nils-herrmann/clean_scirepeval huggingface dataset:
Comparing continuous and intermittent assistance controllers for assistive devices

This paper introduces a novel assistive device (AD) control strategy that provides intermittent assistance and is capable of encouraging user effort during AD use. The assistance regulation controller (ARC) presented here is designed to regulate joint torque and can be used for arbitrary joint and limb motions without requiring any a priori information about the user's intended motion. Data from eight healthy subjects who performed a forearm tracking experiment using a single degree-of-freedom powered orthosis demonstrate that the ARC is capable of encouraging user effort. On average, subjects generated 40% more joint torque when performing the tracking experiment with the ARC instead of a conventional torque-amplifying controller. The results also demonstrate that the ARC is safe to use for dynamic orthosis-assisted joint motions which require several transitions into and out of the assistance and no assistance states of the controller. As such, the results also confirm that the ARC is an effective assist-as-needed control strategy suitable for use with ADs.
Image Restoration by the Method of Convex Projections: Part 1ߞTheory

A projection operator onto a closed convex set in Hilbert space is one of the few examples of a nonlinear map that can be defined in simple abstract terms. Moreover, it minimizes distance and is nonexpansive, and therefore shares two of the more important properties of ordinary linear orthogonal projections onto closed linear manifolds. In this paper, we exploit the properties of these operators to develop several iterative algorithms for image restoration from partial data which permit any number of nonlinear constraints of a certain type to be subsumed automatically. Their common conceptual basis is as follows. Every known property of an original image f is envisaged as restricting it to lie in a well-defined closed convex set. Thus, m such properties place f in the intersection E0 = Ei of the corresponding closed convex sets E1,E2,···Em. Given only the projection operators Pi onto the individual Ei's, i = 1 → m, we restore f by recursive means. Clearly, in this approach, the realization of the Pi's in a Hilbert space setting is one of the major synthesis problems. Section I describes the geometrical significance of the three main theorems in considerable detail, and most of the underlying ideas are illustrated with the aid of simple diagrams. Section II presents rules for the numerical implementation of 11 specific projection operators which are found to occur frequently in many signal-processing applications, and the Appendix contains proofs of all the major results.
Do elderly people prefer a conversational humanoid as a shopping assistant partner in supermarkets?

Assistive robots can be perceived in two main ways: tools or partners. In past research, assistive robots that offer physical assistance for the elderly are often designed in the context of a tool metaphor. This paper investigates the effect of two design considerations for assistive robots in a partner metaphor: conversation and robot-type. The former factor is concerned with whether robots should converse with people even if the conversation is not germane for completing the task. The latter factor is concerned with whether people prefer a communication/function oriented design for assistive robots. To test these design considerations, we selected a shopping assistance situation where a robot carries a shopping basket for elderly people, which is one typical scenario used for assistive robots. A field experiment was conducted in a real supermarket in Japan where 24 elderly participants shopped with robots. The experimental results revealed that they prefer a conversational humanoid as a shopping assistant partner.
Marahel : A Language for Constructive Level Generation

Marahel is a language and framework for constructive generation of 2D tile-based game levels. It is developed with the dual aim of making it easier to build level generators for game developers, and to help solving the general level generation problem by creating a generator space that can be searched using evolution. We describe the different sections of the level generators, and show examples of generated maps from 5 different generators. We analyze their expressive range on three dimensions: percentage of empty space, number of isolated elements, and cell-wise entropy of empty space. The results show that generators that have starkly different output from each other can easily be defined in Marahel.
Detection of unfocused raindrops on a windscreen using low level image processing

In a scene, rain produces a complex set of visual effects. Obviously, such effects may infer failures in outdoor vision-based systems which could have important side-effects in terms of security applications. For the sake of these applications, rain detection would be useful to adjust their reliability. In this paper, we introduce the problem (almost unprecedented) of unfocused raindrops. Then, we present a first approach to detect these unfocused raindrops on a transparent screen using a spatio-temporal approach to achieve detection in real-time. We successfully tested our algorithm for Intelligent Transport System (ITS) using an on-board camera and thus, detected the raindrops on the windscreen. Our algorithm differs from the others in that we do not need the focus to be set on the windscreen. Therefore, it means that our algorithm may run on the same camera sensor as the other vision-based algorithms.
OpenFlow Controllers over EstiNet Network Simulator and Emulator : Functional Validation and Performance Evaluation

In this article, we use the EstiNet OpenFlow network simulator and emulator to perform functional validation and performance evaluation of the widely-used NOX OpenFlow controller. EstiNet uses an unique kernel reentering simulation methodology to enable real applications to run on nodes in its simulated network. As a result, without any modification, the real NOX OpenFlow controller readily runs on a host in an EstiNet simulated network to control thousands of simulated OpenFlow switches. Using EstiNet as the testing and evaluation platform, we studied how NOX implements the learning bridge protocol (LBP) and the spanning tree protocol (STP) based on the OpenFlow 1.0 protocol. Our simulation results show that these protocols, which are implemented as loadable components in NOX, do not synchronize their gathered information well and thus NOX may give wrong forwarding instructions to an OpenFlow switch after a link failure. We also found that when NOX’s STP detects a link failure, it does not send a message to an affected OpenFlow switch to delete obsolete flow entries. As a result, because the obsolete flow entry expires only after an idle period of 5 seconds, it may be matched and used endlessly causing the OpenFlow switch to continue to forward incoming matched packets onto a broken link. Our results reveal that the LBP and STP components provided in NOX serve only as basic implementations and lack information synchronization, and there is much room left to further enhance
Character Identification on Multiparty Conversation: Identifying Mentions of Characters in TV Shows

This paper introduces a subtask of entity linking, called character identification, that maps mentions in multiparty conversation to their referent characters. Transcripts of TV shows are collected as the sources of our corpus and automatically annotated with mentions by linguistically-motivated rules. These mentions are manually linked to their referents through crowdsourcing. Our corpus comprises 543 scenes from two TV shows, and shows the inter-annotator agreement of κ = 79.96. For statistical modeling, this task is reformulated as coreference resolution, and experimented with a state-of-the-art system on our corpus. Our best model gives a purity score of 69.21 on average, which is promising given the challenging nature of this task and our corpus.
A Naturalistic Open Source Movie for Optical Flow Evaluation

Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the imageand flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.
Self-oscillating resonant converter for led applications at 500 Khz

In this paper, an improved design approach is proposed for the self-oscillating command circuit as a driver for resonant converters on LED applications. The improvements, built upon the classic design methodology, contemplate the influence of the MOSFET input capacitance, and make it suitable for high frequency application, where such parasitic elements lead to increased discrepancies. When operated at higher frequencies, resonant converters like the half-bridge series resonant inverter, imply reduced size and weight for the reactive elements, which combined to the simplicity and low cost of the self-oscillating command circuit, translates into a suitable high-power density application. The self-oscillating resonant converter design is based on the describing function method and the extended Nyquist stability criterion, granting the system a proper design prediction around a self-sustained frequency. Simulation and experimental results are shown, comparing the classic design methodology with the improved design approach up to 500 kHz, displaying the feasibility of the design.
Memory augmented neural network for source separation

Recurrent neural network (RNN) based on long short-term memory (LSTM) has been successfully developed for single-channel source separation. Temporal information is learned by using dynamic states which are evolved through time and stored as an internal memory. The performance of source separation is constrained due to the limitation of internal memory which could not sufficiently preserve long-term characteristics from different sources. This study deals with this limitation by incorporating an external memory in RNN and accordingly presents a memory augmented neural network for source separation. In particular, we carry out a neural Turing machine to learn a separation model for sequential signals of speech and noise in presence of different speakers and noise types. Experiments show that speech enhancement based on memory augmented neural network consistently outperforms that using deep neural network and LSTM in terms of short-term objective intelligibility measure.
A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.
Different Feature Selection for Sentiment Classification

Sentiment Analysis (SA) research has increased tremendously in recent times .Sentiment analysis means to extract opinion of users from review documents. Sentiment classification using Machine learning (ML ) methods faces the problem of high dimensionality of feature vector. Therefore, a feature selection method is required to eliminate the irrelevant and noisy features from the feature vector for efficient working of ML algorithms Rough set theory provides an important concept for feature reduction called reduct. The cost of reduct set computation is highly influenced by the attribute size of the dataset where the problem of finding reducts has been proven as NPhard problems.Different feature selection are applied on different data set, Experimental results show that mRMR is better compared to IG for sentiment classification, Hybrid feature selection method based on the RST and Information Gain (IG) is better compared to the previous methods. Proposed methods are evaluated on four standard datasets viz. Movie review, Product (book, DVD, and electronics) reviewed datasets, and Experimental results show that hybrid feature selection method outperforms than feature selection methods for sentimental classification.
IoT Security: Ongoing Challenges and Research Opportunities

The Internet of Things (IoT) opens opportunities for wearable devices, home appliances, and software to share and communicate information on the Internet. Given that the shared data contains a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we begin with general information security background of IoT and continue on with information security related challenges that IoT will encountered. Finally, we will also point out research directions that could be the future work for the solutions to the security challenges that IoT encounters.
Synchronization methods in parallel and distributed discrete-event simulation

This work attempts to provide insight into the problem of executing discrete event simulation in a distributed fashion. The article serves as the state of the art in Parallel DiscreteEvent Simulation (PDES) by surveying existing algorithms and analyzing the merits and drawbacks of various techniques. We discuss the main characteristics of existing synchronization methods for parallel and distributed discrete event simulation. The two major categories of synchronization protocols, namely conservative and optimistic, are introduced and various approaches within each category are presented. We also present the latest efforts towards PDES on emerging platforms such as heterogeneous multicore processors, Web services, as well as Grid and Cloud environment.  Parallel discrete-event simulation With the computing power and advanced software available today, modeling and simulation has become a cost-effective tool for detailed analysis of a broad array of natural and artificial systems. Parallel and distributed simulation is widely accepted as the technology of choice to speed up large-scale discrete-event simulation and to promote reusability and interoperability of simulation components. Parallel Discrete-Event Simulation (PDES) has received increasing interest as simulations become more time consuming and geographically distributed. A rich literature has already been developed in the last three decades, taking advantage of the increasing availability of highly parallel and distributed computing platforms. It has been several years since the last survey by Perumalla  and a refreshed review of the latest developments would be needed for us to ponder new research initiatives, especially on emerging platforms such as many-core processors, Internetscale simulation environments, and cloud-based virtualized infrastructures. In parallel and distributed simulations, the entire simulation task is divided into a set of smaller subtasks with each executed on a different processor or node. Hence, the simulation system is viewed as a collection of concurrent processes, each modeling a different part of the physical system and executing on a dedicated processor in a sequential fashion. These processes communicate with each other by exchanging time-stamped event messages. An event refers to an update to simulation system state at a specific simulation time instant. Throughout the simulation, events arrive at destination processes, and depending on the delivery ordering system of the simulation, they are processed differently. The two commonly used orderings are (1) receive-order and (2) timestamp-order. With the first type, events are delivered to the destination processes when they arrive at the destination. On the other hand, with the timestamp-order, events are delivered in non-decreasing order of their timestamp, requiring runtime checks and buffering to ensure such ordering. . All rights reserved.
A Probabilistic Model for Time-Aware Entity Recommendation

In recent years, there has been an increasing effort to develop techniques for related entity recommendation, where the task is to retrieve a ranked list of related entities given a keyword query. Another trend in the area of information retrieval (IR) is to take temporal aspects of a given query into account when assessing the relevance of documents. However, while this has become an established functionality in document search engines, the significance of time has not yet been recognized for entity recommendation. In this paper, we address this gap by introducing the task of time-aware entity recommendation. We propose the first probabilistic model that takes time-awareness into consideration for entity recommendation by leveraging heterogeneous knowledge of entities extracted from different data sources publicly available on the Web. We extensively evaluate the proposed approach and our experimental results show considerable improvements compared to time-agnostic entity recommendation approaches.
Multi-robot localization in a landmark-free environment using binaural sonar

It's interesting for a collaborative group of mobile robots to be able to identify and localize each other without referring to external beacons. In this context, we are able to develop a localizer model by combining an extended Kalman filter with an ultrasonic sensing model and synchronize them by a communication protocol among mobile agents. The approach we present in this paper consists of embedding on each agent traditional encoders in order to estimate their motion. Then, the EKF is used to correct the position estimate by a collective data fusion technique. The information required here consists of the relative pose between a pair of vehicles. It's an exteroceptive sensor, called binaural sonar, that allows to perform the observation. All along the localization process, these mobile agents share collected data and notify each other when new events procure. This paper also presents simulation results for this approach when applied to non-holonomic constraints mobile robots without using external tools or centralized computational units.
Security Risk Assessment of Cloud Carrier

Cloud computing based delivery model has been adopted by end-users and enterprises to reduce IT costs and complexities. The ability to offload user software and data to cloud data centers has raised many security and privacy concerns over the cloud computing model. Significant research efforts have focused on hyper visor security and low-layer operating system implementations in cloud data centers. Unfortunately, the role of cloud carrier in the security and privacy of user software and data has not been well studied. Cloud carrier represents the wide area network that provides the connectivity and transport of cloud services between cloud consumers and cloud providers. In this paper, we present a risk assessment framework to study the security risk of the cloud carrier between cloud consumers and cloud providers. The risk assessment framework leverages the National Vulnerability Database (NVD) to examine the security vulnerabilities of operating systems of routers within the cloud carrier. This framework provides quantifiable security metrics for cloud carrier, which enables cloud consumers to establish the quality of security services among cloud providers. Such security metric information is very useful in the Service Level Agreement (SLA) negotiation between a cloud consumer and a cloud provider. It can be also be used to build a tool to verify SLA compliance. Furthermore, we implement this framework for the cloud carriers of Amazon Web Services and Windows Azure Platform. Our experiments show that the security risks of cloud carriers on these two commercial clouds are significantly different. This finding provides guidance for a network provider to improve the security of cloud carriers.
Supervised Word Sense Disambiguation using Python

In this paper, we discuss the problem of Word Sense Disambigu ation (WSD) and one approach to solving the lexical sample problem. We use training and te st data fromSENSEVAL-3 and implement methods based on Naı̈ve Bayes calculations, cosi ne comparison of word-frequency vectors, decision lists, and Latent Semantic Analysis. We a lso implement a simple classifier combination system that combines these classifiers into one WSD module. We then prove the effectiveness of our WSD module by participating in the M ultilingual Chinese-English Lexical Sample Task from SemEval-2007.