nils-herrmann/clean_scirepeval huggingface dataset:
Online Resource Scheduling Under Concave Pricing for Cloud Computing

With the booming cloud computing industry, computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. The diverse pricing schemes among different IaaS service providers or even in the same provider form a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers' resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In this paper, we focus on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. We present a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its competitive ratio. Three special cases of the offline concave cost scheduling problem and the corresponding optimal algorithms are introduced. Our simulation shows that ROSA achieves a competitive ratio close to the theoretical lower bound under the special cases. Trace-driven simulation using Google cluster data demonstrates that ROSA is superior to the conventional online scheduling algorithms in terms of cost saving.
Straight leg walking strategy for torque-controlled humanoid robots

Most humanoid robots walk in an unhuman-like way with bent knees due to the use of the simplified Linear Inverted Pendulum Model (LIPM) which constrains the Center of Mass (CoM) in a horizontal plane. Therefore it results in high knee joint torque and extra energy consumption. To address this issue, we propose a simple yet efficient control strategy to realize straight leg walking. First, theoretical analyses of simplified models provide insight into Zero Moment Point (ZMP) deviations during straight knee walking. Based on the finding that the deviation is limited comparing to the support polygon, we decide to keep using the LIPM for high-level planning, but let the robot perform straight leg walking automatically via the optimization-based low-level controller. By setting the desired CoM height slightly over the robot's reachable height, the low-level controller will attempt to straighten the robot's leg to reach this vertical reference, in the meanwhile, also satisfy the constraints (i.e. dynamic feasibility, friction cone, torque limits). The simulation results of the humanoid robot WALK-MAN demonstrate the feasibility of proposed control strategy with relatively high energy efficiency. A typical butterfly shape of CoM trajectory was also observed in the frontal plane which is common in human walking.
Active-Metric Learning for Classification of Remotely Sensed Hyperspectral Images

Classification of remotely sensed hyperspectral images via supervised approaches is typically affected by high dimensionality of the spectral data and a limited number of labeled samples. Dimensionality reduction via feature extraction and active learning (AL) are two approaches that researchers have investigated independently to deal with these two problems. In this paper, we propose a new method in which the feature extraction and AL steps are combined into a unique framework. The idea is to learn and update a reduced feature space in a supervised way at each iteration of the AL process, thus taking advantage of the increasing labeled information provided by the user. In particular, the computation of the reduced feature space is based on the large-margin nearest neighbor (LMNN) metric learning principle. This strategy is applied in conjunction with k-nearest neighbor ( k-NN) classification, for which a new sample selection strategy is proposed. The methodology is validated experimentally on four benchmark hyperspectral data sets. Good improvements in terms of classification accuracy and computational time are achieved with respect to the state-of-the-art strategies that do not combine feature extraction and AL.
SnapShot: Visualization to Propel Ice Hockey Analytics

Sports analysts live in a world of dynamic games flattened into tables of numbers, divorced from the rinks, pitches, and courts where they were generated. Currently, these professional analysts use R, Stata, SAS, and other statistical software packages for uncovering insights from game data. Quantitative sports consultants seek a competitive advantage both for their clients and for themselves as analytics becomes increasingly valued by teams, clubs, and squads. In order for the information visualization community to support the members of this blossoming industry, it must recognize where and how visualization can enhance the existing analytical workflow. In this paper, we identify three primary stages of today's sports analyst's routine where visualization can be beneficially integrated: 1) exploring a dataspace; 2) sharing hypotheses with internal colleagues; and 3) communicating findings to stakeholders.Working closely with professional ice hockey analysts, we designed and built SnapShot, a system to integrate visualization into the hockey intelligence gathering process. SnapShot employs a variety of information visualization techniques to display shot data, yet given the importance of a specific hockey statistic, shot length, we introduce a technique, the radial heat map. Through a user study, we received encouraging feedback from several professional analysts, both independent consultants and professional team personnel.
Survey on Coarse Grained Reconfigurable Architectures

Recent advancement in the semiconductor technology allow the hardware engineers to integrate complex modules like processors, peripheral devices, and memory in a single System-on-a-Chip (SoC); where testability, power minimization and management, area minimization are the important system level considerations. Performances both in terms of processing speed and power consumption are becoming more and more challenging in SOC designing. Novel system on chip architectures should be able to execute multiple performances demanding applications while maintaining low power consumption, small area, non-recurring engineering costs and short time to-market. Hence a lot of research is going on to implement CGRA in SOC because Coarse-grained reconfigurable architecture can provide both performance and flexibility. This paper gives a guided tour over a decade of development in CGRA and their significance in SOC design
Generating test cases to expose concurrency bugs in android applications

Mobile systems usually support an event-based model of concurrent programming. This model, although advantageous to maintain responsive user interfaces, may lead to subtle concurrency errors due to unforeseen threads interleaving coupled with non-deterministic reordering of asynchronous events. These bugs are very difficult to reproduce even by the same user action sequences that trigger them, due to the undetermined schedules of underlying events and threads. In this paper, we proposed RacerDroid, a novel technique that aims to expose concurrency bugs in android applications by actively controlling event schedule and thread interleaving, given the test cases that have potential data races. By exploring the state model of the application constructed dynamically, our technique starts first to generate a test case that has potential data races based on the results obtained from existing static or dynamic race detection technique. Then it reschedules test cases execution by actively controlling event dispatching and thread interleaving to determine whether such potential races really lead to thrown exceptions or assertion violations. Our preliminary experiments show that RacerDroid is effective, and it confirms real data races, while at the same time eliminates false warnings for Android apps found in the wild.
Ambiguity Resolution in a Cognitive Model of Language Comprehension

The Lucia comprehension system attempts to model human comprehension by using the Soar cognitive architecture, Embodied Construction Grammar (ECG), and an incremental, word-by-word approach to grounded processing. Traditional approaches use techniques such as parallel paths and global optimization to resolve ambiguities. Here we describe how Lucia deals with lexical, grammatical, structural, and semantic ambiguities by using knowledge from the surrounding linguistic and environmental context. It uses a local repair mechanism to maintain a single path, and shows a garden path effect when local repair breaks down. Data on adding new linguistic knowledge shows that the ECG grammar grows faster than the knowledge for handling context, and that lowlevel grammar items grow faster than more general ones.
A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems

Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA.
Defeasible Logic Programming: An Argumentative Approach

The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query q will succeed when there is an argument A for q that is warranted, i. e. the argument A that supports q is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent’s knowledge and for providing an argumentation based reasoning mechanism to agents.
A new 2D static hand gesture colour image dataset for ASL gestures

It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset.
A real time hand gesture recognition system based on DFT and SVM

Vision based band gesture recognition provides a more nature and powerful means for human-computer interaction. A fast detection process of hand gesture and an effective feature extraction process are presented. The proposed a hand gesture recognition algorithm comprises four main steps. First use Camshift algorithm to track skin color after closing process. Second, in order to extract feature, we use BEA to extract the boundary of the hand. Third, the benefits of Fourier descriptor are invariance to the starting point of the boundary, deformation, and rotation, and therefore transform the starting point of the boundary by Fourier transformation. Finally, outline feature for the nonlinear non-separable type of data was classified by using SVM. Experimental results showed the accuracy is 93.4% in average and demonstrated the feasibility of proposed system.
Tackling Mental Health by Integrating Unobtrusive Multimodal Sensing

Mental illness is becoming a major plague in modern societies and poses challenges to the capacity of current public health systems worldwide. With the widespread adoption of social media and mobile devices, and rapid advances in artificial intelligence, a unique opportunity arises for tackling mental health problems. In this study, we investigate how users’ online social activities and physiological signals detected through ubiquitous sensors can be utilized in realistic scenarios for monitoring their mental health states. First, we extract a suite of multimodal time-series signals using modern computer vision and signal processing techniques, from recruited participants while they are immersed in online social media that elicit emotions and emotion transitions. Next, we use machine learning techniques to build a model that establishes the connection between mental states and the extracted multimodal signals. Finally, we validate the effectiveness of our approach using two groups of recruited subjects.
Telemanipulation of Snake-Like Robots for Minimally Invasive Surgery of the Upper Airway

This research focuses on developing and testing the highlevel control of a novel eight DOF hybrid robot using a DaVinci master manipulator. The teleoperation control is formulated as weighted, multi objective constrained least square (LS) optimization problems one for the master controller and the other for the slave controller. This allows us to incorporate various virtual fixtures in our control algorithm as constraints of the LS problem based on the robot environment. Experimental validation to ensure position tracking and sufficient dexterity to perform suturing in confined spaces such as throat are presented.
Shape and refractive index recovery from single-view polarisation images

In this paper, we propose an approach to the problem of simultaneous shape and refractive index recovery from multispectral polarisation imagery captured from a single viewpoint. The focus of this paper is on dielectric surfaces which diffusely polarise light transmitted from the dielectric body into the air. The diffuse polarisation of the reflection process is modelled using a Transmitted Radiance Sinusoid curve and the Fresnel transmission theory. We provide a method of estimating the azimuth angle of surface normals from the spectral variation of the phase of polarisation. Moreover, to render the problem of simultaneous estimation of surface orientation and index of refraction well-posed, we enforce a generative model on the material dispersion equations for the index of refraction. This generative model, together with the Fresnel transmission ratio, permit the recovery of the index of refraction and the zenith angle simultaneously. We show results on shape recovery and rendering for real world and synthetic imagery.
BQP and the polynomial hierarchy

The relationship between BQP and PH has been an open problem since the earliest days of quantum computing. We present evidence that quantum computers can solve problems outside the entire polynomial hierarchy, by relating this question to topics in circuit complexity, pseudorandomness, and Fourier analysis.
 First, we show that there exists an oracle relation problem (i.e., a problem with many valid outputs) that is solvable in BQP, but not in PH. This also yields a non-oracle relation problem that is solvable in quantum logarithmic time, but not in AC0.
 Second, we show that an oracle decision problem separating BQP from PH would follow from the Generalized Linial-Nisan Conjecture, which we formulate here and which is likely of independent interest. The original Linial-Nisan Conjecture (about pseudorandomness against constant-depth circuits) was recently proved by Braverman, after being open for twenty years.
Automatic recognition of unified parkinson's disease rating from speech with acoustic, i-vector and phonotactic features

Parkinson’s Disease is a neurodegenerative disease affecting millions of people globally, most of whom present difficulties producing speech sounds. In this paper, we describe a system to identify the degree to which a person suffers from the disease. We use a number of automatic phone recognition-based features and we augment these with i-vector features and utterancelevel acoustic aggregations. On the Interspeech 2015 ComParE challenge corpus, we find that these features allow for prediction well above the challenge baseline, particularly under crossvalidation evaluation.
Applying the Uses and Gratifications Theory to Compare Higher Education Students ’ Motivation for Using Social Networking Sites : Experiences from Iran , Malaysia , United Kingdom , and South Africa

Drawing from the Uses and Gratifications Theory, this study examined the Gratification Sought and the Gratification Obtained from using Social Networking Sites among Iranian, Malaysian, British, and South African higher education students. This comparison allowed to drawing conclusions about how social networking sites fulfill users’ needs with different cultures. Data were collected through a quantitative study applying online and paperbased questionnaire carried out in 2013, using a representative sample (N=320). Findings showed differences in students’ motivation to join to and to use of social network sites in each country. Interestingly, the researchers found that cultural differences may determine the uses and gratifications of social networking sites.
Indoor human localization using PIR sensors and accessibility map

Tracking human's location in indoor environments is very import, for it enables a wide range of applications related to robot service, appliance control and other smart home applications. This paper proposed a new, integrated method for indoor human localization based on passive infrared (PIR) sensors and an accessibility map of the indoor environment. The accessibility map reflects human's visiting habits in that environment, which also contains geometric as well as furniture layout information. First we build the accessibility map based on the human's visiting habits as the prior knowledge. Then the PIR sensor data is used to estimate the rough position. A more practical binary sensing model is used to model the PIR output. A particle filter fuses the PIR sensor data and the accessibility map information to refine the localization. To evaluate our method's performance, simulation experiments are carried out. The results showed that the proposed accessibility map-based PIR localization method outperforms the PIR only localization.
Using Presence Questionnaires in Reality

A between-group experiment was carried out to assess whether two different presence questionnaires can distinguish between real and virtual experiences. One group of ten subjects searched for a box in a real office environment. A second group of ten subjects carried out the same task in a virtual environment that simulated the same office. Immediately after their experience, subjects were given two different presence questionnaires in randomized order: the Witmer and Singer Presence (WS), and the questionnaire developed by Slater, Usoh, and Steed (SUS). The paper argues that questionnaires should be able to pass a reality test whereby under current conditions the presence scores should be higher for real experiences than for virtual ones. Nevertheless, only the SUS had a marginally higher mean score for the real compared to the virtual, and there was no significant difference at all between the WS mean scores. It is concluded that, although such questionnaires may be useful when all subjects experience the same type of environment, their utility is doubtful for the comparison of experiences across environments, such as immersive virtual compared to real, or desktop compared to immersive virtual.
True Random Number Generator Embedded in Reconfigurable Hardware

This paper presents a new True Random Number Generator (TRNG) based on an analog Phase-Locked Loop (PLL) implemented in a digital Altera Field Programmable Logic Device (FPLD). Starting with an analysis of the one available on chip source of randomness the PLL synthesized low jitter clock signal, a new simple and reliable method of true randomness extraction is proposed. Basic assumptions about statistical properties of jitter signal are confirmed by testing of mean value of the TRNG output signal. The quality of generated true random numbers is confirmed by passing standard NIST statistical tests. The described TRNG is tailored for embedded System-On-a-ProgrammableChip (SOPC) cryptographic applications and can provide a good quality true random bit-stream with throughput of several tens of kilobits per second. The possibility of including the proposed TRNG into a SOPC design significantly increases the system security of embedded cryptographic hardware.
Switching Control Strategy to Minimize Dual Active Bridge Converter Losses

A switching control strategy to control the power flow and minimize the total power losses of the dual active bridge converter topology is proposed in this paper. The control strategy consists of driving the bridge with the largest DC voltage to generate a three-level pulsewidth-modulated (PWM) voltage waveform. This PWM is ruled by two manipulated variables: the phase shift between the primary and secondary transformer voltages and the modulation index. These variables are calculated using an algorithm that is deduced on the basis of particular calculation and analysis of converter losses, which are also presented in this paper. An experimental prototype was implemented to validate the theoretical analysis and feasibility of the proposal. The experimental results revealed that the overall efficiency of this converter can be improved up to 10% using the control strategy instead of the conventional one.