Reviewerly/exHarmony huggingface dataset:
Successful Practices in Industry-Academy Collaboration in the Context of Software Agility: A Systematic Literature Review

Research and development (R &D) are the main factors for inserting new technologies and knowledge in the industry, starting from research developments established through collaborations between industry and academia (IAC) within software development organizations. These collaborative practices between these environments enhance the exchange of knowledge and experiences, favoring both communities involved. One of the main processes applied within software development is agile methodologies that present low effort and reduced failure rates in software development. The purpose of this article is to present an exploratory and empirical study of IAC practices in the context of Agile Software Development (ASD), exploring and characterizing solutions and practices, the challenges encountered in the application of IAC, and collaboration. A Systematic Literature Review (SLR) and a Snowballing were conducted in five databases, evaluating/analyzing 8460 articles following the defined criteria. As a result, ten categories of challenges and fourteen categories of good practices for application in collaborative projects were described, in addition to describing seven collaboration models.
A Prospective Study on Regulating Content of the Convergent and Personal Media

This paper proposes a new content regulation framework that responds to the trend of media convergence and personalization. Today, in the area of “convergent media” that reorganizes the internet area based on digital platforms, broadcasting content is only a part of media content. Among them, “personal media”, which is centered on social media platforms, is an area where communication between users takes place, and its essential functions and usage structure are clearly distinguished from broadcasting. Therefore, if broadcasting regulations are applied to the convergent media under the Integrated Broadcasting Act, it is difficult to avoid overlapping with the current communication law regulations and unconstitutionality due to lack of proportionality of regulations. For convergent media content converged into the internet area, it is necessary to consider differential regulatory means according to its properties. The standard for differentially establishing public responsibility, which is the basis for legal regulations, can be understood by abbreviating it to the editorial rights of platform operators corresponding to the broadcasting company’s right to organize and the organization of content producers. Based on the convergent media classification system based on these scales, additional consideration of the harmful categories of content can approach the types and means of reasonable and constitutional content regulations to be applied to each category. From this point of view, I suggest a “semi-regulatory model” to divide the entire media content area into regulatory and non-regulatory areas and to distinguish between administrative and self-regulatory areas. According to this model, the boundaries of self-regulation and the justification limits of administrative regulation coincide, and the boundaries of administrative regulation are legal limitations of self-regulation and define the areas where administrative decisions take precedence in relation to illegal content regulation. As a result, it is possible to secure the predictability of regulatory laws for targets newly incorporated into the regulatory domain, such as personal media, and the semi-regulatory model is meaningful as a legislative design model for the constitutionality of various new types of regulations.
EXTENDED FRAMES AND SEPARATIONS OF LOGICAL PRINCIPLES

Abstract We aim at developing a systematic method of separating omniscience principles by constructing Kripke models for intuitionistic predicate logic $\mathbf {IQC}$ and first-order arithmetic $\mathbf {HA}$ from a Kripke model for intuitionistic propositional logic $\mathbf {IPC}$ . To this end, we introduce the notion of an extended frame, and show that each IPC-Kripke model generates an extended frame. By using the extended frame generated by an IPC-Kripke model, we give a separation theorem of a schema from a set of schemata in $\mathbf {IQC}$ and a separation theorem of a sentence from a set of schemata in $\mathbf {HA}$ . We see several examples which give us separations among omniscience principles.
Ozymandias meets Wikipedia, with notes on natural language generation

I've tweaked Ozymandias to now include short natural language summaries (snippets) for various taxa. This makes the output a little more friendly and informative. For example, here's a snippet from the page on &lt;i&gt; Cephalodesmius &lt;/i&gt; , a dung beetle that makes its own dung. These snippets come from Wikipedia, well actually, from the DBpedia project.
Co-Attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction). To this end, recent works have shifted their focus to learning the interaction between pairs of graphs. Despite their improved performance, these works were still limited in that the interactions were considered at the node-level, resulting in high computational costs and suboptimal performance. To address this issue, we propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks using real-world datasets, while maintaining lower computational complexity.
Traffic Dapper Mobile Application: An Online Solution

Traffic congestion and road violations have become persistent challenges in urban areas, necessitating the development of efficient systems to address these issues. This abstract presents an overview of a mobile application called Traffic Dapper, designed to make traffic management and empower both traffic police and users. The Traffic Dapper mobile application aims to provide a comprehensive solution by enabling users, traffic police, and administrators to effectively collaborate and communicate. The application comprises three main components: the mobile app for users, the mobile app for traffic police, and the web-based administration portal. The Traffic Dapper mobile application strives to improve traffic management and user engagement through seamless communication and digitalization. By leveraging real-time data, the application empowers users to make informed decisions, promotes transparency in law enforcement, and enables administrators to efficiently oversee the system. Keywords: Traffic Dapper, mobile application, traffic management, police stations, complaints, fines.
Searchlights across the black box: Trade secrecy versus access to information

This article challenges the commonly held view that private companies and public authorities who use algorithms to make vital decisions about the lives of individuals can lawfully refuse to disclose those algorithms and information about them by claiming trade secret protection. Article 1(2)(b) of the EU Trade Secret Directive permits disclosures on public interest grounds when EU or national rules require as such. Focusing on EU and national caselaw, this article shows how an individual's right to access information can be – and indeed, has been – used successfully to disclose algorithms and algorithmic information. Some form of disclosure is therefore plausible, lawful and desirable. But disclosures must be meaningful. Even though revealing an algorithm may not be useful to everyone by itself, there are ways to make it meaningful. Namely, by sharing different information to public authorities, regulators, society and individuals. Not only do context-dependant disclosure requirements help balance the competing interests which algorithmic disclosures give rise to, but it will help achieve a greater understanding of algorithms. All in all, this article shows that what is currently being disclosed does not come close to what can and should be disclosed.
Chatbot as a multi-age teaching strategy

The awareness of contemporary students for the learning process comes up against the abundant and accelerated supply of technological resources. This scenario forces teaching to be attractive, dynamic and interactive throughout the construction of autonomous and meaningful knowledge. In this way, this work aims through the active methodology "Project Based Learning" to connect through the teaching of educational robotics students of different levels of education and provide them, through the making and execution of chatbot, a new tool for autonomous study, interactive and integrated to socio-emotional factors. StudyChat SESI-SP 113 was developed by 3rd year high school students using the Scrum method and offered to 5th year elementary school students as a new multidisciplinary study resource.
A secure service framework based on ordered hash tree in blockchain

Blockchain, as a type of distributed ledger technology, is well-suited for the distribution, sharing, certification, and tracing of information. Therefore, DApps implemented on blockchain have been utilized in various industries, where businesses utilize DApps to achieve reliable information flow and certification across enterprises or within an enterprise. However, DApps are limited by the constraints of the blockchain architecture, which results in issues such as poor performance, limited scalability, difficult maintenance and management, and high development costs. As a result, it is difficult for DApps to replace traditional database models in enterprises. In order to address this issue, we conducted an analysis of the shortcomings of DApps and proposed a novel blockchain service framework based on ordered Hash tree architecture, which provides a consistent and reliable solution for data management in DApps. Our work, through analysis, is capable of resolving or improving the challenges faced by DApps and holds practical applications value.
Effects of strengthening exercises on the functional recovery of patients undergoing anterior cruciate ligament reconstruction: An integrative review

Introduction: Anterior cruciate ligament (ACL) injury frequently occurs among practitioners of high impact activities and changes in direction, as these individuals have a greater load of tension and develop minor damage to the ligament, which may cause rupture, requiring surgical reconstruction of the ligament. LCA. Decreased strength after reconstruction has been linked to poor function and altered biomechanics, interfering with routine activities of daily living, thus making it impracticable to return to sports activities. Objective: To analyze the effectiveness of strengthening exercises in functional recovery after ACL reconstruction. Methods: The research is an integrative review based on complete articles published in the last 10 years indexed in PubMed and Portal Periódicos CAPES. The following keywords were used to select the articles: “Anterior Cruciate Ligament Reconstruction” AND “Exercise Therapy”; “Anterior Cruciate Ligament Injuries” AND “Exercise Therapy”. Results and discussion: 36 articles were found and 05 selected, where they were unanimous in presenting significantly positive results in the use of exercises in functional rehabilitation after ACL reconstruction. Conclusion: It is concluded that exercise therapy is effective for individuals undergoing ACL reconstruction, the improvement in functionality and the return to the practice of routine or sports activities are directly related to exercise therapy.
Infrared pedestrian tracking network based on convolution model and transformer model fusion

Camera surveillance plays an important role in maintaining the stability and safety of the social and public environment, and there are further requirements for the role of camera surveillance in building a smart city. This paper proposes a convolutional neural network based on the combination of the convolution module and the Transformer module. The network is applied to the tracking of pedestrian targets in infrared surveillance cameras to fill the shortcomings of surveillance cameras in the night environment. In this paper, the local features of the convolution module and the global features of the Transformer are combined into a comprehensive feature map. <strong>The feature information is used to solve the problem of less target feature information in infrared images, and the advantages of codec network structure design are used to ensure effective target features.</strong> At the same time, considering the embedding and portability of the network model, this paper adopts the method of grouping shared convolution kernels and Transformer nested segmentation in the design of the convolution module and the Transformer module, so as to achieve the purpose of light weight. After several sets of control experiments, the network designed in this paper has a certain improvement in tracking speed and tracking performance, and effectively solves the problem that infrared weak and small targets are not easy to track.
Research on structured and secure data governance

With the development of "Internet+Education", universities are facing the leap from digital campus to smart campus. However, data silos, incomplete data, data leakage and dirty data are common problems in universities, which hinder the construction of smart campus. In order to break through barriers and achieve secure data usage, this paper proposes a data governance framework and a multi-layer protection mechanism. The data governance framework divides all elements involved in the data life cycle into four subjects, and ensures the effectiveness and feasibility of data governance through their mutual cooperation. The multi-layer protection mechanism is designed to ensure the data security as much as possible by segregating the data in layers based on operation attributes. After practice, the level of campus data governance under the guidance of this paper has been greatly improved.
Full-Reference Image Quality Assessment: Addressing Content Misalignment Issue by Comparing Order Statistics of Deep Features

This letter aims to develop advanced full-reference image quality assessment (FR-IQA) models to evaluate content-misaligned image pairs, which are commonly encountered in image reconstruction tasks and texture synthesis tasks. Traditional FR-IQA models tend to be overly sensitive to content shifting and misalignment, thus deviating from subjective evaluations. Herein, we propose a deep order statistical similarity (DOSS) FR-IQA model that compares the order statistics of deep features to address this issue. In DOSS, the reference and distorted images are projected into the deep feature space, and the sorted deep network features are compared with the cosine similarity index to output the final perceptual quality scores. With such a simple design baseline, DOSS offers several advantages. First, it mimics the behavior of the human visual system (HVS) in terms of evaluating content-misaligned image pairs, thereby tolerating slight image shifts and deformations. Second, DOSS possesses an advanced texture perception capability, producing superior quality assessment results on images generated by various texture synthesis algorithms; this indicates that DOSS can be used to select visually appealing texture synthesis results. Finally, experimental results demonstrate that DOSS can also obtain competitive quality assessment results on standard IQA datasets, suggesting that deep feature order statistics can serve as generic features for both content-aligned and content-misaligned IQA. The code for this method is publicly available at https://github.com/Buka-Xing/DOSS.
Developing a Novel and Integrated Datacenter Concept Design Based on Hydrogen Production

Over the past few decades, data centers have made significant strides in enhancing their sustainability practices.These efforts range from minimizing power usage effectiveness (PUE) and carbon usage effectiveness (CUE) to enhancing water usage effectiveness (WUE).However, it's time for the data center industry to take a groundbreaking step forward.By effectively managing waste heat, data centers can become energy providers, boosting their profitability, and improving their energy reuse effectiveness (ERE) metrics.In this paper, we explore the crucial role of efficient heat management in optimizing the performance of an electrolyzer and maximizing the production of green hydrogen.Specifically, we focus on the importance of maintaining the stack temperature of the electrolyzer within a specific range to ensure maximum efficiency.We then propose a novel and integrated design concept for data centers that combines hydrogen production with district heating.An electrolyzer can be connected to both the data center and district heating systems, enabling the heat management system of the data center (e.g., heat pump) for powering the electrolyzer.Additionally, district heating can be utilized for thermal management of the electrolyzer, further improving the overall efficiency of the system.Additionally, the waste heat produced during the electrolysis process can be harnessed and employed to supplement the district heating system.This symbiotic relationship between the two systems results in a reduction in carbon emissions and improved energy efficiency.Our paper examines the technical feasibility of this proposed system and highlights the potential benefits for data centers and district heating systems.
Innovative Pedagogical Technologies as a Condition of Training Specialists for Transport Industry

Relevance: The issues of improving the quality of education require thinking of the lessons within the scope of nature-friendly organization of the educational process in the context of the decline in the population health and the current pandemic period. Objective of the study is to consider special pedagogical technologies as a condition of training specialists for the transport industry. Methods of the study. The article reveals the methodological aspects of domestic and similar foreign experience in organizing training classes in the open air, examines the prototypes of “fresh” training lessons in the pedagogical heritage of the classics, conducted a sociological survey on the research problem. Results of the study. A new type of educational lesson for a university was considered - a “fresh” lesson in the open air, the significance of “fresh” pedagogical technologies for students’ health keeping and the greening of the educational process was revealed, the results of an empirical study on the problem of introducing “fresh” educational classes. Scientific novelty: the concepts of “fresh educational lesson” and “pedagogical fresh technologies” were substantiated and introduced into scientific circulation; empirical data on the problem under study were obtained. Practical relevance: practical recommendations for organizing “fresh” training lessons on the territory of the green zone. The author states in the conclusion that pedagogical fresh technologies should be based on the positions of harmonizing the social and natural environment in the context of the growing environmental crisis, using nature as a means of education.
Generalized Adaptive Kalman Filter of Navigation Measurements Based on Non-periodic Satellite Observations

One of the main problems in collecting the Kalman filter is the unstable (divergent) characteristic of the error vector of system state parameters over time. The ability to use accurate measurements of the system state vector (in a random case, at random times) makes it possible to increase the high accuracy of filtering by choosing appropriate correction estimates. Currently, the correction is carried out mainly by directly changing the current estimates of the state of the system with the corresponding accurate measurements with the parameters of the estimation algorithm itself. Obviously, with such a solution to the problem, the growth of estimation errors in the time interval determined by the time of the next accurate measurement will not decrease (which is observed in the systems mentioned above). In this regard, the paper proposes a new approach that provides the ability to use the available irregular accurate measurements to correct not only the state vector estimates, but also the structure of the filtering algorithm itself – by adapting the a posteriori covariance matrix. Such an adjustment of the parameters of the estimation algorithm sharply increases its accuracy and convergence, which is illustrated by a corresponding example. The simplicity and accuracy of the proposed algorithm provide the possibility of its effective application for the widest class of information and control systems – on-board control systems, radio engineering systems, navigation systems, etc.
Online Training on the MOODLE Platform as the Environment Forming a University Student’s Educational Autonomy

This article aims to summarize the experience of European and Russian scientists on the issue of a university student’s autonomous educational activity. The issue is undoubtedly relevant, since the modern educational model is based on the principles of personal responsibility for the content and quality of one’s education and implies the student’s competence in planning, organizing and managing his/her educational activities not only during the period of study, but throughout his/her life (Lifelong learning). The article describes the experiential training of second-year students at RUDN University. Its results and conclusions showed the effectiveness of e-courses on the MOODLE platform in building the student’s learning autonomy. The experiment also demonstrated another strong point of online learning, i.e. its great contribution to the development of students’ self-study, self-discipline and responsibility. The authors emphasize the universal nature of the skills associated with organizing, regulating and managing one’s educational activities. Mastering these techniques within the training period will be able to fulfill successful self-education and self-development throughout his/her life.
SAM: Query-efficient Adversarial Attacks against Graph Neural Networks

Recent studies indicate that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Particularly, adversarially perturbing the graph structure, e.g., flipping edges, can lead to salient degeneration of GNNs’ accuracy. In general, efficiency and stealthiness are two significant metrics to evaluate an attack method in practical use. However, most prevailing graph structure-based attack methods are query intensive, which impacts their practical use. Furthermore, while the stealthiness of perturbations has been discussed in previous studies, the majority of them focus on the attack scenario targeting a single node. To fill the research gap, we present a global attack method against GNNs, Saturation adversarial Attack with Meta-gradient, in this article. We first propose an enhanced meta-learning-based optimization method to obtain useful gradient information concerning graph structural perturbations. Then, leveraging the notion of saturation attack, we devise an effective algorithm to determine the perturbations based on the derived meta-gradients. Meanwhile, to ensure stealthiness, we introduce a similarity constraint to suppress the number of perturbed edges. Thorough experiments demonstrate that our method can effectively depreciate the accuracy of GNNs with a small number of queries. While achieving a higher misclassification rate, we also show that the perturbations developed by our method are not noticeable.