Reviewerly/exHarmony huggingface dataset:
Weaponized Skepticism

Since at least 2016, many have worried that social media enables authoritarians to meddle in democratic politics. The concern is that trolls and bots amplify deceptive content. This chapter argues that these tactics have a more insidious anti-democratic purpose. Lies implanted in democratic discourse by authoritarians are often intended to be caught. Their primary goal is not to successfully deceive, but rather to undermine the democratic value of testimony. In well-functioning democracies, our mutual reliance on testimony also generates a distinctively democratic value: decentralized testimonial networks evade control by the state or powerful actors. The deliberate exposure of deception in democratic testimonial networks undermines this value by implicating citizens in their own epistemic corruption, weakening the resilience of democratic society against authoritarian pressure. This chapter illustrates that danger through a close reading of recent Russian social media interference operations, showing both their epistemic underpinnings and their ongoing political threat.
Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm

Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.
A-ViT: Adaptive Tokens for Efficient Vision Transformer

We introduce A - ViT, a method that adaptively adjusts the inference cost of vision transformer (ViT) for images of different complexity. A - ViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds. We refor-mulate Adaptive Computation Time (ACT [17]) for this task, extending halting to discard redundant spatial tokens. The appealing architectural properties of vision transformers enables our adaptive token reduction mechanism to speed up inference without modifying the network architecture or inference hardware. We demonstrate that A - ViT requires no extra parameters or sub-network for halting, as we base the learning of adaptive halting on the original network parameters. We further introduce distributional prior regularization that stabilizes training compared to prior ACT approaches. On the image classification task (ImageNet1K), we show that our proposed A - ViT yields high efficacy in filtering informative spatial features and cutting down on the overall compute. The proposed method improves the throughput of DeiT-Tiny by 62% and DeiT-Small by 38% with only 0.3% accuracy drop, outperforming prior art by a large margin.
Depth Super-Resolution via Joint Color-Guided Internal and External Regularizations

Depth information is being widely used in many real-world applications. However, due to the limitation of depth sensing technology, the captured depth map in practice usually has much lower resolution than that of color image counterpart. In this paper, we propose to combine the internal smoothness prior and external gradient consistency constraint in graph domain for depth super-resolution. On one hand, a new graph Laplacian regularizer is proposed to preserve the inherent piecewise smooth characteristic of depth, which has desirable filtering properties. A specific weight matrix of the respect graph is defined to make full use of information of both depth and the corresponding guidance image. On the other hand, inspired by an observation that the gradient of depth is small except at edge separating regions, we introduce a graph gradient consistency constraint to enforce that the graph gradient of depth is close to the thresholded gradient of guidance. We reinterpret the gradient thresholding model as variational optimization with sparsity constraint. In this way, we remedy the problem of structure discrepancy between depth and guidance. Finally, the internal and external regularizations are casted into a unified optimization framework, which can be efficiently addressed by ADMM. Experimental results demonstrate that our method outperforms the state-of-the-art with respect to both objective and subjective quality evaluations.
Convergence of Gradient Descent on Separable Data

We provide a detailed study on the implicit bias of gradient descent when optimizing loss functions with strictly monotone tails, such as the logistic loss, over separable datasets. We look at two basic questions: (a) what are the conditions on the tail of the loss function under which gradient descent converges in the direction of the $L_2$ maximum-margin separator? (b) how does the rate of margin convergence depend on the tail of the loss function and the choice of the step size? We show that for a large family of super-polynomial tailed losses, gradient descent iterates on linear networks of any depth converge in the direction of $L_2$ maximum-margin solution, while this does not hold for losses with heavier tails. Within this family, for simple linear models we show that the optimal rates with fixed step size is indeed obtained for the commonly used exponentially tailed losses such as logistic loss. However, with a fixed step size the optimal convergence rate is extremely slow as $1/\log(t)$, as also proved in Soudry et al (2018). For linear models with exponential loss, we further prove that the convergence rate could be improved to $\log (t) /\sqrt{t}$ by using aggressive step sizes that compensates for the rapidly vanishing gradients. Numerical results suggest this method might be useful for deep networks.
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning

The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to competitive video games. We propose a new benchmark - Obstacle Tower: a high fidelity, 3D, 3rd person, procedurally generated environment. An agent in Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment. In this paper we outline the environment and provide a set of baseline results produced by current state-of-the-art Deep RL methods as well as human players. These algorithms fail to produce agents capable of performing near human level.
Digital Forensics Architecture for Evidence Collection and Provenance Preservation in IaaS Cloud Environment Using SDN and Blockchain Technology

Cloud forensics is an intelligent evolution of digital forensics that defends against cyber-crimes. However, centralized evidence collection and preservation minimizes the reliability of digital evidence. To resolve this severe problem, this paper proposes a novel digital forensic architecture using fast-growing Software-Defined Networking (SDN) and Blockchain technology for Infrastructure-as-a-Service (IaaS) cloud. In this proposed forensic architecture, the evidence is collected and preserved in the blockchain that is distributed among multiple peers. To protect the system from unauthorized users, Secure Ring Verification based Authentication (SRVA) scheme is proposed. To strengthen the cloud environment, secret keys are generated optimally by using Harmony Search Optimization (HSO) algorithm. All data are encrypted based on the sensitivity level and stored in the cloud server. For encryption, Sensitivity Aware Deep Elliptic Curve Cryptography (SA-DECC) algorithm is presented. For every data stored in the cloud, a block is created in the SDN controller and the history of data is recorded as metadata. In each block, the Merkle hash tree is built by using Secure Hashing Algorithm-3 (SHA-3). Our system allows users to trace their data by deploying Fuzzy based Smart Contracts (FCS). Finally, evidence analysis is enabled by constructing Logical Graph of Evidence (LGoE) collected from the blockchain. Experiments are conducted in an integrated environment of java (for cloud and blockchain) and network simulator-3.26 (for SDN). The extensive analysis shows that proposed forensic architecture shows promising results in Response time, Evidence insertion time, Evidence verification time, Communication overhead, Hash computation time, Key generation time, Encryption time, Decryption time and total change rate.
doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset

We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that corresponds to higher-level relations across text sections as well as lower-level relations between discourse units within a section.Then we present these dialogue flows to crowd contributors to create conversational utterances.The dataset includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains.Compared to the prior document-grounded dialogue datasets, this dataset covers a variety of dialogue scenes in information-seeking conversations.For evaluating the versatility of the dataset, we introduce multiple dialogue modeling tasks and present baseline approaches.A9: Would you like to find out whether you are eligible?U10: That's exactly why I contact again!A11: Were there any damages to your clothes that were caused by prosthetic or orthopedic device or your skin medicine?U12: The latter happened.A13: Sorry to hear that.And Is it true that you need this device or skin medicine because of an injury or illness
Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram

In recent years, deep learning techniques are employed in the mammography processing field to reduce radiologists' costs.Existing breast mass classification systems are implemented using deep learning technologies such as a Convolutional Neural Network (CNN).CNN based systems have attained higher performance than the machine learning-based systems in the classification task of mammography images, but a few issues still exist.Some of these issues are; ignorance of semantic features, analysis limitation to the current patch of images, lost patches in less contrast mammography images, and ambiguity in segmentation.These issues lead to increased false information about patches of mammography image, computational cost, decisions based on current patches, and not recovering the variance of patches intensity.In turn, breast mass classification systems based on convolutional neural networks produced unsatisfactory classification accuracy.To resolve these issues and improve the accuracy of classification on low contrast images, we propose a novel Breast Mass Classification system named BMC.It has improved architecture based on a combination of k-mean clustering, Long Short-Term Memory network of Recurrent Neural Network (RNN), CNN, random forest, boosting techniques to classify the breast mass into benign, malignant, and normal.Further, the proposed BMC system is compared with existing classification systems using two publicly available datasets of mammographic images.Proposed BMC system achieves the sensitivity, specificity, F-measure, and accuracy for the DDSM dataset is 0.97%, 0.98%,0.97%,0.96% and for the MIAS dataset is 0.97%, 0.97%,0.98%,and 0.95% respectively.Further Area Under Curve (AUC) rate of the proposed BMC system lies between 0.94% -0.97% for DDSM and 0.94%-0.98%for the MIAS dataset.The BMC method worked comparably better than other mammography classification schemes that have previously been invented.Moreover, the Confidence interval statistical test is also employed to determine the classification accuracy of the BMC system using different configurations and neural network parameters.
Security and Privacy in Cloud-Based E-Health System

Cloud based healthcare computing have changed the face of healthcare in many ways. The main advantages of cloud computing in healthcare are scalability of the required service and the provision to upscale or downsize the data storge, collaborating Artificial Intelligence (AI) and machine learning. The current paper examined various research studies to explore the utilization of intelligent techniques in health systems and mainly focused into the security and privacy issues in the current technologies. Despite the various benefits related to cloud-computing applications for healthcare, there are different types of management, technology handling, security measures, and legal issues to be considered and addressed. The key focus of this paper is to address the increased demand for cloud computing and its definition, technologies widely used in healthcare, their problems and possibilities, and the way protection mechanisms are organized and prepared when the company chooses to implement the latest evolving service model. In this paper, we focused on a thorough review of current and existing literature on different approaches and mechanisms used in e-Health to deal with security and privacy issues. Some of these approaches have strengths and weaknesses. After selecting original articles, the literature review was carried out, and we identified several models adopted in their solutions. We arrived at the reviewed articles after comparing the models used.
The Third DIHARD Diarization Challenge

DIHARD III was the third in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variability in recording equipment, noise conditions, and conversational domain. Speaker diarization was evaluated under two speech activity conditions (diarization from a reference speech activity vs. diarization from scratch) and 11 diverse domains. The domains span a range of recording conditions and interaction types, including read audio-books, meeting speech, clinical interviews, web videos, and, for the first time, conversational telephone speech. A total of 30 organizations (forming 21teams) from industry and academia submitted 499 valid system outputs. The evaluation results indicate that speaker diarization has improved markedly since DIHARD I, particularly for two-party interactions, but that for many domains (e.g., web video) the problem remains far from solved.
Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.
Parallel Implementation of Reinforcement Learning Q-Learning Technique for FPGA

Q-learning is an off-policy reinforcement learning technique, which has the main advantage of obtaining an optimal policy interacting with an unknown model environment. This paper proposes a parallel fixed-point Q-learning algorithm architecture implemented on field programmable gate arrays (FPGA) focusing on optimizing the system processing time. The convergence results are presented, and the processing time and occupied area were analyzed for different states and actions sizes scenarios and various fixed-point formats. The studies concerning the accuracy of the Q-learning technique response and resolution error associated with a decrease in the number of bits were also carried out for hardware implementation. The architecture implementation details were featured. The entire project was developed using the system generator platform (Xilinx), with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.
Deterministic Variational Inference for Robust Bayesian Neural Networks

Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate good predictive performance over alternative approaches.
Mining spatiotemporal association patterns from complex geographic phenomena

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.
Imbalanced Hyperspectral Image Classification With an Adaptive Ensemble Method Based on SMOTE and Rotation Forest With Differentiated Sampling Rates

Rotation forest (RoF) is a powerful ensemble classifier and has been demonstrated the outstanding performance in hyperspectral data classification. However, the classification task suffers from the class imbalanced problem which has been considered to be one of the most important challenges. The traditional construction method of RoF biases classifying the majority classes and ignores recognizing the minority classes samples. This letter proposes a novel adaptive ensemble method based on SMOTE and RoF with differentiated sampling rates (AdaSRoF) for the multiclass imbalance problem. The proposed method adaptively generates several balanced data sets with more diversity and less noise by using SMOTE and a dynamic data sampling ratio for base classifiers. The obtained results on two publicly available hyperspectral images show that the proposed method can get more diversity and better performance than support vector machine (SVM), random forest (RF), and RoF in multiclass imbalance learning.
Stylized Aesthetic QR Code

With the continued proliferation of smart mobile devices, the Quick Response (QR) code has become one of the most-used types of two-dimensional code in the world. Aiming at beautifying the visual-unpleasant appearance of QR codes, existing works have developed a series of techniques. However, these works still leave much to be desired, such as personalization, artistry, and robustness. To address these issues, in this paper, we propose a novel type of aesthetic QR codes, Stylized aEsthEtic (SEE) QR code , and a three-stage approach to automatically produce such robust style-oriented codes. Specifically, in the first stage, we propose a method to generate an optimized baseline aesthetic QR code, which reduces the visual contrast between the noise-like black/white modules and the blended image. In the second stage, to obtain an art style QR code, we tailor an appropriate neural style transformation network to endow the baseline aesthetic QR code with artistic elements. In the third stage, we design a module-based robustness-optimization mechanism to ensure the performance robust by balancing two competing terms: visual quality and readability. Extensive experiments demonstrate that the SEE QR code has high quality in terms of both visual appearance and robustness and also offers a greater variety of personalized choices to users.
DeepMDP: Learning Continuous Latent Space Models for Representation Learning

Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process, we introduce the concept of a DeepMDP, a parameterized latent space model that is trained via the minimization of two tractable losses: prediction of rewards and prediction of the distribution over next latent states. We show that the optimization of these objectives guarantees (1) the quality of the latent space as a representation of the state space and (2) the quality of the DeepMDP as a model of the environment. We connect these results to prior work in the bisimulation literature, and explore the use of a variety of metrics. Our theoretical findings are substantiated by the experimental result that a trained DeepMDP recovers the latent structure underlying high-dimensional observations on a synthetic environment. Finally, we show that learning a DeepMDP as an auxiliary task in the Atari 2600 domain leads to large performance improvements over model-free RL.