nils-herrmann/clean_scirepeval huggingface dataset:
Virtualizing I/O Devices on VMware Workstation's Hosted Virtual Machine Monitor

Virtual machines were developed by IBM in the 1960’s to provide concurrent, interactive access to a mainframe computer. Each virtual machine is a replica of the underlying physical machine and users are given the illusion of running directly on the physical machine. Virtual machines also provide benefits like isolation and resource sharing, and the ability to run multiple flavors and configurations of operating systems. VMware Workstation brings such mainframe-class virtual machine technology to PC-based desktop and workstation computers. This paper focuses on VMware Workstation’s approach to virtualizing I/O devices. PCs have a staggering variety of hardware, and are usually pre-installed with an operating system. Instead of replacing the pre-installed OS, VMware Workstation uses it to host a user-level application (VMApp) component, as well as to schedule a privileged virtual machine monitor (VMM) component. The VMM directly provides high-performance CPU virtualization while the VMApp uses the host OS to virtualize I/O devices and shield the VMM from the variety of devices. A crucial question is whether virtualizing devices via such a hosted architecture can meet the performance required of high throughput, low latency devices. To this end, this paper studies the virtualization and performance of an Ethernet adapter on VMware Workstation. Results indicate that with optimizations, VMware Workstation’s hosted virtualization architecture can match native I/O throughput on standard PCs. Although a straightforward hosted implementation is CPU-limited due to virtualization overhead on a 733 MHz Pentium R III system on a 100 Mb/s Ethernet, a series of optimizations targeted at reducing CPU utilization allows the system to match native network throughput. Further optimizations are discussed both within and outside a hosted architecture.
Paradoxical hypertrichosis and terminal hair change after intense pulsed light hair removal therapy.

Although complications such as blister formation, erosion, and post-inflammatory hypo- and hyperpigmentation are well-known side effects of intense pulsed light (IPL) photoepilation, little is known about the paradoxical hypertrichosis after therapy.



To report the paradoxically increased hair density and coarseness after IPL photoepilation.



Within a period of 23 months, a total of 991 hirsute female patients were treated with IPL for photoepilation. The IPL system used was the Vasculight-SR, a multifunctional laser and IPL system (Lumenis Inc., Santa Clara, CA, USA). The cut-off filters frequently used were 695, 755 and 645 nm.



Paradoxical hypertrichosis and terminal hair change were detected after a few sessions of IPL therapy among 51 out of 991 patients. Our serial digital photographs, schematic diagrams, and hair counts before and after treatment confirmed the patients' claims. The other more commonly seen complications were epidermal burning with blisters, erosion, and crust formation followed by post-inflammatory hypo- and/or hyperpigmentation.



Paradoxical hypertrichosis and terminal hair change is a common complication of IPL photoepilation.
Simulation and performance evaluation of DAG construction with RPL

In this paper, we simulate and analyze the performance of the network formation process with the RPL (IPv6 Routing Protocol for Low Power and Lossy Networks), which is a routing protocol specifically designed for Low power and Lossy Networks (LLN) compliant with 6LoWPAN (IPv6 Low power Wireless Personal Area Networks). One motivation behind this work is that RPL is the first prospective candidate routing protocol for low-power and lossy networks, which are a main component in the next generation Internet-of-Things. RPL is still under development, although having gained maturity, and is open to improvements. Indeed, there is a need to understand well its behavior, and investigate its relevance. Our analysis is based on the ContikiRPL accurate and realistic simulation model developed under Contiki operating system. The performance of RPL is evaluated and analyzed for different network settings to understand the impact of the protocol attributes on the network formation performance, namely in terms energy, storage overhead, communication overhead, network convergence time and the maximum hop count. We argue through simulation that RPL provides several features that make it suitable to large scale networks.
A Future-Adaptable Password Scheme

Many authentication schemes depend on secret passwords. Unfortunately, the length and randomness of user-chosen passwords remain xed over time. In contrast, hardware improvements constantly give attackers increasing computational power. As a result, password schemes such as the traditional UNIX user-authentication system are failing with time. This paper discusses ways of building systems in which password security keeps up with hardware speeds. We formalize the properties desirable in a good password system, and show that the computational cost of any secure password scheme must increase as hardware improves. We present two algorithms with adaptable cost|eksblow sh, a block cipher with a purposefully expensive key schedule, and bcrypt, a related hash function. Failing a major breakthrough in complexity theory, these algorithms should allow password-based systems to adapt to hardware improvements and remain secure well into the future.
Evaluation of Web Service Recommendation Performance via Sparsity Alleviating by Specificity-Aware Ontology-Based Clustering

With the development of information technology, considerable web services are published on the Internet rapidly in the last few years. It becomes a challenging task to recommend applicable web services to users and service recommendation becomes an influential approach to guide users to discover suitable services. In this situation, Collaborative Filtering (CF) based on rating is one of the powerful approaches for service recommendation but suffers from the data sparsity and cold-start problems due to the insufficiency of user-service information. In this paper, we present a novel ontology-based clustering approach that based on the terms specificity and similarity to overcome those limitations. We alleviate the sparsity problem using this novel clustering approach and then service user similarity is calculated using a Pearson Correlation Coefficient (PCC) measurement. Finally, user rating is predicted based on the alleviated user ratings and PCC values and recommendation is based on these predictions. We evaluated in the several viewpoints based on our previous work and results show that our approach can successfully alleviate the sparsity and cold-start problems and works effectively by lower prediction error compared with existing approaches.
Rhythms of social interaction: messaging within a massive online network

We have analyzed the fully-anonymized headers of 362 million messages exchanged by 4.2 million users of Facebook, an online social network of college students, during a 26 month interval. The data reveal a number of strong daily and weekly regularities which provide insights into the time use of college students and their social lives, including seasonal variations. We also examined how factors such as school affiliation and informal online “friend” lists affect the observed behavior and temporal patterns. Finally, we show that Facebook users appear to be clustered by school with respect to their temporal messaging patterns.
Analog-to-digital converter survey and analysis

Analog-to-digital converters (ADC’s) are ubiquitous, critical components of software radio and other signal processing systems. This paper surveys the state-of-the-art of ADC’s, including experimental converters and commercially available parts. The distribution of resolution versus sampling rate provides insight into ADC performance limitations. At sampling rates below 2 million samples per second (Ms/s), resolution appears to be limited by thermal noise. At sampling rates ranging from 2 Ms/s to 4 giga samples per second (Gs/s), resolution falls off by 1 bit for every doubling of the sampling rate. This behavior may be attributed to uncertainty in the sampling instant due to aperture jitter. For ADC’s operating at multi-Gs/s rates, the speed of the device technology is also a limiting factor due to comparator ambiguity. Many ADC architectures and integrated circuit technologies have been proposed and implemented to push back these limits. The recent trend toward single-chip ADC’s brings lower power dissipation. However, technological progress as measured by the product of the ADC resolution (bits) times the sampling rate is slow. Average improvement is only 1.5 bits for any given sampling frequency over the last six–eight years.
Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications

The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.
Autonomic response in the perception of disgust and happiness in depersonalization disorder

Patients with depersonalization disorder have shown attenuated responses to emotional unpleasant stimuli, hence supporting the view that depersonalization is characterised by a selective inhibition on the processing of unpleasant emotions. It was the purpose of this study to establish if autonomic responses to facial emotional expressions also show the same blunting effect. The skin conductance responses (SCRs) of 16 patients with chronic DSM-IV depersonalization disorder, 15 normal controls and 15 clinical controls with DSM-IV anxiety disorders were recorded in response to facial expressions of happiness and disgust. Patients with anxiety disorders were found to have greater autonomic responses than patients with depersonalization, in spite of the fact that both groups had similarly high levels of subjective anxiety as measured by anxiety scales. SCR to happy faces did not vary across groups. The findings of this study provide further support to the idea that patients with depersonalization have a selective impairment in the processing of threatening or unpleasant emotional stimuli.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet <xref ref-type="bibr" rid="ref1">[1]</xref> and Fast R-CNN <xref ref-type="bibr" rid="ref2">[2]</xref> have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a <italic>Region Proposal Network</italic> (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model <xref ref-type="bibr" rid="ref3">[3]</xref> , our detection system has a frame rate of 5 fps (<italic>including all steps</italic>) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.
Numerical simulation of wind effects : a probabilistic perspective

Numerical simulations of wind loads and their effects are critical in the design of structures to ensure their safety under winds. The simulations range from generation of time histories of wind velocity, pressure and force fluctuations to structural response and assessment of attendant functionality and safety under service and design loads, respectively. Typically these schemes employ Monte Carlo-based approaches that encompass model-based simulations or information derived from observed data. The scope of simulations spans uni-variate to multi-variate processes; unidimensional to multi-dimensional fields; Gaussian to non-Gaussian; stationary to non-stationary; conditional and unconditional cases. In order to accomplish these tasks, methods based on the time, frequency, or time–frequency domains are employed. This paper summarizes a historical perspective, recent developments and future challenges. Also included in the discussion are computational tools employed for data analysis, response analysis and its management, and prediction. Examples are presented to illustrate some of the topics discussed.
Towards the Next Generation of Tabletop Gaming Experiences

In this paper we present a novel hardware and software platform (STARS) to realize computer augmented tabletop games that unify the strengths of traditional board games and computer games. STARS game applications preserve the social situation of traditional board games and provide a tangible interface with physical playing pieces to facilitate natural interaction. The virtual game components offer exciting new opportunities for game design and provide richer gaming experiences impossible to realize with traditional media. This paper describes STARS in terms of the hardware setup and the software platform used to develop and play STARS games. The interaction design within STARS is discussed and sample games are presented with regard to their contributions to enhancing user experience. Finally, realworld experiences with the platform are reported.
An Implementation of Data Pre-Processing for Small Dataset

Pre-processing in data mining played essential role for enhancing data quality. The basic concept behind is that, learning with accurate and high quality data may provide more efficient classification results as compared to learning with poor quality of data. In this presented paper a pre-processing technique is implemented with slight modification which is based on the technique given in . In this paper a promising approach of data pre-processing is provided which utilizes a fuzzy technique in order to improve the data quality. The implementation of available technique is performed using MATLAB. Additionally, the improved fuzzy technique is also implemented with it. The results demonstrate the effectiveness in classification accuracy after implementation of both techniques. Finally, the obtained results favour the proposed model for enhancing the performance of classifiers in both manners supervised and unsupervised manner.
Self-presentation and gender on MySpace

Available online 15 August 2008 Within the cultural context of MySpace, this study explores the ways emerging adults experience social networking. Through focus group methodology, the role of virtual peer interaction in the development of personal, social, and gender identities was investigated. Findings suggest that college students utilize MySpace for identity exploration, engaging in social comparison and expressing idealized aspects of the selves they wish to become. The public nature of self and relationship displays introduce feedback mechanisms by which emerging adults can legitimize images as associated with the self. Also, male–female differences in self-presentation parallel, and possibly intensify, gender norms offline. Our study suggests that social networking sites provide valuable opportunities for emerging adults to realize possible selves; however, increased pressure for female sexual objectification and intensified social comparison may also negatively impact identity development. A balanced view, presenting both opportunities and drawbacks, should be encouraged in policies regarding youth participation in social networking sites.
Beating Atari with Natural Language Guided Reinforcement Learning

We introduce the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions. The agent uses a multimodal embedding between environment observations and natural language to selfmonitor progress through a list of English instructions, granting itself reward for completing instructions in addition to increasing the game score. Our agent significantly outperforms Deep Q-Networks (DQNs), Asynchronous Advantage ActorCritic (A3C) agents, and the best agents posted to OpenAI Gym  on what is often considered the hardest Atari 2600 environment : MONTEZUMA’S REVENGE. Videos of Trained MONTEZUMA’S REVENGE  Our Best Current Model. Score 3500. Best Model Currently on OpenAI Gym. Score 2500. Standard A3C Agent Fails to Learn. Score 0.  An agent exploring the first room of MONTEZUMA’S REVENGE.  An example of the list of natural language instructions one might give the agent. The agent grants itself an additional reward after completing the current instruction. “Completion” is learned by training a generalized multimodal embedding between game images and text. ∗The authors contributed equally. 1 ar X iv :1 70 4. 05 53 9v 1 [ cs .A I] 1 8 A pr 2 01 7
Resource-constrained reasoning using a reasoner composition approach

To increase the interoperability and accessibility of data in sensor-rich systems, there has been a recent proliferation of the use of Semantic Web technologies in sensor-rich systems. Quite a range of such applications have emerged, such as hazard monitoring and rescue, context-aware computing, environmental monitoring, field studies, internet of things, and so on. These systems often assume a centralized paradigm for data processing, which does not always hold in reality especially when the systems are deployed in a hostile environment. At runtime, the infrastructure of systems deployed in such an environment is also prone to interference or damage, causing part of the infrastructure to have limited network connection or even to be detached from the rest. A solution to such a problem would be to push the intelligence, such as semantic reasoning, down to the device layer. A key enabler for such a solution is to run semantic reasoning on resourceconstrained devices. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only a “well-suited” amount of reasoning for a given ontology) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms are introduced and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, potentially facilitating greater semantic reasoning on sensor devices.
Recommendations for designers and researchers resulting from the world-wide failure exercise

The World-Wide Failure Exercise (WWFE) contained a detailed assessment of 19 theoretical approaches for predicting the deformation and failure response of polymer composite laminates when subjected to complex states of stress. The leading five theories are explored in greater detail to demonstrate their strengths and weaknesses in predicting various types of structural failure. Recommendations are then derived, as to how the theories can be best utilised to provide safe and economic predictions in a wide range of engineering design applications. Further guidance is provided for designers on the level of confidence and bounds of applicability of the current theories. The need for careful interpretation of initial failure predictions is emphasised, as is the need to allow for multiple sources of non-linearity (including progressive damage) where accuracy is sought for certain classes of large deformation and final failure strength predictions. Aspects requiring further experimental and theoretical investigation are identified. Direction is also provided to the research community by highlighting specific, tightly focussed, experimental and theoretical studies that, if carried out in the very near future, would pay great dividends from the designer’s perspective, by increasing their confidence in the theoretical foundations.
Enduring effects for cognitive behavior therapy in the treatment of depression and anxiety.

Recent studies suggest that cognitive and behavioral interventions have enduring effects that reduce risk for subsequent symptom return following treatment termination. These enduring effects have been most clearly demonstrated with respect to depression and the anxiety disorders. It remains unclear whether these effects are a consequence of the amelioration of the causal processes that generate risk or the introduction of compensatory strategies that offset them and whether these effects reflect the mobilization of cognitive or other mechanisms. No such enduring effects have been observed for the psychoactive medications, which appear to be largely palliative in nature. Other psychosocial interventions remain largely untested, although claims that they produce lasting change have long been made. Whether such enduring effects extend to other disorders remains to be seen, but the capacity to reduce risk following treatment termination is one of the major benefits provided by the cognitive and behavioral interventions with respect to the treatment of depression and the anxiety disorders.
Cognitive Deficits Associated with Nav1.1 Alterations: Involvement of Neuronal Firing Dynamics and Oscillations

Brain oscillations play a critical role in information processing and may, therefore, be essential to uncovering the mechanisms of cognitive impairment in neurological disease. In Dravet syndrome (DS), a mutation in SCN1A, coding for the voltage-gated sodium channel Nav1.1, is associated with severe cognitive impairment and seizures. While seizure frequency and severity do not correlate with the extent of impairment, the slowing of brain rhythms may be involved. Here we investigate the role of Nav1.1 on brain rhythms and cognition using RNA interference. We demonstrate that knockdown of Nav1.1 impairs fast- and burst-firing properties of neurons in the medial septum in vivo. The proportion of neurons that fired phase-locked to hippocampal theta oscillations was reduced, and medial septal regulation of theta rhythm was disrupted. During a working memory task, this deficit was characterized by a decrease in theta frequency and was negatively correlated with performance. These findings suggest a fundamental role for Nav1.1 in facilitating fast-firing properties in neurons, highlight the importance of precise temporal control of theta frequency for working memory, and imply that Nav1.1 deficits may disrupt information processing in DS via a dysregulation of brain rhythms.