Reviewerly/exHarmony huggingface dataset:
Generation of tripartite Einstein–Podolsky–Rosen steering by cascaded nonlinear process*

A scheme is proposed to generate genuine tripartite Einstein–Podolsky–Rosen (EPR) steering in cascaded nonlinear process of the fourth-harmonic generation. The second-harmonic is generated by the first double-frequency process in an optical superlattice. Then, the fourth-harmonic is produced by the second cascaded double-frequency process through quasi-phase-matching technique in the same optical superlattice. The genuine tripartite EPR steering among the pump, the second-harmonic, and the fourth-harmonic beams can be obtained by this cascaded nonlinear process according to a criterion for genuine multipartite quantum steering. The quantum steering properties are discussed by adjusting the parameters related to the cascaded nonlinear system. The present research provides a reference scheme and data for obtaining good multipartite EPR steering in experiment and can advance the applications of quantum steering in the quantum information processing.
Learning to Detect Online Harassment on Twitter with the Transformer

This paper describes our submission to the SIMAH challenge (SocIaL Media And Harassment). The proposed competition addresses the challenge of harassment detection on Twitter posts as well as the identification of a harassment category. Automatically detecting content containing harassment could be the basis for removing it. Accordingly, the task is considered to be an essential step to distinguishing different types of harassment provides the means to control such a mechanism in a fine-grained way. In this work, we classify a set of Twitter posts into non-harassment or harassment tweets where the last ones are classified as indirect harassment, sexual harassment, or physical harassment. We explore how to use self-attention models for harassment classification in order to combine different baselines’ outputs. For a given post, we use the transformer architecture to encode each baseline output exploiting relationships between baselines and posts. Then, the transformer learns how to combine the outputs of these methods with a BERT representation of the post, reaching a macro-averaged F-score of 0.481 on the SIMAH test set.
Talek: Private Group Messaging with Hidden Access Patterns

Talek is a private group messaging system that sends messages through potentially untrustworthy servers, while hiding both data content and the communication patterns among its users. Talek explores a new point in the design space of private messaging; it guarantees access sequence indistinguishability, which is among the strongest guarantees in the space, while assuming an anytrust threat model, which is only slightly weaker than the strongest threat model currently found in related work. Our results suggest that this is a pragmatic point in the design space, since it supports strong privacy and good performance: we demonstrate a 3-server Talek cluster that achieves throughput of 9,433 messages/second for 32,000 active users with 1.7-second end-to-end latency. To achieve its security goals without coordination between clients, Talek relies on information-theoretic private information retrieval. To achieve good performance and minimize server-side storage, Talek introduces new techniques and optimizations that may be of independent interest, e.g., a novel use of blocked cuckoo hashing and support for private notifications. The latter provide a private, efficient mechanism for users to learn, without polling, which logs have new messages.
Characterization of ‘green’ composite laminates after flexural tests by speckle interferometry

For many years, the use of advanced composite materials in aeronautics, automotive, and sports applications has been well established. The characteristics of composite materials in terms of weight reduction, fatigue, and corrosion resistance make them competitive in many cases with respect to conventional ones (i.e., metal alloys). On the other hand, the fabrication process of the most employed composites reinforced by carbon or glass fibers requires complex steps that are not always environmentally friendly. In fact, such composite materials are not themselves “green.” For these reasons, in the last decades, the use of natural reinforcing fibers has gained increasing attention, allowing the development of new materials sharing the same advantages with conventional composite systems while respecting the environment. Due to their structural complexity, these materials are not always compatible with the use of standard nondestructive evaluation methods, e.g., ultrasounds testing. The effective use of speckle holographic techniques as nondestructive evaluation methods in full field and noncontact modality is proved on “green” composite materials. In particular, electronic speckle pattern interferometry is tested on different kinds of specimens preliminarily subjected to flexural tests. Results show that, in most cases, the damage appears more severe on the back side of specimens, opposite to the loaded one, with the sole exception of basalt laminates.
The Ekiden Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts

Although smart contracts inherit the availability and other security assurances of the blockchain, they are impeded by lack of confidentiality and poor performance. We present Ekiden, a system that aims to close these critical gaps by combining the blockchain with trusted execution environments.
Inference with Aggregate Data: An Optimal Transport Approach

We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief propagation type algorithm over tree-structured graphs with polynomial computational complexity as well as a global convergence guarantee. This is in contrast to previous methods that either exhibit prohibitive complexity as the population grows or do not guarantee convergence. Our method is based on optimal transport, or more specifically, multi-marginal optimal transport theory. In particular, we consider an inference problem with aggregate observations, that can be seen as a structured multi-marginal optimal transport problem where the cost function decomposes according to the underlying graph. Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP). We further specialize the SBP algorithm to cases associated with hidden Markov models due to their significance in control and estimation. We demonstrate the performance of our algorithm on applications such as inferring population flow from aggregate observations. We also show that in the special case where the observations are generated by a single individual, our algorithm naturally reduces to the standard belief propagation algorithm.
EFFECTIVE TUNING OF MEMBERSHIP FUNCTION PARAMETERS IN FUZZY SYSTEMS BASED ON MULTI-VALUED INTERVAL LOGIC

The problem of increasing the efficiency of decision making regarding the state of a biophysical object is solved with the use of the computing intelligence apparatus. The architecture of the tools is proposed, which is based on the principles of an advanced method of tuning the parameters of the membership function, taking into account the uncertainty or lack of data, as well as the features and limitations of the subject area of application. The developed tools make it possible to generate and evaluate several alternatives in the solution space, to treat contradictions in the registered data. Software applications for setting the parameters of membership functions with the introduction in the medical field have been developed by means of object-oriented programming. Laboratory operation of the proposed tools confirmed the increase in the reliability of decision-making up to 20%.
Research on Application Strategy of Deep Learning of Internet of Things Based on Edge Computing Optimization Method

Abstract Due to the limitations of network performance IoT deep learning model, in order to optimize the performance of IoT network, this paper proposes an application strategy of IoT deep learning based on edge computing optimization method. Based on the multi-layered structure of learning, the edge calculation method is optimized, and the reduced intermediate data is uploaded at the edge node, thereby reducing the network traffic from the IoT device to the cloud server. On the basis of considering the limitation of edge node service capability in the edge computing process, the optimal strategy of task offload scheduling is formulated to improve the performance of the IoT deep learning model based on edge computing. The experimental results show that the IoT deep learning application strategy based on the edge computing optimization method can efficiently execute multiple deep learning tasks in the edge computing environment, which is superior to other algorithms.
Exploiting Buffered Updates for Fast Streaming Graph Analysis

Streaming graph analysis extracts timely insights from evolving graphs, and has gained increasing popularity. In current practice of streaming graph analysis, incoming updates are simply cached in a buffer, until being applied onto existing graph structure to construct a new snapshot. Graph algorithms then work on the new snapshot to produce up-to-date analysis result. Nevertheless, we find that for widely used monotonic graph algorithms, the analysis process can be accelerated by preprocessing buffered updates. To this end, we propose GraPU, a streaming graph analytics system for monotonic graph algorithms. Before applying updates, GraPU preprocesses buffered updates in three consecutive stages: 1) Components-based Classification first identifies the effective graph data that are actually affected by current updates, by classifying the vertices involved in buffered updates according to the predetermined connected components in underlying graph; 2) In-buffer Precomputation generates the safe and profitable intermediate values that can be later merged onto underlying graph to facilitate convergence on new snapshots, by precomputing the values of vertices involved in buffered updates; 3) Hub-vertices Division eliminates the vertex-level load imbalance for analysis on new snapshots, by automatically identifying the high-degree vertices involved in updates and efficiently distributing their high-cost computation over multiple machines. After buffered updates are applied, GraPU calculates vertex values in new snapshots using the subgraph-centric model. GraPU further presents Load-factors Guided Balancing to achieve load balance at subgraph-level, by reassigning some vertices and edges among subgraphs beforehand. Our experimental result shows that, GraPU outperforms state-of-the-art KineoGraph by up to 20.43x.
Breast Cancer Recurrence Prediction using Machine Learning

The most common cancer among women is breast cancer. Around 12% of women are affected by it all over the world. Recurrent breast cancer is a term used for breast cancer which returns even after a successful treatment. This research aims to use Machine learning to detect and predict the recurrence of breast cancer; and compare all the models by using different metrics like accuracy, precision, etc. The models built can help predict the recurrence of breast cancer effectively. All the models are built using the Wisconsin Prognostic Breast Cancer Dataset(WPBC). The models built are Multiple Linear Regression, Support Vector Machine, which was build by using RBF Kernel and Leave-One-Out(K-fold Cross-Validation) and Decision Tree using metrics like Gini Index, Entropy and Information Gain. Support Vector Machine and K-fold Cross-Validation gave the best results for recurrence and non-recurrence predictions.
Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis

Detecting associations between microbial compositions and sample characteristics is one of the most important tasks in microbiome studies. Most of the existing methods apply univariate models to single microbial species separately, with adjustments for multiple hypothesis testing. We propose a Bayesian analysis for a generalized mixed effects linear model tailored to this application. The marginal prior on each microbial composition is a Dirichlet Process, and dependence across compositions is induced through a linear combination of individual covariates, such as disease biomarkers or the subject's age, and latent factors. The latent factors capture residual variability and their dimensionality is learned from the data in a fully Bayesian procedure. The proposed model is tested in data analyses and simulation studies with zero-inflated compositions. In these settings, within each sample, a large proportion of counts per microbial species are equal to zero. In our Bayesian model a priori the probability of compositions with absent microbial species is strictly positive. We propose an efficient algorithm to sample from the posterior and visualizations of model parameters which reveal associations between covariates and microbial compositions. We evaluate the proposed method in simulation studies, and then analyze a microbiome dataset for infants with type 1 diabetes which contains a large proportion of zeros in the sample-specific microbial compositions.
Task scheduling in Cloud Computing Based on Improved Discrete Particle Swarm Optimization

Cloud computing is a technology that allows users to pay as the demand and offers various kind of services just like using water, and how to efficiently schedule the tasks is an important aspect in cloud. In this paper, an improved discrete particle swarm optimization algorithm abbreviated as IDPSO is implemented in the cloud simulation platform Cloudsim. IDPSO uses a dynamic inertia weight optimization method based on sinusoidal strategy to make particles adaptive to different stages in the process of searching for global optimal solution. The simulation results show that the proposed algorithm outperforms Discrete Particle Swarm Optimization(DPSO) and the basic scheduling algorithm First Come First Serve(FCFS) in terms of completion time and converges better than DPSO.
Path planning and trajectroy tracking of a mobile robot using bio-inspired optimization algorithms and PID control

Path planning and trajectory tacking are the fundamental task in mobile robotic science, and they enable the robot to navigate autonomously. In this work, the path planning task is carried out using three bio-inspired optimization algorithms, including PSO, ABC and FA. The duty of the algorithms is to determine a collision-free path through fixed obstacles in the working environment. The maximum speed of the robot is applied to the optimization problem as a constraint. In order to evaluate the performance of the algorithms, four workspaces with different obstacle layout are simulated in MATLAB, and the quality of path planning task is analyzed statistically and numerically, considering four different criteria, including, convergency quality, convergency time, path length and success rate. In the next step, a control model is designed to track the path curve determined by the path planning algorithms. A PID-based control structure is simulated in MATLAB Simulink and the controller was able to track the pre-determined traj ectories with proper approximation. The controller is applied on a dynamic model of a two-wheeled mobile robot offered by [1]. In order to validate the control inputs it is necessary to apply them on a real platform. The experimental study is implemented on a two-wheeled mobile robot which is designed and built based on the authors' previous paper [2] in various enverioment and obstacles. The result shows control inputs were applied to the real robot and the robot was able to imitate the applied path curve, and find its way toward the target point without colliding obstacles in real and simulation task.
Recent Trends in Natural Language Understanding for Procedural Knowledge

In the last decade, there has been a surge of work contributing to commonsense reasoning and language understanding. Several datasets and knowledge representations for question answering, coreference resolution, and inference tasks have been discussed and built upon in the NLP community. However, these focus on declarative knowledge, or factual knowledge describing an entity or event. In contrast, a large body of research that has not been discussed, is reasoning and language understanding with procedural/how-to knowledge, or the knowledge used in completing a task or process (e.g., navigation instructions, recipes, repair guides, etc.). Several procedural knowledge bases, benchmark datasets, and information extraction methods, have enabled the various natural language understanding tasks with procedural knowledge (also known as procedural knowledge understanding). This paper seeks to provide an overview of the work in procedural knowledge understanding, and information extraction, acquisition, and representation with procedures, to promote discussion and provide a better understanding of procedural knowledge applications and future challenges.
A High Capacity Reversible Data Hiding Technique Based on Pixel Value Ordering Using Interlock Partitioning

Data hiding is a branch of information security that is popularly used for digital watermarking and steganography. Traditional data hiding methods are not suitable for some information sensitive domains such as medical imaging, biometric data protection, military etc. because they can permanently damage the cover image. These fields do not tolerate the cover image to be modified even slightly. There is a need of reversible data hiding in such domains that allows to completely recover the cover-image after the retrieval of secret message. Pixel Value Ordering (PVO) for hiding data reversibly has gained much popularity because it maintains high quality of the resultant stego image. Improved Pixel Value Ordering (IPVO), an extension of PVO, is a high-fidelity method that uses sorting and Prediction Error Expansion(PEE) for hiding data. In IPVO, the cover-image is segregated to form segments of fixed size. These segments, called blocks, are sorted in increasing order of pixel intensities. Then, the prediction error for the largest and the smallest pixels are computed using the second-maximal and the second-minimal pixels, respectively. Thus, the largest-valued and the minimumvalued pixels are utilized to embed data. Each block can embed at most two bits. The data carrying capacity is constrained by the number of blocks. In IPVO, the blocks that are already sorted may produce prediction error of -1 that is not utilized for data embedding. IPVO only makes use of prediction errors 0 and 1, and the error -1 is ignored. In this paper, an improvement of the existing IPVO method is proposed. The proposed method makes use of a novel block partitioning scheme in such a way that a block consists of three pixels. Moreover, the proposed method utilizes a modified IPVO strategy to utilize the prediction error of -1 to hide the data. Hence, the proposed method achieves a high data carrying capacity when compared to the other unconventional methods that use PVO.
Cyber resilience using autonomous agents and reinforcement learning

Cyber resilience usually refers to the ability of an entity to detect, respond to, and recover from cybersecurity attacks to the extent that the entity can continuously deliver the intended outcome despite their presence. This paper presents a method and system for providing cyber resilience by integrating autonomous adversary and defender agents, deep reinforcement learning, and graph thinking. Specifically, the proposed cyber resilience system first predicts the current and future adversary activities and then provides an automated critical asset protection and recovery by enabling agents to take appropriate reactive and pro-active actions for preventing and mitigating adversary activities. In particular, the automated cyber resilience system’s adversary agent makes it possible for cybersecurity adversary activities, patterns, and intentions to be identified and tracked more accurately and dynamically, based on the preprocessed cybersecurity measurements and observations. The automated system’s defender agent is designed to determine and execute cost-effective defensive actions against the adversary activities and intentions predicted by the adversary agent. The game of these adversary and defender agents employ deep reinforcement learning to play a zero-sum observations-aware stochastic game. The experiment results show that the agents perform their tasks efficiently, as the adversary agent is dynamically provided with the input data of infected asset predictions.
A Study of Optimizing Search Engine Results Through User Interaction

In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results.
A Survey on Arabic Handwritten Character Recognition

There are much heavy studies on handwritten character recognition (HCR) for nearly previous four decades. The research on some of the common script like Arabic, Indian and Chinese has been done. This manuscript presents a survey of character recognition on Arabic script, and most of the popular published paper methods are summarized and also analyzed different methods for building a robust system of HCR and included some future research on recognition direction of handwritten character. The paper analyzed and presented various algorithms with respect to preprocessing methods, segmentation methods, feature extraction methods and various classification approaches of the Arabic character recognition.
Detecting Deep-Fake Videos from Appearance and Behavior

Synthetically-generated audios and videos -- so-called deep fakes -- continue to capture the imagination of the computer-graphics and computer-vision communities. At the same time, the democratization of access to technology that can create sophisticated manipulated video of anybody saying anything continues to be of concern because of its power to disrupt democratic elections, commit small to large-scale fraud, fuel dis-information campaigns, and create non-consensual pornography. We describe a biometric-based forensic technique for detecting face-swap deep fakes. This technique combines a static biometric based on facial recognition with a temporal, behavioral biometric based on facial expressions and head movements, where the behavioral embedding is learned using a CNN with a metric-learning objective function. We show the efficacy of this approach across several large-scale video datasets, as well as in-the-wild deep fakes.