Reviewerly/exHarmony huggingface dataset:
NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework

Information retrieval aims to find information that meets users’ needs from the corpus. Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, and so on, while they share the same schema to estimate the relationship between texts. It indicates that a good IR model can generalize to different tasks and domains. However, previous studies indicate that state-of-the-art neural information retrieval (NIR) models, e.g., pre-trained language models (PLMs) are hard to generalize. It is mainly because the end-to-end fine-tuning paradigm makes the model overemphasize task-specific signals and domain biases but loses the ability to capture generalized essential signals. To address this problem, we propose a novel NIR training framework named NIR-Prompt for retrieval and reranking stages based on the idea of decoupling signal capturing and combination. NIR-Prompt exploits Essential Matching Module (EMM) to capture the essential matching signals and gets the description of tasks by Matching Description Module (MDM). The description is used as task-adaptation information to combine the essential matching signals to adapt to different tasks. Experiments under in-domain multi-task, out-of-domain multi-task, and new task adaptation settings show that NIR-Prompt can improve the generalization of PLMs in NIR for both retrieval and reranking stages compared with baselines.
Climbing Technique Evaluation by Means of Skeleton Video Stream Analysis

Due to the growing interest in climbing, increasing importance has been given to research in the field of non-invasive, camera-based motion analysis. While existing work uses invasive technologies such as wearables or modified walls and holds, or focuses on competitive sports, we for the first time present a system that uses video analysis to automatically recognize six movement errors that are typical for novices with limited climbing experience. Climbing a complete route consists of three repetitive climbing phases. Therefore, a characteristic joint arrangement may be detected as an error in a specific climbing phase, while this exact arrangement may not considered to be an error in another climbing phase. That is why we introduced a finite state machine to determine the current phase and to check for errors that commonly occur in the current phase. The transition between the phases depends on which joints are being used. To capture joint movements, we use a fourth-generation iPad Pro with LiDAR to record climbing sequences in which we convert the climber's 2-D skeleton provided by the Vision framework from Apple into 3-D joints using the LiDAR depth information. Thereupon, we introduced a method that derives whether a joint moves or not, determining the current phase. Finally, the 3-D joints are analyzed with respect to defined characteristic joint arrangements to identify possible motion errors. To present the feedback to the climber, we imitate a virtual mentor by realizing an application on the iPad that creates an analysis immediately after the climber has finished the route by pointing out the detected errors and by giving suggestions for improvement. Quantitative tests with three experienced climbers that were able to climb reference routes without any errors and intentionally with errors resulted in precision-recall curves evaluating the error detection performance. The results demonstrate that while the number of false positives is still in an acceptable range, the number of detected errors is sufficient to provide climbing novices with adequate suggestions for improvement. Moreover, our study reveals limitations that mainly originate from incorrect joint localizations caused by the LiDAR sensor range. With human pose estimation becoming increasingly reliable and with the advance of sensor capabilities, these limitations will have a decreasing impact on our system performance.
Predicting Trending Elements on Web Pages Using Machine Learning

AbstractEye-tracking data can be used to understand how users interact with web pages. Understanding the eye-movement sequences of multiple users is a challenging task because the sequence followed by each user tends to be different. Scanpath Trend Analysis (STA) brings multiple individual eye-movement sequences together and identifies a representative sequence as a trending path. However, eye-tracking data on a web page is required to determine the trending path. Our aim here is to investigate whether we can train Machine Learning (ML) algorithms to identify trending elements on a web page without collecting eye-tracking data on that web page. This article presents our experiments with different ML classification algorithms towards achieving that goal. To validate the experiments, we used two datasets from previous research, the first one included browsing and searching tasks and the second one included browsing and synthesis tasks. Our experiments show that the k-nearest neighbors algorithm (KNN) model can successfully identify the trending elements in the first dataset for both browsing (F1=≈91%) and searching tasks (F1=≈88%). However, the second dataset’s synthesis task results were not as successful as its browsing task results. Our work here shows that a model can be created to predict the trending elements in web pages solely with web page features but the task is a critical factor in the success of prediction.Keywords: Eye-trackingtrending pathscanpath trend analysismachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Throughout this paper, we use AOI/segments interchangeably with visual elements of web pages.2 https://developers.google.com/web/tools/puppeteerAdditional informationNotes on contributorsNaziha Shekh KhalilNaziha Shekh Khalil holds BSc and MSc degrees in Computer Engineering from Middle East Technical University Northern Cyprus. Her research focuses on enhancing user experiences in the digital world through human-computer systems.Sukru EraslanSukru Eraslan is a lecturer at Middle East Technical University Northern Cyprus Campus. He completed his PhD in Computer Science at the University of Manchester. He then worked as a research associate at METU NCC and the University of Manchester. His primary research interests are centred around human–computer interaction. https://users.metu.edu.tr/seraslan/index.htmlYeliz YesiladaYeliz Yesilada earned her PhD in Computer Science from the University of Manchester in the UK. She is a Professor at Middle East Technical University Northern Cyprus Campus. Her primary research interest is the human–computer interaction, particularly user behaviour analysis and modelling, the mobile Web, and eye-tracking. https://www.yelizyesilada.info.
Features of the criminal legal characteristics of crimes attacking objects of intellectual property created with the use of information technologies and technologies of artificial intelligence

The article is devoted to the issues of criminal law protection of objects created by artificial intelligence. Currently, there is a legislative vacuum both in regulating the activities of artificial intelligence in general, and in matters of criminal liability for the use of these objects. The author highlights two aspects of the designated problem. First, a decision is needed regarding the possibility of recognising the algorithm as the author of the work it creates and, accordingly, fixing the copyright on this object. Secondly, it is required to study the features of objective and subjective signs of criminal acts in relation to such works. Considering the problem in the context of the theory of intellectual property and the social conditionality of the criminal law protection of intellectual property objects, the author comes to the conclusion that at present there are no grounds for recognising artificial intelligence systems as authors of works. However, the exclusive rights to such objects should belong to the owners of the algorithms. Accordingly, such results of the work of artificial intelligence can be recognised as the subject of crimes against intellectual property. Based on this position, the author describes the features of the criminal law characteristics of crimes against objects created by artificial intelligence, and defines these crimes as committed with direct intent on a large scale, illegal use of an intellectual property object or disclosure of the essence of an industrial property object created using information technologies and artificial intelligence technologies.
ANALISIS SENTIMEN PENGGUNA APLIKASI JAMSOSTEK MOBILE (JMO) PADA APPSTORE MENGGUNAKAN METODE NAIVE BAYES

The use of Jamsostek Mobile has problems that often occur, namely failure to update data on the JMO application, digital cards that do not appear on the JMO application, data update failures and access failures. To overcome this, BPJS participants are faced with BPJS branches or companies. This is an obstacle that should be overcome through optimizing regulations from the BPJS so that there are no complaints from the public regarding this matter.&#x0D; Jamsostek Mobile is an application implemented by BPJS Ketenagakerjaan to make it easier for users to carry out JHT simulations, check JHT balances, check details for JHT contributions and pension benefits, and make JHT claims. This application can be accessed on the App Store and Playstore. The implementation of the application turned out to generate several comments or reviews from users both in the App Store and Play Store.&#x0D; This study aims to analyze sentiment from user reviews on the App Store with the stages of Scraping, Labeling, Cleaning, Preprocessing Text, Class Naive Bayes, TF-IDF, Evaluation, Visualization using Google Collaboratory tools&#x0D; From the results of research on the sentiment analysis of users of the Jamsostek Mobile application on the AppStore platform, which totaled 2001 data and had passed the preprocessing text stage consisting of filtering, tokenization, transformation and classification using the Naïve Bayes algorithm and evaluation of data with a confusion matrix using Google Collaboratory, it can be interpreted that the results from reviews of the use of negative JMO applications with a proportion of 96% in accuracy (accuracy), 96% in value precision, and a success rate (recall) of 100%. This value indicates that the naïve Bayes classification algorithm is considered quite good in processing review data, because the proportion of accuracy is 96%. Based on this value, it proves that the sentiment or reviews of JMO application users on the App Store platform are negative.&#x0D; Keywords: Sentimen Analysis, Naive Bayes, App Store, Jamsostek Mobile, Google Collaboratory
Analytical Studies on the Effectiveness of IoMT for Healthcare Systems

The Internet of Things (IoT) has great importance in the medical industry. The creation of intelligent sensors, intelligent machines, and superior algorithms for lightweight communication made it feasible to connect medical equipment in order to monitor biomedical signals and also to detect illnesses in patients without human intervention. This new IoT and medical equipment connection is called IoMT. This IoMT model is most adapted to this pandemic since every human being has to be interconnected and monitored via a larger communication network. Hence, this article provides an overview of remote healthcare systems, monitoring ingestible sensors, mobile health, smart hospitals, and improved chronic disease management focused on the Internet of Medical Things. Finally, this paper proposes an IoMT architecture and platform to assist with persistent healthcare.
All-weather real-time ore detection method based on near-infrared structured light and zero-crossing points characteristics

To address the low efficiency of ore detection methods caused by the complex lighting environment of mining sites, an all-weather real-time ore detection method based on near-infrared structured light and zero-crossing points characteristics was proposed. Firstly, an image acquisition method based on the infrared–visible radiation detection model (IVRDM) is proposed to avoid structured light imaging being disturbed by a strong sunlight environment; Then, the centerline of the light bar is extracted quickly and accurately based on the zero-crossing point characteristics; Finally, the light bar neighborhood splicing detection method was proposed to realize the real-time detection and 3D measurement of the target ore. The experimental results show that the proposed method can effectively resist strong sunlight interference and faster detection speed, the average extraction time of light bar centerline per frame is below 1.8 ms, and the error rate of ore size detection is <5 %.
Cloud migration framework clustering method for social decision support in modernizing the legacy system

Abstract Cloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K‐means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge‐based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra‐cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system.
Artificial Intelligence in Interdisciplinary Linguistics

Artificial intelligence (AI) is becoming an integral part of various scientific disciplines, industries, and everyday life. AI studies cover quite a number of scientific fields, and the topic needs an integrated and convergent approach to address its multifaceted challenges. This paper provides an extensive survey of existing approaches to define and interpret the AI concept. The research objective was to identify the invariant characteristics of AI that underscore its interdisciplinary nature. The article categorizes the primary drivers, technologies, and key research models that fuel the advancement of AI, which possesses a unique capability to leverage knowledge, acquire additional insights, and attain human-like intellectual performance by analyzing expressions and methods of human cognition. The emulation of human intellectual activity and inherent propensity for continual evolution and adaptability both unlock novel research prospects and complicate the understanding of these processes. Algorithms, big data processing, and natural language processing are crucial for advancing the AI learning technologies. A comprehensive analysis of the existing linguistic research revealed an opportunity to unify various research approaches within this realm, focusing on pivotal tasks, e.g., text data mining, information retrieval, knowledge extraction, classification, abstracting, etc. AI studies make it possible to comprehend its cognitive potential applications across diverse domains of science, industry, and daily life.
Measuring Vulnerability Assessment Tools’ Performance on the University Web Application

This research measured vulnerability assessment tools’ performance on a university web application, including Burp Suite and OWASP ZAP. There are three measurement criteria: (1) the number of vulnerabilities classified under risk and confidence metrics, (2) the number of vulnerability types and URL alerts classified under risk and confidence metrics, and (3) the number of vulnerabilities classified in the 2021 OWASP Top 10 vulnerabilities. Results showed that Burp Suite detected more vulnerabilities and alerts than OWASP ZAP, with a higher proportion of high-risk vulnerabilities. However, OWASP ZAP had a higher proportion of medium-confidence vulnerabilities. The comparison also revealed that the vulnerabilities identified by both tools were ranked differently within the OWASP Top 10, and there were variations in risk prioritisation between the tools. Despite these differences, the vulnerability assessment results obtained from these tools are still helpful for the university’s security analysts and administration, as mitigating cyber threats to the web application is paramount.
Academic Interaction as Part of Developing Professional Identity in Schoolchildren: Informational and Educational One-Industry Urban Environment

Single-industry urban settlements often experience a lack of qualified specialists for their key enterprises. Therefore, professional identification should start at school and be based on a scientifically organized interaction between local education facilities and local industrial enterprises. The authors identified, developed, and tested pedagogical conditions that contribute to career counselling in single-industry urban schools. They developed a digital resource called Proffuture42.rf that modeled informational and academic environment of a single-industry town. The platform united programs of extracurricular activities, additional general education, and career guidance developed by urban education institutions for schoolchildren of various age groups. Education facilities and their industrial partners were able to interact in the form of network training. The research revealed a number of possibilities for industrial organizations and social services to participate in the process of professional identification development. The pedagogical experiment involved observation and questioning. The survey results were subjected to mathematical processing. By monitoring the qualitative changes, the platform was able to facilitate the pedagogical support of career counselling in a single-industry town.
Aplikasi Berbasis Web Menggunakan Apriori Untuk Rekomendasi Bundling Produk Sembako

Data Mining can be utilized in various disciplines, such as Solo Latri Grocery Store, to boost sales and determine grocery sales. The current problem is that the determination of basic food sales at Toko Sembako Solo Latri cannot determine customer purchasing patterns or the number of customers who purchase basic food at the same time. This research seeks to develop a web-based application that integrates the Apriori algorithm into the recommendation process for bundling basic food products. Latri Grocery Store will create a model or association rule to increase sales. By utilizing the Crisp-DM method, it is possible to obtain important information regarding the pattern of transactions made simultaneously using the Apriori algorithm. This investigation is supported by a dataset of sales transactions for the period February 1 to April 20, 2023. Our results show that the Apriori algorithm with a minimum support value of 50% and a minimum confidence value of 50% produces the best seven item set combinations. The best result is a support value of 95.38 percent, a confidence value of one hundred percent, and a lift ratio of 1.97.
Detecting fake reviewers in heterogeneous networks of buyers and sellers: a collaborative training-based spammer group algorithm

Abstract It is not uncommon for malicious sellers to collude with fake reviewers (also called spammers) to write fake reviews for multiple products to either demote competitors or promote their products’ reputations, forming a gray industry chain. To detect spammer groups in a heterogeneous network with rich semantic information from both buyers and sellers, researchers have conducted extensive research using Frequent Item Mining-based and graph-based methods. However, these methods cannot detect spammer groups with cross-product attacks and do not jointly consider structural and attribute features, and structure-attribute correlation, resulting in poorer detection performance. Therefore, we propose a collaborative training-based spammer group detection algorithm by constructing a heterogeneous induced sub-network based on the target product set to detect cross-product attack spammer groups. To jointly consider all available features, we use the collaborative training method to learn the feature representations of nodes. In addition, we use the DBSCAN clustering method to generate candidate groups, exclude innocent ones, and rank them to obtain spammer groups. The experimental results on real-world datasets indicate that the overall detection performance of the proposed method is better than that of the baseline methods.
The analysis of the structural parameter influences on measurement errors in a binocular 3D reconstruction system: a portable 3D system

This study used an analytical model to investigate the factors that affect the reconstruction accuracy composed of the baseline length, lens focal length, the angle between the optical axis and baseline, and the field of the view angle. Firstly, the theoretical expressions of the above factors and measurement errors are derived based on the binocular three-dimensional reconstruction model. Then, the structural parameters' impact on the error propagation coefficient is analyzed and simulated using MATLAB software. The results show that structural parameters significantly impact the error propagation coefficient, and the reasonable range of structural parameters is pointed out. When the angle between the optical axis of the binocular camera and the baseline is between 30° and 55°, the ratio of the baseline length to the focal length can be reasonably reduced. In addition, using the field angle of the view that does not exceed 20° could reduce the error propagation coefficient. While the angle between the binocular optical axis and the baseline is between 40° and 50°, the reconstruction result has the highest accuracy, changing the angle out of this range will lead to an increase in the reconstruction error. The angle between the binocular optical axis and the baseline changes 30° through 60° leads to the error propagation coefficient being in a lower range. Finally, experimental verification and simulation results show that selecting reasonable structural parameters could significantly reduce measurement errors. This study proposes a model that constructs a binocular three-dimensional reconstruction system with high precision. A portable three-dimensional reconstruction system is built in the article.