Reviewerly/exHarmony huggingface dataset:
Order-Adaptive Fractional-ORDER Moment-Based Estimation of Shape Parameter of K-distribution

In the paper, an order-adaptive fractional-order moment-based (OAFOM) estimator is proposed to estimate the shape parameter of K-distribution. The performance of a fractional-order moment-based (FOM) estimator depends on both shape parameter and the order of the fractional-order moment. From numerical experiments, an empirical formula of the best order as a function of the shape parameter is found. An initial estimator of the shape parameter is obtained from one moment-based estimator and the best order is determined from the initial estimate and empirical formula. And then the FOM estimator with the best order is the final estimate of the shape parameter. Simulated results show that the proposed estimator attains better performance than other estimators.
Demonstration of Distributed Quantum Sensing using Nonlinear Optics

Quantum technology consists very often of networks: Quantum communication is inherently a network while some quantum computing architectures are based on networking to realize scalability. Quantum sensing can be also realized in a networked setup employing entangled states and specialized measurements with applications in e.g. magnetic field mapping and precision clocks [1,2].
An Innovative Data Mining Approach for Determine Earthquake Probability Based on Linear Regression Algorithm

Earthquake (EQ) causes many damages to our environment, this paper proposes a new approach to find the Earthquake probability (EQP) using linear regression (LR-EQP) is to minimize this extensive problem with the help of geological data by predicting the intended probability of the level of EQ. This approach is to take the all the geological information which are responsible for EQ such as to create pressure on the land like density of population (DP), soil type (ST), combustible elements (CE), specify the place use latitude and longitude (Lat-Long), distance from nearest tectonic plate (TP). Since TP is the divided part of earth which move independently and responsible earth's seismic activity and LR-EQP deploy these geological data, so LR-EQP approach can determine the EQP easily and accurately. Earthquake prediction using data mining is a process, which uses only three factors: (a) ground water level, (b) chemical changes and (c) radon gas in ground water. But this is a slow approach to determine EQP because in general these factors cannot be determined so easily. LR-EQP can be used for any region by targeting the TP area. This data model can be used also for mobile application for easily detecting the probability of earthquake.
A Reconfigurable Silicon Generator of Four-Photon Entangled States

Large-scale quantum silicon photonic devices can represent a viable route to the generation of large entangled quantum states (graph states)[1], exploiting the generation of multiple pairs of photons on chip. Graph state applications are diverse, spanning from measurement-based quantum computing to quantum error correction and quantum communication [2]. Silicon photonics represents a promising platform for its stability, reconfigurability, high-density and high-performance optical components.
Optimal and Suboptimal Importance Density Functions for Rao-Blackwellized Particle Filter

For the standard particle filter, sampling efficiency is decreasing rapidly with time, especially when the state dimension is large. Rao-Blackwellized particle filter can improve the sampling efficiency by marginalizing over one part of the state and applying particle filter to the other part with lower dimension. However, Rao-Blackwellization will lead to a non-Markovian model, and this causes the existing optimal importance density function theory for the standard particle filter is inapplicable. In this paper, in the sense of the minimum conditional variance of importance weights, we derive the corresponding optimal importance density function for Rao-Blackwellized particle filter. The concrete calculation formula for optimal importance density function is provided for two particular conditionally linear Gaussian models. When optimal importance density function cannot be computed analytically, suboptimal importance density functions are designed by Gaussian approximation methods. The effectiveness of the proposed methods are illustrated through three simulation examples including two typical target tracking models and a fourth order mixed linear/nolinear Gaussian model.
Multiobjective Workload Scheduling in Distributed Datacenters

Cloud computing is now a popular field for indulging infrastructure–as-a-service (IaaS). IaaS defers, on demand virtualized computing resources through datacenters. Cloud users acquire computing infrastructure for the execution of application workloads. However due to increasing data volumes and parallel processing of workloads on limited resources of single data center, workloads are shifted to the other datacenters over the peak time. Therefore scheduling of workloads in distributed datacenter is a challenge as each workload executes across disbursedenvironment having heterogeneous resources and also requires coordination among the datacenters. In this paper, we propose MO-GRASP (Multiobjective Greedy Randomized Adaptive Search Procedure) based heuristic to solve the multi-objective workload scheduling problem. The model objectives are minimization in workload completion time i.e. datacentermakespan and energy consumptionwhilemaximizing the average datacenter utilization. The simulation results show that the proposed algorithm outperforms as compared to other Multiobjective algorithm.
Quantum Key Distribution Exploiting a Faraday Rotator and a Single Spad

Nowadays encryption is exploited for protecting information exchange in several applications. Nevertheless the security of commonly used algorithms of encryption is based on the extremely high computational cost required for message decryption. On the other hand, quantum key distribution (QKD) allows for exchanging a key between two users (Alice and Bob) in a way that has been proved as unconditional secure, thanks to the fundamental principles of quantum physics [1–3]. However to make the QKD a reliable and effective widespread solution, it is essential to reduce the cost and expand the scalability.
Food and Therapy Recommendation System for Autistic Syndrome using Machine Learning Techniques

Autism is a group of disorders that causes social communication, repetitive behaviors, sensory aspects, speech and language challenges commonly in children. There are different therapies such as occupational and speech therapies to treat these autistic children. Nutritious food is a medicine for all diseases. Nutritional therapy is an important therapy to be followed by the children along with other therapies. In this study, the proposed system will recommend food and therapy for the autistic children based on their symptoms. The system uses k-means algorithm for grouping the symptoms based on its types and association rule mining for the recommendation of food and therapies. The food and therapy recommendation system is achieved well and it is helpful for the parents whose children suffering from autism.
“I'm in the center of the vortex”: The affective chain of social media trolling

ABSTRACT Dominant models for the conceptualization of trolling fail to address the subjective experiences of its victims. By bringing affect into perspective, this paper explores the negative impact of trolling on individuals, drawing on data from semi‐structured interviews with 19 respondents who were trolled on popular Chinese social networks (e.g., Weibo , Zhihu , Renren ), blogs, and forums (e.g., Baidu Tieba , Tianya ). Our preliminary findings suggest that: (1) the affective chain of trolling is materialized by the social network platforms' affordability and human actors' desire for intensity, and (2) the victims of trolling were overwhelmed by the accumulated affective intensities in circulation to which they were exposed.
Research on Dynamic Model and Trajectory Optimization Method of Cross-medium Aircraft in Process of Water-Entry

The cross-medium aircraft is a new type of aircraft that can sail in the air and under the water. The water-entry process of the cross-medium aircraft plays a vital role in its performance. This paper begins with an overview of the water-entry problem of a cross-medium aircraft. The research status and development trends of cross-medium aircraft crossing different mediums at home and abroad are introduced in detail. The dynamic model and trajectory optimization problems are studied for the water-entry process of the cross-medium aircraft. Firstly, the shape of the cross-medium aircraft is introduced. Then the force analysis of the water-entry process is carried out, and the dynamic model of the water-entry process is established. Based on the established dynamic model, the water-entry process is treated as a free motion to avoid instability caused by switching complex control strategies. Therefore, the water-entry trajectory of the cross-medium aircraft is determined by the state of motion before reaching the water. To optimize the water-entry trajectory under different mission requirements, particle swarm optimization algorithm is applied, and the water-entry speed, water-entry angle and angle of attack are regarded as the optimization variables. Simulation experiments are conducted to verify the rationality of the dynamical model and the effectiveness of the proposed trajectory optimization algorithm. The results demonstrate that the change law of the water-entry trajectory and the control variables that meets the task requirements is obtained.
Track Me & Unlock: Secured Mutual Authentication System of Integrating Phone Unlock & Women’s Safety Application using MEMS

Abstract In the Existing System, Mobile must be unlocked before using. The widely usage of unlocking types are PIN, password and graph. In the Proposed System, we propose a novel token-based mobile device unlocking approach: transferring the authentication state between two devices by briefly shaking them conjointly. In this system; we propose 4 attributes for phone unlocking. 1. Hand waving pattern in mobile, 2. Creating information from embedded hardware through Bluetooth, 3. MEMS waving pattern by the hardware device, 4.Synchronization of both the device. The unlocking pattern of the mobile will be changing every time for safety reasons, It will be changing randomly. The major advantage of this system, even if hacker knows one of the above specified unlocking password, he have to provide answer based on the required pattern so this is more secured application. We also implement Women’s Safety application along with this implementation. We include two patterns for this application. Pressing the switches in the hardware or Phone’s waving pattern is matched immediately system will understand that Women safety application is initiated. GPS is initiated to save the women by sending location info both to the Guardian and Police.
Research on Tax Inspection Case Selection Model Based on Bayesian Network

Case selection is the first procedure of inspection, and the accuracy of case selection is related to the quality of the whole inspection work.The accuracy of case selection relates to the quality of the whole audit work. Through the selection of financial indicators of enterprises, the Bayesian network method is applied to the field of tax inspection and case selection. With the help of the reasoning function of the network, the probability of occurrence is predicted, and a case selection model is established to reflect the uncertainty and complexity of the case selection system, so as to realize the prediction and diagnosis of the case selection system. Taking the honesty state of the selected enterprise as a probability event, the accuracy of this model is 93.3%. It shows that the model can not only improve the accuracy of tax audit case selection, but also provide the method guidance and decision-making reference for tax audit case selection.
Smart Entrance System Using Computer Vision at Corporate Environment

In this paper, a system has been proposed that can be used to a corporate office to log in their employees. Under this system, process involves, face detection using Viola-Jones Algorithm. The alignment of the face is corrected if there has seen any tortuous neck of an employee by applying Kazemi algorithm. The filtering process is being used to account for the fact that male employees might have different mustache and beard on their face on different days. So filtering is added to the picture to detect how he may look if they have mustache or beard. As a result, the risk of a faulty detection regardless of different facial appearance is minimized. These pictures are then added to database. The face is recognized by using Eignefaces which is easier and quicker in computing an employee under smart entrance system. Under conventional system, it is known that corporate employees get entrance by card punching which is sometimes seen to be fraudulent. But with the innovation and enactment of smart entrance system, which evolves face recognition, these illegitimate tasks can be restrained.
A Survey on Opinion Mining and Sentiment Analysis

Utilizing on the web shopper surveys as electronic verbal exchange to help buy basic leadership has turned out to be progressively prevalent. The Web gives a broad wellspring of shopper audits; however one can scarcely read all surveys to acquire a reasonable assessment of an item or administration. A content preparing system that can condense surveys would hence be attractive. A subtask to be performed by such a structure is locate the general angle classes tended to in survey sentences, for which this paper presents two techniques. As opposed to most existing methodologies, the principal strategy exhibited is an unsupervised technique that applies affiliation control mining on co-event recurrence information acquired from a corpus to discover these angle classifications. While not comparable to best in class directed strategies, the proposed unsupervised strategy performs superior to a few straightforward baselines, a comparative however administered technique, and a managed standard, with a F1-score of 67%. The second technique is a regulated variation that beats existing strategies with a F1-score of 84%.
Making use of observable parameters in evolutionary dynamic optimization

In evolutionary dynamic optimization (EDO), most of the existing studies have assumed that dynamic optimization problems are black boxes. However, for many real-world problems, the dynamic parameters that cause the problems to change are observable. However, determining the utility of these parameters in improving optimization performance has not yet been well studied. In this paper, we propose and compare three strategies for this task: rote learning, fitting data with a feedforward neural network and an ensemble strategy. The main idea of these strategies is to learn the relation between the observable parameters and the optimal solutions and then predict new optima once the environment changes. We also propose a set of test cases representing different kinds of characteristics of real-world problems. In the experiments, the proposed strategies are compared with existing methods that do not use observable parameters, and the results validate our proposed strategies.
Phase Noise Robustness of a Coherent Spatially Parallel Optical Reservoir

Reservoir computing is a machine learning technique that is particularly adapted to process time dependent information [1]. It is also very well suitable to experimental implementations, and in particular photonic implementations [2, 3].
Quantum parameter estimation with feedback control

In this article we study the effect of feedback control on the improvement of the precision limit in quantum parameter estimation. In particular we investigate the case with a qubit in a magnetic field at the presence of spontaneous emission, and by detecting the emitted photons we design a feedback control to improve the precision limit of estimating the magnitude of the magnetic field. We show that the feedback control can improve the precision limit.
Geometric feature based approach for 360° image saliency estimation

This paper presents a 360° image saliency estimation technique. Visual perception of a scene is highly motivated by its geometrical features. The proposed approach combines standard low-level features along with multiple geometry-based features for saliency estimation. Different from the existing saliency approaches, here we combine features such as shape, ridge, corner, and orientation that reflects the geometry of an image. The proposed model is divided into two modules. The first module subsamples input image and generates activation maps for each feature. Subsequently, the second module refines the map by addressing equator biasness, non-uniform illumination and distortion correction due to 360° image projection. Experiments are performed on standard 360° image dataset and the obtained results show the proposed model outperforms existing saliency models.
A Modified Multi-Pedestrian Tracking System

The intelligent vehicle driverless technology has become a very hot topic in the past two decades. To solve the road safety problem, the multi-target tracking system has attracted more and more attention in recent study since it can detect and track multiple objects in traffic scene so as to help the whole driving system plan the safe route. In this work, a novel multi-pedestrian tracking system based on deep neural network is proposed to improve the tracking efficiency while providing high recognition accuracy. The proposed tracking system consists of two parts: pedestrian detector and target tracker. We refer to the structure of SSD detection network to design our own detection network, replacing the traditional pooling layers by image convolution operation implemented in complex frequency domain. Then, a deep appearance model is trained to track the detected targets by their similarity between the continuous frames. To evaluate the performance of our proposed system, we implement the system on MOT16 benchmark. The detector can improve tracking accuracy by up to 9.3% and the tracker updates at a rate of 138Hz, our extensions reducing the number of identity switches by 16.2%. The experimental results show that the proposed multi-pedestrian tracking system provides better real-time performance and accuracy than the state-of-the-art methods, which can provide more accurate object information for the following auto-driving system with higher safety.
Denoising Distant Supervision for Relation Extraction with Entropy Weight Method

Distant supervision for relation extraction has been widely used to construct training set by aligning the triples of the knowledge base, which is an efficient method to reduce human efforts. However, this method inevitably suffers from wrong labeling problems leading too much noise that will severely hurt the performance of relation extraction. To tackle this problem, in this paper, we propose a denoising model based on Entropy Weight Method (EWM) to filter the noise and select most relevant sentences. First, in a pretraining stage, we develop a sentence-level relation aware attention mechanism to distinguish several most relevant sentence, increasing the attention weights for those critical sentences. Second, we filter the noisy sentences by calculating the entropy weight using the above attention matrix, and then we employ intra-bag and inter-bag attentions to aggregate these selected sentence representations. Experiments on the NYT dataset show that our method can significantly reduce the noisy instance and achieve the state-of-the-art model performance.
TEACHING ONLINE READING STRATEGIES USING THE THINK ALOUD TECHNIQUE

The present study aimed to investigate the value of the teacher Think Aloud (TA) instructional technique in providing young readers with effective strategies to enhance the comprehension of online texts. Eighty-nine participants from 10 to 14 years of age were sorted into two groups based on the type of training they had received: the experimental group (n = 47) had been taught with the TA strategies, while the control group (n = 42) had not received any specific training. Both the experimental and control groups were examined twice, before and after the period of training. Pupils had to find the correct answer to a specific question (Access) and to critically analyze online texts, expressing different opinions on a topic (Analysis). The findings indicate a positive effect of the TA technique, mainly in the transferal of strategies aimed at a more correct evaluation of the websites’ reliability. Indeed, while for the control group only a marginal, if any, improvement was recorded from pre-test to post-test, an outstanding amelioration was observed in the experimental group (from 19.1% to 48.9%) when requested to evaluate the reliability of a web site.
Towards an End-User Layer for Data Integrity

Data Integrity (DI) is the ability to ensure that a data retrieved from a database is the same as that stored and processed. It is a major component of data security and has an important role for the quality of decision making. While most of the existing approaches rely on a reinforcement of security mechanisms, e.g., access control or cryptography techniques, to ensure a high data integrity quality, we follow an end-user perspective to this end to complement the security approach. Simple, yet powerful, the proposed approach is promising in the inclusion of the end-user into the complex problem of DI.
Dynamic Patterns for Cloud Application Life-Cycle Management

Cloud applications are by nature dynamic and must react to variations in use, and evolve to adopt new Cloud services, and exploit new capabilities offered by Edge and Fog devices, or within data centers offering Graphics Processing Units (GPUs) or dedicated processors for Artificial Intelligence (AI). Our proposal is to alleviate this complexity by using patterns at all stages of the Cloud application life-cycle: deployment, automatic service discovery, monitoring, and adaptive application evolution. The main idea of this paper is that it is possible to reduce the complexity of composing, deploying, and evolving Cross-Cloud applications using dynamic patterns.
Spatial-Semantic Fusion Network for Semantic Segmentation in Real-time

In this paper, a real-time semantics segmentation algorithm for mobile terminals such as robots is proposed, which captures large-scale and small-scale features respectively by using convolution and dilation convolution to obtain spatial information. It can get semantic information by using decode-encode structure. It contains two branches, one extracts semantic information, the other extracts spatial information, and finally fuses the two branches together through self-attention method. Our algorithm achieves 71.3% mIoU on the Pascal VOC 2012 data sets, and it takes only 16ms to infer a 480*640 picture in GTX1080Ti. It can be applied to most mobile robot platforms.
An Adaptive Corner Detection Method Based on Deep Learning

Corner detection is one of the most important techniques in image processing. But traditional corner detection algorithms are susceptible to complex background, and the threshold needs to be set manually. We propose an adaptive corner detection method based on deep learning in this paper. First, 6900 photos containing the target are taken for training. Then, we detect the target through the Faster R-CNN model to obtain the coordinates of the target area. Finally, we perform an adaptive Harris algorithm on this area to detect corner points. According to the experiment results, the proposed method can not only avoid the influence of complex background, but also improve the efficiency of corner detection.