Reviewerly/exHarmony huggingface dataset:
Improving Sentiment Prediction using Heterogeneous and Homogeneous Ensemble Methods: A Comparative Study

Due to the enormous amount of data in online reviews, various parties, including individuals, businesses, and governments, are becoming more interested in evaluating the sentiments behind these contents. In this paper, we conducted a comparative assessment of the performance of three popular ensemble methods (Bagging, AdaBoost, and Stacking) based on five base learners (Naive Bayes, Linear Regression, Decision Tree, K-Nearest Neighbor, and Support Vector Machine) to predict sentiment classification. Experiments were performed on three different domains of online reviews including restaurant cell-phone, and movies. Results revealed that the ensembles, in general, had better performance than the individual classifiers with an average of 0.83 for precision and 0.82 for recall. When comparing the performance of three ensembles methods, Stacking (heterogeneous ensemble method) was found to be the best method, whereas Bagging (homogeneous ensemble method) recorded the lowest performance. The results offered compelling evidence that ensemble methods, especially heterogeneous with a robust classifier as a based or meta classifier, can positively improve the performance of sentiment classification.
Linear and Kernel Classifiers

Classification is one of the most basic tasks in machine learning. In computer vision, an image classifier is designed to classify input images in corresponding categories. Although this task appears trivial to humans, there are considerable challenges with regard to automated classification by computer algorithms.
Incentive Scoring Mechanism Design in Battle Royale Games

Battle Royale games are popular among players in recent years. The mainstream Battle Royale games utilize a rank-based scoring mechanism which calculates scores for knockout and for surviving time separately. However, our simulating experiments show that the currently used mechanism has little incentive for the high-skill-level teams, which is unfair and may discourage those teams. Besides, we raise three graph-based mechanisms to overcome this shortcoming while keeping a similar performance in ranking the teams. Experimental results also show that one of the mechanisms we raise can also be used for evaluating the skill of all teams.
Baseline-Adaptive Light Field Depth Estimation Based on Stereo Matching

Using the spatial and angular information contained in a light field, the depth map can be generated and the structure of the scene can be inferred. However, it is always challenging to obtain an accurate and robust depth map in different scenes with different resolutions and baselines. In this paper, we propose a resolution and baseline adaptive light field depth estimation method to increase the robustness. Firstly, we use a stereo-based approach to estimate the disparities between specific pairs of views. Secondly, outlier detection is performed to estimate the validity of these disparities. Finally, valid disparities are integrated to obtain a robust depth estimation. Extensive experiments have demonstrated robustness of our method on scenes with different resolution and baseline.
Techniques and Methodologies of Self-Adaptive Software and its Architecture: A Survey

Nowadays, due to the continuously changing en-vironment, modern software systems can work in a dynamic environment. But many changes occur at runtime so, to handle the changes self-adaptive mechanism plays an important role. To encounter the requirement of rapid application development adaptive software is used widely. Adaptive software architecture is based on three steps speculate, collaborate, learn. There are a lot of techniques for adaptive software architecture most appropriate technique is selected by comparing them. In this paper, we perform a survey on different research papers related to the self-adaptive system and self-adaptive architecture. After studied the techniques gaps in the literature are identified and compare those techniques. Different common parameters are identified and considered by studying literature. Then these parameters are used to analyze survey techniques which is helpful for researchers and future directions in this area.
A Riemannian Approach to Extreme Classification Problems

We propose a novel Riemannian method called “RXML” for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models. A constrained optimization problem is formulated as an optimization problem on a matrix manifold and solved using a Riemannian optimization method. A proof of convergence for the proposed Riemannian optimization method is stated. The proposed approach is tested on several real-world large scale multi-label datasets, and its usefulness is demonstrated through numerical experiments. Experimental results show that RXML improves the trade-off between train time and accuracy. At the similar level of accuracy, the train time of RXML was 1.5 to 4 times faster than that of AnnexML and EXMLDS-4, which are the state-of-the-art embedding-based methods.
Edge Guided Attention Based Densely Connected Network for Single Image Super-Resolution

Densely connected neural networks have achieved superior results in the single image super-resolution (SISR) task benefited from the rich feature representations provided by the dense connection structure. However, the rich feature representations also include redundant information, which is useless for final image reconstruction. Besides, during the image reconstruction process, the low-resolution images cannot directly learn the high-frequency information from their corresponding high-resolution images, which can't constrain the high-frequency part of super-resolved images and hinders reconstruct quality. To solve these problems, we apply the neural attention mechanism to extract effective features from the densely connected network and utilize the image edge-prior to guide the super-resolution network to learn high-frequency information effectively. Therefore, we propose an edge-guided attention-based densely connected network (EGADNet) for SISR. EGADNet mainly includes three parts: content branch (CT branch), edge reconstruction branch (ER branch), and information fusion branch (IF branch). Each low-resolution image is first processed by the CT branch and the ER branch to extract the content information and the high-frequency information corresponding to the high-resolution image. After that, the IF branch processes fuse the above information and regress the final super-resolution result. We perform experiments on commonly-used image super-resolution datasets. Both qualitative and quantitative results have proved the effectiveness of our method.
MuST-POS: multiscale spatial-temporal 3D atrous-net and PCA guided OC-SVM for crowd panic detection

Crowd panic detection (CPD) is crucial to control crowd disasters. The recent CPD approaches fail to address crowd shape change due to perspective distortion in the frame and across the frames. To this end, we are motivated to design a simple but most effective model known as multiscale spatial-temporal atrous-net and principal component analysis (PCA) guided one-class support vector machine (OC-SVM), i.e., MuST-POS for the CPD. The proposed model utilizes two multiscale atrous-net to extract multiscale spatial and multiscale temporal features to model crowd scenes. Then we adopted PCA to reduce the dimension of the extracted multiscale features and fed them into an OC-SVM for modeling normal crowd scenes. The outliers of the OC-SVM are treated as crowd panic behavior. Three publicly available datasets: the UMN, the MED, and the Pets-2009, are used to show the effectiveness of the proposed MuST-POS. The MuST-POS achieves the detection accuracy of 99.40%, 97.61%, and 98.37% on the UMN, the MED, and the Pets-2009 datasets, respectively, and performs better to recent state-of-the-art approaches.
An energy-efficient and high-PSNR image denoising pre-processor based on optimized non-local means algorithm for real-time FHD video applications

Image is widely used in many fields as an important information carrier. In embedded devices, denoise as an important part of image processing will obviously affect the result. The progress of sensor resolution and light-sensitive capability has higher requirements for denoise speed and quality. Most of the embedded cameras can only perform noise reduction based on simple local denoising with unsatisfactory results due to resource constraints, while the non-local denoising with better results has high computational complexity and is difficult to achieve real-time processing in embedded applications. In this paper, an energy-efficient and high-PSNR image denoising pre-processor based on optimized NLM algorithm is proposed with following features: i) optimized parameters in the weight function and the utilized method noise to improve the denoising performance; ii) a customized systolic array with full pipelining to achieve high processing throughput; iii) a data reuse strategy to reduce the frequent data movement for high energy-efficiency. The achieved results of 22.8 mJ/frame and 765μJ/frame in 110-nm ASIC and Xillinx Zynq FPGA shows that the proposed design is a competitive energy-efficient solution for many mobile FHD video applications.
SGAT: a Self-supervised Graph Attention Network for Biomedical Relation Extraction

The goal of relation extraction task is to classify texts containing entity pairs into predefined relation types. Biomedical relation extraction can extract high-quality information from massive medical texts, which plays an important role in biomedical research. In this paper, we propose a self-supervised graph attention network to extract biomedical relations from the complex and noisy biomedical texts. The model incorporates self-supervision within the standard graph attention mechanism. Specifically, the model applies the graph attention mechanism to reduce the influence of noisy words and introduces dependency-based parse trees to construct a self-supervised task. With the supervision of dependency-based parse trees, the graph attention network can not only improve its capacity of learning syntactic information but also alleviate its lack of interpretability. Additionally, we use Gumbel Tree-GRU to obtain sentence information for relation classification. Our model achieves state-of-the-art performance on the DDIExtraction 2013 and ChemProt datasets, respectively, which suggests that our proposed model can effectively improve the performance of biomedical relation extraction.
X-Ray Security Inspection Image Detection Based on a Multi-scale Feature Fusion Network

Aiming at the cluttered background in security inspection images and the phenomenon of small size violations, a SSD-based multi-scale X-ray security inspection image detection algorithm (M-SSD) is proposed. Based on the one-stage target detection network SSD, this paper adds feature fusion module (FFM) and asymmetric convolution module (ACM). FFM integrates deep features with shallow features through deconvolution, and obtains more informative fusion features to improve the accuracy of the model’s detection of contraband in complex backgrounds. ACM combines asymmetric convolution and cavity convolution to improve the detection accuracy of small-scale contraband. Compared with the classic SSD algorithm, the mean average precision (mAP) of this algorithm is increased by 2.48%; in addition, the average precision (AP) of this method when detecting small-scale contraband is increased by 9.31%. The detection speed reaches 44.1 frames per second (FPS). The experimental results show that the algorithm in this paper can meet real-time monitoring while improving the detection accuracy.
Revisiting entanglement-induced exclusion

We revisit the problem of absorption by identical atoms with entanglement and symmetrization acting at once. We introduce the recoil of the atoms in the calculations and compare the results with those of distinguishable systems instead of mixtures. These absorption rates have been proposed to test entanglement-induced exclusion. The manifestation of exclusion in this framework is a sharp increase in the absorption rates in its proximity. The control of the superposition coefficients used in this representation is simpler than that of the overlapping degree considered in the initial proposal. Another result of this alternative approach is the inhibition of the entanglement effects for some values of the parameters.
Sentence-Level Automatic Speech Segmentation for Amharic

The extraction of information from a large archive requires extracting both audio file structure and its linguistic content. One of these processes is to add sentence boundaries to the automatic transcription of speech contents. In this work, we present an automatic sentence-level speech segmentation system for the Amharic language. We have used Amharic read speech, and a spontaneous speech corpus for the development of an automatic speech segmentation system. In this work, an automatic speech segmentation system is completed by forced alignment. Monosyllable, tied-state tri-syllable, and mono phone acoustic models have been developed to build forced alignment. Rule-based and AdaBoost have been used to differentiate the accurate boundaries from candidates. The evaluation of the experiments shows that encouraging automatic speech segmentation results are achieved using monosyllable acoustic model forced alignment. We have achieved the best results using a decision tree classifier with a segmentation accuracy of 91.93 and 85% result for read loud and spontaneous speech, respectively.
Development of Hausa dataset a baseline for speech recognition

The Hausa language read-speech dataset was created by recording native Hausa speakers. The recording took place at Nile university of Nigeria audio studio and radio broadcasting studio. The recorded dataset was segmented into unigram and bigram. The Hausa speech dataset contain 47hr of recorded audio speech. The dataset can be used for automatic speech recognition, speech synthesis, Text-to-Speech and speech-to-text application.
Drift Lens: Real-time unsupervised Concept Drift detection by evaluating per-label embedding distributions

Despite the significant improvements made by deep learning models, their adoption in real-world dynamic applications is still limited. Concept drift is among the open issues preventing the widespread exploitation of deep learning models in real-life settings. The dynamic world changes very quickly, and the collected data drifts accordingly. Prediction models, usually trained on static historical data, should be promptly re-trained in case of new real-time drifted data distributions. Although some drift detection methodologies have been proposed over the years, different issues are still open since state-of-the-art solutions show limited effectiveness and efficiency.This paper proposes Drift Lens, a novel real-time unsupervised per-label drift detection methodology based on embedding distribution distances in deep learning models. The preliminary experiments performed on a transformer-based model fine-tuned for topic text classification show promising results in drift detection accuracy, drift characterization, and efficient execution time to support real-time concept drift detection.
A Novel Embedding Model for Knowledge Graph Completion Based on Quaternion

In recent years, knowledge graph completion methods have been extensively studied, in which QuatE learned embeddings of entities and relations in quaternion space and achieved state-of-the-art results. However, QuatE has two main problems: 1) simple modeling operation leads to weak interaction between entities and relations and inflexible representation. 2) complex relations are not to be considered. In this paper, we propose a novel model, en-QuatE, with a dynamic mapping strategy to explicitly capture a variety of relational patterns, enhancing the feature interaction capability between elements of the triplet. The mapping strategy dynamically, associated with the relation, used to learn adaptive the entity embedding vectors in the quaternion space via Hamilton product. Experiment results show en-QuatE achieves significant performance on WNISRR. In particular, the MR (Mean Rank) evaluation has relatively increased by 15% on WNISRR.
A Deep Neural CNN Model with CRF For Breast Mass Segmentation in Mammograms

Malignancy in women's breast is known to be the second most common form of cancer. Early detection can help diagnose the disease effectively, but it continues to grow manifolds due to reasons unknown. Therefore, to aid radiologists in the effective treatment of breast cancer, an end-to-end deep learning-based architecture for ROI-based breast mass segmentation is proposed. The architecture involving Residual connections and Group convolution in U-Net (RGU-Net), contains encoder and decoder blocks with different resolutions and feature sizes. The architecture captures multi-level features from the encoder-decoder architecture using the residual connections and group convolution. Moreover, to improve the field-of-view of the filters, atrous convolutions are added. Later, for better visualization, the predicted masks are labelled (structured learning) using a conditional random field (CRF) to analyse the mass boundaries explicitly. A publicly available INBreast dataset is used to validate the method, which is augmented to produce robust results. The experimental results produced from the proposed approach outperformed the conventional mass segmentation algorithms, demonstrating its effectiveness.
Trend Analysis Neural Networks for Interpretable Analysis of Longitudinal Data

Cohort study is one of the most commonly used study methods in medical and public health researches, which result in longitudinal data. Conventional statistical models and machine learning methods are not capable of modeling the evolution trend of the variables in longitudinal data. In this paper, we propose a Trend Analysis Neural Networks (TANN), which models the evolution trend of the variables by adaptive feature learning. TANN was tested on dataset of Kaiuan research. The task was to predict occurrence of death within 5 years, with 3 repeated medical examinations from 2008 to 2013. The AUC of the TANN is 0.7888, which is a slightly improvement than that of conventional methods, while that of GBDT is 0.7824, that of random forests is 0.7822, and that of logistic regression is 0.7789. The experimental results show that the proposed TANN achieves better prediction performance on death events prediction than conventional models. Furthermore, by analyzing the weights of TANN, we could find out important trends of the indicators. The trend discovery mechanism interprets the model well. TANN is an appropriate trade-off between high performance and interpretability.
An Improved Model for Opinion Mining of Public Reviews using Recurrent Neural Network

Mining of opinions are very crucial in all fields from e commerce websites to social media platforms. The products on any e commerce websites have thousands of reviews which helps customers to make a decision a product. Social media websites also have people with large number of opinions on a particular subject. Mining of opinions can be extensively used in the fields where opinions play a major role. This project caters to this need and classifies the opinions of people as positive and negative. This can further be used by movie recommendation systems and e commerce websites for evaluation of their product. It involved in the classification the opinions as positive opinions and negative opinions with the help of deep learning algorithms by achieving high accuracy. The procedures involved in this project will be of dataset selection, data preprocessing, data tokenization, and data cleansing and building a neural network. We have taken the dataset of reviews for this purpose. Data preprocessing and data cleansing is done so that deep learning algorithms can be easily applied on the data. Deep learning algorithms learn on their own and do not require guidance. The main objective of using deep learning model is for increasing efficiency, performance and accuracy. Here, we have applied three different neural network models to our dataset and compare the performances according to the testing and training accuracy obtained. Analysis of the three models concludes that Recurrent Neural Model (RNN) has least over fitting with considerable testing and training accuracy. Hence, it best suits the problem statement.
Visual Secret Share Creation with Grayscale Image Converted to RGB Images Using Zigzag Scanning Algorithm

In current scenario, lot of information are transmitted via Internet to all over the world at a time. The transmitted data is having problem of hacking. This is the most important problem while sharing the information in cyber world. Visual cryptography is giving more security to avoid this kind of problems. So, visual cryptographic system enables to encrypt the original image as share, and the transparent image is decrypting the image to reveal the original image. Image encrypted information about number of shares and distributed to number of participants, which information cannot get without any knowledge of cryptography. This article presents the study of the basic scheme of the visual cryptography technique and the proposed method for the grayscale image converted into color images. The proposed schema focuses directly on the original image and the shares generated again share 1 and share 2 transparent to the stacked image and identical to the decrypted image. The decryption process is to pile the actions to obtain the secret image. The proposed methodology is to share the creative techniques: shared encryption and decryption. The key generated for the encryption and decryption process is sent securely to the network using zigzag scanning algorithm. The algorithm applies to the grayscale image converted to color images. It is implemented in MATLAB coding.
Towards Comprehensive Analysis of Tor Hidden Service Access Behavior Identification Under Obfs4 Scenario

In this paper, we present a novel approach to identify Tor hidden service access activity with key sequences under Obfs4 scenario. By calculating the key cell signals occurred during Tor hidden service access process, we get the start index and window size of the key TCP package sequence of traffic. In order to verify the effectiveness of this method, we perform comprehensive analysis under nine scenarios of different Obfs4 transmission model. We find through experimental results that there is a TCP package sequence window, which has a great contribution to identifying Tor hidden service access traffic. Only use the key TCP sequence as input, we can achieve more than 90% accuracy as well as recall in nine scenarios.
Blockchain in Healthcare

The privacy and protection of medical data are key concerns during medical treatment. Most people are concerned about the security of their medical data. In order for a healthcare system to function efficiently and effectively, it needs a platform for communicating securely. Several years ago, blockchain emerged as an emerging technology. Some of the features that make blockchain attractive to developers include immutability, decentralization, transparency, and distributed ledgers. HealthCare systems can use Blockchains to securely store medical data, making their use a significant development. In this paper, a secure health care system is described that allows interaction between doctors, patients, insurance companies, and pharmacies. It describes the scope of the system, as well as potential work areas for future work, and compares this to a traditional system.