Reviewerly/exHarmony huggingface dataset:
Analyzing the Impact of Informed Attack Models in Recommender System

Key driver of the modern days ecommerce business is the recommender systems (RS). For the better performance of business, RS's higher accuracy matters a lot. It predicts the rating based on the past interaction of the user with the system. Based upon his preferences in the past and his similarities with other users, recommendations for new items are generated. While predicting the rating several other criteria are also taken care of now a days like features of the item, gender, age of the user etc. These criteria have potential to improve the accuracy of the RS substantially. However, due to providing more detailed recommendations, these systems are highly sensitive against the attacks. Attackers tried to inject the fake user profile to influence the popularity of a particular product. In this paper, we examine the impact of informed attack models on the RS. Empirical results captured from real data demonstrate that these attacks are very effective to manipulate the rating prediction in the RS.
FedVal: Different good or different bad in federated learning

Federated learning (FL) systems are susceptible to attacks from malicious actors who might attempt to corrupt the training model through various poisoning attacks. FL also poses new challenges in addressing group bias, such as ensuring fair performance for different demographic groups. Traditional methods used to address such biases require centralized access to the data, which FL systems do not have. In this paper, we present a novel approach FedVal for both robustness and fairness that does not require any additional information from clients that could raise privacy concerns and consequently compromise the integrity of the FL system. To this end, we propose an innovative score function based on a server-side validation method that assesses client updates and determines the optimal aggregation balance between locally-trained models. Our research shows that this approach not only provides solid protection against poisoning attacks but can also be used to reduce group bias and subsequently promote fairness while maintaining the system's capability for differential privacy. Extensive experiments on the CIFAR-10, FEMNIST, and PUMS ACSIncome datasets in different configurations demonstrate the effectiveness of our method, resulting in state-of-the-art performances. We have proven robustness in situations where 80% of participating clients are malicious. Additionally, we have shown a significant increase in accuracy for underrepresented labels from 32% to 53%, and increase in recall rate for underrepresented features from 19% to 50%.
Improved Environmental Stimulus and Biological Competition Tactics Interactive Artificial Ecological Optimization Algorithm for Clustering

An interactive artificial ecological optimization algorithm (SIAEO) based on environmental stimulus and a competition mechanism was devised to find the solution to a complex calculation, which can often become bogged down in local optimum because of the sequential execution of consumption and decomposition stages in the artificial ecological optimization algorithm. Firstly, the environmental stimulus defined by population diversity makes the population interactively execute the consumption operator and decomposition operator to abate the inhomogeneity of the algorithm. Secondly, the three different types of predation modes in the consumption stage were regarded as three different tasks, and the task execution mode was determined by the maximum cumulative success rate of each individual task execution. Furthermore, the biological competition operator is recommended to modify the regeneration strategy so that the SIAEO algorithm can provide consideration to the exploitation in the exploration stage, break the equal probability execution mode of the AEO, and promote the competition among operators. Finally, the stochastic mean suppression alternation exploitation problem is introduced in the later exploitation process of the algorithm, which can tremendously heighten the SIAEO algorithm to run away the local optimum. A comparison between SIAEO and other improved algorithms is performed on the CEC2017 and CEC2019 test set.
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data. While there have been initial attempts for image-based conversation models, this work addresses the underexplored field of video-based conversation by introducing Video-ChatGPT. It is a multimodal model that merges a video-adapted visual encoder with a LLM. The model is capable of understanding and generating human-like conversations about videos. We introduce a new dataset of 100,000 video-instruction pairs used to train Video-ChatGPT acquired via manual and semi-automated pipeline that is easily scalable and robust to label noise. We also develop a quantiative evaluation framework for video-based dialogue models to objectively analyse the strengths and weaknesses of proposed models. Our code, models, instruction-sets and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
MSR76 Prediction of High End-of-Life Costs Among Adults with Cancer Using a Machine Learning Approach

Healthcare costs among cancer patients tend to increase during end-of-life (EOL) and are disproportionately concentrated in a small proportion of high-cost patients. This study aims to develop and evaluate machine learning models for predicting high-cost patients during EOL (i.e., 6 months before death) among cancer patients 65 years or older.
How digital age shapes the mind of modern youth

The paper explores changes that occur in the human mind as a consequence of life in digital age. The specific subject of the analysis is the impact on the human mind and overall human activity of human existence dominated by screen culture, where mobile phones, tablets, iPods, computers have become inevitable parts of everyday life. Through the analysis of the thought process of teenagers, who are now refered to in literature as "screenagers" and whose life is characterised by copy-paste education, decline of reading culture and new forms of addiction to technology and its impact on their behaviour, this paper will show that this new high-tech culture has led to emergence of increasingly difficult direct, offline interpersonal communication and to a decline of social skills. The main goal of the research is to determine how the mind of a modern man works today in the age of electronic markets, web-economy, social networking, cybercrime, love at first "site" and how the human mind reacts to the effect of technology on our privacy.
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation

New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines ranging from 13 different subjects with 249,587 questions and accompanied by Xiezhi-Specialty and Xiezhi-Interdiscipline, both with 15k questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results indicate that LLMs exceed average performance of humans in science, engineering, agronomy, medicine, and art, but fall short in economics, jurisprudence, pedagogy, literature, history, and management. We anticipate Xiezhi will help analyze important strengths and shortcomings of LLMs, and the benchmark is released in~\url{https://github.com/MikeGu721/XiezhiBenchmark}.
Communication Social Actions of Legislative Candidates in the Election of Representatives in the City of Madiun (Case Studi: Social Exchange of Legislative Candidates to Became DPRD)

Current social phenomena show that the local democratic process that should be running well and correctly in accordance with its purpose is to increase the participation of the people or community politics to elect their representatives in the Regional Representative Council (DPRD) which has not produced maximum election results legislative as a local political activity is carried out based on Law No. 2 of 2011 which was later revised with Law No. 07/2017 concerning elections which regulates the selection of candidates for DPRD which include selection, ratification, appointment, and in terminating board members of the research council this method uses a qualitative approach, where the focus of research uses a case study approach. The purpose of the study was to determine the social communication actions of a prospective legislative candidate in the legislative general election to elect people’s representatives in the DPRD city of Madiun. This research with a qualitative approach is one of the research procedures that produce descriptive data in the form of speech, behaviour, which can be observed from prospective board members. The data collection technique of this research is observation techniques, documentation through snowball sampling on each DPRD candidate who has become a permanent council candidate. To support the conduct of research, researchers used theoretical studies, among others, social exchange theory, Peter Blau and A Weber’s Social Action Theory. The results of this study highlight that; social, legislative communication actions concerning social behaviour, human social movements and social exchanges occur in the election of members of the City Council of Madiun; namely, there are activities for candidates to install campaign propprovide social assistance and transactional practices between voters and candidates at the end of the campaign.
The Specifics and Patterns of Cybercrime in the Field of Payment Processing

In the modern world, cybercrime in the field of payment processing as a phenomenon is developing rapidly. Highly developed, developing and least-developed states become victims of cyberattacks. The purpose of this study is to analyze the experience of the international community and a number of states in combating cybercrime in the field of payment processing. International and regional (on the example of the Council of Europe) legal regulation of the fight against this type of crime were analyzed. The data on the size of losses caused by cybercrime to the world economy are analyzed according to the latest report from the Center for Strategic and International Studies for 2018, the World Economic Forum for 2019, DLA Piper GDPR for the period January-April 2020. Besides, using the example of the Russian Federation, quantitative indicators of the growth of cybercrime and the level of its detection for the period from 2018 to April 2020 were investigated. Comparison of the experience of individual states and its analysis made it possible to single out the best possible measures to counter cybercrime in the field of financial processing. The necessity of interstate cooperation to counter cybercrime in the field of payment processing is indicated. However, due to the presence of significant differences in the legal systems of all states, it is proposed to interact within the framework of regional communities with gradual transfer to international interaction. The priority is given to precisely preventive measures to counter cybercrime in the field of payment processing.
From the Paradigm of Learning to the Paradigm of Self-Education: Modern Technologies of Preparation of Students of University of Culture and Arts

The paper presents the self-education paradigm that takes into account a conscious, focused and rational nature of this phenomenon, substantiates the need to manage this process in the 21st century, considers such concepts in personal development as “to learn” and “to discover”; from the perspective of the organization of the educational process at university, the article studies the correlation of “knowledge” and “education” in the context of self-study of students of universities of culture and arts; it investigates the role and importance of information and communication and time management technologies in students’ self-education.
Knowledge Discovery from Online Reviews

Online reviews have become an important information source which are helping consumers to decide which products to buy and also sellers to understand the buying behavior of consumers. Besides, mining online reviews can help manufacturers proactively review customers’ opinions and unlock insights about the new functionality and features that the market expects. However, the large number of reviews poses a considerable challenge in the process of useful information extraction and knowledge discovery. Moreover, various forms of online reviews including numeric ratings, textual comments, pictures, and videos make it difficult for consumers to summarize all the heterogeneous information for reference. This chapter will focus on three mainstream research methods of online reviews information mining: information extraction, sentiment analysis, and text categorization. It will also introduce some advanced technologies to deal with commercial issues, such as the impact of reviews on sales or product ranking, the usefulness of reviews, etc. It finally points out the expected future of techniques and commercial applications.
Network Detection Platform Based on iMaster-NCE

In order to solve the problems of finding problems in traditional network operation and maintenance, unable to quickly locate the source of the problem, and unable to comprehensively analyze the health index and data of the problem equipment, this paper proposes to design a network detection platform in the form of web based on iMaster-NCE. Through the SDN network architecture iMaster-NCE Campus API, the data is obtained, processed and analyzed, and the processed and analyzed data is displayed in the form of visual charts, which can help the network operation and maintenance personnel accurately and quickly locate network problems, save a lot of time, and improve the efficiency of network operation and maintenance.
Investigating Software Engineering Artifacts in DevOps Through the Lens of Boundary Objects

Software engineering artifacts are central to DevOps, enabling the collaboration of teams involved with integrating the development and operations domains. However, collaboration around DevOps artifacts has yet to receive detailed research attention. We apply the sociological concept of Boundary Objects to describe and evaluate the specific software engineering artifacts that enable a cross-disciplinary understanding. Using this focus, we investigate how different DevOps stakeholders can collaborate efficiently using common artifacts. We performed a multiple case study and conducted twelve semi-structured interviews with DevOps practitioners in nine companies. We elicited participants’ collaboration practices, focusing on the coordination of stakeholders and the use of engineering artifacts as a means of translation. This paper presents a consolidated overview of four categories of DevOps Boundary Objects and eleven stakeholder groups relevant to DevOps. To help practitioners assess cross-disciplinary knowledge management strategies, we detail how DevOps Boundary Objects contribute to four areas of DevOps knowledge and propose derived dimensions to evaluate their use.
A Three-Step Underwater Moving Target Location Algorithm Based on FDOA

The position and velocity estimation of underwater moving targets is an important part of the application of underwater sensor networks. The commonly used fusion of time difference of arrival (TDOA) and angle of arrival (AOA) can only roughly estimate the target position, but also has some limitations for nonlinear problems. So in order to overcome the nonlinear problem and improve the accuracy of underwater target location.Based on TDOA and AOA, this paper adopts a three-step location method which combines frequency difference of arrival (FDOA).At the same time, the whale algorithm is used to optimize the measurement error based on the weighted least square method (Tswls). Finally, the experimental results show that compared with TDOA location method and tdoa/aoa hybrid location algorithm, this method is superior to the above methods in the accuracy and bias of position and velocity estimation.
Design of Digital Piano Audio Signal Transmission

In recent years, the collective teaching mode of digital piano has gradually become a research hotspot of piano teaching.According to the actual use needs and the actual problems encountered in teaching, this paper provides a piano audio signal networking transmission scheme for the digital piano classroom, realizes the networking function of the digital piano collective classroom, provides a feasible operation platform for the digital piano collective teaching, and enables the digital piano collective teaching mode to be better developed and popularized. The bilinear transformation method is used to analyze and process the audio signal, and a suitable filter is designed to filter the noise and restore the original signal, so that the transmission of the audio signal is more stable.
Cultivating the professional philosophy of primary school mathematics teachers in the context of andquot;double reductionandquot;

Teachers&#39; professional philosophy is a fusion of teachers&#39; emotional attitudes and values towards the cause of education, a psychological tendency of teachers&#39; professional behavior, and a concentrated expression of teachers&#39; professional ethics. In the context of the &quot;double reduction&quot; policy, there are problems in the cultivation of primary school mathematics teachers&#39; professional philosophy, such as unclear professional ideals, failure to change teachers&#39; role and failure to correctly establish a modern view of education. In order to promote the professional development of primary school mathematics teachers and to facilitate the implementation of the &quot;double reduction&quot; policy, it is necessary to optimise the policy on the cultivation of primary school mathematics teachers&#39; professional philosophies: to have a strong professional ideal, to establish a correct view of modern education, to shape a new view of the role of teachers and to pursue a positive view of professional interaction.
Research on video-based character recognition method for train cargo cars

After the train reaches the destination station, the actual arriving train number needs to be confirmed to ensure that the arriving vehicle is accurate. The traditional method of obtaining car numbers is by manually transcribing car numbers, which has the disadvantages of large workload, low efficiency and error-prone. Therefore, this paper designs a video-based train carriage character recognition system, and proposes a method of locating and recognizing train carriage characters based on YOLOv5s and CRNN. The four target detection models, SSD, Faster -RCNN, YOLOV5m, and YOLOv5s, are trained until the models converge completely using 10,000 train carriage datasets manually labeled with Labelimg software. The experimental results show that YOLOv5s outperforms the other models in terms of recognition accuracy and recognition efficiency, with YOLOv5s achieving a detection accuracy of 0.99 for the carriage character region and an average detection speed of 31 frames. The average accuracy of the algorithm is 0.96. Finally, the fusion of YOLOv5s and CRNN models results in a stable average frame rate of more than 20 frames for the detection of complete train carriages. The method in this paper can automatically identify the carriage numbers, which can reduce the labor of workers, avoid the errors caused by manual transcription of records, increase the real-time recording, and is of great significance to the development of railroad transportation system.
APPLICATION OF THE COPULA METHOD TO ANALYZE THE RELATIONSHIPS OF MACROECONOMIC FACTORS AFFECTING THE CSPI

The Composite Stock Price Index (CSPI) is a valuable number in assessing the performance of the stocks listed on the stock exchange; by looking at the Composite Stock Price Index, investors can determine their investment strategy. However, the rise and fall of the Composite Stock Price Index depend on a country's macroeconomic conditions; if the economy weakens, the company's performance will also undermine investors' confidence, and confidence decreases. Analysing the relationship between the Composite Stock Price Index with macroeconomic factors can show how much the influence of these factors on the increase or decrease in the Composite Stock Price Index, the macroeconomic factors in question are inflation, interest rates and the rupiah exchange rate. In this study, dependency analysis was carried out with the Copula approach method involving the Tau Kendal method for parameter estimation and the Maximum Likelihood Estimation (MLE) method to choose the best Copula model to explain the relationship between the Composite Stock Price Index and these macroeconomic factors. Research results in it are found that the best Copula that can explain the dependency structure between the Composite Stock Price, The index with inflation and interest rates is the Gumbel Copula with parameters θ ̂= 1.264 and θ ̂= 1.174, While the Copula model is the best that can explain the structure of the dependency between Composite Stock Price Index and the exchange rate is Copula Student-t with parameter θ ̂= −0.6037.
Bayesian Analysis of Multivariate Longitudinal Ordinal Data Using Multiple Multivariate Probit Models

Multivariate longitudinal ordinal data are often involved in longitudinal studies with each individual having more than one longitudinal ordinal measure.However, due to complicated correlation structures within each individual and no explicit likelihood functions, analyzing multivariate longitudinal ordinal data is quite challenging.In this paper, Markov chain Monte Carlo (MCMC) sampling methods are developed to analyze multivariate longitudinal ordinal data by extending multivariate probit (MVP) models for univariate longitudinal ordinal data to multiple multivariate probit models (MMVP) for multivariate longitudinal ordinal data.The identifiable MVP models require the covariance matrix of the latent multivariate normal variables underlying the longitudinal ordinal variables to be a correlation matrix, thus a Metropolis-Hastings (MH) algorithm is usually necessitated, which brings a rigorous task to develop efficient MCMC sampling methods.In contrast to the identifiable MVP models, the non-identifiable MVP models can be constructed to circumvent a MH algorithm to sample a correlation matrix by a Gibbs sampling to sample a covariance matrix, and hence improve the mixing and convergence of the MCMC components.Therefore, both the identifiable MMVP models and the non-identifiable MMVP models for multivariate longitudinal ordinal data are presented, and their corresponding MCMC sampling methods are developed.The performances of these methods are illustrated through simulation studies and an application using data from the Russia Longitudinal Monitoring Survey-Higher School of Economics (RLMS-HSE).