nils-herrmann/clean_scirepeval huggingface dataset:
A decade of reconfigurable computing: a visionary retrospective

The paper surveys a decade of R&D on coarse grain reconfigurable hardware and related CAD, points out why this emerging discipline is heading toward a dichotomy of computing science, and advocates the introduction of a new soft machine paradigm to replace CAD by compilation. 1.  Rapidly increasing number and attendance  of conferences on re-configurable computing and the adoption of this topic area by congresses like ASP-DAC, DAC, DATE, ISCAS, SPIE, and others indicate, that reconfigurable platforms are heading from niche to mainstream, bridging the gap between ASICs and microprocessors (fig. 2). It's time to revisit R&D results: the goal of this paper. (On the implementation status of some projects we have only incomplete information.). Using FPGAs as accelerator platforms is not subject of this paper. In contrast to FPGA use (fine grain reconfigurable) the area of Reconfigurable Computing mostly stresses the use of coarse grain reconfigurable arrays (RAs) with pathwidths greater than 1 bit, because fine-grained architectures are much less efficient, due to a huge routing area overhead and poor routability [2] . Since computational datapaths have regular structure potential, full custom designs of reconfigurable datapath units (rDPUs) can be drastically more area-efficient, than by assembling the FPGA way from single-bit CLBs. Coarse-grained architectures provide operator level CFBs, word level datapaths, as well as powerful and very area-efficient datapath routing switches. A major benefit is the massive reduction of configuration memory and configuration time, as well as drastic complexity reduction of the P&R (placement and routing) problem. Several architectures will be briefly outlined (also see figure1). Some of them introduce multi-granular solutions, where more coarse granularity can be achieved by bundling of resources, such as e. g. 4 ALUs of 4 bits each to obtain a 16 bit ALU. Mesh-based architectures arrange their PEs mainly as a rectangular 2-D array with horizontal and vertical connections which supports rich communication resources for efficient parallelism. and encourages nearest neighbour (NN) links between adjacent PEs (NN or 4NN: links to 4 sides {east, west, CHESS array: fig. 5). Typically, longer lines are added with different lengths for connections over distances larger than 1. DP-FPGA (Datapath FPGA)  has been introduced to implement regularly structured datapaths. It is a FPGA-like mixed fine and coarse grained architecture with 1 and 4 bit paths. Its fabric includes 3 component types: control logic, the datapath, and memory. The datapath block consists of 4 bit-slices: each …
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image

Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven difficult for computer vision algorithms. We define a feed-forward deep neural network algorithm that can reconstruct 3D shapes from 2D landmark points almost perfectly (i.e., with extremely small reconstruction errors), even when these 2D landmarks are from a single image. Our experimental results show an improvement of up to two-fold over state-of-the-art computer vision algorithms; 3D shape reconstruction error (measured as the Procrustes distance between the reconstructed shape and the ground-truth) of human faces is <inline-formula><tex-math notation="LaTeX">$<.004$</tex-math><alternatives> <inline-graphic xlink:href="martinez-ieq1-2772922.gif"/></alternatives></inline-formula>, cars is .0022, human bodies is .022, and highly-deformable flags is .0004. Our algorithm was also a top performer at the 2016 3D Face Alignment in the Wild Challenge competition (done in conjunction with the European Conference on Computer Vision, ECCV) that required the reconstruction of 3D face shape from a single image. The derived algorithm can be trained in a couple hours and testing runs at more than 1,000 frames/s on an i7 desktop. We also present an innovative data augmentation approach that allows us to train the system efficiently with small number of samples. And the system is robust to noise (e.g., imprecise landmark points) and missing data (e.g., occluded or undetected landmark points).
SKIP-GRAM NEGATIVE SAMPLING

Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in “word2vec” software, is usually optimized by stochastic gradient descent. It can be shown that optimizing for SGNS objective can be viewed as an optimization problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.
Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory

We describe our experience in developing a discourse-annotated corpus for community-wide use. Working in the framework of Rhetorical Structure Theory, we were able to create a large annotated resource with very high consistency, using a well-defined methodology and protocol. This resource is made publicly available through the Linguistic Data Consortium to enable researchers to develop empirically grounded, discourse-specific applications.
Querying the Semantic Web with Ginseng: A Guided Input Natural Language Search Engine

The Semantic Web presents the vision of a distributed, dynamically growing knowledge base founded on formal logic. Common users, however, seem to have problems even with the simplest Boolean expression. As queries from web search engines show, the great majority of users simply do not use Boolean expressions. So how can we help users to query a web of logic that they do not seem to understand? We address this problem by presenting Ginseng, a quasi natural language guided query interface to the Semantic Web. Ginseng relies on a simple question grammar which gets dynamically extended by the structure of an ontology to guide users in formulating queries in a language seemingly akin to English. Based on the grammar Ginseng then translates the queries into a Semantic Web query language (RDQL), which allows their execution. Our evaluation with 20 users shows that Ginseng is extremely simple to use without any training (as opposed to any logic-based querying approach) resulting in very good query performance (precision = 92.8%, recall = 98.4%). We, furthermore, found that even with its simple grammar/approach Ginseng could process over 40% of questions from a query corpus without modification.
Vehicle tracking using extended object methods: An approach for fusing radar and laser

Combining data from heterogeneous sensors allows to enhance tracking systems by increasing the field of view, incorporating redundancy, and improving the performance by exploiting complementary sensor characteristics. This paper proposes a new vehicle tracking approach for vehicle environment perception that fuses radar and laser data. A Random-Finite-Set-based tracking filter, which permits a clear mathematical formulation of the multi-object problem, is used as fusion center. In combination with extended object measurement models that work on the raw sensor data directly, the filter uses all available information without the need for further preprocessing routines, considers object interdependencies, and works in ambiguous situations. The results are evaluated using experimental data from a test vehicle.
Blockchain based trust & authentication for decentralized sensor networks

Sensor networks and Wireless Sensor Networks (WSN) are key components for the development of the Internet of Things. These networks are subject of two kinds of constraints. Adaptability by the mean of mutability and evolutivity, and constrained node resources such as energy consumption, computational complexity or memory usage. In this context, none of the existing protocols and models allows reliable peer authentication and trust level management. In the field of virtual economic transactions, Bitcoin has proposed a new decentralized and evolutive way to model and acknowledge trust and data validity in a peer network by the mean of the blockchain. We propose a new security model and its protocol based on the blockchain technology to ensure validity and integrity of cryptographic authentication data and associate peer trust level, from the beginning to the end of the sensor network lifetime.
An inheritance-based theory of the lexicon in combinatory categorial grammar

This thesis proposes an extended version of the Combinatory Categorial Grammar (CCG) formalism, with the following features: 1. grammars incorporate inheritance hierarchies of lexical types, defined over a simple, feature-based constraint language 2. CCG lexicons are, or at least can be, functions from forms to these lexical types This formalism, which I refer to as ‘inheritance-driven’ CCG (I-CCG), is conceptualised as a partially model-theoretic system, involving a distinction between category descriptions and their underlying category models, with these two notions being related by logical satisfaction. I argue that the I-CCG formalism retains all the advantages of both the core CCG framework and proposed generalisations involving such things as multiset categories, unary modalities or typed feature structures. In addition, I-CCG: 1. provides non-redundant lexicons for human languages 2. captures a range of well-known implicational word order universals in terms of an acquisition-based preference for shorter grammars This thesis proceeds as follows: Chapter 2 introduces the ‘baseline’ CCG formalism, which incorporates just the essential elements of category notation, without any of the proposed extensions. Chapter 3 reviews parts of the CCG literature dealing with linguistic competence in its most general sense, showing how the formalism predicts a number of language universals in terms of either its restricted generative capacity or the prioritisation of simpler lexicons. Chapter 4 analyses the first motivation for generalising the baseline category notation, demonstrating how certain fairly simple implicational word order universals are not formally predicted by baseline CCG, although they intuitively do involve considerations of grammatical economy. Chapter 5 examines the second motivation underlying many of the customised CCG category notations — to reduce lexical redundancy, thus allowing for the construction of lexicons which assign (each sense of) open class words and morphemes to no more than one lexical category, itself denoted by a non-composite lexical type.
Ultrafast Terahertz Wireless Communications Technologies

Terahertz wireless communications technologies are attracting a great deal of attention because of their potential to considerably surpass 10 Gb/s. In recent years, progress in semiconductor integrated circuits has enabled indoor transmission experiments of 100-Gb/s-grade terahertz wireless links and outdoor transmission experiment over distances of more than 1 km. In this paper, trends in the research and development of the domestic and foreign terahertz wireless communications technology and the standardization are presented.
Keyword Search Advertising and Limited Budgets

In keyword search advertising, many advertisers operate on a limited budget. Yet how limited budgets affect keyword search advertising has not been extensively studied. This paper offers an analysis of the generalized second-price auction with budget constraints. We find that the budget constraint may induce advertisers to raise their bids to the highest possible amount for two different motivations: to accelerate the elimination of the budget-constrained competitor as well as to reduce their own advertising cost. Thus, in contrast to the current literature, our analysis shows that both budget-constrained and unconstrained advertisers could bid more than their own valuation. We further extend the model to consider dynamic bidding and budget-setting decisions.
Approaching Process Mining with Sequence Clustering: Experiments and Findings

Sequence clustering is a technique of bioinformatics that is used to discover the properties of sequences by grouping them into clusters and assigning each sequence to one of those clusters. In business process mining, the goal is also to extract sequence behaviour from an event log but the problem is often simplified by assuming that each event is already known to belong to a given process and process instance. In this paper, we describe two experiments where this information is not available. One is based on a real-world case study of observing a software development team for three weeks. The other is based on simulation and shows that it is possible to recover the original behaviour in a fully automated way. In both experiments, sequence clustering plays a central role.
A quick assessment tool for human-directed aggression in pet dogs.

Many test series have been developed to assess dog temperament and aggressive behavior, but most of them have been criticized for their relatively low predictive validity or being too long, stressful, and/or problematic to carry out. We aimed to develop a short and effective series of tests that corresponds with (a) the dog's bite history, and (b) owner evaluation of the dog's aggressive tendencies. Seventy-three pet dogs were divided into three groups by their biting history; non-biter, bit once, and multiple biter. All dogs were exposed to a short test series modeling five real-life situations: friendly greeting, take away bone, threatening approach, tug-of-war, and roll over. We found strong correlations between the in-test behavior and owner reports of dogs' aggressive tendencies towards strangers; however, the test results did not mirror the reported owner-directed aggressive tendencies. Three test situations (friendly greeting, take-away bone, threatening approach) proved to be effective in evoking specific behavioral differences according to dog biting history. Non-biters differed from biters, and there were also specific differences related to aggression and fear between the two biter groups. When a subsample of dogs was retested, the test revealed consistent results over time. We suggest that our test is adequate for a quick, general assessment of human-directed aggression in dogs, particularly to evaluate their tendency for aggressive behaviors towards strangers. Identifying important behavioral indicators of aggressive tendencies, this test can serve as a useful tool to study the genetic or neural correlates of human-directed aggression in dogs.
Scalable Anomaly Ranking of Attributed Neighborhoods

Given a graph with node attributes, what neighborhoods1 are anomalous? To answer this question, one needs a quality score that utilizes both structure and attributes. Popular existing measures either quantify the structure only and ignore the attributes (e.g., conductance), or only consider the connectedness of the nodes inside the neighborhood and ignore the cross-edges at the boundary (e.g., density). In this work we propose normality, a new quality measure for attributed neighborhoods. Normality utilizes structure and attributes together to quantify both internal consistency and external separability. It exhibits two key advantages over other measures: (1) It allows many boundaryedges as long as they can be “exonerated”; i.e., either (i) are expected under a null model, and/or (ii) the boundary nodes do not exhibit the subset of attributes shared by the neighborhood members. Existing measures, in contrast, penalize boundary edges irrespectively. (2) Normality can be efficiently maximized to automatically infer the shared attribute subspace (and respective weights) that characterize a neighborhood. This efficient optimization allows us to process graphs with millions of attributes. We capitalize on our measure to present a novel approach for Anomaly Mining of Entity Neighborhoods (AMEN). Experiments on real-world attributed graphs illustrate the effectiveness of our measure at anomaly detection, outperforming popular approaches including conductance, density, OddBall, and SODA. In addition to anomaly detection, our qualitative analysis demonstrates the utility of normality as a powerful tool to contrast the correlation between structure and attributes across different graphs.
ApDeepSense: Deep Learning Uncertainty Estimation without the Pain for IoT Applications

Recent advances in deep-learning-based applications have attracted a growing attention from the IoT community. These highly capable learning models have shown significant improvements in expected accuracy of various sensory inference tasks. One important and yet overlooked direction remains to provide uncertainty estimates in deep learning outputs. Since robustness and reliability of sensory inference results are critical to IoT systems, uncertainty estimates are indispensable for IoT applications. To address this challenge, we develop ApDeepSense, an effective and efficient deep learning uncertainty estimation method for resource-constrained IoT devices. ApDeepSense leverages an implicit Bayesian approximation that links neural networks to deep Gaussian processes, allowing output uncertainty to be quantified. Our approach is shown to significantly reduce the execution time and energy consumption of uncertainty estimation thanks to a novel layer-wise approximation that replaces the traditional computationally intensive sampling-based uncertainty estimation methods. ApDeepSense is designed for neural net-works trained using dropout; one of the most widely used regularization methods in deep learning. No additional training is needed for uncertainty estimation purposes. We evaluate ApDeepSense using four IoT applications on Intel Edison devices. Results show that ApDeepSense can reduce around 88.9% of the execution time and 90.0% of the energy consumption, while producing more accurate uncertainty estimates compared with state-of-the-art methods.
Cost comparison of running web applications in the cloud using monolithic, microservice, and AWS Lambda architectures

Large Internet companies like Amazon, Netflix, and LinkedIn are using the microservice architecture pattern to deploy large applications in the cloud as a set of small services that can be independently developed, tested, deployed, scaled, operated, and upgraded. However, aside from gaining agility, independent development, and scalability, how microservices affect the infrastructure costs is a major evaluation topic for companies adopting this pattern. This paper presents a cost comparison of a web application developed and deployed using the same scalable scenarios with three different approaches: 1) a monolithic architecture, 2) a microservice architecture operated by the cloud customer, and 3) a microservice architecture operated by the cloud provider. Test results show that microservices can help reduce infrastructure costs in comparison with standard monolithic architectures. Moreover, the use of services specifically designed to deploy and scale microservices, such as AWS Lambda, reduces infrastructure costs by 70% or more, and unlike microservices operated by cloud customers, these specialized services help to guarantee the same performance and response times as the number of users increases. Lastly, we also describe the challenges we faced while implementing and deploying microservice applications, and include a discussion on how to replicate the results on other cloud providers.
Novel antiviral agents: a medicinal plant perspective.

Several hundred plant and herb species that have potential as novel antiviral agents have been studied, with surprisingly little overlap. A wide variety of active phytochemicals, including the flavonoids, terpenoids, lignans, sulphides, polyphenolics, coumarins, saponins, furyl compounds, alkaloids, polyines, thiophenes, proteins and peptides have been identified. Some volatile essential oils of commonly used culinary herbs, spices and herbal teas have also exhibited a high level of antiviral activity. However, given the few classes of compounds investigated, most of the pharmacopoeia of compounds in medicinal plants with antiviral activity is still not known. Several of these phytochemicals have complementary and overlapping mechanisms of action, including antiviral effects by either inhibiting the formation of viral DNA or RNA or inhibiting the activity of viral reproduction. Assay methods to determine antiviral activity include multiple-arm trials, randomized crossover studies, and more compromised designs such as nonrandomized crossovers and pre- and post-treatment analyses. Methods are needed to link antiviral efficacy/potency- and laboratory-based research. Nevertheless, the relative success achieved recently using medicinal plant/herb extracts of various species that are capable of acting therapeutically in various viral infections has raised optimism about the future of phyto-antiviral agents. As this review illustrates, there are innumerable potentially useful medicinal plants and herbs waiting to be evaluated and exploited for therapeutic applications against genetically and functionally diverse viruses families such as Retroviridae, Hepadnaviridae and Herpesviridae
A Reinforcement Learning Method for Maximizing Undiscounted Rewards

While most Reinforcement Learning work utilizes temporal discounting to evaluate performance, the reasons for this are unclear. Is it out of desire or necessity? We argue that it is not out of desire, and seek to dispel the notion that temporal discounting is necessary by proposing a framework for undiscounted optimization. We present a metric of undiscounted performance and an algorithm for finding action policies that maximize that measure. The technique, which we call Rlearning, is modelled after the popular Q-learning algorithm . Initial experimental results are presented which attest to a great improvement over Q-learning in some simple cases.
Applicability of Big Data Techniques to Smart Cities Deployments

This paper presents the main foundations of big data applied to smart cities. A general Internet of Things based architecture is proposed to be applied to different smart cities applications. We describe two scenarios of big data analysis. One of them illustrates some services implemented in the smart campus of the University of Murcia. The second one is focused on a tram service scenario, where thousands of transit-card transactions should be processed. Results obtained from both scenarios show the potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport.
Advances in real-time flood forecasting.

This paper discusses the modelling of rainfall-flow (rainfall-run-off) and flow-routeing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the inherent uncertainty that characterizes river-catchment dynamics and the problems of model over-parametrization. The advantages of alternative, efficiently parametrized data-based mechanistic models, identified and estimated using statistical methods, are discussed. It is shown that such models are in an ideal form for incorporation in a real-time, adaptive forecasting system based on recursive state-space estimation (an adaptive version of the stochastic Kalman filter algorithm). An illustrative example, based on the analysis of a limited set of hourly rainfall-flow data from the River Hodder in northwest England, demonstrates the utility of this methodology in difficult circumstances and illustrates the advantages of incorporating real-time state and parameter adaption.