nils-herrmann/clean_scirepeval huggingface dataset:
EFFECT OF HIGH-SPEED AND PLYOMETRIC TRAINING FOR 13-YEAR-OLD MALE SOCCER PLAYERS ON ACCELERATION AND AGILITY PERFORMANCE

Acceleration, sprint and agility performance are crucial in sports like soccer. There are few studies regarding the effect of training on youth soccer players in agility performance and in sprint distances shorter than 30 meter. Therefore, the aim of the recent study was to examine the effect of a high-intensity sprint and plyometric training program on 13-year-old male soccer players. A training group of 14 adolescent male soccer players, mean age (±SD) 13.5 years (±0.24) followed an eight week intervention program for one hour per week, and a group of 12 adolescent male soccer players of corresponding age, mean age 13.5 years (±0.23) served as control a group. Preand post-tests assessed 10-m linear sprint, 20-m linear sprint and agility performance. Results showed a significant improvement in agility performance, pre 8.23 s (±0.34) to post 7.69 s (± 0.34) (p<0.01), and a significant improvement in 0-20m linear sprint, pre 3.54s (±0.17) to post 3.42s (±0.18) (p<0.05). In 0-10m sprint the participants also showed an improvement, pre 2.02s (±0.11) to post 1.96s (± 0.11), however this was not significant. The correlation between 10-m sprint and agility was r = 0.53 (p<0.01), and between 20-m linear sprint and agility performance, r = 0.67 (p<0.01). The major finding in the study is the significant improvement in agility performance and in 0-20 m linear sprint in the intervention group. These findings suggest that organizing the training sessions with short-burst high-intensity sprint and plyometric exercises interspersed with adequate recovery time, may result in improvements in both agility and in linear sprint performance in adolescent male soccer players. Another finding is the correlation between linear sprint and agility performance, indicating a difference when compared to adults. 4 | Mathisen: EFFECT OF HIGH-SPEED...
Scrap your boilerplate: a practical design pattern for generic programming

We describe a design pattern for writing programs that traverse data structures built from rich mutually-recursive data types. Such programs often have a great deal of "boilerplate" code that simply walks the structure, hiding a small amount of "real" code that constitutes the reason for the traversal.Our technique allows most of this boilerplate to be written once and for all, or even generated mechanically, leaving the programmer free to concentrate on the important part of the algorithm. These generic programs are much more adaptive when faced with data structure evolution because they contain many fewer lines of type-specific code.Our approach is simple to understand, reasonably efficient, and it handles all the data types found in conventional functional programming languages. It makes essential use of rank-2 polymorphism, an extension found in some implementations of Haskell. Further it relies on a simple type-safe cast operator.
Automated Versus Manual Blood Pressure Measurement: A Randomized Crossover Trial in the Emergency Department of a Tertiary Care Hospital in Karachi, Pakistan: Are Third World Countries Ready for the Change?

Hypertension has proven to be a strong liability with 13.5% of all mortality worldwide being attributed to elevated blood pressures in 2001. An accurate blood pressure measurement lies at the crux of an appropriate diagnosis. Despite the mercury sphygmomanometer being the gold standard, the ongoing deliberation as to whether mercury sphygmomanometers should be replaced with the automated oscillometric devices stems from the risk mercury poses to the environment.



This study was performed to check the validity of automated oscillometric blood pressure measurements as compared to the manual blood pressure measurements in Karachi, Pakistan.



Blood pressure was recorded in 200 individuals aged 15 and above using both, an automated oscillometric blood pressure device (Dinamap Procare 100) and a manual mercury sphygmomanometer concomitantly. Two nurses were assigned to each patient and the device, arm for taking the reading and nurses were randomly determined. SPSS version 20 was used for analysis. Mean and standard deviation of the systolic and diastolic measurements from each modality were compared to each other and P values of 0.05 or less were considered to be significant. Validation criteria of British Hypertension Society (BHS) and the US Association for the Advancement of Medical Instrumentation (AAMI) were used.



Two hundred patients were included. The mean of the difference of systolic was 8.54 ± 9.38 while the mean of the difference of diastolic was 4.21 ± 7.88. Patients were further divided into three groups of different systolic blood pressure <= 120, > 120 to = 150 and > 150, their means were 6.27 ± 8.39 (p-value 0.175), 8.91 ± 8.96 (p-value 0.004) and 10.98 ± 10.49 (p-value 0.001) respectively. In our study 89 patients were previously diagnosed with hypertension; their difference of mean systolic was 9.43 ± 9.89 (p-value 0.000) and difference of mean diastolic was 4.26 ± 7.35 (p-value 0.000).



Systolic readings from a previously validated device are not reliable when used in the ER and they show a higher degree of incongruency and inaccuracy when they are used outside validation settings. Also, readings from the right arm tend to be more precise.
An Integrated Signature-Based Framework for Efficient Visual Similarity Detection and Measurement in Video Shots

This article presents a framework for speedy video matching and retrieval through detection and measurement of visual similarity. The framework’s efficiency stems from its power to encode a given shot content into a compact fixed-length signature that helps in robust real-time matching. Separate scene and motion signatures are developed and fused together to fully represent and match respective video shots. Scene information is captured through the Statistical Dominant Color Profile (SDCP), while motion information is captured through a graph-based signature called the Dominant Color Graph Profile (DCGP). The SDCP is a fixed-length compact signature that statistically encodes the colors’ spatiotemporal patterns across video frames. The DCGP is a fixed-length signature that records and tracks the gray levels across subsampled video frames, where the graph structural properties are used to extract the signature values. Finally, the overall video signature is generated by fusing the individual scene and motion signatures. The signature-based aspect of the proposed framework is the key to its high matching speed (> 2000 fps) compared to current techniques that rely on exhaustive processing. To maximize the benefit of the framework, compressed-domain videos are utilized as a case study following their wide availability. However, the framework avoids full video decompression and operates on tiny frames rather than full-size decompressed frames. Experiments on various standard and challenging dataset groups show the framework’s robust performance in terms of both retrieval and computational performance.
A Semiautomatic Approach for Accurate and Low-Effort Spreadsheet Data Extraction

Spreadsheets contain valuable data on many topics, but they are difficult to integrate with other sources. Converting spreadsheet data to the relational model would allow relational integration tools to be used, but using manual methods to do this requires large amounts of work for each integration candidate. Automatic data extraction would be useful but it is very challenging: spreadsheet designs generally requires human knowledge to understand the metadata being described. Even if it is possible to obtain this metadata information automatically, a single mistake can yield an output relation with a huge number of incorrect tuples. We propose a two-phase semiautomatic system that extracts accurate relational metadata while minimizing user effort. Based on conditional random fields (CRFs), our system enables downstream spreadsheet integration applications. First, the automatic extractor uses hints from spreadsheets’ graphical style and recovered metadata to extract the spreadsheet data as accurately as possible. Second, the interactive repair component identifies similar regions in distinct spreadsheets scattered across large spreadsheet corpora, allowing a user’s single manual repair to be amortized over many possible extraction errors. Through our method of integrating the repair workflow into the extraction system, a human can obtain the accurate extraction with just 31% of the manual operations required by a standard classification based technique. We demonstrate and evaluate our system using two corpora: more than 1,000 spreadsheets published by the US government and more than 400,000 spreadsheets downloaded from the Web.
Security , Privacy & Trust in Internet of Things : the road ahead

Internet of Things (IoT) is characterized by heterogeneous technologies, which concur to the provisioning of innovative services in various application domains. In this scenario, the satisfaction of security and privacy requirements plays a fundamental role. Such requirements include data confidentiality and authentication, access control within the IoT network, privacy and trust among users and things, and the enforcement of security and privacy policies. Traditional security countermeasures cannot be directly applied to IoT technologies due to the different standards and communication stacks involved. Moreover, the high number of interconnected devices arises scalability issues; therefore a flexible infrastructure is needed able to deal with security threats in such a dynamic environment. In this survey we present the main research challenges and the existing solutions in the field of IoT security, identifying open issues, and suggesting some hints for future research.
Reconfigurable Square-Ring Patch Antenna With Pattern Diversity

A single-feed reconfigurable square-ring patch antenna with pattern diversity is presented. The antenna structure has four shorting walls placed respectively at each edge of the square-ring patch, in which two shorting walls are directly connected to the patch and the others are connected to the patch via pin diodes. By controlling the states of the pin diodes, the antenna can be operated at two different modes: monopolar plat-patch and normal patch modes; moreover, the 10 dB impedance bandwidths of the two modes are overlapped. Consequently, the proposed antenna allows its radiation pattern to be switched electrically between conical and broadside radiations at a fixed frequency. Detailed design considerations of the proposed antenna are described. Experimental and simulated results are also shown and discussed
What is Needed to Develop Critical Thinking in Schools ?

Starting with the fact that school education has failed to become education for critical thinking and that one of the reasons for that could be in how education for critical thinking is conceptualised, this paper presents: (1) an analysis of the predominant approach to education for critical thinking through the implementation of special programs and methods, and (2) an attempt to establish different approaches to education for critical thinking. The overview and analysis of understanding education for developing critical thinking as the implementation of special programs reveal that it is perceived as a decontextualised activity, reduced to practicing individual intellectual skills. Foundations for a different approach, which could be characterised as the ‘education for critical competencies’, are found in ideas of critical pedagogy and open curriculum theory. This approach differs from the predominant approach in terms of how the nature and purpose of critical thinking and education for critical thinking are understood. In the approach of education for critical competencies, it is not sufficient to introduce special programs and methods for the development of critical thinking to the existing educational system. This approach emphasises the need to question and reconstruct the status, role, and power of pupils and teachers in the teaching process, but also in the process of curriculum development.
A unified data mining solution for authorship analysis in anonymous textual communications

The cyber world provides an anonymous environment for criminals to conduct malicious activities such as spamming, sending ransom e-mails, and spreading botnet malware. Often, these activities involve textual communication between a criminal and a victim, or between criminals themselves. The forensic analysis of online textual documents for addressing the anonymity problem called authorship analysis is the focus of most cybercrime investigations. Authorship analysis is the statistical study of linguistic and computational characteristics of the written documents of individuals. This paper is the first work that presents a unified data mining solution to address authorship analysis problems based on the concept of frequent pattern-based writeprint. Extensive experiments on real-life data suggest that our proposed solution can precisely capture the writing styles of individuals. Furthermore, the writeprint is effective to identify the author of an anonymous text from ∗Corresponding author Email addresses: iqbal_f@ciise.concordia.ca (Farkhund Iqbal), h_binsal@ciise.concordia.ca (Hamad Binsalleeh), fung@ciise.concordia.ca (Benjamin C. M. Fung), debbabi@ciise.concordia.ca (Mourad Debbabi)  2011 a group of suspects and to infer sociolinguistic characteristics of the author.
Targeting Infeasibility Questions on Obfuscated Codes

Software deobfuscation is a crucial activity in security analysis and especially, in malware analysis. While standard static and dynamic approaches suffer from well-known shortcomings, Dynamic Symbolic Execution (DSE) has recently been proposed has an interesting alternative, more robust than static analysis and more complete than dynamic analysis. Yet, DSE addresses certain kinds of questions encountered by a reverser namely feasibility questions. Many issues arising during reverse, e.g. detecting protection schemes such as opaque predicates fall into the category of infeasibility questions. In this article, we present the Backward-Bounded DSE, a generic, precise, efficient and robust method for solving infeasibility questions. We demonstrate the benefit of the method for opaque predicates and call stack tampering, and give some insight for its usage for some other protection schemes. Especially, the technique has successfully been used on state-of-the-art packers as well as on the government-grade X-Tunnel malware – allowing its entire deobfuscation. Backward-Bounded DSE does not supersede existing DSE approaches, but rather complements them by addressing infeasibility questions in a scalable and precise manner. Following this line, we propose sparse disassembly, a combination of Backward-Bounded DSE and static disassembly able to enlarge dynamic disassembly in a guaranteed way, hence getting the best of dynamic and static disassembly. This work paves the way for robust, efficient and precise disassembly tools for heavily-obfuscated binaries.
Ameloblastic Fibrosarcoma of the Mandible With Distant Metastases.

Ameloblastic fibrosarcoma is a mixed odontogenic tumor that can originate de novo or from a transformed ameloblastic fibroma. This report describes the case of a 34-year-old woman with a recurrent, rapidly growing, debilitating lesion. This lesion appeared as a large painful mandibular swelling that filled the oral cavity and extended to the infratemporal fossa. The lesion had been previously misdiagnosed as ameloblastoma. Twenty months after final surgery and postoperative chemotherapy, lung metastases were diagnosed after she reported respiratory signs and symptoms.
SMOTE for Regression

Several real world prediction problems involve forecasting rare values of a target variable. When this variable is nominal we have a problem of class imbalance that was already studied thoroughly within machine learning. For regression tasks, where the target variable is continuous, few works exist addressing this type of problem. Still, important application areas involve forecasting rare extreme values of a continuous target variable. This paper describes a contribution to this type of tasks. Namely, we propose to address such tasks by sampling approaches. These approaches change the distribution of the given training data set to decrease the problem of imbalance between the rare target cases and the most frequent ones. We present a modification of the well-known Smote algorithm that allows its use on these regression tasks. In an extensive set of experiments we provide empirical evidence for the superiority of our proposals for these particular regression tasks. The proposed SmoteR method can be used with any existing regression algorithm turning it into a general tool for addressing problems of forecasting rare extreme values of a continuous target variable.
Geographic expansion of dengue: the impact of international travel.

Dengue has emerged as an international public health problem. Reasons for the resurgence of dengue in the tropics and subtropics are complex and include unprecedented urbanization with substandard living conditions, lack of vector control, virus evolution, and international travel. Of all these factors, urbanization has probably had the most impact on the amplification of dengue within a given country, and travel has had the most impact for the spread of dengue from country to country and continent to continent. Epidemics of dengue, their seasonality, and oscillations over time are reflected by the epidemiology of dengue in travelers. Sentinel surveillance of travelers could augment existing national public health surveillance systems.
CoMICon: A Co-Operative Management System for Docker Container Images

Docker containers are becoming an attractive implementation choice for next-generation microservices-based applications. When provisioning such an application, container (microservice) instances need to be created from individual container images. Starting a container on a node, where images are locally available, is fast but it may not guarantee the quality of service due to insufficient resources. When a collection of nodes are available, one can select a node with sufficient resources. However, if the selected node does not have the required image, downloading the image from a different registry increases the provisioning time. Motivated by these observations, in this paper, we present CoMICon, a system for co-operative management of Docker images among a set of nodes. The key features of CoMICon are: (1) it enables a co-operative registry among a set of nodes, (2) it can store or delete images partially in the form of layers, (3) it facilitates the transfer of image layers between registries, and (4) it enables distributed pull of an image while starting a container. Using these features, we describe—(i) high availability management of images and (ii) provisioning management of distributed microservices based applications. We extensively evaluate the performance of CoMICon using 142 real, publicly available images from Docker hub. In contrast to state-of-the-art full image based approach, CoMICon can increase the number of highly available images up to 3x while reducing the application provisioning time by 28% on average.
FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting.
A survey on haptic technologies for mobile augmented reality

Augmented Reality (AR) and Mobile Augmented Reality (MAR) applications have gained much research and industry attention these days. The mobile nature of MAR applications limits users’ interaction capabilities such as inputs, and haptic feedbacks. This survey reviews current research issues in the area of human computer interaction for MAR and haptic devices. The survey first presents human sensing capabilities and their applicability in AR applications. We classify haptic devices into two groups according to the triggered sense: cutaneous/tactile: touch, active surfaces, and mid-air; kinesthetic: manipulandum, grasp, and exoskeleton. Due to the mobile capabilities of MAR applications, we mainly focus our study on wearable haptic devices for each category and their AR possibilities. To conclude, we discuss the future paths that haptic feedbacks should follow for MAR applications and their challenges.
Risk and resilience for psychological distress amongst unaccompanied asylum seeking adolescents.

To investigate the level of posttraumatic stress and depressive symptoms, and background risk and protective factors that might increase or ameliorate this distress amongst unaccompanied asylum-seeking children and adolescents (UASC).



Cross-sectional survey carried out in London. Participants were 78 UASC aged 13-18 years, predominantly from the Balkans and Africa, compared with 35 accompanied refugee children. Measures included self-report questionnaires of war trauma, posttraumatic stress and depressive symptoms.



UASC had experienced high levels of losses and war trauma, and posttraumatic stress symptoms. Predictors of high posttraumatic symptoms included low-support living arrangements, female gender and trauma events, and increasing age only amongst the UASC. High depressive scores were associated with female gender, and region of origin amongst the UASC.



UASC might have less psychological distress if offered high-support living arrangements and general support as they approach the age of 18 years, but prospective studies are required to investigate the range of risk and protective factors.
Algae as nutritional and functional food sources: revisiting our understanding

Global demand for macroalgal and microalgal foods is growing, and algae are increasingly being consumed for functional benefits beyond the traditional considerations of nutrition and health. There is substantial evidence for the health benefits of algal-derived food products, but there remain considerable challenges in quantifying these benefits, as well as possible adverse effects. First, there is a limited understanding of nutritional composition across algal species, geographical regions, and seasons, all of which can substantially affect their dietary value. The second issue is quantifying which fractions of algal foods are bioavailable to humans, and which factors influence how food constituents are released, ranging from food preparation through genetic differentiation in the gut microbiome. Third is understanding how algal nutritional and functional constituents interact in human metabolism. Superimposed considerations are the effects of harvesting, storage, and food processing techniques that can dramatically influence the potential nutritive value of algal-derived foods. We highlight this rapidly advancing area of algal science with a particular focus on the key research required to assess better the health benefits of an alga or algal product. There are rich opportunities for phycologists in this emerging field, requiring exciting new experimental and collaborative approaches.