Reviewerly/exHarmony huggingface dataset:
Effectiveness of Atm and Bank Security: Three Factor Authentications With Systemetic Review

Security is one of the key aspects in despite of time, location and domain. Providing a better security is an essential obligation to all sectors (state and private) and workers. Rendering better security is not only difficult but also fairly impossible due to growth of new technologies and constantly growing Information Technology domain. Rapid Improvement of banking sector provides many acceptable ways of managing the accounts, information and activities. This advancement has been a main driving force to the account activities with the help of Automatic Teller Machine (ATM). With the increase number of ATM usage among the public, hackers also use the technological growth to hack the data / information and apply the fraud actions. The said vulnerabilities have been increasing in a gradual manner and also ATM fraud has spread and become nightmare all around the world. This paper elaborates to mitigate the possible frauds can be happened at the ATM cubical with the hybrid multi-layered security concept. Here, previous studies were reviewed and compared in order to support to develop a system with the less loopholes. A three-layered architecture including PIN, OTP or finger print and pattern lock has been used in this system to mitigate the frauds. 400 students were selected to test the system and 384 students roughly 96% of students have successfully entered their pin number at the first attempt, in the second layer 95% of the students out of 384 students were succeeded and the third layer 99% of the students out of 363 students were success in drawing the patterns. The overall result of this evaluation process shows that, the suggested security tiers can be applicable since the accuracy in performance is better and accepted by the sample selected.
Node Varying Regularization for Graph Signals

While regularization on graphs has been successful for signal reconstruction, strategies for controlling the bias-variance trade-off of such methods have not been completely explored. In this work, we put forth a node varying regularizer for graph signal reconstruction and develop a minmax approach to design the vector of regularization parameters. The proposed design only requires as prior information an upper bound on the underlying signal energy; a reasonable assumption in practice. With such formulation, an iterative method is introduced to obtain a solution meeting global equilibrium. The approach is numerically efficient and has convergence guarantees. Numerical simulations using real data support the proposed design scheme.
Stereo Image Quality Assessment Considering the Asymmetry of Statistical Information in Early Visual Pathway

The design of stereo image quality assessment (SIQA) methods cannot be well based on the biological theory of human vision, so the performance of many SIQA methods cannot achieve good consistency with the subjective perception. The research on the visual system tends to the dorsal and ventral pathways, which ignores the information asymmetry in the early visual pathways. It is worth noting that the ON and OFF receptive fields in retinal ganglion cells (RGCs) respond asymmetrically to the statistical features of images. Inspired by this, we propose a SIQA method based on monocular and binocular visual features, which takes into account the asymmetry of local contrast bright and dark features in early visual pathways. First, this paper extracts the response maps of ON and OFF cell in RGCs to left and right views respectively. And then the different information fusion modes of visual cortex are used to fuse the response maps information of left and right views. Final, monocular and binocular features were extracted and sent to support vector regression (SVR) for quality regression. Experimental results show that the proposed method is superior to several mainstream SIQA metrics on two publicly available databases.
UGNet: Underexposed Images Enhancement Network based on Global Illumination Estimation

This paper proposes a new neural network for enhancing underexposed images. Instead of the decomposition method based on Retinex theory, we introduce smooth dilated convolution to estimate global illumination of the input image, and implement an end-to-end learning network model. Based on this model, we formulate a multi-term loss function that combines content, color, texture and smoothness losses. Our extensive experiments demonstrate that this method is superior to other methods in underexposed image enhancement. It can cover more color details and be applied to various underexposed images robustly.
Saliency Maps for Point Clouds

Algorithms for creating saliency maps are well established for images, even though there is no literature on such methods for point clouds. We use orthographic projections in 2D planes which are subject to well established saliency detection algorithms to create a 3D saliency map. The results of each saliency map are projected to the 3D voxels and the results of the many projections are used to generate a 3D saliency map. Simple compression tests were carried using soft region-of-interest maps. Results have shown an increase in the quality of the voxels inside the selective regions of increased levels of interest.
Aspect Oriented Programming Approach for Variability Feature Implementation in Software Product Line Engineering

Software Product Line Engineering (SPLE) allows developers to build product family software that comes from the same platform. The advantage of this technique is to reduce construction time, effort, costs, and difficulties. So, to build variations of software products, developers do not need to build entirely from scratch and can take advantage of general models that have been prepared previously. The software product line consists of common features and variability features. Common features are found on all product lines produced. While the variability features are determined by the requirements of each user. There are several approaches to implement these variability features, including using patterns, framework, polymorphism or configuration and build tools with compile-time variables. In this study, variability features implementation is carried out using the Aspect-Oriented Programming approach that allows explicit expression and modularization of the variability on a model, code, and generator levels. The proposed approach was implemented in an online store website. Based on the implementation that has been done, an online store website can be built with different features according to user requirements.
A Survey of Dialogue System Evaluation

Dialogue systems provide a very efficient way for humans to interact with computer systems to access various resources and services. The evaluation of a dialogue system can provide valuable feedback for improving the system, but it is a challenging task, so attracts lots of attention from researchers. In this paper, we survey some essential criteria and widely used methods for evaluating dialogue systems, focusing on the latest research progress on this topic. Notably, we discuss machine learning based evaluation method and deep learning based ones. We also compare their advantages and disadvantages. Besides, by analysing the difficulties and challenges that the researchers face in evaluating dialogue systems, we prospect the development tendency of the research on evaluating dialogue systems.
Enhancing statistical power in temporal biomarker discovery through representative shapelet mining

Abstract Motivation Temporal biomarker discovery in longitudinal data is based on detecting reoccurring trajectories, the so-called shapelets. The search for shapelets requires considering all subsequences in the data. While the accompanying issue of multiple testing has been mitigated in previous work, the redundancy and overlap of the detected shapelets results in an a priori unbounded number of highly similar and structurally meaningless shapelets. As a consequence, current temporal biomarker discovery methods are impractical and underpowered. Results We find that the pre- or post-processing of shapelets does not sufficiently increase the power and practical utility. Consequently, we present a novel method for temporal biomarker discovery: Statistically Significant Submodular Subset Shapelet Mining (S5M) that retrieves short subsequences that are (i) occurring in the data, (ii) are statistically significantly associated with the phenotype and (iii) are of manageable quantity while maximizing structural diversity. Structural diversity is achieved by pruning non-representative shapelets via submodular optimization. This increases the statistical power and utility of S5M compared to state-of-the-art approaches on simulated and real-world datasets. For patients admitted to the intensive care unit (ICU) showing signs of severe organ failure, we find temporal patterns in the sequential organ failure assessment score that are associated with in-ICU mortality. Availability and implementation S5M is an option in the python package of S3M: github.com/BorgwardtLab/S3M.
Effects of government regulatory provisions on the innovative behaviour of HEIs and economic advancement: The case of Kazakhstan

This study examines how government initiatives introduced in Kazakhstan’s State Programme for Education Development 2011–2020 are regulated and implemented to stimulate the innovative capacity of universities. The authors then consider the implications of these changes for the government’s aspiration of economic development. As government regulation is accepted as the primary explanatory factor that leads to poor performance, drawing on agency theory the study examines the combinations and conditions of existing regulatory mechanisms. Given that different agency problems are involved, the authors set out the alternatives available for the government to overcome those challenges.
Formalizing the Transaction Flow Process of Hyperledger Fabric

Blockchains leverage the synergy of technologies from networking, cryptography, distributed protocols, security policies, and computer programming, to provide guarantees such as distributed consensus and tamper-resistance over records of data and activities. The interaction of diverse technical elements in a blockchain could create obstacles in precisely understanding the workings of it, and the guarantees delivered by it. We help overcome these obstacles in the case of the transaction flow process of the Hyperledger Fabric blockchain, through theorem-proving techniques. The transaction flow process is an overarching architectural component for the handling of transactions in Hyperledger Fabric. We formalize the transaction flow, and prove that it preserves distributed consensus, in the Coq proof assistant. Our development complements existing formalizations of consensus mechanisms for blockchain systems.
An Efficient Online Estimation Algorithm for Evolving Quantum States

In this paper, we propose an online optimization algorithm for estimating state density in free evolution quantum systems from noisy continuous weak measurements. The problem is formulated via sparsity-promoting semidefinite programming, and an online quantum state estimation algorithm is developed based on online proximal gradient and alternating direction multiplier method. The proposed algorithm is computationally efficient and further features high robustness to measurement noise. The merits of the approach are illustrated by numerical experiments in 1-, 2-, 3-, and 4-qubit systems.
Time-Domain Equalization Using Neural Network with Arbitrary Decision Delay

In this paper, we propose a time-domain equalizer using a neural network (NN) with three fully connected layers. The proposed equalizer uses an arbitrary decision delay and multiple receive antennas to provide performance improvements even when the first tap gain of a channel impulse response is small. In the proposed equalizer, inter-symbol interference is suppressed by minimizing the equalizer output energy while retaining the desired symbol component based on the estimated channel impulse response. Unlike most existing machine learning based approaches, the proposed equalizer requires no transmission of training symbols to adjust the adjustable parameters of the NN. Simulation results show that the bit error rate performance of the proposed equalizer is superior to that of a conventional NN equalizer.
RETRACTED: Design of enterprise employee pension platform based on complex embedded system

Developing countries are increasingly aware of the need to develop and implement an improved system of providing public pensions for the elderly. The purpose of such a system may be smooth consumption, thus providing a reliable income to the elderly, the elderly poverty reduction, the insured no longer risk running out of money to work to promote equality between men and women retirement benefits, even though the average expected lifetime earnings and life may be very different. The market for Enterprise Secure Systems (ESS) continue to grow as companies increasingly global and competitive. More and more in the spotlight as the ES manufacturers are small businesses. Proposal is based on Support Vector Machine (SVM) algorithms to provide (a) the angle of the commercial interests of small and medium enterprises (SMEs) ES a unique supplier / consultant of view, from their ES investments and (b) are expected to review the current approach by considering the conceptual framework for the organization, processes and strategic context of the implementation of SMEs ES implementation. Professionals pointed out that although many ES realize now that a few years ago, small and medium enterprises have only recently begun the process of analyzing the expected return track to achieve business value from their investment in the ES. The results also determine how to perform the ES approach is more suitable for the SME sector to adapt, the ES is an important market for traders given the large enterprise market to achieve saturation ES. In the design and implementation of pension fund management application to replace the manual system to get rid of participating in traditional pension management process barriers.
Joint Host-Network Power Scaling with Minimizing VM Migration in SDN-enabled Cloud Data Centers

In recent times, both industry and academia have paid significant attention to the power management of cloud data centers (CDCs), due to their typically very high electrical energy consumption. While servers remain the components with the highest power-consumption, network stacks can also consume about 10-20 percent of the total energy usage in a data center. Dynamic Virtual Machine (VM) consolidation is one way to reduce the number of active servers, which can be done by live migration of the VMs. But, migration operations in a data center bring several system and service level overheads that include downtime, elephant flows over the network, and potentially higher failure rates. In this work, we propose algorithms for minimizing the number of VM migrations to attain the optimized joint host-network power consumption in a cloud data center. We present a trade-off between the number of migrations, the joint host-network power consumption, and the computational time complexity of the proposed algorithms. Using Mininet and ONOS, an SDN enabled framework is utilised to evaluate the proposed algorithms. Experimental results show that our algorithms can reduce power consumption by about 11 percent, while completing between 18 to 25 percent more VM migrations compared to the baseline algorithm, which only minimizes migration without guaranteeing lowest power consumption.
How software organizations are using the ISO/IEC 29110 standard's processes

ISO/IEC 29110 was developed containing a set of industrially validated practices that can potentially be adopted by software Very Small Entities (VSE). VSEs usually have characteristics that differentiate them from organizations of different sizes, such as extremely limited resources and informal project management processes, tending to adopt Agile methods and having an historical resistance to the adoption of standards, that are in general developed for large organizations. In this sense, our research question arises: "How are software organizations using the ISO/IEC 29110 practices?". To answer this question, a Systematic Mapping Study (SM), and a Survey with software organizations were carried out in order to identify the state of the art and the state of the practice in relation to the use of the standard's practices. The SM identified 21 primary studies reporting the use of the standard in hundreds of software organizations with positive results such as organizational learning, process improvement, improved communication, and also some negative results, such as deployment difficulties in technical areas and the need for additional time and resources. The Survey carried out with 23 software companies identified that, in general, companies do not explicitly know the content of the standard, but partially carry out, in accordance with the standard, practices related to planning, monitoring, control and execution of a project plan, and do not carry out requirements analysis or architecture and detailed design as defined in the standard.
Sampling-based search for a semi-cooperative target

Searching for a lost teammate is an important task for multirobot systems. We present a variant of rapidly-expanding random trees (RRT) for generating search paths based on a probabilistic belief of the target teammate's position. The belief is updated using a hidden Markov model built from knowledge of the target's planned or historic behavior. For any candidate search path, this belief is used to compute a discounted reward which is a weighted sum of the connection probability at each time step. The RRT search algorithm uses randomly sampled locations to generate candidate vertices and adds candidate vertices to a planning tree based on bounds on the discounted reward. Candidate vertices are along the shortest path from an existing vertex to the sampled location, biasing the search based on the topology of the environment. This method produces high quality search paths which are not constrained to a grid and can be computed fast enough to be used in real time. Compared with two other strategies, it found the target significantly faster in the most difficult 60% of situations and was similar in the easier 40% of situations.
The Humanoid Robot/Camera System for Teaching YOGA Exercise Motions

To address the problem of the shortage in caregivers, we have developed a system that can teach learners exercises with a humanoid robot. The system uses a depth camera and a humanoid robot. Here, we present the method we used to obtain the motion data when a learner imitates the exercise motions performed by the robot, and we examine the usefulness of the exercise instruction by the robot.
Group Performance Prediction with Limited Context

Automated prediction of group task performance normally proceeds by extracting linguistic, acoustic, or multimodal features from an entire conversation in order to predict an objective task measure. In this work, we investigate whether we can maintain robust prediction performance when using only limited context from the beginning of the meeting. Graph-based conversation features as well as more traditional linguistic features are extracted from the first minute of the meeting and from the entire meeting. We find that models trained only on the first minute are competitive with models trained on the full conversation. In particular, deriving features from graph-based models of conversational interaction in the first minute of discussion is particularly effective for predicting group performance, and outperforms models using more traditional linguistic features. This work also uses a much larger amount of data than previous work, by combining three similar survival task datasets.
Early and quick function points analysis: Evaluations and proposals

Measuring Function Points following the standard process is sometimes long and expensive. To solve this problem, several early estimation methods have been proposed. Among these, the “NESMA Estimated” method is one of the most widely used; it has also been selected by the International Function Point User Group as the official early function point analysis method, under the name of ‘High-level FPA’ method. A large-scale empirical study has shown that the High-level FPA method – although sufficiently accurate – tends to underestimate the size of software. Underestimating the size of the software to be developed can easily lead to wrong decisions, which can even result in project failure. In this paper we investigate the reasons why the High-level FPA method tends to underestimate. We also explore how to improve the method to make it more accurate. Finally, we propose size estimation models built using different criteria and we evaluate the estimation accuracy of these new models. Our results show that it is possible to derive size estimation models from historical data using simple regression techniques: these models are slightly less accurate than those delivered by the High-level FPA method in terms of absolute estimation errors, but can be used earlier than the High-level FPA method, are cheaper, and do not underestimate software size.
A Mixed Two-stage Object Detector for Image Processing of Power System Applications

Object detection algorithms based on deep learning has become increasingly important in Image Processing. This paper proposes a deep learning method for improving the objects detection accuracy while supporting a real-time operation by optimizing SSD model into a mixed Two-stage detection model with an extra dense small object detection module which can localize the precise position and give the classification of the dense small objects, using a fully convolutional network. Compared with the SOTA detection model such as two-stage Faster R-CNN and single-stage SSD, our approach has a better trade-off between accuracy and efficiency. The experimental results outperform Faster R-CNN in detection speed and considerably better than SSD in detection accuracy.
Associative Classifier for Evidential Data

Associative classifiers are a family of classification techniques that use association rules to classify a new instance. Many works were proposed in this context, with a focus on how to select and combine the relevant association rules for classification. However, most of them do not consider the imperfection of data. In this paper, we tackle the problem of multi-rules based classification for evidential data, i.e., data where imperfection is represented and managed through the Dempster-Shafer theory. In this context, a new algorithm called WEviRC is introduced. This method uses different pruning techniques to eliminate the irrelevant rules and then to combine the selected ones using the powerful combination rules of the Dempster-Shafer theory. To evaluate our introduced method, we conducted extensive experiments on several data sets. Experiments show positive results in terms of classification quality and compare well with the existing methods.