Reviewerly/exHarmony huggingface dataset:
Providentia: Using search-based heuristics to optimize satisficement and competing concerns between functional and non-functional objectives in self-adaptive systems

In general, a system may be subject to a combination of functional requirements (FRs) that dictate behavior and non-functional requirements (NFRs) that characterize how FRs are to be satisfied. NFRs also introduce cross-cutting concerns that may be difficult to predict, where the degree of satisfaction (i.e., satisficement) of one NFR may be impacted by the satisficement of one or more FRs/NFRs. In particular, self-adaptive systems (SASs) can modify system configurations or behaviors at run time to continuously satisfy FRs and NFRs. This paper presents Providentia, a search-based technique to optimize the satisficement of NFRs in an SAS experiencing various sources of uncertainty. Providentia explores different combinations of weighted FRs to maximize NFR/FR satisficement. Experimental results suggest that Providentia-optimized goal models significantly improve the satisficement of an SAS when compared with manually- and randomly-generated weights and subgoals. Additionally, we apply a hyper-heuristic (Providentia-SAW) to balance the contribution of NFRs, FRs, and the number of adaptations and further improve the Providentia technique. We apply Providentia and Providentia-SAW to two case studies in different application domains involving a remote data mirroring network and a robotic vacuum controller, respectively.
Secured Electronic Voting System Using the Concepts of Blockchain

Electronic Voting Machines (EVMs) are replacing paper ballots because of (i) the errors involved in the manual counting process and (ii) the large time period required to count the votes. Even though these digital recording electronic systems are advancements, they are prone to tampering and electoral frauds. The suspected vulnerabilities in EVMs are (i) the possibilities of tampering with the EVM's memory chip or switching it with a fake one, (ii) their simplicity, which enables them to be tampered without requiring much skill and (iii) the chances of double voting. This paper attempts to solve the above problems by storing the vote data in a decentralized network and securing the data using a security mechanism derived from the blockchain framework (which is currently used for securing cryptocurrencies). The vote data is shared among all the devices in the network and peer to peer verification is done to verify the authenticity of the vote data. In order to successfully tamper with the system, the data stored in all the nodes must be changed. This makes the proposed system more efficient and reliable. In this paper, we propose an algorithm, which is based on the concepts of blockchain, to secure the votes stored in an EVM. A fingerprint authentication system is also added as an extra layer of security to prevent double voting. The proposed EVM will be tamperproof, and any attempt to manipulate the registered votes, will be detected and the tampered data will be replaced with the correct data.
Research Support in New Zealand University Libraries

Researcher-facing librarians in New Zealand are providing guidance across more of the research lifecycle than ever before in response to an increasingly complex scholarly ecosystem. This research explored the development of research support through survey responses from all eight New Zealand University Libraries. From the responses it was possible to ascertain the level of services offered or planned in the four key areas of research impact, Research Data Management, scholarly communication and Kaupapa Māori research. Skills gaps and barriers to service development were also identified. Comparison with an earlier study revealed that research impact and Research Data Management services have developed well over the last 6 years. A good level of maturity was identified in scholarly communication services but support for Kaupapa Māori research was identified as an area for development. Barriers to service development included lack of resourcing and low recognition of library value from the wider institution.
A new self-paced method for multiple instance boosting learning

Multi-instance learning is a useful tool for solving label ambiguity. MILBoost is one of the algorithms, which uses boosting method to handle the multiple instance learning problems. Although MILBoosting has achieved good effect on multiple instance learning, little work has been done on the problem of multiple instance learning where a small number of bags are labeled. In this paper, we propose a new approach by incorporating the SPL and boosting into the procedure of multiple instance learning, called Self-Paced Boost Multiple Instance Learning (SP-B-MIL). The proposed approach can improve the effectiveness and robustness of multi-instance learning when a small number of bags are labeled. We first reformulate the multiple instance boosting model with a self-paced loss formulation. Then we propose a self-paced function for realizing desired self-paced scheme, which makes it possible to select instances from different bags during each iteration. Finally, we design a simple and effective algorithm to solve the optimization problem. Experimental results show that the proposed algorithm is comparable to the classical algorithms in some multi-instance learning benchmark data sets.
The Algorithm for Automated Development of Design Drawings of One-Line Diagrams of Distribution Devices for 6-10 kV of Substations

The paper is devoted to the development and software implementation of the algorithm for automatic drawing of schemes for 6-10 kV distribution devices of downstep substations. The algorithm proposed by the authors makes it possible on the basis of technical specifications and decisions made earlier in the design stage to create automatically and import the electrical connections of the switchgear of the designed substation into an editable graphic format. The algorithm developed by the authors and the software in which it is implemented can be used by engineers, designers, which will significantly reduce the time spent on the preparation of project documentation. The work is carried out with the financial support of a grant from the President of the Russian Federation for young scientists - candidates of sciences MK-939.2019.8.
Generic Autonomic Management as a Service in a SOA-based Framework for Industry 4.0

Cyber-physical production systems are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. In order to make these systems interoperable with each other for addressing Industry 4.0 applications a number of service-oriented architecture frameworks are developed. Such frameworks are composed by a number of services, which are inherently dynamic by nature and thus imply the need for self-adaptation. In this paper we propose generic autonomic management as a service and show how it can be integrated in the Arrowhead framework. We propose generic and reusable interfaces for each phase of the autonomic control loop in order to increase the usability of the service for other frameworks and application systems, while reducing the software engineering effort. To show the utility of our approach in the Arrowhead framework we use a climate control application as a representative example.
S-DenseNet: A DenseNet Compression Model Based on Convolution Grouping Strategy Using Skyline Method

DenseNet has strong expressiveness in many computer vision tasks, but the complexity of its model makes it difficult to deploy to devices with limited computing resources. In this paper, we propose S-DenseNet, a compact model of DenseNet which makes the extracting features of DenseNet more comprehensively and reduces the parameter redundancy at the same time. Firstly, we investigate the parameter redundancy of DenseNet and show the possibility of compressing DenseNet. For example, we reduced its parameters by 10% as keeping in the same accuracy. Secondly, we propose an algorithm based on the Skyline method for converting standard convolution into group convolution automatically. It can choose the filter weights automatically and restore the standard convolution filter with a high sparsity one called S-Conv. Finally, we combine S-Conv with dense connections and formed a more effective network architecture, that is S-DenseNet. Experiments show that in Cifar10, Cifar100 and SVHN datasets, we use only 40% parameters and 20% FLOPs of DenseNet, and the accuracy is improved by 1%. Compared with the compression models of DenseNet and other CNNs, we use less complexity in the model and achieve higher or similar accuracy. On ImageNet dataset, compared with lightweight CNN models, traditional CNNs and their compression models, we achieve the higher or similar Top-1 accuracy with less complexity.
Optimising topic coherence with Weighted P<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mover accent="true"><mml:mi mathvariant="normal">o</mml:mi><mml:mo>´</mml:mo></mml:mover></mml:math>lya Urn scheme

Topic models have been widely used to mine hidden topics from documents. However, one limitation of such topic models is that they are prone to generate incoherent topics. To address this limitation, many approaches have been proposed to incorporate the prior knowledge of word semantic relatedness into the topic inference process. One example is the Generalized Po´lya Urn (GPU) scheme. However, GPU-based topic models often require sophisticated algorithms to acquire domain-specific knowledge from data. Moreover, prior knowledge is incorporated into the topic inference process without considering its impact on the intermediate topic sampling results. In this paper, we propose a novel Weighted Po´lya Urn scheme and incorporate it into Latent Dirichlet Allocation framework to build the self-enhancement topic model and generate coherent topics. In specific, semantic prior knowledge based on word embedding is employed to measure the semantic coherence of a word to different topics, which is incorporated into the Weighted Po´lya Urn scheme. Moreover, semantic coherence is updated dynamically based on the semantic similarity between a word and the representative words in different topics. Experiments have been conducted on seven public corpora from different domains to evaluate the effectiveness of the proposed approach. Experimental results show that compared to the state-of-the-art baselines, the proposed approach can generate more coherent topics.
Synthesis of Modal PI-Regulator with Multiple Integration

There is procedure and a mathematical model of a PI-regulator with multiple integration are presented. An approach is proposed to improve the accuracy of the system under the conditions of a disturbance without taking into account the dynamic characteristics on demand. The results of research of the designed system with a specific example are presented.
Semantic concept based video retrieval using convolutional neural network

Retrieval of videos efficiently and effectively has become a challenging issue nowadays and dealing with multi-concept videos is the center of focus. The aim of the work presented here is to propose an improved semantic concept-based video retrieval method using a novel ranked intersection filtering technique and a foreground driven concept co-occurrence matrix. In the proposed ranked intersection filtering technique, an intersection of ranked concept probability scores is taken from key-frames associated with a query shot to identify concepts to be used in retrieval. Convolutional neural network is used as a baseline. The proposed method is implemented using a classifier built with a fusion of asymmetrically trained deep CNNs to deal with data imbalance problem, a novel foreground driven concept co-occurrence matrix to exploit concept co-occurrence information and a ranked intersection filtering approach. Performance is evaluated by a measure, mean average precision on TRECVID multi-label dataset. The results are compared with state-of-the-art other existing methods in its class and shown its superiority.
A Quantum Router For The Entangled Web

Qubit transmission protocols are presently point-to-point, and thus restrictive in their functionality. A quantum router is necessary for the quantum Internet to become a reality. We present a quantum router design based on teleportation, as well as mechanisms for entangled pair management. The prototype was validated using a quantum simulator.
Information Analysis and Knowledge Gain within Graph Data Model

The information gathered within most databases is dedicated to have some interconnections. Part of these connections is described explicitly, like references or foreign keys in relational database model. Others remain implicit and should be discovered by a deeper analysis of relations in data. These hidden relations give a more complex and complete understanding of stored data or imply to be the required knowledge gained from a stored data. Considering data set with explicit references allows us to build a graph data model and apply approaches of graph theory to search for implicit ties. The particular example of an airspace network was investigated and an artificial locust swarm routing algorithm was implemented as an approach to search for a set of paths between airports. The attention of the paper is focused on meta-heuristic approach to reveal the significant subset of implicit relations as a part of knowledge gain procedure.
Responsive Innovation through Perceived Shared Values and Preferences of Customers

Abstract Responsive Innovation can be define as the attempt from the corporate or respected individual to bring the solution from particular problem by offer more ideal practical implementation based on specific requirement in the market. This type innovation can be generated or effectuate through modifying, combining, substituting or eliminating current process of problem solving or decision making in the market to be more precise, efficient and rigid compare to the previous. This study explore the possibilities to identify the characteristics of responsive innovation by developing web and mobile application based on the demand in the market with the several criteria namely number of exist application, higher demand, user experience attachment, attractive market and small routine margin of profit. Thus, this study present the application design called eLo and FunBuz to make it easier the process of order or reservation online.
Hand Gesture Recognition for Human Computer Interaction and Its Applications in Virtual Reality

Computers are emerging as the most utilitarian products in the human society and therefore the interaction between humans and computers will have a very significant influence in the society. As a result, enormous amount of efforts are being made to augment the research in the domain of human computer interaction to develop more efficient and effective techniques for the purpose of reducing the barrier of humans and computers. The primary objective is to develop a conducive environment in which there is feasibility of very natural interaction between humans and computers. In order to achieve this goal, gestures play a very pivotal role and are the core area of research in this domain. Hand gesture recognition is a significant component of virtual Reality finds applications in numerous fields including video games, cinema, robotics, education, marketing, etc. Virtual reality also caters to a variety of healthcare applications involving the procedures used in surgical operations including remote surgery, augmented surgery, software emulation of the surgeries prior to actual surgeries, therapies, training in the medical education, medical data visualization and much more. A lot of tools and techniques have. Been developed to cater to the development of the such virtual environments. Gesture recognition signifies the method of keeping track of gestures of humans, to representing and converting the gestures to meaningful signals. Contact based and vision based devices are used for creating and implementing these systems of recognition effectively. The chapter begins with the introduction of hand gesture recognition and the process of carrying out hand gesture recognition. Further, the latest research which is being in carried out in the domain of hand gesture recognition is described. It is followed by the details of applications of virtual reality and hand gesture recognition in the field of healthcare. Then, various techniques which are applied in hand gesture recognition are described. Finally, the challenges in the field of hand gesture recognition have been explained.
Raising Secure Coding Awareness for Software Developers in the Industry

Many industrial IT security standards and policies mandate the usage of a secure coding methodology in the software development process. This implies two different aspects: secure coding must be based on a set of secure coding guidelines and software developers must be aware of these secure coding practices. On the one side, secure coding guidelines seems a bit like a black-art: while there exist abstract guidelines that are widely accepted, low-level secure coding guidelines for different programming languages are scarce. On the other side, once a set of secure coding guidelines is chosen, a good methodology is needed to make them known by the people which should be using them, i.e. software developers. Motivated both by the secure coding requirements from industry standards and also by the mandate to train staff on IT security by the global industry initiative "Charter of Trust", this paper presents an overview of important research questions on how to choose secure coding guidelines and on how to raise software developer awareness for secure coding using serious games.
Corruption, FDI, and Growth: An Empirical Investigation into the Tunisian Context

The aim of this article is to investigate the dynamic relationship between corruption, investment, and economic growth in Tunisia within a multivariate framework. In the empirical section, we use a data span from 1976 to 2015, and we perform a vector error correction model and cointegartion technique. The main findings of this study show that corruption hampered Tunisia’s economic growth in the short run and the long run as well. Corruption could be the main reason for the slowdown of investment activities and the low inflow of capital. Hence, more efforts are needed to fight corruption in Tunisia.
Deep Learning based Kinship Verification on KinFaceW-I Dataset

Face has been a prominent biometric trait and is widely appropriated for several tasks. One such a task is `kinship verification' which primarily focuses on examining whether a disposed image pair appertains to a same family or not Kinship verification system grabs a large apportion of applications which are advantageous to the society. A convolutional neural network specific algorithm which can function as classifier between `kin' and `non-kin' categories has been proposed in this work. The proposed approach incorporates a novel deep learning based layered neural network architecture. This approach is evident to enact superior performance than a few reported algorithms and the classification entirely depends upon CNN specific feature vectors. We have applied the proposed technique on the Kinship Face in Wild (KinFaceW) dataset version - I which can be publicly obtained. Under a standard experimentation protocol, we have achieved an average verification accuracy of 86.94%.
Multi-scale Information Distillation Network for Image Super Resolution in NSCT Domain

Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. In this paper, we propose a new multi-scale information distillation network (MSID-N) in the non-subsampled contourlet transform (NSCT) domain for single image super resolution (SISR). MSID-N mainly consists of a series of stacked multi-scale information distillation (MSID) blocks to fully exploit features from images and effectively restore the low resolution (LR) images to high-resolution (HR) images. In addition, most previous methods predict the HR images in the spatial domain, producing over-smoothed outputs while losing texture details. Thus, we integrate NSCT and demonstrate the superiority of NSCT over wavelet transform (WT), and formulate the SISR problem as the prediction of NSCT coefficients, which is able to further make MSID-N preserve richer structure details than that in spatial domain. The experimental results on three standard image datasets show that our proposed method is capable of obtaining higher PSNR/SSIM values and preserving complex edges and curves better than other state-of-the-art methods.
Disentangled Representation of Data Distributions in Scatterplots

We present a data-driven approach to obtain a disentangled and interpretable representation that can characterize bivariate data distributions of scatterplots. We first collect tabular datasets from the Web and build a training corpus consisting of over one million scatterplot images. Then, we train a state-of-the-art disentangling model, β-variational autoencoder, to derive a disentangled representation of the scatterplot images. The main output of this work is a list of 32 representative features that can capture the underlying structures of bivariate data distributions. Through latent traversals, we seek for high-level semantics of the features and compare them to previous human-derived concepts such as scagnostics measures. Finally, using the 32 features as an input, we build a simple neural network to predict the perceptual distances between scatterplots that were previously scored by human annotators. We found Pearson's correlation coefficient between the predicted and perceptual distances was above 0.75, which indicates the effectiveness of our representation in the quantitative characterization of scatterplots.
A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers

Traditional clustering algorithms such as k-means and vanilla spectral clustering are known to deteriorate significantly in the presence of outliers. Several previous works in literature have proposed robust variants of these algorithms; however, they do not provide any theoretical guarantees. Extending previous clustering literature on Gaussian mixture models, in their paper “A Robust Spectral Clustering Algorithm for Sub-Gaussian Mixture Models with Outliers,” Prateek R. Srivastava, Purnamrita Sarkar, and Grani A. Hanasusanto developed a new spectral clustering algorithm and provided error bounds for the algorithm under a general sub-Gaussian mixture model setting with outliers. Surprisingly, their derived error bound matches with the best-known bound for semidefinite programs under the same setting without outliers. Numerical experiments on a variety of simulated and real-world data sets further demonstrate that their algorithm is less sensitive to outliers compared with other state-of-the-art algorithms.
A Robo-Advisor Design using Multiobjective RankNets with Gated Neural Network Structure

With rapid developments in deep learning and financial technology, a customized robo-advisory service based on novel artificial intelligence techniques has been widely adopted to realize financial inclusion. This study proposes a novel robo-advisor system that integrates trend prediction, portfolio management, and a recommendation mechanism. A gated neural network structure combining three multiobjective RankNet kernels could rank target financial products and recommend the top-n securities to investors. The gated neural network learns to choose or weigh each RankNet for incorporating the most important partial network inputs, such as earnings per share, market index, and hidden information from the time series. Experimental results indicate that the recommendation results of our proposed robo-advisor based on a gated neural network and multiobjective RankNets can outperform existing models.