nils-herrmann/clean_scirepeval huggingface dataset:
Active Sensor Networks

We propose using application specific virtual machines (ASVMs) to reprogram deployed wireless sensor networks. ASVMs provide a way for a user to define an application-specific boundary between virtual code and the VM engine. This allows programs to be very concise (tens to hundreds of bytes), making program installation fast and inexpensive. Additionally, concise programs interpret few instructions, imposing very little interpretation overhead. We evaluate ASVMs against current proposals for network programming runtimes and show that ASVMs are more energy efficient by as much as 20%. We also evaluate ASVMs against hand built TinyOS applications and show that while interpretation imposes a significant execution overhead, the low duty cycles of realistic applications make the actual cost effectively unmeasurable.
High Voltage Marx Generator Implementation using IGBT Stacks

High voltage Marx generator implementation using IGBT (Insulated Gate Bipolar Transistor) stacks is proposed in this paper. To protect the Marx generator at the moment of breakdown, AOCP (Active Over-Current Protection) part is included. The Marx generator is composed of 12 stages and each stage is made of IGBT stacks, two diode stacks, and capacitors. IGBT stack is used as a single switch. Diode stacks and inductors are used to charge the high voltage capacitor at each stage without power loss. These are also used to isolate input and high voltage negative output in high voltage generation mode. The proposed Marx generator implementation uses IGBT stack with a simple driver and has modular design. This system structure gives compactness and easiness to implement the total system. Some experimental and simulated results are included to verify the system performances in this paper.
Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering

Most work on natural language question answering today focuses on answer selection: given a candidate list of sentences, determine which contains the answer. Although important, answer selection is only one stage in a standard end-to-end question answering pipeline. is paper explores the eectiveness of convolutional neural networks (CNNs) for answer selection in an end-to-end context using the standard TrecQA dataset. We observe that a simple idf-weighted word overlap algorithm forms a very strong baseline, and that despite substantial eorts by the community in applying deep learning to tackle answer selection, the gains are modest at best on this dataset. Furthermore, it is unclear if a CNN is more eective than the baseline in an end-to-end context based on standard retrieval metrics. To further explore this nding, we conducted a manual user evaluation, which conrms that answers from the CNN are detectably beer than those from idf-weighted word overlap. is result suggests that users are sensitive to relatively small dierences in answer selection quality.
Prevalence and Risk Factors of Video Game Dependency in Adolescence: Results of a German Nationwide Survey

In this article, results of a German nationwide survey (KFN schools survey 2007/2008) are presented. The controlled sample of 44,610 male and female ninth-graders was carried out in 2007 and 2008 by the Criminological Research Institute of Lower Saxony (KFN). According to a newly developed screening instrument (KFN-CSAS-II), which was presented to every third juvenile participant (N = 15,168), 3% of the male and 0.3% of the female students are diagnosed as dependent on video games. The data indicate a clear dividing line between extensive gaming and video game dependency (VGD) as a clinically relevant phenomenon. VGD is accompanied by increased levels of psychological and social stress in the form of lower school achievement, increased truancy, reduced sleep time, limited leisure activities, and increased thoughts of committing suicide. In addition, it becomes evident that personal risk factors are crucial for VGD. The findings indicate the necessity of additional research as well as the respective measures in the field of health care policies.
Massive Exploration of Neural Machine Translation Architectures

Neural Machine Translation (NMT) has shown remarkable progress over the past few years, with production systems now being deployed to end-users. As the field is moving rapidly, it has become unclear which elements of NMT architectures have a significant impact on translation quality. In this work, we present a large-scale analysis of the sensitivity of NMT architectures to common hyperparameters. We report empirical results and variance numbers for several hundred experimental runs, corresponding to over 250,000 GPU hours on a WMT English to German translation task. Our experiments provide practical insights into the relative importance of factors such as embedding size, network depth, RNN cell type, residual connections, attention mechanism, and decoding heuristics. As part of this contribution, we also release an open-source NMT framework in TensorFlow to make it easy for others to reproduce our results and perform their own experiments.
Building a Corpus of Temporal-Causal Structure

While recent corpus annotation efforts cover a wide variety of semantic structures, work on temporal and causal relatio ns is still in its early stages. Annotation efforts have typically considere either temporal relations or causal relations, but not bot h, and no corpora currently exist that allow the relation between temporals a nd causals to be examined empirically. We have annotated a co rpus f 1000 event pairs for both temporal and causal relations, focusin g on a relatively frequent construction in which the events a re conjoined by the word and. Temporal relations were annotated using an extension of th e BEFOREandAFTER scheme used in the TempEval competition, and causal relations were annotated using a scheme based on c onnective phrases like and as a result . The annotators achieved 81.2% agreement on temporal relations and 77.8% agreement on caus al relations. Analysis of the resulting corpus revealed som e interesting findings, for example, that over 30% of CAUSAL relations do not have an underlying BEFORErelation. The corpus was also explored using machine learning methods, and while model performanc e ex eeded all baselines, the results suggested that simple grammatical cues may be insufficient for identifying the more difficult te mporal and causal relations.
On Passive Wireless Device Fingerprinting using Infinite Hidden Markov Random Field

This paper presents a new concept of device fingerprinting (or profiling) to enhance wireless security using Infinite Hidden Markov Random Field (iHMRF). Wireless device fingerprinting is an emerging approach for detecting spoofing attacks in wireless network. Existing methods utilize either time-independent features or time-dependent features, but not both concurrently due to the complexity of different dynamic patterns. In this paper, we present a unified approach to fingerprinting based on iHMRF. The proposed approach is able to model both time-independent and time-dependent features, and to automatically detect the number of devices that is dynamically varying. We propose the first iHMRF-based online classification algorithm for wireless environment using variational incremental inference, micro-clustering techniques, and batch updates. Extensive simulation evaluations demonstrate the effectiveness and efficiency of this new approach.
Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation

Pose space deformation generalizes and improves upon both shape interpolation and common skeleton-driven deformation techniques. This deformation approach proceeds from the observation that several types of deformation can be uniformly represented as mappings from a pose space, defined by either an underlying skeleton or a more abstract system of parameters, to displacements in the object local coordinate frames. Once this uniform representation is identified, previously disparate deformation types can be accomplished within a single unified approach. The advantages of this algorithm include improved expressive power and direct manipulation of the desired shapes yet the performance associated with traditional shape interpolation is achievable. Appropriate applications include animation of facial and body deformation for entertainment, telepresence, computer gaming, and other applications where direct sculpting of deformations is desired or where real-time synthesis of a deforming model is required.
The Incredible ELK

ℰ ℒ $\mathcal {E} \mathcal {L}$ is a simple tractable Description Logic that features conjunctions and existential restrictions. Due to its favorable computational properties and relevance to existing ontologies, ℰ ℒ $\mathcal {E} \mathcal {L}$ has become the language of choice for terminological reasoning in biomedical applications, and has formed the basis of the OWL EL profile of the Web ontology language OWL. This paper describes ELK—a high performance reasoner for OWL EL ontologies—and details various aspects from theory to implementation that make ELK one of the most competitive reasoning systems for ℰ ℒ $\mathcal {E} \mathcal {L}$ ontologies available today.
Face Recognition using Template Matching

The traditional approach towards human identification such as fingerprints, identity cards, iris recognition etc. lead to the improvised technique for face recognition. This includes enhancement and segmentation of face image, detection of face boundary and facial features, matching of extracted features against the features in a database, and finally recognition of the face. This research proposes a wavelet transformation for preprocessing the face image, extracting edge image, extracting features and finally matching extracted facial features for face recognition. Simulation is done using ORL database that contains PGM images. This research finds application in homeland security where it can increase the robustness of the existing face recognition algorithms.
Assuring Demanded Read Performance of Data Deduplication Storage with Backup Datasets

Data deduplication has been widely adopted in contemporary backup storage systems. It not only saves storage space considerably, but also shortens the data backup time significantly. Since the major goal of the original data deduplication lies in saving storage space, its design has been focused primarily on improving write performance by removing as many duplicate data as possible from incoming data streams. Although fast recovery from a system crash relies mainly on read performance provided by deduplication storage, little investigation into read performance improvement has been made. In general, as the amount of deduplicated data increases, write performance improves accordingly, whereas associated read performance becomes worse. In this paper, we newly propose a deduplication scheme that assures demanded read performance of each data stream while achieving its write performance at a reasonable level, eventually being able to guarantee a target system recovery time. For this, we first propose an indicator called cache aware Chunk Fragmentation Level (CFL) that estimates degraded read performance on the fly by taking into account both incoming chunk information and read cache effects. We also show a strong correlation between this CFL and read performance in the backup datasets. In order to guarantee demanded read performance expressed in terms of a CFL value, we propose a read performance enhancement scheme called selective duplication that is activated whenever the current CFL becomes worse than the demanded one. The key idea is to judiciously write non-unique (shared) chunks into storage together with unique chunks unless the shared chunks exhibit good enough spatial locality. We quantify the spatial locality by using a selective duplication threshold value. Our experiments with the actual backup datasets demonstrate that the proposed scheme achieves demanded read performance in most cases at the reasonable cost of write performance.
Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles

This paper highlights the role of humans in the next generation of driver assistance and intelligent vehicles. Understanding, modeling, and predicting human agents are discussed in three domains where humans and highly automated or self-driving vehicles interact: 1) inside the vehicle cabin, 2) around the vehicle, and 3) inside surrounding vehicles. Efforts within each domain, integrative frameworks across domains, and scientific tools required for future developments are discussed to provide a human-centered perspective on research in intelligent vehicles.
A comparative study of urban traffic signal control with reinforcement learning and Adaptive Dynamic Programming

This paper proposes a new algorithm that employs Adaptive Dynamic Programming(ADP) to solve the distributed control problem of urban traffic with an infinite horizon. Urban traffic congestions lead to a lot of time consumption and exhaust emissions. So alleviating congested situation will have a good impact on both economy and environment. The signal control at urban intersections is an effective and most important way to reduce the traffic jams and collisions. A lot of control theories including traditional mathematical ways and modern artificial intelligent ways have been exploited. ADP is an effective and amiable intelligent control method. We proposed an algorithm to adjust the signal time plan at urban traffic intersections based on ADP theory. Simulations are taken under a microscopic traffic simulation software, TSIS(Traffic Software Integrated System). Several criteria named MOEs(Measures of Effectiveness) are collected to compare with the widely used pre-timed control, actuated control, also with a machine learning method Q-learning control. Results show that ADP control method have a better adaptability to the various traffic simulating real traffic flows.
A cross-media public sentiment analysis system for microblog

Since classical public sentiment analysis systems for microblog are based on the text sentiment analysis, it is difficult to determine the sentiment of short text without clear sentiment words in microblog posts. Fortunately, a lot of microblog posts contain images which also represent users’ sentiment. To fully understand users’ sentiment, we propose a cross-media public sentiment analysis system for microblog. The best advantage of this novel system is the unified cross-media public sentiment analysis framework which fuses the text sentiment and image sentiment not only from sentiment results, but also from sentiment ontology. To enhance presentation effects, this system presents sentiment results from macroscopic view and microscopic view which details the sentiment results in region, topic, microblog content and user diffusion. In our knowledge, this is the first unified cross-media public sentiment analysis system.
A comparative analysis of image fusion methods

There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images. Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods. Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level. Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method. The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set. An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level.
Semantic Cyberthreat Modelling

Cybersecurity is a complex and dynamic area where multiple actors act against each other through computer networks largely without any commonly accepted rules of engagement. Well-managed cybersecurity operations need a clear terminology to describe threats, attacks and their origins. In addition, cybersecurity tools and technologies need semantic models to be able to automatically identify threats and to predict and detect attacks. This paper reviews terminology and models of cybersecurity operations, and proposes approaches for semantic modelling of cybersecurity threats and attacks.
Defensiveness versus remediation: self-theories and modes of self-esteem maintenance.

How people maintain and repair their self-esteem has been a topic of widespread interest. In this article, the authors ask, What determines whether people will use direct, remedial actions, or defensive actions? In three studies, they tested the hypothesis that a belief in fixed intelligence (entity theory) would produce defensiveness, whereas a belief in improvable intelligence (incremental theory) would foster remediation. In each study, participants assigned to the entity condition opted for defensive self-esteem repair (downward comparison in Studies 1 and 3; a tutorial on already mastered material in Study 2), but those in the incremental condition opted for self-improvement (upward comparison in Studies 1 and 3; a tutorial on unmastered material in Study 2). Experiment 3 also linked these strategies to self-esteem repair; remedial strategies were the most effective in recovering lost self-esteem for those in the incremental condition, whereas defensive strategies were most effective for those in the entity condition.
A Novel Design of Rotor Contour for Variable Reluctance Resolver by Injecting Auxiliary Air-Gap Permeance Harmonics

This paper proposes a novel rotor contour design for variable reluctance (VR) resolvers by injecting auxiliary air-gap permeance harmonics. Based on the resolver model with nonoverlapping tooth-coil windings, the influence of air-gap length function is first investigated by finite element (FE) method, and the detection accuracy of designs with higher values of fundamental wave factor may deteriorate due to the increasing third order of output voltage harmonics. Further, the origins of the third harmonics are investigated by analytical derivation and FE analyses of output voltages. Furthermore, it is proved that the voltage harmonics and the detection accuracy are significantly improved by injecting auxiliary air-gap permeance harmonics in the design of rotor contour. In addition, the proposed design can also be employed to eliminate voltage tooth harmonics in a conventional VR resolver topology. Finally, VR resolver prototypes with the conventional and the proposed rotors are fabricated and tested respectively to verify the analyses.
Content-based retrieval of 3D models

In the past few years, there has been an increasing availability of technologies for the acquisition of digital 3D models of real objects and the consequent use of these models in a variety of applications, in medicine, engineering, and cultural heritage. In this framework, content-based retrieval of 3D objects is becoming an important subject of research, and finding adequate descriptors to capture global or local characteristics of the shape has become one of the main investigation goals. In this article, we present a comparative analysis of a few different solutions for description and retrieval by similarity of 3D models that are representative of the principal classes of approaches proposed. We have developed an experimental analysis by comparing these methods according to their robustness to deformations, the ability to capture an object's structural complexity, and the resolution at which models are considered.
Forecasting the behavior of an elderly using wireless sensors data in a smart home

In this paper, the ability to determine the wellness of an elderly living alone in a smart home using a lowcost, robust, flexible and data driven intelligent system is presented. A framework integrating temporal and spatial contextual information for determining the wellness of an elderly has been modeled. A novel behavior detection process based on the observed sensor data in performing essential daily activities has been designed and developed. The developed prototype is used to forecast the behavior and wellness of the elderly by monitoring the daily usages of appliances in a smart home. Wellness models are tested at various elderly houses, and the experimental results are encouraging. The wellness models are updated based on the time series analysis.
Lane Detection and Kalman-Based Linear-Parabolic Lane Tracking

This paper presents a lane detection and linear-parabolic lane tracking system using kalman filtering method. First, the image horizon is detected in a traffic scene to split the sky and road region. Road region is further analyzed with entropy method to remove the road pixels. Lane boundaries are then extracted from the region using lane markings detection. These detected boundaries are tracked in consecutive video frames with a linear-parabolic tracking model. The model parameters are updated with Kalman filtering method. Error-checking is performed iteratively to ensure the performance of the lane estimation model. Simulation results demonstrate good performance of the proposed Kalman-based linear-parabolic lane tracking system with fine parameters update.
Nirikshan: mining bug report history for discovering process maps, inefficiencies and inconsistencies

Issue tracking systems such as Bugzilla, Mantis and JIRA are Process Aware Information Systems to support business process of issue (defect and feature enhancement) reporting and resolution. The process of issue reporting to resolution consists of several steps or activities performed by various roles (bug reporter, bug triager, bug fixer, developers, and quality assurance manager) within the software maintenance team. Project teams define a workflow or a business process (design time process model and guidelines) to streamline and structure the issue management activities. However, the runtime process (reality) may not conform to the design time model and can have imperfections or inefficiencies. We apply business process mining tools and techniques to analyze the event log data (bug report history) generated by an issue tracking system with the objective of discovering runtime process maps, inefficiencies and inconsistencies. We conduct a case-study on data extracted from Bugzilla issue tracking system of the popular open-source Firefox browser project. We present and implement a process mining framework, Nirikshan, consisting of various steps: data extraction, data transformation, process discovery, performance analysis and conformance checking. We conduct a series of process mining experiments to study self-loops, back-and-forth, issue reopen, unique traces, event frequency, activity frequency, bottlenecks and present an algorithm and metrics to compute the degree of conformance between the design time and the runtime process.