nils-herrmann/clean_scirepeval huggingface dataset:
Cognitive radio based hierarchical communications infrastructure for smart grid

The current centrally controlled power grid is undergoing a drastic change in order to deal with increasingly diversified challenges, including environment and infrastructure. The next-generation power grid, known as the smart grid, will be realized with proactive usage of state-of-the-art technologies in the areas of sensing, communications, control, computing, and information technology. In a smart power grid, an efficient and reliable communication architecture plays a crucial role in improving efficiency, sustainability, and stability. In this article, we first identify the fundamental challenges in the data communications for the smart grid and introduce the ongoing standardization effort in the industry. Then we present an unprecedented cognitive radio based communications architecture for the smart grid, which is mainly motivated by the explosive data volume, diverse data traffic, and need for QoS support. The proposed architecture is decomposed into three subareas: cognitive home area network, cognitive neighborhood area network, and cognitive wide area network, depending on the service ranges and potential applications. Finally, we focus on dynamic spectrum access and sharing in each subarea. We also identify a very unique challenge in the smart grid, the necessity of joint resource management in the decomposed NAN and WAN geographic subareas in order to achieve network scale performance optimization. Illustrative results indicate that the joint NAN/WAN design is able to intelligently allocate spectra to support the communication requirements in the smart grid.
Support Vector Machines for Multiple-Instance Learning

This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. We present experimental results on a pharmaceutical data set and on applications in automated image indexing and document categorization.
A survey and classification of semantic search approaches

A broad range of approaches to semantic document retrieval has been developed in the context of the Semantic Web. This survey builds bridges among them. We introduce a classification scheme for semantic search engines and clarify terminology. We present an overview of ten selected approaches and compare them by means of our classification criteria. Based on this comparison, we identify not only common concepts and outstanding features, but also open issues. Finally, we give directions for future application development and research.
GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB

We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence. Our tracking method combines a convolutional neural network with a kinematic 3D hand model, such that it generalizes well to unseen data, is robust to occlusions and varying camera viewpoints, and leads to anatomically plausible as well as temporally smooth hand motions. For training our CNN we propose a novel approach for the synthetic generation of training data that is based on a geometrically consistent image-to-image translation network. To be more specific, we use a neural network that translates synthetic images to "real" images, such that the so-generated images follow the same statistical distribution as real-world hand images. For training this translation network we combine an adversarial loss and a cycle-consistency loss with a geometric consistency loss in order to preserve geometric properties (such as hand pose) during translation. We demonstrate that our hand tracking system outperforms the current state-of-the-art on challenging RGB-only footage.
Exploratory Study of a Robot Approaching a Person in the Context of Handing Over an Object

This paper presents the results from a Human-Robot Interaction study that investigates the issues of participants’ preferences in terms of the robot approach direction (directionRAD), robot base approach interaction distance (distanceRBAID), robot handing over hand distance (distanceRHOHD), robot handing over arm gesture (gestureRHOAG), and the coordination of both the robot approaching and gestureRHOAG in the context of a robot handing over an object to a seated person. The results from this study aim at informing the development of a Human Aware Manipulation Planner. Twelve participants with some previous human-robot interaction experience were recruited for the trial. The results show that a majority of the participants prefer the robot to approach from the front and hand them a can of soft drink in the front sector of their personal zone. The robot handing over hand position had the most influence on determining from where the robot should approach (i.e distanceRAD). Legibility and perception of risk seem to be the deciding factor on how participants choose their preferred robot arm-base approach coordination for handing over a can. Detailed discussions of the results conclude the paper.
E-learning system use and its outcomes: Moderating role of perceived compatibility

This paper explores the moderating role of perceived compatibility on the relationship between e-learning system use and its outcomes. We conceptualize e-learning outcomes using academic performance, perceived learning assistance, and perceived community building assistance. We further hypothesize that perceived compatibility moderates the relationships between e-learning system use and these outcome variables. The model was tested by collecting data from university students (n = 179) participating in hybrid courses using a popular learning management system, Moodle. The findings suggest that perceived compatibility moderates the relationship between e-learning system use and academic performance. However, it did not moderate the other two relationships, i.e. (1) the relationship between e-learning system use and perceived learning assistance, and (2) the relationship between e-learning system use and perceived community building assistance.
Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks

Obstacle Detection is a central problem for any robotic system, and critical for autonomous systems that travel at high speeds in unpredictable environment. This is often achieved through scene depth estimation, by various means. When fast motion is considered, the detection range must be longer enough to allow for safe avoidance and path planning. Current solutions often make assumption on the motion of the vehicle that limit their applicability, or work at very limited ranges due to intrinsic constraints. We propose a novel appearance-based Object Detection system that is able to detect obstacles at very long range and at a very high speed (~ 300Hz), without making assumptions on the type of motion. We achieve these results using a Deep Neural Network approach trained on real and synthetic images and trading some depth accuracy for fast, robust and consistent operation. We show how photo-realistic synthetic images are able to solve the problem of training set dimension and variety typical of machine learning approaches, and how our system is robust to massive blurring of test images.
On the Practicality of Cryptographically Enforcing Dynamic Access Control Policies in the Cloud

The ability to enforce robust and dynamic access controls on cloud-hosted data while simultaneously ensuring confidentiality with respect to the cloud itself is a clear goal for many users and organizations. To this end, there has been much cryptographic research proposing the use of (hierarchical) identity-based encryption, attribute-based encryption, predicate encryption, functional encryption, and related technologies to perform robust and private access control on untrusted cloud providers. However, the vast majority of this work studies static models in which the access control policies being enforced do not change over time. This is contrary to the needs of most practical applications, which leverage dynamic data and/or policies. In this paper, we show that the cryptographic enforcement of dynamic access controls on untrusted platforms incurs computational costs that are likely prohibitive in practice. Specifically, we develop lightweight constructions for enforcing role-based access controls (i.e., RBAC0) over cloud-hosted files using identity-based and traditional public-key cryptography. This is done under a threat model as close as possible to the one assumed in the cryptographic literature. We prove the correctness of these constructions, and leverage real-world RBAC datasets and recent techniques developed by the access control community to experimentally analyze, via simulation, their associated computational costs. This analysis shows that supporting revocation, file updates, and other state change functionality is likely to incur prohibitive overheads in even minimally-dynamic, realistic scenarios. We identify a number of bottlenecks in such systems, and fruitful areas for future work that will lead to more natural and efficient constructions for the cryptographic enforcement of dynamic access controls. Our findings naturally extend to the use of more expressive cryptographic primitives (e.g., HIBE or ABE) and richer access control models (e.g., RBAC1 or ABAC).
Merging the senses into a robust percept

To perceive the external environment our brain uses multiple sources of sensory information derived from several different modalities, including vision, touch and audition. All these different sources of information have to be efficiently merged to form a coherent and robust percept. Here we highlight some of the mechanisms that underlie this merging of the senses in the brain. We show that, depending on the type of information, different combination and integration strategies are used and that prior knowledge is often required for interpreting the sensory signals.
Heterogeneous Vehicular Networking: A Survey on Architecture, Challenges, and Solutions

With the rapid development of the Intelligent Transportation System (ITS), vehicular communication networks have been widely studied in recent years. Dedicated Short Range Communication (DSRC) can provide efficient real-time information exchange among vehicles without the need of pervasive roadside communication infrastructure. Although mobile cellular networks are capable of providing wide coverage for vehicular users, the requirements of services that require stringent real-time safety cannot always be guaranteed by cellular networks. Therefore, the Heterogeneous Vehicular NETwork (HetVNET), which integrates cellular networks with DSRC, is a potential solution for meeting the communication requirements of the ITS. Although there are a plethora of reported studies on either DSRC or cellular networks, joint research of these two areas is still at its infancy. This paper provides a comprehensive survey on recent wireless networks techniques applied to HetVNETs. Firstly, the requirements and use cases of safety and non-safety services are summarized and compared. Consequently, a HetVNET framework that utilizes a variety of wireless networking techniques is presented, followed by the descriptions of various applications for some typical scenarios. Building such HetVNETs requires a deep understanding of heterogeneity and its associated challenges. Thus, major challenges and solutions that are related to both the Medium Access Control (MAC) and network layers in HetVNETs are studied and discussed in detail. Finally, we outline open issues that help to identify new research directions in HetVNETs.
A scalable two-stage approach for a class of dimensionality reduction techniques

Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data. Many existing dimensionality reduction techniques can be formulated as a generalized eigenvalue problem, which does not scale to large-size problems. Prior work transforms the generalized eigenvalue problem into an equivalent least squares formulation, which can then be solved efficiently. However, the equivalence relationship only holds under certain assumptions without regularization, which severely limits their applicability in practice. In this paper, an efficient two-stage approach is proposed to solve a class of dimensionality reduction techniques, including Canonical Correlation Analysis, Orthonormal Partial Least Squares, linear Discriminant Analysis, and Hypergraph Spectral Learning. The proposed two-stage approach scales linearly in terms of both the sample size and data dimensionality. The main contributions of this paper include (1) we rigorously establish the equivalence relationship between the proposed two-stage approach and the original formulation without any assumption; and (2) we show that the equivalence relationship still holds in the regularization setting. We have conducted extensive experiments using both synthetic and real-world data sets. Our experimental results confirm the equivalence relationship established in this paper. Results also demonstrate the scalability of the proposed two-stage approach.
Transfiguring portraits

People may look dramatically different by changing their hair color, hair style, when they grow older, in a different era style, or a different country or occupation. Some of those may transfigure appearance and inspire creative changes, some not, but how would we know without physically trying? We present a system that enables automatic synthesis of limitless numbers of appearances. A user inputs one or more photos (as many as they like) of his or her face, text queries an appearance of interest (just like they'd search an image search engine) and gets as output the input person in the queried appearance. Rather than fixing the number of queries or a dataset our system utilizes all the relevant and searchable images on the Internet, estimates a doppelgänger set for the inputs, and utilizes it to generate composites. We present a large number of examples on photos taken with completely unconstrained imaging conditions.
Deep Manifold Traversal: Changing Labels with Convolutional Features

Machine learning is increasingly used in high impact applications such as prediction of hospital re-admission, cancer screening or bio-medical research applications. As predictions become increasingly accurate, practitioners may be interested in identifying actionable changes to inputs in order to alter their class membership. For example, a doctor might want to know what changes to a patient’s status would predict him/her to not be re-admitted to the hospital soon. Szegedy et al. (2013b) demonstrated that identifying such changes can be very hard in image classification tasks. In fact, tiny, imperceptible changes can result in completely different predictions without any change to the true class label of the input. In this paper we ask the question if we can make small but meaningful changes in order to truly alter the class membership of images from a source class to a target class. To this end we propose deep manifold traversal, a method that learns the manifold of natural images and provides an effective mechanism to move images from one area (dominated by the source class) to another (dominated by the target class).The resulting algorithm is surprisingly effective and versatile. It allows unrestricted movements along the image manifold and only requires few images from source and target to identify meaningful changes. We demonstrate that the exact same procedure can be used to change an individual’s appearance of age, facial expressions or even recolor black and white images.
Temporal processing deficits of language-learning impaired children ameliorated by training.

Children with language-based learning impairments (LLIs) have major deficits in their recognition of some rapidly successive phonetic elements and nonspeech sound stimuli. In the current study, LLI children were engaged in adaptive training exercises mounted as computer "games" designed to drive improvements in their "temporal processing" skills. With 8 to 16 hours of training during a 20-day period, LLI children improved markedly in their abilities to recognize brief and fast sequences of nonspeech and speech stimuli.
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs

I would like to devote this review to my teachers and colleagues, Nadine Brisson and Gilbert Saint, who passed away too early. I am also grateful to a number of people who contributed directly and indirectly to this paper: Antonio Formaggio and Yosio Shimabokuro and their team from INPE (Sao Jose dos Campos) for shared drafting of a related research proposal for a Brazilian monitoring system; Felix Rembold (JRC Ispra) for discussing the original manuscript; Zoltan Balint, Peris Muchiri and SWALIM at FAO Somalia (Nairobi, Kenya) for contributions regarding the CDI drought index; and Anja Klisch, Francesco Vuolo and Matteo Mattiuzzi (BOKU Vienna) for providing inputs related to vegetation phenology and the MODIS processing chain.
STAR-Vote: A Secure, Transparent, Auditable, and Reliable Voting System

In her 2011 EVT/WOTE keynote, Travis County, Texas County Clerk Dana DeBeauvoir described the qualities she wanted in her ideal election system to replace their existing DREs. In response, in April of 2012, the authors, working with DeBeauvoir and her staff, jointly architected STAR-Vote, a voting system with a DRE-style human interface and a “belt and suspenders” approach to verifiability. It provides both a paper trail and end-toend cryptography using COTS hardware. It is designed to support both ballot-level risk-limiting audits, and auditing by individual voters and observers. The human interface and process flow is based on modern usability research. This paper describes the STAR-Vote architecture, which could well be the next-generation voting system for Travis County and perhaps elsewhere. This paper is a working draft. Significant changes should be expected as the STAR-Vote effort matures.
Millimeter-Wave Wireless LAN and Its Extension toward 5G Heterogeneous Networks

Millimeter-wave (mmw) frequency bands, especially 60 GHz unlicensed band, are considered as a promising solution for gigabit short range wireless communication systems. IEEE standard 802.11ad, also known as WiGig, is standardized for the usage of the 60 GHz unlicensed band for wireless local area networks (WLANs). By using this mmw WLAN, multi-Gbps rate can be achieved to support bandwidthintensive multimedia applications. Exhaustive search along with beamforming (BF) is usually used to overcome 60 GHz channel propagation loss and accomplish data transmissions in such mmw WLANs. Because of its short range transmission with a high susceptibility to path blocking, multiple number of mmw access points (APs) should be used to fully cover a typical target environment for future high capacity multi-Gbps WLANs. Therefore, coordination among mmw APs is highly needed to overcome packet collisions resulting from un-coordinated exhaustive search BF and to increase total capacity of mmw WLANs. In this paper, we firstly give the current status of mmw WLANs with our developed WiGig AP prototype. Then, we highlight the great need for coordinated transmissions among mmw APs as a key enabler for future high capacity mmw WLANs. Two different types of coordinated mmw WLAN architecture are introduced. One is distributed antenna type architecture to realize centralized coordination, while the other is autonomous coordination with the assistance of legacy Wi-Fi signaling. Moreover, two heterogeneous network (HetNet) architectures are also introduced to efficiently extend the coordinated mmw WLANs to be used for future 5th Generation (5G) cellular networks. key words: millimeter wave, IEEE802.11ad, coordinated mmw WLAN, 5G cellular networks, heterogeneous networks
A Survey and Comparison of Characteristics of Motor Drives Used in Electric Vehicles

In recent times, the worldwide price of fuel is showing an upward surge. One of the major factors leading to this can be attributed to the exponential increase in demand. In a country like Canada, where a majority of the people own vehicles, and more being added to the roads, this demand for fuel is surely going to increase in the future and will also be severely damaging to the environment as transportation sector alone is responsible for a larger share of pollutants emitted into the atmosphere. Electric vehicles offer one way to reduce the level of emissions. Electric motor drives are an integral component of an electric vehicle and consist of one or more electric motors. In this paper an effort has been made to compare different characteristics of motor drives used in electric vehicles and also given is a comprehensive list of references papers published in the field of electric vehicles
Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions.
Casual games discussion

Digital games have become a remarkable cultural phenomenon in the last ten years. The casual games sector especially has been growing rapidly in the last few years. However, there is no clear view on what is "casual" in games cultures and the area has not previously been rigorously studied. In the discussions on casual games, "casual" is often taken to refer to the player, the game or the playing style, but other factors such as business models and accessibility are also considered as characteristic of "casual" in games. Views on casual vary and confusion over different meanings can lead to paradoxical readings, which is especially the case when "casual gamer" is taken to mean both "someone who plays casual games" and someone who "plays casually". In this article we will analyse the ongoing discussion by providing clarification of the different meanings of casual and a framework for an overall understanding of casual in the level of expanded game experience.