Reviewerly/exHarmony huggingface dataset:
An Enhanced Location Scattering Based Privacy Protection Scheme

Consumer privacy-preserving is a major concern in location-based services (LBSs). The paramount problem in LBSs is the disclosure of a user’s actual location while interacting with the location-based service provider (LSP). To address this issue, some privacy-preserving mechanisms introduce a trusted middle entity (TME) between a user and the LSP. However, a TME could be compromised, thus posing a serious privacy threat to user’s information. In this paper, we propose a novel dummy location scattering scheme (DLSS) to protect the location privacy of a user. Specifically, DLSS employs a dummy location generation technique to reduce the risk of location information exposure to untrusted entities. In addition, a pseudonym-based mechanism and a time delay technique are adopted to further improve the privacy of a user. We conducted extensive experiments with randomly generated users’ location to evaluate the robustness of our proposed scheme. When the number of users are increased to 300, the computation time of the proposed scheme is below 105 ms. Even for the larger number of points of interest (POIs) (>2000), the computation time is below 1500 ms in comparison with other existing schemes. Simulation results show that the proposed DLSS preserves privacy at low computation time and communication cost in comparison with the existing schemes.
Usefulness of adaptive strategies in asymptotic quantum channel discrimination

Adaptiveness is a key principle in information processing including statistics and machine learning. We investigate the usefulness adaptive methods in the framework of asymptotic binary hypothesis testing, when each hypothesis represents asymptotically many independent instances of a quantum channel, and the tests are based on using the unknown channel and observing outputs. Unlike the familiar setting of quantum states as hypotheses, there is a fundamental distinction between adaptive and nonadaptive strategies with respect to the channel uses, and we introduce a number of further variants of the discrimination tasks by imposing different restrictions on the test strategies. The following results are obtained: (1) We prove that for classical-quantum channels, adaptive and nonadaptive strategies lead to the same error exponents both in the symmetric (Chernoff) and asymmetric (Hoeffding, Stein) settings. (2) The first separation between adaptive and nonadaptive symmetric hypothesis testing exponents for quantum channels, which we derive from a general lower bound on the error probability for nonadaptive strategies; the concrete example we analyze is a pair of entanglement-breaking channels. (3) We prove, in some sense generalizing the previous statement, that for general channels adaptive strategies restricted to classical feed-forward and product state channel inputs are not superior in the asymptotic limit to nonadaptive product state strategies. (4) As an application of our findings, we address the discrimination power of an arbitrary quantum channel and show that adaptive strategies with classical feedback and no quantum memory at the input do not increase the discrimination power of the channel beyond nonadaptive tensor product input strategies.
Robust Multimodal Remote Sensing Image Registration Based on Local Statistical Frequency Information

Multimodal remote sensing image registration is a prerequisite for comprehensive application of remote sensing image data. However, inconsistent imaging environment and conditions often lead to obvious geometric deformations and significant contrast differences between multimodal remote sensing images, which makes the common feature extraction extremely difficult, resulting in their registration still being a challenging task. To address this issue, a robust local statistics-based registration framework is proposed, and the constructed descriptors are invariant to contrast changes and geometric transformations induced by imaging conditions. Firstly, maximum phase congruency of local frequency information is performed by optimizing the control parameters. Then, salient feature points are located according to the phase congruency response map. Subsequently, the geometric and contrast invariant descriptors are constructed based on a joint local frequency information map that combines Log-Gabor filter responses over multiple scales and orientations. Finally, image matching is achieved by finding the corresponding descriptors; image registration is further completed by calculating the transformation between the corresponding feature points. The proposed registration framework was evaluated on four different multimodal image datasets with varying degrees of contrast differences and geometric deformations. Experimental results demonstrated that our method outperformed several state-of-the-art methods in terms of robustness and precision, confirming its effectiveness.
Relationship between adopting servant leadership style and employee commitment: Empirical evidence from Jordanian governmental hospitals

Businesses face a constant struggle in developing leaders who are devoted to serving and developing their people. Therefore, recognizing the link between servant leadership and employee commitment can help organizations establish a more positive working environment, leading to improved employee and organizational performance. This study aims to investigate the relationship between adopting servant leadership by hospital managers and employee commitment within Jordanian governmental hospitals. This paper employs a cross-sectional study design including a quantitative data collection method. Using a simple random sampling method, data were collected from 389 hospital staff employed by Jordanian governmental hospitals. The data were analyzed using Pearson (r) correlation coefficient with hypothesis testing. Research findings revealed a significant positive relationship between the adoption of a servant leadership style by government hospital managers and employee commitment (r = 0.633, Sig. &amp;amp;lt; 0.01). Furthermore, the study showed that demographic variables including gender, age, education, and experience were not associated significantly with employee commitment. The study’s results can help hospital managers develop and practice servant leadership, who are expected to increase employee commitment and achieve organizational objectives. AcknowledgmentThe publication of this study has been supported by the Deanship of Scientific Research and Graduate Studies at Philadelphia University – Jordan.
Automated Filter Pruning Based on High-Dimensional Bayesian Optimization

Filter pruning is necessary to efficiently deploy convolutional neural networks on edge devices that have limited computational resources and power budgets.With conventional filter pruning techniques, the same pruning rate is manually specified for different convolutional layers, which is suboptimal and time-consuming.To extract the features from the coarse level to the fine level, the number of filters in each layer has various distributions.Therefore, it is unsuitable to utilize the same pruning rate for different functional layers.To address this issue, we propose a high-dimensional Bayesian optimization-based filter pruning (HDBOFP) algorithm, which aims to automatically determine the most appropriate pruning rate for each convolutional layer.In addition, the proposed method can automatically identify optimal pruningrate combinations without a time-consuming retraining phase.Compared with conventional filter pruning methods, this automated filter pruning technique exhibits a higher efficiency, which improves accuracy and reduces the required human labor.The effectiveness of our automated filter pruning algorithm is validated through two major computer vision applications, namely image classification and object detection.Specifically, when used in combination with ResNet-110 to classify the CIFAR-10 dataset, HDBOFP reduces the required number of float point operations (FLOPs) by more than 62% without affecting accuracy.Similarly, when HDBOFP is added to the YOLOv5l framework to run detection experiments on the MS-COCO 2017 dataset, FLOPs decrease by more than 43% with only a 1.2% loss in mean average precision, which has advanced the previous studies.
Combined No-Reference Image Quality Metrics for Visual Quality Assessment Optimized for Remote Sensing Images

No-reference image quality assessment is one of the most demanding areas of image analysis for many applications where the results of the analysis should be strongly correlated with the quality of an input image and the corresponding reference image is unavailable. One of the examples might be remote sensing since the transmission of such obtained images often requires the use of lossy compression and they are often distorted, e.g., by the presence of noise and blur. Since the practical usefulness of acquired and/or preprocessed images is directly related to their quality, there is a need for the development of reliable and adequate no-reference metrics that do not need any reference images. As the performance and universality of many existing metrics are quite limited, one of the possible solutions is the design and application of combined metrics. Several possible approaches to their composition have been previously proposed and successfully used for full-reference metrics. In the paper, three possible approaches to the development and optimization of no-reference combined metrics are investigated and verified for the dataset of images containing distortions typical for remote sensing. The proposed approach leads to good results, significantly improving the correlation of the obtained results with subjective quality scores.
Blockchain in IoT and Beyond: Case Studies on Interoperability and Privacy

Since the boom of 2009 sparked by Bitcoin, blockchain has hardly left any field untouched. Blockchain has been countered by the lack of interoperability between different protocols. Privacy and identity management in the distributed ledger technologies have immense potential. These two areas of blockchain have seen steady progress and innovation. In this chapter, we start by first discussing recent works of blockchain and allied technologies in the field of IoT with a focus on how such works can serve as a basis for the next generation of amalgamated solutions. We give a short survey of blockchain in IoT and IIoT followed by proof of concepts of distributed ledger technology used in literature. Then this chapter gives a discussion on distributed identity management with zero knowledge proof followed by self-sovereign identity. Then we move forward with the detailed case study of Hyperledger Indy. Finally, we give case study, interoperability issue, and contemporary survey of Polkadot.
Ten simple rules for finding and selecting R packages

R is an increasingly preferred software environment for data analytics and statistical computing among scientists and practitioners. Packages markedly extend R’s utility and ameliorate inefficient solutions to data science problems. We outline 10 simple rules for finding relevant packages and determining which package is best for your desired use. We begin in Rule 1 with tips on how to consider your purpose, which will guide your search to follow, where, in Rule 2, you’ll learn best practices for finding and collecting options. Rules 3 and 4 will help you navigate packages’ profiles and explore the extent of their online resources, so that you can be confident in the quality of the package you choose and assured that you’ll be able to access support. In Rules 5 and 6, you’ll become familiar with how the R Community evaluates packages and learn how to assess the popularity and utility of packages for yourself. Rules 7 and 8 will teach you how to investigate and track package development processes, so you can further evaluate their merit. We end in Rules 9 and 10 with more hands-on approaches, which involve digging into package code.
Understanding dance semantics using spatio-temporal features coupled GRU networks

The efforts are taken for cultural heritage preservation lead to many digitization initiatives. Indian Classical Dance and its tradition has proven historical importance. Computer-aided archiving and preservation of Indian classical dance resources open enormous opportunities for computational analysis. With the help of recent computational advancements in the field of Computer Vision, these archives can be transformed into intelligent information retrieval systems. In this work, we propose a novel method for understanding the dance semantics by making use of the Spatio-temporal variations of dance features. A video archive is created as a part of this work, from live recordings of different trained dancers and clippings from the internet. The Deep Pose Estimator coupled GRU Model deals with the spatial aspects with a deep learning pose estimator and handles the temporal perspective with GRU Network. The efficiency of the proposed method was compared with benchmark methods such as A 3D-Convolutional Neural Network-based Model, Time Distributed CNN-LSTM Model, and Hybrid Transfer Learning - LSTM Model and the results show the proposed method outperformed others even with different video resolutions.
High Capacity Reversible Data Hiding Scheme based on AMBTC for Encrypted Images

&lt;p&gt;Reversible data hiding for encrypted images is a technique that allows for exact extraction of embedded data and precise reconstruction of the encrypted image to its original version. In this paper, we propose a novel, reversible data hiding technique based on an absolute moment block truncation coding (AMBTC) algorithm for encrypted images. In the proposed scheme, each selected block is represented by an AMBTC code to vacate space to embed data after image encryption. Upon obtaining the encrypted image with the embedded data, the receiver can separately extract the embedded secret data and also recover the cover image losslessly. The experimental results for the proposed scheme achieved excellent performance. The proposed scheme is significantly superior to state-of-the-art schemes in terms of embedding capacity and image quality.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
An efficient cloudlet scheduling via bin packing in cloud computing

&lt;p&gt;In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS).&lt;/p&gt;
Forged Character Detection Datasets: Passports, Driving Licences and Visa Stickers

Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task in digital forensic imaging. Forged characters detection has two big challenges. First challenge is, data for forged characters detection is extremely difficult to get due to several reasons including limited access of data, unlabeled data or work is done on private data. Second challenge is, deep learning (DL) algorithms require labeled data, which poses a further challenge as getting labeled is tedious, time-consuming, expensive and requires domain expertise. To end these issues, in this paper we propose a novel algorithm, which generates the three datasets namely forged characters detection for passport (FCD-P), forged characters detection for driving licence (FCD-D) and forged characters detection for VISA stickers (FCD-V). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm starts by reading plain document images, simulates forging simulation tasks on five different countries' passports, driving licences and VISA stickers. Then it keeps the bounding boxes as a track of the forged characters as a labeling process. Furthermore, considering the real world scenario, we performed the selected data augmentation accordingly. Regarding the stats of datasets, each dataset consists of 15000 images having size of 950 x 550 of each. For further research purpose we release our algorithm code 1 and, datasets i.e. FCD-P 2 , FCD-D 3 and FCD-V 4.
The Radical Unacceptability Hypothesis: Accounting for Unacceptability without Universal Constraints

The Radical Unacceptability Hypothesis (RUH) has been proposed as a way of explaining the unacceptability of extraction from islands and frozen structures. This hypothesis explicitly assumes a distinction between unacceptability due to violations of local well-formedness conditions—conditions on constituency, constituent order, and morphological form—and unacceptability due to extra-grammatical factors. We explore the RUH with respect to classical islands, and extend it to a broader range of phenomena, including freezing, A′ chain interactions, zero-relative clauses, topic islands, weak crossover, extraction from subjects and parasitic gaps, and sensitivity to information structure. The picture that emerges is consistent with the RUH, and suggests more generally that the unacceptability of extraction from otherwise well-formed configurations reflects non-syntactic factors, not principles of grammar.
Physics-aware nonparametric regression models for Earth data analysis

Abstract Process understanding and modeling is at the core of scientific reasoning. Principled parametric and mechanistic modeling dominated science and engineering until the recent emergence of machine learning (ML). Despite great success in many areas, ML algorithms in the Earth and climate sciences, and more broadly in physical sciences, are not explicitly designed to be physically-consistent and may, therefore, violate the most basic laws of physics. In this work, motivated by the field of algorithmic fairness, we reconcile data-driven ML with physics modeling by illustrating a nonparametric and nonlinear physics-aware regression method. By incorporating a dependence-based regularizer, the method leads to models that are consistent with domain knowledge, as reflected by either simulations from physical models or ancillary data. The idea can conversely encourage independence of model predictions with other variables that are known to be uncertain either in their representation or magnitude. The method is computationally efficient and comes with a closed-form analytic solution. Through a consistency-vs-accuracy path diagram, one can assess the consistency between data-driven models and physical models. We demonstrate in three examples on simulations and measurement data in Earth and climate studies that the proposed ML framework allows us to trade-off physical consistency and accuracy.
A Smart-Mutual Decentralized System for Long-Term Care

Existing caretakers of long-term care are assigned constrainedly and randomly to taking care of older people, which could lead to issues of shortage of manpower and poor human quality, especially the proportion of older people increases year after year to let long-term care become more and more important. In addition, due to different backgrounds, inadequate caregivers may cause older people to suffer from spiritual alienation under the current system. Most of the existing studies present a centralized architecture, but even if technology elements are incorporated, such as cloud center services or expert systems, it is still impossible to solve the above-mentioned challenges. This study moves past the centralized architecture and attempts to use the decentralized architecture with Artificial Intelligence and Blockchain technology to refine the model of providing comprehensive care for older people. Using the proposed mapping mutual clustering algorithm in this study, the positions of caregivers and older people can be changed at any time based on the four main background elements: risk level, physiology, medical record, and demography. In addition, this study uses the proposed long-term care decentralized architecture algorithm to solve the stability of care records with transparency to achieve the effect of continuous tracking. Based on previous records, it can also dynamically change the new matching mode. The main contribution of this research is the proposal of an innovative solution to the problem of mental alienation, insufficient manpower, and the privacy issue. In addition, this study evaluates the proposed method through practical experiments. The corporation features have been offered and evaluated with user perceptions by a one-sample t-test; the proposed algorithm to the research model also has been compared with not putting it into the model through ANOVA analysis to get that all hypotheses are supported. The results reveal a high level of accuracy of the proposed mutual algorithm forecasting and positive user perceptions from the post-study questionnaire. As an emerging research topic, this study undoubtedly provides an important research basis for scholars and experts who are interested in continued related research in the future.
Construction of English Translation Model Based on Neural Network Fuzzy Semantic Optimal Control

This work addresses four aspects of the English translation model: consistency, model structure, semantic understanding, and knowledge fusion. To solve the problem of lack of personality consistency in the responses generated by neural networks in English translation models, an English translation model with fuzzy semantic optimal control of neural networks is proposed in this study. The model uses a fuzzy semantic optimal control retrieval mechanism to obtain appropriate information from an externally set English information table; to further improve the effectiveness of the model in retrieving correct information, this work adopts a two-stage training method, using ordinary English translation data for model pretraining and then fine-tuning the model using English translation data with optimal control containing fuzzy semantic information. The model consists of two parts, a sequence generation network that can output the probability distribution of words and an evaluation network that can predict future whole-sentence returns. In particular, the evaluation network can evaluate the impact of currently generated words on whole sentences using deep inheritance features so that the model can consider not only the optimal solution for the current words, as in other generative models, but also the optimal solution for future generated whole sentences. The experimental results show that the English translation model with fuzzy semantic optimal control of the neural network proposed in this study can obtain better semantic feature representation by using a novel bidirectional neural network and a masked language model to train sentence vectors; the combination of semantic features and fuzzy semantic similarity features can obtain higher scoring accuracy and better model generalization. In English translation applications, there are large improvements in scoring accuracy and generality.