Reviewerly/exHarmony huggingface dataset:
FEATURES OF BLENDED LEARNING TECHNOLOGY IN THE SYSTEM OF HIGHER EDUCATION

The essence of the notions of “blended learning”, and “distance learning” and their importance in higher education have been highlighted. The history of “blended learning”(BL) origin is considered as well as various approaches of the researchers regarding the interpretation of that definition are presented. To achieve the goal of the research, such methods as analysis, synthesis, the study of scientific literature, and Internet sources on the particularly pertinent problem of BL implementation within modern higher education conditions are used.&#x0D; Based on the analysis of scientific and pedagogical literature, some tendencies in the implementation of BL in the system of higher education have been summarized. It has been stated that Bl, as a groundbreaking technology, can’t appear on its own and is unable to exist in its pure form but it must be a flexible system of distance learning, added with a direct interaction of a professor and education applicants during traditional learning that, in its turn, will predominantly allow intensifying the educational process, building out and increasing the effectiveness of self-study work. It has been clarified that common to the definition of BL is the interaction of subjects in the educational process by combining three different ways of learning: distance learning (DL), traditional learning, and independent learning.&#x0D; DL is supposed to take into account the changes that have taken place in the educational information structure of modern society, which determines the relevance of the development and implementation of ICT. It can be considered as a means of modernizing the education system, enriching the quality of BL by adding the extensive application of ICT and lifting it to new levels. Its strengths and weaknesses are subjected to scrutiny, where the latter can be weakened or eliminated through the active usage of the advantages of BL as a demanding form of modern education.&#x0D; It has been postulated that, firstly, the pertinence to the introduction of BL is predominantly based on the fact that it is impossible to accomplish academic achievements, by realizing only one pedagogical technology. Secondly, it is not worth contradicting distance (online) and traditional (face-to-face, offline) learning, drawing attention that nowadays both forms of learning are not being applied in a pure form. Thirdly, DL will never displace offline learning and the main purpose of BL lies in not pushing out this form of learning but integrating it effectively. It has been inferred that BL cultivates opportunities to optimize the timing expenses of a teacher and increase the efficiency of the learning process in general. Furthermore, a student has become an active participant in the learning process, who can build up an individual learning trajectory.
TRAINING OF FUTURE TECHNOLOGY TEACHERS FOR WORKSHOP ACTIVITIES IN MODERN CONDITIONS

We revealed the meaning of the workshop classes at extracurricular education institutions, extracurricular and extracurricular work at school. The organization of activities and provision of the educational process in out-of-school education institutions during martial law was analyzed. Regulatory documents and recommendations, which are expedient to be followed by the head of the circle in the modern conditions of providing educational services, are summarized. The peculiarities of the organization and conducting of group classes in remote mode were analyzed and the conditions that arise in the process of such training were determined. The advantages of distance learning and a set of measures for its implementation are revealed. There are means of providing educational materials for students of the group; means of monitoring the performance of assigned tasks; means of interactive cooperation between the teacher and students; the possibility of supplementing the course with new information. Forms of conducting group classes and available software products at the beginning of the organization of remote form of group work are proposed.&#x0D; It is noted that under the guidance of a technology teacher, students are engaged in art-aesthetic, scientific-technical circles and design products from various construction materials. The main stages of product design are described. It is noted that the workshop activity is related to the involvement of students in understanding the world of art, science, technology and engineering. It has been established that the competences are formed in students in classes. There are cognitive, practical, creative, social competences. It is noted that the result of group work depends on the organization and proper planning of activities in classes, chosen forms, traditional and innovative teaching methods. A survey of students was conducted. It was established that group activities are useful and interesting for students. It is necessary to take into account the needs, personal abilities and educational opportunities of pupils. It was found that the importance and relevance of this research is due to the need to find new approaches to the workshop activity to determine the conditions of their effective activity in connection with taking into account modern conditions of education, the importance of developing methodological support for the implementation of a remote form of conducting classes and the further implementation of the STEM approach.
Knowledge Management in Integration with Business Intelligence Systems: A Descriptive Analytical Research

The research aims to test the relationship between knowledge management and business intelligence in the Trade Bank of Iraq, being one of the vital service organizations that have a fundamental role in serving an important segment of the public as well as its vital economic role. Therefore, the adoption of modern administrative concepts such as knowledge management and its role in achieving business intelligence can be It helps it face these changes and achieve its goal of providing the best services in quantity and quality. The research sample was selected using a random sample method, consisting of (64) workers in the bank, and the questionnaire was relied upon as the main tool to obtain research data, which was prepared based on several Ready-made scales after they were subjected to validity and reliability tests, and to test the validity of the hypotheses and answer the research questions, the analytical descriptive approach and statistical tests were used, including: mean, standard deviation, percentages, frequencies, Pearson's correlation coefficient, simple linear regression, and Depending on the statistical program (SPSS.V.25), the research reached a set of conclusions, the most important of which is that the level of knowledge management and business intelligence in the Trade Bank of Iraq is more than average for both variables and that there is a strong correlation between knowledge management and business intelligence. A set of recommendations was presented, the most important of which was the need to continue attracting those with experience in banking, taking into account their desire to share their tacit knowledge with other workers to preserve their expertise from waste, leakage, and forgetfulness, and to intensify the information-digital networking to join the parties inside the bank and stakeholders outside the bank with each other.
On the Characterization of Alternating Groups by Codegrees

Let $G$ be a finite group and $\mathrm{Irr}(G)$ the set of all irreducible complex characters of $G$. Define the codegree of $\chi \in \mathrm{Irr}(G)$ as $\mathrm{cod}(\chi):=\frac{|G:\mathrm{ker}(\chi) |}{\chi(1)}$ and denote by $\mathrm{cod}(G):=\{\mathrm{cod}(\chi) \mid \chi\in \mathrm{Irr}(G)\}$ the codegree set of $G$. Let $\mathrm{A}_n$ be an alternating group of degree $n \ge 5$. In this paper, we show that $\mathrm{A}_n$ is determined up to isomorphism by $\mathrm{cod}(\mathrm{A}_n)$.
Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations

Implicit discourse relation recognition is a challenging task that involves identifying the sense or senses that hold between two adjacent spans of text, in the absence of an explicit connective between them. In both PDTB-2 and PDTB-3, discourse relational senses are organized into a three-level hierarchy ranging from four broad top-level senses, to more specific senses below them. Most previous work on implicit discourse relation recognition have used the sense hierarchy simply to indicate what sense labels were available. Here we do more -- incorporating the sense hierarchy into the recognition process itself and using it to select the negative examples used in contrastive learning. With no additional effort, the approach achieves state-of-the-art performance on the task.
Image Data Augmentation Approaches: A Comprehensive Survey and Future directions

Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on advanced data augmentation techniques. we provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation. For results reproducibility, we compiled available codes of all data augmentation techniques. Finally, we discuss the challenges and difficulties, and possible future direction for the research community. We believe, this survey provides several benefits i) readers will understand the data augmentation working mechanism to fix overfitting problems ii) results will save the searching time of the researcher for comparison purposes. iii) Codes of the mentioned data augmentation techniques are available at https://github.com/kmr2017/Advanced-Data-augmentation-codes iv) Future work will spark interest in research community.
An Enhanced Gradient-Tracking Bound for Distributed Online Stochastic Convex Optimization

Gradient-tracking (GT) based decentralized methods have emerged as an effective and viable alternative method to decentralized (stochastic) gradient descent (DSGD) when solving distributed online stochastic optimization problems. Initial studies of GT methods implied that GT methods have worse network dependent rate than DSGD, contradicting experimental results. This dilemma has recently been resolved, and tighter rates for GT methods have been established, which improves upon DSGD. In this work, we establish more enhanced rates for GT methods under the online stochastic convex settings. We present an alternative approach for analyzing GT methods for convex problems and over static graphs. When compared to previous analyses, this approach allows us to establish enhanced network dependent rates.
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching

Despite surprising performance on zero-shot transfer, pre-training a large-scale multimodal model is often prohibitive as it requires a huge amount of data and computing resources. In this paper, we propose a method (BeamCLIP) that can effectively transfer the representations of a large pre-trained multimodal model (CLIP-ViT) into a small target model (e.g., ResNet-18). For unsupervised transfer, we introduce cross-modal similarity matching (CSM) that enables a student model to learn the representations of a teacher model by matching the relative similarity distribution across text prompt embeddings. To better encode the text prompts, we design context-based prompt augmentation (CPA) that can alleviate the lexical ambiguity of input text prompts. Our experiments show that unsupervised representation transfer of a pre-trained vision-language model enables a small ResNet-18 to achieve a better ImageNet-1K top-1 linear probe accuracy (66.2%) than vision-only self-supervised learning (SSL) methods (e.g., SimCLR: 51.8%, SwAV: 63.7%), while closing the gap with supervised learning (69.8%).
Quantum Encryption in Phase Space using Displacement Operator for QPSK Data Modulation

In 2020, Kuang and Bettenburg proposed Quantum Public Key Distribution (QPKE) which utilized the randomized phase shift gate. Since then, it has been implemented both theoretically through simulations and experimentally over existing fiber optical networks. QPKE can be compared to an RSA-type scheme but in the optical analogue domain. Later on, it was renamed Quantum Encryption in Phase Space (QEPS) to emphasize the encryption of coherent states in phase space. However, the phase shift gate used in QEPS is limited to data modulation schemes with phase shift keying such as quadrature phase shift keying (QPSK) as it may leak data information in amplitude if applied to quadrature amplitude modulation (QAM) schemes. Recently, Kuang and Chan proposed a new version of QEPS known as Quantum Encryption in Phase Space with the displacement gate or QEPS-d, which overcomes the limitation of QEPS with the phase shift gate. This was achieved by introducing a reduced displacement operator that ignores the global phase factor, making the reduced displacement operators commutable, thus aiding the implementation at both transmission and reception. Furthermore, any arbitrary displacement operator can be decoupled into a standard QAM modulation with a phase shift modulation, making encryption and decryption easier. This paper demonstrates the simulation of QEPS-d encryption for QPSK data modulation to illustrate how QEPS-d functions.
k-Means SubClustering: A Differentially Private Algorithm with Improved Clustering Quality

In today's data-driven world, the sensitivity of information has been a significant concern. With this data and additional information on the person's background, one can easily infer an individual's private data. Many differentially private iterative algorithms have been proposed in interactive settings to protect an individual's privacy from these inference attacks. The existing approaches adapt the method to compute differentially private(DP) centroids by iterative Llyod's algorithm and perturbing the centroid with various DP mechanisms. These DP mechanisms do not guarantee convergence of differentially private iterative algorithms and degrade the quality of the cluster. Thus, in this work, we further extend the previous work on 'Differentially Private k-Means Clustering With Convergence Guarantee' by taking it as our baseline. The novelty of our approach is to sub-cluster the clusters and then select the centroid which has a higher probability of moving in the direction of the future centroid. At every Lloyd's step, the centroids are injected with the noise using the exponential DP mechanism. The results of the experiments indicate that our approach outperforms the current state-of-the-art method, i.e., the baseline algorithm, in terms of clustering quality while maintaining the same differential privacy requirements. The clustering quality significantly improved by 4.13 and 2.83 times than baseline for the Wine and Breast_Cancer dataset, respectively.
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior

Many real-world reinforcement learning (RL) problems necessitate learning complex, temporally extended behavior that may only receive reward signal when the behavior is completed. If the reward-worthy behavior is known, it can be specified in terms of a non-Markovian reward function - a function that depends on aspects of the state-action history, rather than just the current state and action. Such reward functions yield sparse rewards, necessitating an inordinate number of experiences to find a policy that captures the reward-worthy pattern of behavior. Recent work has leveraged Knowledge Representation (KR) to provide a symbolic abstraction of aspects of the state that summarize reward-relevant properties of the state-action history and support learning a Markovian decomposition of the problem in terms of an automaton over the KR. Providing such a decomposition has been shown to vastly improve learning rates, especially when coupled with algorithms that exploit automaton structure. Nevertheless, such techniques rely on a priori knowledge of the KR. In this work, we explore how to automatically discover useful state abstractions that support learning automata over the state-action history. The result is an end-to-end algorithm that can learn optimal policies with significantly fewer environment samples than state-of-the-art RL on simple non-Markovian domains.
Quantum Honest Byzantine Agreement as a Distributed Quantum Algorithm

We suggest that the Quantum Honest Byzantine Agreement (QHBA) protocol [1] essentially reduces consensus to coincidence. The volume of coincidence is the parameter that drives a receiver to echo its input. A lack of coincidence results in no output from a receiver. This is a similar mechanism therefore to the learning mechanism in cognitive modular neural architectures like Haikonen's architecture [2]. We introduce a simple feedback mechanism and quantum neuron to realize a hybrid quantum / classical machine learning network of simple nodes.
Taiji Data Challenge for Exploring Gravitational Wave Universe

The direct observation of gravitational waves (GWs) opens a new window for exploring new physics from quanta to cosmos and provides a new tool for probing the evolution of universe. GWs detection in space covers a broad spectrum ranging over more than four orders of magnitude and enables us to study rich physical and astronomical phenomena. Taiji is a proposed space-based GW detection mission that will be launched in the 2030s. Taiji will be exposed to numerous overlapping and persistent GW signals buried in the foreground and background, posing various data analysis challenges. In order to empower potential scientific discoveries, the Mock LISA Data Challenge and the LISA Data Challenge (LDC) were developed. While LDC provides a baseline framework, the first LDC needs to be updated with more realistic simulations and adjusted detector responses for Taiji's constellation. In this paper, we review the scientific objectives and the roadmap for Taiji, as well as the technical difficulties in data analysis and the data generation strategy, and present the associated data challenges. In contrast to LDC, we utilize second-order Keplerian orbit and second-generation time delay interferometry techniques. Additionally, we employ a new model for the extreme-mass-ratio inspiral waveform and stochastic GW background spectrum, which enables us to test general relativity and measure the non-Gaussianity of curvature perturbations. Furthermore, we present a comprehensive showcase of parameter estimation using a toy dataset. This showcase not only demonstrates the scientific potential of the Taiji Data Challenge but also serves to validate the effectiveness of the pipeline. As the first data challenge for Taiji, we aim to build an open ground for data analysis related to Taiji sources and sciences. More details can be found on the official website at http://taiji-tdc.ictp-ap.org.
Human Following Based on Visual Perception in the Context of Warehouse Logistics

Under the background of 5G, Internet, artificial intelligence technol,ogy and robot technology, warehousing, and logistics robot technology has developed rapidly, and products have been widely used. A practical application is to help warehouse personnel pick up or deliver heavy goods at dispersed locations based on dynamic routes. However, programs that can only accept instructions or pre-set by the system do not have more flexibility, but existing human auto-following techniques either cannot accurately identify specific targets or require a combination of lasers and cameras that are cumbersome and do not accomplish obstacle avoidance well. This paper proposed an algorithm that combines DeepSort and a width-based tracking module to track targets and use artificial potential field local path planning to avoid obstacles. The evaluation is performed in a self-designed flat bounded test field and simulated in ROS. Our method achieves the SOTA results on following and successfully reaching the end-point without hitting obstacles.
RGB-T Multi-Modal Crowd Counting Based on Transformer

Crowd counting aims to estimate the number of persons in a scene. Most state-of-the-art crowd counting methods based on color images can't work well in poor illumination conditions due to invisible objects. With the widespread use of infrared cameras, crowd counting based on color and thermal images is studied. Existing methods only achieve multi-modal fusion without count objective constraint. To better excavate multi-modal information, we use count-guided multi-modal fusion and modal-guided count enhancement to achieve the impressive performance. The proposed count-guided multi-modal fusion module utilizes a multi-scale token transformer to interact two-modal information under the guidance of count information and perceive different scales from the token perspective. The proposed modal-guided count enhancement module employs multi-scale deformable transformer decoder structure to enhance one modality feature and count information by the other modality. Experiment in public RGBT-CC dataset shows that our method refreshes the state-of-the-art results. https://github.com/liuzywen/RGBTCC
Physical Realization of Measurement Based Quantum Computation

Harnessing quantum mechanics properties, quantum computers have the potential to outperform classical computers in many applications and are envisioned to affect various aspects of our society. Different approaches are being explored for building such computers. One of such potential approaches is Measurement based quantum computation (MBQC), introduced by Raussendorf and Briegel in 2001. In MBQC a large number of qubits are prepared in a highly entangled clusters, called cluster states. The required quantum computation is then performed by a sequence of measurements. Cluster states are being physically realized using continuous variables (CV) and discrete variables (DV) approaches. CV-based approaches can be further categorized as Frequency domain multiplexing (FDM), Time domain multiplexing (TDM), Spatial domain multiplexing (SDM) and hybrid. We discuss and compare these approaches in detail. We also discuss cluster states generation in DV and report some recent results where photons and superconducting qubits are used.
A Continent-Wide Assessment of Cyber Vulnerability Across Africa

As the internet penetration rate in Africa increases, so does the proliferation of the Internet of Things (IoT) devices. Along with this growth in internet access is the risk of cyberattacks to vulnerable IoT devices mushrooming in the African cyberspace. One way to determine IoT vulnerabilities is to find open ports within Africa s cyberspace. Our research leverages Shodan search engine, a powerful tool for discovering IoT devices facing the public internet, to find open ports across Africa. We conduct an analysis of our findings, ranking countries from most to least vulnerable to cyberattack. We find that South Africa,Tunisia, Morocco, Egypt, and Nigeria are the five countries most susceptible to cyberattack on the continent. Further, 69.8% of devices having one of the five most commonly open internet ports have had past documented vulnerabilities. Following our analysis, we conclude with policy recommendations for both the public and private sector.