Reviewerly/exHarmony huggingface dataset:
The Role of Information Technology Governance on Enhancing Cybersecurity and its Reflection on Investor Confidence

Purpose: The aim of this study is to investigate the relationship between information technology governance and cyber security, and how this relationship affects investor confidence. Theoretical framework: The study drawing upon theories and concepts from information technology, risk management, and finance to provide a comprehensive understanding of the relationship between IT governance, cyber security, and investor confidence. Design/methodology/approach: The statistical software Smart-PLS was utilized to perform an analysis and extract relevant findings from the data collected from the sample group, which comprised 153 individuals. The results obtained through this process were integral to the successful implementation of the research in practice. Findings: The study found that investor confidence is impacted by cyber security, but neither investor confidence nor cyber security are significantly impacted by information technology governance. Research, Practical &amp; Social implications: The study highlights the crucial role of information technology governance dimensions in financial reports of businesses that operate in the IT industry, particularly telecommunications firms and private banks. By including such information in their financial statements, these organizations can effectively enhance investor confidence in their operations. Originality/value: The study's originality and value lie in its critique of the inadequate transparency and lack of interest in information technology governance in the financial reports of banks, telecommunications companies, and other sectors listed on the financial market. The findings underscore the significance of including such information in financial statements to boost investor confidence in these organizations.
Do androids dream of autobiography?

The modern development of ideas and technologies of artificial intelligence adds practical significance to philosophical debates concerning questions of identity and personal identity. The aim of this article is to demonstrate the advantage of the narrative approach for describing the identity of cybernetic organisms. For this purpose, a mental experiment is formulated to download the consciousness of a scientist into the body of an android. At the end of the article the author discusses a potential hierarchy of narrative criteria.
Economic and social sustainability of legacy blockchain for non-crypto use cases: a reality check

Predicted to be a $3+ trillion industry by 2030, blockchain has still not achieved commercial viability beyond its stupendous success in enabling a multi-trillion cryptocurrency industry. Very high transaction cost and vulnerability to centralisation limits blockchain' s full potential. As cryptocurrency thrives, blockchain struggles to find its rightful place. This paper looks at the public blockchain and its failed attempts at non-crypto use cases to arrive at an accurate diagnosis of what's ailing. Based on a de novo review of the literature, this study formulates and supports a hypothesis on blockchain's economic and social unsustainability. Although economic sustainability trumps environmental sustainability all the time, peer-reviewed literature is mostly silent on its economic sustainability, no one conducts environment damaging activity unless there is economic benefit. So far non-crypto use cases of blockchain have shown little or no economic benefit. Testing our hypothesis may help blockchain researchers define the future generation sustainable decentralised solutions.
Dampak Sistem Informasi Akuntansi Penjualan dan Pengeluaran Kas Terhadap Pengendalian Internal

The application of information systems in a business in general can increase competitive advantage by providing added value to the products and services it produces. The application of information systems requires input in the form of data and which will then be processed into information. This information will be needed by the company to assist its operations and also in making decisions. The purpose of this study was to obtain a comprehensive picture of the effect of the Sales and Cash Disbursement Accounting Information System in improving Internal Control. The author in conducting research using descriptive research, using quantitative methods. The researcher uses descriptive research because the questions in this study are asking the relationship between two variables with the questionnaire research data collection procedure (questionnaire) which aims to obtain a clear picture of the research. Where data collection is done through literature, field and questionnaires. Through research conducted by the author, the results obtained include: the results of calculations between the independent variables, namely the Sales Accounting Information System and Cash Expenditures and the dependent variable, namely Internal Control. The results of hypothesis testing in this study indicate that the sales and cash disbursement accounting information system has a significant and positive effect on internal control.&#x0D; &#x0D; Keywords: Accounting Information System, Cash Disbursements, Internal Control
A dataset of human body tracking of walking actions captured using two Azure Kinect sensors

A dataset of body tracking information is presented. The dataset consists of 315 captured walking sequences. Each sequence is simultaneously captured by two Azure Kinect devices. The two captures are interleaved to effectively double the frame rate. Fifteen participants partook in this experiment. Each experiment consists of seven walking actions, and having three predefined trajectories per experiment. That results in 21 sequences per participant. The data were collected using the Azure Kinect Sensor SDK. They were later processed using the official tools and libraries provided by Microsoft. For each sequence and trajectory, the positions and orientations of thirty-two tracked joints were obtained and saved. The dataset is structured as follows. The experiments from each subject are saved in a single directory. Each directory contains multiple JSON files of timestamped body tracking information to enable the fusion of the two device streams. A calibration file is also provided, enabling the mapping of the coordinates between the two Azure Kinect devices capturing the data (mapping the coordinates of the device known as the Subordinate device to the Master device coordinate system). This data can be used to train neural networks for human motion prediction tasks or test pre-existing algorithms on Azure Kinect data. This dataset could also aid in gait recognition and analysis, as well as in performing action recognition and other surveillance activities. The dataset can be found at https://zenodo.org/record/7997856.
Constructing Collaborative Innovation Ecological Environment for College students innovation Ability Cultivation

There are many problems among contemporary college students, such as weak innovation consciousness, insufficient innovation motivation and poor ability of innovation achievements transformation. Through analyzing the relevant elements of innovation chain, ideas about the construction of collaborative innovation ecological environment is proposed for improving the teaching effect of innovation education for college students in this paper. The concrete measures and effects about the practical platform of university-enterprise collaborative innovation, the comprehensive and multi-dimensional innovation education system in universities, and the construction of first-class undergraduate courses in innovation education are introduced in detail. The practice shows that the construction of collaborative innovation ecological environment fundamentally solves the “bottleneck” problem of college students innovation, and provides inexhaustible power for the cultivation of college students innovation ability.
Pengaruh Penerapan Kebijakan Pembelajaran Daring Terhadap Hasil Belajar Mahasiswa Di PG PAUD Universitas PGRI Argopuro Jember

In the implementation of online learning policies during the Covid 19 pandemic at PGRI PAUD Universitas PGRI Argopuro Jember, when viewed from the existing obstacles, it will affect student learning outcomes at PG PAUD Universitas PGRI Argopuro Jember. The intended learning outcomes are the results obtained by students from the results of lecturer assessments in lecture activities in one semester. The research method used in this research uses a quantitative approach. This research is to measure and assess what effect the implementation of online learning policies has on student learning outcomes in the PG PAUD Undergraduate Study Program, PGRI Argopuro University, Jember. Researchers use observation sheets in data collection. The respondents in this study were 90 students consisting of semester 1 and semester 3 students of the PG PAUD Undergraduate Program at PGRI Argopuro University Jember. The results of the research analysis show that there is an influence on the application of online learning policies on student learning outcomes at PGRI PAUD University and PGRI Argopuro Jember. This was obtained because the sign value was between 0.01 and less than 0.05, so the online policy in this study had an effect on student learning outcomes. Meanwhile, the correlation/relationship value is 0.366, and from the results of this output, an R square of 0.127 is also obtained, which means that the influence of online learning policies is 12.7% on student learning outcomes .
Fast Keyword Search over Encrypted Data with Ciphertext in Cloud

At present times , it's accessible for people to store their data on clouds. To secure the privacy, people tend to encode their data before uploading them to clouds. Due to the wide use of cloud services, public key searchable encryption is necessary for users to search the encrypted files efficiently and rightly. still, the being public key searchable encryption schemes supporting monotonic queries suffer from either infeasibility in keyword testing or inefficiency similar as heavy computing cost of testing, large size of ciphertext or lattice, and so on. In this work, we first propose a novel and effective anonymous key- policy attribute- based encryption( KP- ABE). also by applying Shen etal.’s general construction to the proposed anonymous KP- ABE, we capture an effective and suggestive public key searchable encryption, which to the best of our knowledge achieves the best performance in testing among the existing similar schemes
Competencia digital y formación profesional en España: análisis documental sobre su regulación, propuestas y recomendaciones

Digital Competence (DC) is currently presented as an ideal mechanism in Vocational Education and Training (VET) to respond to technological and economic changes originated in recent years. In this context, VET teachers play a fundamental role in preparing students, but they need to have sufficient knowledge in DC to be able to carry out an adequate transfer of knowledge. The objective of this research is to analyze how Training Digital Competence (TDC) for VET Teachers is proposed through official documents in Spain and specifically in Catalonia. The methodology is qualitative and uses a documentary analysis on institutional, legislative sources and reports on TDC and VET. The results show a large effort related to investment in infrastructures to achieve highly technical classrooms and the training of teachers and students in DC to be able to respond to the needs of the productive sector. It is concluded that there is a greater interest in DC training for teachers and students focused on professionalization and economic production rather than on didactic-pedagogical aspects that help strengthen a digitized citizenship.
Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research.

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.
Virtualization, Virtual Reality and Ethics

This chapter discusses the new developments and consequences of virtualization technology and its implications on our participation and how the technology informs our behavior based on our traditional moral and ethical values. In a more detailed way, we define virtualization as a process which embodies both abstraction and reconstruction and as it creates a sense of complete participants’ immersion yet with autonomyAutonomy of participants to vary their chosen new environments to suit individual likings. As defined, virtualization, therefore, conjures uncertainty and fear not of the environment but of the individual who partakes in the activities of the environment. Our discussion of both social and ethical issuesEthicalissues that arise within and outside the environment focused on two types of virtualization: computing resources virtualization and virtual reality (VR)Virtual reality (VR). We note that virtualization is also bringing about easy creation of new human identities in the new virtual environments which makes authenticationAuthentication more difficult but at the same time creating unprecedented potential in self-creation and self-presentation. We note, however, that while these benefits may bring new opportunities and new potential that may empower individuals to new levels of creativity, these unparalleled opportunities of virtualization may come at a price to society.
Quality Traceability for User-centric Context-aware Systems in Intelligent Environment

Context-awareness is an important component of modern software systems. For example, in Ambient Assisted Living (AAL), the concept of context-awareness empowers users by reducing their dependence on others. Due to this role in healthcare, such systems need to be reliable and usable by their intended users. Our research addresses the development, testing and validation of context-aware systems in an emerging field which currently lacks sufficient systems engineering processes and disciplines. One specific issue being that developers often focus on delivering a system that works at some level, rather than engineering a system that meets a specified set of system requirements and their corresponding qualities. Our research aims to contribute towards improving the delivery of system quality by tracing, developing and linking systems development data for requirements, contexts including sensors, test cases and their results, and user validation tests and their results. We refer to this approach as the “quality traceability of context-aware systems”. In order to support the developer, the quality traceability of context-aware systems introduces a systems development approach tailored to context-aware systems in intelligent environments, an automated system testing tool and system validation process. We have implemented a case study to inform the research. The case study is in healthcare and based on an AAL system used to remotely monitor and manage, in real time, an individual prone to depressive symptoms.
Authenticity of Online Publications via IPFS Blockchain

The world of the internet and the advent of technology have altered the digital era and how we access the stuff we publish or post on the Internet, such as books, music, and movies. Make it available on the internet. This was accomplished by integrating two technologies: the Interplanetary File System (IPFS) and blockchain smart contracts. We use IPFS to securely store digital stuff and make it available to anyone. We create a unified framework for creating and editing material using Ethereum smart contracts, enabling oversight and visibility of all content from creation to final publication. In an accessible world, this strategy promotes justice, efficiency, and openness. We offer all of the smart contract code, detail its implementation and testing, and demonstrate its significance.
Energy-Constrained Task Scheduling in Heterogeneous Distributed Systems

The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient.
Item Multi-Information Evolution Network for Click-Through Rate Prediction

Click-through rate prediction plays a critical role in many fields, and many efforts are devoted to analyzing item behavior as a way to find the interest preferences of target user. Most previous work has not taken into account and fully explored the basic information, historical behavior and temporal information of similar users, which can result in missing information and lead to inaccurate CTR prediction. To solve the above problem, we propose Item Multi-Information Evolution Network (IMIEN). Firstly, for the basic information of users, we use RNN sequences to capture the evolutionary dynamics of users interested in the target item. Secondly, we use Evolutionary Interest Extraction Block (EIEB) to mine the evolutionary interest of target user and similar users over time. Finally, we introduce temporal information to find recent user groups with similar interests as the target user, which aids in predicting the probability of the target user clicking on the target item. We achieve the best results on five public datasets compared with previous mainstream models, which validates the effectiveness of IMIEN.
Lightweight Image Dehazing Algorithm Based on Detail Feature Enhancement

Haze can reduce the visibility of the captured image, making it hard to accurately distinguish the details of each object in the captured image scene. Aiming at the problem of detail loss in existing dehazing models, this paper proposes a lightweight end-to-end image dehazing framework called DFE-GAN (Detail Feature Enhancement-GAN). The missing detail contours in the haze image can be predicted by employing a densely connected detail feature prediction network. Supplemented with a patch discriminator and an improved loss function, the restoration of details in the dehazing image is enhanced to improve image quality. We apply inverse residual modules to extract and fuse multi-scale features from images, which can ensure the real-time processing capability of the model. Compared with previous state-of-the-art approaches, solid experimental results on various benchmark datasets validate the robustness and effectiveness of our model.
Covertness Analysis of Snowflake Proxy Request

Snowflake is a special proxy system against IP-based network blocking. As its IP addresses refresh frequently, faster than IP blacklist’s update, users can exploit it to access blocked websites. To block snowflake, existing methods focus on detecting snowflake proxies. But they are susceptible to various factors, for example, proxy’s location and version. In the paper, we propose a new manner to block snowflake. We observe that to adapt fast IP changes, users need to request latest proxies from proxy database before using snowflake. Thus, adversaries can block snowflake by detecting proxy request instead of proxy itself. To verify our method, we analyse covertness of snowflake proxy requests, that has been protected by imitating normal web requests. After comparing with typical web requests, we find the imitation is vulnerable in packet size, direction, time and network speed, such as, the latency time is higher than normal obviously. Using the four vulnerabilities, we train machine learning algorithm to detect snowflake proxy requests in reality. Experimental results demonstrate that proxy request can be detected accurately across different versions at the beginning of connection. In conclusion, our work paves a new way to block snowflake.
Cross-Modal Fusing Vision-Language Network for Referring Image Segmentation

Reference Image Segmentation (RIS) is a challenging task, swhich aims to match the foreground mask of segmented entities with natural language expressions. In order to solve the RIS problem, we propose two modules: Cross-Modal Fusing Vision-Language (CMFVL) module, Vision and Language Sequence Generation (VLSG) module. VLSG receives visual features and language features, generates feature sequences through attention mechanism, and then sends the sequences to CMFVL. CMFVL module first establishes global attention to vision features through Transformer and then inputs vision features and feature sequences generated by VLSG into Transformer for context information interaction. Experiments on three benchmark datasets confirm the effectiveness of our method.
SKRAG: Sampling on Knowledge Graph for Recommendation with Relational Path-aware Graph Neural Network

Recently, the recommender system combining knowledge graph (KG) is gaining traction from researchers. However, current research faces two problems: The first is most existing works ignore the fact that real-world knowledge graphs are often noisy and contain lots of recommendation-irrelevant connections. The second is item-linked relations can reflect different attributes of items, however, previous works usually ignore the different relational semantics carried by different types of relational paths, leading to suboptimal recommendation performance. To address these issues, we propose a novel model: Sampling on Knowledge Graph for Recommendation with Relational Path-aware Graph Neural Network (SKRAG). We design a relational path-aware sampling method to extract recommendation-relevant information in KG, further mitigating the impact of KG noise; Then, we utilize relational path information to capture item long-range knowledge associations with explicit attribute semantics and extract user potential purposes for learning high-quality item and user representations. We conduct extensive experiments on three datasets, and the experimental results show the superiority of our model.