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Real-time-распознавание лиц: методы обучения быстрых и точных моделей для работы на мобильных девайсах

Привет, Хабр! Меня зовут Вадим Селютин, я старший исследователь в компании VisionLabs. Наши решения по распознаванию лиц можно встретить в офисных центрах, московском метро и кассах самообслуживания супермаркетов. Во всех этих кейсах мы используем нейросети, которые адаптируем специально для мобильных устройств.

В этой статье я расскажу про постановку задачи распознавания лиц, подходящие мобильные архитектуры, обучение распознаванию лиц на больших объемах данных и способы повысить точность маленькой архитектуры.

Постановка задачи

Системы распознавания лиц обычно осуществляют верификацию и идентификацию пользователей:

В первом случае определяется, один ли человек представлен в паре изображений, а во втором идет поиск человека с исходного фото в базе.

Эти задачи можно разбить на два класса: closed-set и open-set. В первом случае есть фиксированная база людей, которая не может изменяться. Здесь достаточно по входной фотографии человека определить его класс из обучающей выборки.

В реальных сценариях, как правило, необходимо подготовить модель, которая бы решала open-set-задачи и распознавала любых людей, не включенных в базу в процессе обучения. Для их решения мы используем расстояние между эмбеддингами, которые извлечены из входных изображений.

Можно сказать, что closed-set — это задачи классификации, а open-set — Metric Learning:

[SphereFace: Deep Hypersphere Embedding for Face Recognition, Liu et al, 2017]

Во втором случае необходимо, чтобы представления похожих объектов были близки друг к другу в пространстве эмбеддинга, а разных — далеки.

Метрики распознавания лица

Чтобы распознать лицо, нужно сначала найти его на фотографии, затем выровнять и обрезать лишние детали:

Из обработанного изображения с помощью модели глубокого обучения получаем эмбеддинг или, как он еще называется, дескриптор лица.

Последним шагом идет матчинг лиц, когда мы вычисляем расстояния между полученными дескрипторами и сравниваем с каким-то порогом:

В распознавании лиц обычно используются две метрики: True Positive Rate (TPR) и False Positive Rate (FPR). TPR показывает, как хорошо модель определяет человека по фото. Это отношение числа пар фото, на которых модель правильно ответила, что представлен один человек, к числу всех пар фото с одним и тем же человеком.

FPR, наоборот, является метрикой неправильных ответов. Это отношение числа пар фото, где модель неверно ответила, что это один человек, к числу всех пар фото с разными людьми.

В качестве метрики модели обычно смотрят на значение TPR в низких значениях FPR, что соответствует крайней левой области ROC-кривой.

Чтобы построить сильную модель по распознаванию лиц, нужен баланс трех вещей: функции потерь, архитектуры и данных.

Функция потерь (лосс)

Для классификации изображений подойдет любая модель, поверх которой добавляется дополнительный линейный слой. Он приведет выходной эмбеддинг в нужную размерность дескриптора лица.

В ходе обучения модели для распознавания лиц решается задача классификации, а во время продуктового использования уже идет сравнение эмбеддингов лиц между собой:

Возникает сложность: модель должна не только научиться относить эмбеддинг к нужному классу, но еще и учитывать расстояние между эмбеддингами. В этом помогает лосс-функция:

На скрине выше представлена формула типичного лосса для задачи классификации — кросс-энтропии. Здесь:

х_i — эмбеддинг лица i-изображения;
W — матрица весов последнего классификационного линейного слоя архитектуры;
b — bias слоя;
y_i — номер класса этого изображения в соответствии с разметкой.

Теперь спроецируем эмбеддинги X на гиперсферу фиксированного радиуса S. Тогда мы сможем сравнивать расстояния между векторами в терминах углов между ними. Для этого распишем формулу над экспонентой как скалярное произведение векторов. Далее нормализуем матрицу W и эмбеддинг х_i, а bias занулим. Тогда от формулы останется только косинус:

Проецируем дескрипторы на гиперсферу фиксированного радиуса

Можно усложнить задачу во время обучения и ввести в лосс дополнительные гиперпараметры m1, m2 и m3, которые называются марджинами:

Увеличим расстояние дескриптора до своего центра класса
при помощи добавления марджинов

За счет них расстояние между дескриптором и центром его родного по разметке класса станет в m раз больше, чем его расстояние до любого другого центра класса, который является чужим. Например, в случае двух классов обычный лосс кросс-энтропии позволяет линейно разделить классы между собой:

[SphereFace: Deep Hypersphere Embedding for Face Recognition, Liu et al, 2017]

Если нормализовать векторы и перейти в пространство гиперсферы, внутриклассовые расстояния между точками уменьшатся:

[SphereFace: Deep Hypersphere Embedding for Face Recognition, Liu et al, 2017]

Если еще дополнительно ввести марджины для усложнения задачи, то межклассовые расстояния заметно увеличатся. Этого мы и добивались:

[SphereFace: Deep Hypersphere Embedding for Face Recognition, Liu et al, 2017]

Первыми статьями, где были описаны такие способы добавления марджинов, были SphereFace, ArcFace и CosFace:

Применение марджинов увеличивает разделимость классов
[SphereFace: Deep Hypersphere Embedding for Face Recognition, Liu et al, 2017]
[CosFace: Large Margin Cosine Loss for Deep Face Recognition, Wang et al, 2018]
[ArcFace: Additive Angular Margin Loss for Deep Face Recognition, Deng et al, 2018]

В целом можно делать лосс с их комбинациями. Современные лоссы для распознавания лиц вводят различные адаптивные стратегии обучения, которые основаны на качестве изображения или его сложности для распознавания:

Современные лосс-функции распознавания лиц
Вводят адаптивные стратегии обучения, основанные на сложности лица или качестве фото
[CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition, Huang et al, 2020]
[MagFace: A Universal Representation for Face Recognition and Quality Assessment, Meng et al, 2021]
[AdaFace: Quality Adaptive Margin for Face Recognition, Kim et al, 2022]

Мобильные архитектуры для распознавания лиц

MobileNet

Основной компонент мобильных архитектур — depthwise-свертка. В отличие от обычной свертки, в ней каждый канал обрабатывается отдельно, а результаты конкатенируются и затем агрегируются сверткой с ядром размера 1x1:

[Xception: Deep Learning with Depthwise Separable Convolutions, Chollet, 2017]
[Google’s MobileNets on the iPhone, Hollemans, 2017]

Такая идея была успешно использована в MobileNet. Эта архитектура была предложена в 2017 году и на тот момент получила 70% точности на тесте ImageNet:

Успешный кейс применения depthwise separable-свертки
[MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, Howard et al, 2017]

Стоит отметить, что depthwise-свертка является частным случаем групповой свертки, в которой каналы входной feature map разбиваются на группы, обрабатываются отдельно независимыми свертками, а результаты конкатенируются:

Depthwise-свертка – частный случай групповой свертки
[CondenseNet: An Efficient DenseNet using Learned Group Convolutions, Huang et al, 2018]

Далее вышла статья про MobileNet V2, где авторы заменили обычный residual-блок на inverted residual:

Нововведение относительно MobileNetV1 – inverted residual-блок
[MobileNetV2: Inverted Residuals and Linear Bottlenecks, Sandler et al, 2018]

В нем число каналов входной Feature Map сначала увеличивается, затем применяется depthwise-свертка, после чего число каналов уменьшается к исходному значению.

В третьей версии MobileNet авторы добавили Squeeze-and-Excite-модуль к блоку V2, который адаптивно перевзвешивает каналы Feature Map, исходя из их важности:

Нововведения относительно MobileNetV2: Блок MobileNetV3 = блок MobileNetV2 + модуль Squeeze-and-Excite (SE) для адаптивного перевзвешивания каналов
[Searching for MobileNetV3, Howard et al, 2019]

Другим нововведением была нелинейность h-swish, которая хорошо зарекомендовала себя в мобильных архитектурах:

[Searching for MobileNetV3, Howard et al, 2019]

Третья версия MobileNet смогла превзойти вторую, оставаясь в рамках той же вычислительной сложности.

RegNet

Эта архитектура была предложена в 2020 году. Ее авторы взяли за основу семейство архитектур, похожее на ResNet, и модифицировали ее, получив более точные множества архитектур:

Создание модели:
1) семейство архитектур, похожее на ResNet, в качестве основы: (A)
2) итеративное улучшение семейства путем выбора самых сильных модификаций: (B, C)
[Designing Network Design Spaces, Radosavovic et al, 2020]

Результат их работы — блоки RegNet-X и RegNet-Y. RegNet-Y отличается от RegNet-X добавлением Squeeze-and-Excite-модуля:

Создание модели:
3) Получение архитектур RegNet-X и RegNet-YБлок RegNet-Y = блок RegNet-X + модуль Squeeze-and-Excite
[Designing Network Design Spaces, Radosavovic et al, 2020]

В этой статье были представлены разные размеры архитектур. Дальше я буду рассматривать мобильные.

GhostNet

В этой архитектуре применяется трюк, очень похожий на идею из статьи ShuffleNet. На Feature Map применяется depthwise-свертка, а результат конкатенируется с самой исходной Feature Map:

Нововведения архитектуры:
Ghost-модуль (предложен в GhostNetV1): применение отдельных линейных операций на каналах входа и конкатенация результата со входом
[GhostNet: More Features from Cheap Operations, Han et al, 2020]
[GhostNetV2: Enhance Cheap Operation with Long-Range Attention, Tang et al, 2020]

В статье GhostNet V2 авторы добавили в блок GhostNet еще дополнительный модуль, имитирующий механизм внимания на изображении:

DFC-модуль (предложен в GhostNetV2) имитирует механизм внимания на картинке по X- и Y-осям.
[GhostNet: More Features from Cheap Operations, Han et al, 2020]
[GhostNetV2: Enhance Cheap Operation with Long-Range Attention, Tang et al, 2020]

Такие модификации позволили архитектуре превзойти MobileNet V3 на тесте ImageNet.

ConvNeXt

В 2022 году вышла статья про архитектуру ConvNeXt. Авторы добавили в модель ResNet различные трюки из архитектуры трансформера Swin. Например, LayerNorm-нормализации или большие ядра размера 7х7:

[A ConvNet for the 2020s, Liu et al, 2022]
[Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, Liu et al, 2021]

В этой статье были представлены довольно крупные модели, Поэтому мы обратили внимание на библиотеку Pytorch Image Models с моделями ConvNeXt мобильного размера.

Сравнение архитектур

На скриншоте ниже представлены результаты нашей команды при работе с этими архитектурами:

Здесь вторая колонка — это число параметров модели, третья — ее вычислительная сложность, четвертая — результаты на нашем внутреннем тесте верификации, пятая и шестая — на тесте IJB-C. Чем больше значение, тем лучше

Важно заметить, что это исторические максимумы, которые мы получали для этих архитектур. Каждая модель обучалась со своими гиперпараметрами. Лучшие результаты показывает GhostNet, но у нее больше параметров, чем у остальных моделей. Самые слабые показатели у MobileNet-архитектур. Хороший баланс параметров и результатов тестов показала RegNet-X.

Мы провели замеры скорости выполнения этих моделей:

Замеры были проведены на платформах:
- CPU (batch size = 1): Intel(R) Xeon(R) Gold 6150 CPU @ 2.70GHz
- GPU (batch size = 4): NVIDIA RTX A4500

Заметно, что GhostNet и ConvNeXt медленнее, чем MobileNet и RegNet на CPU, а на GPU они сопоставимы по скорости между собой.

Существуют разные методы ускорения архитектур глубокого обучения. Например, специализированные чипы, а также различные способы квантизации архитектур с переходом в вычисления на меньшей точности. Их я рассматривать в этой статье не буду.

Обучение распознаванию лиц на больших данных

При обучении модели по распознаванию лиц сама природа данных является трудностью:

Каждый id человека является отдельным классом во время обучения модели.
В датасетах содержатся фото от тысяч до миллионов разных людей.
[WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition, Zhu et al, 2021]

Отдельным классом в обучающей выборке являются фотографии одного и того же человека. А в типичных датасетах содержатся изображения тысяч или даже миллионов разных людей. Это приводит к тому, что в пайплайне обучения модели матрица весов классификационного слоя будет иметь очень большие размеры:

Соответственно, растет нагрузка на видеокарту:

Например, если в датасете 2 млн id, то такая матрица займет целиком память видеокарты NVIDIA 2080 Ti
[An Efficient Training Approach for Very Large Scale Face Recognition, Wang et al, 2022]

Типичные методы распараллеливания типа DataParallel не решают эту проблему, поэтому нужен кастомный подход.

Параллельное ускорение

Чтобы уменьшить нагрузку при обучении на нескольких картах, разделим колонки этой матрицы, назовем ее W, на группы равного размера и положим каждую группу на свою видеокарту:

Эта идея была представлена еще в статье ArcFace

Рассмотрим, как будет происходить forward-шаг с таким разбиением. В самом начале у нас есть несколько видеокарт. На каждой карте находится своя группа колонок матрицы W.

Первым шагом собираем эмбеддинги всех минибатчей с каждого процесса и получаем эмбеддинги X для всего батча. Затем на каждом процессе считаем произведение X со своей частью матрицы W. Далее собираем логиты с каждого процесса, после чего можно посчитать итоговый лосс для всего батча.

[Partial FC: Training 10 Million Identities on a Single Machine, An et al, 2020]
[ArcFace: Additive Angular Margin Loss for Deep Face Recognition, Deng et al, 2018]

Во время backward-шага сначала считаем градиенты по локальным логитам каждого процесса. С их помощью обновляем на каждом процессе свою часть весов матрицы W и собираем вместе все локальные градиенты. Последним шагом обновляются веса backbone, копия которых лежит на каждом процессе:

[Partial FC: Training 10 Million Identities on a Single Machine, An et al, 2020]
[ArcFace: Additive Angular Margin Loss for Deep Face Recognition, Deng et al, 2018]

За счет использования такого метода можно заметно сократить нагрузку на каждую видеокарту, даже на датасетах с миллионами классов:

Измерения проводились на 4 картах NVIDIA A100-SXM4-80GB

Метод Partial-FC

У нас этот метод не дал хороших результатов, но поделюсь нашим опытом из-за его популярности.

Рассмотрим каждую колонку весов матрицы W — классификационного слоя модели — в качестве центра своего класса. Для какого-то эмбеддинга X позитивным классом назовем его родной класс по разметке или по лейблу, а негативным — любой другой:

Колонки классификационной матрицы W (D x N)можно рассмотреть как центры каждого из N классов
[SphereFace: Deep Hypersphere Embedding for Face Recognition, Liu et al, 2017]

В 2020 году был представлен метод под названием Partial-FC, который берет из матрицы W центр положительного класса как есть, а центры всех других классов сэмплирует с какой-то низкой вероятностью R:

[Partial FC: Training 10 Million Identities on a Single Machine, An et al, 2020]

Его авторы показали, что можно существенно сократить число негативных центров классов в матрице W и практически не потерять в точности модели:

[Partial FC: Training 10 Million Identities on a Single Machine, An et al, 2020]

Мы попробовали применить этот метод у себя:

Вторая колонка отвечает за процентное количество негативных центров классов, которые мы используем в обучении

Получили заметное ухудшение в точности моделей разных скейлов. Из этого вывод, что первая стратегия распараллеливания колонок по процессам намного эффективнее для уменьшения нагрузки на каждый из них. Плюс она не дает просадок в точности.

[Dynamic Class Queue for Large Scale Face Recognition In the Wild, Li et al, 2021]
[Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset with Limited Computational Resources, Li et al, 2021]
[An Efficient Training Approach for Very Large Scale Face Recognition, Wang et al, 2022]

Есть и другие методы для снижения нагрузки. В основном они фокусируются на введении дополнительных буферов, имитирующих хранение полной матрицы классификационного слоя.

Улучшение качества распознавания мобильной архитектуры

Представим, что мы смогли в supervised-режиме обучить очень точную, но также большую и медленную модель по распознаванию лиц, при помощи типичного лосса классификации. Назовем эту сеть «учителем»:

[Distilling the Knowledge in a Neural Network, Hinton et al, 2015]

Во время обучения заморозим веса «учителя» и будем работать с маленькой сетью — «студентом»:

[Distilling the Knowledge in a Neural Network, Hinton et al, 2015]

В качестве ответов используем не метки классов, а логиты от «учителя». Таким образом можно передать знания от крупной модели к более маленькой и поднять ее точность.

У нас был кейс, когда заказчики попросили сделать две совместимые модели разных размеров, чтобы матчить их дескрипторы между собой. Например, один раз извлечь дескриптор по профильному фото сотрудника с помощью медленной модели. А быструю идентификацию на системе контроля доступа производить с помощью мобильной архитектуры:

Запрос: получить для мобильной архитектуры то же пространство дескрипторов, что и у большой сети, для их совместимости

Для получения совместимости можно во время дистилляции знаний инициализировать матрицу весов классификационного слоя «студента» матрицей весов от «учителя» и заморозить ее на время обучения. Тогда «студент» выучит то же пространство дескрипторов, что и его «учитель»:

[Margin based knowledge distillation for mobile face recognition, Nekhaev et al, 2020]

Этот метод дает такие результаты:

На этом скриншоте строки 1 и 2 — результаты модели «учителя» и «студента». Для получения данных в строке 3 для каждой пары фото нужно взять по одному дескриптору от каждой модели и найти расстояние между ними. Результаты такого кросс-матчинга оказывались между результатами исходных моделей, что показывает их совместимость.

Основные выводы

Здесь я кратко соберу основные идеи по распознаванию лиц на мобильных устройствах, к которым мы пришли в процессе работы:

лучшую скорость и качество показала мобильная модель RegNet-X;
разбиение матрицы весов классификационного слоя лежит в основе обучения всех современных моделей по распознаванию лиц;
негативные центры классов важны во время обучения, поэтому резкое сокращение их количества приводит к ухудшению точности модели;
с помощью дистилляции знаний можно сделать два совместимых варианта моделей: маленькую и быструю и масштабную и медленную. Каждую из них можно использовать для своих задач.

На этом у меня все, готов ответить на ваши вопросы.

Скрытый текст

SphereFace: Deep Hypersphere Embedding for Face Recognition, Liu et al, 2017
https://arxiv.org/abs/1704.08063
CosFace: Large Margin Cosine Loss for Deep Face Recognition, Wang et al, 2018
https://arxiv.org/abs/1801.09414
ArcFace: Additive Angular Margin Loss for Deep Face Recognition, Deng et al, 2018
https://arxiv.org/abs/1801.07698
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition, Huang et al, 2020
https://arxiv.org/abs/2004.00288
MagFace: A Universal Representation for Face Recognition and Quality Assessment, Meng et al, 2021
https://arxiv.org/abs/2103.06627
AdaFace: Quality Adaptive Margin for Face Recognition, Kim et al, 2022
https://arxiv.org/abs/2204.00964
Xception: Deep Learning with Depthwise Separable Convolutions, Chollet, 2017 https://arxiv.org/abs/1610.02357
Google’s MobileNets on the iPhone, Hollemans, 2017
https://machinethink.net/blog/googles-mobile-net-architecture-on-iphone/
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, Howard et al, 2017
https://arxiv.org/abs/1704.04861
CondenseNet: An Efficient DenseNet using Learned Group Convolutions, Huang et al, 2018
https://arxiv.org/abs/1711.09224
MobileNetV2: Inverted Residuals and Linear Bottlenecks, Sandler et al, 2018
https://arxiv.org/abs/1801.04381
Searching for MobileNetV3, Howard et al, 2019
https://arxiv.org/abs/1905.02244
Designing Network Design Spaces, Radosavovic et al, 2020
https://arxiv.org/abs/2003.13678
GhostNet: More Features from Cheap Operations, Han et al, 2020
https://arxiv.org/abs/1911.11907
GhostNetV2: Enhance Cheap Operation with Long-Range Attention, Tang et al, 2020
https://arxiv.org/abs/2211.12905
A ConvNet for the 2020s, Liu et al, 2022
https://arxiv.org/abs/2201.03545
pytorch-image-models
https://github.com/huggingface/pytorch-image-models
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition, Zhu et al, 2021
https://arxiv.org/abs/2103.04098
An Efficient Training Approach for Very Large Scale Face Recognition
https://arxiv.org/abs/2105.10375
Partial FC: Training 10 Million Identities on a Single Machine, An et al, 2020
https://arxiv.org/abs/2010.05222
Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC
https://arxiv.org/abs/2203.15565
Distilling the Knowledge in a Neural Network, Hinton et al, 2015
https://arxiv.org/abs/1503.02531
Margin based knowledge distillation for mobile face recognition, Nekhaev et al, 2020
https://www.researchgate.net/publication/338959991_Margin_based_knowledge_distillation_for_mobile_face_recognition

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12 сент. 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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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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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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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
02:19 briankennedy

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