Reviewerly/exHarmony huggingface dataset:
Security research of blockchain technology in electronic medical records

A blockchain-based Electronic Health Record(EHR) data-sharing scheme was proposed to solve the problems of data sharing difficulties and privacy disclosure.This paper designs a blockchain-based electronic health record model based on the characteristics of blockchain antitampering, decentralization, and distributed storage. Utilize blockchain network and distributed database to store encryption-related access control policies to prevent EHR data from being tampered with and leaked. Data security sharing protocol combines Distributed Key Generation (DKG) and reencryption.The protocol used the Delegated Proof of Stake(DPOS) algorithm to select the proxy node, which reencrypted the EHR to share data between a pair of users. Simulation experiments and comparative analysis showed that DPOS efficiency was higher than Proof of Work (POW) and slightly lower than the Practical Byzantine Fault Tolerance(PBFT).The scheme proposed in this paper is more decentralized and less computationally intensive.
Pengujian Aplikasi Sistem Informasi Akademik Berbasis Web Menggunakan Metode Black Box Testing Equivalence Partitioning

To check the specifications of a software, it is necessary to have a test and validation is something that is needed when testing the software. The inappropriateness of the data input process is caused by the validation process that is not optimal, one of the consequences is that the data addition process fails. Therefore, the application can cause harm to the user. Therefore, improving the quality of accurate validation is very necessary. When the testing process finds an error that was not previously known, then the test can be said to be a good test. One of them is the Black Box testing method, in this test the author chose the iEquivalence Partition testing method. Equivalence Partition is a technique used to reduce the number of test cases that exist at the time of testing. Based on the tests that have been carried out, the authors conclude that this software can be used properly, after repairing the errors that have been found. However, this test is not perfect, because it is only done by using a few samples. From the results of this test, it is expected that the application can be used according to user needs.
Satisfaction of business students with the quality of classes during the pandemic: A mixed study in the Latin American context

Emergency remote education (ERD) adopted by universities amidst the COVID-19 pandemic has pursued to maintain students’ satisfaction. The current research inquiries into perception of the satisfaction towards the quality of classes during ERD. The research is carried out through a mixed sequential approach. Six focus groups in its qualitative phase, and 2074 students from the business undergraduate program in Colombia, Peru, and Mexico in its quantitative phase. Measure of satisfaction towards ERD establishes three dimensions: Concerns about academic quality, teaching strategies used by professors, and perceptions of access limitations. This study identifies a moderating effect of the perceptions of access limitations on the relationship of the teaching strategies and concerns about academic quality. In high constraints, =.-016, p &lt; 0.0001, the relationship is weaker than medium constraints, = −0.22, p &lt; 0.0001. In turn, the medium access limitation condition shows a weaker effect than the high limitation condition, = −0.28, p &lt; 0.0001.
Impact of Combining Syntactic and Semantic Similarities on Patch Prioritization while using the Insertion Mutation Operators

Patch prioritization ranks candidate patches based on their likelihood of being correct.The fixing ingredients that are more likely to be the fix for a bug, share a high contextual similarity.A recent study shows that combining both syntactic and semantic similarity for capturing the contextual similarity, can do better in prioritizing patches.In this study, we evaluate the impact of combining the syntactic and semantic features on patch prioritization using the Insertion mutation operators.This study inspects the result of different combinations of syntactic and semantic features on patch prioritization.As a pilot study, the approach uses genealogical similarity to measure the semantic similarity and normalized longest common subsequence, normalized edit distance, cosine similarity, and Jaccard similarity index to capture the syntactic similarity.It also considers Anti-Pattern to filter out the incorrect plausible patches.The combination of both syntactic and semantic similarity can reduce the search space to a great extent.Also, the approach generates fixes for the bugs before the incorrect plausible one.We evaluate the techniques on the IntroClassJava benchmark using Insertion mutation operators and successfully generate fixes for 6 bugs before the incorrect plausible one.So, considering the previous study, the approach of combining syntactic and semantic similarity can able to solve a total number of 25 bugs from the benchmark, and to the best of our knowledge, it is the highest number of bug solved than any other approach.The correctness of the generated fixes are further checked using the publicly available results of CapGen and thus for the generated fixes, the approach achieves a precision of 100%.
Methods and Results for Quantum Optimal Pulse Control on Superconducting Qubit Systems

The effective use of current Noisy Intermediate-Scale Quantum (NISQ) devices is often limited by the noise which is caused by interaction with the environment and affects the fidelity of quantum gates. In transmon qubit systems, the quantum gate fidelity can be improved by applying control pulses that can minimize the effects of the environmental noise. In this work, we employ physics-guided quantum optimal control strategies to design optimal pulses driving quantum gates on superconducting qubit systems. We test our results by conducting experiments on the IBM Q hardware using their OpenPulse API. We compare the performance of our pulse-optimized quantum gates against the default quantum gates and show that the optimized pulses improve the fidelity of the quantum gates, in particular the single-qubit gates. We discuss the challenges we encountered in our work and point to possible future improvements.
Materials development for immersive augmented reality experiences

Augmented reality (AR) technologies that enable the overlay of digital information on the real world are widely anticipated to be the next wave of computing platforms. For a truly immersive AR experience, the AR glasses should be lightweight, comfortable, stylish, socially acceptable, and efficient that can be worn all day. Traditional optical devices are very bulky and are not suitable for head mounted displays (HMDs) for AR glasses. Hence, there is an increasing interest in developing nano-optics based devices that are significantly smaller and lighter than the conventional devices. Materials innovation is one of the key pillars that could enable the fabrication of such lightweight devices. In this talk, the impact of new materials like highly transparent, high refractive index (HRI) substrates, litho and patterning materials for device fabrication and permanent optical materials to develop all-day wearable AR glasses will be discussed.
Hierarchical Scalable Data Lake Design for Game Engine

With the rapid development of information technology, people are more and more inseparable from smart devices, and people and devices produce a large amount of data all the time. How to store and analyze massive data has become a research hotspot of the times. However, researchers mainly study the high reliability, high availability, high fault tolerance, high throughput, and high concurrent processing of big data systems, but there are few studies on designing hierarchical and scalable data lakes. In this study, a layered and extensible design of the data lake is carried out, which reduces the complexity of implementing business indicators during data analysis, and at the same time improves code reuse, scalability, development efficiency and code security. In addition, in order to avoid the problem of data accumulation or even job execution failure due to the skew of ip dimension table data, this paper designs the ip dimension table rowkey. This paper designs a set of real-time and offline big data analysis data lake architecture based on game engine and applies it to the actual production environment.
Pandemic response across Ohio’s public universities: Novel approaches, emergent challenges and future opportunities

Objective: We describe and analyze case investigation and contact tracing (CICT) efforts across Ohio's public universities in response to COVID-19 to distill challenges and lessons learned and suggest future opportunities for universities to mobilize in the face of emergent public health crises. Participants: Faculty, staff, and graduate students from Ohio's fourteen public universities. Methods: In-depth, semi-structured interviews were conducted with representatives from nine of the 14 universities; representatives from the remaining five universities completed a brief questionnaire. Interviews were transcribed in their entirety and thematically analyzed. Results: Emergent themes include the significance of local relationships for implementing locally tailored solutions; the presence of discrete challenges in doing CICT work with university and local communities, and the importance of university students in pandemic response. Conclusions: There are unique challenges associated with disease control across university populations and surrounding communities, but students from diverse academic background are a potential source of assistance.
Entangled state generation in a linear coupling coupler

The nonlinear coupler, which consists of two nonlinear oscillators linearly coupled together and one or two of these oscillators excited by external coherent ﬁelds, is investigated. We show that evolution of the nonlinear coupler is possible closed in a ﬁnite set of n-photon Fock states and can create Bell-like states. Especially, the entropy of entanglement and the Bell-like states vary dramatically with the different initial conditions are discussed. These results are compared with that obtained previously in the literature.
Applying machine learning techniques on imbalanced datasets for early prediction of high school student dropout

This paper proposes a model for the classification problem on imbalanced datasets, which uses a combination of the SMOTE model and the AdaBoost algorithm for the decision tree algorithm. We make a comparison between the proposed model and the decision tree algorithm using the Gini index and entropy on the collected datasets at Dong Hieu high school, Thai Hoa, Nghe An from 2014 to 2019. The research results can be used as a framework to develop applications supporting the early prediction of the ability of students’ dropout. Based on that results, the managers can analyze and come up with appropriate solutions in order to decrease the school dropout rate.
Experiential activities and application of David A. Kolb model in teaching physics toward developing capacity for students at high schools

This article explores the experiential activities includes definition, content, characteristics, forms, and organizing rules of teaching experiential activities towards developing student’s capacity. Based on defining the role of experiential activities for student competency formation and by exploring the experience model of David A. Kolb, we propose directions, ways of organizing experiential activities in teaching kinetics and dynamics in the 10th grade physics programme at high schools.
Turkish Natural Language Interface: Cevapver

In this paper we developed the system CevapVer presenting a new Natural Language Interface to Database System for Turkish. CevapVer is a portable system, which answers native Turkish questions by translating them into SQL query. It takes questions from a Turkish subset, parses them using a Unification-Based Tree Substitution Grammar into a semantic representation like prolog description, and then translates that model into SQL query. The study also involves the explanations about the morphological analysis, syntactic analysis, semantic analysis and database query modules, which has originally been developed for the CevapVer system.
Synthesis and Upper Bound of Schmidt Rank of Bipartite Controlled‐Unitary Gates

Abstract Quantum circuit model is the most popular paradigm for implementing complex quantum computation. Based on Cartan decomposition, it is shown generalized controlled‐ X (GCX) gates, 6 single‐qubit rotations about the y ‐ and z ‐axes, and single‐partite y ‐ and z ‐rotation‐types which are defined in this paper are sufficient to simulate a controlled‐unitary gate with controlling on . In the scenario of the unitary gate with that is locally equivalent to a diagonal unitary on , GCX gates and single‐partite y ‐ and z ‐rotation‐types are required to simulate it. The quantum circuit for implementing and are presented. Furthermore, it is found that with controlling has Schmidt rank two, and in other cases the diagonalized form of the target unitaries can be expanded in terms of specific simple types of product unitary operators.
Kernel-based Fuzzy Co-clustering in Feature Space with Automated Variable Weighting

Kernel functions have been used successfully in clustering algorithms to deal with the separability of clusters efficiently. Bringing this idea to co-clustering, we propose two kernel-based fuzzy co-clustering algorithms based on the fuzzy double Kmeans (FDK). The first proposed algorithm, the Gaussian kernel fuzzy double Kmeans (GKFDK), is based on FDK and computes the cluster prototypes in the original feature space. The second algorithm, the Weighted gaussian kernel fuzzy double Kmeans (WGKFDK), is an extension of the GKFDK with automated variable weighting, that distinguishes the relevance of the variables in each cluster. Experiments performed with both synthetic and real data, in comparison with previous state-of-the-art co-clustering algorithms, showed the effectiveness of the proposed algorithms.
Semantic Network Model for Sign Language Comprehension

In this study, the authors propose a computational cognitive model for sign language (SL) perception and comprehension with detailed algorithmic descriptions based on cognitive functionalities in human language processing. The semantic network model (SNM) that represents semantic relations between concepts is used as a form of knowledge representation. The proposed model is applied in the comprehension of sign language for classifier predicates. The spreading activation search method is initiated by labeling a set of source nodes (e.g., concepts in the semantic network) with weights or “activation” and then iteratively propagating or “spreading” that activation out to other nodes linked to the source nodes. The results demonstrate that the proposed search method improves the performance of sign language comprehension in the SNM.
Retraction: Research on oil painting creation mode based on multimedia technology in big data Era (J. Phys.: Conf. Ser. 1852 022006)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022
Identifying and Combating Bias in Segmentation Networks by Leveraging Multiple Resolutions

Exploration of bias has significant impact on the transparency and applicability of deep learning pipelines in medical settings, yet is so far woefully understudied. In this paper, we consider two separate groups for which training data is only available at differing image resolutions. For group H, available images and labels are at the preferred high resolution while for group L only deprecated lower resolution data exist. We analyse how this resolution-bias in the data distribution propagates to systematically biased predictions for group L at higher resolutions. Our results demonstrate that single-resolution training settings result in significant loss of volumetric group differences that translate to erroneous segmentations as measured by DSC and subsequent classification failures on the low resolution group. We further explore how training data across resolutions can be used to combat this systematic bias. Specifically, we investigate the effect of image resampling, scale augmentation and resolution independence and demonstrate that biases can effectively be reduced with multi-resolution approaches.
A Study on the Factors Influencing Youth Residents' Satisfaction with Community Governance--Based on the Empirical Analysis of N City

As a socio-professional subject, youth have the most diverse social roles and social needs.Youth attention and participation in community governance can help innovate new models of community governance, improve the concept and efficiency of community governance, and realize modern community governance.This study establishes a hypothesis model based on the analysis of similar related literature and practical research.Sixteen influencing factors of community governance satisfaction among young residents were extracted from four dimensions, including community governance services and performance, community participation, the role of community education, and interpersonal interaction in the community, and the reliability test and correlation analysis were conducted by SPSS to further determine the dimensional division, and the model was validated using AMOS software to finally determine the influence of each dimension on community governance satisfaction among young residents.It was found that, overall, community governance service and performance, community participation and community interpersonal interaction were significantly and positively related to youth residents' community governance satisfaction; community education had no significant effect on satisfaction, but it could influence satisfaction through community governance service and performance and community interaction.Therefore, the four aspects are to improve the level of community governance and enhance the satisfaction of community governance of young residents from the party building leading to promote the collaborative governance of multiple subjects, develop online and offline linkage community participation, strengthen the function of community education governance methods and expand community interaction space.
Accurate Multi-size Circle Detection Using Signature Curves in Hough Transform

In this paper we propose a novel method to reduce the computational complexity of Circular Hough Transform (CHT). This Transform is used in identifying radial shapes in binary images. Hence, a novel adaptive algorithm is presented that reduces the average accumulation storage that is needed during voting process of CHT. The key idea is to use a variable size voting array. For this purpose, our method calculates the Signature Curve of the gradient of the edge points of radial shapes to find the center and radius of the probable circles, thus removing the effect of inconstant background luminance, specifically in presence of the noise. Experimental assessments show that the presented algorithm can considerably improve the identification precision, and decrease the computational burden and storage required.
Development of E-Booklet Media to Improve Students’ Self-Regulated Learning

The purpose of this research is to develop a valid and practical self-regulated learning e-booklet. This study uses the ADDIE development model which has 5 stages (Tegeh, 2014) namely Analysis, Design, Development, Implementation, and Evaluation. Based on the results of research on the development of self-regulated learning E-Booklet media, it can be concluded that self-regulated learning E-Booklet media is prepared using the methods of analysis, planning, development and implementation. Then, the self-regulated learning E-Booklet Media is feasible to use and has a fairly high score in the validation results of the material test and display. The self-regulated learning E-Booklet media is effective in increasing student self-regulated learning and can be used as a medium for delivering information in the realm of guidance and counseling.
Implement strategies to mitigate security risks

LAS VEGAS — From venue lighting, ticket scanners, and signage to websites, cloud services, social media accounts, and wearable devices, technology has become an increasingly critical component of college athletics operations. And in the push and rush to keep up with the latest technology, the security aspect often takes a backseat to speed. But when it comes to cybersecurity, athletics departments really can't afford to cut corners, according to Erik Anderson, Assistant Director of Technical Operations, and Cass Ferguson, Associate Athletic Director for Facilities and Events, both with the University of North Carolina Charlotte Athletics Department. They spoke at the annual convention for the Collegiate Event and Facility Management Association.
CryptoSolve: Towards a Tool for the Symbolic Analysis of Cryptographic Algorithms

Recently, interest has been emerging in the application of symbolic techniques to the specification and analysis of cryptosystems.These techniques, when accompanied by suitable proofs of soundness/completeness, can be used both to identify insecure cryptosystems and prove sound ones secure.But although a number of such symbolic algorithms have been developed and implemented, they remain scattered throughout the literature.In this paper, we present a tool, CryptoSolve, which provides a common basis for specification and implementation of these algorithms, CryptoSolve includes libraries that provide the term algebras used to express symbolic cryptographic systems, as well as implementations of useful algorithms, such as unification and variant generation.In its current initial iteration, it features several algorithms for the generation and analysis of cryptographic modes of operation, which allow one to use block ciphers to encrypt messages more than one block long.The goal of our work is to continue expanding the tool in order to consider additional cryptosystems and security questions, as well as extend the symbolic libraries to increase their applicability.
A Simulation Test Software System of CET-4 Based on J2EE

With the internationalization of China, English, the international common language, has become a compulsory course for colleges and universities. Generally, universities are required to reach CET-4. Teachers usually produce final papers, and the question types are not consistent with those of CET-4. Therefore, the final examination is less helpful for students to pass CET-4. In order to help college students and English teachers better simulate training, we developed software for reforming the system with the latest test questions of CET-4, namely listening, writing, word selection, blank filling, reading comprehension, matching reading comprehension, careful reading, and translation. The technology used in the system is JavaEE + struts.