Reviewerly/exHarmony huggingface dataset:
Epistemic Risk-Sensitive Reinforcement Learning

We develop a framework for interacting with uncertain environments in reinforcement learning (RL) by leveraging preferences in the form of utility functions. We claim that there is value in considering different risk measures during learning. In this framework, the preference for risk can be tuned by variation of the parameter $\beta$ and the resulting behavior can be risk-averse, risk-neutral or risk-taking depending on the parameter choice. We evaluate our framework for learning problems with model uncertainty. We measure and control for \emph{epistemic} risk using dynamic programming (DP) and policy gradient-based algorithms. The risk-averse behavior is then compared with the behavior of the optimal risk-neutral policy in environments with epistemic risk.
Supporting untethered multi-user VR over enterprise wi-fi

In this positioning paper, we propose Chord, a holistic multi-user VR system for untethered mobile devices over 802.11ac/ax Wi-Fi. Taking a cross-layer approach, Chord brings numerous innovations to the application-layer design and VR-aware wireless network optimizations. We present our design and its preliminary evaluation on commodity smartphones and 802.11ac Wi-Fi, to demonstrate the feasibility of Chord.
Neural network integration of classical statistical tests for processing small samples of biometrics data

The Aim of this paper is to increase the power of statistical tests through their joint application to reduce the requirement for the size of the test sample. Methods . It is proposed to combine classical statistical tests, i.e. chi square, Cram r-von Mises and Shapiro-Wilk by means of using equivalent artificial neurons. Each neuron compares the input statistics with a precomputed threshold and has two output states. That allows obtaining three bits of binary output code of a network of three artificial neurons. Results . It is shown that each of such criteria on small samples of biometric data produces high values of errors of the first and second kind in the process of normality hypothesis testing. Neural network integration of three tests under consideration enables a significant reduction of the probabilities of errors of the first and second kind. The paper sets forth the results of neural network integration of pairs, as well as triples of statistical tests under consideration. Conclusions . Expected probabilities of errors of the first and second kind are predicted for neural network integrations of 10 and 30 classical statistical tests for small samples that contain 21 tests. An important element of the prediction process is the symmetrization of the problem, when the probabilities of errors of the first and second kind are made identical and averaged out. Coefficient modules of pair correlation of output states are averaged out as well by means of artificial neuron adders. Only in this case the connection between the number of integrated tests and the expected probabilities of errors of the first and second kind becomes linear in logarithmic coordinates.
Unsupervised Image Noise Modeling with Self-Consistent GAN

Noise modeling lies in the heart of many image processing tasks. However, existing deep learning methods for noise modeling generally require clean and noisy image pairs for model training; these image pairs are difficult to obtain in many realistic scenarios. To ameliorate this problem, we propose a self-consistent GAN (SCGAN), that can directly extract noise maps from noisy images, thus enabling unsupervised noise modeling. In particular, the SCGAN introduces three novel self-consistent constraints that are complementary to one another, viz.: the noise model should produce a zero response over a clean input; the noise model should return the same output when fed with a specific pure noise input; and the noise model also should re-extract a pure noise map if the map is added to a clean image. These three constraints are simple yet effective. They jointly facilitate unsupervised learning of a noise model for various noise types. To demonstrate its wide applicability, we deploy the SCGAN on three image processing tasks including blind image denoising, rain streak removal, and noisy image super-resolution. The results demonstrate the effectiveness and superiority of our method over the state-of-the-art methods on a variety of benchmark datasets, even though the noise types vary significantly and paired clean images are not available.
A novel chaotic bat algorithm based on catfish effect for engineering optimization problems

Purpose Bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel chaotic bat algorithm based on catfish effect (CE-CBA), which can effectively deal with optimization problems in real-world applications. Design/methodology/approach Incorporating chaos strategy and catfish effect, the proposed algorithm can not only enhance the ability for local search but also improve the ability to escape from local optima traps. On the one hand, the performance of CE-CBA has been evaluated by a set of numerical experiment based on classical benchmark functions. On the other hand, five benchmark engineering design problems have been also used to test CE-CBA. Findings The statistical results of the numerical experiment show the significant improvement of CE-CBA compared with the standard algorithms and improved bat algorithms. Moreover, the feasibility and effectiveness of CE-CBA in solving engineering optimization problems are demonstrated. Originality/value This paper proposed a novel BA with two improvement strategies including chaos strategy and catfish effect for the first time. Meanwhile, the proposed algorithm can be used to solve many real-world engineering optimization problems with several decision variables and constraints.
Unsupervised Recurrent Neural Network Grammars

Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. Supervised RNNGs achieve strong language modeling and parsing performance, but require an annotated corpus of parse trees. In this work, we experiment with unsupervised learning of RNNGs. Since directly marginalizing over the space of latent trees is intractable, we instead apply amortized variational inference. To maximize the evidence lower bound, we develop an inference network parameterized as a neural CRF constituency parser. On language modeling, unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese. On constituency grammar induction, they are competitive with recent neural language models that induce tree structures from words through attention mechanisms.
Training Neural Networks for and by Interpolation

In modern supervised learning, many deep neural networks are able to interpolate the data: the empirical loss can be driven to near zero on all samples simultaneously. In this work, we explicitly exploit this interpolation property for the design of a new optimization algorithm for deep learning, which we term Adaptive Learning-rates for Interpolation with Gradients (ALI-G). ALI-G retains the two main advantages of Stochastic Gradient Descent (SGD), which are (i) a low computational cost per iteration and (ii) good generalization performance in practice. At each iteration, ALI-G exploits the interpolation property to compute an adaptive learning-rate in closed form. In addition, ALI-G clips the learning-rate to a maximal value, which we prove to be helpful for non-convex problems. Crucially, in contrast to the learning-rate of SGD, the maximal learning-rate of ALI-G does not require a decay schedule, which makes it considerably easier to tune. We provide convergence guarantees of ALI-G in various stochastic settings. Notably, we tackle the realistic case where the interpolation property is satisfied up to some tolerance. We provide experiments on a variety of architectures and tasks: (i) learning a differentiable neural computer; (ii) training a wide residual network on the SVHN data set; (iii) training a Bi-LSTM on the SNLI data set; and (iv) training wide residual networks and densely connected networks on the CIFAR data sets. ALI-G produces state-of-the-art results among adaptive methods, and even yields comparable performance with SGD, which requires manually tuned learning-rate schedules. Furthermore, ALI-G is simple to implement in any standard deep learning framework and can be used as a drop-in replacement in existing code.
Integrity Verification in Medical Image Retrieval Systems using Spread Spectrum Steganography

The region of interest (ROI) of medical images in content-based image retrieval (CBIR) systems often require content authentication and verification. This is because adversarial modification of the stored image could have lethal effect on research, diagnostic outcome and the outcome of some forensic investigations. In this work, both robust watermarking and Fragile Steganography were combined with image search features to design a medical image retrieval system that incorporates ROI integrity verification. Original ROI features were pre-computed and embedded into archival images and utilised during retrieval for image integrity checks. The average global image PSNR was 38.36dB while the ROI PSNR was maintained at an average of 46dB with all watermark search features retrieved at zero bit error rate (BER) provided the attack on the image is not perceptible.
Generating Question Relevant Captions to Aid Visual Question Answering

Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to improve VQA performance that exploits this connection by jointly generating captions that are targeted to help answer a specific visual question. The model is trained using an existing caption dataset by automatically determining question-relevant captions using an online gradient-based method. Experimental results on the VQA v2 challenge demonstrates that our approach obtains state-of-the-art VQA performance (e.g. 68.4% on the Test-standard set using a single model) by simultaneously generating question-relevant captions.
Mobile Robot Navigation Using Reinforcement Learning in Unknown Environments

In mobile robotics, navigation is considered as one of the most primary tasks, which becomes more challenging during local navigation when the environment is unknown. Therefore, the robot has to explore utilizing the sensory information. Reinforcement learning (RL), a biologically-inspired learning paradigm, has caught the attention of many as it has the capability to learn autonomously in an unknown environment. However, the randomized behavior of exploration, common in RL, increases computation time and cost, hence making it less appealing for real-world scenarios. This paper proposes an informed-biased softmax regression (iBSR) learning process that introduce a heuristic-based cost function to ensure faster convergence. Here, the action-selection is not considered as a random process, rather, is based on the maximum probability function calculated using softmax regression. Through experimental simulation scenario for navigation, the strength of the proposed approach is tested and, for comparison and analysis purposes, the iBSR learning process is evaluated against two benchmark algorithms.
Audio De-identification: A New Entity Recognition Task

Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. In such recordings, audio spans with personal information should be redacted, similar to the redaction of sensitive character spans in de-ID for written text. The application of NER in the context of audio de-identification has yet to be fully investigated. To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected. We then present our pipeline for this task, which involves Automatic Speech Recognition (ASR), NER on the transcript text, and text-to-audio alignment. Finally, we introduce a novel metric for audio de-ID and a new evaluation benchmark consisting of a large labeled segment of the Switchboard and Fisher audio datasets and detail our pipeline's results on it.
ON PERCEPTION OF COMPUTER ALGEBRA SYSTEMS AND MICROSOFT EXCEL BY ENGINEERING STUDENTS

This research presents the analysis of image of the popular program packages: CAS (Computer Algebra Systems) Matlab, Mathcad, Mathematica, and the spreadsheet ‘Microsoft Excel’ among students of engineering specializations studying in Bauman Moscow State Technical University (BMSTU). The research has explored perception of program packages by all students (N=164), and by male students (N=110) and female students (N=54) separately. Image of the program packages, convenience of their interfaces, usage scenarios, and applicability for solution of tasks of certain types have been explored via questioning the students. Moreover, the students assessed likelihood of using the programs in the future. It was established that all the program packages under consideration can be successfully used in the process of education, but some of them (Matlab and Mathematica) require additional lessons to learn their interface and peculiarities. Gender differences in perception of the program packages do not determine the education outcomes. The differences of perception of the programs are mainly caused by their popularity and the students’ skills of using certain programs. The following gender differences have been detected: in the female students’ sample, the programs Mathcad and Excel are leading, Matlab is performed a little worse; Matlab is associated with the statement “difficult / inconvenient interface”. In the male students’ sample, the three programs Matlab, Mathcad, and Microsoft Excel got nearly equal grades. Female students estimate future usage of Microsoft Excel higher than male students do. The CAS Mathematica takes the lowest places for both samples; as a possible cause of such result, it has been found that Mathematica is not widespread as compared to the other programs under consideration. Keywords: computer algebra system, education statistics, engineering education, mathematical education, Mathcad, Mathematica, Matlab, Microsoft Excel.
Data-Driven Choreographies à la Klaim

We propose Klaim as a suitable base for a novel choreographic framework. More precisely we advocate Klaim as a suitable language onto which to project data-driven global specifications based on distributed tuple spaces. These specifications, akin to behavioural types, describe the coordination from a global point of view. Differently from behavioural types though, our specifications express the data flow across distributed tuple spaces rather than detailing the communication pattern of processes. We devise a typing system to validate Klaim programs against projections of our global specifications. An interesting feature of our typing approach is that well-typed systems have an arbitrary number of participants. In standard approaches based on behavioural types, this is often achieved at the cost of considerable technical complications.
Adaptive high‐degree cubature Kalman filter in the presence of unknown measurement noise covariance matrix

Here, the authors address the state estimation problem of non-linear systems in the presence of unknown measurement noise (MN) covariance matrix. Recently, a high-degree cubature Kalman filter (HCKF) has been successfully used in the non-linear-state estimation problem with arbitrary degrees of accuracy in computing the spherical and radial integrals. However, the efficiency of the HCKF depends on a priori knowledge of the MN. To improve the performance of HCKF for non-linear systems with unknown MN covariance matrix, the authors proposed an adaptive HCKF, which combines the high-degree cubature rule with the variational Bayesian (VB) method to jointly estimate the system state and the unknown covariance matrix online. Experimental results demonstrate the effectiveness of the proposed filter.
A Board Game and a Workshop for Co-creating Smart Nature Ecosystems

Younger generations from urban areas spend an increasing amount of time indoors with technology, e.g., with mobiles. GEKI is an exploratory project that investigates how to co-create with younger generations smart nature ecosystems and get them to spend time outdoors. This paper presents the design of a novel board game and a workshop with it for co-creating such an ecosystem with children.
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder

We present a simple and accurate model for semantic parsing with UCCA as our submission for SemEval 2019 Task 1.We propose an encoder-decoder model that maps strings to directed acyclic graphs.Unlike many transitionbased approaches, our approach does not use a state representation, and unlike graph-based parsers, it does not score graphs directly.Instead, we encode input sentences with a bidirectional-LSTM, and decode with selfattention to build a graph structure.Results show that our parser is simple and effective for semantic parsing with reentrancy and discontinuous structures.
Understanding the Interpretability of Search Result Summaries

We examine the interpretability of search results in current web search engines through a lab user study. Particularly, we evaluate search result summary as an interpretable technique that informs users why the system retrieves a result and to which extent the result is useful. We collected judgments about 1,252 search results from 40 users in 160 sessions. Experimental results indicate that the interpretability of a search result summary is a salient factor influencing users' click decisions. Users are less likely to click on a result link if they do not understand why it was retrieved (low transparency) or cannot assess if the result would be useful based on the summary (low assessability). Our findings suggest it is crucial to improve the interpretability of search result summaries and develop better techniques to explain retrieved results to search engine users.
Analysis and measurement of internal usability metrics through code annotations

Nowadays, usability can be meant as an important quality characteristic to be considered throughout the software development process. A great variety of usability techniques have been proposed so far, mostly intended to be applied during analysis, design, and final testing phases in software projects. However, little or no attention has been paid to the analysis and measurement of usability in the implementation phase. Most of the time, usability testing is traditionally executed in advanced stages. However, the detection of usability flaws during the implementation is of utmost importance to foresee and prevent problems in the utilization of the software and avoid significant cost increases. In this paper, we propose a feasible solution to analyze and measure usability metrics during the implementation phase. Specifically, we have developed a framework featuring code annotations that provides a systematic evaluation of the usability throughout the source code. These annotations are interpreted by an annotation processor to obtain valuable information and automatically calculate usability metrics at compile time. In addition, an evaluation with 32 participants has been carried out to demonstrate the effectiveness and efficiency of our approach in comparison with the manual process of analyzing and measuring internal usability metrics. Perceived satisfaction was also evaluated, demonstrating that our approach can be considered as a valuable tool for dealing with usability metrics during the implementation phase.
ICI Reduction by Parallel Concatenated Encoder Using Wavelet Transforms

This paper proposed a new channel encoder technique by parallel concatenated encoders which use Hamming parallel with convolutional to reduce the “intercarrier interference” (ICI) by using the discrete wavelet DWT. The Hamming encoder is simple, and convolutional encoder gives low BER and robust against the burst error. Simulation the two encoder parallel via interleaver reduces the code rate and improves the performance. The simulation results measure the BER after applying the self-cancellation (SC) technique and appear reduction in BER in the results which enhance the system performance.
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"

A recent paper by Liu et al. combines the topics of adversarial training and Bayesian Neural Networks (BNN) and suggests that adversarially trained BNNs are more robust against adversarial attacks than their non-Bayesian counterparts. Here, I analyze the proposed defense and suggest that one needs to adjust the adversarial attack to incorporate the stochastic nature of a Bayesian network to perform an accurate evaluation of its robustness. Using this new type of attack I show that there appears to be no strong evidence for higher robustness of the adversarially trained BNNs.
A comparative study of different eye tracking system algorithms

Eye Gaze Tracking is emerging as a popular method to increase the efficiency as well as to increase the accessibility of computing systems. It has been already employed in various applications as well as many researches are going on to incorporate the concept in various other fields. There are various fields where the systems are employed ranging from ease of accessibility of computing systems [1] to performance of computer graphics processing by the method of foveated rendering [2] and as well as digital marketing analysis [3]. The application of eye gaze trackers is still limited due to its lack of availability as well as the cost of the eye gaze trackers which makes them still a scarce resource for majority of the population. This paper proposes an efficient system for Eye Gaze Tracking and draws a sharp contrast between the different open source software available for Eye Gaze Tracking on the market. The study draws the comparison between two of the most popular open source eye gaze tracking software available in the market. ITU Gaze Tracker [4] PyGame based Eye Tracker [5]. The components proposed for the system are easily available in the market.