nils-herrmann/clean_scirepeval huggingface dataset:
Phase-functioned neural networks for character control

We present a real-time character control mechanism using a novel neural network architecture called a Phase-Functioned Neural Network. In this network structure, the weights are computed via a cyclic function which uses the phase as an input. Along with the phase, our system takes as input user controls, the previous state of the character, the geometry of the scene, and automatically produces high quality motions that achieve the desired user control. The entire network is trained in an end-to-end fashion on a large dataset composed of locomotion such as walking, running, jumping, and climbing movements fitted into virtual environments. Our system can therefore automatically produce motions where the character adapts to different geometric environments such as walking and running over rough terrain, climbing over large rocks, jumping over obstacles, and crouching under low ceilings. Our network architecture produces higher quality results than time-series autoregressive models such as LSTMs as it deals explicitly with the latent variable of motion relating to the phase. Once trained, our system is also extremely fast and compact, requiring only milliseconds of execution time and a few megabytes of memory, even when trained on gigabytes of motion data. Our work is most appropriate for controlling characters in interactive scenes such as computer games and virtual reality systems.
Big data analytics for demand response: Clustering over space and time

The pervasive deployment of advanced sensing infrastructure in Cyber-Physical systems, such as the Smart Grid, has resulted in an unprecedented data explosion. Such data exhibit both large volumes and high velocity characteristics, two of the three pillars of Big Data, and have a time-series notion as datasets in this context typically consist of successive measurements made over a time interval. Time-series data can be valuable for data mining and analytics tasks such as identifying the "right" customers among a diverse population, to target for Demand Response programs. However, time series are challenging to mine due to their high dimensionality. In this paper, we motivate this problem using a real application from the smart grid domain. We explore novel representations of time-series data for BigData analytics, and propose a clustering technique for determining natural segmentation of customers and identification of temporal consumption patterns. Our method is generizable to large-scale, real-world scenarios, without making any assumptions about the data. We evaluate our technique using real datasets from smart meters, totaling ~ 18,200,000 data points, and show the efficacy of our technique in efficiency detecting the number of optimal number of clusters.
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction.

The task of learning an expressive molecular representation is central to developing quantitative structure-activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we employ a convolutional neural network for this embedding task by treating molecules as undirected graphs with attributed nodes and edges. Simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii. By working directly with the full molecular graph, there is a greater opportunity for models to identify important features relevant to a prediction task. Unlike other graph-based approaches, our atom featurization preserves molecule-level spatial information that significantly enhances model performance. Our models learn to identify important features of atom clusters for the prediction of aqueous solubility, octanol solubility, melting point, and toxicity. Extensions and limitations of this strategy are discussed.
Prediction of Hard Drive Failures via Rule Discovery from AutoSupport Data

The ability to accurately predict an impending hard disk failure is important for reliable storage system design. The facility provided by most hard drive manufacturers, called S.M.A.R.T. (selfmonitoring, analysis and reporting technology), has been shown by current research to have poor predictive value. The problem of finding alternatives to S.M.A.R.T. for predicting disk failure is an area of active research. In this paper, we present a rule discovery methodology, and show that it is possible to construct decision support systems that can detect such failures using information recorded in the AutoSupport database. We demonstrate the effectiveness of our system by evaluating it on disks that were returned to NetApp from the field. Our evaluation shows that our system can be tuned either to have a high failure detection rate (i.e., classify a bad disk as bad) or to have a low false alarm rate (i.e., not classify a good disk as bad). Further, our rule-based classifier generates rules that are intuitive and easy to understand, unlike black box techniques.
A Comparative Analysis of Methodologies for Database Schema Integration

One of the fundamental principles of the database approach is that a database allows a nonredundant, unified representation of all data managed in an organization. This is achieved only when methodologies are available to support integration across organizational and application boundaries.
Methodologies for database design usually perform the design activity by separately producing several schemas, representing parts of the application, which are subsequently merged. Database schema integration is the activity of integrating the schemas of existing or proposed databases into a global, unified schema.
The aim of the paper is to provide first a unifying framework for the problem of schema integration, then a comparative review of the work done thus far in this area. Such a framework, with the associated analysis of the existing approaches, provides a basis for identifying strengths and weaknesses of individual methodologies, as well as general guidelines for future improvements and extensions.
Automatic text extraction and character segmentation using maximally stable extremal regions

Text detection and segmentation is an important prerequisite for many content based image analysis tasks. The paper proposes a novel text extraction and character segmentation algorithm using Maximally Stable Extremal Regions as basic letter candidates. These regions are then subjected to thresholding and thereafter various connected components are determined to identify separate characters. The algorithm is tested along a set of various JPEG, PNG and BMP images over four different character sets; English, Russian, Hindi and Urdu. The algorithm gives good results for English and Russian character set; however character segmentation in Urdu and Hindi language is not much accurate. The algorithm is simple, efficient, involves no overhead as required in training and gives good results for even low quality images. The paper also proposes various challenges in text extraction and segmentation for multilingual inputs.
The All Relevant Feature Selection using Random Forest

In this paper we examine the application of the random forest classifier for the all relevant feature selection problem. To this end we first examine two recently proposed all relevant feature selection algorithms, both being a random forest wrappers, on a series of synthetic data sets with varying size. We show that reasonable accuracy of predictions can be achieved and that heuristic algorithms that were designed to handle the all relevant problem, have performance that is close to that of the reference ideal algorithm. Then, we apply one of the algorithms to four families of semi-synthetic data sets to assess how the properties of particular data set influence results of feature selection. Finally we test the procedure using a well-known gene expression data set. The relevance of nearly all previously established important genes was confirmed, moreover the relevance of several new ones is discovered.
Measuring the crowd: a preliminary taxonomy of crowdsourcing metrics

Crowdsourcing initiatives benefit from tapping into diversity. A vast plethora of disparate individuals, organizations, frameworks and skillsets can all play a role in sourcing solutions to a challenge. Nevertheless, while crowdsourcing has become a pervasive phenomenon, there is a paucity of research that addresses how the crowdsourcing process is measured. Whereas research has advanced various taxonomies of crowdsourcing none to date have specifically addressed the issue of measuring either specific stages of the crowdsourcing process or the process as a whole. As a first step towards achieving this goal, this research-in-progress paper examines crowdsourcing at the operational level with a view towards (i) identifying the parts of the process (ii) identifying what can be measured and (iii) categorising operational metrics to facilitate deployment in practice. The taxonomy advanced is overarching in nature and can be deployed across disciplines. Furthermore, the preliminary taxonomy presented will offer practitioners a comprehensive list of metrics that will enable them to facilitate comparison across various crowdsourcing initiatives.
Compact Fast-Waking Light/Heat-Harvesting 0.18- $\mu\text{m}$  CMOS Switched-Inductor Charger

Although microsystems can nowadays consume microwatts, onboard batteries can be so small and leaky that sustaining microwatts for months or years without recharge cycles can be virtually impossible. Tiny photovoltaic cells and thermoelectric generators can help, but only when light or heat is available, and only to the extent that light intensity and thermal gradients allow. This is why energy-harvesting microsystems idle and shut down often and fast-wakeup provisions are important. The 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS charger proposed here is 8.31% more efficient during wakeup than the smallest reported and 7.69% more efficient than the next best, but without a 1:60 off-chip transformer and without vibration energy. Although 3.27% less efficient than the most efficient, the system here uses three fewer off-chip inductors. And after waking, the system is 5% to 46% more efficient than the others. The key innovations are low-power management and design. The system in essence charges a 200-pF capacitor with just enough energy to transfer two energy packets per cycle: one to charge a 1.8-<inline-formula> <tex-math notation="LaTeX">$\mu \text{F}$ </tex-math></inline-formula> battery and the other to replenish the 200 pF. This way, and with 100 nF across the source, the system charges a fully depleted 1.8-<inline-formula> <tex-math notation="LaTeX">$\mu \text{F}$ </tex-math></inline-formula> battery to 0.9 V in 45 ms and draws in steady state 98.8%–99.7% of available input power to deliver 76%–86% of the 40–150 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> drawn.
A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria

People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.
Role of Requirements Elicitation & Prioritization to Optimize Quality in Scrum Agile Development

One of most common aspect with traditional software development is managing requirements. As requirements emerge throughout the software development process and thus are needed to be addressed through proper communication and integration between stakeholders, developers and documentation. Agile methodology is an innovative and iterative process that supports changing requirements and helps in addressing changes throughout the development process. Requirements are elicited at the beginning of every software development process and project (product) and latter are prioritized according to their importance to the market and to the product itself. One of the most important and influencing steps while making a software product is requirements prioritization. Prioritizing requirements helps the software team to understand the existence and importance of a particular requirement, its importance of use and its urgency to time to market. There are many requirements prioritization techniques with their relative strength and weaknesses. Otherwise many of them fail to take account all the factors that must be considered while prioritizing requirements such as cost, value, risk, time to market, number of requirements and effect of non-functional requirements on functional requirements. There are several requirements prioritization methodologies that aid in decision making but importantly many lacks to account the important factors that have significant influence in prioritizing requirements. A requirement prioritization methodology based on several factors such as time to market, cost, risk etc has been proposed. The proposed model is expected to overcome this lack. In sprints, requirements will be prioritized both on the basis of influencing factors such as cost, value, risk, time to market etc. and through the effect of non-functional requirements over functional requirements. This will improve the overall quality of software product when it is included in the development process of scrum. Requirements will not only be prioritized based on sprints, human decision but by critically analyzing the factors (sub characteristics) that can cause the product to success/ fail repeatedly thus ensuring the consistency in right requirements and hence the right prioritized requirements will be selected for a particular sprint at a time. Agile Software Engineering (ASE); Agile Software Development (ASD); Scrum Software Development Process; SCRUM; Product Owner (PO)
Noise and sensitivity analysis of harmonic radar system for vital sign detection

Harmonic radar architectures present systems of wireless detection operating at multiple harmonic carrier frequencies. The noise and sensitivity of a harmonic radar system, which is developed at 12 GHz and 24 GHz for vital signs detection, are studied numerically and experimentally. The received signal power of the radar system is analyzed numerically as a function of the distance, taking into account the radar cross section (RCS) of the patient chest. The numerical data are compared with results obtained with single carrier frequency system. The total noise is a combination of thermal noise, residual phase noise, and flicker noise that presents the most elevated factor influencing the detection at baseband. Experimental results show that with the harmonic radar, the flicker noise can be reduced by 20dB at around 1Hz baseband frequency.
Multidimensional data classification with chordal distance based kernel and Support Vector Machines

In contemporary machine learning multidimensional rather than pure vector like data are frequently encountered. Traditionally, such multidimensional objects, such as color images or video sequences, are first transformed to a vector representation, and then processed by the classical learning algorithms operating with vectors. However, such multi-to-one dimension transformations usually lead to loss of important information. Thus, proposing novel methods for representing and learning with complex and multidimensional data is in focus of current machine learning research. In this paper, we propose a new method for efficient classification of multidimensional data based on a tensor-based kernel applied to the Support Vector Machines. We represent data as tensors, in order to preserve data dimensionality and to allow for processing of complex structures. To allow for an effective classification, we augment a Support Vector Machine (SVM) trained with Sequential Minimal Optimization (SMO) procedure with a chordal distance-based kernel for efficient classification of tensor-like objects. We also discuss different optimization methods for SVM, as well as present implementation details with computational time analysis. The proposed method is evaluated in both binary and multi-class classification problems. Comprehensive experimental analysis carried on a number of multidimensional benchmarks shows high usefulness of the proposed approach. & 2015
Using a multivariate auto-regressive state-space (MARSS) model to evaluate fishery resources abundance in the East China Sea, based on spatial distributional information

The abundance index (AI) is a representative indicator used to assess the state of fishery resources. Conventional AI is generally calculated by summing the catch per unit of effort (CPUE) weighted by the size of each fishing area. However, CPUE data has many missing values owing to annual changes in operational fishing areas, and this can lead to a considerable bias in the estimated AI. To obtain an unbiased AI, a multivariate auto-regressive state-space (MARSS) model was used to estimate and interpolate missing values in a spatially arranged, long-term bottom-trawl CPUE dataset for yellow seabream Dentex hypselosomus and largehead hairtail Trichiurus japonicus in the East China Sea. As expected, increasing the number of analyzed fishing grids improved interpolation accuracy, but remarkably increased the time required for the analysis. Reducing the maximum number of expectation–maximization (EM) iterations in the maximum likelihood procedure was an effective way to practically reduce analysis time, while keeping the accuracy of the estimation. Thus, this EM-reduction MARSS model was applied to the entire CPUE datasets of yellow seabream and largehead hairtail to address the annual shifts in their AIs and their seasonal migration.
Compact Printed Wide-Slot UWB Antenna With 3.5/5.5-GHz Dual Band-Notched Characteristics

A novel compact wide-slot ultrawideband (UWB) antenna with dual band-notched characteristics is presented. The antenna consists of an inverted U-shaped slot on the ground plane and a radiation patch similar to the slot that is fed by a 50-Ω microstrip line. By etching a C-shaped slot on the radiation patch and extruding an L-shaped stub from the ground plane, dual band-notched properties in the WiMAX (3.4-3.69 GHz) and WLAN (5.15-5.825 GHz) are achieved. The proposed antenna has a compact size of 20×27 mm2 and operates from 2.89 to 11.52 GHz. Furthermore, nearly omnidirectional radiation patterns and constant gain are obtained in the working band.
Automatic localization of optic disc based on deep learning in fundus images

The optic disc (OD) contains lots of important information in retinal image analysis. Detecting the region of OD correctly is important for subsequent analysis of retinal images. It is challenging to locate the OD precisely due to the various reasons including low image quality or lesions around the OD. In this paper, we propose a cascading localization method based on deep learning with feedback to improve the accuracy of OD localization. The method employs the model of saliency-based visual attention to find the most salient region and implements deep convolution neural network (CNN) to determine whether it contains OD. If a region is classified as non-OD region, we find the next salient region and input it into the CNN to classify. The algorithm ends when the CNN finds a region with OD. The proposed method is evaluated on the ORIGA and MESSIDOR datasets. Our experimental results show that the proposed method achieved significant improvement in detection of OD compared with previous methods.
Bioconductor: open software development for computational biology and bioinformatics

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.
Detection and location of faults in underground cable using Matlab/Simulink/ANN and OrCad

The paper deals with detection and location of faults in underground cable network. For precision a three step process is followed in this paper, with creation of transmission system model using a Matlab/Simulink and followed by creation of faults in the system. In second step the Fourier analyzed fault voltages and currents obtained from the SIMULINK model are fed to the training set of artificial neural network (ANN) in order to detect the type of fault. In the last step, an independent software OrCad is used to locate the fault distance from the either ends using the principle of time domain reflectometry in a simulated practical underground distribution system.
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings

This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1% (79.0% for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task-2 on identifying fine-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.