Reviewerly/exHarmony huggingface dataset:
Quantified Assertion Verification Method for Non-linear Arithmetic and Mutable Data Structures

Formal verification is an important method to improve the high-confidence of software. However, it cannot be widely used for applications in the industry yet, mainly because of the insufficient proof power of theorem provers. The work of this paper focuses on the verification of quantified assertions describing program properties, and the verification process is based on the verification prototype system. For non-linear arithmetic in the quantified assertions verification, the Splitting-and-Merging method is proposed to transform the quantified assertions about the program, preserving original information and generating new quantified assertions, so that can be proved by automatic theorem prover. A Grouping method is proposed for the pointer programs with multiple quantified assertions of mutable data structures, it helps automatic theorem discover the association and properties between assertions of mutable data structures. Experimental results show that the verification prototype system can effectively solve the non-linear arithmetic problem and mutable data structures problem of the quantified assertions verification in program verification.
Analysis of Lifting Motion in Use of Endoskeleton Type Power Assist Suit Based on Motion Capture and Musculoskeletal Model Analysis Software

In this study, the motion while lifting a heavy object under wearing an endoskeleton-type power assist suit was captured by motion capture device, and the burdens on the lower back were analyzed using the musculoskeletal modeling software AnyBody Modeling System. Then, the muscle activity of the erector spinae muscles, and disc compression force and disc shear force between L4/L5 lumbar vertebrae in the state of no assist force, the state of wearing passive power assist suit and the state of wearing active power assist suit, were evaluated and compared. As a result, effectiveness of the power assist suit on reducing the burden on the lumbar region was verified. In addition, the assist effects of the passive power assist suit and the active power assist suit were almost equivalent.
Other Methods of Column Encryption

We’ve talked a lot in this book about using Always Encrypted to encrypt columns of data, and in general I feel that should be the default tool you choose when wanting to perform such actions. SQL Server does however have other methods you can use to encrypt data and has done since at least the 2005 version of the product. In my opinion these older methods are less secure and more limiting to work with than Always Encrypted – but you may have edge cases where you want to use them. As such we’ll cover them in this chapter, but as Always Encrypted is the main recommendation of this book, we will only cover them in brief.
Blockchain-Based Land Record System

In today’s world, having access to land records is a must and being able to see who owns the land property is important to allow crossverify ownership of the land in order for business purposes or for personal use. In India, maintaining the land record with the current population is a challenging task, and the current system was causing incomplete registries, leading to fraudulent activities. The current system being used by the government is inconsistent and has a lot of intermediaries who have access to the system and make changes to it. The use of Blockchain is increasing day by day, the government is trying to adopt technology, and web3.0 is the future we are heading; adding Land Records in Blockchain is very useful because it provides transparency for everyone. Allowing citizens to view the land record with a visual representation with maps will ensure everyone has access to these land records.
Speeding Up of Patrol Path Planning using Moving Costs Approximated by Lower Boundary

In order to patrol inspection points, a patrol path planning, which plans both a route though the inspection points and paths between the inspection points, is required. The planning has a problem that the more inspection points, the more planning time. We focused on path planning occupies most of the time for patrol route planning, we apply lower bound costs for solving traveling salesman problem. Approximation with the lower bound reduces the number of path planning, which reduces route planning time. The simulation with 25 points inspection showed that the planning time decreased 94% in comparison with the conventional method, whereas route length increased only 0.6%.
Corruption, bureaucracy and export orientation of firms in Africa

This book intends to provide a continuous assessment of the crisis in governance in Africa. As it is, there are huge deficits in the capacity of African states to harness vast human and material resources to promote good governance. This manifests in pervasive corruption, collapsed service delivery, collapsed state-owned enterprises, eroded social trust, capital flight, escalating levels of poverty and wars, human insecurity, and stunted growth. The public sector is the pulse of service delivery because the entire governance system revolves around the sourcing of materials and services, mostly from the private sector, in order to achieve its public policy intents. The procurement process, therefore, ordinarily ought to yield positive economic outcomes and an efficiency-driven system in favour of the government itself and its service recipients. However, this more often than not is not the case. Despite its enormous wealth, the African continent is in an economic quagmire, a dilemma that requires multi-facet research activities. This is the motivation for this book.
Entity relationship extraction model based on entity information enhancement

The entity-relationship extraction task consists of name entity recognition and relationship classification. To address the problem that the current entity-relationship extraction task in the Chinese language domain does not fully utilized the relevant information of entities, we propose a relationship extraction model based on entity information enhancement, using entity category information to construct entity category labels to enrich the original text data, adding the labeled vocabulary as a priori information to the word list of the pre-trained model. At the same time, entity information is considered a kind of crucial information in the relationship extraction task, so this paper increases the entity information in the input data to enhance the model’s ability to process the critical information of the task. Two sub-experiments, entity recognition and relationship classification, are constructed in the form of a Pipeline, where the relationship classification task exists as a downstream task of entity recognition. The model is trained and tested on the DuIE dataset. The results demonstrate the superiority of the proposed model for entity-relationship in the Chinese domain.
Dev Hub

The Salesforce CLI connects to a Developer Hub, or “Dev Hub,” to create and manage scratch orgs. Normally, you would enable and use a Dev Hub in a production org, or in a partner business org if you are a Salesforce AppExchange Partner. If you want to use one of these orgs for your Dev Hub, please jump to the section “Enabling the Dev Hub,” which is after the next section. If you would prefer to experiment with Salesforce DX without touching an existing org, create a free trial org that has a Dev Hub enabled by following the instructions in the next section.
The impact of innovative stem education technologies on the quality of learning of educational material

The peculiarity of professionally oriented training of applicants for higher education based on STEM technologies is the need to take into account interdisciplinary connections as a manifestation of integrative processes of penetration of fundamental disciplines (physics), natural and mathematical knowledge into the cycle of subjects of professionally oriented training of students, which is provided not only by basic physical, mathematical and technical competencies of the 21st century but also by key methodological knowledge taking into account applied aspects. These connections play a leading role in improving the quality of professionally oriented training of future specialists. The purpose of the article is to determine the impact of innovative technologies of STEM education on the quality of learning of educational material through a physical experiment. The article experimentally substantiates the effectiveness of using the STEM approach when performing laboratory work to form the studied physical concepts in comparison with traditional teaching methods. It has been shown that the use of the STEM approach improves the level of assimilation of concepts in comparison with traditional teaching methods. In the case of using the STEM approach, the number of students with a high level of assimilation of concepts increases significantly, while the number of students with a low level decreases, which proves the effectiveness of using the STEM approach for the formation of high-level knowledge and skills. The article highlights the features of the formation of the STEM component in teaching in the innovative educational and scientific environment of the technical university, as well as the features of the teaching methodology taking into account STEM-learning technologies. It has been concluded that the use of the STEM approach significantly improves the level of assimilation of physical concepts in comparison with traditional teaching methods.
Determine distances area of traffic lights using image processing algorithms

Traffic lights recognition and detection considered important step in the advanced driver assistance techniques. The suggested system designed to detect the color and area of the traffic light at different distance. The detection of the traffic color based on designed algorithms to separate colors and calculate area of the signals. The Three colors (red, green, and yellow) distinguished by threshold for each color red, blue, green, and grayscale to give accurate results. The distance between the camera (car) and the traffic light is determined by scale factor (SCF) equation. The real dimension of the traffic measured manually and converted to pixel to calculate the area of each color. The area of each light within the traffic estimated by morphology method. Thus, the light traffic and the area is determined, and the distance between the traffic light and car is determined. The results show that the behavior of the area and distance of the traffic with the video frame shows logic data in pixel domain. Moreover, the light separation is accurate 100% according to the suggested algorithm.
A Research on Performance Improvement of QR Code Embedding Method into Any Image Using Deep Learning

The purpose of this research is embed a QR code image into any image by means of deep learning. In this research, I create Encoder model that embeds QR code image into an image and Decoder model that recovers the information of the embedded QR code image referring to the AutoEncoder model. The loss parameters of the two models are shared and optimized.
Distance Education, Circle Two

The transition of universities to distance learning at the beginning of March 2022 was a complete surprise. Nevertheless, the educational process continued, and in the fall, most universities returned to their usual full-time education. However, the stay in this format was short-lived, and the universities again switched to the remote form, which ended at the end of February of this year. How did teachers and students feel when they switched to remote learning again and when they entered the face-to-face format? How did students evaluate various aspects of the remote form after graduation?
Web-Based Distance Learning Guidance Application in the Covid 19 Era

The purpose of this research is to build a distance learning guidance information system in the era of covid 19. Therefor supporting technology facility is needed in creating a change in teaching and learning procedure into digital form that can be accessed online. The researcher took the initiative to implement learning guidance system using the distance method. The method used in this research is quantitative. The result that have been achieved in this research is to add insight into the latest information systems. The result of the research obtained resulted in a web-based distance learning guidance application system in the covid-19 era. The benefit of this research is that the application of distance learning guidance can make it easier for teacher and student to deliver material quickly. In the process of developing a web-based distance learning guidance system, using the PHP programming language version 7.3.27. Testing the system using the black box testing method to test the functional system so as to produce a system that works well.
A Study on Development and Compilation of Legal English Textbooks Supported by Internet Technology

Textbooks are the basic carrier of teaching. Form innovation of legal English textbooks plays an important guiding role in the digitization reform of legal English teaching. Based on analysis of the current situation of legal English textbooks, this paper explores the important role of Internet technology in the concept, direction and technological innovation of textbook development and compilation, and then puts forward the optimization approach from the principle of three-dimensionality and interactivity, 1)improve the traditional textbooks of legal English; 2)develop digital teaching resources; 3)build a network teaching platform; so as to realize the deep integration of legal English textbooks and Internet technology.
Intellectual Property Protection Lost and Competition: An Examination Using Machine Learning

We examine the impact of lost intellectual property protection on innovation, competition, acquisitions, lawsuits and employment agreements. We consider firms whose ability to protect intellectual property (IP) using patents is weakened following the Alice Corp. vs. CLS Bank International Supreme Court decision. This decision has impacted patents in multiple areas including business methods, software, and bioinformatics. We use state-of-the-art machine learning techniques to identify firms’ existing patent portfolios’ potential exposure to the Alice decision. While all affected firms decrease patenting post-Alice, we find an unequal impact of decreased patent protection. Large affected firms benefit as their sales and market valuations increase, and their exposure to lawsuits decreases. They also acquire fewer firms post-Alice. Small affected firms lose as they face increased competition, product-market encroachment, and lower profits and valuations. They increase R&D and have their employees sign more nondisclosure agreements.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Design and Production of an Osprey-type Drone with 2-DOF Tiltable Mechanisms

Vertical-takeoff-and-landing unmanned aerial vehicles (UAVs) are known as drones and used in a wide range of fields. In general drones, the rotor is fixed to the fuselage, and can only output thrust in one direction, which causes the aircraft to change its attitude when moving. In this research, it is aimed at creating a UAV that can flexibly fly without causing any changes in attitude when navigating. There was a flight limitation due to the total weight of the airframe in a previously studied UAV, which was a tandem-type rotorcraft having 2-DOF tiltable coaxial rotors at front and rear respectively. This research solves this problem and develops a new Osprey-type drone that can fly without such a limitation.
BSB: Bringing Safe Browsing to Blockchain Platform

Programmable Blockchain brings a new type of decentralized applications (Dapps) that facilitate transfer of assets across users without a third party. The popularity of Ethereum Dapps brings themselves great security risks: they have been under various kinds of attacks from cybercriminals to gain profit. As the back-end of Dapps, smart contracts have been exploited their programming errors to steal cryptocurrency or tokens. Multiple approaches have been proposed to detect unsafe contracts. This paper presents a Blockchain Safe Browsing (BSB) platform to effectively disseminate smart contract detection results to contract users, and vulnerable contract owners. Based on those results, contract blacklist can be generated to provide user warning service, which is used to warn users before making transactions with unsafe contracts. Meanwhile, a contract owner notify mechanism is developed to help contract owners study the vulnerability details of their contract so that they can patch the vulnerabilities in time. Among the mechanism, the researchers will gain profits from shared data, which in turn inspire them keep uploading their research results. Moreover, as the most valuable asset for the researchers, vulnerability exploit details will be encrypted before uploading, and can only be decrypted by contract owners, which prevent the details being leaked and utilized by cybercriminals. Extensive evaluations using real datasets (with 2,880 unsafe contracts) demonstrate that our prototype can function as intended without sacrificing user experience, and warn users at the millisecond level.
Improving Continuous Variable Quantum Key Distribution Using Heralded Hybrid Linear amplifier and Heralded Noiseless Linear amplifier When source in the Middle

We propose a protocol to improve the transmission distance and secure key rate of quantum channels, the NLA-HLA QKD. The untrusted entanglement is sent to users by a quantum channel. It is capable of long transmission distances while maintaining high secure key rates and resisting the higher noise. Using the method of calculating secure key rate with the coherent state, the secure key of the NLA-HLA QKD protocol is derived. The effects of the parameters G and g on the key rate are analyzed through simulation experiments. By regulating the values of G and g, the application range of the protocol can be improved. Optimal parameters are used under specific circumstances to ensure the validity of the protocol.
Blind Image Deblurring Via Fast Local Extreme Intensity Prior

Blind image deblurring is a challenging problem in low-level computer vision, which aims to recover blur kernel and latent sharp image from a single blurry input. In recent years, channel priors such as dark channel prior and extreme channel prior have shown excellent results. However, the high computational cost and approximate solution of sub-problem limited the performance of these models. In this paper, a novel fast local extreme intensity prior (LEP) based on maximum a posterior (MAP) framework is presented for kernel estimation. The LEP is inspired by the observation that the blur will damage the local extreme intensity of an image patch. Moreover, we show the LEP is sparser in clear images than blurred ones, thus the change in sparsity of LEP motivated us to explore the kernel estimation model based on LEP. Then, unlike traditional half-quadratic splitting based optimization strategy, an effective and fast optimization algorithm is developed for this non-convex nonlinear problem. Experimental results on image sets show that the proposed algorithm is superior to state-of-the-art methods.
Evaluating the Adaptation of a Secondary Teacher Training Programme: A Case in the Time of Pandemic

This paper has been prompted by the detection of some needs in secondary-school teacher training, especially as a result of the COVID-19 pandemic. In response, several training accessions are made from the initial teacher training programme. First, measures are included designed to facilitate the acquisition of digital competence, especially in teaching in online environments. Communication skills are also developed. The perception of trainee teachers is obtained after they start their period of teaching practice. Both of these actions are perceived as very positive, useful, and enriching for pupils. Teachers’ confidence and experience were improved when they started their teaching practice. It is suggested that both of these subjects become mandatory before teaching is begun. Improvements include new learning environments and the strengthening of practical training.
Development of Visual Comics Physics Media on Atomic Physics Materials for Physics Learning in Senior High School

Physics is one of the subjects in high school which consists of facts, concepts, principles, laws, postulates, and scientific theories and methodologies. Students consider physics to be difficult because many physics concepts are abstract and difficult to understand. In this case, learning media is needed that can be used to assist teachers in clarifying the material. The purpose of this research is to develop comic visual learning media on atomic physics material. The method used in this research is research and development. Data were obtained from validation assessments by 2 material experts and 2 media experts as well as small group trials with a sample of 10 students. This research was conducted in 2022 at SMA Negeri Klakah. The result of the research is that the visual media of physics comics in terms of the material aspect has an average validity of 3.8 with a very high feasibility category. Judging from the media aspect, the physical comic visual media has an average validity of 3.6 with a very high eligibility category. The results of the trial limited to a small group obtained an average of 3.35 in the practical category. The research concludes that based on the categories of validity and practicality, the visual media of physics comics on atomic physics material that has been developed is very feasible and practical to use in learning Atomic Physics in Class XII Senior High School.
Measuring the Big Five Factors from Handwriting Using Ensemble Learning Model AvgMlSC

The Big Five Factors Model (BFFM) is the most widely accepted personality theory used by psychologists today. The theory states that personality can be described with five core factors which are Conscientiousness, Agreeableness, Emotional Stability, Openness to Experience, and Extraversion. In this work, we measure the five factors using handwriting analysis instead of answering a long questionnaire of personality test. Handwriting analysis is a study that merely needs a writing sample to assess personality traits of the writer. It started manually by interpreting the extracted features such as size of writing, slant, and space between words into personality traits based on graphological rules. In this work, we proposed an automated BFFM system called Averaging of SMOTE multi-label SVM-CNN (AvgMlSC). AvgMlSC constructs synthetic samples to handle imbalanced data using Synthetic Minority Oversampling Technique (SMOTE). It averages two learning-based classifiers i.e. Multi-label Support Vector Machine and Multi-label Convolutional Neural Network based on offline handwriting recognition to produce one optimal predictive model. The model was trained using 1066 handwriting samples written in English, French, Chinese, Arabic, and Spanish. The results reveal that our proposed model outperformed the overall performance of five traditional models i.e. Logistic Regression (LR), Naïve Bayes (NB), K-Neighbors (KN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) with 93% predictive accuracy, 0.94 AUC, and 90% F-Score.