nils-herrmann/clean_scirepeval huggingface dataset:
Meditation-induced neuroplastic changes in amygdala activity during negative affective processing.

Recent evidence suggests that the effects of meditation practice on affective processing and resilience have the potential to induce neuroplastic changes within the amygdala. Notably, literature speculates that meditation training may reduce amygdala activity during negative affective processing. Nonetheless, studies have thus far not verified this speculation. In this longitudinal study, participants (N = 21, 9 men) were trained in awareness-based compassion meditation (ABCM) or matched relaxation training. The effects of meditation training on amygdala activity were examined during passive viewing of affective and neutral stimuli in a non-meditative state. We found that the ABCM group exhibited significantly reduced anxiety and right amygdala activity during negative emotion processing than the relaxation group. Furthermore, ABCM participants who performed more compassion practice had stronger right amygdala activity reduction during negative emotion processing. The lower right amygdala activity after ABCM training may be associated with a general reduction in reactivity and distress. As all participants performed the emotion processing task in a non-meditative state, it appears likely that the changes in right amygdala activity are carried over from the meditation practice into the non-meditative state. These findings suggest that the distress-reducing effects of meditation practice on affective processing may transfer to ordinary states, which have important implications on stress management.
Generative Adversarial Networks

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1 2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
Using deep learning for short text understanding

Classifying short texts to one category or clustering semantically related texts is challenging, and the importance of both is growing due to the rise of microblogging platforms, digital news feeds, and the like. We can accomplish this classifying and clustering with the help of a deep neural network which produces compact binary representations of a short text, and can assign the same category to texts that have similar binary representations. But problems arise when there is little contextual information on the short texts, which makes it difficult for the deep neural network to produce similar binary codes for semantically related texts. We propose to address this issue using semantic enrichment. This is accomplished by taking the nouns, and verbs used in the short texts and generating the concepts and co-occurring words with the help of those terms. The nouns are used to generate concepts within the given short text, whereas the verbs are used to prune the ambiguous context (if any) present in the text. The enriched text then goes through a deep neural network to produce a prediction label for that short text representing it’s category.
Perfectly Matched Layers for Elastodynamics : A New Absorbing Boundary

The use of perfectly matched layers (PML) has recently been introduced by Berenger as a material absorbing boundary condition (ABC) for electromagnetic waves. In this paper, we will rst prove that a ctitious elastodynamic material half-space exists that will absorb an incident wave for all angles and all frequencies. Moreover, the wave is attenuative in the second half-space. As a consequence, layers of such material could be designed at the edge of a computer simulation region to absorb outgoing waves. Since this is a material ABC, only one set of computer codes is needed to simulate an open region. Hence, it is easy to parallelize such codes on multiprocessor computers. For instance, it is easy to program massively parallel computers on the SIMD (single instruction multiple data) mode for such codes. We will show two and three dimensional computer simulations of the PML for the linearized equations of elastodyanmics. Comparison with Liao's ABC will be given.
Turing Patterns in Memristive Cellular Nonlinear Networks

The formation of ordered structures, in particular Turing patterns, in complex spatially extended systems has been observed in many different contexts, spanning from natural sciences (chemistry, physics, and biology) to technology (mechanics and electronics). In this paper, it is shown that the use of memristors in a simple cell of a spatially-extended circuit architecture allows us to design systems able to generate Turing patterns. In addition, the memristor parameters play a key role in the selection of the type and characteristics of the emerging pattern, which is also influenced by the initial conditions. The problem of finding the regions of parameters where Turing patterns may emerge in the proposed cellular architecture is solved in an analytic way, and numerical results are shown to illustrate the system behavior with respect to its parameters.
Multi-Sensor Conflict Measurement and Information Fusion

In sensing applications where multiple sensors observe the same scene, fusing sensor outputs can provide improved results. However, if some of the sensors are providing lower quality outputs, e.g. when one or more sensors has a poor signal-tonoise ratio (SNR) and therefore provides very noisy data, the fused results can be degraded. In this work, a multi-sensor conflict measure is proposed which estimates multi-sensor conflict by representing each sensor output as interval-valued information and examines the sensor output overlaps on all possible n-tuple sensor combinations. The conflict is based on the sizes of the intervals and how many sensors output values lie in these intervals. In this work, conflict is defined in terms of how little the output from multiple sensors overlap. That is, high degrees of overlap mean low sensor conflict, while low degrees of overlap mean high conflict. This work is a preliminary step towards a robust conflict and sensor fusion framework. In addition, a sensor fusion algorithm is proposed based on a weighted sum of sensor outputs, where the weights for each sensor diminish as the conflict measure increases. The proposed methods can be utilized to (1) assess a measure of multi-sensor conflict, and (2) improve sensor output fusion by lessening weighting for sensors with high conflict. Using this measure, a simulated example is given to explain the mechanics of calculating the conflict measure, and stereo camera 3D outputs are analyzed and fused. In the stereo camera case, the sensor output is corrupted by additive impulse noise, DC offset, and Gaussian noise. Impulse noise is common in sensors due to intermittent interference, a DC offset a sensor bias or registration error, and Gaussian noise represents a sensor output with low SNR. The results show that sensor output fusion based on the conflict measure shows improved accuracy over a simple averaging fusion strategy.
An Approximation of the Universal Intelligence Measure

The Universal Intelligence Measure is a recently proposed formal definition of intelligence. It is mathematically specified, extremely general, and captures the essence of many informal definitions of intelligence. It is based on Hutter’s Universal Artificial Intelligence theory, an extension of Ray Solomonoff’s pioneering work on universal induction. Since the Universal Intelligence Measure is only asymptotically computable, building a practical intelligence test from it is not straightforward. This paper studies the practical issues involved in developing a real-world UIM-based performance metric. Based on our investigation, we develop a prototype implementation which we use to evaluate a number of different
Modeling and simulation of the BLDC motor in MATLAB GUI

The paper proposes a model of brushless DC motor considering behavior of the motor during commutation. The torque characteristic of BLDC motor presents a very important factor in design of the BLDC motor drive system, so it is necessary to predict the precise value of torque, which is determined by the waveforms of back-EMF. After development of simple mathematical model of the BLDC motor with sinusoidal and trapezoidal waveforms of back-EMF the motor is simulated in the MATLAB/Simulink environment. Based on analysis of the time responses a comparison study of results of both BLDC motor types is presented in Graphical User Interface of MATLAB environment.
Discriminative learning for differing training and test distributions

We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions.
Conductive inks with a "built-in" mechanism that enables sintering at room temperature.

At present there is no metallic ink that enables formation of conductive patterns at room temperature by a single printing step. Printing conductive features by metallic nanoparticle-based inks must be followed by sintering while heating to elevated temperatures, thus preventing their utilization on most plastic substrates used in plastic electronics. In this report we present a new silver nanoparticle-based conductive ink, having a built-in sintering mechanism, which is triggered during drying of the printed pattern. The nanoparticles that are stabilized by a polymer undergo self-sintering spontaneously, due to the presence of a destabilizing agent, which comes into action only during drying of the printed pattern. The destabilizing agent, which contains Cl(-) ions, causes detachment of the anchoring groups of the stabilizer from the nanoparticles' surface and thus enables their coalescence and sintering. It was found that the new metallic ink leads to very high conductivities, by a single printing step: up to 41% of the conductivity of bulk silver was achieved, the highest reported conductivity of a printed pattern that is obtained from nanoparticles at room temperature.
C 4 . 5 classification algorithm with backtrack pruning for accurate prediction of heart disease

In Machine Learning, Decision tree is the mostly used classifier for predictive Modeling. The C4.5 classifier suffers from overfitting; poor attribute split technique, inability to handle continuous valued and missing valued attributes with high learning cost. Among all, overfitting and split attribute has high impact on the accuracies of prediction. The Efficient Back-track pruning algorithm is introduced here to overcome the drawback of overfitting. The proposed concept is implemented and evaluated with the UCI Machine Learning Hungarian database. This database having 294 records with fourteen attributes were used for forecasting the heart disease and relevant accuracies were measured. This implementation shows that the proposed Back-track pruned algorithm is efficient when compared with existing C4.5 algorithm, which is more suitable for the application of large amounts of healthcare data. Its accuracy has been greatly improved in line with the practical Health care Historical data. The result obtained proves that the performance of Back-track pruned C4.5 algorithm is better than C4.5 algorithm.
Millimeter-Wave TE20-Mode SIW Dual-Slot-Fed Patch Antenna Array With a Compact Differential Feeding Network

A millimeter-wave series–parallel patch antenna array is presented, in which the dual-slot feeding structure is handily implemented using the intrinsic field distribution of TE<sub>20</sub> mode in substrate-integrated waveguide (SIW). One 28 GHz patch antenna element fed by the TE<sub>20</sub>-mode SIW is first designed, achieving a 10 dB impedance bandwidth of 10.2% and a simulated peak gain of 6.48 dBi. Based on the antenna element, a <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array with a compact series–parallel differential feeding network is developed accordingly. Due to the novel compact SIW-based series–parallel feeding network, the antenna array can achieve superior radiation performances, which is the highlight of this communication. The simulation and measurement results of the proposed antenna array are in good agreement, demonstrating a performance of 8.5% impedance bandwidth, 19.1 dBi peak gain, symmetrical radiation patterns, and low cross-polarization levels (−30 dB in E-plane and −25 dB in H-plane) in the operating frequency band of 26.65–29.14 GHz.
CityGML-3 D City Models and their Potential for Emergency Response

Virtual 3D city models provide important information for different aspects of disaster management. In order to ensure the unambiguous interpretation of the represented objects, an ontology in the sense of a common information model for urban and regional structures has to be defined. Furthermore, up-to-dateness of and flexible access to 3D city models are of utmost importance. Spatial Data Infrastructures (SDI) provide the appropriate framework to cover this aspect, integrating distributed data sources on demand. In this chapter we present CityGML, which is in the first place an ontology for the three-dimensional, multi-purpose, and multi-scale representation of cities, sites, and regions. The implementation of CityGML is based on the standard GML3 of the Open Geospatial Consortium and thus defines an exchange format for the storage of and interoperable access to 3D city models in SDIs. The class taxonomy distinguishes between buildings and other man-made artifacts, vegetation objects, water bodies, and transportation facilities like streets and railways. Spatial as well as semantic properties are structured in five consecutive levels of detail. Throughout this chapter, special focus is on the utilization of model concepts with respect to different tasks in the context of emergency response.
A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans

The analysis of rich catalogues of genetic variation from population-based sequencing provides an opportunity to screen for functional effects. Here we report a rare variant in APOC3 (rs138326449-A, minor allele frequency ~0.25% (UK)) associated with plasma triglyceride (TG) levels (-1.43 s.d. (s.e.=0.27 per minor allele (P-value=8.0 × 10(-8))) discovered in 3,202 individuals with low read-depth, whole-genome sequence. We replicate this in 12,831 participants from five additional samples of Northern and Southern European origin (-1.0 s.d. (s.e.=0.173), P-value=7.32 × 10(-9)). This is consistent with an effect between 0.5 and 1.5 mmol l(-1) dependent on population. We show that a single predicted splice donor variant is responsible for association signals and is independent of known common variants. Analyses suggest an independent relationship between rs138326449 and high-density lipoprotein (HDL) levels. This represents one of the first examples of a rare, large effect variant identified from whole-genome sequencing at a population scale.
Towards a General Rule for Identifying Deceptive Opinion Spam

Consumers’ purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting deceptive opinion spam— fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. In this paper, we explore generalized approaches for identifying online deceptive opinion spam based on a new gold standard dataset, which is comprised of data from three different domains (i.e. Hotel, Restaurant, Doctor), each of which contains three types of reviews, i.e. customer generated truthful reviews, Turker generated deceptive reviews and employee (domain-expert) generated deceptive reviews. Our approach tries to capture the general difference of language usage between deceptive and truthful reviews, which we hope will help customers when making purchase decisions and review portal operators, such as TripAdvisor or Yelp, investigate possible fraudulent activity on their
SURF-Face: Face Recognition Under Viewpoint Consistency Constraints

We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated. Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized. A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail. The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.
A Study of Steganography Based Data Hiding Techniques

oday the exponential growth in internet users demand secure data communication, for that it is required to send the data in the form of encrypted or hidden form. Many information system security techniques are available. They are classified into three classes as cryptography, steganography and watermarking. This paper presents a review on steganography techniques and their uses and attacks on these. The steganography is commonly known as covert writing and mainly used in hidden communication. A reliable internet communication is free from the attacks on it.  Information system security, cryptography, steganography, watermarking.
Beheading hydras: performing effective botnet takedowns

Devices infected with malicious software typically form botnet armies under the influence of one or more command and control (C&C) servers. The botnet problem reached such levels where federal law enforcement agencies have to step in and take actions against botnets by disrupting (or "taking down") their C&Cs, and thus their illicit operations. Lately, more and more private companies have started to independently take action against botnet armies, primarily focusing on their DNS-based C&Cs. While well-intentioned, their C&C takedown methodology is in most cases ad-hoc, and limited by the breadth of knowledge available around the malware that facilitates the botnet.
 With this paper, we aim to bring order, measure, and reason to the botnet takedown problem. We propose a takedown analysis and recommendation system, called rza, that allows researchers to perform two tasks: 1) a postmortem analysis of past botnet takedowns, and 2) provide recommendations on how to successfully execute future botnet takedowns. As part of our system evaluation, we perform a postmortem analysis of the recent Kelihos, Zeus and 3322.org takedowns. We show that while some of these takedowns were effective, others did not appear to have a significant long-term impact on the targeted botnet. In addition to the postmortem analysis, we provide takedown recommendation metrics for 45 currently active botnets, where we find that 42 of them can likely be disabled entirely by using a DNS-based takedown strategy only.
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution

The creation of facial range models by 3D imaging systems has led to extensive work on 3D face recognition [19]. However, little work has been done to study the usefulness of such data for recognizing and understanding facial expressions. Psychological research shows that the shape of a human face, a highly mobile facial surface, is critical to facial expression perception. In this paper, we investigate the importance and usefulness of 3D facial geometric shapes to represent and recognize facial expressions using 3D facial expression range data. We propose a novel approach to extract primitive 3D facial expression features, and then apply the feature distribution to classify the prototypic facial expressions. In order to validate our proposed approach, we have conducted experiments for person-independent facial expression recognition using our newly created 3D facial expression database. We also demonstrate the advantages of our 3D geometric based approach over 2D texture based approaches in terms of various head poses.
Differentially Private Releasing via Deep Generative Model (Technical Report).

Privacy-preserving releasing of complex data (e.g., image, text, audio) represents a long-standing challenge for the data mining research community. Due to rich semantics of the data and lack of a priori knowledge about the analysis task, excessive sanitization is often necessary to ensure privacy, leading to significant loss of the data utility. In this paper, we present dp-GAN, a general private releasing framework for semantic-rich data. Instead of sanitizing and then releasing the data, the data curator publishes a deep generative model which is trained using the original data in a differentially private manner; with the generative model, the analyst is able to produce an unlimited amount of synthetic data for arbitrary analysis tasks. In contrast of alternative solutions, dp-GAN highlights a set of key features: (i) it provides theoretical privacy guarantee via enforcing the differential privacy principle; (ii) it retains desirable utility in the released model, enabling a variety of otherwise impossible analyses; and (iii) most importantly, it achieves practical training scalability and stability by employing multi-fold optimization strategies. Through extensive empirical evaluation on benchmark datasets and analyses, we validate the efficacy of dp-GAN. (The source code and the data used in the paper is available at: https://github.com/alps-lab/dpgan)