nils-herrmann/clean_scirepeval huggingface dataset:
Techniques for Software Maintenance

Software maintenance constitutes a major phase of the software life cycle. Studies indicate that software maintenance is responsible for a significant percentage of a system’s overall cost and effort. The software engineering community has identified four major types of software maintenance, namely, corrective, perfective, adaptive, and preventive maintenance. Software maintenance can be seen from two major points of view. First, the classic view where software maintenance provides the necessary theories, techniques, methodologies, and tools for keeping software systems operational once they have been deployed to their operational environment. Most legacy systems subscribe to this view of software maintenance. The second view is a more modern emerging view, where maintenance is an integral part of the software development process and it should be applied from the early stages in the software life cycle. Regardless of the view by which we consider software maintenance, the fact is that it is the driving force behind software evolution, a very important aspect of a software system. This entry provides an in-depth discussion of software maintenance techniques, methodologies, tools, and emerging trends.
Wearable Sensors for Human Activity Monitoring: A Review

An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.
Mechanical State Estimation for Overhead Transmission Lines With Level Spans

Electric power transmission lines face increased threats from malicious attacks and natural disasters. This underscores the need to develop new techniques to ensure safe and reliable transmission of electric power. This paper deals with the development of an online monitoring technique based on mechanical state estimation to determine the sag levels of overhead transmission lines in real time and hence determine if these lines are in normal physical condition or have been damaged or downed. A computational algorithm based on least squares state estimation is applied to the physical transmission line equations to determine the conductor sag levels from measurements of tension, temperature, and other transmission line conductor parameters. The estimated conductor sag levels are used to generate warning signals of vertical clearance violations in the energy management system. These warning signals are displayed to the operator to make appropriate decisions to maintain the line within the prescribed clearance limits and prevent potential cascading failures.
A New Approach to Train Convolutional Neural Networks for Real-Time 6-DOF Camera Relocalization

Recent proposed Machine Learning methods for the regression problem of Camera Relocalization achieved satisfactory results by training Convolutional Neural Networks as regressors. However, the main point is that Convolutional Neural Networks are mainly proposed to solve classification problems and perform well when we have sufficient data from each label. In the regression problems such as camera relocalization, the floating point output space has a wide range and number of training data for each label of the output space is low. In this paper, a new approach to train Convolutional Neural Networks for regression problems such as 6-DOF camera relocalization is proposed which converts the problem into a classification one and then generalizes it to regression.
LATERALLY BRACED COLD-FORMED STEEL FLEXURAL MEMBERS WITH EDGE STIFFENED FLANGES

The moment capacity of a laterally braced cold-formed steel flexural member with edge stiffened flanges (e.g., a channel or zee section) may be affected adversely by local or distortional buckling. New procedures for hand prediction of the buckling stress in the local and distortional mode are presented and verified. Numerical investigations are employed to highlight postbuckling behavior unique to the distortional mode. Compared with the local mode, the distortional mode is shown to have (1) heightened imperfection sensitivity, (2) lower postbuckling capacity, and (3) the ability to control the failure mechanism even in cases when the elastic buckling stress in the local mode is lower than in the distortional mode. Traditional design methods do not explicitly recognize distortional buckling, nor do they account for the observed phenomena in this mode. A new design method that integrates distortional buckling into the unified effective width approach, currently used in most cold-formed steel design specifications, is presented. For each element a local buckling stress and a reduced distortional buckling stress are compared to determine the effective width. Comparison with experimental tests shows that the new approach is more consistent and reliable than existing design methods.
Edge computing enabling the Internet of Things

Mobile Edge Computing (MEC), a new concept that emerged about a year ago, integrating the IT and the Telecom worlds will have a great impact on the openness of the Telecom market. Furthermore, the virtualization revolution that has enabled the Cloud computing success will benefit the Telecom domain, which in turn will be able to support the IaaS (Infrastructure as a Service). The main objective of MEC solution is the export of some Cloud capabilities to the user's proximity decreasing the latency, augmenting the available bandwidth and decreasing the load on the core network. On the other hand, the Internet of Things (IoT), the Internet of the future, has benefited from the proliferation in the mobile phones' usage. Many mobile applications have been developed to connect a world of things (wearables, home automation systems, sensors, RFID tags etc.) to the Internet. Even if it is not a complete solution for a scalable IoT architecture but the time sensitive IoT applications (e-healthcare, real time monitoring, etc.) will profit from the MEC architecture. Furthermore, IoT can extend this paradigm to other areas (e.g. Vehicular Ad-hoc NETworks) with the use of Software Defined Network (SDN) orchestration to cope with the challenges hindering the IoT real deployment, as we will illustrate in this paper.
CO-CREATION EXPERIENCES : THE NEXT PRACTICE IN VALUE CREATION

CREATION onsumers today have more choices of products and services than ever before, but they seem dissatisfied. Firms invest in greater product variety but are less able to differentiate themselves. Growth and value creation have become the dominant themes for managers. In this paper, we explain this paradox. The meaning of value and the process of value creation are rapidly shifting from a product-and firm-centric view to personalized consumer experiences. Informed, networked, empowered, and active consumers are increasingly co-creating value with the firm. The interaction between the firm and the consumer is becoming the locus of value creation and value extraction. As value shifts to experiences, the market is becoming a forum for conversation and interactions between consumers, consumer communities, and firms. It is this dialogue, access, transparency, and understanding of risk-benefits that is central to the next practice in value creation.
An Empirical Study on End-to-End Sentence Modelling

Accurately representing the meaning of a piece of text, otherwise known as sentence modelling, is an important component in many natural language inference tasks. We survey the spectrum of these methods, which lie along two dimensions: input representation granularity and composition model complexity. Using this framework, we reveal in our quantitative and qualitative experiments the limitations of the current state-of-the-art model in the context of sentence similarity tasks.
Fleischner Society: glossary of terms for thoracic imaging.

Members of the Fleischner Society compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984 and 1996 for thoracic radiography and computed tomography (CT), respectively. The need to update the previous versions came from the recognition that new words have emerged, others have become obsolete, and the meaning of some terms has changed. Brief descriptions of some diseases are included, and pictorial examples (chest radiographs and CT scans) are provided for the majority of terms.
The mechanics of trust: A framework for research and design

With an increasing number of technologies supporting transactions over distance and replacing traditional forms of interaction, designing for trust in mediated interactions has become a key concern for researchers in HCI. While much of this research focuses on increasing users’ trust, we present a framework that shifts the perspective towards factors that support trustworthy behavior. In a second step, we analyze how the presence of these factors can be signaled. We argue that it is essential to take a systemic perspective for enabling well-placed trust and trustworthy behavior in the long term. For our analysis we draw on relevant research from sociology, economics, and psychology, as well as HCI. We identify contextual properties (motivation based on temporal, social, and institutional embeddedness) and the actor’s intrinsic properties (ability, and motivation based on internalized norms and benevolence) that form the basis of trustworthy behavior. Our analysis provides a frame of reference for the design of studies on trust in technologymediated interactions, as well as a guide for identifying trust requirements in design processes. We demonstrate the application of the framework in three scenarios: call centre interactions, B2C e-commerce, and voice-enabled online gaming.
Dinus Intelligent Assistance (DINA) Chatbot for University Admission Services

Nowadays, universities offer most of their services using corporate website. In higher education services including admission services, a university needs to always provide excellent service to ensure student candidate satisfaction. To obtain student candidate satisfaction apart from the quality of education must also be accompanied by providing consultation services and information to them. This paper proposes the development of Chatbot which acts as a conversation agent that can play a role of as student candidate service. This Chatbot is called Dinus Intelligent Assistance (DINA). DINA uses knowledge based as a center for machine learning approach. The pattern extracted from the knowledge based can be used to provide responses to the user. The source of knowledge based is taken from Universitas Dian Nuswantoro (UDINUS) guest book. It contains of questions and answers about UDINUS admission services. Testing of this system is done by entering questions. From 166 intents, the author tested it using ten random sample questions. Among them, it got eight tested questions answered correctly. Therefore, by using this study we can develop further intelligent Chatbots to help student candidates find the information they need without waiting for the admission staffs's answer.
Solvent-free microwave extraction of essential oil from aromatic herbs: from laboratory to pilot and industrial scale.

Solvent-free microwave extraction (SFME) has been proposed as a green method for the extraction of essential oil from aromatic herbs that are extensively used in the food industry. This technique is a combination of microwave heating and dry distillation performed at atmospheric pressure without any added solvent or water. The isolation and concentration of volatile compounds is performed in a single stage. In this work, SFME and a conventional technique, hydro-distillation HD (Clevenger apparatus), are used for the extraction of essential oil from rosemary (Rosmarinus officinalis L.) and are compared. This preliminary laboratory study shows that essential oils extracted by SFME in 30min were quantitatively (yield and kinetics profile) and qualitatively (aromatic profile) similar to those obtained using conventional hydro-distillation in 2h. Experiments performed in a 75L pilot microwave reactor prove the feasibility of SFME up scaling and potential industrial applications.
Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web

With the growth of the internet, the number of fake-news online has been proliferating every year. The consequences of such phenomena are manifold, ranging from lousy decision-making process to bullying and violence episodes. Therefore, fact-checking algorithms became a valuable asset. To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source. However, most of the widely used Web indicators have either been shutdown to the public (e.g., Google PageRank) or are not free for use (Alexa Rank). Further existing databases are short-manually curated lists of online sources, which do not scale. Finally, most of the research on the topic is theoretical-based or explore confidential data in a restricted simulation environment. In this paper we explore current research, highlight the challenges and propose solutions to tackle the problem of classifying websites into a credibility scale. The proposed model automatically extracts source reputation cues and computes a credibility factor, providing valuable insights which can help in belittling dubious and confirming trustful unknown websites. Experimental results outperform state of the art in the 2-classes and 5-classes setting.
CLOUD COMPUTING AND SECURITY ISSUES IN THE CLOUD

Cloud computing has formed the conceptual and infrastructural basis for tomorrow’s computing. The global computing infrastructure is rapidly moving towards cloud based architecture. While it is important to take advantages of could based computing by means of deploying it in diversified sectors, the security aspects in a cloud based computing environment remains at the core of interest. Cloud based services and service providers are being evolved which has resulted in a new business trend based on cloud technology. With the introduction of numerous cloud based services and geographically dispersed cloud service providers, sensitive information of different entities are normally stored in remote servers and locations with the possibilities of being exposed to unwanted parties in situations where the cloud servers storing those information are compromised. If security is not robust and consistent, the flexibility and advantages that cloud computing has to offer will have little credibility. This paper presents a review on the cloud computing concepts as well as security issues inherent within the context of cloud computing and cloud
Practicing Safe Computing: A Multimedia Empirical Examination of Home Computer User Security Behavioral Intentions

By: Catherine L. Anderson Decision, Operations, and Information Technologies Department Robert H. Smith School of Business University of Maryland Van Munching Hall College Park, MD 20742-1815 U.S.A. Catherine_Anderson@rhsmith.umd.edu Ritu Agarwal Center for Health Information and Decision Systems University of Maryland 4327 Van Munching Hall College Park, MD 20742-1815 U.S.A.
DCFNet: Deep Neural Network with Decomposed Convolutional Filters

Filters in a Convolutional Neural Network (CNN) contain model parameters learned from enormous amounts of data. In this paper, we suggest to decompose convolutional filters in CNN as a truncated expansion with pre-fixed bases, namely the Decomposed Convolutional Filters network (DCFNet), where the expansion coefficients remain learned from data. Such a structure not only reduces the number of trainable parameters and computation, but also imposes filter regularity by bases truncation. Through extensive experiments, we consistently observe that DCFNet maintains accuracy for image classification tasks with a significant reduction of model parameters, particularly with Fourier-Bessel (FB) bases, and even with random bases. Theoretically, we analyze the representation stability of DCFNet with respect to input variations, and prove representation stability under generic assumptions on the expansion coefficients. The analysis is consistent with the empirical observations.
How do you know they are learning? The importance of alignment in higher education

The success of any learning environment is determined by the degree to which there is adequate alignment among eight critical factors: 1) goals, 2) content, 3) instructional design, 4) learner tasks, 5) instructor roles, 6) student roles, 7) technological affordances, and 8) assessment. Evaluations of traditional, online, and blended approaches to higher education teaching indicate that the most commonly misaligned factor is assessment. Simply put, instructors may have lofty goals, high-quality content, and even advanced instructional designs, but most instructors tend to focus their assessment strategies on what is easy to measure rather than on what is important. Adequate assessment should encompass all four learning domains: cognitive, affective, conative, and psychomotor. This paper describes procedures for the development and use of reliable and valid assessments in higher education.
Security Scenario Generator (SecGen): A Framework for Generating Randomly Vulnerable Rich-scenario VMs for Learning Computer Security and Hosting CTF Events

Computer security students benefit from hands-on experience applying security tools and techniques to attack and defend vulnerable systems. Virtual machines (VMs) provide an effective way of sharing targets for hacking. However, developing these hacking challenges is time consuming, and once created, essentially static. That is, once the challenge has been "solved" there is no remaining challenge for the student, and if the challenge is created for a competition or assessment, the challenge cannot be reused without risking plagiarism, and collusion. Security Scenario Generator (SecGen) can build complex VMs based on randomised scenarios, with a number of diverse use-cases, including: building networks of VMs with randomised services and in-thewild vulnerabilities and with themed content, which can form the basis of penetration testing activities; VMs for educational lab use; and VMs with randomised CTF challenges. SecGen has a modular architecture which can dynamically generate challenges by nesting modules, and a hints generation system, which is designed to provide scaffolding for novice security students to make progress on complex challenges. SecGen has been used for teaching at universities, and hosting a recent UK-wide CTF event.
RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations

We present a highly-flexible UIMA-based pipeline for developing structural kernelbased systems for relational learning from text, i.e., for generating training and test data for ranking, classifying short text pairs or measuring similarity between pieces of text. For example, the proposed pipeline can represent an input question and answer sentence pairs as syntacticsemantic structures, enriching them with relational information, e.g., links between question class, focus and named entities, and serializes them as training and test files for the tree kernel-based reranking framework. The pipeline generates a number of dependency and shallow chunkbased representations shown to achieve competitive results in previous work. It also enables easy evaluation of the models thanks to cross-validation facilities.
ENORM: A Framework For Edge NOde Resource Management

Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a PokéMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20%-80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95%. These results highlight the potential of fog computing for improving the quality of service and experience.
Road model prediction based unstructured road detection

Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots. In outdoor unstructured environments such as villages and deserts, the roads are usually not well-paved and have variant colors or texture distributions. Traditional region- or edge-based approaches, however, are effective only in specific environments, and most of them have weak adaptability to varying road types and appearances. In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images. The main difference between our proposed algorithm and previous ones is that, before road detection, an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model. This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road. Moreover, a temporal smoothing mechanism is incorporated, which further makes both model prediction and region classification more stable. Experimental results demonstrate that compared with traditional region- and edge-based algorithms, our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCNmodel improves state-of-the-art baselines on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.