Reviewerly/exHarmony huggingface dataset:
Product Sentiment Analysis Using Natural Language Understanding

In the twenty-first century, there is a need for a quick, effective, and automatic feedback analysis technique to understand client’s needs in real time. Growth of any business depends upon catering to customers’ needs as quickly as possible. The branch of computer science, which helps people to achieve their goals, is known as artificial intelligence and machine learning. This paper proposed one such mechanism to ease out sentiment analysis process. Artificial intelligence covers multiple algorithms ranging from simple SVM classifier to complex neural network. We present an innovative approach for analysing sentiments using hybrid cloud offering called natural language understanding, in which data from a social networking site is analysed. Apart from product review, this system is worthwhile for hospital feedback management system, food review analysis, and blood donation pattern finding.
The Information System Security Assessment Model Based on TOPSIS Method and Linguisitc Viarable

Information is the most valuable asset of most organizations today. At the same time, the problem of information security has also brought huge losses, and has attracted more and more attention from the society. Because of the complexity and uncertainty of information system, the security evaluation of information system inevitably involves a certain degree of fuzziness. A TOPSIS model based on linguistic variables for information system security assessment is proposed. Firstly, according to the feasibility and operability of information system security, a two-level index system of information system security is constructed. Secondly, linguistic variables are used to represent the evaluation values of the secondary indicators, and WAA operators are used to calculate the evaluation values of the primary indicators. Then TOPSIS method is used to evaluate and rank the schemes. The results show that TOPSIS method is more effective and practical for information system security risk assessment.
A Deep Multimodal Approach for Map Image Classification

Map images (e.g., illustrated maps, historical maps, and geographic maps) have been published around the world, not only for giving location but also to attract tourists or hand down the histories of locations. The management of map data, however, has been an open issue for several research fields, including digital library, humanities, and tourism studies. This paper explores an approach for classifying diverse map images by their themes using map content features. Specifically, we present a novel strategy for preprocessing text data that are positioned inside the map images, which are extracted using OCR. The activation of the textual feature-based model is joint with the visual features in an early fusion manner. Finally, we train a classifier model comprising a convolutional layer and a fully connected layer, which predicts the belonging class of the input map. In experiments conducted on a new labeled dataset of map images, we demonstrate that our approach that uses the fused features achieved the best classification performance over single modality. We have made our dataset available on the Internet to facilitate this new task.
Characteristic analysis of large scale fading using wavelet method in urban environment

Analysis of large scale fading characteristics using wavelet method in urban environment is presented in this paper. Path loss, small scale fading and large scale fading variations are extracted due to the multi-scaling property of wavelet method. The optimal wavelet mother function basis is selected by the cross correlation. Large scale fading characteristics including amplitude distribution and autocorrelation are obtained. The conventional log-distance fading model results are also proposed for comparison. Goodness-of-fit test is applied to validate the amplitude distribution results of large scale fading which are obtained from two methods. The results show that it is better to model large scale method characteristic based on wavelet method.
Siamese CNNs for RGB-LWIR Disparity Estimation

Currently, for the task of color (RGB) and thermal infrared (LWIR) disparity estimation, handcrafted feature descriptors such as mutual information are the methods achieving best performance. In this work, we aim to assess if convolutional neural networks (CNNs) can achieve competitive performance in this task. We developed an architecture made of two subnetworks, each consisting of the same siamese network, but taking different image patches as input. Each siamese network, in the feature space, searches for the disparity between the left and right patch. The out-put of the two subnetworks are summed together so that we can be more confident in the predicted disparity by enforcing left-right consistency. We show that having two subnet-works working together in parallel to get the final prediction helps achieve better performance when compared to a single subnetwork by itself. We tested our method on the LITIV dataset and found the results competitive when compared to handcrafted feature descriptors. The source code of our method will be available online upon publication.
Comprehending Taiwan ATM Heist: From Cyber-attack Phases to Investigation Processes

Cybercriminals increasingly use sophisticated tools and advanced methods to attack bank systems. Cyber black markets for hacking tools or services are gaining widespread attention as more advanced persistent threat attacks are relevant to such markets. The recent cyber-attacks on banks or financial institutions have increased the technical expertise of cybercriminals. This study reviews ATM threats and highlights the cybercrime investigation of ATM heist. An incident investigation strategy from ISO/IEC 27043:2015 is proposed to embed cyber-attack phases and detect ATM heist. It demonstrates how this strategy can provide investigators with exceptional abilities to interpret evidence. By integrating an effective cybercrime investigation strategy, investigators can minimize the cost of collecting evidence in a forensically sound manner.
Taguchi-fuzzy Theory Applied to Optimization of Teaching Module

Abstract In this study, a case-reference teaching method was selected, and Taguchi L9 orthogonal table combined with fuzzy control system was adopted to minimize the experimental design, maintain the high precision of the experimental data, and successfully develop the optimal analysis of teaching design mode. This model can effectively improve the quality of teaching. The research results have made a significant contribution to the optimization of teaching design, and can be replicated and followed up.
No-Reference Video Quality Assessment (VQA) Using Novel Inter Sub-band 3-D DWT Features

Major goal of blind video quality assessment (VQA) is to predict visual quality of videos for enhancing the quality of service (QoS) without any reference. However, the conventional VQA model uses video as a two-dimensional image sequence and extracts the features on a frame to frame basis; which completely neglects temporal nature of the video data and the corresponding distortion content as well. In this work, we come up with a novel no-reference VQA model that describes and exploits the inter sub-band statistics of the three-dimensional discrete wavelet transform (3-D DWT) coefficients of video blocks. First, the 3-D DWT transform decomposes the video block of selected size into eight 3-D DWT sub-bands. Then we propose various novel statistical features using the sub-band coefficients. Inter sub-band statistics depicts the spread of the various frequency components and correlation between them. 3-D DWT features automatically take care of the temporal distortions along with spatial distortions and subsequently the support vector regression (SVR) model estimates them to predict the visual quality score of distorted videos. Experimental results on LIVE database demonstrate the superiority of the proposed VQA model over the other state-of-the-art methods.
Context Construction and Virtual Human Based Intelligent Navigation in Virtual Experiment

In view of the problems of high cost and many potential hazards in real middle school experiments, we construct a virtual experiment simulation situation with virtual human guidance in experiment operation, intelligent algorithm assistance in experiment failure, and systematic evaluation at the end of experiment. First of all, the experimental rule library is established, including: experimental process, trigger standard, coding standard and error content. Secondly, based on these libraries, the flow of system algorithm is designed. Finally, take the experiment of heating potassium permanganate to produce oxygen as an example. The flow design, scene design and virtual human design of the experiment are given. Realize intelligent assistance and interactive real-time guidance of the experimental process.
Ontological Approach for Aspect-Based Sentiment Analysis

Sentiment analysis is used to quantify the sentiment of unstructured text, which could be used to draw insights on public opinions about products, services, or any topic that is of relevance. Most of the available sentiment analysis implementations provide an overall sentiment which is not sufficient because it does not provide an aspect-based sentiment. In this work, we present an extendible ontology-based approach for aspect-based sentiment analysis. In this work, we propose a novel and extendible Ontological approach for aspect-based Sentiment Analysis. It also focuses on learning the aspects using topic modelling to automate the aspect detection. The results indicate that the proposed approach can be effective in classifying sentiments under different aspects. The proposed approach may have applications in domains such as e-commerce web sites, product reviewing platforms and social media platforms.
Safety Evaluation Test of Galgeun-tang Applied to Guidelines for the Testing of Chemicals

Background: Galgeun-tang used in traditional Korean medicine, is a mixture of the medicinal plants Cinnamomi Ramulus, Ephedrae Herba and Puerariae Radix, and has been prescribed for the treatment of various ailments, including fever. Although the use of traditional medicinal herbs to treat diseases has recently increased, their safety and toxicity profiles incompletely elucidated. Thus, we evaluated Galgeun-tang’s toxicity in male and female Sprague-Dawley rats.BRMethods and Results: Galgeun-tang (1,000, 2,000 and 4,000 ㎎/㎏) was orally administered to rats for 13 weeks, and then, they were maintained for 4 weeks without administration (recovery period). Their clinical signs, and hematological and urinary properties, were monitored. The results showed that Galgeun-tang administeration slightly increased serum creatinine, urea nitrogen and, aspartate aminotransferase levels. Additionally, 2,000 and 4,000 ㎎/㎏ Galgeun-tang significantly increased urinary bilirubn and protein levels of male and female rats, which were restored during the recovery period.BRConclusions: The no-observed-adverse-effect level of orally administered Galgeun-tang was 4,000 ㎎/㎏ in both female and male rats, and no target organs were identified. In addition, 400 ㎎/㎏ was found to be the no-observed-effect level for toxicity under the study conditions.
Facial Movement Interface for Mobile Devices Using Depth-sensing Camera

In this work, we present a facial based interface as a possible alternative way to control mobile devices applications. The 3D facial movements are obtained using a depth-sensing camera available on many mobile devices. These 3D animation data are analyzed into the form of blend shapes and face direction to determine input actions. These input actions are the conventional touch input actions such as tap, drag, pinch-in, and pinch-out. For this reason, our proposed facial based input methods can be integrated into the existing mobile applications that used touch interface. Through our user studies, we conducted experiments with several subjects on the practical usability of facial movement interface for mobile applications.
Sign Language to Speech Conversion — An Assistive System for Speech Impaired

People with speech and hearing disability have to interact with others with the normally abled people who can talk for their routine work. The persons who are deaf-mute throughout the world use a sign language used in their own country or their local language for the communication with other people. But the person who has been trained is able to understand it. Sign language mainly involves the gestures of hand and head which convey their meaning of either letters or words. The non-verbal behaviors such as the shape, orientation of the hand, head movements and body movements are used to express the thought of the speaker. The speech impaired use sign language to communicate. This often is either misinterpreted or falsely identified. This creates a communication gap between the physically disabled and the normal person. Through sign language, the communication gap is overcome and is possible for a deaf-mute person without acoustic sounds and through the means of signs.
Multi Agent Object Recognition

A multi-agent system is used in various domain application. To what extent it is useful for image processing is yet to be reported. From the review, there are not many works that discuss the development experience in adopting the MAS platform in image processing. This paper reveals the potential of MAS and challenge in adopting MAS-Jade platform in object recognition. We present the experience in designing JADE based object recognition, especially how agent communication is used to coordinate different recognition agents to work together in object recognition. From the findings, multi agent system is able to improve the accuracy of object recognition. However, challenges like complexity in agent modelling; agent population; human agent teamwork; human in the loop in interrupting the intelligent architecture, having an "agent marketplace" for video surveillance are worth to futher explore.
Attributed Network Embedding via a Siamese Neural Network

Recently, network embedding has attracted a surge of attention due to its ability to automatically extract features from graph-structured data. Though network embedding method has been intensively studied, most of the existing approaches pay attention to either structures or attributes. In this paper, we propose a novel attributed network embedding method based on a Siamese neural network, named SANE, to capture both the network structure and node attribute information in a principled way. Specifically, to preserve local semantic proximity, we adopt a Siamese neural network, which can directly learn the similarity of paired nodes with their attributes as input. Then, a skip-gram module is connected with the final shared hidden layer to capture high-order proximity based on the latent representation of node attributes. Thus, we can learn the complex interrelations between nodes. Empirically, we evaluate our model on several real-world datasets and the experimental results have verified the effectiveness of our proposed approach.
A Real Time Implementation of a Bayer Domain Image Deblurring Core for Optical Blur Compensation

In this letter, we present an implementation of deblurring hardware to mitigate blur incurred by optical aberrations in a real-time manner to increase resolution for mobile camera modules. As optical aberrations tend to be variant according to spatial location, algorithm should support spatially variant deblurring. Also, a deblurring problem is known to be rank-deficient, thus requires a regularization and a sparse norm such as L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> norm where 0 ≤ p ≤ 1 has achieved satisfactory solutions. We focus on elaborating algorithm details and a corresponding architecture to efficiently implement with those two requirements. Also, we present a simulation result with the real sensor and this result shows that image resolution can be increased in terms of Modulated Transfer Function (MTF).
Research and Implementation of Data Storage Management System Based on Blockchain Technology

This thesis develops blockchain data storage management system based on the relevant basic theory of blockchain. The system is based on an open source project of Hyperledger. Through the integration of various projects, this paper introduces a blockchain data transmission management system to provide users with a series of services related to data sharing and management across units. The system uses blockchain technology to solve the problems of encryption, disaster tolerance, sharing, and multi-unit coordination. This system uses smart contracts to ensure the security of the data used, without the involvement of third-party authorities or central proxy services, and without the need to set up a separate server. The system mainly reflects the characteristics of system decentralization, trust, secure transmission, collective maintenance, and trusted data management. The system uses the Java language, the Linux development environment, based on the fabric project, the league chain as the technology foundation, and the hierarchical structure design idea.
Ensemble Network For Ranking Images Based On Visual Appeal

We propose a computational framework for ranking images (group photos in particular) taken at the same event within a short time span. The ranking is expected to correspond with human perception of overall appeal of the images. We hypothesize and provide evidence through subjective analysis that the factors that appeal to humans are its emotional content, aesthetics and image quality. We propose a network which is an ensemble of three information channels, each predicting a score corresponding to one of three visual appeal factors. For group emotion estimation, we propose a convolutional neural network (CNN) based architecture for predicting group emotion from images. This new architecture enforces the network to put emphasis on the important regions in the images, and achieves comparable results to the state-of-the-art. Next, we develop a network for the image ranking task that combines group emotion, aesthetics and image quality scores. Owing to the unavailability of suitable databases, we created a new database of manually annotated group photos taken during various social events. We present experimental results on this database and other benchmark databases whenever available. Overall, our experiments show that the proposed framework can reliably predict the overall appeal of images with results closely corresponding to human ranking.
Research on Multi-target Signal Separation and Processing Method

Abstract Nowadays, intelligent cities and transportation are being implemented in major cities around the world. In unmanned vehicles, autonomous identification and separation of moving targets is the essential and important part of intelligent transportation. This paper proposes a multi-object separation algorithm in order to solve the security problems that can promote smart transportation. This paper deals with multi-target signals based on MATLAB. In terms of distance, between the cars (targets) that can be separated, multiple targets are separated by matched filter and FFT algorithm, then the performance of the research is expressed by distance resolution. The separable target distance is up to 200 m, and the resolution is up to 0.6 m; in the terms of speed, multiple targets are separated by the Doppler transform, then the performance of the research is reflected in the form of speed resolution. The speed at which the separated target can be identified is up to 200km/h, and the resolution is up to 1.8km/h, otherwise, both stationary and moving targets can be identified. Finally, all the results are obtained by MTALAB simulation.
Abnormal Behavior Detection Based on the Potential Field Forces in Surveillance Scenarios

Abstract In a social environment, there is a natural mutual force between people. This paper proposes a method for detecting group abnormal behavior based on the potential field method. The movement of a pedestrian is described as the movement towards the target under the combined action of several forces. Forces on pedestrians include the attractive force for themselves and the repulsive force between pedestrians. By calculating the resultant force between them, the behavior of pedestrians is judged according to a set threshold. It is verified through experiments on public data sets that our method has high accuracy and robustness.
Highlighted Word Encoding for Abstractive Text Summarization

The proposed model unites the robustness of the extractive and abstractive summarization strategies. Three tasks indispensable to automatic summarization, namely, apprehension, extraction, and abstraction, are performed by two specially designed networks, the highlighter RNN and the generator RNN. While the highlighter RNN collectively performs the task of highlighting and extraction for identifying the salient facts in the input text, the generator RNN fabricates the summary based on those facts. The summary is generated using word-level extraction with the help of term-frequency inverse document frequency (TFIDF) ranking factor. The union of the two strategies proves to surpass the ROUGE score results on the Gigaword dataset as compared to the simple abstractive approach for summarization.
Recognition of Similar Activities Based on Activity Relationship

With the Internet of Things development, recognizing activities of daily living usually deploy many sensors to objects and environments. However, the sensor events can be triggered by many activities which decreases the accuracy or even fails of inference. In addition, a huge number of personalized activities takes up a lot of space resources and reduces the readability of model. Therefore, this paper adds the duration and period characteristics to improve the inference performance, then adopts the structural model to increase the expendability and standardization. We show that the similar activities inference accuracy has been improved by an average of 8 times. The operating time has been decreased by an average of 0.36 times. This inference method has the good performance and can be used in the future for activity recognition.
Wearable Passive biosensing interface method for gathering bioinformation

Recently, a variety of biometric information systems have been tried and developed to provide IoT-based healthcare and medical information services. Existing biometric information systems actively collect and analyze biometric information around user terminals or smart devices. On the contrary, this study proposes a passive biosensing system and a passive sensing method for collecting and analyzing biometric information using wearable passive patches. In particular, a passive sensing system and method using a passive patch that can be detected by activation of an external power source without an internal battery is proposed. This paper focuses on the following elements of passive biometric information system. The first describes the structure and components of a passive patch system that can be operated from an external source without an internal battery. The second describes passive biometric information algorithms that passively detect biometric information in real time. Finally, we propose a passive sensing analysis modeling that analyzes and evaluates the signal sensitivity of biometric information, and describes an analysis example of bioinformation sensitivity.