while перестает работать.

output:
enter a command: ппокка
отключение

Заключение

В качестве упражнения можно сделать команду по изменению любой переменной и по добавлению новой переменной.

В этом примере я встроил модель для распознавания слов в модель для распознавания команд. Лучше так не делать. Нужно вынести логику распознавания слов отдельно от логики распознавания команд. Но я так сделал для упрощения этого примера, если их отделять, то нужно будет написать больше кода для преобразования обучающих данных для модели команд.

В этой статье я лишь показал простой пример того, как можно использовать методы машинного обучения для создания простой программы. В следующих статьях на эту тему я расскажу про то, как можно создавать знания в системе команд динамически.

Как-то развивать эту идею с использованием моделей машинного обучения я не буду. Так как я исследую возможность создания динамической системы, а не статической, как это происходит традиционно. Логистическая регрессия является статической моделью, то есть ее невозможно дообучить на новых данных, если это сделать, то она перестанет нормально распознавать то, на чем она уже была обучена. Ее можно обучить заново, но я рассматриваю другую идею создания ИИ, которая связана с динамическим созданием знаний в модели, например, импульсные нейроны. Поэтому я буду развивать идею в сторону динамического создания знаний.

Почему я рассматриваю динамическое создание знаний. Я думаю, что это одно из важных свойств, которым должна обладать система, которая обладает интеллектом. С этим свойством знаком каждый, так как каждый человек приобретает знания динамически. Кроме того, сам процесс развития системы предполагает то, что система изменяется со временем.

Подписывайтесь на мой ТГК, чтобы не пропустить мои новые статьи по машинному обучению и идеи о создании цифрового интеллекта.

Папка с кодом из статьи на ГитХабе.

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26 янв. 2025 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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06.09.24 01:44 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

COMMENT ON I NEED A HACKER TO RECOVER MONEY FROM BINARY TRADING. HIRE FASTFUND RECOVERY
25.11.24 02:20 briankennedy

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In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

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26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.
01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

Online crypto investment can seem like a promising opportunity, but it's crucial to recognize that there are no guarantees. My experience serves as a stark reminder of this reality. I was drawn in by the allure of high returns and the persuasive marketing tactics employed by various brokers. Their polished presentations and testimonials made it seem easy to profit from cryptocurrency trading. Everything appeared to be legitimate. I received enticing messages about the potential for substantial gains, and the brokers seemed knowledgeable and professional. Driven by excitement and the fear of missing out, I invested a significant amount of my savings. The promise of quick profits overshadowed the red flags I should have noticed. I trusted these brokers without conducting proper research, which was a major mistake. As time went on, I realized that the promised returns were nothing but illusions. My attempts to withdraw funds were met with endless excuses and delays. It became painfully clear that I had fallen victim. The reality hit hard: my hard-earned money was gone, I lost my peace of mind and sanity. In my desperation, I sought help from a company called DEFTRECOUP. That was the turning point for me as I had a good conversation and eventually filed a complaint via DEFTRECOUP COM. They were quite delicate and ensured I got out of the most difficult situation of my life in one piece.
04.12.24 22:24 andreygagloev

When I first heard about Bitcoin back in 2018, I was skeptical. The idea of a decentralized, digital currency seemed too good to be true. But I was intrigued as I learned more about the technology behind it and its potential. I started small, investing just a few hundred dollars, dipping my toes into the cryptocurrency waters. At first, it was exhilarating to watch the value of my investment grow exponentially. I felt like I was part of the future, an early adopter of this revolutionary new asset. But that euphoria was short-lived. One day, I logged into my digital wallet only to find it empty - my Bitcoin had vanished without a trace. It turned out that the online exchange I had trusted had been hacked, and my funds were stolen. I was devastated, both financially and emotionally. All the potential I had seen in Bitcoin was tainted by the harsh reality that with decentralization came a lack of regulation and oversight. My hard-earned money was gone, lost to the ether of the digital world. This experience taught me a painful lesson about the price of trust in the uncharted territory of cryptocurrency. While the technology holds incredible promise, the risks can be catastrophic if you don't approach it with extreme caution. My Bitcoin investment gamble had failed, and I was left to pick up the pieces, wiser but poorer for having placed my faith in the wrong hands. My sincere appreciation goes to MUYERN TRUST HACKER. You are my hero in recovering my lost funds. Send a direct m a i l ( muyerntrusted ( @ ) mail-me ( . )c o m ) or message on whats app : + 1 ( 4-4-0 ) ( 3 -3 -5 ) ( 0-2-0-5 )
12.12.24 00:35 amandagregory

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY
12.12.24 00:35 amandagregory

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19.12.24 17:07 rebeccabenjamin

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24.12.24 08:33 dddana

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27.12.24 00:21 swiftdream

I lost about $475,000.00 USD to a fake cryptocurrency trading platform a few weeks back after I got lured into the trading platform with the intent of earning a 15% profit daily trading on the platform. It was a hell of a time for me as I could hardly pay my bills and got me ruined financially. I had to confide in a close friend of mine who then introduced me to this crypto recovery team with the best recovery SWIFTDREAM i contacted them and they were able to completely recover my stolen digital assets with ease. Their service was superb, and my problems were solved in swift action, It only took them 48 hours to investigate and track down those scammers and my funds were returned to me. I strongly recommend this team to anyone going through a similar situation with their investment or fund theft to look up this team for the best appropriate solution to avoid losing huge funds to these scammers. Send complaint to Email: info [email protected]
31.12.24 04:53 Annette_Phillips

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04.01.25 04:56 florencestella

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04.01.25 04:57 florencestella

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06.01.25 19:09 michaeljordan15

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18.01.25 12:41 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]
18.01.25 12:41 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]
20.01.25 15:39 patricialovick86

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22.01.25 21:43 DoraJaimes23

Recovery expert. I lost my bitcoin to fake blockchain impostors on Facebook, they contacted me as blockchain official support and i fell stupidly for their mischievous act, this made them gain access into my blockchain wallet whereby 7.0938 btc was stolen from my wallet in total .I was almost in a comma and dumbfounded because this was all my savings i relied on . Then I made a research online and found a recovery expert , with the contact address- { RECOVERYHACKER101 (@) GMAIL . COM }... I wrote directly to the specialist explaining my loss. Hence, he helped me recover a significant part of my investment just after 2 days he helped me launch the recovery program , and the culprits were identified as well , all thanks to his expertise . I hope I have been able to help someone as well . Reach out to the recovery specialist to recover you lost funds from any form of online scam Thanks
23.01.25 02:36 [email protected]

After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3
23.01.25 02:37 [email protected]

After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3
23.01.25 14:20 nellymargaret

DUNAM CYBER SOLUTION BTC-ETH RECOVERY EXPERT
23.01.25 14:20 nellymargaret

DUNAM CYBER SOLUTION BTC-ETH RECOVERY EXPERT I had tried to secure my Bitcoin wallet, maybe a bit too aggressively, enabling every security feature imaginable: two-factor authentication, biometric verification, intricate passwords-the whole shebang. I wanted to make it impossible for anybody to get to my money. I tried to make this impregnable fortress of security and ended up locking myself out of my wallet with $700,000 in Bitcoin. It wasn't until I tried to access my wallet that I realized the trap I had set for myself. I was greeted with an endless series of security checks-passwords, codes, facial recognition, and more. I could remember parts of my multi-layered security setup but not enough to actually get in. In fact, my money was behind this digital fortress, and the more I tried to fix it, the worse it seemed to get. I kept tripping over my own layers of protection, unable to find a way back in. Panic quickly set in when I realized I had made it almost impossible for myself to access my own money. That is when I called DUNAMIS CYBER SOLUTION From that very first call, they reassured me that I wasn't the first person to make this kind of mistake and certainly wouldn't be the last. They listened attentively to my explanation and got to work straight away. Their team methodically began to untangle my overly complicated setup. Patience and expertise managed to crack each layer of security step by step until they had restored access to my wallet. [email protected] +13433030545 [email protected]
26.01.25 03:54 [email protected]

Losing access to my crypto wallet account was one of the most stressful experiences ever. After spending countless hours building up my portfolio, I suddenly found myself locked out of my account without access. To make matters worse, the email address I had linked to my wallet was no longer active. When I tried reaching out, I received an error message stating that the domain was no longer in use, leaving me in complete confusion and panic. It was as though everything I had worked so hard for was gone, and I had no idea how to get it back. The hardest part wasn’t just the loss of access it was the feeling of helplessness. Crypto transactions are often irreversible, and since my wallet held significant investments, the thought that my hard-earned money could be lost forever was incredibly disheartening. I spent hours scouring forums and searching for ways to recover my funds, but most of the advice seemed either too vague or too complicated to be of any real help. With no support from the wallet provider and my email account out of reach, I was left feeling like I had no way to fix the situation.That’s when I found out about Trust Geeks Hack Expert . I was hesitant at first, but after reading about their expertise in recovering lost crypto wallets, I decided to give them a try. I reached out to their team, and from the very beginning, they were professional, understanding, and empathetic to my situation. They quickly assured me that there was a way to recover my wallet, and they got to work immediately.Thanks to Trust Geeks Hack Expert , my wallet and funds were recovered, and I couldn’t be more grateful. The process wasn’t easy, but their team guided me through each step with precision and care. The sense of relief I felt when I regained access to my crypto wallet and saw my funds safely back in place was indescribable. If you find yourself in a similar situation, I highly recommend reaching out to Trust Geeks Hack Expert. contact Them through EMAIL: [email protected] + WEBSITE. HTTPS://TRUSTGEEKSHACKEXPERT.COM + TELE GRAM: TRUSTGEEKSHACKEXPERT