nils-herrmann/clean_scirepeval huggingface dataset:
A game theory based energy efficient clustering routing protocol for WSNs

The energy constraint is one of the inherent defects of the Wireless Sensor Networks (WSNs). How to prolong the lifespan of the network has attracted more and more attention. Numerous achievements have emerged successively recently. Among these mechanisms designing routing protocols is one of the most promising ones owing to the large amount of energy consumed for data transmission. The background and related works are described firstly in detail in this paper. Then a game model for selecting the Cluster Head is presented. Subsequently, a novel routing protocol named Game theory based Energy Efficient Clustering routing protocol (GEEC) is proposed. GEEC, which belongs to a kind of clustering routing protocols, adopts evolutionary game theory mechanism to achieve energy exhaust equilibrium as well as lifetime extension at the same time. Finally, extensive simulation experiments are conducted. The experimental results indicate that a significant improvement in energy balance as well as in energy conservation compared with other two kinds of well-known clustering routing protocols is achieved.
Automatic attendance recording system using mobile telephone

These days, instructors in universities and colleges take the attendance manually either by calling out individual's name or by passing around an attendance sheet for student's signature to confirm his/her presence. Using these methods is both cumbersome and time-consuming. Therefore a method of taking attendance using instructor's mobile telephone has been presented in this paper which is paperless, quick, and accurate. An application software installed in the instructor's mobile telephone enables it to query students' mobile telephone via Bluetooth connection and, through transfer of students' mobile telephones' Media Access Control (MAC) addresses to the instructor's mobile telephone, presence of the student can be confirmed. Moreover, detailed record of a student's attendance can also be generated for printing and filing, if needed.
Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition

We present a simple and effective architecture for fine-grained recognition called <italic>Bilinear Convolutional Neural Networks (B-CNNs)</italic>. These networks represent an image as a <italic>pooled outer product</italic> of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs are related to orderless texture representations built on deep features but can be trained in an end-to-end manner. Our most accurate model obtains 84.1, 79.4, 84.5 and 91.3 percent per-image accuracy on the Caltech-UCSD birds <xref ref-type="bibr" rid="ref1">[1]</xref> , NABirds <xref ref-type="bibr" rid="ref2">[2]</xref> , FGVC aircraft <xref ref-type="bibr" rid="ref3">[3]</xref> , and Stanford cars <xref ref-type="bibr" rid="ref4">[4]</xref> dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at <uri>http://vis-www.cs.umass.edu/bcnn</uri>.
Feasibility study of a real-time wandering detection algorithm for dementia patients

In this paper, we present a new algorithm that automatically classifies wandering patterns (or behaviors) of patients with Alzheimer's disease and other different types of dementia. Experimental results on a real-life dataset show that this algorithm can provide a robust and credible assistive technology for monitoring patients with dementia (PWD) who are prone to wandering. Combined with indoor and outdoor location technologies using ubiquitous devices such as smart phones, we also demonstrate the feasibility of a remote mobile healthcare monitoring solution that is capable of reasoning about wandering behaviors of PWD and real-time detection of abnormalities that require timely intervention from caregivers.
Transformation of the IT Function at British Petroleum

In 1989 the IT function of the exploration and production division of British Petroleum Company set out to transform itself in response to a severe economic environment and poor internal perceptions of IT performance. This case study traces and analyzes the changes made over six years. The authors derive a model of the transformed IT organization comprising seven components which they suggest can guide IT departments in general as they seek to reform themselves in the late 1990's. This model is seen to fit well with recent thinking on general management in that the seven components of change can be reclassified into the Bartlett and Ghoshal (1994) framework of Purpose, Process and People. Some suggestions are made on how to apply the model in other organizations.
Failure mode and effects analysis based on a novel fuzzy evidential method

Failure mode and effect analysis (FMEA) has been widely applied to examine potential failures in systems, designs, and products. The risk priority number (RPN) is the key criteria to determine the risk priorities of the failure modes. Traditionally, the determination of RPN is based on the risk factors like occurrence (O), severity(S) and detection (D), which require to be precisely evaluated. However, this method has many irrationalities and needs to be improved for more applications. To overcome the shortcomings of the traditional FMEA and better model and process uncertainties, we propose a FMEA model based on a novel fuzzy evidential method. The risks of the risk factors are evaluated by fuzzy membership degree. As a result, a comprehensive way to rank the risk of failure modes is proposed by fusing the feature information of O, S and D with Dempster-Shafer (D-S) evidence theory. The advantages of the proposed method are that it cannot only cover the diversity and uncertainty of the risk assessment, but also improve the reliability of the RPN by data fusion. To validate the proposed method, a case study of a micro-electro-mechanical system (MEMS) is performed. The experimental results show that this method is reasonable and effective for real applications.
Parameter selection for health monitoring of electronic products

Please cite this article in press as: Kumar S doi:10.1016/j.microrel.2009.09.016 This paper presents an approach for selecting precursor parameters for health monitoring of electronic products. The approach includes failure modes, mechanisms, and effects analysis (FMMEA) and life cycle profile analysis of a product. The criticality of the failure mechanisms is established using a risk priority number (RPN), where the RPN for each failure mechanism is calculated as a product of the occurrence and the severity of each mechanism. Performance parameters that can be associated with the critical failure mechanisms should be selected for health monitoring of the product. These parameters could be used for diagnostic purposes. A case study is presented to demonstrate the parameter selection approach for a computer server system. FMMEA was performed on the server, and precursor parameters of the server were selected for monitoring based on the failure modes and mechanisms that posed the highest risk. The utilization of identified parameters for fault detection is presented through a diagnostic algorithm. This approach can be used to select parameters for health monitoring of any system.
A Low-Power, Process-and- Temperature- Compensated Ring Oscillator With Addition-Based Current Source

The design of a 1.8 GHz 3-stage current-starved ring oscillator with a process- and temperature- compensated current source is presented. Without post-fabrication calibration or off-chip components, the proposed low variation circuit is able to achieve a 65.1% reduction in the normalized standard deviation of its center frequency at room temperature and 85 ppm/ ° C temperature stability with no penalty in the oscillation frequency, the phase noise or the start-up time. Analysis on the impact of transistor scaling indicates that the same circuit topology can be applied to improve variability as feature size scales beyond the current deep submicron technology. Measurements taken on 167 test chips from two different lots fabricated in a standard 90 nm CMOS process show a 3x improvement in frequency variation compared to the baseline case of a conventional current-starved ring oscillator. The power and area for the proposed circuitry is 87 μW and 0.013 mm2 compared to 54 μ W and 0.01 mm 2 in the baseline case.
Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.
Multicast and unicast real-time video streaming over wireless LANs

In this paper, we address the problem of real-time video streaming over wireless LANs for both unicast and multicast transmission. The wireless channel is modeled as a packet-erasure channel at the IP level. For the unicast scenario, we describe a novel hybrid Automatic Repeat reQuest (ARQ) algorithm that efficiently combines forward error control (FEC) coding with the ARQ protocol. For the multiple-users scenario, we formulate the problem of real-time video multicast as an optimization of a maximum regret cost function across the multicast user space. The proposed solution efficiently combines progressive source coding with FEC coding. We present a theoretical analysis of the unicast and multicast cases, as well as experimental results that demonstrate the performance advantages of the proposed algorithms over existing methods.
Deceptive Opinion Spam Detection Using Neural Network

Deceptive opinion spam detection has attracted significant attention from both business and research communities. Existing approaches are based on manual discrete features, which can capture linguistic and psychological cues. However, such features fail to encode the semantic meaning of a document from the discourse perspective, which limits the performance. In this paper, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam. In particular, given a document, the model learns sentence representations with a convolutional neural network, which are combined using a gated recurrent neural network with attention mechanism to model discourse information and yield a document vector. Finally, the document representation is used directly as features to identify deceptive opinion spam. Experimental results on three domains (Hotel, Restaurant, and Doctor) show that our proposed method outperforms state-of-the-art methods.
RUIS: a toolkit for developing virtual reality applications with spatial interaction

We introduce Reality-based User Interface System (RUIS), a virtual reality (VR) toolkit aimed for students and hobbyists, which we have used in an annually organized VR course for the past four years. RUIS toolkit provides 3D user interface building blocks for creating immersive VR applications with spatial interaction and stereo 3D graphics, while supporting affordable VR peripherals like Kinect, PlayStation Move, Razer Hydra, and Oculus Rift. We describe a novel spatial interaction scheme that combines freeform, full-body interaction with traditional video game locomotion, which can be easily implemented with RUIS. We also discuss the specific challenges associated with developing VR applications, and how they relate to the design principles behind RUIS. Finally, we validate our toolkit by comparing development difficulties experienced by users of different software toolkits, and by presenting several VR applications created with RUIS, demonstrating a variety of spatial user interfaces that it can produce.
Will all Americans become overweight or obese? estimating the progression and cost of the US obesity epidemic.

We projected future prevalence and BMI distribution based on national survey data (National Health and Nutrition Examination Study) collected between 1970s and 2004. Future obesity-related health-care costs for adults were estimated using projected prevalence, Census population projections, and published national estimates of per capita excess health-care costs of obesity/overweight. The objective was to illustrate potential burden of obesity prevalence and health-care costs of obesity and overweight in the United States that would occur if current trends continue. Overweight and obesity prevalence have increased steadily among all US population groups, but with notable differences between groups in annual increase rates. The increase (percentage points) in obesity and overweight in adults was faster than in children (0.77 vs. 0.46-0.49), and in women than in men (0.91 vs. 0.65). If these trends continue, by 2030, 86.3% adults will be overweight or obese; and 51.1%, obese. Black women (96.9%) and Mexican-American men (91.1%) would be the most affected. By 2048, all American adults would become overweight or obese, while black women will reach that state by 2034. In children, the prevalence of overweight (BMI >/= 95th percentile, 30%) will nearly double by 2030. Total health-care costs attributable to obesity/overweight would double every decade to 860.7-956.9 billion US dollars by 2030, accounting for 16-18% of total US health-care costs. We continue to move away from the Healthy People 2010 objectives. Timely, dramatic, and effective development and implementation of corrective programs/policies are needed to avoid the otherwise inevitable health and societal consequences implied by our projections .
Design and Implementation of a FPGA-Based Controller for Resonant Inverters

Design and implementation of a PGA-based controller is presented in this paper. First general control scheme for resonant inverter is introduced and the PGA-based controller construction is given. Then the small signal model of the close loop system for a resonant inverter is analyzed. Thus compensator can be designed. Moreover, the key functional blocks within FPGA including variable frequency pulse width modulator (VFPWM), PLL and power control loop are discussed in detail. Finally experimental architecture and results are given.
Five basic muscle activation patterns account for muscle activity during human locomotion.

An electromyographic (EMG) activity pattern for individual muscles in the gait cycle exhibits a great deal of intersubject, intermuscle and context-dependent variability. Here we examined the issue of common underlying patterns by applying factor analysis to the set of EMG records obtained at different walking speeds and gravitational loads. To this end healthy subjects were asked to walk on a treadmill at speeds of 1, 2, 3 and 5 kmh(-1) as well as when 35-95% of the body weight was supported using a harness. We recorded from 12-16 ipsilateral leg and trunk muscles using both surface and intramuscular recording and determined the average, normalized EMG of each record for 10-15 consecutive step cycles. We identified five basic underlying factors or component waveforms that can account for about 90% of the total waveform variance across different muscles during normal gait. Furthermore, while activation patterns of individual muscles could vary dramatically with speed and gravitational load, both the limb kinematics and the basic EMG components displayed only limited changes. Thus, we found a systematic phase shift of all five factors with speed in the same direction as the shift in the onset of the swing phase. This tendency for the factors to be timed according to the lift-off event supports the idea that the origin of the gait cycle generation is the propulsion rather than heel strike event. The basic invariance of the factors with walking speed and with body weight unloading implies that a few oscillating circuits drive the active muscles to produce the locomotion kinematics. A flexible and dynamic distribution of these basic components to the muscles may result from various descending and proprioceptive signals that depend on the kinematic and kinetic demands of the movements.
What Makes a Review Voted? An Empirical Investigation of Review Voting in Online Review Systems

Research Article Kevin K.Y. Kuan The University of Sydney kevin.kuan@sydney.edu.au Kai-Lung Hui Hong Kong University of Science and Technology klhui@ust.hk Many online review systems adopt a voluntary voting mechanism to identify helpful reviews to support consumer purchase decisions. While several studies have looked at what makes an online review helpful (review helpfulness), little is known on what makes an online review receive votes (review voting). Drawing on information processing theories and the related literature, we investigated the effects of a select set of review characteristics, including review length and readability, review valence, review extremity, and reviewer credibility on two outcomes—review voting and review helpfulness. We examined and analyzed a large set of review data from Amazon with the sample selection model. Our results indicate that there are systematic differences between voted and non-voted reviews, suggesting that helpful reviews with certain characteristics are more likely to be observed and identified in an online review system than reviews without the characteristics. Furthermore, when review characteristics had opposite effects on the two outcomes (i.e. review voting and review helpfulness), ignoring the selection effects due to review voting would result in the effects on review helpfulness being over-estimated, which increases the risk of committing a type I error. Even when the effects on the two outcomes are in the same direction, ignoring the selection effects due to review voting would increase the risk of committing type II error that cannot be mitigated with a larger sample. We discuss the implications of the findings on research and practice.
Automatic tag recommendation for metadata annotation using probabilistic topic modeling

The increase of the complexity and advancement in ecological and environmental sciences encourages scientists across the world to collect data from multiple places, times, and thematic scales to verify their hypotheses. Accumulated over time, such data not only increases in amount, but also in the diversity of the data sources spread around the world. This poses a huge challenge for scientists who have to manually search for information. To alleviate such problems, ONEMercury has recently been implemented as part of the DataONE project to serve as a portal for accessing environmental and observational data across the globe. ONEMercury harvests metadata from the data hosted by multiple repositories and makes it searchable. However, harvested metadata records sometimes are poorly annotated or lacking meaningful keywords, which could affect effective retrieval. Here, we develop algorithms for automatic annotation of metadata. We transform the problem into a tag recommendation problem with a controlled tag library, and propose two variants of an algorithm for recommending tags. Our experiments on four datasets of environmental science metadata records not only show great promises on the performance of our method, but also shed light on the different natures of the datasets.
CHILDREN ’ S WITNESSING OF ADULT DOMESTIC VIOLENCE

Social service professionals are more frequently identifying children who witness adult domestic violence as victims of that abuse. This article expands common definitions of how children witness violence, and adult domestic violence in particular. Over 80 research papers were reviewed and a variety of behavioral, emotional, cognitive and physical functioning problems among children were found to be associated with exposure to domestic violence. Factors that appear to mediate the impact of witnessing violence, such as child gender, age, and time since last exposure to violence are identified. Concerns about research methodology are also raised. CHILDREN’S WITNESSING OF ADULT DOMESTIC VIOLENCE Many people have suggested that family violence – at least to the degree it is observed today – is a recent phenomenon. Yet violence between intimates has long been a part of family life. It has been described repeatedly in religious and historical documents across many centuries, dating as far back as the Roman Empire (Davidson, 1977; Dobash & Dobash, 1979). Some have also argued that current levels of family violence reflect a break-down in the moral structure of the family (see Levine, 1986). This too is unlikely. Rather, as Gordon (1988) suggests, the “ebb-and-flow pattern of concern about family violence...suggests that its incidence has not changed as much as its visibility” (p. 2). Children who witness violence between adults in their homes are only the most recent victims to become visible. These children have been called the “silent,” “forgotten,” and “unintended” victims of adult-to-adult domestic violence (Elbow, 1982; Groves et al., 1993; Rosenbaum & O’Leary, 1981). Studies of archived case records from social service and governmental agencies provide ample evidence that violence has long occurred at levels similar to those measured today and that children are frequently present during violence incidents (Edleson, 1991; Gordon, 1988; Peterson, 1991; Pleck, 1987).
Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction

This paper strives to find the sentence best describing the content of an image or video. Different from existing works, which rely on a joint subspace for image / video to sentence matching, we propose to do so in a visual space only. We contribute Word2VisualVec, a deep neural network architecture that learns to predict a deep visual encoding of textual input based on sentence vectorization and a multi-layer perceptron. We thoroughly analyze its architectural design, by varying the sentence vectorization strategy, network depth and the deep feature to predict for image to sentence matching. We also generalize Word2VisualVec for matching a video to a sentence, by extending the predictive abilities to 3-D ConvNet features as well as a visual-audio representation. Experiments on four challenging image and video benchmarks detail Word2VisualVec’s properties, capabilities for image and video to sentence matching, and on all datasets its state-of-the-art results.