Reviewerly/exHarmony huggingface dataset:
Quantum Generative Model with Variable-depth Circuit

In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with different loss functions. In this study, we address the inflexibility problem by adopting the variable-depth VQC model to automatically change the structure of the quantum circuit according to the qBAS score. The basic idea behind the variable-depth VQC is to consider the depth of the quantum circuit as a parameter during the training. Meanwhile, we compared the performance of the variable-depth VQC model based on four widely used statistical distances set as the loss functions, including Kullback-Leibler divergence (KL-divergence), Jensen-Shannon divergence (JS-divergence), total variation distance, and maximum mean discrepancy. Our numerical experiment shows a promising result that the variable-depth VQC model works better than the original VQC in the generative learning tasks.
Neterature as a Cultural Phenomenon in the Epoch of Digitization

The article is a conceptual assessment and thematic review of the literary space of the Internet, designated in modern humanities by the synthetic term «neterature». The authors of the article indicate the names of specific sites intended for lovers of foreign and Russian literature, potential participants in creative contests, as well as those who want to publish their own poetic or prose works in electronic «self-publish books». In some cases, the phenomenon of digitalization of literature is considered on the example of the activities of students of Kabardino-Balkarian State University.
Emotion Arcs of Student Narratives

This paper studies emotion arcs in student narratives. We construct emotion arcs based on event affect and implied sentiments, which correspond to plot elements in the story. We show that student narratives can show elements of plot structure in their emotion arcs and that properties of these arcs can be useful indicators of narrative quality. We build a system and perform analysis to show that our arc-based features are complementary to previously studied sentiment features in this area.
Network Interaction of Universities in the Context of the Russian Arctic Development

The article considers network interaction of Russian universities to solve strategic problems of macroregions development. Network interaction is a common model of cooperation between universities abroad, and in recent years, in connection with the new challenges facing Russian education, it has become relevant for Russian universities. The article briefly presents the main forms of organization and methods of managing network interaction, its advantages over traditional types of joint activities of universities. Based on the four years of work in the framework of the Association “National Arctic Research and Education Consortium” (NAREC), the authors analyze the experience of cooperation between universities in the interests of the development of the Arctic zone of the Russian Federation. Four key areas of activity are considered: 1) expert and analytical support of personnel training and research; 2) Arctic-focused educational programs and projects; 3) support and promotion of scientific research on Arctic topics among students; 4) creation of joint information space. The article demonstrates the significant results of their implementation for each of these directions. These include monitoring the state of training of personnel with higher education and developing professional standards, developing educational products, including network programs and mass open online courses on Arctic topics, participating in international projects, conducting a national competition for student papers, as well as accumulating information related to training and research on Arctic topics. Based on the best practices of the Association “NAREC” and the difficulties encountered in the implementation of the network interaction model, the authors formulated practical recommendations for the development of interregional scientific and educational cooperation.
A Discussion of the Application of Artificial Intelligence In the Management of Mass Media Censorship in Mainland China

Today, artificial intelligence (AI) has rapidly entered our daily life, such as face recognition technology has been used in all walks of life. In the new era, the management of mass media censorship system in Mainland China is changing through artificial intelligence. The media industry has become more convenient and effective from editing to censorship by using artificial intelligence technology. In the future, the researcher thinks facial recognition could even be used to aid the implementation of "Motion Picture Content Rating System" in Mainland China. Therefore, artificial intelligence will continue to promote the progress of the censorship system of mass media in Mainland China and become the driving force for the booming development of mass media in Mainland China under the new era. This paper will summarize the history of the censorship system of mass media and analyzes the development of the censorship system management of mass media in Mainland China, as well as discuss the using of artificial intelligence and the possibility of its future use.
Artificial Intelligence Designated as Inventor – An Analysis of the Recent EPO Case Law

Abstract This paper will focus on the issue of designating artificial intelligence systems as inventors in the current framework of European patent law. Most recently, the European Patent Office rejected two patent applications which indicated a machine called DABUS as the inventor of the claimed subject-matter. The paper will analyse the grounds of the decisions in detail, thereby reflecting on the current approach of the European Patent Office to such designations and on the concept of inventorship within the European patent system in general.
Improved Lightweight Encryption Algorithm (ILEA)

Lightweight cryptography concept has been a very hot topic for the last few years and considered as a new domain of encryption suitable for big data networks, small devices, phones, cards and embedded systems. These systems require low latency security and low power consuming [1]. An improved lightweight encryption algorithm ILEA is proposed in this paper. ILEA is based on PRINCE lightweight algorithm as his main core with two defacing balanced mixing layers added. ILEA presented in two programming languages: PYTHON, C++ with a comparative study with original PRINCE results and some of another lightweight algorithms.
An Organizational Structure and Self-Adaptive Mechanism for Holonic Multi-Agent Systems

A holonic multi-agent system combines the concept of a holon with a multi-agent system; this combination has been proven to be an effective way to build a complex system. Great progress has been made in this area, but previous studies are fragmented and lack of a task-based perspective to model different systems in the real world. Therefore, this article proposes a formalistic model for HMAS from a task-based perspective. Not only the static organizational structure is designed, but also the dynamic running mechanism, including the self-adaptive mechanism and the task assignment mechanism based on the proposed holonic structure, are also discussed. Finally, a case study is provided to verify the self-adaptive mechanism. The experimental results show that our proposed DHMAS has the ability to adapt to the changing environment, and performs better in terms of the success rate and the response time when the system is heavily loaded.
MOVING SHIP DETECTION AND MOVEMENT PREDICTION IN REMOTE SENSING VIDEOS

Abstract. With the rapid development of remote sensing technology, it is possible to obtain continuous video data from outer space successfully. It is of great significance in military and civilian fields to detect moving objects from the remote sensing image sequence and predict their movements. In recent years, this issue has attracted more and more attention. However, researches on moving object detection and movement prediction in high-resolution remote sensing videos are still in its infancy, which is worthy of further study. In this paper, we propose a ship detection and movement prediction method based on You-Only-Look-Once (YOLO) v3 and Simple Online and Realtime Tracking (SORT). Original YOLO v3 is improved by multi-frame training to fully utilize the information of continuous frames in a fusion way. The simple and practical multiple object tracking algorithm SORT is used to recognize multiple targets detected by multi-frame YOLO v3 model and obtain their coordinates. These coordinates are fitted by the least square method to get the trajectories of multiple targets. We take the derivative of each trajectory to obtain the real-time movement direction and velocity of the detected ships. Experiments are performed on multi-spectral remote sensing images selected on Google Earth, as well as real multi-spectral remote sensing videos captured by Jilin-1 satellite. Experimental results validate the effectiveness of our method for moving ship detection and movement prediction. It shows a feasible way for efficient interpretation and information extraction of new remote sensing video data.
A New Path Planning Method for Flying Probe Test Arms

Whether the circuit components are soldered correctly is an important part to ensure that a printed circuit board (PCB) can be used normally. Calculating the path of detecting each component successively can be regarded as a traveling salesman problem (TSP). This paper presents a novel path planning method for PCB testing by flying probe and introduces the flow and rules of our proposed method. Through a large number of experiments, we can demonstrate that the proposed algorithm reduces the path length and the time required to obtain the best solution effectively.
Criticality Aware Orderer for Heterogeneous Transactions in Blockchain

Blockchain enabled systems are becoming increasingly popular in recent times, spreading across variety of domains such as finance, supply chain management, healthcare, etc. These systems usually involve homogeneous transactions with similar latency requirements. However, as increasingly complex systems are enabled through blockchain, they will include a variety of heterogeneous transactions having different latency requirements. For example, an IoT system such as smart building may include transactions corresponding to varied services like payment, biometric registration, etc. Present blockchain systems are transaction metadata agnostic in their ordering step i.e. during ordering and bundling of transactions into blocks. Since transactions in complex systems are heterogeneous in nature, a framework that prioritizes transactions having low latency requirements would be more suitable. In this work, we propose various transaction criticality aware ordering services and comprehensively evaluate them on a handcrafted smart building scenario. Our experiments demonstrate that a single ordering service is not suitable (in terms of number of transactions missing their latency requirement) for all practical scenarios. We demonstrate that selection of a suitable ordering service may give up to 58.25% improvement over other ordering services.
Does Ubuntu Influence Social Engineering Susceptibility?

Ubuntu refers to living according to a set of values shared by people who believe in a certain way of life. The values of ubuntu include group solidarity, conformity, compassion, respect, human dignity, a humanistic orientation, and collective unity. Some people consciously live by ubuntu values while others do so unconsciously. Ubuntu is ingrained in human beings. The values of ubuntu in aggregate build trust, which inadvertently feeds into the social engineering approach used in information security attacks. Ubuntu appears to increase vulnerability to the success of a social engineering attack. Social engineering is a key threat to information security in many organizations. Social engineers target employees as human beings are naturally vulnerable. This study argues that ubuntu influences the success of social engineering attacks. It views ubuntu as foundational in making humans vulnerable to social engineers. The values that ubuntu is based on lead humans to trust, conform, be compassionate and loyal, and desire to help others. Social engineers exploit these values to obtain information, which they use to breach the information security of the targeted organization. Because of ubuntu, the extensive work of building rapport and elicitation, which would otherwise have been exhausting, is already completed and ready to be exploited by the social engineer. This paper exposes the potential influence of ubuntu on social engineering attacks. It concludes that security awareness, education and training programmes must be adapted to counter these influences and safeguard information assets.
Real-Time Ray-Traced Ambient Occlusion of Complex Scenes using Spatial Hashing

Ambient occlusion is often approximated in real-time using screen-space techniques, leading to visible artifacts. Raytracing provides a unique way to increase the rendering fidelity by accurately sampling the distance to the surrounding objects, but it introduces sampling noise. We propose a real-time ray-traced ambient occlusion technique in which noise is filtered in world space. Using extended spatial hashing for efficient storage, multiresolution AO evaluation and ad-hoc filtering, we demonstrate the usability of our technique as a production feature usable in CAD viewports with scenes comprising hundreds of millions of polygons.
Perceptual Color Image Hashing Based on Quaternionic Local Ranking Binary Pattern

Image hashing is a technique that represents an image as a short unidirectional sequence. In this study, we propose a robust color image hashing technique based on the quaternionic local ranking binary pattern (QLRBP). First, the values of the RGB color channels are expressed as a pure quaternion, and the texture image is generated using the phase difference of the Clifford translation quaternion as the source for local binary pattern (LBP) comparison data. Next, mean pooling is used to reduce the dimensionality of the image texture. For security, the pixels of the dimension-reduced image are randomly selected, and each pixel is quantized to obtain the final image hash. Experimental results showed that the proposed scheme resists a variety of ordinary image operations and achieves good balance between robustness and discrimination. In comparison with other state-of-the-art methods, the receiver operating characteristic (ROC) curves of our scheme demonstrate its superiority in terms of content identification.
A Two-Stage Model for Social Network Investigations in Digital Forensics

This paper proposes a two-stage model for identifying and contextualizing features from artefacts created as a result of social networking activity. This technique can be useful in digital investigations and is based on understanding and the deconstruction of the processes that take place prior to, during and after user activity; this includes corroborating artefacts. Digital Investigations are becoming more complex due to factors such as, the volume of data to be examined; different data formats; a wide range of sources for digital evidence; the volatility of data and the limitations of some of the standard digital forensic tools. This paper highlights the need for an approach that enables digital investigators to prioritize social network artefacts to be further analysed; determine social connections in the context of an investigation e.g. a user’s social relationships, how recovered artefacts came to be, and how they can successfully be used as evidence in court.
Systematic Literature Review of DevOps Models

DevOps can be defined as a set of practices that enables development and operations teams to collaborate in order to produce reliable and high-quality products. Even though DevOps is still a new phenomenon, DevOps practices can be successfully applied in numerous companies. However, it remains unclear what DevOps models currently exist. In this paper, the availability of DevOps models is analyzed by means of a systematic literature review. 24 papers with DevOps models were found and 4 papers were selected as relevant.
A Post-colonial Analysis of Agile Software Development Methods in ICT4D

There is evidence that agile approaches to information system development can improve product quality and developer productivity. However, successful adoption of these approaches appears to depend on adaptation to specific contexts. This research contributes to a broader goal to understand what it means to “be agile” in the presence of adaptations to the specific context. To pursue our research objectives, we have performed 31 semi-structured recorded and transcribed practitioner interviews from three companies in Lebanon. The interview transcripts were analysed using an approach informed by grounded theory. Agile methods enable learning and improvement through team conversations. Yet, the practitioners in our study shun public self-evaluation, finding it difficult to discuss areas for improvement in public. We also found legacy “top down” management practices that undermine team autonomy and local client companies lack experience of engaging with agile processes. In a more positive vein, we found evidence of rich use of various communications channels to overcome geographical distance. On the one hand, agile methods represent a “northern” idea being propagated to the Global South. And yet, on the other hand, the agile concept of self-organising teams has the potential to be empowering and emancipatory. Post-colonial theory helps us understand the phenomenon of agile tailoring, where development process ceremonies are adapted to suite a specific local context.
Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition

This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.
High-Accuracy Facial Depth Models derived from 3D Synthetic Data

In this paper, we explore how synthetically generated 3D face models can be used to construct a high-accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These models provide sophisticated controls over image variations including pose, illumination, facial expressions and camera position. 2D training samples can be rendered from these models, typically in RGB format, together with depth information. Using synthetic facial animations, a dynamic facial expression or facial action data can be rendered for a sequence of image frames together with ground truth depth and additional metadata such as head pose, light direction, etc. The synthetic data is used to train a CNN-based facial depth estimation system which is validated on both synthetic and real images. Potential fields of application include 3D reconstruction, driver monitoring systems, robotic vision systems, and advanced scene understanding.
Creation of Clusters and Tools for Improving the Professional Competence of Future Educators

&lt;p style="text-align:justify"&gt;The objective of the study was to study the perceptions of student teachers and educators of an educational cluster as a tool for improving the professional competence of future educators, and to identify the cluster-related tools to be used to improve the professional competence of future educators. The project evaluation checklist for experts, a questionnaire for participants in the experiment, a semi-structured interview questionnaire to conduct with focus group respondents were used for collecting data. Data were processed using content analysis technique and SPSS Statistics v24 package. The study is quantitative, based on subjective evaluations of study participants and representatives of educational clusters, and does not aim at evaluating students’ achievements. Two simulation models of the educational cluster (“Cluster Educational Initiative” and “Cluster of Educational Opportunities for Educators 2.0”) and four educational activities for educators (discussion "Agglomeration of Subjects, Educational Institutions: Realities and Prospects”; online conference “Ukraine-Brussels - Sharing Experience in Education”; an educational tour to Finland for teachers, parents and managers in the field of education; the exchange of four teachers from two schools) was a key result of this study. It has been found that the creation of clusters for improving the professional competence of future educators and their professional advancement is now part of national education policy in Ukraine and in the world. The tools of this form of organization of educational activity are competence-oriented and aimed at deepening professional experience, development of competences of realization of life-long education, creation of own public image.&lt;/p&gt;