nils-herrmann/clean_scirepeval huggingface dataset:
AN OPTICAL MODEL FOR IMAGE ARTIFACTS PRODUCED BY DUST PARTICLES ON LENSES

Machine vision systems often have to work with cameras that become dusty during use. Dust particles produce image artifacts that can affect the performance of a machine vision algorithm. Modeling these artifacts allows us to add them to test images to characterize an algorithm's sensitivity to dust and help develop counter measures. This paper presents an optics-based model that simulates the size and optical density of image artifacts produced by dust particles. For dust particles smaller than the aperture area the image artifact size is determined by the size of the lens aperture and not the size of the particle, while the artifact’s optical density is determined by the ratio of the particle and aperture areas. We show how the model has been used to evaluate the effect of dust on two machine vision algorithms used on the 2003 Mars Exploration Rovers. 1. DUST ARTIFACTS IN IMAGES In the pinhole camera models used in machine vision exactly one ray of light from a point in object space will pass through the camera's pinhole to strike the image plane. With a real lens however, light from a point in object space is collected from a solid angle of rays and projected through the lens onto the image plane, as illustrated in Fig 1. The extent of this solid angle of rays is limited by the lens elements and by the diameter of any diaphragms along the optical path. The limiting diaphragm is called the aperture stop of the lens. The entrance pupil of the lens is the image of the aperture stop as it would be seen if viewed from an axial position in front of the lens. To model the effect of dust on an image we follow the path of light collected by the lens for a single pixel and consider how dust particles on the lens affect the light reaching the pixel.  Image formation for a simple lens. We can call the solid collection angle subtended by a pixel the collection cone for the pixel. If a dust particle absorbs or scatters light away from the collection cone , the light reaching the pixel will be decreased by a factor equal to the fraction of the collection cone blocked by the particle. We call this a dark dust artifact.  Dust blocking a pixel’s FOV. If, on the other hand, a light shining on the lens window is scattered into the collection cone by a dust particle , then the light reaching the pixel will increase by an additive amount that depends on the intensity of the window illumination. We call this a bright dust artifact.  Dust scattering sunlight into a pixel’s FOV. Bright dust artifacts occur when dust on a lens window is illuminated with an intense light source such as the sun. To help mitigate this lenses are sometimes fitted with sun shades to reduce the range of angles the front lens surface can be illuminated from. Since absorbing or scattering light away from a pixel’s collection cone is much easier to do than scattering light into the collection cone, this paper focuses on dark dust artifacts. We note that this is a purely geometric model. For visible light and the 10 micron and larger particle sizes we are concerned with scattering follows the laws of geometric optics.
A Survey on Zero-Day Polymorphic Worm Detection Techniques

Zero-day polymorphic worms pose a serious threat to the Internet security. With their ability to rapidly propagate, these worms increasingly threaten the Internet hosts and services. Not only can they exploit unknown vulnerabilities but can also change their own representations on each new infection or can encrypt their payloads using a different key per infection. They have many variations in the signatures of the same worm thus, making their fingerprinting very difficult. Therefore, signature-based defenses and traditional security layers miss these stealthy and persistent threats. This paper provides a detailed survey to outline the research efforts in relation to detection of modern zero-day malware in form of zero-day polymorphic worms.
Modelling and Trading the EUR / USD Exchange Rate : Do Neural Network Models Perform Better ?

This research examines and analyses the use of Neural Network Regression (NNR) models in foreign exchange (FX) forecasting and trading models. The NNR models are benchmarked against traditional forecasting techniques to ascertain their potential added value as a forecasting and quantitative trading tool. In addition to evaluating the various models using traditional forecasting accuracy measures, such as root mean squared errors, they are also assessed using financial criteria, such as risk-adjusted measures of return. Having constructed a synthetic EUR/USD series for the period up to 4 January 1999, the models were developed using the same in-sample data, leaving the remainder for out-of-sample forecasting, October 1994 to May 2000, and May 2000 to July 2001, respectively. The out-of-sample period results were tested in terms of forecasting accuracy, and in terms of trading performance via a simulated trading strategy. Transaction costs are also taken into account. It is concluded that NNR models do have the ability to forecast EUR/USD returns for the period investigated, and add value as a forecasting and quantitative trading tool.  Christian Dunis is Girobank Professor of Banking and Finance at Liverpool Business School and Director of CIBEF (E-mail: cdunis@totalise.co.uk). The opinions expressed herein are not those of Girobank.  Mark Williams is an Associate Researcher with CIBEF (E-mail: markpickfordwilliams@hotmail.com).  CIBEF – Centre for International Banking, Economics and Finance, JMU, John Foster Building, 98 Mount Pleasant, Liverpool L3 5UZ.
A Survey of Music Recommendation Aids

This paper provides a review of explanations, visualizations and interactive elements of user interfaces (UI) in music recommendation systems. We call these UI features “recommendation aids”. Explanations are elements of the interface that inform the user why a certain recommendation was made. We highlight six possible goals for explanations, resulting in overall satisfaction towards the system. We found that the most of the existing music recommenders of popular systems provide no explanations, or very limited ones. Since explanations are not independent of other UI elements in recommendation process, we consider how the other elements can be used to achieve the same goals. To this end, we evaluated several existing music recommenders. We wanted to discover which of the six goals (transparency, scrutability, effectiveness, persuasiveness, efficiency and trust) the different UI elements promote in the existing music recommenders, and how they could be measured in order to create a simple framework for evaluating recommender UIs. By using this framework designers of recommendation systems could promote users’ trust and overall satisfaction towards a recommender system thereby improving the user experience with the system.
Appropriating the Returns from Industrial Research and Development

To HAVE the incentive to undertake research and development, a firm must be able to appropriate returns sufficient to make the investment worthwhile. The benefits consumers derive from an innovation, however, are increased if competitors can imitate and improve on the innovation to ensure its availability on favorable terms. Patent law seeks to resolve this tension between incentives for innovation and widespread diffusion of benefits. A patent confers, in theory, perfect appropriability (monopoly of the invention) for a limited time in return for a public
Performance Improvement of a Power Conversion Module by Liquid Micro-Jet Impingement Cooling

Liquid micro-jet array impingement cooling of a power conversion module with 12 power switching devices (six insulated gate bipolar transistors and six diodes) is investigated. The 1200-V/150-A module converts dc input power to variable frequency, variable voltage three-phase ac output to drive a 50HP three-phase induction motor. The silicon devices are attached to a packaging layer [direct bonded copper (DBC)], which in turn is soldered to a metal base plate. DI water micro-jet array impinges on the base plate of the module targeted at the footprint area of the devices. Although the high heat flux cooling capability of liquid impingement is a well-established finding, the impact of its practical implementation in power systems has never been addressed. This paper presents the first one-to-one comparison of liquid micro-jet array impingement cooling (JAIC) with the traditional methods, such as air-cooling over finned heat sink or liquid flow in multi-pass cold plate. Results show that compared to the conventional cooling methods, JAIC can significantly enhance the module output power. If the output power is maintained constant, the device temperature can be reduced drastically by JAIC. Furthermore, jet impingement provides uniform cooling for multiple devices placed over a large area, thereby reducing non-uniformity of temperature among the devices. The reduction in device temperature, both its absolute value and the non-uniformity, implies multi-fold increase in module reliability. The results thus illustrate the importance of efficient thermal management technique for compact and reliable power conversion application
You want to measure coping but your protocol's too long: consider the brief COPE.

Studies of coping in applied settings often confront the need to minimize time demands on participants. The problem of participant response burden is exacerbated further by the fact that these studies typically are designed to test multiple hypotheses with the same sample, a strategy that entails the use of many time-consuming measures. Such research would benefit from a brief measure of coping assessing several responses known to be relevant to effective and ineffective coping. This article presents such a brief form of a previously published measure called the COPE inventory (Carver, Scheier, & Weintraub, 1989), which has proven to be useful in health-related research. The Brief COPE omits two scales of the full COPE, reduces others to two items per scale, and adds one scale. Psychometric properties of the Brief COPE are reported, derived from a sample of adults participating in a study of the process of recovery after Hurricane Andrew.
Recurrent Neural Radio Anomaly Detection

We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies. This approach holds promise in significantly increasing the ability of naive anomaly detection to detect small anomalies in highly complex complexity multi-user radio bands. We demonstrate the efficacy of this approach on a number of common real over the air radio communications bands of interest and quantify detection performance in terms of probability of detection an false alarm rates across a range of interference to band power ratios and compare to baseline methods.
Application of neural networks in optical inspection and classification of solder joints in surface mount technology

The defect detection on manufactures is extremely important in the optimization of industrial processes; particularly, the visual inspection plays a fundamental role. The visual inspection is often carried out by a human expert. However, new technology features have made this inspection unreliable. For this reason, many researchers have been engaged to develop automatic analysis processes of manufactures and automatic optical inspections in the industrial production of printed circuit boards. Among the defects that could arise in this industrial process, those of the solder joints are very important, because they can lead to an incorrect functioning of the board; moreover, the amount of the solder paste can give some information on the quality of the industrial process. In this paper, a neural network-based automatic optical inspection system for the diagnosis of solder joint defects on printed circuit boards assembled in surface mounting technology is presented. The diagnosis is handled as a pattern recognition problem with a neural network approach. Five types of solder joints have been classified in respect to the amount of solder paste in order to perform the diagnosis with a high recognition rate and a detailed classification able to give information on the quality of the manufacturing process. The images of the boards under test are acquired and then preprocessed to extract the region of interest for the diagnosis. Three types of feature vectors are evaluated from each region of interest, which are the images of the solder joints under test, by exploiting the properties of the wavelet transform and the geometrical characteristics of the preprocessed images. The performances of three different classifiers which are a multilayer perceptron, a linear vector quantization, and a K-nearest neighbor classifier are compared. The n-fold cross-validation has been exploited to select the best architecture for the neural classifiers, while a number of experiments have been devoted to estimating the best value of K in the K-NN. The results have proved that the MLP network fed with the GW-features has the best recognition rate. This approach allows to carry out the diagnosis burden on image processing, feature extraction, and classification algorithms, reducing the cost and the complexity of the acquisition system. In fact, the experimental results suggest that the reason for the high recognition rate in the solder joint classification is due to the proper preprocessing steps followed as well as to the information contents of the features
Immune neglect: a source of durability bias in affective forecasting.

People are generally unaware of the operation of the system of cognitive mechanisms that ameliorate their experience of negative affect (the psychological immune system), and thus they tend to overestimate the duration of their affective reactions to negative events. This tendency was demonstrated in 6 studies in which participants overestimated the duration of their affective reactions to the dissolution of a romantic relationship, the failure to achieve tenure, an electoral defeat, negative personality feedback, an account of a child's death, and rejection by a prospective employer. Participants failed to distinguish between situations in which their psychological immune systems would and would not be likely to operate and mistakenly predicted overly and equally enduring affective reactions in both instances. The present experiments suggest that people neglect the psychological immune system when making affective forecasts.
DVB-S2: The Second Generation Standard for Satellite Broad-Band Services

DVB-S2 is the second-generation specification for satellite broad-band applications, developed by the Digital Video Broadcasting (DVB) Project in 2003. The system is structured as a toolkit to allow the implementation of the following satellite applications: TV and sound broadcasting, interactivity (i.e., Internet access), and professional services, such as TV contribution links and digital satellite news gathering. It has been specified around three concepts: best transmission performance approaching the Shannon limit, total flexibility, and reasonable receiver complexity. Channel coding and modulation are based on more recent developments by the scientific community: low density parity check codes are adopted, combined with QPSK, 8PSK, 16APSK, and 32APSK modulations for the system to work properly on the nonlinear satellite channel. The framing structure allows for maximum flexibility in a versatile system and also synchronization in worst case configurations (low signal-to-noise ratios). Adaptive coding and modulation, when used in one-to-one links, then allows optimization of the transmission parameters for each individual user,dependant on path conditions. Backward-compatible modes are also available,allowing existing DVB-S integrated receivers-decoders to continue working during the transitional period. The paper provides a tutorial overview of the DVB-S2 system, describing its main features and performance in various scenarios and applications.
A Survey of Visual Sensor Networks

Visual sensor networks have emerged as an important class of sensor-based distributed intelligent systems, with unique performance, complexity, and quality of service challenges. Consisting of a large number of low-power camera nodes, visual sensor networks support a great number of novel vision-based applications. The camera nodes provide information from a monitored site, performing distributed and collaborative processing of their collected data. Using multiple cameras in the network provides different views of the scene, which enhances the reliability of the captured events. However, the large amount of image data produced by the cameras combined with the network’s resource constraints require exploring new means for data processing, communication, and sensor management. Meeting these challenges of visual sensor networks requires interdisciplinary approaches, utilizing vision processing, communications and networking, and embedded processing. In this paper, we provide an overview of the current state-of-the-art in the field of visual sensor networks, by exploring several relevant research directions. Our goal is to provide a better understanding of current research problems in the different research fields of visual sensor networks, and to show how these different research fields should interact to solve the many challenges of visual sensor networks.
On Map-Matching Vehicle Tracking Data

Vehicle tracking data is an essential “raw” material for a broad range of applications such as traffic management and control, routing, and navigation. An important issue with this data is its accuracy. The method of sampling vehicular movement using GPS is affected by two error sources and consequently produces inaccurate trajectory data. To become useful, the data has to be related to the underlying road network by means of map matching algorithms. We present three such algorithms that consider especially the trajectory nature of the data rather than simply the current position as in the typical map-matching case. An incremental algorithm is proposed that matches consecutive portions of the trajectory to the road network, effectively trading accuracy for speed of computation. In contrast, the two global algorithms compare the entire trajectory to candidate paths in the road network. The algorithms are evaluated in terms of (i) their running time and (ii) the quality of their matching result. Two novel quality measures utilizing the Fréchet distance are introduced and subsequently used in an experimental evaluation to assess the quality of matching real tracking data to a road network.
The distance correlation t-test of independence in high dimension

AMS subject classifications: primary 62G10 secondary 62H20  dCor dCov Multivariate independence Distance covariance Distance correlation High dimension a b s t r a c t Distance correlation is extended to the problem of testing the independence of random vectors in high dimension. Distance correlation characterizes independence and determines a test of multivariate independence for random vectors in arbitrary dimension. In this work, a modified distance correlation statistic is proposed, such that under independence the distribution of a transformation of the statistic converges to Student t, as dimension tends to infinity. Thus we obtain a distance correlation t-test for independence of random vectors in arbitrarily high dimension, applicable under standard conditions on the coordinates that ensure the validity of certain limit theorems. This new test is based on an unbiased es-timator of distance covariance, and the resulting t-test is unbiased for every sample size greater than three and all significance levels. The transformed statistic is approximately normal under independence for sample size greater than nine, providing an informative sample coefficient that is easily interpretable for high dimensional data. 1.  Many applications in genomics, medicine, engineering, etc. require analysis of high dimensional data. Time series data can also be viewed as high dimensional data. Objects can be represented by their characteristics or features as vectors p. In this work, we consider the extension of distance correlation to the problem of testing independence of random vectors in arbitrarily high, not necessarily equal dimensions, so the dimension p of the feature space of a random vector is typically large. measure all types of dependence between random vectors in arbitrary, not necessarily equal dimensions.  Distance correlation takes values in [0, 1] and is equal to zero if and only if independence holds. It is more general than the classical Pearson product moment correlation, providing a scalar measure of multivariate independence that characterizes independence of random vectors. The distance covariance test of independence is consistent against all dependent alternatives with finite second moments. In practice, however, researchers are often interested in interpreting the numerical value of distance correlation, without a formal test. For example, given an array of distance correlation statistics, what can one learn about the strength of dependence relations from the dCor statistics without a formal test? This is in fact, a difficult question, but a solution is finally available for a large class of problems. The …
Multiple memory systems and consciousness.

This Introduction to the Special Issue on Human Memory discusses some of the recent and current developments in the study of human memory from the neuropsychological perspective. A problem of considerable current interest, that of multiple memory systems, is a problem in classification. Much of the evidence for it is derived from clinical and experimental observations of dissociations between performances in memory tasks. The distinction between short-term and long-term memory is considered as an example of classification by dissociation. Current conceptualizations of multiple long-term memory systems are reviewed from the vantage point that distinguishes among three major kinds of memory--episodic, semantic, and procedural. These systems are briefly described and compared, and current views concerning the relation between them are discussed. The role of consciousness in memory is raised against the backdrop of the suggestion that it may be necessary to differentiate among several kinds of consciousness.
An integrated biomechanical analysis of normal stair ascent and descent.

Three normal males of similar height and weight ascended and descended a five step staircase with a riser height of 22 cm and a tread of 28 cm. EMG, force plate and cine data were collected for the stride over the second to fourth step during each mode. Kinematic and kinetic analyses were integrated with EMG to yield an interpretation of the mechanics of normal stair walking. Movement from one step to the next involved simultaneous lifting and horizontal translation of the body, and each stride showed specific phases for progression. The extensor muscles about the knee played a dominant role in progression from one step to the next in both modes coupled with the ankle plantar flexors. The total lower limb extensor pattern, called the support moment, was highly correlated between subjects and to level walking. Intra- and inter-subject variability of the motor patterns were also determined. The greatest variability was seen at the hip, while stereotypic kinetic patterns emerged at the ankle and knee for all subjects across the 24 trials of each mode.