nils-herrmann/clean_scirepeval huggingface dataset:
Cyber security risks for minors: A taxonomy and a software architecture

The explosion of the Internet provides a variety possibilities for communication, finding information and many other activities, turning into an essential tool in our modern everyday life. However, its huge expansion globally has created some serious safety issues, which require a special approach. One of these issues and perhaps the most important one concerns the safety of children on the Internet, as they are more exposed to dangers and threats in comparison with adults. In order to design effective measures against these threats and dangers deep understanding of minors' activities on the Internet, along with their motivation, is a first necessary step. It is shown in this report that minors' Internet activity tends heavily, and in an increasing manner, towards Online Social Networks (OSN). Thus, Internet filtering techniques designed and applied so far for child online protection need to be reconsidered and redesigned in a smarter way such as data analytics, advanced content analysis and data mining techniques are incorporated. OSN fake account identification, sexual content detection and flagging of multiple OSN accounts of the same person are examples that require such sophisticated techniques. This study deals with a literature review concerning the Internet activity and motivation of use by minors and presents in a coherent manner the identified risks and threats that children using the web and online social networks are exposed to. It also presents a systematic process for designing and developing modern and state of the art techniques to prevent minors' exposure to those risks and dangers.
Evaluation of workspace of a spherical robotic wrist

In this paper, the problem of workspace analysis of spherical parallel manipulators (SPMs) is addressed with respect to a spherical robotic wrist. The wrist is designed following a modular approach and capable of a unlimited rotation of rolling. An equation dealing with singularity surfaces is derived and branches of the singularity surfaces are identified. By using the Euler parameters, the singularity surfaces are generated in a solid unit sphere, the workspace analysis and dexterity evaluation hence being able to be performed in the confined region of the sphere. Examples of workspace evaluation of the spherical wrist and general SPMs are included to demonstrate the application of the proposed method.
ST-GAN: Unsupervised Facial Image Semantic Transformation Using Generative Adversarial Networks

The mutual information term I(c;G(z, c)) requires the posterior P (c|G(z, c)), thus, it is hard to maximize directly. ST-GAN uses a technique called Variational Information Maximization Barber and Agakov (2003) by defining an auxiliary distribution Q(c|x) to approximate P (c|x) as InfoGAN Chen et al. (2016) does. The variational lower bound, LI(G,Q), of the local mutual information I(c;G(z, c)) is defined as:
[Lumbar disc herniation and andrological diseases].

Lumbar disc herniation is a common male disease. In the past, More academic attention was directed to its relationship with lumbago and leg pain than to its association with andrological diseases. Studies show that central lumber intervertebral disc herniation may cause cauda equina injury and result in premature ejaculation, erectile dysfunction, chronic pelvic pain syndrome, priapism, and emission. This article presents an overview on the correlation between central lumbar intervertebral disc herniation and andrological diseases, focusing on the aspects of etiology, pathology, and clinical progress, hoping to invite more attention from andrological and osteological clinicians.
Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

Object detection and segmentation represents the basis for many tasks in computer and machine vision. In biometric recognition systems the detection of the region-of-interest (ROI) is one of the most crucial steps in the overall processing pipeline, significantly impacting the performance of the entire recognition system. Existing approaches to ear detection, for example, are commonly susceptible to the presence of severe occlusions, ear accessories or variable illumination conditions and often deteriorate in their performance if applied on ear images captured in unconstrained settings. To address these shortcomings, we present in this paper a novel ear detection technique based on convolutional encoder-decoder networks (CEDs). For our technique, we formulate the problem of ear detection as a two-class segmentation problem and train a convolutional encoder-decoder network based on the SegNet architecture to distinguish between image-pixels belonging to either the ear or the non-ear class. The output of the network is then post-processed to further refine the segmentation result and return the final locations of the ears in the input image. Different from competing techniques from the literature, our approach does not simply return a bounding box around the detected ear, but provides detailed, pixel-wise information about the location of the ears in the image. Our experiments on a dataset gathered from the web (a.k.a. in the wild) show that the proposed technique ensures good detection results in the presence of various covariate factors and significantly outperforms the existing state-of-the-art.
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

Learning policies on data synthesized by models can in principle quench the thirst of reinforcement learning algorithms for large amounts of real experience, which is often costly to acquire. However, simulating plausible experience de novo is a hard problem for many complex environments, often resulting in biases for modelbased policy evaluation and search. Instead of de novo synthesis of data, here we assume logged, real experience and model alternative outcomes of this experience under counterfactual actions, i.e. actions that were not actually taken. Based on this, we propose the Counterfactually-Guided Policy Search (CF-GPS) algorithm for learning policies in POMDPs from off-policy experience. It leverages structural causal models for counterfactual evaluation of arbitrary policies on individual off-policy episodes. CF-GPS can improve on vanilla model-based RL algorithms by making use of available logged data to de-bias model predictions. In contrast to off-policy algorithms based on Importance Sampling which re-weight data, CF-GPS leverages a model to explicitly consider alternative outcomes, allowing the algorithm to make better use of experience data. We find empirically that these advantages translate into improved policy evaluation and search results on a non-trivial grid-world task. Finally, we show that CF-GPS generalizes the previously proposed Guided Policy Search and that reparameterization-based algorithms such Stochastic Value Gradient can be interpreted as counterfactual methods.
Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs

Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we reconstruct the face image based on the synthesized sketch. The proposed Attribute2Sketch2Face framework, which is based on a combination of deep Conditional Variational Autoencoder (CVAE) and Generative Adversarial Networks (GANs), consists of three stages: (1) Synthesis of facial sketch from attributes using a CVAE architecture, (2) Enhancement of coarse sketches to produce sharper sketches using a GANbased framework, and (3) Synthesis of face from sketch using another GAN-based network. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed attributebased three stage face synthesis method.
AN ANALYSIS FOR CREDIT RATING AND MOMENTUM STRATEGY

In recent years, investors are increasingly concerned about whether the credit risk will affect the return on investment. This paper discusses the credit rating and momentum investment strategy relationship. The research period is from January 2005 to December 2010, and the sample is the ordinary shares of companies listed on Taiwan Stock Exchange (TSE). By calculating the cumulative returns of the investment portfolio of the holding period, and grouping the research samples by credit rating, this paper tests the relationship between credit rating and momentum investment strategy in Taiwan’s stock market. Second, in the exploration of the factors affecting credit rating and stock returns, this paper uses variables including firm size, financial leverage, turnover rate, company age and industry to analyze the impact of factors including information asymmetry and industry on the investment strategy. Moreover, this paper probes into the impact of January Effect and business cycle on credit rating. The empirical results reveal that Taiwan’s stock market does not have the momentum effect, although there is reverse investment strategy. In other words, the returns of stocks of investment portfolio of better credit rating are higher than those of poorer credit rating; and the results are reverse if the reverse investment strategy is applied. The empirical results are not affected by adding variables such as firm size, financial leverage, turnover rate, company age and industry. Hence, momentum investment strategy of Taiwan’s stock market is not affected by credit rating.
Direct band gap silicon allotropes.

Elemental silicon has a large impact on the economy of the modern world and is of fundamental importance in the technological field, particularly in solar cell industry. The great demand of society for new clean energy and the shortcomings of the current silicon solar cells are calling for new materials that can make full use of the solar power. In this paper, six metastable allotropes of silicon with direct or quasidirect band gaps of 0.39-1.25 eV are predicted by ab initio calculations at ambient pressure. Five of them possess band gaps within the optimal range for high converting efficiency from solar energy to electric power and also have better optical properties than the Si-I phase. These Si structures with different band gaps could be applied to multiple p-n junction photovoltaic modules.
Safety and antioxidant activity of a pomegranate ellagitannin-enriched polyphenol dietary supplement in overweight individuals with increased waist size.

The consumption of pomegranate juice (PJ), a rich source of antioxidant polyphenols, has grown tremendously due to its reported health benefits. Pomegranate extracts, which incorporate the major antioxidants found in pomegranates, namely, ellagitannins, have been developed as botanical dietary supplements to provide an alternative convenient form for consuming the bioactive polyphenols found in PJ. Despite the commercial availability of pomegranate extract dietary supplements, there have been no studies evaluating their safety in human subjects. A pomegranate ellagitannin-enriched polyphenol extract (POMx) was prepared for dietary supplement use and evaluated in two pilot clinical studies. Study 1 was designed for safety assessment in 64 overweight individuals with increased waist size. The subjects consumed either one or two POMx capsules per day providing 710 mg (435 mg of gallic acid equivalents, GAEs) or 1420 mg (870 mg of GAEs) of extracts, respectively, and placebo (0 mg of GAEs). Safety laboratory determinations, including complete blood count (CBC), chemistry, and urinalysis, were made at each of three visits. Study 2 was designed for antioxidant activity assessment in 22 overweight subjects by administration of two POMx capsules per day providing 1000 mg (610 mg of GAEs) of extract versus baseline measurements. Measurement of antioxidant activity as evidenced by thiobarbituric acid reactive substances (TBARS) in plasma were measured before and after POMx supplementation. There was evidence of antioxidant activity through a significant reduction in TBARS linked with cardiovascular disease risk. There were no serious adverse events in any subject studied at either site. These studies demonstrate the safety of a pomegranate ellagitannin-enriched polyphenol dietary supplement in humans and provide evidence of antioxidant activity in humans.
Scheduling algorithms and operating systems support for real-time systems

This paper summarizes the state of the real-time eld in the areas of scheduling and operating system kernels. Given the vast amount of work that has been done by both the operations research and computer science communities in the scheduling area, we discuss four paradigms underlying the scheduling approaches and present several exemplars of each. The four paradigms are: static table-driven scheduling, static priority preemptive scheduling, dynamic planning-based scheduling, and dynamic best-e ort scheduling. In the operating system context, we argue that most of the proprietary commercial kernels as well as real-time extensions to timesharing operating system kernels do not t the needs of predictable real-time systems. We discuss several research kernels that are currently being built to explicitly meet the needs of real-time applications. This material is based upon work supported by the National Science Foundation under grants CDA8922572 and IRI 9208920, and by the O ce of Naval Research under grant N00014-92-J-1048.
A 2.5 GHz low phase noise oscillator design in 65nm CMOS technology with reduced current consumption

A 2.5 GHz low phase noise oscillator is presented in this paper. The oscillator was design using a Solidly Mounted BAW Resonator (SMR BAW) as resonant element. The resonator exhibits a parallel resonance Q factor around 1300 at 2.54 GHz. The core oscillator was designed using STMicroelectronics 65 nm CMOS technology. It exhibits 632 mV output (zero-to-pic) with phase noise performance of -92 dBc/Hz, -109 dBc/Hz, and -130 dBc/Hz at 2 KHz, 10 KHz and 100 KHz respectively. It consumes 1 mA from a 1.2 V source.
Adaptive image compression in wireless sensor networks

The main obstacle to transmit images in wireless sensor networks is the lack of an appropriate strategy for processing the large volume of data such as images. The high rate packets errors because of what numbers very high packets carrying the data of the captured images and the need for retransmission in case of errors, and more, the energy reserve and band bandwidth is insufficient to accomplish these tasks. This paper presents new effective technique called “Background subtraction” to compress, process and transmit the images in a wireless sensor network. The practical results show the effectiveness of this approach to make the image compression in the networks of wireless sensors achievable, reliable and efficient in terms of energy and the minimization of amount of image data.
Testability Analysis of a UML Class Diagram

Design-for-testability is a very important issue in software engineering. It becomes crucial in the case of OO designs where control flows are generally not hierarchical, but are diffuse and distributed over the whole architecture. In this paper, we concentrate on detecting, pinpointing and suppressing potential testability weaknesses of a UML class diagram. The attribute significant from design testability is called “class interaction”: it appears when potentially concurrent client/supplier relationships between classes exist in the system. These interactions point out parts of the design that need to be improved, driving structural modifications or constraints specifications, to reduce the final testing effort.
Globalization and health: a framework for analysis and action.

Globalization is a key challenge to public health, especially in developing countries, but the linkages between globalization and health are complex. Although a growing amount of literature has appeared on the subject, it is piecemeal, and suffers from a lack of an agreed framework for assessing the direct and indirect health effects of different aspects of globalization. This paper presents a conceptual framework for the linkages between economic globalization and health, with the intention that it will serve as a basis for synthesizing existing relevant literature, identifying gaps in knowledge, and ultimately developing national and international policies more favourable to health. The framework encompasses both the indirect effects on health, operating through the national economy, household economies and health-related sectors such as water, sanitation and education, as well as more direct effects on population-level and individual risk factors for health and on the health care system. Proposed also is a set of broad objectives for a programme of action to optimize the health effects of economic globalization. The paper concludes by identifying priorities for research corresponding with the five linkages identified as critical to the effects of globalization on health.
Hardware implementation of surface electromyogram signal processing: A survey

This paper surveys the previous and ongoing research on surface electromyogram (sEMG) signal processing implementation through various hardware platforms. The development of system that incorporates sEMG analysis capability is essential in rehabilitation devices, prosthesis arm/limb and pervasive healthcare in general. Most advanced EMG signal processing algorithms rely heavily on computational resource of a PC that negates the elements of portability, size and power dissipation of a pervasive healthcare system. Signal processing techniques applicable to sEMG are discussed with aim for proper execution in platform other than full-fledge PC. Performance and design parameters issues in some hardware implementation are also being pointed up. The paper also outlines the trends and alternatives solutions in developing portable and efficient EMG signal processing hardware.
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL

Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickly in the face of unexpected changes. Deep neural network models allow us to represent very complex functions, but lack this capacity for rapid online adaptation. The goal in this paper is to develop a method for continual online learning from an incoming stream of data, using deep neural network models. We formulate an online learning procedure that uses stochastic gradient descent to update model parameters, and an expectation maximization algorithm with a Chinese restaurant process prior to develop and maintain a mixture of models to handle non-stationary task distributions. This allows for all models to be adapted as necessary, with new models instantiated for task changes and old models recalled when previously seen tasks are encountered again. Furthermore, we observe that meta-learning can be used to meta-train a model such that this direct online adaptation with SGD is effective, which is otherwise not the case for large function approximators. In this work, we apply our meta-learning for online learning (MOLe) approach to model-based reinforcement learning, where adapting the predictive model is critical for control; we demonstrate that MOLe outperforms alternative prior methods, and enables effective continuous adaptation in non-stationary task distributions such as varying terrains, motor failures, and unexpected disturbances. Videos available at: https://sites.google.com/berkeley.edu/onlineviameta
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

Deep belief nets have been successful in modeling handwritten characters, but it has proved more difficult to apply them to real images. The problem lies in the restricted Boltzmann machine (RBM) which is used as a module for learning deep belief nets one layer at a time. The Gaussian-Binary RBMs that have been used to model real-valued data are not a good way to model the covariance structure of natural images. We propose a factored 3-way RBM that uses the states of its hidden units to represent abnormalities in the local covariance structure of an image. This provides a probabilistic framework for the widely used simple/complex cell architecture. Our model learns binary features that work very well for object recognition on the “tiny images” data set. Even better features are obtained by then using standard binary RBM’s to learn a deeper model.
How to Engage Users through Gamification: The Prevalent Effects of Playing and Mastering over Competing

Over recent years, gamification has been a frequent strategy to increase user engagement. Gamification of systems is usually associated with incorporating mechanisms for attributing points and badges to guide users' behaviors. However, since the dawn of the digital game industry in the 1980's, Malone's work has shown that the desire to play and master a game are important motivations to engage users. This paper aims to analyze the most engaging factors for gamers in the current context of technology. Using a sample of 717 users whose game preferences were classified into eight categories, representing different emphasis on playing, mastering, and competing, results show that competing is the least important factor to motivate engagement. As a consequence, we question the relevance of some of the most used gamification strategies like attributing points, badges, and reputation to participants. Additionally, we offer some suggestions for development of gamification of systems.
Pregnancy success following surgical correction of imperforate hymen and complete transverse vaginal septum.

Pregnancy success was evaluated in 48 women following surgical correction of a vaginal obstruction due to imperforate hymen (N = 22) or to a complete transverse vaginal septum (N = 26). Pregnancy success was more likely to occur following surgical correction of imperforate hymen (P less than .05). Patients with a complete transverse septum in the middle or upper vagina were less likely to conceive than were patients with a septum in the lower vagina. Prompt diagnosis and surgical correction to drain accumulated blood may preserve preserve fertility possibly through the prevention of endometriosis.