nils-herrmann/clean_scirepeval huggingface dataset:
A Game-Theoretic and Energy-Efficient Algorithm in an Improved Software-Defined Wireless Sensor Network

Because wireless sensor networks (WSNs) are becoming increasingly integrated into daily life, solving the energy efficiency problem of such networks is an urgent problem. Many energy-efficient algorithms have been proposed to reduce energy consumption in traditional WSNs. The emergence of software-defined networks (SDNs) enables the transformation of WSNs. Some SDN-based WSNs architectures have been proposed and energy-efficient algorithms in SDN-based WSNs architectures have been studied. In this paper, we integrate an SDN into WSNs and an improved software-defined WSNs (SD-WSNs) architecture is presented. Based on the improved SD-WSNs architecture, we propose an energy-efficient algorithm. This energy-efficient algorithm is designed to match the SD-WSNs architecture, and is based on the residual energy and the transmission power, and the game theory is introduced to extend the network lifetime. Based on the SD-WSNs architecture and the energy-efficient algorithm, we provide a detailed introduction to the operating mechanism of the algorithm in the SD-WSNs. The simulation results show that our proposed algorithm performs better in terms of balancing energy consumption and extending the network lifetime compared with the typical energy-efficient algorithms in traditional WSNs.
Model-Free Head Pose Estimation Based on Shape Factorisation and Particle Filtering

Head pose estimation is essential for several applications and is particularly required for head pose-free eye-gaze tracking where estimation of head rotation permits free head movement during tracking. While the literature is broad, the accuracy of recent vision-based head pose estimation methods is contingent upon the availability of training data or accurate initialisation and tracking of specific facial landmarks. In this paper, we propose a method to estimate the head pose in realtime from the trajectories of a set of feature points spread randomly over the face region, without requiring a training phase or model-fitting of specific facial features. Conversely, without seeking specific facial landmarks, our method exploits the sparse 3-dimensional shape of the surface of interest, recovered via shape and motion factorisation, in combination with particle filtering to correct mistracked feature points and improve upon an initial estimation of the 3-dimensional shape during tracking. In comparison with two additional methods, quantitative results obtained through our modeland landmark-free method yield a reduction in the head pose estimation error for a wide range of head rotation angles.
Analyzing Perception-Distortion Tradeoff Using Enhanced Perceptual Super-Resolution Network

Convolutional neural network (CNN) based methods have recently achieved great success for image super-resolution (SR). However, most deep CNN based SR models attempt to improve distortion measures (e.g. PSNR, SSIM, IFC, VIF) while resulting in poor quantified perceptual quality (e.g. human opinion score, no-reference quality measures such as NIQE). Few works have attempted to improve the perceptual quality at the cost of performance reduction in distortion measures. A very recent study has revealed that distortion and perceptual quality are at odds with each other and there is always a trade-off between the two. Often the restoration algorithms that are superior in terms of perceptual quality, are inferior in terms of distortion measures. Our work attempts to analyze the trade-off between distortion and perceptual quality for the problem of single image SR. To this end, we use the well-known SR architectureenhanced deep super-resolution (EDSR) network and show that it can be adapted to achieve better perceptual quality for a specific range of the distortion measure. While the original network of EDSR was trained to minimize the error defined based on perpixel accuracy alone, we train our network using a generative adversarial network framework with EDSR as the generator module. Our proposed network, called enhanced perceptual super-resolution network (EPSR), is trained with a combination of mean squared error loss, perceptual loss, and adversarial loss. Our experiments reveal that EPSR achieves the state-of-the-art trade-off between distortion and perceptual quality while the existing methods perform well in either of these measures alone.
Privacy-preserving Biometric Authentication Model for e-Finance Applications

Widespread use of biometric architectures implies the need to secure highly sensitive data to respect the privacy rights of the users. In this paper, we discuss the following question: To what extent can biometric designs be characterized as Privacy Enhancing Technologies? The terms of privacy and security for biometric schemes are defined, while current regulations for the protection of biometric information are presented. Additionally, we analyze and compare cryptographic techniques for secure biometric designs. Finally, we introduce a privacy-preserving approach for biometric authentication in mobile electronic financial applications. Our model utilizes the mechanism of pseudonymous biometric identities for secure user registration and authentication. We discuss how the privacy requirements for the processing of biometric data can be met in our scenario. This work attempts to contribute to the development of privacy-by-design biometric technologies.
An under actuated robotic arm with adjustable stiffness shape memory polymer joints

Various robotic applications including surgical instruments, wearable robots and autonomous mobile robots are often constrained with strict design requirements on high degrees of freedom (DoF) and minimal volume and weight. An intuitive design to meet these contradictory requirements is to embed locking mechanism in under actuated robotic manipulators to direct the actuation from a single and remote source to drive different joints on demand. Mechanical clutches do serve such purposes but often are bulky and require auxiliary mechanism making it difficult to justify the high cost adding the additional DoF, especially in cm scale. Here, we introduce an under-actuated robotic arm with shape memory polymer (SMP) joints. Through controlling the temperature, the stiffness of the joints can be adjusted and selected joints will be activated while the rest are fixed in their position. The presented prototype can control the joints independently with a coupled actuation from two stepper motors. Since we have redundant DoFs in the arm, there can be more than one configuration to reach a given position. We use a probabilistic technique to determine the optimum configuration with the minimum number of active joints that can yield the desired posture. In this paper, we report on the performance of the proposed design for the hardware and the configuration planner.
Neurobiological Correlates in Internet Gaming Disorder: A Systematic Literature Review

Internet Gaming Disorder (IGD) is a potential mental disorder currently included in the third section of the latest (fifth) edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as a condition that requires additional research to be included in the main manual. Although research efforts in the area have increased, there is a continuing debate about the respective criteria to use as well as the status of the condition as mental health concern. Rather than using diagnostic criteria which are based on subjective symptom experience, the National Institute of Mental Health advocates the use of Research Domain Criteria (RDoC) which may support classifying mental disorders based on dimensions of observable behavior and neurobiological measures because mental disorders are viewed as biological disorders that involve brain circuits that implicate specific domains of cognition, emotion, and behavior. Consequently, IGD should be classified on its underlying neurobiology, as well as its subjective symptom experience. Therefore, the aim of this paper is to review the neurobiological correlates involved in IGD based on the current literature base. Altogether, 853 studies on the neurobiological correlates were identified on ProQuest (in the following scholarly databases: ProQuest Psychology Journals, PsycARTICLES, PsycINFO, Applied Social Sciences Index and Abstracts, and ERIC) and on MEDLINE, with the application of the exclusion criteria resulting in reviewing a total of 27 studies, using fMRI, rsfMRI, VBM, PET, and EEG methods. The results indicate there are significant neurobiological differences between healthy controls and individuals with IGD. The included studies suggest that compared to healthy controls, gaming addicts have poorer response-inhibition and emotion regulation, impaired prefrontal cortex (PFC) functioning and cognitive control, poorer working memory and decision-making capabilities, decreased visual and auditory functioning, and a deficiency in their neuronal reward system, similar to those found in individuals with substance-related addictions. This suggests both substance-related addictions and behavioral addictions share common predisposing factors and may be part of an addiction syndrome. Future research should focus on replicating the reported findings in different cultural contexts, in support of a neurobiological basis of classifying IGD and related disorders.
Matlab code for "Centromere detection of human metaphase chromosome images using a candidate based method"

Accurate detection of the human metaphase chromosome centromere is a critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing centromere detection methods tends to perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation. We present a centromere detection algorithm that uses a novel contour partitioning technique to generate centromere candidates followed by a machine learning approach to select the best candidate that enhances the detection accuracy. The contour partitioning technique evaluates various combinations of salient points along the chromosome boundary using a novel feature set and is able to identify telomere regions as well as detect and correct for sister chromatid separation. This partitioning is used to generate a set of centromere candidates which are then evaluated based on a second set of proposed features. The proposed algorithm outperforms previously published algorithms and is shown to do so with a larger set of chromosome images. A highlight of the proposed algorithm is the ability to rank this set of centromere candidates and create a centromere confidence metric which may be used in post-detection analysis. When tested with a larger metaphase chromosome database consisting of 1400 chromosomes collected from 40 metaphase cell images, the proposed algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%.
A new optimised De Bruijn coding strategy for structured light patterns

Coded structured light is an optical technique based on active stereovision that obtains the shape of objects. One shot techniques are based on projecting a unique light pattern with an LCD projector so that grabbing an image with a camera, a large number of correspondences can be obtained. Then, a 3D reconstruction of the illuminated object can be recovered by means of triangulation. The most used strategy to encode one-shot patterns is based on De Bruijn sequences. In This work a new way to design patterns using this type of sequences is presented. The new coding strategy minimises the number of required colours and maximises both the resolution and the accuracy.
The End of Moore’s Law: Opportunities for Natural Computing?

The impending end of Moore’s Law has started a rethinking of the way computers are built and computation is done. This paper discusses two directions that are currently attracting much attention as future computation paradigms: the merging of logic and memory, and brain-inspired computing. Natural computing has been known for its innovative methods to conduct computation, and as such may play an important role in the shaping of the post-Moore era.
A Continuously Growing Dataset of Sentential Paraphrases

A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at ∼70% precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely
Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar

This paper proposes a new approach to solve the problem of real-time vision-based hand gesture recognition with the combination of statistical and syntactic analyses. The fundamental idea is to divide the recognition problem into two levels according to the hierarchical property of hand gestures. The lower level of the approach implements the posture detection with a statistical method based on Haar-like features and the AdaBoost learning algorithm. With this method, a group of hand postures can be detected in real time with high recognition accuracy. The higher level of the approach implements the hand gesture recognition using the syntactic analysis based on a stochastic context-free grammar. The postures that are detected by the lower level are converted into a sequence of terminal strings according to the grammar. Based on the probability that is associated with each production rule, given an input string, the corresponding gesture can be identified by looking for the production rule that has the highest probability of generating the input string.
Persuasive Normative Messages : The Influence of Injunctive and Personal Norms on Using Free Plastic Bags

In this exploratory field-study, we examined how normative messages (i.e., activating an injunctive norm, personal norm, or both) could encourage shoppers to use fewer free plastic bags for their shopping in addition to the supermarket‘s standard environmental message aimed at reducing plastic bags. In a one-way subjects-design (N = 200) at a local supermarket, we showed that shoppers used significantly fewer free plastic bags in the injunctive, personal and combined normative message condition than in the condition where only an environmental message was present. The combined normative message did result in the smallest uptake of free plastic bags compared to the injunctive and personal normative-only message, although these differences were not significant. Our findings imply that re-wording the supermarket‘s environmental message by including normative information could be a promising way to reduce the use of free plastic bags, which will ultimately benefit the environment.
Neural networks-method of moments (NN-MoM) for the efficient filling of the coupling matrix

In this paper, novel radial basis function-neural network (RBF-NN) models are presented for the efficient filling of the coupling matrix of the method of moments (MoM). Two RBF-NNs are trained to calculate the majority of elements in the coupling matrix. The rest of elements are calculated using the conventional MoM, hence the technique is referred to as neural network-method of moments (NN-MoM). The proposed NN-MoM is applied to the analysis of a number of microstrip patch antenna arrays. The results show that NN-MoM is both accurate and fast. The proposed technique is general and it is convenient to integrate with MoM planar solvers.
Development and initial testing of a SWATH boat for shallow-water bathymetry

Students at Santa Clara University have developed a SWATH boat prototype capable of shallow water operation and configured for creating bathymetric maps through the use of a multi-beam sonar. The sonar works in concert with DVL and precision GPS sensors in order to log data that can be used to generate bathymetric maps through the use of the MB System software suite. The boat's physical structure includes pontoons, vertical supports, and a platform housing the vessel's power, sensor, control, and communication systems. Additional systems include a camera and video transmission system for remotely piloted operation, a suite of sensors and controllers for autonomous navigation, and equipment for ballasting the pontoons. An off-board control station aids in navigation computations, provides the pilot/supervisor interface, and links the system to the internet for real-time internet-based piloting and/or monitoring of the mission. An additional winch system has been developed for future operations involving the deployment of a sensor package to various depths. A number of successful test deployments have been completed to date, and operations during the summer of 2008 will include mapping of the Elkhorn Slough, portions of the southern end of San Francisco Bay, portions of Lake Tahoe, and shallow water coastal waters in the San Juan Islands. Ultimately, a more robust model of the boat is planned for deployment at NOAA's Kasitsna Bay Laboratory in Alaska. The system has been developed in partnership with the Monterey Bay Aquarium Research Institute, the University of Alaska Fairbanks, and NOAA's West Coast and Polar Regions Undersea Research Center. This paper will review the technical design of the system and will present the functional performance achieved to date.
A Survey on Internet Traffic Archival Systems

With the popularity of Internet applications and widespread use of mobile Internet, the Internet traffic maintains a rapid growth over the past decades. Internet traffic archival system (ITAS) for packets or flow records becomes more and more widely used in network monitor, network troubleshooting, user behavior and experience analysis etc. In this paper, we survey the design and implementation of several typical traffic archival systems. We analyze and compare the architectures and key technologies backing up Internet traffic archival system, and summarize the key technologies which include packet/flow capturing, packet/flow storage and bitmap index encoding algorithm, and dive into the packet/flow capturing technologies. Then, we propose the design and implementation of TiFaflow traffic archival system. Finally, we summarize and discuss the future direction of Internet traffic archival systems.
Depth Image Layers Separation (DILS) algorithm of image view synthesis based on stereo vision

A new Depth Image Layers Separation (DILS) algorithm for synthesizing inter-view images based on disparity depth map layers representation is presented. The approach is to separate the depth map into several layers identified through histogram-based clustering. Each layer is extracted using inter-view interpolation to create objects based on location and depth. DILS is a new paradigm in selecting interesting image locations based on depth, but also in producing new image representations that allow objects or parts of an image to be described without the need of segmentation and identification. The image view synthesis can reduce the configuration complexity of multi-camera arrays in 3D imagery and free-viewpoint applications. The simulation results show that depth layer separation is able to create inter-view images that may be integrated with other techniques such as occlusion handling processes. The DILS algorithm can be implemented using both simple as well as sophisticated stereo matching methods to synthesize inter-view images.
WORAL: A Witness Oriented Secure Location Provenance Framework for Mobile Devices

Location-based services allow mobile device users to access various services based on the users' current physical location information. Path-critical applications, such as supply chain verification, require a chronological ordering of location proofs. It is a significant challenge in distributed and user-centric architectures for users to prove their presence and the path of travel in a privacy-protected and secure manner. So far, proposed schemes for secure location proofs are mostly subject to tampering, not resistant to collusion attacks, do not offer preservation of the provenance, and are not flexible enough for users to prove their provenance of location proofs. In this paper, we present WORAL, a complete ready-to-deploy framework for generating and validating witness oriented asserted location provenance records. The WORAL framework is based on the asserted location proof protocol and the OTIT model for generating secure location provenance on the mobile devices. WORAL allows user-centric, collusion resistant, tamper-evident, privacy protected, verifiable, and provenance preserving location proofs for mobile devices. This paper presents the schematic development, feasibility of usage, comparative advantage over similar protocols, and implementation of WORAL for android device users including a Google Glass-based client for enhanced usability.
Negotiation-Based Flexible SLA Establishment with SLA-driven Resource Allocation in Cloud Computing

As various consumers tend to use personalized Cloud services, Service Level Agreements (SLAs) emerge as a key aspect in Cloud and Utility computing. The objectives of this doctoral research are 1) to support a flexible establishment of SLAs that enhances the utility of SLAs for both providers and consumers, and 2) to manage Cloud resources to prevent SLA violations. Because consumers and providers may be independent bodies, some mechanisms are necessary to resolve different preferences when they establish a SLA. Thus, we designed a Cloud SLA negotiation mechanism for interactive and flexible SLA establishment. The novelty of this SLA negotiation mechanism is that it can support advanced multi-issue negotiation that includes time slot and price negotiations. In addition, to prevent SLA violations, we provided a SLA-driven resource allocation scheme that selects a proper data center among globally distributed centers operated by a provider. Empirical results showed that the proposed SLA negotiation mechanism supports faster agreements and achieves higher utilities. Also, the proposed SLA-driven resource allocation scheme performs better in terms of SLA violations and the provider's profits.
Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.