Reviewerly/exHarmony huggingface dataset:
Blockchain based Secure Data Exchange between Cloud Networks and Smart Hand-held Devices for use in Smart Cities

In relation to smart city planning and management, processing huge amounts of generated data and execution of non-lightweight cryptographic algorithms on resource constraint devices at disposal, is the primary focus of researchers today. To enable secure exchange of data between cloud networks and mobile devices, in particular smart hand held devices, this paper presents Blockchain based approach that disperses a public/free key to save it on a block within a Blockchain. The proposed system generates public-private key pair to encrypt data digitally to allow data communication. This empowers communication devices to encipher data using keys stored in the Blockchain i.e. the public key. Generated cipher text can be decrypted/deciphered only with the respective private keys, meaning that only the communicating devices can obtain their own plain text in a data exchange process. Smart mobile employed in smart city can then encipher the data using the keys and store them on the cloud. The proposed system is able to decrease the number of overheads that relate to key generation, key delivery and key storage whilst providing solutions for data processing, information exchange and data over-collection, respectively. Thus, the study proposes a robust and secure solution to exchange keys and secure data communication based on Blockchain technology.
On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition

This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo State Network (ESN) input and reservoir weights, in the context of human action recognition (HAR). To ensure stability and echo state property in the reservoir, Recurrent Plots (RPs) and Recurrence Quantification Analysis (RQA) techniques are exploited for explainability and characterization of the reservoir dynamics and hence tuning ESN hyperparameters. The optimized self-organizing reservoirs are cascaded with a Convolutional Neural Network (CNN) to ensure that the activation of internal echo state representations (ESRs) echoes similar topological qualities and temporal features of the input time-series, and the CNN efficiently learns the dynamics and multiscale temporal features from the ESRs for action recognition. The hyperparameter optimization (HPO) algorithms are additionally adopted to optimize the CNN stage in SO-ConvESN. Experimental results on the HAR problem using several publicly available 3D-skeleton-based action datasets demonstrate the showcasing of the RPs and RQA technique in examining the explainability of reservoir dynamics for designing stable self-organizing reservoirs and the usefulness of implementing HPOs in SO-ConvESN for the HAR task. The proposed SO-ConvESN exhibits competitive recognition accuracy.
Analysis and classification of privacy-sensitive content in social media posts

User-generated contents often contain private information, even when they are shared publicly on social media and on the web in general. Although many filtering and natural language approaches for automatically detecting obscenities or hate speech have been proposed, determining whether a shared post contains sensitive information is still an open issue. The problem has been addressed by assuming, for instance, that sensitive contents are published anonymously, on anonymous social media platforms or with more restrictive privacy settings, but these assumptions are far from being realistic, since the authors of posts often underestimate or overlook their actual exposure to privacy risks. Hence, in this paper, we address the problem of content sensitivity analysis directly, by presenting and characterizing a new annotated corpus with around ten thousand posts, each one annotated as sensitive or non-sensitive by a pool of experts. We characterize our data with respect to the closely-related problem of self-disclosure, pointing out the main differences between the two tasks. We also present the results of several deep neural network models that outperform previous naive attempts of classifying social media posts according to their sensitivity, and show that state-of-the-art approaches based on anonymity and lexical analysis do not work in realistic application scenarios.
Offline Bangla Handwritten Text Recognition: A Comprehensive Study of Various Deep Learning Approaches

Offline Handwritten Text Recognition (HTR) is a technique for translating handwritten images into digitally editable text format. Due to the presence of cursive letters, punctuation marks, and compound characters, it is more complex to recognize Bangla handwritten text. Over the years, several approaches to the optical model of the HTR system have been developed, including Hidden Markov Model (HMM) or deep learning techniques such as Convolutional Recurrent Neural Networks (CRNN), and current state-of-the-art Gated-CNN based architectures. Despite this, there are relatively limited works available for Bangla word recognition. In this paper, we introduce an end-to-end system for Bangla word recognition. We used a variety of popular pre-trained CNN architectures, including Xception, MobileNet, and DenseNet, followed by recurrent units such as LSTM or GRU. Furthermore, we experimented with Puigcerver&#x2019;s CRNN based and Flor&#x2019;s Gated-CNN based optical model architectulimited works available in Bangla.res. Flor architecture provided the highest recognition rate in our experiment, with a CER of 12.83% and a WER of 36.01%.
Nuclei Instance Segmentation and Classification in Histopathological Images using a DT-Yolact

Nuclei instance segmentation and classification in histology images is a challenging task, which requires not only a precise semantic segmentation at pixel-level but also an accurate classification at instance-level. To tackle the problem, in this paper, we propose a Double-Tower Yolact network named DT-Yolact, which introduces a bottom-up path augmentation (BPA) module to pair with the feature pyramid network (FPN) module in Yolact, with the purpose of shortening the transmission path and fuse the lower-level and higher-level features extracted from nuclei. Moreover, a dilated convolution (DC) algorithm is employed in the convolutional neural network to expand the convolution receptive field and further improve the accuracy of prediction. The comparison results on PanNuke histology images have shown that: the mAP of the proposed DT-Yolact network is 3.56% and 1.33% higher than those of the state-of-art Mask R-CNN and the original Yolact networks. Therefore, it is promising to apply the proposed DT-Yolact network to the simultaneous nuclei instance segmentation and classification in histopathological medical images.
Privacy Preservation of COVID-19 Contact Tracing Data

Big data are everywhere. Examples of big data include contact tracing data of patients who contracted coro-navirus disease 2019 (COVID-19). On the one hand, mining these contact tracing data can be for social good. For instance, it helps slow down the spread of COVID-19. It also helps people diagnosed with COVID-19 get referrals for services and resources they may need to isolate safely. On the other hand, it is also important to protect the privacy of these COVID-19 patients. Hence, we present in this paper a solution for privacy preservation of COVID-19 contact tracing data. Specifically, our solution preserves the privacy of individuals by publishing only their spatio-temporal representative locations. Evaluation results on real-life COVID-19 contact tracing data from South Korea demonstrate the effectiveness and practicality of our solution in preserving the privacy of COVID-19 contact tracing data.
Multilayer Video Encoding for QoS Managing of Video Streaming in VANET Environment

Efficient delivery and maintenance of the quality of service (QoS) of audio/video streams transmitted over VANETs for mobile and heterogeneous nodes are one of the major challenges in the convergence of this network type and these services. In this context, we propose an inter-layer approach for multimedia stream transmission in a VANET environment (VSMENET). The main idea of our work is based on the dynamic adaptation of the transmission rate according to the physical rate available in the VANET. VSMENET is all about eliminating downtime during video playback by vehicle users. This involves adapting the quality of the video to the actual performance of the VANETs, intelligent encoding of video on the Road Side Units (RSU) side, and finally continuous maintenance of the calculation tasks on the RSU side and sufficient video data on the vehicle node side. Thus, we are interested in the process of evaluating the strict parameters of the VANETs, influencing the video transmission. For example, we propose, on the one hand, an architecture for intelligent data selection and good clock synchronization, and, on the other hand, efficient management of the availability and consumption of video data. We used the NetSim simulator to test the proposed approach performance. To this end, several algorithms such as OCLFEC, MAC, ShieldHEVC, and AntArmour have been implemented for such a performance comparison. Our work suggests that VSMENET is well concerning the average lifetime of the video packets and their delivery rate (more than 9% gain compared with other approaches).
Self-Mutating Network for Domain Adaptive Segmentation of Aerial Images

The domain-adaptive semantic segmentation of aerial images using a deep-learning technique is still challenging owing to the domain gaps between aerial images obtained in different areas. Currently, various convolutional neural network (CNN)-based domain adaptation methods have been developed to decrease the domain gaps. However, they still show poor performance for object segmentation when they are applied to images from other domains. In this paper, we propose a novel CNN-based self-mutating network (SMN), which can adaptively adjust the parameter values of convolutional filters as a response to the domain of an input image for better domain-adaptive segmentation. For the SMN, the parameter mutation technique was devised for adaptively changing parameters, and a parameterfluctuationtechniquewasdevelopedtorandomlyconvulsetheparameters. By adopting the parameter mutation and fluctuation, adaptive self-changing and fine-tuning of parameters can be realized for images from different domains, resulting in better prediction in domain-adaptive segmentation. Meanwhile, the results of the ablation study indicate that the SMN provided 11.19% higher Intersection over Union values than other state-of-the-art methods, demonstrating its potential for the domain-adaptive segmentation of aerial images.
Classification and Detection of Acne on the Skin using Deep Learning Algorithms

Teenagers and young adults are prone to acne. It's a skin condition that arises when oil and dead skin cells clog hair follicles. The face, forehead, upper back, and chest are the most common places where it occurs. Excess bacteria, inflammation, and blocked hair follicles are all reasons that contribute to acne. It is the seventh epidemic and is thought to afflict 9.4% of the world's population. For the identification of acne on diverse skins, we used various pre-trained CNN models such as Inception V3, VGG16, and VGG19. We also used machine learning classifiers for a thorough examination of acne detection. The Inception v3 with the logistic regression classifier provides the best accuracy of 99.5%.
Privacy-preserving collaborative machine learning in biomedical applications

Machine learning (ML) algorithms are now widely used to tackle computational problems in diverse domains. In biomedicine, the rapidly growing amounts of experimental data increasingly necessitate the use of ML to discern complex data patterns. However, biomedical data is often considered sensitive, and the privacy of individuals behind the data is increasingly put at risk as a result. Traditional methods such as anonymization and pseudonymization are not always applicable and have limited effectiveness with respect to risk mitigation. Privacy researchers are actively developing alternative approaches to privacy protection, including strategies based on cryptography, such as homomorphic encryption and secure multiparty computation. This paper discusses recent advances in biomedical applications of these privacy techniques. We first review the key privacy techniques, then provide an overview of their applications in biomedical machine learning. Finally, we highlight the remaining challenges of current approaches and suggest directions for future work.
Consensus-Clustering-Based Automatic Distribution Matching for Cross-Domain Image Steganalysis

Image steganalysis is a technique to detect whether an image contains hidden information. Although the existing cross-domain steganalysis methods have been presented to narrow the distribution gap between different domains, it is still challenging to effectively capture the transferable steganalysis representations under the condition of severe distribution shifts. To address this issue, we propose a novel consensus-clustering-based automatic distribution matching scheme, called CADM, which can automatically and accurately match inconsistent distributions in cross-domain steganalysis scenarios. First, the original steganalysis features are clustered by the spatially constrained fuzzy <i>c</i>-means (SCFCM) algorithm with controllable parameters to fully perceive and mine inherent structural relationships. Subsequently, the cluster consensus knowledge is derived from the perspective of intra-domain and inter-domain to facilitate the clustering and the matching. In this way, the representations of weak stego signals can be augmented by identifying cluster centers that can be combined across domains. Ultimately, the cycle-consistent optimization and adaptation is achieved by gradually adjusting the learning strength of well-aligned and poorly-aligned samples to promote the positive transfer of overlapped clusters and prevent the negative transfer of outlier clusters. Furthermore, extensive experiments on various benchmark databases for cross-domain steganalysis demonstrate the superiority of CADM over the current state-of-the-art methods.
Federated Learning for Data Security and Privacy Protection

In recent years, Artificial intelligence (AI) have been applied in many fields, including driverless cars, smart cities, healthcare, finance, etc. However, data island and data privacy security are still two major challenges for AI, federated learning is proposed as a solution. In federated learning, many clients collaborate to train a common model under the coordination of a central server, while keeping the training data decentralized. Each client's data does not leave the local area, and a global shared model is jointly built by means of parameter exchange under the encryption mechanism, the built model serves only the local target in their respective regions. In this paper, we present the definition, classification, and learning process of the federated learning, and discuss the key challenges faced by federated learning and the solutions that are currently available.
Transfer learning based on lexical constraint mechanism in low-resource machine translation

With the fast development of artificial intelligence (AI) technology, machine translation has become a mainstream field of natural language processing. The low-resource language machine translation tasks have become an essential question. However, traditional machine translation systems usually rely on large amounts of high-quality parallel training data. In terms of this question, data augmentation and transfer learning technique in AI domain have become an effective solution for dealing with low-resource language machine translation. Besides, to better solve the domain mismatch problem of machine translation tasks, leveraging lexical constraint mechanism is a significant measure. We presented an approach which applies the transfer learning techniques for the lexical constraint model in this paper. For the existed problem of the transfer learning and lexical constraint technologies, some improved methods are proposed. We choose the appropriate beam search algorithm for lexical constraint measure and investigate the proper way for transferring parameters across two machine translation models. Besides, we will also investigate the compelling data pre-processing steps to process the low-resource corpus and quote various objective evaluation mechanisms to estimate the performance of our pattern better. The comprehensive experiments and results in the paper demonstrate that our method toward low-resource machine translation tasks is effective.
Application of Digital Image Correlation in Space and Frequency Domains to Deformation Analysis of Polymer Film

Using speckle patterns formed by an expanded and collimated He-Ne laser beam, we apply DIC (Digital Image Correlation) methods to estimate the deformation of LLDPE (linear low-density polyethylene) film. The laser beam was transmitted through the film specimen while a tensile machine applied a load to the specimen vertically. The transmitted laser light was projected on a screen, and the resultant image was captured by a digital camera. The captured image was analyzed both in space and frequency domains. For the space-domain analysis, the random speckle pattern was used to register the local displacement due to the deformation. For the frequency-domain analysis, the diffraction-like pattern, due to the horizontally-running, periodic groove-like structure of the film was used to characterize the overall deformation along vertical columns of analysis. It has been found that when the deformation is small and uniform, the conventional space domain analysis is applicable to the entire film specimen. However, once the deformation loses the spatial uniformity, the space-domain analysis falls short if applied to the entire specimen. The application of DIC to local (windowed) regions is still useful but time consuming. In the non-uniform situation, the frequency-domain analysis is found capable of revealing average deformation along each column of analysis.
Digitalization and Banking Crisis: A Nonlinear Relationship?

Abstract This study assesses the impact that ICT can have on the occurrence of banking crisis for a sample of 113 countries over the period 1996–2017 while considering the interfering role of corruption and the potential existence of a nonlinear relationship for the ICT-banking crisis nexus. We consider countries with varying income levels to check for the stability of the relationship. Our estimation results show that more highly ICT endowed countries can improve the resilience of the banking system. The study provides evidence that ICTs do not produce the same effect regardless of a country’s level of income. Estimation outcomes show the existence of a threshold effect driving the ICT-banking stability nexus. More results show that Information and communication technologies can improve the stability of the banking system only when corruption is relatively low. However, when corruption is endemic, ICT endowment is useless for the stability of the banking system.
Buddy Stacks: Protecting Return Addresses with Efficient Thread-Local Storage and Runtime Re-Randomization

Shadow stacks play an important role in protecting return addresses to mitigate ROP attacks. Parallel shadow stacks, which shadow the call stack of each thread at the same constant offset for all threads, are known not to support multi-threading well. On the other hand, compact shadow stacks must maintain a separate shadow stack pointer in thread-local storage (TLS) , which can be implemented in terms of a register or the per-thread Thread-Control-Block (TCB) , suffering from poor compatibility in the former or high performance overhead in the latter. In addition, shadow stacks are vulnerable to information disclosure attacks. In this paper, we propose to mitigate ROP attacks for single- and multi-threaded server programs running on general-purpose computing systems by using a novel stack layout, called a buddy stack (referred to as Bustk ), that is highly performant, compatible with existing code, and provides meaningful security. These goals are met due to three novel design aspects in Bustk . First, Bustk places a parallel shadow stack just below a thread’s call stack (as each other’s buddies allocated together), avoiding the need to maintain a separate shadow stack pointer and making it now well-suited for multi-threading. Second, Bustk uses an efficient stack-based thread-local storage mechanism, denoted STK-TLS , to store thread-specific metadata in two TLS sections just below the shadow stack in dual redundancy (as each other’s buddies), so that both can be accessed and updated in a lightweight manner from the call stack pointer rsp alone. Finally, Bustk re-randomizes continuously (on the order of milliseconds) the return addresses on the shadow stack by using a new microsecond-level runtime re-randomization technique, denoted STK-MSR . This mechanism aims to obsolete leaked information, making it extremely unlikely for the attacker to hijack return addresses, particularly against a server program that sits often tens of milliseconds away from the attacker. Our evaluation using web servers, Nginx and Apache Httpd , shows that Bustk works well in terms of performance, compatibility, and security provided, with its parallel shadow stacks incurring acceptable memory overhead for real-world applications and its STK-TLS mechanism costing only two pages per thread. In particular, Bustk can protect the Nginx and Apache servers with an adaptive 1-ms re-randomization policy (without observable overheads when IO is intensive, with about 17,000 requests per second). In addition, we have also evaluated Bustk using other non-server applications, Firefox , Python , LLVM , JDK and SPEC CPU2006 , to demonstrate further the same degree of performance and compatibility provided, but the protection provided for, say, browsers, is weaker (since network-access delays can no longer be assumed).
An Explainable Computer Vision in Histopathology: Techniques for Interpreting Black Box Model

Computer vision is a field of artificial intelligence (AI) that is being used increasingly in histopathology to identify pathologies in slide images with a high degree of accuracy. In this paper, we focus on the different interpreting techniques of explainable computer vision (XCV). Analysis of histopathology images is a challenging task, and specialized knowledge is mandatory to make AI decisions. To carry out this analysis, a deep learning model has been used to classify and differentiate the scoring (i.e., benign and malignant) of Prostate cancer (PCa). However, the AI models are complex and opaque, and it is important to understand model decision-making. Therefore, to address this problem, we present three techniques for accountability and transparency of the model, namely Activation Layer Visualization (ALV), Local Interpretable Model-Agnostic Explanation (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM). XCV is AI in which the results of the black-box model can be understood by humans. The robustness of our model has been confirmed by using an external test dataset including 100 histopathology images. The model performance has been evaluated using the receiver operating characteristic (ROC) curve.