Reviewerly/exHarmony huggingface dataset:
Heterogeneous deniable authenticated encryption for location-based services

The location-based services can provide users with the requested location information. But users also need to disclose their current location to the location-based service provider. Therefore, how to protect user’s location privacy is a major concern. In this paper, we propose a heterogeneous deniable authenticated encryption scheme called HDAE for location-based services. The proposed scheme permits a sender in a public key infrastructure environment to transmit a message to a receiver in an identity-based environment. Our design utilizes a hybrid encryption method combing the tag-key encapsulation mechanism (tag-KEM) and the data encapsulation mechanism (DEM), which is well adopted for location-based services applications. We give how to design an HDAE scheme utilizing a heterogeneous deniable authenticated tag-KEM (HDATK) and a DEM. We also construct an HDATK scheme and provide security proof in the random oracle model. Comprehensive analysis shows that our scheme is efficient and secure. In addition, we give an application of the HDAE to a location-based services system.
Bayesian networks: regenerative Gibbs samplings

Gibbs samplings is a Markov Chain Monte Carlo technique for estimating conditional probabilities in Bayesian networks. A major problem of Gibbs sampling is the dependency of the generated chain of samples. Thus the estimates are biased unless the initial value of the chain is drawn from the target distribution. One elegant method to overcome the initial bias is regenerative samplings. We reported elsewhere the “stationary minorization condition” that makes any Markov Chain Monte Carlo technique regenerative. In this paper, we show how this condition can be easily met in the simulations of any Bayesian network.
Traffic Classification of QoS Types Based on Machine Learning Combined with IP Query and Deep Packet Inspection

Due to many network services using encryption protocols, it poses a massive challenge to accurately identify traffic and provide quality of service (QoS). Although deep packet inspection (DPI) technology can identify most traffic, there are still many disadvantages to identifying encrypted traffic. In this paper, we apply the IP (Internet Protocol) autonomous system information query with machine learning (ML) and deep packet detection technology for traffic identification. By combining these methods, it can quickly provide good QoS for different types of encrypted traffic. The experimental results show that different encrypted traffic can be effectively identified. The services with different QoS requirements can be effectively identified even if they use the same protocol.
A Comparative Analysis of Temporal Long Text Similarity: Application to Financial Documents

Temporal text documents exist in many real-world domains. These may span over long periods of time during which there tend to be many variations in the text. In particular, variations or the similarities in a pair of documents over two consecutive years could be meaningful. Most of the textual analysis work like text classification focuses on the entire text snippet as a data instance. It is therefore important to study such similarities besides the entire text document. In Natural Language Processing (NLP), the task of textual similarity is important for search and query retrieval. This task is also better known as Semantic Textual Similarity (STS) that aims to capture the semantics of two texts while comparing them. Also, state-of-the-art methods predominantly target short texts. Thus, measuring the semantic similarity between a pair of long texts is still a challenge. In this paper, we compare different text matching methods for the documents over two consecutive years. We focus on their similarities for our comparative analysis and evaluation of financial documents, namely public 10-K filings to the SEC (Securities and Exchange Commission). We further perform textual regression analysis on six quantitative bank variables including Return on Assets (ROA), Earnings per Share (EPS), Tobin's Q Ratio, Tier 1 Capital Ratio, Leverage Ratio, and Z-score, and show that textual features can be effective in predicting these variables.
Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, a fleet of unmanned ground vehicles (UGVs) was developed as a test and evaluation (T&E) platform to reduce system integration gaps between simulation and live flight hardware. While simulation and hardware-in-the-loop bench testing provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure increase the risks to safety, property, and the project. Given ATTRACTOR’s goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, bridging these gaps was critical to successful project execution and feasibility assessment. In this paper we present the UGV fleet and its role in speeding up system integration, smoothing the transition from simulation to flight, and providing researchers an easy-to-use hardware test bed. An overview of the hardware and software on-board the vehicles is provided along with supporting infrastructure. The system integration process is documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted since the creation of the fleet. Finally, we discuss the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.
Context-Based Evaluation of Dimensionality Reduction Algorithms—Experiments and Statistical Significance Analysis

Dimensionality reduction is a commonly used technique in data analytics. Reducing the dimensionality of datasets helps not only with managing their analytical complexity but also with removing redundancy. Over the years, several such algorithms have been proposed with their aims ranging from generating simple linear projections to complex non-linear transformations of the input data. Subsequently, researchers have defined several quality metrics in order to evaluate the performances of different algorithms. Hence, given a plethora of dimensionality reduction algorithms and metrics for their quality analysis, there is a long-existing need for guidelines on how to select the most appropriate algorithm in a given scenario. In order to bridge this gap, in this article, we have compiled 12 state-of-the-art quality metrics and categorized them into 5 identified analytical contexts. Furthermore, we assessed 15 most popular dimensionality reduction algorithms on the chosen quality metrics using a large-scale and systematic experimental study. Later, using a set of robust non-parametric statistical tests, we assessed the generalizability of our evaluation on 40 real-world datasets. Finally, based on our results, we present practitioners’ guidelines for the selection of an appropriate dimensionally reduction algorithm in the present analytical contexts.
Self-Adaptation of Meta-Parameters for Lamarckian-Inherited Neuromodulated Neurocontrollers in the Pursuit-Evasion Game

Determining meta-parameter settings is a longstanding challenge in evolutionary computing, and often involves running repeated simulations with successive estimated values until acceptable values are found. The term metaevolution is often used to describe the optimization of one or more of the meta-parameters that control, for example, the rates of selection and variation in evolutionary optimization algorithms. Here we use self-adaptation to simultaneously evolve all of the practical meta-parameters together with the other evolved parameters as part of the main evolutionary algorithm. The meta-parameters are added to the gene, and evolved along with the other parameters. The evolved neurocontrollers with self-adaptation are compared to those with manually selected meta-parameters. Secondly, self-adapted meta-parameters from the best neurocontroller are used to evolve a further set of non-self-adapted neurocontrollers, and compared with the selfadapted and manually selected results. The effects of selfadaptation are determined through a series of experiments using a previously demonstrated multi-objective Lamarckianinherited neuromodulated evolutionary neurocontroller. The fitness of the evolved neurocontrollers is determined through their operation of a simulated vehicle pursuing a basic evader vehicle in the pursuit-evasion game. Both vehicles are subject to the effects of mass and drag. It is shown that self-adaptation can be used to automatically tune and control meta-parameters during evolution. Only a trivial amount of computational cost is added, and no degradation in evolutionary performance was observed. Under some circumstances self-adaptation may lead to improved performance of the evolutionary algorithm.
Benefiting from Duplicates of Compressed Data: Shift-Based Holographic Compression of Images

Storage systems often rely on multiple copies of the same compressed data, enabling recovery in case of binary data errors, of course, at the expense of a higher storage cost. In this paper, we show that a wiser method of duplication entails great potential benefits for data types tolerating approximate representations, like images and videos. We propose a method to produce a set of distinct compressed representations for a given signal, such that any subset of them allows reconstruction of the signal at a quality depending only on the number of compressed representations utilized. Essentially, we implement the holographic representation idea, where all the representations are equally important in refining the reconstruction. Here, we propose to exploit the shift sensitivity of common compression processes and generate holographic representations via compression of various shifts of the signal. Two implementations for the idea, based on standard compression methods, are presented: the first is a simple, optimization-free design. The second approach originates in a challenging rate-distortion optimization, mitigated by the alternating direction method of multipliers (ADMM), leading to a process of repeatedly applying standard compression techniques. Evaluation of the approach, in conjunction with the JPEG2000 image compression standard, shows the effectiveness of the optimization in providing compressed holographic representations that, by means of an elementary reconstruction process, enable impressive gains of several dBs in PSNR over exact duplications.
Nonbinary Error-Detecting Hybrid Codes

Hybrid codes simultaneously encode both quantum and classical information, allowing for the transmission of both across a quan- tum channel. We construct a family of nonbinary error-detecting hybrid stabilizer codes that can detect one error while also encod- ing a single classical bit over the residue class rings Zq inspired by constructions of nonbinary non-additive codes.
Optokinetic response for mobile device biometric liveness assessment

As a practical pursuit of quantified uniqueness, biometrics explores the parameters that make us who we are and provides the tools we need to secure the integrity of that identity. In our culture of constant connectivity, an increasing reliance on biometrically secured mobile devices is transforming them into a target for bad actors. While no system will ever prevent all forms of intrusion, even state of the art biometric methods remain vulnerable to spoof attacks. As these attacks become more sophisticated, liveness based attack detection methods provide a potential deterrent. We present a novel optokinetc nystagmus (OKN) based liveness assessment system for mobile applications which leverages phase-locked temporal features of a unique reflexive behavioral response. In this paper we provide proof of concept for eliciting, collecting and extracting the OKN response motion signature on a mobile device. Results of our most successful experimental machine learning classifier are reported for a multi-layer LSTM based model demonstrating a 98.4% single stimulus detection performance for simulated video based attacks.
Modeling and Simulation of Data Centers to Predict Behavior

Data center modeling and simulation help to estimate and predict key parameters under certain a-priori known conditions. Data center systems must adopt end-to-end resource management, covering both cyber and physical components. This paper describes a systematic, easy-to-follow approach for data center modeling and simulation using a cyber-physical systems lens. The methodology is aimed to facilitate the estimation of quality of service, airflow and power requirements, and key indicators to assess data centers, and to then compare them to one another, or compare different scenarios under which data centers operate. The results contribute to communicate and better understand data center behavior and areas of improvement.
Application of Deep Reinforcement Learning in Control of Drawing Robots

Abstract In this paper, the drawing robot studied is after the system extracts the edge of the image, convert its pixels to actual physical coordinates, then control the robot to track these coordinate values and realize image drawing. In many cases, the edges extracted by the edge detection algorithm have a lot of unnecessary details and edge lines of many branch points. Here, some techniques are used to remove these redundant and branch points. When the robot is tracking the trajectory, the quality of its control algorithm directly determines its performance. In this paper, the control system is designed using the deep deterministic strategy gradient (DDPG) algorithm in reinforcement learning, focusing on the setting of its reward function. Here we propose a reward function. After simulation testing, the reward function setting meets the system requirements, the controller can achieve high-precision and high-speed trajectory tracking effect, and it can also suppress chattering caused by external interference and model errors.
Performance Improvement of Heterogeneous Cluster of Big Data Using Query Optimization and MapReduce

The information volumes are bursting, and more data has been created in the past couple of years than in the past history of the human race. Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of fresh information is going to be created every second for each and every person on Earth. So we need a platform to process the large volume of complex information process hardware as well as software. MapReduce frame has received large popularity because of scalable distributed network atmosphere for productive processing of high scale info from this arrangement of Terabytes or longer. Hadoop, open-source implementation of MapReduce combined with Hadoop distributed file system, is broadly applied to encourage bunch computing tasks demanding non-response period. The recent Hadoop implementation supposes the nodes at the bunch have been homogenous in character. In this paper, we proposed a new algorithm to fix these issues for the commercial as well as non-commercial uses can enable the betterment of the community. We have conducted the experiment to establish that if a procedure is defined to handle the different use case situations, an individual could overall decrease the expense of computing and can benefit on relying on distributed systems for rapid executions.
EvoFlow: A Python library for evolving deep neural network architectures in tensorflow

Neuroevolutionary algorithms are one of most effective and extensively applied methods for neural architecture search. While several neuroevolutionary approaches have been proposed, the availability of software that allows a fast development of code to solve problems and test research questions is limited. In this paper we introduce EvoFlow, a Python library for evolving shallow and deep neural network (DNN) architectures. EvoFlow optimizes network structures for DNNs implemented in tensorflow. Single and multi-component DNN architectures are represented by means of descriptors, and the instantiation of the network occurs in the evaluation of the architecture. Genetic operators work by modifying the descriptors. We show how EvoFlow allows efficient architecture optimization of single-component DNNs, such as deep multi-layer perceptrons, but also of multi-component DNNs, such as generative adversarial nets.
Wildfire monitoring using Unmanned Aerial Vehicles operating under UTM (STEReO)

STEReO (Scalable Traffic Management for Emergency Response Operations) project at NASA Ames is designed to provide UTM (UAS Traffic Management) services to unmanned aerial vehicles (UAVs) used for natural disaster response scenarios like wildfire and hurricanes. This will facilitate the use of unmanned aerial vehicles in regions where UAVs are currently prohibited to fly. In this paper we describe a complete architecture of using UAVs for wild fire monitoring in this STEReO environment. We simulate a complete fire monitoring scenario in an high fidelity simulation environment. The simulation consists of a fire drill in the vicinity of Redding airport, one of the test sites for CAL-FIRE. The autonomous vehicle connects to the STEReO systems and gathers information of other operation in the vicinity. The vehicle then uses on-board path planners and decision making algorithms for fire monitoring and mapping. In this paper the vehicle on-board architecture is described in details and the requirements to fly and interact with the STEReO system is discussed.
Indicator Probabilistic Acceleration Velocity Obstacles for Dynamic Collision Avoidance in Uncertain Environments

The ability of autonomous agents like Unmanned Aerial Vehicles (UAVs) and spaceships to avoid collision with static and dynamic obstacles while navigating through an obstacle-cluttered environment is very critical for overall success in any mission. Over the years, There have been significant efforts culminating into some well-established collision avoidance algorithms like Velocity Obstacle (VO), Optimally Reciprocal Collision Avoidance, Nonlinear Velocity Obstacle, Probabilistic Velocity Obstacle (PVO), Collision Cone, etc. to achieve collision avoidance. However, in their basic set-ups several of them assume constant velocity model for the obstacles and does not take into consideration the dynamic constraints of the agent. In the limited setting, when agent-obstacles are alike in terms of planning and control, collision avoiding methods like Acceleration Velocity Obstacle (AVO) do take into account the agent's and obstacles' dynamic constraints. Still they are not robust to sensor noise and environmental uncertainty. The increased involvement of UAVs in our day to day lives and the availability of once expensive, and ever-improving computational power, has necessitated an algorithm to be able to plan for safe dynamically constrained navigation in an environment with uncertainty in the motion of the obstacles, sensor noise and uncertainty beyond the occluded regions. To this end, as an expansion over the PVO and AVO methods, a novel motion planner - Indicator probabilistic Acceleration Velocity Obstacle (IPAVO) method - is developed in this paper for smooth, collision-free navigation of the agent with acceleration constraints in an uncertain, unstructured environments with an element of anticipation for the future environment. Effectiveness of the developed IPAVO algorithm for safe collision-free smooth navigation of the agent is investigated in simulation studies in two environments with obstacles having constant velocity aother with obstacles maneuvering on a trajectory. Then in a hybrid environment consisting of the linear motion obstacle and dynamic maneuvering obstacles, an Extensive simulation studies is performed to elucidate the performance of the proposed IPAVO algorithm on four crucial parameters- target interception time, computational cycle time, minimum obstacle distance, and overall control effort under variable obstacle density. The algorithm deftly handles the environmental uncertainty and with all these desired features, IPAVO has the potential of real-time implementation.
Medical JPEG image steganography method according to the distortion reduction criterion based on an imperialist competitive algorithm

In most of the digital steganography methods provided for natural digital images, the embedding of the confidential message is based on the minimisation of the defined distortion functions. It is often done based on choosing the most optimal criterion of distortion. Although the distortion functions are designed innovatively, steganography algorithms will be optimal. In such approaches, embedding interactions are often overlooked. Unlike usual images that have areas with a variety of tissue features, there are many smooth areas in medical images that will make the changes more visible if they are manipulated. Therefore, this study presents an adaptive approach that comes from the interactions between the changes made during the embedding algorithm to reduce the probability of recognising the message embedded in medical images and reducing the distortion caused by embedding in a discrete cosine transform space and based on the imperialist competitive algorithm for joint photographic experts group images, especially in medical images due to the importance of information steganography in them. The results obtained show the high efficiency of the proposed algorithm in comparison with the state-of-the-art methods that are presented in this area.
Incorporating Domain Knowledge into Health Recommender Systems Using Hyperbolic Embeddings

In contrast to many other domains, recommender systems in health services may benefit particularly from the incorporation of health domain knowledge, as it helps to provide meaningful and personalised recommendations catering to the individual's health needs. With recent advances in representation learning enabling the hierarchical embedding of health knowledge into the hyperbolic Poincare space, this work proposes a content-based recommender system for patient-doctor matchmaking in primary care based on patients' health profiles, enriched by pre-trained Poincare embeddings of the ICD-9 codes through transfer learning. The proposed model outperforms its conventional counterpart in terms of recommendation accuracy and has several important business implications for improving the patient-doctor relationship.