Reviewerly/exHarmony huggingface dataset:
A Systematic Approach for Tracking the Evolution of XAI as a Field of Research

The increasing use of AI methods in various applications has raised concerns about their explainability and transparency. Many solutions have been developed within the last few years to either explain the model itself or the decisions provided by the model. However, the number of contributions in the field of eXplainable AI (XAI) is increasing at such a high pace that it is almost impossible for a newcomer to identify key ideas, track the field’s evolution, or find promising new research directions. Typically, survey papers serve as a starting point, providing a feasible entry point into a research area. However, this is not trivial for some fields with exponential growth in the literature, such as XAI. For instance, we analyzed 23 surveys in the XAI domain published within the last three years and surprisingly found no common conceptualization among them. This makes XAI one of the most challenging research areas to enter. To address this problem, we propose a systematic approach that enables newcomers to identify the principal ideas and track their evolution. The proposed method includes automating the retrieval of relevant papers, extracting their semantic relationship, and creating a temporal graph of ideas by post-analysis of citation graphs. The main outcome of our method is Field’s Evolution Graph (FEG), which can be used to find the core idea of each approach in this field, see how a given concept has developed and evolved over time, observe how different notions interact with each other, and perceive how a new paradigm emerges through combining multiple ideas. As for demonstration, we show that FEG successfully identifies the field’s key articles, such as LIME or Grad-CAM, and maps out their evolution and relationships.
University students’ experience of online space while engaging in synchronous learning via videoconferencing amidst the pandemic

The high infectivity and fatality of COVID-19 has changed the mode of higher education from onsite to online. Although many studies investigated the effectiveness and satisfaction of online education, little is known regarding university students' lived experience of online space during synchronous learning via videoconferencing.The present study explored how university students experienced online space when engaging in synchronous learning via videoconferencing platforms during the outbreak of the pandemic.The phenomenological approach was chosen to primarily explore students' experience of online space as well as their experience of embodiment and relations to self and others. Interviews were conducted with nine university students who voluntarily participated to share their experience of online space.Three core themes were generated from the descriptions of experiences provided by the participants. For each core theme, two sub-themes were emerged and described. The analysis of the themes demonstrated that online space was experienced as being separate from home but also inseparable because it was an extension of the comforts of home. This inseparableness is also reflected in the virtual classroom where the rectangular screen presented on the monitor is always shared with everyone in the class. Moreover, online space was perceived as having no transitional space in which spontaneity and new encounters occur. Finally, the presence of self and others was experienced differently in online space due to the participants' choices of being seen or heard using their microphones and cameras. This led to a different sense of togetherness in online space. The insights gained from the study were discussed in relation to considerations for online learning in the post-pandemic era.Based on the emerged themes from the results, the current study concluded that the online space created by technologies cannot be a complete substitute for traditional face-to-face classrooms and suggested possible implications for designing and using online space in university education.
Mutually unbiased special entangled bases with Schmidt number 2<i>d</i> in ℂ2d+1 ⊗ ℂ4d

In this paper, we construct mutually unbiased special entangled bases with Schmidt number 2d in [Formula: see text]. Precisely, we first provide a necessary and sufficient condition for two special entangled bases with Schmidt number 2d are mutually unbiased, and then use the condition to construct two mutually unbiased special entangled bases with Schmidt number 2 in [Formula: see text] and two mutually unbiased special entangled bases with Schmidt number 4 in [Formula: see text].
Gesture Controlled Virtual Mouse

Abstract: As a result of the pandemic, a technological paradigm shift has happened. One such field of research is the development and usage of a virtual mouse in place of a real one. Despite the fact that a remote mouse or Bluetooth mouse is popular nowadays, it requires various factors such as cost and power. There would be no such cutoff points in the proposed system, assuming signal acknowledgement. The primary breakthroughs used in this review are object recognizable proof and image management. Thus, this study proposes a cutting-edge method for dealing with mouse developments employing an ongoing camera with the help of the hand. It does not necessitate the use of any electrical device. It will use various hand gestures to direct activities such as left click and hauling. For the product's execution, Python and OpenCV will be used. As a result, the proposed mouse framework eliminates the need for a device
A Study on the Effects of Loyalty on Exit in the EVL Framework: Focusing on the Case of Collective Action Against New Medical Policies in South Korea

본 연구는 2020년에 대한민국에서 발생하였던 의료정책 추진 반대 집단행동 사례를 분석하고, 이를 근거로 앨버트 허시먼의 EVL 프레임워크 속 충성심 개념에 대한 이해를 보완하는 것에 목적이 있다. 기존의 EVL 프레임 워크는 집단의 구성원이 갖는 충성심에 이탈 방식을 무력화시키는 효과가 있다고 강조하였다. 그러나 대한전공의 협의회와 대한 의과대학·의학전문대학원 학생협회가 정부의 의료정책 추진에 반대하는 과정에서 보였던 행동 방식은 충성심이 항상 이탈 방식과 음의 관계에 있는 것은 아님을 시사한다. 본 연구는 해당 사례의 시계열적 분석을 통하여 다수의 집단에 소속된 구성원이 모든 집단에 충성심을 가질 수 있으며, 각 집단을 향한 충성심 간의 충돌이 발생할 수 있음을 확인하였다. 또한, 현실에서 집단의 구성원은 허시먼의 설명과 달리 달성하고자 하는 목적 그 자체에도 충성할 수 있었다. 이 같은 경우 집단의 구성원이 갖는 충성심은 오히려 이탈 방식을 활성화할 수도 있는 것으로 밝혀졌다. The purpose of this study is to analyze the collective action against new medical policies that was staged in South Korea in 2020 to supplement the understanding of loyalty in Albert O. Hirschman’s EVL framework. According to the existing EVL framework, members’ loyalty has the effect of holding the exit at bay while activating the voice as a solution to improve the group’s situation. However, the Korean Intern Resident Association’s and the Korean Medical Student Association’s behavior in opposition to the enforcement of the new medical policies has indicated that loyalty is not always inversely proportional to group members’ exit. This study has found that multiple-group members can have loyalty to all of the groups they belong to simultaneously, and that there may be situations in which multiple loyalties collide. It was also confirmed that group members can have loyalty to the goal itself instead of to the group. In such cases, loyalty not only fails to suppress group members’ exit, it also triggers it.
FAIR Data Model for Chemical Substances: Development Challenges, Management Strategies, and Applications

Data models for representation of chemicals are at the core of cheminformatics processing workflows. The standard triple, (structure, properties, and descriptors), traditionally formalizes a molecule and has been the dominant paradigm for several decades. While this approach is useful and widely adopted from academia, the regulatory bodies and industry have complex use cases and impose the concept of chemical substances applied for multicomponent, advanced, and nanomaterials. Chemical substance data model is an extension of the molecule representation and takes into account the practical aspects of chemical data management, emerging research challenges and discussions within academia, industry, and regulators. The substance paradigm must handle a composition of multiple components. Mandatory metadata is packed together with the experimental and theoretical data. Data model elucidation poses challenges regarding metadata, ontology utilization, and adoption of FAIR principles. We illustrate the adoption of these good practices by means of the Ambit/eNanoMapper data model, which is applied for chemical substances originating from ECHA REACH dossiers and for largest nanosafety database in Europe. The Ambit/eNanoMapper model allows development of tools for data curation, FAIRification of large collections of nanosafety data, ontology annotation, data conversion to standards such as JSON, RDF, and HDF5, and emerging linear notations for chemical substances.
Explainable Recommendation Based on Weighted Knowledge Graphs and Graph Convolutional Networks

Knowledge Graphs (KGs) have been shown to have great potential to provide rich and highly defined structured data about Recommender Systems (RSs) items. This paper introduces Explain- KGCN, an Explainable RS based on KGs and Graph Convolutional Networks (GCNs). The system emphasises the importance of semantic information characterisation and high-order connectivity of message passing to explore potential user preferences. Thus, based on a relation-specific neighbourhood aggregation function, it aims to generate for each given item a set of relation-specific embeddings that depend on each semantic relation in the KG. Specifically, the relation-specific aggregator discriminates neighbours based on their relationship with the target node, allowing the system to model the semantics of various relationships explicitly. Experiments conducted on two real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. Besides improving predictive performance in terms of precision and recall, Explain-KGCN fully exploits wealthy structured information provided by KGs to offer recommendation explanation.
EFCMF: A Multimodal Robustness Enhancement Framework for Fine-Grained Recognition

Fine-grained recognition has many applications in many fields and aims to identify targets from subcategories. This is a highly challenging task due to the minor differences between subcategories. Both modal missing and adversarial sample attacks are easily encountered in fine-grained recognition tasks based on multimodal data. These situations can easily lead to the model needing to be fixed. An Enhanced Framework for the Complementarity of Multimodal Features (EFCMF) is proposed in this study to solve this problem. The model’s learning of multimodal data complementarity is enhanced by randomly deactivating modal features in the constructed multimodal fine-grained recognition model. The results show that the model gains the ability to handle modal missing without additional training of the model and can achieve 91.14% and 99.31% accuracy on Birds and Flowers datasets. The average accuracy of EFCMF on the two datasets is 52.85%, which is 27.13% higher than that of Bi-modal PMA when facing four adversarial example attacks, namely FGSM, BIM, PGD and C&amp;W. In the face of missing modal cases, the average accuracy of EFCMF is 76.33% on both datasets respectively, which is 32.63% higher than that of Bi-modal PMA. Compared with existing methods, EFCMF is robust in the face of modal missing and adversarial example attacks in multimodal fine-grained recognition tasks. The source code is available at https://github.com/RPZ97/EFCMF (accessed on 8 January 2023).
Research on Low Code Development Platform Based on Business Middle Platform Technology

In recent years, various enterprises have built various information systems, realized effective management of production data, provided information support for individual businesses, and promoted the overall improvement of management level.With the development of digital transformation and intelligence, it is difficult to meet the personalized needs of standardized software, and the project cycle and cost are not easy to control, resulting in high fixed cost expenditure of the operation and maintenance team, It brings relatively large operating cost pressure to enterprises.Therefore, the traditional chimney enterprise application architecture, data island, thick application architecture and integration relationship lead to the difficulty of personalized demand development, long cycle, high cost, poor resource reusability, and low development efficiency, which cannot quickly respond to market changes.Through the establishment of standardized, shared, componentized and reusable technical resources, we will create a visual development environment, a drag and drop development method, and a simple and easy-to-use low code development platform to reduce development difficulty and improve development efficiency.
User Preference-Based Video Synopsis Using Person Appearance and Motion Descriptions

During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.
Prospects for environmentally safe disinfection of urban sewerage with the help of compositions of non-toxic bactericidal metal complex reagents and surfactants

The prospects of environmentally safe disinfection of urban sewerage using compositions of bactericidal complexes of copper with amino acids and iminodiacetate derivatives of mucopolysaccharides and triglycerides of fatty acids are shown. When they are dosed into wastewater, the complexes penetrate into the structure of Suspended Particulate Matter (SPM) and mucus and within a few minutes bind the structural fragments of pathogens and larvae, which leads to their death with the outflow of cell fluid, the disappearance of fecal odor, cessation of putrification and coagulation of SPM. The complete disinfection is achieved at doses of complexes of 0.8 - 1.5 mmol per 1 kg of dry matter. The composition is dosed into sewage wells. Organo-mineral masses settle from disinfected wastewater. The use of compositions will reduce the negative impact of sewerage on the sanitary-epidemiological and ecological situation in settlements, increase the efficiency of existing treatment facilities and produce organo-mineral masses as commercial products.
Editorial for Special Issue “3D/4D Geological Modeling for Mineral Exploration”

With the development of high-precision geological observation technology, in situ mineral microanalysis technology, isotope geochemical analysis technology, deep geophysical exploration technology, deep drilling, real-time mining, remote sensing high-resolution hyperspectral image technology, and supercomputer and industrial intelligence, geoscience has entered an era of big data and artificial intelligence in the 21st century [...]
A Novel Linkable Ring Signature on Ideal Lattices

In this paper, a novel linkable ring signature scheme is constructed. The hash value of the public key in the ring and the signer's private key are based on random numbers. This setting makes it unnecessary to set the linkable label separately for our constructed scheme. When judging the linkability, it is necessary to determine whether the number of the intersections of the two sets reaches the threshold related to the number of the ring members. In addition, under the random oracle model, the unforgeability is reduced to the SVPγ problem. The anonymity is proved based on the definition of statistical distance and its properties.
Argumentation Frameworks with Attack Classification

Abstract Abstract argumentation frameworks (AAFs), introduced by Dung (1995, Artif. Intell., 228, 321–357), enabled a new way of reasoning with arguments, which does not take into account the internal structure of arguments but only how they are related to each other. The only form of relation considered in AAFs is a binary attack relation on the set of arguments. From the definitions of acceptability semantics of AAFs, it is obvious that attacks actually have a dual role: on the one hand, they generate conflicts; on the other hand, they can defend other arguments from attacks. In this paper, we propose a framework, where the modeller can explicitly specify the role of each attack. For this purpose, we define a set of conflict-generating attacks ${\mathcal {R}_{C}}$ and a set of defending attacks ${\mathcal {R}_{d}}$, as well as a family of semantics that considers the role of each attack while determining which arguments are attacked, which are defended and which will be included in each extension. We study the formal properties of the proposed framework and semantics, show that our framework is a generalization of AAFs and assess its semantics against a set of principles. Finally, we present a web application that provides an interface for creating custom argumentation frameworks and uses ASP to compute their extensions.
Statistical tests suites analysis methods. Cryptographic recommendations

In many applications, it is necessary to work with long sequences of random numbers, or at least those that behave as such (pseudo-random numbers). For this purpose, it is essential to verify the goodness of the sequences under study, e.g., to verify whether the sequences meet the properties of randomness, uniformity, and, in the case of cryptographic applications, unpredictability. To verify these properties, hypothesis tests are used, which are usually grouped into sets of tests known as batteries or suites. The design of these suites is a task of vital importance, and some rules must be followed. On the one hand, the coverage of a suite must be broad; it must check the properties of the sequences from different points of view. On the other hand, a suite with a very large number of tests is not uncommonly expensive in terms of execution time and computational performance. However, this consideration is ignored in most of the test suites in use. There are approximately 50 randomization tests in the literature, and each test suite collects many of them without performing any further analysis on the suite construction. It is important to perform an analysis of the possible relationships between the tests that constitute a suite to eliminate, if necessary, those tests that are redundant and that would slow down the performance of the suite. This paper reviews all of the methods that have been used in the literature to analyze statistical test suites and establishes recommendations for their use in cryptography.
Machine-Synthesized Control of Nonlinear Dynamic Object Based on Optimal Positioning of Equilibrium Points

When solving an optimal control problem with both direct and indirect approaches, the main technique is to transfer the optimal control problem from the class of infinite-dimensional optimization to a finite-dimensional one. However, with all these approaches, the result is an open-loop program control that is sensitive to uncertainties, and for the implementation of which in a real object it is necessary to build a stabilization system. The introduction of the stabilization system changes the dynamics of the object, which means that the optimal control and the optimal trajectory should be calculated for the object already taking into account the stabilization system. As a result, it turns out that the initial optimal control problem is complex, and often the possibility of solving it is extremely dependent on the type of object and functionality, and if the object becomes more complex due to the introduction of a stabilization system, the complexity of the problem increases significantly and the application of classical approaches to solving the optimal control problem turns out to be time-consuming or impossible. In this paper, a synthesized optimal control method is proposed that implements the designated logic for developing optimal control systems, overcoming the computational complexity of the problem posed through the use of modern machine learning methods based on symbolic regression and evolutionary optimization algorithms. According to the approach, the object stabilization system is first built relative to some point, and then the position of this equilibrium point becomes a control parameter. Thus, it is possible to translate the infinite-dimensional optimization problem into a finite-dimensional optimization problem, namely, the optimal location of equilibrium points. The effectiveness of the approach is demonstrated by solving the problem of optimal control of a mobile robot.
Relationship between Personal Traits and Sustainable Competence Development among Librarians in Relation to Value-Added Library Services in a Networked World: A Systematic Literature Review from 2002 to 2022

The objectives of the study were to identify the relationship between the personal traits and sustainable competence development of library professionals, explore trending practices for the development of the personal and professional competencies of librarians, and find out the challenges affecting the development of modern skills in librarians. The authors applied the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines as a research methodology. Thirty-five peer-reviewed articles were selected to conduct the systematic literature review. The results revealed that there is a strong positive correlation between personal traits and the professional development of librarians. The trending practices for the development of personal and professional skills included expert use of IT, continuous training sessions, teamwork, usage of social media forums, and a customer-oriented approach. The results also showed that old curricula, leadership problems, economic issues, and lack of personal interest were barriers to the development of modern skills in librarians relating to the implementation of value-added services in the networked world. The study contributes theoretically by adding valuable knowledge to the existing literature. It outlines practical implications to help devise strategies for capacity building among librarians related to the implementation of user-centered services in libraries.