Reviewerly/exHarmony huggingface dataset:
Design of double-cross-based smartphone unlock mechanism

Due to the advanced features, smartphones have become an essential and widely adopted electronic device around the world, which can provide various benefits, such as online shopping, e-commerce payment, making friends via social media, email checking and more. Such devices make people’s work and life more convenient and flexible, but how to secure the locally stored phone data is a big problem. With more sensitive or private data stored on these portable devices, there is a significant demand to safeguard smartphones from unauthorized access. Currently, one of the most straightforward and intuitive approaches is to implement an unlock mechanism that asks users to input a correct unlock pattern for authentication, e.g., Android unlock patterns. However, such unlock mechanism can be easily compromised when the unlock pattern is leaked. In this work, we advocate that combining behavioral features with unlock mechanisms is a promising solution, and propose a double-cross-based unlock scheme, called Double-X, which requires users to unlock the device by inputting two cross shapes on the selected dots. For authentication, users have to re-input the selected dots and draw the cross shapes. In the evaluation, we conduct two user studies with 100 participants in total to explore and validate the scheme performance, as compared with two similar unlock schemes. The experimental results indicate that under our scheme, users can reach good success rates with better feedback.
A Multi-Agent-Based VM Migration for Dynamic Load Balancing in Cloud Computing Cloud Environment

Cloud computing is the use of remote servers on the internet to store, manage, and process data. It is a demand-based service where users need to pay only for what they use. Cloud computing users are extensively distributed throughout the globe, so it is a big challenge to keep track of this huge data. Load balancing is the distribution of workloads in a smart way among multiple compute resources, like virtual servers. Compute resources can be added or removed from the load balancer according to the needs of the user. A load balancer is primarily used to optimize the use of resources, costs, and VMs, as well as to maximize throughput, reduce response time, and prevent overloading in various VMs. In this paper, multi-agent-based virtual machine migration has been proposed for dynamic load balancing in a cloud computing environment. The proposed algorithm shows better results in terms of makespan time, average response time, and data center processing time than other conventional cloud load balancing algorithms.
Characterizing and quantifying the incompatibility of quantum instruments

Incompatibility of quantum devices is one of the cornerstones of quantum theory, and the incompatibility of quantum measurements and channels has been linked to quantum advantage in certain information theoretic tasks. In this work, we focus on the less well-explored question of the incompatibility of quantum instruments, that is, devices that describe the measurement process in its entirety, accounting for both the classical measurement outcome and the quantum post-measurement state. In particular, we focus on the recently introduced notion of parallel compatibility of instruments, which has been argued to be a natural notion of instrument compatibility. We introduce -- similarly to the case of measurements and channels -- the incompatibility robustness of quantum instruments and derive universal bounds on it. We then prove that post-processing of quantum instruments is a free operation for parallel compatibility. Last, we provide families of instruments for which our bounds are tight, and families of compatible indecomposable instruments.
On Reservoir Computing Approach for Digital Image Encryption and Forecasting of Hyperchaotic Finance Model

Forecasting the dynamical behaviors of nonlinear systems over long time intervals represents a great challenge for scientists and has become a very active area of research. The employment of the well-known artificial recurrent neural networks (RNNs)-based models requires a high computational cost, and they usually maintain adequate accuracy for complicated dynamics over short intervals only. In this work, an efficient reservoir-computing (RC) approach is presented to predict the time evolution of the complicated dynamics of a fractional order hyperchaotic finance model. Compared with the well-known deep learning techniques, the suggested RC-based forecasting model is faster, more accurate for long-time prediction, and has a smaller execution time. Numerical schemes for fractional order systems are generally time-consuming. The second goal of the present study is to introduce a faster, more efficient, and simpler simulator to the fractional order chaotic/hyperchaotic systems. The RC model is utilized in a proposed RC-based digital image encryption scheme. Security analysis is carried out to verify the performance of the proposed encryption scheme against different types of statistical, KPA, brute-force, CCA, and differential attacks.
NUMERICAL SOLUTION OF WAVELET NEURAL NETWORK LEARNING WEIGHTS USING ACCELERATED PARTICLE SWARM OPTIMIZATION ALGORITHM

Artificial Neural Network (ANN) is a supervised learning nonlinear complex model. This characteristic enables ANN to be used in nonlinear system modeling and classification applications. This research work proposed a technique called Wavelet Neural Network (WNN), and in the hidden layers of WNN, Morlet and Mexican are used as an activation function. During the processing, the WNN gets stuck in the local minimum causing slow convergence. For evaluating such kinds of problems, numerous algorithms have been tried and used. Consequently, this proposed research work used a novel meta-heuristic search technique called Accelerated Particle Swarm Optimization (APSO) algorithm combined with the WNN. Due to the effective convergence and fast searching toward an optimal solution, the APSO algorithm is used. In the proposed APSOWNN algorithm, APSO searches for the best sub-search solution. In conclusion, this model is assessed on the basis of total of three different datasets like the 4-bit OR, 7-bit Parity and IRIS benchmark classification problems, and its efficiency is equated with criterion methods such like Wavelet Back Propagation Neural Network (WBPNN), Artificial Bee Colony Wavelet Neural Network (ABCWNN) and WNN. Finally, from the results of simulation, it has been concluded that the proposed algorithm’s performance is much better, as compared to the state-of-the-art algorithms in terms of mean square error.(MSE) and accuracy.
Demonstration of a Principal Component Analysis Trajectory Method to Assess Bioremediation Progress at a <scp>TCE</scp>‐Impacted Site

Abstract In‐situ bioremediation (ISB) is a popular remediation technology for the treatment of a range of compounds, including chlorinated solvents such as tetrachloroethene and trichloroethene (TCE). Large amounts of data are collected before, during, and after ISB applications to determine amendment approaches, monitor progress and evaluate success. The interpretation of these large datasets can be limited by the tools and techniques used for data analysis, and there is considerable potential in applying data reduction and multivariate techniques used elsewhere to performance monitoring during ISB. In this study, a principal component analysis (PCA) trajectory method was applied to a TCE‐impacted ISB site dataset, as an alternative to the inspection of time series data. The method connected each monitoring well's scores through PCA space to account for temporal changes in multiple analytes across the site. The method was used to separate monitoring well locations into categories that included On‐track and Unsuccessful based on their similarity to background wells in PCA space. The results agreed with those generated using traditional methods (e.g., time series plots) and were able to efficiently summarize large amounts of data to facilitate interpretation. It is expected that this PCA trajectory method could provide a useful screening tool to quickly identify site‐wide trends for the evaluation of ISB performance.
Recommendation Method Based on Heterogeneous Information Network and Multiple Trust Relationship

A recommendation method based on heterogeneous information networks and multiple trust relationships is proposed. Firstly, the node sequence in the heterogeneous information network is obtained through the random walk of the meta-path, and the representation vector of each node in different paths is generated. The user similarity based on the meta-path is obtained by calculating the spatial distance between the user node vectors. Then, according to the different relationships among users, different trust-relationship calculation methods are proposed, and the user similarity based on the user’s multiple trust relationship is obtained by fusing multiple trust relationships. Finally, the candidate list of Microblog text is obtained by fusing the two user similarities to achieve a personalized recommendation of Microblog text. The experimental results show that the method proposed in this study is superior to other comparison algorithms in precision, recall, F1 value and NDCG value, which shows that this method is feasible when recommending Microblog text.
A Survey on Ambient Sensor-Based Abnormal Behaviour Detection for Elderly People in Healthcare

With advances in machine learning and ambient sensors as well as the emergence of ambient assisted living (AAL), modeling humans’ abnormal behaviour patterns has become an important assistive technology for the rising elderly population in recent decades. Abnormal behaviour observed from daily activities can be an indicator of the consequences of a disease that the resident might suffer from or of the occurrence of a hazardous incident. Therefore, tracking daily life activities and detecting abnormal behaviour are significant in managing health conditions in a smart environment. This paper provides a comprehensive and in-depth review, focusing on the techniques that profile activities of daily living (ADL) and detect abnormal behaviour for healthcare. In particular, we discuss the definitions and examples of abnormal behaviour/activity in the healthcare of elderly people. We also describe the public ground-truth datasets along with approaches applied to produce synthetic data when no real-world data are available. We identify and describe the key facets of abnormal behaviour detection in a smart environment, with a particular focus on the ambient sensor types, datasets, data representations, conventional and deep learning-based abnormal behaviour detection methods. Finally, the survey discusses the challenges and open questions, which would be beneficial for researchers in the field to address.
Encryption Scheme of Verifiable Search Based on Blockchain in Cloud Environment

While transferring data to cloud servers frees users from having to manage it, it eventually raises new problems, such as data privacy. The concept of searchable encryption has drawn more and more focus in research as a means of resolving the tension between data accessibility and data privacy. Due to the lack of integrity and correctness authentication in most searchable encryption techniques, malicious cloud servers may deliver false search results to users. Based on public key encryption with searching (PEKS), the study suggests a privacy-preserving method for verifiable fuzzy keyword searches based on the Ethernet blockchain in a cloud context to overcome the aforementioned security concerns. The search user can check the accuracy and integrity of the query document using the unalterability characteristics of the Ethernet blockchain system in this scheme to prevent the cloud server from giving incorrect query results. Furthermore, a fair transaction between the cloud server and the data user is achieved and can be tracked back to the malicious user using hash functions and Ethereum smart contracts, even if the user or the cloud is malicious. Finally, the security analysis shows that, under the random oracle model, our technique fulfils the adaptive selection keyword’s semantic security. The performance assessment demonstrates that the proposed scheme outperforms other related schemes in terms of computational efficiency.
The Method of Activating Modern Career Guidance Approaches with the Theses of the Theology of Personality

The subject of the work is the theses of the theology of personality. The aim of the study is to develop practical methods of pedagogy of professional counseling based on the provisions of the theology of personality. The work presents a number of theses of the theology of personality, synthesized on the basis of the works of the theologian and historian Vladimir Nikolaevich Lossky; describes principles of career guidance approaches; theoretical positions are combined into practical conclusions; a training case template and a template for working with it are compiled. The main provisions of the developed method are as follows: there cannot be one exact way to determine the interests of a person; the circle of specialties should not be limited by social factors; vocational guidance should be carried out in accordance with the ultimate goal of human activity — the perception of perfection; care should be taken to interpret the results of diagnostic tests/tasks that are not able to take into account all aspects of the uniqueness of the individual; professional guidance consultations should be carried out with a deep immersion in the understanding of the individual; through development in an area unfamiliar to a person, he can learn a new competence and show interest in it; through the self-giving of the individual in complete immersion in a cause that brings good to people, one can see the perfection of the individual. The method can be used in teaching various disciplines at different levels of education.
Adoption of service robots: exploring the emerging trends through the lens of bibliometric analysis

Purpose The aim of this study is to examine the existing literature on service robots in order to identify prominent themes, assess the present state of service robotics research and highlight the contributions of seminal publications in the business, management and hospitality domain. Design/methodology/approach This study analysed 332 Scopus papers from 1985 to 2022 using bibliometric techniques like citation and co-citation analysis. Findings The study findings highlighted that there has been a consistent rise in publications related to service robots. The paper identifies three significant themes in the service robot literature: adoption of service robots in the context of customer service, anthropomorphism and integration of artificial intelligence in robotic service. Furthermore, this study highlights prominent authors, journals, institutions and countries associated with research on service robots and discusses the future research opportunities in this domain. Originality/value This study contributes to the service robots’ literature in the hospitality context by compilation of various reference materials using a comprehensive bibliometric analysis. Previous studies do not point out crucial themes in this area, nor do they provide an overview of prominent journals, institutions, authors and trends in this field. Therefore, this study attempts to fill the lacunae.
A Joint Domain-Specific Pre-Training Method Based on Data Enhancement

State-of-the-art performances for natural language processing tasks are achieved by supervised learning, specifically, by fine-tuning pre-trained language models such as BERT (Bidirectional Encoder Representation from Transformers). With increasingly accurate models, the size of the fine-tuned pre-training corpus is becoming larger and larger. However, very few studies have explored the selection of pre-training corpus. Therefore, this paper proposes a data enhancement-based domain pre-training method. At first, a pre-training task and a downstream fine-tuning task are jointly trained to alleviate the catastrophic forgetting problem generated by existing classical pre-training methods. Then, based on the hard-to-classify texts identified from downstream tasks’ feedback, the pre-training corpus can be reconstructed by selecting the similar texts from it. The learning of the reconstructed pre-training corpus can deepen the model’s understanding of undeterminable text expressions, thus enhancing the model’s feature extraction ability for domain texts. Without any pre-processing of the pre-training corpus, the experiments are conducted for two tasks, named entity recognition (NER) and text classification (CLS). The results show that learning the domain corpus selected by the proposed method can supplement the model’s understanding of domain-specific information and improve the performance of the basic pre-training model to achieve the best results compared with other benchmark methods.
A Method of Sustainable Development for Three Chinese Short-Text Datasets Based on BERT-CAM

Considering the low accuracy of current short text classification (TC) methods and the difficulties they have with effective emotion prediction, a sustainable short TC (S-TC) method using deep learning (DL) in big data environments is proposed. First, the text is vectorized by introducing a BERT pre-training model. When processing language tasks, the TC accuracy is improved by removing a word from the text and using the information from previous words and the next words to predict. Then, a convolutional attention mechanism (CAM) model is proposed using a convolutional neural network (CNN) to capture feature interactions in the time dimension and using multiple convolutional kernels to obtain more comprehensive feature information. CAM can improve TC accuracy. Finally, by optimizing and merging bidirectional encoder representation from the transformers (BERT) pre-training model and CAM model, a corresponding BERT-CAM classification model for S-TC is proposed. Through simulation experiments, the proposed S-TC method and the other three methods are compared and analyzed using three datasets. The results show that the accuracy, precision, recall, F1 value, Ma_F and Mi_F are the largest, reaching 94.28%, 86.36%, 84.95%, 85.96%, 86.34% and 86.56, respectively. The algorithm’s performance is better than that of the other three comparison algorithms.
Supervised Deep Learning Techniques for Image Description: A Systematic Review

Automatic image description, also known as image captioning, aims to describe the elements included in an image and their relationships. This task involves two research fields: computer vision and natural language processing; thus, it has received much attention in computer science. In this review paper, we follow the Kitchenham review methodology to present the most relevant approaches to image description methodologies based on deep learning. We focused on works using convolutional neural networks (CNN) to extract the characteristics of images and recurrent neural networks (RNN) for automatic sentence generation. As a result, 53 research articles using the encoder-decoder approach were selected, focusing only on supervised learning. The main contributions of this systematic review are: (i) to describe the most relevant image description papers implementing an encoder-decoder approach from 2014 to 2022 and (ii) to determine the main architectures, datasets, and metrics that have been applied to image description.
3D-DIoU: 3D Distance Intersection over Union for Multi-Object Tracking in Point Cloud

Multi-object tracking (MOT) is a prominent and important study in point cloud processing and computer vision. The main objective of MOT is to predict full tracklets of several objects in point cloud. Occlusion and similar objects are two common problems that reduce the algorithm’s performance throughout the tracking phase. The tracking performance of current MOT techniques, which adopt the ‘tracking-by-detection’ paradigm, is degrading, as evidenced by increasing numbers of identification (ID) switch and tracking drifts because it is difficult to perfectly predict the location of objects in complex scenes that are unable to track. Since the occluded object may have been visible in former frames, we manipulated the speed and location position of the object in the previous frames in order to guess where the occluded object might have been. In this paper, we employed a unique intersection over union (IoU) method in three-dimension (3D) planes, namely a distance IoU non-maximum suppression (DIoU-NMS) to accurately detect objects, and consequently we use 3D-DIoU for an object association process in order to increase tracking robustness and speed. By using a hybrid 3D DIoU-NMS and 3D-DIoU method, the tracking speed improved significantly. Experimental findings on the Waymo Open Dataset and nuScenes dataset, demonstrate that our multistage data association and tracking technique has clear benefits over previously developed algorithms in terms of tracking accuracy. In comparison with other 3D MOT tracking methods, our proposed approach demonstrates significant enhancement in tracking performances.
Design of a Telepresence Robot to Avoid Obstacles in IoT-Enabled Sustainable Healthcare Systems

In the Internet of Things (IoT) era, telepresence robots (TRs) are increasingly a part of healthcare, academia, and industry due to their enormous benefits. IoT provides a sensor-based environment in which robots receive more precise information about their surroundings. The researchers work day and night to reduce cost, duration, and complexity in all application areas. It provides tremendous benefits, such as sustainability, welfare improvement, cost-effectiveness, user-friendliness, and adaptability. However, it faces many challenges in making critical decisions during motion, which requires a long training period and intelligent motion planning. These include obstacle avoidance during movement, intelligent control in hazardous situations, and ensuring the right measurements. Following up on these issues requires a sophisticated control design and a secure communication link. This paper proposes a control design to normalize the integration process and offer an auto-MERLIN robot with cognitive and sustainable architecture. A control design is proposed through system identification and modeling of the robot. The robot control design was evaluated, and a prototype was prepared for testing in a hazardous environment. The robot was tested by considering various parameters: driving straight ahead, turning right, self-localizing, and receiving commands from a remote location. The maneuverability, controllability, and stability results show that the proposed design is well-developed and cost-efficient, with a fast response time. The experimental results show that the proposed method significantly minimizes the obstacle collisions. The results confirm the employability and sustainability of the proposed design and demonstrate auto-MERLIN’s capabilities as a sustainable robot ready to be deployed in highly interactive scenarios.