nils-herrmann/clean_scirepeval huggingface dataset:
Design and development of remote-operated multi-direction Unmanned Ground Vehicle (UGV)

The main purpose of this research is to design and develop complete system of a remote-operated multi-direction Unmanned Ground Vehicle (UGV). The development involved PIC microcontroller in remote-controlled and UGV robot, Xbee Pro modules, Graphic LCD 84×84, Vexta brushless DC electric motor and mecanum wheels. This paper show the study the movement of multidirectional UGV by using Mecanum wheels with differences drive configuration. The 16-bits Microchips microcontroller were used in the UGV's system that embed with Xbee Pro through variable baud-rate value via UART protocol and control the direction of wheels. The successful develop UGV demonstrated clearly the potential application of this type of vehicle, and incorporated the necessary technology for further research of this type of vehicle.
Shifty: A Weight-Shifting Dynamic Passive Haptic Proxy to Enhance Object Perception in Virtual Reality

We define the concept of Dynamic Passive Haptic Feedback (DPHF) for virtual reality by introducing the weight-shifting physical DPHF proxy object Shifty. This concept combines actuators known from active haptics and physical proxies known from passive haptics to construct proxies that automatically adapt their passive haptic feedback. We describe the concept behind our ungrounded weight-shifting DPHF proxy Shifty and the implementation of our prototype. We then investigate how Shifty can, by automatically changing its internal weight distribution, enhance the user's perception of virtual objects interacted with in two experiments. In a first experiment, we show that Shifty can enhance the perception of virtual objects changing in shape, especially in length and thickness. Here, Shifty was shown to increase the user's fun and perceived realism significantly, compared to an equivalent passive haptic proxy. In a second experiment, Shifty is used to pick up virtual objects of different virtual weights. The results show that Shifty enhances the perception of weight and thus the perceived realism by adapting its kinesthetic feedback to the picked-up virtual object. In the same experiment, we additionally show that specific combinations of haptic, visual and auditory feedback during the pick-up interaction help to compensate for visual-haptic mismatch perceived during the shifting process.
Adversarial Multiclass Classification: A Risk Minimization Perspective

I Promising empirical results for cost-sensitive and multivariate losses (Asif et al. 2015, Wang et al. 2015) Our Approach: 1. Recast zero-one adversarial classification from ERM perspective by analyzing the Nash equilibrium and define a new multiclass loss 2. Fill the long-standing gap in ERM methods by simultaneously: (1) Guaranteeing Fisher consistency and universal consistency (2) Enabling computational efficiency via kernel trick and dual sparsity (3) Providing competitive performance in practice 3. Significantly improve computational efficiency → no game solving using linear programming is required
Concussions among university football and soccer players.

A study to examine the incidence and characteristics of concussions among Canadian university athletes during 1 full year of football and soccer participation.



Retrospective survey.



Three hundred eighty Canadian university football and 240 Canadian university soccer players reporting to 1999 fall training camp. Of these, 328 football and 201 soccer players returned a completed questionnaire.



Based on self-reported symptoms, calculations were made to determine the number of concussions experienced during the previous full year of football or soccer participation, the duration of symptoms, the time for return to play, and any associated risk factors for concussions.



Of all the athletes who returned completed questionnaires, 70.4% of the football players and 62.7% of the soccer players had experienced symptoms of a concussion during the previous year. Only 23.4% of the concussed football players and 19.8% of the concussed soccer players realized they had suffered a concussion. More than one concussion was experienced by 84.6% of the concussed football players and 81.7% of the concussed soccer players. Examining symptom duration, 27.6% of all concussed football players and 18.8% of all concussed soccer players experienced symptoms for at least 1 day or longer. Tight end and defensive lineman were the positions most commonly affected in football, while goalies were the players most commonly affected in soccer. Variables that increased the odds of suffering a concussion during the previous year for football players included a history of a traumatic loss of consciousness or a recognized concussion in the past. Variables that increased the odds of suffering a concussion during the previous year for soccer players included a past history of a recognized concussion while playing soccer and being female.



University football and soccer players seem to be experiencing a significant amount of concussions while participating in their respective sports. Variables that seem to increase the odds of suffering a concussion during the previous year for football and soccer players include a history of a recognized concussion. Despite being relatively common, symptoms of concussion may not be recognized by many players.
Exact Sampling with Integer Linear Programs and Random Perturbations

We consider the problem of sampling from a discrete probability distribution specified by a graphical model. Exact samples can, in principle, be obtained by computing the mode of the original model perturbed with an exponentially many i.i.d. random variables. We propose a novel algorithm that views this as a combinatorial optimization problem and searches for the extreme state using a standard integer linear programming (ILP) solver, appropriately extended to account for the random perturbation. Our technique, GumbelMIP, leverages linear programming (LP) relaxations to evaluate the quality of samples and prune large portions of the search space, and can thus scale to large tree-width models beyond the reach of current exact inference methods. Further, when the optimization problem is not solved to optimality, our method yields a novel approximate sampling technique. We empirically demonstrate that our approach parallelizes well, our exact sampler scales better than alternative approaches, and our approximate sampler yields better quality samples than a Gibbs sampler and a low-dimensional perturbation method.
Comparison of fully automated urine sediment analyzers H800-FUS100 and LabUMat-UriSed with manual microscopy.

Recent technical developments have focused on the full automation of urinalyses, however the manual microscopic analysis of urine sediment is considered the reference method. The aim of this study was to compare the performances of the LabUMat-UriSed and the H800-FUS100 with manual microscopy, and with each other.



The urine sediments of 332 urine samples were examined by these two devices (LabUMat-UriSed, H800-FUS100) and manual microscopy.



The reproducibility of the analyzers, UriSed and Fus100 (4.1-28.5% and 4.7-21.2%, respectively), was better than that with manual microscopy (8.5-33.3%). The UriSed was more sensitive for leukocytes (82%), while the Fus-100 was more sensitive for erythrocyte cell counting (73%). There were moderate correlations between manual microscopy and the two devices, UriSed and Fus100, for erythrocyte (r = 0.496 and 0.498, respectively) and leukocyte (r = 0.597 and 0.599, respectively) cell counting however the correlation between the two devices was much better for erythrocyte (r = 0.643) and for leukocyte (r = 0.767) cell counting.



It can be concluded that these two devices showed similar performances. They were time-saving and standardized techniques, especially for reducing preanalytical errors such as the study time, centrifugation, and specimen volume for sedimentary analysis; however, the automated systems are still inadequate for classifying the cells that are present in pathological urine specimens.
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations

Deep networks have achieved impressive results across a variety of important tasks. However a known weakness is a failure to perform well when evaluated on data which differ from the training distribution, even if these differences are very small, as is the case with adversarial examples. We propose Fortified Networks, a simple transformation of existing networks, which fortifies the hidden layers in a deep network by identifying when the hidden states are off of the data manifold, and maps these hidden states back to parts of the data manifold where the network performs well. Our principal contribution is to show that fortifying these hidden states improves the robustness of deep networks and our experiments (i) demonstrate improved robustness to standard adversarial attacks in both black-box and white-box threat models; (ii) suggest that our improvements are not primarily due to the gradient masking problem and (iii) show the advantage of doing this fortification in the hidden layers instead of the input space.
B2.7.2 NK Fitness Landscapes

NK fitness landscapes are stochastically generated fitness functions on bit strings, parameterized (with genes and interactions between genes) so as to make them tunably ‘rugged’. Under the ‘natural’ genetic operators of bit-flipping mutation or recombination, NK landscapes produce multiple domains of attraction for the evolutionary dynamics. NK landscapes have been used in models of epistatic gene interactions, coevolution, genome growth, and Wright’s shifting balance model of adaptation. Theory for adaptive walks on NK landscapes has been derived, and generalizations that extend beyond Kauffman’s original framework have been utilized in these applications.
Actions in context

This paper exploits the context of natural dynamic scenes for human action recognition in video. Human actions are frequently constrained by the purpose and the physical properties of scenes and demonstrate high correlation with particular scene classes. For example, eating often happens in a kitchen while running is more common outdoors. The contribution of this paper is three-fold: (a) we automatically discover relevant scene classes and their correlation with human actions, (b) we show how to learn selected scene classes from video without manual supervision and (c) we develop a joint framework for action and scene recognition and demonstrate improved recognition of both in natural video. We use movie scripts as a means of automatic supervision for training. For selected action classes we identify correlated scene classes in text and then retrieve video samples of actions and scenes for training using script-to-video alignment. Our visual models for scenes and actions are formulated within the bag-of-features framework and are combined in a joint scene-action SVM-based classifier. We report experimental results and validate the method on a new large dataset with twelve action classes and ten scene classes acquired from 69 movies.
ANALYSIS OF THE SOLAR POTENTIAL OF ROOFS BY USING OFFICIAL LIDAR DATA

This paper focuses on a field test that locates roof areas with a high solar potential and predicts the solar “harvest” per m. The test analyzes 2.5D LIDAR data provided by official surveying and mapping sources. The primary LIDAR data is prepared by masking the roofs’ contours and afterwards filtering the point cloud by a threshold value. The remaining LIDAR data, which represents the buildings’ roofs, is analyzed according to the slope, the azimuthal exposition and shaded roof areas. The quality assessment of the derived roof areas is carried out by means of a 3D dataset which is semiautomatically acquired from panchromatic stereophotogrammetric aerial photographs.
Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation

Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes. Recent advances in machine learning (ML) enabled by deep neural networks has exacerbated the challenge of evaluating such software due to the opaque nature of these ML-based artifacts. A key related issue is the (in)ability of such systems to generate useful explanations of their outputs, and we argue that the explanation and evaluation problems are closely linked. The paper models the elements of a ML-based AI system in the context of public sector decision (PSD) applications involving both artificial and human intelligence, and maps these elements against issues in both evaluation and explanation, showing how the two are related. We consider a number of common PSD application patterns in the light of our model, and identify a set of key issues connected to explanation and evaluation in each case. Finally, we propose multiple strategies to promote wider adoption of AI/ML technologies in PSD, where each is distinguished by a focus on different elements of our model, allowing PSD policy makers to adopt an approach that best fits their context and concerns.
A CMOS bidirectional 32-element phased-array transceiver at 60GHz with LTCC antenna

An integrated CMOS 60 GHz phased-array antenna module supporting symmetrical 32 TX/RX elements for wireless docking is described. Bidirectional architecture with shared blocks, mm-wave TR switch design with less than 1dB TX loss, and a full built in self test (BIST) circuits with 5deg and +/-1dB measurement accuracy of phase and power are presented. The RFIC size is 29mm2, consuming 1.2W/0.85W at TX and RX with a 29dBm EIRP at -19dB EVM and 10dB NF.
State-of-the-art sub-wavelength grating couplers for silicon-on-insulator platform

We compare state-of-the-art sub-wavelength grating couplers (SWGCs) based on their coupling efficiencies, 1-dB bandwidths and back reflections. Three types of SWGC, the uniform SWGC, the apodized SWGC, and the broadband SWGC, which use one-dimensional sub-wavelength gratings, are compared for the silicon-on-insulator platform. The SWGCs are designed for the fundamental TE mode, operating at 1550nm.
Running 8-bit dynamic fixed-point convolutional neural network on low-cost ARM platforms

In recent years, Convolutional Neural Network (CNN) has been extensively applied in the field of computer vision, which has also made remarkable achievements. However, the CNN models are computation-intensive and memory-consuming, which hinders the deployment of CNN-based methods on resource-limited embedded platforms. Therefore, this paper gives insight into low numerical precision Convolutional Neural Networks. At first, an image classification CNN model is quantized into 8-bit dynamic fixed-point with no more than 1% accuracy drop and then the method of conducting inference on low-cost ARM processor has been proposed. Experimental results verified the effectiveness of this method. Besides, our proof-of-concept prototype implementation can obtain a frame rate of 4.54fps when running on single Cortex-A72 core under 1.8GHz working frequency and 6.48 watts of gross power consumption.
The little bacteria that can – diversity, genomics and ecophysiology of ‘Dehalococcoides’ spp. in contaminated environments

The fate and persistence of chlorinated organics in the environment have been a concern for the past 50 years. Industrialization and extensive agricultural activities have led to the accumulation of these pollutants in the environment, while their adverse impact on various ecosystems and human health also became evident. This review provides an update on the current knowledge of specialized anaerobic bacteria, namely 'Dehalococcoides' spp., which are dedicated to the transformation of various chlorinated organic compounds via reductive dechlorination. Advances in microbiology and molecular techniques shed light into the diversity and functioning of Dehalococcoides spp. in several different locations. Recent genome sequencing projects revealed a large number of genes that are potentially involved in reductive dechlorination. Molecular approaches towards analysis of diversity and expression especially of reductive dehalogenase-encoding genes are providing a growing body of knowledge on biodegradative pathways active in defined pure and mixed cultures as well as directly in the environment. Moreover, several successful field cases of bioremediation strengthen the notion of dedicated degraders such as Dehalococcoides spp. as key players in the restoration of contaminated environments.
Cache-, hash-, and space-efficient bloom filters

A Bloom filter is a very compact data structure that supports approximate membership queries on a set, allowing false positives.
 We propose several new variants of Bloom filters and replacements with similar functionality. All of them have a better cache-efficiency and need less hash bits than regular Bloom filters. Some use SIMD functionality, while the others provide an even better space efficiency. As a consequence, we get a more flexible trade-off between false-positive rate, space-efficiency, cache-efficiency, hash-efficiency, and computational effort. We analyze the efficiency of Bloom filters and the proposed replacements in detail, in terms of the false-positive rate, the number of expected cache-misses, and the number of required hash bits. We also describe and experimentally evaluate the performance of highly tuned implementations. For many settings, our alternatives perform better than the methods proposed so far.
Practical Applications of Homomorphic Encryption

Homomorphic cryptography has been one of the most interesting topics of mathematics and computer security since Gentry presented the first construction of a fully homomorphic encryption (FHE) scheme in 2009. Since then, a number of different schemes have been found, that follow the approach of bootstrapping a fully homomorphic scheme from a somewhat homomorphic foundation. All existing implementations of these systems clearly proved, that fully homomorphic encryption is not yet practical, due to significant performance limitations. However, there are many applications in the area of secure methods for cloud computing, distributed computing and delegation of computation in general, that can be implemented with homomorphic encryption schemes of limited depth. We discuss a simple algebraically homomorphic scheme over the integers that is based on the factorization of an approximate semiprime integer. We analyze the properties of the scheme and provide a couple of known protocols that can be implemented with it. We also provide a detailed discussion on searching with encrypted search terms and present implementations and performance figures for the solutions discussed in this paper.
Fast Regular Expression Matching Using FPGAs

This paper presents an efficient method for finding matches to a given regular expression in given text using FPGAs. To match a regular expression of length n, a serial machine requires 0(2^n) memory and takes 0(1) time per text character. The proposed approach reqiures only 0(n^2) space and still process a text character in 0(1) time (one clock cycle).The improvement is due to the Nondetermineistic Finite Automaton (NFA) used to perform the matching. As far as the authors are aware, this is the first prctical use of a nondeterministic state machine on programmable logic. Furthermore, the paper presents a simple, fast algorithm that quickly constructs the NFA for the given regular expression. Fast NFA construction is crucial because the NFA structure depends on the regular expression, which is known only at runtime. Implementations of the algorithm for conventional FPGAs and the self-reconfigurable Gate Array (SRGA) are described. To evaluate performance, the NFA logic was mapped onto the Virtex XCV100 FPGA and the SRGA. Also, the performance of GNU grep for matching regular expressions was evaluated on an 800 MHz Pentium III machine. The proposed approach was faster than best case grep performance in most cases. It was orders of magnitude faster than worst case grep performance. Logic for the largest NFA considered fit in less than a 1000 CLBs while DFA storage for grep in the worst case consumed a few hundred megabytes.
Characterizing pseudoentropy and simplifying pseudorandom generator constructions

We provide a characterization of pseudoentropy in terms of hardness of sampling: Let (X,B) be jointly distributed random variables such that B takes values in a polynomial-sized set. We show that B is computationally indistinguishable from a random variable of higher Shannon entropy given X if and only if there is no probabilistic polynomial-time S such that (X,S(X)) has small KL divergence from (X,B). This can be viewed as an analogue of the Impagliazzo Hardcore Theorem (FOCS '95) for Shannon entropy (rather than min-entropy).
 Using this characterization, we show that if f is a one-way function, then (f(Un),Un) has "next-bit pseudoentropy" at least n+log n, establishing a conjecture of Haitner, Reingold, and Vadhan (STOC '10). Plugging this into the construction of Haitner et al., this yields a simpler construction of pseudorandom generators from one-way functions. In particular, the construction only performs hashing once, and only needs the hash functions that are randomness extractors (e.g. universal hash functions) rather than needing them to support "local list-decoding" (as in the Goldreich--Levin hardcore predicate, STOC '89).
 With an additional idea, we also show how to improve the seed length of the pseudorandom generator to ~{O}(n3), compared to O(n4) in the construction of Haitner et al.
Speech Perception and Subjective Preference with Fine Structure Coding Strategies

This study aimed to determine differences in speech perception and subjective preference after upgrade from the FSP coding strategy to the FS4 or FS4p coding strategies. Subjects were tested at the point of upgrade (n=10), and again at 1-(n=10), 3-(n=8), 6-(n=8) and 12 months (n=8) after the upgrade to the FS4 or FS4p coding strategy. In between test intervals patients had to use the FS4 or FS4p strategy in everyday life. Primary outcome measures, chosen to best evaluate individual speech understanding, were the Freiburg Monosyllable Test in quiet, the Oldenburg Sentence Test (OLSA) in noise, and the Hochmair-Schulz-Moser (HSM) Sentence Test in noise. To measure subjective sound quality the Hearing Implant Sound Quality Index was used. Subjects with the FS4/FS4p strategy performed as well as subjects with the FSP coding strategy in the speech tests. The subjective perception of subjects showed that subjects perceived a ‘moderate’ or ‘poor’ auditory benefit with the FS4/FS4p coding strategy. Subjects with the FS4 or FS4p coding strategies perform well in everyday situations. Both coding strategies offer another tool to individualize the fitting of audio processors and grant access to satisfying sound quality and speech perception.
A meta-analysis of cloud forensic frameworks and tools

Cloud computing enables users to reduce their infrastructure cost and rely on cloud to deploy their applications. The ability to access cloud from any device and user friendly cloud services allows criminals to perform malicious activities. To perform investigation in cloud environment, digital forensics should be applied to cloud and this is termed as cloud forensics. This paper presents various challenges in the stages of digital forensics and their dissimilarity in the cloud environment. Many researchers proposed various flavors of cloud forensic framework but adversaries increase the malicious usage by taking advantage of lack of a standard cloud forensic framework. This made us to explore various existing frameworks for cloud forensics which could help to come up with a standardized framework. In this paper, existing tools for digital forensics is presented with their applicability to cloud environment.