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Н Новости

MCD-rPPG: на одной волне с телемедициной

Привет, Хабр!

Меня зовут Лиза Неустроева, я студентка четвёртого курса Физтех-школы прикладной математики и информатики МФТИ. Учась на Физтехе, я заинтересовалась машинным обучением. Мне было и остаётся интересным узнавать, как методы искусственного интеллекта применяются в биологии и медицине. Поэтому я была очень рада, когда попала на стажировку в центр медицины Sber AI Lab и получила возможность участвовать в прикладных исследованиях.

Мне хотелось разобраться с методами бесконтактной оценки медицинских параметров, т. е. буквально по видеопотоку с камеры определять, какой у человека пульс! Исследовать и реализовывать настолько амбициозные проекты всегда проще с экспертами в предметной области. Для нас такими проводниками были коллеги из СамГМУ. Их усилиями подготовлен уникальный датасет, который содержит видеозаписи людей и их фотоплетизмограммы (ниже я напишу, что это и почему это очень важно), а также сопутствующую информацию медицинского характера. Я подробнее расскажу про эти данные и покажу первые результаты простых моделей, с которыми будем сравнивать более продвинутые ML-методы.

Надеюсь, что всем ML-исследователям, и особенно тем, кто увлекается компьютерным зрением, будет интересно поэкспериментировать с этим датасетом и сравнить результаты с нашими бейзлайнами.

Про фотоплетизмограммы

Немного терминологии: rPPG — это remote photoplethysmography или, по-русски, бесконтактная фотоплетизмография. Изначально, плетизмография — это метод регистрации изменения объёма органа в результате его наполнения кровью или воздухом. Результат такого измерения называется плетизмограммой, но иногда говорят просто «пульсовая волна». С помощью пульсовой волны можно сделать выводы о сердечном ритме, частоте дыхания, эластичности стенок сосудов и других медицинских параметрах. В частности, пульсовая волна скоррелирована с ЭКГ-сигналом (см. рис. 1).

Рис. 1. Слева: ЭКГ-сигнал и фотоплетизмограмма. Справа: процесс снятия фотоплетизмограммы.

Фотоплетизмография — вариация этого метода, в котором пульсовая волна считывается оптическим способом, то есть регистрируются малые изменения цвета кожи. Кстати, с точки зрения греческого языка название очень логичное, потому что «фото» — это свет, «плетизмос» — наполнение, а «графо» — писать.

Обычно фотоплетизмограмму получают, используя специальный прибор, например многофункциональный пульсоксиметр (на рис. 1 справа). В последнее время развиваются и бесконтактные методы, объединённые названием rPPG. Оказывается, чтобы определить пульсацию крови не обязательно просвечивать ткани: современные камеры, причём даже не профессиональные, а камеры смартфонов, способны улавливать возникающие из-за цикла кровообращения периодические изменения цвета кожи или мельчайшие движения головы (про второй подход есть статья на Хабре). Остаётся обнаружить и аккуратно проанализировать эти изменения и извлечь пульсовую волну. Это не так-то просто, но очень важно. Решение этой задачи потенциально может помочь в телемедицинской диагностике: если извлечь из видео пульсовую волну, то по ней можно определить производные медицинские параметры, что поможет дистанционно консультирующему врачу больше узнать о здоровье пациента. Часть информации пациент сможет получить даже без обращения к врачу: например, с помощью автономного медицинского ассистента.

Как работать с фотоплетизмограммами?

Сейчас для извлечения пульсовой волны из видео используются как статистические методы, так и методы машинного обучения. На их разнообразие можно посмотреть в репозитории rppg-toolbox. Есть и стартапы, которые предсказывают медицинские параметры с помощью фотоплетизмограмм, например, Anura и Binah.

Для разработки и оценки методов определения PPG, а также для разработки моделей удалённой диагностики нужны данные! Уже существуют несколько релевантных датасетов, например:

Каждый из них содержит синхронизированные видеозаписи лица и фотоплетизмограммы, в некоторых также есть дополнительная медицинская информация. Но в каждом из этих датасетов содержатся видеозаписи не более чем ста испытуемых, а камера располагается строго перед человеком на фиксированном расстоянии.

Multi-Camera Dataset for rPPG (или MCD rPPG) предлагает записи о 600 испытуемых, что делает данные существенно более разнообразными, чем в предыдущих датасетах. К тому же видео снимались с трёх различных ракурсов и с использованием разных камер. Такое разнообразие позволит обученным на MCD rPPG моделям работать с видео, снятыми в разных условиях на разные устройства. Также в датасете содержатся сопутствующие медицинские параметры, что позволяет решать не только задачу восстановления фотоплетизмограммы по видео, но и другие задачи удалённой диагностики.

Хочу познакомить вас с этим датасетом и предложить базовые обученные нами модели:

предсказывающие медицинские параметры отдельно по каждому кадру,
предсказывающие медицинские параметры по отрывку видео,
восстанавливающие PPG.

Датасет доступен по ссылке, а код всех экспериментов доступен в репозитории. Поехали!

Про датасет MCD-rPPG

Для каждого из шестисот испытуемых были сделаны два измерения: до и после нагрузки (например, пятнадцати приседаний за тридцать секунд). Каждому измерению соответствует трёхминутное видео, одновременно снятое с трёх разных ракурсов, то есть три ролика (см. рис. 2), синхронизированная с ними фотоплетизмограмма и отдельно записанный ЭКГ-сигнал, всего 3600 записей. Видео записывались в разное время суток в разных помещениях и при различном освещении, чтобы данные были более разнообразными, а обученные на них модели — более устойчивыми и подходящими для использования в разных условиях. Ещё на каждом видео за испытуемыми стоят часы: они нужны для того, чтобы как можно точнее синхронизировать ролики с фотоплетизмограммами.

Рис. 2. Ракурсы съемки.

Ещё для каждого испытуемого известны:

пол (gender);
возраст (age);
рост (height);
вес (weight);
уровень стресса (stress);
уровень гемоглобина (hemoglobin);
уровень гликированного гемоглобина (glycated_hemogloin);
уровень холестерина (cholesterol).

Отдельно до и после нагрузки измерялись:

нижнее или диастолическое артериальное давление (lower_ap);
верхнее или систолическое артериальное давление (upper_ap);
пульс (pulse);
сатурация (saturation);
частота вдохов (respiratory);
эластичность стенок сосудов (rigidity).

Сразу скажу, что для всех дальнейших моделей использовали ИМТ (индекс массы тела) вместо отдельных параметров роста и веса. Это показатель, измеряющийся по формуле:

$ИМТ = \text{вес (кг)}/(\text{рост (м)})^2$

Он связывает рост и вес, поэтому используется, чтобы определить, является ли масса человека недостаточной, нормальной или избыточной при его росте. Поскольку в датасете представлены видеозаписи лиц, а не в полный рост, выбран именно относительный параметр: невозможно определить по лицу рост или вес человека, но можно оценить их соотношение.

Для начала проведём Exploratory Data Analysis (EDA) и посмотрим на распределение параметров в данных.

Во-первых, кто они, эти испытуемые? А точнее, какие у них демографические характеристики? Треть испытуемых — мужчины, остальные — женщины.

Рис. 3. Распределение возраста испытуемых.

90% респондентов не старше 24 лет, оставшиеся 10% — от 25 до 83 лет.

Во-вторых, посмотрим на распределение их ИМТ и сравним его со средним по России по данным за 2018.

Рис. 4. Распределение ИМТ испытуемых

Наши респонденты несколько худее среднестатистического россиянина, может, многие из них студенты :)

В-третьих, изучим, как изменяются некоторые медицинские параметры до и после нагрузки.

Рис. 5. Измерения до и после нагрузки.

Здесь всё ожидаемо: давление повышается, частота вдохов и пульс увеличиваются. Ещё повышается температура, увеличивается эластичность стенок сосудов и уменьшается сатурация. Температура и сатурация — очень низковариабельные параметры, то есть их значения сконцентрированы в узком диапазоне: в случае температуры — около 36,6, в случае сатурации — около 97. Это нормальные параметры для большинства здоровых людей, которыми в основном являются и наши испытуемые. При этом значение сатурации не может сильно отклоняться от этого значения: по определению она не может быть больше 100, а значение 94 уже считается низким — при COVID-19 в таком случае рекомендуют вызвать врача.

Осталось рассмотреть уровень стресса, а также содержание гемоглобина, гликированного гемоглобина и холестерина в крови. Это вариабельные параметры, предсказать которые по фотографии или видеозаписи человеческого лица очень сложно, поскольку гемоглобин, холестерин и гликированный гемоглобин — параметры крови, при малых отклонениях от нормы почти не оказывающие влияния на пульсовую волну и внешность человека, а уровень стресса — субъективный параметр, определяемый по анкете.

Для определения уровня стресса использовалась шкала психологического тестирования PSM-25 (Лемура-Тесье-Филлиона). Испытуемые отвечали на 25 вопросов о наличии физических проявлений стресса: чувстве напряжённости, проблемах с дыханием и других. Каждый вопрос оценивался от 1 до 8 баллов, затем суммарный балл делили на 25. В итоге получалось число в диапазоне от 0 до 8 — чем больше результат, тем в более стрессовом состоянии находится человек.

Рис. 6. Однократные изменения.

Каждый из этих параметров важно определять для дистанционной медицинской диагностики. Кроме, может быть, пола и возраста, потому что их пациенты обычно и сами знают 😀. А ещё многие из параметров сложно определять по снимку лица или по видео: есть разные исследования на эту тему, но они пока не позволяют добиться точных результатов. Так, например, в статье 2023 года верхнее давление определяется с погрешностью 12,35 мм. рт. ст., а нижнее — с погрешностью 9,54 мм. рт. ст.. Задача определения уровня гемоглобина пока решается только классификационно (анемия или норма), а не численно. Нам и вам, всем заинтересованным в этой теме, есть куда продвигаться!

Чтобы было, с чего начинать и с чем сравнивать, предлагаем наши бейзлайны. Во всех из них будем пытаться определить по видео каждый из описанных выше параметров. Определение пола по видеозаписи или фотографии лица — единственная задача классификации в предлагаемых данных. Рассмотрим её позже, а пока обратимся к задачам регрессии.

Нулевой пункт такой: случайно разделим датасет по испытуемым на две части в соотношении 80:20, рассчитаем медиану значений целевого параметра по обучающей части и вычислим ошибку на меньшей. В качестве функции ошибки будем использовать MAE (Mean Absolute Error). Повторим такую процедуру 100 раз и усредним ошибку, а также найдём её среднее отклонение.

$MAE = \frac{1}{n} \sum_{i=1}^n |\hat{y}_i - y_i|, y_i \text{ - истинные значения } \hat{y}_i \text{ - предсказанные.}$

	МАЕ	Среднее отклонение
Возраст	4,39	0,69
ИМТ	3,28	0,25
Нижнее АД	7,42	0,37
Верхнее АД	13,61	0,64
Сатурация	0,87	0,05
Температура	0,09	0,01
Уровень стресса	1,14	0,07
Гемоглобин	1,34	0,07
Гликированный гемоглобин	0,42	0,05
Холестерин	0,65	0,04
Частота вдохов	1,40	0,12
Эластичность стенок сосудов	2,21	0,12
ЧСС	15,04	0,60

Температура и сатурация достаточно точно предсказываются уже даже таким образом: ошибка составляет менее 1 % от максимального значения. Это говорит о том, что значения этих параметров на датасете не очень разнообразны. Перейдём к более сложным моделям!

Модели, работающие с отдельными кадрами

Все описанные ниже модели будем учить, используя данные о 80% пациентов, а результаты будем измерять на отложенной выборке из оставшихся 20%. То есть в обучающей выборке будет 2880 видео, а в тестовой — 720. Затем для каждого видео из обучающей части случайным образом выберем 20 кадров, чтобы на этапе обучения каждый раз давать модели один случайный из них. Из каждого видео тестовой выборки случайным образом выберем один кадр, и именно он каждый раз будет подаваться модели для тестирования.

На каждом кадре с помощью пакета Mediapipe определяем прямоугольник, содержащий лицо, — в задачах детекции он называется bounding box. Далее обрежем кадр, оставляя только внутренность этого прямоугольника, чтобы наша модель работала с фотографиями лиц, не отвлекаясь на фон. Нам же не нужно, чтобы она выучила, что всех испытуемых-студентов снимали на фоне одной стены, а более взрослых испытуемых — на фоне другой. Про пакет Mediapipe, кстати, есть интересная статья на Хабре.

Рис. 7. Обработка кадров.

Немного технических подробностей. На подготовленных таким образом данных мы воспользовались классическим приёмом transfer learning:

Сначала взяли известную модель (энкодер), инициализированную весами, полученными обучением на большом датасете.
Затем ~~оторвали ей голову~~, заменили последний слой (это линейный слой классификатора) двумя линейными слоями с функцией активации ReLU между ними.
Полученную модель дообучили на целевой задаче.

В качестве функции потерь была использована кросс-энтропия для задачи предсказания пола и вышеупомянутое MAE для всех задач регрессии.

Рис. 8. Идея transfer learning.

Ниже приведу результаты для части моделей, но отмечу, что план экспериментов был гораздо шире: подбирали оптимальные гиперпараметры и архитектуру нейросети, экспериментировали со способом обучения, способом инициализации весов и количеством обучаемых параметров. А именно, варьировали скорость обучения (learning rate), функцию потерь (Mean Squared Error или MAE), способ инициализации весов энкодера (случайным образом или из предобученной модели), количество обучаемых параметров («размороженных» слоёв и порядок, в котором эти слои «размораживаются»).

Следуя лучшим практикам компьютерного зрения, пробовали несколько архитектур: ResNet50, ViT, Swin, предобученные на датасете ImageNet, отсюда, а также RexNet150 и EfficientNet_b2 из репозитория HSEmotion. Особенность двух последних моделей заключается в том, что они были специально обучены на датасетах лиц, поэтому должны хорошо подходить нашей задаче.

Посмотрим на результаты. Для анализа качества определения пола мы использовали метрики accuracy и F1 (в качестве положительного класса выбраны женщины).

$accuracy = \frac{|\text{количество правильно классифицированных объектов}|}{\text{|количество объектов|}}$ $F1 = 2\cdot \frac{precision \cdot recall}{precision + recall}$

model	accuracy	F1
ResNet50	0,952	0,961
Swin	0,957	0,964
ReXNet150	0,989	0,991
ViT	0,951	0,960
EfficientNet_b2	0,979	0,983

Все модели показали отличные результаты, они определяют пол почти идеально! Модели, которые были специально предобучены на лицах, справляются немного лучше других. Теперь посмотрим на результаты как для набора моделей, которые определяют один параметр за раз, так и для набора моделей, которые определяют сразу все параметры. Проанализируем работу двух архитектур: RexNet150 и ViT. Этот выбор не случаен: во-первых, они показали результаты немного лучше, чем остальные модели, во-вторых, они являются представителями двух классов современных моделей, борющихся за первенство в разных задачах компьютерного зрения: RexNet150 — свёрточная сеть, а ViT — трансформер.

	Наивный бейзлайн	ReXNet150	ViT	ReXNet150 (все параметры)	ViT (все параметры)
Возраст	4,386	1,689	1,669	1,921	4,461
ИМТ	3,277	2,516	2,534	2,454	2,803
Нижнее АД	4,723	8,581	8,146	8,013	7,870
Верхнее АД	13,606	14,049	13,788	13,178	13,299
Сатурация	0,872	1,814	0,946	0,855	0,876
Температура	0,094	0,614	0,128	0,110	0,103
Стресс	1,144	1,198	1,210	1,117	1,156
Гмоглобин	1,342	1,223	1,154	1,110	1,385
Глик. гемоглобин	0,418	0,533	0,526	0,513	0,487
Холестерин	0,647	0,692	0,710	0,629	0,680
Частота вдохов	1,399	1,453	1,488	1,483	1,455
Эласт-ть сосудов	2,210	2,308	2,320	2,284	2,201
Пульс	15,043	16,564	16,121	14,685	15,316

В остальных задачах предложенные нами решения достигают лишь результатов на уровне бейзлайнов, то есть, выбранные нейросетевые архитектуры не позволяют добиться значимого преимущества. Возможно, эти параметры вообще невозможно определить только по фото, в частности из-за того, что некоторые из них — пульс, частота вдохов — по своей природе имеют динамический характер. Поэтому следующим шагом попробуем модели, принимающие на вход видео. Заметим, что в этих экспериментах модели, специально обученные предсказывать конкретный параметр, не опережают модели, одновременно предсказывающие сразу все параметры, поэтому в следующей главе будем рассматривать только такую стратегию — предсказывающие все таргеты одновременно.

Модели по видео

Если предыдущие модели принимали на вход один кадр и предсказывали по нему один или несколько медицинских параметров, то модели из этого класса будут принимать на вход отрезок видео. Для их обучения мы взяли только видео анфас и вырезали десятисекундные отрывки, а также с помощью Mediapipe обрезали края таким образом, чтобы большую часть кадра занимало лицо. Если точнее, на каждом кадре выделили прямоугольник, ограничивающий лицо, затем нашли минимальный прямоугольник, содержащий все ранее выделенные, и увеличили его стороны на 20%. Наконец, обрезав всё, кроме этого прямоугольника, мы получили видео, на котором лицо испытуемого всё ещё попадает в кадр, а фона как можно меньше.

Рис. 9. Обработка видео.

Также, как и раньше, мы применяли идею transfer learning, но в этот раз в качестве основы взяли модели, заточенные под работу с трёхмерными входными данными, в частности, с видео. Мы взяли предобученные модели, доступные в PyTorch, а именно MViT (аналог трансформера ViT для работы с видео) и три вариации свёрточной сети 3D ResNet. Эти три вариации различаются размерностями ядер свёрток: в первой ядра трёхмерные, во второй есть как трёхмерные, так и двумерные, а в третьей двумерные свёртки по осям высоты и ширины чередуются с одномерным по оси времени.

Все эти модели обучены для работы с короткими видео по 16 кадров, что при нашей частоте съёмки около 30 кадров/с соответствует отрезку видео длиной в полсекунды. Чтобы агрегировать информацию о большем периоде времени, в репозитории мы также предлагаем модели, в которых вместо 16 последовательных кадров на вход подаются 16 кадров с некоторым шагом, или же большой входной отрезок разбивается на несколько частей, в каждой из которых вычисляется средний кадр и на вход модели предлагаются эти 16 «усреднённых» кадров.

Рис. 10. Усреднение последовательных кадров.

В задачах определения пола, возраста и ИМТ эти модели добились результатов, сравнимых с моделями, принимающими на вход отдельные кадры, а вот в остальных задачах тоже не преуспели (подробные результаты по ссылке). Остальные задачи всё ещё бросают вызов исследователям и, мы надеемся, будут решены более специализированными методами, чем наш. Например, интерес представляет комбинация двух уже исследованных подходов: изменения цвета кожи и микродвижений головы.

Восстановление rPPG

Обратимся к анонсированным в самом начале фотоплетизмограммам: будем решать задачу восстановления пульсовой волны по видео. Можно было бы сравнивать, насколько похожи предсказанная и истинная пульсовая волна, но численный результат имел бы сложно интерпретируемый физический смысл, поэтому поступим иначе. Современные методы анализа временных рядов позволяют очень точно определять частоту сердечных сокращений по пульсовой волне. Будем сравнивать пульс, рассчитанный по настоящей фотоплетизмограмме, с пульсом, рассчитанным по предсказанной фотоплетизмограмме. Для сравнения используем MAE. Для восстановления ЧСС по пульсовой волне будет использоваться алгоритм на основе быстрого преобразования Фурье.

Приведу результаты работы двух статистических алгоритмов: POS_WANG и LGI, а также обучим модель TS_CAN. Многие статистические модели и, в частности, выбранные нами, усредняют цвета пикселей по всей картинке или отдельно по каждому цветовому каналу, или даже по всем цветовым каналам сразу, а затем оперируют с полученным временным рядом. Поскольку цель — уловить колебания цвета кожи, наличие пикселей фона или одежды делает сигнал более шумным. Поэтому наряду с классическими статистическими алгоритмами приведу их усовершенствованную версию, в которой сначала с помощью модели сегментации выделяются пиксели кожи, а затем усредняются только их значения. С полученным временным рядом будет работать классический алгоритм.

Как и в предыдущих пунктах, прежде всего нужно подготовить данные. Выберем из каждого видео случайный десятисекундный отрывок. С помощью модели SelfieSegmentation от Mediapipe классифицируем все пиксели на 6 категорий.

Фон.
Волосы.
Кожа лица.
Кожа тела.
Одежда.
Другое (аксессуары).

Затем выделим наименьший прямоугольник, ограничивающий все пиксели кожи лица, увеличим его на 10 % с каждой стороны и обрежем всё, кроме этого прямоугольника. Далее во всех алгоритмах будем использовать либо эти обрезанные видео, либо только пиксели кожи лица на них. Для использования второго варианта сохраним маску пикселей кожи лица для каждого кадра.

Рис. 11. Сегментация изображения.

Для обучения алгоритма TS_CAN использовались только полученные видео, но не маски. Гиперпараметры и предобработка использовались те, что предлагаются в rppg-toolbox для других датасетов.

Алгоритм	POS_WANG	POS_WANG по пикселям кожи	LGI	LGI по пикселям кожи	TS_CAN
MAE пульса (уд./мин.)	4,463	2,968	7,368	5,418	2,841

Замечу, что использование сегментации для статистических алгоритмов даёт прирост точности. Этим удалось добиться MAE меньше 3, что соответствует лучшим результатам на аналогичных датасетах (см. таблицу).

Заключение.

Multi-Camera Dataset rPPG выложен в открытый доступ, поэтому предлагаю вам поэкспериментировать с ним в научных, исследовательских или учебных целях. Задача дистанционного определения медицинских параметров трудная, но важная, и потому кажется нам такой интересной. Думаю, что она понравится и вам, и будем вместе развивать методы удалённой диагностики!

Чтобы облегчить начало работы и чтобы было, с чем сравнивать результаты, выкладываю код бейзлайнов. Пока достаточно уверенно решаются только задачи определения пола, возраста и ИМТ, так что в дальнейших исследованиях есть к чему стремиться.

Надеюсь, что в будущем MCD-rPPG поможет сообществу исследователей продвинуться в разработке методов определения медицинских параметров и внесёт свой вклад в развитие телемедицинских сервисов.

Спасибо, что дочитали. Комментарии открыты для ваших вопросов, идей и предложений :)

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11 июн. 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

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02.10.24 22:27 Emily Hunter

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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

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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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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.