Reviewerly/exHarmony huggingface dataset:
Evaluation and Analysis of Reversible Watermarking Techniques in WSN for Secure, Lightweight Design of IoT Applications: A Survey

Wireless sensor networks (WSNs) are one of the main elements of information acquisition technologies in the Internet of Things (IoT). They are broadly used in managing instruments such as smart meters and health monitoring. The data is prone to manipulation; thus, a secure method is necessary to ensure data integrity. The objectives of this research are to compare lightweight and secure reversible watermarking schemes for data integrity in WSN; elaborate on the influential factors affecting on the system performance; describe the overhead of each security factor in terms of time, memory and energy consumption regarding the computational complexity and, finally, assess the robustness of each scheme to resist against attacks. Taking advantage of the results of this research, we expect that it would be inspiring for researchers to design secure IoT applications such as smart meters by extending these elaborated measurements. We also propose several tips for designing lightweight and secure data integrity methods in IoT.
BIO-VR: Design and Implementation of Virtual Reality-Based Simulated Biology Laboratory Using Google Cardboard with an Emphasis on Virtual Education

Virtual reality (VR) has become one of the most emerging technologies of this decade. Researchers can now exchange and study data like never before with the help of virtual reality-based tools. It’s a key pipeline study in the field of human–computer interaction. In this proposed method, we created a virtual reality-based immersive simulated biology laboratory experience that allows students to learn different laboratory equipment. The immersive method for learning outcomes in the biology laboratory aimed to address the global problem where the underprivileged students lack the access of even the basic laboratory equipment. The proposed method was tested and surveyed among high school students coming from an underprivileged geography specifically in Bangladesh. The substantial finding of the study reflects how these individuals can be benefitted from this virtual reality-based biology laboratory. The results came out positive and along with that the ending calls for future works on immersive VR laboratories on other streams for high school students.
A Comparative Analysis of SVM, LSTM and CNN-RNN Models for the BBC News Classification

Developing technology, easier access to internet networks and the increase in the widespread use of the internet cause more text data to be produced than ever. Analyzing and classifying this data is one of the most difficult problems. Hence, automatic classification of text data is very important. In this study, the word embedding method, machine learning, deep learning and a hybrid model is proposed to classify news texts. GloVe method is used as a word embedding model and SVM (Support Vector Machine), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network) + RNN (Recurrent Neural Networks) models are used to classify data. Also, various optimization algorithms have been tested in deep learning models and the results of these algorithms are in a comparative manner. BBC-News dataset is used to classify news texts. The best accuracy results have been obtained as 98.0% in the SVM model, 97.0% in the LSTM model and 96.0% in the CNN + RNN model.
Machine Learning Applications for Consumer Behavior Prediction

Customers are growing increasingly interested in the degree of service that businesses can provide. Because the services provided by different providers are not significantly different, there is greater competition among firms to maintain and improve the quality of their services. This research study investigates how machine learning can help businesses learn more about their clients and provide them with more personalized services. It also acts as a pattern on how firms can improve the effectiveness of their marketing efforts to acquire new clients and establish long-term connections with existing customers. In marketing, the prediction of consumer behavior is frequently used to schematize service offerings and develop targeted marketing programs. We have implemented six different machine-learning algorithms to improve further our ability to forecast consumer behavior. We have presented six machine learning models to improve performance, including Random Forest, Gradient Boosting, Logistic Regression, LightGBM, XgBoost, and Decision Tree, to achieve better results. Gradient Boosting, according to this study’s findings, is the best overall model for performance and efficiency.
MGRLN-Net: Mask-Guided Residual Learning Network for Joint Single-Image Shadow Detection and Removal

Although significant progress has been made in single-image shadow detection or single-image shadow removal, only few works consider these two problems together. However, the two problems are complementary and can benefit from each other. In this work, we propose a Mask-Guided Residual Learning Network (MGRLN-Net) that jointly estimates shadow mask and shadow-free image. In particular, MGRLN-Net first generates a shadow mask, then utilizes a feature reassembling module to align the features from the shadow detection module to the shadow removal module. Finally, we leverage the learned shadow mask as guidance to generate a shadow-free image. We formulate shadow removal as a masked residual learning problem of the original shadow image. In this way, the learned shadow mask is used as guidance to produce better transitions in penumbra regions. Extensive experiments on ISTD, ISTD+, and SRD benchmark datasets demonstrate that our method outperforms current state-of-the-art approaches on both shadow detection and shadow removal tasks. Our code is available at https://github.com/LeipingJie/MGRLN-Net .
AutoEnhancer: Transformer on U-Net Architecture Search for Underwater Image Enhancement

Deep neural architecture plays an important role in underwater image enhancement in recent years. Although most approaches have successfully introduced different structures (e.g., U-Net, generative adversarial network (GAN) and attention mechanisms) and designed individual neural networks for this task, these networks usually rely on the designer’s knowledge, experience and intensive trials for validation. In this paper, we employ Neural Architecture Search (NAS) to automatically search the optimal U-Net architecture for underwater image enhancement, so that we can easily obtain an effective and lightweight deep network. Besides, to enhance the representation capability of the neural network, we propose a new search space including diverse operators, which is not limited to common operators, such as convolution or identity, but also transformers in our search space. Further, we apply the NAS mechanism to the transformer and propose a selectable transformer structure. In our transformer, the multi-head self-attention module is regarded as an optional unit and different self-attention modules can be used to replace the original one, thus deriving different transformer structures. This modification is able to further expand the search space and boost the learning capability of the deep model. The experiments on widely used underwater datasets are conducted to show the effectiveness of the proposed method. The code is released at https://github.com/piggy2009/autoEnhancer.
Exploring the Relationship Between Learning Rate, Batch Size, and Epochs in Deep Learning: An Experimental Study

Deep learning has promised us great outcomes when enough data are fed to it. Deep learning is a branch of artificial intelligence which employs artificial neural networks to learn. The quality of the performance of these ANNs majorly depends on the data fed to it, architecture of the ANN and hyperparameters. The hyperparameters are the parameters whose values control the process of learning, which in turn controls the performance of ANNs. These hyperparameters are assigned different values usually using hit and trial methods. Hyperparameters such as learning rate, batch size, and epochs are assigned some values independent of each other before training the ANN model. In this study, we introduce a novel method of allowing the learning rate to be a function of batch size and epoch, thereby reducing the number of hyperparameters to be tuned. We later on introduce some randomness to the learning rate to see the effects on accuracy. It was found that the proposed strategy helped increase accuracy by more than 2% in certain cases, when compared to the existing methods.
Superpixel Image Clustering Using Particle Swarm Optimizer for Nucleus Segmentation

In this decade, various superpixel-based techniques have been proposed by various authors for image segmentation where few of them are for medical images. These algorithms are evaluated using several evaluation measures and datasets, resulting in inconsistency in algorithm comparison. We noticed that some superpixel-based algorithms are performing better than other algorithms, for example, clustering-based superpixel methods are more efficient than graph-based superpixel techniques. In this paper, we choose simple linear iterative clustering (SLIC) for its less computation time and great performance on pathology images. In this paper, particle swarm optimization (PSO) and k-means clustering (KM) methods are used both with superpixel preprocessing for kidney renal cell carcinoma images. Clustered images are compared with ground truth images, and SLIC with PSO outperformed all other methods.
Personalization Through User Attributes for Transformer-Based Sequential Recommendation

Sequential recommendation models are able to learn user preferences based solely on the sequences of past user–item interactions. However, there usually exist additional features about the items as well as the user that can be exploited to improve the model performance. Various models have already successfully exploited additional item information, but as fashion taste, for example, is really dependent on the individual person, characterizing the consumer becomes even more important for recommendations. The integration of user attributes into sequential item recommendation models has received far less attention, although user attributes can alleviate the cold-start problem to some degree through providing general preferences of the user at the start of a session. To address this, we propose a way to fuse user attributes directly at the item representation layer and compare this to a pre- and postfusion baseline. To test our approach, we adapt the well-known model BERT4Rec to use these fused user-item representations as input. We also experiment with a unidirectional transformer model inspired by SASRec. We evaluate all models on a real-world dataset from a fashion e-commerce shop as well as on the public Movielens-1m dataset. Our results show that the integration of user attributes improves the recommendation results for BERT4Rec for next item prediction and can also leverage the user information for recommendations in case of a cold-start setting.
Governing the Resource of Data: To What End and for Whom? Conceptual Building Blocks of a Semi-Commons Approach

The 23rd working paperfor Data Governance Network by Anita Gurumurthy and Nandini Chami presents the building blocks of an economic governance regime for data that can contribute to the growth of the digital economy by unlocking the potential of data. The de facto enclosure of data by platform monopolies not only creates a skewed market place with exclusionary effects, it also presents a huge opportunity cost in terms of the unrealized public and social value of data. The paper unpacks the limits of emerging approaches to data governance – both, individualist and collective – arguing why such frameworks do not go far enough. It offers a semi-commons approach to ordering the digital economy as a means to achieve ‘distributive integrity’.
Control System for Robotic Towing Platform for Moving Aircraft

The paper considers the possibility of using a robotic mobile wheeled platform to solve the problems of moving aircraft along the airfield to the runway or hangar. As part of the work, a review of modern principles and methods used to control wheeled objects, including a system of wheeled objects, was carried out. A method for controlling the positioning of a wheeled platform using an optical sensor based on an optocoupler matrix has been developed. The design of the apparatus built according to the scheme of a robot with a differential drive is proposed, and a method for coupling the tug and the aircraft through the elastic element of the nose wheel as well as electronic and electrical equipment of the platform is proposed. The algorithm and logic of the control unit operation are given. Particular attention is paid to the synthesis of a multi-channel controller of the RBAS control system. A method is proposed that allows, based on the decomposition of system movements, to obtain the necessary controller parameters based on the qualitative indicators of the transient process. Mathematical modeling of the controlled motion of a wheeled platform has been carried out.
Development of an Algorithm for Coverage Path Planning for Survey of the Territory Using UAVs

Increasingly, unmanned aerial vehicles (UAVs) are used for surveying areas in search of a specific object, mapping, compiling an orthophotomap, or a three-dimensional terrain model, as well as for processing agricultural fields. At present, the development of algorithms for constructing a trajectory that covers a certain area and takes into account the limited energy resource on board the UAV remains an urgent task. The paper proposes an algorithm for calculating the trajectory for uniform coverage of the territory in the form of a polygon, which provides a complete study of the site by the UAV technical vision system, taking into account its parameters. The paper presents an analysis of existing algorithms for planning the coverage trajectory and proposes a new algorithm that takes into account obstacles in the surveyed area. The resulting trajectory is optimized by reducing the number of maneuvers and smoothing them out. As a result of the experiments, the flight time of the UAV along the trajectories calculated by the developed method on a site with an area of 950,000 m2 was reduced by 10% compared to the flight time along the trajectory calculated by the standard algorithm.
A Distributed Real-time 3D Pose Estimation Framework based on Asynchronous Multiviews

3D human pose estimation is widely applied in various fields, including action recognition, sports analysis, and human-computer interaction.3D human pose estimation has achieved significant progress with the introduction of convolutional neural network (CNN).Recently, several researches have proposed the use of multiview approaches to avoid occlusions in single-view approaches.However, as the number of cameras increases, a 3D pose estimation system relying on a CNN may lack in computational resources.In addition, when a single host system uses multiple cameras, the data transition speed becomes inadequate owing to bandwidth limitations.To address this problem, we propose a distributed real-time 3D pose estimation framework based on asynchronous multiple cameras.The proposed framework comprises a central server and multiple edge devices.Each multiple-edge device estimates a 2D human pose from its view and sends it to the central server.Subsequently, the central server synchronizes the received 2D human pose data based on the timestamps.Finally, the central server reconstructs a 3D human pose using geometrical triangulation.We demonstrate that the proposed framework increases the percentage of detected joints and successfully estimates 3D human poses in real-time.
Projection of the Vitality of the Betawi Language in the Future Time in Jakarta

This research analyzes Betawi language users in the Jakarta area. The purpose of this study is to determine the health vitality of the Betawi language in the future, the factors that can cause the Betawi language to be endangered, and how the solution is to maintain the existence of the Betawi language. The theory used in this study is sociolinguistic, and the supporting theory is cultural sociology. The method used in this research is a qualitative quantity method or a mixed method. This research step combines two forms of research that have existed before: qualitative and quantitative, to obtain more complete, valid, reliable and objective data and describe the results of data collection by researchers through observation of research subject statements. This study used observation and questionnaire techniques (questionnaire). The results obtained are a calculation of the current use of the Betawi language to determine the health vitality of the Betawi language in the future. The conditions for using the Betawi language, from measurements of the frequency of use and the number of speakers, are still very concerning. Although there is still a gap of hope for improvement in the future with the right strategy.
Office Room Smart Lighting Control with Camera and SSD MobileNet Object Localization

There have been many studies that offer smart lighting control for the office. However, there is still room for improvement to enhance the performance of previous studies. This study proposes using a camera and SSD MobileNet object localization to detect the location of people in a room and control the lights according to the person’s position. The smart lighting system is built based on the Internet-of-things (IoT) architecture by implementing the Raspberry Pi (Raspi) and Raspi Camera for image capture and localization. The Raspi communicates with the smart lighting controller consisting of led lights, relays, and NodeMCU. The test bed of this research is an office room, where we create five zones inside it. One light controller controls the illumination of each zone. The test scenario is to ask a person to sit in each zone and sample the light controller 100 times. Then is to gather another 100 samples per zone with no one in the zone. True positive rate (TPR) and true negative rate (TNR) are the metrics to measure the performance of the proposed system. The test results show that the performance of smart lighting control in each zone is TPR=0.942 and TNR=0.956. Our research contributes to smart lighting control in the office room using a camera and object localization with better performance than existing research.
The Ongoing Debate over Teacher Centered Education and Student Centered Education

Teacher-centered education and student-centered education are two different approaches to teaching and learning that have been widely debated in the field of education.Both approaches have their advantages and disadvantages.Teacher-centered education, also known as traditional education, is an approach to teaching in which the teacher is the primary source of knowledge and authority in the classroom.Student-centered learning on the other hand helps students become active participants in their own discovery process.This paper compares these two approaches and endeavours to summarize their advantages.
Lung Cancer Classification Model Using Convolution Neural Network

Lung cancer has one of the worst mortality rates and incidence rates of any prevalent cancers around the world, and sufferers have a better chance of surviving if the illness is detected early. One of the elements essential to determining the type of cancer is the histopathological diagnosis. Because the type of histology, molecular profile, and stage of the cancer all affect how the disease is treated, it is urgently necessary to analyze the histopathology images of lung cancer. Therefore, deep learning techniques are used to speed up the crucial process of diagnosing lung cancer and lessen the workload on pathologists. It focuses on giving computers the ability to perceive, recognize, and process images in a manner similar to that of human vision and subsequently produce the intended results. It is comparable to imparting human intelligence and instincts to a computer. Deep learning techniques, particularly Convolutional Neural Network (CNN), have improved efficiency in the analysis of cancer histopathology slides. The novelty of this work is to investigate the effectiveness of the proposed model in classifying the digital pathology images for those lung cancer images as either benign or malignant. From the LC25000 dataset, which includes 5000 images for each class, a total of 10,000 digital images were obtained. The histological slides have been divided into benign and malignant cells (cancerous cell squamous) using a shallow neural network structure. The applied model has been attained accuracy ranging from 99.3% to 99.8% in classifying lung malignant from benign lesions. It was experimentally proved that there is no tangible difference in average accuracy between the applied experiments. Therefore, the proposed model has been efficient in comparison with the state-of-the-art.
Robust Vehicle Detection by Using Deep Learning Feature and Support Vector Machine

Vehicle detection and classification is an essential task in building an autonomous driving car. However, the accuracy of traditional vehicle detection systems is still not satisfied in real world due to unstable features from the original input images. This study introduces an efficient method to enhance the input feature more stable by considering the benefit of the machine learning approach to discover and extract robust features. The feature generated by the proposed deep learning model is then input to SVM to accurately identify objects. To evaluate the performance of the compared methods, we conducted comprehensive experiments on various datasets, such as KITTI, CCD, and HCI. For the KITTI dataset, the recall of our system, the LS-Support vector machine, the Linear support vector machine, the SVM-HOG, and the support vector machine are 71.4%, 64.3%, 53.8%, 67.8.0%, and 57.1%, respectively.
A search ranking algorithm for web information retrieval

The development of the internet has seen an explosion in the amount of information, which has increased the scope of queries for users but greatly increased the difficulty of searching for valid information. In order to retrieve effective information faster, search ranking algorithms are needed to rank the retrieved information and return it to the user. This paper briefly introduced the RankNet algorithm among web information search ranking algorithms and optimised the loss function to improve its retrieval ranking performance. Simulation tests were carried out with Microsoft public data set MSLR-WEB30K. The improved RankNet algorithm was compared with the ranking support vector machine (SVM) algorithm and the traditional RankNet algorithm. The results showed that as the number of returned retrievals increased, the retrieval ranking performance of all three search ranking algorithms tended to decrease; under the same number of returned retrievals, the improved RankNet algorithm had the best performance.
Education and Awareness of Microwave Activities [President’s Column]

Educators, students, Young Professionals (YPs), and engineers worldwide love to meet and discuss IEEE activities. The IEEE Microwave Theory and Technology Society (MTT-S) is there to help when we discuss activities related to microwave theory and technologies.