nils-herrmann/clean_scirepeval huggingface dataset:
Obstacle detection on around view monitoring system

This paper proposes a method that realizes moving object detection (MOD) and static obstacle detection (SOD) in real time utilizing the fisheye cameras of the around viewing system (AVM). The topview of the AVM is used to calculate the vehicles movement between two frames using homograph estimation. Image features are detected and tracked evenly using cell detection technique. Then the features are projected onto the unit sphere of the fisheye camera model and the epipolar constraint is used to discriminate moving features from static ones. Moving features are clustered into different objects according to their position and orientation. 3D position of the static feature is calculated using triangular principle and potential obstacle is defined as objects above the ground and near the vehicle. The experiment shows the proposed method is robust and accurate for MOD and SOD.
An image analysis framework for the early assessment of hypertensive retinopathy signs

This paper presents a framework for the detection and measurement of retinal vessels in fundoscopy images. The proposed method segments retinal vessels, enabling the actual measurement of the vessel diameter. A Graphical User Interface (GUI) further supports a number of methods for the automatic and interactive measurement of vessel diameters at any selected point or region of interest. In addition, it provides methods for editing the vessel representation in order to recover from possible segmentation misclassifications. All the above steps are integrated into a clinical application capable of supporting vascular risk stratification in persons with hypertension. The performance of the segmentation is assessed on two publicly available DRIVE and STARE datasets. The experimental results show that the method can achieve a high accuracy on both datasets with very low computational cost, being comparative or slightly better than widely used supervised and unsupervised methods.
An accurate algebraic solution for moving source location using TDOA and FDOA measurements

This paper proposes an algebraic solution for the position and velocity of a moving source using the time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) of a signal received at a number of receivers. The method employs several weighted least-squares minimizations only and does not require initial solution guesses to obtain a location estimate. It does not have the initialization and local convergence problem as in the conventional linear iterative method. The estimated accuracy of the source position and velocity is shown to achieve the Crame/spl acute/r-Rao lower bound for Gaussian TDOA and FDOA noise at moderate noise level before the thresholding effect occurs. Simulations are included to examine the algorithm's performance and compare it with the Taylor-series iterative method.
Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.
Mechanisms for widespread hippocampal involvement in cognition.

The quintessential memory system in the human brain--the hippocampus and surrounding medial temporal lobe--is often treated as a module for the formation of conscious, or declarative, memories. However, growing evidence suggests that the hippocampus plays a broader role in memory and cognition and that theories organizing memory into strictly dedicated systems may need to be updated. We first consider the historical evidence for the specialized role of the hippocampus in declarative memory. Then, we describe the serendipitous encounter that motivated the special section in this issue, based on parallel research from our labs that suggested a more pervasive contribution of the hippocampus to cognition beyond declarative memory. Finally, we develop a theoretical framework that describes 2 general mechanisms for how the hippocampus interacts with other brain systems and cognitive processes: the memory modulation hypothesis, in which mnemonic representations in the hippocampus modulate the operation of other systems, and the adaptive function hypothesis, in which specialized computations in the hippocampus are recruited as a component of both mnemonic and nonmnemonic functions. This framework is consistent with an emerging view that the most fertile ground for discovery in cognitive psychology and neuroscience lies at the interface between parts of the mind and brain that have traditionally been studied in isolation.
Influencing the online consumer's behavior: the Web experience

Addresses one of the fundamental issues of e-marketing: how to attract and win over the consumer in the highly competitive Internet marketplace. Analyses the factors affecting the online consumer’s behavior and examines how e-marketers can influence the outcome of the virtual interaction and buying process by focusing their marketing efforts on elements shaping the customer’s virtual experience, the Web experience. Identifying the Web experience components and understanding their role as inputs in the online customer’s decision-making process are the first step in developing and delivering an attractive online presence likely to have the maximum impact on Internet users. Click-and-mortar firms delivering superior Web experience influence their physical clients’ perceptions and attitudes, driving additional traffic to traditional sales outlets. Provides a contribution to the theoretical debate around the factors influencing the online consumer’s behavior and outlines some noticeable similarities and differences between the traditional and virtual consumers.
A practical solution for scripting language compilers

Although scripting languages are becoming increasingly popular, even mature scripting language implementations remain interpreted. Several compilers and reimplementations have been attempted, generally focusing on performance.
 Based on our survey of these reimplementations, we determine that there are three important features of scripting languages that are difficult to compile or reimplement. Since scripting languages are defined primarily through the semantics of their original implementations, they often change semantics between releases. They provide large standard libraries, which are difficult to re-use, and costly to reimplement. They provide C APIs, used both for foreign-function-interfaces and to write third-party extensions. These APIs typically have tight integration with the original implementation. Finally, they support run-time code generation. These features make the important goal of correctness difficult to achieve.
 We present a technique to support these features in an ahead-of-time compiler for PHP. Our technique uses the original PHP implementation through the provided C API, both in our compiler, and an our generated code. We support all of these important scripting language features, particularly focusing on the correctness of compiled programs. Additionally, our approach allows us to automatically support limited future language changes. We present a discussion and performance evaluation of this technique, which has not previously been published.
Web Services for the Internet of Things through CoAP and EXI

According to the Internet of Things (IoT) vision, everyday objects such as domestic appliances, actuators and embedded systems of any kind in the near future will be connected with each other and with the Internet. These will form a distributed network with sensing capabilities that will allow unprecedented market opportunities, spurring new services, including energy monitoring and control of homes, buildings, industrial processes and so forth. In this paper, we concentrate on the actual implementation of the communication technology, adopting the Representational State Transfer (REST) approach. REST only relies on the HTTP methods such as GET and POST. Embedded communication devices are addressed using Universal Resource Indicators (URI) and data is exchanged through standard XML. We present our TinyOS design and implementation of two components that will play a fundamental role in the communication stack of REST-based devices. First, we focus on the Constrained Application Protocol (CoAP), which allows REST--based communications among applications residing in distributed and networked embedded systems. Second, we present our lightweight implementation of a EXI library: an efficient binary compressor for XML data files. Experimental results are provided in terms of compression, decoding (EXI) and access time (CoAP) performance.
Failure as a Service ( FaaS ) : A Cloud Service for Large-Scale , Online Failure Drills

Cloud computing is pervasive, but cloud service outages still take place. One might say that the computing forecast for tomorrow is “cloudy with a chance of failure.” One main reason why major outages still occur is that there are many unknown large-scale failure scenarios in which recovery might fail. We propose a new type of cloud service, Failure as a Service (FaaS), which allows cloud services to routinely perform large-scale failure drills in real deployments.
E2: a framework for NFV applications

By moving network appliance functionality from proprietary hardware to software, Network Function Virtualization promises to bring the advantages of cloud computing to network packet processing. However, the evolution of cloud computing (particularly for data analytics) has greatly benefited from application-independent methods for scaling and placement that achieve high efficiency while relieving programmers of these burdens. NFV has no such general management solutions. In this paper, we present a scalable and application-agnostic scheduling framework for packet processing, and compare its performance to current approaches.
Multilinear Discriminant Analysis for Higher-Order Tensor Data Classification

In the past decade, great efforts have been made to extend linear discriminant analysis for higher-order data classification, generally referred to as multilinear discriminant analysis (MDA). Existing examples include general tensor discriminant analysis (GTDA) and discriminant analysis with tensor representation (DATER). Both the two methods attempt to resolve the problem of tensor mode dependency by iterative approximation. GTDA is known to be the first MDA method that converges over iterations. However, its performance relies highly on the tuning of the parameter in the scatter difference criterion. Although DATER usually results in better classification performance, it does not converge, yet the number of iterations executed has a direct impact on DATER's performance. In this paper, we propose a closed-form solution to the scatter difference objective in GTDA, namely, direct GTDA (DGTDA) which also gets rid of parameter tuning. We demonstrate that DGTDA outperforms GTDA in terms of both efficiency and accuracy. In addition, we propose constrained multilinear discriminant analysis (CMDA) that learns the optimal tensor subspace by iteratively maximizing the scatter ratio criterion. We prove both theoretically and experimentally that the value of the scatter ratio criterion in CMDA approaches its extreme value, if it exists, with bounded error, leading to superior and more stable performance in comparison to DATER.
Mirrors: design principles for meta-level facilities of object-oriented programming languages

We identify three design principles for reflection and metaprogramming facilities in object oriented programming languages. <i>Encapsulation</i>: meta-level facilities must encapsulate their implementation. <i>Stratification</i>: meta-level facilities must be separated from base-level functionality. <i>Ontological correspondence</i>: the ontology of meta-level facilities should correspond to the ontology of the language they manipulate. Traditional/mainstream reflective architectures do not follow these precepts. In contrast, reflective APIs built around the concept of <i>mirrors</i> are characterized by adherence to these three principles. Consequently, mirror-based architectures have significant advantages with respect to distribution, deployment and general purpose metaprogramming.
3D Face Recognition using Kinect

2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of The Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.
Impact of Smartphone Position on Sensor Values and Context Discovery

With near-ubiquitous availability of smartphones equipped with a wide variety of sensors, research in building contextaware services has been growing. However, despite a large number of services proposed and developed as research prototypes, the number of truly context-aware applications available in smartphone applications market is quite limited. A major barrier for the large-scale proliferation of context aware applications is poor accuracy. This paper addresses one of the key reasons for this poor accuracy, which is the impact of smartphone positions. Users carry their smartphones in different positions such as holding in their hand, keeping inside their pants or jacket pocket, keeping in their purse, and so on. This paper addresses the issue of poor application accuracy due to varying smartphone positions. It first shows that smartphone positions significantly affect the values of the sensor data being collected by a context aware application, and this in turn has a significant impact on the accuracy of the application. Next, it describes the design and prototype development of a smartphone position discovery service that accurately detects a smartphone position. This service is based on the sensor data collected from carefully chosen sensors and runs orthogonal to any other context aware application or service. Finally, the paper demonstrates that the accuracy of an existing context aware service or application is significantly enhanced when run in conjunction with the proposed smartphone position discovery service.
Deep Code Search

To implement a program functionality, developers can reuse previously written code snippets by searching through a large-scale codebase. Over the years, many code search tools have been proposed to help developers. The existing approaches often treat source code as textual documents and utilize information retrieval models to retrieve relevant code snippets that match a given query. These approaches mainly rely on the textual similarity between source code and natural language query. They lack a deep understanding of the semantics of queries and source code.
 In this paper, we propose a novel deep neural network named CODEnn (Code-Description Embedding Neural Network). Instead of matching text similarity, CODEnn jointly embeds code snippets and natural language descriptions into a high-dimensional vector space, in such a way that code snippet and its corresponding description have similar vectors. Using the unified vector representation, code snippets related to a natural language query can be retrieved according to their vectors. Semantically related words can also be recognized and irrelevant/noisy keywords in queries can be handled.
 As a proof-of-concept application, we implement a code search tool named DeepCS using the proposed CODEnn model. We empirically evaluate DeepCS on a large scale codebase collected from GitHub. The experimental results show that our approach can effectively retrieve relevant code snippets and outperforms previous techniques.
Adaptive Kalman Filtering Methods for Low-Cost GPS/INS Localization for Autonomous Vehicles

For autonomous vehicles, navigation systems must be accurate enough to provide lane-level localization. Highaccuracy sensors are available but not cost-effective for production use. Although prone to significant error in poor circumstances, even low-cost GPS systems are able to correct Inertial Navigation Systems (INS) to limit the effects of dead reckoning error over short periods between sufficiently accurate GPS updates. Kalman filters (KF) are a standard approach for GPS/INS integration, but require careful tuning in order to achieve quality results. This creates a motivation for a KF which is able to adapt to different sensors and circumstances on its own. Typically for adaptive filters, either the process (Q) or measurement (R) noise covariance matrix is adapted, and the other is fixed to values estimated a priori. We show that by adapting Q based on the state-correction sequence and R based on GPS receiver-reported standard deviation, our filter reduces GPS root-mean-squared error by 23% in comparison to raw GPS, with 15% from only adapting R.
Dense planar SLAM

Using higher-level entities during mapping has the potential to improve camera localisation performance and give substantial perception capabilities to real-time 3D SLAM systems. We present an efficient new real-time approach which densely maps an environment using bounded planes and surfels extracted from depth images (like those produced by RGB-D sensors or dense multi-view stereo reconstruction). Our method offers the every-pixel descriptive power of the latest dense SLAM approaches, but takes advantage directly of the planarity of many parts of real-world scenes via a data-driven process to directly regularize planar regions and represent their accurate extent efficiently using an occupancy approach with on-line compression. Large areas can be mapped efficiently and with useful semantic planar structure which enables intuitive and useful AR applications such as using any wall or other planar surface in a scene to display a user's content.
Preperformance State , Routines , and Automaticity : What Does it Take to Realize Expertise in Self-Paced Events ?

To execute flawlessly and automatically in sports competition is the goal of any serious athlete. Automaticity suggests nonconscious attention to the act itself while executing, and not being aware of and therefore vulnerable to external and internal distractors. Self-paced sports and events in sports allow time for preparing to perform in a stable and predictable situation. More recently, cognitive, behavioral, and psychophysiological measures associated with developing and realizing proficiency in such acts have become increasingly identified. Many themes associated with these topics appear in the scholarly literature: conscious vs. nonconscious, controlled vs. automatic, voluntary vs. involuntary, explicit vs. implicit, systematic vs. heuristic, willed vs. nonwilled, aware vs. unaware, internal vs. externally oriented, and intentional vs. unintentional behaviors. Implications are being made about ways to influence the learning process by modeling expertise behaviors, as well as enhancing the performance of elite athletes. Of particular importance is the immediate preperformance and during-performance routine that serves as a mechanism for self-regulation of arousal level, thoughts, performance expectancy, and attentional focus.
A Hierarchical Neural Model for Learning Sequences of Dialogue Acts

We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs). The input in this task is a sequence of utterances (i.e., conversational contributions) comprising a sequence of tokens, and the output is a sequence of DA labels (one label per utterance). Our model leverages the hierarchical nature of dialogue data by using two nested RNNs that capture long-range dependencies at the dialogue level and the utterance level. This model is combined with an attention mechanism that focuses on salient tokens in utterances. Our experimental results show that our model outperforms strong baselines on two popular datasets, Switchboard and MapTask; and our detailed empirical analysis highlights the impact of each aspect of our model.
Setting our bibliographic references free: towards open citation data

The Open Citation Corpus is a new open repository of scholarly citation data, made available under a Creative Commons CC0 1.0 public domain dedication and encoded as Open Linked Data using the SPAR Ontologies. BLOCKIN Annotation Tools and CiTalO, to facilitate the semantic enhancement of the references in scholarly papers according to Semantic Publishing models and technologies. BLOCKIN publishers BLOCKIN BLOCKIN and BLOCKIN BLOCKIN institutions BLOCKIN BLOCKIN may BLOCKIN BLOCKIN freely BLOCKIN BLOCKIN build BLOCKIN BLOCKIN upon, BLOCKIN BLOCKIN enhance BLOCKIN BLOCKIN and reuse the open citation data for any purpose, without restriction under copyright or database law.
Stock Prediction based on Bayesian-LSTM

Fluctuations in stock market represent the changes in national economic objectively. Some machine learning algorithms, such as linear fitting and sequence mining, are employed to predict the stock market. However, linear fitting faces issue of over-fitting and black relationships with historical data, while sequence mining is short in efficiency and lack dynamic adaptations. This paper builds a modified Bayesian-LSTM (B-LSTM) model for stock prediction. Six indicators of the Chinese stock market in every day are the basic input for LSTM. In order to represent the economic wave, we defined a data set unit by week which means the basic unit in LSTM is data in one week. Furthermore, a Bayesian optimization model is proposed to estimate the unit number dynamically in different economic cycle. The experimental results demonstrated that the B-LSTM increase over 25% prediction than the conventional LSTM.