Reviewerly/exHarmony huggingface dataset:
Blind Image Watermarking for Localization and Restoration of Color Images

Digital images have become easy to generate and share with tremendous growth in communication technology. Therefore, the threat of forgery and tampering in digital images has also been increased. This study proposes a blind fragile watermarking scheme for color images to provide efficient image tamper detection and self-recovery. A secret key based pseudo random binary sequence is used as a fragile watermark for tamper detection. Likewise, the recovery information is preserved in a randomized manner using a secret key. During embedding, each channel of the RGB image is divided into non-overlapping 2 × 4 size blocks. Each block is then watermarked using a LSB (least significant bit) replacement process in 9-base notation structure. The watermark sequence (i.e. 12-bit) for each block contains 6-bits from the fragile watermark and concatenated with the recovery information (i.e. 6 MSB (most significant bit) of block's mean value) of a different block. The experimental results confirm that the scheme is highly efficient to locate tampered region and recover the original image even in case of serious tampering. The scheme offers nearly 99% accurate tamper detection and significant recovery of tampered images (up to 80% tampering rate). Comparative results prove the significance and superiority of the scheme over existing schemes.
A Comparison of Procedural Safety Training in Three Conditions: Virtual Reality Headset, Smartphone, and Printed Materials

Virtual reality (VR) experiences are receiving increasing attention in education and training. Some VR setups can deliver immersive VR training (e.g., on multiple projected screens), while others can deliver nonimmersive VR training (e.g., on standard desktop monitors). Recently, consumer VR headsets make it possible to deliver immersive VR training with six-degrees-of-freedom tracking of trainees' head as well as hand controllers, while most smartphones can deliver nonimmersive VR training without the need for additional hardware. Previous studies compared immersive and nonimmersive VR setups for training, highlighting effects on performance, learning, presence, and engagement, but no study focused on contrasting procedural training with (immersive) VR headsets and (nonimmersive) smartphones. This article conducts a comparison of these two VR setups in the aviation safety domain. The considered training concerned door opening procedures in different aircraft and included a virtual instructor. In addition, we compared the two VR setups with the traditional printed materials used in the considered domain, i.e., safety cards. Results show that both VR setups allowed gaining and retaining more procedural knowledge than printed materials, and led to higher confidence in performing procedures. However, only the VR headset was considered to be significantly more usable than the printed materials, and presence was higher with the VR headset than the smartphone. The VR headset turned out to be important also for engagement and satisfaction, which were higher with the VR headset than both the printed materials and the smartphone. We discuss the implications of these results.
Approximate high utility itemset mining in noisy environments

High utility pattern mining has been proposed to overcome the limitations of frequent pattern mining which cannot reflect the unique profits of items. High utility pattern mining has been actively conducted because it can find more valuable patterns than previous fields of pattern mining. However, its traditional approaches are designed to perform on the assumption that the data stored in databases is faultless. If there are unknown errors, such as noises, in a given database, the mining results traditional high utility pattern mining approaches mined in this database cannot be fully trusted. In this paper, a novel technique considering the noises is suggested in order to overcome this limitation. The proposed technique calculates the ranges of trustworthy utilities for patterns using a utility tolerance factor. By using this factor, the robust high utility patterns, called as approximate high utility patterns, can be extracted from a noisy database. To evaluate the performance of the proposed algorithm, various experiments are designed and conducted in terms of runtime, memory usage, and scalability. The experimental results show that the proposed algorithm outperforms than competitors, an apriori-based approach and UP-Growth.
Simultaneous Earthquake Detection on Multiple Stations via a Convolutional Neural Network

Abstract In recent years, as the amount of seismic data has grown rapidly, it is very important to develop a fast and reliable event detection and association algorithm. Generally, event detection is first performed on individual stations followed by event association through linking phase arrivals to a common event generating them. This study considers earthquake detection as the problem of image classification and convolutional neural networks (CNNs), as some of the widely used deep-learning tools in image processing, can be well used to solve this problem. In contrast to existing studies training the network using seismic data from individual stations, in this study, we train a CNN model jointly using records of multiple stations. Because the CNN automatically synthesizes information among multiple stations, the detector can more reliably detect seismic events and is less affected by spurious signals. The CNN is trained using aftershock data of the 2013 Mw 6.6 Lushan earthquake. We have applied it on two very different datasets of Gofar transform fault, East Pacific Rise and Changning shale gas field in southern Sichuan basin, China. The tests show that the trained CNN has strong generalization ability and is flexible with the number of available stations, different instrument types, and different data sampling rates. It can detect many more events than the conventional short-term average/long-term average detector and is more efficient than template-matching methods.
Incremental On-Device Tiny Machine Learning

Tiny Machine Learning (TML) is a novel research area aiming at designing and developing Machine Learning (ML) techniques meant to be executed on Embedded Systems and Internet-of-Things (IoT) units. Such techniques, which take into account the constraints on computation, memory, and energy characterizing the hardware platform they operate on, exploit approximation and pruning mechanisms to reduce the computational load and the memory demand of Machine and Deep Learning (DL) algorithms.
Disruptor-Aware Interval-Based Response Inconsistency for Correlation Filters in Real-Time Aerial Tracking

Aerial object tracking approaches based on discriminative correlation filter (DCF) have attracted wide attention in the tracking community due to their impressive progress recently. Many studies introduce temporal regularization into the DCF-based framework to achieve a more robust appearance model and further enhance the tracking performance. However, existing temporal regularization approaches usually utilize the information of two consecutive frames, which are not robust enough due to limited information. Although some methods attempt to incorporate abundant training samples and generally improve the tracking performance, these improvements are at the expense of significantly increased computing consumption. Besides, most existing methods introduce historical information directly without denoising, which means that background noises are also introduced into the filter training and may degrade the tracking accuracy. To tackle the drawbacks mentioned earlier, this work proposes a novel aerial object tracking approach to exploit disruptor-aware interval-based response inconsistency, i.e., IBRI tracker. The proposed method is able to incorporate historical interval information by utilizing responses in the filter training process, thereby obtaining a robust tracking performance while maintaining the real-time speed. Moreover, to reduce the disruptions caused by similar object, partial occlusion, and other challenging scenes, a novel disruptor-aware scheme based on response bucketing is introduced to detect the disruptor and enforce a spatial penalty for the disruptive area around the tracked object. Exhausted experiments on multiple well-known challenging aerial tracking benchmarks demonstrate the accuracy and robustness of the proposed IBRI tracker against other 35 state-of-the-art trackers. With a real-time speed of ~32 frames/s on a single CPU, the proposed approach can be applied for typical aerial platforms to achieve aerial visual object tracking efficiently.
ViCrypt to the Rescue: Real-Time, Machine-Learning-Driven Video-QoE Monitoring for Encrypted Streaming Traffic

Video streaming is the killer application of the Internet today. In this article, we address the problem of real-time, passive Quality-of-Experience (QoE) monitoring of HTTP Adaptive Video Streaming (HAS), from the Internet-Service-Provider (ISP) perspective - i.e., relying exclusively on in-network traffic measurements. Given the wide adoption of end-to-end encryption, we resort to machine-learning (ML) models to estimate multiple key video-QoE indicators (KQIs) from the analysis of the encrypted traffic. We present ViCrypt, an ML-driven monitoring solution able to infer the most important KQIs for HTTP Adaptive Streaming (HAS), namely stalling, initial delay, video resolution, and average video bitrate. ViCrypt performs estimations in real-time, during the playback of an ongoing video-streaming session, with a fine-grained temporal resolution of just one second. For this, it relies on lightweight, stream-like features continuously extracted from the encrypted stream of packets. Empirical evaluations on a large and heterogeneous corpus of YouTube measurements show that ViCrypt can infer the targeted KQIs with high accuracy, enabling large-scale passive video-QoE monitoring and proactive QoE-aware traffic management. Different from the state of the art, and besides real-time operation, ViCrypt is not bound to coarse-grained KQI-classes, providing better and sharper insights than other solutions. Finally, ViCrypt does not require chunk-detection approaches for feature extraction, significantly reducing the complexity of the monitoring approach, and potentially improving on generalization to different HAS protocols used by other video-streaming services such as Netflix and Amazon.
Support Vector Machine Accuracy Improvement with Classification

Rapid increase in the information technology through digitalization, leads to fast enhancement in technical industry has expanded the need for effective data mining. Data mining is a combination of tools and mechanisms for taking out of beneficial data set from a gigantic amount of databases. Data mining has three main components: Classification, Sequence Analysis, and Association rule. Data mining provides various functions like; error identification, regression, clustering, classification, association rule, and summarization. This is a machine learning method used to estimate set membership for data instances. Machine learning is defined as the capability of a system to absorb from its past experience. Support vector machine is widely used to classify linear and nonlinear information and instigate the data based on traditional statistics theory. Support vector machine used to draw a decision surface that classifies all training vector into subclasses. There supervised machine learning model (SVM) used as a classifier as well as we tried to achieve better pattern recognition using classification technique of data mining. A comparison of different types of kernels performance using Support Vector Machine has also to be done. These results yield a good pattern prediction rate and low misclassification of data.
Fitness distance correlation and mixed search strategy for differential evolution

The fitness landscape is a theory applied to the evolutionary dynamics of biological evolution to explain the behavior of evolutionary algorithms in the solution of optimization problems. With the continuous advancement of evolutionary algorithm optimization, a fitness landscape can present more abundant feature information, such as the local fitness, fitness distance correlation, and landscape roughness. These landscape features reflect the optimal solution distribution, quantity, and local unimodal topology of the optimization problem from various angles. This paper expresses the adaptability landscape features of typical optimization problems, engages in a quantitative analysis of the fitness distance correlation information, evaluates the difficulty of solving the problem within the search space, and obtains the correlation degree classification result. The search strategy adapts the mixed mutation and the fitness distance correlation for differential evolution. Empirical studies show that, the fitness distance correlation search strategy for the differential evolution algorithm can avoid falling into the local optimum, improve accuracy and convergence, and solve the single-objective optimization problem in a more comprehensive manner.
The Virtual Knowledge Graph System Ontop

Ontop is a popular open-source virtual knowledge graph system that can expose heterogeneous data sources as a unified knowledge graph. Ontop has been widely used in a variety of research and industrial projects. In this paper, we describe the challenges, design choices, new features of the latest release of Ontop v4, summarizing the development efforts of the last 4 years.
Completing the Grand Tour of Asymptotic Quantum Coherence Manipulation

We compute on all quantum states several measures that characterise asymptotic quantum coherence manipulation under restricted classes of operations. We focus on the distillable coherence, i.e. the maximum rate of production of approximate pure bits of coherence starting from independent copies of an input state ρ, and on the coherence cost, i.e. the minimum rate of consumption of pure coherence bits that is needed to generate many copies of ρ with vanishing error. We obtain the first closed-form expression for the distillable coherence under strictly incoherent operations (SIO), proving that it coincides with that obtained via physically incoherent operations (PIO). This shows that SIO and PIO are equally weak at distilling coherence, sheds light on the recently discovered phenomenon of generic bound coherence, and provides us with an explicit optimal distillation protocol that is amenable to practical implementations. We give a single-letter formula for the coherence cost under PIO, showing that it is finite on a set of states with nonzero volume. Since PIO can be realised in a laboratory with incoherent ancillae, unitaries, and measurements, our result puts fundamental limitations on coherence manipulation in an experimentally relevant setting. We find examples of `abyssally bound' states with vanishing PIO distillable coherence yet infinite PIO coherence cost. Our findings complete the picture of asymptotic coherence manipulation under all the main classes of incoherent operations.
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems

Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable.In either case, the knowledge base (KB) plays an essential role in fulfilling user requests.Modularized systems rely on DST to interact with the KB, which is expensive in terms of annotation and inference time.Endto-end systems use the KB directly as input, but they cannot scale when the KB is larger than a few hundred entries.In this paper, we propose a method to embed the KB, of any size, directly into the model parameters.The resulting model does not require any DST or template responses, nor the KB as input, and it can dynamically update its KB via finetuning.We evaluate our solution in five taskoriented dialogue datasets with small, medium, and large KB size.Our experiments show that end-to-end models can effectively embed knowledge bases in their parameters and achieve competitive performance in all evaluated datasets 1 .
Cluster Around Latent Variable for Vulnerability Towards Natural Hazards, Non-Natural Hazards, Social Hazards in West Papua

The diagnosis of a hazard can be classified into three key domains, particularly regarding the natural hazards, non-natural hazards and social hazards.The disasters which have actually happened in West Papua require considerable attention and consideration of the Indonesian Government, despite since they have handled as much as they can to provide solutions and make people feel secure and pleasant.The purpose of this study is to calculate the location-based social vulnerability in West Papua involves the components of Information, Technology, and Communication, Food Access, Natural Disaster, Social Protection Statement, Access to Financial Services, Description of the source of household income, Number of event floods, number of earthquake disasters, COVID-19 death cases, and Number of incidents of protest which are obtained from the National Socio-Economic Survey (SUSENAS) official statistics with the main focus of research on the millennial generation.After employ clustering of variables around latent variables with connectivity value of 3.9400794, Dunn 0.9373, and Silhouette 0.6333.Each factor provide a sign indicating a positive or negative effect on social vulnerability and finally a location cluster will be formed based on the index obtained.
Edge Computing and Its Convergence With Blockchain in 5G and Beyond: Security, Challenges, and Opportunities

The internet is progressing towards a new technology archetype grounded on smart systems, heavily relying on artificial intelligence (AI), machine learning (ML), blockchain platforms, edge computing, and the internet of things (IoT). The merging of IoT, edge computing, and blockchain will be the most important factor of empowering new automatic service and commercial models with various desirable properties, such as self-verifying, self-executing, immutability, data reliability, and confidentiality provided by the advancement in blockchain smart contracts and containers. Motivated by the potential paradigm shift and the security features brought by blockchain from the traditional centralized model to a more robust and resilient decentralized model, this tutorial article proposes a multi-tier integrated blockchain and edge computing architecture for 5G and beyond for solving some security issues faced by resource-constrained edge devices. We begin with a comprehensive overview of different edge computing paradigms and their research challenges. Next, we present the classification of security threats and current defense mechanisms. Then, we present an overview of blockchain and its potential solutions to the main security issues in edge computing. Furthermore, we present the classification of facilitating developers of different architectures to select an appropriate platform for particular applications and offer insights for potential research directions. Finally, we provide key convergence features of the blockchain and edge computing, followed by some conclusions.
HateClassify: A Service Framework for Hate Speech Identification on Social Media

It is indeed a challenge for the existing machine learning approaches to segregate the hateful content from the one that is merely offensive. One prevalent reason for low accuracy of hate detection with the current methodologies is that these techniques treat hate classification as a multiclass problem. In this article, we present the hate identification on the social media as a multilabel problem. To this end, we propose a CNN-based service framework called “HateClassify” for labeling the social media contents as the hate speech, offensive, or nonoffensive. Results demonstrate that the multiclass classification accuracy for the CNN-based approaches particularly sequential CNN (SCNN) is competitive and even higher than certain state-of-the-art classifiers. Moreover, in the multilabel classification problem, sufficiently high performance is exhibited by the SCNN among other CNN-based techniques. The results have shown that using multilabel classification instead of multiclass classification, hate speech detection is increased up to 20%.
Deep Learning Sentiment Analysis of Amazon.Com Reviews and Ratings

Our study employs sentiment analysis to evaluate the compatibility of Amazon.comreviews with their corresponding ratings.Sentiment analysis is the task of identifying and classifying the sentiment expressed in a piece of text as being positive or negative.On e-commerce websites such as Amazon.com,consumers can submit their reviews along with a specific polarity rating.In some instances, there is a mismatch between the review and the rating.To identify the reviews with mismatched ratings we performed sentiment analysis using deep learning on Amazon.comproduct review data.Product reviews were converted to vectors using paragraph vector, which then was used to train a recurrent neural network with gated recurrent unit.Our model incorporated both semantic relationship of review text and product information.We also developed a web service application that predicts the rating score for a submitted review using the trained model and if there is a mismatch between predicted rating score and submitted rating score, it provides feedback to the reviewer.
Perceptions of artificial intelligence: A survey of academic librarians in Canada and the United States

This study aims to evaluate the perceptions of librarians with regard to artificial intelligence in academic libraries. An online survey of 24 questions was distributed through library distribution lists in Canada and the United States at the end of the summer in 2019. Findings suggest that librarians do not agree on a definition of artificial intelligence which is in keeping with this emerging field. The survey responses highlight the fact that academic librarians require more training with regard to artificial intelligence and its potential applications in libraries. Other important implications include a recognition that library patrons are interested in AI and that little to no programming about it has been offered in academic libraries. Very few studies have focused on academic librarians' perceptions of AI. This article highlights some useful practical implications for AI technologies in libraries and how AI could help improve library services and workflows.
BERT-Caps: A Transformer-Based Capsule Network for Tweet Act Classification

Identification of speech acts provides essential cues in understanding the pragmatics of a user utterance. It typically helps in comprehending the communicative intention of a speaker. This holds true for conversations or discussions on any fora, including social media platforms, such as Twitter. This article presents a novel tweet act classifier (speech act for Twitter) for assessing the content and intent of tweets, thereby exploring the valuable communication among the tweeters. With the recent success of Bidirectional Encoder Representations from Transformers (BERT), a newly introduced language representation model that provides pretrained deep bidirectional representations of vast unlabeled data, we introduce BERT-Caps that is built on top of BERT. The proposed model tends to learn traits and attributes by leveraging from the joint optimization of features from the BERT and capsule layer to develop a robust classifier for the task. Some Twitter-specific symbols are also included in the model to observe its influence and importance. The proposed model attained a benchmark accuracy of 77.52% and outperformed several strong baselines and state-of-the-art approaches.