Reviewerly/exHarmony huggingface dataset:
Maximizing Local Rewards on Multi-Agent Quantum Games through Gradient-Based Learning Strategies

This article delves into the complex world of quantum games in multi-agent settings, proposing a model wherein agents utilize gradient-based strategies to optimize local rewards. A learning model is introduced to focus on the learning efficacy of agents in various games and the impact of quantum circuit noise on the performance of the algorithm. The research uncovers a non-trivial relationship between quantum circuit noise and algorithm performance. While generally an increase in quantum noise leads to performance decline, we show that low noise can unexpectedly enhance performance in games with large numbers of agents under some specific circumstances. This insight not only bears theoretical interest, but also might have practical implications given the inherent limitations of contemporary noisy intermediate-scale quantum (NISQ) computers. The results presented in this paper offer new perspectives on quantum games and enrich our understanding of the interplay between multi-agent learning and quantum computation. Both challenges and opportunities are highlighted, suggesting promising directions for future research in the intersection of quantum computing, game theory and reinforcement learning.
A gender perspective in the design of a video-on-demand search engine

Most interaction designs unknowingly discriminate against certain groups of people, and video-on-demand platforms are often included among such designs. This study presents an introduction to the adoption of a gender perspective in the user experience design of the search engine of a video-on-demand platform. User experience design includes, in essence, user needs in order to eliminate the obstacles users may encounter on their way to having a productive, straightforward and rewarding experience. This project was carried out using established user experience methods and techniques. The final design provides a prototype for an advanced search engine within a video-on-demand platform and includes all the elements in the filtering system of the main catalogue. It eliminates the gender gap and promotes women’s empowerment, thereby providing an experience where users feel comfortable and satisfied.
On the role of professional qualifications and professional competencies in various spheres of the public activity

Introduction . Within the framework of the regulatory guillotine mechanism, the regulatory and legal support of educational legislation is currently being reviewed. The use of such a mechanism makes it possible to revise and update the entire array of legal support in order to solve social issues of vocational education. Purpose setting . Modern educational law follows the path of increasing the number of normative acts regulating educational legal relations, which indicates the casuistry of legal regulation. Updated regulations have a limited validity period, and therefore their significance may be reduced by leveling efforts to take into account professional standards when describing professional competencies. This entails social problems in the area under consideration. Methodology and methods of the study . Theoretical and empirical methods were used in the research process. Legal analogy, legal comparison, legal abstraction were used. Results . The article deals with the problems of formation of professional competencies of students. The necessity of the organic development of educational and public service legislation in terms of the formulation of professional competencies is argued, especially taking into account the change since September 2025 in the nomenclature of training areas, specialties and federal state educational standards. The problems of the lack of continuity of professional competencies of a civil servant, professional standards and a graduate of an educational organization are noted. Conclusion . The most important direction for improving educational legislation is the need to establish its priority in regulating the procedure for entering the civil service and determining professional qualifications. In order to use the provisions on the professional qualifications of a civil servant in the development of educational programs of higher education, it is necessary to amend the educational legislation, as well as develop a system of additional professional education.
The Impact of Artificial Intelligence (AI) on Customer Relationship Management: A Qualitative Study

Ever since the commercialization of the Internet in the '90s, technology has been evolving faster than ever with the advent of cloud computing, social media, ubiquitous mobile devices, the Internet of Things (IoT), blockchain, and more. A staggering number of three billion internet users, five billion mobile users, and six billion devices are now connected through this massive global network of networks, facilitating customer information exchange and interaction never before seen in history. Driven by recent technological advances in computing power, big data, high-speed internet connection, and easier access to models built with advanced algorithms, Artificial Intelligence (AI) is the next wave of innovation, which has already come into widespread awareness in the consumer world with the emergence of virtual assistants and chatbots (e.g., Amazon's Alexa, Apple's Siri, Google's Assistant), image recognition (e.g., Facebook Photos, Google ImageNet), personalized recommendations (e.g., Netflix, Amazon) and autonomous driving (e.g., Tesla, Google Waymo). This qualitative research study intends to learn about the impact of AI on customer relationship management (CRM), specifically in the area of customer service of problem resolution. Most prior research focuses on the AI technologies leveraged in CRM systems, such as machine learning, natural language processing, voice recognition, chatbots, data analytics, and cloud infrastructure. Few extant studies have used a qualitative research methodology to gather data from industry experts to truly understand the impact of AI technologies on customer relationship management, especially in the area of customer service and problem resolution. This study aims to fill this research gap. This research contributes to the literature on AI in the context of CRM and is of value to both academics and practitioners as it provides a detailed analysis and documentation of the impact of AI on the customer service domain.
Blockchain in Healthcare: A Comprehensive Survey of Implementations and a Secure Model Proposal

Blockchain's core attributes, including decentralization, transparency, and immutability, have positioned it as a pioneering technology in the realm of financial technology (fintech) and have rendered it highly applicable across diverse industries. The current enterprise ecosystem has faced setbacks primarily due to a lack of trust in the existing infrastructure. This issue can be traced back to the centralized management of healthcare data, making it vulnerable to tampering and fraudulent activities, resulting in financial losses. The existing enterprise ecosystem failed due to the lack of trust in the currently in-place infrastructure. This problem can be attributed to the centralized healthcare data management, which is prone to tampering and fraudulent activities leading to capital loss. The present study relates to a comprehensive survey conducted in timespan from 2018 to 2022 on the implementation of blockchain technology in the healthcare industry, identifying and discussing the key challenges facing the healthcare industry, such as fraud, and scams against healthcare data. It is found that there is an enormous inclination towards the decentralization of patient-centric data. However, a rapid decline is reported due to the privacy and security concerns of the confidential and sensitive data. Moreover, it is noticed that most of the implementations utilized either Ethereum or Hyperledger. Based on the survey's findings, the study proposed a blockchain-based healthcare framework that can address the identified challenges by providing a secure and transparent platform for collecting, storing, and sharing patient health data while prioritizing security and privacy.
Development of Monitoring System Website Based on IoT Devices as a Solution to Plant Planting and Maintenance Process in Water Media

Every industry will adopt technology to carry out its operations so that progress can occur. However, one industry is the urban agriculture industry, which is a sure thing in modernization (smart farming). Therefore, the Internet of Things must also be integrated into smart farming to advance the agricultural industry which will be created as a training system with the IoT framework making it easier for the public to learn more about the measurement levels of each plant. This is made possible using monitoring systems for various variables, including temperature, sunlight, humidity, CO2, and air quality. However, the authors focus on conducting a special monitoring system for water media by detecting the pH level and water temperature. This system uses an IoT sensor that is connected to a hardware device (microcontroller) and the internet which can be accessed via a website or mobile application. The community will use the concept and manufacture of this tool to help smart farming in urban areas, which will be explored and practiced
UTILIZING THE VAL IT 2.0 FRAMEWORK TO ASSESS THE EFFECTIVENESS OF INFORMATION TECHNOLOGY INVESTMENTS FOR IMPLEMENTATION OF THE E-PROCUREMENT SYSTEM IN PT XYZ LIFE INSURANCE

Investments made by business organizations in the field of Technology and Information are a form of investment that is not measurable when the return on investment will be achieved if it is not accompanied by an appropriate IT investment utilization strategy. PT XYZ Insurance has implemented digital transformation in several of the company's operational activities. The Procurement Division at PT BNI Life Insurance has used the e-procurement system since 2019. The Procurement Division of PT XYZ Life Insurance in implementing the e-procurement system will try to be evaluated using the VAL IT 2.0 method. The implementation of VAL IT is one of the frameworks that can be used to conduct an information technology governance management audit or IT Governance. The VAL IT 2.0 Framework analysis used in this study's conclusion to determine the state of the e-procurement system's implementation as part of operational technology management at the company PT XYZ Life Insurance yielded results showing that the highest average GAP Maturity Level is found in the Portfolio Management domain analysis at 1.11, which is different from the level or value 5 of the expected maturity level. PT XYZ Life Insurance must identify all potential e-procurement programs that could be part of the company's IT investment portfolio and prioritize e-procurement programs based on their alignment with strategic objectives, potential value, and risk considerations.
PENDAMPINGAN LITERASI GERAKAN MENUMBUHKAN MINAT BACA BAGI ANAK–ANAK LOKR DI DUSUN WAIPUTIH

Children's reading ability is the basis for mastering various fields. PISA data 2018 shows that the literacy and numeracy abilities of Indonesian children tend to be stagnant and are ranked second from the bottom. To develop children's literacy and numeracy skills, appropriate strategies are needed so that children are interested and trained to develop reading, writing and understanding the information around them. This PKM aims to provide assistance as an effort to strengthen children's literacy. The method in this PKM is by applying the LOKR steps namely Literacy, Orientation, Collaboration and Reflection. The result of this activity is that at the literacy stage the child still has difficulty answering questions from the story that has been read, it is necessary to do repetition and reinforcement at the orientation stage students have started to find answers, the collaboration stage is carried out to stimulate children who are still considered low to understand the meaning of reading, at In the reflection stage, the child can find answers to questions. The conclusion of this activity is that children's literacy skills tend to be at the level of needing intervention and basic. Furthermore, habituation is needed so that children like to read and understand what they read. It was proven that at the third meeting in this PKM activity the children had increased their ability to read and understand the contents of their reading.
Old School: An 8K Multicamera Shoot to Create a Dataset for Computational Cinematography

In our paper, we describe the rationale for, and the process and outcomes of, a shoot that BBC R&D carried out in 2021 to create the Old School dataset, to further research into AI automated editing systems. We introduce our way of thinking about these systems, and explain why we believe a widely available dataset is necessary if research into these systems is to reach its potential. We then explain why a new shoot was necessary to generate such material.
Relation Repository Based Adaptive Clustering for Open Relation Extraction

Clustering-based relation discovery is one of the important methods in the field of open relation extraction (OpenRE). However, samples residing in semantically overlapping regions often remain indistinguishable. In this work, we propose an adaptive clustering method based on a relation repository to explicitly model the semantic differences between clusters to mitigate the relational semantic overlap in unlabeled data. Specifically, we construct difficult samples and use bidirectional margin loss to constrain the differences of each sample and apply self-supervised contrastive learning to labeled data. Combined with contrastive learning of unlabeled data, we construct a relation repository to explicitly model the semantic differences between clusters. Meanwhile, we place greater emphasis on the difficult samples located on the boundary, enabling the model to adaptively adjust the decision boundary, which lead to generate cluster-friendly relation representations to improve the effect of open relation extraction. Experiments on two public datasets show that our method can effectively improve the performance of open relation extraction.
VMemNet: A Deep Collaborative Spatial-Temporal Network With Attention Representation for Video Memorability Prediction

Video memorability measures the degree to which a video is remembered by different viewers and has shown great potential in various contexts, including advertising, education, and health care. While extensive research has been conducted on image memorability, the study of video memorability is still in its early stages. Existing methods in this field primarily focus on coarse-grained spatial feature representation and decision fusion strategies, overlooking the crucial interactions between spatial and temporal domains. Therefore, we propose an end-to-end collaborative spatial-temporal network called VMemNet, which incorporates targeted attention mechanisms and intermediation fusion strategies. This enables VMemNet to capture the intricate relationships between spatial and temporal information and uncover more elements of memorability within video visual features. VMemNet integrates spatially and semantically guided attention modules into a dual-stream network architecture, allowing it to simultaneously capture static local cues and dynamic global cues in videos. Specifically, the spatial attention module is used to aggregate more memorable elements from spatial locations, and the semantically guided attention module is used to achieve semantic alignment and intermediate fusion of the local and global cues. In addition, two types of loss functions with complementary decision rules are associated with the corresponding attention modules to guide the training process of the proposed network. Experimental results obtained on a publicly available dataset verify that the proposed VMemNet approach outperforms all current single- and multi-modal methods in terms of video memorability prediction.
A Neural Database for Answering Aggregate Queries on Incomplete Relational Data

Real-world datasets are often incomplete due to data collection cost, privacy considerations or as a side effect of data integration/preparation. We focus on answering aggregate queries on such datasets, where data incompleteness causes the answers to be inaccurate. To address this problem, assuming typical relational data, existing work generates synthetic data to complete the database, a challenging task, especially in the presence of bias in observed data. Instead, we propose a paradigm shift by learning to directly estimate query answers, circumventing the difficult data generation step. Our approach, dubbed NeuroComplete, learns to answer queries in three steps. First, NeuroComplete generates a set of queries for which accurate answers can be computed given the incomplete dataset. Next, it embeds queries in a feature space, through which each query is effectively represented with the portion of the database that contributes to the query answer. Finally, it trains a neural network in a supervised learning fashion: both query features (input) and correct answers (labels) are known. The learned model generates accurate answers to new queries at test time, exploiting the generalizability of the learned model in the embedding space. Extensive experimental results on real datasets show up to 4 times for AVG queries and 10 times for COUNT queries error reduction compared with the state-of-the-art.
LingoLand: An AI-Assisted Immersive Game for Language Learning

Immersion with a foreign language is key to increased motivation, satisfaction, and learning success. However, this can be hindered by anxiety and lack of access to immersive settings. LingoLand will address this by immersing people in foreign language learning environments designed to mimic real scenarios. Using generative machine learning, LingoLand presents players with personalized missions where they can freely interact with game characters, all endlessly patient and supportive of new language learners. Players receive instant feedback through a natural, voice-based interaction. Through LingoLand, players gain an understanding of different cultures while building practical language skills in a fun way.
Using Personal Situated Analytics (PSA) to Interpret Recorded Meetings

Applications in immersive environments have gained popularity for training, learning, and recreational tasks. Due to the increasing availability of sensors and data-capturing devices, research is extending the use of immersive environments to support visual analytics processes, including sensemaking and strategic immersion for interaction and task completion. In this work, we propose Personal Situated Analytics (PSA) framework to embed users into recorded meetings with support for multiple degrees of immersion in the Reality-Virtuality spectrum. Our proposed framework encompasses various stages such as tracking, data capturing, data cleaning, data synchronization, prototype building, and deploying the final product to end-user hardware. We evaluate this framework on a data analysis scenario between human subjects and a conversational AI agent. Our pilot study (n=12) using this framework compares user experiences when using two different devices: Hololens2 and Quest2.
AmplifiedCoaster: Virtual Roller Coaster Experience using Motorized Ramps and Personal Mobility Vehicle

A novel virtual reality (VR) ride consisting of a head-mounted display (HMD) and an electric wheelchair was developed for a virtual roller coaster experience. The VR ride was integrated with an electric wheeled ramp to amplify the perception of virtual ascent and descent. The ascending and descending motion on a slope was replicated continuously with varying curvatures. The system features an electric wheelchair and two motorized ramps with a fixed 10-degree angle, which the wheelchair can drive on and off to simulate ascending and descending in VR. Moreover, by adjusting the relative speed and position of the wheelchair and ramps, we can simulate pitch angle curvature in VR. We are investigating several methods for providing multiple ascent and descent experiences, and introduce one of them.
Packed to the Brim: Investigating the Impact of Highly Responsive Prefixes on Internet-wide Measurement Campaigns

Internet-wide scans are an important tool to evaluate the deployment of services. To enable large-scale application layer scans, a fast, stateless port scan (e.g., using ZMap) is often performed ahead of time to collect responsive targets. It is a common expectation that port scans on the entire IPv4 address space provide a relatively unbiased view as they cover the complete address space. Previous work, however, has found prefixes where all addresses share particular properties. In IPv6, aliased prefixes and fully responsive prefixes, i.e., prefixes where all addresses are responsive, are a well-known phenomenon. However, there is no such in-depth analysis for prefixes with these responsiveness patterns in IPv4. This paper delves into the underlying factors of this phenomenon in the context of IPv4 and evaluates port scans on a total of 161 ports (142 TCP &amp; 19 UDP ports) from three different vantage points. To account for packet loss and other scanning artifacts, we propose the notion of a new category of prefixes, which we call highly responsive prefixes (HRPs). Our findings show that the share of HRPs can make up 70% of responsive addresses on selected ports. Regarding specific ports, we observe that CDNs contribute to the largest fraction of HRPs on TCP/80 and TCP/443, while TCP proxies emerge as the primary cause of HRPs on other ports. Our analysis also reveals that application layer handshakes to targets outside HRPs are, depending on the chosen service, up to three times more likely to be successful compared to handshakes with targets located in HRPs. To improve future scanning campaigns conducted by the research community, we make our study's data publicly available and provide a tool for detecting HRPs. Furthermore, we propose an approach for a more efficient, ethical, and sustainable application layer target selection. We demonstrate that our approach has the potential to reduce the number of TLS handshakes by up to 75% during an Internet-wide scan while successfully obtaining 99 % of all unique certificates.
Reversible Data Hiding in Encrypted Images Based on Quantization Prediction Error

Reversible data hiding in encrypted images (RDH-EI) can simultaneously protect secret data and the content of transmission carriers, so it has important applications in cloud computing, medicine, and other fields involving data privacy. Aiming at the problem of low embedding capacity in current RDH-EI algorithms, a separate algorithm based on interpolation prediction error quantization is proposed. First, we down-sample the cover image to obtain the sampled pixels. Then interpolate and predict non-sampled pixels to obtain auxiliary data such as prediction errors and classify the auxiliary data. By introducing a quantitative prediction error loss factor, auxiliary data can be compressed to various degrees, reducing the amount of auxiliary data. Next, auxiliary data is embedded into sampled pixels through reversible data hiding (RDH) technology, and the partial of non-sampled pixels. Finally, according to the hiding key, the mark data and secret data are embedded in the encrypted image. At the receiver, a legitimate user extracts secret data and recovers the cover image with owned keys. Experimental results show that the proposed algorithm can ensure the error-free extraction of secret data and provide lossy and lossless versions of the cover image. In lossy versions, the maximum embedding rate can be around 4bpp. Compared to other advanced algorithms, the proposed algorithm has the advantages of more flexibility, a high embedding rate, and recovery quality.