Reviewerly/exHarmony huggingface dataset:
Machine Learning-based Hand Sign Recognition

Sign Language is used by the deaf and voiceless community to be able to communicate with others, but the most commonly faced problem here is that everyone around may not be able to understand sign language. The main motive behind this system is to bridge the communication gap between the communities, therefore, establish the interaction between the speechless community to communicate with others. Hand gestures differ from one person to another person in shape and orientation, therefore, a problem of linearity arises. Recent systems have come up with various ways and algorithms to accomplish the problem and build this system. Algorithms such as KNearest neighbors (KNN), Multi-class Super Vector Machine (SVM), and experiments using hand gloves were used to decode the hand gesture movements before. In this paper, a comparison between KNN, SVM, and CNN algorithms is done to determine which algorithm would provide the best accuracy among all. Approximately 29,000 images were split into test and train data and preprocessed to fit into the KNN, SVM, and CNN models to obtain an accuracy of 93.83%, 88.89%, and 98.49% respectively.
An Effective Mutual Authentication Scheme for Provisioning Reliable Cloud Computing Services

Cloud computing facilitates an economic data storage, expertise software, and scalable computing resources through infrastructure, platform, and software (as a service). However, due to the non-transparent and openness of cloud computing, security is the pressing issue that may hamper the adoption and growth of cloud services. Moreover, conventional authentication management solutions are inadequate and cannot be directly adopted in cloud computing environments. Existing schemes have addressed the above issue, and a comprehensive study of the literature helped in formulating the current research. This research intends to develop an effective mutual authentication scheme for reliable cloud service provisioning. The performance is analysed in terms of computational overhead, communication cost, and resistance to various security characteristics.
The key frame extraction algorithm based on the indigenous disturbance variation difference video

In view of the traditional support vector machine (SVM) learning algorithm for widespread learning parameters is not easy determined in the process of video key frame extraction, the problem of low accuracy, an independent perturbation variable difference SVM algorithm used for video key frame extraction. First of all, the study of the biological mechanism of differential evolution algorithm, put forward an improved way of autonomic disturbance variation. Secondly, combined with improved forms of independent disturbance differential evolution algorithm for SVM parameters optimization, designed the key frame extraction algorithm of the video based on the improved differential of SVM algorithm. Through the standard test functions and video test database experiments show that the improvement of autonomic disturbance variation difference video key frame extraction algorithm can more effectively optimize parameters of support vector machine, so as to contribute to the improvement of the recall of video retrieval rate of (quasi) two algorithms performance evaluation standard.
Breast Cancer Detection-Based Feature Optimization Using Firefly Algorithm and Ensemble Classifier

The accurate detection of the malignant cell of breast cancer, decrease the rate of mortality of women around the world. The process of feature optimization, increase the probability of feature selection and mapping of data. This paper proposed ensemble-based classifier for detection of breast cancer on an early stage. The proposed ensemble classifier support vector machine is a base classifier, and the other is boost classifier. The firefly algorithm reduces the variance of breast cancer features for the selection of feature components for the classification algorithm. The most dominated work is the extraction of features of breast cancer image. For the extraction of features applied wavelet packet transform, wavelet packet transform overcome the limitation of wavelet transform and increase the diversity of feature extraction process. The proposed algorithm implemented in MATLAB software and tested with the very reputed breast cancer image dataset, MIAS and DDSM. The proposed algorithm&#x2019;s performance measured with standard parameters such as accuracy, specificity, sensitivity, and MCC. The evaluated results indicate that the proposed algorithm is better than DWPT, SVM and BMC. The increasing ratio of the classification algorithm is 8&#x0025; instead of existing algorithms.
Autonomous Deblurring Images and Information Extraction from Documents Using CycleGAN and Mask RCNN

In this era of automation technology, there has been a resurgence of interest for extracting data from documents so that information can be used more efficiently. The age of storing sensitive information on paper is on the brink of extinction. Almost every organization in the world is shifting towards an efficient cloud storage system. With the global impact of Covid-19 and the norm of “work from home”, the importance of proper data extraction from digital documents has increased manifold. So, in this paper, we proposed a method to extract data from scanned document images by identifying handwritten texts and respective label fields. While scanning images various issues can appear such as - background noises, blurred images due to camera motion, out of focus images, watermarks, stains, or anything that can cause hindrance to readability of users. So, for our method to work better our first approach was to filer the scanned images by removing noises. We trained our dataset on Cycle Generative Adversarial Networks (Cycle-GAN) to generate clean scanned images from noisy images. Later on, for detecting labels and text fields we trained Mask R-CNN on our cleaned dataset. Finally, we extract the information using Tesseract from the detected fields and assign the labels filed with corresponding information on a text file.
A Dynamic Balance Quantization Method for YOLOv3

Abstract This paper describes a quantization method for pre-trained deep CNN models, and has achieved very good results in YOLOv3. The weights are quantized to int8 and the biases are quantized to int16, by contrasting the float inference result, the mAP loss is less than 0.5 %. While running neural nets on hardware, 8-bit fixed-point quantization is the key to efficient inference. However, it is a very difficult task for running a very deep network on 8-bit hardware, often resulting in a significant decrease in accuracy or spending a lot of time on retraining the network. Our method uses dynamic fixed-point quantization and adds a small amount of bit-shifts to balance the accuracy of each layer in a YOLO network. At the same time, it further shows that this method can also be extended to other computer vision architectures and tasks, such as semantic segmentation and image classification.
Automated Ontology Instantiation of OpenAPI REST Service Descriptions

Web services are published in service registries on the Web by various software vendors and cloud providers so that can be easily discovered and used by the users. The need for standardizing technologies for service publishing and discovery is of crucial importance for their adoption and market success. Web services need to be described in a way that eliminates ambiguities so that they can be uniquely identified by users or machines. OpenAPI Specification is a powerful framework for the description of REST APIs. OpenAPI standard aims to provide service descriptions which are understandable by both humans and machines. Despite its rigorous service language format, OpenAPI service descriptions can be vague. In our previous work we identified the causes of ambiguity in service descriptions and showed that ambiguities can be eliminated by associating ambiguous OpenAPI properties to concepts in a semantic model. Each service is then uniquely described by a JSON object representing its identity, properties, purpose and functionality. Further to the above objective, the Semantic Web vision defines the requirements for unifying the world of Web services and suggests representing the services as semantic objects accessible on the Web. All services should be published on the Web by means of Semantic Web tools (e.g. ontologies), become discovered by Web search engines (e.g. SPARQL) and be reused in applications. Leveraging latest results for hypermedia-based construction of Web APIs (i.e. Hydra) and the latest update of the OpenAPI specification (OpenAPI v3.0), the present work proposes a reference ontology for REST services along with a formal procedure for converting OpenAPI service descriptions to instances of this ontology.
Improving Blockchains With Client-Assistance

Blockchain is a distributed database shared among disparate parties. It promises to enable new applications and solutions to wide-ranging domains. However, today&#x0027;s blockchains suffer from low throughput and high latency. This impedes widespread adoption of more complex blockchain-based applications. We propose a new direction for the future development of blockchains: pushing utmost work to client-side to make the blockchain-core as simple as possible. To show the feasibility and practicability of this idea, we construct both client-assisted consensus and client-assisted smart contracts. The client-assisted consensus only requires a single round-trip between clients and blockchain nodes; it is leaderless and parallelizable. Our experimental results show that it can process thousands of transactions per second when the number of replicas is 400. The client-assisted smart contract pushes the expensive execution to client-side and ensures the correctness by verifiable computation. It avoids duplicated execution, allows parallel execution and reduces transaction/blockchain sizes.
Thyroid Nodule Cell Classification in Cytology Images Using Transfer Learning Approach

Analysis of fine needle aspiration cytology FNAC slides of thyroid nodules is a very crucial test before the preoperative diagnosis of thyroid malignancy. Cytology slides may be composed of different types of cells. Differentiating between cancerous cells and healthy cells plays an important role in the treatment. However, the conventional visual inspection is very time consuming and the process might endure inaccuracies because of the subject-level assessment. To the best of our knowledge, no work has been done for the multi-class cell level classification of thyroid nodules. In this paper, we propose a method for classification of cytology images at the cell level by using fine-tuned VGG-19 and AlexNet models, exploiting the transfer learning approach to better fit the model for classification of our dataset. Model evaluations are done by calculating the precision, recall, F1-score, and accuracy. Although the data is highly imbalanced, but both model have shown very good performance by achieving an accuracy of 93.05% and 92.88% by VGG-19 and AlexNet respectively.
Phishing Attacks and Protection Against Them

Phishing, ransomware and cryptojacking are the main threats to cyber security in recent years. We consider the stages of phishing attacks, examples of such attacks, specifically, attacks using ransomware, malicious PDF files, and banking trojans. The article describes the specifics of phishing emails. Advices on phishing protection are given.
An End-to-End Multiplex Graph Neural Network for Graph Representation Learning

Research on graph classification tasks based on graph neural networks has attracted wide attention. The graphs to be classified may have various graph sizes (i.e., different numbers of nodes and edges) and have various graph properties (e.g., average node degree, diameter, and clustering coefficient). The diverse property of graphs has imposed significant challenges on existing graph learning techniques since diverse graphs have different best-fit hyperparameters. Consequently, it is unreasonable to learn graph representation from a set of diverse graphs by a unified graph neural network. Inspired by this, we design an end-to-end Multiplex Graph Neural Network (MxGNN) that learns graph representations with multiple GNNs, and combines them with a learnable method. The main challenge lies with the combination of multiple representation results. Our new findings show that the a priori graph properties do have an effect on the quality of representation learning, which can be used to guide the learning. Our experiments on graph classification with multiple data sets show that the performance of MxGNN is better than the existing graph representation learning methods.
PIBE: practical kernel control-flow hardening with profile-guided indirect branch elimination

Control-flow hijacking, which allows an attacker to execute arbitrary code, remains a dangerous software vulnerability. Control-flow hijacking in speculated or transient execution is particularly insidious as it allows attackers to leak data from operating system kernels and other targets on commodity hardware, even in the absence of software bugs. Having made the jump from regular to transient execution in recent attacks, control-flow hijacking has become a top priority for developers. While powerful defenses against control-flow hijacking in regular execution are now sufficiently low-overhead to see wide-spread adoption, this is not the case for defenses in transient execution. Unfortunately, current techniques for mitigating attacks in transient execution exhibit high overheads—requiring a costly combination of defenses for every indirect branch.
Cryptanalysis of Lightweight Ciphers Using Metaheuristics

There are several lightweight cryptographic ciphers (LWC) which have been proposed lately for constrained environments. When it comes to selecting one specific cipher for a particular application, the task becomes complicated. Several surveys have been conducted on hardware and software implementations of lightweight cryptographic primitives in order to evaluate and benchmark their characteristics such as throughput, ROM and RAM requirements, power consumption, and so on. But tackling the question about the strength of lightweight cryptographic primitives and their ability to withstand known and potential attacks is not a straightforward activity. The underlying design of the primitives or the key size of LWC is insufficient criteria to measure and compare their cryptographic strengths. This paper attempts to evaluate the relative strength of software implementation of lightweight block ciphers using metaheuristic algorithms and proposes a framework for selection of encryption algorithm. Experiments show that LWC algorithms are as strong as classical AES despite their simple design. Moreover, they execute faster with less resources than conventional AES. Finally, it is observed that the relative strength of LWC is independent of their underlying architectural design.
SurfRiver: Flattening Stream Surfaces for Comparative Visualization

We present SurfRiver, a new visual transformation approach that flattens stream surfaces in 3D to rivers in 2D for comparative visualization. Leveraging the TextFlow-like visual metaphor, SurfRiver untangles the convoluted individual stream surfaces along the flow direction and maps them along the horizontal direction of the abstract river view. It stacks multiple surfaces along the vertical direction of the river view. This visual mapping makes it easy for users to track along the flow direction and align stream surfaces for comparative study. Through brushing and linking, the river view is connected to the spatial surface view for collective reasoning. SurfRiver can be used to examine a single stream surface, investigate seeding sensitivity or variability of a family of surfaces from a group of related seeding curves, or explore a collection of representative surfaces. We describe our optimization solution to achieve the desirable mapping, present SurfRiver interface and interactions, and report results from different flow fields to demonstrate its efficacy. Feedback from a domain expert also indicates the promise of SurfRiver.
Outdoor 3D reconstruction method based on multi-line laser and binocular vision

Three-dimensional (3D) reconstruction of substation fittings is of great significance for live working robots. However, the key problem is that active 3D cameras cannot work in outdoor strong light environment, and the passive 3D cameras cannot extract features of weak texture targets. This paper proposes an outdoor 3D reconstruction method based on multi-line laser and binocular vision. To solve the problem that weak texture target has few features, we use multi-line laser to create artificial features. To reduce the interference of natural light on the laser in the images, the frame-difference method is proposed for natural light filtering. Then we use the gray-centroid method to position the multi-line laser accurately. Finally, the binocular vision model is used to complete 3D reconstruction of the target. The experiments show that, compared with traditional 3D reconstruction methods, our 3D reconstruction method can realize 3D reconstruction of outdoor weak texture targets effectively.
Multi-Authority CP-ABE with Dynamical Revocation in Space-Air-Ground Integrated Network

Space-air-ground integrated network (SAGIN) is emerged as a versatile computing and traffic architecture in recent years. Though SAGIN brings many significant benefits for modern communication and computing services, there are many unprecedented challenges in SAGIN. The one critical challenge in SAGIN is the data security. In SAGIN, because the data will be stored in cleartext on cloud, the sensitive data may suffer from the illegal access by the unauthorized users even the untrusted cloud servers (CSs). Ciphertext-policy attribute-based encryption (CP-ABE), which is a type of attribute-based encryption (ABE), has been regarded as a promising solution to the critical challenge of the data security on cloud. But there are two main blemishes in traditional CP-ABE. The first one is that there is only one attribute authority (AA) in CP-ABE. If the single AA crashs down, the whole system will be shut down. The second one is that the AA cannot effectively manage the life cycle of the users' private keys. If a user on longer has one attribute, the AA cannot revoke the user's private key of this attribute. This means the user can still decrypt some ciphertexts using this invalid attribute. In this paper, to solve the two flaws mentioned above, we propose a multi-authority CP-ABE (MA-CP-ABE) scheme with the dynamical key revocation (DKR). Our key revocation supports both user revocation and attribute revocation. And the our revocation is time friendly. What's more, by using our dynamically tag-based revocation algorithm, AAs can dynamically and directly re-enable or revoke the invalid attributes to users. Finally, by evaluating and implementing our scheme, we can observe that our scheme is more comprehensive and practical for cloud applications in SAGIN.
Non-Linear Hopped Chaos Parameters-Based Image Encryption Algorithm Using Histogram Equalization

Multimedia wireless communications have rapidly developed over the years. Accordingly, an increasing demand for more secured media transmission is required to protect multimedia contents. Image encryption schemes have been proposed over the years, but the most secure and reliable schemes are those based on chaotic maps, due to the intrinsic features in such kinds of multimedia contents regarding the pixels’ high correlation and data handling capabilities. The novel proposed encryption algorithm introduced in this article is based on a 3D hopping chaotic map instead of fixed chaotic logistic maps. The non-linearity behavior of the proposed algorithm, in terms of both position permutation and value transformation, results in a more secured encryption algorithm due to its non-convergence, non-periodicity, and sensitivity to the applied initial conditions. Several statistical and analytical tests such as entropy, correlation, key sensitivity, key space, peak signal-to-noise ratio, noise attacks, number of pixels changing rate (NPCR), unified average change intensity randomness (UACI), and others tests were applied to measure the strength of the proposed encryption scheme. The obtained results prove that the proposed scheme is very robust against different cryptography attacks compared to similar encryption schemes.
Convolutional Neural Network-based Enhancement Method of 3D MRI

Magnetic resonance imaging (MRI) employs a magnetic field and radio frequency signal to generate images of the desired body part. However, due to hardware limitations, the resolution in the slice-select direction is lower than the in-plane direction that generates blurring and anisotropic 3D volume. Several resolution enhancement methods have been widely used that improves the resolution of the slice-select plane. In this paper, a deep learning-based resolution enhancement method is developed to form an isotropic 3D MRI volume with improved slice-select direction resolution. In our proposed method, some isotropic 3D image volumes are trained using a deep learning-based resolution enhancement model for generating a residual volume which is utilized to reconstruct MRI volume with the improved slice-select direction resolution. To assess the performance of the proposed algorithm, we employed both quantitative (e.g., peak signal to noise ratio (PSNR) and structural similarity (SSIM) index) and qualitative measurements. Experimental results showed that the 3D MRI volumes produced by the technique have superior quality to volumes reconstructed using other 3D interpolationbased resolution enhancement methods.
Knowledge Distillation Based on Positive-Unlabeled Classification and Attention Mechanism

With the rapid development of deep learning, convolutional neural networks(CNNs) have achieved great success. But these high-capability CNNs often with a huge burden of computation and memory, which hinders these CNNs from applying to practical application. To solve this problem, in this paper, we proposed a method to train a compact model with high-capacity. The student network with fewer parameters and calculations will learning from the knowledge of the teacher network with more parameters and calculations. To promote the ability of the student network, the more expressive knowledge is extracted from the middle-layer feature of neural networks by attention mechanism, and the knowledge transforms more effective from the teacher network to the student network by the positive- unlabeled(PU) classifier. We validate our method in extensive experiments, showing that it can train the student network to achieve significant performance superior to the teacher network.
Adversarially Training for Audio Classifiers

In this paper, we investigate the potential effect of the adversarially training on the robustness of six advanced deep neural networks against a variety of targeted and non-targeted adversarial attacks. We firstly show that, the ResNet-56 model trained on the 2D representation of the discrete wavelet transform appended with the tonnetz chromagram outperforms other models in terms of recognition accuracy. Then we demonstrate the positive impact of adversarially training on this model as well as other deep architectures against six types of attack algorithms (white and black-box) with the cost of the reduced recognition accuracy and limited adversarial perturbation. We run our experiments on two benchmarking environmental sound datasets and show that without any imposed limitations on the budget allocations for the adversary, the fooling rate of the adversarially trained models can exceed 90%. In other words, adversarial attacks exist in any scales, but they might require higher adversarial perturbations compared to non-adversarially trained models.