Reviewerly/exHarmony huggingface dataset:
Visualizing deep networks using segmentation recognition and interpretation algorithm

Owing to the advancements in artificial intelligence (AI) technology, deep learning has found applications in data analysis and problem-related decision-making processes. However, because of the “black-box” characteristics of deep neural networks, the explanations of decision-making processes are ambiguous. Therefore, model developers face challenges while establishing users’ trust in these processes and the associated decision results. Understanding and explaining the decision-making processes implemented by deep learning-based predictive models has importance in various fields. Based on existing interpretation algorithms, this study proposed a novel interpretation algorithm for deep convolutional neural network (CNN) models. The proposed algorithm, known as the neural network interpreter-segmentation recognition and interpretation (NNI-SRI) algorithm, uses the general ideas of segmentation and recognition to interpret models. Compared with other mainstream interpretation algorithms, this algorithm has the following advantages. First, it has a higher model interpretation speed. Second, it can label the positive and negative features observed during the model prediction process. Third, fewer parameters and hardware resources are required because only the forward propagation is used. Furthermore, the proposed algorithm showed better scene adaptability under the premise of ensuring the accuracy of interpretation, which is consistent with the CNN convolution process. Theoretically, the NNI-SRI algorithm can be applied to any model, particularly CNN models, such as InceptionV3, Xception, and ResNet. In this study, the interpretation algorithm was applied to the standard Inception deep network model and the spider sex recognition model developed by us, which achieved excellent results and verified the feasibility of the algorithm.
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network

Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, researchers have devised methods to automatically segment structures in pathological images. Recently, automated machine and deep learning based methods dominate tissue segmentation research studies. However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixel-wise annotations are expensive and sometimes can be impossible to obtain. This paper introduces a novel unsupervised learning paradigm by integrating an end-to-end deep mixture model with a constrained indicator to acquire accurate semantic tissue segmentation. This constraint aims to centralise the components of deep mixture models during the calculation of the optimisation function. In so doing, the redundant or empty class issues, which are common in current unsupervised learning methods, can be greatly reduced. By validation on both public and in-house datasets, the proposed deep constrained Gaussian network achieves significantly (Wilcoxon signed-rank test) better performance (with the average Dice scores of 0.737 and 0.735, respectively) on tissue segmentation with improved stability and robustness, compared to other existing unsupervised segmentation approaches. Furthermore, the proposed method presents a similar performance (p-value >0.05) compared to the fully supervised U-Net.
Path Guiding with Vertex Triplet Distributions

Abstract Good importance sampling strategies are decisive for the quality and robustness of photorealistic image synthesis with Monte Carlo integration. Path guiding approaches use transport paths sampled by an existing base sampler to build and refine a guiding distribution. This distribution then guides subsequent paths in regions that are otherwise hard to sample. We observe that all terms in the measurement contribution function sampled during path construction depend on at most three consecutive path vertices. We thus propose to build a 9D guiding distribution over vertex triplets that adapts to the full measurement contribution with a 9D Gaussian mixture model (GMM). For incremental path sampling, we query the model for the last two vertices of a path prefix, resulting in a 3D conditional distribution with which we sample the next vertex along the path. To make this approach scalable, we partition the scene with an octree and learn a local GMM for each leaf separately. In a learning phase, we sample paths using the current guiding distribution and collect triplets of path vertices. We resample these triplets online and keep only a fixed‐size subset in reservoirs. After each progression, we obtain new GMMs from triplet samples by an initial hard clustering followed by expectation maximization. Since we model 3D vertex positions, our guiding distribution naturally extends to participating media. In addition, the symmetry in the GMM allows us to query it for paths constructed by a light tracer. Therefore our method can guide both a path tracer and light tracer from a jointly learned guiding distribution.
Detection of Phishing Emails using Machine Learning and Deep Learning

Cyber-attacks or Computer Network Attacks (CNA) are a threat created by cybercriminals by more than one computer against the network. There are various types of attacks, Phishing Attack is one of them. Phishing is a technique of gathering sensitive information of a target user such as username, password, bank details, etc. There are various types of phishing attacks, EMAIL PHISHING attack is one way to get users’ information. Cyber-attackers create fake mail (regarding bank details, shopping websites, etc) using social engineering which looks like legitimate mail. This research study performs a data analysis, data pre-processing, data exploring, training, and predicting by using machine learning and deep learning techniques on an imbalanced dataset, which includes two attributes (EMAIL Text, Label). Further, the performance metrics are used to evaluate the performance of the model. Finally, the proposed model is deployed through the web application FLASK PYTHON. Further, a user input email will be provided by interacting through the web application and it will detect the email by giving information to the user about whether this email is genuine or fraudulent. Based on this detected information, the user will be cautious regarding the mail whether to proceed with that mail or do not open the mail.
Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

Abstract Map‐like visualizations of patient‐specific cardiovascular structures have been applied in numerous medical application contexts. The term map‐like alludes to the characteristics these depictions share with cartographic maps: they show the spatial relations of data attributes from a single perspective, they abstract the underlying data to inCrease legibility, and they facilitate tasks centered around overview, navigation, and comparison. A vast landscape of techniques exists to derive such maps from heterogeneous data spaces. Yet, they all target similar purposes within disease diagnostics, treatment, or research and they face coinciding challenges in mapping the spatial component of a treelike structure to a legible layout. In this report, we present a framing to unify these approaches. On the one hand, we provide a classification of the existing literature according to the data spaces such maps can be derived from. On the other hand, we view the approaches in light of the manifold requirements medical practitioners and researchers have in their efforts to combat the ever‐growing burden of cardiovascular disease. Based on these two perspectives, we offer recommendations for the design of map‐like visualizations of the cardiovascular system.
Classifying Offensive Speech of Bangla Text and Analysis Using Explainable AI

The rapid rise of social networking websites and blogging sites not only provides freedom of expression or speech, but also allows people to express society-prohibited behaviors such as online harassment and cyberbullying, which are known as offensive speech or hate speech. Despite the fact that various research work has been done on detecting hate or abusive speech on social networking websites in the English language, the opportunities for research for detecting offensive or abusive speech in the Bengali language remain open due to the computational resource constraints or the lack of standard-labeled datasets for accurate or effective Natural Language Processing (NLP) of Bangla language. In this paper, an Explainable AI approach is used for analysis as well as for detecting offensive comments or speech in the Bengali language is proposed. Moreover, Convolutional Neural Network (CNN) model is used to extract and classify features. Since the Neural Network is time-consuming for extracting features from the dataset, our proposed approach allows people to save time and effort. In the dataset, we classified all user’s comments from social media comment sections into four categories: religious, personal, geopolitical, and political. Our proposed model successfully detects Bangla offensive speeches from the dataset (Bengali Hate Speech Dataset) by evaluating Machine Learning algorithms like linear and tree-based models and Neural Networks like CNN, Bi-LSTM, Conv-LSTM, and SVM models. Moreover, we calculate scores for completeness and sufficiency to assess the quality of explanations in terms of fidelity, achieving the results with the accuracy of 78% score, significantly outperforming ML and DNN baselines.
Using Low-rank Multilinear Parameter Identification for Anomaly Detection of Building Systems

The paper proposes a new method for anomaly detection based on multilinear low-rank models. No a priori knowledge about the investigated system is needed for data-driven parameter identification of these models. Multilinear parameter identification is able to cover more dynamic phenomena than linear black box identification. A minimal model of rank 1 has a tiny number of parameters which is equal to the dynamic order plus the number of inputs. These multilinear parameters are moreover directly interpretable as each parameter indicates the influence of one corresponding state or input to the next state of the MTI model. As example, the method is demonstrated by an anomaly detection with real data from the HVAC system of a test room.
Fast Patchwise Nonlocal SAR Image Despeckling Using Joint Intensity and Structure Measures

Nonlocal means synthetic aperture radar (SAR) image despeckling approaches have attracted much research attention. However, high computational burden always limits its application in practice, especially using complex similarity measures. We present a fast patchwise nonlocal method using joint intensity and structure measures for SAR image despeckling. Nonlocal methods often define the similarity criterion only based on amplitude or intensity image. In order to preserve structure details, the structure information is also introduced into similarity measure by constructing gradient orientation feature map. The gradient orientation statistical test is performed to determine whether the patches contain the same structural components, and the similar patches are selected through the constant false alarm ratio strategy. Furthermore, we reorganize the patchwise nonlocal despeckling method and accelerate it using fast Fourier transform. Meanwhile, we utilize a Gaussian kernel to aggregate patchwise weights for each pixel in its patch area, so as to reduce the blur effect of classical patchwise nonlocal methods on details. The experiments have demonstrated that the proposed method is an efficient restoration method and has great structure and texture retention ability.
Latent neighborhood-based heterogeneous graph representation

Graph, as a powerful data structure, has shown superior capability on modeling complex systems. Since real-world objects and their interactions are often multi-modal and multi-typed, compared with traditional homogeneous graphs, heterogeneous graphs can represent real-world objects more effectively. Meanwhile, rich semantic information brings great challenges for learning heterogeneous graph representation (HGR). Most existing HGR methods are based on the concept of meta-path, which is constructed based on direct neighbors and define composite semantic relations in heterogeneous graph. However, when the direct neighbor information is inadequate, which always happens due to insufficient observation, the quality of meta-paths cannot be guaranteed. Therefore, we propose a novel HGR framework based on latent direct neighbors. Specifically, random walks are first utilized to discover the potential candidates from indirect neighbors. Then HodgeRank is introduced to determine the latent direct neighbors according to their importance to the target. After that, neighborhood relationships are augmented with the selected latent direct neighbors, and the adjacency tensor of the heterogeneous graph is refactored correspondingly. Finally, Graph Transformer Network is adopted to construct semantic meta-paths automatically and generate HGR. Numerical experiments on different real-world heterogeneous networks show that our new approach can produce more meta-path instances and introduce more complex and diverse semantic information, and consequently achieves more accurate predictions compared with several state-of-the-art baselines.
On the Context-Aware Servicing of User Needs: Extracting and Managing Context Information Supported by Rules and Predictions

The desired context-aware servicing of user needs assumes adequately capturing the user situation, which in turn is often done using sensors. In most cases, the sensor-driven extraction of context information is done counting on pre-defined rules that concern Boolean expressions directly referring to data values for the sake of evaluating the user situation. Further, sensors would be of limited use when considering context indicators (such as intentions) that are not “physical”. Inspired by those challenges, we address the training-data-driven extraction of context information, opting for considering Bayesian Modeling and particularly the Naïve Bayesian Classification Approach because it is: (i) effective as it concerns predictions that are based on training data; (ii) rarely misleading in comparatively “simple” cases, which holds for most real-life cases, as opposed to natural-science-related cases where numerous possible outcomes may apply to any situation; (iii) easily applicable in terms of hardware and software capabilities. Hence, we study the adequacy and usefulness of applying probabilistic approaches together with rules, in establishing and managing the extraction of context information that in turn is needed for the appropriate context-aware servicing of user needs.
A Secure Hardware Implementation for Elliptic Curve Digital Signature Algorithm

Since the end of the 1990s, cryptosystems implemented on smart cards have had to deal with two main categories of attacks: side-channel attacks and fault injection attacks.Countermeasures have been developed and validated against these two types of attacks, taking into account a well-defined attacker model.This work focuses on small vulnerabilities and countermeasures related to the Elliptic Curve Digital Signature Algorithm (ECDSA) algorithm.The work done in this paper focuses on protecting the ECDSA algorithm against fault-injection attacks.More precisely, we are interested in the countermeasures of scalar multiplication in the body of the elliptic curves to protect against attacks concerning only a few bits of secret may be sufficient to recover the private key.ECDSA can be implemented in different ways, in software or via dedicated hardware or a mix of both.Many different architectures are therefore possible to implement an ECDSA-based system.For this reason, this work focuses mainly on the hardware implementation of the digital signature ECDSA.In addition, the proposed ECDSA architecture with and without fault detection for the scalar multiplication have been implemented on Xilinx field programmable gate arrays (FPGA) platform (Virtex-5).Our implementation results have been compared and discussed.Our area, frequency, area overhead and frequency degradation have been compared and it is shown that the proposed architecture of ECDSA with fault detection for the scalar multiplication allows a trade-off between the hardware overhead and the security of the ECDSA.
Towards Developing Privacy-Preserved Data Security Approach (PP-DSA) in Cloud Computing Environment

In the present scenario of rapid growth in cloud computing models, several companies and users started to share their data on cloud servers.However, when the model is not completely trusted, the data owners face several security-related problems, such as user privacy breaches, data disclosure, data corruption, and so on, during the process of data outsourcing.For addressing and handling the security-related issues on Cloud, several models were proposed.With that concern, this paper develops a Privacy-Preserved Data Security Approach (PP-DSA) to provide the data security and data integrity for the outsourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique (EAT) using the Group Signature method that is applied with Third-Party Auditor (TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally, the Cloud Service Provider (CSP) and Data User (DU) can also be the attackers that are to be handled with the EAT.Here, the major objective of the work is to enhance cloud security and thereby, increase Quality of Service (QoS).The results are evaluated based on the model effectiveness, security, and reliability and show that the proposed model provides better results than existing works.
Intervention Algorithm of Horse Racing for Students’ Psychological Disorders Based on Big Data

Horse racing is a competitive activity in which humans control horses. It is a worldwide traditional sports activity. In the history of human-horse racing, horse racing activities in various eras have been followed but also constantly innovated. According to the K-means clustering algorithm, a cluster analysis model is constructed for the model, and the algorithm model is applied to the students’ mental health counseling work. After six iterations to get the classification and six iterations of the cluster center algorithm, it was found that the mixed psychological population accounted for 50%. Horse racing can not only effectively relieve students’ psychological problems and exercise students’ willpower and thinking ability, but also promote traditional sports. Through K-means clustering algorithm research, frame design, and program design, a complete psychological disorder intervention system is established, which provides scientific, objective, and reliable data guarantee for the correct implementation of decision-making strategies. Integrating cluster analysis into the process of students’ mental health analysis makes it easier for decision-makers to understand and master all aspects of students’ information, to analyze and customize solutions for different types of students. It provides a good foundation for providing different psychotherapeutic methods for different groups of students.
Contact Tracing Research: A Literature Review Based on Scientific Collaboration Network

Contact tracing is a monitoring process including contact identification, listing, and follow-up, which is a key to slowing down pandemics of infectious diseases, such as COVID-19. In this study, we use the scientific collaboration network technique to explore the evolving history and scientific collaboration patterns of contact tracing. It is observed that the number of articles on the subject remained at a low level before 2020, probably because the practical significance of the contact tracing model was not widely accepted by the academic community. The COVID-19 pandemic has brought an unprecedented research boom to contact tracing, as evidenced by the explosion of the literature after 2020. Tuberculosis, HIV, and other sexually transmitted diseases were common types of diseases studied in contact tracing before 2020. In contrast, research on contact tracing regarding COVID-19 occupies a significantly large proportion after 2000. It is also found from the collaboration networks that academic teams in the field tend to conduct independent research, rather than cross-team collaboration, which is not conducive to knowledge dissemination and information flow.
Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling

To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text, respectively. Then, a convolution operation is used to extract local key features, while cross-channel feature fusion and multi-head self-attention pooling operations are used to extract global semantic information and filter out key data, before using the multi-granularity feature interaction fusion operation to effectively fuse character-level and word-level semantic information. Finally, the Softmax function is used to output the results. On the weibo_senti_100k dataset, the accuracy and F1 values of the DCCMM model improve by 0.84% and 1.01%, respectively, compared to the best-performing comparison model. On the SMP2020-EWECT dataset, the accuracy and F1 values of the DCCMM model improve by 1.22% and 1.80%, respectively, compared with the experimental results of the best-performing comparison model. The results showed that DCCMM outperforms existing advanced sentiment analysis models.
Consumer preference for formal address and informal address from warm brands and competent brands

Abstract With the exception of Modern English, the world's major languages present advertisers and service providers with a choice: whether to address consumers using informal or formal pronouns (e.g., tu or usted in Spanish). Yet, no research has investigated the impact of informal and formal address on consumer responses. In this paper, we show that brand personality affects consumers' preferences for, and responses to, pronominal address. In five studies, we establish that informal address is more likely to be preferred, and elicits higher preferences and more positive responses, when used by warmer brands; whereas formal address is more likely to be preferred, and elicits higher preferences and more positive responses, when used by more competent brands. These effects are replicated using a variety of contexts and designs. The implications of these findings are discussed.
Decentralized Blockchain-Based IoT Data Marketplaces

In present times, the largest amount of data is being controlled in a centralized manner. However, as the data are in essence the fuel of any application and service, there is a need to make the data more findable and accessible. Another problem with the data being centralized is the limited storage as well as the uncertainty of their authenticity. In the Internet of Things (IoT) sector specifically, data are the key to develop the most powerful and reliable applications. For these reasons, there is a rise on works that present decentralized marketplaces for IoT data with many of them exploiting blockchain technology to offer security advantages. The main contribution of this work is to review the existing works on decentralized IoT data marketplaces and discuss important design aspects and options so as to guide (a) the prospective user to select the IoT data marketplace that matches their needs and (b) the potential designer of a new marketplace to make insightful decisions.