Reviewerly/exHarmony huggingface dataset:
Towards a scalable resource-driven pedestrian detection for low-cost IR sensors

Systems using low-cost infrared (IR) sensors have gained more and more interest in recent years, such as smart video surveillance, driver assistance system, robotics, and military applications. To meet the total cost limit of these systems, low resource processers are desired. In this paper, we propose a scalable resource-driven pedestrian detection approach for low-cost IR sensors. The proposed method introduces a feature-importance-sampling based feature selection for the IR sensors which enables scalable selection of the number of effective features according to the resource requirements. To obtain high performance detection with the few selected effective features and hard-to-obtain data of low-cost IR sensors, we adopt transfer learning from easy-to-obtain visible images to train the pedestrian detector. In this way, optimal IR system design can be determined based on the trade-offs between performance and resources. Using HOG and SVM based pedestrian detection methods, we evaluate the proposed method on Thermal Diode lnfraRed (TDIR) sensor and find that key-points of the human body that observed in the TDIR images, such as the contours of the body, are selected as the most crucial attributes. As a result of training these selected features on visible images, one can reduce memory use by 1/30 and increase detection speed by about 1/6 while maintaining competitive detection performance. To summarize, the proposed work presents a scalable resource-driven method for pedestrian detection for low-cost IR sensors with high accuracy and low resource consumption.
Towards Deeper Graph Neural Networks via Layer-Adaptive

Graph neural networks have achieved state-of-the-art performance on graph-related tasks. Previous methods observed that GNNs’ performance degrades as the number of layers increases and attributed this phenomenon to over-smoothing caused by the stacked propagation. However, we proved experimentally and theoretically that it is overfitting rather than propagation that causes performance degradation. We propose a novel framework: layer-adaptive GNN (LAGNN) consisting of two modules: adaptive layer selection and random Droplayer, which can adaptively determine the number of layers and thus alleviate overfitting. We attached this general framework to two representative GNNs and achieved consistency improvements on six representative datasets.
Self-supervised Pre-training and Semi-supervised Learning for Extractive Dialog Summarization

Language model pre-training has led to state-of-the-art performance in text summarization. While a variety of pre-trained transformer models are available nowadays, they are mostly trained on documents. In this study we introduce self-supervised pre-training to enhance the BERT model’s semantic and structural understanding of dialog texts from social media. We also propose a semi-supervised teacher-student learning framework to address the common issue of limited available labels in summarization datasets. We empirically evaluate our approach on extractive summarization task with the TWEETSUMM corpus, a recently introduced dialog summarization dataset from Twitter customer care conversations and demonstrate that our self-supervised pre-training and semi-supervised teacher-student learning are both beneficial in comparison to other pre-trained models. Additionally, we compare pre-training and teacher-student learning in various low data-resource settings, and find that pre-training outperforms teacher-student learning and the differences between the two are more significant when the available labels are scarce.
1st Workshop on Federated Learning Technologies

AI-based systems, especially those based on machine learning technologies, have become central in modern societies. In the meanwhile, users and legislators are becoming aware of privacy issues. Users are increasingly reluctant in sharing their sensitive information, and new laws have been enacted to regulate how private data is handled (e.g., the GDPR).
Personalization and Recommendations in Search

The utility of a search system for its users can be further enhanced by providing personalized results and recommendations within the search context. However, the research discussions around these aspects of search remain fragmented across different conferences and workshops. Hence, this workshop aims to bring together researchers and practitioners from industry and academia to engage in the discussions of algorithmic and system challenges in search personalization and effectively recommending within search context.
Deep Learning-Based Modified YOLACT Algorithm on Magnetic Resonance Imaging Images for Screening Common and Difficult Samples of Breast Cancer

Computer-aided methods have been extensively applied for diagnosing breast lesions with magnetic resonance imaging (MRI), but fully-automatic diagnosis using deep learning is rarely documented. Deep-learning-technology-based artificial intelligence (AI) was used in this work to classify and diagnose breast cancer based on MRI images. Breast cancer MRI images from the Rider Breast MRI public dataset were converted into processable joint photographic expert group (JPG) format images. The location and shape of the lesion area were labeled using the Labelme software. A difficult-sample mining mechanism was introduced to improve the performance of the YOLACT algorithm model as a modified YOLACT algorithm model. Diagnostic efficacy was compared with the Mask R-CNN algorithm model. The deep learning framework was based on PyTorch version 1.0. Four thousand and four hundred labeled data with corresponding lesions were labeled as normal samples, and 1600 images with blurred lesion areas as difficult samples. The modified YOLACT algorithm model achieved higher accuracy and better classification performance than the YOLACT model. The detection accuracy of the modified YOLACT algorithm model with the difficult-sample-mining mechanism is improved by nearly 3% for common and difficult sample images. Compared with Mask R-CNN, it is still faster in running speed, and the difference in recognition accuracy is not obvious. The modified YOLACT algorithm had a classification accuracy of 98.5% for the common sample test set and 93.6% for difficult samples. We constructed a modified YOLACT algorithm model, which is superior to the YOLACT algorithm model in diagnosis and classification accuracy.
Predictors of perceptions of human rights violations during the Chilean social outburst of 2019

The 2019 social outburst in Chile marked a significant turning point for the nation's politics and society, with widespread reports of human rights violations committed by the armed forces and the Police during demonstrations and riots. Despite the attention given to these events, few studies have systematically analyzed perceptions of human rights violations in such contentious contexts. To investigate the factors influencing perceptions of human rights violations during the 2019 Chilean social outburst, we conducted ordered logistic regressions using data from a nationally representative survey fielded during the unrest. Our findings reveal that participation in demonstrations, use of social media for political information, fear of crime, and proximity to violent protests are correlated with the perception that security forces frequently violated human rights during the outburst. These results contribute to the understanding of public perceptions of human rights violations in the context of the 2019 Chilean social outburst and provide insights for future research on the role of individual and contextual factors in shaping these perceptions.
Dual-channel embedding learning model for partially labeled attributed networks

Network embedding is an important fundamental work in many network application tasks, which encodes the input network from the high-dimensional and sparse topological space into a low-dimensional and dense vector space. Recently, there has been a growing interest in embedding learning on Partially Labeled Attributed Networks (PLANs) due to the increasing occurrence of node attributes and partially available category labels in real-world networks. Semi-supervised embedding learning is a standard approach employed in PLANs, utilizing category labels to supervise the learning process. However, the semi-supervised learning procedure can fail when labels are scarce, noisy, or unreliable. Additionally, most existing embedding algorithms have not successfully integrated heterogeneous information, such as labels, attributes, and structure. To address these issues, we develop a new model, the Dual-Channel Network Embedding (DcNE), which integrates different types of network information into embeddings from a mutual information (MI) perspective. Specifically, we construct a dual-channel information propagation framework to encode the input network in semi-supervised and self-supervised learning paradigms in parallel. Furthermore, a redundancy elimination module is implemented to capture and eliminate the redundant information between the two encoders. Finally, we propose a unified optimization model that integrates the two learning paradigms to collaborate effectively. In the experiments, we demonstrate the effectiveness of DcNE in various network analysis tasks using real-world datasets, establishing its superiority over state-of-the-art baselines.
Improved DASH Architecture for Quality Cloud Video Streaming in Automated Systems

In modern times, multimedia streaming systems that transmit video across a channel primarily use HTTP services as a delivery component. Encoding the video for all quality levels is avoided thanks to fuzzy based encoders' ability to react to network changes. Additionally, the system frequently uses packet priority assignment utilising a linear error model to enhance the dynamic nature of DASH without buffering. Based on a fuzzy encoder, the decision of video quality is made in consideration of the bandwidth available. This is a component of the MPEG DASH encoder. The Fuzzy DASH system seeks to increase the scalability of online video streaming, making it suitable for live video broadcasts through mobile and other devices.
Multimodal Emotion Analysis Based on AttMISA

Multimodal affective analysis is a new research field. Combining multiple modal information to analyze users’ videos is a challenging task. Past studies have tried to fuse information from various modes using complex methods, but the structure of different information has led to differences in data distribution patterns. To address this problem, in past studies, MISA proposed that by creating two different subspaces, within the first subspace, models learn the common features of multiple modal data and reduce their differences. In the second subspace, the model independently learns the characteristics of the data for each mode. On the basis of this research, AttMISA is proposed. On the basis of MISA, attention modules are added to different subspaces. Also summarizes the commonly used datasets of multimodal emotion analysis for social media; Finally, it discusses the difficulty of network public opinion analysis and the future research direction.
Case Studies of Several Popular Text Classification Methods

The amount of data generated by the human race worldwide is increasing at an exponential rate every day. Therefore, data classification has become a necessity, and many researchers are focusing on evaluating automatic language processing techniques and improving text classification methods. Recently, deep learning models have achieved state-of-the-art results in many areas, including a wide variety of NLP applications. In fact, deep learning has the potential to handle and analyze massive data in both supervised and unsupervised modes and in real time. This paper briefly introduces different feature extraction and classification algorithms and analyzes and compares the different textual representations on the performance of various text classification algorithms. The results show that distributed word representations such as word2vec and Glove outperform other feature extraction methods such as BOW. More importantly, contextual embedding, such as BERT, can achieve good performance compared to traditional word embedding and compared to other classification methods.
Interface design guidelines for low literature users: a literature review

The study presented in this paper is part of the investigation project “Echoing the Communal Self (ECHO)”, consisting of the development of an online platform that identifies, interprets and disseminates current self-regulated community practices in social housing neighbourhoods in the city of Vila Nova de Gaia, in Portugal. This online platform intends to reach individuals of similar disadvantaged communities, aiming to stimulate, teach and guide them towards the replication of such good practices. Given that most of these individuals show low levels of education and literacy, this study intends to identify a set of guiding UX and UI principles that will serve as a base to the development of the online platform, guaranteeing its efficient usage by this target group. The methodologies were based on a literature review in the fields of UX and UI; serving as the basis for a first sketch of the interface. This study results show that UIs for low literacy users should be simple, clear and objective, considering the users pains and difficulties.
EGSGP: An Ensemble System Based on Geometric Semantic Genetic Programming

This work is inspired by the idea of seeding Genetic Programming (GP) populations with trained models from a pool of different Machine Learning (ML) methods, instead of using randomly generated individuals. If one considers standard GP, tackling this problem is very challenging, because each ML method uses its own representation, typically very different from the others. However, the task becomes easier if we use Geometric Semantic GP (GSGP). In fact, GSGP allows us to abstract from the representation, focusing purely on semantics. Following this idea, we introduce EGSGP, a novel method that can be seen either as a new initialization technique for GSGP, or as an ensemble method, that uses GSGP to combine different Base Learners (BLs). To counteract overfitting, we focused on the study of elitism and Soft Target (ST) regularization, studying several variants of EGSGP. In particular, systems that use or do not use elitism, and that use (with different parameters) or do not use ST were investigated. After an intensive study of the new parameters that characterize EGSGP, those variants were compared with the used BLs and with GSGP on three real-life regression problems. The presented results indicate that EGSGP outperforms the BLs and GSGP on all the studied test problems. While the difference between EGSGP and GSGP is statistically significant on two of the three test problems, EGSGP outperforms all the BLs in a statistically significant way only on one of them.
INTRODUCTION TO HOTEL OPERATION FOR STUDENTS OF SMK NEGERI 7 CITY OF TANGERANG

The hotel is in the lodging operations section where the main activity is to rent comfortable and safe rooms to guests. The level of service offered in a hotel starts from the core benefit, namely a room to stay. Core benefits are supported by facilitating service or often referred to as hotel operations such as front office, housekeeping, food and beverage, and kitchen/pastry. Vocational High Schools are expected to be able to become quality resources, they are not only taught theoretical classes but are also taught skills according to what is needed by the industry. Therefore, the Community Service Team of the Pradita University Tourism Study Program would like to share knowledge about hotel operations with partners, in this case, SMK Negeri 7 Tangerang City. This activity will begin with an introduction to the campus and after that the participants are divided into VHP practice and kitchen practice. To measure the extent to which participants understand the material that has been made, a pre-test and post-test will be conducted. This activity is guided by lecturers who are experts in the front office and kitchen, assisted by several students. Participants who take part in practical activities using the VHP software gain increased knowledge and can make reservations about the software. Participants who take part in cooking practice activities can make Indonesian dishes such as grilled konro and tekwan soup.
FAKE NEWS DETECTION USING STACKED ENSEMBLE OF CLASSIFIERS

Fake news detection is most important and has an immense impact to our society.On one hand, it will enhance the user experience on the online social media platform and will also save a lot of time of users that they might spend on fake news otherwise.It is less time-consuming, easily conveyance socially relevant news, possibility for obtaining various perspectives of a single news and is updated every minute.On the other hand, news is manipulated by various networking sites based on private opinions or interest.The rapid increase of fake news on social media and Internet is deceiving people to an extent which needs to be stopped.In this paper the use of machine learning techniques is used to detect fake news using Naive Bayes classification, Random forest, and Support vector machine model to predict whether news on social media will labelled as REAL or FAKE.The Stacking Ensemble classifiers is a concept in machine learning model, where multiple models is trained to solve the same problem and uniting them into a single system to get better results.This predicts probability that given statement is real or fake and performs the appropriate classification.
Recognition of Single Japanese Sounds using Surface Electromyography Signals by Machine Learning

The method and results of recognizing single Japanese sounds using surface electromyography (sEMG) signals generated from muscles around the mouth are presented. We determined six features of the waveforms of four muscles (a total of 24 indexes) and recognized 45 single Japanese sounds. We used machine learning with a neural network to improve sound recognition. The neural network has a 24 node input layer, a 100 node intermediate layer, and a 45 node output layer. Each index of a sound was entered into the input layer; the probabilities of the sound were output to the output layer. They were compared, and the output (sound) with the highest probability was determined to be a recognition sound. We used the cross-entropy loss as the loss function and gradient descent as the machine learning method. Machine learning, which built a neural network, has dramatically increased recognition; it stands at approximately 94%.
Design and Implementation of Virtual Simulation Teaching Platform of Sports Action Technology Based on Maya

The application of virtual simulation technology in physical education teaching conforms to the development trend of education information and can improve the effect of physical education teaching. An effective virtual simulation teaching platform construction scheme of sports action technology was proposed. The motion capture system and surface EMG equipment were used to synchronize sports action acquisition, and the motion capture data file was bound to the drawn human muscle model for rendering, which was integrated into the built platform. The platform focuses on the combination of virtual and real, optimizes cognition, improves teaching efficiency and quality, and provides convenience for researchers with the stored movement-related data. The platform is extensibility, which can load the reserved interface for other sports movement data and pave the way for the construction of sports movement database, and has broad application prospects.
English Translation Intelligent Recognition Model Based on Reverse Maximum Matching Segmentation Algorithm

Since there is no obvious delimiter in Chinese, the problem of word segmentation that does not exist in English is a very important step in Chinese. This paper mainly studies the design of English translation intelligent recognition model based on reverse maximum matching segmentation algorithm. In this paper, we first propose the inverse maximum matching segmentation algorithm, and propose the NMT model based on dual learning and mask language model (MLM). The dual learning based NMT model uses one encoder and two decoders (including a source language decoder and a target language decoder). The experimental results show that the method proposed in this paper has a good effect in identifying new words in the article.
A Lightweight Network for Detecting Small Targets in the Air

Fast and accurate detection and identification of small airborne targets are of great importance, to security in the air. Unmanned aerial vehicle detection algorithms are mostly deployed on edge devices, and a yolov5-based aerial target lightweight detector is proposed by compressing channels and network cropping for the limited resource characteristics on edge devices. Firstly, the shallow cross-stage partial module is extended and optimized when designing the feature ex-traction network to maximize the use of shallow features. Secondly, the network is cropped to reduce the number of down-sampling, which makes the computation faster. Finally, the pyramid network used for feature fusion is simplified by modifying from two upsampling operations and two downsampling operations to only one upsampling operation. On the homemade dataset, the proposed Yolo-mini achieves 94.44% mean average accuracy on the test set and the Giga floating-point operations per second of the model is only 3.2, which achieves a better balance of accuracy and computation compared to other lightweight algorithms.
Maximum independent sets of graph vertices searching for software projects improvement

The need is fixed to software project enhancing with seamless integration of technological-descriptive and normative project manage- ment approaches by means of classical Graph Discrete Optimization Problems tailoring for software project management tasks, poorly equipped with best practices within technological approach. Class of software project management tasks is proposed to demonstrate the benefits of such integration. Two Boolean linear programming problems are investigated for searching some maximum size indepen- dent set (Section 1) and an algorithm for searching all possible maximum size independent sets (Section 2). Section 3 presents Problem Statement for searching a given number of non-intersecting independent sets with maximum sum of vertices’ numbers within independent sets. Based on it, Vizing-Plesnevich algorithm is described for coloring the graph vertices with the minimum number of colors. To solve Boolean problems, both specialized mathematical programming language AMPL and corresponding solver program named gu- robi are used. For basic algorithms developed, reference AMPL code versions are given as well as their running results. Illustrative examples of software project enhancing with the algorithms elaborated are considered in Section 4, namely: 25 specialists being conflicted during their previous projects partitioning into coherent conflict-free sub-teams for software projects portfolio; schedule optimization for autonomous testing of reusable components within a critical software system; cores composing for independent teams in a critical software project.