Reviewerly/exHarmony huggingface dataset:
A Non-Negative Matrix-Factorization-Based Network Embedding Approach for Hybrid Recommender Systems

Currently, recommender systems are used in almost all online applications. A lot of algorithms have been proposed to predict user's preference including content filtering, collaboration filtering and hybird model. Traditional hybrid recommender system needs to combine the advantages of single element matrix factorization to reduce time consumption, however, existing single element based collaborative filtering methods need to further optimize node representation through attribute information. To better represent users and items accurately, network embedding method aiming at mining the low dimensional vector embedding of nodes in the user-item bipartite graph of the recommender system, becomes more and more active. In this paper, we propose an non-negative matrix factorization based network embedding approach for hybrid recommender systems, which learning representation for users and items on the basis of three weight edges, and an objective function for network embedding, which is suitable for recommendation system. The function is obtained by jointly optimizing NMF approach, which enables the learned representations of nodes to preserve the microscopic of networks. In addition, we provide complete updating rules to compute parameters of our model, which adopts single element based non-negative matrix factorization model, and it is very suitable for recommender system integrating content-based and collaborative filtering, and improve the accuracy of the model and ensure that it finds the best point. Experimental results on a variety of real-world networks indicate that the performance of the proposed method is superior to the state-of-the-arts.
Scattering Watermark on DICOM Images

In recent years, image has become the most popular format for digital data sharing. Unlike the common image formats such as JPEG, medical images uses special image formats like DICOM. The integrity among the medical image and a patient's identity is undividable for any diagnostic purpose in medical application. This can be achieved by embedding the patient's personal details as invisible watermark on their medical images. As one's personal information needs to be kept confidential, performing encryption on watermark information can serve this purpose. Edges are considered as vital regions in medical images as it contains precise information required for diagnostic purpose and referred as region of interest. This paper suggests a method for the insertion of encrypted watermark on DICOM image via LSB embedding procedure to provide ownership identity and authentication. The proposed method preserves the edge information in the cover image by inserting the watermark only on non edge pixels of the cover image. Besides the encryption, additional security is given to watermark information via the randomness obtained from the PRNG in the selection pixel positions for watermark embedding. The proposed algorithm was implemented using Python. The analysis performed on the watermarked DICOM image through standard metrics such as MSE, PSNR and SSIM confirms the imperceptibility of the embedded watermark information.
Balancing Power And Performance In HPC Clouds

Abstract With energy consumption in high-performance computing clouds growing rapidly, energy saving has become an important topic. Virtualization provides opportunities to save energy by enabling one physical machine (PM) to host multiple virtual machines (VMs). Dynamic voltage and frequency scaling (DVFS) is another technology to reduce energy consumption. However, in heterogeneous cloud environments where DVFS may be applied at the chip level or the core level, it is a great challenge to combine these two technologies efficiently. On per-core DVFS servers, cloud managers should carefully determine VM placements to minimize performance interference. On full-chip DVFS servers, cloud managers further face the choice of whether to combine VMs with different characteristics to reduce performance interference or to combine VMs with similar characteristics to take better advantage of DVFS. This paper presents a novel mechanism combining a VM placement algorithm and a frequency scaling method. We formulate this VM placement problem as an integer programming (IP) to find appropriate placement configurations, and we utilize support vector machines to select suitable frequencies. We conduct detailed experiments and simulations, showing that our scheme effectively reduces energy consumption with modest impact on performance. Particularly, the total energy delay product is reduced by up to 60%.
Design of IOT based facial recognition door access control locker services at tourist attractions in Bali

Abstract Safety and comfort are things that everyone wants to get on a trip. Every tourist who wants a vacation to one of the tourist destinations will prioritize these two things. Leisure in travel is influenced by the availability of supporting facilities. From accommodation, restaurants to facilities provided by tourism actors in each tourism object. One problem is the lack of facilities in attractions such as luggage storage (lockers), many tourists in enjoying tourism objects carrying luggage such as backpacks; groceries to backpacks for tourists are classified as backpackers. Of course this will disrupt the comfort of traveling. In the research conducted, researchers wanted to design a system of goods storage services as a form of supporting industry 4.0 in the tourism environment. By designing an image of the storage media of goods by implementing access control for doors in each locker based on image processing supported by a microcontroller. The method used that focuses on facial recognition as image input using the PCA (Principal Component Analysis) method, which can measure the Euclidean distance in an object’s face image. The design as a blueprint makes it easier to develop the next item of storage media (lockers).The results of this study indicate that the facial recognition method used is able to extract facial features and be recalibrated to activate the locker that is owned by the face object.
Overview and Prospect of secure communication based on Federated learning

Abstract With the increasing amount of data, the in-depth application guided by data prediction has ushered in the climax of development However, due to the limited depth and breadth of the data owned by each unit, the data among different units are not common, so more and more data islands are formed in the city. Data sharing has become a major obstacle to the construction of smart city. To solve the problem of data sharing, in 2016, Google first proposed the concept of Federated learning. However, the bandwidth of wireless communication has not increased significantly. As a result, the bottleneck has shifted from previous computing to today’s communication problems. During my internship in a company, I participated in a project to provide platform support for federal learning. The purpose of the platform is to facilitate the transformation of federal learning academic achievements into commercial products. Based on this project and the current situation of federal learning, this paper summarizes the secure communication problems of federal learning.
Image classification by Distortion-Free Graph Embedding and KNN-Random forest

Image classification algorithms play an important role in various computer vision problems such as object tracking, image labeling, and object segmentation. A number of methodologies have been proposed to tackle this problem. One of the possible approaches employed extensively in the literature is to represent an image as a graph based on its handcrafted features. However, recent advancements in deep neural networks have shown their ability to learn more discriminative and representative features. Therefore, the deep features have become considerable alternatives of hand-crafted ones. In this paper, we propose a novel framework based on distortion-free graph embedding using deep features and KNN-Random forest. Our method outperforms the state-of-the-art graph embedding-based image classification approach for the task of image classification. Particularly, the proposed framework obtains 97.5% top - 1 image classification accuracy for the ImageNet dataset for 5 classes and 93.3% for 10 classes.
Rectification of Camera Captured Document Images using Component Analysis

Image distortion is a common problem when one captures a document page by a digital camera or mobiles from a certain angle. It results a horizontal and vertical distortion of the text lines in the digitized document. This type of distortions will not only impair the readability, but also reduce the accuracy of any Optical Character Recognition system. In this paper we propose a novel technique to identify the entire text-region within the document page and then we apply the homography transformation on the corner points of estimated test-region for rectifying its distortion. Compared to other existing methods, the present system is computationally less expensive. The experimental result shows that, our method is able to correct any type of distortion effectively and outperforms the state-of-the-art methods.
Online Multiple Object Tracking with Reid Feature Extraction Network and Similarity Matrix Network

Abstract In multiple object tracking(MOT), data association is a crucial part. By constructing similarity loss matrix for trajectories and detections, they can be matched correspondingly by using Hungarian matching algorithm. However, the similarity loss is often obtained by calculating the Euclidean distance or other handcraft distance metrics of features extracted between objects, which may not be robust enough, resulting in matching inaccuracy. In this paper, we propose a novel MOT method with applying deep learning to feature extraction and data association. We firstly design the appearance feature extraction network(AFN) to learn effective features by training it on a large-scale person re-identification dataset(reid). Then, we propose the similarity matrix estimation network (SMN) to obtain reliable similarity by training it on the public MOT Challenge dataset, MOT17. Additionally, the similarity matrix output of SMN includes the dummy objects, which are used to deal with the association problems of object missing and object appearance between frames. In the end, our proposed MOT method is evaluated on MOT15, MOT17 and ablation study is carried out.
Spectral Green's-function method in driven open quantum dynamics

A method based on spectral Green's functions is presented for the simulation of driven open quantum dynamics that can be described by the Lindblad master equation in Liouville density operator space. The method extends the Hilbert space formalism and provides simple algebraic connections between the driven and nondriven dynamics in the spectral frequency domain. The formalism shows remarkable analogies to the use of Green's functions in quantum field theory, such as the elementary excitation energies and the Dyson self-energy equation. To demonstrate its potential, we apply the method to a coherently driven dissipative ensemble of two-level systems comprising a single ``active'' subsystem interacting with $N$ ``passive'' subsystems---a generic model with important applications in quantum optics and dynamic nuclear polarization. The method dramatically reduces the computational cost compared with simulations based on solving the full master equation, thus making it possible to study and optimize many-body correlated states up to the physically realistic limit of an arbitrarily large $N$.
Crowd Trajectory Prediction Based on Surveillance Data

In recent years, with the increasing flow of people in public places, people's awareness of safety and personal space have continuously increasing. Therefore, a large number of surveillance cameras are installed in public places, generating a large amount of surveillance data. Trajectory prediction based on the surveillance data has become a hot research direction in the field of computer vision and crowd behavior analysis. It can be used in traffic control and management, road planning, prevention of safety accidents and many other critical domains. The purpose of this paper is to propose a more efficient trajectory prediction algorithm. This paper proposes to use the convLSTM (Convolutional Long Short-Term Memory) neural network to predict pedestrian trajectory, and obtains better results than its original model LSTM (Long Short-Term Memory) network in the acquisition of space-time relationships. Combined with the mixed density network (MDN), the network model uses a probability distribution instead of a definite location to predict the trajectory, which greatly improves the efficiency and accuracy of trajectory prediction.
Gaze Estimation with Easy Calibration Method

In the corneal reflection method for gaze estimation, the user is asked to look at multiple calibration points with known lines of sight, and the central pupil point is captured for each vision angle. Therefore, the center of the eye in the images can be acquired for each known eye rotation angle. However, because the spherical center of the eye is captured in two-dimensional images and because measurement errors are, of course, included, in order to map eye rotation angles from two-dimensional images, normally, the approximate solution is calculated by using the least squares method in the overdetermined system linear equation. Normally, six coefficients are calculated from nine points of regard for calibration. This study proposes a new gaze estimation method that uses fewer calibration points while maintaining accuracy equivalent to using nine points.
Are You Watching Closely? Content-based Retrieval of Hand Gestures

Gestures play an important role in our daily communications. However, recognizing and retrieving gestures in-the-wild is a challenging task which is not explored thoroughly in literature. In this paper, we explore the problem of identifying and retrieving gestures in a large-scale video dataset provided by the computer vision community and based on queries recorded in-the-wild. Our proposed pipeline, I3DEF, is based on the extraction of spatio-temporal features from intermediate layers of an I3D network, a state-of-the-art network for action recognition, and the fusion of the output of feature maps from RGB and optical flow input. The obtained embeddings are used to train a triplet network to capture the similarity between gestures. We further explore the effect of a person and body part masking step for improving both retrieval performance and recognition rate. Our experiments show the ability of I3DEF to recognize and retrieve gestures which are similar to the queries independently of the depth modality. This performance holds both for queries taken from the test data, and for queries using recordings from different people performing relevant gestures in a different setting.
OACD-SA: Online Aspect Category Detection for Sentiment Analysis using Unsupervised Learning

The web is a vast repository of consumer reviews, but it is impossible to go through all reviews to arrive at a fair evaluation when we need to buy any product or service. A recommender system that can quickly categorize reviews is highly needed. A subtask of this system is to categorize the general aspects and check sentiments in consumer reviews to provide a proper suggestion or recommendation to any user. The proposed Online Aspect Category Detection system for Sentiment Analysis (OACD-SA) uses the SemEval 2014 Task 4 as a baseline for `Aspect detection' and then goes on to use online reviews given by users as text corpus for any product or service. Unsupervised learning method along with association rule mining is used to discover aspect categories from a corpus. It uses a novel spreading algorithm to rate every feature which can then be used for evaluation of the overall rating for that product. The unsupervised learning method returns a F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of 68% for aspect detection and an aspect sentiment classification accuracy of 66% on baseline task, thus providing robust aspect categorization with a quick response time for any online product. Therefore, the proposed system can be to improve their product or service quality.
The dynamic behavior investigation of electric power wheelchair during the obstacle avoidance

Abstract The increment of the People with Disabilities (PWDs) keep increasing in each year and an urge of assisting these PWDs is demanding. Commercial manual wheelchair eases the mobility of the PWDs but there is side effect for the manual wheelchair users that is pain on shoulder area due to extensive daily propulsion of manual wheelchair for mobility. Therefore, this paper presents an approach towards the autonomous wheelchair whereas concerning the PWDs that have disabilities from upper to lower limbs. These paper investigates the dynamic behavior of the autonomous wheelchair during the obstacle avoidance. The experiment conducted on the Electric Power Wheelchair (EPW) and several participants divided base on gender and age for this particular investigation. This experiment uses the EPW as a first step before taking next step towards self-navigation system in order to understand the behavior of the autonomous wheelchair by using the intervention of human input on EPW. The participants will maneuver the EPW via joystick with several speed justifications that is determined during the pre-experimental set-up. This study focused on the changes in speed of both left and right tires and yaw angle during the obstacle avoidance. The data will be used as a reference for the autonomous wheelchair during the obstacle avoidance. The data that maneuvered manually by the participants also serve as the human-machine relationship whereas the data will be interpreted into the control systems that will be developed for the autonomous wheelchair. Based on the results, the changes of velocity could be seen from both left and right tires during the obstacle avoidance base on gender is different but the trend of the results significantly same for both male and female.
Reconstruction of Single-Frame Solar Speckle Image with Cycle Consistency Loss and Perceptual Loss

High resolution reconstruction of solar speckle image is an important research subject in astronomy and solar physics. The common method uses the statistical information of a group of low-resolution images to reconstruct a high-resolution image through a specific algorithm. When this method is applied to the reconstruction of solar speckle image, the calculation process is complex and the reconstruction time is long because of the large amount of short-term obtained data. In this paper, based on the idea of image to image conversion, the single frame solar speckle image reconstruction method is proposed by using the generative adversarial networks (GAN), and the mapping from solar image to high-resolution image is established by using the cycle generative adversarial networks (CycleGAN). Due to the lack of high-frequency information in single frame speckle image, perceptual loss is introduced in CycleGAN to solve the problem of too smooth image reconstruction and lack of details. Combined with cycle consistent loss and perceptual loss, the quality of reconstructed image is improved. The experimental results show that the reconstruction accuracy of the proposed method is not less than that of the traditional algorithm, but in the time efficiency of the algorithm, it has more advantages than the traditional algorithm.
The transfer alignment based on UKF for low-cost INS missile-borne weapons

The demands for precision strikes and weapon guidance are increasing in modern warfare. However, for low-precision, low-cost MEMS INS missile-borne weapons, due to the low accuracy and poor stability of the navigation system, the initial attitude error angle of the transfer alignment is relatively large, so the conventional assumption that the small angle is no longer effective. In order to solve the above problems, this paper proposes a MEMS-SINS transfer alignment solution based on large declination-based odorless Kalman filter (UKF). The simulation results show that the attitude error angle estimated by UKF is very close to the real value and meets the application requirements.
Research on a Miss Distance Measurement Method Based on UAV

In order to more intuitively and effectively evaluate the effects of shooting tests and overcome the shortcomings of traditional visualization methods under the background of intelligent construction, this paper proposes a method for miss distance measurement based on Unmanned Aerial Vehicle (UAV). Three aspects of target detection and tracking, water column detection and tracking and miss distance measurement are analyzed and studied, and the effectiveness of miss distance detection method is verified by measured video images.
Cost-Sensitive Tree Induction

In most typical data mining research, there is the assumption that all prediction errors are equally important. Similarly, all features are usually treated as equal whenever available. In real-world applications in medicine or business, such an idealization is not always possible. Often, a cost-sensitive prediction is desirable, and it should account for various cost types, starting with the asymmetric costs (losses) associated with predictive errors.
Multi-approaches on scrubbing data for medium-sized enterprises

Tidy and fit for purpose data are the prerequisite for analyzing data and for guaranteeing good business decisions. Data Scrubbing or data cleaning is the process of identifying errors and inconsistencies in the data and fixing these errors before analyzing the data. Organization's decisions rely on Data Quality which makes data scrubbing a very important step towards their productivity. Untidy data includes; importing data from multiple sources, missing values or corrupt records, data types mismatch, special character removal or discarding duplicates. Current research is lacking the latest data scrubbing techniques practiced by the medium sized enterprises. This article highlights possible data errors, literature review, and data science project life cycle. The document explains how to clean data using Python libraries for exploratory data analysis such as Pandas, NumPy, Scikit- Learn and libraries for data visualization for example matplotlib, Seaborn, and Plotly.
Image Raindrop Removal Method for Generative Adversarial Network Based on Difference Learning

Abstract Due to the interference of the external environment such as rainy weather on the camera, raindrops can easily adhere to the lens and seriously affect the quality of the photos taken. Therefore, it is of great significance to remove raindrops from the image and improve the quality of the photo. In this paper, a raindrop method for generative adversarial network images based on differential learning is proposed. The general generative network is to input images with raindrops and output clean images. The generative network in this paper does not directly output clean images, but learning the difference between images with raindrops and without raindrops, then subtract the learned difference from the image with raindrops to generate a clean image. In order to learn this difference more effectively, adding reconstruction loss to the generative network, the pre-trained VGG-16 network is used to extract the difference between the generated image and the real image features and calculate the mean square error. The experimental results show, the method in this paper can not only remove the raindrops in the image well, but also reconstruct the image information of the part blocked by the raindrops. The image processed by the algorithm in this paper is tested using the yolov3 target detection algorithm, which can significantly improve the recognition accuracy of the detection algorithm.