Reviewerly/exHarmony huggingface dataset:
Knowledge flow: Improve upon your teachers

A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an initialization for fine-tuning a new model. To address this issue, in this paper, we develop knowledge flow which moves 'knowledge' from multiple deep nets, referred to as teachers, to a new deep net model, called the student. The structure of the teachers and the student can differ arbitrarily and they can be trained on entirely different tasks with different output spaces too. Upon training with knowledge flow the student is independent of the teachers. We demonstrate our approach on a variety of supervised and reinforcement learning tasks, outperforming fine-tuning and other 'knowledge exchange' methods.
Effect of Degrees of Freedom on the Sense of Presence Generated by Virtual Reality (VR) Head-Mounted Display Systems: A Case Study on the Use of VR in Early Design Studios

The purpose of this research was to explore the use of virtual reality (VR) in early design studios. In this research project, two different types of Head-Mounted Display (HMD) systems were used. One type of HMD provided six degrees of freedom and the other HMD provided three degrees of freedom. The research findings provide comparison on the functionality of the different types of HMDs and the sense of presence in VR environments. Sense of presence is defined as the sense of “being there” in a computer-simulated environment. The outcomes of this research are (a) development of a new presence questionnaire that focuses on newer VR systems and (b) understanding student perception of using VR in design projects.
How deception can change cyber security defences

The use of deception technology in modern cyber security as a viable means of active, intelligent post-breach defence is a rapidly emerging phenomenon. But like any disruptive technology it comes with misconceptions. As cyber criminals continue to phish, bait, deceive and lure users, cyber defences need to move beyond being based on primarily detecting bad things within an ocean of good activity. Given how attackers are progressing, there is a clear case for invoking an active defence to lure, detect and defend against malware and intruders moving laterally within the network. The use of deception technology in modern cyber security as a viable means of active, intelligent post-breach defence is a rapidly emerging phenomenon. But like any disruptive technology, it comes with misconceptions. Cyber defences need to move beyond being based on primarily detecting bad things. There is a clear case for invoking an active defence to lure, detect and defend against malware and intruders moving laterally within the network. And cyber deception is just now making its way to the main stage for cyber security as a viable option for an active defence, says Andrew Bushby of Fidelis Cybersecurity.
Mining competition in a multi-cryptocurrency ecosystem at the network edge

We model the competition over several blockchains characterizing multiple cryptocurrencies as a non-cooperative game. Then, we specialize our results to two instances of the general game, showing properties of the Nash equilibrium. In particular, leveraging results about congestion games, we establish the existence of pure Nash equilibria and provide efficient algorithms for finding such equilibria.
Tuberculosis Detection Using Shape and Texture Features of Chest X-Rays

Tuberculosis (TB) is a major life-threatening hazard, globally. Mortality rate increases if the disease remains undiagnosed and untreated. Detection of disease in the early stage is the most promising way to increase the lifespan of patients, especially in the regions with limited resources worldwide. We present an automatic TB detection method which uses conventional digital chest radiographs. The method consists of three main stages. We first extract the lung region from the Chest X-Ray (CXR) image using log Gabor filtering technique followed by morphological methods. A set of texture and shape features of segmented dataset is computed. The feature vector thus computed enables the support vector machine to classify the input CXR into healthy and TB-infected. ROC curve and confusion matrix of classifier show its exceptionally good performance. We attain an AUC of 0.98 and 0.96 on MC and CHN dataset, respectively, with 100% specificity.
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space

Despite their attractiveness, popular perception is that techniques for nonparametric function approximation do not scale to streaming data due to an intractable growth in the amount of storage they require. To solve this problem in a memory-affordable way, we propose an online technique based on functional stochastic gradient descent in tandem with supervised sparsification based on greedy function subspace projections. The method, called parsimonious online learning with kernels (POLK), provides a controllable tradeoff? between its solution accuracy and the amount of memory it requires. We derive conditions under which the generated function sequence converges almost surely to the optimal function, and we establish that the memory requirement remains finite. We evaluate POLK for kernel multi-class logistic regression and kernel hinge-loss classification on three canonical data sets: a synthetic Gaussian mixture model, the MNIST hand-written digits, and the Brodatz texture database. On all three tasks, we observe a favorable tradeoff of objective function evaluation, classification performance, and complexity of the nonparametric regressor extracted the proposed method.
SimOptDevice: a library for virtual optical experiments

Abstract. Virtual experiments have become an indispensable tool for the design and the accuracy assessment of novel measurement procedures and instruments. Virtual experiments are particularly relevant in modern optics due to its challenging demands for highly accurate measurements. This paper introduces SimOptDevice, a flexible library for opto-mechanical virtual experiments. After describing the scope and general structure of the library, its underlying mathematical tools used for solving the related numerical tasks are described. Finally, the application of SimOptDevice to a recent interferometric measurement procedure is presented.
On the nature and financial performance of bitcoin

Purpose The purpose of this article is to provide some insights on the true nature of bitcoin and to study empirically its performance by using robust models, widely used in the academic literature. Previous studies assess performance with simple measures such as the Sharpe ratio. Such measures are insufficient because they do not take into account the bitcoin’s specificities, such as the possibilities to diversify risk. Design/methodology/approach The authors use quantitative methodologies to assess the performance of financial assets. Performance is defined as a risk-adjusted return. The authors use regression analysis and measure bitcoin’s performance as the constant term ( α ) of the projection of its returns on the returns of relevant factors of risk. Findings Bitcoin has low correlation with the market index and with factor-mimicking portfolios, which indicates opportunities to diversify risk. The performance of bitcoin ( α ) is positive and significant; this result is robust across period and world region specifications. Research limitations/implications The true nature of bitcoin is subject of debate and needs further research. Furthermore, other factors should be considered in analysing the bitcoin’s performance, such as those related to investors’ behaviour or political risk. Practical implications The empirical results obtained in this paper may be used by professional portfolio managers to diversify risk and to enhance their portfolio’s performance. Originality/value This paper adds to the literature by arguing that bitcoin has the nature of common stock, and therefore, its performance has to be assessed with models that are relevant for this type of securities. This paper is the first using performance models that adjust returns for relevant sources of risk.
Research on Digital Signal Recognition Based on Higher Order Cumulants

This paper proposes a high-order cumulant based digital signal modulation recognition algorithm, which uses the fourth-order cumulant of the signal as the feature recognition parameter to calculate and obtain the fourth-order cumulant of the four signals 8PSK, 16QAM, 4PAM and BPSK. And the classification and identification under the Gaussian white noise channel, the simulation results show that the recognition rate of the algorithm can reach more than 90% when the number of symbols is above 250 and the signal-to-noise ratio is greater than 10dB.
FASSEG: A FAce semantic SEGmentation repository for face image analysis

The FASSEG repository is composed by four subsets containing face images useful for training and testing automatic methods for the task of face segmentation. Threesubsets, namely frontal01, frontal02, and frontal03 are specifically built for performing frontal face segmentation. Frontal01 contains 70 original RGB images and the corresponding roughly labelledground-truth masks. Frontal02 contains the same image data, with high-precision labelled ground-truth masks. Frontal03 consists in 150 annotated face masks of twins captured in various orientations, illumination conditions and facial expressions. The last subset, namely multipose01, contains more than 200 faces in multiple poses and the corresponding ground-truth masks. For all face images, ground-truth masks are labelled on six classes (mouth, nose, eyes, hair, skin, and background).
Hierarchical Quantum Information Splitting of an Arbitrary Two-Qubit Using a Single Quantum Resource

In this paper, we describe a single quantum resource to split the quantum information of arbitrary two-qubit. In addition, we discuss its hierarchical situation, so as to achieve more efficient and convenient information sharing. The sender sends the secret state to the other three parties, and then the sender performs Bell base measurement on himself. Then we use three lines to share the information successfully. We find that when the high privileged recipient restores the secret state, he can succeed only with the help of one of the two low privileged recipients; and when the low privileged recipient restores the secret state, he can succeed only with the help of one of the two low privileged recipients. In the complex secret state, he needs the help of the high privileged receiver and the remaining low privileged receiver, and then successfully realizes the sharing of the secret state through corresponding operations.
Rank-consistent Ordinal Regression for Neural Networks

In many real-world predictions tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, ordinal regression frameworks have been adopted by the deep learning community to take such ordering information into account. Using a framework that transforms ordinal targets into binary classification subtasks, neural networks were equipped with ordinal regression capabilities. However, this method suffers from inconsistencies among the different binary classifiers. We hypothesize that addressing the inconsistency issue in these binary classification task-based neural networks improves predictive performance. To test this hypothesis, we propose the COnsistent RAnk Logits (CORAL) framework with strong theoretical guarantees for rank-monotonicity and consistent confidence scores. Moreover, the proposed method is architecture-agnostic and can extend arbitrary state-of-the-art deep neural network classifiers for ordinal regression tasks. The empirical evaluation of the proposed rank-consistent method on a range of face-image datasets for age prediction shows a substantial reduction of the prediction error compared to the reference ordinal regression network.
Breast Cancer Detection Using Image Processing Techniques

Image processing is a widely used methodology in various medical sectors. Image processing involves performing some operations on images to extract some useful information. Image analysis is very helpful in the early detection of various cancers in which time factor is very crucial. The number of cases of breast cancer has increased worldwide. In this paper, breast cancer disease detection and its subsequent treatment has been discussed. In the proposed work, mammogram and MRI, the two important modalities, have been used to detect the tumorous portion more accurately. The tumorous part from the resultant image has been separated by different segmentation method such as edge detection and threshold method. Further different operators have been applied on resultant image and its quantitatively verified by performance measuring parameter entropy. The early detection of breast cancer can save life and make treatment less complex for medical practitioners.
Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records.

Effective modeling of electronic health records (EHR) is rapidly becoming animportant topic in both academia and industry. A recent study showed thatutilizing the graphical structure underlying EHR data (e.g. relationshipbetween diagnoses and treatments) improves the performance of prediction taskssuch as heart failure diagnosis prediction. However, EHR data do not alwayscontain complete structure information. Moreover, when it comes to claims data,structure information is completely unavailable to begin with. Under suchcircumstances, can we still do better than just treating EHR data as aflat-structured bag-of-features? In this paper, we study the possibility ofutilizing the implicit structure of EHR by using the Transformer for predictiontasks on EHR data. Specifically, we argue that the Transformer is a suitablemodel to learn the hidden EHR structure, and propose the Graph ConvolutionalTransformer, which uses data statistics to guide the structure learningprocess. Our model empirically demonstrated superior prediction performance toprevious approaches on both synthetic data and publicly available EHR data onencounter-based prediction tasks such as graph reconstruction and readmissionprediction, indicating that it can serve as an effective general-purposerepresentation learning algorithm for EHR data.
Designing Deep Reinforcement Learning for Human Parameter Exploration

Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this article, we propose to investigate artificial agents using deep reinforcement learning to explore parameter spaces in partnership with users for sound design. We describe a series of user-centred studies to probe the creative benefits of these agents and adapting their design to exploration. Preliminary studies observing users’ exploration strategies with parametric interfaces and testing different agent exploration behaviours led to the design of a fully-functioning prototype, called Co-Explorer, that we evaluated in a workshop with professional sound designers. We found that the Co-Explorer enables a novel creative workflow centred on human–machine partnership, which has been positively received by practitioners. We also highlight varied user exploration behaviours throughout partnering with our system. Finally, we frame design guidelines for enabling such co-exploration workflow in creative digital applications.
A DPN (Delegated Proof of Node) Mechanism for Secure Data Transmission in IoT Services

The importance of Blockchain and IoT technology have been highlighted in various fields. These are not unaccustomed words anymore in our lives. Although the technologies are in the infancy step and are still many remaining te... | Find, read and cite all the research you need on Tech Science Press
End-to-End Encryption Schemes for Online Social Networks

In a secure Online Social Network (OSN) an attacker with access to the server cannot use the saved data of any user to read the private communication. It should allow users to use the OSN even if they are not technically savvy and have no knowledge about cryptography. We present and discuss an end-to-end encryption based approach that uses the RSA public-key encryption algorithm, as well as the AES symmetric-key encryption algorithm. The result is a fully working personal message service, also known as online chat. Instead of relying on third-party projects with questionable or unknown security levels our prototype is built from scratch in JavaScript.
An Approach to Conversational Recommendation of Restaurants

In this paper, we propose an approach based on the integration of a chatbot module, a location-based service, and a recommendation algorithm. This approach has been deployed for restaurant recommendation, tested on a sample of 50 real users, and compared with some state-of-the-art algorithms. The preliminary experimental results showed the benefits of the proposed approach in terms of performance. An ANOVA test enabled us to verify the statistical significance of the obtained findings.
Detecting cognitive impairments by agreeing on interpretations of linguistic features

Zining Zhu, Jekaterina Novikova, Frank Rudzicz. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Variance Reduced Policy Evaluation with Smooth Function Approximation

Policy evaluation with smooth and nonlinear function approximation has shown great potential for reinforcement learning. Compared to linear function approxi- mation, it allows for using a richer class of approximation functions such as the neural networks. Traditional algorithms are based on two timescales stochastic approximation whose convergence rate is often slow. This paper focuses on an offline setting where a trajectory of $m$ state-action pairs are observed. We formulate the policy evaluation problem as a non-convex primal-dual, finite-sum optimization problem, whose primal sub-problem is non-convex and dual sub-problem is strongly concave. We suggest a single-timescale primal-dual gradient algorithm with variance reduction, and show that it converges to an $\epsilon$-stationary point using $O(m/\epsilon)$ calls (in expectation) to a gradient oracle.
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification

As an essential component of natural language processing, text classification relies on deep learning in recent years.Various neural networks are designed for text classification on the basis of word embedding.However, polysemy is a fundamental feature of the natural language, which brings challenges to text classification.One polysemic word contains more than one sense, while the word embedding procedure conflates different senses of a polysemic word into a single vector.Extracting the distinct representation for the specific sense could thus lead to fine-grained models with strong generalization ability.It has been demonstrated that multiple senses of a word actually reside in linear superposition within the word embedding so that specific senses can be extracted from the original word embedding.Therefore, we propose to use capsule networks to construct the vectorized representation of semantics and utilize hyperplanes to decompose each capsule to acquire the specific senses.A novel dynamic routing mechanism named 'routing-on-hyperplane' will select the proper sense for the downstream classification task.Our model is evaluated on 6 different datasets, and the experimental results show that our model is capable of extracting more discriminative semantic features and yields a significant performance gain compared to other baseline methods.
Regression or classification? Automated Essay Scoring for Norwegian

In this paper we present first results for the task of Automated Essay Scoring for Norwegian learner language. We analyze a number of properties of this task experimentally and assess (i) the formulation of the task as either regression or classification, (ii) the use of various non-neural and neural machine learning architectures with various types of input representations, and (iii) applying multi-task learning for joint prediction of essay scoring and native language identification. We find that a GRU-based attention model trained in a single-task setting performs best at the AES task.
Step phase reconstruction using an anisotropic total variation regularization method in a diffraction phase microscopy

This paper presents an approach to estimate the phase distribution with step features or edge characteristics from an interferogram obtained in diffraction phase microscopy. The proposed approach relies on formulation of a cost function for the interferogram and subsequent application of anisotropic total variation regularization based optimization. In our approach, the cost function is minimized by using a proximal gradient method based iterative approach with alternative updates of the amplitude and the phase. The performance of the proposed approach for retrieving phase maps with rapid edge variations is shown via numerical simulations. Further, the experimental validity of the proposed method is demonstrated for surface topography in diffraction phase microscopy.
A.Eye Drive: Gaze-based semi-autonomous wheelchair interface

Existing wheelchair control interfaces, such as sip & puff or screen based gaze-controlled cursors, are challenging for the severely disabled to navigate safely and independently as users continuously need to interact with an interface during navigation. This puts a significant cognitive load on users and prevents them from interacting with the environment in other forms during navigation. We have combined eyetracking/gaze-contingent intention decoding with computer vision context-aware algorithms and autonomous navigation drawn from self-driving vehicles to allow paralysed users to drive by eye, simply by decoding natural gaze about where the user wants to go: A.Eye Drive. Our "Zero UI" driving platform allows users to look and interact visually with at an object or destination of interest in their visual scene, and the wheelchair autonomously takes the user to the intended destination, while continuously updating the computed path for static and dynamic obstacles. This intention decoding technology empowers the end-user by promising more independence through their own agency.
Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs

Although both positive and negative testing are important for assuring quality of programs, generating a variety of test inputs for such testing purposes is difficult for machine learning software. This paper studies why it is difficult, and then proposes a new method of generating datasets that are test inputs to machine learning programs. The proposed idea is demonstrated with a case study of classifying hand-written numbers.
Deep Kernel Transfer in Gaussian Processes for Few-shot Learning

Humans tackle new problems by making inferences that go far beyond the information available, reusing what they have previously learned, and weighing different alternatives in the face of uncertainty. Incorporating these abilities in an artificial system is a major objective in machine learning. Towards this goal, we adapt Gaussian Processes (GPs) to tackle the problem of few-shot learning. We propose a simple, yet effective variant of deep kernel learning in which the kernel is transferred across tasks, which we call deep kernel transfer. This approach is straightforward to implement, provides uncertainty quantification, and does not require estimation of task-specific parameters. We empirically demonstrate that the proposed method outperforms several state-of-the-art algorithms in few-shot regression, classification, and cross-domain adaptation.