nils-herrmann/clean_scirepeval huggingface dataset:
OpenCL framework for ARM processors with NEON support

The state-of-the-art ARM processors provide multiple cores and SIMD instructions. OpenCL is a promising programming model for utilizing such parallel processing capability because of its SPMD programming model and built-in vector support. Moreover, it provides portability between multicore ARM processors and accelerators in embedded systems. In this paper, we introduce the design and implementation of an efficient OpenCL framework for multicore ARM processors. Computational tasks in a program are implemented as OpenCL kernels and run on all CPU cores in parallel by our OpenCL framework. Vector operations and built-in functions in OpenCL kernels are optimized using the NEON SIMD instruction set. We evaluate our OpenCL framework using 37 benchmark applications. The result shows that our approach is effective and promising.
Efficient Estimation of Word Representations in Vector Space

We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
A protocol for a secure remote keyless entry system applicable in vehicles using symmetric-key cryptography

In our modern society, comfort became a standard. This comfort, especially in cars can only be achieved by equipping the car with more electronic devices. Some of the electronic devices must cooperate with each other and thus they require a communication channel, which can be wired or wireless. In these days, it would be hard to sell a new car operating with traditional keys. Almost all modern cars can be locked or unlocked with a Remote Keyless System. A Remote Keyless System consists of a key fob that communicates wirelessly with the car transceiver that is responsible for locking and unlocking the car. However there are several threats for wireless communication channels. This paper describes the possible attacks against a Remote Keyless System and introduces a secure protocol as well as a lightweight Symmetric Encryption Algorithm for a Remote Keyless Entry System applicable in vehicles.
Color indexing

Computer vision is embracing a new research focus in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, realistic environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the location of a known object. Color can be successfully used for both tasks. This article demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique calledHistogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection, which allows real-time indexing into a large database of stored models. For solving the location problem it introduces an algorithm calledHistogram Backprojection, which performs this task efficiently in crowded scenes.
Identifying Buzzing Stories via Anomalous Temporal Subgraph Discovery

Story identification from online user-generated content has recently raised increasing attention. Existing approaches fall into two categories. Approaches in the first category extract stories as cohesive substructures in a graph representing the strength of association between terms. The latter category includes approaches that analyze the temporal evolution of individual terms and identify stories by grouping terms with similar anomalous temporal behavior. Both categories have limitations. In this work we advance the literature on story identification by devising a novel method that profitably combines the peculiarities of the two main existing approaches, thus also addressing their weaknesses. Experiments on a dataset extracted from a real-world web-search log demonstrate the superiority of the proposed method over the state of the art.
Politeness and psychological distance: a construal level perspective.

According to politeness theory (P. Brown & S. Levinson, 1987), politeness serves to both reflect and regulate social distance. On the basis of this notion and on construal level theory (N. Liberman & Y. Trope, 2008; N. Liberman, Y. Trope, & E. Stephan, 2007), it was predicted that politeness would be related to abstract construal, temporal distance, and spatial distance. Eight studies supported this prediction. Politeness increased when the addressees were construed abstractly (Study 1), were temporally distant (Studies 2, 3), and were spatially distant (Study 4). It was also found that increasing politeness produced abstract construals (Study 5), greater temporal distance (Study 6), and greater spatial distance (Study 7, 8). These findings shed light on the way politeness operates in different cultures and is conveyed in different languages, and they support the idea that dimensions of psychological distance are interrelated.
DialPort: Connecting the spoken dialog research community to real user data

This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog academic research community and gives them access to real users. We introduce a distributed, multi-modal, multi-agent prototype dialog framework that affords easy integration with various remote resources, ranging from end-to-end dialog systems to external knowledge APIs. The portal provides seamless passage from one spoken dialog system to another. To date, the DialPort portal has successfully connected to the multi-domain spoken dialog system at Cambridge University, the NOAA (National Oceanic and Atmospheric Administration) weather API and the Yelp API. We present statistics derived from log data gathered during preliminary tests of the portal on the performance of the portal and on the quality (seamlessness) of the transition from one system to another.
Bullet trains: a study of NIC burst behavior at microsecond timescales

While numerous studies have examined the macro-level behavior of traffic in data center networks---overall flow sizes, destination variability, and TCP burstiness---little information is available on the behavior of data center traffic at packet-level timescales. Whereas one might assume that flows from different applications fairly share available link bandwidth, and that packets within a single flow are uniformly paced, the reality is more complex. To meet increasingly high link rates of 10 Gbps and beyond, batching is typically introduced across the network stack---at the application, middleware, OS, transport, and NIC layers. This batching results in short-term packet bursts, which have implications for the design and performance requirements of packet processing devices along the path, including middleboxes, SDN-enabled switches, and virtual machine hypervisors.
 In this paper, we study the burst behavior of traffic emanating from a 10-Gbps end host across a variety of data center applications. We find that at 10--100 microsecond timescales, the traffic exhibits large bursts (i.e., 10s of packets in length). We further find that this level of burstiness is largely outside of application control, and independent of the high-level behavior of applications.
Academic procrastination of undergraduates : Low self-efficacy to self-regulate predicts higher levels of procrastination

This article reports two studies exploring the academic procrastination of 456 undergraduates. Study 1 explores the relationships among academic procrastination, self-regulation, academic self-efficacy, self-esteem, and self-efficacy for self-regulation. Results reveal that although other self-variables are related to procrastination, self-efficacy for self-regulation is most predictive of procrastination tendencies. Study 2 examines academic and motivation characteristics of ‘‘negative procrastinators,’’ the undergraduates who are most adversely influenced by procrastination. The 25% of 195 participants in Study 2 who were classified as negative procrastinators had significantly lower GPAs, higher levels of daily and task procrastination, lower predicted and actual class grades, and lower self-efficacy for self-regulation. After controlling for GPA, daily procrastination and selfefficacy for self-regulation significantly predicted the negative impact of procrastination. The article concludes with a discussion of the importance that self-efficacy for self-regulation holds for procrastination research, and with suggestions for practitioners who work with students who are adversely affected by procrastination.
Design and evaluation of a wide-area event notification service

The components of a loosely coupled system are typically designed to operate by generating and responding to asynchronous events. An event notification service is an application-independent infrastructure that supports the construction of event-based systems, whereby generators of events publish event notifications to the infrastructure and consumers of events subscribe with the infrastructure to receive relevant notifications. The two primary services that should be provided to components by the infrastructure are notification selection (i. e., determining which notifications match which subscriptions) and notification delivery (i.e., routing matching notifications from publishers to subscribers). Numerous event notification services have been developed for local-area networks, generally based on a centralized server to select and deliver event notifications. Therefore, they suffer from an inherent inability to scale to wide-area networks, such as the Internet, where the number and physical distribution of the service's clients can quickly overwhelm a centralized solution. The critical challenge in the setting of a wide-area network is to maximize the expressiveness in the selection mechanism without sacrificing scalability in the delivery mechanism. This paper presents SIENA, an event notification service that we have designed and implemented to exhibit both expressiveness and scalability. We describe the service's interface to applications, the algorithms used by networks of servers to select and deliver event notifications, and the strategies used to optimize performance. We also present results of simulation studies that examine the scalability and performance of the service.
Metaheuristic Algorithms for Convolution Neural Network

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).
A predictable and command-level priority-based DRAM controller for mixed-criticality systems

Mixed-criticality systems have tasks with different criticality levels running on the same hardware platform. Today's DRAM controllers cannot adequately satisfy the often conflicting requirements of tightly bounded worst-case latency for critical tasks and high performance for non-critical real-time tasks. We propose a DRAM memory controller that meets these requirements by using bank-aware address mapping and DRAM command-level priority-based scheduling with preemption. Many standard DRAM controllers can be extended with our approach, incurring no performance penalty when critical tasks are not generating DRAM requests. Our approach is evaluated by replaying memory traces obtained from executing benchmarks on an ARM ISA-based processor with caches, which is simulated on the gem5 architecture simulator. We compare our approach against previous TDM-based approaches, showing that our proposed memory controller achieves dramatically higher performance for non-critical tasks, without any significant impact on the worstcase latency of critical tasks.
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks

Speech enhancement model is used to map a noisy speech to a clean speech. In the training stage, an objective function is often adopted to optimize the model parameters. However, in the existing literature, there is an inconsistency between the model optimization criterion and the evaluation criterion for the enhanced speech. For example, in measuring speech intelligibility, most of the evaluation metric is based on a short-time objective intelligibility STOI measure, while the frame based mean square error MSE between estimated and clean speech is widely used in optimizing the model. Due to the inconsistency, there is no guarantee that the trained model can provide optimal performance in applications. In this study, we propose an end-to-end utterance-based speech enhancement framework using fully convolutional neural networks FCN to reduce the gap between the model optimization and the evaluation criterion. Because of the utterance-based optimization, temporal correlation information of long speech segments, or even at the entire utterance level, can be considered to directly optimize perception-based objective functions. As an example, we implemented the proposed FCN enhancement framework to optimize the STOI measure. Experimental results show that the STOI of a test speech processed by the proposed approach is better than conventional MSE-optimized speech due to the consistency between the training and the evaluation targets. Moreover, by integrating the STOI into model optimization, the intelligibility of human subjects and automatic speech recognition system on the enhanced speech is also substantially improved compared to those generated based on the minimum MSE criterion.
Simulating LTE/LTE-Advanced Networks with SimuLTE

In this work we present SimuLTE, an OMNeT++-based simulator for LTE and LTE-Advanced networks. Following well-established OMNeT++ programming practices, SimuLTE exhibits a fully modular structure, which makes it easy to be extended, verified and integrated. Moreover, it inherits all the benefits of such a widely-used and versatile simulation framework as OMNeT++, i.e., experiment support and seamless integration with the OMNeT++ network modules, such as INET. This allows SimuLTE users to build up mixed scenarios where LTE is only a part of a wider network. This paper describes the architecture of SimuLTE, with particular emphasis on the modeling choices at the MAC layer, where resource scheduling is located. Furthermore, we describe some of the verification and validation efforts and present an example of the performance analysis that can be carried out with SimuLTE.
Good features to track

No feature-based vision system can work unless good features can be identi ed and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under a ne image transformations. We test performance with several simulations and experiments.
A Network-based End-to-End Trainable Task-oriented Dialogue System

Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring labelled datasets and solving a statistical learning problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable dialogue system along with a new way of collecting task-oriented dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.
Universal Blind Quantum Computation

We present a protocol which allows a client to have a server carry out a quantum computation for her such that the client's inputs, outputs and computation remain perfectly private, and where she does not require any quantum computational power or memory. The client only needs to be able to prepare single qubits randomly chosen from a finite set and send them to the server, who has the balance of the required quantum computational resources. Our protocol is interactive: after the initial preparation of quantum states, the client and server use two-way classical communication which enables the client to drive the computation, giving single-qubit measurement instructions to the server, depending on previous measurement outcomes. Our protocol works for inputs and outputs that are either classical or quantum. We give an authentication protocol that allows the client to detect an interfering server; our scheme can also be made fault-tolerant. We also generalize our result to the setting of a purely classical client who communicates classically with two non-communicating entangled servers, in order to perform a blind quantum computation. By incorporating the authentication protocol, we show that any problem in BQP has an entangled two-prover interactive proof with a purely classical verifier. Our protocol is the first universal scheme which detects a cheating server, as well as the first protocol which does not require any quantum computation whatsoever on the client's side. The novelty of our approach is in using the unique features of measurement-based quantum computing which allows us to clearly distinguish between the quantum and classical aspects of a quantum computation.
Convolutional deep maxout networks for phone recognition

Convolutional neural networks have recently been shown to outperform fully connected deep neural networks on several speech recognition tasks. Their superior performance is due to their convolutional structure that processes several, slightly shifted versions of the input window using the same weights, and then pools the resulting neural activations. This pooling operation makes the network less sensitive to translations. The convolutional network results published up till now used sigmoid or rectified linear neurons. However, quite recently a new type of activation function called the maxout activation has been proposed. Its operation is closely related to convolutional networks, as it applies a similar pooling step, but over different neurons evaluated on the same input. Here, we combine the two technologies, and experiment with deep convolutional neural networks built from maxout neurons. Phone recognition tests on the TIMIT database show that switching to maxout units from rectifier units decreases the phone error rate for each network configuration studied, and yields relative error rate reductions of between 2% and 6%.
Belief space planning assuming maximum likelihood observations

We cast the partially observable control problem as a fully observable underactuated stochastic control problem in belief space and apply standard planning and control techniques. One of the difficulties of belief space planning is modeling the stochastic dynamics resulting from unknown future observations . The core of our proposal is to define deterministic beliefsystem dynamics based on an assumption that the maximum likelihood observation (calculated just prior to the observation) is always obtained. The stochastic effects of future observation s are modeled as Gaussian noise. Given this model of the dynamics, two planning and control methods are applied. In the first, linear quadratic regulation (LQR) is applied to generate policies in the belief space. This approach is shown to be optimal for linearGaussian systems. In the second, a planner is used to find locally optimal plans in the belief space. We propose a replanning approach that is shown to converge to the belief space goal in a finite number of replanning steps. These approaches are characterized in the context of a simple nonlinear manipulation problem where a planar robot simultaneously locates and grasps an object.
Symbiotic Segmentation and Part Localization for Fine-Grained Categorization

We propose a new method for the task of fine-grained visual categorization. The method builds a model of the base-level category that can be fitted to images, producing high-quality foreground segmentation and mid-level part localizations. The model can be learnt from the typical datasets available for fine-grained categorization, where the only annotation provided is a loose bounding box around the instance (e.g. bird) in each image. Both segmentation and part localizations are then used to encode the image content into a highly-discriminative visual signature. The model is symbiotic in that part discovery/localization is helped by segmentation and, conversely, the segmentation is helped by the detection (e.g. part layout). Our model builds on top of the part-based object category detector of Felzenszwalb et al., and also on the powerful Grab Cut segmentation algorithm of Rother et al., and adds a simple spatial saliency coupling between them. In our evaluation, the model improves the categorization accuracy over the state-of-the-art. It also improves over what can be achieved with an analogous system that runs segmentation and part-localization independently.