Reviewerly/exHarmony huggingface dataset:
Runtime Verification of Learning Properties for Reinforcement Learning Algorithms

Reinforcement learning (RL) algorithms interact with their environment in a trial-and-error fashion.Such interactions can be expensive, inefficient, and timely when learning on a physical system rather than in a simulation.This work develops new runtime verification techniques to predict when the learning phase has not met or will not meet qualitative and timely expectations.This paper presents three verification properties concerning the quality and timeliness of learning in RL algorithms.With each property, we propose design steps for monitoring and assessing the properties during the system's operation.
Legal Implications of Ai Navigating Patent Law Challenges in the Age of Ml

This paper examines the legal ramifications of artificial intelligence (AI) in the field of patent law, with a specific emphasis on practical uses and technological benefits. It emphasizes the significance of detecting and eliminating interdependencies among data items in the intricate domain of AI and ML, facilitating concurrent processing, and averting periods of inactivity. The progress of AI has had a substantial impact on patent law, resulting in the creation of laws specifically tailored to AI-related patents. The paper examines the difficulties associated with obtaining patents for AI-related discoveries, with a specific focus on issues related to identifying the inventors and the eligibility of the patents. The study explore issues pertaining to intellectual property, questions about prejudice and fairness, and the need for regulation and governance. The text emphasizes the significance of copyright, patents, trade secrets, and data privacy in safeguarding the progress made in AI and ML. The paper highlights the significance of regulatory frameworks that effectively manage the balance between innovation and user safety. It draws upon ideas from Indian case law and the proposed AI regulatory framework
Survey On Collaborative Learning Platform NexLink

The man in pre-historic times, that is, about 50,000 years ago, was in the cave, recording his social interactive urges in various kinds of drawings and paintings on the walls, thinking that his fellow human beings in his times or posterity would happen on them and try to decode them. That was probably the first seed sown of social media to catch on down the line. The man in the ancient cave is now the man on the web for all especially education. Media students who were selected for the study have recorded their responses towards concurrent social media learning dimensions: Interactivity, Informativeness, Effectiveness, and Stress. These factors help in assessing media students’ perception regarding social media learning.[1] In the ever-evolving landscape of social media, NexLink emerges as a groundbreaking platform that draws inspiration from the most influential giants in the industry—Twitter, LinkedIn. Branded as NexLink, this innovative social network is designed to seamlessly integrate the core functionalities of these renowned platforms, ushering in a new era of online interaction. NexLink inherits the real-time conversational prowess of Twitter, allowing users to engage in dynamic and instant exchanges. Simultaneously, it harnesses the professional networking capabilities synonymous with LinkedIn, empowering individuals to cultivate meaningful connections and advance their careers within their chosen domains. As a result, NexLink is poised to redefine the social media landscape, offering users an all-encompassing digital ecosystem. This platform transcends conventional boundaries, providing a versatile space for networking, knowledge-sharing, real-time engagement, and interactive exploration. With NexLink, the future of social networking takes on a multifaceted and immersive form, promising a dynamic and enriching experience for users across the globe. Keywords: Concurrent social media, Social Media Learning, Collaborative Media Education, NexLink, Twitter, LinkedIn, map functionality, real-time conversations, professional networking, social connectivity, digital ecosystem, online interaction.
WebUltron: An Ultimate Retriever on Webpages Under the Model-Centric Paradigm

Document retrieval has been extensively studied within the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">index-retrieve</i> framework for decades, which has withstood the test of time. However, this approach inherently segregates the indexing and retrieval processes, preventing a cohesive, end-to-end optimization. To bridge this divide, we introduce WebUltron, a revolutionary model-centric indexer for document retrieval. This system embeds the entirety of document knowledge within the model, striving for seamless end-to-end retrieval. Two primary challenges with this indexer are the representation of document identifiers (docids) and the model's training. Current methods grapple with docids that lack semantic depth and the constraints of limited supervised data, making scaling up to larger datasets challenging. Addressing this, we've engineered two novel docid types imbued with richer semantics that also streamline model inference. Further enhancing WebUltron's capabilities, we've developed a three-stage training regimen, leveraging deeper corpus insights and fortifying query-docid relationships. Experiments on two public datasets demonstrate the superiority of WebUltron over advanced baselines for document retrieval.
Pemanfaatan Objek 3D Printing Sebagai Pengembangan Media Pembelajaran Bangun Ruang Berbasis Arduino Untuk Siswa Penyandang Tunanetra

Mantera Media Development is a project aimed at creating effective learning materials for visually impaired students. These materials utilize 3D printing technology and are based on Arduino technology. The development process follows a Research and Development (R&amp;D) methodology with a prototype model approach. This project was conducted at Sriwijaya University's Mechanical Engineering Education Laboratory. The validation results for the learning materials were impressive. Material validation using a Likert scale yielded a perfect score of 100%, indicating high appropriateness and validity. Media validation, assessed using Guttman theory, scored 91%, signifying the media's strong suitability and validity. As a result, these learning materials are considered highly feasible and ready for use in teaching build space concepts to visually impaired students. Mantera's development promises to address the challenges faced in learning space construction, potentially enhancing cognitive and tactile skills. Additionally, it introduces technology-packaged media to the educational landscape.
SLAPPs against journalists in Europe: Exploring the role of self-regulatory bodies

Reports show that media freedom is under pressure worldwide. Violence against journalists has a legal facet that takes the form of Strategic Lawsuits Against Public Participation (SLAPPs) that aim at silencing critical speech. While in some countries there are legal protections against SLAPPs, in Europe a debate is only recently being carried out, mainly fostered by professional journalist associations and civil society. This article explores the role that European journalistic self-regulatory bodies perceive they (could) have in fighting SLAPPs, analysing 16 qualitative answers gathered via a questionnaire. Self-regulation has historically been a way to protect the independence of journalism and uphold its ethical standards. The open-ended responses help to understand how SLAPPs put into question the representations that media councils and professional associations use to construct their ‘professional jurisdiction’, and therefore, their ‘territory’ and the way in which they see their role in society. Our results provide food for thought on the handling of legal attacks against journalists.
The Abnormal Events Detection System by Feature Enhancement-Based Deep Learning Network

To achieve real-time abnormal events detection that can assist the security personnel to manage the video surveillance system more efficient, this paper presents a feature enhancement-based deep learning network. The main strategy is to improve 2D convolutional architecture and employ the channel attention layer to enhance the important features between the layers, and combine DenseNet to strengthen the correlation between the features. Besides, a threshold-scoring strategy is designed to overcome the bad frame problem for substantially improving the accuracy of detecting abnormal events. Thus, the proposed system can effectively detect abnormal events with a higher accuracy rate than other approaches. Experimental results show that the proposed system can effectively detect abnormal events with a higher accuracy rate for various datasets, compared to other approaches, and can also achieve 33 fps (for real-time video processing).
Quantum metric and metrology with parametrically-driven Tavis-Cummings models

We study the quantum metric in a driven Tavis-Cummings model, comprised of multiple qubits interacting with a quantized photonic field. The parametrical driving of the photonic field breaks the system's U(1) symmetry down to a Z2 symmetry, whose spontaneous breaking initiates a superradiant phase transition. We analytically solved the eigenenergies and eigenstates, and numerically simulated the system behaviors near the critical point. The critical behaviors near the superradiant phase transition are characterized by the quantum metric, defined in terms of the response of the quantum state to variation of the control parameter. In addition, a quantum metrological protocol based on the critical behaviors of the quantum metric near the superradiant phase transition is proposed, which enables greatly the achievable measurement precision.
Deep Learning for Channel Estimation and Security Detection in Wireless Communication Systems

Aiming at high complexity of traditional channel estimation algorithms and the problems of intrusion and attack in MIMO communication systems, deep learning algorithms are used for channel estimation and security detection respectively. By taking the pilot signal received at the base station as the input of the BP neural network model, the relationship between the pilot signal and the real channel response is learned, and the complete channel state information (CSI) is obtained. The mean square error and bit error rate are taken as evaluation indexes to analyze the superiority of the proposed method over the traditional channel estimation algorithm. The base station uses the channel state information to recover the data signal, carries on the data dimensionality reduction processing, injects it into the deep neural network, and trains the attack detection classification model. Simulation experiments show that the proposed method can effectively improve the detection accuracy and efficiency, also enhance the security of wireless communication system.
Assistive image caption and tweet development using deep learning

In this rapidly growing world and with the technological boom, there are a huge variety of applications and devices that generate an enormous amount of data every second. With unstructured data being the most difficult to manage and keep track of, there has been a drastic increase in the amount of visual data generation. To keep track of such data for further insights and use, a textual descriptor is often needed and getting it is the primitive step for any analytics. A manual description is subjective and not appropriate for larger data. This issue is addressed by automation, hence opening gates for computer vision and artificial intelligence in the domain. Another area that has changed multimedia communication and has seen a great deal of advancement is social media. Applications like Twitter have become an indispensable part of people&apos;s lives. Moreover, Progressive Web Application (PWA) is a term that has started to be implemented in various applications. It gives an on-par experience with native apps and has become more prevalent. This work Assistive Image Caption and Tweet (AICT) aims to set a new horizon by combining these applications, and setting a base for future applications and devices. It does so by using deep learning techniques such as convolution neural networks (CNN) and Long Short-Term Memory (LSTM) to generate captions for images within milliseconds, natural language processing (NLP) to generate the text in different languages along with the audio to assist visually impaired people, and an automated assistive tweet function that directly tweets the image with its caption in the language desired.
Comparison of artificial intelligence models for prognosis of breast cancer

Breast cancer is the most common cancer not only affecting women but also men. Diagnosis and treatment are crucial stages in the cancer treatment process. However, even after treatment, individuals often experience ongoing challenges, including the regular need for painful procedures such as biopsies, MRIs, and scans as part of their journey towards recovery. We propose that in this case, machine learning (ML) and deep learning analyses may be used to perform longitudinal studies of women with breast cancer. We do a comparative analysis of three situations, in the first situation, we apply ML algorithms just after the primary preprocessing steps, in the second situation, we add balanced class weights hyperparameters, and in the third, we do principal component analysis (PCA). In the first situation, the light gradient boosting machine (LightGBM) gives the best accuracy of 87.87%, and the random forest (RF) gives an accuracy of 87.87% after the hyperparameter of balanced class weights is given. After PCA, logistic regression gives a maximum accuracy of 84.84%.
Development of Augmented Reality Learning Materials for the Hearing Impaired Students in Primary I

There are challenges in the education of students with hearing impairments, including difficulties in reading and writing the Thai language due to a lack of appropriate learning materials. The COVID-19 pandemic has further exacerbated these issues by necessitating the adoption of technology-based learning methods. In response to these challenges, researchers have proposed the development of learning materials to support the education of students with hearing impairments. These materials will not only benefit the students themselves but also their parents who may struggle to support their children’s learning. Furthermore, the use of these materials will contribute to the promotion of inclusivity and equality in education. To investigate the effectiveness of Augmented Reality (AR) books as a learning tool for first-grade students with hearing impairments in primary education, a study was conducted with a target group of ten students (whole level) from Thungmahamek School for the Deaf, selected through stratified random sampling. The results indicated that the development of AR-Books had an effectiveness of 85.33/87, with E1 and E2 values exceeding the established standards. In addition, learning outcomes after using AR-Books were significantly higher than before the intervention at a statistical significance level of 0.05. The students’ satisfaction with the use of AR-Books was also high.
Impact of Technological Modernisations on Employee’s Work Efficiency with Reference to it Organisations

In recent years, IT has had a profound effect in many aspects of life that also includes the personal and professional life of an employee. However, very few research studies have examined the effectiveness of IT. This study will assess the degree to which new systems enable organizations to utilize these opportunities without letting it have an effect on their employees. A considerate effort has been made to review literature on the role, advantages potential limitations of IT in HR processes. Thus, the primary purpose of the present article is to review the current effects of technology on employees of various industries and to give a brief overview for identifying challenges measuring the effectiveness of human capital. In addition, suggestions have been listed on how it can better the employee's work performance without being a threat and how well IT is used to make work life better and effective.
EFL Students' Opinions about Learning English Using Telegram App at the University Level

EFL students now have more options thanks to the quick advancements in mobile technologies and the appearance of new apps like Telegram. Therefore, the purpose of the current study was to find out how EFL College students felt about using Telegram to learn English. 600 EFL college students—400 females and 200 males—were randomly selected from Jordan's Yarmouk University's English and Translation department for the current study. Data for this inquiry was gathered using a survey questionnaire., which employed a quantitative approach. Based on the statistics, most learners of EFL said Telegram was a useful medium for learning English, especially for the four capabilities of vocabulary and grammar acquisition. The findings of this study might help Jordan's higher education institution modernize its teaching techniques.
Effects of the Conversation and Recommendation Mechanism on Chatbots’ Recommendation Effectiveness

This paper delves into the realm of human-chatbot interaction and aims to enhance consumers’ experience with chatbots in products recommendation. Through a situational experimental study, we examine the interaction effect of chatbots’ conversational interactivity and recommended product relevance on recommendation effectiveness. The results indicate that when recommended product relevance is high, conversational interactivity has a positive effect on recommendation effectiveness, while this effect is reversed when recommended product relevance is low. Moreover, we intend to further examine the emotional appeal and expectations discrepancy consumers form with chatbots in a future study to explain the interplay between conversational interactivity and product relevance.
Protótipo de baixo custo para implementar HRI em Robô Social baseado em Raspberry Pi

The success of research in the field of Artificial Intelligence has expanded the application possibilities of Service Robots. Among them, Social Robotics, which includes applications for the elderly, people with disabilities and other areas useful to society. A study indicated that the elderly dependency ratio in Europe would double from 28.4% in 2010 to 58.5% in 2060. Availability of smart home systems at reasonable costs for people with disabilities, may still depend on their adoption by the non-disabled population. This work presents the development of a low-cost Human-Robot Interaction system, based on Raspberry Pi. The system was initially tested with a simple interaction protocol for a small social robot under construction, obtaining good results in terms of object detection accuracy. Dialog processing speed can be improved and at the end of this study some ways to achieve this goal were identified.
Pengembangan e-modul interaktif berbasis gamifikasi pada pembelajaran teks fabel

The change in the learning paradigm demands innovation in learning components. Electronic modules can encapsulate innovation regarding input, learning instruments, and facilities. Based on the needs analysis, fable text is essential because it can instill character. This research aims to develop a gamification-based interactive e-module to provide interactivity experience through games for students. The research method used is the Research and Development (R&amp;D) method, with the development process using the ADDIE model (analyze, design, development, implementation, evaluation). The ADDIE model is systematic, easy, and has an iterative evaluation, so it is appropriate to be applied to develop modules. Media and material experts' results of the validation test stated that the module was categorized as valid. Based on the limited trial, it was found that students' responses to the e-module showed a very good category in terms of ease of use, attractiveness, language use, and media display. The results of the broad trial showed a number less than 0.05, so it can be stated that the Komiknesia e-module was proven effective in improving student learning outcomes.
T2KG: Transforming Multimodal Document to Knowledge Graph

The large amount of textual information, in digital format available today, makes the knowledge extraction task unfeasible by manual means.It is therefore necessary to develop automatic tools that allow us to integrate this knowledge into a structure that is easy to use by both machines and humans.This paper presents T2KG, a framework that can incorporate the relevant information from several structured or unstructured documents into a semantic network.Structured documents are processed based on their annotation scheme.For unstructured documents, T2KG uses a set of Natural Language Processing sensors that identify relevant information to enrich the semantic network created by linking all the knowledge from different documents.
Conclusion and Future Research

This book presented in detail how some key aspects in the design of guidance, navigation, and control systems for unmanned aerial vehicles (UAVs) have been addressed in recent years by experts in the field. This final chapter draws some conclusions on the state-of-the-art and discusses future research directions that embrace not only UAV technology but also futuristic applications such as urban air mobility and advanced air mobility.
RESPONSIBLE DRIVING IN THE AGE OF SMARTPHONES: APPLIED RESEARCH FOR IMPROVING ROAD SAFETY IN THE MOTOR CITY

Smartphones have become ubiquitous commodities in our contemporary lives and essential to our daily experiences. With limited public transportation in the United States and dependence on cars, the combination of smartphones and driving raises important questions of safety. We discuss our collaboration with Chevrolet-GM in Detroit in product development and user experience research, namely beta-testing a mobile phone app that was developed by Chevrolet to eliminate texting while driving, thereby improving car and road safety. We illustrate that the heightened awareness of distracted driving to self and others created spaces for young adult drivers to demonstrate their moral personhood through the care of and for others in their individual use of the app and in their promotion of its use to family members and friends. The practical applications of this finding resulted in a successful app product and its launch campaign, which incorporated the emerging insights from this study.
An Analysis of Employment Policies for Disabled Persons in the Post-epidemic Era in China

People with disabilities are a special social disadvantaged group, facing more difficulties and obstacles in employment compared to the general population. Although the government has been continuously innovating and improving employment policies for people with disabilities since the establishment of the Peoples Republic of China, they still face obstacles such as employment difficulties and limited choices. This study analyzed the development process of employment policies for people with disabilities using the incrementalist model. It examined the employment environment and social barriers faced by people with disabilities under the current policies, as well as the issues related to inadequate organizational and resource support and limitations in preferential incentives during policy implementation. Based on these findings, new recommendations such as building an Equal Employment Environment, strengthening assistance mechanisms, improving Preferential Policies will be proposed to adapt employment policies for people with disabilities to the requirements of the new era and address the needs of this special group.