nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Hand Pose Estimation from a Single Depth Image

We tackle the practical problem of hand pose estimation from a single noisy depth image. A dedicated three-step pipeline is proposed: Initial estimation step provides an initial estimation of the hand in-plane orientation and 3D location, Candidate generation step produces a set of 3D pose candidate from the Hough voting space with the help of the rotational invariant depth features, Verification step delivers the final 3D hand pose as the solution to an optimization problem. We analyze the depth noises, and suggest tips to minimize their negative impacts on the overall performance. Our approach is able to work with Kinect-type noisy depth images, and reliably produces pose estimations of general motions efficiently (12 frames per second). Extensive experiments are conducted to qualitatively and quantitatively evaluate the performance with respect to the state-of-the-art methods that have access to additional RGB images. Our approach is shown to deliver on par or even better results.
A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR

Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. To get a higher precision with less learning time, this paper presents a Hybrid PSO-SVR forecasting method, which uses particle swarm optimization (PSO) to search optimal SVR parameters. In order to find a PSO that is more proper for SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flying out of the searching space Through the comparison of three strategies, we find one of the strategies can make PSO get the optimal parameters more quickly. The PSO using this strategy is called fast PSO. Furthermore, aiming at the problem about the decrease of prediction accuracy caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The results of extensive comparison experiments indicate that the proposed model can get more accurate forecasting results than other state-of-the-art algorithms, and when the data contain noises, the method with historical momentum still gets accurate forecasting results.
Towards the Ontology of ISO/IEC 27005: 2011 Risk Management Standard

The purpose of this paper is to present a solution to manage the concepts related to ISO/IEC 27005:2011 standard in such a way that different stakeholders could access and understand them without misleading their meanings. This paper presents an ontology to structure and organize core concepts of risk assessment phase of ISO/IEC 27005:2011 standard. The method of ontology development ontology follows seven steps guideline. A case scenario of a health clinic is developed to apply the proposed ontology where each entity and relation of the ontology is described. The paper provides a reference point for professionals and researchers by presenting an ontology to describe various concepts of ISO/IEC 27005:2011 in the field of information security risk management.
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels

Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods have been proposed which seek to represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to represent the label assignments in a low-dimensional space. However, by doing so, these methods actually neglect the tail labels labels that are infrequently assigned to instances. We propose an embedding-based method that non-linearly embeds the label vectors using an stochastic approach, thereby predicting the tail labels more accurately. Moreover, the proposed method have excellent mechanisms for handling missing labels, dealing with large-scale datasets, as well as exploiting unlabeled data. With the best of our knowledge, our proposed method is the first multi-label classifier that simultaneously addresses all of the mentioned challenges. Experiments on real-world datasets show that our method outperforms stateof-the-art multi-label classifiers by a large margin, in terms of prediction performance, as well as training time.
Dafny: An Automatic Program Verifier for Functional Correctness

Traditionally, the full verification of a program’s functional correctness has been obtained with pen and paper or with interactive proof assistants, whereas only reduced verification tasks, such as extended static checking, have enjoyed the automation offered by satisfiability-modulo-theories (SMT) solvers. More recently, powerful SMT solvers and well-designed program verifiers are starting to break that tradition, thus reducing the effort involved in doing full verification. This paper gives a tour of the language and verifier Dafny, which has been used to verify the functional correctness of a number of challenging pointer-based programs. The paper describes the features incorporated in Dafny, illustrating their use by small examples and giving a taste of how they are coded for an SMT solver. As a larger case study, the paper shows the full functional specification of the Schorr-Waite algorithm in Dafny.
Spin Transfer Torques

This tutorial article introduces the physics of spin transfer torques in magnetic devices. We provide an elementary discussion of the mechanism of spin transfer torque, and review the theoretical and experimental progress in this field. Our intention is to be accessible to beginning graduate students. This is the introductory paper for a cluster of “Current Perspectives” articles on spin transfer torques published in volume 320 of the Journal of Magnetism and Magnetic Materials. This article is meant to set the stage for the others which follow it in this cluster; they focus in more depth on particularly interesting aspects of spin-torque physics and highlight unanswered questions that might be productive topics for future research.
The ethics of representation and action in virtual reality

This essay addresses ethical aspects of the design and use of virtual reality (VR) systems, focusing on the behavioral options made available in such systems and the manner in which reality is represented or simulated in them. An assessment is made of the morality of ‘immoral’ behavior in virtual reality, and of the virtual modeling of such behavior. Thereafter, the ethical aspects of misrepresentation and biased representation in VR applications are discussed.
A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm

Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster size and weighting factor are optimized and missing values are estimated. The proposed novel hybrid method yields sufficient and sensible imputation performance results. The results are compared with those of fuzzy c-means genetic algorithm imputation, support vector regression genetic algorithm imputation and zero imputation.
Explaining Bargaining Impasse : The Role of Self-Serving Biases

We review studies conducted by ourselves and coauthors that document a "self-serving" bias in judgments of fairness and demonstrate that the bias is an important cause of impasse in negotiations. We discuss experimental evidence showing that (a) the bias causes impasse; (b) it is possible to reduce impasses by debiasing bargainers; and (c) the bias results from selective evaluation of information. We also review results from a field study of negotiations between teachers' unions and school boards in Pennsylvania that both documents the fairness bias in a naturalistic setting and demonstrates its impact on strikes.
Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network

Controller Area Network (CAN) bus in the vehicles is a de facto standard for serial communication to provide an efficient, reliable and economical link between Electronic Control Units (ECU). However, CAN bus does not have enough security features to protect itself from inside or outside attacks. Intrusion Detection System (IDS) is one of the best ways to enhance the vehicle security level. Unlike the traditional IDS for network security, IDS for vehicle requires light-weight detection algorithm because of the limitations of the computing power of electronic devices reside in cars. In this paper, we propose a light-weight intrusion detection algorithm for in-vehicle network based on the analysis of time intervals of CAN messages. We captured CAN messages from the cars made by a famous manufacturer and performed three kinds of message injection attacks. As a result, we find the time interval is a meaningful feature to detect attacks in the CAN traffic. Also, our intrusion detection system detects all of message injection attacks without making false positive errors.
Highly efficient and compact DC-DC converter for ultra-fast charging of electric vehicles

The range of electric vehicles is still limited due to long charging times. With fast charging stations that enable to recharge a vehicle battery in a few minutes, the range limitation can be overcome in principle. For such fast charging stations, compact and efficient DC-DC converters with a high output power are required. Therefore, a multi-phase half-bridge DC-DC converter, which is part of an ultra fast charging station capable of charging a vehicle battery in less than 5 min, is presented in this paper. The ultra fast charging station utilises an intermediate storage battery, in order to avoid power pulsations at the grid and to provide additional functionality as e.g. energy storage for renewables. By splitting the intermediate storage battery, the voltage ratings of the semiconductor devices used in the high power DC-DC converter can be reduced, which increases the system efficiency. For further increasing the efficiency, a triangular current modulation (TCM) scheme is applied, which allows zero voltage switching (ZVS) for all semiconductor devices. To achieve a high power density as well as to implement ripple cancelation on the output current, several power electronic modules are parallel interleaved. A detailed analysis of the proposed system and simulation results are provided in this paper. Based on the above mentioned means the DC-DC converter achieves a system efficiency above 99.5% over a wide operating range.
Bilingual Distributed Word Representations from Document-Aligned Comparable Data

We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in context for polysemous words. Our simple yet effective BWE-based models significantly outperform the MuPTM-based and contextcounting representation models from comparable data as well as prior BWE-based models, and acquire the best reported results on both tasks for all three tested language pairs.
Discriminative Methods for Multi-labeled Classification

In this paper we present methods of enhancing existing discriminative classifiers for multi-labeled predictions. Discriminative methods like support vector machines perform very well for uni-labeled text classification tasks. Multi-labeled classification is a harder task subject to relatively less attention. In the multi-labeled setting, classes are often related to each other or part of a is-a hierarchy. We present a new technique for combining text features and features indicating relationships between classes, which can be used with any discriminative algorithm. We also present two enhancements to the margin of SVMs for building better models in the presence of overlapping classes. We present results of experiments on real world text benchmark datasets. Our new methods beat accuracy of existing methods with statistically significant improvements.
Broadband and High-Gain SIW-Fed Antenna Array for 5G Applications

A broadband and high-gain substrate integrated waveguide (SIW)-fed antenna array is demonstrated for 5G applications. Aperture coupling is adopted as the interconnecting method in the two-layer SIW feeding network. Two pairs of metallic posts act as dipoles and are surrounded by an air-filled cavity. The height of the cavity is designed to be higher than that of the posts to broaden the bandwidth. The measured impedance bandwidth of the proposed <inline-formula> <tex-math notation="LaTeX">$8{\times} 8$ </tex-math></inline-formula> antenna array is 16.3% from 35.4 to 41.7 GHz for <inline-formula> <tex-math notation="LaTeX">$|S_{11}|\leqslant -10$ </tex-math></inline-formula> dB, and the measured peak gain is 26.7 dBi at 40 GHz. The measured radiation efficiency of the antenna array is 83.2% at 40 GHz. The proposed antenna array which is suitable for 5G applications at 37 and 39 GHz bands shows stable radiation patterns, high gain, and broad bandwidth.
Integrating NLP Using Linked Data

We are currently observing a plethora of Natural Language Processing tools and services being made available. Each of the tools and services has its particular strengths and weaknesses, but exploiting the strengths and synergistically combining different tools is currently an extremely cumbersome and time consuming task. Also, once a particular set of tools is integrated, this integration is not reusable by others. We argue that simplifying the interoperability of different NLP tools performing similar but also complementary tasks will facilitate the comparability of results and the creation of sophisticated NLP applications. In this paper, we present the NLP Interchange Format (NIF). NIF is based on a Linked Data enabled URI scheme for identifying elements in (hyper-)texts and an ontology for describing common NLP terms and concepts. In contrast to more centralized solutions such as UIMA and GATE, NIF enables the creation of heterogeneous, distributed and loosely coupled NLP applications, which use the Web as an integration platform. We present several use cases of the second version of the NIF specification (NIF 2.0) and the result of a developer study.
Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays

This paper proposes a method of modeling and simulation of photovoltaic arrays. The main objective is to find the parameters of the nonlinear I-V equation by adjusting the curve at three points: open circuit, maximum power, and short circuit. Given these three points, which are provided by all commercial array data sheets, the method finds the best I-V equation for the single-diode photovoltaic (PV) model including the effect of the series and parallel resistances, and warranties that the maximum power of the model matches with the maximum power of the real array. With the parameters of the adjusted I-V equation, one can build a PV circuit model with any circuit simulator by using basic math blocks. The modeling method and the proposed circuit model are useful for power electronics designers who need a simple, fast, accurate, and easy-to-use modeling method for using in simulations of PV systems. In the first pages, the reader will find a tutorial on PV devices and will understand the parameters that compose the single-diode PV model. The modeling method is then introduced and presented in details. The model is validated with experimental data of commercial PV arrays.
Getting users to pay attention to anti-phishing education: evaluation of retention and transfer

Educational materials designed to teach users not to fall for phishing attacks are widely available but are often ignored by users. In this paper, we extend an embedded training methodology using learning science principles in which phishing education is made part of a primary task for users. The goal is to motivate users to pay attention to the training materials. In embedded training, users are sent simulated phishing attacks and trained after they fall for the attacks. Prior studies tested users immediately after training and demonstrated that embedded training improved users' ability to identify phishing emails and websites. In the present study, we tested users to determine how well they retained knowledge gained through embedded training and how well they transferred this knowledge to identify other types of phishing emails. We also compared the effectiveness of the same training materials delivered via embedded training and delivered as regular email messages. In our experiments, we found that: (a) users learn more effectively when the training materials are presented after users fall for the attack (embedded) than when the same training materials are sent by email (non-embedded); (b) users retain and transfer more knowledge after embedded training than after non-embedded training; and (c) users with higher Cognitive Reflection Test (CRT) scores are more likely than users with lower CRT scores to click on the links in the phishing emails from companies with which they have no account.
Growth Promotion of indigenous Scenedesmus dimorphus strain under different conditions using stirred tank

The effects of culture conditions at different temperature (15-25-35°C) and nitrate concentration (0.1-0.25-0.4g/L) on biomass production and growth rate of scenedesmus dimorphus were determined. The results shown that the dry biomass weight (0.153g/L) at 15°C was the lowest. No significant difference was observed between the dry biomasses at 25°C (0.289g/L) and 35°C (0.240g/L). While the lowest growth rate was at 15°C(0.157d), the highestone was at 25°C (0.229d) and 35°C (0.195d) with insignificant deference, and The nitrate concentration had no effect on both of biomass production and growth rate.
FFT-based Terrain Segmentation for Underwater Mapping

A method for segmenting three-dimensional scans of underwater unstructured terrains is presented. Individual terrain scans are represented as an elevation map and analysed using fast Fourier transform (FFT). The segmentation of the ground surface is performed in the frequency domain. The lower frequency components represent the slower varying undulations of the underlying ground whose segmentation is similar to de-noising / low pass filtering. The cut-off frequency, below which ground frequency components are selected, is automatically determined using peak detection. The user can specify a maximum admissible size of objects (relative to the extent of the scan) to drive the automatic detection of the cut-off frequency. The points above the estimated ground surface are clustered via standard proximity clustering to form object segments. The approach is evaluated using ground truth hand labelled data. It is also evaluated for registration error when the segments are fed as features to an alignment algorithm. In both sets of experiments, the approach is compared to three other segmentation techniques. The results show that the approach is applicable to a range of different terrains and is able to generate features useful for navigation.
SemEval-2014 Task 3: Cross-Level Semantic Similarity

This paper introduces a new SemEval task on Cross-Level Semantic Similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Highquality data sets were constructed for four comparison types using multi-stage annotation procedures with a graded scale of similarity. Nineteen teams submitted 38 systems. Most systems surpassed the baseline performance, with several attaining high performance for multiple comparison types. Further, our results show that comparisons of semantic representation increase performance beyond what is possible with text alone.
Pedestrian detection with a Large-Field-Of-View deep network

Pedestrian detection is of crucial importance to autonomous driving applications. Methods based on deep learning have shown significant improvements in accuracy, which makes them particularly suitable for applications, such as pedestrian detection, where reducing the miss rate is very important. Although they are accurate, their runtime has been at best in seconds per image, which makes them not practical for onboard applications. We present a Large-Field-Of-View (LFOV) deep network for pedestrian detection, that can achieve high accuracy and is designed to make deep networks work faster for detection problems. The idea of the proposed Large-Field-of-View deep network is to learn to make classification decisions simultaneously and accurately at multiple locations. The LFOV network processes larger image areas at much faster speeds than typical deep networks have been able to, and can intrinsically reuse computations. Our pedestrian detection solution, which is a combination of a LFOV network and a standard deep network, works at 280 ms per image on GPU and achieves 35.85 average miss rate on the Caltech Pedestrian Detection Benchmark.