nils-herrmann/clean_scirepeval huggingface dataset:
Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection

This paper presents an intelligent traffic control system to pass emergency vehicles smoothly. Each individual vehicle is equipped with special radio frequency identification (RFID) tag (placed at a strategic location), which makes it impossible to remove or destroy. We use RFID reader, NSK EDK-125-TTL, and PIC16F877A system-on-chip to read the RFID tags attached to the vehicle. It counts number of vehicles that passes on a particular path during a specified duration. It also determines the network congestion, and hence the green light duration for that path. If the RFID-tag-read belongs to the stolen vehicle, then a message is sent using GSM SIM300 to the police control room. In addition, when an ambulance is approaching the junction, it will communicate to the traffic controller in the junction to turn ON the green light. This module uses ZigBee modules on CC2500 and PIC16F877A system-on-chip for wireless communications between the ambulance and traffic controller. The prototype was tested under different combinations of inputs in our wireless communication laboratory and experimental results were found as expected.
K-nearest neighbor search: Fast GPU-based implementations and application to high-dimensional feature matching

The k-nearest neighbor (kNN) search problem is widely used in domains and applications such as classification, statistics, and biology. In this paper, we propose two fast GPU-based implementations of the brute-force kNN search algorithm using the CUDA and CUBLAS APIs. We show that our CUDA and CUBLAS implementations are up to, respectively, 64X and 189X faster on synthetic data than the highly optimized ANN C++ library, and up to, respectively, 25X and 62X faster on high-dimensional SIFT matching.
Topological quantum

We analyzesurface codes , the topological quantum error-correcting codes introduced by Kitaev. In these codes, qubits are arranged in a two-dimensional array on a surface of nontrivial topology, and encoded quantum operations are associated with nontrivial homology cycles of the surface. We formulate protocols for error recovery, and study the efficacy of these protocols. An order-disorder phase transition occurs in this system at a nonzero critical value of the error rate; if the error rate is below the critical value~the accuracy threshold !, encoded information can be protected arbitrarily well in the limit of a large code block. This phase transition can be accurately modeled by a three-dimensional Z2 attice gauge theory with quenched disorder. We estimate the accuracy threshold, assuming that all quantum gates arelocal, that qubits can be measured rapidly, and that polynomial-size classical computations can be executed instantaneously. We also devise a robust recovery procedure that does not require measurement or fast classical processing; however, for this procedure the quantum gates are local only if the qubits are arranged in four or more spatial dimensions. We discuss procedures for encoding, measurement, and performing fault-tolerant universal quantum computation with surface codes, and argue that these codes provide a promising framework for quantum computing architectures.
A Technique for Data Deduplication using Q-Gram Concept with Support Vector Machine

Several systems that rely on consistent data to offer high quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of duplicates, quasi-replicas, or near-duplicate entries in their repositories. Because of that, there have been significant investments from private and government organizations in developing methods for removing replicas from its data repositories.In this paper, we have proposed accordingly. In the previous work, duplicate record detection was done using three different similarity measures and neural network. In the previous work, we have generated feature vector based on similarity measures and then, neural network was used to find the duplicate records. In this paper, we have developed Q-gram concept with support vector machine for deduplication process. The similarity function, which we are used Dice coefficient,Damerau–Levenshtein distance,Tversky index for similarity measurement. Finally, support vector machine is used for testing whether data record is duplicate or not. A set of data generated from some similarity measures are used as the input to the proposed system. There are two processes which characterize the proposed deduplication technique, the training phase and the testing phase the experimental results showed that the proposed deduplication technique has higher accuracy than the existing method. The accuracy obtained for the proposed deduplication 88%.
Violent and nonviolent video games differentially affect physical aggression for individuals high vs. low in dispositional anger.

Although numerous experiments have shown that exposure to violent video games (VVG) causes increases in aggression, relatively few studies have investigated the extent to which this effect differs as a function of theoretically relevant individual difference factors. This study investigated whether video game content differentially influences aggression as a function of individual differences in trait anger. Participants were randomly assigned to play a violent or nonviolent video game before completing a task in which they could behave aggressively. Results showed that participants high in trait anger were the most aggressive, but only if they first played a VVG. This relationship held while statistically controlling for dimensions other than violent content on which game conditions differed (e.g. frustration, arousal). Implications of these findings for models explaining the effects of video games on behavior are discussed.
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.
A Parallel Shortest Path Algorithm Based on Graph-Partitioning and Iterative Correcting

In this paper, we focus on satisfying the actual demands of quickly finding the shortest paths over real-road networks in an intelligent transportation system. A parallel shortest path algorithm based on graph partitioning and iterative correcting is proposed. After evaluating the algorithm using three real road networks, we conclude that our graph-partitioning and iterative correcting based parallel algorithm has good performance. In addition, it achieves more than a 15-fold speedup on 16 processors in an IBM cluster over these real road networks.
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy

This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy.
SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases

A paramount challenge in probabilistic databases is the scalable computation of confidences of tuples in query results. This paper introduces an efficient secondary-storage operator for exact computation of queries on tuple-independent probabilistic databases. We consider the conjunctive queries without self-joins that are known to be tractable on any tuple-independent database, and queries that are not tractable in general but become tractable on probabilistic databases restricted by functional dependencies. Our operator is semantically equivalent to a sequence of aggregations and can be naturally integrated into existing relational query plans. As a proof of concept, we developed an extension of the PostgreSQL 8.3.3 query engine called SPROUT. We study optimizations that push or pull our operator or parts thereof past joins. The operator employs static information, such as the query structure and functional dependencies, to decide which constituent aggregations can be evaluated together in one scan and how many scans are needed for the overall confidence computation task. A case study on the TPC-H benchmark reveals that most TPC-H queries obtained by removing aggregations can be evaluated efficiently using our operator. Experimental evaluation on probabilistic TPC-H data shows substantial efficiency improvements when compared to the state of the art.
Design and Implementation of a 40-kV, 20-kJ/s Capacitor Charger for Pulsed-Power Application

This paper presents the design and implementation of a 40-kV, 20-kJ/s high-voltage capacitor charger based on a series/parallel resonant converter. The inclusion of the parallel resonant capacitor component in this circuit results in the production of a trapezoidal resonant current, which reduces conduction loss. This capacitor is practically realized as a part of the balancing network of the high-voltage rectifiers in the secondary side of the circuit. Particular attention in this paper is paid to the high-voltage transformer, which must be carefully designed to provide the required functionality without negative impact on the resonant circuit. A PSpice simulation has been used to prove that the proposed control method for the circuit is valid. This is supported with the experimental results, which verify that the operation of the circuit is as expected and that the converter is able to meet all the design criteria with an efficiency of up to 96%.
Optimal loop unrolling for GPGPU programs

Graphics Processing Units (GPUs) are massively parallel, many-core processors with tremendous computational power and very high memory bandwidth. With the advent of general purpose programming models such as NVIDIA's CUDA and the new standard OpenCL, general purpose programming using GPUs (GPGPU) has become very popular. However, the GPU architecture and programming model have brought along with it many new challenges and opportunities for compiler optimizations. One such classical optimization is loop unrolling. Current GPU compilers perform limited loop unrolling. In this paper, we attempt to understand the impact of loop unrolling on GPGPU programs. We develop a semi-automatic, compile-time approach for identifying optimal unroll factors for suitable loops in GPGPU programs. In addition, we propose techniques for reducing the number of unroll factors evaluated, based on the characteristics of the program being compiled and the device being compiled to. We use these techniques to evaluate the effect of loop unrolling on a range of GPGPU programs and show that we correctly identify the optimal unroll factors. The optimized versions run up to 70% faster than the unoptimized versions.
Broadband Circularly Polarized Slot Antenna Array Using a Compact Sequential-Phase Feeding Network

A broadband circularly polarized (CP) slot antenna array fed by an asymmetric coplanar waveguide (CPW) with stepped and inverted T-shaped strips is proposed. Using four square slot antenna elements with sequential rotation oblique feed and a modified sequential-phase (SP) feeding network, broadband CP can be achieved. The measured −10 dB reflection coefficient bandwidth and 3 dB axial ratio (AR) bandwidth are 55.4% (1.63–2.88 GHz) and 58% (1.65–3GHz), respectively. Good radiation characteristics with gain more than 6 dBic over the operating band are obtained by the proposed antenna array with a compact size of 155 × 155 × 0.8 mm3. Details of the proposed antenna array design and experimental results are presented and discussed.
Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications

The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.
SVDFeature: a toolkit for feature-based collaborative filtering

In this paper we introduce SVDFeature, a machine learning toolkit for feature-based collaborative filtering. SVDFeature is designed to efficiently solve the feature-based matrix factorization. The feature-based setting allows us to build factorization models incorporating side information such as temporal dynamics, neighborhood relationship, and hierarchical information. The toolkit is capable of both rate prediction and collaborative ranking, and is carefully designed for efficient training on large-scale data set. Using this toolkit, we built solutions to win KDD Cup for two consecutive years.
Predicting statistics of asynchronous SGD parameters for a large-scale distributed deep learning system on GPU supercomputers

Many studies have shown that Deep Convolutional Neural Networks (DCNNs) exhibit great accuracies given large training datasets in image recognition tasks. Optimization technique known as asynchronous mini-batch Stochastic Gradient Descent (SGD) is widely used for deep learning because it gives fast training speed and good recognition accuracies, while it may increases generalization error if training parameters are in inappropriate ranges. We propose a performance model of a distributed DCNN training system called SPRINT that uses asynchronous GPU processing based on mini-batch SGD. The model considers the probability distribution of mini-batch size and gradient staleness that are the core parameters of asynchronous SGD training. Our performance model takes DCNN architecture and machine specifications as input parameters, and predicts time to sweep entire dataset, mini-batch size and staleness with 5%, 9% and 19% error in average respectively on several supercomputers with up to thousands of GPUs. Experimental results on two different supercomputers show that our model can steadily choose the fastest machine configuration that nearly meets a target mini-batch size.
FAST 3 D SCANNING METHODS FOR LASER MEASUREMENT SYSTEMS

3D perception is a promising technology for automatic control, manufacturing and robotics. Compared to 2D vision systems 3D range sensors can provide direct geometric information of the environment. This paper describes 3D scanning systems with laser time-of-flight measurement devices and compares their usability in different application scenes. Additionally new methods that allow fast scanning are contributed. Optimizations are based on efficient scanning methods with a maximum number of measurement points in the area of interest, compensation of systematic measurement errors and improvements of the mechanical scanning system. All described methods are implemented and tested on a mobile robot with a real-time Linux system.
Improving Distant Supervision with Maxpooled Attention and Sentence-Level Supervision

We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FBNYT dataset.
Workload characterization for microservices

The microservice architecture is a new framework to construct a Web service as a collection of small services that communicate with each other. It is becoming increasingly popular because it can accelerate agile software development, deployment, and operation practices. As a result, cloud service providers are expected to host an increasing number of microservices that can generate significant resource pressure on the cloud infrastructure. We want to understand the characteristics of microservice workloads to design an infrastructure optimized for microservices. In this paper, we used Acme Air, an open-source benchmark for Web services, and analyzed the behavior of two versions of the benchmark, microservice and monolithic, for two widely used language runtimes, Node.js and Java. We observed a significant overhead due to the microservice architecture; the performance of the microservice version can be 79.2% lower than the monolithic version on the same hardware configuration. On Node.js, the microservice version consumed 4.22 times more time in the libraries of Node.js than the monolithic version to process one user request. On Java, the microservice version also consumed more time in the application server than the monolithic version. We explain these performance differences from both hardware and software perspectives. We discuss the network virtualization in Docker, an infrastructure for microservices that has nonnegligible impact on performance. These findings give clues to develop optimization techniques in a language runtime and hardware for microservice workloads.
ADLs and dynamic architecture changes

Existing ADLs typically support only static architecture specification and do not provide facilities for the support of dynamically changing architectures. This paper presents a possible solution to this problem: in order to adequately support dynamic architecture changes, ADLs can leverage techniques used in dynamic programming languages. In particular, changes to ADL specifications should be interpreted. To enable interpretation, an ADL should have an architecture construction component that supports explicit and incremental specification of architectural changes, in addition to the traditional architecture description facilities. This will allow software architects to specify the changes to an architecture after it has been built. The paper expands upon the results from an ongoing project -building a development environment for CZstyle architectures.
When dreaming is believing: the (motivated) interpretation of dreams.

This research investigated laypeople's interpretation of their dreams. Participants from both Eastern and Western cultures believed that dreams contain hidden truths (Study 1) and considered dreams to provide more meaningful information about the world than similar waking thoughts (Studies 2 and 3). The meaningfulness attributed to specific dreams, however, was moderated by the extent to which the content of those dreams accorded with participants' preexisting beliefs--from the theories they endorsed to attitudes toward acquaintances, relationships with friends, and faith in God (Studies 3-6). Finally, dream content influenced judgment: Participants reported greater affection for a friend after considering a dream in which a friend protected rather than betrayed them (Study 5) and were equally reluctant to fly after dreaming or learning of a plane crash (Studies 2 and 3). Together, these results suggest that people engage in motivated interpretation of their dreams and that these interpretations impact their everyday lives.
Local trilateral upsampling for thermal image

This paper presents an image upsampling method. Joint-bilateral filtering has been successfully applied to this problem that upsample “target” images using high-resolutional “control” images. In this filtering, the kernel is a product of weights representing spatial proximity and color (or intensity) proximity of the “control” image. However, when the “target” image involves textures that are invisible in the “control” image, these textures are damaged by this upsampling. In the case of thermal target image and visible-light control image, we can often find such textures. For solving this problem, we first propose trilateral upsampling, which is a simple extension of joint-bilateral upsampling incorporating color (or intensity) proximity weight in the “target” image. For improving this method so as to increase the spatial resolution, we introduce weight control based on local mutual information between target and control images. We confirmed that resulted images have higher spatial resolution and lower noise.
SHOT: Unique signatures of histograms for surface and texture description

This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal stems from a taxonomy of existing methods which highlights two major approaches, referred to as Signatures and Histograms, inherently emphasizing descriptiveness and robustness respectively. We formulate a comprehensive proposal which encompasses a repeatable local reference frame as well as a 3D descriptor, the latter featuring an hybrid structure between Signatures and Histograms so as to aim at a more favorable balance between descriptive power and robustness. A quite peculiar trait of our method concerns seamless integration of multiple cues within the descriptor to improve distinctiveness, which is particularly relevant nowadays due to the increasing availability of affordable RGB-D sensors which can gather both depth and color information. A thorough experimental evaluation based on datasets acquired with different types of sensors, including a novel RGBD dataset, vouches that SHOT outperforms state-of-the-art local descriptors in experiments addressing descriptor matching for object recognition, 3D reconstruction and shape retrieval.