Reviewerly/exHarmony huggingface dataset:
Joint Resource Management and Model Compression for Wireless Federated Learning

We consider the problem of convergence time minimization for federated learning (FL) implemented in wireless systems. In such setups, each wireless edge device transmits its local FL model parameters to a base station (BS). The BS then uses the received FL parameters to generate a common FL model and broadcasts it to all edge devices. Since the FL parameters must be transmitted over wireless links, the convergence time depends not only on the number of training steps, but also on the FL parameter transmission delay at each training step, which can be substantial when conveying a large number of parameters. In addition, due to limited wireless resources such as spectrum, only a subset of edge devices can participate in each FL training step, which can further increase convergence time. Our goal therefore is to optimize wireless resource management and user selection for FL, as well as limit the volume of transmitted FL parameters. In this paper, three schemes for facilitating communication efficient FL are introduced: First, a probabilistic device selection scheme is designed such that the devices that can significantly improve the convergence speed and training loss have high probabilities for FL parameter transmission. Then, given the subset of participating devices, an efficient wireless resource allocation scheme is developed. Finally, a quantization method is proposed to reduce the data size. Simulation results demonstrate that the proposed FL method can improve handwritten digit identification accuracy and convergence delay by up to 3% and 90% compared to the conventional FL.
Prioritizing Construction Labor Productivity Improvement Strategies Using Fuzzy Multi-Criteria Decision Making and Fuzzy Cognitive Maps

Construction labor productivity (CLP) is affected by various interconnected factors, such as crew motivation and working conditions. Improved CLP can benefit a construction project in many ways, such as a shortened project life cycle and lowering project cost. However, budget, time, and resource restrictions force companies to select and implement only a limited number of CLP improvement strategies. Therefore, a research gap exists regarding methods for supporting the selection of CLP improvement strategies for a given project by quantifying the impact of strategies on CLP with respect to interrelationships among CLP factors. This paper proposes a decision support model that integrates fuzzy multi-criteria decision making with fuzzy cognitive maps to prioritize CLP improvement strategies based on their impact on CLP, causal relationships among CLP factors, and project characteristics. The proposed model was applied to determine CLP improvement strategies for concrete-pouring activities in building projects as an illustrative example. This study contributes to the body of knowledge by providing a systematic approach for selecting appropriate CLP improvement strategies based on interrelationships among the factors affecting CLP and the impact of such strategies on CLP. The results are expected to support construction practitioners with identifying effective improvement strategies to enhance CLP in their projects.
An FPGA-Based Reconfigurable Accelerator for Low-Bit DNN Training

In recent years, deep neural networks (DNNs) have shown outstanding performance in various tasks. Training DNNs on resource-constrained edge platforms is required for online learning and privacy concerns. However, the training process of DNNs requires enormous computation and memory resources. Therefore, the hardware implementations of the training process with low-precision integer arithmetic are attracting extensive attention, considering their advantages on computation, storage, and energy consumption. In this paper, we propose an FPGA-based reconfigurable accelerator for DNN training with full 8-bit integer arithmetic. First, a reconfigurable processing element in a unified architecture is designed, which is flexible to support various computation patterns during the training process. Second, a two-stage scaling and rounding scheme is introduced to scale intermediate results to low-bit data for minimal memory usage, while retaining data accuracy to the maximum extent. Finally, based on the widely used softmax classification function, an optimized architecture is developed to calculate the cross-entropy loss function on-chip to initiate the backward propagation. Experimental results show that our design can reach 771 GOPS and 47.38 GOPS/W in terms of performance and energy efficiency, respectively. The comparison results demonstrate that our work significantly outperforms prior works.
Directing and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent Modeling

In interactive information-seeking, a user often performs many interrelated queries and interactions covering multiple aspects of a broad topic of interest. Especially in difficult information-seeking tasks the user may need to find what is in common among such multiple aspects. Therefore, the user may need to compare and combine results across queries. While methods to combine queries or rankings have been proposed, little attention has been paid to interactive support for combining multiple queries in exploratory search. We introduce an interactive information retrieval system for exploratory search with multiple simultaneous search queries that can be combined. The user is able to direct search in the multiple queries, and combine queries by two operations: intersection and difference, which reveal what is relevant to the user intent of two queries, and what is relevant to one but not the other. Search is directed by relevance feedback on visualized user intent models of each query. Operations on queries act directly on the intent models inferring a combined user intent model. Each combination yields a new result (ranking) and acts as a new search that can be interactively directed and further combined. User experiments on difficult information-seeking tasks show that our novel system with query operations yields more relevant top-ranked documents in a shorter time than a baseline multiple-query system.
Comparative Analysis for Sentiment in Tweets Using LSTM and RNN

In today’s world, the quantity of the users of social networking sites are increasing day by day so as the users of the Twitter application as tweets to for advertising and inspiring the consumers about their respective products, services, reviews about any particular topic or in several different aspects. Taking this increased need of using the social networking into consideration, we have got decided to make a Sentiment Analysis system on Twitter’s data. In this paper, we have proposed two different models in order to determine which approach is most suited and better. The two models are LSTM and RNN, both the models are good in their own way, but our aim is to classify which one is best suited and straightforward to be used by a user. We have given a well explained description of both the models together with their structure, feasibility, performance, and analysis result. At the end, we came to grasp that both the models have gotten very satisfying results getting a way better results than regular ones.
RETRACTED: Visualization of Basic Mathematics Teaching Based on Artificial Intelligence

Abstract Basic mathematics is an important part of education and teaching in our country. As a basic subject in our country’s teaching, it has always played an important role. However, with the development of time, the traditional mathematics teaching methods can not stimulate students’ interest in mathematics. In order to find out the teaching mode to improve students’ level and enthusiasm, based on artificial intelligence, this paper uses case analysis method, literature analysis method and other methods to collect data from the database, builds a model based on artificial intelligence, and reads and analyzes a large number of relevant literature through literature survey method, according to the research needs, through the research and summary of literature content. Research structure found that through the teaching of artificial intelligence, students’ mathematics scores are about 30% higher than the traditional teaching methods, the sense of cooperation between students reaches 0.8, to solve the contradiction between popularization and improvement, and to solve the polarization and the transformation of backward students and other problems, so that their personality can be fully developed and developed, so that mathematics teaching in Colleges and universities to the track of quality education. This shows that the basic mathematics teaching based on artificial intelligence can play an important role in college education.
Gesture Interaction in Virtual Reality

We explore gestures as interaction methods in virtual reality (VR). We detect hand and body gestures using human pose estimation based on off-the-shelf optical camera images using machine learning, and obtain reliable gesture recognition without additional sensors. We then employ an avatar to prompt users to learn and use gestures to communicate. Finally, to understand how well gestures serve as interaction methods, we compare the studied gesture-based interaction methods with baseline common interaction modalities in VR (controllers, gaze interaction) in a pilot study including usability testing.
Data-Driven Personalized Cervical Cancer Risk Prediction: A Graph-Perspective

Routine cervical cancer screening at regular periodic intervals leads to either over-screening or too infrequent screening of patients. For this purpose, personalized screening intervals are desirable that account for cancer risk development of individual patients. However, developing and training personalized risk prediction models is challenging since cancer screening data are scarce, irregular, and skewed. This paper proposes a personalized time-dependent cervical cancer risk prediction scheme using geometric deep learning (GDL) and spectral geometric matrix completion (SGMC) frameworks. The proposed approach learns row- and column-graphs from irregular and sparse cancer screening data. Then, we leverage the graph structure to reconstruct the continuous latent risk of individuals from screening data. During inference, the completed screening data matrix, comprising estimated individual continuous latent risk, serves as a dictionary for forecasting the cancer risk of new patients. We conducted experiments on synthetic and real-life screening data from the Cancer Registry of Norway.
Implementing the Blockchain Technology in Islamic Financial Industry: Opportunities and Challenges

Blockchain technology is rapidly gaining the attention of financial institutions around the world, especially among decision makers in Islamic financial institutions who are planning to apply this innovative technology into their institutions activities which could be an upright solution for their issues and challenges. This paper contributes to the emerging literature on financial technology by discussing some of the innovative applications of blockchain in Islamic financial institutions. In addition, this paper sheds light on the main opportunities and challenges facing application of Blockchain Technology in the Islamic financial industry and highlight the way forward to address these issues. Relying on the previous literature and analysis; this paper confirms the enormous opportunities for utilizing blockchain technology in various Islamic financial applications such as Waqf, Zakat and Sukuk. On the other hand, the main challenges towards implementing blockchain in this industry are the complexity of Islamic finance products added to the opacity of its application which end up with unclear regulations and lack of standards.
FastPitchFormant: Source-Filter Based Decomposed Modeling for Speech Synthesis

Methods for modeling and controlling prosody with acoustic features have been proposed for neural text-to-speech (TTS) models. Prosodic speech can be generated by conditioning acoustic features. However, synthesized speech with a large pitch-shift scale suffers from audio quality degradation, and speaker characteristics deformation. To address this problem, we propose a feed-forward Transformer based TTS model that is designed based on the source-filter theory. This model, called FastPitchFormant, has a unique structure that handles text and acoustic features in parallel. With modeling each feature separately, the tendency that the model learns the relationship between two features can be mitigated.
Cybercrime and Cybersecurity: FinTech’s Greatest Challenges

The arrival of financial technology (“FinTech”) companies can be traced to a number of regulatory changes made after The Great Depression of the 1920s. Among others, the United States Banking Act—which separated commercial banking from investment banking (and thus, limited the interest that a regular bank depositor could get on his deposit)—spurred, in the 1970s, the emergence of asset management firms and other forms of shadow banks—or what is now called FinTech companies. However, it was the global financial crisis of 2008, and the near-collapse of the financial system, that caused the rise of FinTech companies and activated the FinTech transformation that the world is currently witnessing.From payments and remittances to lending and wealth management, FinTech continues to change the way we live and bank. But by virtue of their operations, FinTech companies constitute a particularly attractive target for cybercriminals. This article examines how cybercrime affects FinTech companies—mostly startups—and how these companies protect their data and infrastructure.
CoDERT: Distilling Encoder Representations with Co-Learning for Transducer-Based Speech Recognition

We propose a simple yet effective method to compress an RNN-Transducer (RNN-T) through the well-known knowledge distillation paradigm. We show that the transducer's encoder outputs naturally have a high entropy and contain rich information about acoustically similar word-piece confusions. This rich information is suppressed when combined with the lower entropy decoder outputs to produce the joint network logits. Consequently, we introduce an auxiliary loss to distill the encoder logits from a teacher transducer's encoder, and explore training strategies where this encoder distillation works effectively. We find that tandem training of teacher and student encoders with an inplace encoder distillation outperforms the use of a pre-trained and static teacher transducer. We also report an interesting phenomenon we refer to as implicit distillation, that occurs when the teacher and student encoders share the same decoder. Our experiments show 5.37-8.4% relative word error rate reductions (WERR) on in-house test sets, and 5.05-6.18% relative WERRs on LibriSpeech test sets.
Multi-Level Sentiment Analysis of Product Reviews Based on Grammar Rules

Vietnamese is a tonal and isolated language. Its highly ambiguity makes the designing of methods for sentiment analysis being difficult. For getting the most effectiveness, the designed method has to analyze sentiment of sentences based on combining the grammar and syllable structures of Vietnamese. In this paper, a method to build a Vietnamese dataset of product reviews with many sentiment levels, including very negative, negative, neutral, positive and very positive, is proposed. This method can be scaled to a large dataset using for analyzing sentiment of product reviews. Moreover, a solution to add more grammar rules of Vietnamese into the pre-processing of sentiment analysis is also constructed. Those rules simulate the sentiment recognition of humans and help to increase the accuracy of sentiment determination. The combination of grammar rules and some methods for sentiment analysis are experimented on the Vietnamese dataset of product reviews to classify them into sentiment-levels. The testing results show that their accuracy and F-measure are improved and suitable to apply in the practical business analyzing of customer behaviors.
A real-time occupancy detection system for unoccupied, normally and abnormally occupied situation discrimination via sensor array and cloud platform in indoor environment

It is significant to detect the occupancy of indoor environment from the view of saving energy. In addition, occupied situation detection can help control the density of people in room to reduce the risk of disease transmission. In preliminary, we trained nine algorithm models on the existing occupancy dataset to select the optimum algorithm. Then, all-subsets regression model is used for feature selection (sensor contribution evaluation) to optimize the size of sensor array. As a result, the voting based weighted extreme learning machine (WV-ELM) model achieved the highest prediction accuracy and the combination of light and CO2 sensors could realize a satisfied classification result. Finally, a real-time occupancy detection system based on the sensor array and cloud platform was proposed. The system used the indoor environmental data collected by the sensor array. WV-ELM model was combined with the proposed system to detect the real-time occupied situation in the indoor environment. To verify their efficiency, the system was implemented in a laboratory to collect occupancy data for one week. According to the actual test results, the proposed system realized a detection accuracy of 97.32% with running time less than 30 s.
Evaluation of regional industrial cluster innovation capability based on particle swarm clustering algorithm and multi-objective optimization

Abstract With the progress of the times and the development of science, industrial clusters have been regarded by all countries in the world as one of the important ways to enhance regional competitiveness, and become an inevitable trend of industrial development. The research on the innovation ability of industrial clusters can not only maintain sustainable development of industrial clusters and obtain sustained competitive advantages, but also provide reference for the government's policy formulation of industrial clusters. This paper aims to study the evaluation of regional industrial clusters' innovation capability based on particle swarm clustering and multi-objective optimization. This paper uses the theory of industrial cluster innovation and takes regional industrial system as the empirical research object to establish a regional industrial system capability evaluation system, which is based on the selection of indicators, combined with analytic hierarchy process and factor analysis to evaluate industrial innovation capability. On this basis, the particle swarm clustering theory is used to verify the innovation ability and evaluation index system of industrial clusters, and provide a reference for the evaluation of the innovation ability of industrial clusters. This paper divides the regional cluster innovation capability into four aspects: innovation input capability, environment support capability, self-development capability and innovation output capability, and systematically analyzes the key elements and in the composition of innovation elements and their relationships. It then constructs the evaluation index system of regional cluster innovation capability. At the same time, this paper introduces clustering analysis algorithm and swarm intelligence algorithm into regional innovation evaluation, combines particle swarm optimization algorithm and K-means clustering algorithm, and optimizes particle swarm clustering algorithm by adjusting adaptive parameters and adding fitness variance. The experimental results of this paper show that from the results of the tested innovation potential of the three industrial clusters, industrial cluster F has the strongest innovation ability, with an evaluation coefficient of 0.851, followed by industrial cluster F, which has a value of 0.623. This result is consistent with the actual innovation status of the selected industry. From this point of view, the established particle swarm clustering model for evaluating the innovation capability of regional industrial clusters is reliable and can be used to evaluate the innovation capability of different industrial clusters.
Virtual Reality (VR) in Superior Education Distance Learning: A Systematic Literature Review

Virtual Reality (VR) is one of the most popular contemporary technologies and it is widely used in the videogame industry, nonetheless, this does not restrict its use in other areas of science, such as medicine or education. Due to the large commotion caused by the appearance of Covid-19, long distance virtual education technologies (e-learning) are being used. With this context, virtual reality is the focus of this study, which had the objective of understanding the work done in superior education at a distance, through the use of VR, by doing a systematic literature review (SLR; LSR in Spanish). The results reveal that the use of VR in education can improve the experience, motivation and the comprehension of abstract concepts for the students, offering them an immersive environment in which they can interact and achieve effective learning. It was concluded that the works that were reviewed regarding the topic evidence a strong growth in the application of VR in education, which in their majority, employ experimental comparative methods between groups of students which use VR when compared to others who use the traditional method.
An empirical study of COVID-19 related posts on Stack Overflow: Topics and technologies

The COVID-19 outbreak, also known as the coronavirus pandemic, has left its mark on every aspect of our lives and at the time of this writing is still an ongoing battle. Beyond the immediate global-wide health response, the pandemic has triggered a significant number of IT initiatives to track, visualize, analyze and potentially mitigate the phenomenon. For individuals or organizations interested in developing COVID-19 related software, knowledge-sharing communities such as Stack Overflow proved to be an effective source of information for tackling commonly encountered problems. As an additional contribution to the investigation of this unprecedented health crisis and to assess how fast and how well the community of developers has responded, we performed a study on COVID-19 related posts in Stack Overflow. In particular, we profiled relevant questions based on key post features and their evolution, identified the most prominent technologies adopted for developing COVID-19 software and their interrelations and focused on the most persevering problems faced by developers. For the analysis of posts we employed descriptive statistics, Association Rule Graphs, Survival Analysis and Latent Dirichlet Allocation. The results reveal that the response of the developers’ community to the pandemic was immediate and that the interest of developers on COVID-19 related challenges was sustained after its initial peak. In terms of the problems addressed, the results show a clear focus on COVID-19 data collection, analysis and visualization from/to the web, in line with the general needs for monitoring the pandemic.
Caption Enriched Samples for Improving Hateful Memes Detection

The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not.Specifically, both unimodal language models and multimodal vision-language models cannot reach the human level of performance.Motivated by the need to model the contrast between the image content and the overlayed text, we suggest applying an off-the-shelf image captioning tool in order to capture the first.We demonstrate that the incorporation of such automatic captions during fine-tuning improves the results for various unimodal and multimodal models.Moreover, in the unimodal case, continuing the pre-training of language models on augmented and original caption pairs, is highly beneficial to the classification accuracy.Our code is publicly available 1 .