Reviewerly/exHarmony huggingface dataset:
Quantum Obfuscation of Generalized Quantum Power Functions with Coefficient

Quantum obfuscation is one of the important primitives in quantum cryptography that can be used to enhance the security of various quantum cryptographic schemes. The research on quantum obfuscation focuses mainly on the obfuscatability of quantum functions. As a primary quantum function, the quantum power function has led to the development of quantum obfuscation because it is applicable to construct new obfuscation applications such as quantum encryption schemes. However, the previous definition of quantum power functions is constrained and cannot be beneficial to the further construction of other quantum functions. Thus, it is essential to extend the definition of the basic quantum power function in a more general manner. In this paper, we provide a formal definition of two quantum power functions called generalized quantum power functions with coefficients, each of which is characterized by a leading coefficient and an exponent that corresponds to either a quantum or classical state, indicating the generality. The first is the quantum power function with a leading coefficient, and the second is the quantum n-th power function, which are both fundamental components of quantum polynomial functions. In addition, obfuscation schemes for the functions are constructed by quantum teleportation and quantum superdense coding, and demonstrations of their obfuscatability are also provided in this paper. This work establishes the fundamental basis for constructing more quantum functions that can be utilized for quantum obfuscation, therefore contributing to the theory of quantum obfuscation.
Hand Gesture Recognition Using Thin Plate Radiation and Gated-Recurrent-Unit, Based on Ultrasound Doppler

In recent years, touchless technologies for human-computer interaction have been widely developed. Doppler sonar makes it possible to extract information from hand gestures by emitting/receiving ultrasounds, and gestures recognition is generally achieved using features extracted from a gesture sequence as input to Convolutional Neural Network. This work aims at achieving an accurate and rich acoustical touchless gesture recognition with a low number of transducers and a low complexity real-time classifier. For this purpose, we use a thin plate as an acoustic antenna, excited by a few piezoelectric actuators, and capture the echoes with microphones around the plate. High amplitude emissions on a large bandwidth are achievable with a better-integrated system. Signal features selected to contain meaningful information on rich 3D gestures are computed and used as an input to a small Gated-Recurrent-Unit neural network. We achieve the detection and classification of 11 3D gestures with an accuracy of 93.5% with our system.
Dynamic Trust Boundary Identification for the Secure Communications of the Entities via 6G

6G is more likely prone to a range of known and unknown cyber-attacks because of its highly distributive nature. Current literature and research prove that a trust boundary can be used as a security door (e.g., gateway/firewall) to validate entities and applications attempting to access 6G networks. Trust boundaries allow these entities to connect or work with entities of other trust boundaries via 6G by dynamically monitoring their interactions, behaviors, and data transmissions. The importance of trust boundaries in security protection mechanisms demands a dynamic multi-trust boundary identification. There exists an automatic trust boundary identification for IoT data. However, it is a binary trust boundary classification and the dataset used in the approach is not suitable for dynamic trust boundary identification. Motivated by these facts, to provide automatic security protection for entities in 6G, in this paper, we propose a mechanism to identify dynamic and multiple trust boundaries based on trust values and geographical location coordinates of 6G communication entities. Our proposed mechanism uses unsupervised clustering and splitting and merging techniques. The experimental results show that entities can dynamically change their boundary location if their trust values and locations change over time. We also analyze the trust boundary identification accuracy in terms of our defined two performance metrics, i.e., trust consistency and the degree of gateway coverage. The proposed scheme allows us to distinguish between entities and control their access to the 6G network based on their trust levels to ensure secure and resilient communication.
Normative Evaluation Method of Long Jump Action Based on Human Pose Estimation

Addressing the issue of the lack of objective quantitative evaluation in training long jump events, this study presents a normative analysis method based on human pose estimation and similarity measures. By training a lightweight human pose estimation model, this method can run on low-delay embedded devices. In line with key movements, the proposed method designs a normative analysis for long jump actions, which yields a measurement of the movements’ adherence to the standard and provides corrective suggestions. Experimental results indicate that the accuracy of this approach in analyzing the standardization of long jump action reaches 91.3%. As a result, it holds significant application value in various scenarios, including students’ long jump training and correction of long jump techniques. Furthermore, it can be extended to other practical applications beyond sports.
Foreword

Welcome to the 2023 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). We proudly continue VL/HCC's tradition as the premier international forum for research on how people learn, express, and understand computational ideas and on the languages, tools, and interventions that can aid people in doing so. Our program this year was diverse as usual, and we also encouraged submissions and keynote talks around low-code / no-code programming systems, especially given the recent popularity of AI coding assistance tools based on large language models (LLMs) such as GitHub Copilot, ChatGPT, and others. With these new technologies developing at such a rapid pace, it is an exciting time to be learning and doing programming.
Interface-type tunable oxygen ion dynamics for physical reservoir computing

Abstract Reservoir computing can more efficiently be used to solve time-dependent tasks than conventional feedforward network owing to various advantages, such as easy training and low hardware overhead. Physical reservoirs that contain intrinsic nonlinear dynamic processes could serve as next-generation dynamic computing systems. High-efficiency reservoir systems require nonlinear and dynamic responses to distinguish time-series input data. Herein, an interface-type dynamic transistor gated by an Hf 0.5 Zr 0.5 O 2 (HZO) film was introduced to perform reservoir computing. The channel conductance of Mott material La 0.67 Sr 0.33 MnO 3 (LSMO) can effectively be modulated by taking advantage of the unique coupled property of the polarization process and oxygen migration in hafnium-based ferroelectrics. The large positive value of the oxygen vacancy formation energy and negative value of the oxygen affinity energy resulted in the spontaneous migration of accumulated oxygen ions in the HZO films to the channel, leading to the dynamic relaxation process. The modulation of the channel conductance was found to be closely related to the current state, identified as the origin of the nonlinear response. In the time series recognition and prediction tasks, the proposed reservoir system showed an extremely low decision-making error. This work provides a promising pathway for exploiting dynamic ion systems for high-performance neural network devices.
PASC23 Scales to New Heights, June 26--28

The Platform for Advanced Scientific Computing (PASC) Conference, co-sponsored by SIGHPC and the Swiss National Supercomputing Centre (CSCS), takes place from June 26 to 28, 2023 at the Congress Center Davos, located in Davos, Switzerland. This year's theme is Computing Across Scales, Domains, and Communities.
Optimal entanglement generation in GHZ-type states

Abstract The entanglement production is key for many applications in the realm of quantum information, but so is the identification of processes that allow to create entanglement in a fast and sustained way. Most of the advances in this direction have been circumscribed to bipartite systems only, and the rate of entanglement in multipartite system has been much less explored. Here we contribute to the identification of processes that favor the fastest and sustained generation of tripartite entanglement in a class of 3-qubit GHZ-type states. By considering a three-party interaction Hamiltonian, we analyze the dynamics of the 3-tangle and the entanglement rate to identify the optimal local operations that supplement the Hamiltonian evolution in order to speed-up the generation of three-way entanglement, and to prevent its decay below a predetermined threshold value. The appropriate local operation that maximizes the speed at which a highly-entangled state is reached has the advantage of requiring access to only one of the qubits, yet depends on the actual state of the system. Other universal (state-independent) local operations are found that conform schemes to maintain a sufficiently high amount of 3-tangle. Our results expand our understanding of entanglement rates to multipartite systems, and offer guidance regarding the strategies that improve the efficiency in various quantum information processing tasks.
Using AI Image Upscaler in Archives

AI image upscaling software can be a valuable tool for archives to improve the visual quality of their digitized collections. It enhances the resolution and quality of historical materials, making them more accessible to researchers and protecting fragile originals. However, it is important to use these techniques responsibly and ethically, with proper attention paid to issues of preservation, copyright, and privacy. There are several software programs that can be used for AI image upscaling in archives, and it is important to choose a program that produces accurate and realistic results.
5 Teaching Research Methods in Library and Information Science Programmes: The Russian Perspective

Intensive transformations are taking place globally in the community, culture, economy, education, environment, information and communications technology, and society at large with significant responses within library and information science worldwide. The new world order and the changes occurring require new approaches to library theory, practice and education based on high-quality research. Russian library and information science (LIS) programmes have a long and fruitful history of professional education and training with a variety of experiences in research methods teaching. The purpose of this chapter is to give an up-to-date overview of the LIS education system in Russia and its characteristics through programmes in LIS teaching research methods provided at the Библиотечно-информационный факультет/ Bibliotechno-informatsionnyj fakul’tet/Library and Information Faculty of the Санкт-Петербургский государственный институт культуры/ Sankt-Peterburgskiy gosudarstvennyy institut kul’tury/St. Petersburg State University of Culture1.
Artificial Intelligence and Human Society (Artificial Intelligence and Education)

The impact of AI technologies on different sectors has been profound, and one area where significant changes have occurred is education. In this abstract the integration of artificial intelligence technologies in education is explored, highlighting their potential advantages, challenges and ethical considerations. There are a wide range of tools and techniques in use for the application of AI to ducation, e.g. Intelligent Coaching Systems, Personalized Learning Platforms, Automated Scoreboards or Virtual Classrooms.
Blockchains In the Classroom: A Deductive Journey into The Abyss of Modern Education

The rapid advancement of blockchain technology has permeated various sectors, heralding a new era of transparency, security, and decentralized control. Education, a cornerstone of societal development, is not exempt from this technological tide. This research embarks on a deductive exploration of the integration of blockchain technology within the educational landscape, aiming to unveil the potential benefits and challenges therein. Through a meticulous review of existing literature, theoretical frameworks, and empirical analysis, this study delineates the transformative potential of blockchain in enhancing deductive reasoning and fostering a conducive learning environment. The research employs a mixed-method approach, encompassing surveys, interviews, and case studies across diverse educational settings. Findings reveal that blockchain technology can significantly bolster the integrity and accessibility of educational resources, streamline administrative processes, and enhance deductive reasoning among learners. Moreover, the study unveils the necessity of a robust regulatory framework and stakeholder education to harness the full potential of blockchain technology in education. The discourse extends to provide pragmatic recommendations for educators, policymakers, and blockchain developers to collaboratively nurture a blockchain-augmented educational ecosystem. This work aspires to contribute to the burgeoning body of knowledge at the nexus of blockchain technology, deductive reasoning, and modern education, propelling further scholarly inquiry and practical initiatives in this multidisciplinary domain.
Inference Engine driven by Situation-Based Correlation

Drawing an intelligible inference is a challenging aspect of correlation studies in data mining. Regression analysis and inferences drawn based on correlation of system state play important role in decision making. However, traditional regression algorithms operate on data in an opaque manner thereby shielding end user from knowing the reasoning behind the inference drawn. Such techniques also fail to learn from repetitive historical conditions occurring in the system over a longer time-span. In this paper, we propose a novel situation- based correlation technique which can be used to not only predict system behavior but also to convey reasoning behind the prediction. “Situation” can be defined as a more inclusive version of the system state, which encompasses variables, parameters, rules, and relationships that describe the behavior of the system over the span of finite time interval. The proposed algorithm identifies similar situations in high dimensional time- series records and produces interpretable digital record of matching situations. We then deploy the proposed situation- based correlation algorithm as core of inference engine to successfully demonstrate fully functional expert system. (Abstract)
English Education Students' Perspective of English Learning Content on Instagram

This research aims to investigate students’ perspective of English learning content on Instagram and to investigate the impact of English education students’ perspective toward English learning content on Instagram. This research employed qualitative research by using descriptive qualitative design. The instrument was used in this research is interview. The data were analyzed by using Miles and Huberman model. In this model, the researcher conducted three analytical activities data simultaneously, namely: (1) data reduction (2) data display and (3) conclusion drawing/verification. The research findings of this research show that English education students give positive perspective toward English learning content on Instagram. There are four things that indicate their positive perspective. Firstly, the benefits of the contents. Secondly, the improvement of English education students from the contents. Thirdly, the usefulness of English learning content on Instagram. Fourthly, English education students’ point of view towards English learning content. As a result of their positive perspective, there are three things that they reveal. Firstly, English learning content good for students. Secondly, Instagram as a learning media. Thirdly, the use of Instagram in teaching and learning activity.&#x0D;
INVESTIGATING TEACHER’S HIGHER ORDER THINGKING SKILLS STRATEGIES IN EFL TEACHING READING AT VOCATIONAL SCHOOL CONTEXT

The current study intends to investigate the strategies used by teachers to improve students' higher order thinking skills in teaching reading and to investigate the challenges faced by teachers in improving students' higher order thinking skills in teaching reading of One Vocational School in Cirebon. This research uses descriptive qualitative research. The subject of the research was one English teacher who teaches in two classes in tenth grade students of One Vocational School in Cirebon. Data collection are obtained through observation and interviews. The findings show that the number of strategies used by teacher is five types of teaching strategies, namely informing learning objectives, divergent questions, group discussions, giving feedback, and motivating. The researcher also found four challenges faced by teacher was time, students learning ability, students mastery, and planning domain. This happens because students still do not understand higher order thinking skills. It is suggested that the teacher must encourage active learning and always motivate students.
The Unseen Guardian: How Blockchain, Java, and AI Stealthily Became the Sherlock Holmes of Cybersecurity

In the intricate and ever-evolving realm of cybersecurity, the safeguarding of digital assets and information becomes paramount, especially considering the sophistication of contemporary cyber threats. This research embarks on a novel exploration of integrating Blockchain, Java, and Artificial Intelligence (AI) to formulate a robust cybersecurity framework, metaphorically likened to the deductive prowess of the fictional detective, Sherlock Holmes. Blockchain technology is heralded for its immutable and decentralized characteristics, offering a fortification against data tampering. Java, with its platform-independent and object-oriented programming capabilities, provides a versatile and secure environment for developing blockchain applications. Concurrently, AI introduces an adaptive and predictive element, enabling the system to anticipate, identify, and mitigate potential cyber threats through pattern recognition and anomaly detection. Through a series of case studies and empirical analyses, this research elucidates the efficacy of the proposed cybersecurity framework in thwarting various cyber-attack vectors, ensuring data integrity, and maintaining user privacy. Furthermore, it navigates through the ethical and legal conundrums associated with deploying intelligent and autonomous cybersecurity systems. The findings underscore the potential of amalgamating Blockchain, Java, and AI in enhancing cybersecurity, providing insights into overcoming associated challenges, and paving the way for future research in deploying intelligent, secure, and ethical cybersecurity frameworks.
Cybersecurity in Lesotho: Current Challenges and Future Opportunities

The rapid growth of digital technologies has transformed the world, enabling new opportunities while also introducing new security risks. Lesotho, like many other countries, faces a range of cybersecurity challenges that threaten the integrity of its information systems, data, and critical infrastructure. This paper provides an overview of the current state of cybersecurity in Lesotho, including the challenges faced by the country and the opportunities for improvement. One prominent cybersecurity concern is the persistent targeting of critical government systems, with a particular focus on the IFMIS (Integrated Financial Management Information System) within the Finance Ministry. This paper examines the vulnerabilities and risks associated with the IFMIS system and analyzes past cyberattacks, such as the successful breach on September 26, 2020, orchestrated by an insider within the Finance Ministry's IFMIS team. It also takes into account a recent attack on the IFMIS system that was encrypted by an unknown entity while synchronizing the backups, highlighting the ongoing threats. The paper reviews the regulatory framework for cybersecurity in Lesotho, the state of cybercrime and cyber threats, and the existing efforts to address these challenges. The paper also proposes a set of recommendations for enhancing cybersecurity in Lesotho, including measures to secure critical government systems like IFMIS to protect against future cyber threats and data breaches.
The role of operational performance in supporting the financial flexibility of the company by applying to a group of companies listed on the Qatar Stock Exchange for the period (2009-2020)

The study aimed to evaluate the operational performance of the company and explain its role in supporting its financial flexibility by following the method of descriptive and quantitative analysis. To achieve this aim, operational cash (OCF, current asset turnover CAT) and financial flexibility measurement based on the indicator (debt flexibility + cash flexibility) have been applied to a group of companies listed on the Qatar Stock Exchange consisting of (6) companies and for the years (2009-2020), Furthermore, showing the impact of Operational performance indicators as an independent variable in financial flexibility as a dependent variable using multiple regression analysis. Results showed that the highest positive significant effect of operational performance on financial flexibility was in (Qatar Fuel Company and Qatar Cement Company), while the rest of the companies did not show a clear effect, as for the results of the regression measurement for the total companies, it was found that there is a significant non-strong impact between operational performance in financial flexibility. Finally, there are a set of recommendations were made, the most important of which is the need to study and identify the influencing factors On the financial flexibility of companies with the need to develop and adopt new models for measuring operational performance.&#x0D;
Diagnosing the dimensions of wise leadership among working leaders in the Mosul Municipality

The current study aims to reveal the availability of dimensions of wise leadership among the leaders working in the Directorate of Municipality of Mosul. In order to achieve what it aimed at, the study adopted the (descriptive-analytical) approach by presenting the theoretical intellectual frameworks, processing the data obtained from the questionnaire, as the main study tool, and analyzing and then interpreting it. after the study community was defined in a number of formations affiliated to the Directorate of Municipality of Mosul, and a random sample was selected, the size of which was (338) individuals, as a representative sample of its community of (2320) individuals, The study used a set of statistical tools and methods to analyze the response of the respondents, which included (the arithmetic mean, standard deviation, response rate), through the use of the statistical packages represented by. (SPSS V.26). The study reached a set of conclusions, the most important of which is the availability of wise leadership dimensions in a good degree in the researched organization, from the point of view of its employees. Giving workers the opportunity to express opinions and participate in decision-making processes, adopting transparency and ensuring the free flow of information and easy access for individuals.