nils-herrmann/clean_scirepeval huggingface dataset:
Experiments with computer vision methods for hand detection

The goal of a fall detection system is to automatically detect cases where a human falls and may have been injured. A natural application of such a system is in home monitoring of patients and elderly persons, so as to automatically alert relatives and/or authorities in case of an injury caused by a fall. This paper describes experiments with three computer vision methods for fall detection in a simulated home environment. The first method makes a decision based on a single frame, simply based on the vertical position of the image centroid of the person. The second method makes a threshold-based decision based on the last few frames, by considering the number of frames during which the person has been falling, the magnitude (in pixels) of the fall, and the maximum velocity of the fall. The third method is a statistical method that makes a decision based on the same features as the previous two methods, but using probabilistic models as opposed to thresholds for making the decision. Preliminary experimental results are promising, with the statistical method attaining relatively high accuracy in detecting falls while at the same time producing a relatively small number of false positives.
Screening for Depression Patients in Family Medicine

GOAL
The aims are to establish the prevalence of newfound, unidentified cases of depressive disorder by screening with the Becks Depression scale; To establish a comparative relationship with self-identified cases of depression in the patients in the family medicine; To assess the significance of the BDI in screening practice of family medicine.



A prospective study was conducted anonymously by Beck's Depression scale (Beck Depression Questionnaire org.-BDI) and specially created short questionnaire. The study included 250 randomly selected patients (20-60 years), users of services in family medicine in "Dom Zdravlja" Zenica, and the final number of respondents with included in the study was 126 (51 male, 75 female; response or response rate 50.4%). Exclusion factor was previously diagnosed and treated mental disorder. Participation was voluntary and respondents acknowledge the validity of completing the questionnaire. BDI consists of 21 items. Answers to questions about symptoms were ranked according to the Likert type scale responses from 0-4 (from irrelevant to very much). Respondents expressed themselves on personal perception of depression, whether are or not depressed.



Depression was observed in 48% of patients compared to 31% in self estimate depression analyzed the questionnaires. The negative trend in the misrecognition of depression is -17% (48:31). Depression was significantly more frequent in unemployed compared to employed respondents (p = 0.001). The leading symptom in both sexes is the perception of lost hope (59% of cases).



All respondents in family medicine care in Zenica showed a high percentage of newly detected (17%) patients with previously unrecognized depression. BDI is a really simple and effective screening tool for the detection and identification of persons with symptoms of depression.
Collaborative Consumption On Mobile Applications: A Study Of Multi-sided Digital Platform GoCatch

This paper examines the role of IT in developing collaborative consumption. We present a study of the multi-sided platform goCatch, which is widely recognized as a mobile application and digital disruptor in the Australian transport industry. From our investigation, we find that goCatch uses IT to create situational-based and object-based opportunities to enable collaborative consumption and in turn digital disruption to the incumbent industry. We also highlight the factors to consider in developing a mobile application to connect with customers, and serve as a viable competitive option for responding to competition. Such research is necessary in order to better understand how service providers extract business value from digital technologies to formulate new breakthrough strategies, design compelling new products and services, and transform management processes. Ongoing work will reveal how m-commerce service providers can extract business value from a collaborative consumption model.
Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study

Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made by layers of internal units (or neurons), each of which computes an affine combination of the output of the units in the previous layer, applies a nonlinear operator, and outputs the corresponding value (also known as activation). A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero. In this (and other similar cases like max pooling, where the max operation involves more than one input value), one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where the continuous variables correspond to the output values of each unit, and a binary variable is associated with each ReLU to model its yes/no nature. In this paper we discuss the peculiarity of this kind of 0-1 MILP models, and describe an effective bound-tightening technique intended to ease its solution. We also present possible applications of the 0-1 MILP model arising in feature visualization and in the construction of adversarial examples. Preliminary computational results are reported, aimed at investigating (on small DNNs) the computational performance of a state-of-the-art MILP solver when applied to a known test case, namely, hand-written digit recognition.
TinyECCK: Efficient Elliptic Curve Cryptography Implementation over GF(2m) on 8-bit MICAz Mote

In this paper, we revisit a generally accepted opinion: implementing Elliptic Curve Cryptosystem (ECC) over GF (2) on sensor motes using small word size is not appropriate because XOR multiplication over GF (2) is not efficiently supported by current low-powered microprocessors. Although there are some implementations over GF (2) on sensor motes, their performances are not satisfactory enough to be used for wireless sensor networks (WSNs). We have found that a field multiplication over GF (2) are involved in a number of redundant memory accesses and its inefficiency is originated from this problem. Moreover, the field reduction process also requires many redundant memory accesses. Therefore, we propose some techniques for reducing unnecessary memory accesses. With the proposed strategies, the running time of field multiplication and reduction over GF (2) can be decreased by 21.1% and 24.7%, respectively. These savings noticeably decrease execution times spent in Elliptic Curve Digital Signature Algorithm (ECDSA) operations (signing and verification) by around 15% ∼ 19%. We present TinyECCK (Tiny Elliptic Curve Cryptosystem with Koblitz curve – a kind of TinyOS package supporting elliptic curve operations) which is the fastest ECC implementation over GF (2) on 8-bit sensor motes using ATmega128L as far as we know. Through comparisons with existing software implementations of ECC built in C or hybrid of C and inline assembly on sensor motes, we show that TinyECCK outperforms them in terms of running time, code size and supporting services. Furthermore, we show that a field multiplication over GF (2) can be faster than that over GF (p) on 8-bit ATmega128L processor by comparing TinyECCK with TinyECC, a well-known ECC implementation over GF (p). TinyECCK with sect163k1 can compute a scalar multiplication within 1.14 secs on a MICAz mote at the expense of 5,592-byte of ROM and 618-byte of RAM. Furthermore, it can also generate a signature and verify it in 1.37 and 2.32 secs with 13,748-byte of ROM and 1,004-byte of RAM. 2 Seog Chung Seo et al.
Implementation of semantic analyzer in Indonesian text-understanding evaluation system

This paper proposes a strategy of semantic processing implemented in an Indonesian text understanding evaluation system. It uses component that already developed in Institut Teknologi Bandung consists of POS Tagger and Syntactic Parser. This research used syntax-driven semantic analysis technique by attaching semantic rules into associated syntactic rule. Semantic rule is used as base of λ-reduction operation, used to produce sentence's knowledge representation in first order logic's form. There is also semantic lexical attachment process, used to give a meaning of each words or phrases in sentence. Semantic processor is implemented in Indonesian text-understanding evaluation system. It has ability to evaluate a reader's understanding of a text by comparing the reader's understanding with computer's understanding. This system consists of text understanding module, question generation module, and understanding evaluation module. Text-understanding module is developed using semantic processor with addition of reference resolution to relate each sentences in text.
Beyond Factoid QA: Effective Methods for Non-factoid Answer Sentence Retrieval

Retrieving finer grained text units such as passages or sentences as answers for non-factoid Web queries is becoming increasingly important for applications such as mobile Web search. In this work, we introduce the answer sentence retrieval task for non-factoid Web queries, and investigate how this task can be effectively solved under a learning to rank framework. We design two types of features, namely semantic and context features, beyond traditional text matching features. We compare learning to rank methods with multiple baseline methods including query likelihood and the state-of-the-art convolutional neural network based method, using an answer-annotated version of the TREC GOV2 collection. Results show that features used previously to retrieve topical sentences and factoid answer sentences are not sufficient for retrieving answer sentences for non-factoid queries, but with semantic and context features, we can significantly outperform the baseline methods.
MoDL: Model-Based Deep Learning Architecture for Inverse Problems

We introduce a model-based image reconstruction framework with a convolution neural network (CNN)-based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with the arbitrary structure. Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion approaches. Thus, reducing the demand for training data and training time. Since we rely on end-to-end training with weight sharing across iterations, the CNN weights are customized to the forward model, thus offering improved performance over approaches that rely on pre-trained denoisers. Our experiments show that the decoupling of the number of iterations from the network complexity offered by this approach provides benefits, including lower demand for training data, reduced risk of overfitting, and implementations with significantly reduced memory footprint. We propose to enforce data-consistency by using numerical optimization blocks, such as conjugate gradients algorithm within the network. This approach offers faster convergence per iteration, compared to methods that rely on proximal gradients steps to enforce data consistency. Our experiments show that the faster convergence translates to improved performance, primarily when the available GPU memory restricts the number of iterations.
Video Identification Solution Using a “ Video Signature ”

NEC’s video identification technology enables instant video content identification by extracting a unique descriptor called a “video signature” from video content. This technology is approved as the MPEG-7 Video Signature Tool; an international standard of interoperable descriptors used for video identification. The video signature is an extremely robust tool for identifying videos with alternations and editing effects. It facilitates search of very short video scenes. It also has a compact design, making ultrafast searches possible via a compact system. In this paper, we propose video identification solutions for the mass media industries. They adopt video signatures as metadata descriptions to enable efficient video registration operations and the visualization of video content relationships.
Design and Testing of a Percutaneously Implantable Fetal Pacemaker

We are developing a cardiac pacemaker with a small, cylindrical shape that permits percutaneous implantation into a fetus to treat complete heart block and consequent hydrops fetalis, which can otherwise be fatal. The device uses off-the-shelf components including a rechargeable lithium cell and a highly efficient relaxation oscillator encapsulated in epoxy and glass. A corkscrew electrode made from activated iridium can be screwed into the myocardium, followed by release of the pacemaker and a short, flexible lead entirely within the chest of the fetus to avoid dislodgement from fetal movement. Acute tests in adult rabbits demonstrated the range of electrical parameters required for successful pacing and the feasibility of successfully implanting the device percutaneously under ultrasonic imaging guidance. The lithium cell can be recharged inductively as needed, as indicated by a small decline in the pulsing rate.
A DC source of current-fed Cockcroft-Walton multiplier with high gain DC-DC converter

As in most of the renewable energy sources it is not possible to generate high voltage directly, the study of high gain dc-dc converters is an emerging area of research. This paper presents a high step-up dc-dc converter based on current-fed Cockcroft-Walton multiplier. This converter not only steps up the voltage gain but also eliminates the use of high frequency transformer which adds to cost and design complexity. N-stage Cockcroft-Walton has been utilized to increase the voltage gain in place of a transformer. This converter also provides dual input operation, interleaved mode and maximum power point tracking control (if solar panel is used as input). This converter is utilized for resistive load and a pulsed power supply and the effect is studied in high voltage application. Simulation has been performed by designing a converter of 450 W, 400 V with single source and two stage of Cockcroft-Walton multiplier and interleaved mode of operation is performed. Design parameters as well as simulation results are presented and verified in this paper.
Identifying categories of zones in scientific papers based on lexical and syntactical features

Scientific papers are continually increasing on the web and it is mandatory for the researchers to grasp on some powerful tools which are helpful in an efficient process of large amounts of data. Zone identification is a Natural Language Processing application which is to classify the sentences of scientific papers into a fixed set of zone categories. In this paper, we will propose an algorithm to identify some categories of zones in scientific papers. Regarding this, we make use of some significant lexical and syntactical features of the sentences standing for these categories in a particular way. In this respect, a sequence of sentences has been used. Experimental results show that these features are capable enough to identify the desired categories in a reasonable manner.
Review of Adult Electrical Burn Injury Outcomes Worldwide: An Analysis of Low-Voltage vs High-Voltage Electrical Injury.

The aims of this article are to review low-voltage vs high-voltage electrical burn complications in adults and to identify novel areas that are not recognized to improve outcomes. An extensive literature search on electrical burn injuries was performed using OVID MEDLINE, PubMed, and EMBASE databases from 1946 to 2015. Studies relating to outcomes of electrical injury in the adult population (≥18 years of age) were included in the study. Forty-one single-institution publications with a total of 5485 electrical injury patients were identified and included in the present study. Fourty-four percent of these patients were low-voltage injuries (LVIs), 38.3% high-voltage injuries (HVIs), and 43.7% with voltage not otherwise specified. Forty-four percentage of studies did not characterize outcomes according to LHIs vs HVIs. Reported outcomes include surgical, medical, posttraumatic, and others (long-term/psychological/rehabilitative), all of which report greater incidence rates in HVI than in LVI. Only two studies report on psychological outcomes such as posttraumatic stress disorder. Mortality rates from electrical injuries are 2.6% in LVI, 5.2% in HVI, and 3.7% in not otherwise specified. Coroner's reports revealed a ratio of 2.4:1 for deaths caused by LVI compared with HVI. HVIs lead to greater morbidity and mortality than LVIs. However, the results of the coroner's reports suggest that immediate mortality from LVI may be underestimated. Furthermore, on the basis of this analysis, we conclude that the majority of studies report electrical injury outcomes; however, the majority of them do not analyze complications by low vs high voltage and often lack long-term psychological and rehabilitation outcomes after electrical injury indicating that a variety of central aspects are not being evaluated or assessed.
User Acceptance of Voice Recognition Technology: An Empirical Extension of the Technology Acceptance Model

Voice recognition technology-enabled devices possess extraordinary growth potential, yet some research indicates that organizations and consumers are resisting their adoption. This study investigates the implementation of a voice recognition device in the United States Navy. Grounded in the social psychology and information systems literature, the researchers adapted instruments and developed a tool to explain technology adoption in this environment. Using factor analysis and structural equation modeling, analysis of data from the 270 participants explained almost 90% of the variance in the model. This research adapts the technology acceptance model by adding elements of the theory of planned behavior, providing researchers and practitioners with a valuable instrument to predict technology adoption.
Synthesis of Wideband Multicoupled Resonators Filters

A method for the synthesis of multicoupled resonators filters with frequency-dependent couplings is presented. A circuit model of the filter that accurately represents the frequency responses over a very wide frequency band is postulated. The two-port parameters of the filter based on the circuit model are obtained by circuit analysis. The values of the circuit elements are synthesized by equating the two-port parameters obtained from the circuit analysis and the filtering characteristic function. Solutions similar to the narrowband case (where all the couplings are assumed frequency independent) are obtained analytically when all coupling elements are either inductive or capacitive. The synthesis technique is generalized to include all types of coupling elements. Several examples of wideband filters are given to demonstrate the synthesis techniques.
Design and Calibration of an Optical Micro Motion Sensing System for Micromanipulation Tasks

An optical sensing system has been developed using a pair of orthogonally placed position sensitive detectors (PSD) to track 3D displacement of a microsurgical instrument tip in real-time. An infrared (IR) diode is used to illuminate the workspace. A ball is attached to the tip of an intraocular shaft to reflect IR rays onto the PSDs. Instrument tip position is then calculated from the centroid positions of reflected IR light on the respective PSDs. The system can be used to assess the accuracy of hand-held microsurgical instruments and operator performance in micromanipulation tasks, such as microsurgeries. In order to eliminate inherent nonlinearity of the PSDs and lenses, calibration is performed using a feedforward neural network. After calibration, percentage RMS error is reduced from about 5.46 % to about 0.16%. The system RMS noise is about 0.7 mum. The sampling rate of the system is 250 Hz.
Mistaking minds and machines: How speech affects dehumanization and anthropomorphism.

Treating a human mind like a machine is an essential component of dehumanization, whereas attributing a humanlike mind to a machine is an essential component of anthropomorphism. Here we tested how a cue closely connected to a person's actual mental experience-a humanlike voice-affects the likelihood of mistaking a person for a machine, or a machine for a person. We predicted that paralinguistic cues in speech are particularly likely to convey the presence of a humanlike mind, such that removing voice from communication (leaving only text) would increase the likelihood of mistaking the text's creator for a machine. Conversely, adding voice to a computer-generated script (resulting in speech) would increase the likelihood of mistaking the text's creator for a human. Four experiments confirmed these hypotheses, demonstrating that people are more likely to infer a human (vs. computer) creator when they hear a voice expressing thoughts than when they read the same thoughts in text. Adding human visual cues to text (i.e., seeing a person perform a script in a subtitled video clip), did not increase the likelihood of inferring a human creator compared with only reading text, suggesting that defining features of personhood may be conveyed more clearly in speech (Experiments 1 and 2). Removing the naturalistic paralinguistic cues that convey humanlike capacity for thinking and feeling, such as varied pace and intonation, eliminates the humanizing effect of speech (Experiment 4). We discuss implications for dehumanizing others through text-based media, and for anthropomorphizing machines through speech-based media.
Methods for Multisweep Automation

Sweeping has become the workhorse algorithm for cre ating conforming hexahedral meshes of complex model s. This paper describes progress on the automatic, robust generat ion of MultiSwept meshes in CUBIT. MultiSweeping ex t nds the class of volumes that may be swept to include those with mul tiple source and multiple target surfaces. While no t yet perfect, CUBIT’s MultiSweeping has recently become more reliable, an d been extended to assemblies of volumes. Sweep For ging automates the process of making a volume (multi) sweepable: Sweep V rification takes the given source and target sur faces, and automatically classifies curve and vertex types so that sweep lay ers are well formed and progress from sources to ta rge s.
Impact of nutrition environmental interventions on point-of-purchase behavior in adults: a review.

Nutrition interventions targeted to individuals are unlikely to significantly shift US dietary patterns as a whole. Environmental and policy interventions are more promising for shifting these patterns. We review interventions that influenced the environment through food availability, access, pricing, or information at the point-of-purchase in worksites, universities, grocery stores, and restaurants.



Thirty-eight nutrition environmental intervention studies in adult populations, published between 1970 and June 2003, were reviewed and evaluated on quality of intervention design, methods, and description (e.g., sample size, randomization). No policy interventions that met inclusion criteria were found.



Many interventions were not thoroughly evaluated or lacked important evaluation information. Direct comparison of studies across settings was not possible, but available data suggest that worksite and university interventions have the most potential for success. Interventions in grocery stores appear to be the least effective. The dual concerns of health and taste of foods promoted were rarely considered. Sustainability of environmental change was never addressed.



Interventions in "limited access" sites (i.e., where few other choices were available) had the greatest effect on food choices. Research is needed using consistent methods, better assessment tools, and longer durations; targeting diverse populations; and examining sustainability. Future interventions should influence access and availability, policies, and macroenvironments.