Reviewerly/exHarmony huggingface dataset:
A Learning Based Image Quality Assessment Model Assisted with Visual Saliency and Gradient Features

Deep learning has been widely used for image quality assessment (IQA). When designing convolutional neural network (CNN) model for IQA, researcher usually take image patches as input of the network and predict quality score for each patch. In the CNN-based models, excepting the design of convolution layers, there are also two important issues should be well settled, which will affect the quality evaluation results very much. The first one is the ground truth assignment of image patches in the training set, while the second one is the pooling strategy to fuse scores of all patches together into a final quality score for one image. In this paper, we proposed a new IQA model based on visual saliency and gradient features. Particularly, we take the visual saliency map as a weighting map to change the patch's label. Moreover, after obtaining the predicted scores of patches using the CNN-IQA model, we combine the patch's scores according to the image gradient to get the final image score. This framework is trained on TID2013 dataset, and shows state-of-the-art performance on the LIVE and TID2008 dataset.
Study of Aspect level Sentiment Analysis and Word Embeddings

Reviews play critical part in decision making of consumers. It's becoming tedious for consumers to go through an ever-increasing number of reviews. Hence, it is becoming more important to analyze all these reviews and present in easily understandable format. User is unable to comprehend the essence of all these reviews from average ratings and also, they are unsuitable for providing information related to various features of that entity. Aspect or feature level sentiment analysis captures sentiments of features related to entities providing quick overview of each aspect of entities. This paper studies various methods and approaches for aspect detection and corresponding sentiment analysis. Also, various challenges faced while both sentiment analysis and aspect detection, and their approaches are discussed. Word embeddings for efficient sentiment analysis are also investigated. A system is proposed which uses machine learning as well as word embedding for aspect detection and corresponding sentiment analysis.
Trusted Phishing: A Model to Teach Computer Security Through the Theft of Cookies

Social engineering is a common practice to obtain information through the manipulation of users’ trust; while phishing refers to a computer attack model that is executed through social engineering. Combined with Cross-Site scripting (XSS), users’ curiosity to access their cookies and steal information from their sessions could be abused. The objective of this proposal was to teach concepts about cookie theft through vulnerable blogs. Our idea was to develop a blog vulnerable to XSS attacks to steal information from a test cookie that was created on the computer of users who accessed this site. Subsequently, the information corresponding to the attack was organized to present a new publication on the blog in order to explain to users how, with great care, we stole their cookies. Our goal was to challenge the trust and curiosity of our contacts in the social network Facebook and in the WhatsApp messaging application, so that they were tempted to visit this compromised blog whose content was false information. The results show that 182 contacts accessed the compromised blog and 100% of the users assumed that the blog was reliable. It was also shown that through this controlled attack, all 182 contacts learned about the theft of cookies that can be produced through fake blogs.
Speckle Noise Removal for SAR Image Based on G<sup>0</sup> Distribution Combining Total Variation and Total Curvature

In order to remove speckle noise of synthetic aperture radar (SAR) images, we proposed a high order variational model based on G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> distribution. Specifically, the model combines the total variation(TV) and total curvature(TC) regularizations. Besides, considering the terrain backscatter, we derived a new data fidelity term. Thus the new variational model can reduce the staircase effect, meanwhile effectively preserve the image features such as the edge, corner and fine details. Since the model has the characteristics of nonlinear, non-convex and non-smooth, we transformed it into an alternating optimization problem by importing auxiliary variables. Then, we designed a fast numerical approximation iterative scheme for proposed model. Qualitative and quantitative experiments on both synthetic and real SAR images were implemented to indicate the advantages of the proposed model and the high computation efficiency of the designed algorithm.
NetMerger: Predicting Cross-network Links in Merged Heterogeneous Networks

Previous work on link prediction focuses on single network settings or transferring knowledge between two networks for predicting intra-network links. However, many real-world applications involve multiple networks, and we need to predict cross-network links in these networks instead. For example, when mergers or acquisitions happen between two companies, the two business networks need to be merged into one. We may want to promote cross-network links, such as connections between the users in PayPal with those in x.com, to speed up the merger and integration of the two business networks. Considering recommending cross-network links can significantly speed up the business integration, in this paper, we study the problem of cross-network link prediction in merged heterogeneous networks. The goal is to predict potential links between multiple kinds of nodes across the two merged networks. It is also highly challenging because little or no information is previously known about the cross-network links, i.e., a cold-start problem. We proposed an approach, called NetMerger, which first learns network embeddings of the merged networks based upon their network structures and then makes cross-network link predictions. We compared our method with baselines on two real-world datasets. The results demonstrated the effectiveness of our proposal.
The Research on Minimum System Construction Management Based on Water Temperature Detector

Abstract The operation of the system greatly affects our work efficiency. We need to improve our system construction management. Therefore, we will use the eight-way detection method to study how to manage the minimum system with the water temperature detector. The research results show that the design and construction of the minimum system has passed the temperature detection of DS18B20 in eight, the system is stable, and the effect of the minimum system of process management is achieved.
An inertial measurement unit aided binocular stereo vision calibration for field applications

Digital image correlation is an effective way for accurate dimensional measurements with merits of non-contact and high precision abilities. Accurate calibration for the binocular stereo vision system is critical. A simple calibration method has been proposed in this study with the use of an inertial measurement unit (IMU) on each imaging station. As the IMU is aligned with the optical axis of the camera, the orientation of the camera is known. This helps in determining the rotation matrix of the extrinsic parameter of the camera based on the epipolar constraint relationship. The experimental results show that the proposed method has good accuracy and flexibility
Sign Sequence based Gesture Recognatiton Using MEMS Accelerometer for Augumentad Reality Applications

Abstract The Increase in Human Machine Interactions in our daily life is getting important day by day. Physical gesture will greatly ease the interaction process and enable humans to more naturally command the machinery like computers. In this hand gestures are mostly used as the control mechanism in virtual reality. To ease these physical gestures MEMS accelerometer can be used which can recognize the several hand gestures. In the real world the physical gesture machinery control is already in existence such as vision system or data glow but these are relatively expensive. By the usage of MEMS, the cost can be reduced and method can be implemented more effectively. This project deals with an accelerometer-based gesture recognition application. This application consists of a tri-axial accelerometer and same is connected to raspberry pi through wire. The triaxial accelerometer measures the acceleration signals generated by a users hand motions to identify the character. The code for application program in raspberry pi is written using python programming language. The text is displayed on the monitor of the PC.
Design and Analysis of K-SVD Algorithm for the Noise Removal Using MATLAB

Abstract This paper was proposed to developed a dictionary learning algorithm to minimize or remove the noise from the noisy pixels present in the given data sets without distorting the edges and preserving the fine particulars and details of the image for removing the noise there are three stages named INTERPOLATION, OMP(orthogonal matching pursuit)and finally K-SVD dictionary learning matrix algorithm in this algorithm there are different methods for removing different level of noises present in the image by using the masking by using this algorithm noise can be from the pixel range by checking the each and every pixel whether the pixel is a noisy pixel or noise free pixel only noisy pixels are passed the noise removal process so noise free pixels would not get degraded or blurred or changed in their property this K-SVD algorithm can remove the noise Color Images, Black and White Images and Grey Scale Images The algorithm can handle noise level
Selection of Precise Sensors for Wig-Craft Altitude Control

The paper describes the problem of ekranoplane (WIG-craft) altitude stabilization with great accuracy above the disturbed sea surface. Influence of sea waves and water spray makes the conditions for any location altimeter operation at ekranoplane unfavorable and forces to select carefully the type of altimeter to ensure high measurement accuracy and reliability. The opportunities of different types of altimeters use at ekranoplane are analyzed.
Image Automatic Counting Method by Using Deep Transfer Learning

Abstract In view of the narrow long room, fast speed and roll sport while outside the room, single camera couldn’t achieve the measurement of big cargo extraction. This paper puts forward an intersection measurement system of multiple ultra-short focus cameras and high-speed cameras. The system can achieve the parameters of wholly sport process by measuring the marked points on cargo, coordinate conversion and data registration. Measurement data show the trajectory and attitude parameters truly. Measurement precision satisfies the requirement of subject.
A Data-driven Approach to Solve a Production Constrained Build-order Optimization Problem

Production constrained build-order optimization problems challenge artificial intelligence research in computer game applications due to an uncertain set of constraints. Traditional approaches provide subjective values in the constraint formulation therefore resulting in unexpected performance of the optimal build-order in a game. In this article, we propose a data-driven approach to solve a build-order optimization problem in StarCraft. We formulate the constraint by learning the parameter values from game replay data, which complements more precise problem formulation. To solve the optimization, we use the improved genetic algorithm by learning initial solutions from the data. We show the performance of the data-driven methods in a StarCraft simulation platform.
Autonomous Navigation with Improved Hierarchical Neural Network Based on Deep Reinforcement Learning

Compared with traditional navigation strategies in normal environments, the unmanned vehicles in battlefield environments require better navigation strategies. This research formulates the autonomous navigation in battlefield environments as a markov decision process (MDP) and introduces deep deterministic policy gradient (DDPG) to obtain the continuous control signal. Meanwhile, the curriculum learning is employed to increase utilization of samples in this research. Inspired by the biological mechanism, an improved hierarchical neural network is proposed to refine the input information, which plays a better role in coordinating the choice of agent's behavior. Experimental results show that the models we proposed are able to acquire effective navigation strategies without knowing the whole information of environment. At the same time, it is proved that the hierarchical neural network and the curriculum learning are effective for improving efficiency of learning and generalization capability of models.
Conversion of two Photons with Different Color by a Single Waveguide for Quantum Networks

Quantum communication would enable opportunities such as secure communication [1]. A quantum network with quantum nodes and quantum channels is necessary for this. The realization of such a quantum network requires the ability to process, store and send photons over long distances. It is not very likely that a single physical system can accomplish all these operations. Therefore, dissimilar quantum systems have to be connected in future quantum networks.
A Fault Detection AES Scheme for Resource-Constrained Embedded Systems

The fault injection attack is an effective cryptanalysis technique against symmetric and asymmetric encryption algorithms., several countermeasures that detect errors in processing have been proposed in order to protect the Advanced Encryption Standard (AES) against Differential fault analysis (DFA). In this paper, a parity error-detection scheme has been presented in order to secure AES 32-bits embedded systems with resource constraints. This scheme is based on parity comparison between the correct parity of the round output and the predicted parity according to the processing steps of the AES round. The proposed AES hardware scheme was designed and synthesized using the Xilinx Virtex-5 FPGA. The experimental results show that the area overhead and the decreasing time are about 2.5% and 22% respectively. The proposed countermeasure achieves high fault coverage reaching 93 %.
Large-scale Global Optimization based on ABC Algorithm with Gbest-guided Strategy and Opposite-based Learning Technique

A novel approach for better solving large scale global optimization (LSGO) problem, which incorporates an enhanced artificial bee colony (ABC) algorithm and adaptive neighborhood search technique, is presented in this paper. In the enhanced ABC algorithm, the gbest-guided strategy is employed to better exploit a local area, while an opposite-based learning technique is simultaneously used to enhance its exploration capability. In addition, a self-adaptive neighborhood search strategy with Gaussian and Cauchy operators is used to balance the local and global search process. Experimental results on 20 benchmark functions of CEC2010 can not only verify the effectiveness and efficiency of this method, but also indicate the enhanced ABC algorithm with gbest-guided strategy and opposite-based learning technique can achieve comparable or better solution quality with respect to some other approaches in LSGO.
A Prediction Approach of Hot Electric Power Researches for Heterogeneous Time Spans

Abstract Recently with the rapid growth of electric power literatures, it is hard to artificially track and process hot electric scientific researches. In the past most, professionals use simple statistics to get high-frequency words, which is time-consuming and ignores the similarity between words. Moreover, different researchers have different requirements for prediction time span. In the paper we propose a prediction system for hot electric scientific researches and gives its implementation. It is based on our previous work and we improve it to suit indefinite prediction period. The proposed embedded RNN prediction model is flexible for heterogeneous time spans and can return prediction results rapidly and accurately. Our extensive experiments demonstrated that our approach has acceptable precision ratio as well as training time in comparison to SVM, RNN and linear regression algorithms. It also performs better when the embedded layers are multiple.
My Smartwatch Is Mine – Machine Learning Based Theft Detection of Smartwatches

Smartwatches are small but powerful devices which make daily life easier and are without a doubt desirable objects for thieves. In this paper, we present a first machine learning based theft detection approach running in a user’s domain, relying solely on data of his smartwatch and thus not involving third parties. Hence, we collect data from multiple persons to first show that there is an exploitable structure within data provided by a smartwatch’s inertial sensors and perform user identification on the basis of that data. Then we will present and thoroughly evaluate our robust, efficient and fast (within seconds) theft detection algorithm which has both a low false rejection rate and an even lower false acceptance rate.
Modelling Soft Tissue Deformation Based on Spatial Kriging

Abstract People can only obtain incomplete information when needle get into soft tissue. The actual conditions limit monitoring results. The kriging, as a statistical interpolation method, can effectively transform discrete point information into facial continuous information. On the basis of displacement data of pork soft tissue, the correlation parameter setting and optimal semi-variance model selection of kriging were studied to determine the kriging interpolation results. The results show that the accuracy of the kriging model can reach millimetre level, the maximum error is about 0.8mm, the average standard error is 0.35mm, and the calculation is simple. It can help doctors provide references when the soft tissue cannot be directly observed, and predict the deformation within the soft tissue.
Three Dimensional Image Reconstruction Method Based on Two Dimensional Radar Image and Optical Image

Since the building room consists mainly of three brick walls and one glass wall, Unmanned Aerial Vehicle (UAV) equipped with optical sensor and radar sensor detects targets only from a specific view during monitoring the building fire explosion site. The unknown height information of combustible objects in the two dimensional (2-D) radar image and the unknown range information of combustible objects in the 2-D optical image makes it difficult to get comprehensive information. By fusion of the range and azimuth information extracted by the 2-D radar image and the height and azimuth information extracted by the 2-D optical image, in this paper, a new algorithm is proposed to reconstruct a three dimensional (3-D) image with the correct range, azimuth and height information of combustible objects. The simulation results validate the correctness and feasibility of the algorithm.
Relation and Fact Type Supervised Knowledge Graph Embedding via Weighted Scores

Knowledge graph embedding aims at learning low-dimensional representations for entities and relations in knowledge graph. Previous knowledge graph embedding methods use just one score to measure the plausibility of a fact, which can't fully utilize the latent semantics of entities and relations. Meanwhile, they ignore the type of relations in knowledge graph and don't use fact type explicitly. We instead propose a model to fuse different scores of a fact and utilize relation and fact type information to supervise the training process. Specifically, scores by inner product of a fact and scores by neural network are fused with different weights to measure the plausibility of a fact. For each fact, besides modeling the plausibility, the model learns to classify different relations and differentiate positive facts from negative ones which can be seen as a muti-task method. Experiments show that our model achieves better link prediction performance than multiple strong baselines on two benchmark datasets WN18 and FB15k.
Advanced Robust Data Hiding Using Visual Cryptography

Visual cryptography is an algorithm used for encrypting digital media like images, text etc in which the decryption can be performed by visual mechanical operations rather than using a computer. In this paper, a novel visual cryptography scheme with k-n secret sharing is proposed which are then hidden in envelope cover mages using LSB technique for secure data transmission. As the creation of n shares uses random data input, a random number generator algorithm is used for obtain inputs for share generation. This technique needs very less mathematical calculation compare with other existing techniques of visual cryptography on color images.
Motion Gesture Tracking and Recognition Using the Segmentation Modeling and Machine Learning

In the human-computer interaction, the moving gesture is prone to morphological changes while projected into a two-dimensional video space. In this paper, the Adaboost classifier is firstly used to detect the presence of gestures in a video frame to start the tracker, and the k-means algorithm is used for accurate Colour Space Modeling of hand gestures. The back projection is decomposed into a gesture motion region and a color interference region, and the Surendra algorithm is used to model the color interference region. Then the correctness of the tracking is performed by the Bhattacharyya Distance and the Perceptual Hash Coefficient. When the object is lost, the motion position of the gesture is re-detected by the Gaussian Mixture Model and the Bayesian Skin Color model. Finally, the tracked gesture is identified by a machine learning algorithm. The new method uses a combination of the feature space segmentation modeling and the probability density map to overcome the interference problem in motion tracking, and has a high tracking accuracy.
An End-to-End Method for Data Filtering on Tibetan-Chinese Parallel Corpus via Negative Sampling

In the field of machine translation, parallel corpus serves as the most important prerequisite for learning complex mappings between targeted language pairs. However, in practice, the scale of parallel corpus is not necessarily the only factor to be taken into consideration for improving performance of translation models due to the quality of parallel data itself also has tremendous impact on model capacity. In recent years, neural machine translation systems have become the de facto choice of implementation in MT research, but they are more vulnerable to noisy disturbance presented in training data compared with traditional statistical machine translation models. Therefore, data filtering is an indispensable procedure in NMT pre-processing pipeline. Instead of utilizing discrete feature representations of basic language units to build a ranking function of given sentence pairs, in this work, we proposed a fully end-to-end parallel sentence classifier to estimate the probability of given sentence pairs being equivalent translation for each other. Our model was tested in three scenarios, namely, classification, sentence extraction and NMT data filtering tasks. All testing experiments showed promising results, and especially in Tibetan-Chinese NMT experiments, 3.7 BLEU boost was observed after applying our data filtering method, indicating the effectiveness of our model.