Reviewerly/exHarmony huggingface dataset:
Leveraging Federated Learning &amp; Blockchain to counter Adversarial Attacks in Incremental Learning

Whereas data labelling in IoT applications is costly, it is also time consuming to train a supervised Machine Learning (ML) algorithm. Hence, a human oracle is required to gradually annotate the data patterns at run-time to improve the models' learning behavior, through an active learning strategy in form of User Feedback Process (UFP). Consequently, it is worth to note that during UFP there may exist malicious content that may subject the learning model to be vulnerable to adversarial attacks, more so, manipulative attacks. We argue in this position paper, that there are instances during incremental learning, where the local data model may present wrong output, if retraining is done using data that has already been subjected to adversarial attack. We propose a Distributed Interactive Secure Federated Learning (DISFL) framework that utilizes UFP in the edge and fog node, that subsequently increases the amount of labelled personal local data for the ML model during incremental training. Furthermore, the DISFL framework addresses data privacy by leveraging federated learning, where only the model's knowledge is moved to a global unit, herein referred to as Collective Intelligence Node (CIN). During incremental learning, this would then allow the creation of an immutable chain of data that has to be trained, which in its entirety is tamper-free while increasing trust between parties. With a degree of certainty, this approach counters adversarial manipulation during incremental learning in active learning context at the same time strengthens data privacy, while reducing the computation costs.
Enhancing citizens trust in technologies for data donation in clinical research: validation of a design prototype

Mobile phones, wearable trackers and Internet of Things devices continuously produce data about our health and lifestyle that can be used for medical research. However, how data is accessed, by whom and for what purpose is not always understood. This lack of transparency undermines citizens trust in the use of such technologies for research purposes. This paper proposes a set of 6 use cases and related mock-up interfaces for citizen science, mobile-based health research: “Curated information about the institution”, “Sequential consent of shared data”, “Updates from the institution”, “Privacy notifications”, “Overview of donated data” and “Personal impact in medical research”. Interviews and Kano analysis of the interfaces with 6 prospective users show that all except “Privacy notifications” are perceived as important and beneficial for increasing users’ trust. The defined use cases can guide the development of future data collection platforms.
A hardware architecture for Multiscale Retinex with Chromacity Preservation on an FPGA

Image-processing algorithms based on Retinex theory aim to model human color perception to enhance images with low contrast or poor illumination. In particular, the Multiscale Retinex with Chromacity Preservation (MSRCP) algorithm improves on the original Retinex by processing the image at multiple scales and adding a color balance step in postprocessing. Despite their advantages, multiscale Retinex algorithms are computationally intensive, and real-time video processing is not generally possible with general-purpose processor architectures. In this paper, we present a special-purpose hardware accelerator for the MSRCP algorithm. The accelerator introduces tradeoffs to the original formulation of MSRCP by reducing the magnitude of the scales and using a cumulative histogram in the color-balance stage. Despite these modifications, we show that the accelerator produces images that are visually almost identical to a software implementation of the original MSRCP algorithm. We implement our design on a Xilinx XC7A200T-1SBG484C FPGA, which is capable of processing 1280×720-pixel video at up to 94 frames per second, a speedup of 123x compared to a desktop computer running a software version of the algorithm.
Auditing the Sensitivity of Graph-based Ranking with Visual Analytics

Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall we recommend to a given user on an e-commerce platform? The answers to such questions are typically returned in the form of a ranked list, and graph-based ranking methods are widely used in industrial information retrieval settings. However, these ranking algorithms have a variety of sensitivities, and even small changes in rank can lead to vast reductions in product sales and page hits. As such, there is a need for tools and methods that can help model developers and analysts explore the sensitivities of graph ranking algorithms with respect to perturbations within the graph structure. In this paper, we present a visual analytics framework for explaining and exploring the sensitivity of any graph-based ranking algorithm by performing perturbation-based what-if analysis. We demonstrate our framework through three case studies inspecting the sensitivity of two classic graph-based ranking algorithms (PageRank and HITS) as applied to rankings in political news media and social networks.
Clickthrough Testing for Real-World Phishing Simulations

In this paper, we begin by discussing the challenges associated with phishing email simulations. In part, we note how challenging it is to achieve realism in such a simulation and what this may mean for the results obtained. Next, we detail a real-world phishing simulation that targeted participants involved in an unrelated research project.
Bounded biharmonic blending of Möbius transformations for flexible omnidirectional image rectification

We propose a new method to correct distortions in equirectangular images, i.e., images that use the longitude/latitude representation to describe the full spherical field of view around a given viewpoint. We show that Möbius transformations are the correct mathematical tool to deal with the conflicting distortions in this setting: they are conformal, are able to rectify lines, perform rotations, translations and scales on the sphere and are bijective. Multiple transformations are specified through points, lines and cages on the sphere. We associate three points that uniquely define a Möbius transformation to each one of these geometric handles. Linear blend skinning with bounded biharmonic weights combine the different transformations into a single full spherical modified image. We present a collection of results in challenging settings and show that our method is more flexible and produces higher quality results when compared to previous methods.
Comparison of reliability- and design-based code calibrations

Some of the early probabilistic code calibrations minimized an error term defined as difference between the optimal or target design and the design resulting with the trial partial factors and the code format. In contrast to these design-based calibrations, in recent years, the error was defined in terms of a reliability deviation. Aiming at minimizing societal costs, reliability-based calibrations probably provide more accurate results than design-based code calibrations, however at the cost of a significantly lower computational efficiency. The long duration of reliability-based code calibrations impedes repeated code calibrations, which are needed in the development of a code format or for a more profound understanding of it. This paper compares the reliability-based with the design-based code calibration procedure theoretically and by using an example. The results showed that the design-based code calibration is efficient for the development of a code format. However, the differences between both calibration procedures regarding the calibrated partial factors are significant. As the reliability-based calibration has a more solid theoretical background, it is preferred for the final calibration of the code format.
Developing an Epidemiological Model to Study Spread of Toxicity on YouTube

The increased global salience of social media has fueled the propagation of various forms of toxicity. As digital technologies and interactive websites proliferate, cyberspace has become a venue through which toxic users torment their victims. Numerous platforms are trying to combat this phenomenon by training computational methods that are capable of automatically recognizing these toxic contents and removing them from the user-generated text on their platforms. However, given the immensity and speed of content posted on online platforms, identifying and deterring these behaviors at scale remains challenging. Aiming to address this challenge, in this paper, we describe a novel approach to understand and explain the spread of toxicity on YouTube by using well-known epidemic models. Our underlying hypothesis is that the spread of toxicity on YouTube is comparable to the spread of disease. Since mathematical epidemiology has been widely studied, being able to express the spread of toxicity as an epidemic model will enable us to use a wide range of tools and techniques that have been proven to be analytically rich and operationally useful. We leveraged the YouTubeTracker application to extract comments on YouTube. We then employed Google’s Perspective API to assign a toxicity score to each comment. The results of our analysis show that our approach can be fruitfully combined with other strategies to moderate the spread of toxicity on online platforms.
Task-distribution-aware Meta-learning for Cold-start CTR Prediction

Nowadays, click-through rate (CTR) prediction has achieved great success in online advertising. However, making desirable predictions for unseen ads is still challenging, which is known as the cold-start problem. To address such a problem in CTR prediction, meta-learning methods have recently emerged as a popular direction. In these approaches, the predictions for each user/item are regarded as individual tasks, then training a meta-learner on them to implement zero-shot/few-shot learning for unknown tasks. Though these approaches have effectively alleviated the cold-start problem, two facts are not paid enough attention, 1) the diversity of the task difficulty and 2) the perturbation of the task distribution. In this paper, we propose an adaptive loss that ensures the consistency between the task weight and difficulty. Interestingly, the loss function can also be viewed as a description of the worst-case performance under distribution perturbation. Moreover, we develop an algorithm, under the framework of gradient descent with max-oracle (GDmax), to minimize such an adaptive loss. Then we prove the algorithm can return to a stationary point of the adaptive loss. Finally, we implement our method on top of the meta-embedding framework and conduct experiments on three real-world datasets. The experiments show that our proposed method significantly improves the predictions in the cold-start scenario.
Neural Networks in Diagnosis of Breast Cancer

Purpose: The paper is devoted to a creation of mathematical model of an artificial neural network for detection of breast cancer based on microwave radiothermometry and anamnesis.Design/Methodology/Approach: One of the most complex and urgent challenges of modern medicine is arranging of effective mammological screening. The solution to this problem will significantly reduce the mortality from breast cancer by detecting a tumor before clinical manifestation, as well as cut the costs of treating patients. Currently, the main trend in increasing the effectiveness of screening is the transition to modern digital technologies, including the use of methods and algorithms of artificial intelligence.One of the newest methods for early diagnosis of breast cancer is the method of microwave radiothermometry. Over the past two decades, it has already proved itself in some areas of medicine. In addition, it is one of a few methods recommended for use in mammological screening of women under the age of 40. One of the problems for implementation of this method is a complexity of analysis and interpretation of thermometric data.Findings: The solution to this problem involves the development of advisory intelligent diagnostic systems. Such systems use the effective classification algorithms, including ones based on artificial neural networks.Originality/Value: A distinctive feature of the neural network proposed in this study is the use of a specific attribute space as an input layer. This space built on the basis of mathematical functions that simulate the nuances of the behavior of temperature fields in patients of various diagnostic classes.
Joint Self-expression with Adaptive Graph for Unsupervised Feature Selection

Feature selection usually takes unsupervised way to preprocess the data before clustering. In the unsupervised feature selection, the embedding based method can capture more discriminative information contained in data compared to the other methods. Considering many existing methods learn a cluster indicator matrix which may bring noise, and at the same time, these kinds of methods does not make good use of the geometry structure of the data. In order to address the existing problems, we propose a novel model based on joint self-expression model with adaptive graph constraint. The joint self-expression module is utilized to explore the relationship between features. Different from the conventional self-expression, our joint self-expression module contains two types self-expression, i.e., conventional self-expression and the convex non-negative matrix factorization (CNMF) which can extract more representative features. Furthermore, we introduce manifold learning to maintain the structural characteristics of the original data. Adaptive graph regularization term is also incorporated based on the principle of maximum entropy into our model. In order to solve the final model, an alternative algorithm is well designed. Finally, experiment is well designed on five benchmark datasets and the experimental results show that the model proposed in this paper is more effectiveness than the state-of-the-art comparison models.
TUTOR: Training Neural Networks Using Decision Rules as Model Priors

The human brain has the ability to carry out new tasks with limited experience. It utilizes prior learning experiences to adapt the solution strategy to new domains. On the other hand, deep neural networks (DNNs) generally need large amounts of data and computational resources for training. However, this requirement is not met in many settings. To address these challenges, we propose the TUTOR (training neural networks using decision rules as model priors) DNN synthesis framework. TUTOR targets tabular datasets. It synthesizes accurate DNN models with limited available data and reduced memory/computational requirements. It consists of three sequential steps. The first step involves generation, verification, and labeling of synthetic data. The synthetic data generation module targets both categorical and continuous features. TUTOR generates synthetic data from the same probability distribution as the real data. It then verifies the integrity of the generated synthetic data using a semantic integrity classifier module. It labels synthetic data based on a set of rules extracted from the real dataset. Next, TUTOR uses two training schemes that combine synthetic and training data to learn the DNN model parameters. These two schemes focus on two different ways in which synthetic data can be used to derive a prior on the model parameters and, hence, provide a better DNN initialization for training with real data. In the third step, TUTOR employs a grow-and-prune synthesis paradigm to learn both the weights and the architecture of the DNN to reduce model size while ensuring its accuracy. We evaluate the performance of TUTOR on nine datasets of various sizes. We show that in comparison to fully connected DNNs, TUTOR, on an average, reduces the need for data by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.9\times $ </tex-math></inline-formula> (geometric mean), improves accuracy by 3.4%, and reduces the number of parameters (floating-point operations) by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.7\times $ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.3\times $ </tex-math></inline-formula> ) (geometric mean). Thus, TUTOR enables less data-hungry, more accurate, and more compact DNN synthesis.
Semantic Storytelling Automation: A Context-Aware and Metadata-Driven Approach

Multimedia content production is nowadays widespread due to technological advances, namely supported by smartphones and social media. Although the massive amount of media content brings new opportunities to the industry, it also obfuscates the relevance of marketing content, meant to maintain and lure new audiences. This leads to an emergent necessity of producing these kinds of contents as quickly and engagingly as possible. Creating these automatically would decrease both the production costs and time, particularly by using static media for the creation of short storytelling animated clips. We propose an innovative approach that uses context and content information to transform a still photo into an appealing context-aware video clip. Thus, our solution presents a contribution to the state-of-the-art in computer vision and multimedia technologies and assists content creators with a value-added service to automatically build rich contextualized multimedia stories from single photographs.
Modelization and analysis of dynamic heterogeneous redundant system

Summary With the development and popularization of Internet technology, network security has become the focus of attention. Vulnerabilities and back doors are regarded as two of the main reasons of network security problems. Dynamic heterogeneous redundant (DHR) architecture is a typical framework of cyberspace mimic defense. It can make use of untrusted hardware and software components to construct a high reliable and high security information system. In this article, a mathematical model is set up for the DHR architecture, and the system security is characterized by vulnerability consistency rate and success rate of system attack. The antiattack ability of the DHR system is analyzed using the mathematical model.
Data Augmentation vs. Domain Adaptation—A Case Study in Human Activity Recognition

Recent advances in big data systems and databases have made it possible to gather raw unlabeled data at unprecedented rates. However, labeling such data constitutes a costly and timely process. This is especially true for video data, and in particular for human activity recognition (HAR) tasks. For this reason, methods for reducing the need of labeled data for HAR applications have drawn significant attention from the research community. In particular, two popular approaches developed to address the above issue are data augmentation and domain adaptation. The former attempts to leverage problem-specific, hand-crafted data synthesizers to augment the training dataset with artificial labeled data instances. The latter attempts to extract knowledge from distinct but related supervised learning tasks for which labeled data is more abundant than the problem at hand. Both methods have been extensively studied and used successfully on various tasks, but a comprehensive comparison of the two has not been carried out in the context of video data HAR. In this work, we fill this gap by providing ample experimental results comparing data augmentation and domain adaptation techniques on a cross-viewpoint, human activity recognition task from pose information.
A Cross-modality and Progressive Person Search System

This demonstration presents an instant and progressive cross-modality person search system, called 'CMPS'. Through the system, users can instantly find the lost children or elderly persons by simply describing their appearance through speech. Unlike most existing person search applications which have to cost much time to find the probe images, CMPS will save more valuable time in the early stage of losing. The proposed CMPS is one of the first attempts towards instant and progressive person search leveraging the audio, text, and visual modalities together. In detail, the system first takes the speech that describes the appearance of a person as the input to obtain a textual description by speech-to-text conversion. Then the cross-modal search is performed by matching the textual embedding with the visual representations of images in the learned latent space. The searched images can be used as candidates for query expansion. If the candidates are not right, the user can quickly adjust their description through speech. Once a right image is found, the user can directly click it as a new query. Finally the system will give the complete track of the lost person by once-click. On the built CUHK-PEDES-AUDIOS dataset, the system can achieve 82.46% rank-1 accuracy in real-time speed. Our code of CMPS is available at https://github.com/SheldongChen/Search-People-With-Audio.
A novel method for network traffic classification based on robust <scp>support vector machine</scp>

ABSTRACT The increasing demand for high‐speed transmission and traffic classification has been widely applied for network resource management, quality of service, dynamic access control, and others. Some normal machine learning algorithms for network traffic classification are becoming obsolete, as the increasing deployment of encryption in protocols and dynamic ports, as well as some new technology are available, such as HTML5 protocols and HTTP adaptive streaming in video traffic. This article presents a novel classification algorithm for video traffic. We produce a new video traffic dataset and propose an original parameter adaptive algorithm for robust support vector machine with a fuzzy membership function. The results show that the proposed model is an effective alternative to be applied in actual network environment.
Correlation immunity of three-valued logic functions

The current stage in the development of cryptography is characterized by attention of researchers to the methods of many-valued logic. Of particular importance are the methods of three-valued logic, which are used in quantum cryptography, as well as in three-valued cryptographic algorithms with an increased level of cryptographic strength, implemented on binary computers. This circumstance leads to the need for further improvement of methods for constructing of high-quality cryptographic primitives. This paper is devoted to the generalization of definition of the correlation immunity of three-valued logic functions in the context of the properties of matrices of correlation coefficients of S-boxes built on their basis. The paper proposes methods for the synthesis of complete classes of 3-functions of length N = 9, the output of which does not depend on the input variable x1 or x2, as well as the full set of correlation immune 3-functions. The relationship between the introduced criterion of the correlation immunity of 3-functions and the criterion for the absence of a correlation interconnection between the output and input vectors of S-boxes of length N = 9 is also established.
Some Limit Properties of Markov Chains Induced by Recursive Stochastic Algorithms

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 24 April 2019Accepted: 22 July 2020Published online: 15 October 2020Keywordsstochastic gradient descent, empirical dynamic programming, constant stepsize Q learning, iterative random maps, Feller Markov chainsAMS Subject Headings93E35, 60J20, 68Q32Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq