Reviewerly/exHarmony huggingface dataset:
A document recommendation system of stemming and stopword removal impact: A web-based application

Abstract Stemming and stopword removal is process that requires a lot of resources in the text pre-processing. The resources used in stemming and stopword removal are directly proportional to the amount of stopword, text, and document. Elimination of stemming and stopwords is one of many options which can reduce process in document-based recommendation system. However, the elimination of stemming and stopword removal have an impact on the recommendation system accuracy. This study determines the impact of stemming and stopword removal in document-based recommendation system. The system used in this study is recommendation system that recommend final project supervisor based on similarity student preliminary research proposal (UPP) document and lecturers’ scientific publications. Student preliminary proposal and lecturers’ scientific publications are document in Bahasa Indonesia. This study begins with analysis to map out the components to be used in each recommendation systems testing. Then proceed with the rearrangement of recommended system components based on testing focus. The study result is precision, recall, and f-mesure values comparison between each recommendation system component elimination. Elimination of stemming and stopwords show that the elimination generates precision and f-measure value worse than the system with stemming and stopword removal. However, system with elimination give a better result at recall value. In the future study, recommendation system needs some development to improve the precision, recall, and f-mesure value with modification in stemming method using Sarawi project and increase the amount of lecturer’s publication.
Sequential unbiased converted measurement non‐linear filter with range rate in ECEF coordinates

Air-to-air target tracking is a challenging task for practical military and civilian applications. In this study, an unbiased converted measurement Kalman filter in Earth-centred Earth-fixed (ECEF) coordinates (E-UCMKF) is proposed for air-to-air target tracking, which takes account for noisy measurements from the airborne radar and the inertial navigation system/global navigation satellite system. The unbiased measurement conversion approach from spherical coordinates to ECEF coordinates is presented, and the Kalman filter is implemented for target state estimation with the unbiased converted measurements and the corresponding measurement error covariances. Besides, when range-rate measurements are available to the processor, a sequential unbiased converted measurement non-linear filter in ECEF coordinates with range-rate measurements (E-SUCMNFwR) is developed, which constructs a pseudo measurement to decorrelate the range-rate measurement noise from the unbiased converted measurement noise, and estimates the state sequentially by the E-UCMKF and a modified cubature Kalman filter. Target tracking scenarios with different sensor accuracies are presented to demonstrate the validity of the proposed algorithms. Simulation results validate the consistency of the proposed unbiased measurement conversion. The results also show that the proposed E-UCMKF and E-SUCMNFwR outperform the conventional algorithms in terms of the position and velocity root-mean-square error, demonstrating their effectiveness and practicality.
Deep Encoder-Decoder Neural Network Architectures for Graph Output Signals

Neural networks (NNs) and graph signal processing have emerged as important actors in data-science applications dealing with complex (non-linear, non-Euclidean) datasets. In this work, we introduce a novel graph-aware NN architecture to learn the mapping between graph signals that are defined on two different graph datasets. The novel proposed architecture is based on two NNs and a common latent space. In particular, we consider an underparametrized graph-aware NN encoder that maps the input graph signal to a latent space, followed by an underparametrized graph-aware NN decoder which maps the latent representation to the output graph signal. The parameters of the two NN are jointly learned by using a training set and the back-propagation algorithm. The resulting architecture can then be viewed as an underparametrized graph-aware encoder/decoder NN operating over two different graphs. The proposed scheme outperforms the corresponding benchmark NN architectures in the literature.
A counterexample to the existence of a general central limit theorem for pairwise independent identically distributed random variables

The Central Limit Theorem (CLT) is one of the most fundamental results in statistics. It states that the standardized sample mean of a sequence of $n$ mutually independent and identically distributed random variables with finite first and second moments converges in distribution to a standard Gaussian as $n$ goes to infinity. In particular, pairwise independence of the sequence is generally not sufficient for the theorem to hold. We construct explicitly a sequence of pairwise independent random variables having a common but arbitrary marginal distribution $F$ (satisfying very mild conditions) for which the CLT is not verified. We study the extent of this 'failure' of the CLT by obtaining, in closed form, the asymptotic distribution of the sample mean of our sequence. This is illustrated through several theoretical examples, for which we provide associated computing codes in the R language.
Prediction of Web Service Anti-patterns Using Aggregate Software Metrics and Machine Learning Techniques

Service-Oriented Architecture(SOA) can be characterized as an approximately coupled engineering intended to meet the business needs of an association/organization. Service-Based Systems (SBSs) are inclined to continually change to enjoy new client necessities and adjust the execution settings, similar to some other huge and complex frameworks. These changes may lead to the evolution of designs/products with poor Quality of Service (QoS), resulting in the bad practiced solutions, commonly known as Anti-patterns. Anti-patterns makes the evolution and maintenance of the software systems hard and complex. Early identification of modules, classes, or source code regions where anti-patterns are more likely to occur can help in amending and maneuvering testing efforts leading to the improvement of software quality. In this work, we investigate the application of three sampling techniques, three feature selection techniques, and sixteen different classification techniques to develop the models for web service anti-pattern detection. We report the results of an empirical study by evaluating the approach proposed, on a data set of 226 Web Service Description Language(i.e., WSDL)files, a variety of five types of web-service anti-patterns. Experimental results demonstrated that SMOTE is the best performing data sampling techniques. The experimental results also reveal that the model developed by considering Uncorrelated Significant Predictors(SUCP) as the input obtained better performance compared to the model developed by other metrics. Experimental results also show that the Least Square Support Vector Machine with Linear(LSLIN) function has outperformed all other classifier techniques.
A Method to Improve the Fresh Data Query Efficiency of Blockchain

In order to accelerate the improvement of the blockchain's efficiency of tracing fresh data, this paper proposes a novel method of tracing data based on LevelDB. In the process of blockchain source tracing, it is necessary to query data in the history database This paper proposes an improved method for querying high-fresh data in memory while consuming a small amount of space on the commonly used database LevelDB in the blockchain. Hash indexes are applied to LevelDB's memory and build hash indexes on MemTable and Immutable MemTable respectively. When MemTable is converted to Immutable MemTable and data is stored from memory to disk, the index will be converted and deleted accordingly to prevent the index from occupying too much space. Indexing the data in memory makes the query speed of fresh data faster and because of the amount of data will not be too large, the serious hash conflicts will not be resulted in. The experimental results show that the query speed for data in memory has been improved with the limition of writing performance impaction.
An Efficient Machine Reading Comprehension Method Based on Attention Mechanism

Machine Reading Comprehension (MRC) answers the questions related to the specified passage to accurately locate the position of the answer. In recent years, many deep learning models based on attention mechanisms have evolved because they provide flexible and effective information interaction. Attention mechanism can further improved by introducing a multi-head self-attention mechanism to obtain the information in different subspaces. In this work, we propose a new model called MHT-Reader by improving multi-head self-attention mechanism with residual network, and convolutional neural network (CNN) combined with highway network to learn the vector representation of passage and question. The experimental results show that on DuReader Search, the BELU-4 is 24.8%, and the Rouge-L is 31.7%, they increased by 1.7% and 2.1% respectively compared with Bidirectional Attention Flow (BiDAF). On DuReader Zhidao, the BELU-4 is 42.1%, and the Rouge-L is 45.6%, they increased by 2.0% and 1.0% respectively compared with BiDAF. Experimental results show that our model has a better performance than BiDAF on the DuReader dataset.
Efficient Low-Resource Neural Machine Translation with Reread and Feedback Mechanism

How to utilize information sufficiently is a key problem in neural machine translation (NMT), which is effectively improved in rich-resource NMT by leveraging large-scale bilingual sentence pairs. However, for low-resource NMT, lack of bilingual sentence pairs results in poor translation performance; therefore, taking full advantage of global information in the encoding-decoding process is effective for low-resource NMT. In this article, we propose a novel reread-feedback NMT architecture (RFNMT) for using global information. Our architecture builds upon the improved sequence-to-sequence neural network and consists of a double-deck attention-based encoder-decoder framework. In our proposed architecture, the information generated by the first-pass encoding and decoding process flows to the second-pass encoding process for more sufficient parameters initialization and information use. Specifically, we first propose a “reread” mechanism to transfer the outputs of the first-pass encoder to the second-pass encoder, and then the output is used for the initialization of the second-pass encoder. Second, we propose a “feedback” mechanism that transfers the first-pass decoder’s outputs to a second-pass encoder via an important weight model and an improved gated recurrent unit (GRU). Experiments on multiple datasets show that our approach achieves significant improvements over state-of-the-art NMT systems, especially in low-resource settings.
In-depth Analysis of the Micro-Doppler Features to Discriminate Drones and Birds

This paper presents the classification result of various features obtained from the micro-Doppler(MD) signature of drones and birds considering the real flight scenarios. Classification results using the rotating blade and the flapping wing made of the plate and the ellipsoid demonstrate that the MD bandwidth is the most efficient feature.
Pedestrian Detection Using Optimized YOLOv3 in UAV Scenario

Pedestrian detection is of great importance in computer vision task. It is a hard work since the performance is affected by environmental factors such as illumination, occlusion, and background clutters, especially in UAV scenario where the pedestrian is always with a small scale. In this paper, we propose adaptive random multi-scale variation method to detect small object in UAV scenario. First, the special pedestrian dataset used for UAV is collected to train the proposed method. Then, we improve YOLOv3, which is a classical one-stage object detection method, by data sample classification (normal, far, game scenario), adaptive random multi-scale variation, and k-means clustering to make the model robust to scale variation. Compared with the original YOLOv3 pedestrian detection result, the proposed model has a high detection precision (90.91%) and accuracy (80.59%).
Arrival-time picking uncertainty: Theoretical estimations and their application to microseismic data

Traveltime-based methods depend on the accurate determination of the arrival times of seismic waves. They further benefit from information on the uncertainty with which the arrival times are determined. Among other applications, arrival-time uncertainties are used to weight data in inversion algorithms and to define the resolution of reconstructed velocity models. The most physically meaningful approaches for the estimation of arrival-time uncertainties are based on probabilistic formulations. The two approaches for the assessment of the lower bound of arrival-time uncertainties, the Cramér–Rao Bound (CRB) and the Ziv–Zakai Bound (ZZB), have been reviewed. The CRB determines the minimum-achievable estimation error under the assumption of a high signal-to-noise ratio (S/N) but underestimates said error for small S/N. The ZZB provides a better result for noisy data because it utilizes a priori information. The CRB and ZZB require knowledge of the spectral variance of the signal, which often is hard to determine in seismic experiments. Furthermore, both bounds assume additive white Gaussian noise (AWGN), which does not hold for seismic data. To overcome these problems, alternative expressions have been proposed, which yield comparable estimates as CRB and ZZB but are solely based on the S/N and the dominant period in the data. Moreover, a recipe to correct the S/N and account for the difference between the seismic noise and AWGN has been provided. For a case study of downhole microseismic monitoring, it is determined that the new expressions provide station-dependent arrival-time uncertainties, which are used as weights to improve source location uncertainties.
RGB-T Saliency Detection via Robust Graph Learning and Collaborative Manifold Ranking

Visual saliency detection inspired by brain cognitive mechanisms is an important component of computer vision, aiming at automatically highlight salient visual objects from the image background. In complex real scenarios, integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting saliency detection performance. In this paper, we propose a novel collaborative algorithm for saliency detection by adaptively incorporating information from grayscale and thermal images. Specifically, we first construct an affinity graph model via robust graph learning to characterize the feature similarities and location relationships between two different image patches under each modal. Based on these affinity graphs, a collaborative manifold ranking method cross-modality consistent constraints is designed to effectively and efficiently infer the salient score for each patch. Moreover, we introduce a weight for each modality to describe the reliability, and integrate them into the graph-based manifold ranking scheme to achieve an adaptive weighted fusion of different source data. For optimization, we propose some iterative algorithms to efficiently solve the models with several subproblems. Experiments on the grayscale-thermal datasets demonstrate the superior performance of the new method over the state-of-the-art approaches.
Deep visual ensemble similarity (DVESM) approach for visually aware recommendation and search in smart community

Recommendation systems (RS) play an important role in e-commerce applications as they help the consumers in choosing the required items within reduced time. The traditional methods of collaborative filtering, fail to capture the visual data associated with the items. Visually-aware recommendation systems are upcoming in e-commerce applications that use the visual features of the products rather than the user profiles. Deep learning techniques are used for the classification and prediction in visual recommendation systems. However, the criticality of the visual recommendation system lies in identifying the similar images for a given target image. In this paper, a visual recommendation system is proposed based on Deep Visual Ensemble similarity metric (DVESM) using Convolutional Autoencoder (CAE) neural network for classification. The basic idea is to get a set of trained feature vectors for the image catalogue using CAE and find the similarity between the trained feature vectors and the target feature vector using DVESM method. The proposed methodology of using DVESM has been demonstrated with the state-of-the-art methods on Amazon 2014, 2015 and Street2Shop datasets. The results show that the DVESM method is most suitable for visually aware recommendation systems as it learns an ensemble of metrics to provide recommendations.
Analysis and Implementation of IWT-SVD Scheme for Video Steganography

In the world of computer, the skill of sending and displaying secret data particularly in public places faced several challenges and had received more attention now. Protecting information on communication media is a vital need in information transmission technology. We can protect information through encryption process over the transmission media but due to advanced computing techniques, encrypted information can easily be detected and decrypted. So, for avoiding the unauthorized user from accessing information, we need some more advanced techniques. For this purpose, we use steganography which is a technique to conceal secret message over communication networks. In this paper, IWT-SVD scheme is proposed to conceal watermark image in video cover file. Concealing watermark in video cover file gives higher concealing capacity. It has been shown that concealing watermark in HH and LL sub-band results in good perceptual quality, more robustness, and less computational cost. Simulation results also show that this new scheme outperforms adaptive steganography based on IWT-SVD in term of PSNR, MSE, and concealing capacity.
Performance evaluation of fuzzy clustered case-based reasoning

Case-based reasoning (CBR) is a nature-inspired machine learning technique. It solves a new problem using the existing similar problems with their solutions stored in central repository known as case-base. It results in continuous growth of the case-base enhancing the problem solving capability of the system but at the same time compromising the performance. First performance challenge is continuous growth of the case-base. Second performance challenge is to handle the performance bottleneck without compromising the relevance of cases with their neighbourhood to solve new problems. Different approaches have been introduced in literature to address this performance challenge. In this work, a knowledge-base maintenance approach using fuzzy clustering has been presented which takes care of the performance bottleneck and does not compromise the problem solving capability of CBR. Performance of the proposed approach has been evaluated on different case-bases and results have been compared with the conventional CBR approach.
Multi-Group Transfer Learning on Multiple Latent Spaces for Text Classification

Transfer learning aims to leverage valuable information in one domain to promote the learning tasks in the other domain. Some recent studies indicated that the latent information, which has a close relationship with the high-level concepts, are more suitable for cross-domain text classification than learning raw features. To obtain more latent information existing in the latent feature space, some previous methods constructed multiple latent feature spaces. However, those methods ignored that the latent information of different latent spaces may lack the relevance for promoting the adaptability of transfer learning models, even may lead to negative knowledge transfer when there exists a glaring discrepancy among the different latent spaces. Additionally, since those methods learn the latent space distributions using a strategy of direct-promotion, their computational complexity increases exponentially as the number of latent spaces increases. To tackle this challenge, this paper proposes a Multiple Groups Transfer Learning (MGTL) method. MGTL first constructs multiple different latent feature spaces and then integrates the adjacent ones that have a similar latent feature dimension into one latent space group. Along this way, multiple latent space groups can be obtained. To enhance the relevance among these latent space groups, MGTL makes the adjacent groups contain one same latent space at least. Then, different groups will have more relevance than raw latent spaces. Second, MGTL utilizes an indirect-promotion strategy to connect different latent space groups. The computational complexity of MGTL increases linearly as the number of latent space groups increases and is superior to those multiple latent space methods based on direct-promotion. In addition, an iterative algorithm is proposed to solve the optimization problem. Finally, a set of systematic experiments demonstrate that MGTL outperforms all the compared existing methods.
Data Security in Mobile Cloud Computing

Mobile Cloud Computing (MCC) is a rapid growing technology these days as number of mobile users are increasing day by day. Data privacy and security due to the use of mobile devices is an aspect of major concern. MCC can offer infrastructure, computational power, software and platform services to any ordinary smartphone. There are several issues regarding the security of MCC such as network security, web application security, data access, authentication, authorization, data confidentiality, and data breach. Mobile devices do not have enough storage and CPU speed so data storage capacity in these devices is less. To achieve a secure MCC environment, security threats have to be studied and analyzed accordingly. We propose an algorithm to enhance the security of the mobile cloud which will ensure data integrity and confidentiality. This mechanism uses AES and RSA to provide better security. We also discuss the security threats in MCC environments and propose solutions for these.
Detecting Temporal Correlation via Quantum Random Number Generation

All computing devices, including quantum computers, must exhibit that for a given input, an output is produced in accordance with the program.The outputs generated by quantum computers that fulfill these requirements are not temporally correlated, however.In a quantum-computing device comprising solid-state qubits such as superconducting qubits, any operation to rest the qubits to their initial state faces a practical problem.We applied a statistical analysis to a collection of random numbers output from a 20-qubit superconducting-qubit cloud quantum computer using the simplest random number generation scheme.The analysis indicates temporal correlation in the output of some sequences obtained from the 20 qubits.This temporal correlation may be not related to the relaxation time of each qubit.