Reviewerly/exHarmony huggingface dataset:
Distributed Bernstein–Vazirani algorithm

Distributed quantum computation has been considered an alternative and beneficial application in the noisy intermediate-scale quantum (NISQ) era, which needs fewer qubits and quantum gates. In this paper, we consider the Bernstein–Vazirani (BV) problem that needs to identify the hidden string s∈{0,1}n of Boolean function fs(x)=〈s⋅x〉=∑i=0n−1si⋅ximod2:{0,1}n→{0,1}. In the first instance, we subtly generate t subfunctions fSnj(mj)=〈Snj⋅mj〉:{0,1}nj→{0,1} according to fs(x), where Snj∈{0,1}nj, ∑j=0t−1nj=n, and j∈{0,1,…,t−1}. After that, we propose a distributed Bernstein–Vazirani algorithm (DBVA) with 2≤t≤n computing nodes, which will exactly acquire s=Sn0Sn1⋯Snt−1∈{0,1}n. To be more specific, (1) the query complexity of DBVA is O(1) rather than O(n); (2) the circuit depth of DBVA is 2max(n0,n1,…,nt−1)+3 less than the circuit depth of the BV algorithm 2n+3; (3) we request neither auxiliary qubits nor further classical queries; (4) we explicate how DBVA decomposes a 6-qubit BV algorithm into two 3-qubit or three 2-qubit BV algorithms on MindQuantum (a quantum software), which validates the correctness and practicable of DBVA. Eventually, by simulating the algorithms running in the depolarized channel, it further illustrates that distributed quantum algorithms have the superiority of resisting noise.
A multi-scale self-supervised hypergraph contrastive learning framework for video question answering

Video question answering (VideoQA) is a challenging video understanding task that requires a comprehensive understanding of multimodal information and accurate answers to related questions. Most existing VideoQA models use Graph Neural Networks (GNN) to capture temporal-spatial interactions between objects. Despite achieving certain success, we argue that current schemes have two limitations: (i) existing graph-based methods require stacking multi-layers of GNN to capture high-order relations between objects, which inevitably introduces irrelevant noise; (ii) neglecting the unique self-supervised signals in the high-order relational structures among multiple objects that can facilitate more accurate QA. To this end, we propose a novel Multi-scale Self-supervised Hypergraph Contrastive Learning (MSHCL) framework for VideoQA. Specifically, we first segment the video from multiple temporal dimensions to obtain multiple frame groups. For different frame groups, we design appearance and motion hyperedges based on node semantics to connect object nodes. In this way, we construct a multi-scale temporal-spatial hypergraph to directly capture high-order relations among multiple objects. Furthermore, the node features after hypergraph convolution are injected into a Transformer to capture the global information of the input sequence. Second, we design a self-supervised hypergraph contrastive learning task based on the node- and hyperedge-dropping data augmentation and an improved question-guided multimodal interaction module to enhance the accuracy and robustness of the VideoQA model. Finally, extensive experiments on three benchmark datasets demonstrate the superiority of our proposed MSHCL compared with stat-of-the-art methods.
Education 4.0 Maturity Models for Society 5.0: Systematic literature review

Society 5.0 is a transformative vision for the future driven by integrating digital technologies and human-centered approaches, fusing with cyber-physical spaces to create a smart society that addresses megatrends through innovative and collaborative solutions by stakeholders. This article analyzes Maturity Models (MMs) in Higher Education to identify the components of Education 4.0 that aim to achieve Society 5.0, seeking the dimensions and levels associated with the quintuple helix and the mission of the HEIs. We conducted a systematic literature review (SLR) guided by research questions to highlight studies that address Maturity Models worldwide, identify components of Education 4.0 in the MM, research methods and instruments used, internal and external stakeholders, and some characteristics of the university missions. The study method employed was a Systematic Review analyzing 26 articles from 2018 to 2023 found in WoS and Scopus databases using inclusion and exclusion criteria under a Prisma workflow. The findings were that the HEIs must: a) analyze dimensions of various Education 4.0 components in a balanced way, b) incorporate comparative designs and mixed methods of the MM models, c) provide technology transfer services, training, and data centers, d) provide fully open environments to educate all citizens inclusively, and e) use sustainable MMs for a significant difference in impact. This review offers educational leaders and policymakers the methodologies for measuring the Education 4.0 path to Society 5.0.
Research on the innovative teaching mode of rhythmic gymnastics course under the condition of multimedia and network technology

With the rapid development of multimedia and network technology, rhythmic gymnastics courses need to be innovated in the teaching mode. This paper aims to explore innovative teaching mode and apply multimedia and network technology to rhythmic gymnastics courses to improve students' learning effect and interest. Through the implementation of this innovative teaching mode, it aims to provide valuable reference for the field of rhythmic gymnastics education, enhance students 'interest in rhythmic gymnastics courses, and promote students' comprehensive development in skills, performance, creativity and other aspects.
Machine learning methods for speech emotion recognition on telecommunication systems

The manuscript is devoted to the study of human behavior in stressful situations using machine learning methods, which depends on the psychotype, socialization and a host of other factors. Global mobile subscribers lost approximately $53 billion in 2022 due to phone fraud and unwanted calls, with almost half (43%) of subscribers having spam blocking or caller ID apps installed. Phone scammers build their conversation focusing on the behavior of a certain category of people. Previously, a person is introduced into a state of acute stress, in which his further behavior to one degree or another can be manipulated. We were allowed to single out the target audience by research by Juniper Research. These are men under the age of 44 who have the highest risk of being deceived by scammers. This significantly narrows the scope of research and allows us to limit the behavioral features of this particular category of subscribers. In addition, this category of people uses modern gadgets, which allows researchers not to consider outdated models; has stable health indicators, which allows not to conduct additional studies of people with diseases of the heart system, because. Their percentage in this sample is minimal; and also most often undergoes a polygraph interview, for example, when applying for a job, and this allows us to get a sample sufficient for training the neural network. To teach the method, polygrams were used, marked by a polygraph examiner and a psychologist of healthy young people who underwent a scheduled polygraph test for company loyalty. For testing, the readings of the PPG sensor built into the smart bracelet were taken and analyzed within a month from young people who underwent a polygraph test. We have developed a modification of the wavelets capsular neural network—2D-CapsNet, allowing to identify the state of panic stupor by classification quality indicators: Accuracy—86.0%, Precision—84.0%, Recall = 87.5% and F-score—85.7%, according to the photoplethysmogram graph (PPG), which does not allow him to make logically sound decisions. When synchronizing a smart bracelet with a smartphone, the method allows real-time tracking of such states, which makes it possible to respond to a call from a telephone scammer during a conversation with a subscriber. The proposed method can be widely used in cyber-physical systems in order to detect illegal actions.
The influence of a highly-valued market on M&amp;A activity

Merger and Acquisition decisions are about timing, opportunity, and decision. In the business world,&#x0D; these three words are partially influenced by the strength of the stock market. The timing is when the&#x0D; stock market is highly valued which results in high stock valuation, after that is the opportunity which&#x0D; means to find a target or if an existing target has lower stock valuation and finally comes the decision to&#x0D; make whether to use cash or equity for the transaction. Since companies can use its stock as leverage to&#x0D; acquire companies, a highly valued market gives the companies the resource with which to make purchases.&#x0D; Therefore, the question arises whether the stock market influences the decision for companies&#x0D; to engage in M&amp;A transactions. This question is addressed in this study using a relatively large panel&#x0D; data of M&amp;A activity for the period 2008-2017. The econometric tool used in this study is regression&#x0D; analysis. The measurement that this study used to value the stock market is by measuring the stock&#x0D; market capitalization to GDP ratio of a country. The results displayed that the stock market to GDP ratio&#x0D; is statistically significant at 5% risk on the M&amp;A activity and with a 1% increase in the stock market&#x0D; we expect a 0.1729% increase in the M&amp;A activity. Additionally, the paper suggests the development of&#x0D; a regression model that can measure the FDI inflows in Albania, and this is achieved by adjusting the&#x0D; regression model variables in order to become more suitable for its economy.
Protection of documents against sophisticated photocopying and scanning

A novel method, for designing pantographs, is presented. An iterative algorithm is used to design a distribution of bright and dark spots to be embedded in the original document. This distribution should make the word “void”, “invalid”, or “copy” visible on the photocopy or the scanned version of the original document. We used an iterative algorithm to optimize the pantograph. We can start from a random distribution, or from a deterministic distribution based on Dirac pulses properties.
Exploration and Practice of College English Teaching Model Based on PAD Class

Addressing the problems in College English reading, writing and translation teaching, the study adopts the "condensation-expansion" and "AB double Texts" based on PAD Class teaching model, which solves problems in the traditional College English teaching. This study includes three parts: guiding ideology, teaching mode, and operating methods of "condensation-expansion PAD and AB double text" in English reading, writing, and translation teaching. It mainly addresses the long-standing problems of imbalanced teaching objectives, educational orientation bias, and lack of skills in stimulating learning interests in English reading, writing, and translation teaching and provides effective methods at the operational level for the specific implementation of teachers, and makes breakthrough contributions to solving the operational problems of stimulating students' learning motivation in teaching practice.
Growth Reduction of Similarity-Transformed Electronic Hamiltonians in Qubit Space

Accurately solving the electronic structure problem through the variational quantum eigensolver (VQE) is hindered by the available quantum resources of current and near-term devices. One approach to relieving the circuit depth requirements for VQE is to "pre-process" the electronic Hamiltonian by a similarity transformation incorporating some degree of electronic correlation, with the remaining correlation left to be addressed by the circuit ansatz. This often comes at the price of a substantial increase in the number of terms to measure in the similarity-transformed Hamiltonian. In this work, we propose an efficient approach to sampling elements from the complete Pauli group for N qubits which minimizes the onset of new terms in the transformed Hamiltonian while facilitating substantial energy lowering. We benchmark the growth-mitigating generator selection technique for ground state energy estimations applied to models of the H4, N2, and H2O molecular systems. It is found that utilizing a selection procedure which obtains the growth-minimizing generator from the set of operators with the maximal energy gradient is the most competitive approach to reducing the onset of Hamiltonian terms while achieving systematic energy lowering of the reference state.
FNRegion: A fast NAM‐based region extraction algorithm

Abstract Region extraction is usually used by many computer vision tasks as a pre‐processing step to extract image features. However, how to efficiently extract effective regions remains a challenging problem. In this paper, inspired by the non‐symmetry and anti‐packing pattern representation model (NAM) and the FatRegion algorithm, a fast NAM‐based region extraction algorithm which is called FNRegion is proposed. A NAM‐based homogeneous block generation algorithm is first presented to represent an image as a combination of multiple homogeneous blocks, each of which is a square region with visually indistinguishable intra‐region colour difference. Then, these homogeneous blocks are merged into larger regions according to their colour and shape information. To group these regions into larger ones in order to progressively build a region tree, a distance function is defined using variety of regional information to measure the distance between adjacent regions. Also, a multi‐feature region merging algorithm with linear complexity both in time and space is presented.The proposed algorithm has been evaluated on multiple public datasets in comparison with the state‐of‐the‐art region extraction algorithms. The experimental results show that in the case of almost the same or even less running time as other fast region extraction algorithms, the proposed algorithm is able to extract higher‐quality regions.
Users of Online Child Sexual Abuse Material

Abstract Online communities on darknet contribute to sexual violence against children. They provide offender access to Child Sexual Abuse Material and to a group of peers that supports criminal activities. This article sheds light on online child sexual abusers and their justifications for Child Sexual Abuse (CSA). It describes different offender profiles and motivations for CSA, followed by a section on policing these offences. The article then lays out psychological models, such as cognitive distortions, that are used to understand pedophiles’ behavior. The discussion is complemented by direct citations from a darknet community of CSA offenders, showing that these models are not just theoretical considerations. The last part of the article concentrates on online communities of CSA offenders. It describes different types of members of such communities and explains how the communities support individual offenders and how they provide learning models that facilitate criminal behavior. The article concludes with a short reflection of its findings, including novel insights for investigators of these crimes and proposed venues for further research.
Extracting Business Activities for Digital Transformation in the SET Healthcare Sector Using Verb Phrases Analysis

Without standardized roadmap, firms in the healthcare sectors have found it challenging to adopt digital transformation. This study, based on signaling theory, alleviated the issue by applying automated information extraction to retrieve such demanding knowledge from firms’ information disclosure while avoiding a tedious and time-consuming task of manually extracting information from huge text documents. The main objectives of this study were 1) to extract evolving business activities relating to digital healthcare transformation and 2) to identify whether extracted business activities align with a particular digital maturity model. The samples were firms listed in the healthcare sector of the Stock Exchange of Thailand. The data was obtained from firms’ annual reports covered period 2017–2021. To extract business activities, this study employed phrase extraction technique based on words’ parts of speech tagging. The extracted phrases then were filtered using custom-made dictionary. The findings indicated that while investing in key infrastructure and leveraging digital technology to improve business processes made up the majority of a company’s early period business activities, later timeframe business activities were more focused on value generation activities and new business models creation. The changes of activities in different periods also suggested that some business activities might require prerequisite activities. Assigning extracted business activities into relevant dimensions of the specific maturity model further proved that the extracted business activities adhere to the elements of chosen digital maturity model. The elements carried out by firms expressed the nature of the sector as well as how highly they prioritize digital transformation.
The Initial Coin Offering: Is It a Profitable Tool for Investment?

Initial Coin Offerings (ICOs) have established themselves since 2017 as an attractive alternative for financing innovative companies. However, these new modes of financing from the world of blockchain technology have a very volatile return on investment (ROI) in the short term, and very ambivalent in the medium and long term: 90% of ICOs issued since 2017 have failed. This article attempts to shed some light on the question of the ROI of ICOs. The analysis of some databases of the most significant platforms for ICOs reveals that the highest ROIs reached on average 3100% at their peak and 30% on average over the period 2017- 2022. The results of the analysis suggest that given the level of risk of ICOs, they do not represent a good financial asset over a long period; on the other hand, they would be a good lever for speculation and possibly a good financial strategy to boost the financial performance of a diversified portfolio.
Consumer Experience and Augmented Reality Wine Label Application

Augmented reality (AR) applications are regarded as effective experiential marketing practices that can help companies promote their products/services in an interactive manner and deliver exceptional consumer experiences. The purpose of the present study is to evaluate a wine-label AR mobile application by examining its impact on consumer experience dimensions, satisfaction, and re-usage intentions towards the application, as well as attitude and purchase intentions towards the wine product. Moreover, to test the effect of product-consumption related factors (consumption frequency, amount of spending, wine expertise, and attention to wine labels) and technology-related factors (consumers’ familiarity with smartphone applications, number of AR applications used in smartphone, and extent of information search for wine-related information through smartphones) on the experiential dimensions of entertainment, flow, escapism, and education. Towards this end, a wine AR label application was developed and evaluated using a quantitative survey. In total, 306 respondents answered a self-administered questionnaire after interacting with the application. Results indicate that the AR application induced the entertainment and educational dimensions of consumer experience. The AR experience was also able to increase respondents’ satisfaction with the application and in turn help them form positive attitudes and purchase intentions for the wine. Moreover, the present study revealed that respondents’ expertise for wine, attention to wine label, familiarity with smartphone applications, and information search for wine-related information through smartphones are important factors that have an impact on the experience lived by consumers when using the AR label application.
Fact-checking Vietnamese Information Using Knowledge Graph, Datalog, and KG-BERT

In the era of digital information, ensuring the accuracy and reliability of information is crucial, making fact-checking a vital process. Currently, English fact-checking has thrived due to various language processing tools and ample datasets. However, the same cannot be said for Vietnamese fact-checking, which faces significant challenges due to the lack of such resources. To address these challenges, we propose a model for checking Vietnamese facts by synthesizing three popular technologies: Knowledge Graph (KG), Datalog, and KG-BERT. The KG serves as the foundation for the fact-checking process, containing a dataset of Vietnamese information. Datalog, a logical programming language, is used with inference rules to complete the knowledge within the Vietnamese KG. KG-BERT, a Deep Learning (DL) model, is then trained on this KG to rapidly and accurately classify information that needs fact-checking. Furthermore, to put Vietnamese complex sentences into the fact-checking model, we present a solution for extracting triples from these sentences. This approach also contributes significantly to the ease of constructing foundational datasets for the Vietnamese KG. To evaluate the model's performance, we create a Vietnamese dataset comprising 130,190 samples to populate the KG. Using Datalog, we enrich this graph with additional knowledge. The KG is then utilized to train the KG-BERT model, achieving an impressive accuracy of 95%. Our proposed solution shows great promise for fact-checking Vietnamese information and has the potential to contribute to the development of fact-checking tools and techniques for other languages. Overall, this research makes a significant contribution to the field of data science by providing an accurate solution for fact-checking information in Vietnamese language contexts.
Respon Pembaca Terhadap Kumpulan Puisi Hujan Bulan Juni Sepilihan Sajak Karya Sapardi Djoko Damono

&#x0D; &#x0D; &#x0D; &#x0D; This study describes the reader's response to the ease and interest of students of SMK Negeri 1 Pekanbaru in understanding the elements of physical structure in the 10 poems in the collection of the poetry of Hujan Bulan Juni Sepilih Sajak by Sapardi Djoko Damono, who is one of the most famous writers in Indonesia with his various literary works, through distribution. a questionnaire of 50 students of SMK Negeri 1 Pekanbaru taken purposively. This research is a qualitative research. This research uses descriptive method. Data collection techniques in this study were obtained from questionnaires and interviews as supporters. The data analysis technique in this study begins with the steps, namely running a questionnaire, classifying the results of the response, grouping, discussing, connecting, making results, and drawing conclusions to get a picture of the object under study. Based on the results of data analysis, it was found that 10 poems from the collection of Hujan Bulan Juni Sepilih Sajak by Sapardi Djoko Damono received positive responses from readers, namely as many as 4,922 and negative responses from readers as many as 1,578, so if the two responses were put together, the results were 6,500 responses.&#x0D; &#x0D; &#x0D; &#x0D;