Reviewerly/exHarmony huggingface dataset:
An Enterprise Feature Ontology for Feature‐Based Product Line Engineering

ABSTRACT Feature‐based software and systems product line engineering (feature‐based PLE) has emerged as a modern, repeatable, codified, and proven‐in‐practice specialization of generic product line engineering (PLE) practice. Feature‐based PLE involves an automation‐supported configuration of engineering and operations artifacts from across an enterprise to reflect the feature choices a product embodies. Configuration of artifacts based on feature choices is a powerful narrative, but what actually constitutes a feature? A feature is a distinguishing characteristic that sets products apart (Kang et al. 1990), but in practice this concept encompasses a broad spectrum of granularity. Stakeholders who are concerned with the finest‐grained differences are very different from those who care about the coarsest‐grained differences. This paper presents an ontology for features that is capable of supporting an entire enterprise of product line engineering and business operations, and shows how various stakeholders throughout the enterprise can interact with these different kinds of features.
Grey Wolf Optimizer-Based Fractional MPPT for Thermoelectric Generator

The energy harvested from a thermoelectric generator (TEG) relies mostly on the difference in temperature between the hot side and cold side of the TEG along with the connected load.Hence, a reliable maximum power point tracker is needed to force the TEG to operate close to the maximum power point (MPP) with any variation during the operation.In the current work, an optimized fractional maximum power point tracker (OFMPPT) is proposed to improve the performance of the TEG.The proposed tracker is based on fractional control.The optimal parameters of the OFMPPT have been determined using the grey wolf optimizer (GWO).To prove the superiority of GWO, the results are compared with particle swarm optimization (PSO) and genetic algorithm (GA).The largest fitness function, the lowest standard deviation, and the maximum efficiency are achieved by GWO.The goal of the suggested OFMPPT is to overcome the two main issues in conventional trackers.They are the slow dynamic of the traditional incremental resistance tracker (INRT) and high steady-state fluctuation around the MPP in the very common perturb & observe tracker (POT).The main finding confirmed the superiority of OFMPPT compared with INRT and POT for both dynamic and steady-state responses.
Comprehensive assessment of groundwater quality using heavy metal pollution indices and geospatial technique: a case study from Wanaparthy watershed of upper Krishna River basin, Telangana, India

The present study is to characterize groundwater quality using heavy metal pollution indices and geospatial variations. A total of 58 samples from hand pump/submersible bore wells were collected from the Wanaparthy watershed of the upper Krishna River basin according to the grid size (5×6 km2). The trace elements' concentration in groundwater samples are found in the order of Zn (38.67%) > B (32.67%) > Ba (13.59%) > As (8.49%) > Hg (3.71%) > Cr (1.28%) > Ni (0.52%) > Cd (0.47%). Among these trace elements, arsenic (22.4%) and mercury (5.1%) were found above the permissible limits of WHO drinking water guideline values. A positive correlation between TH versus EC/TDS indicates the presence of trace elements due to chemical reaction (rock–water interaction). Arsenic correlation with EC/TDS/TH indicates artificial intervention. Drainage network analysis enumerates high concentration of parameters at near or joining to upper order of drainage system, which might be due to input of runoff water (interaction of variable rocks composition) and later stage infiltration to subsurface and reached to an aquifer. Heavy metal pollution index (HPI) showed 86.2% of samples are in the category of low class, whereas 12.07% of samples fall within medium class. According to metal index (MI) classification, 12.07% samples are in very pure, 24.14% samples are pure, while the remaining 63.79% samples are in the slightly to strongly affect category. This study suggested the main source of trace elements in groundwater might be from the dominant granitoid rocks because the area is mostly devoid of industrialization.
Multi-Channel Interactive Reinforcement Learning for Sequential Tasks

The ability to learn new tasks by sequencing already known skills is an important requirement for future robots. Reinforcement learning is a powerful tool for this as it allows for a robot to learn and improve on how to combine skills for sequential tasks. However, in real robotic applications, the cost of sample collection and exploration prevent the application of reinforcement learning for a variety of tasks. To overcome these limitations, human input during reinforcement can be beneficial to speed up learning, guide the exploration and prevent the choice of disastrous actions. Nevertheless, there is a lack of experimental evaluations of multi-channel interactive reinforcement learning systems solving robotic tasks with input from inexperienced human users, in particular for cases where human input might be partially wrong. Therefore, in this paper, we present an approach that incorporates multiple human input channels for interactive reinforcement learning in a unified framework and evaluate it on two robotic tasks with 20 inexperienced human subjects. To enable the robot to also handle potentially incorrect human input we incorporate a novel concept for self-confidence, which allows the robot to question human input after an initial learning phase. The second robotic task is specifically designed to investigate if this self-confidence can enable the robot to achieve learning progress even if the human input is partially incorrect. Further, we evaluate how humans react to suggestions of the robot, once the robot notices human input might be wrong. Our experimental evaluations show that our approach can successfully incorporate human input to accelerate the learning process in both robotic tasks even if it is partially wrong. However, not all humans were willing to accept the robot's suggestions or its questioning of their input, particularly if they do not understand the learning process and the reasons behind the robot's suggestions. We believe that the findings from this experimental evaluation can be beneficial for the future design of algorithms and interfaces of interactive reinforcement learning systems used by inexperienced users.
An efficient approach for reviewing security-related aspects in agile requirements specifications of web applications

Defects in requirement specifications can have severe consequences during the software development life cycle. Some of them may result in poor product quality and/or time and budget overrun due to incorrect or missing quality characteristics, such as security. This characteristic requires special attention in web applications because they have become a target for manipulating sensible data. Several concerns make security difficult to deal with. For instance, security requirements are often misunderstood and improperly specified due to lack of security expertise and emphasis on security during early stages of software development. This often leads to unspecified or ill-defined security-related aspects. These concerns become even more challenging in agile contexts, where lightweight documentation is typically produced. To tackle this problem, we designed an approach for reviewing security-related aspects in agile requirements specifications of web applications. Our proposal considers user stories and security specifications as inputs and relates those user stories to security properties via natural language processing. Based on the related security properties, our approach identifies high-level security requirements from the Open Web Application Security Project (OWASP) to be verified and generates a reading technique to support reviewers in detecting defects. We evaluate our approach via three experimental trials conducted with 56 novice software engineers, measuring effectiveness, efficiency, usefulness and ease of use. We compare our approach against using: (1) the OWASP high-level security requirements and (2) a perspective-based approach as proposed in contemporary state of the art. The results strengthen our confidence that using our approach has a positive impact (with large effect size) on the performance of inspectors in terms of effectiveness and efficiency.
Fast Nested Key Equation Solvers for Generalized Integrated Interleaved Decoder

Generalized integrated interleaved (GII) codes nest Reed-Solomon (RS) or BCH sub-codewords to generate codewords belonging to stronger RS or BCH codes. Their hyper-speed decoding and good error-correction capability make them one of the best candidates for next-generation terabit/s digital storage and communications. The key equation solver (KES) in the nested decoding stage causes clock frequency bottleneck and takes a large portion of the GII decoder area. Recent architectures reduce the critical path to two multipliers and rely on the application of the slow-down technique to further reduce it to one. The slow-down technique requires two sub-codewords to be interleaved in the nested KES. However, most of the time, the nested decoding only needs to be carried out on one sub-codeword and half of the clock cycles are wasted. This paper proposes two fast nested KES algorithms, both of which have one multiplier in the critical path without applying slow-down and accordingly reduce the latency of the nested KES to almost a half. The short critical path is achieved by algorithmic reformulations that enable the pre-computation of the scalars in parallel with polynomial updating. Our second design adopts scaled versions of the polynomials to enable product term sharing so that the number of multipliers in each pair of processing elements is reduced from 8 as in the first design to 4. Novel scaling and combined scalar computations are developed to keep the critical path one multiplier. For an example GII code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(2^{8})$ </tex-math></inline-formula> that has 3 nested codewords, our designs achieve 49.9% reduction on the number of clock cycles needed in the nested KES compared to prior designs. Besides, our second design requires 22% less area than the first one under the same timing constraint.
Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis

Sentiment Analysis intends to get the basic perspective of the content, which may be anything that holds a subjective supposition, for example, an online audit, Comments on Blog posts, film rating and so forth. These surveys and websites might be characterized into various extremity gatherings, for example, negative, positive, and unbiased keeping in mind the end goal to concentrate data from the info dataset. Supervised machine learning strategies group these reviews. In this paper, three distinctive machine learning calculations, for example, Support Vector Machine (SVM), Maximum Entropy (ME) and Naive Bayes (NB), have been considered for the arrangement of human conclusions. The exactness of various strategies is basically inspected keeping in mind the end goal to get to their execution on the premise of parameters, e.g. accuracy, review, f-measure, and precision.
A Lightweight Blockchain for IoT in Smart City (IoT-SmartChain)

The smart city is a technological framework that connects the city's different components to create new opportunities.This connection is possible with the help of the Internet of Things (IoT), which provides a digital personality to physical objects.Some studies have proposed integrating Blockchain technology with IoT in different use cases as access, orchestration, or replicated storage layer.The majority of connected objects' capacity limitation makes the use of Blockchain inadequate due to its redundancy and its conventional processing-intensive consensus like PoW.This paper addresses these challenges by proposing a NOVEL model of a lightweight Blockchain framework (IoT-SmartChain), with a lightweight consensus and a lightweight structure.The framework architecture presents a role hierarchy of connected objects according to their computational and storage capacity.This organization allows all things to be linked even indirectly via different interfaces and to benefit from the power of high-capacity objects such as Fog and Edge computing nodes.Data is validated and added to the blockchain ledger by running a lightweight consensus called Proof of Random Participation (PoRP), which reduces the blockchain nodes' high computing power requirement.The TOPIC subscription-based data storage strategy called Assisted Selected Relevant Data in Local Ledger (ASRDLL) reduces the data size of a node's local ledger and the entire network's data size.This strategy is assisted by a centralized algorithm that optimizes the overall network size by adjusting the choice of TOPICS.The storage capacity, computational power, and energy consumption have been evaluated by a proof of concept implementation under NodeJS.
Literally, Ourselves

Previous articleNext article No AccessLiterally, OurselvesKris CohenKris Cohen Search for more articles by this author Kris Cohen is associate professor of art and humanities at Reed College. He works on the relationships between art, economy, and media technology, focusing especially on the aesthetics of collective life. His first book, Never Alone, Except for Now (2017), addresses these concerns in the context of electronic networks. His current research explores the way that black artists, working in the wake of the Black Arts Movement, engaged a set of earlier computational technologies.PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Critical Inquiry Volume 46, Number 1Autumn 2019 Article DOIhttps://doi.org/10.1086/705299 Views: 740Total views on this site © 2019 by The University of Chicago. All rights reserved.PDF download Crossref reports no articles citing this article.
Solution of Differential Equations

Many practical problems involve the study of how rates of change in two or more variables are interrelated. Often the independent variable is time. These problems give rise naturally to differential equations which enable us to understand how the real-world works and how it changes dynamically. In this chapter, we describe a range of widely used algorithms to solve differential equations.
Mathematical foundations

This chapter introduces some of the mathematical foundations so as to help gain in-depth insight into the algorithms and their algorithmic structures in later chapters. It includes convexity, complexity, entropy, norms, probability, sampling, Monte Carlo, and others.
Neurodesign Live

Neurodesign is a novel field of research, education and practice that emerges as a cross-disciplinary initiative. In 2019, the Hasso Plattner Institute (HPI) offered for the first time a neurodesign curriculum. The objective of neurodesign as we pursue it is to explore synergies at the intersection of (i) neuroscience, (ii) engineering and (iii) design thinking · creativity · collaboration · innovation. In this chapter, we share insights into the development of a curriculum that quickly became more comprehensive than we had anticipated for this initial implementation phase. Neurodesign evolves serendipitously driven by the passions of numerous protagonists who contribute their expertise, ideas and work results in a uniquely collaborative fashion. The chapter briefly summarizes input provided by neuroscientists and creative engineers from several countries and different continents, who contributed guest expert talks at the HPI to help build up a joint knowledge base. The major part of the chapter is a review of neurodesign projects that have emerged, often in collaboration with guest experts of the program. Overall, these projects indicate how intersections of neurodesign (i)–(ii)–(iii) open up cornucopias of opportunities. Especially the integration of engineering expertise has introduced many favourable dynamics. In terms of strategic reflections, this chapter shares “missions” we pursue in the development of neurodesign. These directions for further initiatives also commence a brief outlook on upcoming neurodesign developments.
Experiences on teaching alloy with an automated assessment platform

This paper presents Alloy4Fun, a web application that enables online editing and sharing of Alloy models and instances (including dynamic ones developed with the Electrum extension), to be used mainly in an educational context. By introducing secret paragraphs and commands in the models, Alloy4Fun allows the distribution and automated assessment of simple specification challenges, a mechanism that enables students to learn the language at their own pace. Alloy4Fun stores all versions of shared and analyzed models, as well as derivation trees that depict how they evolved over time: this wealth of information can be mined by researchers or tutors to identify, for example, learning breakdowns in the class or typical mistakes made by Alloy users. A data analysis library is also provided to support this process. Alloy4Fun has been used in formal methods graduate courses for 3 years and for the latest editions we present results regarding its adoption by the students, as well as preliminary insights regarding the most common bottlenecks when learning Alloy (and Electrum).
Bayesian Classification

This chapter covers the first of four image classificationImage classification methods in the book. It begins with a brief introduction of Bayesian theorem and demonstrates how Bayesian theorem works using two examples. The rest of the chapter focuses on applications of Bayesian theorem on image classificationImage classification . It begins with describing how images can be classified using a simple Naïve BayesianNaïve Bayesian (NB) classifier and its variants. The NBNaïve Bayesian (NB) methods give readers the opportunity to relate image data with Bayesian theorem before contacting with other sophisticated Bayesian methods. After these preparations, three more powerful and nonparametric Bayesian methods are described in algorithmic forms and illustrations. In the next, more advanced methods such as parametric method and Gaussian processGaussian process are discussed with illustrations.
A Study on Decentralized Autonomous Organizations Based Intelligent Transportation System enabled by Blockchain and Smart Contract

With the rapid development of the transportation industry, Intelligent Transportation System (ITS) tries to adapt to industry changes through constructing new organizational forms, management methods, and incentive mechanisms. However, many new governance issues have arisen due to the transportation system’s complexity, diversity, and uncertainty. DAO(Decentralized autonomous organization) enabled by blockchain and smart contracts are regarded as effective methods to solve the organizational governance problems for complex systems. Therefore, this article analyzes the main issues currently faced by ITS. Then, this paper proposed a 6-layer DAO structure framework for ITS and discussed its key research issues. Finally, a case study for the Macau transportation system is carried out, and further research directions are proposed. The DAO-based transportation system will represent the future trend of ITS development. This paper aims to motivate more discussion for this new direction and provide helpful guidance and reference for future research work.
An innovative topic-based customer complaints sentiment classification system

Text mining helps to convert big text data into a small amount of most relevant data for a particular problem, and also helps providing knowledge provenance and interpreting patterns. Text mining has to do with further analysing the relevant data to discover the actionable knowledge that can be directly useful for decision making or many other tasks. In the previous work, authors have used conditional entropy with maxima/minima boundaries in identifying aspects from given text in identifying aspects. In the present work, we concentrated on mining word associations using mutual information in measuring co-occurrence of words. The objective of our research is to propose a novel probabilistic latent aspect rating regression (LARR) model. In which, natural language processing is used to represent the text, discovering and analysing topics using expectation maximisation (EM) algorithm. In addition to that, we compared the performance of the proposed model with multiple ordinal logistic regression classifier (MLRC) and fuzzy the rating obtained from the model. The finding in our research is the use of mutual information to mine synatagmatic relations helps in identifying the aspect at document level at greater extend, intern helps to multi level sentiment polarity prediction.
Secure search service based on word2vec in the public cloud

For the retrieval of data in the cloud, existing studies mostly generate vectors based on dictionaries, but these vectors do not respond well to the semantic information of articles. In this paper, we propose two safety retrieval schemes based on word2vec (SSSW-1 and SSSW-2). By establish a model through word2vec training method, we will generate index vectors directly for keywords. And the index vector can also reflect the semantic relationship of the document so as to improve the accuracy of the retrieval. Cosine measure will be used to calculate the similarity scores. Experiments based on real data show that the two schemes are effective and feasible.
Channel Attention based Network for LiDAR Super-resolution

LiDAR is indispensable in the field of autonomous driving. LiDARs with more scan-lines captures more environ-mental information and provides denser cloud points, which is beneficial for tasks like structure from motion (SFM), SLAM and autonomous driving. However, the cost for a high-resolution LiDAR is unignorable. Applying super-resolution (SR) on point cloud obtained from low-resolution LiDAR to get denser data is a promising solution. Based on the previous use of neural network SR methods, we proposed a channel attention network for LiDAR point clouds SR. Specifically ,the proposed network can restore a higher precision Hi-res point cloud via range image SR. Additionally, for the problem of edge reconstruction after projection into a range image, we proposed to use circle padding to achieve a good result. Furthermore, we conduct the experiments using dataset collected in the simulation environment and compare out methods with the traditional approaches and network methods. Results show that our method gets the best effect on the proposed evaluation metric.
Feature Selection

As detailed in Chap. 4 , features have been extracted from the pre-processed data. Too many features may lead to the curse of dimensionality issues. This chapter explains how to obtain a set of relevant features which is the process of feature selection. Feature selection is the process in which most informative variables are selected for the generation of the model. It helps to remove the redundant data and contributes to proper classification. While minimizing the redundancy, we should keep in mind that the predicted information must be preserved as much as possible. This chapter describes various feature selection methods such as Principal Component Analysis (PCA)-based approach, Mutual Information (MI), Bhattacharyya Distance (BD), and Independent Component Analysis (ICA). A novel feature selection based on graphical review has also been elaborated later in this chapter.