nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Parallel Scan Algorithms for GPUs

Scan and segmented scan algorithms are crucial building blocks for a great many data-parallel algorithms. Segmented scan and related primitives also provide the necessary support for the flattening transform, which allows for nested data-parallel programs to be compiled into flat data-parallel languages. In this paper, we describe the design of efficient scan and segmented scan parallel primitives in CUDA for execution on GPUs. Our algorithms are designed using a divide-and-conquer approach that builds all scan primitives on top of a set of primitive intra-warp scan routines. We demonstrate that this design methodology results in routines that are simple, highly efficient, and free of irregular access patterns that lead to memory bank conflicts. These algorithms form the basis for current and upcoming releases of the widely used CUDPP library.
The Effect of Syntactic Representation on Semantic Role Labeling

Almost all automatic semantic role labeling (SRL) systems rely on a preliminary parsing step that derives a syntactic structure from the sentence being analyzed. This makes the choice of syntactic representation an essential design decision. In this paper, we study the influence of syntactic representation on the performance of SRL systems. Specifically, we compare constituent-based and dependencybased representations for SRL of English in the FrameNet paradigm. Contrary to previous claims, our results demonstrate that the systems based on dependencies perform roughly as well as those based on constituents: For the argument classification task, dependencybased systems perform slightly higher on average, while the opposite holds for the argument identification task. This is remarkable because dependency parsers are still in their infancy while constituent parsing is more mature. Furthermore, the results show that dependency-based semantic role classifiers rely less on lexicalized features, which makes them more robust to domain changes and makes them learn more efficiently with respect to the amount of training data.
Reliable Early Classification on Multivariate Time Series with Numerical and Categorical Attributes

Early classification on multivariate time series has recently emerged as a novel and important topic in data mining fields with wide applications such as early detection of diseases in healthcare domains. Most of the existing studies on this topic focused only on univariate time series, while some very recent works exploring multivariate time series considered only numerical attributes and are not applicable to multivariate time series containing both of numerical and categorical attributes. In this paper, we present a novel methodology named REACT (Reliable EArly ClassificaTion), which is the first work addressing the issue of constructing an effective classifier on multivariate time series with numerical and categorical attributes in serial manner so as to guarantee stability of accuracy compared to the classifiers using full-length time series. Furthermore, we also employ the GPU parallel computing technique to develop an extended mechanism for building the early classifier efficiently. Experimental results on real datasets show that REACT significantly outperforms the state-of-the-art method in terms of accuracy and earliness, and the GPU implementation is verified to substantially enhance the efficiency by several orders of magnitudes.
Spoken English Grading: Machine Learning with Crowd Intelligence

In this paper, we address the problem of grading spontaneous speech using a combination of machine learning and crowdsourcing. Traditional machine learning techniques solve the stated problem inadequately as automatic speaker-independent speech transcription is inaccurate. The features derived from it are also inaccurate and so is the machine learning model developed for speech evaluation. We propose a framework that combines machine learning with crowdsourcing. This entails identifying human intelligence tasks in the feature derivation step and using crowdsourcing to get them completed. We post the task of speech transcription to a large community of online workers (crowd). We also get spoken English grades from the crowd. We achieve 95% transcription accuracy by combining transcriptions from multiple crowd workers. Speech and prosody features are derived by force aligning the speech samples on these highly accurate transcriptions. Additionally, we derive surface and semantic level features directly from the transcription. We demonstrate the efficacy of our approach by predicting expert graded speech sample of 566 adult non-native speakers across two different countries - India and Philippines. Using the regression modeling technique, we are able achieve a Pearson correlation of 0.79 on the Philippines set and 0.74 on the Indian set with expert grades, an accuracy much higher than any previously reported machine learning approach. Our approach has an accuracy that rivals that of expert agreement. We show the value of the system through a case study in a real-world industrial deployment. This work is timely given the huge requirement of spoken English training and assessment.
One time password authentication scheme based on elliptic curves for Internet of Things (IoT)

Establishing end-to-end authentication between devices and applications in Internet of Things (IoT) is a challenging task. Due to heterogeneity in terms of devices, topology, communication and different security protocols used in IoT, existing authentication mechanisms are vulnerable to security threats and can disrupt the progress of IoT in realizing Smart City, Smart Home and Smart Infrastructure, etc. To achieve end-to-end authentication between IoT devices/applications, the existing authentication schemes and security protocols require a two-factor authentication mechanism. Therefore, as part of this paper we review the suitability of an authentication scheme based on One Time Password (OTP) for IoT and proposed a scalable, efficient and robust OTP scheme. Our proposed scheme uses the principles of lightweight Identity Based Elliptic Curve Cryptography scheme and Lamport's OTP algorithm. We evaluate analytically and experimentally the performance of our scheme and observe that our scheme with a smaller key size and lesser infrastructure performs on par with the existing OTP schemes without compromising the security level. Our proposed scheme can be implemented in real-time IoT networks and is the right candidate for two-factor authentication among devices, applications and their communications in IoT.
Conception, development and implementation of an e-Government maturity model in public agencies

Governments worldwide are encouraging public agencies to join e-Government initiatives in order to provide better services to their citizens and businesses; hence, methods of evaluating the readiness of individual public agencies to execute specific e-Government programs and directives are a key ingredient in the successful expansion of e-Government. To satisfy this need, a model called the eGovernment Maturity Model (eGov-MM) was developed, integrating the assessment of technological, organizational, operational, and human capital capabilities, under a multi-dimensional, holistic, and evolutionary approach. The model is strongly supported by international best practices, and provides tuning mechanisms to enable its alignment with nation-wide directives on e-Government. This article describes how the model was conceived, designed, developed, field tested by expert public officials from several government agencies, and finally applied to a selection of 30 public agencies in Chile, generating the first formal measurements, assessments, and rankings of their readiness for eGovernment. The implementation of the model also provided several recommendations to policymakers at the national and agency levels.
Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes to use the vehicle's position (e.g., available via GPS) to query a multipath fingerprint database, which provides prior knowledge of potential pointing directions for reliable beam alignment. The approach is the inverse of fingerprinting localization, where the measured multipath signature is compared to the fingerprint database to retrieve the most likely position. The power loss probability is introduced as a metric to quantify misalignment accuracy and is used for optimizing candidate beam selection. Two candidate beam selection methods are developed, where one is a heuristic while the other minimizes the misalignment probability. The proposed beam alignment is evaluated using realistic channels generated from a commercial ray-tracing simulator. Using the generated channels, an extensive investigation is provided, which includes the required measurement sample size to build an effective fingerprint, the impact of measurement noise, the sensitivity to changes in traffic density, and beam alignment overhead comparison with IEEE 802.11ad as the baseline. Using the concept of beam coherence time, which is the duration between two consecutive beam alignments, and parameters of IEEE 802.11ad, the overhead is compared in the mobility context. The results show that while the proposed approach provides increasing rates with larger antenna arrays, IEEE 802.11ad has decreasing rates due to the higher beam training overhead that eats up a large portion of the beam coherence time, which becomes shorter with increasing mobility.
Optimizing Stencil Computations for NVIDIA Kepler GPUs

We present a series of optimization techniques for stencil computations on NVIDIA Kepler GPUs. Stencil computations with regular grids had been ported to the older generations of NVIDIA GPUs with significant performance improvements thanks to the higher memory bandwidth than conventional CPU-only systems. However, because of the architectural changes introduced with the latest generation of the GPU architecture, Kepler, we show that existing implementation strategies used for such older GPUs are not as effective on Kepler as before. To fully exploit the potential performance of the latest generation of the GPU architecture, our implementation method uses shared memory for better data locality combined with warp specialization for higher instruction throughput. Our method achieves approximately 80% of the estimated peak performance by the roofline model, and even higher performance with temporal blocking.
Random image steganography in spatial domain

Steganography is an art of hiding information in some media. This paper describes various image steganography techniques, based on spatial domain and by considering pixel values in binary format. Spatial domain is based on physical location of pixels in an image. Generally 8 bit gray level or colour images can be used as a cover to hide data. Again binary representations of these pixels are considered to hide secret information. Random bits from these bytes are used to replace the bits of secret. In this paper, many steganography techniques can be used like Least Significant Bit (LSB), layout management schemes, replacing only 1's or only zero's from lower nibble from the byte are considered for hiding secret message in an image. Along with these techniques, some more methods are proposed, based on selection of random pixels from an image and again secret data is hidden in random bits of these randomly selected pixels. For this purpose, many parameters of an image are considered like physical location of pixels, intensity value of pixel, etc.
Modeling the customer in electronic commerce.

This paper reviews interface design of web pages for e-commerce. Different tasks in e-commerce are contrasted. A systems model is used to illustrate the information flow between three subsystems in e-commerce: store environment, customer, and web technology. A customer makes several decisions: to enter the store, to navigate, to purchase, to pay, and to keep the merchandize. This artificial environment must be designed so that it can support customer decision-making. To retain customers it must be pleasing and fun, and create a task with natural flow. Customers have different needs, competence and motivation, which affect decision-making. It may therefore be important to customize the design of the e-store environment. Future ergonomics research will have to investigate perceptual aspects, such as presentation of merchandize, and cognitive issues, such as product search and navigation, as well as decision making while considering various economic parameters. Five theories on e-commerce research are presented.
Measuring abstract reasoning in neural networks

Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation ‘regimes’ in which the training and test data differ in clearlydefined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with a structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model’s ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction.
A Unified Framework for Painting Classification

In the past few years, the number of fine-art collections that are digitized and publicly available has been growing rapidly. With the availability of such large collections of digitized artworks comes the need to develop multimedia systems to archive and retrieve this pool of data. Measuring the visual similarity between artistic items is an essential step for such multimedia systems, which can benefit more high-level multimedia tasks. In order to model this similarity between paintings, we should extract the appropriate visual features for paintings and find out the best approach to learn the similarity metric based on these features. We investigate a comprehensive list of visual features and metric learning approaches to learn an optimized similarity measure between paintings. We develop a machine that is able to make aesthetic-related semantic-level judgments, such as predicting a painting's style, genre, and artist, as well as providing similarity measures optimized based on the knowledge available in the domain of art historical interpretation. Our experiments show the value of using this similarity measure for the aforementioned prediction tasks.
A compact, high voltage 25 kW, 50 kHz DC-DC converter based on SiC JFETs

In the area of power electronics there is a general trend to higher power densities and efficiency. In order to continue this trend new devices, which enable high switching frequencies at higher power levels or show reduced losses at moderate switching frequencies are required. High voltage switches based on a series connection of SiC JFETs and one MOSFET in cascode connection meet these demands. For investigating the performance of the SiC based switch and its influence on the power density/efficiency a dual active bridge, which could transfer 25 kW bidirectionally between a 5 kV and a 700 V dc bus at a switching frequency of 50 kHz, is presented in this paper. There, especially the design of the high voltage/high frequency transformer and the switching as well as the static behaviour of the SiC switch is investigated in detail by simulations and experimental results in this paper.
EEG dynamics in patients with Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder characterized by cognitive and intellectual deficits and behavior disturbance. The electroencephalogram (EEG) has been used as a tool for diagnosing AD for several decades. The hallmark of EEG abnormalities in AD patients is a shift of the power spectrum to lower frequencies and a decrease in coherence of fast rhythms. These abnormalities are thought to be associated with functional disconnections among cortical areas resulting from death of cortical neurons, axonal pathology, cholinergic deficits, etc. This article reviews main findings of EEG abnormalities in AD patients obtained from conventional spectral analysis and nonlinear dynamical methods. In particular, nonlinear alterations in the EEG of AD patients, i.e. a decreased complexity of EEG patterns and reduced information transmission among cortical areas, and their clinical implications are discussed. For future studies, improvement of the accuracy of differential diagnosis and early detection of AD based on multimodal approaches, longitudinal studies on nonlinear dynamics of the EEG, drug effects on the EEG dynamics, and linear and nonlinear functional connectivity among cortical regions in AD are proposed to be investigated. EEG abnormalities of AD patients are characterized by slowed mean frequency, less complex activity, and reduced coherences among cortical regions. These abnormalities suggest that the EEG has utility as a valuable tool for differential and early diagnosis of AD.
A Behavioral Model of Digital Music Piracy

The increasing pervasiveness of the internet, broadband connections and the emergence of digital compression technologies have dramatically changed the face of digital music piracy. Digitally compressed music files are essentially a perfect public economic good, and illegal copying of these files has increasingly become rampant. This paper presents a study on the behavioral dynamics which impact the piracy of digital audio files, and provides a contrast with software piracy. Our results indicate that the general ethical model of software piracy is also broadly applicable to audio piracy. However, significant enough differences with software underscore the unique dynamics of audio piracy. Practical implications that can help the recording industry to effectively combat piracy, and future research directions are highlighted.
Training and Inference with Integers in Deep Neural Networks

Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfully reduced precision in inference, transferring both training and inference processes to low-bitwidth integers has not been demonstrated simultaneously. In this work, we develop a new method termed as “WAGE” to discretize both training and inference, where weights (W), activations (A), gradients (G) and errors (E) among layers are shifted and linearly constrained to low-bitwidth integers. To perform pure discrete dataflow for fixed-point devices, we further replace batch normalization by a constant scaling layer and simplify other components that are arduous for integer implementation. Improved accuracies can be obtained on multiple datasets, which indicates that WAGE somehow acts as a type of regularization. Empirically, we demonstrate the potential to deploy training in hardware systems such as integer-based deep learning accelerators and neuromorphic chips with comparable accuracy and higher energy efficiency, which is crucial to future AI applications in variable scenarios with transfer and continual learning demands.
Applying Supervised Opinion Mining Techniques on Online User Reviews

In recent years, the spectacular development of web technologies, lead to an enormous quantity of user generated information in online systems. This large amount of information on web platforms make them viable for use as data sources, in applications based on opinion mining and sentiment analysis. The paper proposes an algorithm for detecting sentiments on movie user reviews, based on naive Bayes classifier. We make an analysis of the opinion mining domain, techniques used in sentiment analysis and its applicability. We implemented the proposed algorithm and we tested its performance, and suggested directions of development.
Casalendar: a temporal interface for automated homes

Smart homes with advanced building technologies can react to sensor triggers in a variety of preconfigured ways. These rules are usually only visible within designated configuration interfaces. For this reason inhabitants who are not actively involved in the configuration process can be taken by surprise by the effects of such rules, such as for example the unexpected automated actions of lights or shades. To provide these inhabitants with better means to understand their home, as well as to increase their motivation to actively engage with its configuration, we propose Casalendar, a visualization that integrates the status of smart home technologies into the familiar interface of a calendar. We present our design and initial findings about the application of a temporal metaphor in smart home interfaces.
MobiGUITAR: Automated Model-Based Testing of Mobile Apps

As mobile devices become increasingly smarter and more powerful, so too must the engineering of their software. User-interface-driven system testing of these devices is gaining popularity, with each vendor releasing some automation tool. However, these tools are inappropriate for amateur programmers, an increasing portion of app developers. MobiGUITAR (Mobile GUI Testing Framework) provides automated GUI-driven testing of Android apps. It's based on observation, extraction, and abstraction of GUI widgets' run-time state. The abstraction is a scalable state machine model that, together with test coverage criteria, provides a way to automatically generate test cases. When applied to four open-source Android apps, MobiGUITAR automatically generated and executed 7,711 test cases and reported 10 new bugs. Some bugs were Android-specific, stemming from the event- and activity-driven nature of Android.
Online Reviews : Do Consumers Use Them ?

The use of the WWW as a venue for voicing opinions, complaints and recommendations on products and firms has been widely reported in the popular media. However little is known how consumers use these reviews and if they subsequently have any influence on evaluations and purchase intentions of products and retailers. This study examines the effect of negative reviews on retailer evaluation and patronage intention given that the consumer has already made a product/brand decision. Our results indicate that the extent of WOM search depends on the consumer’s reasons for choosing an online retailer. Further the influence of negative WOM information on perceived reliability and purchase intentions is determined largely by familiarity with the retailer and differs based on whether the retailer is a pure-Internet or clicks-and-mortar firm. Managerial implications for positioning strategies to minimize the effect of negative word-ofmouth have been discussed.