nils-herrmann/clean_scirepeval huggingface dataset:
Experimental study platform of the automatic transfer switch used to power supplies back-up

This paper presents the practical implementation of an experimental platform used for the automatic transfer switch to the back-up power source. The automatic transfer switch concept represents the procedure of fast connection of the electrical consumers to a back-up electric power source. The connection to the back-up power source is necessary in the event of failure of the main power source. The experimental platform is designed to operate in automatic standard mode, manual mode and through a programmable logic controller. The selection's mode of operation is determined by the operation's key. The simulation of grid faults on the power supply is done by means of the controls and the variable voltage regulators located on the experimental platform. The automatic transfer switch is conditioned by the effective status of the power supply and minimum voltage allowed by devices. The signaling status of the power supply is performed based on the information received from relays of interface and programmable logic controllers. The platform presents to users two technologies able to achieve the same function. Thus, in case of the classic mode of the automatic transfer switch, the wired technology is used and this involves many components and related links. In case of the automatic transfer switch based on programmable logic controller, a technology based on programming is also implemented. Using this platform, the advantage of implementation of programmable logic controllers versus wired technology in industrial applications can be observed.
An efficient classification model for detecting advanced persistent threat

Among most of the cyber attacks that occured, the most drastic are advanced persistent threats. APTs are differ from other attacks as they have multiple phases, often silent for long period of time and launched by adamant, well-funded opponents. These targeted attacks mainly concentrated on government agencies and organizations in industries, as are those involved in international trade and having sensitive data. APTs escape from detection by antivirus solutions, intrusion detection and intrusion prevention systems and firewalls. In this paper we proposes a classification model having 99.8% accuracy, for the detection of APT.
Data Governance Framework for Big Data Implementation with a Case of Korea

Big Data governance requires a data governance that can satisfy the needs for corporate governance, IT governance, and ITA/EA. While the existing data governance focuses on the processing of structured data, Big Data governance needs to be established in consideration of a broad sense of Big Data services including unstructured data. To achieve the goals of Big Data, strategies need to be established together with goals that are aligned with the vision and objective of an organization. In addition to the preparation of the IT infrastructure, a proper preparation of the components is required to effectively implement the strategy for Big Data services. We propose the Big Data Governance Framework in this paper. The Big Data governance framework presents criteria different from existing criteria at the data quality level. It focuses on timely, reliable, meaningful, and sufficient data services, focusing on what data attributes should be achieved based on the data attributes of Big Data services. In addition to the quality level of Big Data, the personal information protection strategy and the data disclosure/accountability strategy are also needed to achieve goals and to prevent problems. This paper performed case analysis based on the Big Data Governance Framework with the National Pension Service of South Korea. Big Data services in the public sector are an inevitable choice to improve the quality of people's life. Big Data governance and its framework are the essential components for the realization of Big Data service.
Security Aspects of the Extended Playfair Cipher

The well known multiple letter encryption cipher is the Playfair cipher. Here the digrams in the plaintext are treated as single units and converted into corresponding cipher text digrams. However because of the drawbacks inherent in the 5*5 Playfair cipher which adversely affects the security we proposed an 8*8 Playfair cipher and then coupled it with LFSR (Linear Feedback Shift Register) to make the traditional Playfair cipher at par with the advanced ciphers available like AES and DES. For details one can refer to . Now for all practical applications, performance and speed are also prime concerns besides security. The LFSR not only enhances the security up to a considerable level by generating random sequences but also provides a much faster rate of encryption and decryption. This paper deals in with the security issues of the new proposed system. Various types of cryptography attacks have been taken under consideration and the resistance of the proposed cipher to all these attacks has been discussed. At the end we find that the proposed cipher is not vulnerable to attacks.
Robust 3D Action Recognition with Random Occupancy Patterns

We study the problem of action recognition from depth sequences captured by depth cameras, where noise and occlusion are common problems because they are captured with a single commodity camera. In order to deal with these issues, we extract semi-local features called random occupancy pattern (ROP) features, which employ a novel sampling scheme that effectively explores an extremely large sampling space. We also utilize a sparse coding approach to robustly encode these features. The proposed approach does not require careful parameter tuning. Its training is very fast due to the use of the high-dimensional integral image, and it is robust to the occlusions. Our technique is evaluated on two datasets captured by commodity depth cameras: an action dataset and a hand gesture dataset. Our classification results are superior to those obtained by the state of the art approaches on both datasets.
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)

The simple, but general formal theory of fun and intrinsic motivation and creativity (1990-2010) is based on the concept of maximizing intrinsic reward for the active creation or discovery of novel, surprising patterns allowing for improved prediction or data compression. It generalizes the traditional field of active learning, and is related to old, but less formal ideas in aesthetics theory and developmental psychology. It has been argued that the theory explains many essential aspects of intelligence including autonomous development, science, art, music, and humor. This overview first describes theoretically optimal (but not necessarily practical) ways of implementing the basic computational principles on exploratory, intrinsically motivated agents or robots, encouraging them to provoke event sequences exhibiting previously unknown, but learnable algorithmic regularities. Emphasis is put on the importance of limited computational resources for online prediction and compression. Discrete and continuous time formulations are given. Previous practical, but nonoptimal implementations (1991, 1995, and 1997-2002) are reviewed, as well as several recent variants by others (2005-2010). A simplified typology addresses current confusion concerning the precise nature of intrinsic motivation.
UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory

In this work we train in an end-to-end manner a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a unified architecture. Such a network can act like a swiss knife for vision tasks, we call it an UberNet to indicate its overarching nature. The main contribution of this work consists in handling challenges that emerge when scaling up to many tasks. We introduce techniques that facilitate (i) training a deep architecture while relying on diverse training sets and (ii) training many (potentially unlimited) tasks with a limited memory budget. This allows us to train in an end-to-end manner a unified CNN architecture that jointly handles (a) boundary detection (b) normal estimation (c) saliency estimation (d) semantic segmentation (e) human part segmentation (f) semantic boundary detection, (g) region proposal generation and object detection. We obtain competitive performance while jointly addressing all tasks in 0.7 seconds on a GPU. Our system will be made publicly available.
A multifaceted approach to understanding the botnet phenomenon

The academic community has long acknowledged the existence of malicious botnets, however to date, very little is known about the behavior of these distributed computing platforms. To the best of our knowledge, botnet behavior has never been methodically studied, botnet prevalence on the Internet is mostly a mystery, and the botnet life cycle has yet to be modeled. Uncertainty abounds. In this paper, we attempt to clear the fog surrounding botnets by constructing a multifaceted and distributed measurement infrastructure. Throughout a period of more than three months, we used this infrastructure to track 192 unique IRC botnets of size ranging from a few hundred to several thousand infected end-hosts. Our results show that botnets represent a major contributor to unwanted Internet traffic - 27% of all malicious connection attempts observed from our distributed darknet can be directly attributed to botnet-related spreading activity. Furthermore, we discovered evidence of botnet infections in 11% of the 800,000 DNS domains we examined, indicating a high diversity among botnet victims. Taken as a whole, these results not only highlight the prominence of botnets, but also provide deep insights that may facilitate further research to curtail this phenomenon.
Cloud computing-based forensic analysis for collaborative network security management system

Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service (DDoS) attacks on victim hosts. New emerging botnet attacks degrade the status of Internet security further. To address these problems, a practical collaborative network security management system is proposed with an effective collaborative Unified Threat Management (UTM) and traffic probers. A distributed security overlay network with a centralized security center leverages a peer-to-peer communication protocol used in the UTMs collaborative module and connects them virtually to exchange network events and security rules. Security functions for the UTM are retrofitted to share security rules. In this paper, we propose a design and implementation of a cloud-based security center for network security forensic analysis. We propose using cloud storage to keep collected traffic data and then processing it with cloud computing platforms to find the malicious attacks. As a practical example, phishing attack forensic analysis is presented and the required computing and storage resources are evaluated based on real trace data. The cloudbased security center can instruct each collaborative UTM and prober to collect events and raw traffic, send them back for deep analysis, and generate new security rules. These new security rules are enforced by collaborative UTM and the feedback events of such rules are returned to the security center. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly
Perching and takeoff of a robotic insect on overhangs using switchable electrostatic adhesion

For aerial robots, maintaining a high vantage point for an extended time is crucial in many applications. However, available on-board power and mechanical fatigue constrain their flight time, especially for smaller, battery-powered aircraft. Perching on elevated structures is a biologically inspired approach to overcome these limitations. Previous perching robots have required specific material properties for the landing sites, such as surface asperities for spines, or ferromagnetism. We describe a switchable electroadhesive that enables controlled perching and detachment on nearly any material while requiring approximately three orders of magnitude less power than required to sustain flight. These electroadhesives are designed, characterized, and used to demonstrate a flying robotic insect able to robustly perch on a wide range of materials, including glass, wood, and a natural leaf.
Image-based visual navigation for mobile robots

We introduce a new image-based visual navigation algorithm that allows the Cartesian velocity of a robot to be defined with respect to a set of visually observed features corresponding to previously unseen and unmapped world points. The technique is well suited to mobile robot tasks such as moving along a road or flying over the ground. We describe the algorithm in general form and present detailed simulation results for an aerial robot scenario using a spherical camera and a wide angle perspective camera, and present experimental results for a mobile ground robot.
Multilingualism (but not always bilingualism) delays the onset of Alzheimer disease: evidence from a bilingual community.

A recent paper by Bialystok et al in Neuropsychologia (vol. 45, pgs. 459 to 464) suggested that early bilingualism produced a statistically significant 4.1-year delay in onset of memory loss symptoms in older individuals with Alzheimer disease, possibly reflecting an increase in the cognitive reserve of these individuals. That study focused on multilingual elderly patients of whom 90% were immigrants. Our memory clinic, in Montreal Canada, has the advantage of having a large set of individuals who are either multilingual immigrants to Canada, or who are nonimmigrants but raised in both official languages of Canada--French and English. We thus attempted to replicate the above findings using a larger cohort in a different setting. We examined age at diagnosis of Alzheimer disease and age at symptom onset for all unilingual versus multilingual participants, and then for those who were nonimmigrant English/French bilinguals. Overall, we found a small but significant protective effect of more than 2 languages spoken, but we found no significant benefit in bilinguals overall in relation to age at diagnosis or age at symptom onset. However, in the immigrant group, the results mirrored those of Bialystok et al with 2 or more languages delaying the diagnosis of Alzheimer disease by almost 5 years. A trend toward the same effect was also seen in nonimmigrants whose first language was French. In contrast, in nonimmigrants whose first language was English, no such effect was found. These results are discussed in relation to the earlier findings and the theory of cognitive reserve.
Independence and Bipolarity in the Structure of Current Affect

The independence of positive and negative affect has been heralded as a major and counterintuitive finding in the psychology of mood and emotion. Still, other findings support the older view that positive and negative fall at opposite ends of a single bipolar continuum. Independence versus bipolarity can be reconciled by considering (a) the activation dimension of affect, (b) random and systematic measurement error, and (c) how items are selected to achieve an appropriate test of bipolarity. In 3 studies of self-reported current affect, random and systematic error were controlled through multiformat measurement and confirmatory factor analysis. Valence was found to be independent of activation, positive affect the bipolar opposite of negative affect, and deactivation the bipolar opposite of activation. The dimensions underlying D. Watson, L. A. Clark, and A. Tellegen's (1988) Positive and Negative Affect schedule were accounted for by the valence and activation dimensions.
Social Issues in wireless sensor networks with healthcare perspective

The recent advances in Wireless Sensor Networks have given rise to many application areas in healthcare. It has produced new field of Wireless Body Area Networks. Using wearable and non-wearable sensor devices humans can be tracked and monitored. Monitoring from the healthcare perspective can be with or without the consent of the particular person. Even if it is with the consent of the person involved, certain social issues arise from this type of application scenario. The issues can be privacy, security, legal and other related issues. Healthcare sensor networks applications have a bright future and it is a must to take up these issues at the earliest. The issues should be carefully studied and understood or else they can pose serious problems. In this paper we try to raise and discuss these issues and find some answers to them.
Deep Learning Neural Network with Semi supervised Segmentation for Predicting Retinal and Cancer Cell Diseased Images.

In medical field, diagnosis of diseases competently carried out by using the image processing. So that to retrieve the relevant data from the amalgamation of resulting image is too difficult. Here the segmentation done by semi supervised learning then the result is tuned by using Deep Learning Neural Network. Higher tuning of results will leads to efficient detection of disease. The experiment done by using retinal image data sets in order to predict any disease affected or not. The aim of this paper is to keenly predict diseased image or not by the efficient tuning of image. segmentation; semi supervised learning; Neural network; Deep learning neural network.
A Passive Gait-Based Weight-Support Lower Extremity Exoskeleton With Compliant Joints

This paper presents the design and analysis of a passive body weight (BW)-support lower extremity exoskeleton (LEE) with compliant joints to relieve compressive load in the knee. The biojoint-like mechanical knee decouples human gait into two phases, stance and swing, by a dual snap fit. The knee joint transfers the BW to the ground in the stance phases and is compliant to free the leg in the swing phases. Along with a leg dynamic model and a knee biomechanical model, the unmeasurable knee internal forces are simulated. The concept feasibility and dynamic models of the passive LEE design have been experimentally validated with measured plantar forces. The reduced knee forces confirm the effectiveness of the LEE in supporting human BW during walking and also provide a basis for computing the internal knee forces as a percentage of BW. Energy harvested from the hip spring reveals that the LEE can save human walking energy.
Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology

Efforts to develop more effective depression treatments are limited by assessment methods that rely on patient-reported or clinician judgments of symptom severity. Depression also affects speech. Research suggests several objective voice acoustic measures affected by depression can be obtained reliably over the telephone. Thirty-five physician-referred patients beginning treatment for depression were assessed weekly, using standard depression severity measures, during a six-week observational study. Speech samples were also obtained over the telephone each week using an IVR system to automate data collection. Several voice acoustic measures correlated significantly with depression severity. Patients responding to treatment had significantly greater pitch variability, paused less while speaking, and spoke faster than at baseline. Patients not responding to treatment did not show similar changes. Telephone standardization for obtaining voice data was identified as a critical factor influencing the reliability and quality of speech data. This study replicates and extends previous research with a larger sample of patients assessing clinical change associated with treatment. The feasibility of obtaining voice acoustic measures reflecting depression severity and response to treatment using computer-automated telephone data collection techniques is also established. Insight and guidance for future research needs are also identified.
IT Governance Maturity: Developing a Maturity Model Using the Delphi Method

To advance in maturity, organizations should pay attention to both the hard and soft sides of IT governance (ITG). The hard side is related to processes and structure, the soft side to social aspects like behavior and organizational culture. This paper describes a study to develop an ITG maturity model (MM) that includes both. Our research method is based on literature study, the Delphi method and makes use of a Group Decision Support System. We chose to design a focus area MM. In this type of MM maturity is determined by a set of focus areas. The study reveals one MM as being appropriate for hard ITG. For soft ITG we found no single model appropriate. Soft governance needs more specific capabilities defined for each focus area individually. Based on knowledge from literature and experts we selected models for each focus area. Three alternatives for informal organization need further research.
Generalized Simulated Annealing

We propose a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and can be quicker than both.  Simulated annealing; Nonconvex optimization; Gradient descent; Generalized Statistical Mechanics.
A design process for the development of innovative smart clothing that addresses end-user needs from technical, functional, aesthetic and cultural view points

The research and development of attractive smart clothing, with truly embedded technologies, demands the merging of science and technology with art and design. This paper looks at a comparatively new and unique design discipline that has been given little prior consideration. The concept of 'wearables' crosses the boundaries between many disciplines. A gap exists for a common 'language' that facilitates creativity and a systematic design process. Designers of smart clothing require guidance in their enquiry, as gaining an understanding of issues such as; usability, manufacture, fashion, consumer culture, recycling and end user needs can seem forbidding and difficult to prioritise. This paper presents a design tool in a format that can be understood by practitioners who come from a range of backgrounds. The representation of the 'critical path' is intended as a tool to guide the design research and development process in the application of smart technologies.