Reviewerly/exHarmony huggingface dataset:
A Novel Architecture for a Two-Pass Opinion Mining Classifier

Opinions given by others usually have a vast influence on the actions of human beings and opinions are vital to almost all decision-making activities. The rapid growth of social media has extensively came up with user data which includes opinions, comments, reviews, events, and other services. The data generated will be helpful for both manufacturer as well as the customer. The manufacturers can get an online reality check about the weaknesses and strengths of the product which totally depends on the sentiments of the customer. Opinion mining is a well-known problem in Natural Language Processing (NLP) that has been increasing attention in recent years. In opinion mining, the greater part of the researchers have dealt with general domains, like electronic items, movies, restaurants, etc., but the domain of health care has been neglected or paid less attention. Therefore, in this study, we proposed a novel two-pass classifier architecture which focuses on classifying the drug-satisfaction level among the patients who have already experienced a particular drug. The two-pass classifier is an intelligent classification system designed with the combination of support vector machine and artificial neural network. The results suggest that the proposed classifier provides better results in terms of precision and recall as compared to traditional individual approaches.
Measuring Sentence Similarity based on Image Association and Siamese Network

Modeling sentence similarity plays an important role in natural language processing tasks such as question answering and plagiarism detection. Measuring semantic relationship of two sentences is challenging because of the variability and ambiguity of linguistic expression. Previous studies on sentence similarity are focusing on the configuration of input data and classification model structure. However, we focus on the sentence understanding process of human. Human brain stimulates association effect when one tries to understand a sentence describing landscape or object. The association effect that transforms text into image makes human robust to expression changes and word order changes in a sentence. To implement the association effect, we propose a new sentence similarity model based on Siamese network and Text2image generative adversarial network (GAN). The role of Siamese network is to compute the similarity between two sentences with the shared network weights. Inside the Siamese network, two subnetworks are composed of Text2image GAN which transforms the text data into image data. Once the sentences are transformed into image, latent features are extracted through VGGNet. The sentence similarity is computed from the normalized distance between two feature vectors. To verify our proposed method, we modify the MSCOCO dataset and experimental results show that the proposed method outperforms the benchmarked models without association process.
Interferometrically estimating a quadratic form for any immanant of a matrix and its permutations

We devise a multiphoton interferometry scheme for sampling a quadratic function of a specific immanant for any submatrix of a unitary matrix and its row permutations. The full unitary matrix describes a passive, linear interferometer, and its submatrix is used when photons enter in and are detected at subsets of possible input and output channels. Immanants are mathematical constructs that interpolate between the permanent and determinant; contrary to determinants and permanents, which have meaningful physical applications, immanants are devoid of physical meaning classically but here are shown to be meaningful in a quantum setting. Our quadratic form of immanants is sampled by injecting vacuum and single photons into interferometer input ports such that the photon arrival times are entangled, in contrast to previous methods that control arrival times without entangling. Our method works for any number of photons, and we solve explicitly the quadratic form for the two-, three- and four-photon cases.
TimeSets: Temporal Sensemaking in Intelligence Analysis

TimeSets is a temporal data visualization technique designed to reveal insights into event sets, such as all the events linked to one person or organization. In this article, we describe two TimeSets-based visual analytics tools for intelligence analysis. In the first case, TimeSets is integrated with other visual analytics tools to support open-source intelligence analysis with Twitter data, particularly the challenge of finding the right questions to ask. The second case uses TimeSets in a participatory design process with analysts that aims to meet their requirements of uncertainty analysis involving fake news. Lessons learned are potentially beneficial to other application domains.
Measuring and mitigating OAuth access token abuse by collusion networks

We uncovered a thriving ecosystem of large-scale reputation manipulation services on Facebook that leverage the principle of collusion. Collusion networks collect OAuth access tokens from colluding members and abuse them to provide fake likes or comments to their members. We carried out a comprehensive measurement study to understand how these collusion networks exploited popular third-party Facebook applications with weak security settings to retrieve OAuth access tokens. We infiltrated popular collusion networks using honeypots and identified more than one million colluding Facebook accounts by "milking" these collusion networks. We disclosed our findings to Facebook and collaborated with them to implement a series of countermeasures that mitigated OAuth access token abuse without sacrificing application platform usability for third-party developers.
PRINCIPLES OF WORKING WITH SOUND IN M. SHALYGIN'S ESSAY “RED BELLS OF JUAN MIRO”

&#x0D; &#x0D; &#x0D; The aim of the research is to establish the specifics of M. Shalygin's work with sound (using the work “Red Bells of Juan Miro" as an example), which will allow a better understanding of the originality of his music, as well as the composer's method of organizing sound and its (sound) interpretation. Thus, using the integrated method which combines the auditory and structural methods of analysis, as well as the analysis of the spectrogram of the work, the properties of the timbre elements, the principles of the organization of musical tissue, as well as the features of composer thinking and creative methods will be established od M. Shalygin.&#x0D; &#x0D; &#x0D;
Research on Shared Service Discovery Model Based on Collaborative Filtering Algorithm

With the advent of the Internet transformation wave of enterprise informationization, the sharing and efficient use of enterprise information services have become the focus of enterprise informationization construction. The widely popular public service platform model and microservice technology architecture of Internet companies have solved the problems of service sharing and efficient utilization from two aspects of business and technology. After enterprise-level applications adopt the microservice architecture, how to discover shared services from hundreds of thousands of microservices has become a difficult point in the construction of shared service platforms. Based on the collaborative filtering algorithm, this paper realizes the service discovery model of shared services, solves the problem of shared service discovery, and realizes the automated construction of enterprise shared service platforms.
Hierarchical Trellis Based Decoder for Total Variation Sequence Detection (TVSD) in Space-Time Trellis Coded (STTC) Wireless Image/Video Communication

This paper proposes a robust reconstruction scheme for image/ video transmission in space-time trellis coding (STTC) based MIMO wireless multimedia sensor networks (WMSN). The information bits of the image/ video stream are modulated using STTC prior to transmission over the MIMO wireless channel between the multimedia sensors and the cluster head. At the receiver, a novel total variation sequence detector (TVSD) is developed that employs an anisotropic total variation (TV) regularized cost function to leverage the bounded variation property of the image/ video stream, in addition to the coding and diversity advantages of the STTC, for improved image/ video recovery. For reconstruction, a novel bi-layered hierarchical trellis based modified Viterbi decoder is developed to decode the optimal bit sequence corresponding to the least TV cost function using appropriately modified node and branch metrics. Simulation results using several test images and video sequences demonstrate the improved reconstruction performance of the proposed scheme, especially in the low SNR regime (0 - 10 dB), in comparison to the conventional maximum likelihood (ML) decoder, which makes it ideally suited for implementation in practical WMSN.
For a Quantitative Evaluation of Risk Assessment -Behavior-based Safety

There are novel structures of society such as Industry 4.0 and Connected Industries. Under the structures, a novel safety management system considering man-machine interaction is very important topic. Safety 2.0, a new safety control, is proposed from Japan. It seems that direct and quantitative validity of risk assessment and risk reduction measure such as reinforcement of safety behavior and/or reduction of unsafe behavior is now not sufficiently accomplished. Some quantitative assessment for residual risks focusing on worker's behavior is necessary to make Vision Zero function better. We introduce to a principle of Behavior-Based Safety (BBS) which might be applicable to reduce residual risk for safety of workers. BBS, focused on behavioral modification using principle of reinforcement, is one group of Behavior Analysis of Psychology. Goals of BBS is prediction, control and quantitative measurement of target behavior.
SHAD: Privacy-Friendly Shared Activity Detection and Data Sharing

Nowadays, there is a growing demand for sharing multimedia data among participants in the same activity. With existing social applications, users need to conduct friending and data sharing operations manually, which is troublesome due to changing attendees and highly diverse data content of different activities. To tackle this issue, in this work we propose a novel system SHAD to achieve privacy-friendly shared activity detection and multimedia data auto-sharing based on users' historical multimodal data. Facing noisy, incomplete and asynchronous data, as well as inaccurate recognition results of machine learning models, we design an algorithm to aggregate multimodal data relevant to the same activity and propose an activity-semantic graph to comprehensively characterize each activity by fusing knowledge of multimodal data. Based on the activity-semantic graph, the privacy-preserving shared activity detection and data sharing method is designed, which protects both raw data and semantic information of data. We implemented our system and conducted comprehensive evaluations with real-life multimodal data (including photos and motion sensor data). The results show the efficacy of our system. We can achieve 94.9% precision and 91.5% recall for shared activity detection.
Reducing Data Redundancy and Analysing Video Using Deep Learning

Video surveillance has for ages been used to track protection delicate areas such as banks, department stores and highways and crowded people places and boundaries. Traditionally the video sparks are processed on the web by human operators and also the increase in many cameras from the now utilized surveillance technologies overload the storage devices with substantial quantities of data and make it rather tricky for your human operators to correctly track the videos. Inside this paper, we offer a remedy to eliminate redundant data from the surveillance procedures using a movement detection algorithm. In addition to that, we now apply thing detection about videos to form them that they can be easily processed from the human operators.
Video-Based Traffic Flow Monitoring Algorithm for Single Phase Position at An Intersection

Road traffic flow monitoring is the main information for traffic safety management, traffic condition evaluation and decision-making. This paper mainly improved the accuracy of real-time traffic flow information by adding de-noising to preprocessing images, and has certain reference significance for improving road traffic conditions. At the same time, this article details some of the video processing technologies that play a major role in ITS.
Object Detection and Danger Warning of Transmission Channel Based on Improved YOLO Network

In view of the wide monitoring area of the transmission channel, the proportion of object in the captured pictures is small, especially the problem of early warning of targets in inspection images under complex backgrounds such as mutual occlusion, forests, mountains and rivers and multiple interference factors. A YOLOv3-MobileNet algorithm for power transmission channel danger warning is proposed. According to the characteristics of the transmission channel image, the number of network layers is increased while maintaining a small amount of calculation, and the feature mapping module is enriched to provide more accurate semantic information for the prediction layer. An IoU-Kmeans algorithm is proposed, which improves the predicted position of the target. Experimental results show that the optimized algorithm's detection accuracy (mAP) is 90%. Compared with SSD and YOLOv3 object detection algorithms, the detection accuracy of the algorithm is improved by 75% and 3.5%.
Constructing de Bruijn Sequences Based on a New Necessary Condition

In this paper, we propose a new necessary condition for feedback functions of de Bruijn sequences and discuss its application in constructing de Bruijn sequences. It is shown that a large number of de Bruijn sequences could be easily constructed by precomputing an [Formula: see text]-stage nonlinear feedback shift register (NFSR) with a special cycle structure—that is, if a state [Formula: see text] is on a cycle generated by this NFSR, then all the states with the same Hamming weight as [Formula: see text] are also on this cycle. Moreover, if there are [Formula: see text] different cycles in the state graph of the precomputed NFSR, then we can construct [Formula: see text] de Bruijn sequences by the different choices of conjugate state pairs, where [Formula: see text].
Adaptive Fuzzy Network based Transfer Learning for Image Classification

With the introduction of Convolutional Neural Networks (CNN) the computer vision domain has witnessed a tremendous increase in novel architectures achieving results on vision tasks that exceed human performance. Neuro-fuzzy hybrid systems are a great avenue for enhancing the interpretability of neural networks. A lot of research in recent times has explored the technique of transfer learning applied to CNNs for computer vision applications. However, a pre-trained deep convolutional network with a subsequent adaptive fuzzy based network is yet to be explored. Hence in this paper, a novel adaptive fuzzy network based convolutional network is proposed. The paper focuses on using non-hybrid learning based adaptive fuzzy networks in conjunction with pre-trained convolutional networks for the task of image classification. The results illustrate the proposed approach eclipses over existing architectures used for image classification.
Using Distributed Ledger Technology to Mitigate Challenges with Flight Information Exchange

Uniquely identifying flights and the exchange of information about flights between aviation systems in a global aviation network poses many complex challenges. This paper explores the possibilities, benefits, and challenges of applying Distributed Ledger Technology (DLT) to some of the challenges posed by global flight identification and information exchange.
An Expert System Framework for Bug Tracking and Management

Open Source software wanders, fair as closed software exercises and firms, utilize the Issue tracking framework. The data of Issue tracking frameworks is uncommonly critical for distinctive sorts of investigate moreover within the correct Program Building. For Open Source Software ventures a noteworthy degree of this data is available transparently and with approximately unhindered get to. Intentionally dismembered, this data can appear a awesome bargain and deliver unused bits of information around an Open Source ventures - for occasion with regard to the task’s region or its progression. The amassed information from an colossal degree of Issue tracking frameworks can allow choices approximately Open Source software progression practices as a run the show. Along these lines, it may be a critical test to form the information accessible and discharge its potential for exploratory investigate. This investigate will display a utilization that enables us to standardize and utilize the information of a colossal degree of Issue tracking.
Deep Cascaded Forest-Based Facial Beauty Prediction

The facial beauty not only has the individualism but also the commonality. In order to find the inherent of the facial beauty, deep cascaded forest-based facial beauty prediction method is proposed in this paper. We extract features in image by multi-grand scanning and get the final facial beauty level prediction results by input these features to deep cascaded forest. This method achieved the higher performance and prediction accuracy in the 10,000 facial beauty images dataset. And a series of experiments we did have proved the effectiveness of facial beauty prediction task.
Reference Model Storage Covert Channel for Secure Communications

Covert channel is a channel which is not intended for communication. It generally used to transfer unintended form of information. The primary purpose of any kinds of covert channels is to secret communication and not bandwidth. In this paper, the proposed model is to enhance the bandwidth of covert communication using TCP sequence number header field and IP identification field header. Our reference model is proposed for combination of 32-bit sequence number and 16-bit identification number, so that the total bandwidth of storage covert channel for this model is 48 bits per packet.
Pvalite CLN: Lightweight Object Detection with Classfication and Localization Network

We developed a 2D convolution object detection network called CLN layer to let a network with a small amount of parameters and have a better detection result as well. Compared to YOLOv2 detector and SSD with lightweight Pvalite network, the proposed Classification and Localization Network (CLN) layer has better accuracy and higher recall on the object which is located on the border of image. Additionally, we revise the original open source to make this architecture more platform portable than other architectures. The proposed system is implemented on the embedded platforms with the performance of 640x480@10 fps under nVidia Jetson TX-2 and 480x320@5fps under Renesas R-car H3.
Gesture Recognition Using Deep Learning

Deep Learning is a revolution that has potential to cause tremendous technological upheavals across all walks of humanity. Here I present an application to translate alphabets of Indian Sign Language in real time and a pipeline to make similar applications. The main steps include Data set Generation, Model Training, Frame by Frame Classification. The application works in real time and in varying backgrounds and the user can type alphabets by doing corresponding gestures in front of webcam. While some other implementations require several expensive devices that user might need to wear, my application only needs a camera.
An Improved Knowledge Base for Chinese Chess Game

In order to solve the problem of time resource and hardware resource waste caused by repeated situations, based on a method proposed by the predecessors to construct a chess computer game knowledge base, this article improves the structure of the knowledge base and uses the hierarchical architecture method to expand the knowledge. The total storage capacity of the library increases the speed of operation. This article's knowledge base master records the best moves and current board situation each time the computer “thinks” through the historical heuristic Alpha-Beta algorithm. Next time, when encountering the same situation, directly search the knowledge base. To get the best move. A comparison of time consumption in the Visual Studio C++ environment demonstrates the validity of the proposed knowledge base.
REdiREKT: Extracting Malicious Redirections from Exploit Kit Traffic

This paper proposes REdiREKT, a system which utilises the open-source Zeek Intrusion Detection System (IDS) to map HTTP redirection chains observed in Exploit Kit (EK) attacks and extracts distinguishing features to assist machine learning (ML). We build a ground-truth dataset of EK samples, ensuring that the redirection chains for every sample are accurate and reusable in future experiments. By processing a unique combination of 9 redirection techniques, REdiREKT was able to correctly extract 96.52% of malicious domains from 1279 EK samples, spanning 28 families and 8 campaigns, and, only failed to extract 0.7% of malicious chains. Using the VirusTotal API to filter out domains flagged as malicious, we build a benign dataset from the Alexa top 10k websites, extracting 12,783 domains from 5910 redirection chains. The malicious redirection data is divided into yearly and family-based categories and compared to the benign results. Based on our analysis of the collected data, we extract and store 48 key features from websites within the redirection chains that could aid future ML-based detection efforts. Finally, we evaluate the performance of REdiREKT, compare it with existing research, and, suggest use-cases and future areas of work.
Acceptability of Virtual Reality among Older People

By 2050, it is estimated that 80% of older people will be living in low- and middle-income countries. The older people are seen to limit their physical activity after the age of 65 years. The aim of this study was to explore the influence of sex and age towards active ageing to answer and agree a usability and acceptance of Virtual Reality (VR). This pilot study involved 30 older people who voluntarily participated in March to May 2018. They were asked to use VR for 15 minutes twice a week for 6 weeks. We used ordinal logistic regression to see whether sex and age influenced the answer in the Technology Acceptance Model (TAM) questionnaire. SPSS vers.21 was used to perform statistical analyses of the data. We found that most of them have agreed to the acceptance of VR use for each variables of according to the sex and age.
Reflectance based semi-empirical model to determine nature and metamorphic grade of almora group of rocks, Kumaon (Lesser) Himalaya

Reflectance spectroscopy can determine the nature and geothermal gradient of metamorphic assemblage. This contemplates that the spectral identification of primary mineral phase is insufficient in ascertaining the nature and gradient of metamorphism, thus, role of associated mineral phases needs to be incorporated. A series of algorithms, statistical distance mapping, spectral simulation of metamorphic assemblages from the primary spectral data has been applied to determine the nature and grade of metamorphism and isograd boundaries. The constrained randomization of (∼5500) semi-empirical simulations on single scattering albedo of the mixture components has been implemented. The model has been designed for a spectral range from 0.426 µm to 2.365 µm of hyperspectral bandwidth with RMSE of 0.09. The model was applied to field collected spectroradiometer reflectance data of Almora nappe of Lower Himalayas. An inverted thermal profile was observed along the thrust and higher grade of metamorphism was observed in the central region.