Reviewerly/exHarmony huggingface dataset:
Application of neural network based on Caffe framework for object detection in Hilens

The semantic analysis of computer vision is to let the machine automatically understand, analyze and generate reasonable semantic concepts for the content contained in visual signals such as images or videos. At present, many experts and scholars have carried out in-depth and extensive research on image recognition, and with the rise of neural networks, the use of deep learning should become the current research trend. In this paper, we use the industry-wide deep learning framework caffe training data set, and the results are deployed to Huawei's latest product, hilens, to obtain the final result through the startup file, and display the image and accuracy.
A Cloud Framework Design for A Disease Symptom Self-inspection Service

The establishment of a symptom self-inspection service faces will face many challenges related to how to best acquire, store, and analyze the available data. In view of the problems, a cloud framework symptom self-inspection service model is proposed in this article. A Hadoop cluster is set up to store massive medical data and provide indexing so as to produce acceptable electronic medical record search response times. A cluster of the distributed search nodes based on Lucene can be used for real-time retrieval, data analysis, and privacy filtering from a massive collection of electronic medical records. The implementation of symptom check-up services is discussed, including the selection of search nodes, the establishment of medical records index files, the ranking and sorting of medical records similarity. Experimental results demonstrate that our proposed cloud framework model serves as a scalable and effective self-inspection health symptom service.
Quantum search on Hanoi network

Hanoi network (HN) has a one-dimensional periodic lattice as its main structure with additional long-range edges, which allow having efficient quantum walk algorithm that can find a target state on the network faster than the exhaustive classical search. In this paper, we use regular quantum walks and lackadaisical quantum walks, respectively, to search for a target state. From the curve fitting of the numerical results for HN of degrees three and four, we find that their running time for the regular quantum walks are followed by amplitude amplification scales as [Formula: see text] and [Formula: see text], respectively. For the search by lackadaisical quantum walks, the running time scales are as [Formula: see text] and [Formula: see text], respectively.
Automated Language Scoring System by Employing Neural Network Approaches

Present approaches of automated language scoring lack the ability to investigate the multiple-level and several contexts of sequential features which are helpful to examine the language proficiency for the responses (monologue and dialogue). This paper aimed to identify the different levels of sequential features with respect to various contexts to evaluate the speaker's proficiency of a language. We have employed Neural Network based automated assessment. We have combined 3 attention based Bidirectional Long-Short-Term-Memory to effectively consider the three dimensions of a speech as delivery, grammar and content. The resultant outcomes demonstrated that our methodology have outperformed the traditional ways of language scoring.
Beyond Quantum Supremacy

Demonstrations of quantum supremacy have become convincing enough that it's time to think seriously about what comes next.
Sentiment Analysis using Few Short Learning

Many social websites and android applications whether being Facebook, WhatsApp or Twitter, in this highly advanced and modernized world is flooded with views and data. One of the most popular social platform is Twitter which has been observed as the main source of sentiments where almost every enthusiastic or social person tends to express his or her views in the form of comments. This research aims to analyze the problem of sentiments on small datasets, namely Sentiment Strength-Tweet, Health Care Reform, airline and Whatsapp dataset. In this work, a Convolutional neural networks-Bidirectional long short term memory based model is presented which uses four conv1d and two maxpool layers along with Bidirectional long short term memory. The proposed method has been compared with various machine learning based methods. The experimental results show that the proposed method outperforms the existing methods over the considered datasets.
Texture-Based Multiresolution Steganalytic Features for Spatial Image Steganography

Steganography is a technique of hiding information in digital media whereas steganalysis is the detection and recovery of such hidden information. This paper focuses on extracting more efficient steganalytic features to improve the classification accuracy in spatial image steganography. Textural features are extracted using the well-known Threshold Local Binary Pattern (TLBP) method which is an extended version of a local binary pattern. A set of high-order derivative filters are incorporated before performing the TLBP operation to strengthen the stego features. Various textural features are extracted using the Grey Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Contourlet Transform (CT). The ada-boost classifier is used for classification. Performance of the classifier is analyzed with various combinations of feature sets and the best performance is obtained for the combination of statistical and multi-resolution feature set. An accuracy of 93.87% is obtained.
SOON: Specifically Optimized One-Stage Network for Object Detection in Remote Sensing Imagery

With great significance in military and civilian applications, detecting indistinguishable small objects in wide-scale remote sensing images is still a challenging topic. In this work, we propose a specially optimized one-stage network (SOON) focusing on extracting spatial information of high-resolution images by understanding and analyzing the combination of feature and semantic information of small objects, which consists of feature enhancement, multi-scale detection, and feature fusion. The first part is implemented by constructing a receptive field enhancement (RFE) module and incorporating it into the specific parts of the network where the information of small objects mainly exists. The second part is achieved by four detectors with different sensitivities accessing to the fused and enhanced features, which enables the network to make full use of features in different scales. The third part consolidates the high-level and low-level features by adopting up-sampling, concatenation and convolution operations to build a feature pyramid structure, which explicitly yields strong feature representation and semantic information. In addition, we introduce the Soft-NMS to preserve accurate bounding boxes in the post-processing stage for densely arranged objects. Note that the split and merge strategy, as well as the multi-scale training strategy, are employed in this work. Extensive experiments and thorough analysis are performed on the NWPU VHR-10-v2 dataset and the ACS dataset as compared with several state-of-the-art methods, in which satisfactory performance verifies the effectiveness of the design and optimization. The code will be released for reproduction.
A Method to Estimate Request Sentences using LSTM with Self-Attention Mechanism

Recently, many opinions are accumulated on the review sites. These reviews are very useful for the service providers and users. Since it requires much cost to extract the useful information manually, there is a need for processing the information automatically. Sentiment analysis is a representative example of the processes. In the sentiment analysis, opinions are classified into positive opinions and negative opinions. The general sentiment analysis have focused on sentiment sentences (positive / negative), however, have not focused on request sentences such as Please set an electric pot in the room. In this research, we aimed to estimate request sentences using deep learning, especially Recurrent Neural, Network (RNN) with multiple Self-Attention mechanisms. We proposed a request estimation method using LSTM with four Self-Attention mechanisms to represent the sentences from multiple perspectives. As the result, we confirmed the effectiveness of the proposed method.
Validating BGP Update Using Blockchain-Based Infrastructure

A number of solutions have been proposed to secure the Border Gateway Routing (BGP) protocol by validating BGP update path and origin information. These solutions make use of centralized database, centralized Public Key Infrastructure (PKI) and some conventional PGP variants as their security mechanism. These solutions are prone to successful attack by state actors and often build database to verifying BGP updates without proper means of validating data stored in this database. Therefore, there is a need for alternative approach to secure the BGP routing protocol. In this chapter, we propose a blockchain based technology used to create a distributed or decentralized immutable database that relies on consensus of participating Autonomous System (AS), to build this blockchain. Every BGP route update received by an AS peer is validated against the content of the blockchain distributed database to detect updates with falsified path and origin information. The limitation of throughput and scalability associated with the blockchain would not affect the proposed blockchain solution once it is fully operational. This is because the data stored in the distributed ledger has a frequency or rate of change that is far lower than that of the blockchain transaction rate. Furthermore, with the blockchain solution, the centralized PKI root of trust is eliminated and AS are now capable of detecting and mitigating IP prefix hijack attack in real time, without outsourcing this service to a third party.
Comparative Study on Swarm Based Algorithms for Feature Reduction in Twitter Sentiment Analysis on Figurative Language

In this paper, we deal with the issue of feature selection by comparing different approaches based on Gravitational Search Algorithm, Particle Swarm Optimisation and Genetic Algorithm. The comparison is drawn on the parameters of feature reduction percentage, accuracy and time taken. The optimization is performed with the following supervised predictive models - Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbour and Multilayer Perceptron. Datasets were acquired from SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11), which provided a set of 5198 tweets scored with finely grained sentiment score (−5 to +5 including 0). 11538 features were generated from the datasets and the experiments performed have been successful in reducing an average of 55% of the features, without any decline in the accuracy.
Towards a Professionalising of Communication for Development: A Strategy for Improving Aid Effectiveness

Communication for development is an emerging academy and professionalism whose presence in development practice is more likely to increase aid effectiveness and improve the success rate of development programmes. This article presents the academy and professionalism communication for development (C4D). In particular, it describes the author’s familiarity with C4D and his attempt to raise it into an academy as well as establish it in the field as a profession. He pulls together his experiences in Africa, his authorships and his most recent funding activities in Pakistan to demonstrate how C4D can be an effective role player in the development process.
Realization of the Werner–Holevo and Landau–Streater Quantum Channels for Qutrits on Quantum Computers

We realize Landau-Streater (LS) and Werner-Holevo (WH) quantum channels for qutrits on the IBM quantum computers. These channels correspond to interaction between the qutrit and its environment that result in the globally unitarily covariant qutrit transformation violating multiplicativity of the maximal $p$-norm. Our realization of LS and WH channels is based on embedding qutrit states into states of two qubits and using single-qubit and two-qubit CNOT gates to implement the specific interaction. We employ the standard quantum gates hence the developed algorithm suits any quantum computer. We run our algorithm on a 5-qubit and a 20-qubit computer as well as on a simulator. We quantify the quality of the implemented channels comparing their action on different input states with theoretical predictions. The overall efficiency is quantified by fidelity between the theoretical and experimental Choi states implemented on the 20-qubit computer.
Predicting User Reported Symptoms Using a Gated Neural Network

Detection of Adverse Drug Events (ADE) or side effects of different treatments are necessary to minimize potential health risks of patients. Given the prevalence of user reported content on the web, recent research has focused on the automatic discovery of potential side effects from these online platforms. However, it is not clear whether the symptoms that a patient experiences, are solely side effects of a particular treatment (or a combination of them), or there are other confounding factors influencing them. In this work, we characterize the reported symptoms along with their severity for patients, based on their past interactions with various treatments and their pre-existing medical conditions. We analyze a large dataset from a symptoms tracking app, and observe a strong correlation between a patient's existing health condition(s) and the symptoms he or she experiences across different treatments. We develop a multi-objective neural network with gating mechanism, to predict the possible symptoms and their overall severity level for a set of treatments, for a given patient. Experimental results demonstrate the effectiveness of our model over state-of-the-art approaches. Furthermore, our adaptation of the gating mechanism imbues the network with the ability of justifying its predictions.
YOLO based Logo detection

Artificial Intelligence(AI) is an art of creating machines which can perform functions that requires intelligence when performed by people. Object detection, which comes under AI, can be applied to various domains like traffic sign detection, face detection, video surveillance, ball tracking in sports, pedestrian detection, people counting etc. Recently, AI has brought many innovations in the retail sector such as shelf monitoring and self checkouts. This work introduces object detection to the retail domain, that is, detection of logos in various products. Dataset used for this purpose was not publicly available and hence the images are taken from different stores with different background, different angle and different lighting conditions. Some images are also taken from Internet sources. Logos of various objects are detected using You Only Look Once(YOLO). Instead of local features, it uses global features to predict labels and bounding boxes. Our trained network efficiently detects logos of different products with more than 95% accuracy.
Image Recognition with MapReduce Based Convolutional Neural Networks

Convolutional neural networks (CNNs) have gained global recognition in advancing the field of artificial intelligence and have had great successes in a wide array of applications including computer vision, speech and natural language processing. However, due to the rise of big data and increased complexity of tasks, the efficiency of training CNNs have been severely impacted. To achieve state-of-art results, CNNs require tens to hundreds of millions of parameters that need to be fine-tuned, resulting in extensive training time and high computational cost. To overcome these obstacles, this work takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. Close examination of the implementation of MapReduce based CNNs as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high accuracy in classification and improvements in speedup, scaleup and sizeup compared to the standard algorithm.
Object Tracking Based on Kernel Correlation Filter and Multi-feature Fusion

Target tracking is a hotpot issue in computer vision. This study proposed a robust tracking algorithm based on kernel correlation filter and multi-feature fusion. Firstly, an effective feature fusion strategy is designed which combines GRAY, HOG and LAB features for improving the robustness of the tracker. Secondly, we proposed a novel strategy to solve the occlusion challenge, which overcomes the shortcomings of KCF tracker in occlusion. Finally, a multi-scale filter is introduced in the model for solving the problem of scale change, and the target model is updated to alleviate tracking drift. The comprehensive evaluations are conducted, and the results show that the proposed tracker perform well against other algorithms.
Control of Quadrotor Drone with Partial State Observation via Reinforcement Learning

In this paper, we propose a quadrotor control algorithm which use the historical strengthened partial state observations as input information to control the quadrotor drone using reinforcement learning algorithm. Reinforcement learning method could enable the agent to learn a policy which could map the observations to control commands, which, in our work, is the actuator command of the quadrotor. Besides, we conduct our method in the control task via simulation, the results of which show excellent performance.
Autonomous Vehicle Trajectory Planning and Control Based on Traffic Motion Prediction

The autonomous vehicle should be properly operated according to the road geometry and behavior of the surrounding vehicles. For this purpose, we propose a supervisor that predicts the motion of the surrounding vehicle using the artificial potential field method, determines the collision risk of the vehicle with the collision check algorithm and generates the velocity distribution map considering the velocity of the surrounding vehicles. In addition, the trajectory planner is designed to minimize the jerk for passenger comfort, considering the road shape and the velocity distribution determined by the supervisor. Finally, the nonlinear model predictive controller is formulated considering the limit of the actuator and collision avoidance.
Using the Lexicon from Source Code to Determine Application Domain

Context: The vast majority of software engineering research is reported independently of the application domain: techniques and tools usage is reported without any domain context. As reported in previous research, this has not always been so: early in the computing era, the research focus was frequently application domain specific (for example, scientific and data processing).
A Survey on Spam Review Detection and Recommendation of Superior Results in Netspam Framework

Major Society of people using internet trust the contents of internet. The possibility that anyone can take survey about anything provides a great opportunity to spammers to make fake reviews about product, its services. So to identify the spammers and their spams is really a debatable issue for research and despite that there are many studies in this context yet none of them has a great significance. In this application, we use a structure, stated as NetSpam, which proposes spam features for giving a practical hotel review datasets to design a spam review detection method into a classification issue. Utilizing the role of spam features helps us to have good outcome in context of different metrics on review datasets. The results shows that NetSpam outcome with the previous methods and encompassed by features of the four categories; involving behavior and language of review feature, behavior and language of user feature, better outcomes can be obtained from first type of feature rather than other ones. The contribution work is when user will search query it will display all top products as well as there is recommendation of the product.
Optimized Design of ECC Point Multiplication Algorithm Over GF(2m)

In order to improve the efficiency of the Elliptic Curve Cryptography (ECC) point multiplication algorithm, this paper presents a new high-efficient algorithm and architecture of ECC point multiplication. Firstly, we analyze and optimize the designs of the modular addition, the modular multiplication, the modular square and the modular inversion. Then, we propose an integral serial and partial parallel structure of point multiplication over GF(2m), and design the overall hardware circuit using 2 modular multiplication units. Over GF (2233), the experimental results using FPGA show that our approach can reduce time required for point multiplication to 5.87us, while the resource cost is kept a reasonable amount to 8682 slices.
Cross Domain Descriptor for Sketch Based Image Retrieval using Siamese network

Sketch based image retrieval (SBIR) is a methodology in which the system can recognize an image and retrieve from the database, given an input query sketch. Socio economic importance and relevance of Sketch based image retrieval systems are high as the users of touch screen based devices are rapidly increasing even in remote areas. The main challenge of this task is that sketches contain only an abstract visual information compared to images. The proposed method uses Siamese CNN for the implementation of SBIR. Siamese CNN joins two convolutional neural networks with one loss function. The transfer learning approach is implemented to learn the feature space of both sketch input query and the images. The VGG19 pre-trained model is used to obtain feature descriptors of sketches and images. The dataset Sketchy Database containing 75,471 sketches of 125 categories is used. The results obtained shows that proposed method gives better performance compared to the similar methods.
Research on Fault Diagnosis Method of Axial Flow Induced Draft Fan of Power Plant Based on Machine Learning

A fault diagnosis model based on machine learning is proposed for the fault diagnosis of axial flow induced draft fans. The data of the time series data in the PI database is matched and filtered by the historical fault data in the SAP database of the power plant, and the feature and data preprocessing are further extracted to construct a fault diagnosis model for machine learning. Finally, the model diagnosis results are evaluated by the confusion matrix, and the fault diagnosis of the axial flow induced draft fan of a power plant is realized.
Adaptive Speaker Recognition Based on Hidden Markov Model Parameter Optimization

The Hidden Markov Model (HMM) is a widely used method for speaker recognition. During its training, the composite order of the measurement probability matrix and the number of re-evaluations of the initial model affect the speed and accuracy of a recognition system. However, theoretical analysis and related quantitative methods are rarely used for adaptively acquiring them. In this paper, a quantitative method for adaptively selecting the optimal composite order and the optimal number of re-evaluations is proposed based on theoretical analysis and experimental results. First, the standard deviation (SD) is introduced to calculate the recognition rate considering its relationship with Mel frequency cepstrum coefficients (MFCCs) dimension, then the composite order is optimized according to its relationship curve with the SD. Second, the composited measurement probability with different number of re-evaluations is calculated and the number of re-evaluations is optimized when a convergence condition is satisfied. Experiments show that the recognition rate with the optimal composite order obtained in this paper is 97.02%, and the recognition rate with the optimal number of re-evaluations is 98.9%.