pwc-archive/papers-with-abstracts huggingface dataset:
MPEG: A Multi-Perspective Enhanced Graph Attention Network for Causal Emotion Entailment in Conversations
Authors: Tiantian Chen, Ying Shen, Xuri Chen, Lin Zhang, Shengjie Zhao
Date: 2023-09-15 00:00:00
Proceeding: IEEE Transactions on Affective Computing 2023 9
Tasks: Causal Emotion Entailment, Causal Inference, Graph Attention

Emotion causes constitute a pivotal component in the comprehension of emotional conversations. Recently, a new task named Causal Emotion Entailment (CEE) has been proposed to identify the causal utterances for the target emotional utterance in a conversation. Although researchers have achieved some progress in solving this problem, they failed to adequately incorporate speaker characteristics and overlooked the effects of temporal relations in conversation structures. To fill such a research gap to some extent, we propose a novel causal emotion entailment framework, namely MPEG (Multi-Perspective Enhanced Graph attention network). The training of MPEG consists of three stages. First, we utilize a speaker-aware pre-trained model and two attention mechanisms to obtain the utterance representations that incorporate local contexts as well as the speaker and emotional information. Then, these representations are fed into a graph attention network to model the conversation structures and emotional dynamics from both local and global perspectives. Finally, a fully-connected network is implemented to predict the relationships between emotional utterances and causal utterances. Experimental results show that MPEG achieves state-of-the-art performance.
Establishment of a feed intake prediction model based on eating time, ruminating time and dietary composition
Authors: Li Shangru, Zhang Chengrui, Wang Ruixue, Song Jiamei, Xin Hangshu
Date: 2022-08-09 00:00:00
Proceeding: Computers and Electronics in Agriculture 2022 8

The individual feed intake of dairy cows is an indicator of oestrus and health status and can be used as an ab­
normality warning in health monitoring. However, existing methods for monitoring individual feed intake are
limited by the feeding environment and equipment and are difficult to apply in commercial production. In this
experiment, a feed intake monitoring system for dairy cows was used to determine the daily feed intake of 10
dairy cows. The dairy cows were fed 8 diets with different concentration-to-forage ratios according to different
phases or time intervals. The adaptation period of the cows to each diet was 7 days, and the trial period was 7
days. Smart collars were used to monitor the daily eating time and ruminating time of the dairy cows, which
were used along with dietary components as input parameters to establish five feed intake prediction models: a
linear regression model (LRM), an artificial neural network model, a support vector machine model, a K-nearest
neighbour model and a chi-square automatic interaction detector model. The research results showed that R2 was
0.73 for the LRM and ≥0.82 for the other four models, which indicated that the relationship between feed intake
and the predictors may not be a simple linear relationship. The five models had root mean square errors (RMSEs)
≤ 1.5 kg/d and standard deviations (SDs) ≤ 0.87 kg/d, and in external verification, the five models achieved R2
values ≥ 0.80, RMSEs ≤ 0.75 kg/d, and SDs ≤ 0.90 kg/d. This study shows that adding dietary ingredient data to
collar-collected data can improve prediction model accuracy.
专利质押、 融资约束与企业劳动雇佣
Authors: 余明桂，王俐璇，赵文婷，胡彦琦
Date: 2022-09-05 00:00:00
Proceeding: 数量经济技术经济研究 2022 9

本文研究专利质押融资试点政策对企业劳动雇佣的影响及其作用机制。以 ２００５ ～２０１５ 年沪深 Ａ 股上市公司的数据为样本， 采用双重差分法评估专利质押融资政策试点对企业劳动雇佣的影响。 研究发现， 专利质押融资试点政策显著增加了企业员工总数和高技术员工人数， 并且这一作用主要通过缓解融资约束、 增加银行信贷、 降低债务融资成本以及缓解经营风险不确定性来实现。 进一步检验发现，
专利质押融资试点政策对企业劳动雇佣的影响主要存在于专利价值高、 轻资产型和地区金融发展水平较低的企业。 本文研究结果表明， 专利质押融资试点政策具有促进充分就业和提高劳动配置效率的作用， 为专利质押融资的试点提供了新的经验证据。
Can machine learning algorithms perform better than multiple linear regression in predicting nitrogen excretion from lactating dairy cows
Authors: Xianjiang Chen1, 2, Huiru Zheng2*, Haiying Wang2* & Tianhai Yan1
Date: 2022-12-01 00:00:00
Proceeding: Scientific Reports 2022 12
Tasks: Prediction, regression

This study aims to compare the performance of multiple linear regression and machine learning
algorithms for predicting manure nitrogen excretion in lactating dairy cows, and to develop new
machine learning prediction models for MN excretion. Dataset used were collated from 43 total diet
digestibility studies with 951 lactating dairy cows. Prediction models for MN were developed and
evaluated using MLR technique and three machine learning algorithms, artificial neural networks,
random forest regression and support vector regression. The ANN model produced a lower RMSE
and a higher CCC, compared to the MLR, RFR and SVR model, in the tenfold cross validation.
Meanwhile, a hybrid knowledge-based and data-driven approach was developed and implemented
to selecting features in this study. Results showed that the performance of ANN models were greatly
improved by the turning process of selection of features and learning algorithms. The proposed new
ANN models for prediction of MN were developed using nitrogen intake as the primary predictor.
Alternative models were also developed based on live weight and milk yield for use in the condition
where nitrogen intake data are not available (e.g., in some commercial farms). These new models
provide benchmark information for prediction and mitigation of nitrogen excretion under typical dairy
production conditions managed within grassland-based dairy systems.
Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees
Authors: Sophie Hall, Francesco Micheli, Giuseppe Belgioioso, Ana Radovanović, Florian Dörfler
Date: 2024-10-28 00:00:00
Tasks: Cloud Computing, Computational Efficiency, Scheduling

Data centers are significant contributors to carbon emissions and can strain power systems due to their high electricity consumption. To mitigate this impact and to participate in demand response programs, cloud computing companies strive to balance and optimize operations across their global fleets by making strategic decisions about when and where to place compute jobs for execution. In this paper, we introduce a load shaping scheme which reacts to time-varying grid signals by leveraging both temporal and spatial flexibility of compute jobs to provide risk-aware management guidelines and job placement with provable performance guarantees based on distributionally robust optimization. Our approach divides the problem into two key components: (i) day-ahead planning, which generates an optimal scheduling strategy based on historical load data, and (ii) real-time job placement and (time) scheduling, which dynamically tracks the optimal strategy generated in (i). We validate our method in simulation using normalized load profiles from randomly selected Google clusters, incorporating time-varying grid signals. We can demonstrate significant reductions in carbon cost and peak power with our approach compared to myopic greedy policies, while maintaining computational efficiency and abiding to system constraints.
Facial Image Reconstruction and its Influence to Face Recognition
Authors: Filip Pleško, Tomáš Goldmann, Kamil Malinka
Date: 2023-12-15 00:00:00
Proceeding: International Conference of the Biometrics Special Interest Group (BIOSIG) 2023 12
Tasks: Face Recognition, Image Reconstruction

This paper focuses on reconstructing damaged facial images using GAN neural networks. In addition, the effect of generating the missing part of the face on face recognition is investigated. The main objective of this work is to observe whether it is possible to increase the accuracy of face recognition by generating missing parts while maintaining a low false accept rate (FAR). A new model for generating the missing parts of a face has been proposed. For face-based recognition, state-of-the-art solutions from the DeepFace library and the QMagFace solution have been used.
Comparative Analysis of PI and PID Controllers for Level and Flow Control in Coupled Tank Systems
Authors: Divyam R. Kakani, Dineshkumar Subendran, Shivam J. Singh
Date: 2024-10-29 00:00:00

The comparative study of Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers applied to level and flow control in coupled tank systems is presented in this research work. The coupled tank system, characterized by its nonlinear behavior, was selected due to its relevance in chemical processing industries where precision in liquid level control is crucial. The study evaluates the performance of both controllers under varying conditions, focusing on their ability to handle disturbances and maintain stability. Through experimental data and graphical analysis, it was observed that PID controllers, with their derivative action, provide faster response times and higher accuracy but are more sensitive to noise and harder to tune. In contrast, PI controllers, though slower, offer more stability and are easier to configure for systems where precise control is less critical. These findings highlight the trade-offs between the two control strategies, providing insights into their application depending on system requirements.
StegFormer: Rebuilding the Glory of the Autoencoder-Based Steganography
Authors: Xiao Ke, Huanqi Wu, Wenzhong Guo
Date: 2024-03-24 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2024 3
Tasks: Image Steganography

Image hiding aims to conceal one or more secret images within a cover image of the same resolution. Due to strict capacity requirements, image hiding is commonly called large-capacity steganography. In this paper, we propose StegFormer, a novel autoencoder-based image-hiding model. StegFormer can conceal one or multiple secret images within a cover image of the same resolution while preserving the high visual quality of the stego image. In addition, to mitigate the limitations of current steganographic models in real-world scenarios, we propose a normalizing training strategy and a restrict loss to improve the reliability of the steganographic models under realistic conditions. Furthermore, we propose an efficient steganographic capacity expansion method to increase the capacity of steganography and enhance the efficiency of secret communication. Through this approach, we can increase the relative payload of StegFormer to 96 bits per pixel without any training strategy modifications. Experiments demonstrate that our StegFormer outperforms existing state-of-the-art (SOTA) models. In the case of single-image steganography, there is an improvement of more than 3 dB and 5 dB in PSNR for secret/recovery image pairs and cover/stego image pairs.
HLM FOOTBALL FR
Authors: Yuson_kp
Date: 2024-10-30 00:00:00
Proceeding: 10/30 2024 10

HLM Football FR est une plateforme média numérique dynamique dédiée au monde du football, fondée en 2020 et dont le siège est à Lomé au Togo. Forte d'une forte présence sur les réseaux sociaux populaires tels que TikTok, YouTube et Instagram, HLM Football FR s'est rapidement imposé comme une référence incontournable pour les passionnés de football.

La plateforme est particulièrement connue pour ses séries de vidéos captivantes qui plongent dans des « anecdotes inconnues sur le football », offrant aux téléspectateurs des aperçus uniques et des histoires divertissantes sur la riche histoire de ce sport. Cette approche distinctive a permis à HLM Football FR d’attirer un public croissant et de favoriser une communauté dynamique de fans partageant une passion pour le jeu.

Fondée par Helmut Patahini, l'organisation fonctionne avec une petite équipe de cinq employés dévoués qui s'engagent à créer du contenu de haute qualité en français. Depuis sa création, HLM Football FR a cherché à combler le fossé entre les supporters et le sport, rendant le football plus accessible et plus agréable pour tous.
HLM FOOTBALL FR
Authors: HLM FOOTBALL FR
Date: 2024-10-30 00:00:00
Proceeding: 10/30 2024 10

HLM Football FR est une plateforme média numérique dynamique dédiée au monde du football, fondée en 2020 et basée à Lomé au Togo. Ayant pour mission d’engager et de divertir les passionnés de football, HLM Football FR s’est rapidement taillé une place dans le paysage numérique. La plateforme bénéficie d'une présence active sur les réseaux sociaux populaires, notamment TikTok, YouTube et Instagram, où elle se connecte avec des fans de tous âges.

Ce qui distingue HLM Football FR, c'est sa concentration unique sur la découverte d'histoires moins connues et d'anecdotes fascinantes du monde du football. À travers une série captivante de vidéos, la plateforme partage des informations intrigantes qui passent souvent inaperçues, mettant en lumière la riche histoire et la culture de ce sport. Des histoires en coulisses de joueurs légendaires aux faits insolites sur des matchs emblématiques, HLM Football FR apporte une nouvelle perspective au football, le rendant à la fois divertissant et éducatif pour son public.

Fondée par Helmut Patahini, HLM Football FR est animée par une équipe restreinte mais passionnée de cinq salariés. Ensemble, ils se consacrent à produire un contenu de haute qualité qui trouve un écho auprès des téléspectateurs et favorise un sentiment de communauté parmi les fans de football. L'équipe travaille sans relâche pour garantir que chaque élément de contenu est non seulement informatif mais également engageant, encourageant l'interaction et la discussion entre les abonnés.

Depuis sa création, HLM Football FR a connu une croissance importante, attirant un public fidèle qui apprécie son approche innovante des médias sportifs. La plateforme vise à combler le fossé entre les supporters et le jeu, en rendant le football plus accessible et plus agréable pour tous, quel que soit leur niveau de connaissance du sport.

Opérant principalement en français, HLM Football FR cherche à impliquer le public francophone du Togo et au-delà, en puisant dans une riche veine de liens culturels à travers le football. L'engagement de l'équipe à présenter des histoires uniques permet aux fans de découvrir de nouveaux aspects du sport qu'ils aiment, approfondissant ainsi leur appréciation du football en tant que jeu et phénomène culturel.

Alors que HLM Football FR continue d'évoluer, elle reste concentrée sur sa mission principale : inspirer, éduquer et divertir les amateurs de football grâce à des contenus numériques innovants. Que ce soit grâce à une narration captivante, des discussions animées ou un engagement interactif sur les réseaux sociaux, HLM Football FR est prêt à avoir un impact durable sur la communauté du football.
Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading
Authors: Avinash Maurya, Jie Ye, M. Mustafa Rafique, Franck Cappello, Bogdan Nicolae
Date: 2024-10-26 00:00:00
Tasks: CPU, GPU

Transformers and large language models~(LLMs) have seen rapid adoption in all domains. Their sizes have exploded to hundreds of billions of parameters and keep increasing. Under these circumstances, the training of transformers is very expensive and often hits a ``memory wall'', i.e., even when using 3D parallelism (pipeline, tensor, data) and aggregating the memory of many GPUs, it is still not enough to hold the necessary data structures (model parameters, optimizer state, gradients, activations) in GPU memory. To compensate, state-of-the-art approaches offload the optimizer state, at least partially, to the host memory and perform hybrid CPU-GPU computations. However, the management of the combined host-GPU memory is often suboptimal and results in poor overlapping between data movements and computations. This leads to missed opportunities to simultaneously leverage the interconnect bandwidth and computational capabilities of CPUs and GPUs. In this paper, we leverage a key observation that the interleaving of the forward, backward and update phases generate fluctuations in the GPU memory utilization, which can be exploited to dynamically move a part of the optimizer state between the host and the GPU memory at each iteration. To this end, we design and implement \proj, a novel technique to split the LLM into subgroups, whose update phase is scheduled on either the CPU or the GPU based on our proposed performance model that addresses the trade-off between data movement cost, acceleration on the GPUs vs the CPUs, and competition for shared resources. We integrate our approach with DeepSpeed and demonstrate 2.5$\times$ faster iterations over state-of-the-art approaches using extensive experiments.
Comprehensive dataset of user-submitted articles with ideological and extreme bias from Reddit
Authors: Kamalakkannan Ravi, Adan Ernesto Vela
Date: 2024-08-12 00:00:00
Proceeding: Data in Brief 2024 8
Tasks: Articles, Holdout Set, News Classification, News Recommendation, Political Salient Issue Orientation Detection, Sentiment Analysis, Text Classification

Our study aims to collect data to understand ideological and extreme bias in text articles shared across various online communities, particularly focusing on the language used in subreddits associated with extremism and targeted violence.
Initially, we gathered data from related online communities, specifically the r/Liberal and r/Conservative communities on Reddit, utilizing the Reddit Pushshift API to collect URLs shared within these subreddits. Our aim was to gather news, opinion, and feature articles, resulting in a corpus of 226,010 articles. We also curated a balanced subset of 45,108 articles and annotated 4000 articles to validate their relevance, facilitating understanding of language usage within ideological Reddit communities and insights into ideological bias in media content.
Expanding beyond binary ideologies, we introduced a new category termed “Restricted” to encompass articles shared in private or banned subreddits. This third category encompasses articles shared in restricted, privatized, quarantined, or banned subreddits characterized by radicalized and extremist ideologies. This expansion yielded a large dataset of 377,144 articles. Additionally, we included articles from subreddits with unspecified ideologies, creating a holdout set of 922,522 articles. In total, our combined dataset of 1.3 million articles collected from 55 different subreddits will assist in examining radicalized communities and providing discourse analysis in associated subreddits, enhancing understanding of the language used in articles shared within radicalized Reddit communities and offering insights into extreme bias in media content.
In summary, we collected 1.52 million articles to understand ideological and extreme bias, providing a comprehensive dataset that aids in understanding language usage within text articles posted in ideological and extreme Reddit communities.
Deep Residual Network based Automatic Image Grading for Diabetic Macular Edema
Authors: Santhosh Kumar Sukumar, Kamalakkannan Ravi, Supriti Mulay, Keerthi Ram, Mohanasankar Sivaprakasam
Date: 2018-07-01 00:00:00
Proceeding: Engineering in Medicine and Biology Society (EMBC), 2018 40th Annual International Conference of the IEEE 2018 7
Tasks: Fovea Detection, image-classification, Image Classification, Medical Image Analysis, Medical Image Classification, Optic Disc Detection, Transfer Learning

Diabetic Macular Edema (DME) is an advanced symptom of diabetic retinopathy that affects central vision of diabetes patients. An automated system for early detection of DME symptom has been proposed herein to elude vision impairment and assist in effective treatment. Transfer learning based on Deep Residual Networks (ResNets) which has proven to be a very successful model in many image classification applications and is used in the proposed system for automatic grading of DME images. Validation of the developed system on Indian Diabetic Retinopathy Image Dataset (IDRID 2018) results in 86.56 % detection accuracy.
Breast cancer histology classification using Deep Residual Networks
Authors: Kamalakkannan Ravi, Sakthivel Selvaraj, JM Poorneshwaran, Keerthi Ram, Mohanasankar Sivaprakasam
Date: 2018-07-01 00:00:00
Proceeding: Engineering in Medicine and Biology Society (EMBC), 2018 40th Annual International Conference of the IEEE 2018 7
Tasks: Breast Cancer Histology Image Classification, Classification, Transfer Learning

In this work, in order to improve the computer aided diagnosis systems’ performance on histopathological image analysis, we have proposed an approach with image pre-processing followed by a deep learning method to classify the breast cancer histology images into four classes; (i) normal tissue, (ii) benign lesion, (iii) in-situ carcinoma, and (iv) invasive carcinoma. The images are preprocessed for intensity and stain normalization using histogram equalization method. The Fine-tuning ConvNet transfer learning method is used with ResNet152 to train and classify the images. This proposed approach yields an average fivefold cross validation accuracy of 83%, a substantial improvement over the state-of-the-art.
Exploring Multi-Level Threats in Telegram Data with AI-Human Annotation: A Preliminary Study
Authors: Kamalakkannan Ravi, Adan Ernesto Vela, Elizabeth Jenaway, Steven Windisch
Date: 2023-12-15 00:00:00
Proceeding: 2023 22nd IEEE International Conference on Machine Learning and Applications (ICMLA) 2023 12
Tasks: Information Retrieval, Text Classification, Transfer Learning, Violence and Weaponized Violence Detection

This research addresses the crucial challenge of effectively measuring threats in social media comments targeting voting, public officials, and institutions in the United States. Our understanding of these online threats and their links to real-world risks is limited, making it difficult to assess their seriousness. To overcome these limitations, we propose a comprehensive threat level scale from 0 to 5 and collect a dataset of 1.3 million Telegram responses for developing and rigorously testing these threat levels. Additionally, we explore OpenAI-human annotation to efficiently label this vast dataset. Our innovative two-step transfer learning approach initially employs a pre-existing, pre-trained model for labeling, followed by expert validation. Next, we use the AI-annotated samples to develop independent models, and expert annotators verify their predictions. Notably, our findings demonstrate that the GPT-2 model, despite its fewer annotated training set, performs comparably to OpenAI's anno-tations, showcasing its potential for cost-effective threat detection with more annotated samples. With the long-term objective of establishing continuous threat-level monitoring, we identify the strengths and limitations of our current approach and propose a roadmap for enhancing threat detection.