nils-herrmann/clean_scirepeval huggingface dataset:
Using Kinect for hand tracking and rendering in wearable haptics

Wearable haptic devices with poor position sensing are combined with the Kinect depth sensor by Microsoft. A heuristic hand tracker has been developed. It allows for the animation of the hand avatar in the virtual reality and the implementation of the force rendering algorithm: the position of the fingertips is measured by the hand tracker designed and optimized for Kinect, and the rendering algorithm computes the contact forces for wearable haptic display. Preliminary experiments with qualitative results show the effectiveness of the idea of combining Kinect and wearable haptics.
How Many Topics? Stability Analysis for Topic Models

Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic. Despite the diversity of topic modeling algorithms that have been proposed, a common challenge in successfully applying these techniques is the selection of an appropriate number of topics for a given corpus. Choosing too few topics will produce results that are overly broad, while choosing too many will result in the“over-clustering” of a corpus into many small, highly-similar topics. In this paper, we propose a term-centric stability analysis strategy to address this issue, the idea being that a model with an appropriate number of topics will be more robust to perturbations in the data. Using a topic modeling approach based on matrix factorization, evaluations performed on a range of corpora show that this strategy can successfully guide the model selection process.
Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.
SLSA: A Sentiment Lexicon for Standard Arabic

Sentiment analysis has been a major area of interest, for which the existence of highquality resources is crucial. In Arabic, there is a reasonable number of sentiment lexicons but with major deficiencies. The paper presents a large-scale Standard Arabic Sentiment Lexicon (SLSA) that is publicly available for free and avoids the deficiencies in the current resources. SLSA has the highest up-to-date reported coverage. The construction of SLSA is based on linking the lexicon of AraMorph with SentiWordNet along with a few heuristics and powerful back-off. SLSA shows a relative improvement of 37.8% over a state-of-theart lexicon when tested for accuracy. It also outperforms it by an absolute 3.5% of F1-score when tested for sentiment analysis.
Wi-fi backscatter: internet connectivity for RF-powered devices

RF-powered computers are small devices that compute and communicate using only the power that they harvest from RF signals. While existing technologies have harvested power from ambient RF sources (e.g., TV broadcasts), they require a dedicated gateway (like an RFID reader) for Internet connectivity. We present Wi-Fi Backscatter, a novel communication system that bridges RF-powered devices with the Internet. Specifically, we show that it is possible to reuse existing Wi-Fi infrastructure to provide Internet connectivity to RF-powered devices. To show Wi-Fi Backscatter's feasibility, we build a hardware prototype and demonstrate the first communication link between an RF-powered device and commodity Wi-Fi devices. We use off-the-shelf Wi-Fi devices including Intel Wi-Fi cards, Linksys Routers, and our organization's Wi-Fi infrastructure, and achieve communication rates of up to 1 kbps and ranges of up to 2.1 meters. We believe that this new capability can pave the way for the rapid deployment and adoption of RF-powered devices and achieve ubiquitous connectivity via nearby mobile devices that are Wi-Fi enabled.
Short text understanding through lexical-semantic analysis

Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing methods cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text processing such as topic modeling. Third, short texts are usually more ambiguous. We argue that knowledge is needed in order to better understand short texts. In this work, we use lexical-semantic knowledge provided by a well-known semantic network for short text understanding. Our knowledge-intensive approach disrupts traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are effective in harvesting semantics of short texts.
Computationally Efficient Truncated Nuclear Norm Minimization for High Dynamic Range Imaging

Matrix completion is a rank minimization problem to recover a low-rank data matrix from a small subset of its entries. Since the matrix rank is nonconvex and discrete, many existing approaches approximate the matrix rank as the nuclear norm. However, the truncated nuclear norm is known to be a better approximation to the matrix rank than the nuclear norm, exploiting a priori target rank information about the problem in rank minimization. In this paper, we propose a computationally efficient truncated nuclear norm minimization algorithm for matrix completion, which we call TNNM-ALM. We reformulate the original optimization problem by introducing slack variables and considering noise in the observation. The central contribution of this paper is to solve it efficiently via the augmented Lagrange multiplier (ALM) method, where the optimization variables are updated by closed-form solutions. We apply the proposed TNNM-ALM algorithm to ghost-free high dynamic range imaging by exploiting the low-rank structure of irradiance maps from low dynamic range images. Experimental results on both synthetic and real visual data show that the proposed algorithm achieves significantly lower reconstruction errors and superior robustness against noise than the conventional approaches, while providing substantial improvement in speed, thereby applicable to a wide range of imaging applications.
Closed-loop IGBT gate drive featuring highly dynamic di/dt and dv/dt control

In this paper, a closed-loop active IGBT gate drive providing highly dynamic diC/dt and dvCE/dt control is proposed. By means of using only simple passive measurement circuits for the generation of the feedback signals and a single operational amplifier as PI-controller, high analog control bandwidth is achieved enabling the application even for switching times in the sub-microsecond range. Therewith, contrary to state of the art gate drives, the parameter dependencies and nonlinearities of the IGBT are compensated enabling accurately specified and constant diC/dt and dvCE/dt values of the IGBT for the entire load and temperature range. This ensures the operation of an IGBT in the safe operating area (SOA), i.e. with limited turn-on peak reverse recovery current and turn-off overvoltage, and permits the restriction of electromagnetic interference (EMI). A hardware prototype is built to experimentally verify the proposed closed-loop active gate drive concept.
A portable ARINC 653 standard interface

The ARINC 653 specification defines the functionality that an operating system (OS) must guarantee to enforce robust spatial and temporal partitioning as well as an avionics application programming interface for the system. The standard application interface - the ARINC 653 application executive (APEX) - is defined as a set of software services a compliant OS must provide to avionics application developers. The ARINC 653 specification defines the interfaces and the behavior of the APEX but leaves implementation details to OS vendors. This paper describes an OS independent design approach of a portable APEX interface. POSIX, as a programming interface available on a wide range of modern OS, will be used to implement the APEX layer. This way the standardization of the APEX is taken a step further: not only the definition of services is standardized but also its interface to the underlying OS. Therefore, the APEX operation does not depend on a particular OS but relies on a well defined set of standardized components.
Intelligent adaptive cruise control system design and implementation

Advanced driver assistance systems (ADAS) have a critical role in the development of the active safety systems for vehicles. There are various sub technologies like Adaptive cruise control (ACC), Collision avoidance system, Blind spot detection etc. under ADAS. All these technologies are also accepted as the preliminary technology of autonomous driving. Therefore, during development of these technologies using a system of system (SOS) control approach would help both decreasing the development costs and unifying all these technologies under autonomous driving. In this paper, a SOS based intelligent ACC system design is proposed. The ACC system has high level control, low level control and sensor units.
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.
Location-Based Recommendation System Using Bayesian User's Preference Model in Mobile Devices

As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to user’s physical location. As increasing information and services, it becomes difficult to find a proper service that reflects the individual preference at proper time. Due to the small screen of mobile devices and insufficiency of resources, personalized services and convenient user interface might be useful. In this paper, we propose a map-based personalized recommendation system which reflects user's preference modeled by Bayesian Networks (BN). The structure of BN is built by an expert while the parameter is learned from the dataset. The proposed system collects context information, location, time, weather, and user request from the mobile device and infers the most preferred item to provide an appropriate service by displaying onto the mini map.
Differences in finger length ratios between self-identified "butch" and "femme" lesbians.

There is indirect evidence that heightened exposure to early androgen may increase the probability that a girl will develop a homosexual orientation in adulthood. One such putative marker of early androgen exposure is the ratio of the length of the index finger (2D) to the ring finger (4D), which is smaller in male humans than in females, and is smaller in lesbians than in heterosexual women. Yet there is also evidence that women may have different sexual orientations at different times in their lives, which suggests that other influences on female sexual orientation, presumably social, are at work as well. We surveyed individuals from a gay pride street fair and found that lesbians who identified themselves as "butch" had a significantly smaller 2D:4D than did those who identified themselves as "femme." We conclude that increased early androgen exposure plays a role in only some cases of female homosexuality, and that the sexual orientation of "femme" lesbians is unlikely to have been influenced by early androgens.
Green Manufacturing: An Evaluation of Environmentally Sustainable Manufacturing Practices and Their Impact on Competitive Outcomes

Increasingly, stakeholders are asking or requiring organizations to be more environmentally responsible with respect to their products and processes; reasons include regulatory requirements, product stewardship, public image, and potential competitive advantages. This paper presents an exploratory study of the relationships between specific environmentally sustainable manufacturing practices, and specific competitive outcomes in an environmentally important but under-researched industry, the U.S. commercial carpet industry. In general, empirical research on the impact of environmental practices on organizational outcomes is inconclusive, partly due to limitations of earlier studies. This paper addresses some of these limitations, and surveys the entire U.S. commercial carpet industry; respondents represent 84 of the market. Findings suggest that environmentally sustainable manufacturing practices may be positively associated with competitive outcomes. In particular, different types of environmentally sustainable manufacturing practices (e.g., pollution prevention, product stewardship) are associated with different competitive outcomes (e.g., manufacturing cost, product quality). These specific findings can be helpful to engineering and operations managers as they respond to environmental and competitive demands.
MT Quality Estimation for E-Commerce Data

In this paper we present a system that automatically estimates the quality of machine translated segments of e-commerce data without relying on reference translations. Such approach can be used to estimate the quality of machine translated text in scenarios in which references are not available. Quality estimation (QE) can be applied to select translations to be postedited, choose the best translation from a pool of machine translation (MT) outputs, or help in the process of revision of translations, among other applications. Our approach is based on supervised machine learning algorithms that are used to train models that predict post-editing effort. The post-editing effort is measured according to the translation error rate (TER) between machine translated segments against their human post-edits. The predictions are computed at the segment level and can be easily extended to any kind of text ranging from item titles to item descriptions. In addition, our approach can be applied to different kinds of e-commerce data (e.g. different categories of products). Our models explore linguistic information regarding the complexity of the source sentence, the fluency of the translation in the target language and the adequacy of the translation with respect to its source sentence. In particular, we show that the use of named entity recognition systems as one source of linguistic information substantially improves the models’ performance. In order to evaluate the efficiency of our approach, we evaluate the quality scores assigned by the QE system (predicted TER) against the human posteditions (real TER) using the Pearson correlation coefficient.
Big-data security management issues

Big data phenomenon arises from the increasing number of data collected from various sources, including the internet. Big data is not only about the size or volume. Big data posses specific characteristics (volume, variety, velocity, and value - 4V) that make it difficult to manage from security point of view. The evolution of data to become big data rises another important issues about data security and its management. NIST defines guide for conducting risk assessments on data, including risk management process and risk assessment. This paper looks at NIST risk management guidance and determines whether the approach of this standard is applicable to big data by generally define the threat source, threat events, vulnerabilities, likelihood of occurence and impact. The result of this study will be a general framework defining security management on Big data.
Gaussian random number generators

Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.
Tracing Information Flows Between Ad Exchanges Using Retargeted Ads

Numerous surveys have shown that Web users are concerned about the loss of privacy associated with online tracking. Alarmingly, these surveys also reveal that people are also unaware of the amount of data sharing that occurs between ad exchanges, and thus underestimate the privacy risks associated with online tracking. In reality, the modern ad ecosystem is fueled by a flow of user data between trackers and ad exchanges. Although recent work has shown that ad exchanges routinely perform cookie matching with other exchanges, these studies are based on brittle heuristics that cannot detect all forms of information sharing, especially under adversarial conditions. In this study, we develop a methodology that is able to detect clientand server-side flows of information between arbitrary ad exchanges. Our key insight is to leverage retargeted ads as a tool for identifying information flows. Intuitively, our methodology works because it relies on the semantics of how exchanges serve ads, rather than focusing on specific cookie matching mechanisms. Using crawled data on 35,448 ad impressions, we show that our methodology can successfully categorize four different kinds of information sharing behavior between ad exchanges, including cases where existing heuristic methods fail. We conclude with a discussion of how our findings and methodologies can be leveraged to give users more control over what kind of ads they see and how their information is shared between ad exchanges.
Halo: a technique for visualizing off-screen objects

As users pan and zoom, display content can disappear into off-screen space, particularly on small-screen devices. The clipping of locations, such as relevant places on a map, can make spatial cognition tasks harder. Halo is a visualization technique that supports spatial cognition by showing users the location of off-screen objects. Halo accomplishes this by surrounding off-screen objects with rings that are just large enough to reach into the border region of the display window. From the portion of the ring that is visible on-screen, users can infer the off-screen location of the object at the center of the ring. We report the results of a user study comparing Halo with an arrow-based visualization technique with respect to four types of map-based route planning tasks. When using the Halo interface, users completed tasks 16-33% faster, while there were no significant differences in error rate for three out of four tasks in our study.
MATLAB / Simulink Based Modelling of Solar Photovoltaic Cell

This paper focuses on a Matlab/SIMULINK model of a photovoltaic cell. This model is based on mathematical equations and is described through an equivalent circuit including a photocurrent source, a diode, a series resistor and a shunt resistor. The developed model allows the prediction of PV cell behaviour under different physical and environmental parameters. The model can also be used to extract the physical parameters for a given solar PV cell as a function of temperature and solar radiation. In addition, this study outlines the working principle of PV module as well as PV array. In order to validate the developed model, an experimental test bench was built and the obtained results exhibited a good agreement with the simulation ones. Matlab, SIMULINK, PV, solar cell model, solar array model, solar radiation, maximum power point
Challenges, issues and trends in fall detection systems

Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls.
Efficient 3D Pointing Selection in Cluttered Virtual Environments

In this article, we study the impact of such eye-hand visibility mismatch on selection tasks performed with hand-rooted pointing techniques. We propose a new mapping for ray control, called Ray Casting from the Eye (RCE), which attempts to overcome this mismatch's negative effects. In essence, RCE combines the benefits of image-plane techniques (the absence of visibility mismatch and continuity of the ray movement in screen space) with the benefits of ray control through hand rotation (requiring less physical hand movement). This article builds on a previous study on the impact of eye-to-hand separation on 3D pointing selection. Here, we provide empirical evidence that RCE clearly outperforms classic ray casting (RC) selection, both in sparse and cluttered scenes.