Reviewerly/exHarmony huggingface dataset:
Semantic vector space mapping in edge of network multi-domain operations

Artificial Intelligence applications are increasingly making use of vector embedding techniques to achieve impressive results in many application domains. Semantic Vector Spaces (SVS’s) are constructed using semantic vector embedding techniques that learn vector representations of data across multiple domains. An important application area enabled by such techniques is the capability to represent software services and service workflows as semantic hypervectors. Previous work has shown how these hypervector representations have significant advantages over alternative schemes for decentralized service workflow construction, particularly in low communications bandwidth and energy constrained environments that are typical in multi-domain operations. SVS construction usually assumes that all the data required to construct the semantic vector space is available centrally. However, in multi-domain operations different partners may not be willing to share the training data necessary to construct a common multi-domain SVS. Hence semantic hypervectors representing similar services or workflows but constructed from different training data by different partners cannot be discovered and used. In this paper we focus on how it is possible to map semantic hypervectors between partner SVS’s, so that complementary services and workflows developed and owned by different partners can be discovered and used to achieve mission goals. The paper describes techniques for generating the required mapping that require a minimum exchange of information between the different partners; demonstrate how it is possible to do this for semantic hypervectors that use different types of encoding (eg., real valued, binary, sparse slot-encoding); and illustrates how the mapping can be implemented in various multi-domain operational settings.
MEDIA INFLUENCE AS A MEANS OF THE ARCHITECTURAL SPACE QUALITIES EXPANDING ON THE EXAMPLE OF SHOPPING AND ENTERTAINMENT COMPLEXES

This article covers the issues of the architectural space qualities expanding under the influence of electronic media. The research was based on specialized scientific works and in-formation from related fields of knowledge. Extensive practical experience in solving media-active architecture objects, such as shopping and entertainment complexes, has become the basis for de-velopment and testing of theoretical concepts. During this work, there were identified two basic components of media influence: technical and informational, there was established their integration nature into the architectural space. To describe new properties that surfaces acquire in a media-active architectural environment, there were used concepts of media facture and media texture, some practical examples of operating with these concepts were also considered. There was studied formational potential of information mes-sage or content, there were distinguished architecturally-oriented, third-party and localized types of content. There was established a principle of sequential "cooling" (as defined by McLuhan) of media influence according movement from the outside to the inside, from the exterior to the inte-rior, for shopping and entertainment complexes. Due to the interdisciplinary nature of the media-active architecture concept, the article at-tempts to find links between the study issues and the experience of related fields of knowledge, including the cinema art and the laws of dramatic works construction. There was established a number of acquired qualities of architectural space that emerged via media influence, such as variability and dynamism, multi-layered visuality, expanded content, plot, temporal-spatial organization of the composition, or plot.
Synthetic data for automatic target recognition from small drones

Automatic target recognition (ATR) technology is likely to play an increasingly prevalent role in maintaining situational awareness in the modern battlefield. Progress in deep learning has enabled considerable progress in the development of ATR algorithms; however, these algorithms require large amounts of high-quality annotated data to train and that is often the main bottleneck. Synthetic data offers a potential solution to this problem, especially given recent proliferation of tools and techniques to synthesize custom data. Here, we focus on ATR, in the visible domain, from the perspective of a small drone, which represents a domain of growing importance to the Army. We describe custom simulators built to support synthetic data for multiple targets in a variety of environments. We describe a field experiment where we compared a baseline (YOLOv5) model, trained on off-the-shelf large generic public datasets, with a model augmented with specialized synthetic data. We deployed the models on a VOXL platform in a small drone. Our results showed a considerable boost in performance when using synthetic data of over 40% in target detection accuracy (average precision with at least 50% overlap). We discuss the value of synthetic data for this domain, the opportunities it creates, but also the novel challenges it introduces.
ClipSim: A GPU-friendly Parallel Framework for Single-Source SimRank with Accuracy Guarantee

SimRank is an important metric to measure the topological similarity between two nodes in a graph. In particular, single-source and top-k SimRank has numerous applications in recommendation systems, network analysis, and web mining, etc. Mathematically, given a vertex, the computation of single-machine and single-source SimRank mainly lies in matrix-matrix operations. However, it is almost impossible to directly compute on large graphs. Thus, existing works yield to two main operations: a series of random walks, and sparse matrix and dense vector multiplication operations. This brings about high computation cost for SimRank on large graphs. In real-world applications, there is always the query time and accuracy trade-off, which hinders the computation of high-precision SimRank on large-scale graphs. To handle this problem, this paper proposesClipSim, the first GPU-friendly parallel framework that accelerates the single-source SimRank on GPU with accuracy guarantee. We design a novel data structure and GPU-friendly parallel algorithms for efficient computation of all the operations of SimRank on GPU. Moreover, our theoretical derivation enables ClipSim to largely reduce the number of random walks required for each node, while maintaining the same theoretical accuracy as the state-of-the-art algorithm, ExactSim. We conduct extensive experiments on real-world and synthetic datasets to demonstrate the accuracy and efficiency of ClipSim. The results show that compared with ExactSim, ClipSim obtains single-source SimRank vectors with the same accuracy and up to 160× faster computation time.
A novel QKD protocol utilizing temporal correlations in photon pairs

We introduce a novel QKD protocol which utilizes the intrinsic temporal correlations found in photon pairs from a two-mode squeezed vacuum (TMSV) state. Upon generation, the idler photons are measured right away by Alice, and their time stamps recorded. This idler detection heralds the corresponding signal photons traveling in the channel towards Bob, which ensures the temporal configuration of the photons is identical between the two sets of photons when Bob measures them as a later time. By converting the time intervals between these photons into an uniformly-distributed integer stream, we generate two matching streams of integers which can be used to generate a secure key. The security of this key-generation scheme is discussed and investigated, as well as the utilization of this system in a real-world environment. The introduction of dropped photons and dark counts requires the use of non-standard error correcting codes. We discuss the use of Marker codes in the system and how correction and privacy amplification can occur without allowing knowledge of the full key to transmit to an eavesdropper.
Automatic Evaluation for Machine Translation

Language is the primary mode of communication. Communication is the only way to convey our thoughts and emotions to others. But there are many languages that we don't know how to speak and we can't learn all languages so quickly which is why Machine Translation systems were invented to help us communicate with anyone from anywhere. Researchers started working on Machine Translation Systems in the 1950s and have developed various amazing techniques to make our communication easy. Machine Translation Evaluation (MTE) Methodology checks the accuracy of the translations done by Machine Translation Tools. It is extremely important because while developing the translation systems, it constantly checks the performance and helps us to make appropriate changes in the system for bringing accuracy. Mostly it is done by comparing the output of machine translation systems with the translation(s) done by Human beings, there are also some techniques that do not require any reference sentences. In our project, we are using four Automated Machine Translation Evaluation metrics which are TER (Translation Error Rate), METEOR (Metric for Evaluation of Translation with Explicit Ordering), BLEU (Bilingual Evaluation Understudy), and NIST (National Institution of Standards and Technology) for English-Hindi Translation.
DIGSM 4.0 CURRICULUM LIFECYCLE BASED ON COMPONENT ORGANISED LEARNING METHOD

The research under consideration is how to build COL (Component Organised Learning) based curriculum lifecycle that teachers can apply in different fields. The problem is that the degree studies curriculum is not always applied to working students as the needs of the companies they work for differ. Companies find it more valuable when students can use the existing curriculum topics to solve today's company problems. Current degree studies take considerable time and are unsuitable for full-time employed specialists. The proposed Education 4.0 concept brings the knowledge and skills required by the market for full-time employed students in a time-efficient way. One possible solution to apply this concept in practice is to create a micro-credential curriculum for companies. The authors suggest using the intensive week approach for analysis, where the students will solve the real company problem using previously studied solutions. They will also use the Moodle courses linked to the developed COL to speed up access to the materials. Authors suggest organising such an event in collaboration with consortium partners and involving all teaching staff who worked on course development. After introducing problems brought by the company and visiting the company on site, asking the questions, students define the task to be solved in groups. After that, the Authors recommend providing intensive courses where each partner will present the developed Moodle materials. The target is that the students will solve the problem in 24h and present solutions to the company representative.
Argument and Counter-Argument Generation: A Critical Survey

Argument Generation (AG) is becoming an increasingly active research topic in Natural Language Processing (NLP), and a large variety of terms has been used to highlight different aspects and methods of AG such as argument construction, argument retrieval, argument synthesis and argument summarization, producing a vast literature. This article aims to draw a comprehensive picture of the literature concerning argument generation and counter-argument generation (CAG). Despite the increasing interest on this topic, no attempt has been made yet to critically review the diverse and rich literature in AG and CAG. By confronting works from the relevant subareas of NLP, we provide a holistic vision that is essential for future works aiming to produce understandable, convincing and ethically sound arguments and counter-arguments.
Creativity of High School Teachers Over Time

This article deals with the creativity of teachers of higher education in the process of their professional activity. The teacher's creativity is substantiated as an important factor that determines the success of the teacher's professional activity and, as a consequence, a better educational process. An active professional thesaurus of a teacher is considered as a means of supporting indicators of changes in biological creativity. The data were obtained from direct surveys of teachers, administrators and managers of universities, using D. Johnson's creativity questionnaire, adapted by E.V. Tunik, as well as from the review of the results of scientific and pedagogical activities of university teachers: original articles, creative classes, and translation of creative behavior. Based on the data of the study, the dependence of the level of creativity of a higher education teacher on the level of a professional thesaurus is revealed. In particular, the article presents the level of creativity of teachers who combine educational work with research, teachers who are mainly engaged in educational activities and representatives of the administrative and managerial personnel of universities, combining managerial functions with teaching.
A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method.
The Legal Regulation of Electronic Companies

Electronic companies are considered one of the prerequisites of the technical revolution in the field of communications and informatics, through which companies aim to provide their services electronically. Traditional companies have taken to convert to electronic companies or create exclusive electronic companies; this is because this form of company has the advantage of saving effort and time. Hence, the importance of this study is to identify the legal regulation of electronic companies in Palestine, which takes place in an intangible environment, and to demonstrate the adequacy of the relevant legal rules for their regulation under the Electronic Transactions Law and the relevant laws, by indicating the meaning of the electronic company, its distinctive characteristics and the procedures for its establishment.
Las TIC en el proceso evaluativo virtual en tiempos del covid 19: percepción docente

During the period Spring 2020 to Spring 2021, due to the SARS-CoV-2 Pandemic, face-to-face classes were suspended at all educational levels, including the higher level, forcing the actors to use information and communication technologies (ICT) as main tools in the teaching-learning process, action that was initially carried out empirically and individually, but which forced higher education institutions (HEIs) to adapt study programs to the virtual environment, hire and improve institutional platforms, and initiate teacher training courses, impacting on the prevailing teaching-learning process, especially in the form of evaluation. The objectives of this educational research work are to know the level of satisfaction in the undergraduate teachers of Facultades de Contaduria Publica and Ingenieria Quimica from Benemerita Universidad Autonoma de Puebla (BUAP), through the analysis of a significant sample, with the new evaluation process in which they were participants during the aforementioned period, using a mixed methodology (quantitative and qualitative), descriptive and interpretive; and with this, generate a proposal to improve said evaluation process, which allows maintaining and/or raising the level of teacher satisfaction to continue in the virtual mode or with hybrid educational models; and so achieve the learning purposes of each subject and facilitate the learning-teaching process with a focus on competencies, determined by BUAP's modelo universitario Minerva (DGES BUAP, 2016). To do this, an electronic survey type research instrument was designed, analogous to the Likert scale; The data treatment was carried out by creating a database in Excel Spreadsheet and, later, the SPSS Software was used to build the descriptive statistical graphics, obtaining satisfactory results.
Corrigendum: An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction

[This corrects the article DOI: 10.3389/fncom.2023.1145209.].
Leveraging Natural Language Processing to Improve Electronic Health Record Suicide Risk Prediction for Veterans Health Administration Users

ABSTRACT Background: Suicide risk prediction models frequently rely on structured electronic health record (EHR) data, including patient demographics and health care usage variables. Unstructured EHR data, such as clinical notes, may improve predictive accuracy by allowing access to detailed information that does not exist in structured data fields. To assess comparative benefits of including unstructured data, we developed a large case-control dataset matched on a state-of-the-art structured EHR suicide risk algorithm, utilized natural language processing (NLP) to derive a clinical note predictive model, and evaluated to what extent this model provided predictive accuracy over and above existing predictive thresholds. Methods: We developed a matched case-control sample of Veterans Health Administration (VHA) patients in 2017 and 2018. Each case (all patients that died by suicide in that interval, n = 4,584) was matched with 5 controls (patients who remained alive during treatment year) who shared the same suicide risk percentile. All sample EHR notes were selected and abstracted using NLP methods. We applied machine-learning classification algorithms to NLP output to develop predictive models. We calculated area under the curve (AUC) and suicide risk concentration to evaluate predictive accuracy overall and for high-risk patients. Results: The best performing NLP-derived models provided 19% overall additional predictive accuracy (AUC = 0.69; 95% CI, 0.67, 0.72) and 6-fold additional risk concentration for patients at the highest risk tier (top 0.1%), relative to the structured EHR model. Conclusions: The NLP-supplemented predictive models provided considerable benefit when compared to conventional structured EHR models. Results support future structured and unstructured EHR risk model integrations. J Clin Psychiatry 2023;84(4):22m14568 Author affiliations are listed at the end of this article.
Understanding the relationship between Technical Debt, New Code Cost and Rework Cost in Open-Source Software Projects: An Empirical Study

Making sub-optimal design decisions during software development leads to the accumulation of Technical Debt (TD) in software projects. There are tools to identify TD Items in software code through static code analysis. However, quantifying TD to support decision-making on whether to keep taking on TD or if it is time to refactor TD is a difficult task, and proposed approaches for this still lack consensus. Prior work observed that TD Interest could be further decomposed into constituents ‘New Code Cost’ and ‘Rework Cost’, which gives an interesting direction of research to explore TD quantification in terms of these costs. Therefore, through our empirical study, we plan to explore the relationship between TD, New Code Cost and Rework Cost in Open-Source Software Projects. This paper reports on an initial motivating study, our plan for future work and implications for researchers.
Analysis of Bug Report Qualities with Fixing Time using a Bayesian Network

Most client software employs a bug-tracking system, which utilizes user-submitted reports (bug reports) that contain information necessary for software developers to fix bugs. The quality of bug reports drastically differs. Bug reports can include severity, priority, and associated issues determined by researching the addressed bug. Herein we investigate the influence of bug report qualities on successfully fixing a bug and estimating the fixing time. We also examine the claim in previous studies that bias and differences in the treatment of bug reports exist due to broad expertness among the reporters. Our approach examines the relationship between the qualities within the bug-fixing cycle and modeling graphical causal dependencies through a Bayesian Network. Bug reports with attachments, dependencies on another bug, and frequent discussions are more likely to be fixed. In addition, bug reports with a high severity tend to be fixed faster. Moreover, the difficulty of the bug itself may influence the fixing rate such that a straightforward bug will be fixed easier and faster regardless of the bug report quality.
Ara-Gaya of the Ancient tomb with a gigantic mound and System

The start of the Ara-Gaya the ancient tomb with a gigantic mound and System began in the mid-5th century, which is now Tomb No. 4 at Marisan Tombs(末伊山古墳群). The Ara-Gaya of the ancient tomb with a gigantic mound and System begins when the tomb ritual is changed to a stone tomb, and at the same time, the tomb ritual is changed. The biggest change in the burial ritual is the implementation of the Sacrificial burial(殉葬), and it inherits the structure of the large wooden chamber tombs from the previous period, but the pattern of the burial site changes. The transition period of civil engineering technology has not been confirmed in the construction of the ancient tomb with a gigantic mound, and it shows a relatively complete form from the time of its appearance. In addition, it shows a flexible appearance by applying a suitable civil engineering method according to the topography. It is confirmed that the same civil engineering method is applied to large civil engineering structures such as Saturn, fortress, embankment, and large buildings in the same province. Through this, it can be inferred that Ara-Gaya’s advanced civil engineering technology and its management and operation system were well equipped. The Ara-Gaya of the ancient tomb with a gigantic mound and System starts at the top of the fourth ridge of the Marisan Tombs and continues to the ninth ridge. Type A-1 is arranged at regular intervals on the top of the main ridge and the ridge of the branch ridge, and type A-2 and 3 are arranged as tombs attached to the periphery. The tomb of a large mound is in the middle of the 5th century. It was intensively contrasted in a relatively short time of about 70 years until the beginning of the 6th century. At the same time, considering the construction of large-scale mound tombs of type A on each ridge, it is believed that when the hills of Marisan Tombs were built as Scope of the Royal Tomb, the right of burial was not unified construction of royalty(多系列王權). On the other hand, from the stage of moving the the scope of a royal tomb to the Namnunoe Tombs(南門外古墳群) at the same time as turning into the ancient tomb with a gigantic mound and System, it is possible to get a glimpse of the establishment of a specific series of kingship(一元的 王權系列).