Reviewerly/exHarmony huggingface dataset:
A Lightweight Hybrid Cryptographic Algorithm for WSNs Tested by the Diehard Tests and the Raspberry Pi

Cryptographic techniques are crucial in ensuring the security of Wireless Sensor Networks data to limit the effect of such offensive operations. As a consequence, many cryptographic techniques are used to assure network security. Cryptography makes use of two techniques: symmetric-key and asymmetric-key cryptography. However, specific concerns remain. In this paper, a hybrid security algorithm is proposed. It provides both high security and fast encryption and decryption processes by using symmetric-key and asymmetric-key techniques. In addition, it provides three cryptographic primitives: confidentiality, authentication, and integrity. The concept is to encrypt using Blowfish, Rivest Cipher 4, and the Advanced Encryption Standard. Message Digest 5 is used for integrity, and Elliptic Curve Diffie Hellman is used for authentication. Two computers and a Raspberry Pi test the proposed algorithm&#x0027;s performance. The proposed algorithm exceeds numerous current algorithms in ciphertext size, encryption time, decryption time, and throughput. Using a Raspberry Pi 4 Model B and a plaintext size of 184,162 bytes, the proposed algorithm achieved a 37&#x0025; reduction in ciphertext size, a 90.5&#x0025; reduction in encryption time, a 62.7&#x0025; reduction in decryption time, and a 1052&#x0025; increase in throughput compared to previously published works.
Effect of Gafchromic™ EBT2 radiochromic film's components on the detection of UVA solar radiation selectively

The response of Gafchromic™ EBT2 film selectively to UVA solar radiation was studied. A set of EBT2 films was exposed from the topside (80 μm) and the other set from the polyester substrate (175 μm) for different durations. The EBT2 topside components absorb wavelengths shorter than 400 nm, while the polyester substrate absorbs wavelengths shorter than 315 nm. Accordingly, EBT2 film can detect solar UVA radiation (315–400 nm) by calculating the difference (net change) between the irradiated sets. For EBT2 film exposed from polyester substrate, the red, green and grey color channels are decay exponentially with exposure time, where the green color channel has the highest sensitivity. When EBT2 film is exposed from the topside, red, green and grey color channels are linearly correlated with exposure time, but showing poor sensitivity. For EBT2 exposed from topside, the absorbance peaks at 580 ± 2 nm and 632 ± 2 nm are slightly increasing with the increase in the exposure time, while for EBT2 films exposed from polyester substrate; the characteristic peaks are correlated exponentially with exposure time and saturated after 80 min. The net effect of solar ultraviolet UVA radiation on EBT2 films increases exponentially with exposure time with tendency to saturate at prolonged exposure. It is found that the EBT2 films digitization with flatbed scanner has a higher sensitivity but a narrower dynamic range, while analysis with UV–Vis spectrophotometer has lower sensitivity but a wider dynamic range. This new findings pave the way to measure UVA radiation (315–400 nm) using a simple equipment including Gafchromic™ EBT2 film and flatbed scanner.
Monitoring Maritime Traffic with Ship Detection via YOLOv4

In India a large part of goods transportation is carried out by sea, leading to an emerging requirement for remote maritime patrolling system, which also serves as an asset during wartime and peacetime for defence. In this research paper, we propose an automated maritime patrolling solution by making a Deep Learning Model Pipeline for Ship Detection from satellite images using existing State of the art Object Detection Algorithms like Faster-RCNN, SSD, YOLOv3, and YOLOv4. We compare results based on various Evaluation Metrics. Further we also release our own dataset which consists of around 300 satellite images of the top 13 busiest Sea-ports of India. After performing the validations, we found that the YOLO v4 displayed the best re-sults with a balanced mAP and FPS score to detect the ships in the satellite images.
Hybrid Binary Atom Search Optimization Approaches with Statistical Dependence for Feature Selection

The feature selection process aims to obtain the vital information contained in the dataset. Determining the high-impact features has a key role in improving the classification process, applied in many scientific and medical fields within our study. This paper proposes a hybrid SD-BASO algorithm between the statistical dependence (SD) technique and the binary atom search optimization (BASO) algorithm. This algorithm depends on a proposed fitness function through which the essential features that affect the classification process are obtained. The experimental results on the datasets showed that the proposed algorithm, which we refer to as SD-BASO, is superior to the classical algorithm in terms of accuracy in the results and the number of features selected.
Language Effect on Speaker Gender Classification Using Deep Learning

In speech processing, identifying the speaker’s gender has been considered a topic of interest by many studies. Various approaches and methods have been proposed to detect the gender of a speaker with high accuracy. However, they are limited to isolated and specific languages. In this research, the speaker’s gender is classified from a mixed languages speech point of view, constituting six different languages using Bidirectional Long Short-Term Memory (BLSTM) network classifiers. Also, gender classification is performed using each specific language independently. The main aim of this approach is to tackle the effect of the language on speakers’ genders classification. Performance evaluation of the language effect on speaker gender classification is conducted on the open-source Mozilla datasets. We achieved an average gender classification accuracy of 90.42%, 97.42%, 82.44%, 98.39%, 100%, and 85.04% on Arabic, Chinese, English, French, Russian, and Spanish datasets, respectively. These results uncover some dependencies of speakers’ gender classification on the language.
Gaussian process-assisted active learning for autonomous data acquisition of impact echo

The goal of this study is to pursue sampling efficiency and achieve reasonable accuracy for the quantity of interest (damage) in impact-echo testing. Because the locations of damages are not always obvious, the conventional grid-based measurement requires subjective judgment. The proposed solution is the implementation of Gaussian process (GP)-assisted active learning to enable the sequential sampling of the quantity of interest with autonomous decision-making for data acquisition. Throughout sampling, an automated model selection is employed to select a suitable kernel for GP modeling dynamically. Active learning is then applied to identify potential damage locations without any human assistance. The proposed method iterates the two modules to explore unknown damage and improve the accuracy of the quantity of interest until the maximum sample size is reached. Through numerical and experimental validation, this work demonstrates that the proposed method exhibits more accurate and efficient results.
Fully used reliable data and attention consistency for semi-supervised learning

Large labeled datasets represent human labor’s costly consumption of resources. Therefore, semi-supervised learning leverages a large amount of unlabeled data to improve the training results in limited labels. Many methods of semi-supervised learning utilize diverse data augmentations to improve model learning and the classification rule from these changes, requiring models to spend a lot of time to adapt to the changes. Besides, reducing the noise in trained unlabeled data is also an issue that is often discussed in semi-supervised learning so that the inference from error predictions can be reduced. It may define that the data, of which the probability predicted from the model is higher than a threshold, as confident and then only train on those high-confidence unlabeled data so that the model avoids the influence from deviation of the error caused by unlabeled data predictions. However, it also leads to the fact that many unlabeled data cannot be effectively used. Thus, this study proposes a semi-supervised framework, including Attention Consistency (AC) and One Supervised (OS) algorithms, which improves efficiency and performance of the model learning by guiding the model to pay attention to classified features and judging whether the model cannot be effectively trained in existing reliable data. This way, the model fully uses unlabeled data to train. The experiment results and comparisons show that similar results can be reached using other methods within a shorter training process. This paper also analyzes the distribution of feature results and proposes a new measurement to find out distribution information.
Application of Digital Technologies

The application of digital technologies in supply chain and logistics is immense and the most important happening in the last decade. The advent of COVID-19 has forced changes in the supply chain and some cases disrupted it. In some cases, it has been a do-or-die situation as social distancing and passenger travel and essentials have affected most economies. China controlled about 40% of the global supply chain, and most countries were made to change and initiate alternative supply chains to reduce the risk. The purpose of this study is to study how the emerging technologies of blockchain, virtual reality, artificial intelligence, machine learning are the few additions to enhance the supply chain. The supply chain digitalization will lead to cost reduction, improved efficiency, data security, and storage of the information. The emerging technologies are poised to revolutionize the supply chain and logistics. The study uses qualitative approach to showcase the expert opinion of stakeholders on this topic and contribute to the scarce data available currently.
On the implementation of large period piece-wise linear Arnold cat map

Abstract This paper presents a piece-wise linear cat map (PWLCM) obtained by perturbing the conventional quantized Arnold cat map (QACM) with a nonlinear term. The effect of the nonlinear term on the dynamics of the QACM is investigated. We show that the eigenvalues, hence the Lyapunov exponents of the PWLCM depend on the initial conditions, which is not the case for the QACM. As a result, the proposed PWLCM is a generalized form of the QACM, whose the period exponentially increases with respect to the precision, thus taking as value 1.09 × 10 513 for only 10-bit precision; while that of the corresponding QACM is only 768. The nonlinear term increases the sensitivity of the system to the initial conditions, which contributes to increase its period, hence to enhance its complexity. An electronic implementation of both the QACM and the PWLCM in the case of 4-bit precision using Multisim is presented. The proposed architecture of both the QACM and the PWLCM are implemented using Verilog and prototyped on the Zynq 7020 FPGA board. For 4-bit precision, the FPGA implementation performs 1.072 Gbps throughput at 134 MHz maximum frequency. We verified that experimental and simulation behaviors of the proposed system perfectly match, thus confirming the effectiveness of the proposed electronic circuit for exhibiting the expected dynamics in real-time.
Sustainable Technical Debt-Aware Computing Model for Virtual Machine Migration (TD4VM) in IaaS Cloud

In the cloud, optimal CPU and memory utilization can lead to low energy consumption, which is an important aspect of green computing. However, constantly changing workloads may contribute to resource over- or underutilization. The former violates the service level agreement’s quality of service constraints. The latter indicates that as workload decreases, virtual machine resource utilization decreases. They introduce difficult decision-making tasks when dynamically adapting (e.g., migrating) a virtual machine in order to maximize its resource utilization over time. To address these challenges, we propose a newer mathematical model called the technical debt-aware computing model for virtual machine migration (TD4VM). The model promotes a holistic approach to dynamic virtual machine adaptation for cloud service providers and addresses existing issues regarding logical aspects of virtual machine adaptation in a highly dynamic cloud environment, which includes a measurement mechanism and estimation guidelines for estimating future debt and utility. Technical debt-aware models make decisions based on VM operating costs, quality, minimizing SLA violations, and incurring technical debt. This approach connects decisions about virtual machine migration that affect overall utility over time. Our method can determine whether a virtual machine should be moved when it is over or underutilized based on its technical debt. The experimental results on a dataset obtained from the Materna-trace-1 demonstrate that the proposed approach outperforms other state-of-the-art methods on a variety of performance metrics. A numerical comparison shows that TD4VM outperforms the other approaches, with VM resource economies of 171.84%, 91.33%, 97.85%, and 93.89% for TD4VM, LRMMT, IQRMC, and IQRMMT, respectively. Additionally, we quantify the debt amassed using TD4VM and state-of-the-art techniques. When compared to LRMMT, IQRMC, and IQRMMT, which cost (in $) 0.77, 0.73, and 0.76, respectively, TD4VM accumulates the minimum debt of 0.17.
Experimental simulation of a monogamy relation between quantum contextuality and nonlocality on an NMR quantum processor

The quantum no-disturbance principle imposes a fundamental monogamy relation on quantum contextuality and quantum nonlocality. We experimentally demonstrated the simulation of this monogamy relation on a ququart-qubit system using three NMR qubits. We constructed experimental tests of the violation of the Klyachko-Can-Binicioglu-Shumovski (KCBS) inequality and the Clauser-Horne-Shimony-Holt (CHSH) inequality. We measured the average CHSH and KCBS values at the single point where the quantum boundary meets the no-disturbance boundary, at which point the tradeoff inequality becomes an equality. The tradeoff relation is always satisfied, implying that the violation of either the KCBS or the CHSH inequality automatically forbids the violation of the other.
To social with social distance: a case study on a VR-enabled graduation celebration amidst the pandemic

This paper introduces a timely case study on a 3D virtual reality-enabled graduation celebration project that provided an alternative approach to celebrating university graduation amidst the COVID-19 pandemic. The project was carefully designed with fun, engaging, and interactive activities to compensate for the cancelation of the conventional commencement in spring 2020 due to the pandemic. More than 20 graduating students, faculty and staff members participated in a 2-h virtual reality live event appearing as 3D avatars on their own computers. Quantitative and qualitative data collected from 10 participants were analyzed for user experience, cognitive contribution, technology acceptance, and the feasibility. Results revealed that such a project was technologically sound, functionally acceptable, user-friendly, and practically implementable. The results also informed the lessons learned from the current design and the places to improve.The online version contains supplementary material available at 10.1007/s10055-022-00646-2.
Word Translation using Cross-Lingual Word Embedding: Case of Sanskrit to Hindi Translation

Sanskrit is a low resource language for which large parallel data sets are not available. Large parallel data sets are required for Machine Translation. Cross-Lingual word embedding helps to learn the meaning of words across languages in a shared vector space. In the present work, we propose a translation technique between Sanskrit and Hindi words without a parallel corpus-base. Here, fastText pre-trained word embedding for Sanskrit and Hindi are used and are aligned in the same vector space using Singular Value Decomposition and a Quasi bilingual dictionary. A Quasi bilingual dictionary is generated from similar character string words in the monolingual word embeddings of both languages. Translations for the test dictionary are evaluated on the various retrieval methods e.g. Nearest neighbor, Inverted Sofmax approach, and Cross-domain Similarity Local Scaling, in order to address the issue of hubness that arises due to the high dimensional space of the vector embeddings. The results are compared with the other Unsupervised approaches at 1, 10, and 20 neighbors. While computing the Cosine similarity, we observed that the similarity between the expected and the translated target words is either close to unity or equal to unity for the cases that were even not included in the Quasi bilingual dictionary that was used to generate the orthogonal mapping. A test dictionary was developed from the Wikipedia Sanskrit-Hindi Shabdkosh to test the translation accuracy of the system. The proposed method is being extended for sentence translation.
Deep Isometric Maps

Isometric feature mapping is an established time-honored algorithm in manifold learning and non-linear dimensionality reduction. Its prominence can be attributed to the output of a coherent global low-dimensional representation of data by preserving intrinsic distances. In order to enable an efficient and more applicable isometric feature mapping, a diverse set of sophisticated advancements have been proposed to the original algorithm to incorporate important factors like sparsity of computation, conformality, topological constraints and spectral geometry. However, a significant shortcoming of most approaches is the dependence on large-scale dense-spectral decompositions and the inability to generalize to points far away from the sampling of the manifold. In this paper, we explore an unsupervised deep learning approach for computing distance-preserving maps for non-linear dimensionality reduction. We demonstrate that our framework is general enough to incorporate all previous advancements and show a significantly improved local and non-local generalization of the isometric mapping. Our approach involves training with only a few landmark points and avoids the need for population of dense matrices as well as computing their spectral decomposition.
A composite framework coupling FCRM, LSSVM and improved hybrid IHHOMFO optimization for Takagi–Sugeno fuzzy model identification

This paper proposes a novel Takagi–Sugeno fuzzy model identification method by combining fuzzy c-regression model clustering (FCRM), least squares support vector machine (LSSVM) and intelligent optimization algorithm. Firstly, in order to improve the performance of FCRM for the complex nonlinear dataset, in this paper the method of FCRM based on LSSVM (FCRM-LSSVM) is proposed to discover the data structure and obtain the antecedent parameters. And then, a newly developed intelligent optimization algorithm by hybridizing Harris hawks optimization and moth-flame optimization algorithm (IHHOMFO) is proposed to further optimize the antecedent membership function parameters obtained by the FCRM-LSSVM. Finally, the proposed novel T-S fuzzy model identification combines FCRM, LSSVM and IHHOMFO for solving actual model identification problems. Experiments on five different datasets demonstrate that the proposed method is more efficient than conventional methods, such as T-S model identification based on fuzzy c-means (FCM), FCRM and FCRM-LSSVM, in standard measurement indexes. This study thus demonstrates that the proposed method is a credible and competitive fuzzy model identification method. The novel method contributes not only to the theoretical aspects of fuzzy model, but is also widely applicable in data mining, image recognition and prediction problems.
Image Captioning using Neural Network Model

The field of Artificial Intelligence had made a huge change in all sectors of computer technology. It had given a comfort zone and user friendly environment for human beings. Similarly the most trending topic also made a lots of improvement in Image captioning. Image captioning is most widely used technology in computer field. In this paper we will be implementing generation of image captions using neural network models and understand what the role of this models is in caption generation. The models used for implementation are CNN, RNN with LSTM. In this paper the dataset used is Flickr 8k dataset which consist images of different varieties and different activities performed by human beings. The main aim is to extract all the features from the given input image and establish an appropriate relationship from the extracted features and tried to get the most accurate captions for given input images.
Balancing regional and global information: An interactive segmentation framework for ultrasound breast lesion

Acquiring adequate annotated data to train a deep convolutional neural network (CNN) is commonly challenging, especially when it comes to medical data. Manual annotation is tedious and time-consuming. To address this issue, we introduce a novel end-to-end trainable framework, which requires only four clicks, for interactive segmentation of breast lesions in ultrasound images. The interference induced by varying sizes and class imbalance problem brings huge challenges to precise segmentation, thus we propose an Region of Interest (RoI) focusing module to fix RoI features to a specific dimension and force the network to focus only on the lesions by discarding backgrounds. In addition, to fully and wisely utilize both RoI and global features, we introduce an RoI & Global re-calibration module, which re-weights each channel of the entire feature map and the RoI feature map before concatenation, such that regional and global information are utilized in a well-balanced way for more accurate segmentation. Experimental results report Intersection over Union (IoU) 89.33 ± 5.16%, Dice similarity coefficient (Dice) 94.28 ± 3.11% and pixel accuracy (PA) 99.25 ± 0.83% achieved by the proposed framework on an unseen test set with 120 cases (240 images) of breast lesion ultrasound images, showing the effectiveness of our method.
Just-in-time defect prediction based on AST change embedding

Just-in-time (JIT) defect prediction can help developers quickly identify whether a change is defective or not. The features extracted from changes play an essential role in building an accurate prediction model. In recent years, it has been considered effective to extract the semantic features of software code files by using code representation technology. However, how to extract semantic information from broken changing code snippets is still a challenging problem. We propose a new feature to represent code semantics based on Abstract Syntax Trees (ASTs), called ACE (AST Change Embedding), by comparing the abstract syntax tree of source code before and after a change and extracting AST change sequences, and then mapping them into numeric vectors by using word embedding technology. At the same time, we utilize the gated mechanism to build a gated hierarchical model, called GH-ACE, to combine the traditional manual features and semantic features. We conduct experiments on within-project and cross-project defect prediction tasks and evaluate the effectiveness of our proposed model in non-effort-aware scenarios and effort-aware scenarios. The results show that, on average, our model is 4.0 percent higher than the best baseline method for within-project defect prediction and 2.4 percent higher than the best baseline method for cross-project defect prediction.