Reviewerly/exHarmony huggingface dataset:
EthExplorer: A Tool for Forensic Analysis of the Ethereum Blockchain

This paper presents EthExplorer, a graph-based tool for analysing the Ethereum blockchain. EthExplorer has been designed for the assessment of Ethereum transactions, which represent diverse and complex activities in a large-scale distributed system. EthExplorer shows Ethereum addresses as nodes and transactions as directed arcs between addresses. The graph is annotated in several ways: arcs are scaled according to the amount of Ether they carry and the nodes are colour encoded to indicate types of addresses, such as exchanges, miners or mining pools. Ether transfer transactions and smart contracts are distinguished by line styles. EthExplorer can be used to trace the flow of Ether between addresses. For a given address all its output or input transactions with the corresponding receiver or sender addresses can be found. The set of considered addresses can be increased by adding selected addresses to the set of analysed addresses.
Compressing Closed Frequent Itemsets with Controlled Information Loss

Closed frequent itemsets (CFIs) condense frequent itemsets without loss of information. For large and dense datasets like big data and unbound big data streams, even the number of CFIs generated can be enormous. In such scenarios approximation is preferred against an accurate solution. Subset Significance Threshold (SST) is an effective constraint variable in mining significant CFIs. The support of the insignificant CFIs is approximated to the support of their immediate superset. However, few insignificant CFIs are approximated beyond specified SST due to chaining effect. To overcome this limitation in SST, the authors are proposing an enhancement to the SST (e-SST) in this paper to improve the degree of accuracy of the approximated insignificant CFIs. The merging of insignificant CFIs to thier superset is limited to one level so that the approximation is bound within specified SST. Experimental results show that the e-SST technique is efficient than SST in limiting the approximation of the support of insignificant CFIs within the specified threshold, thus reducing the information loss.
Research on an Improved SVM Training Algorithm

A new SVM training algorithm is proposed in the paper to improve the validity and efficiency of image annotation. These annotation tasks are related to one another due to the correlation among the labels. The model will implicitly learn a linear output kernel during training. Simulation results show that compared with independent SVMs training, Joint SVM improves classification accuracy and efficiency substantially.
Qualitative Analysis of Verbal Fluency Tasks in Alzheimerʼs Disease and Mild Cognitive Impairment

言語流暢性課題の得点がアルツハイマー病 (以下 AD) や軽度認知障害 (以下 MCI) の評価に役立つ指標であることがすでに報告されている。そして, 言語流暢性課題では反応の質的な分析を行うことで, 語彙検索機能や自己モニタリング機能について検討できる可能性がある。また, 語彙検索機能については, 検索の方略という視点から検討されているが, 英語話者を対象とした先行研究で扱われている方略は日本語話者にとって一般的とはいえないため, 日本語話者特有の方略に基づいて分析する必要がある。そこで本研究では, 日本語話者の AD と MCI の語彙検索機能と自己モニタリング機能の特徴を明らかにすることを目的とした。動物の名前を挙げる意味課題と「か」からはじまる語を挙げる文字課題を, AD と MCI, そして健常高齢者 (以下 NC) に実施した。語彙検索機能の評価には日本語話者特有の方略を考慮した上でクラスター数とスイッチ数を用い, 自己モニタリング機能の評価には, 産生語の重複エラー率と検査者への質問の有無の割合を用いた。その結果, 意味課題において AD では NC と比べてクラスター数とスイッチ数が少なく, 重複エラー率が高かったことから, 語彙検索機能と自己モニタリング機能の低下が示唆された。また, MCI では NC と比べてクラスター数とスイッチ数は少なかったものの, 重複エラー率には違いが示されなかったことから, 自己モニタリング機能に先行して語彙検索機能が障害される可能性が示唆された。
Setting of SOI MOSFET Model Parameters based on BP Neural Networks Algorithm

Abstract With development of SOI technology, SOI MOSFET technology is developed as well. Most traditional MOSFET parameter models adopt semi-empirical and semi-physical model, and simplification assumption is introduced during modeling. However, as SOI MOSFET devices keep downsizing, the short-channel effect and quantum effect are more obvious so that it is more complicate to calculate and extract characteristic parameters of SOI MOSFET. This paper proposes a kind of SOI MOSFET characteristic parameter modeling method based on BP neutral networks algorithm. Compared to other semi-empirical models, this method needs not to calculate characteristic parameters of devices. In stead, it calculates current and voltage output characteristics and transfer characteristics of devices through BP neutral network models according to test data. Through verification, the trained and predicted output relative error is within 5%. The model has short operation time, high calculation precision and good stability. The models established may be applied extensively to other types of transistor, and feasible for practical engineering application.
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces

Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the pairs of classes are treated the same way, some classes are closer than the others. Weighted FDA assigns weights to the pairs of classes to address this shortcoming of FDA. In this paper, we propose a cosine-weighted FDA as well as an automatically weighted FDA in which weights are found automatically. We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights. Our experiments on the ORL face recognition dataset show the effectiveness of the proposed weighting schemes.
If a Picture is Worth a Thousand Words – How Many in An Emoji? Emojis in Common Law Criminal Courts

Emojis are now recognised as standard additions to technology-mediated communications and are beginning to be viewed as a language in themselves. Necessarily, these peculiar icons have found their way into courtrooms across the globe, raising questions as to how they are best dealt with, particularly when a finding of criminal guilt turns on their interpretation. This paper analyses the approaches of criminal courts in three common law jurisdictions: the United States, Australia, and New Zealand – concluding that the current approach is characterised by a worrying lack of consistency and coherence. Additionally, the unique legal challenges presented by emojis are explained and analysed. These amusing characters are marred by practically limitless potential for misinterpretation, a gulf in understanding between their typical users and legal decision-makers, lack of an authoritative reference source, and unclear procedural rules. Finally, recommendations are put to the New Zealand judiciary as to how they can ‘look forward’ and establish legal principles for dealing with emojis, to avoid frustrating fair outcomes.
Improvement of Key Management Mechanism for RSSP-II and Its Formal Modeling and Verification

As the RSSP-II protocol has hidden dangers in the management of transport keys and authentication keys, in order to strengthen the safety of the key management of RSSP-II and make the communication between the safety-related entities of the train control system safer and more reliable, an improved scheme is presented in this paper. This scheme adopted the Raft algorithm combined with elliptic curve cryptography and time-triggered mechanism to get all safety-related devices in a certain area of the system to update and consistently share an authentication key in a way that works without key management center and reduces human intervention. Then, the specification language TLA+ is used to model the consensus process, and the TLC model checker is used to verify the properties of the model. The results show that the scheme is feasible, safe and can simultaneously avoid the deadlock problem. At last, the safety analysis shows that the scheme is safe and meets EN50159 standard.
Image Scaling Algorithm Based on Elliptic Curve

When Digital Video image transformation from general screen (i.e., 4:3, etc.) to widescreen (i.e., 16:9, etc.), the length-width ratio of source image will change. So, it is difficult to adjust image resolution and convert the format. In this paper, a method of nonlinear scaling is proposed, aiming at overcoming the image distortion problems caused by conventional transform methods. The proposed method is that the vertical direction is transformed by linear interpolation, and nonlinear scaling transformation is conducted in the horizontal direction. The elliptic curve scaling function is used on both sides of the image and appropriate linear scaling factor is added in the central area, achieving the overall image of the nonlinear scaling factor change continuously. It can greatly reduce the image distortion in human vision. The simulation result shows that the distortion of the scaled image is acceptable, the proposed method is more efficient than conventional methods.
A Shape-Based Model with Zone-Wise Hough Transformation for Handwritten Digit Recognition

A novel method for different-shape-features-based handwritten digit recognition is proposed in this paper. A digit database is constructed with shape features obtained by calculating normalized and signed width vector of handwritten digit. The differences between left most and right most point in different heights make the width vector of the digit. If it is completely left of central point of total image, then a negative sign is attached with width. The signed width vector is normalized by dividing it by maximum width to make the width vector invariant to scaling. In the same way, normalized signed width vector of the test digit is calculated and compared with the normalized signed width vectors of training digits those already stored in digit database. K-nearest neighbor algorithm and random forest-based classification are used to classify the test digit. Hole-based preprocessing is used to reduce the recognition time complexity. Hough transformation-based line characteristics analysis is used to increase the accuracy of classification. The image is divided into different zones, and Hough line characteristics of the specific zone for specific digit are analyzed. Proposed model classifies digits with 98.3% accuracy using k-nearest neighbor-based classification and 98.8% using random forest-based classification, which is comparable to other state-of-the-art digit recognition models. The time complexity of training and testing are very low.
Implementation of Human Tracking System under Different Video Degradations

In this paper, a new strategy for human tracking is proposed to enhance the human detection and tracking in the presence of video degradations using Kalman filter. The average number of assigned tracks (indicating the correct detection and tracking) was calculated. We have measured the human tracking accuracy in the presence of both speckle and salt & pepper video disturbances. Experimental results show that both median and disk filters have targeted the most efficient masking system that easily restore the accurate number of the average detections and predictions.
Mixture-preference bayesian matrix factorization for implicit feedback datasets

Recommendation with implicit feedback has been extensively studied in recent years. It is more difficult to provide users with stable and accurate recommendation compared to recommendation with explicit feedback, due to the reason that interactions from implicit feedback datasets do not clearly indicate the level of user preference. Most existing methods dealing with implicit feedback have achieved excellent performance by focusing on other aspects rather than directly inferring user preference. In this paper, we offer accurate recommendation to users by addressing the problem of directly inferring user preference from implicit feedback with such less information and huge uncertainty. We propose a novel mixture-preference model (MPBMF), which introduces a set of pseudo-preference values to surmise the true user preference. More specifically our proposed model can be described as a Gaussian mixture model in which each single model is trained with pseudo-preferences which show the user's different views for items. Then the predicted user preference is estimated by the models under different pseudo-preferences with different contributions. We conduct extensive experiments on three real-world datasets, and the superior performance demonstrates the effectiveness of our model.
Augmenting Moving Planar Surfaces and Spatial Control of Interactive Surfaces Application through Virtual Touch Screen (VIRTOS)-A Case Study

The virtual touch screen framework (VIRTOS) is intended to exertion in the space, whereas surrounding light environments can change. The VIRTOS consistently displays touch area, when a vast front area (non-foundation, a hand/ shadow) protects and breaks on the catch; the shade of anticipated touch catch is changed quickly with the end goal to recognize the shadow from the hand. Likewise VIRTOS bolsters the slider (scrollbar) in light of these virtual touch-catch instruments. The basic detraction procedure exists is utilized to fragment the frontal area. The reference image meant for each catch is utilized for basic detraction, is ceaselessly refreshed to represent changes in encompassing light. The experiments show that the ease of VIRTOS, is in it needs of two applications: for slide introductions by two virtual catches towards to explore the slides, and further is an activity diversion measured by the virtual slider effective. Maintenance of Virtos is really easy. Among all the benefits of Virtos, the most important thing is that we are using a solar panel and we know that solar energy is a truly renewable energy source.
Network Traffic Sampling System Based on Storage Compression for Application Classification Detection

With the development of the Internet, numerous new applications have emerged, the features of which are constantly changing. It is necessary to perform application classification detection on the network traffic to monitor the changes in the applications. Using RelSamp to sample traffic can provide the sampled traffic with sufficient application features to support application classification. RelSamp separately assigns counters for each flow to record the statistical features and introduces a collision chain into the hash flow table to resolve hash conflicts in the table entries. However, in high-speed networks, owing to the number of concurrent flows and heavy-tailed nature of the traffic, the storage allocation method of RelSamp results in a significant waste of storage on the traffic sampling device. Moreover, the hash conflict resolution of RelSamp causes the collision chains of several hash table entries to be excessively deep, thereby reducing the search efficiency of the flow nodes. To overcome the shortcomings of RelSamp, this study presents a sampling model known as MiniSamp. Based on the RelSamp sampling mechanism, MiniSamp introduces shared counter trees to compress the storage space of the counters during the sampling process and integrates an efficient search tree into the hash table. The search tree structure is adjusted according to the network environment to improve the search efficiency of the flow nodes. The experimental results demonstrate that MiniSamp can effectively aid network operators to classify traffic in the high-speed network.
When Models Meet Data

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
UAV intelligent optical communication based on conditional generation against network

Abstract This paper first introduces the advancement and importance of laser communication technology for drones., then introduced Deep Neural Network (DNN) to develop wireless end-to-end communication systems. Accurate channel state parameters (CSI) are necessary for computing DNN, but in laser communication systems, channel information is difficult to gain in advance or changes quickly with time and environment. This paper will use conditional generation network (GAN) to represent channels. The coded signal of the transmitter and the pilot data are used as condition information. By simulating the simulation results of the lognormal channel, the method can achieve high-quality transmission without clear CSI.
Fuzzy classification involved in fusion of existing decision and pre-known task applied for integrated input space

Although the Takagi-Sugeno-Kang (TSK) fuzzy classifier has achieved great success, how to further improve its classification performance and enhance its interpretability is still one of the most difficult challenges. Involved with the fusion of existing decision information and pre-known classifica tion task, a newly proposed deep/hierarchical TSK fuzzy classifier (EDIPK-TSK) with interpretable fuzzy rules makes full use of the classification advantages of each base classifier to construct a multi-layer deep learning structure. This study first considers that the existing decision information of each training sub-block is sequentially projected into the subsequent sub-blocks for training. Undoubtedly, the existing decision information has played a guiding role in the current learning process to some extent. Simultaneously, the pre-known classification task is fused into the decision information for fine-tuning of it, which can significantly improve the efficiency of guidance and accelerate the fitting speed of the model. In each layer, the use of interpretable integration input space guarantees that EDIPK-TSK is not a black box. The proposed deep classifier can realize learning by using short fuzzy rules, which ensures the satisfactory interpretability of the classifier. The final experimental results also verify that EDIPK-TSK has strong classification advantages and interpretability.
Mapping the Strength of Research in the Focus Areas for Transportation based on SCOPUS 2014 - 2018

Abstract This study investigated scientific research on mapping the strength of transportation focus research BASED on the 2014-2018scopus data. The Ministry of Research Technology and (KEMENRISTEKDIKTI), especially the Directorate General of Research and Development (RISBANG) has a huge Role on improving the quality of higher education. One way to improve higher education quality is through mapping the strengths of research. The strength of the research that form the basis of this mapping, refers to RPJPN 2005-2025 and RPJMN 2015-2019 which focuses on Transportation. This study, used three steps methodology; (1). Collecting Data, were collected from secondary Scopus data year 2014-2018 from KEMENRISTEKDIKTI in scientific publication focus on Transportation, (2) identifying Scopus data based on sub-focus of Transportation, (3). Data analysis using frequency distribution, (Determine the range of data, determine the classes, and determine the class interval). This study concluded that 91, 7 % (143) of 156 universities published journal indexed Scopus on range 1-19 journal publications within 5 years. ITB (Bandung Institute of Technology) has the most journal publications of Transportation focus.
A Takagi-Sugeno Based Fuzzy Inference System for Predicting Electrical Output in a Combined Cycle Power Plant

The work highlighted in this paper presents a system for speculating the electrical output for a combined cycle power plant based on the input attributes: temperature, pressure, humidity, and vacuum. An accurate prediction is essential to maximize the electrical output by adjusting the input for optimal results. The paper uses the Takagi-Sugeno fuzzy inference system. The dataset used for the purpose is the UCI machine learning datasets for combined cycle power plant. The dataset consists of 9568 data points accumulated from a combined cycle power plant over a span of 6 years ranging from 2006 to 2011.
Integration of Pedagogical Approaches and Their Use in the Educational Process

The paper considers the process of applying pedagogical approaches in education, their integration; provides an example of formation of students' educational and cognitive competencies and their thinking in the process of studying, based on the integration of competence, technological and activity approaches, which allow to increase the level of development and effectiveness of university education.
Natural Scene Mongolian Text Detection Based on Convolutional Neural Network and MSER

Maximum Stable Extreme Region (MSER) is the most influential algorithm in text detection. However, due to the complex and varied background of Mongolian text in natural scene images, it is difficult to distinguish between text and non-text connected regions, thus reducing the robustness of the MSER algorithm. Therefore, this paper proposes to extract the connected regions in the natural scene pictures by applying MSER, and then uses the convolutional neural network (CNN) to train a high-performance text classifier to classify the extracted connected regions, and finally obtaining the final detection results. This paper evaluates the proposed method on the CSIMU-MTR dataset established by the School of Computer Science, Inner Mongolia University. The recall rate is 0.75, the accuracy rate is 0.83, and the F-score is 0.79, which is significantly higher than the previous method. It shows the effectiveness of the proposed Mongolian text detection method for natural scenes.
Comparative Study of Stereo Correspondence Techniques for Underwater Images

The aim of the chapter is to provide an overview of the computer vision techniques involved in stereo correspondence of underwater images, which is one of the important steps in the 3D reconstruction of underwater objects and scenes. The author provide briefly the various categories of techniques for 3D reconstruction of objects. Also, the author provides steps involved in the 3D reconstruction of objects using stereo vision technique, particularly, more focused on stereo correspondence step and its techniques available in the literature. Further, they present some of the local and global stereo correspondence methods employed for underwater stereo images with simulation results. Finally, the author presents a visual comparison of local and global stereo correspondence techniques employed for underwater stereo images.