nils-herrmann/clean_scirepeval huggingface dataset:
Shrinking microwave filters

An adaptive predistortion technique has been presented and verified through the design and fabrication of practical filters in both the C and Ku bands. The method allows the realization of microwave filters at a lower cost, lighter mass, smaller volume, and better performance with minimum insertion loss penalties.The concept of lossy filters has been presented from a practical perspective. A simple lossy synthesis technique using any synthesized lossless (nontransversal) filter was shown, which can be used with hyperbolic rotations for loss distribution. Moreover, the limitation on the minimum Q of lossy resonators has been studied using a one-pole filter as a fundamental building block. Lossy four-pole Chebyshev and quasi-elliptic synthesis examples were presented. A four- pole Chebyshev lossy filter in the Ku band has been synthesized, modeled, and fabricated successfully using mixed combline and microstrip technologies. The design has the advantage of having all input-output paths going through more than one resonator, which minimizes unwanted source-to-load coupling, especially at high frequencies. The lossy approach is still at its early stages of development and needs more research and development effort to become as mature as the predistorted filters.
Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network

Estimation of in-field biomass and crop composition is important for both farmers and researchers. Using close-up high resolution images of the crops, crop species can be distinguished using image processing. In the current study, deep convolutional neural networks for semantic segmentation (or pixel-wise classification) of cluttered classes in RGB images was explored in case of catch crops and volunteer barley cereal. The dataset consisted of RGB images from a plot trial using oil radish as catch crops in barley. The images were captured using a high-end consumer camera mounted on a tractor. The images were manually annotated in 7 classes: oil radish, barley, weed, stump, soil, equipment and unknown. Data argumentation was used to artificially increase the dataset by transposing and flipping the images.  Preliminary results using this network show a pixel accuracy of 79% and a frequency weighted intersection over union of 66%. These preliminary results indicate great potential in deep convolutional networks for segmentation of plant species in cluttered RGB images.
Dynamic TXOP HCCA reclaiming scheduler with transmission time estimation for IEEE 802.11e real-time networks

802.11e HCCA reference scheduler guarantees Quality of Service only for Constant Bit Rate traffic streams, whereas its assignment of scheduling parameters (transmission time TXOP and polling period) is too rigid to serve Variable Bit Rate (VBR) traffic.
 This paper presents a new scheduling algorithm, Dynamic TXOP HCCA (DTH). Its scheduling scheme, integrated with the centralized scheduler, uses both a statistical estimation of needed transmission duration and a bandwidth reclaiming mechanism with the aim of improving the resource management and providing an instantaneous dynamic Transmission Opportunity (TXOP), tailored to multimedia applications with variable bit rate. Performance evaluation through simulation, confirmed by the scheduling analysis, shows that DTH is suitable to reduce the transmission queues length. This positively impacts on the delay and on packets drop rate experienced by VBR traffic streams.
A mm-Wave Patch Antenna with Broad Bandwidth and a Wide Angular Range

A novel mm-wave microstrip-fed patch antenna with broad bandwidth and wide angular coverage suitable for integration in planar arrays is designed, analyzed, and verified by measurements. The antenna provides a bandwidth of 13.1% between 34.1 and 38.9 GHz, which is achieved by a slotted multiple resonances microstrip patch and a matching circuit in microstrip technology. The antenna is built on RO3003 substrate with top and ground layers, which is low cost compared with other techniques. For simple integration with microstrip and frontend circuits, the feeding happens at the top layer with a microstrip coupling gap feed. The wide half-power beamwidth (HPBW) is achieved by suitably designed parasitic patches for the first resonant mode. The second resonant mode has a wide HPBW by default. The HPBW is between 100° and 125° within the matched bandwidth, which is a very good value for a microstrip patch antenna radiating over a ground plane. The measured input impedance and radiation characteristic show very good agreement with simulation results.
Neural Image Restoration for Decoding 1-D Barcodes using Common Camera Phones

The existing open-source libraries for 1-D barcodes recognition are not able to recognize the codes from images acquired using simple devices without autofocus or macro function. In this article we present an improvement of an existing algorithm for recognizing 1-D barcodes using camera phones with and without autofocus. The multilayer feedforward neural network based on backpropagation algorithm is used for image restoration in order to improve the selected algorithm. Performances of the proposed algorithm were compared with those obtained from available open-source libraries. The results show that our method makes possible the decoding of barcodes from images captured by mobile phones without autofocus.
Behavior Analysis for Elderly

Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU’15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.
Adversarially Learned Anomaly Detection

Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task. ALAD then uses reconstruction errors based on these adversarially learned features to determine if a data sample is anomalous. ALAD builds on recent advances to ensure data-space and latent-space cycle-consistencies and stabilize GAN training, which results in significantly improved anomaly detection performance. ALAD achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published GAN-based method.
Modeling Agent's Preferences Based On Prospect Theory

It is well known that human preferences in decisions under risk do not always complies with expected utility theory (EUT). In fact, there are several effects that are inconsistent with basic tenets of EUT. Alternative theories have been proposed and perhaps the most well studied is Prospect Theory (PT). Recent work showed experimental results that support the idea that financial professionals may behave according to PT and violate EUT. Meanwhile, some argue that economy needs agent-based modeling, because it may be a better way to help guide financial policies than mathematical models. If financial professional behave according to PT in markets, then agent-based modeling needs PT based agents. Our idea is creating trading agents based on PT to simulate a market. However, the creation of an artificial agent based on PT as originally proposed is very hard and limited to two outcome prospects. We propose an agent model based on an extension of PT called Smooth Prospect Theory (SPT). We used this model to create agents to populate an artificial market with SPT and EUT agents. It was used to predict real market behavior for short periods. SPT agents provided more accurate predictions in crisis periods than EUT agents.
IBM Mega Traffic Simulator

IBM Mega Traffic Simulator (Megaffic) is an agent-based simulator of traffic flows with two unique features. First, Megaffic can build its model of simulation by directly estimating some of the parameters of the model from probe-car data. This capability is in contrast to existing agent-based simulators of traffic flows, where the values of their parameters are calibrated with iterative simulation. Second, Megaffic can run on massively parallel computers and simulate the microscopic traffic flows in the scale of an arbitrary city or even the whole Japan. This manuscript gives an overview of the design and capability of Megaffic. ∗5-6-52 Toyosu, Koto-ku, Tokyo 135-8511, Japan
Assessment of Factors Associated with Malnutrition among Under Five Years Age Children at Machakel Woreda, Northwest Ethiopia: A CaseControl Study

; malnutrition continues to be a significant public health and development concern not only in developing country but also in the world. It is a serious problem because it is causing the deaths of 3.5 million children under 5 years old peryear. Its magnitude is still highest in Ethiopia as well as in Amhara Region that remains a major public health problem.  The main aim of this study is to assess associated factors of malnutrition on under five years children in Machakel Woreda.  Unmatched Case control study was conducted. Cases were children of aged 6-59 months who have malnutrition (weight for height <2sd, weight for age ≤ 2Sd, height for age ≤ 2Sd, Mid-Upper Arm Circumstance (MUAC) <12 cm, if there is edema) and controls were 6-59 months of children those who have not malnutrition (weight for height ≥ 2sd, weight for age ≥ 2Sd, height for age ≥ 2sd, MUAC>12 cm, there is no edema). A consecutive sampling technique was employ to select study subjects for this study. Logistic regression was used to analyze data by using backward variable selection technique.  A total of 102 cases and 201 controls were included in the study with overall response rate of 94.4%. Sixty five (63.20%) of cases and 49 (24.40%) controls had fathers that cannot read and write. Thirty nine (38.23%) of cases and 44(21.89%) of controls had history of diarrheal episode. Those children whose family use drinking water from unprotected source were 3 times more likely to have malnutrition as compared to those children whose family use drinking water from protected source with [AOR=3.04, 95%CI (1.01, 9.17)].  The fining of this study revealed that inappropriate child carrying and feeding practice were strongly associated with under five malnutrition. Therefore, the responsible body should implement on nutritional intervention activities at all level of the community.
Commonsense Reasoning in and Over Natural Language

ConceptNet is a very large semantic network of commonsense knowledge suitable for making various kinds of practical inferences over text. ConceptNet captures a wide range of commonsense concepts and relations like those in Cyc, while its simple semantic network structure lends it an ease-of-use comparable to WordNet. To meet the dual challenge of having to encode complex higher-order concepts, and maintaining ease-of-use, we introduce a novel use of semi-structured natural language fragments as the knowledge representation of commonsense concepts. In this paper, we present a methodology for reasoning flexibly about these semi-structured natural language fragments. We also examine the tradeoffs associated with representing commonsense knowledge in formal logic versus in natural language. We conclude that the flexibility of natural language makes it a highly suitable representation for achieving practical inferences over text, such as context finding, inference chaining, and conceptual analogy. 1  (www.conceptnet.org) is the largest freely available, machine-useable commonsense resource. Structured as a network of semi-structured natural language fragments, ConceptNet presently consists of over 250,000 elements of commonsense knowledge. We were inspired dually by the range of commonsense concepts and relations in Cyc (Lenat, 1995), and by the ease-of-use of WordNet (Fellbaum, 1998), and hoped to combine the best of both worlds. As a result, we adopted the semantic network representation of WordNet, but extended the representation in several key ways. First, we extended WordNet’s lexical notion of nodes to a conceptual notion of nodes, but we kept the nodes in natural language, because one of the primary strengths of WordNet in the textual domain is that its knowledge representation is itself textual. ’s nodes are thus natural language fragments which are semi-structured according to an ontology of allowable syntactic patterns, and accommodate both first-order concepts given as noun phrases (e.g. “potato chips”), and second-order concepts given as verb phrases (e.g. “buy potato chips”). Second, we extended WordNet’s small ontology of semantic relations, which are primarily taxonomic in nature, to include a richer set of relations appropriate to concept-level nodes. At present there are 19 semantic relations used in ConceptNet, representing categories of, inter alia, temporal, spatial, causal, and functional knowledge. By combining higher order nodes with this relational ontology, it is possible to represent richer kinds of knowledge in ConceptNet beyond what can be represented in WordNet . For example, we can represent a layman’s common sense observation that “you may be hurt if you get into an accident” in ConceptNet as EffectOf(“get into accident”, “be hurt”). Note that because the knowledge representation is semi-structured natural language, there are often various ways to represent the same knowledge. This is a source of ambiguity, but as we will argue in this paper, maintaining some ambiguity lends us greater flexibility for reasoning.
Lampung - a new handwritten character benchmark: database, labeling and recognition

This research paper deals with our effort of creation and recognition of isolated Lampung characters, a script originated from Indonesia. The aim is to describe this new script with all its peculiarities, propose a labeling scheme to manage a large isolated character dataset and finally a recognition scheme based on water reservoir concept. The Lampung script originally descending from Brahmi script is used in Lampung Province and it is close to extinction if no such initiative as ours will direct the focus to this cultural heritage. The collected dataset contains isolated characters coming from fairy tales transcriptions and were annotated with a semi-automatic labeling method using a limited human effort. Our attention is focused not only on the database collection but on recognition as well. For this purpose a water reservoir based feature set is proposed exploiting the different cavities and the subsequent measures of the character shapes. The experimental results (94.27%) prove the efficiency of the method considering a brand new script and feature set.
Mariana: Tencent Deep Learning Platform and its Applications

Deep learning gains lots of attentions in recent years and is more and more important for mining values in big data. However, to make deep learning practical for a wide range of applications in Tencent Inc., three requirements must be considered: 1) Lots of computational power are required to train a practical model with tens of millions of parameters and billions of samples for products such as automatic speech recognition (ASR), and the number of parameters and training data is still growing. 2) The capability of training larger model is necessary for better model quality. 3) Easy to use frameworks are valuable to do many experiments to perform model selection, such as finding an appropriate optimization algorithm and tuning optimal hyper-parameters. To accelerate training, support large models, and make experiments easier, we built Mariana, the Tencent deep learning platform, which utilizes GPU and CPU cluster to train models parallelly with three frameworks: 1) a multi-GPU data parallelism framework for deep neural networks (DNNs). 2) a multi-GPU model parallelism and data parallelism framework for deep convolutional neural networks (CNNs). 3) a CPU cluster framework for large scale DNNs. Mariana also provides built-in algorithms and features to facilitate experiments. Mariana is in production usage for more than one year, achieves state-of-the-art acceleration performance, and plays a key role in training models and improving quality for automatic speech recognition and image recognition in Tencent WeChat, a mobile social platform, and for Ad click-through rate prediction (pCTR) in Tencent QQ, an instant messaging platform, and Tencent Qzone, a social networking service.
Big Data analytics. Three use cases with R, Python and Spark

Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained by the direct use of three reference environments : R, Python Scikit-learn, ∗Université de Toulouse – INSA, Institut de Mathématiques, UMR CNRS 5219 †Institut de Mathématiques, UMR CNRS 5219 ‡Université de Toulouse – UT3, Institut de Mathématiques, UMR CNRS 5219 1 ar X iv :1 60 9. 09 61 9v 1 [ st at .A P] 3 0 Se p 20 16 Spark MLlib on three public use cases : character recognition, recommending films, categorizing products. As main result, it appears that, if Spark is very efficient for data munging and recommendation by collaborative filtering (non-negative factorization), current implementations of conventional learning methods (logistic regression, random forests) in MLlib or SparkML do not ou poorly compete habitual use of these methods (R, Python Scikit-learn) in an integrated or undistributed architecture.
Hyracks: A flexible and extensible foundation for data-intensive computing

Hyracks is a new partitioned-parallel software platform designed to run data-intensive computations on large shared-nothing clusters of computers. Hyracks allows users to express a computation as a DAG of data operators and connectors. Operators operate on partitions of input data and produce partitions of output data, while connectors repartition operators' outputs to make the newly produced partitions available at the consuming operators. We describe the Hyracks end user model, for authors of dataflow jobs, and the extension model for users who wish to augment Hyracks' built-in library with new operator and/or connector types. We also describe our initial Hyracks implementation. Since Hyracks is in roughly the same space as the open source Hadoop platform, we compare Hyracks with Hadoop experimentally for several different kinds of use cases. The initial results demonstrate that Hyracks has significant promise as a next-generation platform for data-intensive applications.
A –$21.2$ -dBm Dual-Channel UHF Passive CMOS RFID Tag Design

Previous research results showed that UHF passive CMOS RFID tags had difficulty to achieve sensitivity less than -20 dBm. This paper presents a dual-channel 15-bit UHF passive CMOS RFID tag prototype that can work at sensitivity lower than -20 dBm. The proposed tag chip harvests energy and backscatters uplink data at 866.4-MHz (for ETSI) or 925-MHz (for FCC) channel and receives downlink data at 433-MHz channel. Consequently, the downlink data transmission does not interrupt our tag from harvesting RF energy. To use the harvested energy efficiently, we design a tag chip that includes neither a regulator nor a VCO such that the harvested energy is completely used in receiving, processing, and backscattering data. Without a regulator, our tag uses as few active analog circuits as possible in the receiver front-end. Instead, our tag uses a novel digital circuit to decode the received data. Without a VCO, the design of our tag can extract the required clock signal from the downlink data. Measurement result shows that the sensitivity of the proposed passive tag chip can reach down to -21.2 dBm. Such result corresponds to a 19.6-m reader-to-tag distance under 36-dBm EIRP and 0.4-dBi tag antenna gain. The chip was fabricated in TSMC 0.18- μm CMOS process. The die area is 0.958 mm ×0.931mm.
Food Addiction and Bulimia Nervosa: New Data Based on the Yale Food Addiction Scale 2.0.

Previous research on 'food addiction' as measured with the Yale Food Addiction Scale (YFAS) showed a large overlap between addiction-like eating and bulimia nervosa. Most recently, a revised version of the YFAS has been developed according to the changes made in the diagnostic criteria for substance use disorder in the Diagnostic and Statistical Manual of Mental Disorders fifth edition. The current study examined prevalence and correlates of the YFAS2.0 in individuals with bulimia (n = 115) and controls (n = 341). Ninety-six per cent of participants with bulimia and 14% of controls received a YFAS2.0 diagnosis. A higher number of YFAS2.0 symptoms was associated with lower interoceptive awareness, higher depressiveness, and higher impulsivity in both groups. However, a higher number of YFAS2.0 symptoms was associated with higher body mass and weight suppression in controls only and not in participants with bulimia. The current study is the first to show a large overlap between bulimia and 'food addiction' as measured with the YFAS2.0, replicating and extending findings from studies, which used the previous version of the YFAS. Compensatory weight control behaviours in individuals with bulimia likely alleviate the association between addiction-like eating and higher body mass. Thus, the large overlap between bulimia and 'food addiction' should be taken into consideration when examining the role of addiction-like eating in weight gain and obesity.
A Hybrid Algorithm for LTL Games

In the game theoretic approach to the synthesis of reactive systems, specifications are often given in linear time logic (LTL). Computing a winning strategy to an infinite game whose winning condition is the set of LTL properties is the main step in obtaining an implementation. We present a practical hybrid algorithm—a combination of symbolic and explicit algorithm—for the computation of winning strategies for unrestricted LTL games that we have successfully applied to synthesize reactive systems with up to 10 states.
Real-Time Semantic Segmentation with Label Propagation

Despite of the success of convolutional neural networks for semantic image segmentation, CNNs cannot be used for many applications due to limited computational resources. Even efficient approaches based on random forests are not efficient enough for real-time performance in some cases. In this work, we propose an approach based on superpixels and label propagation that reduces the runtime of a random forest approach by factor 192 while increasing the segmentation accuracy.