nils-herrmann/clean_scirepeval huggingface dataset:
A Descriptive Algorithm for Sobel Image Edge Detection

Image edge detection is a process of locating the e dg of an image which is important in finding the approximate absolute gradient magnitude at each point I of an input grayscale image. The problem of getting an appropriate absolute gradient magnitude for edges lies in the method used. The Sobel operator performs a 2-D spatial gradient measurement on images. Transferri ng a 2-D pixel array into statistically uncorrelated data se t enhances the removal of redundant data, as a result, reduction of the amount of data is required to represent a digital image. The Sobel edge detector uses a pair of 3 x 3 convolution masks, one estimating gradient in the x-direction and the other estimating gradient in y–direction. The Sobel detector is incredibly sensit ive o noise in pictures, it effectively highlight them as edges. Henc e, Sobel operator is recommended in massive data communication found in data transfer.
Evaluation of face recognition system in heterogeneous environments (visible vs NIR)

Performing facial recognition between Near Infrared (NIR) and visible-light (VIS) images has been established as a common method of countering illumination variation problems in face recognition. In this paper we present a new database to enable the evaluation of cross-spectral face recognition. A series of preprocessing algorithms, followed by Local Binary Pattern Histogram (LBPH) representation and combinations with Linear Discriminant Analysis (LDA) are used for recognition. These experiments are conducted on both NIR→VIS and the less common VIS→NIR protocols, with permutations of uni-modal training sets. 12 individual baseline algorithms are presented. In addition, the best performing fusion approaches involving a subset of 12 algorithms are also described.
Combining strengths, emotions and polarities for boosting Twitter sentiment analysis

Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express sentiments about specific topics or entities with different strengths and intensities, where these sentiments are strongly related to their personal feelings and emotions. A number of methods and lexical resources have been proposed to analyze sentiment from natural language texts, addressing different opinion dimensions. In this article, we propose an approach for boosting Twitter sentiment classification using different sentiment dimensions as meta-level features. We combine aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources. Our research shows that the combination of sentiment dimensions provides significant improvement in Twitter sentiment classification tasks such as polarity and subjectivity.
A 94-GHz passive imaging receiver using a balanced LNA with embedded Dicke switch

A fully-integrated silicon-based 94-GHz direct-detection imaging receiver with on-chip Dicke switch and baseband circuitry is demonstrated. Fabricated in a 0.18-µm SiGe BiCMOS technology (fT/fMAX = 200 GHz), the receiver chip achieves a peak imager responsivity of 43 MV/W with a 3-dB bandwidth of 26 GHz. A balanced LNA topology with an embedded Dicke switch provides 30-dB gain and enables a temperature resolution of 0.3–0.4 K. The imager chip consumes 200 mW from a 1.8-V supply.
Recurrent Memory Addressing for Describing Videos

In this paper, we introduce Key-Value Memory Networks to a multimodal setting and a novel key-addressing mechanism to deal with sequence-to-sequence models. The proposed model naturally decomposes the problem of video captioning into vision and language segments, dealing with them as key-value pairs. More specifically, we learn a semantic embedding (v) corresponding to each frame (k) in the video, thereby creating (k, v) memory slots. We propose to find the next step attention weights conditioned on the previous attention distributions for the key-value memory slots in the memory addressing schema. Exploiting this flexibility of the framework, we additionally capture spatial dependencies while mapping from the visual to semantic embedding. Experiments done on the Youtube2Text dataset demonstrate usefulness of recurrent key-addressing, while achieving competitive scores on BLEU@4, METEOR metrics against state-of-the-art models.
SIW-Fed Yagi Antenna and Its Application on Monopulse Antenna

Planar Yagi antennas are commonly hard to possess simple structure, high gain, and broad bandwidth at the same time. In this letter, a new substrate integrated waveguide (SIW)-fed Yagi antenna is proposed with nonplanar director element, and it achieves bandwidth of 25.7% and gain of 9.46 dBi at 10.0 GHz. Integrated of an SIW hybrid ring coupler, a Yagi monopulse antenna is designed. Measured gain of the sum pattern is higher than 13 dBi, whereas the maximum null depth of the difference pattern is lower than -20 dB in 9.4-10.05 GHz. This type of monopulse antenna presents an excellent candidate in directional-finding system.
A causal framework for explaining the predictions of black-box sequence-to-sequence models

The bipartite version of the graph partitioning problem with edge uncertainty considered by Fan et al. (2012) has the following form. Assume we want to partition U and V into K subsets each, say {Ui} and {Vj}, with each Ui having cardinality in [cmin, c u max] and each Vj in [c v min, c v max]. Let xik be the binary indicator of ui ∈ Uk, and analogously for xjk and vj . In addition, we let yij be a binary variable which takes value 1 when ui, vj are in different corresponding subsets (i.e. ui ∈ Uk, vj ∈ Vk′ and k 6= k′). We can express the constraints of the problem as:
Constant transconductance bias circuit with an on-chip resistor

A method to generate stable transconductance (gm) without using precise external components is presented. The off-chip resistor in a conventional constant-gm bias circuit is replaced with a variable on-chip resistor. A MOSFET biased in triode region is used as a variable resistor. The resistance of the MOSFET is tuned by a background tuning scheme to achieve the stable transconductance that is immune to process, voltage and temperature variation. The transconductance generated by the constant-gm bias circuit designed in 0.18mum CMOS process with 1.5F supply displays less than 1% variation for a 20% change in power supply voltage and less than plusmn1.5% variation for a 60degC change in temperature. The whole circuit draws approximately 850muA from a supply
High-Q Tunable Filters: Challenges and Potential

High-Q tunable filters are in demand in both wireless and satellite applications. The need for tunability and configurability in wireless systems arises when deploying different systems that coexist geographically. Such deployments take place regularly when an operator has already installed a network and needs to add a new-generation network, for example, to add a long-term evolution (LTE) network to an existing third-generation (3G) network. The availability of tunable/reconfigurable hardware will also provide the network operator the means for efficiently managing hardware resources, while accommodating multistandards requirements and achieving network traffic/capacity optimization. Wireless systems can also benefit from tunable filter technologies in other areas; for example, installing wireless infrastructure equipment, such as a remote radio unit (RRU) on top of a 15-story high communication tower, is a very costly task. By using tunable filters, one installation can serve many years since if there is a need to change the frequency or bandwidth, it can be done through remote electronic tuning, rather than installing a new filter. Additionally, in urban areas, there is a very limited space for wireless service providers to install their base stations due to expensive real estate and/or maximum weight loading constrains on certain installation locations such as light poles or power lines. Therefore, once an installation site is acquired, it is natural for wireless service providers to use tunable filters to pack many functions, such as multistandards and multibands, into one site.
Cognitive and system factors contributing to diagnostic errors in radiology.

In this article, we describe some of the cognitive and system-based sources of detection and interpretation errors in diagnostic radiology and discuss potential approaches to help reduce misdiagnoses.



Every radiologist worries about missing a diagnosis or giving a false-positive reading. The retrospective error rate among radiologic examinations is approximately 30%, with real-time errors in daily radiology practice averaging 3-5%. Nearly 75% of all medical malpractice claims against radiologists are related to diagnostic errors. As medical reimbursement trends downward, radiologists attempt to compensate by undertaking additional responsibilities to increase productivity. The increased workload, rising quality expectations, cognitive biases, and poor system factors all contribute to diagnostic errors in radiology. Diagnostic errors are underrecognized and underappreciated in radiology practice. This is due to the inability to obtain reliable national estimates of the impact, the difficulty in evaluating effectiveness of potential interventions, and the poor response to systemwide solutions. Most of our clinical work is executed through type 1 processes to minimize cost, anxiety, and delay; however, type 1 processes are also vulnerable to errors. Instead of trying to completely eliminate cognitive shortcuts that serve us well most of the time, becoming aware of common biases and using metacognitive strategies to mitigate the effects have the potential to create sustainable improvement in diagnostic errors.
Extraction of Visual Features for Lipreading

The multimodal nature of speech is often ignored in human-computer interaction, but lip deformations and other body motion, such as those of the head, convey additional information. We integrate speech cues from many sources and this improves intelligibility, especially when the acoustic signal is degraded. This paper shows how this additional, often complementary, visual speech information can be used for speech recognition. Three methods for parameterizing lip image sequences for recognition using hidden Markov models are compared. Two of these are top-down approaches that fit a model of the inner and outer lip contours and derive lipreading features from a principal component analysis of shape or shape and appearance, respectively. The third, bottom-up, method uses a nonlinear scale-space analysis to form features directly from the pixel intensity. All methods are compared on a multitalker visual speech recognition task of isolated letters.
Mining sequential patterns for classification

While a number of efficient sequential pattern mining algorithms were developed over the years, they can still take a long time and produce a huge number of patterns, many of which are redundant. These properties are especially frustrating when the goal of pattern mining is to find patterns for use as features in classification problems. In this paper, we describe BIDE-Discriminative, a modification of BIDE that uses class information for direct mining of predictive sequential patterns. We then perform an extensive evaluation on nine real-life datasets of the different ways in which the basic BIDE-Discriminative can be used in real multi-class classification problems, including 1-versus-rest and model-based search tree approaches. The results of our experiments show that 1-versus-rest provides an efficient solution with good classification performance.
Active damping control of a high-power PWM current-source rectifier for line-current THD reduction

The use of active damping to reduce the total harmonic distortion (THD) of the line current for medium-voltage (2.3-7.2 kV) high-power pulsewidth-modulation (PWM) current-source rectifiers is investigated. The rectifier requires an LC filter connected at its input terminals, which constitutes an LC resonant mode. The lightly damped LC filter is prone to series and parallel resonances when tuned to a system harmonic either from the utility or from the PWM rectifier. These issues are traditionally addressed at the design stage by properly choosing the filter resonant frequency. This approach may result in a limited performance since the LC resonant frequency is a function of the power system impedance, which usually varies with power system operating conditions. In this paper, an active damping control method is proposed for the reduction in line current THD of high-power current-source rectifiers operating at a switching frequency of only 540 Hz. Two types of LC resonances are investigated: the parallel resonance excited by harmonic currents drawn by the rectifier and the series resonance caused by harmonic pollution in the source voltage. It is demonstrated through simulation and experiments that the proposed active damping control can effectively reduce the line-current THD caused by both parallel and series resonances.
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis

Emotion is expressed in multiple modalities, yet most research has considered at most one or two. This stems in part from the lack of large, diverse, well-annotated, multimodal databases with which to develop and test algorithms. We present a well-annotated, multimodal, multidimensional spontaneous emotion corpus of 140 participants. Emotion inductions were highly varied. Data were acquired from a variety of sensors of the face that included high-resolution 3D dynamic imaging, high-resolution 2D video, and thermal (infrared) sensing, and contact physiological sensors that included electrical conductivity of the skin, respiration, blood pressure, and heart rate. Facial expression was annotated for both the occurrence and intensity of facial action units from 2D video by experts in the Facial Action Coding System (FACS). The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection. The entire corpus will be made available to the research community.
Cancer classification using gene expression data

The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. However, most previous cancer classification studies are clinical-based and have limited diagnostic ability. Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis and drug discovery. The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. With this abundance of gene expression data, researchers have started to explore the possibilities of cancer classification using gene expression data. Quite a number of methods have been proposed in recent years with promising results. But there are still a lot of issues which need to be addressed and understood. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the problem, the proposed solutions and the related issues all together. In this survey paper, we present a comprehensive overview of various proposed cancer classification methods and evaluate them based on their computation time, classification accuracy and ability to reveal biologically meaningful gene information. We also introduce and evaluate various proposed gene selection methods which we believe should be an integral preprocessing step for cancer classification. In order to obtain a full picture of cancer classification, we also discuss several issues related to cancer classification, including the biological significance vs. statistical significance of a cancer classifier, the asymmetrical classification errors for cancer classifiers, and the gene contamination problem.
Towards an Applied Gamification Model for Tracking, Managing, & Encouraging Sustainable Travel Behaviours

In this paper we introduce a gamification model for encouraging sustainable multi-modal urban travel in modern European cities. Our aim is to provide a mechanism that encourages users to reflect on their current travel behaviours and to engage in more environmentally friendly activities that lead to the formation of sustainable, long-term travel behaviours. To achieve this our users track their own behaviours, set goals, manage their progress towards those goals, and respond to challenges. Our approach uses a point accumulation and level achievement metaphor to abstract from the underlying specifics of individual behaviours and goals to allow an Simon Wells University of Aberdeen, Computing Science, Meston building, Meston Walk, Aberdeen, AB24 3UE  simon.wells@abdn.ac.uk Henri Kotkanen University of Helsinki, Department of Computer Science, P.O. 68 (Gustaf Hllstrmin katu 2b), FI00014 UNIVERSITY OF HELSINKI, FINLAND e-mail: henri.kotkanen@helsinki.fi Michael Schlafli University of Aberdeen, Computing Science, Meston building, Meston Walk, Aberdeen, AB24 3UE  Aberdeen, AB24 3UE  Antti Jylhä University of Helsinki, Department of Computer Science, P.O. 68 (Gustaf Hllstrmin katu 2b), FI00014 UNIVERSITY OF HELSINKI, FINLAND e-mail: antti.jylha@cs.helsinki.fi Paula Forbes University of Aberdeen, Computing Science, Meston building, Meston Walk, Aberdeen, AB24 3UE
SVM PMSM Drive With Low Resolution Hall-Effect Sensors

In this paper, a back-electromotive force-based method is used in conjunction with three very low-cost integrated circuits based on the Hall-effect to estimate the rotor position of the direct-drive permanent-magnet synchronous motors (PMSMs) to be used for traction purpose of an electric wheelchair. A speed estimator based on a rotor frame machine model has been implemented for the PMSM drives, and then the rotor position estimation is achieved by means of a discrete integration of the estimated speed. The three Hall effect sensors are used to detect the rotor initial position as well to reset the error on the rotor position estimation every 60 electrical degrees. In the specific application, support vector machine technique has been implemented together with the field oriented control. Simulations and experimental results are shown in the paper
Financial fraud detection using vocal, linguistic and financial cues

a r t i c l e i n f o Corporate financial fraud has a severe negative impact on investors and the capital market in general. The current resources committed to financial fraud detection (FFD), however, are insufficient to identify all occurrences in a timely fashion. Methods for automating FFD have mainly relied on financial statistics, although some recent research has suggested that linguistic or vocal cues may also be useful indicators of deception. Tools based on financial numbers, linguistic behavior, and non-verbal vocal cues have each demonstrated the potential for detecting financial fraud. However, the performance of these tools continues to be poorer than desired, limiting their use on a stand-alone basis to help identify companies for further investigation. The hypothesis investigated in this study is that an improved tool could be developed if specific attributes from these feature categories were analyzed concurrently. Combining features across categories provided better fraud detection than was achieved by any of the feature categories alone. However, performance improvements were only observed if feature selection was used suggesting that it is important to discard non-informative features. Efficient allocation of capital is critical to stimulate economic growth, the engine that drives improvements in social welfare. The financial world has been rocked by a wave of accounting scandals, from Enron and WorldCom in the early 2000s to the collapse of Lehman Brothers in 2008 leading to a significant degradation of trust in capital markets and concomitant inefficient capital allocation decisions. In order to restore trust in the capital markets, regulators have passed a series of reforms, such as the Sarbanes Oxley Act that impose penalties on those who commit financial misdeeds. However, such penalties will be a helpful deterrent if and only if frauds are detected in a timely fashion. The current resources committed to fraud detection by the Securities and Exchange Commission (the regulatory body that oversees capital markets in the United States) are insufficient to identify all occurrences of frauds in a timely fashion. Moreover, the Center for Audit Quality, which serves the financial auditing profession, recently called for the identification of new ways to better detect financial fraud so as to restore investor confidence . Thus, an automated method for accurate financial fraud detection (FFD) has the potential to deter financial misdeeds and stabilize capital markets. Most corporate FFD methods documented in accounting and finance research have focused on financial information (see …
A Multicast Tree Router for Multichip Neuromorphic Systems

We present a tree router for multichip systems that guarantees deadlock-free multicast packet routing without dropping packets or restricting their length. Multicast routing is required to efficiently connect massively parallel systems' computational units when each unit is connected to thousands of others residing on multiple chips, which is the case in neuromorphic systems. Our tree router implements this one-to-many routing by branching recursively-broadcasting the packet within a specified subtree. Within this subtree, the packet is only accepted by chips that have been programmed to do so. This approach boosts throughput because memory look-ups are avoided enroute, and keeps the header compact because it only specifies the route to the subtree's root. Deadlock is avoided by routing in two phases-an upward phase and a downward phase-and by restricting branching to the downward phase. This design is the first fully implemented wormhole router with packet-branching that can never deadlock. The design's effectiveness is demonstrated in Neurogrid, a million-neuron neuromorphic system consisting of sixteen chips. Each chip has a 256 × 256 silicon-neuron array integrated with a full-custom asynchronous VLSI implementation of the router that delivers up to 1.17 G words/s across the sixteen-chip network with less than 1 μs jitter.
Shady dealings: Robust, long-term visual localisation using illumination invariance

This paper is about extending the reach and endurance of outdoor localisation using stereo vision. At the heart of the localisation is the fundamental task of discovering feature correspondences between recorded and live images. One aspect of this problem involves deciding where to look for correspondences in an image and the second is deciding what to look for. This latter point, which is the main focus of our paper, requires understanding how and why the appearance of visual features can change over time. In particular, such knowledge allows us to better deal with abrupt and challenging changes in lighting. We show how by instantiating a parallel image processing stream which operates on illumination-invariant images, we can substantially improve the performance of an outdoor visual navigation system. We will demonstrate, explain and analyse the effect of the RGB to illumination-invariant transformation and suggest that for little cost it becomes a viable tool for those concerned with having robots operate for long periods outdoors.