nils-herrmann/clean_scirepeval huggingface dataset:
Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries

Query expansion is an important and commonly used technique for improving Web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click-through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new Web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico-semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph-based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi-step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. The experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo-relevance feedback.
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce

Collaborative filtering is one of the most widely adopted and successful recommendation approaches. Unlike approaches based on intrinsic consumer and product characteristics, CF characterizes consumers and products implicitly by their previous interactions. The simplest example is to recommend the most popular products to all consumers. Researchers are advancing CF technologies in such areas as algorithm design, human- computer interaction design, consumer incentive analysis, and privacy protection.
Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The lowresolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.
Compact square quadrifilar spiral antenna with circular polarization for UHF mobile RFID reader

A compact square quadrifilar spiral antenna (QSA) with right-hand circular polarization for mobile UHF passive radio frequency identification (RFID) reader is presented. The proposed QSA consists of four spiral antennas fed by microstrip feed network. The proposed reader antenna matching is optimized by using the matching condition for a small 4-port antenna. Experimental results show that the QSA of size 60 × 60 × 17 mm3 has a 3-dB beamwidth of more than 130, the front-to-back ratio of more than 15 dB, the peak gain of 2.5 dBic and the axial ratio under 2 dB.
An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting

We propose the use of dilated filters to construct an aggregation module in a multicolumn convolutional neural network for perspective-free counting. Counting is a common problem in computer vision (e.g. traffic on the street or pedestrians in a crowd). Modern approaches to the counting problem involve the production of a density map via regression whose integral is equal to the number of objects in the image. However, objects in the image can occur at different scales (e.g. due to perspective effects) which can make it difficult for a learning agent to learn the proper density map. While the use of multiple columns to extract multiscale information from images has been shown before, our approach aggregates the multiscale information gathered by the multicolumn convolutional neural network to improve performance. Our experiments show that our proposed network outperforms the state-of-the-art on many benchmark datasets, and also that using our aggregation module in combination with a higher number of columns is beneficial for multiscale counting.
IoT based control and automation of smart irrigation system: An automated irrigation system using sensors, GSM, Bluetooth and cloud technology

India has a population of more than a billion and its requirement for water increases each year as the demand for food increases hence management of water resources to sustain this massive population is of high importance. The agricultural sector, an important sector of our economy accounts for a good percentage of our nation's GDP and of the exports. With advancement in technology we can establish a system that automates the irrigation process such that there is efficient usage of water and create an ease of work load for the farmers. With embedded technology and Internet of Things, in this work we have designed IoT based automated irrigation system for the Indian scenario. Our system is able to deliver optimal water to the plants based on moisture, light and temperature levels which are obtained through sensors. The farmer will be able to monitor the parameters through the mobile app which is integrated with cloud storage. By analyzing and comparing previous year's data and our current data we are able to efficiently find a way to save water.
Multiple social identities and stereotype threat: imbalance, accessibility, and working memory.

In 4 experiments, the authors showed that concurrently making positive and negative self-relevant stereotypes available about performance in the same ability domain can eliminate stereotype threat effects. Replicating past work, the authors demonstrated that introducing negative stereotypes about women's math performance activated participants' female social identity and hurt their math performance (i.e., stereotype threat) by reducing working memory. Moving beyond past work, it was also demonstrated that concomitantly presenting a positive self-relevant stereotype (e.g., college students are good at math) increased the relative accessibility of females' college student identity and inhibited their gender identity, eliminating attendant working memory deficits and contingent math performance decrements. Furthermore, subtle manipulations in questions presented in the demographic section of a math test eliminated stereotype threat effects that result from women reporting their gender before completing the test. This work identifies the motivated processes through which people's social identities became active in situations in which self-relevant stereotypes about a stigmatized group membership and a nonstigmatized group membership were available. In addition, it demonstrates the downstream consequences of this pattern of activation on working memory and performance.
CAD of Waveguide Low-Pass Filters for Satellite Applications

Corrugated-waveguide low-pass filters are analysed by successive reflection coefficient transformations. The discontinuities are modelled by waveguide equivalent circuits, including interaction of higher order modes. The filter response is determined by TEm0-transfer functions (m=1,2...) each of which can be calculated independently. Implementing this method in an optimization routine allows complete computer-aided design according to given specifications.
Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features

Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to the users, such authentication mechanisms pose security and privacy concerns. Passive authentication systems can tackle these challenges by frequently and unobtrusively monitoring the user’s interaction with the device. In this paper, we propose a Siamese Long Short-Term Memory network architecture for passive authentication, where users can be verified without requiring any explicit authentication step. We acquired a dataset comprising of measurements from 30 smartphone sensor modalities for 37 users. We evaluate our approach on 8 dominant modalities, namely, keystroke dynamics, GPS location, accelerometer, gyroscope, magnetometer, linear accelerometer, gravity, and rotation sensors. Experimental results find that, within 3 seconds, a genuine user can be correctly verified 97.15% of the time at a false accept rate of 0.1%.
Action understanding and active inference

We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action–observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference.
The case for ethernet in automotive communications

Several factors seem to favor the introduction of Ethernet technology in automotive communications. The spreading of Ethernet as an in-vehicle network for today's cars or for those in the near future is being broadly announced by spokespersons for major carmakers and automotive electronics companies. Even in the scientific community there is a growing interest in the topic, shown by the increasing number of studies on the performance of Ethernet-based technologies such as Switched Ethernet or Time-Triggered Ethernet in automotive embedded systems.
 This position paper provides an overview on facts and trends towards the introduction of Ethernet in automotive communications and discusses how and to what extent Ethernet technology is likely to step in and provide benefits to the different automotive functional domains. The paper will also discuss which Ethernet technologies would be the possible candidates for the automotive industry.
Approaches , methods , metrics , measures , and subjectivity in ontology evaluation : A survey

Ontology evaluation is concerned with ascertain two important aspects of ontologies: quality and correctness. The distinction between the two is attempted in this survey as a way to better approach ontology evaluation. The role that ontologies play on the semantic web at large has been has been argued to have catalyzed the proliferation of ontologies in existence. This has also presented the challenge of deciding one the suitability of an given ontology to one’s purposes as compared with another ontology in a similar domain. This survey intends to analyze the state of the art in ontology evaluate spanning such topics as the approaches to ontology evaluation, the metrics and measures used. Particular interest is given to Data-driven ontology evaluation with special emphasis on the notion of bias and it’s relevance to evaluation results. Chief among the outputs of this survey is the gap analysis on the topic of ontology evaluation.
Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval

Recently, promising results have been shown on face recognition researches. However, face recognition and retrieval across age is still challenging. Unlike prior methods using complex models with strong parametric assumptions to model the aging process, we use a data-driven method to address this problem. We propose a novel coding framework called Cross-Age Reference Coding (CARC). By leveraging a large-scale image dataset freely available on the Internet as a reference set, CARC is able to encode the low-level feature of a face image with an ageinvariant reference space. In the testing phase, the proposed method only requires a linear projection to encode the feature and therefore it is highly scalable. To thoroughly evaluate our work, we introduce a new large-scale dataset for face recognition and retrieval across age called Cross-Age Celebrity Dataset (CACD). The dataset contains more than 160,000 images of 2,000 celebrities with age ranging from 16 to 62. To the best of our knowledge, it is by far the largest publicly available crossage face dataset. Experimental results show that the proposed method can achieve state-of-the-art performance on both our dataset as well as the other widely used dataset for face recognition across age, MORPH
Derivation of Human Trophoblast Stem Cells.

Trophoblast cells play an essential role in the interactions between the fetus and mother. Mouse trophoblast stem (TS) cells have been derived and used as the best in vitro model for molecular and functional analysis of mouse trophoblast lineages, but attempts to derive human TS cells have so far been unsuccessful. Here we show that activation of Wingless/Integrated (Wnt) and EGF and inhibition of TGF-β, histone deacetylase (HDAC), and Rho-associated protein kinase (ROCK) enable long-term culture of human villous cytotrophoblast (CT) cells. The resulting cell lines have the capacity to give rise to the three major trophoblast lineages, which show transcriptomes similar to those of the corresponding primary trophoblast cells. Importantly, equivalent cell lines can be derived from human blastocysts. Our data strongly suggest that the CT- and blastocyst-derived cell lines are human TS cells, which will provide a powerful tool to study human trophoblast development and function.
Future directions for behavioral information security research

Information Security (InfoSec) research is far reaching and includes many approaches to deal with protecting and mitigating threats to the information assets and technical resources available within computer based systems. Although a predominant weakness in properly securing information assets is the individual user within an organization, much of the focus of extant security research is on technical issues. The purpose of this paper is to highlight future directions for Behavioral InfoSec research, which is a newer, growing area of research. The ensuing paper presents information about challenges currently faced and future directions that Behavioral InfoSec researchers should explore. These areas include separating insider deviant behavior from insider misbehavior, approaches to understanding hackers, improving information security compliance, cross-cultural Behavioral InfoSec research, and data collection and measurement issues in Behavioral InfoSec research. a 2012 Elsevier Ltd. All rights reserved.
Fire and smoke detection in video with optimal mass transport based optical flow and neural networks

Detection of fire and smoke in video is of practical and theoretical interest. In this paper, we propose the use of optimal mass transport (OMT) optical flow as a low-dimensional descriptor of these complex processes. The detection process is posed as a supervised Bayesian classification problem with spatio-temporal neighborhoods of pixels;feature vectors are composed of OMT velocities and R,G,B color channels. The classifier is implemented as a single-hidden-layer neural network. Sample results show probability of pixels belonging to fire or smoke. In particular, the classifier successfully distinguishes between smoke and similarly colored white wall, as well as fire from a similarly colored background.
Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach

Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media. However, these sources are in general unstructured and, construction of surveillance tools such as taxonomical correlations and trace mapping involves considerable human supervision. In this paper, we motivate a disease vocabulary driven word2vec model (Dis2Vec) to model diseases and constituent attributes as word embeddings from the HealthMap news corpus. We use these word embeddings to automatically create disease taxonomies and evaluate our model against corresponding human annotated taxonomies. We compare our model accuracies against several state-of-the art word2vec methods. Our results demonstrate that Dis2Vec outperforms traditional distributed vector representations in its ability to faithfully capture taxonomical attributes across different class of diseases such as endemic, emerging and rare.
COMPACT WIDE-SLOT TRI-BAND ANTENNA FOR WLAN/WIMAX APPLICATIONS

In this paper, a wide-slot triple band antenna fed by a coplanar waveguide (CPW) for WLAN/WiMAX applications is proposed. The antenna mainly comprises a ground with a wide square slot in the center, a rectangular feeding strip and two pairs of planar inverted L strips (PIL) connecting with the slotted ground. By introducing the two pairs of PIL’s, three resonant frequencies, 2.4/5.5GHz for WLAN, and 3.5 GHz for WiMAX, are excited. Prototypes of the antenna are fabricated and tested. The simulated and measured results show that the proposed antenna has three good impedance bandwidths (S11 better than −10 dB) of 300MHz (about 12.6% centered at 2.39 GHz), 280 MHz (about 8% centered at 3.49GHz) and 790 MHz (about 14.5% centered at 5.43GHz), which make it easily cover the required bandwidths for WLAN band (2.4– 2.48GHz, 5.15–5.35 GHz, and 5.725–5.825GHz) and WiMAX (3.43.6GHz) applications. Moreover, the obtained radiation patterns demonstrate that the proposed antenna has figure-eight patterns in E-plane, and is omni-directional in H-plane. The gains of the antenna at operation bands are stable.
Characteristics of Excessive Cellular Phone Use in Korean Adolescents

The objective of this study was to evaluate the possible psychological problems related to excessive cellular phone use in adolescents. Results from 595 participants showed that the potentially excessive user group had a tendency to identify themselves with their cellular phones and to have difficulties in controlling usage. They expressed more depressive symptoms, higher interpersonal anxiety, and lower self-esteem. A positive correlation was also observed between excessive cellular phone use and Internet addiction.
Improving Text Proposals for Scene Images with Fully Convolutional Networks

Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text recognition. In this paper we propose an improvement over the original Text Proposals algorithm of , combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.
Analytical Solution of Air-Gap Field in Permanent-Magnet Motors Taking Into Account the Effect of Pole Transition Over Slots

We present an analytical method to study magnetic fields in permanent-magnet brushless motors, taking into consideration the effect of stator slotting. Our attention concentrates particularly on the instantaneous field distribution in the slot regions where the magnet pole transition passes over the slot opening. The accuracy in the flux density vector distribution in such regions plays a critical role in the prediction of the magnetic forces, i.e., the cogging torque and unbalanced magnetic pull. However, the currently available analytical solutions for calculating air-gap fields in permanent magnet motors can estimate only the distribution of the flux density component in the radial direction. Magnetic field and forces computed by the new analytical method agree well with those obtained by the finite-element method. The analytical method provides a useful tool for design and optimization of permanent-magnet motors.
Autophagy: Renovation of Cells and Tissues

Autophagy is the major intracellular degradation system by which cytoplasmic materials are delivered to and degraded in the lysosome. However, the purpose of autophagy is not the simple elimination of materials, but instead, autophagy serves as a dynamic recycling system that produces new building blocks and energy for cellular renovation and homeostasis. Here we provide a multidisciplinary review of our current understanding of autophagy's role in metabolic adaptation, intracellular quality control, and renovation during development and differentiation. We also explore how recent mouse models in combination with advances in human genetics are providing key insights into how the impairment or activation of autophagy contributes to pathogenesis of diverse diseases, from neurodegenerative diseases such as Parkinson disease to inflammatory disorders such as Crohn disease.
KeyGraph: Automatic Indexing by Co-Occurrence Graph Based on Building Construction Metaphor

In this paper, we present an algorithm for extracting keywords representing the asserted main point in a document, without relying on external devices such as natural language processing tools or a document corpus. Our algorithm KeyGraph is based on the segmentation of a graph, representing the co-occurrence between terms in a document, into clusters. Each cluster corresponds to a concept on which author's idea is based, and top ranked terms by a statistic based on each term's relationship to these clusters are selected as keywords. This strategy comes from considering that a document is constructed like a building for expressing new ideas based on traditional concepts. The experimental results show that thus extracted terms match author's point quite accurately, even though KeyGraph does not use each term's average frequency in a corpus, i.e., KeyGraph is a contentsensitive, domain independent device of indexing.