Reviewerly/exHarmony huggingface dataset:
Victory by KO

We present a set of attacks on the OpenPGP specification and implementations of it which result in full recovery of users' private keys. The attacks exploit the lack of cryptographic binding between the different fields inside an encrypted private key packet, which include the key algorithm identifier, the cleartext public parameters, and the encrypted private parameters. This allows an attacker who can overwrite certain fields in OpenPGP key packets to perform cross-algorithm attacks, causing a user's software to, for example, misinterpret an ECC private key as being a DSA key. It also allows an attacker to replace the legitimate public parameters with adversarially chosen ones, e.g. allowing them to select the DSA group. We refer to this class of attacks as Key Overwriting (KO) attacks. We provide a detailed analysis of the vulnerability of different OpenPGP libraries to KO attacks, showing in particular that in some cases additional key validation steps performed by libraries that should prevent the attacks in fact allow variant attacks. We also assess the applicability of KO attacks in the context of specific OpenPGP-based applications that reflect different threat models. Finally, we explain how KO attacks can be completely prevented (and the need for key validation obsoleted) at the OpenPGP specification level by expanding the existing proposal of using AEAD schemes for key packet protection to have all the security-relevant public fields included as Associated Data.
Review of Algorithms, Frameworks and Implementation of Deep Machine Learning Algorithms

Machine Learning (ML) is increasingly being used in intelligent systems that can perform Artificial Intelligence (AI) functions. Analytical model development and solving problems related with it may be automated by machine learning, which explains the ability of computers to learn from problem-specific learning algorithm. Depending on artificial neural networks, "deep learning" is a kind of machine learning. The performance of deep learning techniques is superior to that of superficial machine learning techniques and conventional methods of data analysis in many situations. Deep Machine Learning (DML) algorithms and frameworks that have been implemented to and supported by wireless communication systems have been thoroughly analyzed in this paper. User associations, power latency and allocation; bandwidth assignment and user selections, and; cloud computing technology on the edge have both been suggested as potential DML implementations.
Grow Your Plant: A Plant-Based Game For Creating Awareness About Sustainability Behaviour by Using Renewable Energy

Environmental sustainability has become more and more a very important topic that will also change our way of life in the near future. A question is how people can be influenced into becoming more aware of environmental sustainability. Since technology is considered a means to simplify people’s lives, it can also be used as a tool to support people’s awareness of these aspects. This paper investigates how we can use a mobile phone in combination with a solar panel as a wearable in a playful way to support people’s awareness of plant-based environments. For this purpose, the first version of a mobile game was developed with the goal that the players can not only learn what a plant needs to grow healthy but also learn about the sun as a renewable energy source.
TChecker

PHP applications provide various interfaces for end-users to interact with on the Web. They thus are prone to taint-style vulnerabilities such as SQL injection and cross-site scripting. For its high efficiency, static taint analysis is widely adopted to detect taint-style vulnerabilities before application deployment. Unfortunately, due to the high complexity of the PHP language, implementing a precise static taint analysis is difficult. The existing taint analysis solutions suffer from both high false positives and high false negatives because of their incomprehensive inter-procedural analysis and a variety of implementation issues.
Distributed, Private, Sparse Histograms in the Two-Server Model

We consider the computation of sparse, (ε, ϑ)-differentially private~(DP) histograms in the two-server model of secure multi-party computation~(MPC), which has recently gained traction in the context of privacy-preserving measurements of aggregate user data. We introduce protocols that enable two semi-honest non-colluding servers to compute histograms over the data held by multiple users, while only learning a private view of the data. Our solution achieves the same asymptotic l∞-error of O(log(1/ϑoverε) as in the central model of DP, but without relying on a trusted curator. The server communication and computation costs of our protocol are independent of the number of histogram buckets, and are linear in the number of users, while the client cost is independent of the number of users, ε, and ϑ. Its linear dependence on the number of users lets our protocol scale well, which we confirm using microbenchmarks: for a billion users, ε = 0.5, and ϑ = 10-11, the per-user cost of our protocol is only 1.08 ms of server computation and 339 bytes of communication. In contrast, a baseline protocol using garbled circuits only allows up to 106 users, where it requires 600 KB communication per user.
DangZero

Use-after-free vulnerabilities remain difficult to detect and mitigate, making them a popular source of exploitation. Existing solutions incur impractical performance/memory overhead, require specialized hardware, and/or guarantee only protection, but not detection.
Fast Fully Secure Multi-Party Computation over Any Ring with Two-Thirds Honest Majority

We introduce a new MPC protocol to securely compute any functionality over an arbitrary black-box finite ring (which may not be commutative), tolerating t < n/3 active corruptions whileguaranteeing output delivery (G.O.D.). Our protocol is based on replicated secret-sharing, whose share size is known to grow exponentially with the number of parties n. However, even though the internal storage and computation in our protocol remains exponential, the communication complexity of our protocol is constant, except for a light constant-round check that is performed at the end before revealing the output.
Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, ehealthcare, etc.Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN).Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches.This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model.The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites.The SRO-MLCOSN model initially employs Glove technique for word embedding process.Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB.Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model.The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.
A combination of simple and dilated convolution with attention mechanism in a feature pyramid network to segment leukocytes from blood smear images

Leukemia, a type of blood cancer, is amongst the most deadly cancers worldwide. Since it affects leukocytes in the bloodstream, fast and early detection of abnormal leukocytes is required. Thus, precise detection of leukemia highly relies on accurate segmentation of leukocytes from blood smear images. The segmentation process has become quite robust with the development of deep neural networks, especially convolutional neural networks (CNNs). Such models have also shown superior results compared to traditional machine learning algorithms. This work represents a deep learning-based encoder–decoder model that focuses on salient multiscale leukocyte features. It is accomplished by combining features derived from standard and dilated convolutions. Using a convolutional block attention module (CBAM) in the network facilitates the extraction of refined features. We evaluated the performance of the proposed approach by conducting ablation studies on three publicly available datasets: ALL_IDB1, CellaVision and LISC. The first study is conducted to finalize the architecture of the dilated encoder path. In the subsequent study, a series of experiments are performed to obtain the most effective attention module. The last set of experiments deals with only a single encoder path that encapsulates dilated convolutions' importance. The resultant values of the proposed method are also compared with the state-of-the-art techniques using four performance indices: Dice score, IoU, PPV and NPV and qualitatively by visual results.
Novel Framework for Alzheimer Early Diagnosis using Inductive Transfer Learning Techniques

The early diagnosis of Alzheimer’s disease (AD) is gaining popularity due to massive applications in the medical field. The estimated 50 million people affected by AD worldwide are increasing rapidly, and the condition has an approximately 1 dollar trillion economic cost. This necessitates developing scalable, economical, and reliable methods for detecting Alzheimer’s disease. We describe a unique architecture that uses auditory, cognitive, and linguistic aspects to create a multimodal ensemble system. Scores from the Mini-Mental State Examination are used to assess AD severity, which is detected using specialized artificial neural networks with temporal properties. We first test it using the ADReSS challenge dataset, a balanced dataset available through Dementia Bank that contains no biases against any patient. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 93.30% each, as well as a state-of-the-art test Root Mean Squared Error (RMSE) of 4.60, when it comes to the classification of AD. To our knowledge, the algorithm achieves cutting-edge AD classification accuracy of 88.0 % when tested against the benchmark Dementia Bank Pitt database. In order to build a robust inductive transfer learning model, our study highlights the applicability and transferability of spontaneous speech and demonstrates generalizability by utilising a task-agnostic feature space.
Dialectal Arabic Speech Recognition using CNN-LSTM Based on End-to-End Deep Learning

Arabic dialect is the type of Arabic language which is used for daily communication in the Arab world. Each Arab country has a unique dialect. Due to the challenges of recognizing the spoken Arabic dialects, such as the significant variations in Arabic dialects and the lack of standard structure, there are few research attempts in the literature that address this problem. The end-to-end deep learning approach provides a promising approach for improving the performance of speech recognition systems. However, with deep learning techniques, overfitting is still the main problem with little data. In this paper, we investigate the end-to-end model to improve dialectal Arabic speech recognition (DASR) based on deep learning. Data augmentation is one of the significant steps in the proposed approach that is applied to the employed dataset to increase training data. The proposed approach is based on a hybrid model that is composed of a convolutional neural network (CNN) and long short-term memory (LSTM) and is referred to as (CNN-LSTM). This model is utilized along with attention-based encoder-decoder methods for building the acoustic model and performing the decoding process. To the best of our knowledge, there is no prior research that employed CNN-LSTM and attention-based models in dialectal Arabic ASR systems. In addition, the language model is built based on recurrent neural network (RNN) and LSTM methods. The proposed approach is validated using two speech datasets, namely SASSC and MGB-3. Experimental results showed that the proposed approach achieved a word error rate (WER) of 57.02%. This result is superior to those achieved by the other approaches in the literature. Lower WER often indicates that the ASR system is more accurate.
3D SOC-Net: Deep 3D reconstruction network based on self-organizing clustering mapping

Image-based 3D reconstruction from a single-view image is critical and fundamental in many areas and can be integrated into many applications to provide useful functions. However, there are several crucial difficulties and challenges in accomplishing this process. For example, the issues of self-occlusion and lack of object information in a different perspective of viewing. Thus, the quality of the generated 3D shape from a single-view image may not be satisfactory and robust, hence affecting its feasibility in further applications. Conventionally, the 3D reconstruction process requires multiple input images such that the context of the target object can be fully conveyed. In this paper, we propose a new and simple, yet powerful framework that improves the quality of the generated point cloud from a single-view image. Concretely, the significant representatives are first discovered and selected by adopting a network architecture that contains both encoder and decoder models. Finally, the resultant point clouds are obtained by extracting the mean shape using the methods of Chamfer Distance (CD), Earth Mover’s Distance (EMD), and Self-Organizing Map (SOM). As a result, the proposed algorithm is capable to demonstrate its robustness and effectiveness when compared to state-of-the-art 3D reconstruction methods. The best mean loss exhibited is 4.45 when evaluated on 12 classes in the ShapeNetCoreV1 dataset. In addition, qualitative results are presented to further verify the reliability of the proposed method.
Blockchain-based medical health record access control scheme with efficient protection mechanism and patient control

The patient's medical health record (PMHR) has always provided a large amount of research data to medical institutions and pharmaceutical companies, etc., and has contributed to the development in medical research. However, such PMHR data contains the patient's personal privacy and should be shared under the control of the patients, not the hospital where this data is acquired. In order to protect the privacy of PMHR data while realizing efficient data sharing, this paper proposes a blockchain-based sharing and protection scheme. In this solution, the PMHR data are encrypted and stored in a cloud server, which is equipped with an access control scheme implemented as a smart contract on a blockchain. Different from previous works, in order to ensure efficient access and reduce the workload of patients, the types of users who can apply for access are limited to hospitals and pharmaceutical companies. In order to resist the potential Man-in-the-middle (MITM) attack, we have introduced an improved proxy re-encryption scheme to ensure the secrecy of PMHR data while reducing the computational complexity. The whole system is implemented using Solidity and tested on 10 nodes for function verification. Experimental result shows that the proposed system is more efficient than previous systems. Security under the MITM attack is also ensured by security analysis.
Selecting and Composing Learning Rate Policies for Deep Neural Networks

The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training time of Deep Neural Networks (DNNs). This article presents a systematic approach to selecting and composing an LR policy for effective DNN training to meet desired target accuracy and reduce training time within the pre-defined training iterations. It makes three original contributions. First, we develop an LR tuning mechanism for auto-verification of a given LR policy with respect to the desired accuracy goal under the pre-defined training time constraint. Second, we develop an LR policy recommendation system (LRBench) to select and compose good LR policies from the same and/or different LR functions through dynamic tuning, and avoid bad choices, for a given learning task, DNN model, and dataset. Third, we extend LRBench by supporting different DNN optimizers and show the significant mutual impact of different LR policies and different optimizers. Evaluated using popular benchmark datasets and different DNN models (LeNet, CNN3, ResNet), we show that our approach can effectively deliver high DNN test accuracy, outperform the existing recommended default LR policies, and reduce the DNN training time by 1.6-6.7× to meet a targeted model accuracy.
A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies

Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control.
Source-free Unsupervised Domain Adaptation with Trusted Pseudo Samples

Source-free unsupervised domain adaptation (SFUDA) aims to accomplish the task of adaptation to the target domain by utilizing pre-trained source domain model and unlabeled target domain samples, without directly accessing any source domain data. Although many SFUDA works use the pseudo-labeling strategy to improve the accuracy of pseudo-labels in the target domain, these strategies ignore the influence of domain shift on calculating the reference distribution of pseudo-labels. In this article, we propose a novel kind of SFUDA with trusted pseudo samples (SFUDA-TPS), which uses reliable feature reference distribution to solve the SFUDA problem. In SFUDA-TPS, we design a target feature correcting classifier to alleviate the problem of feature reference distribution deviating from target domain samples distribution. On this basis, the more reliable feature reference distribution is calculated by selecting the target domain samples with a high amount of information, i.e., low entropy in the fixed source domain classifier and target feature correcting classifier. The implicit alignment between the source domain and target domain is realized by learning the source domain distributions hidden in the fixed source domain classifier. Experimental evaluations illustrate the effectiveness of our proposed method in solving SFUDA tasks.
Federated learning based on stratified sampling and regularization

Abstract Federated learning (FL) is a new distributed learning framework that is different from traditional distributed machine learning: (1) differences in communication, computing, and storage performance among devices (device heterogeneity), (2) differences in data distribution and data volume (data heterogeneity), and (3) high communication consumption. Under heterogeneous conditions, the data distribution of clients varies greatly, which leads to the problem that the convergence speed of the training model decreases and the training model cannot converge to the global optimal solution. In this work, an FL algorithm based on stratified sampling and regularization (FedSSAR) is proposed. In FedSSAR, a density-based clustering method is used to divide the overall client into different clusters, then, some available clients are proportionally extracted from different clusters to participate in training which realizes unbiased sampling for the overall client and reduces the aggregation weight variance of the client. At the same time, when calculating the model local loss function, we limit the update direction of the model by a regular term, so that heterogeneous clients are optimized in the globally optimal direction. We prove the convergence of FedSSAR theoretically and experimentally, and demonstrate the superiority of FedSSAR by comparing it with other FL algorithms on public datasets.
Adaptive human bodies and adaptive built environments for enriching futures

As humans, we spend most of our lives inside human-built environments, such as homes, offices, and schools. In these built environments, humans co-create and share a collective awareness, social practices, and knowledge while computing machinery is designed to maintain the built environment and support our interactions. The effects of these technologically enriched built environments on humans are how our bodies adapt to the practices they promote and how these practices, in return, affect the built environment and the natural environment. This perspective paper uses inbodied interaction to frame the constant adaption of our bodies to our surrounding environment as an opportunity to inform the design of technology and its practices and offer a vision where humans, the built environment, and the natural environment coexist in a mutually beneficial relationship.
Flexible patch moving modes for pixel-value-ordering based reversible data hiding methods

Pixel-value-ordering (PVO) is one of the most popular frameworks in the research of reversible data hiding (RDH) in recent years. In most PVO-based methods, the cover image is divided into non-overlapped blocks to embed secret data into the maximum and minimum pixels of each block. In these methods, the image division processing is just like moving a patch with constant step lengths in both horizontal and vertical directions. As a result, the block amount, as well as the embedding capacity (EC), is limited by the image size and the patch size. In this paper, we propose one-dimensional and two-dimensional flexible patch moving (FPM) modes to move the patch with adaptive step lengths. After enlarging the EC by employing FPM, an average-difference-based complexity computation is also proposed to cooperate with FPM to further improve the embedding performance. Finally, multiple pairwise embedding schemes are adaptively applied on blocks with different complexities to enhance the embedding performance once again. Experimental results illustrate that the proposed FPM modes work well on many PVO-based methods. Moreover, the proposed method achieves an obvious improvement in fidelity when compared with some state-of-the-art RDH schemes.