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Как мы построили сервис по поиску видеоконтента с помощью текста

Кто мы

Привет, Habr!
Мы — команда нейро-сети.рф, специализирующаяся на разработке AI-сервисов для решения бизнес-задач и внедрения искусственного интеллекта в реальный сектор экономики. Мы специализируемся в GenAI (LLM, Диффузионные модели), NLP, CV, Machine Learning. В этой статье мы хотим поделиться нашим опытом создания сервиса по поиску видеоконтента с помощью текста.

Цель данной статьи — подробно рассказать о процессе создания нашего сервиса, решениях, которые мы приняли на каждом этапе, и о том, какие технологии использовали. Мы хотим показать, как сложные технические задачи могут быть решены простыми и эффективными методами.

Целевая аудитория статьи, и что вы получите в конце статьи:

IT-специалистов: получат глубокое понимание технических аспектов реализации сервиса и новых технологий.
Продукт-менеджеров: узнают, как подобный сервис может улучшить их продукт, а также о рыночных возможностях и конкурентных преимуществах.
Руководителей проектов: получат информацию для оценки рисков и ресурсов.
Владельцев IT-бизнесов: найдут стратегии для улучшения маркетинговых кампаний.

Проблема

Рынок коротких видеороликов стремительно растет:

Создателям контента все сложнее быстро находить релевантные видеоролики для маркетинговых целей. Наш сервис помогает им эффективно искать и использовать видеоконтент, оптимизируя процессы создания и продвижения.

Альтернативные решения

Тут представлены сервисы для генерации коротких роликов на основе текста, но они решают ту же проблему, что и мы :

Решение

Повторюсь, я буду объяснять технические моменты в ИИ простым языком для людей, которые не касались этой темы, однако технически подкованные люди тоже могут подчеркнуть для себя технологии и фишки. Для этого расскажу про каждую технологию в общих чертах, а потом более подробнее объясню, поэтому каждый для себя сможет найти полезную информацию.

Постановка задачи

Пользователь вводит текст «собака гуляет на берегу моря», и сервис с помощью магии ИИ должен найти видео, где показано, как собака гуляет на берегу моря —- это задача Text2Video Retrieval, то есть поиск релевантного видео по текстовому запросу

Метрики

Метрики нужны, чтобы понимать качество модели (в нашем случае ИИ), которая находится внутри сервиса и выполняет основную логику. Нам важно, чтобы найденные видеоролики совпадали с запросом пользователя. Также нам важна точность поиска релевантного видео, поэтому будем оптимизировать Precision@k.

Что такое Precision@k простыми словами?

Precision@k — это метрика, которая показывает, насколько хорошо наша модель находит нужные видеоролики среди первых k результатов поиска. Представьте, что вы ищете видео и получаете список из k видео. Precision@k показывает, сколько из этих k видео действительно подходят под ваш запрос. Чем выше precision@k, тем больше подходящих видео среди первых k результатов. Подробнее можно почитать здесь.

Пример. На левой картинке среди 9 первых элементов релевантные 6, Precision@9 = 6/9=0.6. На правой картинки среди 5 первых элементов релевантные 4, Precision@5 = 4/5=0.8.

Данные

Хранилище данных

Для хранения данных мы использовали облачное хранилище Amazon S3. На начальном этапе данные хранились на нашем сервере в базе данных MongoDB. Этот вариант позволил быстро начать работу и выполнить поставленные задачи.

Сбор данных

Для успешного поиска видеоконтента нам нужно было собрать как можно больше видеороликов в базе данных (БД). Чем больше видеороликов, тем выше вероятность найти релевантный контент по запросу пользователя.

Мы рассмотрели три основных подхода к сбору видеоконтента:

1. Сбор видео с определенных сайтов, в том числе YouTube, и их предварительная загрузка в базу данных, с которой видео позже будут извлекаться/подгружаться при вводе запроса пользователем.

Плюсы:

Высокая скорость поиска: Видео уже находятся в базе данных, нет необходимости искать их в Интернете.
Высокое качество: Можно заранее проверить качество видео по картинке, звуку и содержанию.

Минусы:

Высокая вероятность ошибки: Если база данных недостаточно насыщена, сервис может не найти нужное видео.
Большое требование к памяти: Нужно много места для хранения видео на сервере.

2. При запросе пользователя сервис начинает поиск видео в Интернете, то есть не заходя в базу данных.

Плюсы:

Низкая вероятность ошибки: Можно найти релевантные видео в Интернете и затем выбрать лучшие для пользователя.
Меньше требований к памяти: Видео хранятся в Интернете, не нужно постоянно хранить их на сервере.

Минусы:

Низкое качество найденных роликов: Сложно оценить качество видео в реальном времени.
Низкая скорость: Пользователю придётся ждать не только поиска, но и скачивания видео.

3. Комбинированный подход

Используем базу данных для быстрого поиска. Если нужное видео не найдено, начинаем искать его в специализированных источниках в Интернете.

Плюсы:

Баланс скорости и качества: Быстрый поиск в базе данных и возможность найти дополнительные видео в Интернете.
Оптимизация ресурсов: Экономия памяти на сервере за счет хранения только самых релевантных видео.
Гибкость: Возможность адаптироваться к изменяющимся запросам пользователей и обновлению контента.

Минусы:

Сложность реализации: Требуется разработка более сложного алгоритма для интеграции двух методов.
Задержка в поиске: В случае отсутствия нужного видео в базе данных, пользователю придется ждать дополнительное время на поиск в Интернете.
Вопросы качества: Возможны проблемы с оценкой качества видео, найденных в Интернете в режиме реального времени.

Предобработка данных

Мы создали сервис с расчётом на то, что пользователь получит видео длиной 5-10 секунд. Но в Интернете в основном длинные ролики и их нужно делить на сцены. Ввиду нашего алгоритма из них нужно взять несколько картинок. Поэтому мы использовали библиотеку pyscenedetect.

Давайте рассмотрим алгоритм преодобработки видео на реальном примере (видео).

Делим видео на сцены с помощью pyscenedetect
Каждая сцена делится на картинки

Вот код:

import scenedetect
import torch
import yaml
from PIL import Image
from scenedetect import (
    AdaptiveDetector,
    ContentDetector,
    ThresholdDetector,
    FrameTimecode,
    open_video,
    split_video_ffmpeg,
)
from pathlib import Path
import os

class SceneExtractor:
    def __init__(self):
        self.manager = scenedetect.SceneManager()
        self.detector = scenedetect.ContentDetector()
        self.manager.add_detector(self.detector)
    
    def get_scenes(self, video_path, num_images=3, output_pics_dir=Path("../Pics")):
        # Открываем видео файл
        video = open_video(str(video_path))
        
        # Запускаем процесс обнаружения сцен
        self.manager.detect_scenes(video)
        
        # Получаем список сцен
        scenes_list = self.manager.get_scene_list()
        
        # Инициализируем списки для хранения информации о сценах
        scenes_timecodes = []
        scenes_list_new = []
        
        # Проходим по каждой сцене, чтобы извлечь временные коды начала и конца
        for scene_start, scene_end in scenes_list:
            start_time = scene_start.get_seconds()
            end_time = scene_end.get_seconds()
            
            scenes_timecodes.append((start_time, end_time))
            scenes_list_new.append((scene_start, scene_end))
        
        # Сохраняем изображения для каждой сцены
        imgs_scenes = scenedetect.scene_manager.save_images(
            scenes_list_new,
            video,
            num_images,
            frame_margin=15,
            output_dir=str(output_pics_dir),
            scale=0.25,
        )
        
        # Формируем пути к сохраненным изображениям
        scenes_images_paths = [
            [os.path.join(output_pics_dir, img_name) for img_name in imgs_scene]
            for imgs_scene in list(imgs_scenes.values())
        ]
        
        # Возвращаем временные коды сцен и пути к изображениям
        return scenes_timecodes, scenes_images_paths

Секретный секрет нашего ИИ

После сбора и обработки данных мы подходим к самому интересному — создание алгоритма поиска на основе ИИ.

Разберём основные моменты перед тем, как расскажу про алгоритм.

Vision Language Model — Описываем картинку по нашему желанию

Vision Language Model — нейронная сеть, которая описывает картинку по запросу. Представьте себе коробочку с ИИ внутри. Вы подаёте на вход картинку и запрос «опиши, что изображено на картинке». На выходе вы получаете описание, например: «на картинке два енота обнимают собаку».

Таких нейросетей может быть очень большое количество с разным качеством генерации описания картинки. В нашем проекте мы возьмём QWEN-VL — бесплатная модель от Алибабы, которая в приемлемом качестве генерирует текст.

ANN (Approximate Nearest Neighbor): Найдите то, что вам нужно

ANN (Approximate Nearest Neighbor) — это алгоритм поиска ближайших соседей.
Представьте, что у вас есть огромная галерея с миллионами картин. Каждая картина сопровождается текстовым описанием, например: «на картине изображен закат над океаном». Теперь вам нужно найти все картины, которые по описанию похожи на запрос «морской пейзаж при закате».

Вот тут приходит на помощь ANN. Этот алгоритм помогает быстро и эффективно найти текстовые описания, которые наиболее близки к вашему запросу. Он не ищет точное совпадение слов, а находит те описания, которые имеют схожий смысл. Это как если бы вы искали картины по ключевым словам и получили список самых подходящих вариантов, даже если слова в описаниях немного отличаются.

На вход в ANN:

1. Промпт: Это текст, который вы хотите использовать для поиска. Например, «морской пейзаж при закате».

2. База данных описаний: Это коллекция всех текстовых описаний картин, которые у вас есть. Например:

«закат над океаном с облаками»
«закат над уровнем океана»
«Город с закатом и облаками»
«Закат над океаном с горами»
«Закат над горами»

На выход:

ANN обрабатывает ваш запрос и возвращает список текстовых описаний, которые наиболее близки к вашему запросу. В нашем примере:

При промпте: «морской пейзаж при закате».

Получаем отсортированный список по «похожести»:

Описание 1: «закат над уровнем океана»
Описание 2: «закат над океаном с облаками»
Описание 3: «Город с закатом и облаками»
Описание 4: «Закат над океаном с горами»
Описание 5: «Закат над горами»

Encoder: Превращаем текст в числа

Но вот в чём загвоздка. ANN может работать только с числами, а не с текстом. Представьте себе, что ANN — это робот-исследователь, который понимает только язык чисел. Чтобы он понял ваш запрос, нужно сначала перевести текст на его язык.

Здесь на сцену выходит Encoder. Это нейронная сеть, которая превращает текст в набор чисел, называемый вектором. Зачем это нужно? Векторы позволяют компьютеру понимать и сравнивать тексты. Таких Encoder`ов много, в нашей задаче мы выбрали bge.

Например, когда вы вводите запрос «морской пейзаж при закате», Encoder преобразует его в вектор — что-то вроде этого: [0.1, 0.24, 0.41, ... 0.12]. Этот вектор — цифровое представление вашего запроса.

Почему это важно?

Векторы помогают быстро и точно находить видеоконтент, который соответствует вашему запросу. Они работают как координаты на карте: если два вектора находятся близко друг к другу, значит их тексты похожи по смыслу.

На входе:

Промпт: Вы вводите текст «морской пейзаж при закате».

На выходе:

Вектор: Вектор из чисел — [0.1, 0.24, 0.41, ... 0.12].

Склеиваем блоки в алгоритм

Итак, у нас есть три ключевых компонента:

Vision Language Model: Нейронная сеть, которая генерирует текст на основе вашего запроса и входящей картинки.
ANN (Approximate Nearest Neighbor): Алгоритм, который находит ближайшие элементы из базы данных по входящему элементу.
Encoder: Нейронная сеть, которая превращает текст в числа.

Теперь давайте соберем всё это вместе и посмотрим, как работает алгоритм.

Формируем Базу Данных:
1. Предположим, что данные уже собраны. Мы разбили все видео на отдельные кадры и теперь нужно их описать.
2. Vision Language Model создаёт текстовое описание для каждого кадра из нашей Базы Данных.
3. Encoder превращает все текстовые описания в вектора и складывает числовые вектора в Базу Данных.
Ищем видео по запросу пользователя — теперь представим, что вы хотите найти видео по запросу «морской пейзаж при закате»:
1. Промпт: Вы вводите текст «морской пейзаж при закате».
2. Преобразование: Encoder превращает этот текст в вектор.
3. Поиск: Сервис сравнивает этот вектор с векторами из базы данных с помощью ANN.
4. Результат: Вы получаете список ID, которые наиболее точно соответствуют вашему запросу. С помощью этих ID мы подтягиваем нужные видео.

Как ещё пробовали ?

В процессе создания нашего сервиса поиска видеоконтента мы экспериментировали с различными подходами. Один из таких подходов — попытка извлекать векторные представления непосредственно из изображений. Мы решили попробовать отказаться от генерации текстовых описаний и вместо этого использовать векторные представления самих картинок для поиска с помощью ANN.

Представьте себе: у нас есть кадр из видео, скажем, красивый закат над океаном. Вместо того чтобы описывать этот кадр текстом, как, например, «закат над океаном», мы обучили нейросеть создавать векторное представление для изображения. Этот вектор — набор чисел, который должен был описать картинку также эффективно, как и текст.

Почему это может работать?

Идея была проста: если нейросеть сможет эффективно "понимать" картинку, то мы могли бы обойтись без текстовых описаний, что сэкономило бы время и ресурсы на их генерацию. Мы полагали, что такие векторы могли бы быть еще более точными, ведь они создаются напрямую на основе визуальных данных.

В итоге:

Наш эксперимент показал, что использование текстовых описаний картинок более эффективно для поиска нужного видеоконтента. Конечно, это добавляет дополнительный шаг — генерацию описаний, но выигрыши в точности поиска этого стоят.

Как мы обучали ИИ

Зачем нужно обучать ИИ?

Обучение ИИ — это как обучение нового сотрудника. Мы берём уже обученные модели, но их данные могут отличаться от наших. Чтобы наш ИИ понимал данные и точно интерпретировал запросы пользователей, нужно дообучить его на наших данных.

Важность обучения

Обучение моделей на специфических данных улучшает точность и релевантность результатов. Например, если вы бы обучили опытного повара готовить по вашему семейному рецепту блюда

Как мы обучали Encoder с помощью контрастного обучения

Мы дообучили Encoder с помощью контрастного обучения (contrastive learning). Этот метод помогает модели различать похожие и непохожие объекты. Представьте два набора карточек: один с кошками, другой — с собаками. Контрастное обучение помогает модели понять, какие признаки отличают кошек от собак.

Процесс:

Взяли пары текстов и соответствующих изображений из базы данных.
Encoder преобразовывал тексты в вектора.
Использовали контрастное обучение, чтобы научить модель отличать правильные пары (текст и изображение соответствуют) от неправильных (текст и изображение не соответствуют).

Данные мы собрали с помощью копирайтеров и расширили с помощью API OpenAI.

Теперь наш Encoder лучше понимает, как текстовые запросы соотносятся с описаниями картинок от Vision Language Model.

Метрики: как мы измеряли успех

Для оценки качества работы нашей модели мы использовали метрику Precision@k, где k=1, 5, 10. Эта метрика показывает, насколько хорошо модель отбирает нужные видео; чем меньше k, тем строже метрика относится к отбору видео.

- Precision@1: До обучения этот показатель составлял 0.65, а после — 0.74. Это значит, что в 74% случаев первое видео будет релевантным.

- Precision@5: До обучения он составлял 0.84, а после — 0.92. Это значит, что в 92% случаев одно из первых пяти видео будет релевантным.

- Precision@10: До обучения он составлял 0.91, а после — 0.95. Это значит, что в 95% случаев одно из первых десяти видео будет релевантным.

Благодаря дообучению Encoder с помощью контрастного обучения, мы значительно улучшили точность и релевантность поиска видео по текстовым запросам пользователей.

Как мы деплоим наш сервис

Немного терминов:

Процесс деплоя — это как финальный этап подготовки спектакля перед его премьерой. Представьте, что у вас есть театральная постановка: сценарий написан, актёры отрепетировали свои роли, декорации готовы. Теперь нужно сделать так, чтобы это было представлено на сцене перед зрителями.

В мире IT деплой — это процесс, когда готовое приложение (или обновления к нему) переносится из среды разработки в рабочую среду, где им могут пользоваться реальные пользователи.

Многоконтейнерное приложение - это приложение (сервис), которое состоит из нескольких отдельных частей (контейнеров). Каждый контейнер выполняет свою задачу. Представьте ресторан, где кухня, бар и касса находятся в разных комнатах. Каждая комната выполняет свою функцию, но вместе они составляют один ресторан.

Docker — это как магическая коробка для вашего приложения. Вы кладёте в неё всё необходимое (код, библиотеки, настройки), и она работает одинаково на любом компьютере. Dockerfile — это рецепт, который описывает, как собрать эту коробку, чтобы ваше приложение всегда было готово к запуску.

Для описания и управления многоконтейнерным приложением используется docker-compose. С его помощью можно объединить несколько контейнеров в одно целое приложение, описав их взаимодействие и зависимости в одном файле (YAML-файле).

Термины закончились, приступим к описанию деплоя сервиса:

Мы используем Docker и docker-compose для деплоя нашего многоконтейнерного приложения. Вот как контейнеры работают у нас:

Бэкенд: Это сердце нашего приложения, которое обрабатывает данные, то есть в этом контейнере хранится наш магический ИИ, который принимает запрос пользователя и отдаёт ID видео. Мы создаем его с помощью специального файла инструкций (Dockerfile). Бэкенд хранит данные в отдельных местах (томах) и использует графические процессоры (GPU) для сложных задач. Если что-то ломается, он сам перезапускается.
База данных: Здесь хранятся все данные нашего приложения. Мы используем готовую программу (образ MongoDB), которая тоже хранит данные в своём месте (томе) и автоматически перезапускается при сбое.
Фронтенд: Это то, что видят пользователи. Он тоже создаётся с помощью файла инструкций (Dockerfile) и ждёт, пока бэкенд будет готов, чтобы взаимодействовать с ним.

docker-compose позволяет описать все части в одном файле (YAML файл). Это упрощает их запуск и управление. Данные и ресурсы хранятся в отдельных хранилищах (томах), что делает систему гибкой и лёгкой для масштабирования. Автоматический перезапуск сервисов обеспечивает стабильную работу.

Взаимодействие между контейнерами:

Представьте, что пользователь заходит на наш сайт и вводит запрос, например, «морской пейзаж при закате». Вот как происходит магия за кулисами:

Фронтенд: Это первая точка контакта с пользователем. Фронтенд принимает запрос и передаёт его дальше по цепочке.
Бэкенд: Получив запрос, бэкенд запускает свой ИИ-алгоритм. Этот алгоритм анализирует запрос и обращается к базе данных.
База данных: Здесь хранятся все наши видео. Бэкенд запрашивает нужное видео на основе анализа запроса.
Бэкенд: Получив видео из базы данных, ИИ-алгоритм сопоставляет его с запросом пользователя и генерирует уникальный идентификатор (ID) видео.
Фронтенд: Бэкенд отправляет ID видео обратно на фронтенд, который красиво отображает результат пользователю.

Таким образом, каждый контейнер выполняет свою роль, слаженно работая вместе. Фронтенд принимает запросы, бэкенд обрабатывает их и взаимодействует с базой данных, а затем возвращает результат пользователю.

Итоги

Создание нашего сервиса по поиску видеоконтента на основе текстовых запросов является примером того, как можно применить передовые технологии ИИ для решения актуальных бизнес-задач. Мы обсудили ключевые аспекты разработки, включая выбор подхода к сбору данных, использование технологий для предобработки видео, и построение алгоритмов поиска и обработки текстовых описаний.

Наш сервис позволяет эффективно и быстро находить релевантные видеоролики, что является важным преимуществом в условиях растущего рынка коротких видеороликов. Внедрение AI в процессы поиска и использования видеоконтента способствует оптимизации рабочих процессов, снижению затрат времени и ресурсов, и повышению качества создаваемого контента.

Наш сервис по поиску видеоконтента по тексту может стать основой для многих приложений с короткими видео. Он подходит для создания тик-токов, instagram-stories (соцсеть Instagram принадлежат Meta*, заблокирована и запрещена в России) и других форматов. Вот как он может помочь бизнесу:

Автоматизация контента: Маркетологи смогут быстро находить и комбинировать видеоролики, сокращая время на подготовку рекламных кампаний.
Персонализированные ролики: Создание видеорекламы для разных сегментов аудитории увеличивает её эффективность.
Оптимизация архивов: Медиа-компании смогут быстро находить нужные материалы в видеоархивах.
Обзоры продуктов: Автоматическое создание видеороликов с обзорами на основе текстовых описаний и отзывов.

И это не все примеры, где сервис может применяться для автоматизации процесса работы с короткими видеороликами.

Для продакт-менеджеров и владельцев IT-бизнесов наш опыт поможет вдохновиться и станет наглядным примером того, как современные технологии могут решать практические задачи, улучшая пользовательский опыт и повышая конкурентоспособность на рынке.

Будущее улучшение сервиса:

Мы продолжаем развивать наш сервис и видим множество возможностей для его улучшения:

Расширение базы данных видео: Интеграция новых источников видеоконтента увеличит количество доступных видео, повышая вероятность нахождения нужного контента.
Обучение модели: Обучение модели не имеет границ, оно зависит от качества данных, методов обучения и архитектур моделей, которые стоят в основе нашей модели.
Усовершенствование алгоритмов поиска и предобработки: Внедрение новых технологий улучшит скорость и качество поиска.
Интеграция дополнительных метрик оценки качества: Дополнительные метрики помогут точнее оценивать работу сервиса и находить новые пути для его оптимизации.

В итоге наш сервис представляет собой мощный инструмент для поиска и использования видеоконтента, делая его незаменимым для современных IT-бизнесов и профессионалов в сфере цифровых технологий.

Контакты

нейро-сети.рф — Наша команда, мы занимаемся внедрением искусственного интеллекта в бизнес.

@eboutdatascience — Мой личный Telegram-канал, где я рассказываю про ИИ, Data Science, Собеседования, Найм и проектирование ИИ систем.

@ngmdite — Мой Telegram

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23 июл. 2024 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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27.08.24 12:50 James889900

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06.09.24 01:44 Celinagarcia

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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.
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02.10.24 22:27 Emily Hunter

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.
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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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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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26.11.24 21:59 [email protected]

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

FAST SOLUTION FOR CYPTOCURRENCY RECOVERY SPARTAN TECH GROUP RETRIEVAL
26.11.24 23:12 rickrobinson8

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