Reviewerly/exHarmony huggingface dataset:
A network attack evaluation model based on variable weight theory

With the development of the network, people pay more and more attention to network security. Network attack assessment is a very important item in network security assessment. Starting from three aspects: network attack evaluation attribute index selection, index weight determination, and network attack evaluation model establishment, based on the shortcomings of existing evaluation methods, this paper proposes a variable weight evaluation algorithm and a network attack evaluation model based on TOPSIS. Spatio-temporal correlation attributes are introduced into the attack effect evaluation index system, and a method of fusion of constant weight and variable weight is proposed to determine the weight of the index system. While ensuring the rationality of the indicator system, the compensation mechanism between indicators is overcome. Finally, the algorithm was verified through simulation experiments, and the experimental results show that the algorithm improves the accuracy and effectiveness of the evaluation.
A Corpus-Based Study of Semantic Categorizations of Attracted Adjectives to the <i>it BE ADJ clause</i> Construction

By implementing such corpus approaches as collexeme analysis and hierarchical cluster analysis, this paper investigates the semantic categorizations of adjectives that are extremely significantly attracted to the it BE ADJ clause construction in English. The findings demonstrate that these adjectives could be at least subdivided into seven different semantic clusters that denote (1) importance, necessity, and possibility; (2) appropriateness, reasonability, and unreality; (3) impracticability and irrelevance; (4) undeniability and axiomaticity; (5) obviousness; (6) dubiety, desirability, and ease; (7) improbability and anomalousness.
Machine Learning

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Analysis of Solar Power Plants with the Combination Systems of PV Module-Reflective Mirror

Solar energy is a combination of light and heat produced by the sun, where this energy is utilized by humans through solar collector technology consisting of PV modules to be converted into electrical energy. The development of PV module technology is carried out to improve its performance, where one of these technologies uses a reflecting mirror to increase the amount of sun radiation captured by the surface of the PV module. This research method uses performance analysis of the utilization of reflector mirrors added to the PV module system by using two different cases, among others are the use of two and four mirrors along the sides of the PV module. The results showed that the application of four reflective glass can direct the sun's radiation to the surface of the PV module with the amount of radiation intention doubled. This result is a kind of technology that gives us a good result to utilize it in building the solar power plant.
Development of Student Worksheets Write Project Based Learning-based Explanatory Text for Class xi Senior High School

Aim: The aim of this research is to produce student worksheets using project based learning model, describe the feasibility of students worksheets using project based learning, and determine the effectiveness of students worksheets using project based learning model.&#x0D; Study Design: Research and Development.&#x0D; Background: The problem in this research is related to the development of Student Worksheets writing project-based learning texts of class XI high school learners. The purpose of this study is to produce students worksheet teaching materials to write project-based learning explanatory texts, knowing the feasibility and effectiveness of student worksheets in writing explanatory texts in grade XI high school learners.&#x0D; Research Methods: Methods used in this research and development (R&amp;D) research and also uses two method, qualitative and quantitative methods. Data collection techniques with observations, interviews and deployment of questionnaires at three schools in Metro City.&#x0D; Research Results: The results showed that the product was successfully developed in the form of students worksheet writing project based learning explanatory text, the feasibility of students work sheet as a whole was declared "very feasible" by material experts, learning experts and practitioners with percentages of 82%, 84%, and 85.71%. Students worksheet effectively improves explanatory text writing capabilities with N-Gain by (0.38) (0.38) and (0.37) falls into the "medium" category.
Making Data Right

Learning how to wield data ethically and responsibly is a critical skill for data scientists, but one that is often lacking from traditional curriculum. Libraries have a long history of teaching data stewardship and sharing, and, in collaboration with collegiate research support entities, are good candidates to expose students engaging in data science to data ethics. This chapter presents four case studies on how the University of Minnesota Libraries and its partners have deeply integrated ethics into data management instruction. The chapter will highlight ethics for general data management instruction to undergraduate and graduate students from various disciplines, human subject data de-identification, qualitative methods and sharing, and biodiversity location data. Together, the case studies show how libraries and their partners are a natural fit to advance the work of data science curricula when it comes to managing data and the myriad ethical considerations that go along with this work.
An Analysis of Correlations between the Level of Disgust against Anatomy and Experience, Biology Teaching Self-efficacy and Awareness of the Importance of Anatomy in Elementary School Teachers for the Gifted

This study was to analyze the correlation between the level of disgust against anatomy and past experience of anatomy, and find out the effect of the level of disgust on biology teaching self-efficacy and awareness of the importance of anatomy. As research subjects, this study selected 72 elementary school teachers for the gifted who participated in an elementary school science teacher training program for the gifted. The analysis results can be summarized as follows. Firstly, it was found that the level of disgust was higher in those with disgust against anatomy than those without disgust. Secondly, those without disgust had more experience of anatomy in the past than those with disgust. As a negative correlation was clearly found between the level of disgust and past experience of anatomy, this study found out that the more past experience of anatomy they had, the less disgust they felt against anatomy. Thirdly, those without disgust had more biology teaching self-efficacy after anatomy experiments, so this study found out that the level of disgust affected the improvement of biology teaching self-efficacy. Lastly, those without disgust were found to aware the importance of anatomy in biology class much more than those with disgust.
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models

Natural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e.g., co-occurrence) correlates with meaning.We propose that n-grams composed of random character sequences, or garble, provide a novel context for studying word meaning both within and beyond extant language.In particular, randomly generated character n-grams lack meaning but contain primitive information based on the distribution of characters they contain.By studying the embeddings of a large corpus of garble, extant language, and pseudowords using CharacterBERT, we identify an axis in the model's high-dimensional embedding space that separates these classes of n-grams.Furthermore, we show that this axis relates to structure within extant language, including word part-ofspeech, morphology, and concept concreteness.Thus, in contrast to studies that are mainly limited to extant language, our work reveals that meaning and primitive information are intrinsically linked.
Introduction

Crime scene investigators (CSIs) are responsible for writing technical reports that explain complicated examinations of crime scenes and processing. The only current references for CSIs regarding report writing are police reports and police report writing manuals or texts. The introduction portion of this text will explain the police report and the CSI report, their relevance, and the distinct differences between them.
Conclusion

Report writing for crime scene investigators is a pivotal part of their career. Not only do CSIs need to know the fundamentals of writing such as proper grammar and punctuation, but they must also be able to succinctly and factually describe complex scientific processes. This text is meant to guide the CSI to proper report writing that can be adjusted for any agency’s policies and procedures. By implementing the guidance in this text, the CSI will be successful in writing reports that will present them as professional, well organized, and knowledgeable from their first involvement in the case until the conclusions in court.
Research on Intelligent Parsing of Business English Semantics based on Root Data Network Mining

Based on network mining of root data, the basic features of business English semantics are analyzed, and the application of this theory in business English semantic analysis is discussed. Based on the fourth-generation semantic analysis tool of CQ Pweb, the collocation features of high-frequency business English words were extracted and reduced in multiple directions through research on collocations, class connections, semantic tendency and semantic prosody, and the data volume was compressed to 51.2%. Using the high-precision definition algorithm of root data network mining to reorganize, collocate and parse semantic features, the experimental results show that the effect of business English parsing is increased by 6.7%.
Improving the Automatic Segmentation of Elongated Organs Using Geometrical Priors

Deep neural networks are widely used for automated organ segmentation as they achieve promising results for clinical applications. Some organs are more challenging to delineate than others, for instance due to low contrast at their boundaries. In this paper, we propose to improve the segmentation of elongated organs thanks to Geometrical Priors that can be introduced during training, using a local Tversky loss function, or at post-processing, using local thresholds. Both strategies do not introduce additional training parameters and can be easily applied to any existing network. The proposed method is evaluated on the challenging problem of pancreas segmentation. Results show that Geometrical Priors allow us to correct the systematic under-segmentation pattern of a state-of-the-art method, while preserving the overall segmentation quality.
Seismic retrofitting manuals for highway systems

Under the sponsorship of the U.S. Federal Highway Administration, the National Center for Earthquake Engineering Research is developing a set of guidelines for conducting seismic hazard evaluations and retrofitting of complete highway systems. These guidelines provide assistance in performing seismic vulnerability screening, evaluation and retrofitting of all major highway system structural components (bridges, retaining structures, slopes, tunnels, culverts, and pavements) and in determining the impacts of scenario earthquakes on highway system performance (traffic flows, economic consequences, etc.). The guidelines are contained in the 3-volume “Seismic Retrofitting Manuals for Highway Systems,” in which volume I contains procedures for conducting a seismic risk assessment of highway systems and regional networks, volume II contains procedures for seismic screening, evaluation and retrofitting of highway bridges, and volume III contains similar procedures for retaining structures, slopes, tunnels, culverts and pavements.
The hydrothermal sinter and kaolinite-Au-Ag occurrences of Ixtacamaxtitlán (Puebla, Mexico): preliminary results

The hydrothermal deposits of the Ixtacamaxtitlán area are hosted by volcanic and intrusive rocks of the Mexican Ignimbritic belt. These deposits show quartz-chalcedony-sulfide banded veins and stockworks, kaolinite formation, and silica caps on top, interpreted as hydrothermal sinter. The occurrence of kaolinite is due to acid-sulfate alteration, possibly derived from boiling at shallow depths. The alteration assemblage ranges from clorite-sericite to acid-sulfate from the deepest parts. The characteristics of the ensemble, plus the geochemical anomalies in Au and Ag in the stockworks, may indicate the presence of economic mineralisation at depth. The deposits are interpreted as hot spring-type low-sulphidation epithermal deposits.
New magnetotelluric data in the suture zone of Fennoscandia and Sarmatia

Presents the results of magnetotelluric soundings acquired in 2020 along the profile Rzhev– Velikie Luki, in the suture zone of three parts of Precambrian crust of East European platform: Fennoscandia, Sarmatia and Volga–Ural. In the central part of the profile, anomaly zone of increased conductivity in the crust was revealed. The crust conductor is weakly expressed on the components of the telluric tensor due to the screening effect of the conductive sedimentary cover; therefore, the focus in the qualitative analysis of data and inversion was made on magnetovariational parameters. According to the results of two–dimensional inversion carried out in the program of A.E. Kaminsky ZONDMT2D, on the geoelectric model a subvertical zone of low resistivity in the crust is identified, which is probably associated with the paleosubduction zone of the Belarusian Ocean Plate.
The Emotional Impact of Multi-Factor Authentication for University Students

Abilene Christian University recently rolled out multi-factor authentication (MFA) to the entire student body. Previous work has found frequent negative reactions and dissent experienced in university settings in regard to MFA. This can lead to decreased compliance or the use of less secure passwords to compensate. We hypothesize these responses are tied to the emotional impact of using required MFA for critical tasks. We present an empirical study on user perception of adopting two-factor authentication (n=465). Our findings indicate that, due to the time sensitive nature of many tasks that required MFA, university students are likely to experience strong negative emotions towards MFA that drastically lower their perceptions of its utility and usability. However, our findings also show that these negative emotions can be at least partially mitigated when users feel more personally secure due to MFA, which can in part be controlled by rollout strategy and communication.
KaraokeVUI: Utilizing Karaoke Subtitles for Voice User Interfaces to Navigate Users What They Would Say

Voice user interfaces (VUIs) have the problems in discoverability and learnability. Conventional VUIs tried to solve these problems by listing voice commands for using features on the screen of devices in combination with GUIs. However, with these VUI help tools, it is difficult to know how accurately the commands must be spoken and to determine whether the target operation is being executed if the words are rephrased or spoken incorrectly during speaking. Therefore, users are too preoccupied with speaking phrases correctly, and the advantage of VUIs, which can be used without awareness of the operation method, has not been fully demonstrated. To address these issues, we propose KaraokeVUI, which is a VUI help tool for supporting voice operation with feedback by displaying spoken words through filling in the blanks or overlaying on phrases, just like on a karaoke screen. In this paper, we evaluate and verify KaraokeVUI usefulness and usability.
Demonstrating 3D Reconstruction to Modify 2D Laser Cutting Plans

We demonstrate software tools to convert 2D cutting plans to 3D models either through user interaction (Assembler3[2]) or automatically using a heuristics-based beam search algorithm (autoAssembler [3]). This conversion allows users to make 3D manipulations (here using kyub [1]) to models for laser cutting, commonly shared in the form of 2D cutting plans. By sharing the models again in a 3D format, others can build on work and increase the complexity of what is shared in the community. The resulting more advanced models and the ability to customize them to individual users’ needs, increase the value of models for laser cutting shared online. In this demonstration, participants use the different software tools, and customize models to their liking.
The intersection of data protection rights and trade secret privileges in algorithmic transparency

Algorithmic decision-making is entering all areas of society. Algorithms now decide which grades we are awarded, whether we are granted visas, or receive social benefits. However, we know little about when, why and how algorithms are used, despite onerous transparency and information requirements under the General Data Protection Regulation. In this chapter we set out how the Trade Secret Directive is used by data controllers to prevent the disclosure of algorithms and algorithmic data, which we find to be somewhat at odds with the requirements in the GDPR. By examining the legal requirements under both laws, we map argue that that there may be a yet to be explored public interest ground that could compel companies to be more forthcoming with their algorithms and algorithmic data in support of the objectives of the GDPR. We therefore see a path forward where the suggested differences between these two EU laws can be reconciled.
Math Accessibility for Blind People in Society Using Machine Learning

Math plays a crucial role in each and every sector belonging to human beings in society. Sometimes, it is very difficult to recognize math equations and symbols due to variation in writing, change in stroke, touching symbols, and many more. The blind people get very low success in math recognition as compared to character and digit recognition. It is needed to develop blind math application in which various math equations and symbols have to be recognized. The proposed system uses a machine learning approach to identify the various math equations and symbols by extracting various statistical and complex features with well-known classifier viz. support vector machine, neural network, and K-nearest neighbor. The math documents have to be scanned and recognized. Finally, a text to speech converter has been made to get the contents of math documents for blind people in society. The proposed system will be helpful for blind math applications and it will not affect the health of blind people in terms of stress on the eye to recognize so the health can be maintained and documents can be read.The implemented system can be used by blind people to read the math documents, digits on a bank check, calculate the valuation of currency, etc.
Toolkits &amp; Wearables: Developing Toolkits for Exploring Wearable Designs

Designing wearables is a complex task that includes many layers, such as wearability, interactivity, functionality, social and cultural considerations. For decades now, prototyping toolkits are proposed to aid diverse types of audiences in exploring the design of smart accessories and garments. However, the task of designing toolkits for wearables has not received a comprehensive discussion and systematic reflection. In this workshop, we look into challenges, opportunities, and lessons learned in using, developing and evaluating wearable toolkits by focusing on their target groups, purposes, effects on the final designs. By bringing together researchers and practitioners who are experienced with the design, use and assessment of wearable toolkits, we see a particular opportunity in providing a broader perspective on defining the future of wearable toolkit designs.
Banknote Denomination Recognition on Mobile Devices

Several mobile applications have been developed to facilitate denomination detection for blind users. However, none of the existing applications allow for detecting multiple notes in a single frame and relaying the total denomination, nor is there a dataset available for the new Indian currency notes, annotated for object detection training. We describe the development of a detection application that aims to improve on the previously existing solutions by enabling multi-note detection, continuous audio feedback, automatic torch usage, and minimal user-application interaction. YOLOv4 allowed the training of a lightweight and fast object detection model with high accuracy on a custom-created dataset post-demonetization of Indian currencies that is deployed on a mobile device.
The Generating Super Resolution of Thermal Image based on Deep Learning

The need for a high resolution to the thermal image is urgent and essential. The high resolution of the thermal image can give accurate information on the heat distribution map of the objects. The accurate heat distribution maps can give accurate temperature information. This accurate temperature measurement is used for measuring many objects such as electric motors, engines, the human body, and so on—this information is used to detect the anomalies of the object to find the damaged parts. The anomalies are considered damaged parts found in solar panels, agricultural fields, buildings, bridges, etc. As the super-resolution of thermal images is very important, generating them is compulsory. The camera for obtaining super-resolution thermal images is rare, not available in the common market. Furthermore, this kind of device is costly too. Therefore not all the users, such as farmers or technicians, can have them. In order to handle the problem, the proposed method has the purpose of generating super-resolution thermal images economically and is more accessible through the deep learning method. The dataset is taken from the solar panel. The results show that the proposed method can handle the low-resolution problem of thermal images.&#x0D;
Interprocessus distances based on gender using panoramic radiographs

Objectives: This research is aimed do find out the differences of interprocessus distances between males and females using panoramic radiographs.&#x0D; Materials and Methods: This research is using observational analytic with cross sectional study design. Secondary data was taken from 96 panoramic radiographs consisting of 48 males and 48 females, from RSKGM-P Universitas Airlangga. Measurements were carried out using ImageJ Software by 3 observers. The measurement results were then analyzed by independent t-test and the inter-measuring reliability was tested using the Intraclass Correlation Coefficient (ICC) on IBM SPSS software.&#x0D; Results: The result showed that the significance value (2-tailed) of interprocessus distances measurement on males and females was &lt;0.05 in both regions. Reliability test of inter-observer using ICC obtained the overall measurement results was more than 0.75 which means excellent reliability.&#x0D; Conclusion: There was a significant difference between the interprocessus distances on males and females as measured by panoramic radiographs.
Wearable Technology Design and Accessibility Considerations Course

The course proposed here will focus on design and accessibility considerations for wearable technology. In this course we will explore how to develop a robust set of design and accessibility considerations (guidelines) for wearable technology. The course will begin a presentation on wearability and accessibility then participants will engage in an activity using a new Wearable Technology Designer’s Web Tool. The tool can be accessed again after the course and shared with students and colleagues. The course will end with a discussion about what design considerations might need to be added to the tool and what human factors information or other research should be updated.