nils-herrmann/clean_scirepeval huggingface dataset:
Abnormalities in emotion processing within cortical and subcortical regions in criminal psychopaths: evidence from a functional magnetic resonance imaging study using pictures with emotional content

Neurobiology of psychopathy is important for our understanding of current neuropsychiatric questions. Despite a growing interest in biological research in psychopathy, its neural underpinning remains obscure.



We used functional magnetic resonance imaging to study the influence of affective contents on brain activation in psychopaths. Series containing positive and negative pictures from the International Affective Picture System were shown to six male psychopaths and six male control subjects while 100 whole-brain echo-planar-imaging measurements were acquired. Differences in brain activation were evaluated using BrainVoyager software 4.6.



In psychopaths, increased activation through negative contents was found right-sided in prefrontal regions and amygdala. Activation was reduced right-sided in the subgenual cingulate and the temporal gyrus, and left-sided in the dorsal cingulate and the parahippocampal gyrus. Increased activation through positive contents was found left-sided in the orbitofrontal regions. Activation was reduced in right medial frontal and medial temporal regions.



These findings underline the hypotheses that psychopathy is neurobiologically reflected by dysregulation and disturbed functional connectivity of emotion-related brain regions. These findings may be interpreted within a framework including prefrontal regions that provide top-down control to and regulate bottom-up signals from limbic areas. Because of the small sample size, the results of this study have to be regarded as preliminary.
Combinatorial Bounds for Broadcast Encryption

Abst rac t . A broadcast encryption system allows a center to communicate securely over a broadcast channel with selected sets of users. Each time the set of privileged users changes, the center enacts a protocol to establish a new broadcast key that only the privileged users can obtain, and subsequent transmissions by the center are encrypted using the new broadcast key. We study the inherent trade-off between the number of establishment keys held by each user and the number of transmissions needed to establish a new broadcast key. For every given upper bound on the number of establishment keys held by each user, we prove a lower bound on the number of transmissions needed to establish a new broad~ cast key. We show that these bounds are essentially tight, by describing broadcast encryption systems that come close to these bounds.
Genital anatomy in non-abused preschool girls.

To describe the normal variations in genital anatomy in preschool girls selected for non-abuse.



A total of 2731 girls aged 5 or 6 y were invited to take part in the study; 195 girls were recruited. Inclusion was based on self-selection, whereby parents who did not suspect any occurrence of sexual abuse of their children gave informed consent to participate. Several steps were taken to exclude abused girls and girls with previous accidental genital injuries. The genital examination, using a colposcope and a camera, was performed in supine position using a separation and traction technique, and in the prone knee-chest position.



A number of genital anatomical features and hymenal measurements were described and found consistent with previous studies. An important finding was outward folding of the posterior hymenal rim in many girls, a feature that could be difficult to distinguish from attenuation of the posterior hymen. A gaping hymenal orifice, previously suggested to be a supportive sign of sexual abuse, was fairly frequently found and significantly associated with a large horizontal hymenal diameter.



To distinguish between girls with outward folding of the posterior hymen and those with attenuated hymens, we recommend the use of the saline irrigation method. Even though normative hymenal measurement data now exist from a reasonable number of girls, these measurements should be used with caution in sexual abuse evaluations.
Penetration testing: Concepts, attack methods, and defense strategies

Penetration testing helps to secure networks, and highlights the security issues. In this paper investigate different aspects of penetration testing including tools, attack methodologies, and defense strategies. More specifically, we performed different penetration tests using a private networks, devices, and virtualized systems and tools. We predominately used tools within the Kali Linux suite. The attacks we performed included: smartphone penetration testing, hacking phones Bluetooth, traffic sniffing, hacking WPA Protected Wifi, Man-in-the-Middle attack, spying (accessing a PC microphone), hacking phones Bluetooth, and hacking remote PC via IP and open ports using advanced port scanner. The results are then summarized and discussed. The paper also outlined the detailed steps and methods while conducting these attacks.
Symbolic execution with mixed concrete-symbolic solving

Symbolic execution is a powerful static program analysis technique that has been used for the automated generation of test inputs. Directed Automated Random Testing (DART) is a dynamic variant of symbolic execution that initially uses random values to execute a program and collects symbolic path conditions during the execution. These conditions are then used to produce new inputs to execute the program along different paths. It has been argued that DART can handle situations where classical static symbolic execution fails due to incompleteness in decision procedures and its inability to handle external library calls.
 We propose here a technique that mitigates these previous limitations of classical symbolic execution. The proposed technique splits the generated path conditions into (a) constraints that can be solved by a decision procedure and (b) complex non-linear constraints with uninterpreted functions to represent external library calls. The solutions generated from the decision procedure are used to simplify the complex constraints and the resulting path conditions are checked again for satisfiability. We also present heuristics that can further improve our technique. We show how our technique can enable classical symbolic execution to cover paths that other dynamic symbolic execution approaches cannot cover. Our method has been implemented within the Symbolic PathFinder tool and has been applied to several examples, including two from the NASA domain.
Multi-clip video editing from a single viewpoint

We propose a framework for automatically generating multiple clips suitable for video editing by simulating pan-tilt-zoom camera movements within the frame of a single static camera. Assuming important actors and objects can be localized using computer vision techniques, our method requires only minimal user input to define the subject matter of each sub-clip. The composition of each sub-clip is automatically computed in a novel L1-norm optimization framework. Our approach encodes several common cinematographic practices into a single convex cost function minimization problem, resulting in aesthetically pleasing sub-clips which can easily be edited together using off-the-shelf multi-clip video editing software. We demonstrate our approach on five video sequences of a live theatre performance by generating multiple synchronized subclips for each sequence.
Genetic algorithms and VRP : the behaviour of a crossover operator

In the paper, we investigate the crossover operators for a vehicle routing problem where only feasible solutions are taken into account. New crossover operators are proposed that are based on the common sequence in the parent solutions. Random insertion heuristic is used as a reconstruction method in a crossover operator to preserve stochastic characteristics of the genetic algorithm. The genetic algorithm together with the new crossover operators can be applied to different VRP problems or other problems that can be expressed as a graph and depend on a sequence of elements. The proposed crossover operators are compared with other crossovers that deal with feasible solutions and insertion heuristics.
A region-based image caption generator with refined descriptions

Describing the content of an image is a challenging task. To enable detailed description, it requires the detection and recognition of objects, people, relationships and associated attributes. Currently, the majority of the existing research relies on holistic techniques, which may lose details relating to important aspects in a scene. In order to deal with such a challenge, we propose a novel region-based deep learning architecture for image description generation. It employs a regional object detector, recurrent neural network (RNN)-based attribute prediction, and an encoderdecoder language generator embedded with two RNNs to produce refined and detailed descriptions of a given image. Most importantly, the proposed system focuses on a local based approach to further improve upon existing holistic methods, which relates specifically to image regions of people and objects in an image. Evaluated with the IAPR TC-12 dataset, the proposed system shows impressive performance, and outperforms state-of-the-art methods using various evaluation metrics. In particular, the proposed system shows superiority over existing methods when dealing with cross-domain indoor scene images.
Ridge gap waveguide antenna array using integrated coaxial power divider

A slot array antenna is designed using ridge gap waveguide technology in two sub arrays. Each sub array is fed through a power divider which is designed on a separate ridge- groove gap waveguide. The coupling between two gap waveguides is conducted using coaxial transmission lines. The power divider of ridge-groove gap is fed using coaxial line that can provide a bandwidth of 22% at 14 GHz. The integrated antenna operates within the frequency range of 13.5-15.6 GHz with a gain of 13.9-15.5 dB. Design of the transmission line, power divider and sub-array antenna is presented.
An AUC-based permutation variable importance measure for random forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings. The standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website:
Pure Pursuit Revisited: Field Testing of Autonomous Vehicles in Urban Areas

In this paper, we aim to explore path following. We implement a path following component by referring to the existing Pure Pursuit algorithm. Using the simulation and field operational test, we identified the problem in the path following component. The main problems identified were with respect to vehicles meandering off the path, turning a corner, and the instability of steering control. Therefore, we apply some modifications to the Pure Pursuit[1] algorithm. We have also conducted the simulation and field operational tests again to evaluate these modifications.
Sentiment expression via emoticons on social media

Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and other NLP tasks as features to machine learning algorithms or as entries of sentiment lexicons. In this paper, we argue that while emoticons are strong and common signals of sentiment expression on social media the relationship between emoticons and sentiment polarity are not always clear. Thus, any algorithm that deals with sentiment polarity should take emoticons into account but extreme caution should be exercised in which emoticons to depend on. First, to demonstrate the prevalence of emoticons on social media, we analyzed the frequency of emoticons in a large recent Twitter data set. Then we carried out four analyses to examine the relationship between emoticons and sentiment polarity as well as the contexts in which emoticons are used. The first analysis surveyed a group of participants for their perceived sentiment polarity of the most frequent emoticons. The second analysis examined clustering of words and emoticons to better understand the meaning conveyed by the emoticons. The third analysis compared the sentiment polarity of microblog posts before and after emoticons were removed from the text. The last analysis tested the hypothesis that removing emoticons from text hurts sentiment classification by training two models with and without emoticons in the text, respectively. The results confirms the arguments that: 1) a few emoticons are strong and reliable signals of sentiment polarity and one should take advantage of them in any sentiment analysis; 2) a large group of the emoticons conveys complicated sentiment hence they should be treated with extreme caution.
Implications of modified waterfall model to the roles and education of health IT professionals

Electronic Health Records (EHRs) are believed to have the potential to enhance efficiency and provide better health care. However, the benefits could be easily compromised if EHRs are not used appropriately. This paper applies a modified waterfall life cycle model to evaluate the roles of health IT professionals in the adoption and management of EHRs. We then present our development of a Masters' program in Medical Informatics for the education of health IT professionals. We conclude that health IT professionals serve key roles in addressing the problems and concerns and help fulfill envision of EHRs.
Software model synthesis using satisfiability solvers

We introduce a novel approach for synthesis of software models based on identifying deterministic finite state automata. Our approach consists of three important contributions. First, we argue that in order to model software, one should focus mainly on observed executions (positive data), and use the randomly generated failures (negative data) only for testing consistency. We present a new greedy heuristic for this purpose, and show how to integrate it in the state-of-the-art evidence-driven state-merging (EDSM) algorithm. Second, we apply the enhanced EDSM algorithm to iteratively reduce the size of the problem. Yet during each iteration, the evidence is divided over states and hence the effectiveness of this algorithm is decreased. We propose—when EDSM becomes too weak—to tackle the reduced identification problem using satisfiability solvers. Third, in case the amount of positive data is small, we solve the identification problem several times by randomizing the greedy heuristic and combine the solutions using a voting scheme. The interaction between these contributions appeared crucial to solve hard software models synthesis benchmarks. Our implementation, called DFASAT, won the StaMinA competition.
Discovering Parallel Text from the World Wide Web

Parallel corpus is a rich linguistic resource for various multilingual text management tasks, including crosslingual text retrieval, multilingual computational linguistics and multilingual text mining. Constructing a parallel corpus requires effective alignment of parallel documents. In this paper, we develop a parallel page identification system for identifying and aligning parallel documents from the World Wide Web. The system crawls the Web to fetch potentially parallel multilingual Web documents using a Web spider. To determine the parallelism between potential document pairs, two modules are developed. First, a filename comparison module is used to check filename resemblance. Second, a content analysis module is used to measure the semantic similarity. The experiment conducted to a multilingual Web site shows the effectiveness of the system.
Design and implementation of a 1.3 kW, 7-level flying capacitor multilevel AC-DC converter with power factor correction

This work presents a 1.3 kW, single phase, AC-DC converter with power factor correction based on a 7-level flying capacitor multilevel converter. The topology features a tradeoff of active circuit complexity for dramatic reduction on the magnetic component size, while maintaining a high efficiency. In this work, we demonstrate these features through theoretical analysis as well as a hardware prototype. It has been experimentally demonstrated that the prototype can operate at an universal AC input from 90–230 VRMS and frequencies from 47–63 Hz with an output voltage of 400 V, achieving a box power density of 1.21 W/cm3 (19.8 W/in3) and a peak efficiency of 97.6%. This prototype is the first successful demonstration of a 7-level flying capacitor multilevel boost topology as an AC-DC converter with fully implemented digital PFC control and self-powered start-up from a universal AC input.
A Joint Segmentation and Classification Framework for Sentiment Analysis

In this paper, we propose a joint segmentation and classification framework for sentiment analysis. Existing sentiment classification algorithms typically split a sentence as a word sequence, which does not effectively handle the inconsistent sentiment polarity between a phrase and the words it contains, such as “not bad” and “a great deal of ”. We address this issue by developing a joint segmentation and classification framework (JSC), which simultaneously conducts sentence segmentation and sentence-level sentiment classification. Specifically, we use a log-linear model to score each segmentation candidate, and exploit the phrasal information of top-ranked segmentations as features to build the sentiment classifier. A marginal log-likelihood objective function is devised for the segmentation model, which is optimized for enhancing the sentiment classification performance. The joint model is trained only based on the annotated sentiment polarity of sentences, without any segmentation annotations. Experiments on a benchmark Twitter sentiment classification dataset in SemEval 2013 show that, our joint model performs comparably with the state-of-the-art methods.
Learning to Generate Corrective Patches using Neural Machine Translation

Bug fixing is generally a manually-intensive task. However, recent work has proposed the idea of automated program repair, which aims to repair (at least a subset of) bugs in different ways such as code mutation, etc. Following in the same line of work as automated bug repair, in this paper we aim to leverage past fixes to propose fixes of current/future bugs. Specifically, we propose Ratchet, a corrective patch generation system using neural machine translation. By learning corresponding pre-correction and post-correction code in past fixes with a neural sequence-to-sequence model, Ratchet is able to generate a fix code for a given bug-prone code query. We perform an empirical study with five open source projects, namely Ambari, Camel, Hadoop, Jetty and Wicket, to evaluate the effectiveness of Ratchet. Our findings show that Ratchet can generate syntactically valid statements 98.7% of the time, and achieve an F1-measure between 0.41-0.83 with respect to the actual fixes adopted in the code base. In addition, we perform a qualitative validation using 20 participants to see whether the generated statements can be helpful in correcting bugs. Our survey showed that Ratchet’s output was considered to be helpful in fixing the bugs on many occasions, even if fix was not 100%
Amino acid mediated synthesis of silver nanoparticles and preparation of antimicrobial agar/silver nanoparticles composite films.

Silver nanoparticles (AgNPs) were synthesized using amino acids (tyrosine and tryptophan) as reducing and capping agents, and they were incorporated into the agar to prepare antimicrobial composite films. The AgNPs solutions exhibited characteristic absorption peak at 420 nm that showed a red shift to ∼434 nm after forming composite with agar. XRD data demonstrated the crystalline structure of AgNPs with dominant (111) facet. Apparent surface color and transmittance of agar films were greatly influenced by the AgNPs. The incorporation of AgNPs into agar did not exhibit any change in chemical structure, thermal stability, moisture content, and water vapor permeability. The water contact angle, tensile strength, and modulus decreased slightly, but elongation at break increased after AgNPs incorporation. The agar/AgNPs nanocomposite films possessed strong antibacterial activity against Listeria monocytogenes and Escherichia coli. The agar/AgNPs film could be applied to the active food packaging by controlling the food-borne pathogens.
Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings

Collecting a large dataset with high quality annotations is expensive and timeconsuming. Recently, Shrivastava et al. (2017) propose Simulated+Unsupervised (S+U) learning: It first learns a mapping from synthetic data to real data, translates a large amount of labeled synthetic data to the ones that resemble real data, and then trains a learning model on the translated data. Bousmalis et al. (2017b) propose a similar framework that jointly trains a translation mapping and a learning model. While these algorithms are shown to achieve the state-of-the-art performances on various tasks, it may have a room for improvement, as they do not fully leverage flexibility of data simulation process and consider only the forward (synthetic to real) mapping. Inspired by this limitation, we propose a new S+U learning algorithm, which fully leverage the flexibility of data simulators and bidirectional mappings between synthetic and real data. We show that our approach achieves the improved performance on the gaze estimation task, outperforming (Shrivastava et al., 2017).
Modeling and control active suspension system for a full car model

The purpose of this paper is to investigate the performance of a full car model active suspension system using LQR controller. Dynamic model used in this study is a linear model. A linear model can capture basic performances of vehicle suspension such as body displacement, body acceleration, wheel displacement, wheel deflection, suspension travels, pitch and yawn. Performance of suspension system is determined by the ride comfort and vehicle handling. It can be measured by car body displacement and wheel displacement performance. Two types of road profiles are used as input for the system. Simulation is based on the mathematical model by using MATLAB/SIMULINK software. Results show that the performance of body displacement and wheel displacement can be improved by using Linear Quadratic Regulator control (LQR).