Reviewerly/exHarmony huggingface dataset:
A Meta-Model to Automatically Generate Evolutionary Prototypes from Software Requirements

Requirement validation is the most vital phase of Requirement Engineering (RE) process which provides clear, complete and consistent requirements to software development team. Although several requirement validation approaches have been proposed such as review checklists, prototyping and model-based validation. Among all these existing techniques, prototyping being model-based approach is most widely utilized. It is analyzed that functional implementation and iterations often makes traditional manual prototyping to be inefficient, time consuming and expensive. In order to bridge this gap, a meta-model for automatic generation of prototypes is proposed in this paper. This prototype will be developed directly from the elicited requirements for the purpose of efficient validation of requirements. This approach provides thorough knowledge, reactions foresight and design analysis of a system, steering towards the validation of the necessary, verifiable, traceable and reusable requirements. In addition, after performing requirement validation using proposed approach, rework on design and implementation is avoided.
Generative Low-Dose CT Image Denoising

The continuous development and extensive use of CT in medical practice have raised a public concern over the associated radiation doseRadiation dose to patients. Reducing the radiation doseRadiation dose may lead to increased noise and artifacts, which can adversely affect radiologists’ judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean squared error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio (PSNR) is improved, MSE- or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoisingImage denoising method based on the generative adversarial network (GAN) Generative Adversarial Network (GANs) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory, and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoisingImage denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.
Decentralized Access Control with Anonymous Authentication of Data Stored in Clouds

The paper proposes a new decentralized access control scheme for secure data storage in clouds that supports anonymous authentication. In the proposed scheme, the cloud verifies the authenticity of the series without knowing the user's identity before storing data. Our scheme also has the added feature of access control in which only valid users are able to decrypt the stored information. The scheme prevents replay attacks and supports creation, modification, and reading data stored in the cloud. We also address user revocation. Moreover, our authentication and access control scheme are decentralized and robust, unlike other access control schemes designed for clouds which are centralized. The communication, computation, and storage overheads are comparable to centralized approaches.
Prediction of User Intentions Using Web History

In the present internet has become much more necessary thing to humans and we use it as a way of sharing information and way of communication. If the networks can identify the user's intentions, it will be affecting to increase productivity and personalization. Predicting user intention(s) is interesting and useful for many applications such as threat identification, imposing restrictions and cashing web details. The aim of this research is to develop a method to predict user intention using supervised machine learning methods with user's past historical behaviours. Experiments in this study used access log on a local server and focused on creating single user prediction and multiuser generalize prediction models. Experimental models were created based on several multi-classifier algorithms, such as Support Vector Machine (SVM), Multilayer Perceptron (MLP) and K-Nearest Neighbor (KNN). KNN based models outperform other used algorithms. Also results in this study show that there is some sort of behavioural patterns for peoples to use the internet according to the time and the groups they interact.
Model Mismatch Effect in Coarse Resolution Heterogeneous Clutter Cells

Passive radar imaging with today's signals of opportunity result in coarse range resolution images with heterogeneous clutter mixed with targets in a single resolution cell. Although land use land cover maps can aid in determining clutter composition, it is difficult to accurately model heterogeneous clutter in a coarse resolution cell. Furthermore, the target and its shadow will replace some amount of clutter in the target present hypothesis. Target detector performance prediction will be in error if signal model does not match the true physics of the situation. This paper characterizes how much variation occurs in receiver operating characteristic curves when the target present model does not account for a reduction in clutter due to the target and shadows. Results show the mismatched model over-predicts probability of detection by as much as 43% compared to the model that matches the scene physics. Examples assume complex random variables with Weibull magnitude and uniform phase, with complex Gaussian random variables as a special case.
Instance Selection for the Nearest Neighbor Classifier: Connecting the Performance to the Underlying Data Structure

Instance selection is one of the most successful solutions to low noise tolerance of the nearest neighbor classifier. Many algorithms have been proposed in the literature, but further research in this area is still needed to complement the existing findings. Here we intend to go beyond a simple comparison of instance selection methods and correspondingly, we carry out a qualitative analysis of why some algorithms perform better than others under different conditions. In summary, this paper investigates the impact of instance selection on the underlying structure of a data set by analyzing the distribution of sample types, with the purpose of linking the performance of these methods to changes in the data structure.
Context-Aware Computing: Telecom Perspective

A pervasive computing system has been used in several domains. This chapter elaborates on its use in the telecom industry. The mobile user needs various information like traffic updates and weather conditions. From the perception of mobile users, this chapter begins with the layered architecture of context-aware system. The interaction of the telecom industry with the media context has been increased in the last few years. This chapter discusses several context-aware services offered by the telecom industry. The services include content, location, session and identity. The chapter continues to discuss telecom SOA architecture. The chapter finally concludes with a discussion on a context-aware business model which includes perception from the user, network and third party.
Decentralized Energy Management for Smart Home System of Systems

Today's energy production is confronted with the problem of energy transformation [1]. Renewable energies like photovoltaic or wind energy become increasingly important for power supply. On the other hand, the amount of energy, which is produced in the conventional way, is already reduced and will decrease further. As one consequence, the supply stability is getting endangered. Power production, as established until today, can be predicted and, more important, be controlled quite precisely. Wind and solar energy production in contrast depends on the weather situation and is much harder to control. Therefore, the need of innovative demand side measures increases, even more regarding the goals of phasing out nuclear energy production. The private household sector, as the second major energy consumer in energy market, is forced to transform towards an innovative and sustainable part of the energy transition. With regard to the demand side, it turns out, that the stability of energy supply can be positively influenced by an demand side energy management. Nevertheless, there are a lot of complex systems like washing machines, tumble dryers, which have to interact with each other to fulfill the management. Therefore, this contribution discusses an energy management system, based on a decentralized multi-agent smart home system.
Covert Communications-Based Information Reconciliation for Quantum Key Distribution Protocols

The rate at which a secure key can be generated in a quantum key distribution (QKD) protocol is limited by the channel loss and the quantum bit-error rate (QBER). Increases to the QBER can stem from detector noise, channel noise, or the presence of an eavesdropper, Eve. Eve is capable of obtaining information of the unsecure key by performing an attack on the quantum channel or by listening to all discussion performed via a noiseless public channel. Conventionally a QKD protocol will perform the information reconciliation over the authenticated public channel, revealing the parity bits used to correct for any quantum bit errors. In this invited paper, the possibility of limiting the information revealed to Eve during the information reconciliation is considered. Using a covert communication channel for the transmission of the parity bits, secure key rates are possible at much higher QBERs. This is demonstrated through the simulation of a polarization based QKD system implementing the BB84 protocol, showing significant improvement of the SKRs over the conventional QKD protocols.
All-Optical Readout for Integrated Photonic Reservoir Computing

Photonic neuromorphic computing has gained a lot of attention for its strong potential to deliver machine learning computation capability at high bitrates (> 32 Gbps) with very low energy consumption. Reservoir computing is one of the strong candidates that delivers a huge advantage on a real hardware implementation. However, one of the challenges is that current readout systems are the bottleneck of the high-speed link involving heavy power consumption from opto-electrical conversions. In this paper, we present our design of an integrated all-optical readout system that overcomes the challenges with optical weighting elements, which works at 32 Gbps and can deliver computation capability on-chip with one final readout signal channel. We especially compare the memory capability difference between the optical readout scheme with conventional electrical readout and show that optical readout is superior. Furthermore, this paper discusses the problem of some non-volatile optical weighting elements having limited weighting resolution. As a result, our readout system can still perform very well at an low resolution (4 bit) compared to using full resolution weighting elements.
Recognizing Union-Find Trees is NP-Complete, Even Without Rank Info

Disjoint-Set forests, consisting of Union-Find trees, are data structures having a widespread practical application due to their efficiency. Despite them being well-known, no exact structural characterization of these trees is known (such a characterization exists for Union trees which are constructed without using path compression) for the case assuming union-by-rank strategy for merging. In this paper we provide such a characterization by means of a simple PUSH operation and show that the decision problems whether a (ranked or unranked) tree is a Union-Find tree are NP-complete, complementing our earlier similar result for the union-by-size strategy.
Virtual Machine Integration &amp; Fault Recovery in Crowd-Resourcing Virtual Laboratory

Crowd-Resourcing virtual laboratory (CRVL) uses some resources which were shared by the crowd as the contributors to run virtual laboratory exercises. The shared resource is in the form of a virtual machine (VM) that must be automatically integrated into the virtual laboratory system. In this paper, we propose a mechanism of integrating a VM into a CRVL system. It starts with verifying the VM integrity, followed by configuring the VM until the VM is ready to be used to run a Virtual Laboratory exercise. We use WebSocket for communication between the VM and the Virtual Laboratory Server, to be able to monitor the VM Integrity. We also propose a VM fault recovery approach that could automatically divert user connections to other VMs. For the fault recovery purpose, we use SSHFS to mount a remote file system on a file server to store the data of a VM. Our experiments showed that our approaches could work appropriately, and the time for Team Recovery is similar to the time for Team Creation.
A Formal Graphical Language of Interdependence in Teamwork

Agents in teamwork may be highly interdependent on each other, and the awareness of interdependences is an important requirement for designing and consequently implementing a multiagent system. In this article, we propose a formal graphical and domain-independent language that can facilitate the identification of comprehensive interdependences among the agents in teamwork. Moreover, a formal semantics is also introduced to precisely express and explain the properties of a graphical structure. The novel feature of the graphical language is that it complements the Interdependence Analysis Color Scheme in a way that explicitly models negative influences and, in addition, provides a visual-communication aid for developers. To demonstrate the applicability and sufficiency of the graphical language in a variety of domains, our case studies include a multirobot scenario and a human-robot scenario.
Data generated by evaluating the seasonal variability and trend analysis of the solar energy resource in the Northeastern Brazilian region

The solar radiation is the primary energy resource for several human activities. Nowadays, the environmental issues and climate concerning are boosting the substitution of fossil fuel resources by renewable energy resources, including the adoption of solar energy for power generation. Although the solar power stands for 0.2% of the Brazilian electricity mix, the solar energy resource in the Northeastern Brazilian region (NEB) is higher than in countries where the solar energy market is already consolidated. Nowadays, it is crucial to deepen the comprehension of solar resource time and spatial variability in NEB to support and promote the solar energy market and save water to other purposes than power generation. The paper presents the data generated by Lima et al. (2019). The database, based on meteorological observations at 129 automated weather stations, provides reliable information on the spatial and seasonal variability of the incoming solar irradiation in NEB.
Context-Sensitive Case-Based Software Security Management System

An increasing number of security attacks on software have motivated the need for including secure development practices within the software development life cycle. With this urgent need, software security management system has received considerable attention and there are various efforts that can be found in this direction. In this paper, we highlighted the need for including application context-sensitive modeling within case-based software security management system proposed by the authors in [3]. Therefore, in this paper, we extend the previous work [3] to include application context modeling. The proposed idea constructs software security models using an application context.
National Cyber Governance Awareness Policy and Framework

Abstract Despite an ongoing drive by governments and law enforcers around the world to improve the sophistication of their risk mitigation measures, cyber-attacks are continually increasing. A study from Computer Crime and Intellectual Property Section (CCIPS) shows more than 4,000 ransomware attacks occurred daily in 2016. That's a 300 percent increase over 2015, where 1,000 ransomware attacks were seen per day. Cyber criminals are successfully penetrating even the most high-profile companies and governmental agencies. The breach at the NSA was truly alarming and just one recent example of the dire situation the country, and world, face as cybercrime intensifies and the cyber security talent shortage becomes more serious. Accordingly, the purpose of this research is to focus on cyber education at the national, government, and law enforcement level examining the methodology to set the tone from the top ensuring alignment between governments, law enforcers, private sector, and academic level. It also examines the gaps in cyber laws and educational governance initiatives and their impact on efficient execution of cyber policies for various regions of the world with a focus on the United Arab Emirates and the United States of America. Finally, this article recommends policy guidelines and a compliance manual framework for governments and law enforcers to consider ensuring that cyber risks are properly addressed and mitigated in a structured and coherent way.
Area Efficient Box Filter Acceleration by Parallelizing with Optimized Adder Tree

Box filters are widely used in image and video processing applications. To achieve the real-time performance for these applications, designers may need to parallelize these box filters. However, it is very challenging to implement a parallel box filter on modern programmable system-on-chip (SoC). On one hand, the dependency between the operations of a box filter is too strong to achieve parallelism. On the other hand, more adder trees are required as the degree of parallelism increases. In this paper, we propose a performance and area efficient box filter. It uses the partial sum difference, which needs much less resource, to effectively calculate the box filter. We make the full use of this reusable partial sum to optimize the adder trees for parallel processing. We also make two case studies of the box filter by applying it to the guided filter and the stereo matching algorithm on a programmable SoC using a C-based design flow. Our method removes the dependencies between the parallel operations of the box filter. Compare to the state-of-the-art, results show that the computational complexity of the adder tree for a single pixel has been reduced from O(R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O((R+N)lgN/N ) on average. There are orders of magnitude reduction in resource usage with large filter size R and parallelization degree N. The throughput can be increased by N times, where N is up to 72 in the case of Xilinx FPGA board XCZU9EG-2FFVB1156E.
Efficient Motion Planning for Autonomous Vehicle Maneuvers Using Duality-Based Decomposition

A method to decompose a motion-planning problem into several segments is presented. It is based on a modification of the original problem, such that certain variables at the splitting points are considered to be precomputed and thus fixed and the remaining variables are obtained by performing Lagrange relaxation. The resulting dual problem is split into several subproblems, allowing parallel computation. The method is formalized as a computational algorithm and evaluated in a safety critical double lane-change situation. The resulting maneuver has close-to-optimal behavior and, for certain initialization strategies, it is obtained in shorter computational time compared to computing the full maneuver in one step.
Context Aware Self Learning Voice Assistant for Smart Navigation with Contextual LSTM

The gift of vision for humans is a valuable blessing but regrettably there are around 37 million people who are visually impaired. Among them, 15 million people are from India. They undergo numerous challenges in their daily lives. They are always dependent on others for traveling to different places. It is noted that context-awareness has a key responsibility in the lives of the visually impaired. There are many mobile applications contributing to ease them, but due to dependence on many additional resources, it has become a nightmare. To sophisticate the above challenge, the proposed mobile-cloud context-aware application will act as a voice chat-bot that provides context-aware travel assistance to the visually challenged people which is implemented in specific public environments. It is an interactive application and provides them with a help desk where they can query their necessary information through speech interface. This application relies on the Location based services including providers and Geo-coordinates for manipulating the latitude and longitude of places. The present location of the user is tracked by using location services. The distance from the user’s exact location to the destination is pre-determined and this application will assist them with the route to travel through audible directions. This would completely assist them with the travel by replying to the queries asked by them and it helps them to travel independently. The application flow initially takes the voice instruction and converts that into the text instructions. The contextual LSTM (Long-short Term Memory) in the application takes care of the conversational strategy, analyzes it and advocates all the users with answers for whatever questions are been posted. It also drives the visually handicapped to destination by identifying the obstacle and detection of the object in the way. The application uses the computational resources from cloud servers such as location specific resources and in turn pushes all the data in cloud server for reference and future usage.
QR CODES AND THE POSSIBILITIES OF THEIR USE IN THE EDUCATIONAL PROCESS

The modern educational process includes a number of innovative tools, teaching methods and educational technologies. This article discusses the possibilities of introducing a QR code into the educational process.
Limited-Time Convergent ZNN for Computing Time-Dependent Complex-Valued Matrix Pseudoinverse

A special class of recurrent neural network, termed zeroing neural network (ZNN), has been recently proposed for computing various real-valued time-dependent problems, and it has shown wonderful performance in declining the computing error. When extended to complex-valued domain, ZNN's characteristics have become complicated. Specifically, the activation function is a tremendous challenge for the complex-valued ZNN. In many previous studies, the linear activation function is adopted, which leads to a slow convergence speed. Adopting an ingenious nonlinear activation function, this work first proposes a new limited-time convergent ZNN (LTCZNN) model for solving time-dependent complex-valued matrix pseudoinverse. Theoretical analyses are conducted, and results show that the LTCZNN model can converge to the optimal solution in the limited time. Moreover, the upper bound of the convergence time is inferred in theory. Finally, the numerical experiment results further confirm those of the theoretical analysis, indicating the superior convergence of the presented LTCZNN model for solving time-dependent complex-valued matrix pseudoinverse.