Reviewerly/exHarmony huggingface dataset:
Designing an Efficient and Highly Dynamic Substitution-Box Generator for Block Ciphers Based on Finite Elliptic Curves

Developing a substitution-box (S-box) generator that can efficiently generate a highly dynamic S-box with good cryptographic properties is a hot topic in the field of cryptography. Recently, elliptic curve (EC)-based S-box generators have shown promising results. However, these generators use large ECs to generate highly dynamic S-boxes and thus may not be suitable for lightweight cryptography, where the computational power is limited. The aim of this paper is to develop and implement such an S-box generator that can be used in lightweight cryptography and perform better in terms of computation time and security resistance than recently designed S-box generators. To achieve this goal, we use ordered ECs of small size and binary sequences to generate certain sequences of integers which are then used to generate S-boxes. We performed several standard analyses to test the efficiency of the proposed generator. On an average, the proposed generator can generate an S-box in 0.003 seconds, and from 20,000 S-boxes generated by the proposed generator, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>93</mn> <mo>%</mo> </math> S-boxes have at least the nonlinearity 96. The linear approximation probability of 1000 S-boxes that have the best nonlinearity is in the range [0.117, 0.172] and more than 99% S-boxes have algebraic complexity at least 251. All these S-boxes have the differential approximation probability value in the interval [0.039, 0.063]. Computational results and comparisons suggest that our newly developed generator takes less running time and has high security against modern attacks as compared to several existing well-known generators, and hence, our generator is suitable for lightweight cryptography. Furthermore, the usage of binary sequences in our generator allows generating plaintext-dependent S-boxes which is crucial to resist chosen-plaintext attacks.
Adaptive feature selection with shapley and hypothetical testing: Case study of EEG feature engineering

Feature selection aims to explore the characteristics of a problem that is under investigation instead of focusing on extracting (deep) features or classification tasks. The pending issues being explored are as follows: 1) to minimize the interference of uncertain irrelevant features and 2) to construct the (full) set of relevant features as an individual feature that might be too weak, such as those extracted from bio-signals, for example, electroencephalograms (EEGs). This study fosters an adaptive feature selection approach with the Shapley value and hypothetical testing (abbrev. ShapHT+) via adaptive relevance evaluation. The tree SHAP (SHapley Additive exPlanations) method first can be used to quantify the importance of each candidate feature. An adaptive threshold is then derived to evaluate the feature’s relevance to a priori information, and all relevant features are then selected through the hypothesis testing for binomial distribution. The benchmarks indicate that ShapHT+ significantly outperforms its mainstream counterparts in ruling out interferences and the efficiency and accuracy in selecting all relevant features. The case study of EEG feature engineering for autism spectrum disorder (ASD) evaluation indicates that the features selected by ShapHT+ 1) can achieve the highest classification accuracy (82.44%) and 2) well match the observations in recent ASD research.
Secured Compression for 2D Medical Images Through the Manifold and Fuzzy Trapezoidal Correlation Function

In biomedical imaging, the imaging of secured storage and maintaining medical images like MRI, CT, and ultrasound scans are challenging with ever-growing tremendous image data. This article has proposed a systematic approach for secured compression of the image data that would compress the image data at multiple levels at each instance that would substitute with a smaller size data block through dictionary mechanism. The resultant image is encrypted through a 256-bit symmetric key dynamically generated through the hashing-based technique for multiple rounds. In each round, a 16-bit key sequence obtained from the hashing-based technique is an integral part of the 256-bit key used in the encryption process, and the same key sequence is being used in the decryption phase. Finally, the resultant image is stored for future reference for further medical examinations. In reconstructing the original image, the same approach is performed in reverse order to get back the original image without any significant impact on the image standard through the Fuzzy Trapezoidal correlation method. The proposed mechanism is being practically implemented over the medical images, and the outcome seems to be very pleasing compared to the counterparts. It is observed on implementation. The medical images are compressed to 58% of their original size without significant impact on the quality of the image that is being reconstructed. The approximated entropy in the majority of the cases is less than zero has proven the proposed mechanism is robust for secured compression of the medical images for secured storage.
SBTMS: Scalable Blockchain Trust Management System for VANET

With many advances in sensor technology and the Internet of Things, Vehicle Ad Hoc Network (VANET) is becoming a new generation. VANET’s current technical challenges are deploying decentralized architecture and protecting privacy. Because Blockchain features are decentralized, distributed, mass storage, and non-manipulation features, this paper designs a new decentralized architecture using Blockchain technology called Blockchain-based VANET. Blockchain-based VANET can effectively resolve centralized problems and mutual distrust between VANET units. To achieve this, it is needed to provide scalability on the blockchain to run for VANET. In this system, our focus is on the reliability of incoming messages on the network. Vehicles check the validity of the received messages using the proposed Bayesian formula for trust management system and some information saved in the Blockchain. Then, based on the validation result, the vehicle computes a rate for each message type and message source vehicle. Vehicles upload the computed rates to Roadside Units (RSUs) in order to calculate the net reliability value. Finally, RSUs using a sharding consensus mechanism generate blocks, including the net reliability value as a transaction. In this system, all RSUs collaboratively maintain the latest updated Blockchain. Our experimental results show that the proposed system is effective, scalable and dependable in data gathering, computing, organization, and retrieval of trust values in VANET.
Mining Waste as a Potential Additional Source of HREE and U for the European Green Deal: A Case Study of Bangka Island (Indonesia)

The European Commission has adopted the European Green Deal strategy, which aims to achieve climate neutrality in the EU by 2050. To achieve this goal, it is necessary to shift the economy toward the use of green and renewable energy. Critical raw materials (CRMs), Li, Co, REE, Te, Sc and others, are used in renewable energy sources (RES) production. The EU lacks its own CRM deposits, and additionally, the access to already identified deposits is limited, which is making the EU countries search for alternative CRM sources. One such source of CRMs may be mining waste generated on the Indonesian island of Bangka as a result of processing cassiterite-bearing sands. Studies of the mineral composition of the waste using the XRD method reveal rich contents of xenotime (0.79–17.55 wt%), monazite (1.55–21.23 wt%), zircon (1.87–64.35 wt%) and other minerals, carriers of valuable metals, such as Sn, Ti, Nb, Ta. The point mineral chemistry analyses were performed using EPMA. Xenotime is the main carrier of heavy rare earth elements (HREE), especially the “most critical” HREEs: Gd2O3 (1.42–7.16 wt%), Dy2O3 (2.28–11.21 wt%), Er2O3 (2.44–7.85 wt%), and Yb2O3 (1.71–7.10 wt%). Xenotime is characterized by a complex internal structure resulting from metasomatic processes occurring during their formation. In SEM-BSE imaging, they show zonation of internal structure, which is the effect of an HREE, Y, Si and U substitution in the crystal structure. On the other hand, thorite ThSiO4 and uranothorite (Th,U)SiO4 inclusions are present in xenotimes. The ICP-MS/ES studies of tailings reveal very high contents of HREE + Y (up to 7.58 wt%), U (up to 0.11), Th (up to 0.75 wt%) and Sc (132 ppm). A CRM source diversification is part of the strategy to ensure the security of raw materials for countries of the European Union and the green transformation of the continent. Bilateral EU–Indonesia cooperation in the geological exploration and development of primary and secondary sources may contribute to an increase in the supply of HREEs to the global market.
Echo State Networks for Practical Nonlinear Model Predictive Control of Unknown Dynamic Systems

Nonlinear model predictive control (NMPC) of industrial processes is changeling in part because the model of the plant may not be completely known but also for being computationally demanding. This work proposes an extremely efficient reservoir computing (RC)-based control framework that speeds up the NMPC of processes. In this framework, while an echo state network (ESN) serves as the dynamic RC-based system model of a process, the practical nonlinear model predictive controller (PNMPC) simplifies NMPC by splitting the forced and the free responses of the trained ESN, yielding the so-called ESN-PNMPC architecture. While the free response is generated by the forward simulation of the ESN model, the forced response is obtained by a fast and recursive calculation of the input-output sensitivities from the ESN. The efficiency not only results from the fast training inherited by RC but also from a computationally cheap control action given by the aforementioned novel recursive formulation and the computation in the reduced dimension space of input and output signals. The resulting architecture, equipped with a correction filter, is robust to unforeseen disturbances. The potential of the ESN-PNMPC is shown by application to the control of the four-tank system and an oil production platform, outperforming the predictive approach with a long-short term memory (LSTM) model, two standard linear control algorithms, and approximate predictive control.
Time-Predictable Acceleration of Deep Neural Networks on FPGA SoC Platforms

This work focuses on the time-predictable execution of Deep Neural Networks (DNNs) accelerated on FPGA Systemon-Chips (SoCs).The modern DPU accelerator by Xilinx is considered.An extensive profiling campaign targeting the Zynq Ultrascale+ platform has been performed to study the execution behavior of the DPU when accelerating a set of state-of-the-art DNNs for Advanced Driver Assistance Systems (ADAS).Based on the profiling, an execution model is proposed and then used to derive a response-time analysis.A custom FPGA module named DICTAT is also proposed to improve the predictability of the acceleration of DNNs and tighten the analytical bounds.A rich set of experimental results based on both analytical bounds and measurements from the target platform is finally presented to assess the effectiveness and the performance of the proposed approach on ADAS applications.
Cost-sensitive learning strategies for high-dimensional and imbalanced data: a comparative study

High dimensionality and class imbalance have been largely recognized as important issues in machine learning. A vast amount of literature has indeed investigated suitable approaches to address the multiple challenges that arise when dealing with high-dimensional feature spaces (where each problem instance is described by a large number of features). As well, several learning strategies have been devised to cope with the adverse effects of imbalanced class distributions, which may severely impact on the generalization ability of the induced models. Nevertheless, although both the issues have been largely studied for several years, they have mostly been addressed separately, and their combined effects are yet to be fully understood. Indeed, little research has been so far conducted to investigate which approaches might be best suited to deal with datasets that are, at the same time, high-dimensional and class-imbalanced. To make a contribution in this direction, our work presents a comparative study among different learning strategies that leverage both feature selection, to cope with high dimensionality, as well as cost-sensitive learning methods, to cope with class imbalance. Specifically, different ways of incorporating misclassification costs into the learning process have been explored. Also different feature selection heuristics have been considered, both univariate and multivariate, to comparatively evaluate their effectiveness on imbalanced data. The experiments have been conducted on three challenging benchmarks from the genomic domain, gaining interesting insight into the beneficial impact of combining feature selection and cost-sensitive learning, especially in the presence of highly skewed data distributions.
Efficient Multilevel Federated Compressed Reinforcement Learning of Smart Homes Using Deep Learning Methods

Internet of Things connects all real time devices using the wireless nature for collecting, sharing and processing of data. These data are analyzed using machine learning models based on the structure of data. Reinforcement learning is a type of learning method which performs with past experience of data. Traditional algorithms use data with a specific environment with a learning process for prediction. Federated Learning (FL) is achieved through the integration of the various learning models for achieving accuracy. The proposed learning algorithm uses multilevel FL over the smart homes with two house data for analysis of the user behavior. Various kinds of sensors are used for analyzing the behavior, namely local and global. The data is shared with agents and servers with the use of communication networks. It suffers the bandwidth issues because of heterogeneity in the data, so this is overcome by using FL compression method. Multilevel FL compression method achieves reduced latency with efficient interaction. The proposed technique achieves better accuracy when compared to existing RL method with maximum performance and reliability.
Deep Listwise Triplet Hashing for Fine-Grained Image Retrieval

Hashing is a practical approach for the approximate nearest neighbor search. Deep hashing methods, which train deep networks to generate compact and similarity-preserving binary codes for entities (e.g. images), have received lots of attention in the information retrieval community. A representative stream of deep hashing methods is triplet-based hashing that learns hashing models from triplets of data. The existing triplet-based hashing methods only consider triplets that are in the form of (q,q+,q-) , where q and q+ are in the same class and q and q- are in different classes. However, the number of possible triplets is approximately the cube of training examples, triplets used in the existing methods are only a small fraction of all possible triplets. This motivates us to develop a new triplet-based hashing method that adopts many more triplets in training phase. We propose Deep Listwise Triplet Hashing (DLTH) that introduces more triplets into batch-based training and a novel listwise triplet loss to capture the relative similarity in new triplets. This method has a pipeline of two steps. In Step 1, we propose a novel way to generate triplets from the soft class labels obtained by knowledge distillation module, where the triplets in the form of (q,q+,q-) are a subset of the newly obtained triplets. In Step 2, we develop a novel listwise triplet loss to train the hashing network, which seeks to capture the relative similarity between images in triplets according to soft labels. We conduct comprehensive image retrieval experiments on four benchmark datasets. The experimental results show that the proposed method has superior performances over state-of-the-art baselines.
Building In-the-Cloud Network Functions: Security and Privacy Challenges

Network function virtualization (NFV) has been promising to improve the availability, programmability, and flexibility of network function deployment and communication facilities. Meanwhile, with the advancements of cloud technologies, there has been a trend to outsource network functions through virtualization to a cloud service provider, so as to alleviate the local burdens on provisioning and managing such hardware resources. Promising as it is, redirecting the communication traffic to a third-party service provider has drawn various security and privacy concerns. Traditional end-to-end encryption can protect the traffic in transmit, but it also hinders data usability. This dilemma has raised wide interests from both industry and academia, and great efforts have been made to realize privacy-preserving network function outsourcing that can guarantee the confidentiality of network communications while preserving the ability to inspect the traffic. In this article, we conduct a comprehensive survey of the state-of-the-art literature on network function outsourcing, with a special focus on privacy and security issues. We first give a brief introduction to NFV and pinpoint its challenges and security risks in the cloud context. Then, we present detailed descriptions and comparisons of existing secure network function outsourcing schemes in terms of functionality, efficiency, and security. Finally, we conclude by discussing possible future research directions.
“The Boundaries are Blurry…”: How Comment Moderators in Germany See and Respond to Hate Comments

Aggressive and hateful user comments on news sites and social media threaten discussions on the Internet and pose a difficult challenge for content regulation. Previous research has mainly focused on the analysis of moderation strategies in dealing with such comments. In contrast, little attention was paid to the issue of which comments are considered problematic by content moderators in the first place. The answer to this question has more than theoretical relevance, but practical significance against the backdrop of increasing efforts to automate the detection of hate speech or toxicity in user comments. Based on 20 interviews, this paper explores what comment moderators in Germany consider to be hate comments, how they moderate them, and how differences in moderation practices can be explained. Our findings show strong agreement regarding extreme cases of hate comments, whereby there is overlap with the theoretical concept of hate speech, but also forms of incivility. Moreover, the interviews revealed differences in the perception and handling of hate comments, which can be linked to explanatory factors at the levels of the individual, professional routines, and the organization.
Design and Manufacture of Data Gloves for Rehabilitation Training and Gesture Recognition Based on Flexible Sensors

This work takes the production and usage scenarios of the data glove as the research object and studies the method of applying the flexible sensor to the data glove. Many studies are also devoted to exploring the transplantation of flexible sensors to data gloves. However, this type of research still lacks the display of specific application scenarios such as gesture recognition or hand rehabilitation training. A small amount of experimental data and theoretical analysis are difficult to promote the development of flexible sensors and flexible data gloves design schemes. Therefore, this study uses the self-made flexible sensor of the research group as the core sensing unit to produce a flexible data glove to monitor the bending changes of the knuckles and then use it for simple gesture recognition and rehabilitation training.
Self‐adaptation on parallel stream processing: A systematic review

Summary A recurrent challenge in real‐world applications is autonomous management of the executions at run‐time. In this vein, stream processing is a class of applications that compute data flowing in the form of streams (e.g., video feeds, images, and data analytics), where parallel computing can help accelerate the executions. On the one hand, stream processing applications are becoming more complex, dynamic, and long‐running. On the other hand, it is unfeasible for humans to monitor and manually change the executions continuously. Hence, self‐adaptation can reduce costs and human efforts by providing a higher‐level abstraction with an autonomic/seamless management of executions. In this work, we aim at providing a literature review regarding self‐adaptation applied to the parallel stream processing domain. We present a comprehensive revision using a systematic literature review method. Moreover, we propose a taxonomy to categorize and classify the existing self‐adaptive approaches. Finally, applying the taxonomy made it possible to characterize the state‐of‐the‐art, identify trends, and discuss open research challenges and future opportunities.
Infrared image enhancement algorithm using local entropy mapping histogram adaptive segmentation

The traditional histogram equalization algorithm may cause problems such as local over-enhancement and noise amplification while enhancing the image. To solve these problems, this paper proposes an infrared image enhancement method using local entropy mapping histogram adaptive segmentation. Firstly, a local entropy mapping histogram is built through the modified 'sigmoid' function to describe the detailed distribution of the infrared image. Then the LOESS algorithm and local minimum examination are used to adaptively segment the local entropy mapping histogram into multiple sub-histograms. And follow, the double plateau constraint of shape preservation is adopted, and the double plateau thresholds of each interval of the local entropy mapping histogram are adaptively optimized according to the genetic algorithm. Finally, multiple sub-histograms are equalized to obtain an enhanced image. Comparative experiments on real infrared images show that our method is ahead of other superior methods in qualitative and quantitative evaluation.
A bimodal BI-RADS-guided GoogLeNet-based CAD system for solid breast masses discrimination using transfer learning

Numerous solid breast masses require sophisticated analysis to establish a differential diagnosis. Consequently, complementary modalities such as ultrasound imaging are frequently required to evaluate mammographically further detected masses. Radiologists mentally integrate complementary information from images acquired of the same patient to make a more conclusive and effective diagnosis. However, it has always been a challenging task. This paper details a novel bimodal GoogLeNet-based CAD system that addresses the challenges associated with combining information from mammographic and sonographic images for solid breast mass classification. Each modality is initially trained using two distinct monomodal models in the proposed framework. Then, using the high-level feature maps extracted from both modalities, a bimodal model is trained. In order to fully exploit the BI-RADS descriptors, different image content representations of each mass are obtained and used as input images. In addition, using an ImageNet pre-trained GoogLeNet model, two publicly available databases, and our collected dataset, a two-step transfer learning strategy has been proposed. Our bimodal model achieves the best recognition results in terms of sensitivity, specificity, F1-score, Matthews Correlation Coefficient, area under the receiver operating characteristic curve, and accuracy metrics of 90.91%, 89.87%, 90.32%, 80.78%, 95.82%, and 90.38%, respectively. The promising results indicate that the proposed CAD system can facilitate bimodal suspicious mass analysis and thus contribute significantly to improving breast cancer diagnostic performance.