nils-herrmann/clean_scirepeval huggingface dataset:
FaceTube: predicting personality from facial expressions of emotion in online conversational video

The advances in automatic facial expression recognition make possible to mine and characterize large amounts of data, opening a wide research domain on behavioral understanding. In this paper, we leverage the use of a state-of-the-art facial expression recognition technology to characterize users of a popular type of online social video, conversational vlogs. First, we propose the use of several activity cues to characterize vloggers based on frame-by-frame estimates of facial expressions of emotion. Then, we present results for the task of automatically predicting vloggers' personality impressions using facial expressions and the Big-Five traits. Our results are promising, specially for the case of the Extraversion impression, and in addition our work poses interesting questions regarding the representation of multiple natural facial expressions occurring in conversational video.
Object Detection with Mask-based Feature Encoding

Region-based Convolutional Neural Networks (R-CNNs) have achieved great success in the field of object detection. The existing R-CNNs usually divide a Region-of-Interest (ROI) into grids, and then localize objects by utilizing the spatial information reflected by the relative position of each grid in the ROI. In this paper, we propose a novel featureencoding approach, where spatial information is represented through the spatial distributions of visual patterns. In particular, we design a Mask Weight Network (MWN) to learn a set of masks and then apply channel-wise masking operations to ROI feature map, followed by a global pooling and a cheap fully-connected layer. We integrate the newly designed feature encoder into the Faster R-CNN architecture. The resulting new Faster R-CNNs can preserve the object-detection accuracy of the standard Faster R-CNNs by using substantially fewer parameters. Compared to R-FCNs using state-of-art PS ROI pooling and deformable PS ROI pooling, the new Faster R-CNNs can produce higher object-detection accuracy with good runtime efficiency. We also show that a specifically designed and learned MWN can capture global contextual information and further improve the object-detection accuracy. Validation experiments are conducted on both PASCAL VOC and MS COCO datasets.
Neurotoxins: Current Concepts in Cosmetic Use on the Face and Neck--Upper Face (Glabella, Forehead, and Crow's Feet).

There are 3 Food and Drug Administration-approved botulinum toxin formulations now being successfully used for treatment in the upper face. The most common areas for botulinum toxin treatment are the upper face, including the glabella, forehead, brows, and lateral canthal lines or crow's feet. The frozen look is no more desired in patients. Thus, physicians are more commonly individualizing dosage based on the patient's variation in anatomy, muscle mass, asymmetry, and, most importantly, desired outcome.
Suture suspension technique for midface and neck rejuvenation.

Seventeen patients averaging 51 years of age underwent 23 surgical procedures, including suture suspension for both midface and neck rejuvenations. A 3/0 polypropylene thread with bioabsorbable cones with multiple point fixations in addition to 2 x 0.5-cm polypropylene surgical mesh are used in this technique. The mean postoperative, follow-up time was 9 months. Of the 17 patients, 12 underwent this procedure for midface rejuvenations, 3 for facial palsy, 5 for neck aesthetic procedures, 2 for brow ptosis, and 1 for brow asymmetry. The average number of sutures used for each face was 4 and 2 were used for each neck. The authors present an anatomic study for the safe placement of sutures, the surgical technique, and a microscopic photo documentation of the fibrosis around the suture knot and cone. All patients developed temporary edema. Two patients had a moderate aesthetic improvement of the face, and 1 patient underwent resuspension of the sutures 4 months postsurgery. Overall early patient satisfaction at 9 months was 90%. This technique has the potential to be a useful and effective clinical tool for minimally invasive face and neck rejuvenations.
The effect of "open access" on citation impact: An analysis of ArXiv's condensed matter section

This article statistically analyses how the citatio n impact of articles deposited in the Condensed Matter section of the preprint server ArX iv (hosted by Cornell University), and subsequently published in a scientific journal, compares to that of articles in the same journal that were not deposited in that archive. Its principal aim is to further illustrate and roughly estimate the effect of two factors, ‘early view’ and ‘quality bias’, upon differences in citation impact between these two se ts of papers, using citation data from Thomson Scientific’s Web of Science. It presents es imates for a number of journals in the field of condensed matter physics. In order to discriminate between an ‘open access’ effect and an early view effect, longitudinal citat ion data was analysed covering a time period as long as 7 years. Quality bias was measure d by calculating ArXiv citation impact differentials at the level of individual aut hors publishing in a journal, taking into account co-authorship. The analysis provided evide nce of a strong quality bias and early view effect. Correcting for these effects, there is in a sample of 6 condensed matter physics journals studied in detail, no sign of a general ‘open access advantage’ of paper s deposited in  The study does provide evidence that ArXiv accelerates citation, due to the fact that that ArXiv makes papers arlier available rather than that it makes papers freely available.
Multi-Modal Locomotion Robotic Platform Using Leg-Track-Wheel Articulations

Other than from its sensing and processing capabilities, a mobile robotic platform can be limited in its use by its ability to move in the environment. Legs, tracks and wheels are all efficient means of ground locomotion that are most suitable in different situations. Legs allow to climb over obstacles and change the height of the robot, modifying its viewpoint of the world. Tracks are efficient on uneven terrains or on soft surfaces (snow, mud, etc.), while wheels are optimal on flat surfaces. Our objective is to work on a new concept capable of combining different locomotion mechanisms to increase the locomotion capabilities of the robotic platform. The design we came up with, called AZIMUT, is symmetrical and is made of four independent leg-track-wheel articulations. It can move with its articulations up, down or straight, allowing the robot to deal with three-dimensional environments. AZIMUT is also capable of moving sideways without changing its orientation, making it omnidirectional. By putting sensors on these articulations, the robot can also actively perceive its environment by changing the orientation of its articulations. Designing a robot with such capabilities requires addressing difficult design compromises, with measurable impacts seen only after integrating all of the components together. Modularity at the structural, hardware and embedded software levels, all considered concurrently in an iterative design process, reveals to be key in the design of sophisticated mobile robotic platforms.
Time required for Judgements of Numerical Inequality

AN educated adult can tell which of two digits is the larger with virtually no uncertainty. By what process is this accomplished ? On the one hand, it is conceivable that such judgements are made in the same way as judgements of stimuli varying along physical continua. On the other hand, numerical judgements may be made at a different, less perceptual and more cognitive, level. For instance, the task may be one of memory access, each possible pair of numerals being stored with a corresponding inequality sign ; or perhaps some sort of digital computation is performed, such as counting the space between the two numerical values.
A Q-band (40–45 GHz) 16-element phased-array transmitter in 0.18-μm SiGe BiCMOS technology

A 16-element phased-array transmitter based on 4-bit RF phase shifters is designed in 0.18-mum SiGe BiCMOS technology for Q-band applications. The phased-array shows 12.5plusmn1.5 dB of power gain per channel at 42.5 GHz for all phase states and the 3-dB gain bandwidth is 40-45.6 GHz. The input and output return loss is less than -10 dB at 37.5-50 GHz. The transmitter also results in les8.8deg of RMS phase error and les1.2 dB of RMS gain error for all phase states at 30-50 GHz. The maximum saturated output power is -2.5plusmn1.5 dBm per channel at 42.5 GHz. The RMS gain variation and RMS phase mismatch between all 16 channels is les0.5 dB and les4.5deg, respectively. The chip consumes 720 mA from a 5 V supply voltage and overall chip size is 2.6times3.2 mm2. To our knowledge, this is the first implementation of a 16-element phased array on a silicon chip with the RF phase shifting architecture at any frequency.
Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: a meta-analysis.

Peer victimization is related to an increased chance of suicidal ideation and suicide attempts among children and adolescents.  To examine the relationship between peer victimization and suicidal ideation or suicide attempts using meta-analysis.  Ovid MEDLINE, PsycINFO, and Web of Science were searched for articles from 1910 to 2013. The search terms were bully*, teas*, victim*, mobbing, ragging, and harassment in combination with the term suic*. Of the 491 studies identified, 34 reported on the relationship between peer victimization and suicidal ideation, with a total of 284,375 participants. Nine studies reported on the relationship between peer victimization and suicide attempts, with a total of 70,102 participants.  Studies were eligible for inclusion if they reported an effect size on the relationship between peer victimization and suicidal ideation or suicide attempt in children or adolescents.



Two observers independently coded the effect sizes from the articles. Data were pooled using a random effects model.  This study focused on suicidal ideation and suicide attempts. Peer victimization was hypothesized to be related to suicidal ideation and suicide attempts.  Peer victimization was found to be related to both suicidal ideation (odds ratio, 2.23 [95% CI, 2.10-2.37]) and suicide attempts (2.55 [1.95 -3.34]) among children and adolescents. Analyses indicated that these results were not attributable to publication bias. Results were not moderated by sex, age, or study quality. Cyberbullying was more strongly related to suicidal ideation compared with traditional bullying.  Peer victimization is a risk factor for child and adolescent suicidal ideation and attempts. Schools should use evidence-based practices to reduce bullying.
Locality-Driven Dynamic Flash Cache Allocation

Flash-based SSDs are widely deployed as the storage caches to boost the system performance. However, unlike the traditional cache devices such as SRAM and DRAM, SSDs have internal garbage collection activities, which can severely degrade the cache performance. Moreover, SSD can only sustain limited P/E cycles. Therefore, traditional cache hit ratio oriented optimizations might not obtain the optimal performance for the SSD cache and can even shorten the device lifetime, especially for the SSD write cache, which could introduce more internal garbage collection processes and lifetime concerns. In this paper, we propose a reuse distance aware cache management to improve both the performance and lifetime of SSD-based write cache by compromising the cache hit ratio and the internal garbage collection overhead.
An Approach to Capture Role-Based Access Control Models from Spring Web Applications

To mitigate potential misinterpretation and security violations, software developers should use tools that reflect the state of web applications and visualise them as graphical models. Modelling helps to ensure that functionality and access control mechanisms are consistently interconnected. In this paper, we propose an approach to support Web application development using the Spring platform. Our proposal is supported by the Eclipse IDE plugin tool, which recognises Spring Security configuration captures, its notations, and visualises them in the role-based access control (RBAC) models. The RBAC models are represented using SecureUML modelling language. The plugin is validated through survey taken by software developers.
Rethinking the Value of Network Pruning

Network pruning is widely used for reducing the heavy computational cost of 1 deep models. A typical pruning algorithm is a three-stage pipeline, i.e., training (a 2 large model), pruning and fine-tuning. In this work, we make a rather surprising 3 observation: fine-tuning a pruned model only gives comparable or even worse 4 performance than training that model with randomly initialized weights. Our 5 results have several implications: 1) training a large, over-parameterized model is 6 not necessary to obtain an efficient final model, 2) learned “important” weights 7 of the large model are not necessarily useful for the small pruned model, 3) the 8 pruned architecture itself, rather than a set of inherited weights, is what leads to the 9 efficiency benefit in the final model, which suggests that some pruning algorithms 10 could be seen as performing network architecture search.
Java-based graphical user interface for MRUI, a software package for quantitation of in vivo/medical magnetic resonance spectroscopy signals

This article describes a Java-based graphical user interface for the magnetic resonance user interface (MRUI) quantitation package. This package allows MR spectroscopists to easily perform time-domain analysis of in vivo/medical MR spectroscopy data. We have found that the Java programming language is very well suited for developing highly interactive graphical software applications such as the MRUI system. We also have established that MR quantitation algorithms, programmed in the past in other languages, can easily be embedded into the Java-based MRUI by using the Java native interface (JNI).
Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets

Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis'04 contest), the ionization front instability data set (Vis'08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.
Soft-switching inverter for variable frequency induction heating using magnetic energy recovery switch (MERS)

Variable frequency induction heating has great potential for industrial heating applications due to the possibility of heating distribution control. Conventional high frequency converters are not suitable for the variable frequency operation; therefore, large scale induction heating with variable frequency has not yet been introduced to practical use. This paper proposes a soft-switching converter using a concept of a magnetic energy recovery switch. Operation principles and characteristics are investigated and compared with other existing high frequency inverters. The proposed converter can be characterized by current source topology and wide frequency operating range with soft-switching. From discussions, the proposed converter is found to be suitable for high power variable frequency induction heating. The proposed converter has the challenge that semi-conductor switches must handle all of the load current and required load voltage to achieve wide frequency operation range, although conventional topologies utilize resonance capacitor to reduce semi-conductor ratings. This paper also describes current rating reduction by using shunt capacitor for the proposed converter. A simplified configuration to achieve the same concept is also proposed.
Education-specific Tag Recommendation in CQA Systems

Systems for Community Question Answering (CQA) are well-known on the open web (e.g. Stack Overflow or Quora). They have been recently adopted also for use in educational domain (mostly in MOOCs) to mediate communication between students and teachers. As students are only novices in topics they learn about, they may need various scaffoldings to achieve effective question answering. In this work, we focus specifically on automatic recommendation of tags classifying students' questions. We propose a novel method that can automatically analyze a text of a question and suggest appropriate tags to an asker. The method takes specifics of educational domain into consideration by a two-step recommendation process in which tags reflecting course structure are recommended at first and consequently supplemented with additional related tags.
 Evaluation of the method on data from CS50 MOOC at Stack Exchange platform showed that the proposed method achieved higher performance in comparison with a baseline method (tag recommendation without taking educational specifics into account).
SPARQL Graph Pattern Processing with Apache Spark

A common way to achieve scalability for processing SPARQL queries is to choose MapReduce frameworks like Hadoop or Spark. Processing basic graph pattern (BGP) expressions generating large join plans over distributed data partitions is a major challenge in these frameworks. In this article, we study the use of two distributed join algorithms, partitioned join and broadcast join, for the evaluation of BGP expressions on top of Apache Spark. We compare five possible implementation and illustrate the importance of cautiously choosing the physical data storage layer and of the possibility to use both join algorithms to efficiently take account of existing data partitioning schemes. Our experimentations with different SPARQL benchmarks over real-world and synthetic workloads emphasize that hybrid join plans introduce more flexibility and often achieve better performance than single kind join plans.
Security Threats to Automotive CAN Networks - Practical Examples and Selected Short-Term Countermeasures

The IT security of automotive systems is an evolving area of research. To analyse the current situation and the potentially growing tendency of arising threats we performed several practical tests on recent automotive technology. With a focus on automotive systems based on CAN bus technology, this article summarises the results of four selected tests performed on the control systems for the window lift, warning light and airbag control system as well as the central gateway. These results are supplemented in this article by a classification of these four attack scenarios using the established CERT taxonomy and an analysis of underlying security vulnerabilities, and especially, potential safety implications. With respect to the results of these tests, in this article we further discuss two selected countermeasures to address basic weaknesses exploited in our tests. These are adaptations of intrusion detection (discussing three exemplary detection patterns) and IT-forensic measures (proposing proactive measures based on a forensic model). This article discusses both looking at the four attack scenarios introduced before, covering their capabilities and restrictions. While these reactive approaches are short-term measures, which could already be added to today’s automotive IT architecture, long-term concepts also are shortly introduced, which are mainly preventive but will require a major redesign. Beneath a short overview on respective research approaches, we discuss their individual requirements, potential and restrictions.
Schema Theory for Semantic Link Network

Semantic link network (SLN) is a loosely coupled semantic data model for managing Web resources. Its nodes can be any types of resources. Its edges can be any semantic relations. Potential semantic links can be derived out according to reasoning rules on semantic relations. This paper proposes the schema theory for SLN including the concepts, rule-constraint normal forms and relevant algorithms. The theory provides the basis for normalized management of SLN and its applications. A case study demonstrates the proposed theory.
The Cultural Evolution of Religion

After almost a century of dwelling in two “non-overlapping magisteria,” as Steven Jay Gould once put it, religion and science are coming together again. Long the exclusive province of the humanities and left outside of the mainstream of psychology and the behavioural sciences, religion is gaining scientific attention at a rapid pace. The dismantling of the taboos that have kept religion out of the scientific spotlight will take time (Dennett, 2006). Nevertheless, these are exciting times, and we can now safely say that religion—to paraphrase Chomsky about language—has been upgraded from scientific mystery to scientific puzzle (Boyer, 2001). This growing scientific interest promises to offer a naturalistic account for a deeply affecting aspect of human lives that is widespread in all known cultures in the world.
Minimum classification error rate methods for speech recognition

A critical component in the pattern matching approach to speech recognition is the training algorithm, which aims at producing typical (reference) patterns or models for accurate pattern comparison. In this paper, we discuss the issue of speech recognizer training from a broad perspective with root in the classical Bayes decision theory. We differentiate the method of classifier design by way of distribution estimation and the discriminative method of minimizing classification error rate based on the fact that in many realistic applications, such as speech recognition, the real signal distribution form is rarely known precisely. We argue that traditional methods relying on distribution estimation are suboptimal when the assumed distribution form is not the true one, and that “optimality” in distribution estimation does not automatically translate into “optimality” in classifier design. We compare the two different methods in the context of hidden Markov modeling for speech recognition. We show the superiority of the minimum classification error (MCE) method over the distribution estimation method by providing the results of several key speech recognition experiments. In general, the MCE method provides a significant reduction of recognition error rate.