nils-herrmann/clean_scirepeval huggingface dataset:
Effective resource management for enhancing performance of 2D and 3D stencils on GPUs

GPUs are an attractive target for data parallel stencil computations prevalent in scientific computing and image processing applications. Many tiling schemes, such as overlapped tiling and split tiling, have been proposed in past to improve the performance of stencil computations. While effective for 2D stencils, these techniques do not achieve the desired improvements for 3D stencils due to the hardware constraints of GPU.
 A major challenge in optimizing stencil computations is to effectively utilize all resources available on the GPU. In this paper we develop a tiling strategy that makes better use of resources like shared memory and register file available on the hardware. We present a systematic methodology to reason about which strategy should be employed for a given stencil and also discuss implementation choices that have a significant effect on the achieved performance. Applying these techniques to various 2D and 3D stencils gives a performance improvement of 200-400% over existing tools that target such computations.
A comparative study between RadViz and Star Coordinates

RadViz and star coordinates are two of the most popular projection-based multivariate visualization techniques that arrange variables in radial layouts. Formally, the main difference between them consists of a nonlinear normalization step inherent in RadViz. In this paper we show that, although RadViz can be useful when analyzing sparse data, in general this design choice limits its applicability and introduces several drawbacks for exploratory data analysis. In particular, we observe that the normalization step introduces nonlinear distortions, can encumber outlier detection, prevents associating the plots with useful linear mappings, and impedes estimating original data attributes accurately. In addition, users have greater flexibility when choosing different layouts and views of the data in star coordinates. Therefore, we suggest that analysts and researchers should carefully consider whether RadViz's normalization step is beneficial regarding the data sets' characteristics and analysis tasks.
Firewall Rules Analysis

In this paper, we propose a method to analyze the firewall policy or rule-set using Relational Algebra and Raining 2D-Box Model. It can discover all the anomalies in the firewall rule-set in the format that is usually used by many firewall products such as Cisco Access Control List, IPTABLES, IPCHAINS and Check Point Firewall-1. While the existing analyzing methods consider the anomalies between any two rules in the firewall rule-set, we consider more than two rules together at the same time to discover the anomaly. Therefore we can find all the hidden anomalies in the firewall rule-set. Results from analyzing can be used with the proposed rules-combination method presented in this paper, to minimize the firewall rule without changing the policy. Finally, we have developed an application based on the proposed analyzing method. This application could help administrator to analyze and modify a complex firewall policy with less error.
The Introduction of a Single-Use Carrier Bag Charge in Wales : Attitude Change and Behavioural Spillover Effects

Disclaimer All opinions expressed in this working paper are those of the author(s) alone and should not be regarded as the views of the Welsh School of Architecture or of Cardiff University. The copyright is retained by the author(s).  Wales is the first country in the United Kingdom to have introduced a charge for single-use carrier bags. From the 1st of October 2011 shoppers in Wales have to pay a compulsory five pence for each single-use carrier bag at point of sale. A controlled field experiment, comparing Welsh and English samples, was set up to evaluate the effectiveness and further attitudinal and behavioural impacts of the charge. The introduction of the carrier bag charge was used to examine whether environmental policies may become more popular after their introduction and could lead to behaviour spillover (i.e., when adoption of one behaviour leads to the adoption of another behaviour). The study found that the charge was highly effective in changing its target behaviour. While own bag use increased in both countries, the increase was much greater in Wales than in England. The study also found evidence for the policy becoming more popular after its implementation. Despite support for the carrier bag charge already being high before its introduction, the Welsh population became even more supportive afterwards. Although no support was found for positive behavioural spillover, the study found changes in environmental identity that could produce spillover effects in the longer term. The theoretical and policy implications of the findings are discussed.
Virtual reality-augmented rehabilitation for patients following stroke.

Recent evidence indicates that intensive massed practice may be necessary to modify neural organization and effect recovery of motor skills in patients following stroke. Virtual reality (VR) technology has the capability of creating an interactive, motivating environment in which practice intensity and feedback can be manipulated to create individualized treatments to retrain movement.



Three patients (ML, LE, and DK), who were in the chronic phase following stroke, participated in a 2-week training program (3 1/2 hours a day) including dexterity tasks on real objects and VR exercises. The VR simulations were targeted for range of motion, movement speed, fractionation, and force production.



ML's function was the most impaired at the beginning of the intervention, but showed improvement in the thumb and fingers in range of motion and speed of movement. LE improved in fractionation and range of motion of his thumb and fingers. DK made the greatest gains, showing improvement in range of motion and strength of the thumb, velocity of the thumb and fingers, and fractionation. Two of the 3 patients improved on the Jebsen Test of Hand Function.



The outcomes suggest that VR may be useful to augment rehabilitation of the upper limb in patients in the chronic phase following stroke.
Explorer Merlin : An Open Source Neural Network Speech Synthesis System

We introduce the Merlin speech synthesis toolkit for neural network-based speech synthesis. The system takes linguistic features as input, and employs neural networks to predict acoustic features, which are then passed to a vocoder to produce the speech waveform. Various neural network architectures are implemented, including a standard feedforward neural network, mixture density neural network, recurrent neural network (RNN), long short-term memory (LSTM) recurrent neural network, amongst others. The toolkit is Open Source, written in Python, and is extensible. This paper briefly describes the system, and provides some benchmarking results on a freelyavailable corpus.
Eval all, trust a few, do wrong to none: Comparing sentence generation models

In this paper, we study recent neural generative models for text generation related to variational autoencoders. These models employ various techniques to match the posterior and prior distributions, which is important to ensure a high sample quality and a low reconstruction error. In our study, we follow a rigorous evaluation protocol using a large set of previously used and novel automatic metrics and human evaluation of both generated samples and reconstructions. We hope that it will become the new evaluation standard when comparing neural generative models for text.
A Framework for Modeling the Appearance of 3D Articulated Figures

This paper describes a framework for constructing a linear subspace model of image appearance for complex articulated 3D figures such as humans and other animals. A commercial motion capture system provides 3D data that is aligned with images of subjects performing various activities. Portions of a limb’s image appearance are seen from multiple views and for multiple subjects. From these partial views, weighted principal component analysis is used to construct a linear subspace representation of the “unwrapped” image appearance of each limb. The linear subspaces provide a generative model of the object appearance that is exploited in a Bayesian particle filtering tracking system. Results of tracking single limbs and walking humans are presented.
Weighted Low-Rank Approximations

Weighted-norms can arise in several situations. A zero/one weighted-norm, for example, arises when some of the entries in the matrix are not observed. External estimates of the noise variance associated with each measurement may be available (e.g. gene expression analysis) and using weights inversely proportional to the noise variance can lead to better reconstruction of the underlying structure. In other applications, entries in the target matrix represent aggregates of many samples. When using unweighted low-rank approximations (e.g. for separating style and content ), we assume a uniform number of samples for each entry. By incorporating weights, we can account for varying numbers of samples in such situations. Low-rank approximations are also used in the design of two-dimensional digital filters, in which case weights might arise from constraints of varying importance .
Generating Notifications for Missing Actions: Don't Forget to Turn the Lights Off!

We all have experienced forgetting habitual actions among our daily activities. For example, we probably have forgotten to turn the lights off before leaving a room or turn the stove off after cooking. In this paper, we propose a solution to the problem of issuing notifications on actions that may be missed. This involves learning about interdependencies between actions and being able to predict an ongoing action while segmenting the input video stream. In order to show a proof of concept, we collected a new egocentric dataset, in which people wear a camera while making lattes. We show promising results on the extremely challenging task of issuing correct and timely reminders. We also show that our model reliably segments the actions, while predicting the ongoing one when only a few frames from the beginning of the action are observed. The overall prediction accuracy is 46.2% when only 10 frames of an action are seen (2/3 of a sec). Moreover, the overall recognition and segmentation accuracy is shown to be 72.7% when the whole activity sequence is observed. Finally, the online prediction and segmentation accuracy is 68.3% when the prediction is made at every time step.
Issues in Dataflow Computing

Early advocates of dataflow computers predicted that the characteristics of the dataflow model of execution would provide plenty of computing power. However, a direct implementation of computers based on the dataflow model has been found to be a monumental challenge. This article presents a survey of issues and evolutionary developments in dataflow computing. The discussion includes the major problems encountered in past dataflow projects and the emergence novel ideas which have shed a new light in dataflow computing. ISSUES IN DATAFLOW COMPUTING A. R. Hurson* and Ben Lee** *The Pennsylvania State University Department of Electrical and Computer Engineering University Park, PA 16802 E-mail: A2H@ecl.psu.edu Phone: (814) 863-1167 **Oregon State University Department of Electrical and Computer Engineering Corvallis, Oregon 97331-3211  Early advocates of dataflow computers predicted that the characteristics of the dataflow model of execution would provide plenty of computing power. However, a direct implementation of computers based on the dataflow model has been found to be a monumental challenge. This article presents a survey of issues and evolutionary developments in dataflow computing. The discussion includes the major problems encountered in past dataflow projects and the emergence novel ideas which have shed a new light in dataflow computing.Early advocates of dataflow computers predicted that the characteristics of the dataflow model of execution would provide plenty of computing power. However, a direct implementation of computers based on the dataflow model has been found to be a monumental challenge. This article presents a survey of issues and evolutionary developments in dataflow computing. The discussion includes the major problems encountered in past dataflow projects and the emergence novel ideas which have shed a new light in dataflow computing.
Sequence to Sequence Learning with Neural Networks

Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT’14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM’s BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM’s performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
An Integrated Microwave Imaging Radar With Planar Antennas for Breast Cancer Detection

The system design of an integrated microwave imaging radar for the diagnostic screening of breasts cancer is presented. A custom integrated circuit implemented in a 65-nm CMOS technology and a pair of patch antennas realized on a planar laminate are proposed as the basic module of the imaging antenna array. The radar operates on the broad frequency range from 2 to 16 GHz with a dynamic range of 107 dB. Imaging experiments carried out on a realistic breast phantom show that the system is capable of detecting tumor targets with a resolution of 3 mm.
On Space-Time Interest Points

Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for interpretation of spatio-temporal events. To detect spatio-temporal events, we build on the idea of the Harris and Förstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We estimate the spatio-temporal extents of the detected events by maximizing a normalized spatio-temporal Laplacian operator over spatial and temporal scales. To represent the detected events, we then compute local, spatio-temporal, scale-invariant N-jets and classify each event with respect to its jet descriptor. For the problem of human motion analysis, we illustrate how a video representation in terms of local space-time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds.
Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches

Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like Go. While RL is emerging as a practical component in reallife systems, most successes have been in Single Agent domains. This report will instead specifically focus on challenges that are unique to Multi-Agent Systems interacting in mixed cooperative and competitive environments. The report concludes with advances in the paradigm of training Multi-Agent Systems called Decentralized Actor, Centralized Critic, based on an extension of MDPs called Decentralized Partially Observable MDPs, which has seen a renewed interest lately. 1 ar X iv :1 80 7. 09 42 7v 1 [ cs .A I] 2 5 Ju l 2 01 8
Monitoring of Distributed Pipeline Systems by Wireless Sensor Networks

This paper describes a sensor network platform for pipeline system monitoring. Pipeline systems are widely used for distribution and transportation of petroleum, natural gas, water, and sewage. Leaks and ruptures due to an aging and fast decaying pipeline system infrastructure cost millions of dollars a year; they also make clear the necessity for continuous, automatic monitoring systems that can provide early detection and early warning of defects, such as corrosion and leaks, before they reach the magnitude of a major disaster. In this paper, we discuss how sensor networks can detect, localize, and quantify bursts, leaks and other anomalies in pipeline systems. Lamb waves are guided ultrasonic waves that can propagate for considerable distances in plates. Research has shown that it is possible to detect flaws over a large area with active sensing devices such as Lead Zirconate Titanate (PZT) for simultaneous actuation and sensing. PZT sensors can be mounted on the curve surface of the pipelines for generating and measuring guided waves that propagate along the pipes. A network of PZT actuators/sensors provide a real-time continuous and automatic monitoring of the health of the pipeline systems. Complications that encountered in Lamb wave propagation include the existence of multiple propagation modes and the dispersive nature of the modes, which makes the sensing, communication, and control a difficult task. In this paper, we will address the detection and localization problems for the proposed acoustic sensor networks using signal processing techniques. We will discuss strategies to deal with the Lamb wave dispersive propagation to achieve reasonable performance for real-time monitoring.
An Hybrid Approach to Quality Evaluation across Big Data Value Chain

While the potential benefits of Big Data adoption are significant, and some initial successes have already been realized, there remain many research and technical challenges that must be addressed to fully realize this potential. The Big Data processing, storage and analytics, of course, are major challenges that are most easily recognized. However, there are additional challenges related for instance to Big Data collection, integration, and quality enforcement. This paper proposes a hybrid approach to Big Data quality evaluation across the Big Data value chain. It consists of assessing first the quality of Big Data itself, which involve processes such as cleansing, filtering and approximation. Then, assessing the quality of process handling this Big Data, which involve for example processing and analytics process. We conduct a set of experiments to evaluate Quality of Data prior and after its pre-processing, and the Quality of the pre-processing and processing on a large dataset. Quality metrics have been measured to access three Big Data quality dimensions: accuracy, completeness, and consistency. The results proved that combination of data-driven and process-driven quality evaluation lead to improved quality enforcement across the Big Data value chain. Hence, we recorded high prediction accuracy and low processing time after we evaluate 6 well-known classification algorithms as part of processing and analytics phase of Big Data value chain.
Sound Signal Processing Based on Seq 2 Tree Network

Most state-of-the-art solutions to sound signal processing tasks such as the speech and noise separation task and the music style classification task are based on Recurrent Neural Network (RNN) architecture or Hidden Markov Model (HMM). Both RNN and HMM assume that the input is chain-structured so that each element in the chain is equally dependent on all its previous units. However in real-life scenes the units alone do not carry much meaning. Only when several units group to be segments will they be semantically informative. This characteristic of sound signals clearly prefers emphasizing dependencies among units in the same segment, which leads to a natural selection of tree-structured models instead of chain-structured ones. In this paper we introduce Seq2Tree network and two models based on Seq2Tree architecture solving 1) speech and noise separation task and 2) music style classification task, respectively. Experiments show that our Seq2Tree-based models outperform the state-of-the-art systems in both tasks, which agrees with our hypothesis that sound signals have potential tree-structured dependencies among their sound elements. Also the experiment results prove the advancement of the Seq2Tree network architecture in sound signal processing tasks.
Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach

Millions of tweets are generated each day on multifarious issues. Topical diversity in content demands domain-independent solutions for analysing twitter sentiments. Scalability is another issue when dealing with huge amount of tweets. This paper presents an unsupervised method for analysing tweet sentiments. Polarity of tweets is evaluated by using three sentiment lexicons—SenticNet, SentiWordNet and SentislangNet. SentislangNet is a sentiment lexicon built from SenticNet and SentiWordNet for slangs and acronyms. Experimental results show fairly good $$F$$ F -score. The method is implemented and tested in parallel python framework and is shown to scale well with large volume of data on multiple cores.
NITE: A Neural Inductive Teaching Framework for Domain Specific NER

In domain-specific NER, due to insufficient labeled training data, deep models usually fail to behave normally. In this paper, we proposed a novel Neural Inductive TEaching framework (NITE) to transfer knowledge from existing domain-specific NER models into an arbitrary deep neural network in a teacher-student training manner. NITE is a general framework that builds upon transfer learning and multiple instance learning, which collaboratively not only transfers knowledge to a deep student network but also reduces the noise from teachers. NITE can help deep learning methods to effectively utilize existing resources (i.e., models, labeled and unlabeled data) in a small domain. The experiment resulted on Disease NER proved that without using any labeled data, NITE can significantly boost the performance of a CNN-bidirectional LSTM-CRF NER neural network nearly over 30% in terms of F1-score.
Inner Product Similarity Search using Compositional Codes

This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of the selected elements. The inner product between a query vector and a database vector is efficiently estimated from the query vector and the short code of the database vector. We show the superior performance of the proposed group M -selection algorithm that selects M elements from M source dictionaries for vector approximation in terms of search accuracy and efficiency for compact codes of the same length via theoretical and empirical analysis. Experimental results on large-scale datasets (1M and 1B SIFT features, 1M linear models and Netflix) demonstrate the superiority of the proposed approach.