Reviewerly/exHarmony huggingface dataset:
Trailblazing With Technology

This chapter explores the evolution of technology use in the K-12 educational setting, and how it has made a significant change over time. Frameworks guide educators in seeing the big picture of what and how they are instructing students. The 4S culturally responsive technology framework combines culturally responsive pedagogical practices with the 3T framework for technology innovation. This allows the technology to amplify and enhance what is being learned while celebrating the culture and language of the learner. Supplemental frameworks (Bloom's taxonomy, social justice standards, or CASEL) can be added to a lesson to personalize the learning experience for each community to ensure equitable practices are being met for all students. Technology in the hands of innovative educators and students can lead to transformational learning that extends the funds of knowledge for every learner.
To answer or to ignore? The impact of questioners and questions on continuous knowledge contributions in virtual Q&amp;A communities

ABSTRACTContinuous knowledge contributions are a key component of the success of virtual question and answer (Q&A) communities. Continuous knowledge contributions comprise answerers’ responses to questioners and their questions. As antecedent information to continuous knowledge contributions, based on stimulus–organism–response theory and information dual-process theory, a research model that reflects the impact of the characteristics of questions and questioners on answerers’ continuous knowledge contributions is established. Then, the model is empirically examined by using short panel data containing 44,198 question and answer interaction data points collected from 1,830 answerers on the Chinese Software Developer Network. Meanwhile, the mediation effects of outcome expectations and perceived achievement on the characteristics of questions and questioners are investigated. The two-way fixed-effects negative binomial regression results show that (1) the label similarity, question readability, norms of reciprocity and reputation of questioners all have a positive impact on answerers’ continuous knowledge contributions in virtual question and answer communities; (2) outcome expectations fully mediate the effect of label similarity, while they partially mediate the effect of question readability, the norms of reciprocity and the reputation of questioners; and (3) perceived achievement mediates the effects of label similarity, the norms of reciprocity and the reputation of questioners.KEYWORDS: Continuous knowledge contributionsthe characteristics of questions and questionersperceived achievementoutcome expectationsmediation effectsvirtual Q&A communities Data availability statementThe raw data that support the findings of this study are available on reasonable request to the corresponding author.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Humanity and Social Science Youth Foundation of Ministry of Education of China [grant number 21YJCZH070].
The double-edged sword effect of chatbot anthropomorphism on customer acceptance intention: the mediating roles of perceived competence and privacy concerns

This study examines the double-edged sword effect of chatbot agent anthropomorphism on customer acceptance in retail e-commerce using social response theory. Scenario-based experiments with a between-subject design were conducted to test hypotheses. Study 1 investigated the differences in positive and negative psychological perceptions (i.e. perceived competence, privacy concerns) and acceptance intention among 138 participants for different anthropomorphism levels of chatbots. Study 2 randomly assigned 282 subjects to 2 (anthropomorphism: high vs. low) × 2 (time pressure: high vs. low) experiments. Data collected from users of chatbot services in the retail e-commerce platforms were analysed using methods of reliability and validity analyses, one-sample t-test, independent-sample t-test, one-way ANOVA, and PROCESS bootstrap analyses. The results showed that chatbot anthropomorphism increased customers’ perceptions of chatbot competence and thus increased their intention to accept chatbot services, but also increased customers’ privacy concerns and thus decreased acceptance intention. Under high (vs. low) time pressure, customers perceived highly anthropomorphic chatbots as more competent, but time pressure did not moderate the effect of anthropomorphism on privacy concerns. This study extends research on social response theory and time pressure in the e-commerce management field, and provides guidance for retail e-commerce enterprises to develop chatbot anthropomorphism level differentiation strategies.
Generalized Net Model of Heavy Oil Products’ Manufacturing in Petroleum Refinery

Generalized nets (GNs) are a suitable tool for the modeling of parallel processes. Through them, it is possible to describe the functioning and results of the performance of complex real processes running in time. In a series of articles, we consistently describe the main processes involved in the production of petroleum products taking place in an oil refinery. The GN models can be used to track the actual processes in the oil refinery in order to monitor them, make decisions in case of changes in the environment, optimize some of the process components, and plan future actions. This study models the heavy oil production process in a refinery using the toolkit of GNs. Five processing units producing ten heavy-oil-refined products in an amount of 106.5 t/h from 443 t/h atmospheric residue feed, their blending, pipelines, and a tank farm devoted to storage of finished products consisting of three grades of fuel oil (very low sulfur fuel oil (0.5%S) —3.4 t/h; low sulfur fuel oil (1.0%S) —4.2 t/h; and high sulfur fuel oil (2.5%S) —66.9 t/h), and two grades of road pavement bitumen (bitumen 50/70 —30 t/h and bitumen 70/100 —2 t/h) are modeled in a GN medium. This study completes the process of modeling petroleum product production in an oil refinery using GNs. In this way, it becomes possible to construct a highly hierarchical model that incorporates the models already created for the production of individual petroleum products into a single entity, which allows for a comprehensive analysis of the refinery’s operations and decision making concerning the influence of various factors such as disruptions in the feedstock supply, the occurrence of unplanned shutdowns, optimization of the production process, etc.
Provably Secure Data Access Control Protocol for Cloud Computing

Currently, cloud storage servers are controlled by a third-party administrator. This semi-trusted approach gives rise to security concerns. Therefore, in cloud computing, some protocols use a key manager to encrypt the user’s private data before uploading the data to the cloud. However, the security concerns that arise from the use of a key manager are not yet solved. In this respect, in this paper, a provably secure user cloud data access control protocol (DAC) is proposed based on existing cloud storage. Empirical tests confirm that the proposed approach is highly secure against adaptive selective ciphertext attacks and has excellent resistance to message attacks. A comprehensive performance evaluation, including time measurements, is conducted and the protocol is compared to other protocols, revealing the efficient file upload and download processes of the proposed approach. The results demonstrate the protocol’s strong security, practicality, and operational efficiency.
Blockchain-Based Multistage Continuous Authentication for Smart Devices

With the increasing connectivity between multiple smart devices in the Internet of Vehicles, privacy and security have become stringent threats due to unauthorized access. To overcome this issue, designing continuous authentication systems has become an important research topic because of the advantages of continuous monitoring of users after the initial access to the smart devices. Unfortunately, the existing systems are based on a third-party centralized structure, and most of them suffer storage pressure on equipment, thus resulting in significant security hazards and limited performance. In this paper, we propose a multistage continuous authentication system based on blockchain technology and the IPFS, which achieves decentralization and reduces storage pressure. In the first stage of authentication, we adopt Hyperledger Fabric to implement the underlying technical architecture of the blockchain to enhance the security and reliability of identity parameters. The preoutputs of the first-stage authentication are compared against behavioral biometric characteristics stored in the IPFS that aim to accomplish the final authentication. In particular, we use fuzzy extractors to deal with behavioral biometric feature templates, thus solving the privacy problem caused by user information leakage. To evaluate the security of our system, we prove the correctness of the communication protocol and two-way authentication of the scheme using BAN Logic. Furthermore, we use Hyperledger Caliper to analyze the impact of the sending rate of authentication requests on various performance parameters such as throughput, memory, and CPU utilization of the authentication system. Security and experimental results show that: (i) We solve the problem of centralized authentication and can resist replay attacks. (ii) Our scheme can maintain high throughput and effectively reach consensus. Compared to related works, the throughput is improved by 8.6%.
Artificial Neural Networks Using Quiver Representations of Finite Cyclic Groups

In this paper, we propose using quiver representations as a tool for understanding artificial neural network algorithms. Specifically, we construct these algorithms by utilizing the group algebra of a finite cyclic group as vertices and convolution transformations as maps. We will demonstrate the neural network using convolution operation in the group algebra. The convolution operation in the group algebra that is formed by a finite cyclic group can be seen as a circulant matrix. We will represent a circulant matrix as a map from a cycle permutation matrix to a polynomial function. Using the permutation matrix, we will see some properties of the circulant matrix. Furthermore, we will examine some properties of circulant matrices using representations of finite symmetric groups as permutation matrices. Using the properties, we also examine the properties of moduli spaces formed by the actions of the change of basis group on the set of quiver representations. Through this analysis, we can compute the dimension of the moduli spaces.
End Face Attitude Detection of Special Steel Bars Based on Improved DBSCAN

An end face attitude detection system for special steel bars is designed to solve the problem of defect localization for steel bar grinding. A circle detection method based on improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed for calculating special steel bars’ end face attitude. Firstly, the images are subjected to edge detection, connected region marking, and improved DBSCAN in accordance with the image characteristics. After that, the arcs belonging to the same circle are clustered into the same category to create virtual connected regions. Then, circle parameters of a virtual connected region are clustered using an improved DBSCAN algorithm. The actual circle parameter is obtained by calculating the centroid of each category. Finally, the vector is generated under the set coordinate system, passing through the center of the circumcircle of the steel bar end and one endpoint of the two-dimensional code, and the angle of the vector is calculated to determine the attitude of the special steel bar’s end face. The experimental results demonstrate that the method can obtain an attitude angle resolution of 0.2 degrees with an error range of ±0.1 degrees. This will provide accurate defect localization support for the digitization and intelligence of the grinding platform on the special steel bar production line.
Dual-Pyramid Wide Residual Network for Semantic Segmentation on Cross-Style Datasets

Image segmentation is the process of partitioning an image into multiple segments where the goal is to simplify the representation of the image and make the image more meaningful and easier to analyze. In particular, semantic segmentation is an approach of detecting the classes of objects, based on each pixel. In the past, most semantic segmentation models were for only one single style, such as urban street views, medical images, or even manga. In this paper, we propose a semantic segmentation model called the Dual-Pyramid Wide Residual Network (DPWRN) to solve the segmentation on cross-style datasets, which is suitable for diverse segmentation applications. The DPWRN integrated the Pyramid of Kernel paralleled with Dilation (PKD) and Multi-Feature Fusion (MFF) to improve the accuracy of segmentation. To evaluate the generalization of the DPWRN and its superiority over most state-of-the-art models, three datasets with completely different styles are tested in the experiments. As a result, our model achieves 75.95% of mIoU on CamVid, 83.60% of F1-score on DRIVE, and 86.87% of F1-score on eBDtheque. This verifies that the DPWRN can be generalized and shows its superiority in semantic segmentation on cross-style datasets.
The Effect Of Simon Says Game Method On The Listening Ability For The Eighth Grade Students Of MTS AL-WASHLIYAH Serbelawan

This research entitled The Effect of Simon Says Game Method on the Listening Ability for the Eighth Grade Students of MTSS Al-Washliyah Serbelawan. It concered with the problem: Does Simon Says Game Method effect on the listening ability for the eighth grade students of MTSS Al-Washliyah Serbelawan?. To answer the research, some theories are used : (Arikunto, 2014), (Cresswell, 2008), (Nation &amp; Newton, 2009), (Nunan, 2003), (Sudijono, 2017), (Sugiyono, 2013), (Tyagi, 2013) and (Wright, 2006). This research is design into quantitative method by following pre-experimental design of one group pre-test and post-test design. The population is 31 students and the sample is taken by saturated sampling. The instrument is essay test. The data analysis is started from scoring the students’ answer sheets, tabulating students’ pre-test and post-test scores, classifying the students’ listening ability, calculating mean pre-test and post-test, calculating the standard deviation, calculating mean difference and testing hypothesis. Based on the data analysis, it was found that before the treatment od Simon Says Game method, the students’ listening is low since the mean pre-test is 26,45 and low level is dominated. After the treatment, the student’s listening is improved since the mean post-test is 79,03 and good level has been dominated. After analyzing the data, it is calculated that Simon Says Game method effects students’ listening ability for the eighth grade MTSS Al-Washliyah Serbelawan by contributing significant effect value about 52,58. So it is suggested that Simon Says Game effect on the listening ability fot the eighth grade students of MTSS Al -Washliyah Serbelawan in academic year 2021/2022 and should use appropriate method in improving students’ listening ability method
An Analysis Of Students’ Difficulties In Writing Hortatory Exposition Text At Eleventh Grade Of SMA TAMAN SISWA Pematangsiantar

The purpose of this research are to know: 1) The students’ ability in writing Hortatory exposition text at the eleventh grade students of SMA Taman Siswa Pematang siantar in 2021/2022 academic year 2) The difficulties that faced by the students’ in writing Hortatory exposition text at the eleventh grade students of SMA Taman Siswa Pematang siantar in 2021/2022 academic year. This research is used mixed method that is combination of qualitative and quantitative research. This is research was done by using documentation and students text essay hortatory exposition text. The informants of this research are 66 that representative from the eleventh grade students. The data were collected used randomly sampling. Test was made to know the students’ ability in writing Hortatory exposition text and the difficulties that faced by the students’ in writing Hortatory exposition text ability at the eleventh grade, observation was made to know all of the activities or behaviours of the students’ in writing Hortataory exposition text. The highest score 75, the lowest score 38, and the average score in writing Hortatory exposition text 57.33 and it was categorized difficult, it means that students ability in writing Hortatory exposition text are still difficult. The students had difficulty in using content 80%, using organization 73.33%, using vocabulary 73.33%, mastering grammar 93.33%, and using mechanics 20%. It means that the students need to learn more about Hortatory exposition text.while the students difficulties in writing was language features and generic structure of hortatory exposition text consist of thesis and arguments, lack vocabulary and grammar mastery so majority of students was difficult to create the sentences and write correct spelling words, based on the result, the researcher taken a conclusion that the students have not understanding in writing hortatory exposition text
Introduction to informatics in a Peruvian penitentiary using cs unplugged: from university to penitentiary

Abstract Within the field of social reintegration and re-education, this paper presents an educational experience carried out at the Iquitos Penitentiary Center, Lima, Peru, with the aim of providing an introduction to informatics to 25 inmates who volunteered to take part in the project. Twenty students and a teacher from the Scientific University of the South also in Peru, were responsible for initiating the transmission of knowledge from the university to inmates, with the collaboration and participation of the penitentiary coordinator. The main objectives of the case study were to validate both the suitability of the CS unplugged proposal and the adaptability of the L2T2L pedagogic strategy to the transmission of knowledge to adults, specifically penitentiary inmates. This strategy had been originally designed to transmit informatics knowledge from university to primary school. The validity and effectiveness of the experience was assessed using surveys. Results confirm that inmates achieved a good level of understanding when endeavoring to resolve most of the CS unplugged assignments designed for them. It was also seen that L2T2L is adaptable and valid for different scenarios other than those for which it was initially designed. Indeed, it was proven to be valid for transmitting knowledge to the prison population. Finally, it should be pointed out that the experience is easily replicable and that it brings an opportunity to introduce informatics into education programs in prisons, something which can contribute enormously to social reintegration and re-education, facilitating the subsequent reentry of inmates into the community once their period of imprisonment has ended.
Thetacrypt

With decentralized systems becoming prominent, often involving financial applications, several companies started looking for stronger security guarantees that could fit the new model. Threshold cryptography is being proposed as a solution for several central issues in the blockchain space, such as MEV prevention, wallet-key management, and randomness generation. The absence of a commonly available library for threshold cryptography is a major concern, as demonstrated by the recent NIST project [10]. In this paper, we present Thetacrypt, the first distributed service that aims at providing threshold cryptography as a service implementing the most requested threshold schemes.
Adaptive Thresholding for Fair and Robust Biometric Authentication

Thresholds play a crucial role in state-of-the-art biometric authentication systems as they determine the level of confidence required for a match between the presented biometric sample (e.g., facial image) and the stored reference template. These thresholds help balance security and convenience in biometric authentication. They are typically determined a priori based on some test data, and then fixed during system operation after deployment, and hence the same for all users. In this doctoral research, we investigate a.o. attacks against static thresholds by exploiting the non-uniform distributions of biometric characteristics, and research an extensible middleware solution for adaptive thresholding to offer the same level of security despite individual and demographic differences in biometric modalities. The ultimate goal of this study is to adjust state-of-the-art solutions of biometric authentication systems to dynamically adapt the threshold for enhanced security, robustness, and fairness. It goes without saying that incorporating dynamic adaptation within a distributed deployment environment introduces a new potential point of vulnerability in the attack surface. The projected middleware must duly address this critical issue.
Decentralized Service Composition Applications (dXapps)

As the blockchain landscape evolves, the emergence of diverse blockchain platforms designed for specific use cases provides an opportunity to combine their strengths. One way to take advantage on this is to compose specialized services across multiple heterogeneous blockchains. Just as microservices revolutionized cloud platforms, decentralized service composition can similarly transform the way blockchain-based software systems are developped and unlock new use case.
Towards the Automatic Adaptation of Stateful Microservices Applications across Edge and Cloud

The Edge/Cloud continuum is a promising infrastructure for deploying latency-sensitive collaborative applications. Many such applications are based on a collection of interacting microservices. Placing instances of the microservices involved in answering latency-sensitive queries can benefit local user communities across different edge locations. We focus in this Ph.D. project on the automatic placement and adaptation of microservices across the continuum, with a specific focus on stateful microservices and the sharding of their state over multiple, geo-distributed instances.
Towards Effective yet Frugal Blockchain Network Emulation

Benchmarking blockchain systems is still a persistent issue. The Diablo benchmark suite allows strategically stressing and testing blockchain solutions thanks to parallel requests to their nodes network. Here, the efficiency of network infrastructure plays a paramount role. Kollaps, a research testbed, emulates network architecture, delivering accuracy levels on par with physical configurations. This research explores integrating Kollaps and Diablo to streamline the benchmarking process while reducing implementation costs through a cluster-based environment.
Fabric-Visualizer

Hyperledger Fabric is a popular permissioned blockchain that follows an optimistic concurrency control model, which often leads to transaction dependency conflicts. We developed a web application that extracts data from a live Fabric network and generates transaction dependency graphs. Apart from visually understanding the transaction dependency distribution, the user also gets additional information regarding each transaction, such as the validation status, dependent transactions, smart contract function, accessed keys, endorsers and clients. Further, our tool also checks the serializability of the generated graph and identifies the number of transactions to abort to achieve serializability. We demonstrate how our tool can be used with multiple realistic workloads to identify performance optimization opportunities. We also highlight scenarios where our tool can be used to identify smart contract improvements. Our tool is demonstrated with multiple realistic workloads.