Reviewerly/exHarmony huggingface dataset:
An Efficient Online Placement Scheme for Cloud Container Clusters

Containers represent an agile alternative to virtual machines (VMs), for providing cloud computing services. Containers are more flexible and lightweight, and can be easily instrumented. Enterprise users often create clusters of inter-connected containers to provision complex services. Compared to traditional cloud services, key challenges in container cluster (CC) provisioning lie in the optimal placement of containers while considering inter-container traffic in a CC. The challenge further escalates, when CCs are provisioned in an online fashion. We propose an online algorithm to address the above challenges, aiming to maximize the aggregate value of all served clusters. We first study a one-shot CC placement problem. Leveraging techniques of exhaustive sampling and ST rounding, we design an efficient one-shot algorithm to determine the placement scheme of a given CC. We then propose a primal-dual online placement scheme that employs the one-shot algorithm as a building block to make decisions upon the arrival of each CC request. Through both theoretical analysis and trace-driven simulations, we verify that the online placement algorithm is computationally efficient and achieves a good competitive ratio.
Non-cumulative only solar photovoltaics for electricity load-matching

Reference rules-of-thumb advise equator-facing orientations and slopes near the site's latitude as the optimal conditions for photovoltaic (PV) systems to maximize annual solar electricity production. However, if the electricity consumption profile is considered, this layout will most likely increase net load variance on the electricity grid at sunrise and sunset, which ought to be avoided. Making use of a variety of orientations and inclinations can help to minimize this impact, especially in cities where plentiful area of diversely oriented façades and rooftops is particularly relevant for broadening the peak of PV production throughout the day. Providing electricity not only around solar noon but also in the morning and late afternoon, when demand from residential buildings increases, helps to maximize self-consumption/-sufficiency and reduces costs for end-users and utilities. Until recently, due to high installation costs, there was little interest in this non-optimal PV systems configurations. In the last five years, however, the paradigm started to shift, triggered by the growing interest from municipalities that seek energy transition through currently more affordable PV systems and, on the other hand, by the duck-curve phenomena reported in pioneering “solar cities”. This paper reviews studies that address the use of non-optimal azimuths and tilts to better match utility- and distributed-scale demand and supply.
Development of sustainable landscape design guidelines for a green business park using virtual reality

Green business parks (GBPs) are attractive to employees because of their environmental and social benefits. Recently, sustainability has become an international trend that is intended to tackle climate change. Therefore, there is a persistent need for sustainable landscapes in GBPs to ensure that workplaces are productive and healthy. The main focus of this research is developing a methodology of the GBPs` landscapes, in order to guide the stakeholders in the decision-making process. Moreover, getting users` and experts` opinions using Virtual Reality (VR) techniques plays an essential role to correctly predicting how a space will perform. By considering these contexts and their significance to sustainable development in a hot-dry climate area, the study evaluates the practice of landscape design for a GBP in terms of compactness and greenness. Consequently, three different landscape design models—formal, xeriscape, and biomimicry designs—were constructed as 2D and 3D models on AutoCAD and SketchUp. Then, the three alternatives are evaluated by experts and users using VR headset and mobile devices (MDs). The main finding is merging the sustainable principles with the aesthetic appearance which satisfies the users` opinions. The essential aim of this study is creating guidelines for sustainable landscape design for the future GBPs.
Distributed Ledger Technology based Property Transaction System with Support for IoT Devices

Blockchain-based distributed ledger technology (DLT) is transforming the existing operational models of economy, financial transactions and other government machineries so as to allow these to operate in a much more secure and decentralized manner. This research focuses on providing framework for decentralized and secure P2P infrastructure for handling e-stamp and property registration mechanism along with interface for verification of document originality. The proposed efficient consensus mechanism reduces the overhead of broadcasting a new block by more than 50% coupled with saving CPU computation power along with network bandwidth. To ensure that even people at remote locations with constrained resources are able to participate and harness these benefits, a cloud server architecture &amp; web interface for verification of property registered deed is also proposed.
Bending device and anticlastic surface measurement of solids under large deformations and displacements

Large bending of elastic bodies gives rise to significant transverse effects. Based on a recent theoretical model in the context of finite elasticity, both the longitudinal and anticlastic curvatures in bent solids under large deformation and displacement can be accurately assessed. In order to experimentally investigate the anticlastic deformation induced by large inflexion and corroborate the theoretical predictions, a properly designed mechanical bending device is here proposed. By imposing a rotation at the ends of the sample, both the longitudinal and anticlastic curvatures are measured by DIC (digital image correlation) monitoring instrumentation and compared with the theoretical results, finding good agreement. Compact analytical formulae for assessing the radii of curvature within the thickness of the sample are provided. Conversely to existing studies of the anticlastic surface induced by infinitesimal bending, the present analysis takes into account large through-to-thickness curvature variations, whose knowledg can plays a key role for a wide class of mechanical applications.
Ten years of critical review on database forensics research

The database is at the heart of any digital application. With the increased use of high-tech applications, the database is used to store important and sensitive information. Sensitive information storage leads to crimes related to computer activities. Digital forensics is an investigation process to discover any un-trusted or malicious movement, which can be presented as testimony in a court of law. Database forensics is a subfield of digital forensics which focuses on detailed analysis of a database including its contents, log files, metadata, and data files depending on the type of database used. Database forensics research is in its mid age and has not got awareness as compare to digital forensics research. The reason behind this is the internal complications of the database as well as the different dimensions to be considered for analysis. This review paper is focusing on the last ten years of research related to forensic analysis of relational and NoSQL databases along with the study of artifacts to be considered for database forensics. This review of the current state of database forensics research will serve as a resource to move forward as far as research and investigation are concerned.
Generative learning for imbalanced data using the Gaussian mixed model

Imbalanced data classification, an important type of classification task, is challenging for standard learning algorithms. There are different strategies to handle the problem, as popular imbalanced learning technologies, data level imbalanced learning methods have elicited ample attention from researchers in recent years. However, most data level approaches linearly generate new instances by using local neighbor information rather than based on overall data distribution. Differing from these algorithms, in this study, we develop a new data level method, namely, generative learning (GL), to deal with imbalanced problems. In GL, we fit the distribution of the original data and generate new data on the basis of the distribution by adopting the Gaussian mixed model. Generated data, including synthetic minority and majority classes, are used to train learning models. The proposed method is validated through experiments performed on real-world data sets. Results show that our approach is competitive and comparable with other methods, such as SMOTE, SMOTE-ENN, SMOTE-TomekLinks, Borderline-SMOTE, and safe-level-SMOTE. Wilcoxon signed rank test is applied, and the testing results show again the significant superiority of our proposal.
An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism

Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks.
A fork() in the road

The received wisdom suggests that Unix's unusual combination of fork() and exec() for process creation was an inspired design. In this paper, we argue that fork was a clever hack for machines and programs of the 1970s that has long outlived its usefulness and is now a liability. We catalog the ways in which fork is a terrible abstraction for the modern programmer to use, describe how it compromises OS implementations, and propose alternatives.
A Trust-Aware Mechanism for Cloud Federation Formation

Cloud providers can form cloud federations by pooling their resources together to balance their loads, reduce their costs, and manage demand spikes. However, forming cloud federations is a challenging problem, especially when considering the incentives of the cloud providers making their own decisions to participate in cloud federations. In this paper, we model the formation of cloud federations necessary to provide resources to execute Map-heavy/Reduce-heavy programs while considering the trust and reputation among the participating cloud providers. The objective is to form cloud federations with highly reputable cloud providers that achieve maximum profit for their participation. This is an NP-hard bicriteria optimization problem. We introduce a coalitional graph game, called trust-aware cloud federation formation game, to model the cooperation among cloud providers. We design a mechanism for cloud federation formation that enables the cloud providers with high reputation to organize into federations reducing their costs. Our proposed mechanism guarantees the highest profits for the participating cloud providers in the federations, and ensures high reliability of the formed federations in executing the applications. We perform extensive experiments to characterize the properties of the proposed mechanism. The results show that our proposed mechanism produces Pareto optimal and stable cloud federations that not only guarantee that the participating cloud providers have high reputation, but also high individual profits.
dAppER: Decentralised Application for Examination Review

dAppER is to provide an automated quality assurance mechanism for the internal procedures put in place for the production of exam papers and their respective assessment schemes. Higher Education Providers (HEP) in the UK, or for that matter Universities worldwide, have a duty to meet the quality standards and requirements set out in their respective curricula and programme specifications. Such requirements usually include internal moderation of unseen examination papers and independent review by an external examiner to ensure necessary checks have taken place. These checks are likely to be scrutinised during audit trails. Permissioned blockchain facilitates the secure distribution of the exam paper, whilst maintaining an immutable and trusted ledger for audits. The system allows transparency that is ideal for External Examiners and Auditors to view. A prototype was developed using Hy-perLedger and implemented on three mock exam papers. The results demonstrate that dAppER can be successfully implemented and provide some insights into whether the development of decentralised applications complements quality assurance systems in general.
Bio-inspired voice evaluation mechanism

Decision support systems become a very important part of our lives. Technologies make them applicable almost everywhere. These systems can simplify solutions to the numerous problems, speed up analysis of the results of medical research and contribute to the rapid classification of input patterns. Practical applications may not only contribute to efficient technology, but also improve important aspects of our lives. In this article, we propose a model of decision support system for speech processing. Proposed mechanism can be used in various applications, where voice sample can be evaluated by the use of the proposed methodology. The proposed solution is based on analysis of the speech signal through the developed intelligent technique in which the signal is processed by the composed mathematical transform cooperating with bio-inspired algorithm and spiking neural network to evaluate possible voice problems. A novelty of our idea is the approach to the topic from a different side, because graphical representations of audio signals and heuristic methods are composed for feature extraction. The results are discussed after extensive comparisons in terms of advantages and disadvantages of the proposed approach. As a part of the conducted research, we demonstrated which transformations and heuristic algorithms work better in the process of voice analysis.
Subword Regularization and Beam Search Decoding for End-to-end Automatic Speech Recognition

In this paper, we experiment with the recently introduced subword regularization technique [1] in the context of end-to-end automatic speech recognition (ASR). We present results from both attention-based and CTC-based ASR systems on two common benchmark datasets, the 80 hour Wall Street Journal corpus and 1,000 hour Librispeech corpus. We also introduce a novel subword beam search decoding algorithm that significantly improves the final performance of the CTC-based systems. Overall, we find that subword regularization improves the performance of both types of ASR systems, with the regularized attention-based model performing best overall.
Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment

The artificial potential field approach provides a simple and effective motion planner for robot navigation. However, the traditional artificial potential field approach in practice can have a local minimum problem, i.e., the attractive force from the target position is in the balance with the repulsive force from the obstacle, such that the robot cannot escape from this situation and reach the target. Moreover, the moving object detection and avoidance is still a challenging problem with the current artificial potential field method. In this paper, we present an improved version of the artificial potential field method, which uses a dynamic window approach to solve the local minimum problem and define a danger index in the speed field for moving object avoidance. The new danger index considers not only the relative distance between the robot and the obstacle, but also the relative velocity according to the motion of the moving objects. In this way, the robot can find an optimized path to avoid local minimum and moving obstacles, which is proved by our experimental results.
An ontological approach to representing the product life cycle

The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organiza tions. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end of life. We call this suite the Product Life Cycle (PLC) Ontologies. The suite extends proximately from the Common Core Ontologies (CCO) used widely in defense and intelligence circles, and ultimately from the Basic Formal Ontology (BFO), which serves as top level ontology for the CCO and for some 300 further ontologies. The PLC Ontologies were developed together, but they have been factored to cover particular domains such as design, manufacturing processes, and tools. We argue that these ontologies, when used together with standard public domain alignment and browsing tools created within the context of the Semantic Web, may offer a low-cost approach to solving increasingly costly problems of data management in the manufacturing industry.
Sign Language Detection “in the Wild” with Recurrent Neural Networks

We propose a multi-layer RNN for sign language detection. The system uses features extracted automatically from a 2-stream convolutional neural network (CNN) that takes video image data and motion data as input. We also created a dataset of videos containing signing "in the wild" to be used for training and evaluation purposes. We compare our system against the state-of-the-art, and attain an improvement of around 18%, indicating that our network is able to leverage dynamic information of hand motion during detection.
A Readability Analysis of Online Cardiovascular Disease-Related Health Education Materials

Online cardiovascular health materials are easily accessible with an Internet connection, but the readability of its content may limit practical use by patients.The goal of our study was to assess the readability of the most commonly searched Internet health education materials for cardiovascular diseases accessed via Google.We selected 20 commonly searched cardiovascular disease terms: aneurysm, angina, atherosclerosis, cardiomyopathy, congenital heart disease, coronary artery disease, deep vein thrombosis, heart attack, heart failure, high blood pressure, pericardial disease, peripheral arterial disease, rheumatic heart disease, stroke, sudden death, valvular heart disease, mini-stroke, lower extremity edema, pulmonary embolism, and exertional dyspnea. Terms were selected on Google and selected up to 10 results in order of presentation in the search results by reviewing a maximum of 15 pages of Google search results specifically providing education toward patients to yield 196 total patient education articles.All readability measures assessing grade level measures found the 196 articles were written at a mean 10.9 (SD = 1.8) grade reading level. Moreover, 99.5% of the articles were written beyond the 5th- to 6th-grade level recommended by the American Medical Association.Given the prominent use of online patient education material, we consider readability as a quality metric that should be evaluated prior to online publication of any health education materials. Further study of how to improve the readability of online materials may enhance patient education, engagement, and health outcomes.
Improving Genetic Programming with Novel Exploration - Exploitation Control

Low population diversity is recognized as a factor in premature convergence of evolutionary algorithms. We investigate program synthesis performance via grammatical evolution. We focus on novelty search – substituting the conventional search objective – based on synthesis quality, with a novelty objective. This prompts us to introduce a new selection method named knobelty. It parametrically balances exploration and exploitation by creating a mixed population of parents. One subset is chosen based on performance quality and the other subset is chosen based on diversity. Three versions of this method, two that adaptively tune balance during evolution solve program synthesis problems more accurately, faster and with less duplication than grammatical evolution with lexicase selection.
A Low-latency Sparse-Winograd Accelerator for Convolutional Neural Networks

Low-latency and low-power implementations of Convolutional Neural Network (CNN) are highly desired for budget-restricted scenarios. Pruning and Winograd algorithm are two representative approaches to reduce the computation complexity of CNNs. Coupling them is very attractive, but the Winograd transformation removes data sparsity brought by pruning. In this paper, we present a low-latency sparse-Winograd CNN accelerator (LSW-CNN) for pruned Wino-grad CNN models. The ReLU-modified algorithm is employed to solve the zero refilling issue. Our design fully leverages the sparsity in both weights and activations, and thus eliminates all unnecessary computation and cycles. Moreover, a novel fast mask indexing algorithm for sparse data compression is developed. Accumulation buffers are scaled to reduce the latency brought by irregular serial channel merging. On VGG-16, experimental results demonstrate that the latency of LSW-CNN is reduced by 5.1 and 1.7 times, respectively, compared with state-of-the-art dense-Winograd and sparse-Winograd accelerators. Besides, the consumed hardware resource is also significantly reduced.
Quantitative Measurement of Tool Embodiment for Virtual Reality Input Alternatives

Virtual reality (VR) strives to replicate the sensation of the physical environment by mimicking people's perceptions and experience of being elsewhere. These experiences are of-ten mediated by the objects and tools we interact with in the virtual world (e.g., a controller). Evidence from psychology posits that when using the tool proficiently, it becomes em-bodied (i.e., an extension of one's body). There is little work,however, on how to measure this phenomenon in VR, andon how different types of tools and controllers can affect the experience of interaction. In this work, we leverage cognitive psychology and philosophy literature to construct the Locus-of-Attention Index (LAI), a measure of tool embodiment. We designed and conducted a study that measures readiness-to-hand and unreadiness-to-hand for three VR interaction techniques: hands, a physical tool, and a VR controller. The study shows that LAI can measure differences in embodiment with working and broken tools and that using the hand directly results in more embodiment than using controllers.