Reviewerly/exHarmony huggingface dataset:
Degree-Quant: Quantization-Aware Training for Graph Neural Networks

Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make them more efficient at inference time. In this work, we explore the viability of training quantized GNNs, enabling the usage of low precision integer arithmetic during inference. For GNNs seemingly unimportant choices in quantization implementation cause dramatic changes in performance. We identify the sources of error that uniquely arise when attempting to quantize GNNs, and propose an architecturally-agnostic and stable method, Degree-Quant, to improve performance over existing quantization-aware training baselines commonly used on other architectures, such as CNNs. We validate our method on six datasets and show, unlike previous quantization attempts, that models generalize to unseen graphs. Models trained with Degree-Quant for INT8 quantization perform as well as FP32 models in most cases; for INT4 models, we obtain up to 26% gains over the baselines. Our work enables up to 4.7x speedups on CPU when using INT8 arithmetic.
A new speech encryption algorithm based on dual shuffling Hénon chaotic map

Over the past few decades, many algorithms have been proposed to improve the performance of speech encryption over un-secure channel (i.e., Internet). In this paper, the security level was enhanced using a dynamic dual chaotic based on Hénon chaotic map. In the proposed algorithm, the speech elements are shuffled in a random fashion. Moreover, when both Hénon state variables are free to be used for shuffling the index is toggled randomly between them according to toggle bit. After index shuffling each speech element is modified with XOR operation between the original speech element value and the key that is selected randomly from the updated key table. The same chaotic map is used to initiate the empty or full table and provide new table entries from the values that are already shuffled. The experimental results show that the proposed crypto-system is simple, fast with extra random toggling behavior. The high order of substitution make it sensitive to initial condition, common cryptanalysis attacks such as linear and differential attacks are infeasible.
Cancer Detection Using Convolutional Neural Network

Cancer is a dreadful heterogeneous disease that refers to abnormal growth of cell tissue. Millions of people die every year due to unnoticed recognition or late detection of the disease. Hence, detecting presence of cancer cells correctly becomes important. Putting into attention the number of people affected by cancer, we need to find better ways of diagnosis and treatment. Neural networks are a burning research area in medical science, especially in the areas of radiology, cardiology, oncology, urology and etc.. A variety of these techniques as Decision Trees (DTs), Support Vector Machines (SVM) and Artificial Neural Network (ANN) have been widely applied in cancer detection. This paper presents a method of classification of cancer cells into Benign and Malignant using deep learning Convolutional Neural Network.
Let’s Talk About CUIs: Putting Conversational User Interface Design Into Practice

As CUIs become more prevalent in both academic research and the commercial market, it becomes more essential to design usable and adoptable CUIs. Though research on the usability and design of CUIs has been growing greatly over the past decade, we see that many usability issues are still prevalent in current conversational voice interfaces, from issues in feedback and visibility, to learnability, to error correction, and more. These issues still exist in the most current conversational interfaces in the commercial market, like the Google Assistant, Amazon Alexa, and Siri. The aim of this workshop therefore is to bring both academics and industry practitioners together to bridge the gaps of knowledge in regards to the tools, practices, and methods used in the design of CUIs. This workshop will bring together both the research performed by academics in the field, and the practical experience and needs from industry practitioners, in order to have deeper discussions about the resources that require more research and development, in order to build better and more usable CUIs.
Neural Data Augmentation via Example Extrapolation

In many applications of machine learning, certain categories of examples may be underrepresented in the training data, causing systems to underperform on such "few-shot" cases at test time. A common remedy is to perform data augmentation, such as by duplicating underrepresented examples, or heuristically synthesizing new examples. But these remedies often fail to cover the full diversity and complexity of real examples. We propose a data augmentation approach that performs neural Example Extrapolation (Ex2). Given a handful of exemplars sampled from some distribution, Ex2 synthesizes new examples that also belong to the same distribution. The Ex2 model is learned by simulating the example generation procedure on data-rich slices of the data, and it is applied to underrepresented, few-shot slices. We apply Ex2 to a range of language understanding tasks and significantly improve over state-of-the-art methods on multiple few-shot learning benchmarks, including for relation extraction (FewRel) and intent classification + slot filling (SNIPS).
Addressing Real-Time Demands for Robotic Path Planning Systems: A Routing Protocol Approach

Path quality and computational time have formed together a well-known trade-off problem for path planning techniques. Due to this trade-off, contributions were usually considering improving only one of the two aspects, either increasing the swiftness as in real-time robotic path planning algorithms or enhancing the path quality as in shortest path query algorithms. Producing a path planning technique that targets both aspects is a challenging problem for robotic systems. However, this paper proposes a novel path planning framework that controls the motion of robotic systems and aims to overcome this traditional trade-off challenge, by targeting both, decreasing the computational time, and improving the path quality represented by the path length and smoothness. The shortest path is obtained by minimizing a novel objective function inspired by the artificial potential field methodology. To accelerate the execution, the Particle Swarm Optimization (PSO) technique is adopted to obtain the optimal solution in a real-time hop-by-hop manner imitating the procedures performed by computer network routing protocols. Testbed experimental results have proven the effectiveness of the proposed technique and showed superior performance over other meta-heuristic optimization techniques and over classical path planning approaches such as A*, D*, and PRM.
Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems

We introduce a trainable coded modulation scheme that enables joint optimization of the bit-wise mutual information (BMI) through probabilistic shaping, geometric shaping, bit labeling, and demapping for a specific channel model and for a wide range of signal-to-noise ratios (SNRs). Compared to probabilistic amplitude shaping (PAS), the proposed approach is not restricted to symmetric probability distributions, can be optimized for any channel model, and works with any code rate k/m, m being the number of bits per channel use and k an integer within the range from 1 to m-1. The proposed scheme enables learning of a continuum of constellation geometries and probability distributions determined by the SNR. Additionally, the PAS architecture with Maxwell-Boltzmann (MB) as shaping distribution was extended with a neural network (NN) that controls the MB shaping of a quadrature amplitude modulation (QAM) constellation according to the SNR, enabling learning of a continuum of MB distributions for QAM. Simulations were performed to benchmark the performance of the proposed joint probabilistic and geometric shaping scheme on additive white Gaussian noise (AWGN) and mismatched Rayleigh block fading (RBF) channels.
Learnable MFCCs for Speaker Verification

We propose a learnable mel-frequency cepstral coefficients (MFCCs) front-end architecture for deep neural network (DNN) based automatic speaker verification.Our architecture retains the simplicity and interpretability of MFCC-based features while allowing the model to be adapted to data flexibly.In practice, we formulate data-driven version of four linear transforms in a standard MFCC extractor -windowing, discrete Fourier transform (DFT), mel filterbank and discrete cosine transform (DCT).Results reported reach up to 6.7% (VoxCeleb1) and 9.7% (SITW) relative improvement in term of equal error rate (EER) from static MFCCs, without additional tuning effort.
Creating a robot localization monitor using particle filter and machine learning approaches

Abstract Robot localization is a fundamental capability of all mobile robots. Because of uncertainties in acting and sensing, and environmental factors such as people flocking around robots, there is always the risk that a robot loses its localization. Very often behaviors of robots rely on a reliable position estimation. Thus, for dependability of robot systems it is of great interest for the system to know the state of its localization component. In this paper we present an approach that allows a robot to asses if the localization is still correct. The approach assumes that the underlying localization approach is based on a particle filter. We use deep learning to identify temporal patterns in the particles in the case of losing/lost localization. These patterns are then combined with weak classifiers from the particle set and sensor perception for boosted learning of a localization estimator. Through the extraction of features generated by neural networks and its usage for training strong classifiers, the robots localization accuracy can be estimated. The approach is evaluated in a simulated transport robot environment where a degraded localization is provoked by disturbances cased by dynamic obstacles. Results show that it is possible to monitor the robots localization accuracy using convolutional as well as recurrent neural networks. The additional boosting using Adaboost also yields an increase in training accuracy. Thus, this paper directly contributes to the verification of localization performance.
Experimental determination and finite element analysis of coefficients of the multi-parameter Williams series expansion in the vicinity of the crack tip in linear elastic materials. Part I

This study aims at obtaining coefficients of the multi-parameter Williams series expansion for the stress field in the vicinity of the central crack in the rectangular plate and in the semi-circular notched disk under bending by the use of the digital photoelasticity method. The higher-order terms in the Williams asymptotic expansion are retained. It allows us to give a more accurate estimation of the near-crack-tip stress, strain and displacement fields and extend the domain of validity for the Williams power series expansion. The program is specially developed for the interpretation and processing of experimental data from the phototelasticity experiments. By means of the developed tool, the fringe patterns that contain the whole field stress information in terms of the difference in principal stresses (isochromatics) are captured as a digital image, which is processed for quantitative evaluations. The developed tool allows us to find points that belong to isochromatic fringes with the minimal light intensity. The digital image processing with the aid of the developed tool is performed. The points determined with the adopted tool are used further for the calculations of the stress intensity factor, T-stresses and coefficients of higher-order terms in the Williams series expansion. The iterative procedure of the over-deterministic method is utilized to find the higher order terms of the Williams series expansion. The procedure is based on the consistent correction of the coefficients of the Williams series expansion. The first fifteen coefficients are obtained. The experimentally obtained coefficients are used for the reconstruction of the isochromatic fringe pattern in the vicinity of the crack tip. The comparison of the theoretically reconstructed and experimental isochromatic fringe patterns shows that the coefficients of the Williams series expansion have a good match.
Deep Equals Shallow for ReLU Networks in Kernel Regimes

Deep networks are often considered to be more expressive than shallow ones in terms of approximation. Indeed, certain functions can be approximated by deep networks provably more efficiently than by shallow ones, however, no tractable algorithms are known for learning such deep models. Separately, a recent line of work has shown that deep networks trained with gradient descent may behave like (tractable) kernel methods in a certain over-parameterized regime, where the kernel is determined by the architecture and initialization, and this paper focuses on approximation for such kernels. We show that for ReLU activations, the kernels derived from deep fully-connected networks have essentially the same approximation properties as their shallow two-layer counterpart, namely the same eigenvalue decay for the corresponding integral operator. This highlights the limitations of the kernel framework for understanding the benefits of such deep architectures. Our main theoretical result relies on characterizing such eigenvalue decays through differentiability properties of the kernel function, which also easily applies to the study of other kernels defined on the sphere.
Turning everyday objects into passive tangible controllers

In augmented reality (AR), gesture/hand-based interactions are becoming more common place over tangible user interfaces (TUIs) and physical controllers. Major concerns regard portability and battery-life with TUIs and physical controllers in an AR environment. For a TUI or physical controller to be usable "on the go" and in the field, they need to be compact such that they can be easily deployable. Subsequently, it is desirable to be low-energy or powerless, such that the device remains functional after long periods. In this paper, we present our initial design towards a powerless, passive controller that leverages the optical camera of a AR head-mounted display (such as the Hololens or Magic Leap) for tracking. We discuss related work, design motivations, high and low fidelity designs, opportunistic/adaptable input modalities, and use cases. We conclude with propositions for future work.
Spectral analysis of product formulas for quantum simulation

Abstract We consider the time-independent Hamiltonian simulation using the first order Lie–Trotter–Suzuki product formula under the assumption that the initial state is supported on a low-dimension subspace. By comparing the spectral decomposition of the original Hamiltonian and the effective Hamiltonian, we obtain better upper bounds for various conditions. Especially, we show that the Trotter step size needed to estimate an energy eigenvalue within precision ϵ using quantum phase estimation can be improved in scaling from ϵ to ϵ 1/2 for a large class of systems. Our results also depend on the gap condition of the simulated Hamiltonian.
Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application

Mobile learning has been increased in past years and has attracted the interests of academicians and educators in the past many years especially in higher education. The mobile-based online test is the buzzing in the current pandemic time. Institutions need to use online learning as a powerful tool for conducting exams and assess the students effectively. Integrating technology in education can be advantageous for universities and help engage better results for students. Therefore, it is important to understand each student their capacities and create a different test based on the required difficulty. Students should be graded based on their capabilities. The purpose of the research study is to develop the progressive model with the calibration of difficulty level according to the student capacity. To achieve the goal, a test of 20 python questions was conducted on 120 students with each question having difficulty given by 8 field experts. To verify the model, 5 categories were formed with different difficulty levels which in turn gave satisfactory results. To find a relation between the initial difficulty and the calculative difficulty based on the student response, a correlation test was conducted. After careful analysis of the question difficulty and student responses, it was observed that both are highly dependent on each other wherein the difficulty level of any question can be calculated using incorrect answers. The correlation coefficient obtained between them was 0.9833. Upon collecting the difficulty of the questions and student responses, respective grading could be done using the stated formula. Later on, the progressive model was simulated with five different cases (Best case, above-average case, below average case, the average case, worst case). The model outperformed in all the cases with appropriate difficulty levels. Online Tests have ushered a revolution in the assessment of students but yet they tend to be unpopular in India as the evaluation based on pen-paper approach is preferred. The main reasons for this are difficult to grade everyone at the same level, susceptible to cheating, and transition to open books. Using our study, universities can identify obstacles, and prepare an appropriate result-driven plan of action for implementing the mobile-based online test and make easy migration from paper-based test to online test.
Blockchain and Decentralized Money System: Principles of Building and Ways of Its Development

The article considers the origins of the fourth industrial revolution, the impact of new IT processes and their integration into society and economy. The authors focus on the idea of creating a cryptocurrency Bitcoin and a new payment system based on the blockchain platform. The innovative components of blockchain technology are presented. The technological changes caused by its development are analyzed. The problems of the financial industry and the technological processes occurring in the banking market are analyzed in detail. The prerequisites and the innovation process of the formation of a decentralized monetary system are explained. The authors propose a new concept of «parallel money systems» and formulate the principles of centra­lized and decentralized approaches to the organization of the monetary system. The features of building a decentralized monetary system are described; its advantages and disadvantages are formulated. A number of arguments in favor of the validity of the adoption of the new form of the monetary system is given and substantiated; the difficulties of its development are analyzed.
Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion

Capturing hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person’s finger joint. Due to the modular design, the ECSMS’s sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers.
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations

Recently we see a rising number of methods in the field of eXplainable Artificial Intelligence. To our surprise, their development is driven by model developers rather than a study of needs for human end users. The analysis of needs, if done, takes the form of an A/B test rather than a study of open questions. To answer the question "What would a human operator like to ask the ML model?" we propose a conversational system explaining decisions of the predictive model. In this experiment, we developed a chatbot called dr_ant to talk about machine learning model trained to predict survival odds on Titanic. People can talk with dr_ant about different aspects of the model to understand the rationale behind its predictions. Having collected a corpus of 1000+ dialogues, we analyse the most common types of questions that users would like to ask. To our knowledge, it is the first study which uses a conversational system to collect the needs of human operators from the interactive and iterative dialogue explorations of a predictive model.