Reviewerly/exHarmony huggingface dataset:
Image Deblurring Using Multi-Stream Bottom-Top-Bottom Attention Network and Global Information-Based Fusion and Reconstruction Network

Image deblurring has been a challenging ill-posed problem in computer vision. Gaussian blur is a common model for image and signal degradation. The deep learning-based deblurring methods have attracted much attention due to their advantages over the traditional methods relying on hand-designed features. However, the existing deep learning-based deblurring techniques still cannot perform well in restoring the fine details and reconstructing the sharp edges. To address this issue, we have designed an effective end-to-end deep learning-based non-blind image deblurring algorithm. In the proposed method, a multi-stream bottom-top-bottom attention network (MBANet) with the encoder-to-decoder structure is designed to integrate low-level cues and high-level semantic information, which can facilitate extracting image features more effectively and improve the computational efficiency of the network. Moreover, the MBANet adopts a coarse-to-fine multi-scale strategy to process the input images to improve image deblurring performance. Furthermore, the global information-based fusion and reconstruction network is proposed to fuse multi-scale output maps to improve the global spatial information and recurrently refine the output deblurred image. The experiments were done on the public GoPro dataset and the realistic and dynamic scenes (REDS) dataset to evaluate the effectiveness and robustness of the proposed method. The experimental results show that the proposed method generally outperforms some traditional deburring methods and deep learning-based state-of-the-art deblurring methods such as scale-recurrent network (SRN) and denoising prior driven deep neural network (DPDNN) in terms of such quantitative indexes as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and human vision.
Blockchain and Industrial Internet of Things: Applications for Industry 4.0

Internet of Things (IoT) is commanding a great role in connecting electronic appliances through the cloud. It is an expanding area of research and is finding association with other emerging technologies like 3D printing and machine vision. The growing popularity of the technology is an extension of the Internet boom. An expanding Internet user base, cheaper smart devices and greater on-cloud memory are encouraging the spread of IoT. From fridges to water geysers, any electronic appliance may be connected to the IoT network. The advantages of remote access, master control and cloud storage give it unprecedented advantage over conventional means of communication. In this paper, we attempt to review some latest applications of IoT across various sectors: location tracking, sports, smart devices and blockchain-based security. IoT remains a rapidly developing topic with significant interdisciplinary research and development potential. Further integration of sensors for inputs will propagate the spread of IoT.
Readiness for information security of teachers as a function of their personality traits and their assessment of threats

Purpose This study examines the impact of personality traits on the degree of challenge experienced by individuals with respect to the threat on their information, the evaluation of their self-efficacy to secure the information and hence, their readiness to secure information. Design/methodology/approach The study's population consisted of 157 teachers from various educational institutions across Israel. We used five questionnaires to gather data. Findings Findings reveal a link between participants' personality traits, situation evaluation indicators and their readiness to secure information. Further, the greater subjects' information security awareness and familiarity with information security concepts, the better their application of the tools for securing information will be. Originality/value The importance of this research lies primarily in that it highlights the importance of individual differences while dealing with information security awareness. The findings constitute a theoretical and empirical basis for building tools toward guiding teachers to protect their information, as well as for devising educational and pedagogic programs for making a cultural change.
Relative Examination of Texture Feature Extraction Techniques in Image Retrieval Systems by Employing Neural Network: An Experimental Review

In this paper, the performance of the four most significant texture feature extraction techniques of content-based image retrieval (CBIR) systems are technically analyzed and compared in two phases. In the first phase, simply texture features are extracted by various techniques on three important CBIR datasets which are Wang, Corel-5K and Corel-10K and retrieval parameters are evaluated. In the next phase, artificial neural network, i.e., cascade forward back propagation neural network (CFBPNN) is employed as a classifier after the step of feature extraction which increases the accuracy and average precision of the system. The main issue with CBIR systems is the proper selection of techniques for the extraction of low-level features of the images which comprises of color, texture, and edge. Among these features, texture is one of the most influential features. This selection of features completely depends upon the type of images to be retrieved from the database. The four important texture techniques i.e. Grey level co-occurrence matrix (GLCM), discrete wavelet transforms (DWT), Gabor transform and local binary pattern (LBP) are experimented here and performance parameters are evaluated. So this analysis will help the researchers in selection of proper texture feature techniques depending upon the type of images for CBIR systems. During phase 1, which is without classifier the LBP technique has the highest precision which is around 82, 70 and 66% on Wang, Corel-5K, and Corel-10K respectively. The results spectacles a significant improvement with the use of CFBPNN and these results now comes up to around 94, 83, and 79%, respectively, on the same datasets.
Enhancing search-based product line design with crossover operators

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA designing has been formulated as a multi-objective optimization problem and successfully solved by a state-of-the-art search-based approach. However, the majority of empirical studies optimize PLA designs without applying one of the fundamental genetic operators: the crossover. An operator for PLA design, named Feature-driven Crossover, was proposed in a previous study. In spite of the promising results, this operator occasionally generated incomplete solutions. To overcome these limitations, this paper aims to enhance the search-based PLA design optimization by improving the Feature-driven Crossover and introducing a novel crossover operator specific for PLA design. The proposed operators were evaluated in two well-studied PLA designs, using three experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results show the usefulness and efficiency of the presented operators on reaching consistent solutions. We also observed that the two operators complement each other, leading to PLA design solutions with better feature modularization than the baseline experiment.
Tightly Close It, Robustly Secure It: Key-Based Lightweight Process for Propping up Encryption Techniques

Securing invaluable information has been, and will be, the highest priority whether for individuals or organizations. Researchers are working diligently to meet this priority by offering different types of protection techniques. The encryption techniques stand out as de-facto mechanisms for ensuring proper protection for information. Many encryption techniques are available that have passed basic security tests and ensure reasonable levels of protection. The greatest challenge to these techniques is the formidably–ever–advancing cryptanalysis tools. Given this real challenge, we believe that these encryption techniques will sooner or later face the same destiny as other techniques (e.g. DES). That is, unless we keep boosting their capabilities, these techniques may fail to resist the tricky cryptanalysis tools, offering perfect opportunity for privacy–intruding lovers to threaten the information’s privacy. This paper addresses this problem by offering a specific way. In particular, it proposes a closing stage that forms an additional (and highly effective) line of defense against security attacks by concealing the final output of the encryption techniques in highly random and enormously complicated codes. This method can be integrated with any encryption technique as a final stage to increase its resistance against cryptanalysis tools. The proposed method is implemented and subjected to rigorous security testing. These tests showed that the method provides very effective camouflaging mechanisms to hide data.
Research and analysis of intelligent English learning system based on improved neural network

It is of great significance to explore the prospects of English intelligent learning in the field of basic education and to understand the current status and practical needs of mobile learning technology. Based on the intelligent English learning and teaching needs, this study constructs an intelligent English learning system based on improved neural network. The system uses wavelets to replace the neurons in the traditional neural network, establishes the connection between the wavelet transform and the network coefficients through affine transformation, and uses the wavelet neural network to correct the network parameters, so as to avoid the problem of being partially optimal due to the sensitivity of the initial value to some extent. Moreover, the English learning system adopts a mature C/S structure, and the system is divided into two parts and four layers. In addition, this study designed the experiment to analyze the performance of the English learning system, set the experimental group and the control group to conduct practical analysis. The research results show that the system constructed in this paper has certain effects and can provide theoretical reference for subsequent related research.
Weakly Supervised Fine-Grained Image Classification via Salient Region Localization and Different Layer Feature Fusion

The fine-grained image classification task is about differentiating between different object classes. The difficulties of the task are large intra-class variance and small inter-class variance. For this reason, improving models’ accuracies on the task heavily relies on discriminative parts’ annotations and regional parts’ annotations. Such delicate annotations’ dependency causes the restriction on models’ practicability. To tackle this issue, a saliency module based on a weakly supervised fine-grained image classification model is proposed by this article. Through our salient region localization module, the proposed model can localize essential regional parts with the use of saliency maps, while only image class annotations are provided. Besides, the bilinear attention module can improve the performance on feature extraction by using higher- and lower-level layers of the network to fuse regional features with global features. With the application of the bilinear attention architecture, we propose the different layer feature fusion module to improve the expression ability of model features. We tested and verified our model on public datasets released specifically for fine-grained image classification. The results of our test show that our proposed model can achieve close to state-of-the-art classification performance on various datasets, while only the least training data are provided. Such a result indicates that the practicality of our model is incredibly improved since fine-grained image datasets are expensive.
Prediction for Dengue Fever in Indonesia Using Neural Network and Regression Method

Abstract Dengue fever is the most hurriedly diffused mosquito-borne viral disease in the world. More than 33% of the total population in the world is under risk. Currently the prediction of dengue can save a person ‘s life by alerting them to take proper diagnosis and care. The Objectives of this research is (1) To Predict The area with the most potential to suspend dengue fever In Indonesia, And (2) to Predict dengue fever cases. (3). To analyz how many percent the effect factor of dengue fever. There are many ways to predict one of them is Regression and Deep learning Approach. Reseacher tried to analyz the most accurate such as Regression Multyplied, Neural Network, and Sensitivity Analysis. Set of data have been used is timeseries from 1997—2017. The Variable has been used for this research is Humadity, Temperature, Wind, Airpressure, Rainfall Index, income, sunlight, Population density, and output is cases. The Result of this research is first The area with the most potential to suspend Dengue fever In Indonesia 2019 is Jambi, Lampung, Bangka Belitung, West Sumatera, Central java with average Accuracy 87,16%. The Prediction dengue fever cases 2019 is 80233 Cases with accuracy 87,16%. The Third All variable ( X1 s.d X8) have been to effect to Partial and Simultaneous to (Y) in the amount of 0.16 (16 % ) with a significance level of 0.001 (99%). While the remaining 100% - 16% = 84% is influenced by other variables outside of this research.
Random group recommendation model based on fuzzy clustering

The group recommendation system is a viral requirement for the Internet service provider to provide recommendation services for all the users in a group. Due to the shared or different interests among users in the group, it is difficult for traditional personal recommendation algorithms to predict items that can meet the requirements of all the users in the group. In this paper, a random group recommendation model is proposed to recommend the top K most appealing items for all the users in a random group. By analyzing item ratings of all the users in the group, the recommendation model can abstract the group as a virtual user. Then a personal recommendation algorithm is applied to suggest the top K most appealing items for the virtual user. And the preference score and fuzzy clustering algorithm based on multiclass are applied to optimize the recommendation result of the group recommendation model. Finally, the MovieLens-100K dataset is applied to verify the efficiency of the recommendation model. The experimental results show that the items recommended by the proposed group recommendation model are more popular for all the users in the group than the items recommended by traditional group recommendation algorithms.
Random Centroid Selection for K-means Clustering: A Proposed Algorithm for Improving Clustering Results

Primary motivation towards this study was to obtain better clustering results from K-means clustering algorithm. Several studies had provided relevant findings showing normal clustering algorithm and scope of improvements regarding clustering accuracy. With the aim to increase clustering accuracy, a conceptual notion of Genetic Algorithm is utilized in K-means clustering algorithm. Depending on the Genetic algorithm concepts, an improved clustering technique is proposed in this study for obtaining more accurate and more precise clustering outcomes. The contribution from this study could be essential in terms of topic-modelled data and clustering text documents.
Deeper Siamese Network With Stronger Feature Representation for Visual Tracking

Siamese network based visual tracking has drawn considerable attention recently due to the balanced accuracy and speed.This type of method mostly trains a relatively shallow twin network offline, and measures the similarity online using cross-correlation operation between the feature maps generated by the last convolutional layer of the target and search regions to locate the object.Nevertheless, a single feature map extracted from the last layer of shallow networks is insufficient to describe target appearance, as well as sensitive to the distractors, which could mislead the similarity response map and make the tracker easily drift.To enhance the tracking accuracy and robustness while maintaining the real-time speed, based on the above tracking paradigm, three improvements including reform of backbone network, fusion of hierarchical features and utilization of channel attention mechanism, have been made in this paper.Firstly, we introduce a modified deeper VGG16 backbone network, which could extract more powerful features contributing to distinguishing the target from distractors.Secondly, we fuse diverse features extracted from deep layers and shallow layers to take advantage of both semantic and spatial information of the target.Thirdly, we incorporate a novel lightweight residual channel attention mechanism into the backbone network, which expands the weight gap between different channels and helps the network pay more attention on dominant features.Extensive experimental results on OTB100 and VOT2018 benchmarks demonstrate that our tracker performs better in accuracy and efficiency against several state-of-the-art methods in real-time scenarios.
Experimental test of ECDSA digital signature robustness from timing-lattice attack

An experimental test of robustness to timing attack is reported for the widely used public-key cryptographic algorithm in IoT transducers, the Elliptic Curve Digital Signature Algorithm (ECDSA). To this aim, a timing-lattice attack is addressed on ECDSA of the firmware Library MbedTLS for ARM microcontrollers. Timing is assessed by measuring the execution time of ecdsa_write_signature of MbedTLS library implemented on an ARM Cortex-M4 microcontroller. The time intervals required to sign the messages, the messages themselves, and the signatures, are used to mount a lattice attack in order to discover ECDSA private key. Experimental results highlight the security of ECDSA function in MbedTLS library to the implemented attack.
A Specificity-Based Approach to Semantic Interpretation and Hierarchical Complexity Reduction in Fuzzy Models

The existing approaches to interpretability in data-driven fuzzy models are reliant, heavily, on constraining different components of the model, and subsequently on sophisticated learning methods. This article, however, puts forward an alternative view of the interpretability of fuzzy models based on the concept of specificity. In addition to being free of constraints, this view can effectively deal with the subjectivity inherent in the linguistic interpretation of fuzzy sets and rules rather than suppressing it, as the constraint-based approaches do. Second to this semantic improvement, a specificity-based hierarchical fuzzy structure is also proposed to deal with the complexity-based aspect of interpretability. It achieves this purpose by means of a two-level hierarchy, in which a number of general rules at the first level localize both the inference and learning processes of the second level to a small number of relevant, specific rules. Such a localization, besides employment of the recursive least squares algorithm for parameter learning, reduces the computational burden of the model significantly, making it capable of real-time operation. The experimental results of applying the proposed fuzzy modeling approach to a variety of synthetic and real-world datasets confirm its efficacy in the sense of interpretability, accuracy, and computational simplicity.
A Smartphone-Based Cell Segmentation to Support Nasal Cytology

Rhinology studies the anatomy, physiology, and diseases affecting the nasal region—one of the most modern techniques to diagnose these diseases is nasal cytology, which involves microscopic analysis of the cells contained in the nasal mucosa. The standard clinical protocol regulates the compilation of the rhino-cytogram by observing, for each slide, at least 50 fields under an optical microscope to evaluate the cell population and search for cells important for diagnosis. The time and effort required for the specialist to analyze a slide are significant. In this paper, we present a smartphones-based system to support cell segmentation on images acquired directly from the microscope. Then, the specialist can analyze the cells and the other elements extracted directly or, alternatively, he can send them to Rhino-cyt, a server system recently presented in the literature, that also performs the automatic cell classification, giving back the final rhinocytogram. This way he significantly reduces the time for diagnosing. The system crops cells with sensitivity = 0.96, which is satisfactory because it shows that cells are not overlooked as false negatives are few, and therefore largely sufficient to support the specialist effectively. The use of traditional image processing techniques to preprocess the images also makes the process sustainable from the computational point of view for medium–low end architectures and is battery-efficient on a mobile phone.
Text Classification Using Word Embeddings

Growth in the internet made immense impact on the data generation. Most of the data in the world is in textual format. There is need to access and use this data efficiently and easily hence, text classification is widely studied problem in research community. The applications of text classification are also in diverse domains such as news filtering, opinion mining, information retrieval and so on. At core of these applications is machine learning algorithms which need input of fixed length as a vector. Bag-of-Words is popular model used to represent text in numeric vector format. But it has several disadvantages such as word order is ignored, high dimensionality and sparse representation if vocabulary is large. Word embeddings are distributed vector representation of words. These representation inherit semantic notion of similarity also they have shown state-of-art results in many core natural language processing tasks. In presented work, we present text classification using these word embeddings and measure the performance.
Bayesian linear regression models with flexible error distributions

This work introduces a novel methodology based on finite mixtures of Student-t distributions to model the errors' distribution in linear regression models. The novelty lies on a particular hierarchical structure for the mixture distribution in which the first level models the number of modes, responsible to accommodate multimodality and skewness features, and the second level models tail behaviour. Moreover, the latter is specified in a way that no estimation of the degrees of freedom parameters is required. This way, the known statistical difficulties when dealing with those parameters are mitigated and yet model flexibility is not compromised. The inference is performed via a carefully designed Markov chain Monte Carlo algorithm and simulation studies are conducted to evaluate the performance of the proposed methodology. The analysis of two real data sets is also presented.