Reviewerly/exHarmony huggingface dataset:
Secure Electronic Voting Machine using Multi-Modal Biometric Authentication System, Data Encryption, and Firewall

Electronic voting machines have replaced paper ballot systems, which were being used in early Indian elections.But, with the advancement of technology, a series of security issues have been raised regarding the present voting system, such as EVM tamp ering in order to register fraudulent votes.The proposed system attempts to solve the problem of bogus voting by introdu cing a multi-modal biometric authentication system.It makes the voting system more secure by using data encryption and firewalls to protect the voter database.It increases accessibility by allowing voters to cast their vote in the elections of their respective constituency from any polling booth across the country.It also increases transparency in the election process by notifying voters on successful ca sting of their vote.This paper proposes a Biometric Voting Machine with a robust system architecture that is able to withstand malicious attacks and fraudulent behaviours.
BackNet: An Enhanced Backbone Network for Accurate Detection of Objects with Large Scale Variations

Deep Convolutional Neural Networks (CNNs) have induced significant progress in the field of computer vision including object detection and classification. Two-stage detectors like Faster RCNN and its variants are found to be more accurate compared to its one-stage counter-parts. Faster RCNN combines an ImageNet pretrained backbone network (e.g VGG16) and a Region Proposal Network (RPN) for object detection. Although Faster RCNN performs well on medium and large scale objects, detecting smaller ones with high accuracy while maintaining stable performance on larger objects still remains a challenging task. In this work, we focus on designing a robust backbone network for Faster RCNN that is capable of detecting objects with large variations in scale. Considering the difficulties posed by small objects, our aim is to design a backbone network that allows signals extracted from small objects to be propagated right through to the deepest layers of the network. This being our motivation, we propose a robust network: BackNet, which can be integrated as a backbone into any two-stage detector. We evaluate the performance of BackNet-Faster RCNN on MS COCO dataset and show that the proposed method outperforms five contemporary methods.
Software Knowledge Representation to Understand Software Systems

A software development process involves numerous persons, including customers, domain experts, software engineers, managers, evaluators and certifiers. Together, they produce some software that satisfies its requirements and its quality criteria at a certain point in time. This software contains faults and flaws of different levels of severity and at different phases of its production (specification, design, etc.) so maintenance is needed in order to correct it. Perfective and adaptive maintenance is also needed to cope with changes in the environment or with new requirements, e.g. new functionalities. In this work, we introduce the Persistent Knowledge Monitor (PKM), which is being developed within the DECODER H2020 project for handling (i.e. storing, retrieving, merging and checking for consistency) all kinds of knowledge and information related to a software project. The PKM will be part of a platform capable of taking advantage of all the artefacts available in a software ecosystem, not only the source code, but also its version control system, abstract specifications, informal documents or reports, etc. for representing the software knowledge and improving the workflow of software developers.
Using machine learning to predict total organic content – case study: Canning Basin, Western Australia

SummaryTotal Organic Content (TOC), one of the most important parameters for sweet spot mapping of unconventional plays is estimated using a RockEval Pyrolysis technique. This process being expensive and time-consuming, the sampling rate per well is very low. In this paper, we use Machine Learning to develop a model that can use conventional wireline logs to estimate TOC data. This model is based on wells in the Canning Basin, Western Australia.
Competitions of Multi-agent Systems for Teaching Artificial Intelligence

This paper presents an approach based on competitions of multi-agent systems as the basis for teaching advanced topics in Artificial Intelligence. The method was applied in the Cognitive Robotics course with students of the 5th-year in Computer Science from the University of Mumbai in India, in the domain of Soccer. The championships are played between different teams to allow students to assess and compare the results. The motivation that is reached is fundamental for creating interest in the study of Artificial Intelligence techniques and in research. The developed experiences are described, as well as an analysis of the method and its impact for the academy and the research.
Histograms of Oriented Gradients for cats-dogs detection

Abstract We study the question based on grids of Histograms of Oriented Gradient (HOG) and support vector machine(SVM),adopting different kernel functions to distinguish cat from dog with robust visual object recognition [1]. We show experimentally that the quadratic descriptors significantly outperform others kernel functions and with the increasing number of the samples, the performance of the quadratic is getting better and better. Beside, in order to reduce the complexity, we also apply principal Component Analysis, which is known as pca, and we also study the influence of the number of features on performance, concluding that the line is quadratic.
Pembudayaan Literasi Kritis

This research has a long-term goal, namely how to apply the culture of critical literacy in the face of globalization so that students are able to compete at the national level and even at the global level. This research method is an explanatory research process. Information about Literacy Management in the face of globalization. Can conclude that students only read interest in 1-2 times each week. This requires special attention from teachers and parents in encouraging children / students to be more interested in reading at least reading textbooks taught in school. When it is considered normal, then the child will never be able to point to other good things, and the knowledge he has will be very narrow, whereas when children want to do literacy critically, then their thinking will be broad, sociable, sociable and be responsive to what happens.
Computational Challenges and Opportunities in Financial Services

As one of the fastest growing areas of applied scientific computing, financial services uses high performance computing techniques to respond to both governmental regulatory bodies as well as to deal with a fast-paced business environment. Financial services industry is data driven and aims to resolve mathematical challenges to make sense out of data to solve complex problems in pricing, risk management, and portfolio optimization. These challenges are solved by financial institutions regularly, and the goal here is to provide a short survey of approaches and techniques used to solve these problems. Cloud is one of the areas of interest, since said challenges can benefit from the dynamicity and metered pricing of Cloud computing, plus being virtually limitless in scale. FPGA- and GPU-as-a-Service will also be explored as they are showing a great deal of benefit in solving such problems.
Mineralization signatures of the magnetite-dominant Acropolis prospect, Olympic Dam IOCG district, South Australia

SummaryThe Acropolis prospect is a vein-style magnetite (±apatite ±hematite) system located ~20 km southwest from the giant Olympic Dam iron-oxide copper gold (IOCG) deposit, South Australia. A whole rock dataset comprising 4,864 core samples from fourteen drillholes was analysed using multivariate statistics to understand and identify geochemical signatures of mineralization at Acropolis. Statistical analyses included principal component analysis, hierarchical and k-means clustering. The results of statistical analyses are overlaid and interpreted relative to a 3D implicit geological model of the prospect, and encompass a projection of mineralization signature as PC1.The mineralization footprint of Acropolis is multi-element and typified by a distinct ‘magnetite’ signature of Fe-V-Ni-Co. Such a signature is developed in the western part of the prospect and represents samples containing >60 wt% Fe. In contrast, the ‘hematite’ signature displays an association of REE, W, Sn, Sb, U, Th, Ca, and P and is present throughout the prospect. Furthermore, kriged values of Cu (> 200 ppm) demonstrate an offset from Ferich veins, thus supporting a genetic model in which (earlier) Cu-Au-deficient veins in which magnetite is the dominant Fe-oxide are subsequently overprinted by Cu-bearing hematite-dominant mineralization.Results obtained provide insights into the evolution from magnetite- to hematite-dominant IOCG systems and may offer a proxy for exploration and discovery of economically significant IOCG deposits at shallower levels in the eastern Gawler Craton.
Collision Attack on 4 Secure PGV Hash Function Schemes based on 4-Round PRESENT-80 with Iterative Differential Approach

Preneel-Govaerts-Vandewalle (PGV) hash function schemes is a single-block-length hash function based on block cipher introduced by Preneel et al. in 1993. They proposed 64 basic ways to construct a (collision-resistant) hash functions from a block cipher. They regarded 12 of these 64 schemes are secure, though no proofs or formal claims were given. We take 4 schemes from those 12 schemes to analyzed. In this paper, we show different approach to find collisions in four secure PGV hash function schemes. The use of block cipher that has iterative differential such as PRESENT can let us to find collisions. In 2007, Wang found four iterative characteristics for 4-round PRESENT-80. Based on the analysis, we indicate that those four secure PGV schemes based on 4-round PRESENT-80 are not fulfilled collision resistance property.
Deep Reinforcement Learning for Autonomous Model-Free Navigation with Partial Observability

Navigation is known to be a hard Sequential Decision-Making problem that attracts the attention of a large number of fields like Artificial Intelligence or Robotics. In this work, we approach the problem of partially observable navigation with a reactive system trained by model-free Reinforcement Learning. The advantages of this learned approach include reducing the engineering effort at the cost of more computing power during training. We designed an agent and an environment with a focus on being able to navigate independently of the map. We use well-tested general Reinforcement Learning algorithms without any hyper-parameter tuning and achieve promising results. Our results show that several general purpose Reinforcement Learning algorithms can reach the target in our navigation setup more than 85% of the episodes. Hence, these algorithms may provide a significant step forward towards autonomous navigation systems.
Prosodic Boundaries Prediction in Russian Using Morphological and Syntactic Features

The paper presents a comparison between three approaches towards prosodic boundary prediction in Russian text, namely a rule-governed method and methods involving statistical classifier and deep learning technique. The methods aim to predict all possible prosodic boundaries in text applying morphological and syntactic information. All used features were described in terms of Universal Dependencies framework by means of SyntaxNet parser. The rule-governed method runs in a bottom-up fashion, using the information about syntax group edges and applying data-driven and hand-written linguistic rules. For machine learning methods, conditional random fields classifier and bidirectional LSTM model were built, with such features as part-of-speech tag, syntactic dependency type, syntactic relation embedding and presence of syntactic link between the current and adjacent words. As experimental material, we used the data of CORPRES corpus, containing over 30 hours of professionally read speech. Used separately, morphological features are slightly superior to syntactic ones, and their combination improves the results. BiLSTM yields the highest F1 measure value of 90.4, as compared to 88.8 for CRF and 83.1 for rule-based method.
A Time-Context-Dependent Resource Diffusion Algorithm Based on User Splitting

Abstract Personalized recommendation for smartphone applications is studied, and a time-context-dependent resource diffusion algorithm based on user splitting is proposed. The algorithm introduces time context information. According to this idea, the algorithm can make full use of user preference data information, cluster mobile applications to get more user preference categories and optimize recommendation results. In the personalized recommendation process of mobile applications, because users and projects are dynamic changes, this paper analyses the dynamic change process of the algorithm in detail, and enhances the applicability of the algorithm. Experimental results show that the proposed algorithm improves the accuracy of recommendation results compared with the previous resource diffusion algorithm.
Self-organizing Maps and k-Means Clustering in Non Life Insurance

Feed-forward neural networks are algorithms with supervised learning. It means that we have to a priori identify the most relevant variables and to know the desired outputs for combinations of these variables. For example, forecasting the frequency of car accidents with a perceptron requires an a priori segmentation of some explanatory variables like the driver’s age into categories, in a similar manner to Generalized Linear Models. The misspecification of these categories can induce a large bias in the forecast. On the other hand, the presence of collinearity between covariates affects the accuracy of the prediction. In this situation, the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data. Self-organizing maps offer an elegant solution to segment explanatory variables and to detect dependence among covariates.
Total quality management practice of urban water environment rehabilitation project

Abstract The urban water environment rehabilitation project is a comprehensive strategic social project with large investment, wide scope and complicated construction conditions, which determines the uncertainty and particularity of the quality management of urban water environment rehabilitation projects. In order to improve the quality management level of urban water environment rehabilitation projects and promote urban water environment rehabilitation, this paper analyzes the characteristics of quality management of urban water environment rehabilitation projects from the viewpoint of the total quality management. Taking the Maozhou River urban water environment rehabilitation project in Shenzhen, Guangdong, China as an example, using the total quality management theory and causal analysis, the authors summed up the new quality management model, dual-collaborative quality management, for the water environment rehabilitation project, and some new technologies such as resident supervision system, closed circuit television inspection and data analysis technology were applied.
Filtered prioritized and cluster-based optimization towards correction of antipatterns

When systems evolve, the maintainability and the complexity of the system increase, resulting in the poor antidesign patterns or antipatterns in software maintenance. Refactoring is an operation used to improve the structure of an existing system by changing its internal structure without affecting its external behavior. Many studies revealed that refactoring is underused by software development team in industry due to its increased effort and lack of knowledge about existing approaches. Thus, a prioritized correction of antipatterns based on relevance of classes, analyzed development history, risk to refactor, minimized code changes, and maintainers context and preferences is needed for good refactoring recommendations. We propose an approach based on filtered relevant classes with generic linkage-based clustering and adapted nondominated sorting genetic algorithm III to find good refactoring recommendations, which maximize the software quality and reduce the refactoring effort. Our approach uses our own node centrality-based sub-graph isomorphism algorithm to dynamically understand the system with good accuracy prior to optimization. Our approach is evaluated on six open-source systems and the results have shown the effectiveness of our approach compared to the other existing ones.
Temporal-Variation-Aware Profit-Maximized and Delay-Bounded Task Scheduling in Green Data Center

An increasing number of enterprises deploy their business applications in green data centers (GDCs) to address irregular and drastic natures in task arrival of global users. GDCs aim to schedule tasks in the most cost-effective way, and achieve the profit maximization by increasing green energy usage and reducing brown one. However, prices of power grid, revenue, solar and wind energy vary dynamically within tasks’ delay constraints, and this brings a high challenge to maximize the profit of GDCs such that their delay constraints are strictly met. Different from existing studies, a Temporal-variation-aware Profit-maximized Task Scheduling (TPTS) algorithm is proposed to consider dynamic differences, and intelligently schedule all tasks to GDCs within their delay constraints. In each interval, TPTS solves a constrained profit maximization problem by a novel Simulated-annealing-based Chaotic Particle swarm optimization (SCP). Compared to several state-of-the-art scheduling algorithms, TPTS significantly increases throughput and profit while strictly meeting tasks’ delay constraints.
On k-error linear complexity over F <sub>N</sub> of interleaved binary sequences derived from four N-periodic ones

Abstract We estimate the k -error linear complexity of some interleaved sequences with period 4 N over the finite field of order N ( N an odd prime). Furthermore, we obtain the exact value of the k -error linear complexity for small value of k of the interleaved sequences obtained from the cyclotomic sequences. In particular, we study the interleaved sequences obtained from Legendre sequences and Hall’s sextic residue sequences, respectively. Our results show that these sequences are quite stable.
Reconfiguring Human-centred Design of Technology as a Technohuman Intervention

Human-centred design has been conceptualised as the simultaneous splitting and synthesising of the human and the technology, for the purpose of reconfiguring orders and positions of people and things within organisational innovation practices. Describing this function of design as an intervention, the paper makes visible the designer subject position being enacted as the mediating of different knowledges within the technohuman designer body. Using these insights into design practices, a reconceptionalisation is proposed, away from the current individual designerly agency of the technohuman designer, towards an intervention through collaborative technohuman reconfiguration by design.
Auto Data Augmentation for Testing Set

Testing phase augmentation is a fast way to further improve the performance of image classification when CNN (Convolutional Neural Network) is already trained for hours. Limited attempts have been made to find the best augmentation strategy for testing set. We propose a reinforcement learning based augmentation strategy searching method for testing phase augmentation. With the augmentation strategy, we augment each testing image and integrate features of its augmented images into one feature. The reinforcement learning method searches the best parameters in the augmentation strategy which is formed as a matrix in this paper. Using the proposed method, we achieve competitive accuracies on image classification and face verification.
Cloud technologies as a way to form and develop evolutionary concepts in future teachers of biology

Aim. The article is devoted to the substantiation of theoretical and methodical principles of professional training of teachers of biology in the conditions of the informational and educational environment; multilevel and differentiated approaches in training with the possibility of continuous receipt of online and off-line assistance. Methods. Analysis of psychological and pedagogical, methodical literature; synthesis, a generalization; psycho-pedagogical observation, questionnaire, pedagogical experiment. Results. The authors proposed a methodology for involving information technologies, in particular cloud services, the inclusion of their varieties in all activities of the teacher in order to facilitate interaction between the subjects of the educational process, improve the content, forms and ways of interaction of the teacher and students in the process of formation and development of evolutionary concepts. Conclusions. Teaching and methodological support, developed in conditions of cloud-oriented environment, will be open and dynamic, elementarily variable and perceptible for the formation and development of scientific concepts, in particular, evolutionary ones.&#x0D; Keywords: "cloud technologies", formation, development, evolutionary concepts, means of training.
Future Linear Matrix Equation of Generalized Sylvester Type Solved by Zeroing Neural Dynamics and 5-Instant ZeaD Formula

In this paper, a new discrete-time zeroing dynamics (or say, Zhang dynamics, ZD) model is proposed, analyzed and investigated for solving generalized-Sylvester-type future linear matrix equation (GS-type FLME). First of all, based on ZD design formula, a continuous-time ZD (CTZD) model is proposed for solving generalized-Sylvester-type continuous-time linear matrix equation (i.e., GS-type CTLME). Secondly, a novel one-step-ahead discretization formula (termed Zhang et al. discretization formula, ZeaD formula) is presented for the first-order derivative approximation with higher computational precision. Then, by exploiting the presented ZeaD formula to discretize the CTZD model, a novel discrete-time ZD (DTZD) model, i.e., ZeaD-type DTZD-E model, is proposed, analyzed and investigated for solving GS-type FLME. Theoretical analyses on the convergence and precision of the proposed DTZD models are presented. Comparative numerical experimental results further substantiate the efficacy and superiority of proposed ZeaD-type DTZD model for solving the GS-type FLME.
Deep Learning Training Management Platform Based on Distributed Technologies in Resource-Constrained Scenarios

Deep learning has attracted a lot of research attention in the past few years for its efficiency and accuracy. However, there exist two problems of their study. Firstly, the computing power of one single machine is limited and not suitable for handling with training deep learning models with massive cells. Secondly, it costs much to train models on different deep learning frameworks. Motivated by these problems, this paper proposed a deep learning training management platform based on distributed technologies, which integrates different kinds of deep learning frameworks through virtualization technologies and coordinates machines through distributed technologies. Specially, specific algorithms are proposed to solve the multi-task scheduling problem, the computing resources allocation problem and the fault tolerance problem in resource limited scenarios. It turns out that the platform can be widely used in small and medium-sized research teams.