Reviewerly/exHarmony huggingface dataset:
Translating Algorithms to Handle Fully Homomorphic Encrypted Data

An algorithm is defined as a self-contained step-by-step set of operations to be performed to solve a particular problem. The concept of simplicity and elegance related to any algorithm informally appears in Knuth’s saying “ $$\ldots $$ we want good algorithms in some loosely defined aesthetic sense. One criterion is the length of time taken to perform the algorithm... Other criteria are adaptability of the algorithm to computers...” In this chapter, we discuss how to develop suitable synthesis techniques and methodologies to handle algorithms which operate on encrypted data and to be executed on underlying unencrypted processor.
Beyond Outliers and on to Micro-clusters: Vision-Guided Anomaly Detection

Given a heatmap for millions of points, what patterns exist in the distributions of point characteristics, and how can we detect them and separate anomalies in a way similar to human vision? In this paper, we propose a vision-guided algorithm, EagleMine, to recognize and summarize point groups in the feature spaces. EagleMine utilizes a water-level tree to capture group structures according to vision-based intuition at multiple resolutions, and adopts statistical hypothesis tests to determine the optimal groups along the tree. Moreover, EagleMine can identify anomalous micro-clusters (i.e., micro-size groups), which exhibit very similar behavior but deviate away from the majority. Extensive experiments are conducted for large graph scenario, and show that our method can recognize intuitive node groups as human vision does, and achieves the best performance in summarization compared to baselines. In terms of anomaly detection, EagleMine also outperforms state-of-the-art graph-based methods by significantly improving accuracy in synthetic and microblog datasets.
Vehicle Categorical Recognition for Traffic Monitoring in Intelligent Transportation Systems

Automatic vehicle detection and recognition play a vital role in intelligent transport systems (ITS). However, study results in this field remain certain limitations in terms of accuracy and processing time. This article proposes a solution to improve the accuracy of vehicle recognition in order to support traffic monitoring on vehicle restricted roads. The proposed solution to vehicle recognition consists of two basic stages: (1) Vehicle detection, (2) vehicle recognition. This study focuses on proposing solutions for improving the accuracy of vehicle recognition (stage 2). The vehicle recognition solution is based on the combination of architectural development in deep neural networks, SVM model, and data augmenting solutions. It aims at achieving a greater accuracy than traditional approaches. The proposed solution is experimented, evaluated, and compared with different approaches to the same set of data. Experimental results have shown that the proposed solution brings a higher accuracy than other approaches. Along with an acceptable processing time, this promising solution is able to be applied in practical systems.
Approximate Multiobjective Multiclass Support Vector Machine Restricting Classifier Candidates Based on k-Means Clustering

In this paper, we propose a reduction method for the multiobjective multiclass support vector machine (MMSVM), one of all-together method of the SVM. The method can maintain the discrimination ability, and reduce the computational complexity of the original MMSVM. First, we derive an approximate convex multiobjective optimization problem for the MMSVM by linearizing some constraints, and we secondly restrict the normal vectors of classifier candidates by using centroids obtained from the k-means clustering for each class dataset. The derived problem can be solved by the reference point method based on the centers of gravity of class datasets, in which the geometric margins between all pairs are exactly maximized. Some numerical experiments for benchmark problems show that the proposed method can reduce the computational complexity without decreasing its generalization ability widely.
Generation of Fill-in-the-Blank Questions from Concept Map and Preliminary Comparison between Multiple-Choice Task and Kit-Build Task

In previous research, we proposed a framework for a kit-build concept map (KB map), in which a learner is provided a set of components with which to build a concept map. In that framework, the learner's constructed map can be diagnosed automatically. The task of building a concept map out of provided components is a promising exercise for strengthening and assessing a learner's comprehension of a learned topic. In general, the multiple-choice task of fill-in-the-blank (FIB) questions can also be used to strengthen and assess a learner's comprehension, and the answers can be automatically evaluated. In this paper, we discuss our process for designing a set of FIB questions that can be generated from a concept map. Then, we compare the KB map task with the FIB multiple choice task. Both tasks can be generated from the same concept map: that is, from the same content. We compare the two tasks, using three science lessons for fifth graders in two classes. One class uses the KB map and the other uses the FIB questions. In this experiment, the KB task evaluated the learners' comprehension more adequately than did the FIB question task, and the students in the KB class performed at a higher level than did the students in the FIB class.
A Study on the Development and Effect of Smart Manufacturing System in PCB Line

A production system is a management system that supports all activities to perform production operations at the manufacturing site. From the point-of-view of a smart factory, smart manufacturing systems redesigned the concept of onsite production systems to fit the entire system and its necessary functional composition. In this study, we select the key functions needed to build a smart factory for a PCB line and propose a new six-step model for the deployment of a smart manufacturing system by integrating essential functions. The smart manufacturing system newly classified the production and operation tasks of PCB manufacturing and selected necessary functions through requirement analysis and benchmarking of advanced companies. The selected production operation tasks are mapped to the functions of the system and configured into seven modules, and the optimal deployment model is presented to allow flexible responses to the characteristics of the tasks. These methodologies are first presented in this study, and the proposed model was applied to the PCB line to confirm that they had significant changes in the work method, qualitative effects, and quantitative effects. Typically, lead time and WIP have reduced by about 50%.
Vulnerabilities and Security Breaches in Cryptocurrencies

Nowadays, 1375 different cryptocurrencies exist, and their market value totals up to $444.8 billion, at the time of writing. The interest revolving around cryptocurrencies is constantly growing, and this hype caused an increase of criminal attacks on various cryptocurrencies. In this paper, we cover the main aspects that concern cryptocurrencies vulnerabilities and related security breaches. Then, we propose possible solutions to prevent them, or to decrease the attackers’ profit margins by increasing the costs they have to face, in order to strike some of these attacks. Alongside, we briefly describe a few attacks that have occurred in the past.
Design and Development of Mechanical and Electronic Jacquard Handloom for Fine-Korai-Mat Weaving

The market for Design of Fine-Korai-Mat started dwindling day by day due to laborious work involved in using primitive and outdated handicraft methods in its production resulting in higher cost. The young generation shy away from this craft as the working condition is not good and not remunerative. This handicraft is practiced by a community living in Pathamadai (Tirunelveli Dist., TN) for many years and the products are also known by its place of production (Pathamadai Pai). RuTAG, IITM, Chennai on realizing its status has taken initiative for the technology development of Fine-Korai-Mat Weaving handicraft so as to improve its ergonomics (working condition) that will result in more comfort and less fatigue to weavers, and to enable them to produce more results in increased productivity and better earning apart from increase in the design capability. Mechanical Jacquard Handloom of 120 hooks has been developed initially to give better ergonomics that produce symmetrical Design of Fine-Korai-Mat resulted in 200% increase in productivity. Subsequently, Electronic Jacquard of 192 hooks has been developed to produce full-size design of Fine-Korai-Mats and Marriage Mat that contains names resulting in 300% increase in productivity apart from other benefits such as ease of storage and retrieval of design and elimination of card punching and its recurring cost, etc.
Runtime Service Composition Modification Supporting Situational Sensor Data Correlation

Although IoT service and service composition provide effective means to develop IoT applications, the dynamic and time-varying correlation among massive sensors rises up new challenges to the traditional model-based approaches, and the extra uncertainty and complexity of service composition become apparent. This paper proposes a data-driven service composition method based on our previous proactive data service model. We utilize real-time correlation analysis of sensor data to refine model-based service composition at runtime. The correlation among sensor data is usually asynchronous. In this paper, we adopt and improve a Dynamic Time Warping (DTW) algorithm to obtain one-way lag-correlation, and realize dynamic sensor data correlation through refining existing service composition. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate the effectiveness of our service composition method.
Visualizing Results of Digital Ink Stroke Matching in Chinese Based on Multi-Perceived Hierarchy

Stroke matching is the mainstream method for evaluating the quality of Chinese characters. Visualization of matched strokes is necessary to evaluate the validity of the matching method and the prerequisite for manual correction. To reduce the cognitive burden, visualization based on multi-perceived hierarchy is proposed. According to the features of matched strokes and information contained, the approach proposes a multi-perceive hierarchy composed of colors, graphic symbols and number indexes. After experimental verification, the visualization is effective in operational complexity and efficiency.
Discovering Fuzzy Rules with Parallelized Linguistic Variable Elimination

Fuzzy rule discovery in collected data with linguistic variable elimination has been successfully used to create classifiers. These classifiers are composed of a group of fuzzy rules which are interpretable by humans. However, large amounts of collected data cause difficulties to obtain accurate and interpretable fuzzy rules speedily enough. In this paper, several parallelization variations of linguistic variable elimination for fuzzy rule discovery in collected data are presented with the purpose of increasing the speed of the discovery. The parallelization variations are based on parallel programming patterns tailored to the elimination so that the discovery uses several cores of the processor and the parallelization is thought in terms of tasks rather than threads. Four implemented parallel variations are compared with two sequential implementations of the original algorithm for linguistic variable elimination. Experiments show speed improvements with parallelization for various given inputs with different amounts of instances and linguistic variables.
Facial Recognition Technique with Combination Method of Local Binary Pattern Histogram and Image Euclidean Distance

This study aims to determine the level of precision, recall, and accuracy of performance of local binary pattern histogram and image euclidean distance for face image recognition. This research uses local binary pattern histogram method for segmenting face image and euclidean distance image for face image recognition. The proposed method started with the process of converting pixel value into a binary value, which then the value is used as a decimal value and the histogram is calculated, for then saved in the database. The trained facial image histogram will be compared with the histogram of the test data using the euclidean distance image formula. The result of this research shows realtime test result get 0.699 recall value and 0.7 precision value.
The Analytical Approach to the Parameterized Fuzzy Operators Design

In this paper we discussed the problem of generation and efficient software- and hardware-based implementation of branches of fuzzy logic parametric operations that can be used as basic operations of reconfigurable fuzzy systems (embedded fuzzy controllers). The ways of design of strict t-norms and t-conorms on fuzzy systems in presented paper based on the mathematical theory of geometrical aggregation operations from the Rvatchev constructive geometry. Presented theoretical results may be implemented for a new branches of membership functions design and in the hardware-based units of analytical fuzzy inference systems.
Novel Authentication System for Personal and Domestic Network Systems Using Image Feature Comparison and Digital Signatures

With the advent of the digital age, there has been an increased need for the robustness and the versatility of authentication systems. Many techniques have been developed since the last few decades in digital security to secure and encrypt data. As much as these techniques are versatile and robust, authentication systems remain the weakest link in any given cyber-physical system. Human intervention does not necessarily make a system as robust as it ought to be, more so often, the chances are it might result in adverse and also opposite of the desired effect. In order for a system to be robust and secure, all forms of precaution must be in place for it to function securely. Authentication systems, since its inception are designed largely in order to minimize human errors and to securely authenticate and verify an individual’s identity in question. Traditional authentication systems are either not efficient and or can be easily bypassed. This could be largely rectified or reduced using modern authentication systems like bio-metrics, iris detection systems etc. However, these systems are not easy to setup and configure, being highly sophisticated and require trained personnel to maintain. Through this paper, we propose a novel authentication system which is relatively inexpensive, easily made mobile and secure to setup. The system targets domestic networks and aims to secure personal and small scale systems. This novel authentication scheme proposed operates based on image feature comparison and digital signatures. The image comparison is used for the authentication process itself while a digital signature is used to provide non-repudiation. Through this novel approach, we aim to demonstrate and highlight the efficiency of the proposed system to the other existing authentication schemes used today. This approach not only provides an easy way to authenticate, it provides a way to determine if the person has indeed the authorization to access the system.
Design of medical database for medical decision support system in laboratory diagnosis of acute leukaemia

This article discusses aspects of the design and physical implementation of the medical database for the subsequent development of medical information system (hereinafter, MIS), capable of intellectually supporting the doctor in the laboratory diagnosis of acute leukemia. The database will allow you to store huge amounts of information on patient histories, laboratory studies and structure the data at the request of the doctor. Also, the main stages of database design are described in detail, taking into account the specifics of the selected subject area. The question of choosing the optimal database management system (hereinafter, DBMS) is highlighted.
A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services

Nowadays there are many social networking services supporting three-party communication such as Skype, Line, and Facebook Messenger. To ensure the message security, a cryptographic encryption scheme is a commonly adopted measure. However, the traditional asymmetric encryption only allows one designated recipient to decrypt the ciphertext with his/her private key. It is thus difficult for two parties to share the same ciphertext without exposing their private keys. In this paper, the author comes up with a novel dual authenticated encryption (DAE) scheme designed for three-party communication environments. Specifically, a DAE scheme enables a party to generate a single ciphertext that could be solely decrypted by the other two participants without sharing their private keys. It is also formally shown that the proposed scheme achieves the crucial security properties using the random oracle proof model.
A Study on Analyzing Risk Scenarios About Vulnerabilities of Security Monitoring System: Focused on Information Leakage by Insider

Information leakage by insider results in financial losses and ethical issues, thus affects business sustainability as well as corporate reputation. In Korea, information leakage by insiders occupies about 80% of the security incidents. Most companies are establishing preventive and prohibited security policies. Nevertheless, security incidents are unceasing. Such restrictive security policies inhibit work efficiency or make employees recognize security negatively. Due to these problems, the rapid detection capability of leakage signs is required. To detect the signs of information leakage, security monitoring is an essential activity. This study is an exploratory case study that analyzed the current state of security monitoring operated by three companies in Korea and provides some risk scenarios about information leakage. For the case analysis, this study collected each company’s security policy, systems linked with security monitoring system, and system log used. As a result, this study identified vulnerabilities that were difficult to be detected with the current security monitoring system, and drew 4 risk scenarios that were likely to occur in the future. The results of this study will be useful for the companies that are planning to establish effective security monitoring system.
Word Characters and Phone Pronunciation Embedding for ASR Confidence Classifier

Confidence classifier is an integral component of an automatic speech recognition (ASR) system. These classifiers predict the accuracy of an ASR hypothesis by associating a confidence score in [0,1] range, where larger score implies higher probability of the hypothesis being correct. Confidence scores have significant applications in ASR system design, training data selection, model adaptation, and other ASR applications. In this work we focus on word embedding features to improve confidence classifier, and introduce character and phone embeddings as confidence features. We motivate these features in the context of representing and factorizing acoustic scores along the proposed features. We evaluate our work on large scale ASR tasks, and demonstrate significant improvement in the confidence performance with the proposed features. At our typical operating point, we report 8% relative reduction in false alarm (FA) for limited vocabulary enUS Xbox task, and 9.9% relative reduction in FA for large vocabulary enUS server task. We also conducted server experiments for our proposed features in combination with natural language Glove embeddings, and improved the overall relative reduction in FA to 16%.
Metadata Challenges for Long Tail Research Data Infrastructures

Abstract Research Data has emerged as a 1 st class research citizen, providing prospects for new services, projects, and ultimately users. In the context of long tail research data, we aim to make this data available to researchers across disciplines via a set of metadata-based services. The metadata component – GeRDI schema – represents one of the key challenges faced in this undertaking.
The Design of Power Quality Detecting System Based on ADSP-BF606

The low cost and the multi-function have been the main contradiction of power quality detecting devices. For it, this paper designs a power quality detecting system based on the latest ADI’s ADSP-BF606 dual-core processor. With the high-speed AD7606 chip, the system achieves real-time data acquisition, processing, and analysis. The hardware design makes the system highly integrated, lower cost and easy to be integrated into the enterprise information management system. The fast flourier transformation with window and interpolation is adapted to detect harmonic, and S-Transform is used to detect and locate interferences automatically and quickly, wavelet package transform and binary tree support vector machine are also used to make interference classifications. Experiment results show that this design meets the requirements and different algorithms make the system more automatic and intelligent. The system with low cost and the multi-function is better than others.
Position Independent Mobile User Authentication Using Keystroke Dynamics

In this chapter, a novel technique to authenticate a mobile phone user irrespective of his/her typing position is presented. The user is never always in sitting position while using mobile phone. Thus, it becomes very important to check the accuracy of keystroke dynamics technique while taking input in all positions but authenticating the user irrespective of these positions. Three user positions were considered for input – sitting, walking, and relaxed. The input was taken in uncontrolled environment to get realistic results. Hold time, latency, and motion features using accelerometer data were extracted, and the analysis was done using random forest and KNN classifiers. The accelerometer data provides additional features like mean of all X, Y, and Z axis values. The inclusion of these features improved the results drastically and played a very significant role in determining the user typing behavior. An EER of 4.3% was achieved with a best FAR of 0.9% and an FRR of 15.2%.
The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems

In this work we investigate the practicability of stochastic gradient descent and recently introduced variants with variance-reduction techniques in imaging inverse problems, such as space-varying image deblurring. Such algorithms have been shown in machine learning literature to have optimal complexities in theory, and provide great improvement empirically over the full gradient methods. Surprisingly, in some tasks such as image deblurring, many of such methods fail to converge faster than the accelerated full gradient method (FISTA), even in terms of epoch counts. We investigate this phenomenon and propose a theory-inspired mechanism to characterize whether a given inverse problem should be preferred to be solved by stochastic optimization technique with a known sampling pattern. Furthermore, to overcome another key bottleneck of stochastic optimization which is the heavy computation of proximal operators while maintaining fast convergence, we propose an accelerated primal-dual SGD algorithm and demonstrate the effectiveness of our approach in image deblurring experiments.