nils-herrmann/clean_scirepeval huggingface dataset:
Natural Eye Movement & its application for paralyzed patients

The noble aim behind this project is to study and capture the Natural Eye movement detection and trying to apply it as assisting application for paralyzed patients those who cannot speak or use hands such disease as amyotrophic lateral sclerosis (ALS), Guillain-Barre Syndrome, quadriplegia & heniiparesis. Using electrophySiological genereted by the voluntary contradictions of the muscles around the eye. The proposed system which is based on the design and application of an electrooculogram (EOG) based an efficient human–computer interface (HCI). Establishing an alternative channel without speaking and hand movements is important in increasing the quality of life for the handicapped. EOG-based systems are more efficient than electroencephalogram (EEG)-based systems as easy acquisition, higher amplitude, and also easily classified. By using a realized virtual keyboard like graphical user interface, it is possible to notify in writing the needs of the patient in a relatively short time. Considering the bio potential measurement pitfalls, the novel EOG-based HCI system allows people to successfully communicate with their environment by using only eye movements.  Classifying horizontal and vertical EOG channel signals in an efficient interface is realized in this study. The nearest neighbourhood algorithm will be use to classify the signals. The novel EOG-based HCI system allows people to successfully and economically communicate with their environment by using only eye movements.  An Electrooculography is a method of tracking the ocular movement, based on the voltage changes that occur due to the medications on the special orientation of the eye dipole. The resulting signal has a myriad of possible applications.  In this dissertation phase one, the goal was to study the Eye movements and respective signal generation, EOG signal acquisition and also study of a Man-Machine Interface that made use of this signal. As per our goal we studied eye movements and design simple EOG acquisition circuit. We got efficient signal output in oscilloscope. I sure that result up to present stage will definitely leads us towards designing of novel assisting device for paralyzed patients. Thus, we set out to create an interface will be use by mobility impaired patients, allowing them to use their eyes to call nurse or attended person and some other requests.  Electro Oculogram, Natural Eye movement Detection, EOG acquisition & signal conditioning, Eye based Computer interface GUI, Paralysed assisting device, Eye movement recognization
AutoStyle: Toward Coding Style Feedback at Scale

While large-scale automatic grading of student programs for correctness is widespread, less effort has focused on automating feedback for good programming style:} the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We hypothesize that with a large enough (MOOC-sized) corpus of submissions to a given programming problem, we can observe a range of stylistic mastery from naïve to expert, and many points in between, and that we can exploit this continuum to automatically provide hints to learners for improving their code style based on the key stylistic differences between a given learner's submission and a submission that is stylistically slightly better. We are developing a methodology for analyzing and doing feature engineering on differences between submissions, and for learning from instructor-provided feedback as to which hints are most relevant. We describe the techniques used to do this in our prototype, which will be deployed in a residential software engineering course as an alpha test prior to deploying in a MOOC later this year.
Rigour and grounded theory research.

Grounded theory methodology is a suitable qualitative research approach for clinical inquiry into nursing practice, leading to theory development in nursing. Given the variations in, and subjectivity attached to, the manner in which qualitative research is carried out, it is important for researchers to explain the process of how a theory about a nursing phenomenon was generated. Similarly, when grounded theory research reports are reviewed for clinical use, nurses need to look for researchers' explanations of their inquiry process.



The focus of this article is to discuss the practical application of grounded theory procedures as they relate to rigour.



Reflecting on examples from a grounded theory research study, we suggest eight methods of research practice to delineate further Beck's schema for ensuring, credibility, auditability and fittingness, which are all components of rigour.



The eight methods of research practice used to enhance rigour in the course of conducting a grounded theory research study were: (1) let participants guide the inquiry process; (2) check the theoretical construction generated against participants' meanings of the phenomenon; (3) use participants' actual words in the theory; (4) articulate the researcher's personal views and insights about the phenomenon explored; (5) specify the criteria built into the researcher's thinking; (6) specify how and why participants in the study were selected; (7) delineate the scope of the research; and (8) describe how the literature relates to each category which emerged in the theory.



The eight methods of research practice should be of use to those in nursing research, management, practice and education in enhancing rigour during the research process and for critiquing published grounded theory research reports.
Softworms: the design and control of non-pneumatic, 3D-printed, deformable robots.

Robots that can easily interact with humans and move through natural environments are becoming increasingly essential as assistive devices in the home, office and hospital. These machines need to be safe, effective, and easy to control. One strategy towards accomplishing these goals is to build the robots using soft and flexible materials to make them much more approachable and less likely to damage their environment. A major challenge is that comparatively little is known about how best to design, fabricate and control deformable machines. Here we describe the design, fabrication and control of a novel soft robotic platform (Softworms) as a modular device for research, education and public outreach. These robots are inspired by recent neuromechanical studies of crawling and climbing by larval moths and butterflies (Lepidoptera, caterpillars). Unlike most soft robots currently under development, the Softworms do not rely on pneumatic or fluidic actuators but are electrically powered and actuated using either shape-memory alloy microcoils or motor tendons, and they can be modified to accept other muscle-like actuators such as electroactive polymers. The technology is extremely versatile, and different designs can be quickly and cheaply fabricated by casting elastomeric polymers or by direct 3D printing. Softworms can crawl, inch or roll, and they are steerable and even climb steep inclines. Softworms can be made in any shape but here we describe modular and monolithic designs requiring little assembly. These modules can be combined to make multi-limbed devices. We also describe two approaches for controlling such highly deformable structures using either model-free state transition-reward matrices or distributed, mechanically coupled oscillators. In addition to their value as a research platform, these robots can be developed for use in environmental, medical and space applications where cheap, lightweight and shape-changing deformable robots will provide new performance capabilities.
The Probabilistic Model Checking Landscape*

Randomization is a key element in sequential and distributed computing. Reasoning about randomized algorithms is highly non-trivial. In the 1980s, this initiated first proof methods, logics, and model-checking algorithms. The field of probabilistic verification has developed considerably since then. This paper surveys the algorithmic verification of probabilistic models, in particular probabilistic model checking. We provide an informal account of the main models, the underlying algorithms, applications from reliability and dependability analysis---and beyond---and describe recent developments towards automated parameter synthesis.
Individual differences in children's materialism: the role of peer relations.

Associations between materialism and peer relations are likely to exist in elementary school children but have not been studied previously. The first two studies introduce a new Perceived Peer Group Pressures (PPGP) Scale suitable for this age group, demonstrating that perceived pressure regarding peer culture (norms for behavioral, attitudinal, and material characteristics) can be reliably measured and that it is connected to children's responses to hypothetical peer pressure vignettes. Studies 3 and 4 evaluate the main theoretical model of associations between peer relations and materialism. Study 3 supports the hypothesis that peer rejection is related to higher perceived peer culture pressure, which in turn is associated with greater materialism. Study 4 confirms that the endorsement of social motives for materialism mediates the relationship between perceived peer pressure and materialism.
Becoming what we love: autogynephilic transsexualism conceptualized as an expression of romantic love.

The increasing prevalence of male-to-female (MtF) transsexualism in Western countries is largely due to the growing number of MtF transsexuals who have a history of sexual arousal with cross-dressing or cross-gender fantasy. Ray Blanchard proposed that these transsexuals have a paraphilia he called autogynephilia, which is the propensity to be sexually aroused by the thought or image of oneself as female. Autogynephilia defines a transsexual typology and provides a theory of transsexual motivation, in that Blanchard proposed that MtF transsexuals are either sexually attracted exclusively to men (homosexual) or are sexually attracted primarily to the thought or image of themselves as female (autogynephilic), and that autogynephilic transsexuals seek sex reassignment to actualize their autogynephilic desires. Despite growing professional acceptance, Blanchard's formulation is rejected by some MtF transsexuals as inconsistent with their experience. This rejection, I argue, results largely from the misconception that autogynephilia is a purely erotic phenomenon. Autogynephilia can more accurately be conceptualized as a type of sexual orientation and as a variety of romantic love, involving both erotic and affectional or attachment-based elements. This broader conception of autogynephilia addresses many of the objections to Blanchard's theory and is consistent with a variety of clinical observations concerning autogynephilic MtF transsexualism.
I Still Know What You Visited Last Summer: Leaking Browsing History via User Interaction and Side Channel Attacks

History sniffing attacks allow web sites to learn about users' visits to other sites. The major browsers have recently adopted a defense against the current strategies for history sniffing. In a user study with 307 participants, we demonstrate that history sniffing remains feasible via interactive techniques which are not covered by the defense. While these techniques are slower and cannot hope to learn as much about users' browsing history, we see no practical way to defend against them.
Falls in young, middle-aged and older community dwelling adults: perceived cause, environmental factors and injury

Falls in older people have been characterized extensively in the literature, however little has been reported regarding falls in middle-aged and younger adults. The objective of this paper is to describe the perceived cause, environmental influences and resultant injuries of falls in 1497 young (20-45 years), middle-aged (46-65 years) and older (> 65 years) men and women from the Baltimore Longitudinal Study on Aging.



A descriptive study where participants completed a fall history questionnaire describing the circumstances surrounding falls in the previous two years.



The reporting of falls increased with age from 18% in young, to 21% in middle-aged and 35% in older adults, with higher rates in women than men. Ambulation was cited as the cause of the fall most frequently in all gender and age groups. Our population reported a higher percentage of injuries (70.5%) than previous studies. The young group reported injuries most frequently to wrist/hand, knees and ankles; the middle-aged to their knees and the older group to their head and knees. Women reported a higher percentage of injuries in all age groups.



This is the first study to compare falls in young, middle and older aged men and women. Significant differences were found between the three age groups with respect to number of falls, activities engaged in prior to falling, perceived causes of the fall and where they fell.
The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation

Deep reinforcement learning (DRL) demonstrates its potential in learning a model-free navigation policy for robot visual navigation. However, the data-demanding algorithm relies on a large number of navigation trajectories in training. Existing datasets supporting training such robot navigation algorithms consist of either 3D synthetic scenes or reconstructed scenes. Synthetic data suffers from domain gap to the real-world scenes while visual inputs rendered from 3D reconstructed scenes have undesired holes and artifacts. In this paper, we present a new dataset collected in real-world to facilitate the research in DRL based visual navigation. Our dataset includes 3D reconstruction for real-world scenes as well as densely captured real 2D images from the scenes. It provides highquality visual inputs with real-world scene complexity to the robot at dense grid locations. We further study and benchmark one recent DRL based navigation algorithm  and present our attempts and thoughts on improving its generalizability to unseen test targets in the scenes.
Vision and GPS-based autonomous vehicle navigation using templates and artificial neural networks

This paper presents a vehicle control system capable of learning to navigate autonomously. Our approach is based on image processing, road and navigable area identification, template matching classification for navigation control, and trajectory selection based on GPS way-points. The vehicle follows a trajectory defined by GPS points avoiding obstacles using a single monocular camera. The images obtained from the camera are classified into navigable and non-navigable regions of the environment using neural networks that control the steering and velocity of the vehicle. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.
CNTFET based Logic Circuits : A Brief Review

In this paper at first a brief review of carbon nanotubes (CNTs) is given and then an extensive survey of CNTFET (carbon nanotube field effect transistor) based logic circuits are discussed with the most recently reported CNTFET logic circuits.  MOSFETs, technology scaling, CNTFETs, carbon nanotubes (CNT), chiriality.
A circularly-polarized aperture array antenna with a corporate-feed hollow-waveguide circuit in the 60 GHz-band

The authors proposed a circular-polarized array antenna by using hexagonal radiating apertures in the 60 GHz-band. The hexagonal radiating aperture is designed, and the good axial ratio characteristics are achieved in the boresight. We analyze the full structure of the 16×16-element array that combines the 2×2-element subarrays and a 64-way divider. The reflection is less than −14dB over 4.9% bandwidth where the axial ratio is less than 2.5dB. High antenna efficiency of 88.7% is obtained at 61.5GHz with the antenna gain of 33.3dBi including losses. The 1dB-down gain bandwidth is 6.8%.
Consumerization and IT Department Conflict

After years of banning consumer devices (e.g., iPads and iPhone) and applications (e.g., DropBox, Evernote, iTunes) organizations are allowing employees to use their consumer tools in the workplace. This IT consumerization phenomenon will have serious consequences on IT departments which have historically valued control, security, standardization and support (Harris et al. 2012). Based on case studies of three organizations in different stages of embracing IT consumerization, this study identifies the conflicts IT consumerization creates for IT departments. All three organizations experienced similar goal and behavior conflicts, while identity conflict varied depending upon the organizations’ stage implementing consumer tools (e.g., embryonic, initiating or institutionalized). Theoretically, this study advances IT consumerization research by applying a role conflict perspective to understand consumerization’s impact on the IT department.
Literature Review of Information Technology Adoption Models at Firm Level

Today, information technology (IT) is universally regarded as an essential tool in enhancing the competitiveness of the economy of a country. There is consensus that IT has significant effects on the productivity of firms. These effects will only be realized if, and when, IT are widely spread and used. It is essential to understand the determinants of IT adoption. Consequently it is necessary to know the theoretical models. There are few reviews in the literature about the comparison of IT adoption models at the individual level, and to the best of our knowledge there are even fewer at the firm level. This review will fill this gap. In this study, we review theories for adoption models at the firm level used in information systems literature and discuss two prominent models: diffusion on innovation (DOI) theory, and the technology, organization, and environment (TOE) framework. The DOI found that individual characteristics, internal characteristics of organizational structure, and external characteristics of the organization are important antecedents to organizational innovativeness. The TOE framework identifies three aspects of an enterprise's context that influence the process by which it adopts and implements a technological innovation: technological context, organizational context, and environmental context. We made a thorough analysis of the TOE framework, analysing the studies that used only this theory and the studies that combine the TOE framework with other theories such as: DOI, institutional theory, and the Iacovou, Benbasat, and Dexter model. The institutional theory helps us to understand the factors that influence the adoption of interorganizational systems (IOSs); it postulates that mimetic, coercive, and normative institutional pressures existing in an institutionalized environment may influence the organization’s predisposition toward an IT-based interorganizational system. The Iacovou, Benbasat, and Dexter model, analyses IOSs characteristics that influence firms to adopt IT innovations. It is based on three contexts: perceived benefits, organizational readiness, and external pressure. The analysis of these models takes into account the empirical literature, and the difference between independent and dependent variables. The paper also makes recommendations for future research.
A mechanistic performance model for superscalar out-of-order processors

A mechanistic model for out-of-order superscalar processors is developed and then applied to the study of microarchitecture resource scaling. The model divides execution time into intervals separated by disruptive miss events such as branch mispredictions and cache misses. Each type of miss event results in characterizable performance behavior for the execution time interval. By considering an interval's type and length (measured in instructions), execution time can be predicted for the interval. Overall execution time is then determined by aggregating the execution time over all intervals. The mechanistic model provides several advantages over prior modeling approaches, and, when estimating performance, it differs from detailed simulation of a 4-wide out-of-order processor by an average of 7%.
 The mechanistic model is applied to the general problem of resource scaling in out-of-order superscalar processors. First, we use the model to determine size relationships among microarchitecture structures in a balanced processor design. Second, we use the mechanistic model to study scaling of both pipeline depth and width in balanced processor designs. We corroborate previous results in this area and provide new results. For example, we show that at optimal design points, the pipeline depth times the square root of the processor width is nearly constant. Finally, we consider the behavior of unbalanced, overprovisioned processor designs based on insight gained from the mechanistic model. We show that in certain situations an overprovisioned processor may lead to improved overall performance. Designs where a processor's dispatch width is wider than its issue width are of particular interest.
Post-ranking query suggestion by diversifying search results

Query suggestion refers to the process of suggesting related queries to search engine users. Most existing researches have focused on improving the relevance of suggested queries. In this paper, we introduce the concept of diversifying the content of the search results from suggested queries while keeping the suggestion relevant. Our framework first retrieves a set of query candidates from search engine logs using random walk and other techniques. We then re-rank the suggested queries by ranking them in the order which maximizes the diversification function that measures the difference between the original search results and the results from suggested queries. The diversification function we proposed includes features like ODP category, URL and domain similarity and so on. One important outcome from our research which contradicts with most existing researches is that, with the increase of suggestion relevance, the similarity between the queries actually decreases. Experiments are conducted on a large set of human-labeled data, which is randomly sampled from a commercial search engine's log. Results indicate that the post-ranking framework significantly improves the relevance of suggested queries by comparing to existing models.
Towards a Big Data Architecture for Facilitating Cyber Threat Intelligence

Internet and organizational network security is still threatened by devastating malicious activities. Given the continuous escalation of such attacks in terms of their frequency, sophistication and stealthiness, it is of paramount importance to generate effective cyber threat intelligence that aim at inferring, attributing, characterizing and mitigating such misdemeanors. Nevertheless, such imperative tasks are partially impeded by the lack of approaches that can produce prompt and accurate actionable intelligence by investigating various network traffic sources. In this paper, we propose and evaluate a big data architecture that is rooted in real-time network traffic processing, distributed messaging and scalable data storage. The key innovation behind the proposed architecture is that it automates the analysis of heterogeneous network data, allowing the focus to remain on devising effective cyber threat intelligence analytics, rather than being hindered by data management, aggregation, reconciliation and formatting. Empirical evaluations investigating the application of machine learning analytics by exploiting the artifacts of the proposed architecture and by using 100 GB of real network traffic, indeed demonstrate the practicality, effectiveness, and addedvalue of the proposed architecture.