Reviewerly/exHarmony huggingface dataset:
Efficient Privacy-Preserving Geographic Keyword Boolean Range Query Over Encrypted Spatial Data

With the widespread popularity of mobile devices and geolocation-related services, spatial keyword data has exploded in recent years. As an application, people are accustomed to using specific keywords to search for data in a given geometric range. To protect user privacy, searchable encryption technologies are used to encrypt data and user queries. Most existing works focus on either spatial attributes or keyword attributes over encrypted spatial keyword data, which cannot solve the problem of geographic keyword range queries directly. And several other works considering these two attributes have some limitations in terms of query efficiency and security assurance. In this article, we propose an efficient privacy-preserving geographic keyword Boolean range query (EPBRQ) scheme to solve existing challenges in the current work. In particular, we design a recoding algorithm to break the limits of the current work to achieve lower time complexity and employ secure Knn computation to protect user data privacy comprehensively. The security analysis shows that our solution can well protect the privacy of data and queries from cloud server threats. And numerous experiments based on real-world data also show that our scheme provides better query efficiency than existing works.
Few-Shot Learning for Radar Signal Recognition Based on Tensor Imprint and Re-Parameterization Multi-Channel Multi-Branch Model

In an ever-increasingly complex electromagnetic environment, automatic radar signal recognition is becoming vital. Convolutional neural networks have been widely used for radar signal recognition, but deep learning-based algorithms only recognize trained classes. Recognizing novel radar signals with few-shot samples in an open environment is still a challenging research problem. In this letter, a few-shot learning algorithm based on the tensor imprint algorithm and convolutional classification layer is proposed for radar signal recognition, and the proposed convolutional classification layer can avoid spatial information loss caused by the global pooling layer and the fully connected layer. In addition, the lightweight re-parameterization multi-channel multi-branch convolutional neural network (RepMCMBNet) is proposed for feature extraction. The model is trained on a dataset containing 8 types of radar signals, and achieves high recognition accuracy in a test dataset containing 12 types of radar signals. The overall recognition accuracy of the proposed tensor imprint algorithm achieves 93.9&#x0025; at &#x2212;6 dB when the number of samples is 5.
Virtual Keyboards With Real-Time and Robust Deep Learning-Based Gesture Recognition

In head-mounted display devices for augmented reality and virtual reality, external signals are often entered using a virtual keyboard (VKB). Among various user interfaces for VKBs, hand gestures are widely used because they are fast and intuitive. This work proposes a gesture-recognition (GR)-based VKB algorithm that is accurate in any environment and operates in real time. Specifically, the proposed ambidextrous VKB layouts reduce the total finger travel distance on one-hand VKB layouts. Additionally, a fast typing action is proposed to use characteristics when previous and current keys are adjacent. To be robust in any environment, we utilize a deep learning (DL)-based GR method in the proposed VKB algorithm. To train DL networks, seven classes are defined and an automated dataset generation method is proposed to reduce the necessary time and effort. The proposed one-hand VKB layout with the fast typing action shows a 1.5× faster typing speed than the popular ABC keyboard layout. Furthermore, the proposed ambidextrous VKB layout brings an additional 52% improvement compared with the proposed one-hand VKB layout. The proposed DL-based GR method implemented on the well-known YOLOv3 machine learning framework shows a mean average precision rate of 95% for images including background colors similar to skin color. The proposed DL-based GR method for one-hand and ambidextrous VKBs achieves around 41 frames per second on a software platform, which allows real-time processing.
Research on imbalance machine learning methods for MR$$T_1$$WI soft tissue sarcoma data

Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma are imbalanced. In this paper, we propose an effective solution to find the optimal imbalance machine learning model for predicting the classification of soft tissue sarcoma data.In this paper, a large number of features are first obtained based on [Formula: see text]WI images using the radiomics methods.Then, we explore the methods of feature selection, sampling and classification, get 17 imbalance machine learning models based on the above features and performed extensive experiments to classify imbalanced soft tissue sarcoma data. Meanwhile, we used another dataset splitting method as well, which could improve the classification performance and verify the validity of the models.The experimental results show that the combination of extremely randomized trees (ERT) classification algorithm using SMOTETomek and the recursive feature elimination technique (RFE) performs best compared to other methods. The accuracy of RFE+STT+ERT is 81.57% , which is close to the accuracy of biopsy, and the accuracy is 95.69% when using another dataset splitting method.Preoperative predicting pathological grade of soft tissue sarcoma in an accurate and noninvasive manner is essential. Our proposed machine learning method (RFE+STT+ERT) can make a positive contribution to solving the imbalanced data classification problem, which can favorably support the development of personalized treatment plans for soft tissue sarcoma patients.
Enhanced Signal Area Estimation Based on Edge Detection and Flood Fill

Spectrograms are a common form of time-frequency representation of wireless communication signals.In many practical scenarios spectrograms need to be processed to identify accurately the time-frequency region occupied by each individual radio transmission, which in this work is referred to as Signal Area (SA).Several methods have been proposed in the literature for Signal Area Estimation (SAE), however their performance degrades significantly in the low SNR regime.In this context, this work proposes a novel approach for SAE based on the use of two well-known techniques from the field of image processing, namely edge detection and flood fill.Edge detection is first employed to identify the edges of potential SAs and flood fill is then used to fill the area inside the estimated edges in order to produce a more accurate estimation of the SAs present in a spectrogram.The performance of three popular edge detection methods (gradient magnitude, Laplacian of Gaussian and Canny) is assessed both with simulations and experimental data.The obtained results show that the proposed strategy can improve significantly the performance of existing SAE methods in the low SNR regime (with estimation accuracy improvements up to 38-45% within the SNR interval from -20 dB to -10 dB) when used as a pre/post-processing stage, thus improving their sensitivity and effectively extending their overall SNR range of operation.
A person-following method based on monocular camera for quadruped robots

This paper proposes a person-following method based on monocular vision, which allows quadruped robots to track a target person in both indoor and outdoor environments with different illumination conditions. Our method is composed of a person detector, a Kalman filter (KF) tracker, and a re-identification module. To be more specific, the person detector uses a human pose estimation method to detect persons. The KF is applied to predict the position of the target person and update its state with detection results. A re-identification module is proposed to deal with distractions, where the Convolutional Channel Features (CCF) is used to extract appearance features and Online Boosting is used to distinguish the target person from others. Especially, we design a target recapture mechanism based on the Recurrent Neural Network (RNN). Combining motion information with appearance features, the system can accurately re-identify the target person. Without extra customized markers, our method can track the target person steadily in real-time only using a monocular camera. Experiments results can validate the robustness and effectiveness of the proposed method.
The COVID-19 Pandemic: Web 2.0 Tools as an Alternative Instruction for Science in Secondary Schools

COVID-19 pandemic had caused an unprecedented impact on teachers and students in their teaching and learning process. Diverse teaching and learning tools are essential to ensure that education continues. This confinement provides an opportunity to enculture a new norm for learning among Malaysian students and teachers. Usage of web 2.0 tools has indirectly encouraged students to study on their own and communicate dynamically with peers and teachers during online lessons. A mixed-method quasi-experimental study involving pre-test and post-test was conducted to investigate the impact of using web 2.0 tools on students’ achievement. 60 Form Two students were divided into control and experimental groups where the experimental group had lessons using The Product, Process, Person, and Press model of creative approach with web 2.0 tools while the control group was taught using the teacher-centered instruction. The creative approach using web 2.0 tools has not only produced advancement in students’ achievement in the science subject but has also motivated them to complete the assessments given during online lessons. The findings also emanated that students’ motivation to learn science through online learning was due to the usage of 2.0 tools. Hence, science teachers ought to be better prepared with the skills as well as better-designed learning modules and experiences to incorporate integrated online learning and face-to-face class with a blended learning approach seamlessly.
Trine: Cloud-Edge-Device Cooperated Real-Time Video Analysis for Household Applications

Real-time mobile video analysis like object detection and tracking is key to various household applications such as AR, cognitive assistance and smart home. Such applications rely on heavy DNN models, which are not suitable for mobile devices due to resource limitation. The long latency of cloud offloading is unacceptable for the real-time requirements, and the direct edge offloading relies on powerful edge servers, which is impractical for household scenarios. To solve this challenge, we take advantage of the computing devices that are low-cost or already exist in our lives, and propose Trine, a cloud-edge-device cooperated framework, in which complicated computation tasks are offloaded from the device to the cloud with the edge as the key bond to coordinate. In addition, due to the heterogeneity of edge devices, which leads to no one-fits-all algorithm that is optimal in all situations, we propose a profile-based algorithm to customize trackers for various edge devices. We implemented Trine on an android phone and three edge devices. The experiments demonstrate that Trine achieves 8-36% higher real-time accuracy and 25-89% higher robustness than state-of-the-art.
Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

In today's digital era, the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine (SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology, edge detection, histograms of oriented gradients (HOG), and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position, phrase position, numerical data, inverted comma, sentence length, and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system's performance.The experiment included news articles from various genres, such as Bollywood, politics, and sports.The proposed method's accuracy for Hindi character recognition is 96.97%, which is good compared with baseline approaches, and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72% at a compression ratio of 50% and a precision of 60% at a compression ratio of 25%, in comparison to state-of-the-art methods, this is a decent result.
Re-Identification in Differentially Private Incomplete Datasets

Efforts to counter COVID-19 reaffirmed the importance of rich medical, behavioral, and sociological data. To make data available to many researchers who can conduct statistical analyses and machine learning, personally identifiable information must be excluded to protect individual privacy. It is essential to remove explicit identifiers, sample population data, and apply differential privacy, the de facto standard privacy metric. Despite the general belief that the risk of re-identification is insignificant when these techniques are applied, this study shows that even after applying these techniques, the risk of being re-identified is highly significant for some data. This study proposes in detail an algorithm for estimating the number of people in a population who have certain attribute values based on incomplete, differentially private databases. If the estimated number is one, the probability that only one person with that attribute value is present in the population is high, which means that there is a high probability of re-identification. Therefore, this study concludes that the re-identification risk must be evaluated even after applying state-of-the-art techniques to protect privacy.
Kinematics and trajectory planning analysis based on hybrid optimization algorithms for an industrial robotic manipulators

In industrial applications and automation, the robotic manipulators exhibit a significant role. Several complex robotic systems performed a number of industrial works named spray painting, welding, assembly, pick and place action, etc. The end-effector’s position and the joint angles play a vital role since any task is activated inside the pre-defined robotic manipulator’s work space. Also, the problem of trajectory planning is a very challenging task in the robotic fields. To solve these problems, this paper proposes a kinematics analysis and trajectory planning of an Industrial robotic manipulators (IRMs) based on the hybrid optimization algorithms. Here, three IRMs such as PUMA 560 (6 DOF), KUKA LBR iiwa 14 R820 (7DOF) and ABB IRB 140 (6DOF) are considered. For each robot, the forward and the inverse kinematics (IK) are analysed and also the trajectory planning of each robot is discussed using the hybrid optimization algorithms. In this work, 18 optimization algorithms such as PSO (particle swarm optimization), SSO (social spider optimization), DFO (dragonfly optimization), BOA (butterfly optimization), CSA (crow search algorithm), BSA (bird swarm algorithm), SHO (selfish herd optimization), KHO (krill herd optimization), ALO (antlion optimization), ACO (ant colony optimization), GWO (Grey wolf optimization), GOA (grasshopper optimization), SBO (satin bowerbird optimizer), WCO (world cup optimization), COA (cuckoo optimization algorithm), CFA (cuttlefish algorithm), SOA (seagull optimization algorithm), and TSA (tunicate swarm algorithm) are utilized for both forward and inverse kinematic analysis and the trajectory planning problem. For the PUMA 560, KUKA LBR iiwa 14 R820, and ABB IRB 140 IRMs, forward kinematics (FK) are solved by the hybrid combination of PSO-SSO, SHO-KHO, and SBO-WCO individually. Also, the IK are solved by the DFO-BOA, ALO-ACO and COA-CFA for each IRMs. The trajectory planning problem is solved by the CSA-BSA, GWO-GOA and SOA-TSA for each robot individually. These optimization techniques give number of solution for kinematics and trajectory problem but it converses the best solution for the minimum multi-objective function value. Each robot obtained the minimum travelling time for without and with an obstacle which is 0.0118 and 0.0313 s for PUMA and 0.0117 and 0.0310 s for KUKA, and 0.0114 and 0.0120 s for ABB IRB 140 robot. The advantages of these hybrid algorithm are shorter computation time, and fewer iterations. The kinematics and trajectory analysis of each IRM is simulated using robotic tool box in MATLAB with GUI interface. For each IRM, optimized position values of the end effector, joint angles and the best optimal path are computed with minimum objective function. Finally, the performance of each robot is compared to the existing robotic works.
Estimating clear-sky PV electricity production without exogenous data

The global shift towards the utilization of renewable energy sources has driven the development of photovoltaics (PVs) and the need for their mass integration in energy markets. Although the penetration of PVs is dramatically increasing during the last decades, the ‘problem’ of PV power output fluctuations due to uncertain environmental parameters still remains an issue. A key challenge is to provide accurate predictions of PV power output since this will assist grid operators for efficient capacity management and scheduling. In this paper, a data-driven method was developed, using time-series of PV power output from a PV station in Limassol, Cyprus to estimate the clear-sky PV electricity production without any exogenous data. The time-series was divided into monthly intervals and analysed by using the mean value, the maximum value and the standard deviation. The predicted clear-sky PV signals calculated were compared to the best visibly smooth signal in terms of Root Mean Square Error, Mean Absolute Deviation, and Mean Absolute Percent Error. Results indicate that the proposed method can provide a good approximation of the true clear-sky signal and thus it can be argued that PV data alone can be used for clear-sky PV output calculation without any information from the manufacturer or location specific data.
General Framework to Reversible Data Hiding for JPEG Images With Multiple Two-Dimensional Histograms

In this paper, a general reversible data hiding (RDH) framework for joint photographic experts group (JPEG) images with multiple two dimensional histograms (2DHs) is proposed. Regardless of whether zero alternating current (AC) coefficients are included to join data embedding or only non-zero AC coefficients are applied, the performance in terms of visual quality and file size increment is improved by using the proposed framework. This framework is mainly composed of the following three parts: histogram generation, adaptive 2DH mapping selection, and improved discrete particle swarm optimization (IDPSO). Unlike existing 2DH-based JPEG RDH methods, in which a uniform threshold is utilized to construct multiple histograms, in histogram generation, thresholds for different histograms are adaptively assigned according to the local properties of histogram coefficients. As a result, as many coefficients in complex regions as possible are excluded from the construction of each histogram. We subtly design multiple 2DH mappings, and adaptively select 2DH mappings for different 2DHs based on their distribution characteristics. Through slight adjustments, each 2DH mapping can be employed in cases where either zero AC coefficients or only non-zero AC coefficients are used for data embedding. Adaptive threshold and 2DH mapping selection provide a better image quality at a given embedding capacity but inevitably cause considerable complexity cost. To significantly reduce the computational cost, we propose IDPSO by combining differential evolution. IDPSO has the advantages of rapid convergence speed as well as satisfactory qualities of the best solutions. With the help of differential evolution, IDPSO expands the diversity of particles and efficiently avoids local optimal trapping problems. The experimental results also demonstrate the effectiveness of the proposed method in terms of visual quality, file size increment and complexity cost.
<i>SUBPLEX</i>: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level

Understanding the interpretation of machine learning (ML) models has been of paramount importance when making decisions with societal impacts, such as transport control, financial activities, and medical diagnosis. While local explanation techniques are popular methods to interpret ML models on a single instance, they do not scale to the understanding of a model's behavior on the whole dataset. In this article, we outline the challenges and needs of visually analyzing local explanations and propose SUBPLEX, a visual analytics approach to help users understand local explanations with subpopulation visual analysis. SUBPLEX provides steerable clustering and projection visualization techniques that allow users to derive interpretable subpopulations of local explanations with users' expertise. We evaluate our approach through two use cases and experts' feedback.
An efficient chaos-based image encryption algorithm using real-time object detection for smart city applications

Currently, an increasing number of digital images are being generated by and for the general public and professionals. Simultaneously, the protection of these images is crucial for various emerging applications. Encryption is a popular technique for protecting image data confidentiality and privacy. There are multiple existing studies on image encryption, however, most of them are computationally overhead and do not achieve high security. Considering that time cost and security have become the most important factors, designing an efficient encryption algorithm for digital images are necessary. In this paper, a secure chaos-based image encryption algorithm for smart city applications is proposed. First, the You Only Look Once v3 object detection algorithm is used to quickly identify the objects of an image. Second, initialisation and generation of a key is performed with confusion and diffusion operations to get an encrypted object image. This algorithm for encryption has the advantage of strong security performance combined with speed, which is not available in the general encryption system. Experimental results show that this proposed algorithm outperforms other state-of-the-art schemes. • A secure chaos-based image encryption algorithm for smart city applications is proposed. • A neural network based object detection method is used to identify the significant object(s) of an image before encryption. • Proposed algorithm is secure and efficient than similar techniques.