nils-herrmann/clean_scirepeval huggingface dataset:
Teaching Machines to Describe Images via Natural Language Feedback

Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users. In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a learning agent in the form of natural language. We argue that a descriptive sentence can provide a much stronger learning signal than a numeric reward in that it can easily point to where the mistakes are and how to correct them. We focus on the problem of image captioning in which the quality of the output can easily be judged by non-experts. In particular, we first train a captioning model on a subset of images paired with human written captions. We then let the model describe new images and collect human feedback on the generated descriptions. We propose a hierarchical phrase-based captioning model, and design a feedback network that provides reward to the learner by conditioning on the human-provided feedback. We show that by exploiting descriptive feedback on new images our model learns to perform better than when given human written captions on these images.
The mix estimation algorithm for battery State-of-Charge estimator- Analysis of the sensitivity to measurement errors.

The problem considered in this paper is the analysis of a battery State-of-Charge estimation algorithm: in particular the mix estimation algorithm. This algorithm provides the estimation mixing two estimation approaches: namely the Coulomb-Counting and the Model-Based. The mix algorithm is qualitatively able to provide a more robust and accurate estimation with respect to the estimation provided by the approaches the algorithm mixes together. The aim of this paper is to analyze the differences between the three algorithms and the advantages produced by the mixing procedure. In particular the paper presents the comparison of the mix algorithm behavior with the Coulomb-Counting and the Model-Based behaviors in case of measure errors.
Towards Deep Learning Models Resistant to Adversarial Attacks

Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models.
BLADYG: A Novel Block-Centric Framework for the Analysis of Large Dynamic Graphs

Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, GraphLab, and Trinity. These systems can be divided into two categories: (1) vertex-centric and (2) block-centric approaches. In vertex-centric approaches, each vertex corresponds to a process, and message are exchanged among vertices. In block-centric approaches, the unit of computation is a block, a connected subgraph of the graph, and message exchanges occur among blocks. In this paper, we are considering the issues of scale and dynamism in the case of block-centric approaches. We present BLADYG, a block-centric framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of BLADYG on top of AKKA framework. We experimentally evaluate the performance of the proposed framework.
Speaker-Independent Speech Separation With Deep Attractor Network

Despite the recent success of deep learning for many speech processing tasks, single-microphone, speaker-independent speech separation remains challenging for two main reasons. The first reason is the arbitrary order of the target and masker speakers in the mixture permutation problem, and the second is the unknown number of speakers in the mixture output dimension problem. We propose a novel deep learning framework for speech separation that addresses both of these issues. We use a neural network to project the time-frequency representation of the mixture signal into a high-dimensional embedding space. A reference point attractor is created in the embedding space to represent each speaker which is defined as the centroid of the speaker in the embedding space. The time-frequency embeddings of each speaker are then forced to cluster around the corresponding attractor point which is used to determine the time-frequency assignment of the speaker. We propose three methods for finding the attractors for each source in the embedding space and compare their advantages and limitations. The objective function for the network is standard signal reconstruction error which enables end-to-end operation during both training and test phases. We evaluated our system using the Wall Street Journal dataset WSJ0 on two and three speaker mixtures and report comparable or better performance than other state-of-the-art deep learning methods for speech separation.
Aspect-based sentiment analysis for Indonesian restaurant reviews

Aspect-based sentiment analysis summarizes what people like and dislike from reviews of products or services. In this paper, we adapt the first rank research at SemEval 2016 to improve the performance of aspect-based sentiment analysis for Indonesian restaurant reviews. We use six steps for aspect-based sentiment analysis i.e.: preprocess the reviews, aspect extraction, aspect categorization, sentiment classification, opinion structure generation, and rating calculation. We collect 992 sentences for experiment and 383 sentences for evaluation. We conduct experiment to find best feature combination for aspect extraction, aspect categorization, and sentiment classification. The aspect extraction, aspect categorization, and sentiment classification have F1-measure value of 0.793, 0.823, and 0.642 respectively.
Pazl: A mobile crowdsensing based indoor WiFi monitoring system

WiFi in indoor environments exhibits spatio-temporal variations in terms of coverage and interference in typical WLAN deployments with multiple APs, motivating the need for automated monitoring to aid network administrators to adapt the WLAN deployment in order to match the user expectations. We develop Pazl, a mobile crowdsensing based indoor WiFi monitoring system that is enabled by a novel hybrid localization mechanism to locate individual measurements taken from participant phones. The localization mechanism in Pazl integrates the best aspects of two well known localization techniques, pedestrian dead reckoning and WiFi fingerprinting; it also relies on crowdsourcing for constructing the WiFi fingerprint database. Compared to existing WiFi monitoring systems based on static sniffers, Pazl is low cost and provides a user-side perspective. Pazl is significantly more automated than wireless site survey tools such as Ekahau Mobile Survey tool by drastically reducing the manual point-and-click based measurement location determination. We implement Pazl through a combination of Android mobile app and cloud backend application on the Google App Engine. Experimental evaluation of Pazl with a trial set of users shows that it yields similar results to manual site surveys but without the tedium.
Satellite Oil Spill Detection Using Artificial Neural Networks

Oil spills represent a major threat to ocean ecosystems and their health. Illicit pollution requires continuous monitoring and satellite remote sensing technology represents an attractive option for operational oil spill detection. Previous studies have shown that active microwave satellite sensors, particularly Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish oil spills from `look-alikes'. This paper describes the development of a new approach to SAR oil spill detection employing two different Artificial Neural Networks (ANN), used in sequence. The first ANN segments a SAR image to identify pixels belonging to candidate oil spill features. A set of statistical feature parameters are then extracted and used to drive a second ANN which classifies objects into oil spills or look-alikes. The proposed algorithm was trained using 97 ERS-2 SAR and ENVSAT ASAR images of individual verified oil spills or/and look-alikes. The algorithm was validated using a large dataset comprising full-swath images and correctly identified 91.6% of reported oil spills and 98.3% of look-alike phenomena. The segmentation stage of the new technique outperformed the established edge detection and adaptive thresholding approaches. An analysis of feature descriptors highlighted the importance of image gradient information in the classification stage.
Body Area Networks: A Survey

Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications. M. Chen · S. Gonzalez · H. Cao · V. C. M. Leung Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada M. Chen School of Computer Science and Engineering, Seoul National University, Seoul, South Korea A. Vasilakos (B) Department of Computer and Telecommunications Engineering, University of Western Macedonia, Macedonia, Greece e-mail: vasilako@ath.forthnet.gr
Trustworthiness in electronic commerce: the role of privacy, security, and site attributes

While the growth of business-to-consumer electronic commerce seems phenomenal in recent years, several studies suggest that a large number of individuals using the Internet have serious privacy concerns, and that winning public trust is the primary hurdle to continued growth in e-commerce. This research investigated the relative importance, when purchasing goods and services over the Web, of four common trust indices (i.e. (1) third party privacy seals, (2) privacy statements, (3) third party security seals, and (4) security features). The results indicate consumers valued security features significantly more than the three other trust indices. We also investigated the relationship between these trust indices and the consumer’s perceptions of a marketer’s trustworthiness. The findings indicate that consumers’ ratings of trustworthiness of Web merchants did not parallel experts’ evaluation of sites’ use of the trust indices. This study also examined the extent to which consumers are willing to provide private information to electronic and land merchants. The results revealed that when making the decision to provide private information, consumers rely on their perceptions of trustworthiness irrespective of whether the merchant is electronic only or land and electronic. Finally, we investigated the relative importance of three types of Web attributes: security, privacy and pleasure features (convenience, ease of use, cosmetics). Privacy and security features were of lesser importance than pleasure features when considering consumers’ intention to purchase. A discussion of the implications of these results and an agenda for future research are provided.
Estimating Taxi-out times with a reinforcement learning algorithm

Flight delays have a significant impact on the nationpsilas economy. Taxi-out delays in particular constitute a significant portion of the block time of a flight. In the future, it can be expected that accurate predictions of dasiawheels-offpsila time may be used in determining whether an aircraft can meet its allocated slot time, thereby fitting into an en-route traffic flow. Without an accurate taxi-out time prediction for departures, there is no way to effectively manage fuel consumption, emissions, or cost. Dynamically changing operations at the airport makes it difficult to accurately predict taxi-out time. This paper describes a method for estimating average taxi-out times at the airport in 15 minute intervals of the day and at least 15 minutes in advance of aircraft scheduled gate push-back time. A probabilistic framework of stochastic dynamic programming with a learning-based solution strategy called Reinforcement Learning (RL) has been applied. Historic data from the Federal Aviation Administrationpsilas (FAA) Aviation System Performance Metrics (ASPM) database were used to train and test the algorithm. The algorithm was tested on John F. Kennedy International airport (JFK), one of the busiest, challenging, and difficult to predict airports in the United States that significantly influences operations across the entire National Airspace System (NAS). Due to the nature of departure operations at JFK the prediction accuracy of the algorithm for a given day was analyzed in two separate time periods (1) before 4:00 P.M and (2) after 4:00 P.M. On an average across 15 days, the predicted average taxi-out times matched the actual average taxi-out times within plusmn5 minutes for about 65 % of the time (for the period before 4:00 P.M) and 53 % of the time (for the period after 4:00 P.M). The prediction accuracy over the entire day within plusmn5 minutes range of accuracy was about 60 %. Further, application of the RL algorithm to estimate taxi-out times at airports with multi-dependent static surface surveillance data will likely improve the accuracy of prediction. The implications of these results for airline operations and network flow planning are discussed.
A Wideband Differentially Fed Dual-Polarized Antenna with Stable Radiation Pattern for Base Stations

A new wideband differentially fed dual-polarized antenna with stable radiation pattern for base stations is proposed and studied. A cross-shaped feeding structure is specially designed to fit the differentially fed scheme and four parasitic loop elements are employed to achieve a wide impedance bandwidth. A stable antenna gain and a stable radiation pattern are realized by using a rectangular cavity-shaped reflector instead of a planar one. A detailed parametric study was performed to optimize the antenna’s performances. After that, a prototype was fabricated and tested. Measured results show that the antenna achieves a wide impedance bandwidth of 52% with differential standing-wave ratio <1.5 from 1.7 to 2.9 GHz and a high differential port-to-port isolation of better than 26.3 dB within the operating frequency bandwidth. A stable antenna gain ( $\approx 8$  dBi) and a stable radiation pattern with 3-dB beamwidth of 65° ±5° were also found over the operating frequencies. Moreover, the proposed antenna can be easily built by using printed circuit board fabrication technique due to its compact and planar structure.
CoBoLD — A bonding mechanism for modular self-reconfigurable mobile robots

Within this paper, CoBoLD — short for Cone Bolt Locking Device — a bonding mechanism for modular self-reconfigurable autonomous mobile robots is presented. The docking unit is used to physically connect modular robots and/or toolboxes in order to form complex structures and robotic organisms. CoBoLD is specially designed to combine essential features like genderlessness, symmetry, high stiffness, integrated force sensors and electrical contacts, while still using a simple and low cost, easy to manufacture setup. Next to a detailed description of CoBoLDs assembly, first trails and their results are shown in this paper. CoBoLD is used at several different robotic platforms and toolboxes within the EU founded projects SYMBRION and REPLICATOR.
HIGH-GAIN LOW SIDE LOBE LEVEL FABRY PEROT CAVITY ANTENNA WITH FEED PATCH ARRAY

In this paper, a high gain, low side lobe level Fabry Perot Cavity antenna with feed patch array is proposed. The antenna structure consists of a microstrip antenna array, which is parasitically coupled with an array of square parasitic patches fabricated on a FR4 superstrate. The patches are fabricated at the bottom of superstrate and suspended in air with the help of dielectric rods at 0.5λ0 height. Constant high gain is obtained by resonating parasitic patches at near close frequencies in 5.725–5.875GHz ISM band. The structure with 9× 9 square parasitic patches with 1.125λ0 spacing between feed elements is fabricated on 5λ0 × 5λ0 square ground. The fabricated structure provides gain of 21.5 dBi associated with side lobe level less than −25 dB, cross polarization less than −26 dB and front to back lobe ratio of more than 26 dB. The measured gain variation is less than 1 dB and VSWR is less than 2 over 5.725–5.875 GHz ISM band. The proposed structures are good candidates for base station cellular systems, satellite systems, and point-to-point links.
Reliable SPICE Simulations of Memristors , Memcapacitors and Meminductors

Memory circuit elements, namely memristive, memcapacitive and meminductive systems, are gaining considerable attention due to their ubiquity and use in diverse areas of science and technology. Their modeling within the most widely used environment, SPICE, is thus critical to make substantial progress in the design and analysis of complex circuits. Here, we present a collection of models of different memory circuit elements and provide a methodology for their accurate and reliable modeling in the SPICE environment. We also provide codes of these models written in the most popular SPICE versions (PSpice, LTspice, HSPICE) for the benefit of the reader. We expect this to be of great value to the growing community of scientists interested in the wide range of applications of memory circuit elements.
A Fog-Based DSS Model for Driving Rule Violation Monitoring Framework on the Internet of Things

In recent days for logistics as well as common peoples of developing and developed countries are in need of transportation policy including road safety, which can be achieved only when well-managed driving rule violation monitoring takes place to monitor the flow of increasing number of vehicles. Manual detection of driving rule breakers leads to overhead of today’s traffic monitoring body. Switching to an automated decision support driving rule violation monitoring system based on sensor network is one of the paradigms to solve this issue. Upcoming cloud researchers have already felt the demand of designing such a sensor-based driver’s driving rule violation monitoring cloud application that can release the overhead of the current traffic monitoring bodies. To accomplish the client’s expectation researchers have realized such cloud application based on sensor network or Internet of Things (IoT) is not so satisfactory. To solve these bottleneck conditions of the cloud computing, researchers have introduced the future of cloud computing called fog computing in this aspect, where computing services reside at the network edge. This paper proposed novel fog-based intelligent decision support system (DSS) for driver safety and traffic violation monitoring based on the IoT. Our conceptual framework could easily be adapted in current scenario and can also become a de facto decision support system model for future hassle-free driving rule violation monitoring system.
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition

Facial expression is central to human experience, but most previous databases and studies are limited to posed facial behavior under controlled conditions. In this paper, we present a novel facial expression database, Real-world Affective Face Database (RAF-DB), which contains approximately 30 000 facial images with uncontrolled poses and illumination from thousands of individuals of diverse ages and races. During the crowdsourcing annotation, each image is independently labeled by approximately 40 annotators. An expectation–maximization algorithm is developed to reliably estimate the emotion labels, which reveals that real-world faces often express compound or even mixture emotions. A cross-database study between RAF-DB and CK+ database further indicates that the action units of real-world emotions are much more diverse than, or even deviate from, those of laboratory-controlled emotions. To address the recognition of multi-modal expressions in the wild, we propose a new deep locality-preserving convolutional neural network (DLP-CNN) method that aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatter. Benchmark experiments on 7-class basic expressions and 11-class compound expressions, as well as additional experiments on CK+, MMI, and SFEW 2.0 databases, show that the proposed DLP-CNN outperforms the state-of-the-art handcrafted features and deep learning-based methods for expression recognition in the wild. To promote further study, we have made the RAF database, benchmarks, and descriptor encodings publicly available to the research community.