nils-herrmann/clean_scirepeval huggingface dataset:
The Impact of Degree of Exposure to Violent Video Games, Family Background, and Other Factors on Youth Violence.

Despite decades of study, no scholarly consensus has emerged regarding whether violent video games contribute to youth violence. Some skeptics contend that small correlations between violent game play and violence-related outcomes may be due to other factors, which include a wide range of possible effects from gender, mental health, and social influences. The current study examines this issue with a large and diverse (49 % white, 21 % black, 18 % Hispanic, and 12 % other or mixed race/ethnicity; 51 % female) sample of youth in eighth (n = 5133) and eleventh grade (n = 3886). Models examining video game play and violence-related outcomes without any controls tended to return small, but statistically significant relationships between violent games and violence-related outcomes. However, once other predictors were included in the models and once propensity scores were used to control for an underlying propensity for choosing or being allowed to play violent video games, these relationships vanished, became inverse, or were reduced to trivial effect sizes. These results offer further support to the conclusion that video game violence is not a meaningful predictor of youth violence and, instead, support the conclusion that family and social variables are more influential factors.
Self-adaptive differential evolution with multi-trajectory search for large-scale optimization

In this paper, Self-adaptive DE is enhanced by incorporating the JADE mutation strategy and hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS) to solve large scale continuous optimization problems. The JADE mutation strategy, the “DE/current-to-pbest” which is a variation of the classic “DE/current-to-best”, is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation in SaDE, both trial vector generation strategies and their associated control parameter values are gradually selfadapted by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation strategy along with associated parameter settings will be determined adaptively to match different phases of the search process. MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every MMTS step. The success rates of both SaDE and the MMTS are determined and compared, consequently, future function evaluations for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed to solve the 19 numerical optimization problems in Special Issue of Soft Computing on Scalability of Evolutionary Algorithms for Large Scale Continuous Optimization Problems and competitive results are presented. Keyword: differential evolution, large scale continuous optimization, self-adaptation, strategy adaptation, parameter adaptation, multi-trajectory search, JADE mutation. S. Z. Zhao and P. N. Suganthan are with School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave., 639798 Singapore (zh0047ng@ntu.edu.sg, epnsugan@ntu.edu.sg) Swagatam Das is with Department of Electronics and Telecommunication, Jadavpur University, Kollkata, India. (swagatamdas19@yahoo.co.in)
Configurable Structure Tree as a Means to Manage Configurable Business Processes

A configurable Business Process (BP) is an abstract BP that engineers customize with respect to specific requirements. To keep track of the multiple and recurrent customizations that lead to a set of derived BPs, this paper proposes a knowledge-based approach that uses a new Process Structure Tree called configurable PST (cPST). A cPST abstracts a separate variability option of the configurable BP. All cPSTs should be equivalent to the set of all PSTs associated with the derived BPs that could originate from the same configurable BP. This paper also proposes a logic-based configuration model for capturing configuration details on the cBP and describing the cPST computing.
Simultaneous segmentation and reconstruction: a level set method approach for limited view computed tomography.

An iterative tomographic reconstruction algorithm that simultaneously segments and reconstructs the reconstruction domain is proposed and applied to tomographic reconstructions from a sparse number of projection images.



The proposed algorithm uses a two-phase level set method segmentation in conjunction with an iterative tomographic reconstruction to achieve simultaneous segmentation and reconstruction. The simultaneous segmentation and reconstruction is achieved by alternating between level set function evolutions and per-region intensity value updates. To deal with the limited number of projections, a priori information about the reconstruction is enforced via penalized likelihood function. Specifically, smooth function within each region (piecewise smooth function) and bounded function intensity values for each region are assumed. Such a priori information is formulated into a quadratic objective function with linear bound constraints. The level set function evolutions are achieved by artificially time evolving the level set function in the negative gradient direction; the intensity value updates are achieved by using the gradient projection conjugate gradient algorithm.



The proposed simultaneous segmentation and reconstruction results were compared to "conventional" iterative reconstruction (with no segmentation), iterative reconstruction followed by segmentation, and filtered backprojection. Improvements of 6%-13% in the normalized root mean square error were observed when the proposed algorithm was applied to simulated projections of a numerical phantom and to real fan-beam projections of the Catphan phantom, both of which did not satisfy the a priori assumptions.



The proposed simultaneous segmentation and reconstruction resulted in improved reconstruction image quality. The algorithm correctly segments the reconstruction space into regions, preserves sharp edges between different regions, and smoothes the noise within each region. The proposed algorithm framework has the flexibility to be adapted to different a priori constraints while maintaining the benefits achieved by the simultaneous segmentation and reconstruction.
Composite right-/left-handed interdigital leaky-wave antenna on a substrate integrated waveguide

The successful concept of a composite right-/left-handed (CRLH) leaky-wave antenna based on a periodically loaded and dielectrically-filled rectangular hollow waveguide as proven in [1]-[4] is realised by single layer printed technology, which simplifies fabrication substantially. The clue is the constitution of one cell with an interdigital series capacitor, which produces an enhanced series capacitance. The eigenvalues are determined by the method presented in  to circumvent a rather time-consuming simulation with a fully numerical eigenmode solver. The dispersion results are moreover validated by the estimation technique Matrix-Pencil (MP)  and the input reflection as well as the radiation behaviour of a fabricated prototype is measured.
SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)

The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drugdrug interactions that appear in biomedical literature. We propose two subtasks for the DDIExtraction 2013 Shared Task challenge: 1) the recognition and classification of drug names and 2) the extraction and classification of their interactions. Both subtasks have been very successful in participation and results. There were 14 teams who submitted a total of 38 runs. The best result reported for the first subtask was F1 of 71.5% and 65.1% for the second one.
Micro-punch technique for treatment of Fordyce spots: a surgical approach for an unpleasant condition of the male genital.

Fordyce spots are ectopic sebaceous glands, ranging between 2 and 3 mm in diameter. These benign lesions are most frequently located in the oral mucosa and the genital skin. Especially in the male genital region they can cause itching, discomfort during sexual activities and are aesthetically unpleasant. So far, a variety of therapeutic procedures have been reported with varying success and recurrence rates. In the present retrospective study (n = 23 patients between 2003 and 2011), we present our surgical approach by means of the micro-punch technique. Using this effective method, we achieved very satisfactory functional and cosmetic results. There were no signs of recurrence during postoperative observations from 12 up to 84 months (median = 51.3 months).
Identifying Product Opportunities Using Social Media Mining: Application of Topic Modeling and Chance Discovery Theory

As an emerging voice of the customer (VOC) containing feedback, such as opinions and expectations about products, social media data have the potential use for product improvement and new product development. However, most prior studies have focused on determining customer concerns, while neglecting to incorporate them into a systematic approach to identify product opportunities. In response, this paper suggests an approach to identify product opportunities from customer reviews in social media. This approach employs topic modeling to identify the product topics discussed by customers from large-scale review posts related to a given product. A keygraph is then constructed based on the co-occurrences among the topics contained in each post. The chance discovery theory is then applied to generate new product opportunities from the chance nodes obtained from the keygraph. Our approach contributes to the systematic ideation process for product opportunity analysis based on large-scale and real-time VOC.
Adaptive Droop Control Applied to Voltage-Source Inverters Operating in Grid-Connected and Islanded Modes

This paper proposes a novel control for voltage-source inverters with the capability to flexibly operate in grid-connected and islanded modes. The control scheme is based on the droop method, which uses some estimated grid parameters such as the voltage and frequency and the magnitude and angle of the grid impedance. Hence, the inverter is able to inject independently active and reactive power to the grid. The controller provides a proper dynamics decoupled from the grid-impedance magnitude and phase. The system is also able to control active and reactive power flows independently for a large range of impedance grid values. Simulation and experimental results are provided in order to show the feasibility of the control proposed.
A Slow-Wave Microstrip Line With a High-Q and a High Dielectric Constant for Millimeter-Wave CMOS Application

A slow-wave microstrip line (S-MSL) designed in 0.13 μm CMOS technology with a high-Q and a high dielectric constant is proposed in this letter. Grounded metal strips with two metal layers are located in zigzags in order to prevent penetration of the electric field into the silicon substrate. These metal strips equate the potentials of the ground planes and thus eliminate the parasitic coupled slotline mode without requiring additional air-bridges. Also, locating the ground planes in the SiO2 layers instead of on the top metal reduces the size of the gap between the signal line and ground planes relative to the conventional structure. This allows relaxation of the metal density rule of the CMOS processes. Measured results for the proposed S-MSL show that the relative permittivity is 25 and the quality factor ranges from 18 to 37.7 between 20 and 60 GHz. The wavelength of the measured 253 μm S-MSL is 4/λ at 60 GHz.
Circular Pulley Versus Variable Radius Pulley: Optimal Design Methodologies and Dynamic Characteristics Analysis

Human-centered robotics has received growing interest in low-impedance actuations. In particular, pneumatic artificial muscles (PAMs) provide compliance and high force-to-weight ratio, which allow for safe actuation. However, several performance drawbacks prevent PAMs from being more pervasive. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs, such as restricted workspace and torque capacity, remain to be addressed. This paper analyzes the characteristics and limitations of PAMs-driven joints and subsequently provides an optimization strategy for circular pulleys (CPs) in order to improve joint torque capacity over a large workspace. In addition to CPs, this paper proposes a design methodology to synthesize a pair of variable radius pulleys (VRPs) for further improvement. Simulation and experimental results show that newly synthesized VRPs significantly improve torque capacity in the enlarged workspace without loss of dynamic performance. Finally, the characteristics of CPs and VRPs are discussed in terms of physical human-robot interaction.
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching

End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be trained for a given disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel crossentropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.
A proactive approach based on online reliability prediction for adaptation of service-oriented systems

Service computing is an emerging technology in System of Systems Engineering (SoS Engineering or SoSE), which regards System as a Service (i.e. SaaS), and aims to construct a robust and value-added complex system by outsourcing external component systems through service composition technology. A service-oriented SoS runs under a dynamic and uncertain environment. To successfully deploy SoS’s run-time quality assurance, online reliability time series prediction, which aims to predict the reliability in near future for a service-oriented SoS arises as a grand challenge in SoS research. In this paper, we tackle the prediction challenge by exploiting two novel prediction models. We adopt motifs-based Dynamic Bayesian Networks (or m DBNs) model to perform one-step-ahead time series prediction, and propose a multi-steps trajectories DBNs (or multi DBNs) to further revise the future reliability prediction. Finally, an proactive adaption strategy is achieved based on the reliability prediction results. Extensive experiments conducted on real-world Web services demonstrate that our models outperform other well-known approaches consistently.
Bandwidth Distributed Denial of Service: Attacks and Defenses

The Internet is vulnerable to bandwidth distributed denial-of-service (BW-DDoS) attacks, wherein many hosts send a huge number of packets to cause congestion and disrupt legitimate traffic. So far, BW-DDoS attacks have employed relatively crude, inefficient, brute force mechanisms; future attacks might be significantly more effective and harmful. To meet the increasing threats, we must deploy more advanced defenses.
START (screening tool to alert doctors to the right treatment)--an evidence-based screening tool to detect prescribing omissions in elderly patients.

Inappropriate prescribing encompasses acts of commission i.e. giving drugs that are contraindicated or unsuitable, and acts of omission i.e. failure to prescribe drugs when indicated due to ignorance of evidence base or other irrational basis e.g. ageism. There are considerable published data on the prevalence of inappropriate prescribing; however, there are no recent published data on the prevalence of acts of omission. The aim of this study was to calculate the prevalence of acts of prescribing omission in a population of consecutively hospitalised elderly people.



A screening tool (screening tool to alert doctors to the right treatment acronym, START), devised from evidence-based prescribing indicators and arranged according to physiological systems was prepared and validated for identifying prescribing omissions in older adults. Data on active medical problems and prescribed medicines were collected in 600 consecutive elderly patients admitted from the community with acute illness to a teaching hospital. On identification of an omitted medication, the patient's medical records were studied to look for a valid reason for the prescribing omission.



Using the START list, we found one or more prescribing omissions in 57.9% of patients. In order of prevalence, the most common prescribing omissions were: statins in atherosclerotic disease (26%), warfarin in chronic atrial fibrillation (9.5%), anti-platelet therapy in arterial disease (7.3%) and calcium/vitamin D supplementation in symptomatic osteoporosis (6%).



Failure to prescribe appropriate medicines is a highly prevalent problem among older people presenting to hospital with acute illness. A validated screening tool (START) is one method of systematically identifying appropriate omitted medicines in clinical practice.
Mirroring others' emotions relates to empathy and interpersonal competence in children

The mirror neuron system (MNS) has been proposed to play an important role in social cognition by providing a neural mechanism by which others' actions, intentions, and emotions can be understood. Here functional magnetic resonance imaging was used to directly examine the relationship between MNS activity and two distinct indicators of social functioning in typically-developing children (aged 10.1 years+/-7 months): empathy and interpersonal competence. Reliable activity in pars opercularis, the frontal component of the MNS, was elicited by observation and imitation of emotional expressions. Importantly, activity in this region (as well as in the anterior insula and amygdala) was significantly and positively correlated with established behavioral measures indexing children's empathic behavior (during both imitation and observation) and interpersonal skills (during imitation only). These findings suggest that simulation mechanisms and the MNS may indeed be relevant to social functioning in everyday life during typical human development.
Sentiment analysis of a document using deep learning approach and decision trees

The given paper describes modern approach to the task of sentiment analysis of movie reviews by using deep learning recurrent neural networks and decision trees. These methods are based on statistical models, which are in a nutshell of machine learning algorithms. The fertile area of research is the application of Google's algorithm Word2Vec presented by Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean in 2013. The main idea of Word2Vec is the representations of words with the help of vectors in such manner that semantic relationships between words preserved as basic linear algebra operations. The extra advantage of the mentioned algorithm above the alternatives is computational efficiency. This paper focuses on using Word2Vec model for text classification by their sentiment type.
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.
Regime Changes and Financial Markets

Regime switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several periods after such a change. While the regimes captured by regime switching models are identified by an econometric procedure, they often correspond to different periods in regulation, policy, and other secular changes. In empirical estimates, the regime switching means, volatilities, autocorrelations, and cross-covariances of asset returns often differ across regimes, which allow regime switching models to capture the stylized behavior of many financial series including fat tails, heteroskedasticity, skewness, and time-varying correlations. In equilibrium models, regimes in fundamental processes, like consumption or dividend growth, strongly affect the dynamic properties of equilibrium asset prices and can induce non-linear risk-return trade-offs. Regime switches also lead to potentially large consequences for investors’ optimal portfolio choice.
Prior Art Search and its Evaluation

Prior Art Search is an information seeking task where searchers, for instance patent examiners, search for published literature to determine whether the claimed invention in a patent application is novel. In Prior Art Search, search tasks are often timesensitive and consist of rich information needs with multiple aspects/subtopics. In this thesis, we explore information retrieval techniques and evaluation metrics for prior art search. The work consists of three parts. Given a patent application document, we rst manage to retrieve relevant patent documents by performing retrieval at both the document level and the passage level. At the document level, we focus on automatically formulating e ective search queries. The queries are formed by extracting terms from claims, titles and hyphenating phrases in a patent application and re ned based on Inverse Document Frequency (IDF) and Part-of-Speech (POS) tagging. At the passage level, we propose a TF-IDF-based retrieval algorithm to calculate the relevance score for each passage and select the most relevant passages. Second, we propose a novel evaluation metric for prior art search. The new evaluation metric, termed the Cube Test (CT), is based on the proposed conceptual user utility model the Water Filling Model, which describes the process of prior art search. We compare our metric with existing prior art search evaluation metrics, as well as existing Web search evaluation metrics in correlation and discriminative power. Experiment results show that our metric e ectively captures the characteristics of prior art search.