Reviewerly/exHarmony huggingface dataset:
On the Effectiveness of Using Graphics Interrupt as a Side Channel for User Behavior Snooping

Graphics Processing Units (GPUs) are now a key component of many devices and systems, including those in the cloud and data centers, thus are also subject to side-channel attacks. Existing side-channel attacks on GPUs typically leak information from graphics libraries like OpenGL and CUDA, which require creating contentions within the GPU resource space and are being mitigated with software patches. This article evaluates potential side channels exposed at a lower-level interface between GPUs and CPUs, namely the graphics interrupts. These signals could indicate unique signatures of GPU workload, allowing a spy process to infer the behavior of other processes. We demonstrate the practicality and generality of such side-channel exploitation with a variety of assumed attack scenarios. Simulations on both Nvidia and Intel graphics adapters showed that our attack could achieve high accuracy, while in-depth studies were also presented to explore the low-level rationale behind such effectiveness. On top of that, we further propose a practical mitigation scheme which protects GPU workloads against the graphics-interrupt-based side-channel attack by piggybacking mask payloads on them to generate interfering graphics interrupt “noises”. Experiments show that our mitigation technique effectively prohibited spy processes from inferring user behaviors via analyzing runtime patterns of graphics interrupt with only trivial overhead.
Crafting On-Skin Interfaces: An Embodied Prototyping Journey

This pictorial presents a design exploration of On-Skin Interfaces for recreational running. By integrating principles of interaction design, art and psychology, we explore the design of unconventional interfaces that facilitate the intuitive understanding of biofeedback and physiological-related information. We explored how principles from agency and bodily ownership can be applied in the design of sport- related wearables. Through our embodied prototyping journey, we gained insights on the implications of using the skin as an interactive design material. We focused on diverse materiality explorations to uncover and highlight the possibilities and challenges of materializing both functional and appealing On-Skin Interfaces. We synthesize and refl on our theoretical and practical explorations and deliver actionable insights for this growing fi of bodily and unconventional interfaces.
Multi-Generator Adversarial Networks For Light Field Saliency Detection

Compared to traditional cameras, light field imaging can additionally record the direction of incoming light. Based on this, light fields can be represented in focus stacks that focus at different depths and all-in-focus images. The refocusing information of focal stacks can provide supplementary information for saliency detection. In this paper, we propose a novel multi-generator adversarial network for saliency detection that consists of a cascaded multi-generator and a discriminator. The multi-generator extract saliency features from all-in-focus images and focal stacks. Besides, we set the predicted map multiplied by the all-in-focus image as the input of the discriminator. With this multiplication, we can preserve color and texture information of the salient objects, and reduce the computational cost. We conduct experiments on three public datasets. Compared with existing methods, our method achieves competitive results.
Detection and Classification of Toxic Comments by Using LSTM and Bi-LSTM Approach

With the advancement in the technology, a lot of comments has been produced on a regular basis through the various online communication platforms like Wikipedia, twitter, Glassdoor etc. Although, many of these comments really benefit the people, but the various high toxic comments are also responsible for the increasing online harassment, mental depression and even personal attacks. Toxic Comment Classification is one of the active research topics at present. In the following study, a multi-label classification model is presented to classify the various toxic comments into six classes namely toxic, severe toxic, obscene, threat, insult and identity hate. The proposed classification model has been built using deep learning algorithms explicitly Long Short-Term Memory (LSTM) and Bi-Directional Long Short-Term Memory (Bi-LSTM) along with the word embeddings by adapting insights from previous proposed works. The dataset for this research is obtained from the Kaggle and is provided by the Conversation AI team (a research ingenuity co-founded by Google as well as Jigsaw). The accuracy score of both the proposed techniques is evaluated and compared. Finally, the empirical results show that Bi-LSTM algorithm achieved better in comparison to LSTM with an increased accuracy of 98.07%.
Moving Target and Implementation Diversity Based Countermeasures Against Side-Channel Attacks

Side-channel attacks (SCAs) are among the major threats to embedded systems’ security, where implementation characteristics of cryptographic algorithms are exploited to extract secret parameters. The most common SCAs take advantage of electromagnetic (EM) leakage or power consumption recorded during device operation by placing an EM probe over the chip or measuring the voltage drop across an internal resistor, respectively. In this work, two SCA countermeasures are presented which address these two types of leakage vectors. The first countermeasure supports implementation diversity and moving target defense, while the second one generates random algorithmic noise. These concepts are implemented using the dynamic partial reconfiguration (DPR) feature of modern FPGA devices. Both of the countermeasures are easily scalable, and the effect of scalability on the area overhead and security strength is presented. We evaluate our design by measuring EM emanations from a state-of-the-art System-on-Chip (SoC) with 16 nm production technology. With the most secure variant, we are able to increase the resistance against Correlation Power Analysis (CPA) by a factor of 95 compared to an unprotected AES implementation.
Protecting the Privacy of IoT-Based Health Records Using Blockchain Technology

In recent years, billions of sensors are controlled via the Internet with the rapid development of technology. The received data from the sensors are stored in local or cloud databases and can be accessed from anywhere. This convenience provided by the Internet of Things (IoT) makes it usable in the health system. In particular, remote patient monitoring provides convenience to healthcare workers in the treatment and care of patients. In addition to these amenities, there have been serious privacy risks and security concerns in the protection of health data. In this research, a model that is based on blockchain technology is proposed for the secure transfer of data received from IoT-based sensors used for remote patient monitoring to healthcare professionals. The system has been established that transfers the data received from the pulse and galvanic skin response sensors to the cloud server with Raspberry Pi (RAP) for the study ecosystem. Sensor data is transmitted to the cloud server, with a smart contract blockchain structure on the fabric-based system. The blockchain-based model aims to contribute to the implementation of health services within the framework of data privacy.
Developing a cloud-based service-oriented architecture for fuzzy logic systems

Fuzzy logic systems are customarily related to specific hardware or software systems. Nevertheless, it has been observed that distributed and cloud-based architectures of various intelligent systems are pouring intensifying attention. While the distributed architectures can potentially add values in developing fuzzy systems, a lack of standard methods and practices may limit their public use. This study aims to provide a standard solution for developing cloud-based service-oriented architectures for fuzzy logic systems, based on extending IEEE-1855 (2016) in the defining system and exchanging data. Experiments were performed employing simulation concerning collection, processing and monitoring of data in a distributed manner over the web. A real-time human activity recognition simulated scenario is also demonstrated through a cloud-based fuzzy system.
Obstacle Avoidance and Formation Regrouping for Follower UAV in Leader-Follower Formation

Currently, multi UAVs formation is very hot. Since it can greatly increase task efficiency. UAV formation has many research points like trajectory optimization, formation holding, formation optimal configuration, formation switch, obstacle avoidance, and formation regrouping, etc. But in our paper, we mainly focus on follower UAV obstacle avoidance and formation regrouping. Firstly, we propose an obstacle avoidance algorithm which mainly used on follower UAV. Secondly, we introduced PX4's position controller which used for formation regrouping. To the end, we make some simulations to verify the obstacle avoidance strategy, PX4's position controller and UAV formation.
Querying for Interactions

Advances in Deep Learning and Computer Vision enabled sophisticated information extraction out of images and video frames. Recent research aims to make objects, their types and relative locations as the video evolves, first class citizens for query processing purposes.In this paper, we initiate research to explore declarative style of querying for real time video streams involving objects and their interactions. We seek to efficiently identify frames in a streaming video in which an object is interacting with another in a specific way, such as for example a human kicking a ball. We first propose an algorithm called progressive filters (PF) that deploys a sequence of inexpensive and less accurate models (filters) to detect the presence of the query specified objects on frames. We demonstrate that PF derives a least cost sequence of filters given the current selectivities of query objects. Since selectivities may vary as the video evolves, we present a dynamic statistical test to determine when to trigger re-optimization of the filters. Finally, we present a filtering approach called Interaction Sheave (IS) that utilizes learned spatial information about objects and interactions to effectively prune frames that are unlikely to involve the query specified action between them, thus improving the frame processing rate further.We present the results of a thorough experimental evaluation involving real data sets, demonstrating the performance benefits of each of our proposals. In particular we experimentally demonstrate that our techniques can improve query performance substantially (up to an order of magnitude in our experiments) while maintaining essentially the same F1-score as alternatives.
Tactical Intention Recognition in Wargame

Opponent modeling is a significant method in imperfect information games. And intention recognition is regarded as the important but difficult in opponent modeling. This paper focuses on the task of tactical intention recognition in computational wargame. We propose an approach to recognize opponents' intention which models the intention as long-term trajectories. The approach consists of situation encoding model and position prediction model. The first model uses attention mechanism to attach the statistic map data with dynamic feature and adopt CNN to learn the representation of battlefield situation. The position prediction model then predicts the long-term trajectories of opponents, based on well-represented situation vectors. Experiment indicates that our approach is proven to be effective on the task of tactical intention recognition in wargame. Meanwhile, a high-quality replay data set for analyzing the actions' characteristics is also provided in this paper.
Technologies of active learning in the context of the risks of modern professional education

The article includes a comprehensive analysis of the specifics of the system of active learning methods and a description of its resources in the professional training of future specialists in the context of modern modernization of higher education. Based on the results of the analysis of current trends in the development of a variety of technologies and formats of training in higher education, the criteria for determining their feasibility are identified. The paper presents an overview of the significant psychological characteristics of the teacher, acting as internal determinants of successful activity based on active learning methods. Various components of students' project activity are considered. The advantages of active teaching methods in the development of professional thinking, reflection of students' experience and competencies in ensuring the productivity of their advancement in educational and professional activities are revealed. Based on the analysis of the results of empirical research, the article describes the content features of modern students ' ideas about the resources and risks of active learning methods for their role in achieving a high level of professional competence development. The psychological factors that determine the success of the use of active learning methods in professional education are identified.
Detecting Regional Arabic Dialect based on Recurrent Neural Network

In recent times, Arabic text analysis has attracted great interest, due to the widespread and use of the Arabic language by social media platforms, applications, and communities, and others. Each Arabian country has a special dialect that distinguishes it from others. Accordingly, the work on classifying these dialects is an interesting area of research, as it has implications for other areas, such as; sentiment analysis and machine translation. In this paper, we build a multi-task classification model for dialects based on utilizing Recurrent Neural Networks, where the dialects are classified into four categories, namely; Maghreb, Levantine, Gulf (in addition to Iraqi), and the Nile. The used dataset is taken from the MADAR corpus, which contained 110,000 sentences, these belong to dialects of different countries in the four regions. Based on experimentations, the results revealed that the classifiers are able to distinguish between the four dialects with an accuracy of up to 84.76%, which in turn is considered a promising result in this field.
Cross talk compensation in multimode continuous-variable entanglement distribution

Two-mode squeezed states are scalable and robust entanglement resources for continuous-variable and hybrid quantum information protocols at a distance. We consider the effect of a linear cross talk in the multimode distribution of two-mode squeezed states propagating through parallel similar channels. First, to reduce degradation of the distributed Gaussian entanglement, we show that the initial two-mode squeezing entering the channel should be optimized already in the presence of a small cross talk. Second, we suggest simultaneous optimization of relative phase between the modes and their linear coupling on a receiver side prior to the use of entanglement, which can fully compensate the cross talk once the channel transmittance is the same for all the modes. For the realistic channels with similar transmittance values for either of the modes, the cross talk can be still largely compensated. This method relying on the mode interference overcomes an alternative method of entanglement localization in one pair of modes using measurement on another pair and feed-forward control. Our theoretical results pave the way to more efficient use of multimode continuous-variable photonic entanglement in scalable quantum networks with cross talk.
Adaptive Weighted Discriminator for Training Generative Adversarial Networks

Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data respectively. One challenge associated with an equally weighted sum of two losses is that the training may benefit one loss but harm the other, which we show causes instability and mode collapse. In this paper, we introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions or aw-loss functions. Using the gradients of the real and fake parts of the loss, we can adaptively choose weights to train a discriminator in the direction that benefits the GAN's stability. Our method can be potentially applied to any discriminator model with a loss that is a sum of the real and fake parts. For our experiments, SN-GAN, AutoGAN, and BigGAN are used. Experiments validated the effectiveness of our loss functions on unconditional and conditional image generation tasks, improving the baseline results by a significant margin on CIFAR-10, STL-10, and CIFAR-100 datasets in Inception Scores (IS) and Fréchet Inception Distance (FID) metrics.
Rereading, art-making and other joys: toward a theory of information, repetition and the good life

Purpose This paper offers a conceptual discussion of repetition and joy in the context of information and their relation to the good life. Design/methodology/approach Joy is defined as an integrative element of the good life which can be achieved through repetition. This may be surprising, given that our most ready-to-hand associations with “repetition” are negative in tenor rather than positive. Building on the work of repetition theorists Søren Kierkegaard and Gertrude Stein, we can discern three different forms of repetition: that looking backwards (e.g. rereading), that looking forwards (e.g. art-making) and that looking inwards (e.g. chiasmus). Throughout this paper, information-related examples are given and discussed as vignettes that move the conversation forward. Findings These examples lead to a nascent theory of why the repetition of information can spark joy and not just tedium. First, its stability and predictability that instill comfort in us. Second, its unifying force that brings us to experience wholeness. Third, its invitation to keep the repetition going through creation, further helping us feel part of the world. And finally, its paradoxicality—as strict repetition is impossible—which requires change, paving the way for satisfying surprises and delights. Originality/value Repetition is a ubiquitous and theoretically interesting phenomenon when it comes to information, and though it is implicit in some information science research, it has not yet been theorized directly. Moreover, this paper connects this issue to an emerging “positive” orientation in information studies.
Towards effective GP multi-class classification based on dynamic targets

In the multi-class classification problem GP plays an important role when combined with other non-GP classifiers. However, when GP performs the actual classification (without relying on other classifiers) its classification accuracy is low. This is especially true when the number of classes is high. In this paper, we present DTC, a GP classifier that leverages the effectiveness of the dynamic target approach to evolve a set of discriminant functions (one for each class). Notably, DTC is the first GP classifier that defines the fitness of individuals by using the synergistic combination of linear scaling and the hinge-loss function (commonly used by SVM). Differently, most previous GP classifiers use the number of correct classifications to drive the evolution. We compare DTC with eight state-of-art multi-class classification techniques (e.g., RF, RS, MLP, and SVM) on eight popular datasets. The results show that DTC achieves competitive classification accuracy even with 15 classes, without relying on other classifiers.
An Empirical Study of Dehazing Techniques for Chest X-Ray in Early Detection of Pneumonia

Pneumonia is one of the major infectious disease which affects most of the population. In day-to-day life, the use of technologies is increasing rapidly. Imaging plays a major role in the detection of many diseases. Chest-X-Ray images in medical imaging play the main role in the diagnosis of pneumonia patients. But chest X-Ray images are not much clear and this creates a problem in the detection of pneumonia. Presents of haze can destroy the quality of these images. If the quality of an image is low, doctors may not able to predict early whether the Pneumonia present or not. This is the main reason because of which the mortality rate increases. Thus image dehazing techniques are used to improvise the quality. The main aim of this paper is to perform experimentation of dehazing techniques on X-Ray images and analyze the results which improve the quality of images.
A novel fuzzy clustering method based on GA, PSO and Subtractive Clustering

Data clustering is a challenging problem in data science, requiring both accurate determination of the number of clusters and correct clustering of the data. While Fuzzy C-means (FCM) is a powerful algorithm that can cluster data into overlapping groups and converge quickly to local optima, it however depends on the choice of initial parameters and may not always reach a global optimum. In addition, FCM by itself cannot determine the correct number of clusters. In this study, we combine FCM with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Subtractive Clustering (SC) algorithms for a novel algorithm, FZGPS, that both determines the correct number of clusters and efficiently constructs these clusters from a given dataset. We show that FZGPS performs effectively on both artificial and experimentally-derived gene expression datasets.
Distributed representations of diseases based on co-occurrence relationship

The co-occurrence relationship among diseases facilitates the knowledge discovery in the medical field. However, due to limited data, previous researches are mainly based on clinician experience and simple statistics which make it difficult to discover deep associations among diseases. Treating the diagnoses in an electronic medical record (EMR) as interrelated random variables, we use Markov random fields to model the co-occurrence relationship among diseases and propose Di2Vec to learn distributed representations of diseases. The diseases having high co-occurrence frequency will be very close to each other in the embedding space. Considering the hierarchical structure in each diagnosis code, we introduce the subword embedding and explore its impact on the quality of embeddings, where the embedding of each diagnosis is expressed as the sum of its subword embedding. Qualitative and Quantitative experiments show that our Di2Vec can make the embeddings of diseases with high co-occurrence frequency close to each other, and can also outperform Skip-gram and CBOW when use these embeddings as the feature representations for medical expense prediction. Using subword embedding will make the disease embeddings to have better clustering property, but to a certain extent, it loss the co-occurrence information contained in the disease embeddings.