nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating convolutional neural network with FFT on tiny cores

FueXeA by ILSVRC and COCO competitions, Convolutional Neural Network (CNN) has become important in computer vision, and natural language processing. However state-of-the-art CNNs are computationally and memory intensive, thus energy efficient implementation on embedded platform is challenging. Recently VGGNet and ResNet showed that deep neural networks with more convolution layers (CV) and few fully connected layer (FC) can achieve lower error rates, thus reducing the complexity of convolution layers is of utmost importance. To reduce computations and shared memory usage in convolution layers, in this paper we evaluate the performance of direct convolution (Direct-Conv), Fast Fourier Transform (FFT) based convolution (FFT-Conv), and Overlap and Add FFT convolution (FFT-OVA-Conv) in embedded architecture including a low power domain specific many-core architecture called Power Efficient Nano Clusters (PENC) and ARM Cortex A53 CPU. To demonstrate the efficiency of FFT-Conv and FFT-OVA-Conv, we map ResNet-20 for the CIFAR-10 dataset on PENC as well as in ARM Cortex A53 CPU. Results are evaluated and compared with respect to throughput per watt, energy delay product, and execution time for three methods. Using built-in FFT instruction in PENC, the FFT-OVA-Conv performs 2.9× and 1.65× faster and achieves 6.7× and 2.3× better throughput per watt than Direct-Conv and FFT-Conv respectively. In ARM A53 CPU, the FFT-OVA-Conv achieves 3.36× and 1.38× improvement in execution time and 2.72× and 1.32× better throughput than Direct-Conv and FFT-Conv.
Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation

Sponsored search advertising is largely based on bidding on individual terms. The richness of natural languages permits web searchers to express their information needs in myriad ways. Advertisers have difficulty discovering all the terms that are relevant to their products or services. We examine the performance of logistic regression and collaborative filtering models on two different data sources to predict terms relevant to a set of seed terms describing an advertiser’s product or service.
Prolate spheroidal wave functions, fourier analysis, and uncertainty — V: the discrete case

A discrete time series has associated with it an amplitude spectrum which is a periodic function of frequency. This paper investigates the extent to which a time series can be concentrated on a finite index set and also have its spectrum concentrated on a subinterval of the fundamental period of the spectrum. Key to the analysis are certain sequences, called discrete prolate spheroidal sequences, and certain functions of frequency called discrete prolate spheroidal functions. Their mathematical properties are investigated in great detail, and many applications to signal analysis are pointed out.
Cloudlet-based just-in-time indexing of IoT video

As video cameras proliferate, the ability to scalably capture and search their data becomes important. Scalability is improved by performing video analytics on cloudlets at the edge of the Internet, and only shipping extracted index information and meta-data to the cloud. In this setting, we describe interactive data exploration (IDE), which refers to human-in-the-loop content-based retrospective search using predicates that may not have been part of any prior indexing. We also describe a new technique called just-in-time indexing (JITI) that improves response times in IDE.
Beamspace channel estimation for millimeter-wave massive MIMO systems with lens antenna array

By employing the lens antenna array, beamspace MIMO can utilize beam selection to reduce the number of required RF chains in mmWave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beamspace channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. Specifically, we propose to decompose the total beamspace channel estimation problem into a series of sub-problems, each of which only considers one sparse channel component. For each channel component, we first reliably detect its support by utilizing the structural characteristics of mmWave beamspace channel. Then, the influence of this channel component is removed from the total beamspace channel estimation problem. After the supports of all channel components have been detected, the nonzero elements of the sparse beamspace channel can be estimated with low pilot overhead. Simulation results show that the proposed SD-based channel estimation outperforms conventional schemes and enjoys satisfying accuracy, even in the low SNR region.
Baidu driving dataset and end-to-end reactive control model

End-to-end autonomous driving system has obtained great progress recently. In this paper, we will introduce our open source dataset: Baidu Driving Dataset(BDD), and our end-to-end reactive control model trained on BDD. The BDD comes from Baidu street view project, which generates millions of kilometers driving data every year. Among them, we publish 10000 kilometers driving data for end-to-end autonomous driving research. The BDD consists of two parts: forward images and vehicle motion attitude. The vehicle motion attitude is derived from real time kinematic GPS location data with standard deviation of 3 centimeters. Our reactive control model consists of lateral control and longitudinal control. We employ curvature instead of steering angle for lateral control, and leverage acceleration, not throttle or brake, for longitudinal control. CNN network is employed for lateral control model, mapping a single image from forward camera directly to corresponding curvature. For longitudinal control, stacked convolutional LSTM is used to extract spatial and temporal features from a sequence of frames, and to map the features with longitudinal control commands. The demo and data are in http://roadhackers.baidu.com. To the best of our knowledge, it is the first time that both lateral and longitudinal control are implemented in an end-to-end style.
Iris recognition using shape-guided approach and game theory

Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The novelty of this research effort is that we propose to apply a variational model to localize the iris region belonging to given shape space using active contour method, a geometric shape prior, and the Mumford–Shah functional. This variational model is robust against noise, poor localization and weak iris/sclera boundaries. Furthermore, we apply the Modified Contribution-Selection Algorithm (MCSA) for iris feature ranking based on the Multi-Perturbation Shapley Analysis (MSA), a framework which relies on cooperative game theory to estimate the effectiveness of the features iteratively and select them accordingly, using either forward selection or backward elimination approaches. The verification and identification performance of the proposed scheme is validated using the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and WVU Nonideal datasets.
Fault Tolerance for Stream Processing Engines

Distributed Stream Processing Engines (DSPEs) target applications related to continuous computation, online machine learning and real-time query processing. DSPEs operate on high volume of data by applying lightweight operations on real-time and continuous streams. Such systems require clusters of hundreds of machine for their deployment. Streaming applications come with various requirements, i.e., low-latency, high throughput, scalability and high availability. In this survey, we study the fault tolerance problem for DSPEs. We discuss fault tolerance techniques that are used in modern stream processing engines that are Storm, S4, Samza, SparkStreaming and MillWheel. Further, we give insight on fault tolerance approaches that we categorize as active replication, passive replication and upstream backup. Finally, we discuss implications of the fault tolerance techniques for different streaming application requirements.
Path Planning and Steering Control for an Automatic Perpendicular Parking Assist System

This paper considers the perpendicular reverse parking problem of front wheel steering vehicles. Relationships between the widths of the parking aisle and the parking place, as well as the parameters and initial position of the vehicle for planning a collision-free reverse perpendicular parking in one maneuver are first presented. Two types of steering controllers (bang-bang and saturated tanh-type controllers) for straightline tracking are proposed and evaluated. It is demonstrated that the saturated controller, which is continuous, achieves also quick steering avoiding chattering and can be successfully used in solving parking problems. Simulation results and first experimental tests confirm the effectiveness of the proposed control scheme.
An Efficient Computational Architecture for a Collision Early-Warning System for Vehicles, Pedestrians, and Bicyclists

We describe a computational architecture of a collision early-warning system for vehicles and other principals. Early-warnings allow drivers to make good judgments and to avoid emergency stopping or dangerous maneuvering. With many principals (vehicles, pedestrians, bicyclists, etc.) coexisting in a dense intersection, it is difficult to predict, even a few seconds in advance, since there are many possible scenarios. It is a major challenge to manage computational resources and human attention resources so that only the more plausible collisions are tracked, and of those, only the most critical collisions prompt warnings to drivers. In this paper, we propose a two-stage collision risk assessment process, including the following: 1) a preliminary assessment via simple efficient geometric computations, which thoroughly considers surrounding principals and identifies likely potential accidents, and 2) a specialized assessment that computes more accurate collision probabilities via sophisticated statistical inference. The whole process delivers an expected utility assessment to available user interfaces (UIs), allowing the UIs to make discriminating choices of when to warn drivers or other principals.
Prevalence of diabetes mellitus and impaired fasting glucose levels in the Eastern Province of Saudi Arabia: results of a screening campaign.

This study aimed to estimate the prevalence of diagnosed and undiagnosed diabetes mellitus (DM) in the Eastern Province of Saudi Arabia, and to study its relationship with socioeconomic factors.



The study targeted all Saudi subjects aged 30 years and above who resided in the Eastern Province in 2004. DM screening was conducted by taking the capillary fasting blood glucose (CFBG) after eight hours or more of fasting, or the casual capillary blood glucose (CCBG). A positive screening test for hyperglycaemia was defined as CFBG more than or equal to 100 mg/dl (5.6 mmol/l), or CCBG more than or equal to 140 mg/dl (7.8 mmol/l). A positive result was confirmed on another day through the measurement of fasting plasma glucose (FPG) levels from a venous sample. A diagnosis of DM was considered if FPG was more than or equal to 126 mg/dl (7.0 mmol/l), or when there was a history of a previous diagnosis.



Out of 197,681 participants, 35,929 (18.2 percent) had a positive history of DM or a positive screening test for hyperglycaemia. After confirmation by venous blood testing, the prevalence of DM dropped to 17.2 percent while the prevalence of newly diagnosed DM was 1.8 percent. The prevalence increased with age and was higher in women, widows, divorcees, those who had a low education level and the unemployed.



The prevalence of DM in Saudi Arabia is one of the highest reported in the world, and its yield of screening is high.
Perpetual development: A model of the Linux kernel life cycle

Software evolution is widely recognized as an important and common phenomenon, whereby the system follows an ever-extending development trajectory w ith intermittent releases. Nevertheless there have been only few lifecycle models that attempt to por tray such evolution. We use the evolution of the Linux kernel as the basis for the formulation of such a model, integrating the progress in time with growth of the codebase, and differentiating bet ween development of new functionality and maintenance of production versions. A unique elem nt of the model is the sequence of activities involved in releasing new production versions, and how this has changed with the growth of Linux. In particular, the release follow-up phase before th forking of a new development version, which was prominent in early releases of production ve rsions, has been eliminated in favor of a concurrent merge window in the release of 2.6.x versions . We also show that a piecewise linear model with increasing slopes provides the best descr iption of the growth of Linux. The perpetual development model is used as a framework in which c ommonly recognized benefits of incremental and evolutionary development may be demonstra ted, nd to comment on issues such as architecture, conservation of familiarity, and failed p rojects. We suggest that this model and variants thereof may apply to many other projects in additio n to Linux.
Color categories: Evidence for the cultural relativity hypothesis

The question of whether language affects our categorization of perceptual continua is of particular interest for the domain of color where constraints on categorization have been proposed both within the visual system and in the visual environment. Recent research (Roberson, Davies, & Davidoff, 2000; Roberson et al., in press) found substantial evidence of cognitive color differences between different language communities, but concerns remained as to how representative might be a tiny, extremely remote community. The present study replicates and extends previous findings using additional paradigms among a larger community in a different visual environment. Adult semi-nomadic tribesmen in Southern Africa carried out similarity judgments, short-term memory and long-term learning tasks. They showed different cognitive organization of color to both English and another language with the five color terms. Moreover, Categorical Perception effects were found to differ even between languages with broadly similar color categories. The results provide further evidence of the tight relationship between language and cognition.
Undervalued or Overvalued Customers : Capturing Total Customer Engagement Value

Customers can interact with and create value for firms in a variety of ways. This article proposes that assessing the value of customers based solely upon their transactions with a firm may not be sufficient, and valuing this engagement correctly is crucial in avoiding undervaluation and overvaluation of customers. We propose four components of a customer’s engagement value (CEV) with a firm. The first component is customer lifetime value (the customer’s purchase behavior), the second is customer referral value (as it relates to incentivized referral of new customers), the third is customer influencer value (which includes the customer’s behavior to influence other customers, that is increasing acquisition, retention, and share of wallet through word of mouth of existing customers as well as prospects), and the fourth is customer knowledge value (the value added to the firm by feedback from the customer). CEV provides a comprehensive framework that can ultimately lead to more efficient marketing strategies that enable higher long-term contribution from the customer. Metrics to measure CEV, future research propositions regarding relationships between the four components of CEV are proposed and marketing strategies that can leverage these relationships suggested.
Skill Acquisition and the LISP Tutor

An onolysis of student learning with the LISP tutor indicates thot while LISP is complex, learning it is simple. The key to factoring out the complexity of LiSP is to monitor the leorning of the 500 productions in the LISP tutor which describe the progromming skill. The learning of these productions follows the power-low learning curve typical of skill acquisition. There is transfer from other progromming experience to the extent that this programming experience involves the some productions. Subjects oppeor to differ only on the general dimensions of how well they acquire the productions ond how well they retain the productions. lnstructionol monipulotions such OS remediotion, content of feedback. ond timing of feedback ore effective to the extent they give students more practice progromming, and explain to students why correct solutions work.
Visualization of trends in subscriber attributes of communities on mobile telecommunications networks

Churn, the decision for a subscriber to leave a provider, is frequently of interest in the telecommunications industry. Previous research provides evidence that social influence can be a factor in mobile telecommunications churn. In our work, presented at ASONAM, we presented a system, called ChurnVis, to visualize the evolution of mobile telecommunications churn and subscriber actions over time. First, we infer a social network from call detail records. Then, we compute components based on an overlay of this social network and churn activity. We compute summaries of the attributes associated with the subscribers and finally, we visualize the components in a privacy preserving way. The system is able to present summaries of thousands of churn components in graphs of hundreds of millions of edges. One of the drawbacks of the original approach was that churn components were sometimes very large, leading to over-aggregation in the summary data. In this extension of the ASONAM paper, we adapt the ChurnVis approach to operate on the output of a community finding algorithm and present new results based on this adaptation.
An anatomy of a YouTube meme

Launched in 2005 as a video-sharing website, YouTube has become an emblem of participatory culture. A central feature of this website is the dazzling number of derivative videos, uploaded daily by many thousands. Using the ‘meme’ concept as an analytic tool, this article aims at uncovering the attributes common to ‘memetic videos’ – popular clips that generate extensive user engagement by way of creative derivatives. Drawing on YouTube popularity-measurements and on user-generated playlists, a corpus of 30 prominent memetic videos was assembled. A combined qualitative and quantitative analysis of these videos yielded six common features: focus on ordinary people, flawed masculinity, humor, simplicity, repetitiveness and whimsical content. Each of these attributes marks the video as incomplete or flawed, thereby invoking further creative dialogue. In its concluding section, the article addresses the skyrocketing popularity of mimicking in contemporary digital culture, linking it to economic, social and cultural logics of participation.
Self-compassion and social anxiety disorder.

Self-compassion refers to having an accepting and caring orientation towards oneself. Although self-compassion has been studied primarily in healthy populations, one particularly compelling clinical context in which to examine self-compassion is social anxiety disorder (SAD). SAD is characterized by high levels of negative self-criticism as well as an abiding concern about others' evaluation of one's performance. In the present study, we tested the hypotheses that: (1) people with SAD would demonstrate less self-compassion than healthy controls (HCs), (2) self-compassion would relate to severity of social anxiety and fear of evaluation among people with SAD, and (3) age would be negatively correlated with self-compassion for people with SAD, but not for HC. As expected, people with SAD reported less self-compassion than HCs on the Self-Compassion Scale and its subscales. Within the SAD group, lesser self-compassion was not generally associated with severity of social anxiety, but it was associated with greater fear of both negative and positive evaluation. Age was negatively correlated with self-compassion for people with SAD, whereas age was positively correlated with self-compassion for HC. These findings suggest that self-compassion may be a particularly important target for assessment and treatment in persons with SAD.
High Performance DES Encryption in Virtex(tm) FPGAs Using Jbits(tm)

A JBits implementation of the Data Encryption Standard (DES) algorithm in a Virtex FPGA is described. The Virtex architecture eÆciently implements the DES primitive operations, and permits a high degree of pipelining. JBits provides a Java-based Application Programming Interface (API) for the run time creation and modi cation of the con guration bitstream. This allows dynamic circuit specialization based on a speci c key and mode (encrypt or decrypt). The key schedule is computed entirely in software, and is part of the bitstream. As a result, all cryptographic key input and subkey generation logic are removed from the fully unrolled datapath. When combined with a speed eÆcient layout, the result is a throughput of over 10 Gigabits per second. This is suÆcient bandwidth for SONET OC-192c (optical) networks, and exceeds the speed reported for a recently announced DES ASIC.
Strategic Aspects of Cyber Risk Information Sharing

Cyber risk management largely reduces to a race for information between defenders of ICT systems and attackers. Defenders can gain advantage in this race by sharing cyber risk information with each other. Yet, they often exchange less information than is socially desirable, because sharing decisions are guided by selfish rather than altruistic reasons. A growing line of research studies these strategic aspects that drive defenders’ sharing decisions. The present survey systematizes these works in a novel framework. It provides a consolidated understanding of defenders’ strategies to privately or publicly share information and enables us to distill trends in the literature and identify future research directions. We reveal that many theoretical works assume cyber risk information sharing to be beneficial, while empirical validations are often missing.
Characterizing Task Completion Latencies in Fog Computing

Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about the potential of fog computing, few comprehensive quantitative characteristics of the properties of fog computing architectures have been conducted. In this paper we examine the properties of task completion latencies in fog computing. First, we present the results of our empirical benchmarking-based study of task completion latencies. The study covered a range of settings, and uniquely considered both traditional and serverless fog computing execution points. It demonstrated the range of execution point characteristics in different locations and the relative stability of latency characteristics for a given location. It also highlighted properties of serverless execution that are not incorporated in existing fog computing algorithms. Second, we present a framework we developed for co-optimizing task completion quality and latency, which was inspired by the insights of our empirical study. We describe fog computing task assignment problems we formulated under this framework, and present the algorithms we developed for solving them.