Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Defense For Black Box Classifiers Against On-Line Adversarial Attacks

Deep learning models have been shown to be vulnerable to adversarial attacks. Adversarial attacks are imperceptible perturbations added to an image such that the deep learning model misclassifies the image with a high confidence. Existing adversarial defenses validate their performance using only the classification accuracy. However, classification accuracy by itself is not a reliable metric to determine if the resulting image is "adversarial-free". This is a foundational problem for online image recognition applications where the ground-truth of the incoming image is not known and hence we cannot compute the accuracy of the classifier or validate if the image is "adversarial-free" or not. This paper proposes a novel privacy preserving framework for defending Black box classifiers from adversarial attacks using an ensemble of iterative adversarial image purifiers whose performance is continuously validated in a loop using Bayesian uncertainties. The proposed approach can convert a single-step black box adversarial defense into an iterative defense and proposes three novel privacy preserving Knowledge Distillation (KD) approaches that use prior meta-information from various datasets to mimic the performance of the Black box classifier. Additionally, this paper proves the existence of an optimal distribution for the purified images that can reach a theoretical lower bound, beyond which the image can no longer be purified. Experimental results on six public benchmark datasets namely: 1) Fashion-MNIST, 2) CIFAR-10, 3) GTSRB, 4) MIO-TCD, 5) Tiny-ImageNet, and 6) MS-Celeb show that the proposed approach can consistently detect adversarial examples and purify or reject them against a variety of adversarial attacks.
Eye-Tracking Analysis with Deep Learning Method

The eyes are a rich source of information about mental activities as well as providing the perception of the outside world. Because they cannot be consciously controlled, they can reveal unique characteristics such as preferences and intentions. For this reason, eye-tracking technology is widely used in medicine, gaming, and commercial applications. In this study, an estimate of what type of text is read was made using the analysis of eye movements during daily reading activity. In the study, deep learning approaches were preferred due to the insufficient results of machine learning approaches before. Multiplexing was performed using a dataset with 52 features consisting of 20 participants (10 males, 10 females). 627 data were obtained as a result of multiplexing from 20 data. As a result of the creation of visual representations (spectrograms) of the data produced in sufficient numbers and processing with deep learning architectures, a good success rate of 97.88% was achieved with AlexNet. While the best values in news and text types were obtained with AlexNet and Resnet101, better results were produced with ResNet18 and ResNet50 in comedy with high visual content. It was noticed that the success rate in women was higher in documents with visual content.
Seeds of SEED: New Security Challenges for Persistent Memory

Persistent Memeory Object (PMO) is a general system abstraction for holding persistent data in persistent main memory, managed by an operating system. PMO programming model breaks inter-process isolation as it results in sharing of persistent data between two processes as they alternatively access the same PMO. The uncoordinated data-access opens a new avenue for cross-run and cross-process security attacks.In this paper, we discuss threat vulnerabilities that are either new or increased in intensity under PMO programming model. We also discuss security implications of using the PMO, highlighting sample PMO-based attacks and potential strategies to defend against them.
A Metaphoric Design of Electronic Medical Record (EMR) for Periodic Health Examination Reports

The exponential growth of big data demands an efficient knowledge discovery. The electronic medical records of patients on medical data Clouds contain implicit medical information. Although the periodic health examination (PHE) reports describing a set of screening tests for healthy individuals performed periodically, common individuals require the assistance of an expert to interpret the results for a medical opinion. This research study proposes a metaphoric design of Electronic Medical Record (EMR) for PHE reports of patients. The outcomes of this study glimpses useful findings for the common people in the self-interpretation of their medical reports. Besides, among a variety of solutions, the study uses the metaphoric representation to convert the numerical data and medical terminology to familiar graphic representations from real life. The study identifies the detailed requirements to propose a conceptual architecture for metaphoric EMR reports. The future work will result in a prototype design, evaluation, and refinement of metaphors based on stakeholders' feedback.
Multi-Branch Neural Architecture Search for Lightweight Image Super-Resolution

Deep convolutional neural networks (CNNs) are widely used to improve the performance of image restoration tasks, including single-image super-resolution (SISR). Generally, researchers are manually designing more complex and deeper CNNs to further increase the given problems’ performance. Instead of this hand-crafted CNN architecture design, neural architecture search (NAS) methods have been developed to find an optimal architecture for a given task automatically. For example, NAS-based SR methods find optimized network connections and operations by reinforcement learning (RL) or evolutionary algorithms (EA). These methods enable finding an optimal system automatically, but most of them need a very long search time. In this paper, we propose a new search method for the SISR that can significantly reduce the overall design time by applying a weight-sharing scheme. We also employ a multi-branch structure to enlarge the search space for capturing multi-scale features, resulting in better reconstruction on the textured region. Experiments show that the proposed method finds an optimal SISR network about twenty times faster than the existing methods, while showing comparable performance in terms of PSNR vs. parameters. Comparison of visual quality validates that the obtained SISR network reconstructs texture areas better than the previous methods because of the enlarged search space to find multi-scale features.
Classically simulating quantum circuits with local depolarizing noise

We study the effect of noise on the classical simulatability of quantum circuits defined by computationally tractable (CT) states and efficiently computable sparse (ECS) operations. Examples of such circuits, which we call CT-ECS circuits, are IQP, Clifford Magic, and conjugated Clifford circuits. This means that there exist various CT-ECS circuits such that their output probability distributions are anti-concentrated and not classically simulatable in the noise-free setting (under plausible assumptions). First, we consider a noise model where a depolarizing channel with an arbitrarily small constant rate is applied to each qubit at the end of computation. We show that, under this noise model, if an approximate value of the noise rate is known, any CT-ECS circuit with an anti-concentrated output probability distribution is classically simulatable. This indicates that the presence of small noise drastically affects the classical simulatability of CT-ECS circuits. Then, we consider an extension of the noise model where the noise rate can vary with each qubit, and provide a similar sufficient condition for classically simulating CT-ECS circuits with anti-concentrated output probability distributions.
Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title

Abstract Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.
OUStralopithecus

In many parts of the world, censors are continuously increasing their capacity to fingerprint, identify, and block censorship resistance tools to maintain control over what can and can not be accessed over the Internet. In response, traffic replacement, which involves co-opting a steady stream of uncensored overt traffic to serve as a perfect cover for censored covert content, has been developed in an effort to provide undetectable access to the open Internet for those in censored regions. Despite the promise of this technique, creating a suitable stream of uncensored overt traffic that is high throughput, fingerprint and identification resistant, and does not overburden the user to generate, is an underexplored area that is critical to traffic replacement's success.
Comparative Analysis of Micro Expression Recognition using Deep Learning and Transfer Learning

Micro Expressions are those involuntary muscular movements of the facial muscles produced in response to a stimulus. They are short-lived expressions that last for anywhere between 0.04 to 0.2 seconds and are extremely subtle in their amplitude. Given their fleeting and elusive nature, it becomes almost impossible to detect these expressions through the naked eye. Recent developments in Deep Learning models have shown great success in efficiently identifying and analyzing Micro Expressions. In this paper, various models have been implemented on the SAMM dataset. The models studied are namely– VGG16, ResNet50, MobileNet, InceptionV3, and Xception. The experimental results have helped us carefully analyze the various metrics related to the models and compare them with each other to ascertain which one outperformed the others and is best suited for real-world applications. The MobileNet model has surpassed all other models in terms of its efficiency with respect to the domain of this paper. It has been able to describe and understand all the information that can be found in the various Micro Expressions.
GCONV Chain: Optimizing the Whole-Life Cost in End-to-end CNN Acceleration

The acceleration of CNNs has gained increasing attention since their success in computer vision. Since the heterogeneous layers cannot be processed by accelerators proposed for convolution layers only, modern end-to-end CNN acceleration solutions either transform diverse computation into matrix/vector arithmetic, which loses data reuse opportunities in convolution, or introduce dedicated functional unit to each kind of layer, which results in underutilization and high update expenses. To enhance the whole-life cost efficiency, we need a solution that is efficient in processing CNN layers and has the generality to apply to all kinds of existing and emerging layers. To this end, we propose GCONV Chain, a method to convert the entire CNN computation into a chain of standard general convolutions (GCONV) that can be efficiently processed by existing CNN accelerators with low-overhead hardware support. This paper comprehensively analyzes the GCONV Chain model and proposes a full-stack implementation to support GCONV Chain. Our results on various CNNs demonstrate that GCONV Chain improves the performance and energy efficiency of existing CNN accelerators by an average of 3.4x and 3.2x respectively. Furthermore, we show that GCONV Chain provides low whole-life costs for CNN acceleration, including both developer efforts and total cost of ownership.
Adaptive Clustering-Based Model Aggregation for Federated Learning with Imbalanced Data

This paper studies the data imbalance issue in federated learning. We propose a new model aggregation method based on adaptive clustering, called weighted clustered federated learning (CFL). At each round of model aggregation, we first cluster the participating edge devices based on the cosine similarity of their local gradients, where the similarity threshold can adapt to the time varying divergence of local gradients. Then we perform weighted per-cluster model aggregation on the edge sever. We analyze the convergence rate of the proposed learning framework with respect to the data balance feature of the training dataset. Based on this convergence analysis, the weights for model aggregation are designed to balance the contribution of each cluster. Experimental results demonstrate that the proposed weighted CFL can achieve faster model convergence rate and higher learning accuracy than existing methods in the scenario with imbalanced data.
QoE-driven Mobile 360 Video Streaming: Predictive View Generation and Dynamic Tile Selection

In mobile video streaming, 360-degree videos can provide users with immersive and memorable experience. Due to the panoramic and high resolution features, limited bandwidth and stringent latency requirements, the transmission of full high-definition 360-degree video may cause severe stalling, significantly lowering the users' quality of experience (QoE). As the video content seen by the user largely relies on the user's viewing direction and the size of field of view, in this paper, we investigate viewpoint prediction and dynamic tile selection to improve users' QoE for mobile 360-degree video streaming. Specifically, we first design a recurrent neural network integrated with attention mechanism to predict the user's viewpoint in the next video segment. We then propose a dynamic tile-selection method which selects and transmits the tiles that are most likely to be viewed in a segment through online learning. Experimental results based on a real-world dataset show that, the proposed viewpoint prediction neural network and dynamic tile selection method can effectively improve the prediction accuracy and improve the users' QoE.
Causal inference via string diagram surgery

Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.
Development of machine learning models based on air temperature for estimation of global solar radiation in India

Abstract The designing of solar thermal systems need accurate information on global solar radiation (GSR). In the present study, six machine learning models, for example, random forest, k‐nearest neighbors, Gaussian process regression, support vector machine, multilayer perception, and XGBoost, are developed for GSR prediction with only air temperature as input for different climatic zones of India. The performance of machine learning models is also compared with some well‐known empirical models. The results show that generally, the performance of the machine learning models is better than empirical models, though, for a few climatic zones, empirical models give a better prediction. The top‐performing models are k‐nearest neighbors and XGBoost. Thus, we highly recommend temperature‐based models to predict GSR in the regions of India where only air temperature data are available. The accurate information of future GSR can be easily obtained by combining future air temperature forecasts with KNN/XGBoost models. These models can be extremely helpful in designing solar thermal systems in those regions where solar radiation facility is not available.
Assessing Collaborative Explanations of AI using Explanation Goodness Criteria

Explainable AI represents an increasingly important category of systems that attempt to support human understanding and trust in machine intelligence and automation. Typical systems rely on algorithms to help understand underlying information about decisions and establish justified trust and reliance. Researchers have proposed using goodness criteria to measure the quality of explanations as a formative evaluation of an XAI system, but these criteria have not been systematically investigated in the literature. To explore this, we present a novel collaborative explanation system (CXAI) and propose several goodness criteria to evaluate the quality of its explanations. Results suggest that the explanations provided by this system are typically correct, informative, written in understandable ways, and focus on explanation of larger scale data patterns than are typically generated by algorithmic XAI systems. Implications for how these criteria may be applied to other XAI systems are discussed.
Long Text Classification Based on BERT

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models.
Profit Maximization in a Federated Cloud by Optimal Workload Management and Server Speed Setting

In an environment with multiple heterogeneous multiservers across multiple clouds and data centers, optimal workload management and server speed setting makes strong impact on the aggregated performance, cost, and profit of a cloud service provider. Such a situation provides us a more challenging opportunity to address and discuss profit maximization of a service provider in a wider scale and to generate more significant influence. In this paper, we investigate profit maximization by optimal workload management and server speed setting for multiple heterogeneous multiservers in a federated cloud or a geo-distributed data center. The heterogeneous multiservers have different sizes, speeds, power consumption models, workload, performance, costs, and profit. To conduct rigorous study, each multiserver system is modeled by an M/M/m queueing system, such that the profit of a multiserver system can be characterized analytically. We address two problems, i.e., workload management without server speed setting and workload management with server speed setting. Both problems are formulated as multi-variable optimization problems. We develop numerical algorithms to solve these problems. We also provide numerical data for the purpose of illustration. To the best of the author's knowledge, this is the first paper which analytically discusses profit maximization for multiple heterogeneous multiservers in a federated cloud or a geo-distributed data center using queueing systems.
Residual-guided In-loop Filter Using Convolution Neural Network

The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, and so on. To compensate for those artifacts, extensive filtering techniques were proposed in the loop of video codecs, which are capable of boosting the subjective and objective qualities of reconstructed videos. Recently, neural network-based filters were presented with the power of deep learning from a large magnitude of data. Though the coding efficiency has been improved from traditional methods in High-Efficiency Video Coding (HEVC), the rich features and information generated by the compression pipeline have not been fully utilized in the design of neural networks. Therefore, in this article, we propose the Residual-Reconstruction-based Convolutional Neural Network (RRNet) to further improve the coding efficiency to its full extent, where the compression features induced from bitstream in form of prediction residual are fed into the network as an additional input to the reconstructed frame. In essence, the residual signal can provide valuable information about block partitions and can aid reconstruction of edge and texture regions in a picture. Thus, more adaptive parameters can be trained to handle different texture characteristics. The experimental results show that our proposed RRNet approach presents significant BD-rate savings compared to HEVC and the state-of-the-art CNN-based schemes, indicating that residual signal plays a significant role in enhancing video frame reconstruction.
Enhanced Cognitive Training using Virtual Reality: Examining a Memory Task Modified for Use in Virtual Environments

Head-mounted display (HMD) based cognitive training enables a number of unique visual features, including 3D depth and immersive visuals. We examine the impact of these features by measuring task performance and EEG power with a cognitive task that has been modified for use in virtual environments. Results were then compared with those obtained using an unmodified version of the task. Some modified versions of the task resulted in increased cognitive load, but the differences did not correlate with changes in task performance. The increased EEG power observed may therefore only reflect changes at the perceptual level, rather than task-contingent cognitive function. On the whole, these results suggest that adapting a standard cognitive training task to a virtual environment in order to take advantage of the inherent benefits of an HMD should pose few if any problems.