Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks

Crowdsensing platforms collect, process, transmit, and analyze spectrum data worldwide to optimize radio frequency spectrum usage. However, Internet of Things (IoT) spectrum sensors, performing some of the previous tasks, are exposed to software manipulation aiming to execute spectrum sensing data falsification (SSDF) attacks to compromise data integrity and spectrum optimization. Novel intrusion detection systems (IDSs) combining device fingerprinting with machine and deep learning (ML/DL) improve the limitation of traditional solutions and remove the necessity of redundant sensors and reputation mechanisms. However, they fail when detecting SSDF attacks accurately while protecting sensors privacy. This work proposes a novel host-based and federated learning-oriented IDS for IoT spectrum sensors that consider unsupervised ML/DL and fingerprints based on system calls. The framework detection performance and consumption of resources are analyzed in local and federated scenarios with six spectrum sensors deployed on Raspberry Pis. The obtained results significantly improve related work when detecting SSDF attacks while protecting sensors privacy, and consuming CPU, memory, and storage of sensors in a reduced manner.
Multi-layer Integrated Extreme Learning Machine for Mechanical Fault Diagnosis of High-Voltage Circuit Breaker

Mechanical fault caused by abnormal joint clearance jeopardizes the safety of high-voltage circuit breakers (HVCB). Accurate fault diagnosis is of great significance to ensure the reliable service. Existing prediction approaches are generally based on individual classifiers, which may fail to achieve satisfactory performance, especially in multi-class fault diagnosis. In this paper, a multi-layer integrated extreme learning machine (IELM) is proposed. At first, clearance joint most likely to induce mechanical fault is found by finite element analysis. Then experiments under various working conditions are carried out, and the collected vibration is decomposed by variational mode decomposition. In feature extraction, the energy distribution in the different frequency domains is considered as the model input. In IELM, each ELM module in different layers is trained independently, and its optimal hidden layer number is adaptively searched. Then it is employed as a sub-classifier for the final diagnosis model. The fault diagnosis process of IELM is similar to a multi-stage filter since the data diagnosed by the previous layer is dynamically removed. It is demonstrated that the proposed method not only achieves the highest fault diagnosis accuracy than its counterparts but also maintains a certain recognition accuracy of unknown mechanical fault.
Boosting Supervised Dehazing Methods via Bi-level Patch Reweighting

Natural images can suffer from non-uniform haze distributions in different regions. However, this important fact is hardly considered in existing supervised dehazing methods, in which all training patches are accounted for equally in the loss design. These supervised methods may fail in making promising recoveries on some regions contaminated by heavy hazes. Therefore, for a more reasonable dehazing losses design, the varying importance of different training patches should be taken into account. Such rationale is exactly in line with the process of human learning that difficult concepts always require more practice in learning. To this end, we propose a bi-level dehazing (BILD) framework by designing an internal loop for weighted supervised dehazing and an external loop for training patch reweighting. With simple derivations, we show the gradients of BILD exhibit natural connections with policy gradient and can thus explain the BILD objective by the rewarding mechanism in reinforcement learning. The BILD is not a new dehazing method per se, it is better recognized as a flexible framework that can seamlessly work with general supervised dehazing approaches for their performance boosting.
Privacy-preserving collaborative social network data publishing against colluding data providers

Preserving individuals' privacy is a vital issue in a collaborative social network data publishing setup. Existing data publishing approaches for collaborative social network data prevent identity disclosure and attribute disclosure performed by external data recipients only. These approaches are vulnerable to insider attack - an attack performed by colluding data providers. In an insider attack, attackers' malicious intent is to compromise the privacy of those individuals whose data records are contributed by other data providers. Additionally, existing approaches utilise generalisation and/or suppression operations to preserve privacy and hence, do not retain the data originality that eventually results in compromise of the data utility. In this paper, we present an approach that overcomes these shortcomings aiming to prevent identity disclosure and attribute disclosure of collaborative social network data against insider attack and to provide higher utility. Experimental outcomes affirm the security of the approach with adequate data utility.
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing

In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.
iCycleGAN: An Improved CycleGAN for Rain Streak Removal From Single Image

On the one hand, although the supervised learning methods have been used for image rain removal task, such methods have obvious limitations because maybe there is no or only few paired images with-without rain. On the other hand, problems such as color distortion and the inpainting of background information is not clear enough also limit the processing effect of unsupervised methods for image rain removal. An improved CycleGAN (iCycleGAN) was proposed to remove rain streak from a single image. First of all, CycleGAN's transfer learning ability and cyclic structure can solve the problem of the lack of paired data sets. Secondly, a densely connected convolutional network (DenseNet) was added to the generator backbone network to improve the protection of high-frequency information such as background textures, and a CBAM attention mechanism was added to the generator to focus on the repaired area near the rain streak and obtain a clearer repaired image. Finally, feature perceptual loss was introduced to strengthen the constraint of image feature restoration and obtain more realistic results. In order to verify the effectiveness of the proposed method, training was conducted on Rain100L and Rian800 data sets. The comparison of experimental results shows that the model is superior to the existing unsupervised methods in the overall repair effect, and also has comparable inpainting effect compared with mainstream supervised methods.
Combination of Cross Stage Partial Network and GhostNet with Spatial Pyramid Pooling on Yolov4 for Detection of Acute Lymphoblastic Leukemia Subtypes in Multi-Cell Blood Microscopic Image

Purpose: Acute Lymphoblastic Leukemia (ALL) Detection with microscopic blood images can use a deep learning-based object detection model to localize and classify ALL cell subtypes. Previous studies only performed single cell-based detection objects or binary classification with leukemia and normal classes. Detection of ALL subtypes is crucial to support early diagnosis and treatment. Therefore, an object detection model is needed to detect ALL subtypes in multi-cell blood microscopic images.Methods: This study focuses on detecting the ALL subtype using YOLOV4 with a modified neck using Cross Stage Partial Network (CSPNet) and GhostNet. CSPNet is combined with Spatial Pyramid Pooling (SPP) to become SPPCSP to get various features map before the YOLOv4 final layer. Ghostnet was used to reduce the computation time of the modified YOLOV4 neck.Result: Experimental results show that YOLOv4 SPPCSP outperformed the recall value of 14.6%, the value of mAP@.5 0.8%, and reduced the computation time by 4.7 ms compared to the original YOLOv4.Novelty: The combination of CSPNet and GhostNet for YOLOV4 neck modification can increase the variety of features map and reduce computing time compared to the Original YOLOv4.
Few-Shot SAR Ship Image Detection Using Two-Stage Cross-Domain Transfer Learning

Synthetic Aperture Radar is superior to optical sensors in that it can identify ships at all hours and on all days. Deep learning-based object detection relies on huge amounts of data, yet SAR ship images are challenging to obtain and label. A few-shot cross-domain transfer learning approach for SAR image ship detection is used in this paper. It is divided into two stages: the first uses a large volume of optical remote sensing ship images as the source domain training detection framework, and the second employs SAR ship images and optical remote sensing ship images to create a few-shot balanced subset fine-tuning detection framework. Use a metric learning-based prediction box classifier instead of a fully connected prediction box classifier. When fine-tuning the whole detection frame using the metric learning-based pre-diction frame classifier, the experiments show that an AP50 of 55.99% can be reached with only 10 SAR ship images.
Ensemble Multifactorial Evolution With Biased Skill-Factor Inheritance for Many-Task Optimization

Current years have witnessed an increment in the number of research activities on improving the efficacy of multitasking algorithms for tackling challenging optimization problems. However, current approaches often present two potential problems. Firstly, although tasks may have different characteristics, existing literature usually utilizes only one search operator for all of them. Secondly, while multitasking environments comprise tasks of varying difficulty, previous proposals treat them equally. This paper proposes an algorithm named Ensemble Multifactorial Evolution with Biased Skill Factor Inheritance (EME-BI) for optimizing a large number of tasks simultaneously. In EME-BI, an effective parameter adaptation based on the knowledge transfer quality with Biased Skill-Factor Inheritance mechanism is designed to minimize negative transfer and allocate generated offspring to tasks that need resources. Besides, instead of using only one fixed search operator, EME-BI can automatically select the most appropriate one for each task at each evolutionary stage. Finally, the proposed algorithm is armed with a dynamically adjusted population size to promote exploitation. Empirical studies on various many-task benchmark problems and a real-world problem are conducted to verify the efficiency of EME-BI. The results portrayed that EME-BI achieves highly competitive performance compared to several state-of-the-art algorithms regarding the solution quality, convergence trend, and computation time. This proposal also won first prize at the CEC2021 Competition on Evolutionary Multi-task Optimization, Multi-task single-objective optimization.
Digitalisation in the Health Sector: A South African Public Law Perspective

The landscape of the health sector in South Africa as seen from a regulatory perspective is rapidly changing under the disruptive impact of digitalisation. Drawing on a paradigm of "strong rights" protection, particularly a robust privacy law fit for the digital age and sourced in the nation's Constitution, the operationalisation and application of health privacy regulation in post-apartheid society is briefly described. The note then enumerates and assesses a number of specific digital health technologies currently in use in interventions in South Africa. To do so, we adopt the international World Health Organisation (WHO) classification of digital health interventions. We also cover the recent South African response to the COVID-19 pandemic, noting the establishment in South Africa of the COVID-19 Tracing Database and subsequent technological interventions aimed at enhancing contact tracing and other responses to the pandemic. The establishment of the initial database was a development at the interface of the law enforcement and health sectors, which raised concerns regarding its risks to privacy, but it also raised hopes regarding its potential rewards in protecting public health.
MPI: Memory Protection for Intermittent Computing

Batteryless devices harvest energy from sporadic ambient sources, enabling a wide range of long-lived, stand-alone, and environmentally-friendly sustainable applications. Software on these devices operates intermittently due to frequent power failures. Each power failure leads the device to lose its computational state that hinders the forward progress of computation and memory consistency. One solution to remedy this situation is to pair programs with checkpoints to save a snapshot of the intermediate program state to non-volatile memory before a power loss. Due to the lack of protection mechanisms in the state-of-the-art intermittent systems, checkpoints can be altered either by programmer errors or deliberately by an attacker. This situation leads to catastrophic effects since the program execution might be corrupted, and in turn, the device might malfunction. In this paper, we propose MPI, a memory protection mechanism for intermittent computing systems. In particular, MPI is a minimal intermittent-compliant trusted computing base acting as a hypervisor that fully manages and protects the underlying memory of a batteryless device. MPI enables a reliable and secure generation and restoration of checkpoints, maintaining their integrity and access control in the presence of remote software-based attacks without trusting the user program or requiring programmer intervention. Notable is that MPI neither requires hardware modifications nor depends on hardware features that might not exist in all batteryless platforms. Our experiments on a real batteryless platform show that MPI provides stronger security guarantees compared to the state-of-the-art approaches, with a comparable time and energy overhead.
Multirobot Transport of Deformable Objects With Collision Avoidance

In the problem of autonomous transport of deformable objects, we propose a multirobot approach for steering a large object to a target configuration (object dimensions, orientation, and position). Firstly, we present a deformation model based on the evolution over time of the dimensions and rotation of the object's bounding box (BB). We consider the object is grasped by a set of mobile robots with double-integrator dynamics. Then, we propose a set of nominal controllers that allows reaching a desired configuration of the BB that models the deformable object. In order to prevent collisions of the object with static or dynamic obstacles, we formulate a control barrier function (CBF) that exploits our deformation model. Finally, we integrate the nominal controllers and the CBF into a quadratic-programming controller, which includes overstretching avoidance and velocity constraints. We report simulation results to show the performance of this approach in different test scenarios.
A Model for Gujarati News Search Engine by Link Builder and Web Crawler Algorithms

The web serves a paradoxical role in language learning. Providing a fertile ground for neologisms and redefinitions, the web incubates a growing translation gap between one’s native tongue and his or her target foreign language. However, the web can also be used to bridge languages even as they change, because it contains a vast and ever-expanding set of human-translated web pages. Search engines that mine high-quality human translations can unlock the web’s pedagogical potential, and form the basis for next-generation language learning tools. So, it has become essential to develop crawlers in such a way that it can help in local language optimization with topic-search. Based on study of many research papers motivated to do research in Gujarati Search Engine. Moreover, proposed a new model for it.
Robot Target Location Based on the Difference in Monocular Vision Projection

Visual guidance is widely used in industrial robots.At present, the traditional method of template matching and positioning is often used in industrial robots under monocular vision guidance.However, for complex workpieces with height differences, if the angle and position of the workpiece are not consistent with the template, the projection will be different, and the positioning accuracy will be obviously reduced.A new method is proposed to solve this problem by dividing the whole contour of the workpiece and using the weighted method of the contour module to correct and match the workpiece image.First, the contour region of the template image is divided according to the position of the grasping points.Then the weight is assigned according to the distance from each contour region to the grasping points.Then, a fast feature point matching method is used to match the initial image of the workpiece to be measured.Finally, accurate contour matching is carried out for each weighted contour region so that the robot can grasp the object accurately.A large number of experiments show that the method has the design requirements of high stability and high accuracy.
Pengaruh Stimulasi Tugas Terhadap Motivasi dan Pemahaman Membaca Mahasiswa Dengan Rancangan One Shot case study

This study seeks to increase intrinsic reading motivation by using situational interest to encourage the development of long-term individual reading motivation. Stimulating tasks are used to increase situational interest. Simultaneously, the researcher provides books on the topic of stimulating tasks to satisfy the curiosity arising from these tasks. Students who have high task stimulation show an increase in reading comprehension after controlling for initial comprehension. Students' reading motivation predicts the level of reading comprehension after controlling for initial comprehension. Task stimulation in reading increases situational interest, which increases long-term intrinsic motivation and reading comprehension.
Pose estimation for six-axis industrial robots based on pose distillation

Industrial robot posture monitoring can be used to detect abnormal movements of industrial robots and thus avoid safety accidents. Most of the current research on industrial robot pose estimation has focused on improving the accuracy aspect, and there is a lack of research on its recognition efficiency. In this work, we build a model for recognizing the pose of industrial robots based on the human pose estimation model DensePose, combined with a strategy of pose distillation. Individual industrial robot images are fed into a highly accurate pose estimation network model and the model is compressed by using distillation with a fitted loss function to achieve accurate and efficient industrial robot pose recognition, laying the foundation for safe monitoring of industrial robots. The evaluation shows that compared to the original model without the FDPD method compression, the model compressed by the FDPD method is 6.5% more efficient while maintaining high accuracy.
A Comparative Analysis of Camera, LiDAR and Fusion Based Deep Neural Networks for Vehicle Detection

Self-driving cars are an active area of interdisciplinary research spanning Artificial Intelligence (AI), Internet of Things (IoT), embedded systems, and control engineering. One crucial component needed in ensuring autonomous navigation is to accurately detect vehicles, pedestrians, or other obstacles on the road and ascertain their distance from the self-driving vehicle. The primary algorithms employed for this purpose involve the use of cameras and Light Detection and Ranging (LiDAR) data. Another category of algorithms consists of a fusion between these two sensor data. Sensor fusion networks take input as 2D camera images and LiDAR point clouds to output 3D bounding boxes as detection results. In this paper, we experimentally evaluate the performance of three object detection methods based on the input data type. We offer a comparison of three object detection networks by considering the following metrics - accuracy, performance in occluded environment, and computational complexity. YOLOv3, BEV network, and Point Fusion were trained and tested on the KITTI benchmark dataset. The performance of a sensor fusion network was shown to be superior to single-input networks.
A Study on Segmentation of Leukocyte Image With Shannon's Entropy

In recent years, a considerable number of approaches have been proposed by the researchers to evaluate infectious diseases by examining the digital images of peripheral blood cell (PBC) recorded using microscopes. In this chapter, a semi-automated approach is proposed by integrating the Shannon's entropy (SE) thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures. This work implements a two-step practice with cuckoo search (CS) and SE-based pre-processing and DRLS-based post-processing procedure to examine the PBC pictures. During the experimentation, the PBC pictures are adopted from the database leukocyte images for segmentation and classification (LISC). The proposed approach is implemented by considering the RGB scale and gray scale version of the PBC pictures, and the performance of the proposed approach is confirmed by computing the picture similarity and statistical measures computed with the extracted stained blood cell with the ground truth image.