Reviewerly/exHarmony huggingface dataset:
Evaluation of the extraction of methodological study characteristics with JATSdecoder

Abstract This paper introduces and evaluates the study.character module from the JATSdecoder package which extracts several key methodological study characteristics from NISO-JATS coded scientific articles. study.character splits the text into sections and applies its heuristic-driven extraction procedures to the text of the method and result section/s. When used individually, study.character ’s functions can also be applied to any textual input. An externally coded data set of 288 PDF articles serves as an indicator of study.character ’s capabilities in extracting the number of sub-studies reported per article, the statistical methods applied and software solutions used. Its precision of extraction of the reported $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -level, power, correction procedures for multiple testing, use of interactions, definition of outlier, and mentions of statistical assumptions are evaluated by a comparison to a manually curated data set of the same collection of articles. Sensitivity, specificity, and accuracy measures are reported for each of the evaluated functions. study.character reliably extracts the methodological study characteristics targeted here from psychological research articles. Most extractions have very low false positive rates and high accuracy ( $$\ge 0.9$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>≥</mml:mo> <mml:mn>0.9</mml:mn> </mml:mrow> </mml:math> ). Most non-detections are due to PDF-specific conversion errors and complex text structures, that are not yet manageable. study.character can be applied to large text resources in order to examine methodological trends over time, by journal and/or by topic. It also enables a new way of identifying study sets for meta-analyzes and systematic reviews.
RESTCluster: Automated Crash Clustering for RESTful API

RESTful API has been adopted by many other notable companies to provide cloud services. Quality assurance of RESTful API is essential. Several automated RESTful API testing techniques have been proposed to overcome this problem. However, automated tools often generate a large number of failed test cases. Since validating each test case is a lot of work for developers, automatic failure clustering is a promising solution to help debug cloud services.
Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics

Software metrics capture information about software development processes and products. These metrics support decision-making, e.g., in team management or dependency selection. However, existing metrics tools measure only a snapshot of a software project. Little attention has been given to enabling engineers to reason about metric trends over time—longitudinal metrics that give insight about process, not just product. In this work, we present PRIME (PRocess MEtrics), a tool to compute and visualize process metrics. The currently-supported metrics include productivity, issue density, issue spoilage, and bus factor. We illustrate the value of longitudinal data and conclude with a research agenda. The tool’s demo video can be watched at https://bit.ly/ase2022-prime. Source code can be found at https://github.com/SoftwareSystemsLaboratory/prime.
‘Who built this crap?’ Developing a Software Engineering Domain Specific Toxicity Detector

Since toxicity during developers’ interactions in open source software (OSS) projects show negative impacts on developers’ relation, a toxicity detector for the Software Engineering (SE) domain is needed. However, prior studies found that contemporary toxicity detection tools performed poorly with the SE texts. To address this challenge, I have developed ToxiCR, a SE-specific toxicity detector that is evaluated with manually labeled 19,571 code review comments. I evaluate ToxiCR with different combinations of ten supervised learning models, five text vectorizers, and eight preprocessing techniques (two of them are SE domain-specific). After applying all possible combinations, I have found that ToxiCR significantly outperformed existing toxicity classifiers with accuracy of 95.8% and an F1 score of 88.9%.
Artificial intelligence based model for establishing the histopathological diagnostic of the cutaneous basal cell carcinoma

Abstract Introduction : Artificial intelligence (AI), a component of computer science, has the ability to process the multitude of medical data existing in the medical system around the world. The goal of our study is to build an AI model, based on Machine Learning, capable of assisting pathologists around the world in the diagnosis of the basal cell carcinoma of the skin. Material and Method : Our study is represented by the development of a Mask-RCNN (Mask Region-based Convolutional Neural Network) model, for the detection of cells with typical basal cell carcinoma tumoral changes. A number of 258 digitized histological images were used. The images emerged from Hematoxylin&amp;Eosin stained pathology slides, diagnosed with cutaneous basal cell carcinoma between January 2018 and December 2021, at the Pathology Service of the Mureș County Clinical Hospital. Results : All the used images have the unique resolution of 2560x1920 pixels. For the learning process, we divided these images into two datasets: the learning dataset, representing 80% of the total images; and the test dataset, representing 20% of the total images. The AI model was trained using 1000 epochs with a learning rate of 0.00025 and only one classification category: basal cell carcinoma. Conclusions : The AI model successfully identified in 85% of the cases the areas with pathological changes present in the input images.
Thailand's HCI Education and UX Practice: Perspectives from Bangkok ACM SIGCHI Chapter and UX Thailand Community

Based on experiences from running the Bangkok ACM SIGHCHI chapter and moderating the UX Thailand Facebook group, this paper offers perspectives on the current situation of human-computer interaction (HCI) education and user experience (UX) practice in Thailand. The status of HCI and UX education is described concerning the lack of HCI-focus curricula offered at the higher education level and other educational efforts from UX professionals. It also briefly discusses local publication venues for HCI research. Regarding the UX practice in Thailand, the paper shortly examines the growing adoption of UX in Thai businesses and the support system sustaining the community of Thai UX practitioners. This paper is considered a starting point for discussion among SEACHI workshop participants.
PROVISION OF READING GARDEN AS A SOLUTION TO INCREASE INTEREST IN READING IN SINDANG MANDI VILLAGE

Reading is something that everyone should do, from children and teenagers to adults and parents, because reading allows them to keep up with all the changes happening in the world. Through reading, people can generate new ideas, obtain information, and increase knowledge, so that their vision becomes wider and develops. However, this reading habit has not been fully realized in developing countries such as Indonesia, especially in Sindang Mandi Village, Anyar. Therefore, the service team carried out this activity as a solution to foster reading interest in Sindang Mandi Village. It is hoped that after this Community Reading Park (TBM) activity, interest in reading in Sindang Mandi will increase
Blockchain-enabled Secure Distributed Data Aggregation and Verification Mechanism for IIoT

The traditional Industrial Internet of Things (IIoT) is a centralized system that fully trusts and relies on a central cloud server to process and store the data collected by the equipments. As the number of devices increases, this centralized model not only faces severe network load issues and single point of failure crises, but also raises various security and privacy concerns. Fortunately, blockchain can provide decentral-ization, high trustworthiness and security. Therefore, to design a distributed IIoT system, a blockchain-based IIoT would be a reasonable choice. This paper combines the consortium chain and the homomorphic Paillier algorithm, and proposes a secure distributed data aggregation and verfication (SDAV) mechanism based on the consortium chain. This mechanism combines edge computing and blockchain technology to build a distributed data aggregation framework, which effectively supports the secure collection and storage of data, and avoids single point of failure and tampering crisis. Secondly, based on the (k, t)-threshold Paillier algorithm, a secure cryptographic verification mechanism is designed to protect the privacy and confidentiality of data on the blockchain. Finally, the analysis proves the security of the proposed mechanism and demonstrates the efficiency advantage through simulation experiments.
Distortions of Body Perception during Immersion in Computer Virtual Reality Using Full-Body Tracking

Person’s immersion in computer virtual reality (VR) is accompanied by numerous distortions in his/her perception due to the replacement of sensory stimuli coming through visual, auditory and partially proprioceptive channels. In this case, the person’s own body becomes an immersion tool, since its movements indirectly affect the movement of the avatar in VR. Performing actions in VR on behalf of the avatar contributes to the appearance of distortions in the perception of one’s own body due to the diffuse effect of actualizing the operational image at the moment of purposeful activity (the subjective body image is modified in accordance with the need to adapt to VR conditions). There are various ways of immersing in VR, taking into account the different degree of involvement of individual parts of the recipient’s real body in controlling a digital character. Thus, the full-body tracking (FBT) technology is becoming widespread, allowing the use of almost all human gross motor skills for projection onto the movements of the avatar. The purpose of the study was to establish the specific features of the distortion of a person’s perception of the size of his/her own body, after its being immersed in computer virtual reality, and the control over the avatar using the FBT technology. The study was conducted in two stages (in 2020 and 2021) in order to compare the intensity and direction of body image distortions of the subjects when they were immersed with and without the FBT technology. The OhShape VR app for mobile immersion without FBT and a modification of the VR Chat app for mobile immersion with FBT were used as experimental exposures. Psychometric data on the subjects’ perception of their own bodies were obtained using the psychometric data on the subjects’ perception of their own bodies were obtained using Moshe Feldenkrais’ methods for physical measurements. According to the results of the study, the use of FBT during immersion in VR leads to distortions in the perception of various body sizes by the subjects, including the trunk and legs, while mobile immersion without the use of FBT only causes distortions in the perception of the dimensions of the upper shoulder girdle. It should be noted that this observation testifies to the connection of distortions with the facts of the involvement of the corresponding parts of the real body of the subjects in the process of controlling the avatar. It is concluded that there are specific distortions in the perception of a person’s own body when being immersed in VR using FBT. Finally, an assumption is made about the possible connection of these distortions with the success of performing intra-environment mobile tasks.
EmoThreat@FIRE2022: Shared Track on Emotions and Threat Detection in Urdu

Many languages with a wealth of resources have been researched to solve the challenges of emotion and targeted abuse detection, i.e. threat. But when it comes to languages, such as Urdu, it is noted that there is a severe lack of both resources and approaches in terms of Urdu language processing. Therefore, this study concentrated on offering resources for Urdu by organizing a shared task called “EmoThreat: Emotions and Threat detection in Urdu". The task offered two tasks: (i) multi-label emotion classification (Task A), and (ii) binary threat detection (Task B). Task B was a multi-class problem since it was further subdivided into the identification of threats posed by groups and individuals. This paper provides an overview of the methodology and results obtained by each of the 10 distinct teams who participated in the shared task. In addition, each group presented a detailed error analysis as part of their submission for the best model. The top-performing system in Task A received a macro-F1 score of 0.687. In contrast, subtask 1 of Task B received a score of 0.716 macro-F1 while subtask 2 of Task B obtained a 0.539 macro-F1 score.
Online Resource Optimization for Elastic Stream Processing with Regret Guarantee

Recognizing the explosion of large-scale real-time analytics needs, a plethora of stream processing systems, such as Apache Storm and Flink, have been developed to support such applications. Under these systems, a stream processing application is realized as a directed acyclic graph (DAG) of operators, where the resource configuration of each operator has a significant impact on its overall throughput and latency performance. However, there is a lack of dynamic resource allocation schemes, which are theoretically sound and practically implementable, especially under the drastically changing offered load. To address this challenge, we present Dragster1, an online-optimization-based dynamic resource allocation scheme for elastic stream processing. By combining the online optimization framework with upper confidence bound (UCB) techniques, Dragster can guarantee, in expectation, a sub-linear increase in the throughput regret w.r.t. time. To demonstrate the efficacy, we implement Dragster to improve the throughput of Flink applications over Kubernetes. Compared to the state-of-the-art algorithm Dhalion, Dragster can achieve a 1.8X-2.2X speed-up in converging to the optimal configuration. It can contribute to 20.0%-25.8% gain in tuple-processing goodput and 14.6%-15.6% cost-savings.
Research on the Application of Virtual Reality Technology in Environmental Art Design

Virtual reality technology is a comprehensive display technology developed on the basis of visual simulation technology and multimedia technology. Through equipment simulation, a three-dimensional virtual world is generated, and visual, auditory and tactile sensing are realized, bringing a real experience and an immersive feeling. With the help of VR, designers can easily and quickly complete environmental scene simulation, effectively grasp the specific characteristics of the corresponding environmental scene, and effectively carry out the design. The related technologies of VR applied to environmental art design include environment modeling technology, stereoscopic display technology, haptic feedback technology, system interaction technology and system integration technology. In the process of realizing the design, we should give full play to the advantages of these technologies to improve and optimize the effect of environmental art design.
From GTC to Reset: Generating Reset Proof Systems from Cyclic Proof Systems

We consider cyclic proof systems in which derivations are graphs rather than trees. Such systems typically come with a condition that isolates which derivations are admitted as 'proofs', known as a the soundness condition. This soundness condition frequently takes the form of either a global trace condition, a property dependent on all infinite paths in the proof-graph, or a reset condition, a 'local' condition depending on the simple cycles only which, as a result, is typically stable under more proof transformations. In this article we present a general method for constructing cyclic proof systems with reset condition from cyclic proof with global trace conditions. In contrast to previous approaches, this method of generation is entirely independent of logic's semantics, only relying on combinatorial aspects of the notion of 'trace' and 'progress'. We apply this method to present reset proof systems for three cyclic proof systems from the literature: cyclic arithmetic, cyclic G\"odel's T and cyclic tableaux for the modal {\mu}-calculus.
Practical Provably Secure Flooding for Blockchains

In recent years, permisionless blockchains have received a lot of attention both from industry and academia, where substantial effort has been spent to develop consensus protocols that are secure under the assumption that less than half (or a third) of a given resource (e.g., stake or computing power) is controlled by corrupted parties. The security proofs of these consensus protocols usually assume the availability of a network functionality guaranteeing that a block sent by an honest party is received by all honest parties within some bounded time. To obtain an overall protocol that is secure under the same corruption assumption, it is therefore necessary to combine the consensus protocol with a network protocol that achieves this property under that assumption. In practice, however, the underlying network is typically implemented by flooding protocols that are not proven to be secure in the setting where a fraction of the considered total weight can be corrupted. This has led to many so-called eclipse attacks on existing protocols and tailor-made fixes against specific attacks. To close this apparent gap, we present the first practical flooding protocol that provably delivers sent messages to all honest parties after a logarithmic number of steps. We prove security in the setting where all parties are publicly assigned a positive weight and the adversary can corrupt parties accumulating up to a constant fraction of the total weight. This can directly be used in the proof-of-stake setting, but is not limited to it. To prove the security of our protocol, we combine known results about the diameter of Erdős–Rényi graphs with reductions between different types of random graphs. We further show that the efficiency of our protocol is asymptotically optimal. The practicality of our protocol is supported by extensive simulations for different numbers of parties, weight distributions, and corruption strategies. The simulations confirm our theoretical results and show that messages are delivered quickly regardless of the weight distribution, whereas protocols that are oblivious of the parties’ weights completely fail if the weights are unevenly distributed. Furthermore, the average message complexity per party of our protocol is within a small constant factor of such a protocol.
The Semantic Scholar Open Data Platform

The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature. We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction to build the Semantic Scholar Academic Graph, the largest open scientific literature graph to-date, with 200M+ papers, 80M+ authors, 550M+ paper-authorship edges, and 2.4B+ citation edges. The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings. In this paper, we describe the components of the S2 data processing pipeline and the associated APIs offered by the platform. We will update this living document to reflect changes as we add new data offerings and improve existing services.
POSGen: Personalized Opening Sentence Generation for Online Insurance Sales

The insurance industry is shifting their sales mode from offline to online, in expectation to reach massive potential customers in the digitization era. Due to the complexity and the nature of insurance products, a cost-effective online sales solution is to exploit chatbot AI to raise customers' attention and pass those with interests to human agents for further sales. For high response and conversion rates of customers, it is crucial for the chatbot to initiate a conversation with personalized opening sentences, which are generated with user-specific topic selection and ordering. Such personalized opening sentence generation is challenging because (i) there are limited historical samples for conversation topic recommendation in online insurance sales and (ii) existing text generation schemes often fail to support customized topic ordering based on user preferences. We design POSGen, a personalized opening sentence generation scheme dedicated for online insurance sales. It transfers user embeddings learned from auxiliary online user behaviours to enhance conversation topic recommendation, and exploits a context management unit to arrange the recommended topics in user-specific ordering for opening sentence generation. POSGen is deployed on a real-world online insurance platform. It achieves 2.33x total insurance premium improvement through a two-month global test.
The use of mobile applications in the study of environmental safety monitoring issues in a foreign language

Due to climate changes caused by scientific and technological progress, there is a need to monitor and track environmental indicators. A new mobile learning technology (m Learning) has been proposed to monitor indicators and track them in mobile applications. There is a need to adjust and update the modern educational process by introducing mobile technologies, including weather and environmental informers. This increases awareness about the state of the environment in the region where students live, simultaneously stimulates their motivation to learn a foreign language and contributes to the interest of society in preserving the environment. The article emphasizes that it is necessary to maintain a balance between the use of the traditional educational process and mobile learning, which is due to the psychology of students' perception of mobile devices and applications. The article is aimed at finding ways to introduce existing mobile applications into the educational process in order to increase public interest in the processes of negative climate change and environmental safety
Multimodal learning strategy role in the formation of value orientations of the student youth

The purpose of this research is to study the exceptional nature of modern conditions for inculturation in higher education is, in particular, due to the introduction of a multimodal learning strategy into the educational process, focused on the training of broad specialists in terms of transitional economy. The paper presents a comparative analysis of the values of two generations of the student youth, formed in two fundamentally different systems of training highly qualified personnel. Modern methods of data analysis have made it possible to draw up a portrait of a modern student of higher education, including his/her needs and interests. The analysis showed that the use of the possibilities of a multimodal learning strategy contributed to the strengthening of individualism, a broad worldview were formed among young people. To improve the quality of training highly qualified personnel, it is proposed to shift the vector of a multimodal training strategy from solving urgent problems of economic markets to the mental needs of a person, which is a key link in the sustainable development of the economy.