nils-herrmann/clean_scirepeval huggingface dataset:
Use of e-books in an academic and research environment: A case study from the Indian Institute of Science

E-books are not as popular as other types of e-publications, such as e-journals and e-newspapers. The possible reasons for this could be because the technology for creating/accessing e-books (both hardware and software) is not yet matured, users’ perspectives about e-books need to be changed and the awareness of e-books needs to be raised. The purpose of this study is to investigate the use and usability of e-books from users’ perspectives in an academic and research environment.
R-FCN: Object Detection via Region-based Fully Convolutional Networks

We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN [6, 18] that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Our method can thus naturally adopt fully convolutional image classifier backbones, such as the latest Residual Networks (ResNets) , for object detection. We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20× faster than the Faster R-CNN counterpart. Code is made publicly available at: https://github.com/daijifeng001/r-fcn.
Multiple nutrient deficiency detection in paddy leaf images using color and pattern analysis

Paddy being the staple food of India is majorly affected by deficiency of primary nutrient elements like nitrogen, phosphorus and potassium. Leaves can be deficient with multiple nutrient elements at a same time. This can alter natural color of paddy leaves. Such leaves are considered as defective. The proposed work is to automate multiple nutrient element deficiency identification of paddy leaves. Pattern analysis RGB color features are extracted to identify defective paddy leaves. Firstly the database of healthy, nitrogen, phosphorus and potassium defected paddy leaves are created. For any test image effective comparison at different levels are employed such as multiple color comparison, multiple pattern comparison and combination of color and patterns comparison, so that defectiveness is accurately identified for combination of deficiency such as nitrogen-phosphorus(NP), nitrogen-potassium(NK) and phosphorous-potassium (KP).
How to make large self-organizing maps for nonvectorial data

The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generalized median of symbol strings.
Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social

The traditional humanism of the twentieth century, inspired by the culture of the book, systematically distanced itself from the new society of digital information; the Internet and tools of information processing revolutionized the world, society during this period developed certain adaptive characteristics based on coexistence (Human Machine), this transformation sets based on the impact of three technology segments: devices, applications and infrastructure of social communication, which are involved in various physical, behavioural and cognitive changes of the human being; and the emergence of new models of influence and social control through the new ubiquitous communication; however in this new process of conviviality new models like the "collaborative thinking" and "InfoSharing" develop; managing social information under three Human ontological dimensions (h) Information (i) Machine (m), which is the basis of a new physical-cyber ecosystem, where they coexist and develop new social units called "virtual communities ". This new communication infrastructure and social management of information given discovered areas of vulnerability "social perspective of risk", impacting all social units through massive impact vector (i); The virtual environment "H + i + M"; and its components, as well as the life cycle management of social information allows us to understand the path of integration "Techno Social" and setting a new contribution to cybernetics, within the convergence of technology with society and the new challenges of coexistence, aimed at a new holistic and not pragmatic vision, as the human component (h) in the virtual environment is the precursor of the future and needs to be studied not as an application, but as the hub of a new society.
A Multilayer LTCC Solution for Integrating 5G Access Point Antenna Modules

An integrated solution for the development of multilayer antenna modules for fifth-generation (5G) communications, based on low temperature cofired ceramic (LTCC), is presented. The design exploits the 3-D integration capabilities of the LTCC process, enabling the realization of a full-corporate feed network (CFN) in vertical configuration. A novel implementation of the CFN employing dielectric-embedded parallel plate waveguides (PPWs) is proposed. The PPW lines are delimited by via-rows. As opposed to standard substrate-integrated waveguide feed networks, guided fields are orthogonal to the via-rows and propagate along the vertical axis of the structure. The CFN feeds four long slots, without any coupling structure, and provides broadband operation. The final prototype comprises 18 LTCC tapes, with a total thickness of 3.4 mm. The measured -10-dB impedance bandwidth spans from 51.2 to 66 GHz (>25.2%). The module generates a fixed broadside beam, but multibeam operation in H-plane can be easily achieved. In the 50-66-GHz band, the peak gain is 14.25 dBi and the average first side-lobe level in H-plane is -20.6 dB. The proposed technology and the design concept are suited for highly integrated millimeterwave systems, such as access points in the future V-band high data-rate wireless networks.
Design of over-current protection circuit for power transistors

The paper presents an over-current protection circuit designed for high voltage power switches. The proposed protection mechanism employs a current limiter to monitor the current flowing through the power switch. The current limiter protects the power switch from potential damage by automatically switching off the device once the current exceeds the preset rating value. Simulated results exhibit that the capability of the designed current limiter can work up to 1 MHz where the maximum current is above 1A using 0.25 μm Bipolar-CMOS-DMOS (BCD) technology.
Peephole: Predicting Network Performance Before Training

The quest for performant networks has been a significant force that drives the advancements of deep learning in recent years. While rewarding, improving network design has never been an easy journey. The large design space combined with the tremendous cost required for network training poses a major obstacle to this endeavor. In this work, we propose a new approach to this problem, namely, predicting the performance of a network before training, based on its architecture. Specifically, we develop a unified way to encode individual layers into vectors and bring them together to form an integrated description via LSTM. Taking advantage of the recurrent network’s strong expressive power, this method can reliably predict the performances of various network architectures. Our empirical studies showed that it not only achieved accurate predictions but also produced consistent rankings across datasets – a key desideratum in performance prediction.
XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings

Image translation refers to the task of mapping images from a visual domain to another. Given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce xgan, a dual adversarial auto-encoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the learned embedding to preserve semantics shared across domains. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset we collected for this purpose, ”CartoonSet”, is also publicly available as a new benchmark for semantic style transfer at https://google.github.io/cartoonset/index.html.
Survey of Large-Scale MIMO Systems

The escalating teletraffic growth imposed by the proliferation of smartphones and tablet computers outstrips the capacity increase of wireless communications networks. Furthermore, it results in substantially increased carbon dioxide emissions. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon commensurately increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. Therefore, this paper surveys the state of the art of LS-MIMO systems. First, we discuss the measurement and modeling of LS-MIMO channels. Then, some typical application scenarios are classified and analyzed. Key techniques of both the physical and network layers are also detailed. Finally, we conclude with a range of challenges and future research topics.
Developing Competency Models to Promote Integrated Human Resource Practices • 309 DEVELOPING COMPETENCY MODELS TO PROMOTE INTEGRATED HUMAN RESOURCE PRACTICES

Today, competencies are used in many facets of human resource management, ranging from individual selection, development, and performance management to organizational strategic planning. By incorporating competencies into job analysis methodologies, the Office of Personnel Management (OPM) has developed robust competency models that can form the foundation for each of these initiatives. OPM has placed these models into automated systems to ensure access for employees, human resources professionals, and managers. Shared access to the data creates a shared frame of reference and a common language of competencies that have provided the basis for competency applications in public sector agencies.
ANALYSIS OF VITAL SIGNS MONITORING USING AN IR-UWB RADAR

Ultra-wide Band (UWB) technology is a new, useful and safe technology in the field of wireless body networks. This paper focuses on the feasibility of estimating vital signs — specifically breathing rate and heartbeat frequency — from the spectrum of recorded waveforms, using an impulse-radio (IR) UWB radar. To this end, an analytical model is developed to perform and interpret the spectral analysis. Both the harmonics and the intermodulation between respiration and heart signals are addressed. Simulations have been performed to demonstrate how they affect the detection of vital signs and also to analyze the influence of the pulse waveform. A filter to cancel out breathing harmonics is also proposed to improve heart rate detection. The results of the experiments are presented under different scenarios which demonstrate the accuracy of the proposed technique for determining respiration and heartbeat rates. It has been shown that an IR-UWB radar can meet the requirements of typical biomedical applications such as non-invasive heart and respiration rate monitoring.
Stronger generalization bounds for deep nets via a compression approach

Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but do not as yet result in sample complexity bounds better than naive parameter counting. The current paper shows generalization bounds that’re orders of magnitude better in practice. These rely upon new succinct reparametrizations of the trained net — a compression that is explicit and efficient. These yield generalization bounds via a simple compression-based framework introduced here. Our results also provide some theoretical justification for widespread empirical success in compressing deep nets. Analysis of correctness of our compression relies upon some newly identified “noise stability”properties of trained deep nets, which are also experimentally verified. The study of these properties and resulting generalization bounds are also extended to convolutional nets, which had eluded earlier attempts on proving generalization.
Event-Driven Semantic Concept Discovery by Exploiting Weakly Tagged Internet Images

Analysis and detection of complex events in videos require a semantic representation of the video content. Existing video semantic representation methods typically require users to pre-define an exhaustive concept lexicon and manually annotate the presence of the concepts in each video, which is infeasible for real-world video event detection problems. In this paper, we propose an automatic semantic concept discovery scheme by exploiting Internet images and their associated tags. Given a target event and its textual descriptions, we crawl a collection of images and their associated tags by performing text based image search using the noun and verb pairs extracted from the event textual descriptions. The system first identifies the candidate concepts for an event by measuring whether a tag is a meaningful word and visually detectable. Then a concept visual model is built for each candidate concept using a SVM classifier with probabilistic output. Finally, the concept models are applied to generate concept based video representations. We use the TRECVID Multimedia Event Detection (MED) 2013 as our video test set and crawl 400K Flickr images to automatically discover 2, 000 visual concepts. We show significant performance gains of the proposed concept discovery method over different video event detection tasks including supervised event modeling over concept space and semantic based zero-shot retrieval without training examples. Importantly, we show the proposed method of automatic concept discovery outperforms other well-known concept library construction approaches such as Classemes and ImageNet by a large margin (228%) in zero-shot event retrieval. Finally, subjective evaluation by humans also confirms clear superiority of the proposed method in discovering concepts for event representation.
Fast Method to Optimize RF Bumper Transparency for Wide-Band Automotive Radar

This article describes novel methods to enhance the RF integration of wideband automotive radars. The first part of the paper is introducing the context and the considered scenario. Whereas the second part is focusing on two solutions to improve the radar integration in car chassis environment. The aim is to have good RF performances on wideband operation (76–81 GHz) and on a wide range of angle of incidence for the radar having wide field of view (e.g. Blind spot radar, Pedestrian detection, …).
Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies.

Polyphenols are abundant micronutrients in our diet, and evidence for their role in the prevention of degenerative diseases is emerging. Bioavailability differs greatly from one polyphenol to another, so that the most abundant polyphenols in our diet are not necessarily those leading to the highest concentrations of active metabolites in target tissues. Mean values for the maximal plasma concentration, the time to reach the maximal plasma concentration, the area under the plasma concentration-time curve, the elimination half-life, and the relative urinary excretion were calculated for 18 major polyphenols. We used data from 97 studies that investigated the kinetics and extent of polyphenol absorption among adults, after ingestion of a single dose of polyphenol provided as pure compound, plant extract, or whole food/beverage. The metabolites present in blood, resulting from digestive and hepatic activity, usually differ from the native compounds. The nature of the known metabolites is described when data are available. The plasma concentrations of total metabolites ranged from 0 to 4 mumol/L with an intake of 50 mg aglycone equivalents, and the relative urinary excretion ranged from 0.3% to 43% of the ingested dose, depending on the polyphenol. Gallic acid and isoflavones are the most well-absorbed polyphenols, followed by catechins, flavanones, and quercetin glucosides, but with different kinetics. The least well-absorbed polyphenols are the proanthocyanidins, the galloylated tea catechins, and the anthocyanins. Data are still too limited for assessment of hydroxycinnamic acids and other polyphenols. These data may be useful for the design and interpretation of intervention studies investigating the health effects of polyphenols.
Category-Based Induction

An argument is categorical if its premises and conclusion are of the form All members ofC have property F, where C is a natural category like FALCON or BIRD, and P remains the same across premises and conclusion. An example is Grizzly bears love onions. Therefore, all bears love onions. Such an argument is psychologically strong to the extent that belief in its premises engenders belief in its conclusion. A subclass of categorical arguments is examined, and the following hypothesis is advanced: The strength of a categorical argument increases with (a) the degree to which the premise categories are similar to the conclusion category and (b) the degree to which the premise categories are similar to members of the lowest level category that includes both the premise and the conclusion categories. A model based on this hypothesis accounts for 13 qualitative phenomena and the quantitative results of several experiments.
Information Retrieval with Verbose Queries

Recently, the focus of many novel search applications shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like Facebook's Graph Search or Google's Knowledge Graph. However the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. However, thus far, there was not a coherent and organized tutorial on this topic. In this tutorial, we aim to put together various research pieces of the puzzle, provide a comprehensive and structured overview of various proposed methods, and also list various application scenarios where effective verbose query processing can make a significant difference.
DALEX: explainers for complex predictive models

Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand. This paper describes a consistent collection of explainers for predictive models, a.k.a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method despite its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable. Every explainer presented in this paper works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended. The current implementation supports the most popular frameworks for classification and regression.
Design , construction and verification of a self-balancing vehicle

The Segway Personal Transporter is a small footprint electrical vehicle designed by Dean Kamen to replace the car as a more environmentally friendly transportation method in metropolitan areas. The dynamics of the vehicle is similar to the classical control problem of an inverted pendulum, which means that it is unstable and prone to tip over. This is prevented by electronics sensing the pitch angle and its time derivative, controlling the motors to keep the vehicle balancing (1). This kind of vehicle is interesting since it contains a lot of technology relevant to an environmentally friendly and energy efficient transportation industry. This thesis describes the development of a similar vehicle from scratch, incorporating every phase from literature study to planning, design, vehicle construction and verification. The main objective was to build a vehicle capable of transporting a person weighing up to 100 kg for 30 minutes or a distance of 10 km, whichever comes first. The rider controls are supposed to be natural movements; leaning forwards or backwards in combination with tilting the handlebar sideways should be the only rider input required to ride the vehicle. The vehicle was built using a model-based control design and a top-down construction approach. The controller is a linear quadratic controller implemented in a 100 Hz control loop, designed to provide as fast response to disturbances as possible without saturating the control signal under normal operating conditions. The need for adapting the control law to rider weight and height was investigated with a controller designed for a person 1,8 m tall weighing 80 kg. Simulations of persons having weights between 60-100 kg and heights between 1,6-1,9 m were performed, showing no need to adapt the controller. The controller could safely return the vehicle to upright positions even after angle disturbances of ±6 degrees, the highest angle deviation considered to occur during operation.