Reviewerly/exHarmony huggingface dataset:
Using Text Mining to Categorize the Purpose of Public Spending for the Benefit of Transparency and Accountability

Advertising is a fundamental and structuring principle of Public Administration, standing out as an instrument of supervision and social control. In Brazil, public managers are required by law to be accountable and transparent to public spending, and the "Tribunal de Contas" (public controllership and finance) are responsible for overseeing and ensuring that the information provided is clear and complete. This paper presents a comparative study between the Support Vector Machine, Naïve Bayes, and Logistic Regression models to classify public spending according to the purpose of the expenditure, allowing to identify those that were omitted by managers. The constructed model detected 124 unpublished records totaling $ 3,1 million.
Towards a Multi-engine Query Optimizer for Complex SQL Queries on Big Data

In an era where big data analytics has become a first-class requirement for both the industrial and the academic community, multiple engines are built to execute distributed domain-specific analytics. SQL-based big data analytics is a very popular but also challenging domain due to its complexity that requires multiple runtime query optimizations. Popular frameworks, such as Presto and SparkSQL, commonly retrieve data from multiple sources and process them locally using domain-specific optimizers. However, recent work indicates that no single engine offers the optimal all-in-one solution for all types of SQL queries. Taking this into account, we envision building an optimizer to facilitate faster distributed SQL analytics over multiple engines, which will perform operator-level optimization using Machine Learning techniques and will exploit the sophisticated data-driven local engine optimizations.
Fuzzy Resembler: An Approach for Evaluation of Fuzzy Sets

The efficiency of a fuzzy logic-based system is catalyzed by the system design. Fuzzy sets generalize classical crisp sets by incorporating concepts of membership for a fuzzy variable. Each fuzzy set is associated with linguistic concepts that are germane to a particular application. This paper presents an approach for evaluating the region of certainty and uncertainty represented in design of fuzzy linguistic variables. Fuzzy Resembler (FuzR) attempts to capture the goodness of a fuzzy system design using a geometric approach; it can be used for evaluating the design of fuzzy membership space. FuzR is the ratio between region of certainty to region of uncertainty. From the results, it can be inferred that FuzR presents meaningful observations of a fuzzy variable, characterized by trapezoidal, triangular, and gaussian membership functions. FuzR can be used as a design evaluation parameter for evolving fuzzy systems. Knowledge engineers can use it to optimize design of fuzzy systems in the absence of domain experts. Moreover, the level of abstraction provided by FuzR makes it an intuitive design parameter. The significance of this work lies more in its point-of-view than voracious results; the theory and formulation are still young and much more is yet to be conceptualized and tested.
Workload Diffusion Modeling for Distributed Applications in Fog/Edge Computing Environments

This paper addresses the problem of workload generation for distributed applications in fog/edge computing. Unlike most existing work that tends to generate workload data for individual network nodes using historical data from the targeted node, this work aims to extrapolate supplementary workloads for entire application / infrastructure graphs through diffusion of measurements from limited subsets of nodes. A framework for workload generation is proposed, which defines five diffusion algorithms that use different techniques for data extrapolation and generation. Each algorithm takes into account different constraints and assumptions when executing its diffusion task, and individual algorithms are applicable for modeling different types of applications and infrastructure networks. Experiments are performed to demonstrate the approach and evaluate the performance of the algorithms under realistic workload settings, and results are validated using statistical techniques.
Automatic Target Recognition for CT-based Airport Screening System

Abstract To construct an automatic target recognition CT-based screening system for airport, this paper introduces a method by combining two hot technology, CT Image Processing and Machine Learning together. With the grey-scale features and the Histogram of Oriented Gradient (HOG) features extracted from the CT images, we can train different classifier (SVM, KNN) to recognize the targets (saline, rubber, clay) we want. By comparing the recall rate and precision rate of each classifier, we may find the best classifier for this project.
Multimodal Representation Learning for Human Robot Interaction

We present a neural network based system capable of learning a multimodal representation of images and words. This representation allows for bidirectional grounding of the meaning of words and the visual attributes that they represent, such as colour, size and object name. We also present a new dataset captured specifically for this task.
Minimizing Last-Minute Inter-Datacenter Backup with Risk-Awareness

Network failure caused by disasters results in communication disruption and huge data loss in backbone Datacenter (DC) networks. To minimize such losses, in this paper, we consider an early- warning triggered risk-aware mutual backup model, which significantly reduces the last-minute backup duration. We specifically consider the joint optimization of probabilistic backup path and site selection and the amount of data to be backed up. We propose a mixed integer linear program (ILP) for backup time minimization using dual DCs at backup sites. Further, we perform extensive numerical simulations to show that, in case of disasters, dual DC backup with Risk-aware probabilistic path selection gives significantly shorter backup windows as compared to existing algorithms.
Multi-stage Real-time Human Head Pose Estimation

The paper explores a human head pose estimation method combining face detection and face dichotomy optimization algorithm. The estimation of head pose is mainly to obtain the angle information of face orientation. The human head pose estimation algorithm in this paper mainly estimates the 3D Euler Angle of the input face patch. That is to say, a regressor is trained in a data-driven way, which can directly predict the input face blocks. In this paper, we use three models to make the final prediction of human head posture. First, the first model is used to detect faces. So as to prevent the loss of face detection in realtime face detection, we set a small threshold on the face detection model. Second, on the basis of face detection, a face dichotomy model is added to optimize the detected face to determine whether it is a face. Third, if the result of the second step is a face, the head pose estimation of the detected face is carried out. This paper mainly aims at the change of head pose angle to judge people's attention, and puts forward multi-stage head pose estimation to meet the demand of real-time engineering calculation. In this paper, the first novelty is that the average speed on Windows with CPU(i5-7500 3.40GHz) is 35ms/frame, and the second novelty is that we have collected 64101 face images under the surveillance camera, and the third novelty is that we have mixed depthwise separable convolution for head pose recognition. The method is tested on the UMDFaces Dataset, and the consequences show that compared with extra approaches, the proposed method can obtain improvements in efficiency.
Pyramid Entropy Source for True Random Number Generator on FPGA

True random number generator (TRNG) is a crucial part of any modern cryptographic primitives. Designing high entropy TRNG is still a challenging task to compete. Usually, TRNGs use physical mean as random source to generate random numbers which are unpredictable. TRNG has different application but not limited to symmetric key cryptography, random simulation, nonce, user authentication protocols, and gaming. In this work, ring oscillator (RO)-based pyramid entropy source is proposed for generating device-independent true random numbers. The de-synchronization technique has been utilized for extracting the randomness from entropy source wherein linear-feedback shift register (LFSR) is used as post-processor to improve the statistical characteristics of proposed TRNG. The design of TRNG has been implemented on Altera Cyclone II EP2C20F484C6 FPGA which consumed 892 logic elements (5%), 67.75 mW power to generate 131,072 true random bits. It yields maximum entropy of 0.999987 through entropy analysis. Statistical properties of the proposed TRNG have been evaluated using NIST tests. Restart experiment is also conducted to evidence the true randomness of the proposed TRNG.
The WebID Protocol Enhanced with Biometrics and a Federated Enrollment Protocol

We are seamlessly integrating biometrics into the WebID decentralized authentication protocol. This involves constructing a method of user verification that allows for simple, easy, and safe access. WebIDs provide such things when compared to the traditional username and password user authentication. However, with the use of WebIDs, important information can be stolen if an attacker either gains direct access to the user's computer. This possibility can be blocked with the inclusion of biometrics to the authentication process since biometric data (e.g., fingerprints, iris scan) is unique and not easily duplicated. We first created an enrollment protocol that verifies whether a user has a WebID while attempting to access a server. If they do, we permit the use to have access to the server, and if they do not, we register the user by accessing their own server. Implementing these features in the WebID protocol would significantly improve the security of user authentication.
Quantum Secure Routing for Future Internet

In this paper, we numerically evaluated an optically switched multi-user Quantum Key Distribution (QKD) network for the end-to-end secure signal transmission with the metropolitan area network (MAN) applications. The multi-user network performance can be enhanced via MEMS (Micro-Electro-Mechanical Systems) technology to get the maximum secure key rates due to the less insertion loss and crosstalk for switching operation. We have analyzed a four-state, quadrature-phase shift keying (QPSK) transmitter to generate quantum keys, i.e. Alice, while a classical coherent receiver aided by the digital post-processing unit is being used as Bob. To route the secure QKD traffic to the end-users, 1×2, 1×4, 1×8 and 1×12 switching configurations have been analyzed for the transmission distance ranging from 0 to 60 km. We have achieved the secure key rates of 30 Mbit/s and 100 Mbit/s for the 1×12 and 1×2 switching configurations, respectively. We have also compared the performance of routed QKD traffic with a simple point-to-point QKD system and the classical DV-QKD protocol. The QKD networks need an effective low-loss fibre channel and a distribution system to enhance the transmission distance of the secure keys and applications. This paper provides multi-user used case as an important paradigm with the corresponding scheme and simulation performances.
Variant of Northern Bald Ibis Algorithm for Unmasking Outliers

Clustering, one of the most attractive data analysis concepts in data mining, are frequently used by many researchers for analysing data of variety of real-world applications. It is stated in the literature that traditional clustering methods are trapped in local optima and fail to obtain optimal clusters. This research work gives the design and development of an advanced optimum clustering method for unmasking abnormal entries in the clinical dataset. The basis is the NOA, a recently proposed algorithm, driven by mimicking the migration pattern of Northern Bald Ibis (Threskiornithidae) birds. First, we developed the variant of the standard NOA by replacing C1 and C2 parameters of NOA with chaotic maps turning it into the VNOA. Later, we utilized the VNOA in the design of a new and advanced clustering method. VNOA is first benchmarked on a 7 unimodal (F1–F7) and 6 multimodal (F8–F13) mathematical functions. We tested the numerical complexity of proposed VNOA-based clustering methods on a clinical dataset. We then compared the obtained graphical and statistical results with well-known algorithms. The superiority of the presented clustering method is evidenced from the simulations and comparisons.
Analysis of Chinese Remainder Theorem for Data Compression

Chinese remainder theorem (CRT) forms the foundation of recent simultaneous encryption and compression schemes for image data. In the schemes moduli sequence is being used as the secret key while, the number of moduli is being considered as the achievable compression ratio. However, some studies have discredited these schemes and reported the compression performance of CRT as poor or even negative. In this study, we have investigated parameters that effect the compression performance of CRT. We have performed our experiments on KODAK dataset. Our analysis links the degraded compression performance of CRT with the number of moduli i.e. the block size to be compressed. Further, our results have shown an improved performance for smaller number of moduli. We have achieved an average compression ratio of about 8% on KODAK dataset.
Big Data Feature Selection to Achieve Anonymization

In the age of big data, data is increasing in a tremendous way in many fields and the data shared by the users is in a great risk. To preserve privacy of an individual anonymization-based algorithm like k-anonymity-related algorithm and differential privacy is proposed to make sure that the resulting dataset is free from privacy disclosure. However, majority of these anonymization algorithms are applied in isolated environment, without considering the utility in knowledge task making the dataset less informative. Also the presence of redundant data also decreases the performance and reduces accuracy of anonymization. Hence a preprocessing-based anonymization is required to increase the utility and to achieve accuracy in anonymization. This paper aims to apply the feature selection fast correlation-based filter (FCBF) solution to select the relevant features and remove the redundant data. Then k-anonymity is applied to dataset to achieve data anonymization. Comparisons on real-world dataset were made with anonymized dataset with preprocessing and without preprocessing and result was produced.
Efficiency Estimation of the Inter-Frame Coding of Modern Video Codecs

In the article an experimental efficiency estimation of inter-frame compression for video streams in modern video codecs, operating in such standard, like MPEG-4- XviD, H.264 and, H.265 is provided. Also the experimental results of image compression based on the motion compensation of video objects in the processing of videos with local, global and combined movement are presented.
The Use Of Technology In Corpus Linguistics

Corpus linguistics can be termed as the study of language founded on evidence derived from corpora. While “corpora” is a general word defining the use of corpus software in the extraction of patterns derived from corpus. It revolves around unfolding occurrences of a phrase or word within the corpus as well as accounting on its occurrence frequency in comparison to others. This paper explores the application of technological advances within corpus linguistics as a new avenue within research and pedagogy. There have been a couple of studies that was captured and utilized methodologies and insights within the subject which at the end have revived theoretical intelligence of corpus within the writing field. However, many gaps have been left unserved mostly on its application on the teaching area as well as its feasibility. To this end, it's of consensus that the modern world demands an innovative industry in all areas of concern and thus makes this rationale more researchable. This is a qualitative research design that involves a vast array of techniques that captures: open-ended interviews, observations and a grounded survey meant to expound on this topic in order to add more literature on linguistics.
Imbalanced Dataset Analysis with Neural Network Model

The comprehensive real-time dataset is skewed. The dataset is often imbalanced and hard to classify with the existing balanced dataset. The dataset may be skewed in the range of 10:1 ratio. Due to the data imbalance and errors, the data classification accuracy rate can be reached only to 90%. The classification accuracy rate can be increased if the number of errors in dataset is minimized. Hence, in this paper, we propose to identify error in SCRUM dataset by linear neural network model. For analysis, two datasets with errors and without errors are taken and analyzed with neural network model. The model train to determine error in dataset. With the proposed method, the model determines error in dataset with 98% accuracy.
Characteristic Analysis of Side Channel Attacks and Various Power Analysis Attack Techniques

Embedded systems have a plethora of security solutions and encryption protocols that can protect them against a multitude of attacks. Hardware engineers infuse lot of time and effort in implementing cryptographic algorithms, keeping the analysis of design constraints into rumination. When it comes to designs in potential hostile environment, engineers face a challenge for building resistance-free embedded systems against attacks called side channel attacks. Therefore, there is a strong need to address issues related to side channel attacks. This chapter will provide an insight into the field of hardware security, and will provide a deep investigation of various types of side channel attacks and better understanding of various power analysis tools, which will further give researchers a vision to build efficient and secure systems in order to thwart attacks. This chapter mainly focuses on passive attacks as compared to active attacks since passive attacks are easy to perform and lot of research is going on these attacks.
View Synthesis from multi-view RGB data using multilayered representation and volumetric estimation

Aiming at free-view exploration of complicated scenes, this paper presents a method for interpolating views among multi RGB cameras. In this study, we combine the idea of cost volume, which represent 3D information, and 2D semantic segmentation of the scene, to accomplish view synthesis of complicated scenes. We use the idea of cost volume to estimate the depth and confidence map of the scene, and use a multi-layer representation and resolution of the data to optimize the view synthesis of the main object. /Conclusions By applying different treatment methods on different layers of the volume, we can handle complicated scenes containing multiple persons and plentiful occlusions. We also propose the view-interpolation→multi-view reconstruction→view interpolation pipeline to iteratively optimize the result. We test our method on varying data of multi-view scenes and generate decent results.
Changes in Motivation Factors in Language Education of Senior Learners

Nowadays, education of seniors has become an important topic as the number of seniors in the population is continually growing all over Europe. In Slovakia, as well as in other European countries, Universities of the Third Age offer a variety of educational programs, out of which language courses have become most preferred. Seniors, as a specific group of language learners, have their own needs, wants and lacks which influence their motivation to step again into the role of a learner. The purpose of our study is to focus on those motivation factors that play an essential role in senior’s decision making to sign in the English language course and complete it. Based on the knowledge of motivation, we divide seniors’ motivational factors into two categories: primary factors (cognitive, social and personal) are present at the initial stage of signing in the course. Secondary factors (social relations within the group, teacher, teaching methods and success) have a significant impact on seniors in affecting them to continue or drop out the course. Analyzing four case studies, we attempt to find out how the intensity and combination of motivational factors change during the language course and what impact it has on learners.
Part of Speech Tagging for Setswana African Language

Part of speech (POS) tagging is the technique that assigns appropriate lexical categories to words in a sentence. It is a crucial step in Natural Language Processing (NLP) applications such as Machine Translation, Spell and Grammar checking, Word Predictions, Information Retrieval, etc.. A lot of work has been done on POS tagging mainly for European and Asiatic languages, while in Africa, more work is needed mostly due to the lack of the annotated corpus. Some significant works have been done on African languages, such as Arabic, Igbo, Swahili and Yoruba, South African official languages. However, African languages are generally under-resourced, in particular, in terms of lexical semantics annotated corpora, necessary for effective NLP tools and applications. Hence, advances in this direction have been limited. The main aim of the work reported in this paper is the development of a POS tagger model for an under-resourced Setswana African language. A review of some POS taggers for different African languages is conducted, challenges and techniques used in creating the POS taggers are elicited, and a POS tagger model for Setswana language using SVMTool is presented.
Research on Digital Image Correlation Method used for Strain Detection

Abstract Strain measurement is an important means to study structural strength. Strain gauge measurement method is widely used in different industry area. The strain gauge should be stuck on the surface of the measured object to detect displacement or strain. As the measurement mode is contact measurement, strain gauge can have a certain impact on the measured object itself. So research of strain detection is increasingly moving from contact method to non-contact method. In this paper, we design a digital image correlation non-contact method used for strain detection. The basic principle of image correlation method is introduced. And the image process algorithm is detailed described. A set of laser lighting hardware is especially used for object illumination to get a random distribution light spot instead of sticking target bar on object. The measurement results show that this method can detect strain with good effect and can be applied to non-contact strain detection test.
Model-Based Design of Body Motion Sensing Technology using Systems Modeling Language and Coloured Petri Nets

Millions of people run, while movement scientists study the relationships between running kinematics and fatigue, injury or exercise economy. By using optoelectronic technologies to capture human motion, it has been possible to demonstrate that kinematics of running is highly individual and it is often not possible to characterize it using simple variables. In the present study, the model-based design methodology was applied to define the architecture of a body motion sensing technology for estimating the most significant parameters and indexes of running gait, using the Systems Modeling Language for graphic description of behavior and structure, complemented with Coloured Petri Nets for verification and a physical implementation in order to validate the design by means of automatic code generation. The approach shown in this article models the interactive behavior between components of the system using states and transitions, as well as conditions and events as the main semantics. It also allows rigorous static and dynamic analysis, providing benefits such as improved understanding of key issues in the design, fewer errors, reduced costs and risks, allowing a faster development process.