Reviewerly/exHarmony huggingface dataset:
An Augmented Reality Model Physical Transformation Learning

Every object is made up of small particles such as atoms, molecules or ions. In other words, each particle attracts other particles on its own. There are three forms of common objects, namely solid, liquid and gas. Objects can change from one form to another when heat energy is absorbed or released. This change is known as the physical transformation of an object. This study aims to create an application that can model the process of physical transformation of an object through 3D shapes and animation. The purpose of this research can be problems in the form of changes in a form that is felt to be less implementative, so that the model of this technology exists to be able to fill that gap. The augmented reality method used is the Tracking Based Marker. The results of testing the device shows that the application is made to run smoothly and is suitable for use on Android versions 6.0 and above with a 16: 9 screen aspect ratio. While the results of the study based on questionnaires distributed to senior high school students and college students show that each question from the questionnaire received agree and strongly agree answers ranging from 68% to 89% which indicates positive results.
Term Frequency-Inverse Document Frequency Answer Categorization with Support Vector Machine on Automatic Short Essay Grading System with Latent Semantic Analysis for Japanese Language

In this paper, conducted a research to increase accuracy of Japanese language automatic short essay grading system. Japanese short answers are processed with a supervised machine learning algorithm; Support Vector Machine (SVM) before entering the system that used Latent Semantic Analysis (LSA). The SVM is used to classify short answers topics that minimize error in assessing the essay. TF-IDF process is done as an input to the SVM to weigh every keyword in a sentence. Then, the result will be processed with LSA. LSA uses Singular Value Decomposition (SVD) as the main process and Frobenius Norm as the final calculation from the result of SVD. Using linear kernel in SVM, the accuracy obtained in classifying short answers topics from Japanese-written short answers is 96.36% with 10.0 to 100.0 penalty values and 0.5 training portion. The accuracy score obtained from LSA is as much as 87.15% average with the input of TDM that shows frequency of a word's occurrence.
Intuitive gesture-based control system with collision avoidance for robotic manipulators

Purpose This paper aims to present an intuitive control system for robotic manipulators that pairs a Leap Motion, a low-cost optical tracking and gesture recognition device, with the ability to record and replay trajectories and operation to create an intuitive method of controlling and programming a robotic manipulator. This system was designed to be extensible and includes modules and methods for obstacle detection and dynamic trajectory modification for obstacle avoidance. Design/methodology/approach The presented control architecture, while portable to any robotic platform, was designed to actuate a six degree-of-freedom robotic manipulator of our own design. From the data collected by the Leap Motion, the manipulator was controlled by mapping the position and orientation of the human hand to values in the joint space of the robot. Additional recording and playback functionality was implemented to allow for the robot to repeat the desired tasks once the task had been demonstrated and recorded. Findings Experiments were conducted on our custom-built robotic manipulator by first using a simulation model to characterize and quantify the robot’s tracking of the Leap Motion generated trajectory. Tests were conducted in the Gazebo simulation software in conjunction with Robot Operating System, where results were collected by recording both the real-time input from the Leap Motion sensor, and the corresponding pose data. The results of these experiments show that the goal of accurate and real-time control of the robot was achieved and validated our methods of transcribing, recording and repeating six degree-of-freedom trajectories from the Leap Motion camera. Originality/value As robots evolve in complexity, the methods of programming them need to evolve to become more intuitive. Humans instinctively teach by demonstrating the task to a given subject, who then observes the various poses and tries to replicate the motions. This work aims to integrate the natural human teaching methods into robotics programming through an intuitive, demonstration-based programming method.
A Dynamic Threshold Based Energy Efficient Method for Cloud Datacenters

Cloud computing has opened new avenues in the computing resource provisioning. It is providing affordable computing services to the users and opportunities to the cloud service providers to scale up their business. However, the upscaling resulted in the creation of huge datacenters running round the clock. Energy consumption in these datacenters is becoming a burning issue due to environmental hazards and operating costs. A novel method using dynamic threshold has been proposed in this article. Thresholds are used to migrate the virtual machines (VM) on physical machines (PM) for consolidation. A dynamic threshold selection method is used to reduce energy consumed by physical machines of the data centers. The upper and lower thresholds are set dynamically based upon the previous pattern of the CPU utilization. Experiments conducted on PlanetLab data show significant improvement in the energy efficiency without compromising SLA.
An Automatic Compensation System for Unclear Area in 360-degree Images Using Pan-tilt Camera

We conduct research using a 360-degree camera THETA. The ability to capture a wider range with a single camera is very advantageous for object recognition and classification. However, processing of images taken with a 360degree camera has problems such as image distortion caused by the lens and insufficient resolution in recognizing distant objects, so that it is difficult to classify the object in the corresponding area. In this paper, we propose a method for modifying unclear areas of 360-degree camera images using images taken with a couple of Pan-Tilt (PT) cameras those are installed on a same platform as THEA. By using this system with three cameras, it can be expected that object classification in an unclear area, which has been difficult in the past, is performed with high accuracy.
Forgery attacks on two provably secure certificateless signature schemes

Recently, Hashimoto and Ogata proposed a constant-size certificateless aggregate signature scheme based on bilinear pairings. Karati et al. constructed a new certificateless signature scheme without bilinear pairings. The schemes were proven secure against both Type I and Type II adversaries in the random oracle model under the hardness assumptions of the Elliptic Curve discrete logarithm problem and the Computational Diffie–Hellman problem. In this paper, we first show that Hashimoto and Ogata is insecure against a Super-Type I adversary who knows the user secret key associated to the replaced public key and suggest its improvement to prevent our attack. We also show that Karati et al.’s scheme is entirely broken: anyone can forge certificateless signatures on any messages for any identities from only publicly known information without using any secret information. Thus, the scheme is trivially insecure against the Type I adversary who can replace user public keys and the Type II adversary who knows the master secret key. The attack shows that their security proofs against the Type I and Type II adversaries are flawed since these forgery attacks are never reflected in the proofs. Thus, it should be proposed security models that cover various forgery attacks due to algebraic relations in the underlying groups.
Quantum speed limit for a three-qubit system in spin-chain environment with multisite interaction

We study the relationship between the quantum speed limit (QSL) time of a three-qubit system, and the quantum phase transitions (QPTs) of a spin-chain environment with the three-spin interaction. We find that the three-spin interaction can effectively manipulate the critical value of the QSL time. It makes the QSL time mark more clearly the quantum phase transition of the one-dimensional spin-chain models, especially the XX model. The dynamical evolution of the QSL time presents a periodic behavior in quantum-critical environment, whereas the three-spin interaction and external magnetic field can destroy this periodicity.
Highway Visibility Detection Method Based on Surveillance Video

Low visibility weather has seriously affected daily traffic management and safety of life and property. Aiming at the problem that highway in low visibility environment is prone to traffic accidents, a highway visibility detection method based on video surveillance is proposed, and the surveillance video data of each section are analyzed. Based on the traditional dark channel prior, the highway visibility detection method converted the surveillance video frame into gray image, and extracted the air transmittance and atmospheric light value contained in the surveillance image by combining the adaptive guided filtering algorithm and quadtree image segmentation algorithm. Then based on the visibility calculation model, the visibility distance of the road was estimated according to the actual distance and offset angle between the monitoring equipment and the detection object. The experimental results show that the optimized image processing method can solve the problems of low image clarity, color distortion, and poor scene adaptability. The detection of visibility error rate meets the accuracy requirements of highway visibility detection in China. Finally, a road real-time visibility detection and warning system is applied based on the corresponding early warning strategies of each visibility level.
OpenHI2 — Open source histopathological image platform

Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine. Pathological diagnoses are sensitive to many external factors and is known to be subjective. Only systems that can meet strict requirements in pathology would be able to run along pathological routines and eventually digitized the area, and the developed platform should comply with existing pathological routines and international standards. Currently, there are a number of available software tools which can perform histopathological tasks including virtual slide viewing, annotating, and basic image analysis, however, none of them can serve as a digital platform for pathology. Here we describe OpenHI2, an enhanced version Open Histopathological Image platform which is capable of supporting all basic pathological tasks and file formats; ready to be deployed in medical institutions on a standard server environment or cloud computing infrastructure. In this paper, we also describe the development decisions for the platform and propose solutions to overcome technical challenges including responsive region retrieval and viewing, virtual slide magnification, recording of diagnostic areas. These factors would promote OpenHI2 be used as a platform for histopathological images in real-world clinical settings. Furthermore, in research, OpenHI2 inherited the annotation functionality from the previous version, thus acquired annotations can be directly utilized by the newly added machine learning module which include popular machine learning models to perform tasks such as histology image classification and segmentation in the same environment. Addition can be made to the platform since each component is modularized and fully documented. OpenHI2 is free, open-source, and available at https://gitlab.com/BioAI/OpenHI.
A Study on Automatic Generation of Chinese Discharge Summary

Discharge summary, which summarizes a patient's health information during hospitalization, is very important for transferring information between the hospitalist and primary care physician. Discharge summary writing is a necessary but time-consuming job for physicians. Automatically generating discharge summaries using information technology is helpful to physicians, but challenging as discharge summaries are typically long and contain amounts of information to outline patient's reason for admission, labtests, examinations, diagnostic findings, treatments, and medication care plan. In this study, we propose a framework based on deep learning methods for automatic discharge summary generation. In the framework, we split information of discharge summary into two parts: 1) history information such as reason for admission, admission diagnosis; 2) outcome information such as discharge diagnosis and medication care plan, and deploy different neural networks to generate them separately. Hierarchical sequence labeling methods are proposed to select key sentences from existing documents, e.g., admission notes, progress notes, examination reports as the history information, and multi-task learning methods to predict the outcomes, e.g., diagnosis and medication care plan. Experiments on a Chinese corpus show that our approach has the ability to generate effective discharge summaries.
Building user Profiles Based on user Interests and Preferences for Recommender Systems

Nowadays, Web 2.0 allows users to express their opinions about items provided by various Internet services through reviews. These reviews towards particular items could contain a valuable information, such as the reasons why user like or dislike the item. This information can help more accurately describe a user for recommender system and, by that increase the accuracy of recommendations. Moreover, such reasons can reflect user preferences. However, extraction the user preferences from reviews is still a quite challenging task as reviews represented in the free text form. In this paper, a personalized recommendation approach based on user profiles of interests and preferences is proposed. In order to increase the accuracy of personal recommendations, user interests are obtained from item general description attributes such as item tags or categories, whereas user preferences are extracted from users' reviews. To evaluate proposed user profiles, we provided the top personalized recommendations for users. The experiments are conducted on a real-world dataset. We select candidates for recommendations based on user interests profile, and then, narrow and sort them by taking into account user preferences profile. We compare the results with several stateof-the-art recommendation algorithms using precision, recall and F1 metrics for evaluation. The results show the effectiveness and efficiency of our proposed approach in comparison with different recommendation algorithms.
An FPGA based Parallel Implementation for Point Cloud Neural Network

Point cloud is an important type of data structure for 3d visual information processing. Convolutional Neural Networks(CNNs) like Pointnet [4] are proposed for effective feature learning of point clouds recently. Pointnet is a novel type of neural network that directly consumes point clouds whcih can select informative points. In this paper, we design a fast, low-power FPGA accelerator for Pointnet. Most of the current FPGA accelerators currently do not handle point cloud data very well. The network designed for the FPGA implement, called O-Pointnet, optimizes Pointnet's nonlinear implementation, multi-layer sensing layer and maximum pooling layer by analyzing the implementation of the network and the space-time complexity. Experiments show that the computational performance is 1.208 GMAC/s with 2.149W power dissipation, and the the accuracy rate of the O-Pointnet is 88.48%, which outperforms previous approaches.
Crypto-watermarking of images for secure transmission over cloud

AbstractAbstractWith ever increasing usage of Cloud based data storage in various domains, the security and authentication of such data is a matter of concern for the end user. Data centres being the third party servers are always prone to data leakage. Moreover investigating an illegal activity over cloud is not possible. The proposed scheme provides a solution to the highly confidential medical data(image) of patients to be shared with authentic users (hospital staff) over the cloud. This work proposes a collective blind crypto-watermarking scheme that provides not only security but also authentication and ownership protection to the medical information of the patients. The scheme is based on a bit level extraction and embedding of watermark in spatial domain. A chaotic mapping based substitution initially obfuscates the host image then a watermark image is embedded into the host image. At next stage the watermarked image is encrypted in fractional Hartley transform domain. This crypto-watermarked image is shared over vulnerable cloud storage. The chaotic parameters and the transform orders serve as the secret keys. The results and analysis indicate that proposed scheme is secure against potential attacks and provides a solution to the security risks associated with the cloud based data storage services.Subject Classification: Information ScienceKeywords: Crypto-watermarkingCompound chaotic mapFractional Hartley transformCloud computingBlind watermark extraction
Genetic Architecture Search for Binarized Neural Networks

In order for deep learning applications to run efficiently on low-power edge devices, including mobile and internet-of-things systems, it is important to reduce their computational and memory requirements. Binarized neural networks have shown promise in this area, but these are typically designed using existing architectures based on floating-point number representations. A more promising approach is to apply network architecture search algorithms to find optimized binarized architectures. In this paper, encoding schemes for the genetic algorithm search of binarized networks are described. The simulation results demonstrate the effectiveness of the proposed method.
Research on New Energy Grid Connection and Electric Vehicle Charging Fault Resolution Method

With the development of energy industry, the power generation and transportation industry have developed from traditional petrochemical energy to renewable energy (RE). Many wind farms and photovoltaic (PV) power plants have been set up. More and more electric vehicles (EV) and EV charging stations are connected to the power grid which has brought new challenges to the safe and stable operation of the power grid. The EV grid-connected charging is accompanied by various faults. The unstable RE also induced lots of new problems. In this paper an intelligent test platform was designed to realize the grid-connected RE station and EV charging station online diagnose and fault prediction. IoT, big data analysis technologies were used to process the collected various characteristic dimensions of multi-heterogeneous data. Based on Random Forest (RF) algorithm machine learning technology was used to realize the online diagnose and fault prediction.
Particle Filtering-Based Low-Elevation Target Tracking With Multipath Interference Over the Ocean Surface

As radar signals propagate above the ocean surface to determine the trajectory of a target, the signals that are reflected directly from the target arrive at the receiver along with indirect signals reflected from the ocean surface. These unwanted signals must be properly filtered; otherwise, their interference may mislead the signal receiver and significantly degrade the tracking performance of the radar. To this end, we propose a low-elevation target tracking mechanism considering the specular and diffuse reflection effects of multipath propagation over the ocean surface simultaneously. The proposed mechanism consists of a state-space model and a particle filtering algorithm and promises considerable improvements in the capacity and accuracy of the radar tracking systems. The efficiency and accuracy of the developed target tracking method are tested and compared with an unscented Kalman filtering method in two- and three-dimensional space using a series of simulation experiments.
Multi-Modal Weights Sharing and Hierarchical Feature Fusion for RGBD Salient Object Detection

Salient object detection (SOD) aims to identify and locate the most attractive regions in an image, which has been widely used in various vision tasks. Recent years, with the development of RGBD sensor technology, depth information of scenes becomes available for image understanding. In this paper, we systematically investigate and evaluate on how to integrate depth cues in a pre-trained deep network and learn informative features for SOD. First, we propose a CNN-based cross-modal transfer learning, which learn knowledge from sufficient labeled RGB salient object datasets and guide the depth domain feature extraction. Then we design a feature fusion module to fuse the complementary features in a hierarchical manner. At last, the final saliency map is obtained by integrating multi-scale information step by step. Extensive experiments on five popular RGBD benchmark datasets demonstrate that our proposed approach achieves significant improvements and outperforms the state-of-the-art methods.
A Location Privacy Attack Based on the Location Sharing Mechanism with Erroneous Distance in Geosocial Networks

Geographical social networks (GSN) is an emerging research area. For example, Foursquare, Yelp, and WeChat are all well-known service providers in this field. These applications are also known as location-based services (LBS). Previous studies have suggested that these location-based services may expose user location information. In order to ensure the privacy of the user's location data, the service provider may provide corresponding protection mechanisms for its applications, including spatial cloaking, fuzzy location information, etc., so that the user's real location cannot be easily cracked. It has been shown that if the positioning data provided by the user is not accurate enough, it is still difficult for an attacker to obtain the user's true location. Taking this factor into consideration, our attack method is divided into two stages for the entire attack process: (1) Search stage: cover the area where the targeted user is located with unit discs, and then calculate the minimum dominating set. Use the triangle positioning method to find the minimum precision disc. (2) Inference phase: Considering the existence of errors, an Error-Adjusted Space Partition Attack Algorithm (EASPAA) was proposed during the inference phase. Improved the need for accurate distance information to be able to derive the user's true location. In this study, we focus on the Location Sharing Mechanism with Maximal Coverage Limit to implement the whole attack. Experimental results show that the proposed method still can accurately infer the user's real location even when there is an error in the user's location information.