Reviewerly/exHarmony huggingface dataset:
Photo Forensics From Rounding Artifacts

Many aspects of JPEG compression have been successfully used in the domain of photo forensics. Adding to this literature, we describe a JPEG artifact that can arise depending upon seemingly innocuous implementation details in a JPEG encoder. We describe the nature of these artifacts and show how a generic JPEG encoder can be configured to explain a wide range of these artifacts found in real-world cameras. We also describe an algorithm to simultaneously estimate the nature of these artifacts and localize inconsistencies that can arise from a wide range of image manipulations.
On cloud computing infrastructure for existing code-clone detection algorithms

Microservice Architecture (MSA) is becoming a design standard for modern cloud-based software systems. However, even though cloud-based applications have been thoroughly explored with regards to networking, scalability, and decomposition of existing monolithic applications into MSA based applications, not much research has been done showing the viability of MSA in new problem domains. In this paper, we explore the application of MSA to the code-clone detection problem domain to identify any improvements that can be made over existing local code-clone detection applications. A fragment of source code that is identical or similar to another is a code-clone. Code-clones make it difficult to maintain applications as they create multiple points within the code that bugs must be fixed, new rules enforced, or design decisions imposed. As applications grow larger and larger, the pervasiveness of code-clones likewise grows. To face the code-clone related issues, many tools and algorithms have been proposed to find and document code-clones within an application. In this paper, we show that many improvements can be made by utilizing emerging cloud-based technologies.
Authentication and Key Agreement Based on Anonymous Identity for Peer-to-Peer Cloud

Cross-cloud data migration is one of the prevailing challenges faced by mobile users, which is an essential process when users change their mobile phones to a different provider. However, due to the insufficient local storage and computational capabilities of the smart phones, it is often very difficult for users to backup all data from the original cloud servers to their mobile phones in order to further upload the downloaded data to the new cloud provider. To solve this problem, we propose an efficient data migration model between cloud providers and construct a mutual authentication and key agreement scheme based on elliptic curve certificate-free cryptography for peer-to-peer cloud. The proposed scheme helps to develop trust between different cloud providers and lays a foundation for the realization of cross-cloud data migration. Mathematical verification and security correctness of our scheme is evaluated against notable existing schemes of data migration, which demonstrate that our proposed scheme exhibits a better performance than other state-of-the-art scheme in terms of the achieved reduction in both the computational and communication cost.
The Progressive Multilevel Embedding Method for Audio Steganography

Abstract Audio steganography refers to hiding a secret message inside a cover audio file. The existing methods follow almost a sequential embedding which leads to poor utilization of the cover. The proposed method aims to achieve the maximum feasible capacity of the audio file without lowering the quality. The proposed method is based on seven levels of thresholds and six LSB layers, while the embedding is carried out progressively. The method allocates the message bits over the cover signal horizontally to ensure fair payload distribution over the signal. Hence, the selected samples might be visited multiple times till the message is totally embedded. The experimental results show that the method is able to reach a hiding capacity of 40% of the file size or 266.6 kbps while maintaining an SNR and PSNR values of 40 and 58 dB respectively. It is concluded that the proposed progressive method is highly adaptive to the message size and capable of providing high-quality stego even when big payloads are used. The comparative study shows that the progressive method provides the best message distribution based on the nature of the cover.
IoT Based Smart Video Surveillance System Using Convolutional Neural Network

Video surveillance plays an important role in our times. It is a great help in reducing the crime rate, and it can also help to monitor the status of facilities. The performance of the video surveillance system is limited by human factors such as fatigue, time efficiency, and human resources. It would be beneficial for all if fully automatic video surveillance systems are employed to do the job. The automation of the video surveillance system is still not satisfying regarding many problems such as the accuracy of the detector, bandwidth consumption, storage usage, etc. This scientific paper mainly focuses on a video surveillance system using Convolutional Neural Networks (CNN), IoT and cloud. The system contains multi nods, each node consists of a microprocessor(Raspberry Pi) and a camera, the nodes communicate with each other using client and server architecture. The nodes can detect humans using a pretraining MobileNetv2-SSDLite model and Common Objects in Context(COCO) dataset, the captured video will stream to the main node(only one node will communicate with cloud) in order to stream the video to the cloud. Also, the main node will send an SMS notification to the security team to inform the detection of humans. The security team can check the videos captured using a mobile application or web application. Operating the Object detection model of Deep learning will be required a large amount of the computational power, for instance, the Raspberry Pi with a limited in performance for that reason we used the MobileNetv2-SSDLite model.
Personal Health Train on FHIR: A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare

Big data and machine learning applications focus on retrieving data on a central location for analysis. However, healthcare data can be sensitive in nature and as such difficult to share and make use for secondary purposes. Healthcare vendors are restricted to share data without proper consent from the patient. There is a rising awareness among individual patients as well regarding sharing their personal information due to ethical, legal and societal problems. The current data-sharing platforms in healthcare do not sufficiently handle these issues. The rationale of the Personal Health Train (PHT) approach shifts the focus from sharing data to sharing processing/analysis applications and their respective results. A prerequisite of the PHT-infrastructure is that the data is FAIR (findable, accessible, interoperable, reusable). The aim of the paper is to describe a methodology of finding the number of patients diagnosed with hypertension and calculate cohort statistics in a privacy-preserving federated manner. The whole process completes without individual patient data leaving the source. For this, we rely on the Fast Healthcare Interoperability Resources (FHIR) standard.
Automated model-based performance analysis of software product lines under uncertainty

Context: A Software Product Line (SPL) can express the variability of a system through the specification of configuration options. Evaluating performance characteristics, such as the system response time and resource utilization, of a software product is challenging, even more so in the presence of uncertain values of the attributes. Objective: The goal of this paper is to automate the generation of performance models for software products derived from the feature model by selection heuristics. We aim at obtaining model-based predictive results to quantify the correlation between the features, along with their uncertainties, and the system performance. This way, software engineers can be informed on the performance characteristics before implementing the system. Method: We propose a tool-supported framework that, starting from a feature model annotated with performance-related characteristics, derives Queueing Network (QN) performance models for all the products of the SPL. Model-based performance analysis is carried out on the models obtained by selecting the products that show the maximum and minimum performance-based costs. Results: We applied our approach to almost seven thousand feature models including more than one hundred and seventy features. The generation of QN models is automatically performed in much less than one second, whereas their model-based performance analysis embeds simulation delays and requires about six minutes on average. Conclusion: The experimental results confirm that our approach can be effective on a variety of systems for which software engineers may be provided with early insights on the system performance in reasonably short times. Software engineers are supported in the task of understanding the performance bounds that may encounter when (de)selecting different configuration options, along with their uncertainties.
Research development of Bitcoin: a network and concept linking analysis

Purpose This study aims to provide researchers a holistic approach for comprehensive understanding of the Bitcoin-related research by discovering its trends, subjects, relations, keywords and concepts. Design/methodology/approach An integrated approach of bibliometric analysis, network analysis and concept linking analysis was proposed for exploring Bitcoin-related studies from 70 countries in the Scopus database. Findings The bibliometric analysis shows that electronic money and blockchain are the mainstream issues of Bitcoin, and the domain distribution of the literature is mainly in engineering-related fields. Through the network analysis of cocitations, co-occurrences and cowords, research clusters were discovered respectively from different perspectives. The authors also have mastered a multilevel concept linking diagram for six related major concepts. Originality/value The major contribution of this research is about providing an integrated and comprehensive approach to extract the mainstream issues that can help researchers conduct Bitcoin-relevant research. This study shows the development trend, context and clusters of Bitcoin-related studies from various perspectives networks and produces a visual concept linking diagram that will enable researchers to quickly understand the contextual relationship between Bitcoin keywords during literature analysis. In addition, the most crucial studies in the main topics are extracted to save the considerable time and labor that would be required to manually read all the literature and summarize the issues.
Exploiting Topic Modelling to Classify Sentiment from Lyrics

With the increase in an enormous amount of data, text analysis has become a challenging task. Techniques like classification, categorization, summarization and topic modeling have become part of every natural language processing activity. In this experiment, we aim to perform sentiment class extraction from lyrics using topic modeling techniques. We have use generative statistical model Latent Dirichlet Allocation (LDA) which is also the most widely explored model in topic modeling and another nonparametric bayesian based approach model Heuristic Dirichlet Process (HDP) to extract the topics from 150 lyrics samples of Manipuri songs written using Roman script. We observe this unsupervised techniques, able to obtain the underlying different sentiment class of lyrics in the form of topics.
The Impact of Jerk on Quality of Experience and Cybersickness in an Immersive Wheelchair Application

This paper presents a quality of experience (QoE) and cybersickness study of an immersive and interactive wheelchair training simulator. The study had two independent groups and each group experienced the virtual wheelchair with either high or low jerk effect levels. Jerk is the rate of change of an object's acceleration over time and it is well accepted as an important consideration in dynamic immersive experiences. The QoE influencing factors (content, human and system) were considered as part of the comparison between groups. Content influencing factors were simulator configurations (high/low jerk effect) and presence levels (Igroup presence questionnaire). Human influencing factors were cognitive task load (NASA task load assessment), emotional (self-assessment manikin) and physiological response (electrodermal activity and heart rate variability). System influencing factors were device-related (immersive headset) and usability levels (system usability scale). The key focus of this work was to understand the influence of jerk on cybersickness and this was captured by using the simulator sickness questionnaire. The current findings indicate that the simulator with low jerk effect resulted in a higher arousal response and reduced simulator sickness symptoms, based on the self-assessment manikin and simulator sickness questionnaire. In addition, in terms of implicit metrics, the same group also showed more positive electrodermal activity responses and lower heart rates compared to the group that experienced the simulator with a high jerk effect. A significant correlation was found between heart rates observed during collision periods and simulator sickness scores. This correlation is moderately positive (r=0.558), which suggests a higher heart rate variability after collisions may be indicative of a higher tendency towards cybersickness.
Augmented scientific investigation

Augmented reality (AR) has great potential to radically change science education by making abstract science concepts visible and interactive. In this paper, we describe initial investigations into high school students' perceptions of learning science with an AR technology (i.e., SmartIR) through analyzing semi-structured interviews. SmartIR is an app that supports the investigation of science, such as thermodynamics. Specifically, it can show changes in thermal imaging over time and provides a data analytics function that visualizes data for analyzing and interpreting the changes. Our analysis of 31 interviews shows that students perceived the exploration of science phenomena with "fine details", including a full vision of second-by-second changes in thermal imaging, as helpful and engaging to understand science concepts. In future work, these findings will be triangulated with logging data of their interactions with SmartIR and student-generated lab reports.
Obstacle Avoidance Algorithm for Small Autonomous Mobile Robot Equipped with Ultrasonic Sensors

Detection and avoiding obstacles in real time is an important design requirement for any autonomous mobile platforms. In this paper we propose a new algorithm for real time obstacle avoidance. To improve the localization of a small autonomous mobile robot equipped with a microcontroller and four ultrasonic sensors, a linear recursive Kalman filter was used. The application of the Kalman filter enables the robot to avoid additional obstacles when transferring data from the sensor, adaptively adjust the intensity of noise, which ensures a smoother movement of the robot. Simulation results show that the developed approach enables to obtain the adequate realtime localization and demonstrate the efficiency in obstacle avoidance with resulting RMSE of 4.15 s and 0.07 m.
Visual place recognition using directed acyclic graph association measures and mutual information-based feature selection

Visual localization is a challenging problem, especially over the long run, since places can exhibit significant variation due to dynamic environmental and seasonal changes. To tackle this problem, we propose a visual place recognition method based on directed acyclic graph matching and feature maps extracted from deep convolutional neural networks (DCNN). Furthermore, in order to find the best subset of DCNN feature maps with minimal redundancy, we propose to form probability distributions on image representation features and leverage the Jensen–Shannon divergence to rank features. We evaluate the proposed approach on two challenging public datasets, namely the Bonn and the Freiburg datasets, and compare it to the state-of-the-art methods. For image representations, we evaluated the following DCNN architectures: AlexNet, OverFeat, ResNet18 and ResNet50. Due to the proposed graph structure, we are able to account for any kind of correlations in image sequences, and therefore dub our approach NOSeqSLAM. Algorithms with and without feature selection were evaluated based on precision–recall curves, area under the curve score, best recall at 100% precision score and running time, with NOSeqSLAM outperforming the counterpart approaches. Furthermore, by formulating the mutual information-based feature selection specifically for visual place recognition and by selecting the feature percentile with the best score, all the algorithms, and not just NOSeqSLAM, exhibited enhanced performance with the reduced feature set.
Adaptive Pseudo-Random Sequence Generation for Spread Spectrum Image Steganography

This article discusses methods for hiding data in digital images by using pseudo-random sequences and direct spread spectrum technology. We propose a new method of forming sequences that takes into account the statistical properties of cover images. This allows you to achieve low correlation, which provides reliable and safe hiding of information in digital images. The results of experimental studies show that the bit error rate in the recovered messages is significantly reduced. At the same time, distortions of cover images remain at the same level.
A Model-Based RF Hand Motion Detection System for Shadowing Scenarios

With the explosive growth of mobile computing, new modes of human-computer interaction (HCI) are emerging and becoming feasible. Compared to vision-based systems that require lighting, radio frequency (RF)-based hand motion detection systems are becoming more popular in HCI applications. In real RF hand motion detection scenarios, the line-of-sight between the transmitter (Tx) and receiver (Rx) is usually blocked. Hence, shadowing significantly affects the detection accuracy. To design better RF hand motion detection systems, we propose a simple diffraction and interference model (DIM) to interpret the received signal strength (RSS) variation caused by hand motions in the shadowing scenario. Based on theories of knife-edge diffraction and mutual radio interference, DIM provides a simple theoretical foundation for analyzing the RSS variation with hand size, signal frequency, and Tx-Rx distance. Furthermore, a model-based RF hand motion detection system benefiting from DIM is presented. Unlike existing systems that require a large number of motion features to train a motion classifier, the model-based system achieves training-free motion classification, which has potential for hand motion detection on a real-time basis. Empirical data collected from a vector network analyzer validate our system as well as demonstrate a simple diffraction model can help hand motion detection processing for commonly growing HCI applications.
Learning Feature Representation and Partial Correlation for Multimodal Multi-Label Data

User-provided annotations in existing multimodal datasets sometimes are inappropriate for model learning and can hinder the task of cross-modal retrieval. To handle this issue, we propose a discriminative and noise-robust cross-modal retrieval method, called FLPCL, which consists of deep feature learning and partial correlation learning. Deep feature learning is implemented by utilizing label supervised information to guide the training of deep neural network for each modality, which aims to find modality-specific deep feature representations that preserve the similarity and discrimination information among multimodal data. Based on deep feature learning, partial correlation learning is proposed to infer direct association between different modalities by removing the effect of common underlying semantics from each modality. It is achieved by maximizing the canonical correlation of the feature representations of different modalities conditioned on the label modality. Different from existing works that build indirect association between modalities via incorporating semantic labels, our FLPCL method can learn more effective and robust multimodal latent representations by explicitly preserving both intra-modal and inter-modal relationship among multimodal data. Extensive experiments on three cross-modal datasets show that our method outperforms state-of-the-art methods on cross-modal retrieval tasks.
Attention-Based English to Mizo Neural Machine Translation

Machine Translation alleviates the need of human translators for source to target languages translation by enabling instant translation in multiple languages. Neural Machine Translation (NMT) has exhibited remarkable results in case of high-resource languages. However, for resource scare languages, NMT does not perform equivalently well. In this paper, various NMT models based on different configurations such as unidirectional and bidirectional Long Short Term Memory (LSTM), deep and shallow networks and optimization methods like Stochastic Gradient Descent (SGD) and Adam has been trained and tested for resource scare English to Mizo language pair. The quality of output translations have been evaluated using automatic evaluation metrics and analyzed the predicted translations based on best and worst performances of test data.
Prediction by Fuzzy Clustering and KNN on Validation Data With Parallel Ensemble of Interpretable TSK Fuzzy Classifiers

For many application scenarios where raw and even multidomain training data can be easily collected, and at the same time, validation data (as ground-truth data) are available, it becomes naturally desirable for us to perform an enhanced classification/prediction on only validation data with the appropriate leverage of training data. In this article, a novel ensemble framework EP-TSK-FK of Takagi–Sugeno–Kang (TSK) fuzzy subclassifiers, is proposed to achieve the following distinctive characteristics: 1) each interpretable TSK fuzzy subclassifier on each training subset can be quickly built in parallel such that its outputs provide the values of the corresponding augmented features of the original validation data space; 2) as a novel ensemble method of fuzzy subclassifiers, EP-TSK-FK trains all the interpretable TSK fuzzy subclassifiers only once and does not explicitly reuse them while predicting a testing sample, which thereby reduces the computational complexity of the ensemble process for prediction; 3) after running the proposed iterative fuzzy c-means clustering algorithm iterative fuzzy C-means clustering (IFCM) on the augmented validation data to obtain the representative centroids, the fast classification/prediction of EP-TSK-FK on the testing samples is realized by using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor (KNN) method on the representative centroids with the original features; and 4) enhanced classification performance by the IFCM & KNN method is theoretically revealed, and the experimental results on the benchmarking datasets indicate the effectiveness of EP-TSK-FK and its parallel learning method in the sense of enhanced classification performance, running time, and interpretability.