Reviewerly/exHarmony huggingface dataset:
Weighted attack graphs and behavioral cyber game theory for cyber risk quantification

Operating and engineering secure systems is challenging yet a necessary prerequisite for modern life as we know it, flourishing economic systems and society as a whole. This is as digitalization penetrated broad aspects of every facet of life realizing many opportunities. However, digital transformation also leads to increasing vulnerability to cyber threats. Cyber risk quantification thereby has a crucial role as anything that “is not measured cannot be improved. [And] what is not improved will always degrade” (Thomas Kelvin). However, quantifying cyber risks respectively as an inverse quantifying cyber security is still in its infancy and a largely unsolved problem. We propose a novel methodology for cyber risk quantification based on weighted attack graphs. By doing so, we introduce a multi-layered attack ontology which is the basis of the attack graph. The attack graph is developed relying on cyber threat intelligence. We weight each attack path using computational models of motivation. The attack graph is the basis of a defender-attacker game. We analyze and solve the game for deriving quantitative measures describing the risk of getting attacked.
Detective Eye: Exclusively for Face and Object Detection, Identification and Localization

Dynamic Visual surveillance System is basically used for examination and explanation of article approaches to acting. It contains static and moving article acknowledgment, video following to appreciate the events that occur in scene. The principal objective of this study paper is to choose the various methodologies in static and moving article area as well as following of moving things. Any video scene contains objects that not altogether firmly established by object area technique. There are various classes of recognized article like tree, fogs, individual and other moving things. Area for moving thing is a very going after for any video perception structure. Object Following is used to find the district where articles are available and condition of things in each packaging in more critical level application. In this paper, we used different procedures like establishment allowance, genuine system, and transient edge differencing for the recognizable proof of moving articles. We also portrayed different following methodologies like Point following, Diagram Following and Piece continuing in our review paper.
Web Service Multidimensional Data Mining in the Optimization Middleware of Secondary Guiding Management System in Private Colleges

Web Service multidimensional data mining in the optimization middleware of the secondary guiding management system in private colleges is studied in the paper. The proposed service model performs query rewriting according to the query input requirements of each selected service, that is, by passing the user’s query conditions to the input conditions of the service, and calling the service through service binding in order to complete the extraction of the core required information. For the Web Service, the information layer is essential. This layer is in the core of sharing the heterogeneous information sources. Then, the multidimensional data mining is applied to the whole system. The novel UI is finally designed through the test.
A Systematic Literature Review: Limitation of Video Conference

The Covid-19 pandemic that has hit the world has resulted in limited human activities. Video conferencing is one solution for carrying out activities. Video conferencing helps people reduce the spread of Covid-19 and connects people. With video conferencing, people can meet without being limited by space and time. However, video conferencing still has various drawbacks, such as the inability to interact. Because we only see the video, the lack of technology due to video conferencing requires us to use technology such as computers or cell phones that a stable and fast internet network must support to ensure smooth video conferencing. In this paper, the researcher conducted a literature study to determine the limitations of video conferencing. The results show the various reasons for video conferencing restrictions. Some of these reasons are experienced by lecturers, students, or the lack of infrastructure. Students are challenged to improve their level of study due to the lack of technological support that supports only using video conferencing.
A Method of Car Driver's Phone Call Recognition Based on Human Joint Points

The distracted driving state of the car driver has caused serious harm to traffic safety and the personal safety of the driver. The most frequent and common distraction is the distracted driving behavior of the driver on the phone. In order to accurately identify the car driver's phone call is distracting driving behavior. A driver's phone call behavior recognition method based on the joint point information of the driver's body was proposed in this manuscript. After using the OpenPose network structure to extract the joint points of the human body, the driver’s angle of the driver's upper and lower arms and the normalized distance between the joint points are calculated and compared to relatively accurate identification of car drivers' phone calls and distracted driving behaviors The experimental results show that the method proposed in this paper has an accuracy rate of 97.23% for the distracted driving state of the car driver's phone call.
Conversational AI Assistant

Abstract: A conversational assistant is an intelligent conversational computing system designed to mimic human speech to provide automated online guidance and support. The growing benefits of conversational support have led to widespread adoption across many industries to provide virtual support to their customers. Conversation assistance uses methods and algorithms from his two fields of artificial intelligence: natural language processing and machine learning. However, the application has many challenges and limitations. This research reviews recent advances in conversation support using artificial intelligence and natural language processing. We highlight the main challenges and limitations of the current work and provide recommendations for future research investigations.
Effects of Conversation Politeness on Hiring Decision in Online Labor Markets: An Inverted U-Shaped Relationship Exploration

This study examined the effect of politeness, as a key reflection of linguistic features of conversation in the online labor marketplace, on hiring behavior. Drawing on the politeness theory, a non-linear relationship was theorized. A hypothesis was put forward and examined against a large-scale archival dataset from a Chinese online labor market. Using an econometric model, the results demonstrated that there was an inverted U-shaped relationship between politeness and hiring decisions. The study offers theoretical implications to the online labor market literature and politeness theory by providing empirical insights on the role of politeness in hiring decision. In addition, our findings offer beneficial and practical contributions for vendors and platform operators.
Cloud Workload and Data Center Analytical Modeling and Optimization Using Deep Machine Learning

Predicting workload demands can help to achieve elastic scaling by optimizing data center configuration, such that increasing/decreasing data center resources provides an accurate and efficient configuration. Predicting workload and optimizing data center resource configuration are two challenging tasks. In this work, we investigate workload and data center modeling to help in predicting workload and data center operation that is used as an experimental environment to evaluate optimized elastic scaling for real data center traces. Three methods of machine learning are used and compared with an analytical approach to model the workload and data center actions. Our approach is to use an analytical model as a predictor to evaluate and test the optimization solution set and find the best configuration and scaling actions before applying it to the real data center. The results show that machine learning with an analytical approach can help to find the best prediction values of workload demands and evaluate the scaling and resource capacity required to be provisioned. Machine learning is used to find the optimal configuration and to solve the elasticity scaling boundary values. Machine learning helps in optimization by reducing elastic scaling violation and configuration time and by categorizing resource configuration with respect to scaling capacity values. The results show that the configuration cost and time are minimized by the best provisioning actions.
A linear logic framework for multimodal logics

Abstract One of the most fundamental properties of a proof system is analyticity , expressing the fact that a proof of a given formula F only uses subformulas of F . In sequent calculus, this property is usually proved by showing that the $\mathsf{cut}$ rule is admissible, i.e., the introduction of the auxiliary lemma H in the reasoning “if H follows from G and F follows from H , then F follows from G ” can be eliminated. The proof of cut admissibility is usually a tedious, error-prone process through several proof transformations, thus requiring the assistance of (semi-)automatic procedures. In a previous work by Miller and Pimentel, linear logic ( $\mathsf{LL}$ ) was used as a logical framework for establishing sufficient conditions for cut admissibility of object logical systems (OL). The OL’s inference rules are specified as an $\mathsf{LL}$ theory and an easy-to-verify criterion sufficed to establish the cut-admissibility theorem for the OL at hand. However, there are many logical systems that cannot be adequately encoded in $\mathsf{LL}$ , the most symptomatic cases being sequent systems for modal logics. In this paper, we use a linear-nested sequent ( $\mathsf{LNS}$ ) presentation of $\mathsf{MMLL}$ (a variant of LL with subexponentials), and show that it is possible to establish a cut-admissibility criterion for $\mathsf{LNS}$ systems for (classical or substructural) multimodal logics. We show that the same approach is suitable for handling the $\mathsf{LNS}$ system for intuitionistic logic.
Description Of Keyword Analysis: A Bibliometric Study Of Interpersonal Communication

The aim of this study was to assess the most relevant themes related to interpersonal communication by bibliometric analysis using the keyword "interpersonal communication" as input to gain a full understanding of interpersonal communication and understand the current research concentration. The research method used is literature review by collecting data through the Publish of Perish application because it is considered the best choice for bibliometric analysis. As a bibliometric analysis tool for visualizing a network of authors, quotes and keywords, the VOSviewer software results show that Every year, there are more and more papers on interpersonal communication, a trend indicating increasing interest in the subject. A Purwanto is the author with the most citations overall. Bibliometric analysis offers relevant details on the main researched issues of interpersonal communication.
Research on Cause of Coal Face Accident Based on Topic Model Mining

As the first production site of coal, mining working face is the place where coal mine accidents occur frequently.In order to avoid or reduce the occurrence of accidents, it is necessary to eliminate and discover the hidden danger of accidents in mining face in time.However, in the past, the cause of accidents mainly adopts the method of qualitative analysis, and lacks the effective analysis model of the cause of accidents, so that the potential value of a large number of hidden danger data of accidents has not been applied.Therefore, the LDA topic mining algorithm is used to build the accident cause topic mining model, and the Perplexity index is used to determine the best number of topics, and the semantic network graph is used to analyze the internal relationship between the hidden dangers of accidents, which directly reflects the main accident cause and the correlation between the cause factors of coal mining face.Taking 18925 accident hidden danger data of a mine in Shandong Province from August 2013 to September 2019 as an example, the topic of accident causes was extracted by mining the topic model, and the semantic network diagram was drawn.The
MONITORING AND WARNING OF SMART GRID FAULT INFORMATION USING DEEP LEARNING

To ensure the stable operation of a smart grid, it is necessary to monitor fault information and release timely warnings.In this paper, a monitoring algorithm based on a convolutional neural network (CNN) was designed using the deep learning method to determine the fault category of transformers and monitor them.An improved particle swarm optimizationlong short-term memory (IPSO-LSTM) model was established to realize early warning of faults based on the prediction of dissolved gas concentration in oil.Experiments were conducted on the data of a power company.It was found that the CNN model had higher fault monitoring accuracy than the support vector machine (SVM) model and other methods; compared with the LSTM model, the IPSO-LSTM model had a smaller error in gas concentration prediction.Taking H2 as an example, the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the IPSO-LSTM model was 0.0412, 0.0432, and 6.2145%, superior to the LSTM model, so the IPSO-LSTM model could achieve the early warning of faults.The experimental results prove the effectiveness of the proposed method.The method can be further applied to the actual smart grid.
An Enhanced Light Object Detection for Indiscernible Object in the Special Scene

We present a transfer learning method named Special Application Transfer (SAT) for special object detection in a real life scenario. Our method improves fine-tuning hyper-parameter and adds unrecognized samples to detect special samples when training object detection neural networks for classification. We implement the model of NanoDet on special supervised datasets and fine-tune the hyper-parameter on a target task. More importantly, we combine a few carefully selected samples in training and simple heuristic fine-tuning to achieve good performance on special object detection in real-life scenarios. Our method (SAT) performs well across surprisingly the small dataset the medium dataset and the large dataset. SAT achieves 95% AP (Average Precision) on the small dataset, 94.8% AP on the medium dataset, and 94.5% AP on the large dataset. The performances of AP run-time and training convergence are perfect, compared with the original method and well-established famous methods on the challenging COCO dataset and our dataset. We hope our work could promote and complete the practical application in more real life scenarios.
Retracted: Research on High Robust Multimedia Image Encryption Based on Gyrator Transform Domain Model

Research on High Robust Multimedia Image Encryption Based on Gyrator Transform Domain Model" [1] due to concerns that the peer review process has been compromised.Following an investigation conducted by the Hindawi Research Integrity team [2], signifcant concerns were identifed with the peer reviewers assigned to this article; the investigation has concluded that the peer review process was compromised.We therefore can no longer trust the peer review process, and the article is being retracted with the agreement of the editorial board.
The Effectiveness of Developing Interactive Learning Media on Learning Basic Concept Material

This study’s aim was to determine the interactive learning media effectiveness on the Learning Basic Concept material. The ADDIE Robert Maribe Branch approach is being used in this research and development. Regarding product effectiveness, in the field trial conducted, the t-test result was obtained tcount = 5.09 while ttable = 1.699 at significance level of 0.05. This shows that tcount &gt; ttable, then Ho is rejected and Ha is accepted, so that interactive learning media based Lectora Inspire is effectively used in the learning process.
MALWARE DETECTION AND CYBER SECURITY

In this research, we trained machine learning models specifically, support vector machines on malware families over sliding windows of time, based on a large number of PE file features.Our approach focused on the static features extracted from PE files.Other features such as -byte grams can also be explored using our Support Vector Machine dependent technique.Features extracted by dynamic analysis can be add significant information to the Support Vector Machine dependent feature tracking strategy.
Preface: 3rd International Conference on Mechanical Engineering, Civil Engineering and Material Engineering (MECEME 2022)

It is with great pleasure to introduce the proceedings of the 2022 3rd International Conference on Mechanical Engineering, Civil Engineering and Material Engineering (MECEME 2022). The conference is an international seminar organized by China University of Geosciences, Manipal University and University of Minho. The conference was held during August 13-14, 2022 in Shenyang, China.&#x0D; MECEME series provides a forum for accessing to the most up-to-date and authoritative knowledge from both industrial and academic worlds, sharing best practice in the fields of mechanical engineering, civil engineering, material engineering, industrial engineering and green manufacturing. The meeting provides an opportunity to highlight recent developments and to identify emerging and future areas of growth in these exciting fields.&#x0D; MECEME 2022 has the main purpose to bring researchers and scholars to share their knowledge and experiences. The conference serves as an excellent opportunity to meet each other and to exchange ideas with theme of "Opportunities and Challenges for Research and Technology Enhancement in Post Covid-19 era".&#x0D; Scientific Committee of MECEME 2022&#x0D; Shenyang, China
Structural repetitions and discourse relations in English-Turkish translations of TED talks

This study attempts to analyse translations of the discourse relations of the arguments with structural repetitions in the TED-MDB corpora consisting three texts (English) and their translations (Turkish). The main questions addressed in this paper are: a) Are connective relations used explicitly or implicitly together with repetitions? b) Which discourse relations are being used with repetitions? c) Do translators translate repetitions or omit them? The analysis shows that speakers of TED Talks mostly prefer to use these repetitions in their speeches with implicit discourse relations (66.6%), and translators translate them through implicit forms (54.8%). Moreover, the dominantly explicit usage of and in repetitive structures shows that the relations can be associated with an explicit connective and. Accordingly, ExpansionConjunction (addition) relations are frequently being used with repetitions at the beginning (73.8%). Also, mostly, the translators tend to omit repetitions at the beginning (92.9%).
New Associate Editor

It is with great pleasure that I welcome Sanu K. Mathew to the editorial board of the IEEE Journal of Solid-State Circuits as a new Associate Editor. Mathew is an expert on digital circuits and systems.
A kriging-based sequential optimization algorithm with hybrid infill sampling strategy

The appropriate infill sampling criteria to generate more promising updated points is crucial for kriging-based global optimization. For this purpose, a kriging-based sequential optimization algorithm with hybrid infill sampling strategy (KSO-HIS) is proposed. In each iteration, three efficient sampling criteria (i.e., predicted objective minimization criterion, improved expected improvement and new curvature maximization criterion) based on kriging are respectively optimized to produce three optimal solutions by TR (Trust Region) method. Then, a new screening strategy is adopted to determine final expensive evaluation points from the three optimal solutions. The proposed method is compared with three other optimization methods. The test results of eight benchmark functions and a simulation case verify that KSO-HIS can deliver better sampling and convergence performance.
Bazı Dembowski-Ostrom Polinomlarının Planaritesi Üzerine

Planar mappings, defined by Dembowski and Ostrom, are identified as a means to construct projective planes. Then, many important applications of planar mappings appear in different fields such as cryptography and coding theory. In this paper, we provide sufficient and necessary conditions for the planarity of certain Dembowski-Ostrom polynomials over the finite field extension of degree three with odd characteristic. In particular, we completely determine the coefficients of the given Dembowski-Ostrom polynomials to be planar.
Designs

Intellectual property remains not just economically significant, but also of daily importance to most businesses and individuals. The digital age brings many opportunities, but also presents continuing challenges to IP law, and the EU's programme of harmonisation unfolds in this context. Taking account of numerous changes, the third edition of this detailed yet accessible book offers a fully updated account of the law as it affects all the major rights, free movement and competition matters, and enforcement. It sets the substantive law in its international and policy context, and is essential reading for postgraduate students, academic researchers, and legal practitioners alike.
The Effectiveness of the Two Stay Two Stray Learning Model Against UPT Student Mathematics Learning Outcomes. SMP Negeri 1 Laut Tador

This study aims to find out whether the Two Stay Two Stray learning model is effective for use in learning and to find out whether there is an increase in student learning outcomes by using the Two Stay Two Stray learning model for mathematics learning outcomes of UPT.SMP Negeri 1 Laut Tador Academic Year 2022/ 2023. The population of this study were all students of class VII UPT.SMP Negeri 1 Laut Tador, totaling 130 students. While the sample used in this study was UPT.SMP Negeri 1 Laut Tador class VII-1 students, totaling 26 students. This study uses a type of quantitative research with a research design that is one-group pretest-posttest design. The data analysis technique used in this study is the normality test using the Kolmogorov Smirnov test and hypothesis testing using the t test. the data tested will be processed using the SPSS program. the results of the research that has been carried out show that the value of the g factor ranges from -10.75 (low category) to 49.5 (high category). With the percentage of the g factor which is in the low category which is 26.6% and the percentage of the g factor which is in the high category which is 73.3%. Whereas in the medium category and the false category is 0% and from the data it can be obtained that the average value of the g factor is 9.71%, so it is included in the high-quality category. So, it can be concluded that the use of the Two Stay Two Stray model is effective on student learning outcomes in Mathematics at UPT.SMP Negeri 1 Laut Tador TP. 2022/2023
Escape Room in Pre-School Education Learning

This paper analyzes the use of ICT in education, which is demanding in the new generations, who must be involved from an early age. To this is added a virtualized education, due to the pandemic by Covid-19. For these reasons, this research aims to demonstrate that the Escape Room can be used as an effective
Picture Books to Support the Teaching of Disciplinary Literacy in the Primary School Classroom

This study seeks to investigate the use of picture books in relation to the burgeoning area of disciplinary literacy in the Irish primary school context. At the centre of this research is the recognition that picture books play a central role in teaching and learning across the curriculum in primary schools; with this research deciphering