nils-herrmann/clean_scirepeval huggingface dataset:
Lossy compression on IoT big data by exploiting spatiotemporal correlation

As the volume of data generated by various deployed IoT devices increases, storing and processing IoT big data becomes a huge challenge. While compression, especially lossy ones, can drastically reduce data volume, finding an optimal balance between the volume reduction and the information loss is not an easy task given that the data collected by diverse sensors exhibit different characteristics. Motivated by this, we present a feasibility analysis of lossy compression on agricultural sensor data by comparing fidelity of reconstructed data from various signal processing algorithms and temporal difference encoding. Specifically, we evaluated five real-world sensor data from weather stations as one of major IoT applications. Our experimental results indicate that Discrete Cosine Transform (DCT) and Fast Walsh-Hadamard Transform (FWHT) generate higher compression ratios than others. In terms of information loss, Lossy Delta Encoding (LDE) significantly outperforms others nonetheless. We also observe that, as compression factor is increased, error rates for all compression algorithms also increase. However, the impact of introduced error is much severe in DCT and FWHT while LDE was able to maintain a relatively lower error rate than other methods.
Hybrid sampling Bayesian Occupancy Filter

Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which each cell is tracked at a sub-object level. The Bayesian Occupancy Filter  is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30×30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices.
Counting Graphlets: Space vs Time

Counting graphlets is a well-studied problem in graph mining and social network analysis. Recently, several papers explored very simple and natural approaches based on Monte Carlo sampling of Markov Chains (MC), and reported encouraging results. We show, perhaps surprisingly, that this approach is outperformed by a carefully engineered version of color coding (CC) , a sophisticated algorithmic technique that we extend to the case of graphlet sampling and for which we prove strong statistical guarantees. Our computational experiments on graphs with millions of nodes show CC to be more accurate than MC. Furthermore, we formally show that the mixing time of the MC approach is too high in general, even when the input graph has high conductance. All this comes at a price however. While MC is very efficient in terms of space, CC's memory requirements become demanding when the size of the input graph and that of the graphlets grow. And yet, our experiments show that a careful implementation of CC can push the limits of the state of the art, both in terms of the size of the input graph and of that of the graphlets.
Automatic Wrinkle Detection Using Hybrid Hessian Filter

Aging as a natural phenomenon affects different parts of the human body under the influence of various biological and environmental factors. The most pronounced changes that occur on the face is the appearance of wrinkles, which are the focus of this research. Accurate wrinkle detection is an important task in face analysis. Some have been proposed in the literature, but the poor localization limits the performance of wrinkle detection. It will lead to false wrinkle detection and consequently affect the processes such as age estimation and clinician score assessment. Therefore, we propose a hybrid Hessian filter (HHF) to cope with the identified problem. HHF is composed of the directional gradient and Hessian matrix. The proposed filter is conceptually simple, however, it significantly increases the true wrinkle localization when compared with the conventional methods. In the experimental setup, three coders have been instructed to annotate the wrinkle of 2D forehead image manually. The inter-reliability among three coders is 93% of Jaccard similarity index (JSI). In comparison to the state-of-the-art Cula method (CLM) and Frangi filter, HHF yielded the best result with a mean JSI of 75.67%. We noticed that the proposed method is capable of detecting the medium to coarse wrinkle but not the fine wrinkle. Although there is a gap between human annotation and automated detection, this work demonstrates that HHF is a remarkably strong filter for wrinkle detection. From the experimental results, we believe that our findings are notable in terms of the JSI.
A low-kickback-noise latched comparator for high-speed flash analog-to-digital converters

In traditional comparators especially for flash ADCs, one serious problem is the kick back noise, which disturbs the input signal voltages and consequently might cause errors at the outputs of the ADCs. In this paper, we propose a novel CMOS latched comparator with very low kickback noise for high-speed flash ADCs. The proposed comparator separates analog preamplifier from the positive feedback digital dynamic latch so as to reduce the influence of the kickback noise. Simulation results based on a mixed signal CMOS 0.35 /spl mu/m technology show that, this comparator can work at a maximum clock frequency of 500 MHz with very reduced kickback noise compared with conventional architectures.
Automated invoice handling with machine learning and OCR

Companies often process invoices manually, therefore automation could reduce manual labor. The aim of this thesis is to evaluate which OCR-engine, Tesseract or OCRopus, performs best at interpreting invoices. This thesis also evaluates if it is possible to use machine learning to automatically process invoices based on previously stored data. By interpreting invoices with the OCR-engines, it results in the output text having few spelling errors. However, the invoice structure is lost, making it impossible to interpret the corresponding fields. If Naïve Bayes is chosen as the algorithm for machine learning, the prototype can correctly classify recurring invoice lines after a set of data has been processed. The conclusion is, neither of the two OCR-engines can interpret the invoices to plain text making it understandable. Machine learning with Naïve Bayes works on invoices if there is enough previously processed data. The findings in this thesis concludes that machine learning and OCR can be utilized to automatize manual labor.
Developing and Assessing College Student Teamwork Skills

Astronaut Jim Lovell’s words during the Apollo 13 lunar mission, “Houston, we have a problem,” launched a remarkable tale of effective teamwork and creative problem solving by NASA engineers working to try to save the lives of the imperiled crew when two oxygen tanks exploded en route to the moon. Details of the dramatic and successful resolution to the problem became widely known in the motion picture Apollo 13, but it’s not just during dramatic moments when the importance of good teamwork is needed or recognized. In fact, some form of team-oriented work is employed in most, if not all, organizations today (Hills, 2007; Kozlowski and Bell, 2003; Lawler, Mohrman, and Ledford, 1995; Morgeson, DeRue, and Karam, 2010). It would seem, then, that an important role for higher education should involve developing critical teamwork skills among students so as to prepare them for success in life. This very point was highlighted in a 2009 poll conducted on behalf of the Association of American Colleges and Universities (AACU), in which 71 percent of employers said they wanted colleges to place greater emphasis on “teamwork skills and the ability to collaborate with others in diverse group settings” (Hart, 2010, p. 2). Many studies, in fact, have identifi ed teamwork as one of the most valued and necessary skills among college graduates. For example, a report by the Conference Board (2008) indicated that for four-year college graduates, prospective employers rated the importance of effective teamwork and collaboration second only to oral communication in contributing to job success. This is consistent with fi ndings from our own institutional surveys at the U.S. Air Force Academy, This chapter describes research on team member contributions to overall team effectiveness, and various applications of this research to developing and assessing teamwork by students on team and group projects and assignments.
An Ultra-Wide-Band 0.4–10-GHz LNA in 0.18-$\mu$m CMOS

A two-stage ultra-wide-band CMOS low-noise amplifier (LNA) is presented. With the common-gate configuration employed as the input stage, the broad-band input matching is obtained and the noise does not rise rapidly at higher frequency. By combining the common-gate and common-source stages, the broad-band characteristic and small area are achieved by using two inductors. This LNA has been fabricated in a 0.18-mum CMOS process. The measured power gain is 11.2-12.4 dB and noise figure is 4.4-6.5 dB with -3-dB bandwidth of 0.4-10 GHz. The measured IIP3 is -6 dBm at 6 GHz. It consumes 12 mW from a 1.8-V supply voltage and occupies only 0.42 mm2
Formal Modeling and Verification of Smart Contracts

Smart contracts can automatically perform the contract terms according to the received information, and it is one of the most important research fields in digital society. The core of smart contracts is algorithm contract, that is, the parties reach an agreement on the contents of the contract and perform the contracts according to the behaviors written in certain computer algorithms. It not only needs to make sure about the correctness of smart contracts code, but also should provide a credible contract code execution environment. Blockchain provides a trusted execution and storage environment for smart contracts by the distributed secure storage, consistency verification and encryption technology. Current challenge is how to assure that smart contract can be executed as the parties' willingness. This paper introduces formal modeling and verification in formal methods to make smart contract model and verify the properties of smart contracts. Formal methods combined with smart contracts aim to reduce the potential errors and cost during contract development process. The description of a general and formal smart contract template is provided. The tool of model checking, SPIN, is used to verify the correctness and necessary properties for a smart contract template. The research shows model checking will be useful and necessary for smart contracts.
Beyond Tone Mapping: Enhanced Depiction of Tone Mapped HDR Images

High Dynamic Range (HDR) images capture the full range of luminance present in real world scenes, and unlike Low Dynamic Range (LDR) images, can simultaneously contain detailed information in the deepest of shadows and the brightest of light sources. For display or aesthetic purposes, it is often necessary to perform tone mapping, which creates LDR depictions of HDR images at the cost of contrast information loss. The purpose of this work is two-fold: to analyze a displayed LDR image against its original HDR counterpart in terms of perceived contrast distortion, and to enhance the LDR depiction with perceptually driven colour adjustments to restore the original HDR contrast information. For analysis, we present a novel algorithm for the characterization of tone mapping distortion in terms of observed loss of global contrast, and loss of contour and texture details. We classify existing tone mapping operators accordingly. We measure both distortions with perceptual metrics that enable the automatic and meaningful enhancement of LDR depictions. For image enhancement, we identify artistic and photographic colour techniques from which we derive adjustments that create contrast with colour. The enhanced LDR image is an improved depiction of the original HDR image with restored contrast information.
Mutual coupling compensation in small phased array antennas

A new technique to compensate for the mutual coupling effect in small phased array antennas is presented. This method is a modification to the classical approach for correcting the array output vector by multiplication of a decoupling matrix to achieve a less unperturbed array response. Two different methods of calculating coupling coefficients between array elements are presented. The performance is demonstrated by a simulation based on a numerical analysis with the finite element method (FEM) and a practical application to a digital beamforming (DBF) array.
Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People

This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities. Surveillance, multiple camera, people tracking, topology.
Enhancement of suggestibility and imaginative ability with nitrous oxide

Imaginative suggestibility, a trait closely related to hypnotic suggestibility, is modifiable under some circumstances. Nitrous oxide (laughing gas) is commonly used for sedation in dentistry and is reported to be more effective when combined with appropriate suggestions. The aim of this study was to determine whether nitrous oxide inhalation alters imaginative suggestibility and imagery vividness. Thirty participants were tested twice in a within-subjects design, once during inhalation of 25% nitrous oxide and once during inhalation of air plus oxygen. Before the study, participants’ expectancies regarding the effects of nitrous oxide were assessed. Participants were blinded to drug administration. During each session, participants were verbally administered detailed measures of imagination and suggestibility: the Sheehan–Betts Quality of Mental Imagery scale and the Stanford Hypnotic Susceptibility Scale Form C, minus the hypnotic induction. Imaginative suggestibility and imaginative ability (imagery vividness) were both elevated in the nitrous oxide condition. This effect was unrelated to participants’ expectations regarding the effects of the drug. Nitrous oxide increased imaginative suggestibility and imaginative ability. Possible explanations of these findings are discussed with respect to the effects of N-methyl-d-aspartate antagonists and to other pharmacological effects upon suggestibility and imagination.
Gender Classification Based on Fusion of Different Spatial Scale Features Selected by Mutual Information From Histogram of LBP, Intensity, and Shape

In this paper, we report our extension of the use of feature selection based on mutual information and feature fusion to improve gender classification of face images. We compare the results of fusing three groups of features, three spatial scales, and four different mutual information measures to select features. We also showed improved results by fusion of LBP features with different radii and spatial scales, and the selection of features using mutual information. As measures of mutual information we use minimum redundancy and maximal relevance (mRMR), normalized mutual information feature selection (NMIFS), conditional mutual information feature selection (CMIFS), and conditional mutual information maximization (CMIM). We tested the results on four databases: FERET and UND, under controlled conditions, the LFW database under unconstrained scenarios, and AR for occlusions. It is shown that selection of features together with fusion of LBP features significantly improved gender classification accuracy compared to previously published results. We also show a significant reduction in processing time because of the feature selection, which makes real-time applications of gender classification feasible.
An Introduction to Concept-Level Sentiment Analysis

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis, and other online collaborative media. The distillation of knowledge from the huge amount of unstructured information on the Web can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product or brand. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. To this end, concept-level sentiment analysis aims to go beyond a mere wordlevel analysis of text and provide novel approaches to opinion mining and sentiment analysis that enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.
The Body Surface as a Communication System: The State of the Art after 50 Years

The suggestion that the body surface might be used as an additional means of presenting information to human-machine operators has been around in the literature for nearly 50 years. Although recent technological advances have made the possibility of using the body as a receptive surface much more realistic, the fundamental limitations on the human information processing of tactile stimuli presented across the body surface are, however, still largely unknown. This literature review provides an overview of studies that have attempted to use vibrotactile interfaces to convey information to human operators. The importance of investigating any possible central cognitive limitations (i.e., rather than the peripheral limitations, such as related to sensory masking, that were typically addressed in earlier research) on tactile processing for the most effective design of body interfaces is highlighted. The applicability of the constraints emerging from studies of tactile processing under conditions of unisensory (i.e., purely tactile) stimulus presentation, to more ecologically valid conditions of multisensory stimulation, is also discussed. Finally, the results obtained from recent studies of tactile information processing under conditions of multisensory stimulation are described, and their implications for haptic/tactile interface design elucidated.
The Role of Network Analysis in Industrial and Applied Mathematics

Many problems in industry — and in the social, natural, information, and medical sciences — involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this commentary, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry. We focus, in particular, on discussing a physical-applied-mathematics approach to the study of networks. 1 ar X iv :1 70 3. 06 84 3v 2 [ cs .S I] 1 4 Se p 20 17 The Role of Network Analysis in Industrial and Applied Mathematics: A Physical-Applied-Mathematics Perspective Mason A. Porter and Sam D. Howison Department of Mathematics, University of California, Los Angeles, Los Angeles, California 90095, USA Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK
Miniaturized Wilkinson power dividers utilizing capacitive loading

The authors report the miniaturization of a planar Wilkinson power divider by capacitive loading of the quarter wave transmission lines employed in conventional Wilkinson power dividers. Reduction of the transmission line segments from /spl lambda//4 to between /spl lambda//5 and /spl lambda//12 are reported here. The input and output lines at the three ports and the lines comprising the divider itself are coplanar waveguide (CPW) and asymmetric coplanar stripline (ACPS), respectively. The 10 GHz power dividers are fabricated on high resistivity silicon (HRS) and alumina wafers. These miniaturized dividers are 74% smaller than conventional Wilkinson power dividers, and have a return loss better than +30 dB and an insertion loss less than 0.55 dB. Design equations and a discussion about the effect of parasitic reactance on the isolation are presented for the first time.
THE DIGITAL WORKFORCE AND THE WORKPLACE OF THE FUTURE

This editorial is part of a series written by editors and co-authored with a senior executive, thought leader, or scholar from a different field, to explore new content areas and grand challenges with the goal of expanding the scope, interestingness, and relevance of the work presented in the Academy of Management Journal. The principle is to use the editorial notes as “stage setters” to open up fresh, new areas of inquiry for management research.
Survey of clustering algorithms for MANET

Many clustering schemes have been proposed for ad hoc networks. A systematic classification of these clustering schemes enables one to better understand and make improvements. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance. Protocols try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control protocol. The clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. A large variety of approaches for ad hoc clustering have been developed by researchers which focus on different performance metrics. This paper presents a survey of different clustering schemes.