nils-herrmann/clean_scirepeval huggingface dataset:
SPECTRE: A dependable introspection framework via System Management Mode

Virtual Machine Introspection (VMI) systems have been widely adopted for malware detection and analysis. VMI systems use hypervisor technology for system introspection and to expose malicious activity. However, recent malware can detect the presence of virtualization or corrupt the hypervisor state thus avoiding detection. We introduce SPECTRE, a hardware-assisted dependability framework that leverages System Management Mode (SMM) to inspect the state of a system. Contrary to VMI, our trusted code base is limited to BIOS and the SMM implementations. SPECTRE is capable of transparently and quickly examining all layers of running system code including a hypervisor, the OS, and user level applications. We demonstrate several use cases of SPECTRE including heap spray, heap overflow, and rootkit detection using real-world attacks on Windows and Linux platforms. In our experiments, full inspection with SPECTRE is 100 times faster than similar VMI systems because there is no performance overhead due to virtualization.
Deep learning for stock market prediction from financial news articles

This work uses deep learning methods for intraday directional movements prediction of Standard & Poor's 500 index using financial news titles and a set of technical indicators as input. Deep learning methods can detect and analyze complex patterns and interactions in the data automatically allowing speed up the trading process. This paper focus on architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which have had good results in traditional NLP tasks. Results has shown that CNN can be better than RNN on catching semantic from texts and RNN is better on catching the context information and modeling complex temporal characteristics for stock market forecasting. The proposed method shows some improvement when compared with similar previous studies.
Sampling for Bayesian Program Learning

Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data. Within this setting, we propose an algorithm that uses a symbolic solver to efficiently sample programs. The proposal combines constraint-based program synthesis with sampling via random parity constraints. We give theoretical guarantees on how well the samples approximate the true posterior, and have empirical results showing the algorithm is efficient in practice, evaluating our approach on 22 program learning problems in the domains of text editing and computer-aided programming.
Stream programming on general-purpose processors

In this paper we investigate mapping stream programs (i.e., programs written in a streaming style for streaming architectures such as Imagine and Raw) onto a general-purpose CPU. We develop and explore a novel way of mapping these programs onto the CPU. We show how the salient features of stream programming such as computation kernels, local memories, and asynchronous bulk memory loads and stores can be easily mapped by a simple compilation system to CPU features such as the processor caches, simultaneous multi-threading, and fast inter-thread communication support, resulting in an executable that efficiently uses CPU resources. We present an evaluation of our mapping on a hyperthreaded Intel Pentium 4 CPU as a canonical example of a general-purpose processor. We compare the mapped stream program against the same program coded in a more conventional style for the general-purpose processor. Using both micro-benchmarks and scientific applications we show that programs written in a streaming style can run comparably to equivalent programs written in a traditional style. Our results show that coding programs in a streaming style can improve performance on today¿s machines and smooth the way for significant performance improvements with the deployment of streaming architectures.
Tactile interfaces for small touch screens

We present the design, implementation, and informal evaluation of tactile interfaces for small touch screens used in mobile devices. We embedded a tactile apparatus in a Sony PDA touch screen and enhanced its basic GUI elements with tactile feedback. Instead of observing the response of interface controls, users can feel it with their fingers as they press the screen. In informal evaluations, tactile feedback was greeted with enthusiasm. We believe that tactile feedback will become the next step in touch screen interface design and a standard feature of future mobile devices.
Remembering. A Study in Experimental and Social Psychology.Frederick C. Bartlett

THE most persistent problems of recall all concern the ways in which past experiences and past reactions are utilised when anything is remembered. From a general point of view it looks as if the simplest explanation available is to suppose that when any specific event occurs some trace, or some group of traces, is made and stored up in the organism or in the mind. Later, an immediate stimulus re-excites the trace, or group of traces, and, provided a further assumption is made to the effect that the trace somehow carries with it a temporal sign, the re-excitement appears to be equivalent to recall. There is, of course, no direct evidence for such traces, but the assumption at first sight seems to be a very simple one, and so it has commonly been made.
A Digital QRS Detector Based on the Principle of Contour Limiting

The article describes a detector for physiological phenomena, e.g., the QRS-complex, having a trigger accuracy of 0.5 ms. The configuration to be recognized first is preprocessed then fed into a pair of amplifiers with adjustable gain and offset, representing the upper and lower contour-limit derived from the actual signal. These signals are sampled and A/D converted, then stored in two memories during the ``read''-operation.
Wide Pulse Combined With Narrow-Pulse Generator for Food Sterilization

This paper proposes a wide pulse combined with a narrow-pulse generator for solid-food sterilization. The proposed generator is composed of a full-bridge converter in phase-shift control to generate a high dc-link voltage and a full-bridge inverter associated with an L-C network and a transformer to generate wide pulses combined with narrow pulses. These combined pulses can prevent undesired strong air arcing in free space, reduce power consumption, and save power components, while sterilizing food effectively. The converter and inverter can be operated at high frequencies and with pulse width-modulation control; thus, its weight and size can be reduced significantly, and its efficiency can correspondingly be improved. Experimental results obtained from a prototype with plusmn10-kV wide pulses combined with plusmn10-kV narrow pulses and with 10- to 50-kW peak output power, depending on pulsewidth of the output pulses, have demonstrated its feasibility.
A data mining approach to solve the goal scoring problem

In soccer, scoring goals is a fundamental objective which depends on many conditions and constraints. Considering the RoboCup soccer 2D-simulator, this paper presents a data mining-based decision system to identify the best time and direction to kick the ball towards the goal to maximize the overall chances of scoring during a simulated soccer match. Following the CRISP-DM methodology, data for modeling were extracted from matches of major international tournaments (10691 kicks), knowledge about soccer was embedded via transformation of variables and a Multilayer Perceptron was used to estimate the scoring chance. Experimental performance assessment to compare this approach against previous LDA-based approach was conducted from 100 matches. Several statistical metrics were used to analyze the performance of the system and the results showed an increase of 7.7% in the number of kicks, producing an overall increase of 78% in the number of goals scored.
A GNU Radio-based IEEE 802 . 15 . 4 Testbed

We present a Software Defined Radio (SDR) based IEEE 802.15.4 transceiver testbed for GNU Radio. Our testbed is Open Source and fully interoperable with off-the-shelf TelosB sensor motes and the Contiki sensor mote operating system, from the physical layer to the network stack. The testbed can be setup and configured easily via a graphical user interface and applications can interface with the SDR using TCP sockets. Furthermore, the SDR is able to log traffic in PCAP format to investigate networks with common software like Wireshark. We believe the main applications of the transceiver to be twofold. First, the communication stack has a modular, layered structure, which allows for rapid prototyping, is educational, and is easy to grasp, lowering the steep learning curve that SDRs typically have. Secondly, the integration of a network stack in GNU Radio pushes interoperability from the physical to the network and application layer and thus enables the investigations of higher layer metrics with SDRs.
Mechanical Tuning of Substrate Integrated Waveguide Resonators

This letter presents a novel approach for providing substrate-integrated waveguide tunable resonators by means of placing an additional metalized via-hole on the waveguide cavity. The via-hole contains an open-loop slot on the top metallic wall. The dimensions, position and orientation of the open-loop slot defines the tuning range. Fabrication of some designs reveals good agreement between simulation and measurements. Additionally, a preliminary prototype which sets the open-loop slot orientation manually is also presented, achieving a continuous tuning range of 8%.
Visual Field Maps in Human Cortex

Much of the visual cortex is organized into visual field maps: nearby neurons have receptive fields at nearby locations in the image. Mammalian species generally have multiple visual field maps with each species having similar, but not identical, maps. The introduction of functional magnetic resonance imaging made it possible to identify visual field maps in human cortex, including several near (1) medial occipital (V1,V2,V3), (2) lateral occipital (LO-1,LO-2, hMT+), (3) ventral occipital (hV4, VO-1, VO-2), (4) dorsal occipital (V3A, V3B), and (5) posterior parietal cortex (IPS-0 to IPS-4). Evidence is accumulating for additional maps, including some in the frontal lobe. Cortical maps are arranged into clusters in which several maps have parallel eccentricity representations, while the angular representations within a cluster alternate in visual field sign. Visual field maps have been linked to functional and perceptual properties of the visual system at various spatial scales, ranging from the level of individual maps to map clusters to dorsal-ventral streams. We survey recent measurements of human visual field maps, describe hypotheses about the function and relationships between maps, and consider methods to improve map measurements and characterize the response properties of neurons comprising these maps.
Gravitational Approach for Point Set Registration

In this paper a new astrodynamics inspired rigid point set registration algorithm is introduced-the Gravitational Approach (GA). We formulate point set registration as a modified N-body problem with additional constraints and obtain an algorithm with unique properties which is fully scalable with the number of processing cores. In GA, a template point set moves in a viscous medium under gravitational forces induced by a reference point set. Pose updates are completed by numerically solving the differential equations of Newtonian mechanics. We discuss techniques for efficient implementation of the new algorithm and evaluate it on several synthetic and real-world scenarios. GA is compared with the widely used Iterative Closest Point and the state of the art rigid Coherent Point Drift algorithms. Experiments evidence that the new approach is robust against noise and can handle challenging scenarios with structured outliers.
Dynamics of human trust in recommender systems

The trust that humans place on recommendations is key to the success of recommender systems. The formation and decay of trust in recommendations is a dynamic process influenced by context, human preferences, accuracy of recommendations, and the interactions of these factors. This paper describes two psychological experiments (N=400) that evaluate the evolution of trust in recommendations over time, under personalized and non-personalized recommendations by matching or not matching a participant's profile. Main findings include: Humans trust inaccurate recommendations more than they should; when recommendations are personalized, they lose trust in inaccurate recommendations faster than when recommendations are not personalized; and participants report less trust and lower overall ratings of personalized but inaccurate recommendations compared to not-personalized inaccurate recommendations. We make connections to the possible implications of these psychological findings to the design of recommender systems.
A Wideband Aperture Efficient 60-GHz Series-Fed E-Shaped Patch Antenna Array With Copolarized Parasitic Patches

A novel series-fed E-shaped patch antenna array with copolarized parasitic patches for enhanced aperture efficiency at 60-GHz unlicensed wideband applications is proposed. The additional radiation from the inserted copolarized parasitic patches between series-fed patches improves the gain flatness remarkably as well as the gain and aperture efficiency, because of the offset resonant frequency. The four-array antenna with parasitic patches presents 0.8-dB gain flatness over the whole 57-66 GHz unlicensed band, 14.5 dBi peak gain, and 63.6% aperture efficiency while that without parasitic patches shows 1.4-dB flatness, 13.4 dBi peak gain, and 49.2% aperture efficiency, without any change in the antenna size, $6.0\times 14.7\times 0.25$ mm2.
Enriching object detection with 2D-3D registration and continuous viewpoint estimation

A large body of recent work on object detection has focused on exploiting 3D CAD model databases to improve detection performance. Many of these approaches work by aligning exact 3D models to images using templates generated from renderings of the 3D models at a set of discrete viewpoints. However, the training procedures for these approaches are computationally expensive and require gigabytes of memory and storage, while the viewpoint discretization hampers pose estimation performance. We propose an efficient method for synthesizing templates from 3D models that runs on the fly - that is, it quickly produces detectors for an arbitrary viewpoint of a 3D model without expensive dataset-dependent training or template storage. Given a 3D model and an arbitrary continuous detection viewpoint, our method synthesizes a discriminative template by extracting features from a rendered view of the object and decorrelating spatial dependences among the features. Our decorrelation procedure relies on a gradient-based algorithm that is more numerically stable than standard decomposition-based procedures, and we efficiently search for candidate detections by computing FFT-based template convolutions. Due to the speed of our template synthesis procedure, we are able to perform joint optimization of scale, translation, continuous rotation, and focal length using Metropolis-Hastings algorithm. We provide an efficient GPU implementation of our algorithm, and we validate its performance on 3D Object Classes and PASCAL3D+ datasets.
MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning

Compressed sensing (CS) utilizes the sparsity of magnetic resonance (MR) images to enable accurate reconstruction from undersampled k-space data. Recent CS methods have employed analytical sparsifying transforms such as wavelets, curvelets, and finite differences. In this paper, we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the image simultaneously from highly undersampled k-space data. The sparsity in this framework is enforced on overlapping image patches emphasizing local structure. Moreover, the dictionary is adapted to the particular image instance thereby favoring better sparsities and consequently much higher undersampling rates. The proposed alternating reconstruction algorithm learns the sparsifying dictionary, and uses it to remove aliasing and noise in one step, and subsequently restores and fills-in the k-space data in the other step. Numerical experiments are conducted on MR images and on real MR data of several anatomies with a variety of sampling schemes. The results demonstrate dramatic improvements on the order of 4-18 dB in reconstruction error and doubling of the acceptable undersampling factor using the proposed adaptive dictionary as compared to previous CS methods. These improvements persist over a wide range of practical data signal-to-noise ratios, without any parameter tuning.
Awesome Typography: Statistics-Based Text Effects Transfer

In this work, we explore the problem of generating fantastic special-effects for the typography. It is quite challenging due to the model diversities to illustrate varied text effects for different characters. To address this issue, our key idea is to exploit the analytics on the high regularity of the spatial distribution for text effects to guide the synthesis process. Specifically, we characterize the stylized patches by their normalized positions and the optimal scales to depict their style elements. Our method first estimates these two features and derives their correlation statistically. They are then converted into soft constraints for texture transfer to accomplish adaptive multi-scale texture synthesis and to make style element distribution uniform. It allows our algorithm to produce artistic typography that fits for both local texture patterns and the global spatial distribution in the example. Experimental results demonstrate the superiority of our method for various text effects over conventional style transfer methods. In addition, we validate the effectiveness of our algorithm with extensive artistic typography library generation.
Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM. To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.
Circumferential Traveling Wave Slot Array on Cylindrical Substrate Integrated Waveguide (CSIW)

Traveling wave slot array on cylindrical substrate integrated waveguide (CSIW) is designed, fabricated and measured at K-band. CSIW is formed by wrapping the substrate integrated waveguide (SIW) around the cylinder in the circumferential direction. 16 element longitudinal slot array on the broad wall of single CSIW is designed by the Elliot's design procedure. The spacings between the slot elements are determined to reduce the half power beam width (HPBW) and to obtain good matching at 25 GHz. A 4 × 16 slot array is formed using 4 CSIW array each having 16 traveling wave longitudinal slots and the array is fed by 1 × 4 SIW power divider structure. About 10 ° beam steering is achieved when frequency is swept from 24 to 26 GHz. Gain of the antenna is 14 dB. Very good agreement between the simulated and measured results is obtained.
A reliable Internet of Things based architecture for oil and gas industry

Anomaly detection systems deployed for monitoring in oil and gas industries are mostly WSN based systems or SCADA systems which all suffer from noteworthy limitations. WSN based systems are not homogenous or incompatible systems. They lack coordinated communication and transparency among regions and processes. On the other hand, SCADA systems are expensive, inflexible, not scalable, and provide data with long delay. In this paper, a novel IoT based architecture is proposed for Oil and gas industries to make data collection from connected objects as simple, secure, robust, reliable and quick. Moreover, it is suggested that how this architecture can be applied to any of the three categories of operations, upstream, midstream and downstream. This can be achieved by deploying a set of IoT based smart objects (devices) and cloud based technologies in order to reduce complex configurations and device programming. Our proposed IoT architecture supports the functional and business requirements of upstream, midstream and downstream oil and gas value chain of geologists, drilling contractors, operators, and other oil field services. Using our proposed IoT architecture, inefficiencies and problems can be picked and sorted out sooner ultimately saving time and money and increasing business productivity.
A Review on the Computational Methods for Emotional State Estimation from the Human EEG

A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.
A dynamic self-structuring neural network model to combat phishing

Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing website classification problem and it shows competitive results with respect to various evaluation measures such as harmonic mean (F1-score), precision, and classification accuracy.