nils-herrmann/clean_scirepeval huggingface dataset:
Will Simulation Sickness Slow Down the Diffusion of Virtual Environment Technology?

The widespread diffusion of immersive virtual environments (VE) is threatened by persistent reports that some users experience simulation sickness, a form of motion sickness that accompanies extended use of the medium. Experience with the problem of simulation sickness is most extensive in the military where the illness has accompanied the use of various simulators since the 1950s. This article considers the obstacles presented by simulation sickness to the diffusion of VE systems, its physiological and technological causes, and, finally, the remedies that have been suggested to fix the problems. This issue is also considered in light of previous reports of purported illnesses that accompanied the diffusion of other communication technologies.
A Cache Timing Attack on AES in Virtualization Environments

We show in this paper that the isolation characteristic of system virtualization can be bypassed by the use of a cache timing attack. Using Bernstein’s correlation in this attack, an adversary is able to extract sensitive keying material from an isolated trusted execution domain. We demonstrate this cache timing attack on an embedded ARMbased platform running an L4 microkernel as virtualization layer. We also show that an attacker who gained access to the untrusted domain can extract the key of an AES-based authentication protocol used for a financial transaction. We provide measurements for different public domain AES implementations. Our results indicate that cache timing attacks are highly relevant in trusted execution environments.
Distributional Reinforcement Learning With Quantile Regression

In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term return. Traditionally, reinforcement learning algorithms average over this randomness to estimate the value function. In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean. That is, we examine methods of learning the value distribution instead of the value function. We give results that close a number of gaps between the theoretical and algorithmic results given by Bellemare, Dabney, and Munos (2017). First, we extend existing results to the approximate distribution setting. Second, we present a novel distributional reinforcement learning algorithm consistent with our theoretical formulation. Finally, we evaluate this new algorithm on the Atari 2600 games, observing that it significantly outperforms many of the recent improvements on DQN, including the related distributional algorithm C51.
Measuring the Diversity of Automatic Image Descriptions

Automatic image description systems typically produce generic sentences that only make use of a small subset of the vocabulary available to them. In this paper, we consider the production of generic descriptions as a lack of diversity in the output, which we quantify using established metrics and two new metrics that frame image description as a word recall task. This framing allows us to evaluate system performance on the head of the vocabulary, as well as on the long tail, where system performance degrades. We use these metrics to examine the diversity of the sentences generated by nine state-of-the-art systems on the MS COCO data set. We find that the systems trained with maximum likelihood objectives produce less diverse output than those trained with additional adversarial objectives. However, the adversarially-trained models only produce more types from the head of the vocabulary and not the tail. Besides vocabulary-based methods, we also look at the compositional capacity of the systems, specifically their ability to create compound nouns and prepositional phrases of different lengths. We conclude that there is still much room for improvement, and offer a toolkit to measure progress towards the goal of generating more diverse image descriptions.
Smart fabrics and interactive textile enabling wearable personal applications: R&D state of the art and future challenges

Smart fabrics and interactive textiles (SFIT) are fibrous structures that are capable of sensing, actuating, generating/storing power and/or communicating. Research and development towards wearable textile-based personal systems allowing e.g. health monitoring, protection & safety, and healthy lifestyle gained strong interest during the last 10 years. Under the Information and Communication Programme of the European Commission, a cluster of R&D projects dealing with smart fabrics and interactive textile wearable systems regroup activities along two different and complementary approaches i.e. “application pull” and “technology push”. This includes projects aiming at personal health management through integration, validation, and use of smart clothing and other networked mobile devices as well as projects targeting the full integration of sensors/actuators, energy sources, processing and communication within the clothes to enable personal applications such as protection/safety, emergency and healthcare. The integration part of the technologies into a real SFIT product is at present stage on the threshold of prototyping and testing. Several issues, technical as well user-centred, societal and business, remain to be solved. The paper presents on going major R&D activities, identifies gaps and discuss key challenges for the future.
Adjustable hysteresis CMOS Schmitt triggers

Adjustable hysteresis CMOS Schmitt trigger design strategies are investigated and two new inverter based designs are proposed. The sizing of the two feedback inverters controls the two trip points of the structure independently. By the addition of voltage controlled current sinking and/or sourcing transistors, the hysteresis window can be easily moved without changing its width. Moreover the new designs are immune to the kick-back noise coming from the succeeding blocks.
Dynamic Multi-View Hashing for Online Image Retrieval

Advanced hashing technique is essential in large scale online image retrieval and organization, where image contents are frequently changed. While traditional multi-view hashing method has achieve promising effectiveness, its batch-based learning based scheme largely leads to very expensive updating cost. Meanwhile, existing online hashing scheme generally focuses on single-view data. Good effectiveness can not be expected when searching over real online images, which typically have multiple views. Further, both types of hashing methods only can generate hash codes with fixed length. Thus they have limited capability on comprehensive characterization of streaming image data. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated in the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-theart hashing methods.
Dynamic time warping for gesture-based user identification and authentication with Kinect

The Kinect has primarily been used as a gesture-driven device for motion-based controls. To date, Kinect-based research has predominantly focused on improving tracking and gesture recognition across a wide base of users. In this paper, we propose to use the Kinect for biometrics; rather than accommodating a wide range of users we exploit each user's uniqueness in terms of gestures. Unlike pure biometrics, such as iris scanners, face detectors, and fingerprint recognition which depend on irrevocable biometric data, the Kinect can provide additional revocable gesture information. We propose a dynamic time-warping (DTW) based framework applied to the Kinect's skeletal information for user access control. Our approach is validated in two scenarios: user identification, and user authentication on a dataset of 20 individuals performing 8 unique gestures. We obtain an overall 4.14%, and 1.89% Equal Error Rate (EER) in user identification, and user authentication, respectively, for a gesture and consistently outperform related work on this dataset. Given the natural noise present in the real-time depth sensor this yields promising results.
Ethical frontiers of ICT and older users: cultural, pragmatic and ethical issues

The reality of an ageing Europe has called attention to the importance of e-inclusion for a growing population of senior citizens. For some, this may mean closing the digital divide by providing access and support to technologies that increase citizen participation; for others, e-inclusion means access to assistive technologies to facilitate and extend their living independently. These initiatives address a social need and provide economic opportunities for European industry. While undoubtedly desirable, and supported by European Union initiatives, several cultural assumptions or issues related to the initiatives could benefit from fuller examination, as could their practical and ethical implications. This paper begins to consider these theoretical and practical concerns. The first part of the paper examines cultural issues and assumptions relevant to adopting e-technologies, and the ethical principles applied to them. These include (1) the persistence of ageism, even in e-inclusion; (2) different approaches to, and implications of independent living; and (3) the values associated with different ethical principles, given their implications for accountability to older users. The paper then discusses practical issues and ethical concerns that have been raised by the use of smart home and monitoring technologies with older persons. Understanding these assumptions and their implications will allow for more informed choices in promoting ethical application of e-solutions for older persons.
Beautiful Math, Part 2: Aesthetic Patterns Based on Fractal Tilings

A fractal tiling (f-tiling) is a tiling whose boundary is fractal. This article presents two families of rare, infinitely many f-tilings. Each f-tiling is constructed by reducing tiles by a fixed scaling factor, using a single prototile, which is a segment of a regular polygon. The authors designed invariant mappings to automatically produce appealing seamless, colored patterns from such tilings.
Stochastic modeling of immersed rigid-body dynamics

The simulation of immersed rigid-body dynamics involves the coupling between objects and turbulent flows, which is a complicated task in computer animation. In this paper, we propose a stochastic model of the dynamics of rigid bodies immersed in viscous flows to solve this problem. We first modulate the dynamic equations of rigid bodies using generalized Kirchhoff equations (GKE). Then, a stochastic differential equation called the Langevin equation is proposed to represent the velocity increments due to the turbulences. After the precomputation of the Kirchhoff tensor and the kinetic energy of a synthetic turbulence induced by the object moving, we utilize a fractional-step method to solve the GKE with vortical loads of drag and lift dynamics in runtime. The resulting animations include both inertial and viscous effects from the surrounding flows for arbitrary geometric objects. Our model is coherent and effective to simulate immersed rigid-body dynamics in real-time.
Porokeratosis with follicular involvement: report of three cases and review of literatures.

Porokeratosis is characterized clinically by annular plaques with a distinct peripheral keratotic ridge and histologically by the cornoid lamella. Porokeratosis with follicular involvement is rarely reported. To provide the basis of that follicular porokeratosis is a clinical variant or not. Biopsy was taken from three patients who were diagnosed porokeratosis. Routine stain was made and reviewed the literatures about well-documented cases of porokeratosis with follicular involvement. Porokeratosis with follicular involvement may have some clinical features: asymptomatic, erythematous, brownish or skin-color, less than 1 cm in the areas excluding palm and plantar, which commonly involved on middle-age. But there have still not enough proof as an independent clinical variant.
Privacy preserving big data mining: association rule hiding using fuzzy logic approach

Data repositories contain sensitive information which must be protected from unauthorized access. Existing data mining techniques can be considered as a privacy threat to sensitive data. Association rule mining is one of the utmost data mining techniques which tries to cover relationships between seemingly unrelated data in a data base.. Association rule hiding is a research area in privacy preserving data mining (PPDM) which addresses a solution for hiding sensitive rules within the data problem. Many researches have be done in this area, but most of them focus on reducing undesired side effect of deleting sensitive association rules in static databases. However, in the age of big data, we confront with dynamic data bases with new data entrance at any time. So, most of existing techniques would not be practical and must be updated in order to be appropriate for these huge volume data bases. In this paper, data anonymization technique is used for association rule hiding, while parallelization and scalability features are also embedded in the proposed model, in order to speed up big data mining process. In this way, instead of removing some instances of an existing important association rule, generalization is used to anonymize items in appropriate level. So, if necessary, we can update important association rules based on the new data entrances. We have conducted some experiments using three datasets in order to evaluate performance of the proposed model in comparison with Max-Min2 and HSCRIL. Experimental results show that the information loss of the proposed model is less than existing researches in this area and this model can be executed in a parallel manner for less execution time
On the coexistence of IEEE 802.11ac and WAVE in the 5.9 GHz Band

The IEEE Wireless Access in Vehicular Environment, or WAVE, system uses 5.9 GHz band. Recently, however, the FCC announced its intention to share the band for WLANs based on new standards such as IEEE 802.11ac. In this article, we discuss the problems in the possible coexistence scenario of WAVE and WLAN, such as direct competition between WLAN and WAVE devices for bandwidth and the hidden terminal problem. The problems are exacerbated by the different bandwidth used by the two technologies, so the only usable sensing method between them is energy detection. In this article, we consider one way to address the problems within the current standards framework, which is the combined control of interframe space and the receiver sensitivity on the WLAN devices cued by the energy detection.
Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features

The European standard for access control imposes stringent performance requirements on commercial biometric technologies that few existing recognition systems are able to meet. In this correspondence paper, we present the first mouse dynamics biometric recognition system that fulfills this standard. The proposed system achieves notable performance improvement by developing separate models for separate feature groups involved. The improvements are achieved through the use of a fuzzy classification based on the Learning Algorithm for Multivariate Data Analysis and using a score-level fusion scheme to merge corresponding biometric scores. Evaluation of the proposed framework using mouse data from 48 users achieves a false acceptance rate of 0% and a false rejection rate of 0.36%.
A Facial Expression Recognition System Using Convolutional Networks

Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.
Design of rare-earth free five-phase outer-rotor IPM motor drive for electric bicycle

This paper presents the design considerations of a five-phase outer rotor interior permanent magnet motor (IPM) for an electric bicycle (e-bicycle). Conventional e-bicycles employ three phase outer rotor surface permanent magnet motors which are less reliable compared to the five-phase IPM motors. Moreover, the traditional electric motors use rare-earth permanent magnet materials such as neodymium which are expensive. Ferrite based magnets can be a better substitute to rare earth materials to make the motor more affordable. In this study, the design of a highly reliable and economical five-phase ferrite based IPM motor to be accommodated in a hub motor drive (HMD) for an e-bicycle is presented. The proposed motor design is optimized using a lumped parameter model (LPM) based optimizer. Detailed analysis on the design parameters and constraints considered for optimization is discussed. Thorough finite element simulations in terms of torque developed, cogging torque, back-EMF, and mechanical stress have been conducted to verify the effectiveness of the proposed motor design.
The processes of inference

Mental models represent possibilities, and the theory of mental models postulates three systems of mental processes underlying inference: (0) the construction of an intensional representation of a premise’s meaning – a process guided by a parser; (1) the building of an initial mental model from the intension, and the drawing of a conclusion based on heuristics and the model; and (2) on some occasions, the search for alternative models, such as a counterexample in which the conclusion is false. System 0 is linguistic, and it may be autonomous. System 1 is rapid and prone to systematic errors, because it makes no use of a working memory for intermediate results. System 2 has access to working memory, and so it can carry out recursive processes, such as the construction of alternative models. However, it too is fallible when the limited processing capacity of working memory becomes overburdened. The three systems are embodied in a unified computational implementation of the model theory, called mReasoner, which is a recent departure in the theory. We review its three systems as they apply to reasoning about the properties of sets of individuals, and we explore how these systems can be extended to other domains of reasoning.
Detecting deception: the scope and limits

With the increasing interest in the neuroimaging of deception and its commercial application, there is a need to pay more attention to methodology. The weakness of studying deception in an experimental setting has been discussed intensively for over half a century. However, even though much effort has been put into their development, paradigms are still inadequate. The problems that bedevilled the old technology have not been eliminated by the new. Advances will only be possible if experiments are designed that take account of the intentions of the subject and the context in which these occur.
A study on the position of CDO for improving competitiveness based big data in cluster computing environment

So many companies became enthusiastic over accumulating and managing data, decision making through data analysis, and improvement of competitiveness. This definitely requires budget and work force for data management and utilization. However, organizations do not try to raise budget and increase work force recklessly, for they must be within the range of being managed. For this reason, Chief Data Officer (CDO) was introduced. Even though CDO is highly necessary and important in organizations, CDO is not even recognized by many organizations in Korea. Also, it is hard to find any studies or reports about CDO. Also, CDO is a sub-department or sub-organization of CIO so it has a weak position in organizations. For this reason, the purpose of this study is to support to strengthen competiveness of current organization by suggesting a plan for a desirable position and role of CDO in organizations. To achieve the purpose, actual status of CDO organization in Korea is surveyed and analyzed. This study suggests the alternative to enhance the desirable position for CDO based on the implication from analysis.
Context Adaptive Neural Network for Rapid Adaptation of Deep CNN Based Acoustic Models

Using auxiliary input features has been seen as one of the most effective ways to adapt deep neural network (DNN)-based acoustic models to speaker or environment. However, this approach has several limitations. It only performs compensation of the bias term of the hidden layer and therefore does not fully exploit the network capabilities. Moreover, it may not be well suited for certain types of architectures such as convolutional neural networks (CNNs) because the auxiliary features have different time-frequency structures from speech features. This paper resolves these problems by extending the recently proposed context adaptive DNN (CA-DNN) framework to CNN architectures. A CA-DNN is a DNN with one or several layers factorized in sub-layers associated with an acoustic context class representing speaker or environment. The output of the factorized layer is obtained as the weighted sum of the contributions of each sub-layer, weighted by acoustic context weights that are derived from auxiliary features such as i-vectors. Importantly, a CA-DNN can compensate both bias and weight matrices. In this paper, we investigate the use of CA-DNN for deep CNN-based architectures. We demonstrate consistent performance gains for utterance level rapid adaptation on the AURORA4 task over a strong network-in-network based deep CNN architecture.