Reviewerly/exHarmony huggingface dataset:
Bayes and the Law

Steve Fienberg and I shared both an interest in the law and an attraction to Bayesian statistics. Just what that relationship is has been discussed over the years by many. The purpose of this paper is to review some of the major contributions to thought on this subject and to raise some attendant questions.
Statistical Issues in Assessing the Reliability of Eyewitness Identification

Among the 375+ wrongful convictions identified by the Innocence Project that were later overturned by DNA evidence resurrected from the crime scene, nearly ∼70% involved eyewitness testimony ( www.innocenceproject.org ). Courtroom identifications from an eyewitness can be tremendously powerful evidence in a trial. Yet memory is not a perfect video recording of events, and one’s recollection of the events surrounding an incident is even less reliable. In October 2014, the National Academy of Sciences (NAS) issued a landmark report evaluating the scientific research on memory and eyewitness identification. The Committee of researchers (psychologists, statisticians, sociologists) and judicial representatives (judges, attorneys) reviewed the published research on the factors that influence the accuracy and consistency of eyewitnesses’ identifications, conducted via laboratory and field studies. Steve Fienberg chaired the Committee that reviewed and ultimately authorized the release of that report. Unbeknownst to many, however, Steve himself played an important role behind the scenes by providing advice to the Committee that greatly strengthened the report. In this chapter, I will describe some of the research on memory and recollection relevant to eyewitness identification, the shortcomings in the statistical methods that had been used in evaluating laboratory studies, and Committee’s recommendations for standardizing procedures and further research using statistically designed experiments. The astounding insights that Steve provided to the Committee illustrate another one of many instances where Steve left his mark and made the world a better place.
Development of a Digilog Learning Model for Training on the Principles of Artificial Intelligence Learning in Elementary Education

The importance of nurturing human resources who will lead the 4th Industrial Revolution is increasing, and artificial intelligence is a core factor of innovative technologies. Therefore, developing various and interesting teaching methods for principles of artificial intelligence is necessary. This article suggests teaching principles of artificial intelligence by convergence of digital and analogue, called digilog. Students get to know how machines can learn and operate, which is digital, with paper worksheets and several physical teaching aids, which are analogue. In digilog way, students figure out the principles of image recognition. There are two methods, MAX and filtration box. The principles of artificial intelligence are too abstract to understand for elementary learners who are yet at concrete operational period, according to Piaget. Therefore, the convergence of digital and analogue is effective for teaching and learning about artificial intelligence in elementary education. Elementary learners examine colorful virtual images in their worksheet and use their hands and pencils to trace artificial intelligence’s work. They end up with figuring out how artificial intelligence compresses inserted images into smaller reference images step by step. With the offered method and developing more diverse digilog elements, elementary learners’ knowledge and experiences necessary for the future society will be increased.
SMART HOUSE OPPORTUNITIES IN PRESENT

The author performed an analysis why testing is important for e-commerce application. The work contains basic information about testing, software development and e-commerce.
Pengembangan modul pembelajaran atletik berbantuan QR code

This study aims to develop an athletic learning module assisted by QR code for grade X students of MA Mutiara Al-Qur'an. The research method used was the research and development model of Dick and Carey with nine steps. Data collection techniques in research using interviews, observation, questionnaires. The data analysis technique used the steps of analyzing the validity of the media expert, getting a value of 87.03%, the linguist with a value of 82.14%, and the material expert getting a value of 95.83% so that from these results the validity of the module was categorized as very valid, then the practical questionnaire data analysis One to one and small group test students got a score of 87.22% so that the practicality of the athletic learning module assisted by the QR code for the tenth grade students of MA Mutiara Al-Qur'an was categorized as having very strong practicality.
Beam Stack Search-Based Reconstruction Of Unhealthy Coronary Artery Wall Segmentations In CCTA-CPR Scans

The estimation of the coronary artery wall boundaries in CCTA scans is a costly but essential task in the diagnosis of cardiac diseases. To automatize this task, deep learning-based image segmentation methods are commonly used. However, in the case of coronary artery wall, even state-of-the-art segmentation methods fail to produce an accurate boundary in the presence of plaques and bifurcations. Post-processing reconstruction methods have been proposed to further refine segmentation results, but when applying general-purpose reconstruction to artery wall segmentations, they fail to reproduce the wide variety of boundary shapes. In this paper, we propose a novel method for reconstructing coronary artery wall segmentations, the Tube Beam Stack Search (TBSS). By leveraging the voxel shape of adjacent slices in a CPR volume, our TBSS is capable of finding the most plausible path of the artery wall. Similarly to the original Beam Stack Search, TBSS navigates along the voxel probabilities output by the segmentation method, reconstructing the inner and outer artery walls. Finally, skeletonization is applied on the TBSS reconstructions to eliminate noise and produce more refined segmentations. Also, since our method does not require learning a model, the lack of annotated data is not a limitation. We evaluated our method on a dataset of coronary CT angiography with curved planar reconstruction (CCTA-CPR) of 92 arteries. Experimental results show that our method outperforms the state-of-the-art work in reconstruction.
Research on Key Technologies of Computer Graphics and Image Processing

Under the new background of the rapid development of Internet technology in the information age, Internet technology and graphics and image processing technology have made unprecedented progress. In the process of development, it pointed out the development direction of electrical mode processing in the new era. Computer graphics in computer processing technology needs to be used more optimally, and its own research content involves a wide range of aspects. Its research content includes graphics and image visualization, standardization research, graphics modeling, and image interaction research. At present, China has entered the era of Internet technology development, and various needs and problems have arisen in the development process. At this stage, we should give full play to the application value of information technology in the Internet technology era in graphics and image processing. This article analyzes the structure and technical guarantee realization of technical improvement. The author first briefly explained the current necessity of paying attention to electrical mode processing, and then analyzed the construction content of the technical improvement of image information recognition. The author looks forward to the development prospects of technical improvement under the data detection system in the new era, and hopes to be helpful to the in-depth promotion of technical improvement in the next stage.
A distributed integrated energy trading solution

Blockchain technology can bring profound changes to the energy industry and is now becoming a new driving force for the rapid development of this sector. In order to build a secure, reliable, and stable energy service system, it is necessary to study the framework and interaction mechanism of the energy trading system. This paper introduces a distributed integrated energy trading solution, proposes a new overall architecture, and analyses the blockchain model. This paper presents an algorithm to calculate the score directly in the client. Through this algorithm, users can choose service providers, get rid of cloud control, and realize users' free choice of electricity purchase and interaction between users through the smart contract. The test results show that the system has high performance and efficiency, can meet the needs of integrated energy transactions, and provides a solution for the application of blockchain in energy trading.
Algebraic Bayesian Networks: Checking Backbone Connectivity

The paper investigates one of the problems arising in machine learning of bases of knowledge fragments with uncertainty, presented in the form of algebraic Bayesian networks, the construction of an adjacency graph as a global network structure based on its primary structure. The aim of the research is to propose methods for solving the inverse problem. As the results, algorithms for checking a graph for belonging to a family of joint graphs and a family of minimal joint graphs are proposed, and estimates of their computational complexity are made. An improved version for the special case and an improvement for the general case on average are also proposed for the algorithm for checking membership in a family of joint graphs. The problem of recognition of joint graphs has not been previously researched; this issue is being addressed for the first time as currently drafted. The theoretical significance lies in the possibilities for applying the results in further research of graph-theoretic invariants in the global structures of algebraic Bayesian networks.
A Store Entity Identification Method Based on Deep Learning

In recent years, the development of deep learning has led to unprecedented advances in computer vision, making it possible to use artificial intelligence to identify shop names. This paper proposes a store entity identification system based on deep learning, which consists of three modules. The text detection module adopts Cascade Mask R-CNN. The text recognition module adopts Attention LSTM. The named entity recognition module adopts Bert-BiLSTM-CRF. It can recognize all the text information in the picture and extract the shop name accurately. The final score can reach 91.12%. Our research saves the time of traditional manual extraction of the shop name in the picture, and provides technical support for the intelligent development of the shop management system.
Digital Copyright Management Model of University Library Based on Blockchain Technology

Renewal of digital copyright in university libraries is not timely and easy to be infringed, which hinders the creator's right to confirm in time. The introduction of blockchain technology to monitor and update the use of digital rights ensures the interests of digital rights holders. In this paper, we construct a digital copyright management model for university libraries based on blockchain technology, including a federated chain and a side chain. The model can trace the whole process of digital copyright creation and improve the efficiency of digital copyright use. The research of this paper provides a model for the management of digital copyright in university libraries. The model traces the use of copyright to ensure the legitimate interests of copyright owners.
Analysis of Blockchain Technology and its Application in the Field of Radio Monitoring

With the increasing rise and rapid development of blockchain technology, the advantages of decentralized, distributed consensus and secure, tamper-evident and reliable database of blockchain technology will become increasingly prominent. Facing the radio monitoring system that generates massive data in the field of radio monitoring, by using blockchain timestamp, distributed consensus and data encryption, it will effectively solve the problem of centralized system of radio monitoring, reduce the construction cost of radio monitoring system, improve monitoring efficiency and ensure data security.
Application of BP Neural Network in Higher Education System

The higher education system is an important part of the country’s education of citizens. In order to adapt to this trend, this paper establishes an evaluation model for the higher education system. The evaluation elements include the health and sustainability of the higher education system. We use this model to evaluate and predict the higher education system. Then, we propose practical policies and implementation schedule. Finally, we evaluate the effectiveness of the policy and possible difficulties.
Analysis of blockchain digital currency and market risk based on VAR

As a kind of financial asset, blockchain digital currency attracts investors' attention. Based on bitcoin price data and three kinds of stock market data, this paper constructs a second-order lag VAR model to test their influence mechanism. Conclusion: there is no causal relationship between bitcoin and stock market index. Verify the original hypothesis: bitcoin can better avoid market risk and has the property of market hedging assets. This paper makes a contribution to the cross study of special currency and financial market.
Research on the construction of agricultural product quality and safety traceability platform based on blockchain technology

In order to solve the problems of low credibility, information missing, and low efficiency in the current agricultural product traceability system due to the centralized storage of data, a blockchain-based agricultural product quality and safety traceability platform has been established. This system chose Hyperledger as the implementation of the blockchain and configured multiple organizational nodes. The chain codes have been written in Go language, while the server and client programs have been written in Java., traceability chain is formed based on the sequential relationship of links, and the operation information of each link is recorded in detail, and different departments of different enterprises can participate in the same traceability chain at the same time. The traceability information of agricultural products can be uploaded to the distributed ledger in real time. It has been tested in practice that the platform has been launched and operated stably, all traceability information including production information, storage information, circulation information and sales information of agricultural products can be obtained through client query. This platform could be applied to a variety of agricultural products and ensure the credibility and integrity of the traceability results by the distributed storage of key traceability information.
Minimizing Task Completion Time in the Cloud based on Random Neural Network

With the development of IoT and 5G, the number of devices accessing the Internet is increasing every day. While mobile edge computing effectively reduces the pressure on cloud centers, cloud centers still face the challenge of task scheduling and resource allocation for a large amount of SaaS applications. In this paper, the conditions for minimizing the average task completion time are derived by a simplified queuing model and an adaptive dynamic scheduling algorithm for minimizing the average task completion time is proposed in combination with stochastic neural networks, which is based on online measurements and takes up very little resources and computation. A diverse range of algorithms are tested in many different environments as a way to analyze algorithm performance. The simulation results show that our proposed algorithm is effective in reducing the average task completion time in a variety of environments.
Stealing Kernel Function of Support Vector Machine through Cache Side Channel

With breakthroughs in various statistical learning methods, machine learning is playing an increasingly important role in production and life. Although machine learning models perform well in practice, the security issues of machine learning models have attracted widespread attention from academia and industry. For example, machine learning models require a lot of manpower, material and financial resources for training, which has huge commercial value. Generally speaking, machine learning models are trade secrets and company patents. This paper uses Flush+Reload technique to extract support vector machines (SVM) kernel function information through the cache side channel, which seriously threatens the security of the machine learning model. Our experiment attacked the scikit-learn machine learning framework. Only two attacks are required to accurately infer the kernel function used by the victim’s SVM training program.
Text Reading Ability in Everyday Life for Mild Aphasic Patients : A Study Using a Readability Measure

Readability とは文章の読みやすさのことである。日本語において, 文章を公式に当てはめ readability score を算出する研究は行われているが, 失語症を対象としたものはない。そこで失語症者がどの程度の文章であれば読解可能かを調査することを目的に, 日常生活上, 目にしやすい文章の readability score を算出し, 失語症者の読解結果と比較した。まず, 区報・新聞・週刊誌の記事の readability score を算出した。また, 軽度失語症者と健常者に対し独自に作成した読解検査を実施し, 読解検査の正答数を比較した。結果, 区報・新聞・週刊誌の順で難易度が高く, 記事のカテゴリーによって難易度が異なった。 さらに, 失語症者と健常者の正答数の差には有意差が認められた。よって, 失語症検査の文読解課題で高い得点をとったとしても, 日常目にしやすい文章の理解が困難な可能性が示唆された。
PEMANFAATAN INTERACTIVE e-BOOK BERBASIS ANDROID UNTUK MENINGKATKAN HASIL BELAJAR MAHASISWA

The covid-19 pandemic that struck the world has forced educational institutions to carry out distance learning both online and offline. The main problem in distance learning is the unstable internet network in some areas far from urban areas and the teaching materials are not interactive. As a result, student motivation to learn independently is very lacking which results in low learning achievement. This research is a classroom action research in an effort to improve student learning achievement and learning motivation in the numerical method course, where the teaching material used is an interactive e-book. The results showed that learning achievement increased and completed 100% and student learning motivation was in the very high category. Interactive e-books are very effective in overcoming unstable internet network problems, because they can be accessed without being connected to the internet.&#x0D;
Using Hashing to Improve Efficiency in Cross-image Duplicate Detection in Research Publications

Cases of research misconduct had increasingly exhibited themselvesthrough the duplicate figures that they contain; Bik et al. [1] examined over 20 thousand biomedical published papers and found that 3.8% had inappropriate duplicate figures, with this percentage on the rise in recent years. Currently, the identification of Figure duplicates is mainly carried out by human reviewers; the process is slow and requires specialized training. There have been attempts to develop large-scale screening tools for image duplicates, but they are either unpublished [2] or do not perform very well. There exists prior research in the field of copy-move forgery detection. These deal with duplicate regions on a single image, but the methods could be modified and applied to cross-image matching, as we intend to. However, cross-image matching implies a much larger feature set to match between, and feature matching is currently the slowest step in the process [3]. Currently, there are two directions to address this problem. One is to use keypoint-based features, such as SIFT, to decrease the size of the feature set. The other is to apply hashing to the features and use hash lookup to quickly eliminate those features that definitely don’t match; Bayram et al. [4] demonstrates that using bloom filters in place of traditional methods increased the matching speed at some loss of result accuracy. We plan to devise a method that applies hashing to matching SIFT features in order to reliably perform faster than prior methods on cross-image matching in large biomedical image sets. We expect the resulting method to perform faster than current methods with little to no loss of accuracy.
Immersive Technology in Integrating STEM Education

Educators are often faced with the challenge of exposing students to new environments and information in an educational and timely manner. The use of immersive technology in a classroom allows teachers to expose students to simulations or conditions that could be difficult or not possible to explore without this technology without ever needing to leave their desks. These technologies not only provide students with lessons in the science fields, but also help them develop social and emotional skills that are critical to success in higher education and the work field. The immersive technology integrated in STEM motivates kids to employ their problem-solving abilities to find answers to impossible challenges and help to shape their future better. This paper is an attempt to analyze the impact of immersive technology in STEM education by providing advantages, challenges, and drawbacks of integrating this technology to an educational system. Additionally, the state of art papers in the filed of STEM learning using immersive technology are reviewed.
Video Duygu Analizi

In this study, emotional analysis was carried out with the CNN deep learning model on the human faces detected from the images in the video.The results of this analysis were recorded in seconds and an emotion analysis graph was created.The study consists of 3 main stages.The first is to find and label the emotional images required for the CNN model, the second is to create a CNN deep learning model that can conduct emotion analysis, and the third is to identify the facial images from the videos.In order to create a training data set, thousands of photographs from 61 selected films were analyzed.These include films with different feelings such as Yes Man, The Babadook, Scarface, Seven Pounds.First, faces were collected for 7 types of emotions.These feelings are boredom, fear, happiness, calmness, confusion, irritability and sadness.Haarcascade technique is used in the face detection section.Assistance was received from the Amazon Face Recognition web service for tagging detected faces according to emotions.In the study, about 50 thousand face samples were obtained.However, in the subsequent controls, many non-facial images were detected and removed from the images found with Haarcascade.In addition, approximately 40% false emotion detection was determined in the emotional analysis returned from the Amazon web service and removed from the training data set.As a result of the clearing of the whole data set, 20 thousand photos were tagged for 7 emotions.