Reviewerly/exHarmony huggingface dataset:
Graph collaborative filtering-based bug triaging

Issue tracking systems are widely used for collecting bug reports. A target of intelligent software engineering is to automate assigning bugs to appropriate developers. Recently, the momentum of artificial intelligence has brought many successful studies that triage bugs by classifying their reports with NLP-based methods. Some studies also try to introduce context information to represent developers. Nevertheless, they take a fundamental assumption that developers and bugs, closely related entities in real-world scenarios, should be modeled independently. To capture the bug-developer correlations in bug triaging activities, we propose a Graph Collaborative filtering-based Bug Triaging framework: (1) bug-developer correlations are modeled as a bipartite graph; (2) natural language processing-based pre-training is implemented on bug reports to initialize bug nodes; (3) spatial–temporal graph convolution strategy is designed to learn the representation of developer nodes; (4) information retrieval-based classifier is proposed to match bugs and developers. Extensive experiments across mainstream datasets show the competence of our GCBT. Moreover, We believe that GCBT could generally benefit the modeling of correlations in other software engineering scenarios.
Artificial Intelligence in Forensic Science

Artificial intelligence is a rapidly evolving technology that is being used in a variety of industries. This report provides an overview of some artificial intelligence applications used in forensic science. It also describes recent research in various fields of forensics, and we implemented a model for a use case in digital forensics.
Lightweight Encoder-Decoder Model for Semantic Segmentation of Hand Postures

Hand gesture recognition (HGR) is becoming more important in human-computer interaction as people's expectations for interactive experiences rise. It has a wide range of applications in the automobile industry, consumer electronics, home automation, and other areas. Proposal of an alternative for gesture recognition without extra sensor requirement of depth images, by using semantic segmentation images of the hand for real-time hand gesture recognition is done. Segmentation is a challenging problem by itself, due to different restrictions such as illumination variation, a complicated background, and so on. The use of an encoder-decoder architecture for pixel wise semantic segmentation is done which is conducive for real-time application with a segmentation accuracy of 83.57%. A comparison is drawn between the performance of different existing categorization models as encoder or discriminator to attain a balance of efficiency and accuracy as well as observed the performance upon fine tuning on pretrained encoders on ImageNet to achieve the task of segmenting hand postures. The results of applying semantic segmentation for HGR have been demonstrated, highlighting the proposed model's computational efficacy of 87.8 GFLOPS.
Efficient Big Data Clustering Using Adhoc Fuzzy C Means and Auto-Encoder CNN

Clustering, a well-known unsupervised machine learning technique is effective in handling massive amount of data for a variety of applications. Clustering is the process of grouping identical data into clusters that belong to a specific category. Big data consists of massive amounts of data generated every second; hence, clustering this data is quite challenging. Deep learning techniques are used to analyze large amounts of data, which necessitates the use of a large number of samples for training, resulting in a time-consuming and inefficient process. The adhoc fuzzy C means method can be used to overcome this limitation. In this research study, a fusion approach known as fusion clustering is built by combining auto-encoder features with adhoc fuzzy C means (AFCM) technique to improve the fusion approach. The auto-encoder, in combination with the CNN approach, is used for feature engineering to improve the performance, resulting in increased computational speed. The efficiency of the proposed model is improved by evaluating each performance metrics in detail by using three different data types: MNIST, Fashion-MNIST dataset, and USPS. The comparison of each dataset demonstrates that the proposed adhoc fuzzy C means (AFCM) model outperforms other state-of-the-art techniques.
Establishing a Security Champion in Agile Software Teams: A Systematic Literature Review

Security is increasingly recognized as an important aspect of software development processes. In agile software development, adoption of security practices is still facing a lot of challenges due to the perception and management of software teams. A security champion is an important strategic mechanism for creating a better security culture, however it is little known about how they can be achieved. In this paper, we present the results of a systematic literature review investigating approaches to establishing and maintaining a security champion in an organization with Agile teams. Gathering empirical evidence from 11 primary studies, we presented how security champion is characterized, the conditions for establishing and reported challenges in maintaining security champion programs. One of our main findings is a classification schema of 14 steps and 32 actions can be taken to establish a security champion program. The study has practical recommendations for organizations who want to establish or improve their security program in Agile teams.
Implementation of the Asset Administration Shell Concept to Industrial Augmented Reality Applications

To unify communication across partners in production systems, the German Plattform Industrie 4.0 network specified the Asset Administration Shell (AAS) both as a general concept and a specific data exchange format for assets in Internet of Things contexts. This contribution presents an approach to apply this concept to quality inspection (QI) processes supported by Augmented Reality (AR) assistant systems. It defines the submodels that contain all data required to provide shop floor personnel with necessary information during the process. Furthermore, the approach contains the representation of necessary system and positioning information for correct AR overlay of the QI information on top of the real-world object. Finally, a prototype AAS system implementation for Microsoft HoloLens 2 based on Eclipse Basyx is presented for evaluation and validation purposes.
Simple Task Allocation Algorithm in a Collaborative Robotic System

The paper presents an algorithm of assigning a sequence of tasks in the collaborative robotic system (CRS). These tasks are represented both as a directed graph, and a timeline with the operations’ start events. The algorithm focuses on assigning the operations based on the availability of CRS members and their effectiveness. After the assignment, the execution time of the operation is recalculated according to the efficiency of the assigned humans and cobots, modifying the weight of the corresponding graph edge and rebuilding the time sequence after the current time event. If these conditions are not met, the assignment for the operation is postponed until the next event. The algorithm makes the transition from the current time event to the nearest one and traverses the graph from the first vertex to the last one. The analysis of modeling results shows that the use of the algorithm for a sequence of interdependent operations that requires human–robot collaboration allows reducing the time of execution of both the technological process and its individual operations. The algorithm can be used to test various hypotheses for the optimal number of participants to perform the operations by the CRS as a team of heterogeneous participants.
Preliminary Study of Reasoning Existing Projects’ Descriptions Based on Classname Word Elements

In the field of software development, there are many cases where products developed in the past have technical liabilities or are not managed as assets. As a solution to this situation, we are investigating the concept of software upcycling, which converts past products into valuable resources. In order to perform upcycling, the purpose of this paper is to understand the purpose and function of existing projects. Our key idea is to focus on the words that constitute class names. Moreover, examine how useful these words are for inferring the purpose and function of the system. Specifically, we obtain all the class names from the project by repository mining and extract the words that constitute each class file name. We consider the obtained words to reflect the characteristics of the project from which they were extracted. Furthermore, we also conduct a preliminary experiment to verify whether it is possible to infer the purpose and function of the project by referring only to these words.
Where authorities fail and experts excel: Influencing internet users’ compliance intentions

Cyber threats continue to rise because Internet users are often unwilling to adopt simple security behaviors. Public security messages sent by leaders could formally and informally influence Internet users to take action to protect themselves from threats. However, scant research has examined how leaders' power or expertise influences Internet users' willingness to comply with requested security behaviors. In this paper, we develop a research model using the bases of power theory and three related theories (social influence theory, psychological reactance theory, and attribution theory) to explain how security messages from authoritative and expert leaders, respectively, shape users' intentions to comply with the recommendations of a security message. Analysis of data gathered in a field experiment suggests that security messages from leaders can have positive and negative consequences. If sent by leaders with formal authority (i.e., those high in coercive power), security messages may backfire because they fail to stimulate security cognitions. In contrast, if sent by expert leaders (i.e., those high in expertise), security messages are more likely to work as intended. Our findings offer insights into how public security messages can be made more effective.
Description framework of makerspaces: Examining the relationship between spatial arrangement and diverse user populations

Abstract As makerspaces continue to proliferate in academic and public libraries, researchers and educators are increasingly concerned with ensuring these STEM‐rich learning environments are inclusive to historically marginalized student communities. This article offers a new framework, the Description Framework of Makerspaces, to outline the relationship between the spatial qualities of makerspaces and the user population it attracts. This study represents the first phase of a 5‐year research program dedicated to analyzing the everyday life information seeking practices that students (un)intentionally make when deciding to engage with a STEM­rich learning environment such as a makerspace. Using constructivist grounded framework to analyze interview data from 17 academic makerspace leaders, we theorize 2 propositions from the main findings: (a) the act of defining a makerspace is difficult and in tension with several imaginings of a makerspace: imagined, ideal, and experienced and (b) a makerspace is significantly composed of affective features that are often unarticulated and abstract. By conceptualizing makerspaces as environments that are configured by both physical and affective characteristics, we reveal insights regarding a baseline conceptualization of the features of a conventional academic makerspace and the design decisions that makerspace leaders make and are confronted with.
Data-driven logistic function for weighting of geophysical evidence layers in mineral prospectivity mapping

Logistic function has been extremely executed to transform values of evidence layers into the range [0,1] to produce evidence maps assimilated or to predict mineralization zones as fuzzy prospectivity models. Many researchers have recently presented fuzzy layers transformed with continuous weights in range from [0, 1]. But thay did not try to consider efficiency or inefficiency of achieved results by overcoming exploratory bias. However, majority of integration methods comprising an inherent bias due to employing expert's judgments or applying trial-and-error procedure for determining some parameters of their functions. This research demonstrated, the application of data-driven fuzzy logic to assign continuous weights to geophysical evidence maps which can be integrated to generate prospectivity map with no biasness (with a 91% prediction ability). Results of the fuzzy transformation as continuous weights and discrete weights were compared and validated using prediction-area plot and locations of some mineralization clues.
Research on Four-Dimensional Innovative Intelligent Education Platform Based on Cloud Edge-End Architecture

In order to solve the problem of backward talent training mode in agriculture-related colleges and universities, this paper proposed a scheme to build a smart teaching platform by using cloud architecture, combining virtualization and twinning technology. The intelligent teaching platform is developed using the 5G converged network architecture and cloud edge system architecture. The intelligent teaching platform has realized such teaching modes as real scene teaching, combination of virtual and real teaching, immersive teaching, multi-teacher collaborative teaching and live interactive teaching. The smart teaching platform has established a new model of digital education, with the functions of teaching, teaching research, teaching management and teaching evaluation, and provides smart teaching cloud services for teachers and students of agriculture-related colleges and universities as well as external tutors. The research of multi-dimensional evaluation system solves the precise management of teaching process. The teaching effect has been significantly improved, and the management cost has been reduced, which meets the goal of training new agricultural talents in agricultural and forestry colleges.
Predicate counterparts of modal logics of provability: High undecidability and Kripke incompleteness

Abstract In this paper, the predicate counterparts, defined both axiomatically and semantically by means of Kripke frames, of the modal propositional logics $\textbf {GL}$, $\textbf {Grz}$, $\textbf {wGrz}$ and their extensions are considered. It is proved that the set of semantical consequences on Kripke frames of every logic between $\textbf {QwGrz}$ and $\textbf {QGL.3}$ or between $\textbf {QwGrz}$ and $\textbf {QGrz.3}$ is $\Pi ^1_1$-hard even in languages with three (sometimes, two) individual variables, two (sometimes, one) unary predicate letters, and a single proposition letter. As a corollary, it is proved that infinite families of modal predicate axiomatic systems, based on the classical first-order logic and the modal propositional logics $\textbf {GL}$, $\textbf {Grz}$, $\textbf {wGrz}$ are not Kripke complete. Both $\Pi ^1_1$-hardness and Kripke incompleteness results of the paper do not depend on whether the logics contain the Barcan formula.
Inconclusive conclusions in forensic science: rejoinders to Scurich, Morrison, Sinha and Gutierrez

To the Editor, We thank professors Scurich, Morrison, Sinha and Mr Gutierrez for their thoughtful comments on our article (Arkes and Koehler, 2021). We agree with Scurich (2023) that when an examiner knows that he or she is being tested, the results of such a test are highly suspect. If an examiner can avoid making errors by deeming a comparison to be inconclusive and if inconclusives are never deemed to be indicative of an error, then a ‘strategic’ examiner can inflate accuracy levels by rendering an inconclusive decision for any difficult test. Such a test will not provide an unbiased measure of an examiner’s accuracy. But this is not reason enough to change the way accuracy is measured. In support of a different view on the matter, Scurich (2023) provides an analogy offered by Kaye et al. (2022) in which a student answers ‘I don’t know’ to a true–false test question. If the student should know the answer, then Kaye et al. (2022) say the ‘I don’t know’ answer should be counted as an error. We don’t think that Kaye’s analogy is apt in this context. Teachers are charged with determining whether a student should know the answer to a question. For example, if the topic was covered in the required reading or in a lecture, then the student should know the answer. In a forensic test, one cannot say whether the examiner should know the answer. Dror and Scurich (2020) suggested strategies that might help one determine whether the examiner should know the answer, but in our response to Dror and Scurich (Arkes and Koehler, 2022) we offered reasons why we think those strategies are inadequate.
Comparing immersive VR and non-immersive VR on social skill acquisition for students in middle school with ASD

AbstractAbstractVirtual reality (VR) has evolved to include non-immersive to fully-immersive experiences for the classroom. This study seeks to understand the potential effects VR may offer, specifically characteristics associated with how a student experiences a VR intervention and the effects of using a VR device for learning. A group experimental design was used to compare a fully-immersive VR intervention and an identical non-immersive VR intervention for a group of middle school participants with ASD (N = 22). Participants were randomly assigned a screen-based VR experience or a head-mounted display VR experience through rolling randomization. Results indicate that while the more immersive condition did not produce higher acquisition than the non-immersive condition, both device conditions did produce significant increases in learning.Keywords: Virtual realitysocial skillsautisminstructional technologyassistive technologymixed realityspecial education Additional informationNotes on contributorsAdam CarreonAdam Carreon is an assistant professor of special education in the Department of Elementary and Special Education at Georgia Southern University. Dr. Carreon's research centers around innovative technology to support students with disabilities and special education teachers.Sean J. SmithSean J. Smith is a professor of special education in the Department of Special Education at the University of Kansas. Dr. Smith's research centers on innovations and technology solutions to support struggling learners.Bruce FreyBruce Frey is a professor of special education in the Department of Educational Psychology at the University of Kansas. Dr. Frey's research centers on classroom assessment, instrument development, and program evaluation.Amber RowlandAmber Rowland is an associate research professor with the Center for Research on Learning and the co-director of the Advanced Learning Technologies Division at the University of Kansas. Dr. Rowland's research centers on supporting professional learning at a distance and pursuing methods for tapping into the collective capacity of educators and students.Maggie MosherMaggie Mosher, Ph.D. ABD, is the Social Skill Content Strategist for Virtual-Reality Opportunities to Implement Scaffolded Skills (VOISS). She specializes in social-emotional development, innovative technology, multisensory instructional methods, tiered systems of support, and individualized instruction.
DaeMon: Architectural Support for Efficient Data Movement in Fully Disaggregated Systems

Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices, organized as independent failure-isolated components interconnected by a high-bandwidth network. A critical challenge, however, is the high performance penalty of accessing data from a remote memory module over the network. Addressing this challenge is difficult as disaggregated systems have high runtime variability in network latencies/bandwidth, and page migration can significantly delay critical path cache line accesses in other pages. This paper conducts a characterization analysis on different data movement strategies in fully disaggregated systems, evaluates their performance overheads in a variety of workloads, and introduces DaeMon, the first software-transparent mechanism to significantly alleviate data movement overheads in fully disaggregated systems. First, to enable scalability to multiple hardware components in the system, we enhance each compute and memory unit with specialized engines that transparently handle data migrations. Second, to achieve high performance and provide robustness across various network, architecture and application characteristics, we implement a synergistic approach of bandwidth partitioning, link compression, decoupled data movement of multiple granularities, and adaptive granularity selection in data movements. We evaluate DaeMon in a wide variety of workloads at different network and architecture configurations using a state-of-the-art simulator. DaeMon improves system performance and data access costs by 2.39× and 3.06×, respectively, over the widely-adopted approach of moving data at page granularity.
Versatile quantum-enabled telecom receiver

We experimentally demonstrate a quantum-measurement-based receiver for a range of modulation schemes and alphabet lengths in a telecom C-band. We attain symbol error rates below the shot noise limit for all the studied modulation schemes and the alphabet lengths 4≤M≤16. In doing so, we achieve the record energy sensitivity for telecom receivers. We investigate the trade-off between energy and bandwidth use and its dependence on the alphabet length. We identify the combined (energy and bandwidth) resource efficiency as a figure of merit and experimentally confirm that the quantum-inspired hybrid frequency/phase encoding has the highest combined resource efficiency.
Focused NPIs in Statements and Questions

Abstract Negative Polarity Items (NPIs) with emphatic prosody such as ANY or EVER, and minimizers such as lift a finger or sleep a wink are known to generate particular contextual inferences that are absent in the case of non-emphatic NPIs such as unstressed any or ever. It remains an open question, however, what the exact status of these inferences is and how they come about. In this paper, we analyze these cases as NPIs bearing focus, and examine the interaction between focus semantics and the lexical semantics of NPIs across statements and questions. In the process, we refine and expand the empirical landscape by demonstrating that focused NPIs give rise to a variety of apparently heterogeneous contextual inferences, including domain widening in statements and inferences of negative bias in questions. These inferences are further shown to be modulated in subtle ways depending on the specific clause-type in which the NPI occurs (e.g., polar questions vs. wh-questions) and the type of emphatic NPI involved (e.g., ANY vs. lift a finger). Building on these empirical observations, we propose a unified account of NPIs which posits a single core semantic operator, even, across both focused and unfocused NPIs. What plays a central role in our account is the additive component of even, which we formulate in such a way that it applies uniformly across statements and questions. This additive component of even, intuitively paraphrased as the implication that all salient focus alternatives of the prejacent of the operator must be settled in the doxastic state of the speaker, is selectively activated depending on the presence of focus alternatives, and is shown to be able to derive all the observed contextual inferences stemming from focused NPIs, both in statements and in questions.