nils-herrmann/clean_scirepeval huggingface dataset:
Street lighting control system based on large-scale WSN: A step towards a smart city

This paper presents the field testing of a street lighting monitoring and control system. The system is based on a WSN network of the large-scale type that enables the remote control of street lighting lamps. The system also enables savings in terms of the electric power and maintenance costs. The architecture uses integrated Doppler sensors that allows for vehicle detection and help complete the power efficiency objective. Thus, when a vehicle is detected the light intensity of the lamps is increased to a preset level, so as not to affect road traffic safety, and reduced in the opposite case. Moreover, the system uses current sensors so as to allow for the identification of any possible malfunctions and thus facilitate the maintenance process. According to the obtained results, the system allows for an increased performance level and can be integrated in the Smart City concept.
Measuring and Explaining Management Practices Across Firms and Countries Preliminary

We use an innovative survey tool to collect management practice data from 732 medium sized manufacturing firms in the US and Europe (France, Germany and the UK). Our measures of managerial best practice are strongly associated with superior firm performance in terms of productivity, profitability, Tobin’s Q, sales growth and survival. We also find significant intercountry variation with US firms on average better managed than European firms, but a much greater intra-country variation with a long tail of extremely badly managed firms. This presents a dilemma – why do so many firms exist with apparently inferior management practices, and why does this vary so much across countries? We find this is due to a combination of: (i) low product market competition and (ii) family firms passing management control down to the eldest sons (primo geniture). European firms in our sample report facing lower levels of competition, and substantially higher levels of primo geniture. These two factors appear to account for around half of the long tail of badly managed firms and half of the average US-Europe gap in management performance.
The inverse kinematics solutions of industrial robot manipulators

The inverse kinematics problem of robot manipulators is solved analytically in order to have complete and simple solutions to the problem. This approach is also called as a closed form solution of robot inverse kinematics problem. In this paper, the inverse kinematics of sixteen industrial robot manipulators classified by Huang and Milenkovic were solved in closed form. Each robot manipulator has an Euler wrist whose three axes intersect at a common point. Basically, five trigonometric equations were used to solve the inverse kinematics problems. Robot manipulators can be mainly divided into four different group based on the joint structure. In this work, the inverse kinematics solutions of SN (cylindrical robot with dome), CS (cylindrical robot), NR (articulated robot) and CC (selectively compliant assembly robot arm-SCARA, Type 2) robot manipulator belonging to each group mentioned above are given as an example. The number of the inverse kinematics solutions for the other robot manipulator was also summarized in a table.
Autonomicity in Oracle Database Management System

Human world is becoming more and more dependent on computers and information technology (IT). The autonomic capabilities in computers and IT have become the need of the day. These capabilities in software and systems increase performance, accuracy, availability and reliability with less or no human intervention (HI). Database has become the integral part of information system in most of the organizations. Databases are growing w.r.t size, functionality, heterogeneity and due to this their manageability needs more attention. Autonomic capabilities in Database Management Systems (DBMSs) are also essential for ease of management, cost of maintenance and hide the low level complexities from end users. With autonomic capabilities administrators can perform higher-level tasks. The DBMS that has the ability to manage itself according to the environment and resources without any human intervention is known as Autonomic DBMS (ADBMS). The paper explores and analyzes the autonomic components of Oracle by considering autonomic characteristics. This analysis illustrates how different components of Oracle manage itself autonomically. The research is focused to find and earmark those areas in Oracle where the human intervention is required. We have performed the same type of research over Microsoft SQL Server and DB2 [1, 2]. A comparison of autonomic components of Oracle with SQL Server is provided to show their autonomic status.
Outdoor Flight Testing of a Pole Inspection UAV Incorporating High-speed Vision

We present a pole inspection system for outdoor environments comprising a high-speed camera on a vertical take-off and landing (VTOL) aerial platform. The pole inspection task requires a vehicle to fly close to a structure while maintaining a fixed stand-off distance from it. Typical GPS errors make GPS-based navigation unsuitable for this task however. When flying outdoors a vehicle is also affected by aerodynamics disturbances such as wind gusts, so the onboard controller must be robust to these disturbances in order to maintain the stand-off distance. Two problems must therefor be addressed: fast and accurate state estimation without GPS, and the design of a robust controller. We resolve these problems by a) performing visual + inertial relative state estimation and b) using a robust line tracker and a nested controller design. Our state estimation exploits high-speed camera images (100Hz ) and 70Hz IMU data fused in an Extended Kalman Filter (EKF). We demonstrate results from outdoor experiments for pole-relative hovering, and pole circumnavigation where the operator provides only yaw commands. Lastly, we show results for image-based 3D reconstruction and texture mapping of a pole to demonstrate the usefulness for inspection tasks.
A novel hybrid planar SIW magic Tee

In this paper, a novel planar SIW magic Tee is proposed. Compared with traditional rectangle magic Tee, the new design lays down the E arm to one side and makes it possible to compact the magic Tee. In the meanwhile, by introducing balun in E arm, the SIW magic Tee maintains the balance excitation of E Tee and the isolation between E arm and H arm. In order to measure the S parameter of this magic Tee, special waveguide-coaxial converter is designed and implemented. The measured result shows the good performance of the SIW magic Tee.
Very Low-Cost Sensing and Communication Using Bidirectional LEDs

A novel microprocessor interface circuit is described which can alternately emit and detect light using only an LED, two digital I/O pins and a single current limiting resistor. This technique is first applied to create a smart illumination system that uses a single LED as both light source and sensor. We then present several devices that use an LED as a generic wireless serial data port. An important implication of this work is that every LED connected to a microprocessor can be thought of as a wireless two-way communication port. We present this technology as a solution to the “last centimeter problem”, because it permits disparate devices to communicate with each other simply and cheaply with minimal design modification. To appear in UbiComp 2003, Seattle, Washington, October 12-15, 2003 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Traceability in a food supply chain: Safety and quality perspectives

The food industry is becoming more customer-oriented and needs faster response times to deal with food scandals and incidents. Good traceability systems help to minimize the production and distribution of unsafe or poor quality products, thereby minimizing the potential for bad publicity, liability, and recalls. The current food labelling system cannot guarantee that the food is authentic, good quality and safe. Therefore, traceability is applied as a tool to assist in the assurance of food safety and quality as well as to achieve consumer confidence. This paper presents comprehensive information about traceability with regards to safety and quality in the food supply chain.
Building and Using Home Automation Systems: A Field Study

These last years, several new home automation boxes appeared on the market, the new radio-based protocols facilitating their deployment with respect to previously wired solutions. Coupled with the wider availability of connected objects, these protocols have allowed new users to set up home automation systems by themselves. In this paper, we relate an in situ observational study of these builders in order to understand why and how the smart habitats were developed and used. We led 10 semi-structured interviews in households composed of at least 2 adults and equipped for at least 1 year, and 47 home automation builders answered an online questionnaire at the end of the study. Our study confirms, specifies and exhibits additional insights about usages and means of end-user development in the context of home automation.
Image Popularity Prediction in Social Media Using Sentiment and Context Features

Images in social networks share different destinies: some are going to become popular while others are going to be completely unnoticed. In this paper we propose to use visual sentiment features together with three novel context features to predict a concise popularity score of social images. Experiments on large scale datasets show the benefits of proposed features on the performance of image popularity prediction. Exploiting state-of-the-art sentiment features, we report a qualitative analysis of which sentiments seem to be related to good or poor popularity. To the best of our knowledge, this is the first work understanding specific visual sentiments that positively or negatively influence the eventual popularity of images.
A Sober Look at Clustering Stability

Stability is a common tool to verify the validity of sample based algorithms. In clustering it is widely used to tune the parameters of the algorithm, such as the number k of clusters. In spite of the popularity of stability in practical applications, there has been very little theoretical analysis of this notion. In this paper we provide a formal definition of stability and analyze some of its basic properties. Quite surprisingly, the conclusion of our analysis is that for large sample size, stability is fully determined by the behavior of the objective function which the clustering algorithm is aiming to minimize. If the objective function has a unique global minimizer, the algorithm is stable, otherwise it is unstable. In particular we conclude that stability is not a well-suited tool to determine the number of clusters it is determined by the symmetries of the data which may be unrelated to clustering parameters. We prove our results for center-based clusterings and for spectral clustering, and support our conclusions by many examples in which the behavior of stability is counter-intuitive.
Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds

Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar instances for manual annotation. More recently, there have been attempts towards a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. In this paper, we propose two novel batch mode active learning (BMAL) algorithms: BatchRank and BatchRand. We first formulate the batch selection task as an NP-hard optimization problem; we then propose two convex relaxations, one based on linear programming and the other based on semi-definite programming to solve the batch selection problem. Finally, a deterministic bound is derived on the solution quality for the first relaxation and a probabilistic bound for the second. To the best of our knowledge, this is the first research effort to derive mathematical guarantees on the solution quality of the BMAL problem. Our extensive empirical studies on 15 binary, multi-class and multi-label challenging datasets corroborate that the proposed algorithms perform at par with the state-of-the-art techniques, deliver high quality solutions and are robust to real-world issues like label noise and class imbalance.
EasyGO: Gene Ontology-based annotation and functional enrichment analysis tool for agronomical species

It is always difficult to interpret microarray results. Recently, a handful of tools have been developed to meet this need, but almost none of them were designed to support agronomical species. This paper presents EasyGO, a web server to perform Gene Ontology based functional interpretation on groups of genes or GeneChip probe sets. EasyGO makes a special contribution to the agronomical research community by supporting Affymetrix GeneChips of both crops and farm animals and by providing stronger capabilities for results visualization and user interaction. Currently it supports 11 agronomical plants, 3 farm animals, and the model plant Arabidopsis. The authors demonstrated EasyGO's ability to uncover hidden knowledge by analyzing a group of probe sets with similar expression profiles. EasyGO is a good tool for helping biologists and agricultural scientists to discover enriched biological knowledge that can provide solutions or suggestions for original problems. It is freely available to all users at http://bioinformatics.cau.edu.cn/easygo/ .
Arabic sign language fingerspelling recognition from depth and intensity images

Automatic Arabic sign language recognition (ArSL) and fingerspelling considered to be the preferred communication method among deaf people. In this paper, we propose a system for alphabetic Arabic sign language recognition using depth and intensity images which acquired from SOFTKINECT™ sensor. The proposed method does not require any extra gloves or any visual marks. Local features from depth and intensity images are learned using unsupervised deep learning method called PCANet. The extracted features are then recognized using linear support vector machine classifier. The performance of the proposed method is evaluated on dataset of real images captured from multi-users. Experiments using a combination of depth and intensity images and also using depth and intensity images separately are performed. The obtained results show that the performance of the proposed system improved by combining both depth and intensity information which give an average accuracy of 99:5%.
AutoStyle: Automatic Style Transfer from Image Collections to Users' Images

Stylizing photos, to give them an antique or artistic look, has become popular in recent years. The available stylization filters, however, are usually created manually by artists, resulting in a narrow set of choices. Moreover, it can be difficult for the user to select a desired filter, since the filters’ names often do not convey their functions. We investigate an approach to photo filtering in which the user provides one or more keywords, and the desired style is defined by the set of images returned by searching the web for those keywords. Our method clusters the returned images, allows the user to select a cluster, then stylizes the user’s photos by transferring vignetting, color, and local contrast from that cluster. This approach vastly expands the range of available styles, and gives each filter a meaningful name by default. We demonstrate that our method is able to robustly transfer a wide range of styles from image collections to users’ photos.
Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

We propose a new exact Euclidean distance transformation (DT) by propagation, using bucket sorting. A fast but approximate DT is first computed using a coarse neighborhood. A sequence of larger neighborhoods is then used to gradually improve this approximation. Computations are kept short by restricting the use of these large neighborhoods to the tile borders in the Voronoi diagram of the image. We assess the computational cost of this new algorithm and show that it is both smaller and less image-dependent than all other DTs recently proposed. Contrary to all other propagation DTs, it appears to remain o(n2) even in the worst-case scenario.
Underwater SLAM: Challenges, state of the art, algorithms and a new biologically-inspired approach

The unstructured scenario, the extraction of significant features, the imprecision of sensors along with the impossibility of using GPS signals are some of the challenges encountered in underwater environments. Given this adverse context, the Simultaneous Localization and Mapping techniques (SLAM) attempt to localize the robot in an efficient way in an unknown underwater environment while, at the same time, generate a representative model of the environment. In this paper, we focus on key topics related to SLAM applications in underwater environments. Moreover, a review of major studies in the literature and proposed solutions for addressing the problem are presented. Given the limitations of probabilistic approaches, a new alternative based on a bio-inspired model is highlighted.
High dimensions and heavy tails for natural evolution strategies

The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization. NES follows the natural gradient of the expected fitness on the parameters of its search distribution. While general in its formulation, previous research has focused on multivariate Gaussian search distributions. Here we exhibit problem classes for which other search distributions are more appropriate, and then derive corresponding NES-variants.
 First, for separable distributions we obtain SNES, whose complexity is only O(d) instead of O(d3). We apply SNES to problems of previously unattainable dimensionality, recovering lowest-energy structures on the Lennard-Jones atom clusters, and obtaining state-of-the-art results on neuro-evolution benchmarks. Second, we develop a new, equivalent formulation based on invariances. This allows for generalizing NES to heavy-tailed distributions, even those with undefined variance, which aids in overcoming deceptive local optima.
Persistent Authenticated Dictionaries and Their Applications

We introduce the notion of persistent authenticated dictionaries, that is, dictionaries where the user can make queries of the type “was element e in set S at time t?” and get authenticated answers. Applications include credential and certificate validation checking in the past (as in digital signatures for electronic contracts), digital receipts, and electronic tickets. We present two data structures that can efficiently support an infrastructure for persistent authenticated dictionaries, and we compare their performance.
CoLT: Coalesced Large-Reach TLBs

Translation Look aside Buffers (TLBs) are critical to system performance, particularly as applications demand larger working sets and with the adoption of virtualization. Architectural support for super pages has previously been proposed to improve TLB performance. By allocating contiguous physical pages to contiguous virtual pages, the operating system (OS) constructs super pages which need just one TLB entry rather than the hundreds required for the constituent base pages. While this greatly reduces TLB misses, these gains are often offset by the implementation difficulties of generating and managing ample contiguity for super pages. We show, however, that basic OS memory allocation mechanisms such as buddy allocators and memory compaction naturally assign contiguous physical pages to contiguous virtual pages. Our real-system experiments show that while usually insufficient for super pages, these intermediate levels of contiguity exist under various system conditions and even under high load. In response, we propose Coalesced Large-Reach TLBs (CoLT), which leverage this intermediate contiguity to coalesce multiple virtual-to-physical page translations into single TLB entries. We show that CoLT implementations eliminate 40\% to 58\% of TLB misses on average, improving performance by 14\%. Overall, we demonstrate that the OS naturally generates page allocation contiguity. CoLT exploits this contiguity to eliminate TLB misses for next-generation, big-data applications with low-overhead implementations.
Residual memory networks: Feed-forward approach to learn long-term temporal dependencies

Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep RNN. In case of feed-forward networks training deep structures is simple and faster while learning long-term temporal information is not possible. In this paper we propose a residual memory neural network (RMN) architecture to model short-time dependencies using deep feed-forward layers having residual and time delayed connections. The residual connection paves way to construct deeper networks by enabling unhindered flow of gradients and the time delay units capture temporal information with shared weights. The number of layers in RMN signifies both the hierarchical processing depth and temporal depth. The computational complexity in training RMN is significantly less when compared to deep recurrent networks. RMN is further extended as bi-directional RMN (BRMN) to capture both past and future information. Experimental analysis is done on AMI corpus to substantiate the capability of RMN in learning long-term information and hierarchical information. Recognition performance of RMN trained with 300 hours of Switchboard corpus is compared with various state-of-the-art LVCSR systems. The results indicate that RMN and BRMN gains 6 % and 3.8 % relative improvement over LSTM and BLSTM networks.
Hierarchical load forecasting : Gradient boosting machines and Gaussian processes

This report discusses methods for forecasting hourly loads of a US utility as part of the load forecasting track of the Global Energy Forecasting Competition 2012 hosted on Kaggle. The methods described (gradient boosting machines and Gaussian processes) are generic machine learning / regression algorithms and few domain specific adjustments were made. Despite this, the algorithms were able to produce highly competitive predictions and hopefully they can inspire more refined techniques to compete with state-of-the-art load forecasting methodologies.