nils-herrmann/clean_scirepeval huggingface dataset:
An End-to-End Multitask Learning Model for Fact Checking

With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. Here we decompose this problem into two parts: an entity linking task (retrieving relative Wikipedia pages) and recognizing textual entailment between the claim and selected pages. In this paper, we present an end-to-end multi-task learning with bi-direction attention (EMBA) model to classify the claim as “supports”, “refutes” or “not enough info” with respect to the pages retrieved and detect sentences as evidence at the same time. We conduct experiments on the FEVER (Fact Extraction and VERification) paper test dataset and shared task test dataset, a new public dataset for verification against textual sources. Experimental results show that our method achieves comparable performance compared with the baseline system.
The boomRoom: mid-air direct interaction with virtual sound sources

In this paper we present a system that allows to "touch", grab and manipulate sounds in mid-air. Further, arbitrary objects can seem to emit sound. We use spatial sound reproduction for sound rendering and computer vision for tracking. Using our approach, sounds can be heard from anywhere in the room and always appear to originate from the same (possibly moving) position, regardless of the listener's position. We demonstrate that direct "touch" interaction with sound is an interesting alternative to indirect interaction mediated through controllers or visual interfaces. We show that sound localization is surprisingly accurate (11.5 cm), even in the presence of distractors. We propose to leverage the ventriloquist effect to further increase localization accuracy. Finally, we demonstrate how affordances of real objects can create synergies of auditory and visual feedback. As an application of the system, we built a spatial music mixing room.
Examining the psychometric properties of a sport-related concussion survey: a Rasch measurement approach

Awareness of sport-related concussion (SRC) is an essential step in increasing the number of athletes or parents who report on SRC. This awareness is important, as there is no established data on medical care at youth-level sports and may be limited to individuals with only first aid training. In this circumstance, aside from the coach, it is the players and their parents who need to be aware of possible signs and symptoms. The aim of this study was to examine the psychometric properties of a parent and player concussion survey intended for use before and after an education campaign regarding SRC. 1441 questionnaires were received from parents and 284 questionnaires from players. The responses to the sixteen-item section of the questionnaire’s ‘recognition of signs and symptoms’ were submitted to psychometric analysis using the dichotomous and polytomous Rasch model via the Rasch Unidimensional Measurement Model software RUMM2030. The Rasch model of Modern Test Theory can be considered a refinement of, or advance on, traditional analyses of an instrument’s psychometric properties. The main finding is that these sixteen items measure two factors: items that are symptoms of concussion and items that are not symptoms of concussion. Parents and athletes were able to identify most or all of the symptoms, but were not as good at distinguishing symptoms that are not symptoms of concussion. Analyzing these responses revealed differential item functioning for parents and athletes on non-symptom items. When the DIF was resolved a significant difference was found between parents and athletes. The main finding is that the items measure two ‘dimensions’ in concussion symptom recognition. The first dimension consists of those items that are symptoms of concussion and the second dimension of those items that are not symptoms of concussion. Parents and players were able to identify most or all of the symptoms of concussion, so one would not expect to pick up any positive change on these items after an education campaign. Parents and players were not as good at distinguishing symptoms that are not symptoms of concussion. It is on these items that one may possibly expect improvement to manifest, so to evaluate the effectiveness of an education campaign it would pay to look for improvement in distinguishing symptoms that are not symptoms of concussion.
Investigation of a new GaN AC/DC topology for battery charging application

Switching losses are reduced by gallium nitride (GaN) technology, which allows faster device switching speeds. At the same time, the weight and size of converters utilizing these devices are greatly reduced in several applications by using high frequency (HF) transformers. A new, low voltage high current, GaN-based AC/DC topology for battery charging applications is simulated, modeled, and investigated in this paper. The proposed topology is designed to operate at 1 MHz for 120Vac to 48Vdc/60Vdc conversion for 1.4 kW load. The integration of GaN totem-pole power factor correction rectifier (TP-PFC), high-frequency series-resonant converter (SRC), and current doubler rectifier (CDR) presents an opportunity to further increase the efficiency and compactness of this GaN battery charging system. In this work, a new GaN topology to combine the advantages of TP-PFC and SRC with CDR in a battery charging converter is investigated, simulated, and modelled. Loss analysis of the proposed topology is addressed.
A meta-analysis of continuing medical education effectiveness.

We undertook a meta-analysis of the Continuing Medical Education (CME) outcome literature to examine the effect of moderator variables on physician knowledge, performance, and patient outcomes.



A literature search of MEDLINE and ERIC was conducted for randomized controlled trials and experimental design studies of CME outcomes in which physicians were a major group. CME moderator variables included the types of intervention, the types and number of participants, time, and the number of intervention sessions held over time.



Thirty-one studies met the eligibility criteria, generating 61 interventions. The overall sample-size weighted effect size for all 61 interventions was r = 0.28 (0.18). The analysis of CME moderator variables showed that active and mixed methods had medium effect sizes (r = 0.33 [0.33], r = 0.33 [0.26], respectively), and passive methods had a small effect size (r = 0.20 [0.16], confidence interval 0.15, 0.26). There was a positive correlation between the effect size and the length of the interventions (r = 0.33) and between multiple interventions over time (r = 0.36). There was a negative correlation between the effect size and programs that involved multiple disciplines (r = -0.18) and the number of participants (r = -0.13). The correlation between the effect size and the length of time for outcome assessment was negative (r = -0.31).



The meta-analysis suggests that the effect size of CME on physician knowledge is a medium one; however, the effect size is small for physician performance and patient outcome. The examination of moderator variables shows there is a larger effect size when the interventions are interactive, use multiple methods, and are designed for a small group of physicians from a single discipline.
Fingerprint biometric based Access Control and Classroom Attendance Management System

Fingerprint is a reliable biometric feature having a wide range of applications that require authentication. Person-specific verification is needed in many scenarios such as access-control, classroom attendance and financial transactions etc. In this paper, two applications of fingerprint biometric are proposed.  Fingerprints are stored dynamically in a database for computing the different statistics, e.g., month-wise or semester-wise trends in the case of CAMS. The CAMS can also provide a solution to the problem of late-coming. Experiments are conducted for measuring the recognition accuracy, i.e., fingerprint match. The initial results of recognition accuracy at 87% for ACS and 92% for CAMS are encouraging. The proposed systems can be further scaled-up for real-time deployment, in applications such as employee attendance and controlled access to high-security areas etc.
SiC MOSFETs connected in series with active voltage control

Silicon Carbide (SiC) MOSFETs offer rapid switching and low on-state voltages. Connecting SiC MOSFETs in series will enable high voltage high frequency applications. Nonetheless, the output capacitances of SiC MOSFETs are often found to ring with the significant stray inductance inevitably found in circuits with series connected devices. The active voltage control gate drive method is used here to clamp the MOSFET voltages to ensure low and stable overshoot voltages, good voltage balancement and a near ideal turn on. It is concluded that SiC MOSFETs and SiC diodes may be connected in series with significant advantages compared to Silicon (Si) IGBTs and Si diode technologies, and the benefits may be realized with the use of active voltage control.
CalvinFS: Consistent WAN Replication and Scalable Metadata Management for Distributed File Systems

Existing file systems, even the most scalable systems that store hundreds of petabytes (or more) of data across thousands of machines, store file metadata on a single server or via a shared-disk architecture in order to ensure consistency and validity of the metadata. This paper describes a completely different approach for the design of replicated, scalable file systems, which leverages a high-throughput distributed database system for metadata management. This results in improved scalability of the metadata layer of the file system, as file metadata can be partitioned (and replicated) across a (shared-nothing) cluster of independent servers, and operations on file metadata transformed into distributed transactions. In addition, our file system is able to support standard file system semantics—including fully linearizable random writes by concurrent users to arbitrary byte offsets within the same file—across wide geographic areas. Such high performance, fully consistent, geographically distributed files systems do not exist today. We demonstrate that our approach to file system design can scale to billions of files and handle hundreds of thousands of updates and millions of reads per second— while maintaining consistently low read latencies. Furthermore, such a deployment can survive entire datacenter outages with only small performance hiccups and no loss of availability.
Horovod: fast and easy distributed deep learning in TensorFlow

Training modern deep learning models requires large amounts of computation, often provided by GPUs. Scaling computation from one GPU to many can enable much faster training and research progress but entails two complications. First, the training library must support inter-GPU communication. Depending on the particular methods employed, this communication may entail anywhere from negligible to significant overhead. Second, the user must modify his or her training code to take advantage of inter-GPU communication. Depending on the training library’s API, the modification required may be either significant or minimal. Existing methods for enabling multi-GPU training under the TensorFlow library entail non-negligible communication overhead and require users to heavily modify their model-building code, leading many researchers to avoid the whole mess and stick with slower single-GPU training. In this paper we introduce Horovod, an open source library that improves on both obstructions to scaling: it employs efficient inter-GPU communication via ring reduction and requires only a few lines of modification to user code, enabling faster, easier distributed training in TensorFlow. Horovod is available under the Apache 2.0 license at https://github.com/uber/horovod.
Detection of money laundering groups using supervised learning in networks

Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates that suspicious activity is successfully detected. Importantly, the system exhibits a low rate of false positives, and is therefore suitable for use in a live intelligence environment.
A question answering system based on VerbNet frames

The precision of the answer is now essential for a question answering system, because of the large amount of free texts on the Internet. Attempting to achieve a high precision, we propose a question answering system supported by case grammar theory and based on VerbNet frames. It extracts the syntactic, thematic and semantic information from the question to filter out unmatched sentences in semantic level and to extract answer chunk (a phrase or a word that can answer the question) from the answer sentence. VerbNet is applied in our system to detect the verb frames in question and candidate sentences, so that the syntactic and thematic information as well as semantic information can be therefore obtained. Our question answering system works well especially for answering factoid questions. The experiments show that our approach is able to filter out semantically unmatched sentences effectively and therefore rank the correct answer (s) higher in the result list.
Diagnosing the Periphery: Using the Rey–Osterrieth Complex Figure Drawing Test to Characterize Peripheral Visual Function

Peripheral vision is strongly limited by crowding, the deleterious influence of neighboring stimuli on target perception. Many quantitative aspects of this phenomenon have been characterized, but the specific nature of the perceptual degradation remains elusive. We utilized a drawing technique to probe the phenomenology of peripheral vision, using the Rey-Osterrieth Complex Figure, a standard neuropsychological clinical instrument. The figure was presented at 12° or 6° in the right visual field, with eye tracking to ensure that the figure was only presented when observers maintained stable fixation. Participants were asked to draw the figure with free viewing, capturing its peripheral appearance. A foveal condition was used to measure copying performance in direct view. To assess the drawings, two raters used standard scoring systems that evaluated feature positions, spatial distortions, and omission errors. Feature scores tended to decrease with increasing eccentricity, both within and between conditions, reflecting reduced resolution and increased crowding in peripheral vision. Based on evaluation of the drawings, we also identified new error classes unique to peripheral presentation, including number errors for adjacent similar features and distinctive spatial distortions. The multifaceted nature of the Rey-Osterrieth Complex Figure-containing configural elements, detached compound features, and texture-like components-coupled with the flexibility of the free-response drawing paradigm and the availability of standardized scoring systems, provides a promising method to probe peripheral perception and crowding.
Multi-Information Source Optimization

We consider Bayesian methods for multi-information source optimization (MISO), in which we seek to optimize an expensive-to-evaluate black-box objective function while also accessing cheaper but biased and noisy approximations (“information sources”). We present a novel algorithm that outperforms the state of the art for this problem by using a Gaussian process covariance kernel better suited to MISO than those used by previous approaches, and an acquisition function based on a one-step optimality analysis supported by efficient parallelization. We also provide a novel technique to guarantee the asymptotic quality of the solution provided by this algorithm. Experimental evaluations demonstrate that this algorithm consistently finds designs of higher value at less cost than previous approaches.
Internet of Things Security Analysis

Internet of Things is an up-and-coming information and technology industry, and in such an environment of the concept and the content and the extension of Internet of Things are not very distinctive, the project of Internet of Things which is a piece of small area with small-scale and self-system obtain gratifying achievement and bright future, it can promote the development of Internet of Things in some extent. But there are some serious hidden danger and potential crisis problems. The paper focuses on the application of Internet of Things in the nation and even in the global in the future, analysing the existed security risks of the Internet of Things 's network points, transmission, finally we propose some suggestive solutions due to these problems.
High Accuracy Android Malware Detection Using Ensemble Learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates. Keywordsmobile security; Android; malware detection; ensemble learning; static analysis; machine learning; data mining; random forest
Improving localization accuracy of android's Fused Location Provider API using Kalman Filter

This paper intends to improve the location accuracy of Google's Fused Location Provider API, for Android handheld device using Kalman Filter. Since the Fused Location Provider was built for managing the battery and accuracy tradeoff between GPS provider and Network Provider, the estimate is likely to be noisy and the track obtained contains jumps. So by using Kalman filter, the jumps can be devoid, and a smooth track can be obtained. A real time experiment is carried out to check the improvements. The results show that the proposed location path is smoother than the path travelled using the conventional Fused Location Provider API.
A novel ultra-high compliance, high output impedance low power very accurate high performance current mirror

In this paper a novel ultra-high compliance, low power, very accurate and high output impedance current mirror/source is proposed. Deliberately composed elements and a good combination (for a mutual auto control action) of negative and positive feedbacks in the proposed circuit made it unique in gathering ultra-high compliances, high output impedance and high accuracy ever demanded merits. The principle of operation of this unique structure is discussed, its most important formulas are derived and its outstanding performance is verified by HSPICE simulation in TSMC 0.18 mm CMOS, BSIM3 and Level49 technology. Simulation results with 1 V power supply and 8 mA input current show an input and output minimum voltages of 0.058 and 0.055 V, respectively, which interestingly provide the highest yet reported compliances for current mirrors implemented by regular CMOS technology. Besides an input resistance of 13.3 O, an extremely high output resistance of 34.3 GO and 3 dB cutoff frequency of 210 MHz are achieved for the proposed circuit while it consumes only 42.5 mW and its current transfer error (at bias point) is the excellent value of 0.02%. & 2010 Elsevier Ltd. All rights reserved.
A Conceptual Model on Cloud Computing and SME Performance: Resource-Based View

Despite the promising future of cloud computing to be successfully adopted for SMEs's daily operations, researchers, policy makers and SME owners are still grappling with how effective cloud computing could be customized to monitor respective SME performances. The literature review revealed that there is still a lack of research on the post-adoption of cloud computing, particularly concerning the key variables and interrelationships which are mostly divergent and that produce varied results. Therefore, the main purpose of this paper is to develop an integrative cloud computing model underpinned by the Resources-Based View Theory (RBV) and also via applying the concept of IT business value. This study seeks to bridge the knowledge gap of cloud computing complementary organizational capabilities identified as the internal factor. As future research guidelines, this research will fill the theoretical gap especially concerning the impacts of cloud computing among Malaysian SME adopters.
Steady-State Analysis of the Series Resonant Converter

The series resonant converter is analyzed in steady state, and for constant switching frequency the output current and voltage characteristics are found to be ellipses. The converter operating point can then be easily obtained by superimposing a load line on these elliptical characteristics. Peak resonant capacitor voltage and inductor current are also plotted in the output plane and are dependent to first order only on output current. When peak voltage and current are plotted in this manner, the dependence of component stresses on operating point is clearly revealed. The output characteristics are modified to include the effect of transistor and diode voltage drops, and experimental verification is presented.
Giving patients a lift - the robotic nursing assistant (RoNA)

Nursing has ranked as one of the top 10 occupations for causing the work-related musculoskeletal injuries in U.S. Constantly and manually lifting and repositioning patients around bed and transferring them from bed to bed have been recognized as the major reasons causing nurses' workrelated musculoskeletal injuries. We believe that advanced robotic technologies can assist nurses in performing the labor intensive tasks and preventing the musculoskeletal injuries among medical workers and nurses. In this paper, we present Hstar Technologies' 2nd generation Robotic Nursing Assistant (RoNA) systemRoNA. Compared to the 1st generation RoNA system released in 2011, the RoNA has more powerful arms which can lift up to 500 pounds patient. The RoNA has been equipped with many intelligent sensors that allow nurse easily and intuitively guide it performing the patient lifting. The paper also discusses the design improvement, control system as well as the software architecture of the RoNA system.