Reviewerly/exHarmony huggingface dataset:
Monitoring Cloud Service Unreachability at Scale

We consider the problem of network unreachability in a global-scale cloud-hosted service that caters to hundreds of millions of users. Even when the service itself is up, the "last mile" between where users are, and the cloud is often the weak link that could render the service unreachable. We present NetDetector, a tool for detecting network-unreachability based on measurements from a client-based HTTP-ping service. NetDetector employs two models. The first, GA (Gaussian Alerts) models temporally averaged raw success rate of the HTTP-pings as a Gaussian distribution and flags significant dips below the mean as unreachability episodes. The second, more sophisticated approach (BB, or Beta-Binomial) models the health of network connectivity as the probability of an access request succeeding, estimates health from noisy samples, and alerts based on dips in health below a client-network-specific SLO (service-level objective) derived from data. These algorithms are enhanced by a drill-down technique that identifies a more precise scope of the unreachability event. We present promising results from GA, which has been in deployment, and the experimental BB detector over a 4-month period. For instance, GA flags 49 country-level unreachability incidents, of which 42 were labelled true positives based on investigation by on-call engineers (OCEs).
Fashion Style Forecasts based on Different Price Ranges

Fashion trend forecasting is of great significance to both fashion workers and consumers. Although the existing methods can effectively capture the trend of fashion style, most of them are aimed at the change of clothing style trend, and the factors affecting the trend of style are not fully considered. To solve these problems, we introduce a method to capture fashion elements and discover styles in the products, and an end-to-end LSTM encoding and decoding framework that can accurately forecast future trends of fashion styles in different price ranges is proposed. We demonstrate our model in the Amazon data set and the results indicate that our model has the best forecasting performance compared with baselines menthods.
RUPEX Search: Online Tool for Analyzing Multichannel Discourse

The paper presents an online search system built for the “Russian Pear Chats & Stories” corpus (RUPEX). RUPEX is a collection of communication sessions used for studying multichannel discourse. It aims at discovering how interlocutors use verbal, prosodic, oculomotor, manual gesticulation and other channels as they take part in natural monologic and dialogic communication. The sessions were recorded with microphones, video cameras, and eye trackers, and an integrated multichannel annotation scheme was implemented to them. The search system operates on these data and provides an intuitive graphic user interface that offers the following options: creating simple and complex queries, viewing search results in a multi-layered annotation format associated with video fragments, specifying search domain. To illustrate this functionality, we discuss search queries relevant to a specific multichannel research, that of dialogic elicitations.
Development of Excessive-Smoke-Emitting-Car Fault Diagnostic System (ESECFDS) based on Case-Base Reasoning (CBR) Methodology

This paper proposes a diagnostic system, ESECFDS, to solve the problem of excessive smoke emission from a faulty car, based on Case-Based Reasoning (CBR) Methodology that accepts a user query as a new case, compares it with several stored cases in a Case-Base (CB) based on a similarity score using Jaccard Similarity method and provides feedback result to the user. The CBR cycle R4 Model of Aamodt and Plaza has been modified to R5 Model to improve the time complexity of the proposed algorithm of the system over the popular R4 Model.
The SPECpowerNext Benchmark Suite, its Implementation and New Workloads from a Developer's Perspective

Innovation needs a competitive and fair playing field on which products can be compared and informed choices can be made. Standard benchmarks are a necessity to create such a level playing field among competitors in the server market for more energy-efficient servers. That, in turn, motivates their engineers to design more energy-efficient hardware. The SPECpower_ssj 2008 benchmark drove the increase of server energy efficiency by 113 times for single CPU servers, or 19 times on average. Yet, with added functionality and load, they are expected to consume a rising amount of energy. Additionally, server usage in data centers has changed over time with new application types. To continue the effort of increasing server energy efficiency, a new version, SPECpowerNext, is under development. In this work, after a short introduction to SPECpower_ssj 2008, we present the new implementation of SPECpowerNext together with the standardized way to collect server information in heterogeneous data centers. We also give insight, including preliminary measurements, into two of SPECpowerNext new workloads, the Wiki and the APA workload, in addition to an overview of both workloads.
Secure Encrypted Data Transmission of Brainwaves Using AES

In recent scenario, Wireless Sensor Network (WSN) plays vital role in the current environmental application specifically in Wireless Multimedia Sensor Network (WMSN). In medical field application of WMSN is tremendous. Patient's healthcare information plays an important role in the treatment plan. It is very difficult to appoint health care staffs to monitor the patients who need continuous supervision. WSN can provide an important solution to this problem. Using wearable or implantable sensors the patient's conditions and their data can be gathered in the care unit and are put away in the database. Patient's detailed information can be accessed remotely by the different authorities like healthcare staff, researchers, government agencies, insurance companies, and patients. Data that are transmitted over a network must be transferred in a secure way. Many research works are going on this field to provide the effective solution but till the security threats exists. The proposed work of this paper is ensured to provide the secure data transmission over a network. A Symmetric key method is used which helps to avoid the overwriting or modification of the patient's details. In existing work, they give the security to the database utilizing a few systems and fine-grained information get to control it. In the proposed, Symmetric key encryption/decoding method provides secure mechanism. This assists the patients and health care unit to work in a secure manner with keeping away from the change of patient's details.
Multi-agent Formation Control with Obstacles Avoidance under Restricted Communication through Graph Reinforcement Learning

Multi-agent formation control with obstacles avoidance (MAFC-OA) is one of the attractive tasks of multi-agent cooperation. Although a number of algorithms can achieve formation control effectively, they ignore the nature structure feature of the graph formed by agents. Given this problem, a model, MAFC-OA, which is composed of observation attention network, action attention network and Multi-long short-term memory (Multi-LSTM) is proposed. With MAFC-OA, the agents can be trained to form the desired formation and avoid dynamic obstacles in the environments with restricted communication. Specifically, the above two attention networks not only incorporate the influence of the nearby agents' observation and actions, but also enlarge the agents' receptive field (communication range) through the chain propagation characteristics to promote cooperation among agents. Moreover, the Multi-LSTM allows the agents to take obstacles into consideration in the order of distance and to avoid the obstacles effectively. Simulations demonstrate that the agents can form the desired formation and avoid dynamic obstacles effectively.
Cryptography Using Chaos In Communication Systems

Abstract A new and unexplored area in the field of cryptography is the use of chaotic systems to encrypt data. The purpose of this paper is to present how the principles of chaos theory can be applied in the field of cryptography. A cipher that uses chaos dynamics is presented that has very good statistical properties.
Construction of S-Box Based on Parametric Mixing of Chaotic Maps

This paper proposes a new method of constructing S-box based on parametric mixing of chaotic maps. The proposed method employs two 1-D Logistic maps and a 2-D Henon map. The pseudo random sequence is generated by mixing X and Y sequences of Henon map and the selection of X and Y sequence is based on two Logistic maps. The mixed chaotic sequence is used to generate S-box. The performance analysis of the proposed S-box generation method is carried out based on criteria such as bijective property, nonlinearity, strict avalanche criterion, bit independence criterion and differential probability. The proposed method is comparable to existing methods and achieves superior differential probability.
Reliability based micro-economic cost model for cloud computing systems

An increasing number of industries are shifting their operations to cloud computing platforms. A large number of these users outsource their needs to different cloud providers. In such a scenario it becomes imperative to study the economics of these systems. In this work we present a micro-economic model to maximize the profit of a cloud service providers. The profits of such firms depends highly on the reliability of the system and the quantity of resources employed. A profit function formulated has been modified to consider the reliability of the system as a major component on the profitability of the firm. The cloud system has been modelled using queuing theory and a profit optimization problem has been formulated using the principles of producer side micro-economics.
Wearable Sensors based Smart Secured Remote Health Monitoring System

There is a growing need of remote health monitoring systems that can monitor an array of health parameters and alert concerned individuals about the same, thereby maintaining the security of private medical data. Through our sensor array, consisting of electro-cardiogram, pulse rate, oximeter and accelerometer sensor whose output signals were conditioned through tuning of the exponential moving average parameter, we encrypted and transmitted it to the connected fog node, wherein some computations are performed before the data were being pushed to the Cloud platform for human activity monitoring and disorder detection though our designed algorithms. Alerts are released immediately to the concerned individuals, including the medical experts, with immediate data visualization through Android application. The system has achieved about 90% accuracy in our testing samples.
On Parametric Model Mismatch in Nonlinear EKF Approximations

Standard state estimation techniques, either for linear or non-linear systems, typically assume a perfect knowledge of the system, i.e., known process and measurement functions, and the corresponding noise statistics. The filter performance strongly depends on such knowledge and a possible model mismatch may lead to a significant performance degradation, but closed-form expressions for the resulting bias and co-variance are not available. In this contribution we explore the impact in extended Kalman filter approximations of a parametric model mismatch on both system functions, and provide expressions for the estimator bias and covariance error induced by the mismatch. An illustrative example is shown to support the discussion.
HoughNet: Integrating Near and Long-Range Evidence for Visual Detection

This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method. Inspired by the Generalized Hough Transform, HoughNet determines the presence of an object at a certain location by the sum of the votes cast on that location. Votes are collected from both near and long-distance locations based on a log-polar vote field. Thanks to this voting mechanism, HoughNet is able to integrate both near and long-range, class-conditional evidence for visual recognition, thereby generalizing and enhancing current object detection methodology, which typically relies on only local evidence. On the COCO dataset, HoughNet's best model achieves 46.4 AP (and 65.1 AP50), performing on par with the state-of-the-art in bottom-up object detection and outperforming most major one-stage and two-stage methods. We further validate the effectiveness of our proposal in other visual detection tasks, namely, video object detection, instance segmentation, 3D object detection and keypoint detection for human pose estimation, and an additional "labels to photo'' image generation task, where the integration of our voting module consistently improves performance in all cases.
Real-Time Solar Tracking System with GPS

In the present world, people are shifting towards renewable sources of energy and solar energy is a widely explored area. Due to the less efficiency and small duration of peak power, it cannot be a reliable source. Here, the power output can be maximized from a solar power system. A solar tracking technique is used based on GPS to achieve maximum power output. This system is especially proposed for the moving platforms that keep on changing their location with time. In this article, various subsystems and the maximum power output in an effective way and its advantages over the existing techniques were discussed.
BFastPay

Bitcoin is the most popular cryptocurrency which supports payment services via the Bitcoin peer-to-peer network. However, Bitcoin suffers from a fundamental problem. In practice, a secure Bitcoin transaction requires the payee to wait for at least 6 block confirmations (one hour) to be validated. Such a long waiting time thwarts the wide deployment of the Bitcoin payment services because many usage scenarios require a much shorter waiting time. In this paper, we propose BFastPay to accelerate the Bitcoin payment validation. BFastPay employs a smart contract called BFPayArbitrator to host the payer's security deposit and fulfills the role of a trusted payment arbitrator which guarantees that a payee always receives the payment even if attacks occur. BFastpay is a routing-free solution that eliminates the requirement for payment routing in the traditional payment routing network (e.g., Lightning Network). The theoretical and experimental results show that BFast is able to significantly reduce the Bitcoin payment waiting time (e.g., from 60 mins to less than 1 second) with nearly no extra operation cost.
Empirical Analysis of Policy Gradient Algorithms where Starting States are Sampled accordingly to Most Frequently Visited States

In this paper, we propose an extension to the policy gradient algorithms by allowing starting states to be sampled from a probability distribution that may differ from the one used to specify the reinforcement learning task. In particular, we suggest that, between policy updates, starting states should be sampled from a probability density function which approximates the state visitation frequency of the current policy. Results generated from various environments clearly demonstrate a performance improvement in terms of mean cumulative rewards and substantial update stability compared to vanilla policy gradient algorithms where the starting state distributions are either as specified by the environment or uniform distributions over the state space. A sensitivity analysis over a subset of the hyper-parameters of our algorithm also suggests that they should be adapted after each policy update to maximise the improvements of the policies.
Semantic analysis based on fusion of audio/visual features for soccer video

In order to analysis the semantic content of soccer video, the audio/visual features are effectively extracted. Based on the theory that the variation of video content would cause the fluctuation of viewers’ affection, the highlight time curve (HTC) is generated by fusion of affection arousal factors to reveal the excitement of the game. The semantic boundaries of highlights are determined by HTC combined with the domain knowledge of soccer video. With the help of distinguishable highlight feature vectors (HFVs), highlights are classified into goal, shoot, and foul. Compared with the existing works, the main contributions of this paper are as follows. We proposed a novel Hough transform based whistle detection algorithm and achieves more effective performance. A robust goalmouth detection algorithm is presented and contributed to the highlight classification phase. The highlights with semantic boundaries are accurately extracted and classified. Experiments conducted on real world soccer videos demonstrated the good performance of the proposed framework.
On Machine Learning-based Stage-aware Performance Prediction of Spark Applications

The data volume of large-scale applications in various science, engineering, and business domains has experienced an explosive growth over the past decade, and has gone far beyond the computing capability and storage capacity of any single server. As a viable solution, such data is oftentimes stored in distributed file systems and processed by parallel computing engines, as exemplified by Spark, which has gained increasing popularity over the traditional MapReduce framework due to its fast in-memory processing of streaming data. Spark engines are generally deployed in cloud environments such as Amazon EC2 and Alibaba Cloud. However, storage and computing resources in these cloud environments are typically provisioned on a pay-as-you-go basis and thus an accurate estimate of the execution time of Spark workloads is critical to making full utilization of cloud resources and meeting performance requirements of end users. Our insight is that the execution pattern of many Spark workloads is qualitatively similar, which makes it possible to leverage historical performance data to predict the execution time of a given Spark application. We use the execution information extracted from Spark History Server as training data and develop a stage-aware hierarchical neural network model for performance prediction. Experimental results show that the proposed hierarchical model achieves higher accuracy than a holistic prediction model at the end-to-end level, and also outperforms other existing regression-based prediction methods.
A Two-Dimensional mKdV Linear Map and Its Application in Digital Image Cryptography

Cryptography is the science and study of protecting data in computer and communication systems from unauthorized disclosure and modification. An ordinary difference equation (a map) can be used in encryption–decryption algorithms. In particular, the Arnold’s cat and the sine-Gordon linear maps can be used in cryptographic algorithms for encoding digital images. In this article, a two-dimensional linear mKdV map derived from an ordinary difference mKdV equation will be used in a cryptographic encoding algorithm. The proposed encoding algorithm will be compared with those generated using sine-Gordon and Arnold’s cat maps via the correlations between adjacent pixels in the encrypted image and the uniformity of the pixel distribution. Note that the mKdV map is derived from the partial discrete mKdV equation with Consistency Around the Cube (CAC) properties, whereas the sine-Gordon map is derived from the partial discrete sine-Gordon equation, which does not have CAC properties.
Multi-node trust model construction based on blockchain: modelling and simulation

Multi-node trust model construction based on the blockchain is introduced in this manuscript. The blockchain edge network is responsible for storing all transactions in the system, uploading the packaged block consensus on the chain, and broadcasting the latest block information in the system. Based on this feature, this paper designs the efficient trust model. Therefore, from an individual point of view, the agent's autonomous performance adjusts its state and also output information spontaneously according to the input information. Besides this, the Agent and neural network models are combined to construct the feedback framework. The expeirment compared with the latest models shows that the designed trust model can communication with higher efficiency.
On the Use of Artificial Intelligence for Student Distribution in Dormitories: MEPhI University Campus Case

this article describes the research on the development of a program in PyCharm environment using Python. The program creates a database of MEPhI University applicants. It searches for data about students from social networks and from MEPhI home site: their gender, citizenship, study groups in which they are enrolled, their friends and interests, and in accordance with them distributes students in the dormitory. The article outlines the main stages of work, as well as analyzes in detail information of each step of the project. The research is based on programming in Python and HTML; Excel database and API: HomeMephi, Vkontakte.
Realignment separability criterion assisted with filtration for detecting continuous-variable entanglement

We introduce a weak form of the realignment separability criterion which is particularly suited to detect continuous-variable entanglement and is physically implementable (it requires linear optics transformations and homodyne detection). Moreover, we define a family of states, called Schmidt-symmetric states, for which the weak realignment criterion reduces to the original formulation of the realignment criterion, making it even more valuable as it is easily computable especially in higher dimensions. Then, we focus in particular on Gaussian states and introduce a filtration procedure based on noiseless amplification or attenuation, which enhances the entanglement detection sensitivity. In some specific examples, it does even better than the original realignment criterion.
Dynamic Privacy-preserving Collaborative Schemes for Average Computation

In this paper, we consider the privacy-preserving problem in collaborative computing. Based on a two-step average computation framework, we propose three privacy-aware schemes, all of which achieve different levels of privacy protections depending on data servers’ trust degrees. Further, by carefully designing noises injected to the distributed computing process, we obtain dynamic privacy-preserving schemes, whose privacy preserving levels are measured by Kullback-Leibler differential privacy. In addition, we prove that the proposed schemes achieve convergence in different senses. Numerical experiments are finally conducted to verify the obtained privacy properties and convergence guarantees.
Integration of Dimension-adaptive Obstacle Avoidance Algorithm in Fuzzy-based Autonomous Vehicle Parking

Sensor-based obstacle avoidance and Autonomous vehicle parking have been immensely researched in recent times. An integration of both will increase the usability of autonomous parking systems in dynamic and uncertain environments. The fuzzy logic theory is widely used to learn expert human skills for machines. However, existing fuzzy-based expert systems generally fail to mimic the natural adaptive skills of humans. The expert driver has a natural tendency to adapt to machine dynamics, especially vehicle-related. This paper proposes a novel non-holonomic dimension-based obstacle avoidance parking algorithm that integrates obstacle avoidance capabilities to a standalone parking controller. This algorithm is developed based on adaptive fuzzy membership inferences concerning passenger cars' different sizes and segments. It is tested for various vehicles in simulation results to show the effectiveness of the algorithm.