nils-herrmann/clean_scirepeval huggingface dataset:
Use of SIMD Vector Operations to Accelerate Application Code Performance on Low-Powered ARM and Intel Platforms

Augmenting a processor with special hardware that is able to apply a Single Instruction to Multiple Data(SIMD) at the same time is a cost effective way of improving processor performance. It also offers a means of improving the ratio of processor performance to power usage due to reduced and more effective data movement and intrinsically lower instruction counts. This paper considers and compares the NEON SIMD instruction set used on the ARM Cortex-A series of RISC processors with the SSE2 SIMD instruction set found on Intel platforms within the context of the Open Computer Vision (OpenCV) library. The performance obtained using compiler auto-vectorization is compared with that achieved using hand-tuning across a range of five different benchmarks and ten different hardware platforms. On the ARM platforms the hand-tuned NEON benchmarks were between 1.05× and 13.88× faster than the auto-vectorized code, while for the Intel platforms the hand-tuned SSE benchmarks were between 1.34× and 5.54× faster.
Distributional Word Clusters vs. Words for Text Categorization

We study an approach to text categorization that combines di stributional clustering of words and a Support Vector Machine (SVM) classifier. This word-cluster r presentation is computed using the recently introducedInformation Bottleneckmethod, which generates a compact and efficient representation of documents. When combined with the classifica tion power of the SVM, this method yields high performance in text categorization. This novel combination of SVM with word-cluster representation is compared with SVM-based categorization using the simpler bag-of-words (BOW) representation. The comparison is performed over three kno wn datasets. On one of these datasets (the 20 Newsgroups) the method based on word clusters signifi ca tly outperforms the word-based representation in terms of categorization accuracy or repr esentation efficiency. On the two other sets (Reuters-21578 and WebKB) the word-based representation s lightly outperforms the word-cluster representation. We investigate the potential reasons for t his behavior and relate it to structural differences between the datasets.
CC-Hunter: Uncovering Covert Timing Channels on Shared Processor Hardware

As we increasingly rely on computers to process and manage our personal data, safeguarding sensitive information from malicious hackers is a fast growing concern. Among many forms of information leakage, covert timing channels operate by establishing an illegitimate communication channel between two processes and through transmitting information via timing modulation, thereby violating the underlying system's security policy. Recent studies have shown the vulnerability of popular computing environments, such as cloud computing, to these covert timing channels. In this work, we propose a new micro architecture-level framework, CC-Hunter, that detects the possible presence of covert timing channels on shared hardware. Our experiments demonstrate that Chanter is able to successfully detect different types of covert timing channels at varying bandwidths and message patterns.
Reviewing automatic language identification

The Oregon Graduate Institute Multi-language Telephone Speech Corpus (OGI-TS) was designed specifically for language identification research. It currently consists of spontaneous and fixed-vocabulary utterances in 11 languages: English, Farsi, French, German, Hindi, Japanese, Korean, Mandarin, Spanish, Tamil, and Vietnamese. These utterances were produced by 90 native speakers in each language over real telephone lines. Language identification is related to speaker-independent speech recognition and speaker identification in several interesting ways. It is therefore not surprising that many of the recent developments in language identification can be related to developments in those two fields. We review some of the more important recent approaches to language identification against the background of successes in speaker and speech recognition. In particular, we demonstrate how approaches to language identification based on acoustic modeling and language modeling, respectively, are similar to algorithms used in speaker-independent continuous speech recognition. Thereafter, prosodic and duration-based information sources are studied. We then review an approach to language identification that draws heavily on speaker identification. Finally, the performance of some representative algorithms is reported.<<ETX>>
Using mobile phones to write in air

Numerous sensors in modern mobile phones enable a range of people-centric applications. This paper envisions a system called PhonePoint Pen that uses the in-built accelerometer in mobile phones to recognize human writing. By holding the phone like a pen, a user should be able to write short messages or draw simple diagrams in the air. The acceleration due to hand gestures can be translated into geometric strokes, and recognized as characters. We prototype the PhonePoint Pen on the Nokia N95 platform, and evaluate it through real users. Results show that English characters can be identified with an average accuracy of 91.9%, if the users conform to a few reasonable constraints. Future work is focused on refining the prototype, with the goal of offering a new user-experience that complements keyboards and touch-screens.
Seamless Outdoors-Indoors Localization Solutions on Smartphones: Implementation and Challenges

The demand for more sophisticated Location-Based Services (LBS) in terms of applications variety and accuracy is tripling every year since the emergence of the smartphone a few years ago. Equally, smartphone manufacturers are mounting several wireless communication and localization technologies, inertial sensors as well as powerful processing capability, to cater to such LBS applications. A hybrid of wireless technologies is needed to provide seamless localization solutions and to improve accuracy, to reduce time to fix, and to reduce power consumption. The review of localization techniques/technologies of this emerging field is therefore important. This article reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this article is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones. Furthermore, the taxonomy of smartphone-location techniques is highlighted with a special focus on the detail of each technique and its hybridization. The article compares the indoor localization techniques based on accuracy, utilized wireless technology, overhead, and localization technique used. The pursuit of achieving ubiquitous localization outdoors and indoors for critical LBS applications such as security and safety shall dominate future research efforts.
Designing Usable Interactive Visual Analytics Tools for Dimension Reduction

In this paper, we present principles focused on humancentered usability for developing interactive visual analytic systems that enable users to tweak model parameters directly or indirectly so that they may explore high-dimensional data. To exemplify our principles, we refer to our application, Andromeda, that implements interactive weighted multidimensional scaling (WMDS). Through its use, we uncovered design principles of effective, interactive, visual analytic tools. These design principles focus on two main areas: (1) the visualization and interaction and (2) the design of the communication between interface and algorithm.
Brain Tumor Segmentation using hybrid Genetic Algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS)

Medical image segmentation plays an important role in treatment planning, identifying tumors, tumor volume, patient follow up and computer guided surgery. There are various techniques for medical image segmentation. This paper presents a image segmentation technique for locating brain tumor(AstrocytomaA type of brain tumor).Proposed work has been divided in two phases-In the first phase MRI image database(Astrocytoma grade I to IV) is collected and then preprocessing is done to improve quality of image. Second-phase includes three steps-Feature extraction, Feature selection and Image segmentation. For feature extraction proposed work uses GLCM (Grey Level co-occurrence matrix).To improve accuracy only a subset of feature is selected using hybrid Genetic algorithm(Genetic Algorithm+fuzzy rough set) and based on these features fuzzy rules and membership functions are defined for segmenting brain tumor from MRI images of .ANFIS is a adaptive network which combines benefits of both fuzzy and neural network .Finally, a comparative analysis is performed between ANFIS, neural network, Fuzzy ,FCM,K-NN, DWT+SOM,DWT+PCA+KN, Texture combined +ANN, Texture Combined+ SVM in terms of sensitivity ,specificity ,accuracy.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSING

Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Dynamic Hash Tables

Linear hashing and spiral storage are two dynamic hashing schemes originally designed for external files. This paper shows how to adapt these two methods for hash tables stored in main memory. The necessary data structures and algorithms are described, the expected performance is analyzed mathematically, and actual execution times are obtained and compared with alternative techniques. Linear hashing is found to be both faster and easier to implement than spiral storage. Two alternative techniques are considered: a simple unbalanced binary tree and double hashing with periodic rehashing into a larger table. The retrieval time of linear hashing is similar to double hashing and substantially faster than a binary tree, except for very small trees. The loading times of double hashing (with periodic reorganization), a binary tree, and linear hashing are similar. Overall, linear hashing is a simple and efficient technique for applications where the cardinality of the key set is not known in advance.
Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods

Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.
PRISM: Probabilistic Symbolic Model Checker

In this paper we describe PRISM, a tool being developed at the University of Birmingham for the analysis of probabilistic systems. PRISM supports three probabilistic models: discrete-time Markov chains, continuous-time Markov chains and Markov decision processes. Analysis is performed through model checking such systems against specifications written in the probabilistic temporal logics PCTL and CSL. The tool features three model checking engines: one symbolic, using BDDs (binary decision diagrams) and MTBDDs (multi-terminal BDDs); one based on sparse matrices; and one which combines both symbolic and sparse matrix methods. PRISM has been successfully used to analyse probabilistic termination, performance, dependability and quality of service properties for a range of systems, including randomized distributed algorithms , polling systems [22], workstation clusters [18] and wireless cell communication [17].
A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.
Model-based performance prediction in software development: a survey

Over the last decade, a lot of research has been directed toward integrating performance analysis into the software development process. Traditional software development methods focus on software correctness, introducing performance issues later in the development process. This approach does not take into account the fact that performance problems may require considerable changes in design, for example, at the software architecture level, or even worse at the requirement analysis level. Several approaches were proposed in order to address early software performance analysis. Although some of them have been successfully applied, we are still far from seeing performance analysis integrated into ordinary software development. In this paper, we present a comprehensive review of recent research in the field of model-based performance prediction at software development time in order to assess the maturity of the field and point out promising research directions.
It Usually Works: The Temporal Logic of Stochastic Systems

In this paper the branching time logic pCTL is deened. pCTL expresses quantitative bounds on the probabilities of correct behavior ; it can be interpreted over discrete Markov processes. A bisim-ulation relation is deened on nite Markov processes, and shown to be sound and complete with respect to pCTL. We extend the universe of models to generalized Markov processes in order to support notions of re-nement, abstraction, and parametrization. Model checking pCTL over generalized Markov processes is shown to be elementary by a reduction to RCF. We conclude by describing practical and theoretical avenues for further work.
Machine Learning for the New York City Power Grid

Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce (1) feeder failure rankings, (2) cable, joint, terminator, and transformer rankings, (3) feeder Mean Time Between Failure (MTBF) estimates, and (4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real-time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid.
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation

While many recent hand pose estimation methods critically rely on a training set of labelled frames, the creation of such a dataset is a challenging task that has been overlooked so far. As a result, existing datasets are limited to a few sequences and individuals, with limited accuracy, and this prevents these methods from delivering their full potential. We propose a semi-automated method for efficiently and accurately labeling each frame of a hand depth video with the corresponding 3D locations of the joints: The user is asked to provide only an estimate of the 2D reprojections of the visible joints in some reference frames, which are automatically selected to minimize the labeling work by efficiently optimizing a sub-modular loss function. We then exploit spatial, temporal, and appearance constraints to retrieve the full 3D poses of the hand over the complete sequence. We show that this data can be used to train a recent state-of-the-art hand pose estimation method, leading to increased accuracy.
Decentralized Semantic Identity

This paper examines a semantic approach for identity management, namely the W3C WebID, as a representation of personal information, and the WebID-TLS as a decentralized authentication protocol, allowing individuals to manage their own identities and data privacy. The paper identifies a set of important usability, privacy and security issues that needs to be addressed, and proposes an end to end authentication mechanism based on WebID, JSON Web Tokens (JWT) and the blockchain. The WebID includes a personal profile with its certificate, and the social relationship information described as the RDF-based FOAF ontology. The JWT is a standardized container format to encode personal related information in a secure way using "claims". The distributed, irreversible, undeletable, and immutable nature of the blockchain has appropriate attributes for distributed credential storage and decentralized identity management.
Depth map enhancement method based on joint bilateral filter

Considering the emergence of Microsoft Kinect has attracted attention not only from customers but also from researchers in the field of computer vision. Nonetheless, Kinect-generated depth maps usually contain various holes, especially at the edges of the front ground. In order to fill in the holes existing in the depth map, some methods that adopted color map guided in-painting or joint bilateral filter have been proposed to obtain missing depth pixels. However, these methods often caused edge blur. In this paper, the method is proposed that the holes and noises of the depth map are filled by the weighted joint bilateral filter and the fast marching algorithm based on the edge priority is further used to keep the edges smooth. To efficiently validate our method, we perform experiments on both the Kinect data and the Middlebury dataset, which provide qualitative and quantitative results. Experimental results show that our method has obtained the best performance of improvement of depth map for the smooth and edge regions and can perform real-time processing.
Action of cannabidiol on the anxiety and other effects produced by delta 9-THC in normal subjects.

The object of the experiment was to verify whether cannabidiol (CBD) reduces the anxiety provoked by delta 9-THC in normal volunteers, and whether this effect occurs by a general block of the action of delta 9-THC or by a specific anxiolytic effect. Appropriate measurements and scales were utilized and the eight volunteers received, the following treatments in a double-blind procedure: 0.5 mg/kg delta 9-THC, 1 mg/kg CBD, a mixture containing 0.5 mg/kg delta 9-THC and 1 mg/kg CBD and placebo and diazepam (10 mg) as controls. Each volunteer received the treatments in a different sequence. It was verified that CBD blocks the anxiety provoked by delta 9-THC, however this effect also extended to marihuana-like effects and to other subjective alterations induced by delta 9-THC. This antagonism does not appear to be caused by a general block of delta 9-THC effects, since no change was detected in the pulse-rate measurements. Several further effects were observed typical of CBD and of an opposite nature to those of delta 9-THC. These results suggest that the effects of CBD, as opposed to those of delta 9-THC, might be involved in the antagonism of effects between the two cannabinoids.
Powering the Future of Industry: High-Power Adjustable Speed Drive Topologies

The HP-ASD technology has experienced a huge development in the last decade. This can be appreciated by the large number of recently introduced drive configurations on the market. In addition, many industrial applications are reaching MV operation and megawatt range or have experienced changes in requirements on efficiency, performance, and power quality, making the use of HP-ASDs more attractive. It can be concluded that, HP-ASDs is an enabling technology ready to continue powering the future of industry for the decades to come.
Failsafe Drive Performance of FRID Electric Vehicles With the Structure Driven by the Front and Rear Wheels Independently

Failsafe performance is clarified for electric vehicles (EVs) with the drive structure driven by front and rear wheels independently, i.e., front and rear wheel independent drive type (FRID) EV. A simulator based on the four-wheel vehicle model, which can be applied to various types of drive systems like four in-wheel motor-drive-type EVs, is used for the clarification. Yaw rate and skid angle, which are related to drivability and steerability of vehicles and which further influence the safety of vehicles during runs, are analyzed under the condition that one of the motor drive systems fails while cornering on wet roads. In comparison with the four in-wheel motor-drive-type EVs, it is confirmed that the EVs with the structure focused in this paper have little change of the yaw rate and that hardly any dangerous phenomena appear, which would cause an increase in the skid angle of vehicles even if the front or rear wheel drive systems fail when running on wet roads with low friction coefficient. Moreover, the failsafe drive performance of the FRID EVs with the aforementioned structure is verified through experiments using a prototype EV.