Reviewerly/exHarmony huggingface dataset:
Quantitative Analysis on Attack Capacity in Meltdown-Type Attacks

In recent years, modern CPUs have been suffering from Meltdown-type attacks. These attacks are delivered by exploiting transient execution created by a faulting load operation. A secret value is encoded into the cache by transient instructions, which in turn is deduced from a microarchitectural covert channel such as Flush+Reload. Recent studies on these attacks mainly focus on finding new vulnerable microarchitectural structures, while lacking interest in how many transient instructions can be executed in the transient execution. If attackers know the exact attack capacity, i.e., the maximum number of instructions available within a transient execution window, they will be able to maximize information leakage by executing additional transient instructions. In order to devise security solutions against Meltdown-type attacks, it is of crucial importance to measure and evaluate the attack capacity. In this paper, we quantitatively analyze the attack capacity in terms of the number of \(\mu \)ops, the latency of transient instructions, and the size of the Reorder Buffer (ROB). Specifically, we present our method in detail that measures the capacity by reconstructing the original implementations of Meltdown-type attacks. We analyze the attack capacity by conducting experiments with various CPU models and identify several elements that affect the capacity. Based on our findings, we propose two methods that reinforce the Meltdown-type attacks.
Effectiveness Factors for Algorithm Based Team Formation with Data Project Case Application

Teams must be formed for all kinds of projects and purposes. Team formation is a key activity for innovation, entrepreneurship, class projects, and industry initiatives. In parallel work, we have proposed a generalized framework for Algorithm-based Team Formation. We are interested to apply this framework to the specific task of forming teams for a Data Science project course. In this paper, we have focused on the characteristics teams as correlated by the success of the project itself. We find that by examining approximately 30 project teams, there are characteristics which may be used to set feature values for algorithms that can best match student projects together. In particular, teams who work well together with trust and common backgrounds did perform better. Teams with greater domain experience in coding and ML generally also performed better. Surprisingly, diversity in background did not seem to indicate better performance. However, we did observe that individuals who characterize themselves as optimistic and wide comfort zone among others were more strongly present on teams that performed well, speaking to a correlation of individual characteristics/behaviors to team performance.
LibGuides: Kentucky Christian University's Young Library Summer Forever Virtual Vacation: Let's Travel the Equator!

Take a trip around the equator, visiting 7 enchanting cities. See what life is like when the days are long, the nights are short, and there's always something fun to do. Introduction of our November 2020 Virtual Vacation
Efficient and Physics-based Facial Blendshapes based on ODE sweeping Surface and Newton’s second law

Online games require small data of 3D models for low storage costs, quick transmission over the Internet, and efficient geometric processing to achieve real-time performance, and new techniques of facial blendshapes to create natural facial animation. Current geometric modelling and animation techniques involve big data of geometric models and widely applied facial animation using linear interpolation cannot generate natural facial animation and create special facial animation effects. In this paper, we propose a new approach to integrate the strengths of ODE (ordinary differential equation) sweeping surfaces and Newton’s second law-based facial blendshapes to create 3D models and their animation with small data, high efficiency, and ability to create special facial effects.
The Science of Writing Characters, Kira-Anne Pelican (2020)

Review of: The Science of Writing Characters , Kira-Anne Pelican (2020) New York: Bloomsbury Academic, 200 pp., ISBN 978-1-50135-724-4, p/bk, $26.95; h/bk, $90.00; Kindle, $24.25
Cheating and Plagiarism Among University Students: Ways of Solving the Problem

Today, various electronic devices and gadgets have become an integral part of university students learning. However, the use of modern technologies can significantly complicate the work of a teacher, as it becomes difficult to determine the degree of students' independence and authorship when assessing their tests and papers. The purpose of this study is to define the strategies that develop students’ responsibility and conscientiousness. The objectives of the study are the following: to conduct a survey among the teachers to determine the degree of their concern regarding this problem; to conduct a survey among the students to find out their attitude towards cheating and plagiarism, and the ways they resort to while cheating. The survey allowed the authors to identify the reasons for students’ cheating during tests. The study shows that it is the foreign language teachers who are most concerned about the problem of cheating and plagiarism, while most students questioned consider cheating normal. The authors conclude that it is vital to address the problem of cheating and plagiarism by conducting regular educational sessions to develop their sense of responsibility.
Optimization of Computer Aided Mandarin Proficiency Test System

With the in-depth development of computer technology, it has brought earth-shaking changes to our lives and studies. More and more schools use computers to assist in teaching in various subjects. The requirements for the equipment of computer teaching facilities and teachers to master computer technology methods are becoming more and more demanding. In particular, the use of computers for Mandarin proficiency test not only brings different experiences to teachers, but also presents many advantages, reducing the human capital investment and capital investment in the process of Mandarin proficiency test, but it is inevitable in the process of using it. There are also some drawbacks. This article proposes a computer-assisted Mandarin proficiency test system to optimize some of the existing drawbacks.
Unsupervised Person Re-Identification with Transformer-based Network for Intelligent Surveillance Systems

Person re-identification (re-ID) is an important topic in computer vision. In this paper, we study the unsupervised person re-ID which aims to identify target identity across multiple non-overlapping cameras for intelligent surveillance systems. The main challenge of unsupervised person re-ID lies in how to learn discriminative features without leveraging any annotated data. In this paper, we apply the Vision Transformer (ViT) to unsupervised person re-identification (re-ID) task. Combined with Multi-label Classification, the performance outperforms most CNN-based methods. We evaluate the proposed model on Market-1501, DukeMTMC-reID and MSMT17 and achieves 56.6%, 49.4%, 14.5% in mAP, respectively, which outperforms the baseline by a clear margin and achieves the state-of-the-art unsupervised re-ID methods.
Efficient Secret Sharing-Based Partial Matching Search Using Error Correction Code

In searchable encryption, there are two types of search methods: exact matching search, in which all search characters are matched, and partial matching search, in which a certain difference is allowed. In an exact matching search, there is only one specified character string that needs to be searched: however, in a partial matching search, multiple search processes are required to be performed on every different pattern of the search query (depending on the number of errors allowed). For example, in methods that make use of multiple indexes to realize partial matching search, all the possible pattern of the partial match for a single keyword needs to be prepared. In addition, the number of searches required will also increase. As far as we know, there are almost no methods that could realize partial matching search efficiently. Therefore, in this paper, by using error correction code, we propose an efficient partial matching search without the need for multiple indexes, where we only need to perform a partial matching search once regardless of the number of mismatched characters allowed. Our method could perform the search process directly on the encrypted document by shifting and searching through each character. Moreover, our method could also be used in index search, therefore realizing a more efficient method of partial matching search.
A Study on American Football Player Tracking Based on Video Through Deep Learning and GPS Convergence

When tracking objects (players, referees, etc.) in a game using deep learning, tracking often fails due to occlusion between objects. In this paper, we track the location of objects in the stadium through video tracking using deep learning. And we fused the GPS(Global Positioning System), which has a large error but can maintain the ID of the object even when the objects overlap so that the tracking can be done correctly even in the overlapping phenomenon between objects. From the experiment results, we could confirm that the object tracking failure rate can be reduced and the accuracy of the object location can be increased through the convergence of deep learning and GPS.
Characteristic space-based algorithm for detecting abnormal monitoring data in underground engineering

Abnormal points in underground space monitoring data contain valuable information. An anomaly detection algorithm based on characteristic space is proposed in this document. First, based on a temporal edge operator, the edge amplitude of each point in time series data is calculated. The monitoring data curve is linearly segmented by selecting the points with larger edge amplitudes. Second, the data characteristics of each subsegment are extracted, and the original monitoring curve is mapped into the characteristic space. Finally, the abnormal features are identified by the local anomaly factor outlier algorithm, from which abnormal data points can be further obtained. The effectiveness of the proposed algorithm was first verified by identifying artificial abnormal data serials or discrete data point from a normal dataset. The feasibility and applicability of the algorithm was further verified by applying the developed algorithm to detect the anomaly of monitoring data for a real tunnel project.
TGKG: New Data Graph Based on Game Ontology

With the advent of the big data era, knowledge graph embodies great advantages. Especially in game domain, building a knowledge graph is of great value. However, some open-domain knowledge bases is not designed for games and the amount of game data is relatively limited. In this paper, we propose a game ontology. In addition, in the process of constructing game knowledge graph, we found it is import to identify an entity is a game entity or not. Thus, we propose a new DLC entity identification model to help us use the ontology to build the graph.
Micro-vision Image Stitching System for Large-scale and Fine-featured Circuit Substrates

With the improvement of the manufacturing process and packaging technology of the precision electronic manufacturing industry, various precision circuits with large scale and fine features are widely used in high-end precision electronic products and instruments. At present, large-scale circuit substrates can reach very fine pitch and line width in only several micron-meter level. So the quality inspection equipment should have the inspection capabilities of large-scale and high-precision detection. Since a single view of image with high-resolution only have a small field of view, image stitching by registration and fusion is an effective method to inspect the whole large scale substrate. Hence, this paper proposes a micro vision image stitching method with high precision through coarse and fine registration. The real-time position information of the platform is used to calculate the approximate pixel offset between images to achieve coarse registration. Then, two convolutional neural regression networks connected by a spatial transformation network are used to achieve fine registration. A high-precision nano-positioning platform is used to establish image registration dataset, which is used for the training of convolutional neural regression networks. Experimental results show that under the condition of resolution of 0.5um//pixel and stitching overlap of 20%, our method can achieve less than 0.2 pixel registration error and less than 25ms registration time for a single image.
Statistics Analysis of Multi-Modality and Text-Derived Representations on Concept Learning

When learning concepts, cognitive psychology research has revealed that there are two types of concept representation in the human brain: language-derived codes and sensory-derived codes. Thus, in terms of computing, do the multimodal concept vectors and linguistic concept vectors have differences and similarities as human beings? Four experiments are presented in this work, all focused on multi-modality representations labeled by psychologists and text-derived representations generated by computer scientists for concept learning, and the results demonstrate that 1)for the same concept, both forms of representations can properly reﬂect the concept, but 2) the representational similarity analysis ﬁndings reveal that the two types of representations are signiﬁcantly different, 3) as the concreteness of a concept grows larger, the multi-modality representation of the concept becomes closer to human beings than the text-derived representation, and 4) we veriﬁed that combining the two improves the concept representation.
Classification of Multi-Criteria Group Decision Making in Dynamic Environments for Resolving Corruption Cases

In group decision making, decision-makers have their criteria for making decisions so that the decision-making parameters have a multi-criteria nature. Things like this often happen in dynamic environments. One example of a multicriteria problem in a dynamic environment is when the Inspectorate Work Unit examines corruption cases. In one corruption case, there are many criteria (multi-criteria), and it is dynamic to solve them. For this reason, the Inspectorate can choose the criteria used by the classification method, including Neural Network, Logistic Regression, SVC, Gradient Boosting Classifier, Extra Trees Classifier, Bagging Classifier, AdaBoost Classifier, Gaussian NB, MLP Classifier, XGB Classifier, LGBM Classifier, K Nearest Neighbor Classifier, Decision Tree Classifier, and Random Forest Classifier. The data used is the Inspectorate's criteria data to examine corruption cases. The five criteria are classified into three criteria with several classification methods. Each classification method can choose criteria, and it can be seen the accuracy of the selection of criteria. The highest accuracy is 100%, so it can be concluded that the classification method can be used to select criteria and support group decision making.
RCWI: A Dataset for Chinese Complex Word Identification

Reasonable evaluation of lexical complexity is the premise of multiple downstream NLP tasks such as text simplification. At present, there lacks of reliable Chinese lexical complexity datasets, while most of the existing foreign datasets only focus on the words that cause reading difficulty. This paper constructs a RCWI-Dataset for native Chinese speakers, which contains 40613 examples and three complexity categories. Each example is annotated by at least three annotators. We adopt comparison method to annotate words that are more difficult than average lexical complexity in sentences, so that we can get more information about word complexity and improve the reliability of our dataset. We provide baseline experiments based on feature engineering, the results show the validity of RCWI-Dataset.
An Application to Semantic Networks

In Chap. 4, we gave R-calculi \(\mathbf{S}^\mathrm{DL}, \mathbf{T}^\mathrm{DL},\mathbf{U}^\mathrm{DL}\) for description logic, where \(\mathbf{S}^\mathrm{DL}, \mathbf{T}^\mathrm{DL},\mathbf{U}^\mathrm{DL}\) are sound and complete with respect to \(\subseteq \)-minimal change, \(\preceq \)-minimal change and \(\vdash _\preceq \)-minimal change, respectively, where we assume that theories does not contain statements of form \(C\sqsubseteq D.\)
Application of Internet of Things in Online Teaching Platform

In the era of knowledge economy in the 21st century, modern science and technology has penetrated into the field of education, which has brought great changes to China’s education. Artificial intelligence has been gradually applied to education, greatly innovating educational methods, and has become an important trend in the development of educational informatization in China in the future. Machine learning is an important part of artificial intelligence. With the development of educational big data, machine learning is an educational application. It can effectively analyze and predict a large number of educational data and better realize intelligent education, starting from the significance of artificial intelligence. This paper analyzes the necessity of independent research and development of women’s university network education platform, and introduces the architecture, design idea and function of the system.
Before Copernicus and Copernicus

A discussion of Before Copernicus: The Cultures and Contexts of Scientific Learning in the Fifteenth Century edited by Rivka Feldhay and F. Jamil Ragep. Published Online (2021-04-30)Copyright © 2021 by Matjaž Vesel Article PDF Link: https://jps.library.utoronto.ca/index.php/aestimatio/article/view/37603/28608 Corresponding Author: Matjaž Vesel, Research Centre of the Slovenian Academy of Sciences and ArtsE-Mail: matjaz.vesel@zrc-sazu.si
Challenges of Professional Adaptation: Difference Between Novice University Teachers with and Without Pedagogical Training

Novice teachers face challenges in the process of adaptation to professional activity. These problems can be caused by a heavy workload, lack of confidence in the level of professional training, lack of creativity and teaching skills, and the need to cope with a large amount of information that is not directly related to the preparation and conduct of classes. All these difficulties entail an unfavourable balance between work and personal life. The aim of the study is to identify the challenges novice university teachers face in the beginning of their career and compare the frequency of the detected problems within the groups of respondents with and without pedagogical training. 93 novice educators of Peter the Great St. Petersburg Polytechnic University took part in the series of surveys. The researchers applied the quantitative method to collect data using a questionnaire. The study involved quantitative data analysis containing the descriptive statistical analysis and the correlation analysis. It was found out that novice teachers face problems in professional, psychological, and motivational spheres of adaptation. The difference between the values of novice teachers with and without a teaching degree is, in most cases, statistically significant. It makes it possible to conclude that university teachers without pedagogical education need support via additional educational programmes. The research is particularly relevant for improving the professional culture of specialists in the field of education and contributes to the solution of the professional adaptation problem faced by novice educators.
Alastria mission and vision. A Multidisciplinary research

Governance of permissioned blockchains is a key issue for the short-term success and further development and consolidation of decentralized ledger technology (DLT) general purpose networks. The present book, written by thirty authors and directed by Prof. Ibanez, one of the Consorcio Red Alastria blockchain community cofounders, shows how this pioneer worldwide public-permissioned national ecosystem, Alastria, is to be governed and organized. The different sections of this collective work analyze the mission and vision of Alastria from a multidisciplinary viewpoint encompassing the technological, economic and legal perspectives of its mission and vision. This collective task also encompasses key issues concerning self-sovereign digital identity, decentralized applications, smart contracts and other legal issues related with the creation, expansion and strategic anchorage of the aforementioned ecosystemic community, unique in Europe and in the world, showing the way for other permissioned consortia to boot in the forecoming years
Pemanfaatan Alat Evaluasi Untuk Meningkatkan Hasil Belajar Pada Materi APBN &amp; APBD Siswa Kelas XI IPA

The purpose of this study is to analyze the differences in learning outcomes and to analyze the effectiveness of the use of evaluation tools on learning outcomes in State Expenditure Revenue Budget and Regional Expenditure Budget materials toward students in science class XI. Because there is a decrease in student learning outcomes during distance learning so that, this study utilizes experimental research that uses a nonequivalent control group design, with a population of 170 students with majors in Science. In selecting the sample, researcher uses purposive sampling technique with Science class XI 3 as the experimental class and Science class XI 4 as the control class, but the only difference in the experimental class is given as additional evaluation tools. This study utilizes hypothetical test of pre-test value and post-test with One Way Anova. Based on these results, in the use of evaluation tools there was no significant difference in student learning outcomes in State Expenditure Revenue Budget and Regional Expenditure Budget materials toward students in science class XI and the use of evaluation tools had effectiveness as a support for improving learning outcomes with State Expenditure Revenue Budget and Regional Expenditure Budget materials toward student ins science class XI
IMPROVING STUDENTS’ VOCABULARY MASTERY THROUGH WORD WALL MEDIA IN READING SKILL (AN EXPERIMENTAL STUDY AT GRADE EIGHTH OF SMP 1 WOYLA WEST ACEH)

Abstract Name: Marlinda Student No: 1306102020009 Study Program: English Education Title: Improving Students’ Vocabulary Mastery through Word Wall Media in Reading Skill. Keywords: Word Wall, Vocabulary, Media. The aim of this action research study is to improve students’ English vocabulary mastery of VIII/1 students of SMPN 1 Woyla, Aceh Barat Regency through word wall media. This research was a classroom action research which was conducted at SMPN 1 Woyla, Aceh Barat Regency in the academic year 2020/2021. The research subjects in this research were VIII/1 students of SMPN 1 Woyla which consist 30 students in the classroom. The method of this study was quantitative. Vocabulary tests were used as the instruments of collecting the data. The result of the research shows the success in the improvement of students’ vocabulary mastery. The result showed that there was an increase between the pre-test (45.28) and the post-test (79.47). The differences between the pre-test and post-test was statistically significant (0.00˂0.05). This indicates that word wall media increased students’ vocabulary achievement. Respectively. This suggests that word wall media can be used as alternative media in teaching vocabulary.