Reviewerly/exHarmony huggingface dataset:
Single image haze removal using sharpness evaluation index

In order to reproduce clear scenes of visible light images in hazy weather, and effectively suppress the image contrast and clarity degradation caused by haze degradation. General defogging methods do not take into account the uneven distribution of fog concentration and defog the whole image directly. Outdoor scenes of defogging is required to take into account the distribution of fog concentration. In this paper, a natural image defogging method for clarity evaluation indicators is proposed. We select representative indicators for fog concentration classification based on depth information and also obtain global transmittance maps. Compared with traditional method and the deep learning defogging method, The results outperform the other algorithms in different metrics. Experiments show that our results outperform other algorithms in various metrics and are robust to inhomogeneous fog.
Multi-Class Medical Image Classification Based on Feature Ensembling using DeepNets

Medical image classification plays a key role in computer-aided detection and diagnosis systems. Conventional machine learning, neural network-based approaches strongly depends on the type of features i.e. texture, edge, shape, and/or blob along with their amalgamations, extracted from the medical image. Since most of the feature extraction algorithms are specific to the modalities and problem. The machine learning or neural network-based model built using these features lack- of generalization and high-level problem representation ability. Deep Learning (DL) methods based on deep features deliver an active way to build an edge-to-edge model which has the ability to reckon the classification labels with the input raw pixels of medical domain images. Due to the unavailability of massive medical data, high noise rate, and resolution of medical images, DL approaches grieve in terms of classification rate. To improve the classification rate, we propose to fuse the contemporary features (high-level features) extracted from a deep convolution neural network (DCNN) and conventional texture features. The building of the planned model comprises the subsequent steps. Firstly, DCNNs (MobileNet, GoogelNet, and Resnet.) are trained as feature extractors, and the feature vectors are extracted. Second, we extract the traditional texture features of medical images. Lastly, we fuse the deep features and texture features which represent robust high-level features for medical image classification. We assess the proposed method on standard medical image benchmark datasets: MEDMNIST We attain classification accuracy of 99.1&#x0025;,experimental results confirm that fusing texture features and deep learning features results in the increase of accuracy of medical image classification.
Research on face recognition method based on opening operation and Softmax-BP

Face recognition technology is a kind of biometric recognition technology which uses human face feature to recognize identity. In recent years, with the development of Internet of Things(IOT)hzi and the construction of Smart City, face recognition has been applied more and more widely. Focusing on existing several key problems of face recognition, this paper presents a face recognition algorithm based on improved BP neural network with Softmax regression, in the face detection phase, a face feature extraction method based on brightness detection and grey value opening operation is introduced to fully mine facial features and effectively improve the accuracy of face recognition.
Plot-Topic based Movie Recommendation System using WordNet

With the immense increase of content, finding the desired movie/content is a challenging task as it is being limited by genre, language filtering. Recommending the same genre of movies collectively doesn&#x0027;t interest users anymore. Users are interested in finding movies with particular storyline irrespective of the genre. A content-based filtering method is proposed in this paper, where recommendations will be based on a particular topic representing an aspect portrayed in the movie. Topics are assigned to movies using WordNet and topic modeling over their plot summaries. The dataset used consists of movies scraped from Internet Movie Database (IMDb) users created movie lists which are based on the movie&#x0027;s plot-aspect. Precision of predicted plot topics is calculated manually over movies from IMDb&#x0027;s most popular Telugu movies. This is a na&#x00EF;ve approach to identify topics involved in a movie. Further research can be done to predict movie lists based on plot-aspects by identifying topic correlations.
DeepGBASS: Deep Guided Boundary-Aware Semantic Segmentation

Image semantic segmentation is ubiquitously used in scene understanding applications, such as AI Camera, which require high accuracy and efficiency. Deep learning has significantly advanced the state-of-the-art in semantic segmentation. However, many of recent semantic segmentation works only consider class accuracy and ignore the accuracies at the boundaries between semantic classes. To improve the semantic boundary accuracy, we propose low complexity Deep Guided Decoder (DGD) networks, trained with a novel Semantic Boundary-Aware Learning (SBAL) strategy. Our ablation studies on Cityscapes and the ADE20K-32 confirm the effectiveness of our approach with network of different complexities. We show that our DeepGBASS approach significantly improves the mIoU by up to 11% relative gain and the mean boundary F1-score (mBF) by up to 39.4% when training MobileNetEdgeTPU DeepLab on ADE20K-32 dataset.
Self-Ensemble Variance Regularization for Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a label-rich source domain to a different yet related fully-unlabeled target domain. Existing approaches utilize self-training scheme to learn discriminative target features and thus enforce class-level distribution alignment implicitly across the source and target domains. However, inherent noise of the pseudo labels due to domain shift could compromise the training process to negatively affect the adapted model performance. In this paper, we propose Self-Ensemble Variance Regularization for Domain Adaptaton (VRDA) method to rectify the learning with pseudo labels. To be specific, we regard the prediction distinction between the student and its self-ensemble teacher model as prediction variance, to regularize target domain prediction bias from pseudo labels. The experimental results reveal that the proposed VRDA achieves the state-of-the-art performance on several standard UDA datasets.
A Decentralized Approach for Evidence Management in Archaeology

Archaeological artifacts are important evidence to history. Hence, it becomes critical to store the data safely to prevent their tampering and destruction. The paper proposes a system that safely stores necessary archaeological data, such as the analysis and findings of these artifacts using Blockchain. This technology makes the data immutable and transparent. With the help of this system, tracing their details and tracking any changes made in their possession or data will become effortless. This functionality is achieved using the smart contracts of the Ethereum blockchain, which can authenticate, track, and hence secure the data about these artifacts at each step. Unlike other majority blockchain-based applications, the proposed approach is lightweight and faster. It considerably reduces computational power by integrating a distributed file storage system with blockchain.
Robustness of Zero-Knowledge Proofs using RSA Problem

The Robustness of any cryptographic technique gives us an idea about how that technique is asymptotically secure (asymptotic security), efficient, and can defeat different types of attacks on it. In this research, analysis and study have been done about how non-black-box technique called zero-knowledge proofs, can be used with RSA (Rivest, Shamir, Adleman) problem. One of the better algorithms for factoring needed by the RSA problem is general number field sieve factoring. The efficiency of general number field sieve factoring for RSA problem and discrete logarithm problem is analyzed and compared with each other; covariance between their asymptotic functions is calculated which clearly shows that they are strongly correlated with each other.
Study Of Security Postures In Payment Gateways Using a Case Study Approach

The emergence of e-commerce has given birth to many big and small online businesses which are operating through apps and websites. These apps and websites use payment gateways to facilitate the online monetary transactions between the business and the customer. As these payment gateways handle sensitive customer data and act as enablers for online transactions, it is most vital to pay attention to the security of their framework. The objective of this paper is to find ways to optimize the security of the payment gateways by performing secondary research and analysing all the former cases of breaches that happened at the payment gateway level and then come up with a standardized set of security measures to prevent future attacks. This paper is unique as it will help the government to find out whether the current regulations are needed to be revised and updated. The study will also aid the companies and the payment aggregators to adopt certain must-have security aspects in their operation framework to prevent future breaches.
An Approach to Identify Covid Infected People Due to Contact by using Machine Learning

Since the year 2020, the world has faced an unprecedented situation resulting from the spread of a deadly and highly contagious Coronavirus disease. Ever since the inception of the Coronavirus disease 2019 (Covid- 19), scientists all over the world have started preparing methods to fight the disease to save as many human lives as possible. One preliminary solution was hidden in the spreading mechanism of the virus, which was physical contact. So, the solution was to follow social distancing as much as possible. Another solution, which was the second stage solution, was to find the affected patients and isolate them to prevent the disease from spreading. Thus, contact tracing mechanisms were developed to find potential patients in as little time as possible. The Density-based spatial clustering of applications with noise (DBSCAN) algorithm proves to be an efficient solution to implement the proposed solution by making use of the physical locations of patients and geotagging them. This paper focuses on the development of a standalone web application that performs the task of contact tracing by making use of machine learning clustering algorithms. This reduces the time taken by competent authorities and helps them to find probable covid -positive patients in a much lesser period.
Improving End-To-End Speech Translation Model with Bert-Based Contextual Information

This paper proposes an end-to-end speech translation system that utilizes contextual information. Contextual information helps clarify the meaning of the utterances. However, conventional end-to-end speech translation (E2E-ST) is primarily designed to handle single-utterance. Thus, we introduce a context encoder that extracts contextual information from previous translation results. Here, the context encoder obtains high-quality contextual information by adopting the BERT model. Then, we combine it with speech information extracted from speech signals to generate translation results. On the widely used TED-based speech translation corpus, we show that the results of the contextual E2E-ST model are significantly better than those of the single utterance-based E2E-ST model. Furthermore, we demonstrate that contextual information contributes to the processing of unclearly spoken utterances as well as ambiguity caused by pronouns and homophones.
Style transfer and evaluation: an intuitive style transfer quantitative evaluation method

With China's entry into an aging society, the phenomenon of the elderly living alone has become an important issue of general concern. Accidental fall is a major factor endangering the safety of the elderly. Therefore, the design of intelligent nursing system with fall detection function is of great significance to ensure the safety of the elderly. For this demand, a fall detection method that can be applied to intelligent nursing equipment is proposed in this paper, which is based on the convolutional neural network (CNN). Since deep learning requires a large number of samples, however, it is not easy to obtain large-scale labelled images, transfer learning technology based on pre-trained model of Inception v3 is applied to construct new CNN model, which is implemented with the commonly used TensorFlow and Keras. The basic layers are transferred from Inception v3 and keep the weights untrainable. The full connection layer, dropout layer and output layer are added for fine-tuned training. A small-scale image data set is established for training and test. Cross entropy loss function and Adam optimization algorithm are used during training. Finally, the experimental results show that the trained model can effectively realize the automatic detection of falls, with an accuracy of 95.38%, which has a certain practical significance.
Research on anomaly detection algorithm in complex electromagnetic environment

Under the condition of modern information technology, the scale of battlefield electromagnetic information increases exponentially, which makes the electromagnetic environment more complex, and brings great opportunities and challenges to the development of anomaly detection algorithms in complex electromagnetic environment. Since the research in this field is still in its infancy and the research work system is not strong, this paper sorted out and analyzed relevant literatures at home and abroad, and sorted out and summarized traditional methods and deep learning methods respectively according to different anomaly detection algorithms. At the same time, the research difficulties and development trend of anomaly detection algorithm in complex electromagnetic environment are proposed to provide reference and suggestions for subsequent research.
Hybrid Learning Challenges and Knowledge Management: Role of Information Technology

Coronavirus disease of 2019 (COVID-19) has made the situation challenging for higher education institutions. Students have been given the option to study either in offline or online mode. This has forced higher education institutions to conduct classes in a hybrid mode. Teaching in a hybrid mode has its unique challenges. Apart from the issues in delivering lectures, it has made Knowledge Management difficult. This study examines the problems faced by the educators from higher education institutions in delivering lectures in the hybrid mode and the problems faced by the institutions in Knowledge Management. The role of Information Technology in facilitating Knowledge Management is studied. This study uses a qualitative approach. The findings show that educators are facing challenging to deliver lecturers in a hybrid mode dually at the same time assimilation and dissemination of knowledge has become a challenge. It is also observed that Information Technology tool like Internet of Things (IoT) can play a significant role in capturing data and Knowledge Management.
SecMPNN: 3-Party Privacy-Preserving Molecular Structure Properties Inference

Compound screening is a key step in the development of new drugs. Current high-throughput screening methods cannot be widely adopted by laboratories due to their expensive equipment and low efficiency. The booming deep learning in recent years has provided a new answer to this question. The message passing neural network (MPNN) can directly predict molecular properties from molecular structure so that compound screening can be completed without experimentation. In the face of large-scale molecular data, outsourcing this task to a professional cloud server can further accelerate prediction efficiency and reduce costs. In order to solve the privacy protection problem of computing on cloud servers, we propose a 3-party molecular structure properties inference privacy protection framework SecMPNN based on additive secret sharing. We design brand-new cryptographic protocols to ensure the privacy and security in the prediction process, and through experiments show that the single inference time of our protocol on different networks is 40.85% faster than CRYPTEN and 18.5% faster than SecureNN.
Explicitly Modeling Importance and Coherence for Timeline Summarization

Timeline summarization (TLS) identifies major events and generates short summaries on how the event evolves in a period of time. Existing timeline summarization methods generate summaries by considering the coverage and diversity of the content and temporized information but ignore the importance and coherence of sentences used in summary. However, ignoring such information often causes missing important facts in the generated TLS and confuses users. We propose a better approach for TLS by explicitly optimizing importance and coherence on top of coverage and diversity. We apply our approach to both direct and pipeline TLS frameworks. Experimental results show that our approach achieves better performance when compared with two state-of-the-art TLS methods.
Exploring Effective Data Utilization for Low-Resource Speech Recognition

Automatic speech recognition (ASR) has suffered great performance degradation when facing low-resource languages with limited training data. In this work, we propose a series of training strategies to exploring more effective data utilization for low-resource speech recognition. In low-resource scenarios, multilingual pretraining is of great help for the above purpose. We exploit relationships among different languages for better pretraining. Then, the knowledge extracted from the language classifier is utilized for data weighing on training samples, making the model more biased towards the target low-resource language. Moreover, dynamic curriculum learning as a warm-up strategy and length perturbation as data augmentation are also designed. All these three methods form a newly improved training strategy for low-resource speech recognition. Meanwhile, we evaluate the proposed strategies using rich-resource languages for pretraining (PT) and finetuning (FT) the model on the target language with limited data. The experimental results show that on the CommonVoice dataset, compared with the commonly used multilingual PT+FT method, the proposed strategies achieve a relative 15-25% reduction in word error rate on different target languages, which shows the significant effects of the proposed data utilization strategy.
A Novel Chaotic Kernel Framework for Support Vector Machines using Probability-Based Feature Extraction Method

This manuscript proposes a framework to reduce the uncertainty of the data points for chaotic kernel. The work applies a probability based feature extraction method to find the threshold value of the data point and determine the uncertain support vectors (SVs), The reduction of uncertainty in data points was achieved through the framework. The computational time was also optimized. The experimental details of the same are proposed henceforth.
Accuracy Optimization with Weighted Ensemble for Multi-class Anomaly Recognition in Surveillance Videos

Nowadays, due to the ubiquitous use of surveillance systems it is intractable for humans to analyze large data. In video systems, anomalies occur only for a short duration of time. Hence manual monitoring results decrease in reliability and accuracy. In this work, we aim to find and combine models to detect multiclass abnormalities, so that their various parameters can contribute to the analysis. We propose MCPME-AD (Multiclass prediction model ensemble for anomaly detection). It is a model-centered ensemble that trains three predictive convolution neural networks with different parameters and combines their accuracy scores with a weighted vote classification approach and determines the best-weighted function through grid search. This method improves the detection performance. The major findings of this study are twofold. Firstly, we use ensemble-weighted learning to optimize the impact of each base model. Secondly, most studies deal with binary classification, but the reported studies do not deal with other abnormal events such as abuse, Stalinism, road accidents, robbery, etc. Our approach provides a framework for recognizing different anomalous activities using a weighted ensemble approach. This method is tested on a multi-class UCF crime video dataset and experimental results achieve higher accuracy scores than other advanced approaches, that demonstrate the effectiveness of the proposed methodology.
A Universal Detection Method for Adversarial Examples and Fake Images

Deep-learning technologies have shown impressive performance on many tasks in recent years. However, there are multiple serious security risks when using deep-learning technologies. For examples, state-of-the-art deep-learning technologies are vulnerable to adversarial examples that make the model's predictions wrong due to some specific subtle perturbation, and these technologies can be abused for the tampering with and forgery of multimedia, i.e., deep forgery. In this paper, we propose a universal detection framework for adversarial examples and fake images. We observe some differences in the distribution of model outputs for normal and adversarial examples (fake images) and train the detector to learn the differences. We perform extensive experiments on the CIFAR10 and CIFAR100 datasets. Experimental results show that the proposed framework has good feasibility and effectiveness in detecting adversarial examples or fake images. Moreover, the proposed framework has good generalizability for the different datasets and model structures.
A Framework for Supporting Ambient Assisted Living for Users of Special Needs

Healthcare improvements cannot be ignored, especially in the face of advanced technology. Technology also plays a critical role in controlling the spread of viral diseases and providing assistance to physically challenged people. The organization of many health services is heterogeneous, and they often work in isolation from each other. This adversely affects the provision of a better quality of services, integration between them, and creates new issues. This paper has proposed a framework for supporting ambient assisted living. The framework integrates all services in one place and enables cooperation amongst them to make them smart services. In doing so, a review of the available services, used in the proposed framework, is also conducted. The proposed framework is designed to act as a broker between service providers and end-users, which would enhance the accessibility and usability of the services.
Classification of large ruminant carriers as an effort in selecting the feasibility of livestock drivers with the Naive Bayes Algorithm

Abstract Livestock is one of the leading commodities in Indonesia. Transportation costs and the quality of the produced are the problems that lead to the lack of competitiveness of these commodities. The main focus of this paper, is the transportation factor with the driver component as the object, the Knowledge Data Discovery we use as a methodology, and Naïve Bayes as one of the algorithms that can classify the driver and provide knowledge to the transportation managers, K-Means, and PCA we use as a supporting model. The results showed that the use of Naïve Bayes with a small number of datasets resulted in accuracy values of 0.80, the precision of 83%, recall 80%, and 81% of F1score, this value indicates that the resulting classification is in a good category, and this can be the basis of the manager’s decision in the selection of drivers.