nils-herrmann/clean_scirepeval huggingface dataset:
Treating the Banana Fold with the Dermotuberal Anchorage Technique: Case Report

The banana fold, or the infragluteal fold, is a fat deposit on the posterior thigh close to the gluteal crease and parallel to it. A banana fold may form for different reasons, among which an iatrogenic cause is recurrent. Although banana fold is a common problem, unrelished by most women, few procedures are targeted specifically to fight it. This report presents a severe case of iatrogenic banana fold corrected by a modification of the dermotuberal anchorage buttock-lifting technique, as reported by the author for gluteal ptosis. The operation is performed by tucking in part of the banana fold tissue caudal to the gluteal crease, sliding that tissue, after depithelization, under the buttock, and pulling it up toward the ischial tuberosity until the redundant skin on the posterior thigh is tight and the banana fold is reduced. Assessment of the results 1 year after surgery showed that the technique provided a good scar kept within the subgluteal crease, and that it satisfactorily corrected the patient’s major complaint: the banana-shaped fold.
Cannabis use and psychosis: a longitudinal population-based study.

Cannabis use may increase the risk of psychotic disorders and result in a poor prognosis for those with an established vulnerability to psychosis. A 3-year follow-up (1997-1999) is reported of a general population of 4,045 psychosis-free persons and of 59 subjects in the Netherlands with a baseline diagnosis of psychotic disorder. Substance use was assessed at baseline, 1-year follow-up, and 3-year follow-up. Baseline cannabis use predicted the presence at follow-up of any level of psychotic symptoms (adjusted odds ratio (OR) = 2.76, 95% confidence interval (CI): 1.18, 6.47), as well as a severe level of psychotic symptoms (OR = 24.17, 95% CI: 5.44, 107.46), and clinician assessment of the need for care for psychotic symptoms (OR = 12.01, 95% CI: 2.24, 64.34). The effect of baseline cannabis use was stronger than the effect at 1-year and 3-year follow-up, and more than 50% of the psychosis diagnoses could be attributed to cannabis use. On the additive scale, the effect of cannabis use was much stronger in those with a baseline diagnosis of psychotic disorder (risk difference, 54.7%) than in those without (risk difference, 2.2%; p for interaction = 0.001). Results confirm previous suggestions that cannabis use increases the risk of both the incidence of psychosis in psychosis-free persons and a poor prognosis for those with an established vulnerability to psychotic disorder.
Classical Statistics and Statistical Learning in Imaging Neuroscience

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques.
Bandwidth-Enhanced Low-Profile Cavity-Backed Slot Antenna by Using Hybrid SIW Cavity Modes

A bandwidth enhanced method of a low-profile substrate integrated waveguide (SIW) cavity-backed slot antenna is presented in this paper. Bandwidth enhancement is achieved by simultaneously exciting two hybrid modes in the SIW-backed cavity and merging them within the required frequency range. These two hybrid modes, whose dominant fields are located in different half parts of the SIW cavity, are two different combinations of the and resonances. This design method has been validated by experiments. Compared with those of a previously presented SIW cavity-backed slot antenna, fractional impedance bandwidth of the proposed antenna is enhanced from 1.4% to 6.3%, its gain and radiation efficiency are also slightly improved to 6.0 dBi and 90%, and its SIW cavity size is reduced about 30%. The proposed antenna exhibits low cross polarization level and high front to back ratio. It still retains advantages of low-profile, low fabrication cost, and easy integration with planar circuits.
Delite: A Compiler Architecture for Performance-Oriented Embedded Domain-Specific Languages

Developing high-performance software is a difficult task that requires the use of low-level, architecture-specific programming models (e.g., OpenMP for CMPs, CUDA for GPUs, MPI for clusters). It is typically not possible to write a single application that can run efficiently in different environments, leading to multiple versions and increased complexity.  are a promising avenue to enable programmers to use high-level abstractions and still achieve good performance on a variety of hardware. This is possible because DSLs have higher-level semantics and restrictions than general-purpose languages, so DSL compilers can perform higher-level optimization and translation. However, the cost of developing performance-oriented DSLs is a substantial roadblock to their development and adoption. In this article, we present an overview of the Delite compiler framework and the DSLs that have been developed with it. Delite simplifies the process of DSL development by providing common components, like parallel patterns, optimizations, and code generators, that can be reused in DSL implementations. Delite DSLs are embedded in Scala, a general-purpose programming language, but use metaprogramming to construct an Intermediate Representation (IR) of user programs and compile to multiple languages (including C++, CUDA, and OpenCL). DSL programs are automatically parallelized and different parts of the application can run simultaneously on CPUs and GPUs. We present Delite DSLs for machine learning, data querying, graph analysis, and scientific computing and show that they all achieve performance competitive to or exceeding C++ code.
Vehicular communication systems: Enabling technologies, applications, and future outlook on intelligent transportation

Numerous technologies have been deployed to assist and manage transportation. But recent concerted efforts in academia and industry point to a paradigm shift in intelligent transportation systems. Vehicles will carry computing and communication platforms, and will have enhanced sensing capabilities. They will enable new versatile systems that enhance transportation safety and efficiency and will provide infotainment. This article surveys the state-of-the-art approaches, solutions, and technologies across a broad range of projects for vehicular communication systems.
Aurasium: Practical Policy Enforcement for Android Applications

The increasing popularity of Google’s mobile platform Android makes it the prime target of the latest surge in mobile malware. Most research on enhancing the platform’s security and privacy controls requires extensive modification to the operating system, which has significant usability issues and hinders efforts for widespread adoption. We develop a novel solution called Aurasium that bypasses the need to modify the Android OS while providing much of the security and privacy that users desire. We automatically repackage arbitrary applications to attach user-level sandboxing and policy enforcement code, which closely watches the application’s behavior for security and privacy violations such as attempts to retrieve a user’s sensitive information, send SMS covertly to premium numbers, or access malicious IP addresses. Aurasium can also detect and prevent cases of privilege escalation attacks. Experiments show that we can apply this solution to a large sample of benign and malicious applications with a near 100 percent success rate, without significant performance and space overhead. Aurasium has been tested on three versions of the Android OS, and is freely available.
A survey and comparative study of data deduplication techniques

Increase in enormous amount of digital data needs more storage space, which in turn significantly increases the cost of backup and its performance. Traditional backup solutions do not provide any inherent capability to prevent duplicate data from being backed up. Backup of duplicate data significantly increases the backup time and unnecessary consumption of resources. Data Deduplication plays an important role in eliminating this redundant data and reducing the storage consumption. Its main intend is how to reduce more duplicates efficiently, removing them at high speed and to achieve good duplicate removal ratio. Many mechanisms have been proposed to meet these objectives. It has been observed that achieving one objective may affect the other. In this paper we classify and review the existing deduplication techniques. We also present evaluation of deduplication algorithms by measuring their performance in terms of complexity and efficiency with unstructured files. We propose an efficient means to achieve high deduplication ratio with minimum backup window. We also indicate some key issues need to focus in the future work.
FontMatcher: Font Image Paring for Harmonious Digital Graphic Design

One of the important aspects in graphic design is choosing the font of the caption that matches aesthetically the associated image. To obtain a good match, users would exhaustively examine a long font list requiring them a substantial effort. This paper presents FontMatcher, which supports users to design digital graphic works harmoniously pairing fonts with an image. The system provides three features, recommendation, explaination and feedback. If a warm feeling image is given as input, the system recommends warm feeling fonts, and then explains what is the distinguishing features of the recommendation, e.g. a cursive shape. Users can also provide feedback to find fonts which correspond to their intention. Our evaluation results show that the recommended fonts scored better than selected fonts by novices and provides competing results with the ones chosen by experienced graphic designers. The system also provides explanations that help increasing the reliability of the recommended results.
2D Gabor filters-based steganalysis of content-adaptive JPEG steganography

To improve the detection accuracy for content-adaptive JPEG steganography, which often constrains the embedding changes to complex texture regions, a new steganalysis feature called the GRF (Gabor Rich Feature) based on two-dimensional (2D) Gabor filters is proposed. First, the diverse 2D Gabor filters are generated and used to filter the decompressed JPEG image. Second, five types of statistical features are extracted from the filtered images and these features are merged according to their respective symmetry. Third, all the features are combined and feature selection is performed to reduce dimensionality. Last, an ensemble classifier is used to assemble the steganalysis feature as well as the final steganalyzer. The experimental results show that the proposed steganalysis feature can achieve a performance that is competitive with the state-of-the-art steganalysis features when used for the detection of the latest content-adaptive JPEG steganography algorithms.
Geo-Spatial Multimedia Sentiment Analysis in Disasters

Sentiment analysis of disaster-related posts in social media can contribute to the situation awareness and better understanding of the dynamics of disaster events by identifying the polarity of sentiments expressed by the public. However, Even though many sentiment analysis techniques have been developed and available, there are still limitations in reliably using sentiment analysis since there is no dominantly accepted technique in disasters. Taking advantage of existing state-of-the-art sentiment classifiers, this paper proposes a novel framework for geo-spatial sentiment analysis of disaster-related social media data objects. Our framework addresses three types of challenges: the inaccuracy and discrepancy associated with various text and image sentiment classifiers, the geo-sentiment discrepancy among data objects in a local geographical area, and observing diverse sentiments from multimedia data objects (i.e., text and image). The extracted sentiments are aggregated geographically for the purpose of extracting more accurate local regional insights. For the evaluation of the framework, we explored Twitter and Flickr datasets at the time of Hurricane Sandy and Napa Earthquake and showed how our approach can provide a better understanding of disaster events.
Study of battery modeling using mathematical and circuit oriented approaches

Energy storage improves the efficiency and reliability of the electric utility system. The most common device used for storing electrical energy is batteries. To investigate power converter-based charge and discharge control of a battery storage device, effective battery models are critically needed. This paper presents a comparison study of mathematical and circuit-oriented battery models with a focus on lead-acid batteries that are normally used for large power storage applications. The paper shows how mathematical and circuit-oriented battery models are developed to reflect typical battery electrochemical properties. The relation between mathematical and circuit-oriented battery models is analyzed in the paper. Comparison study is made to investigate the difference and complexity in parameter extraction using the two different modeling approaches. The paper shows that the fundamental battery electromechanical relationship is usually built into the battery mathematical model but is not directly available in the circuit-oriented model. In terms of the computational complexity, the circuit-oriented battery model requires much expensive computing resources. Performance study is conducted to evaluate various factors that may affect the behavior of the mathematical battery models.
Counter-forensics in machine learning based forgery detection

With the powerful image editing tools available today, it is very easy to create forgeries without leaving visible traces. Boundaries between host image and forgery can be concealed, illumination changed, and so on, in a naive form of counter-forensics. For this reason, most modern techniques for forgery detection rely on the statistical distribution of micro-patterns, enhanced through high-level filtering, and summarized in some image descriptor used for the final classification. In this work we propose a strategy to modify the forged image at the level of micro-patterns to fool a state-of-the-art forgery detector. Then, we investigate on the effectiveness of the proposed strategy as a function of the level of knowledge on the forgery detection algorithm. Experiments show this approach to be quite effective especially if a good prior knowledge on the detector is available.
Predicting the perceived quality of a first person shooter: the Quake IV G-model

This paper describes the development of an end-to-end quality measurement method that allows us to quantify the perceived quality of Interactive Gaming, with an emphasis on the so-called First Person Shooter (FPS) game Quake IV. We conducted a number of subjective experiments to quantify the impact of network parameters on the perceived quality of this recent FPS game. Making use of a multi-dimensional regression analysis we developed the Quake IV G-model which enables us to predict a gamer's Quake IV quality rating (expressed in a Mean Opinion Score) based on measured ping and jitter values. Our G-model shows a very high correlation (R = 0.98) with the subjective data.
A Slacker Coherence Protocol for Pull-based Monitoring of On-line Data Sources �

An increasing number of online applications operate on data from disparate, and often wide-spread, data sources. This paper studies the design of a system for the automated monitoring of on-line data sources. In this system a number of ad-hoc data warehouses, which maintain client-specified views, are interposed between clients and data sources. We present a model of coherence, referred to here as slacker coherence, to address the freshness problem in the context of pull-based protocols. We experimentally examine various techniques for estimating update rates and polling adaptively. We also look at the impact on the coherence model performance of the request scheduling algorithm at the source.
Aligning Qualitative, Real-Time, and Probabilistic Property Specification Patterns Using a Structured English Grammar

Formal methods offer an effective means to assert the correctness of software systems through mathematical reasoning. However, the need to formulate system properties in a purely mathematical fashion can create pragmatic barriers to the application of these techniques. For this reason, Dwyer et al. invented property specification patterns which is a system of recurring solutions to deal with the temporal intricacies that would make the construction of reactive systems very hard otherwise. Today, property specification patterns provide general rules that help practitioners to qualify order and occurrence, to quantify time bounds, and to express probabilities of events. Nevertheless, a comprehensive framework combining qualitative, real-time, and probabilistic property specification patterns has remained elusive. The benefits of such a framework are twofold. First, it would remove the distinction between qualitative and quantitative aspects of events; and second, it would provide a structure to systematically discover new property specification patterns. In this paper, we report on such a framework and present a unified catalogue that combines all known plus 40 newly identified or extended patterns. We also offer a natural language front-end to map patterns to a temporal logic of choice. To demonstrate the virtue of this new framework, we applied it to a variety of industrial requirements, and use PSPWizard, a tool specifically developed to work with our unified pattern catalogue, to automatically render concrete instances of property specification patterns to formulae of an underlying temporal logic of choice.
DesnowNet: Context-Aware Deep Network for Snow Removal

Existing learning-based atmospheric particle-removal approaches such as those used for rainy and hazy images are designed with strong assumptions regarding spatial frequency, trajectory, and translucency. However, the removal of snow particles is more complicated because they possess additional attributes of particle size and shape, and these attributes may vary within a single image. Currently, hand-crafted features are still the mainstream for snow removal, making significant generalization difficult to achieve. In response, we have designed a multistage network named DesnowNet to in turn deal with the removal of translucent and opaque snow particles. We also differentiate snow attributes of translucency and chromatic aberration for accurate estimation. Moreover, our approach individually estimates residual complements of the snow-free images to recover details obscured by opaque snow. Additionally, a multi-scale design is utilized throughout the entire network to model the diversity of snow. As demonstrated in the qualitative and quantitative experiments, our approach outperforms state-of-the-art learning-based atmospheric phenomena removal methods and one semantic segmentation baseline on the proposed Snow100K dataset. The results indicate our network would benefit applications involving computer vision and graphics.
Human-Robot Similarity and Willingness to Work with a Robotic Co-worker

Organizations now face a new challenge of encouraging their employees to work alongside robots. In this paper, we address this problem by investigating the impacts of human-robot similarity, trust in a robot, and the risk of physical danger on individuals' willingness to work with a robot and their willingness to work with a robot over a human co-worker. We report the results from an online experimental study involving 200 participants. Results showed that human-robot similarity promoted trust in a robot, which led to willingness to work with robots and ultimately willingness to work with a robot over a human co-worker. However, the risk of danger moderated not only the positive link between the surface-level similarity and trust in a robot, but also the link between intention to work with the robot and willingness to work with a robot over a human co-worker. We discuss several implications for the theory of human-robot interaction and design of robots.
Reduction of THD in Diode Clamped Multilevel Inverter employing SPWM technique

Conventional two-level pulse width modulation (PWM) inverters generate high dv/dt, high frequency common mode voltages and introduces harmonics which is very harmful in electric drives applications. It may damage motor bearings, conducted electromagnetic interferences, and malfunctioning of electronic equipments. Multilevel inverter (MLI) technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. Neutral-pointclamped (NPC) inverters are the most widely used topology of multilevel inverters in high-power applications (several megawatts). This paper presents the most relevant control and modulation method developed for inverters to reduce total harmonic distortion (THD): multilevel sinusoidal pulse width modulation (SPWM). More relevant applications of these converters are laminators, conveyor belts, and unified powerflow controllers. Simulation results obtained in Matlab/Simulink confirms the effectiveness with negligible THD.
Fault segmentation in fabric images using Gabor wavelet transform

Gabor wavelets have been successfully applied for a variety of machine vision applications such as Texture segmentation, Edge detection, Boundary detection etc. As the Fourier transform is not suitable for detecting local defects, and the Wavelet transforms posses only limited number of orientations, Gabor wavelet transform is chosen and applied to detect the defects in fabrics. Gabor filters scheme that imitates the early human vision process is applied to the sample under inspection. Defects can be automatically segmented from the regular texture by applying the proposed method. Proper thresholding ensures segmentation of the defect from the texture background. The results obtained using this method confirms its efficiency. This can also be applied to detect defects on surfaces and materials that have regular periodic texture.
Multi-Sensor Context Aware Clothing

Inspired by perception in biological systems, distribution of a massive amount of simple sensing devices is gaining more support in detection applications. A focus on fusion of sensor signals instead of strong analysis algorithms, and a scheme to distribute sensors, results in new issues. Especially in wearable computing, where sensor data continuously changes, and clothing provides an ideal supporting structure for simple sensors, this approach may prove to be favourable. Experiments with a body-distributed sensor system investigate the influence of two factors that affect classification of what has been sensed: an increase in sensors enhances recognition, while adding new classes or contexts depreciates the results. Finally, a wearable computing related scenario is discussed that exploits the presence of many sensors.