Reviewerly/exHarmony huggingface dataset:
Attention-based cropping and erasing learning with coarse-to-fine refinement for fine-grained visual classification

Fine-grained visual classification is challenging due to similarities within classes and discriminative features located in subtle regions. Conventional methods focus on extracting features from the most discriminative parts, which may underperform when these parts are occluded or invisible. And the limited training data also leads to serious overfitting problem. In this paper, we propose an Attention-based Cropping and Erasing Network (ACEN) with a coarse-to-fine refinement strategy to address these problems. By convolving the feature maps from CNN, we obtain a set of attention maps which focus on discriminative object parts. Guided by the attention maps, we propose attention region cropping and erasing operations to augment training data. Moreover, the attention region cropping enhances local discriminative feature learning, and the attention region erasing promotes multi-attention learning. During inference phase, the coarse-to-fine refinement strategy is proposed to refine the model prediction. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on challenging benchmarks, including CUB-200-2011, FGVC-Aircraft and Stanford Cars.
Building a Personalized Dialogue System with Prompt-Tuning

Dialogue systems without consistent responses are not fascinating.In this study, we build a dialogue system that can respond based on a given character setting (persona) to bring consistency.Considering the trend of the rapidly increasing scale of language models, we propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained largescale language models.The results of automatic and manual evaluations in English and Japanese show that it is possible to build a dialogue system with more natural and personalized responses using less computational resources than fine-tuning.
An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

With the advent of modern technologies, the healthcare industry is moving towards a more Personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence. These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient&amp;rsquo;s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users&amp;rsquo; device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five publicly available datasets from Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.
No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions. Among the employed local features, the statistics of popular local feature descriptors, such as SURF, FAST, BRISK, or KAZE, are proposed for NR-IQA; other features are also introduced to boost the performances of local features. The proposed method was compared to 12 other state-of-the-art algorithms on popular and accepted benchmark datasets containing RGB images with authentic distortions (CLIVE, KonIQ-10k, and SPAQ). The introduced algorithm significantly outperforms the state-of-the-art in terms of correlation with human perceptual quality ratings.
Ethical Decision Making in Soft lifting-A UAE Based Case Study

Soft lifting is a type of software piracy that is prevalent in today's computer-dependent world. Soft lifting is a type of software piracy in which software is installed or duplicated in a system in violation of its license agreement. In software development, ethical decisions are critical, as they have a significant impact on end users, organizations, and the environment. As the problem of copying affects soft lifting behaviour, the research lends some credence to the postulates model. Most notably, the study found that one's ethical perspective of soft lifting has no bearing on one's softlifting activity. These findings have significant implications for both software companies and academics who are aiming to prevent piracy through ethics education.
An Ultra-Lightweight Data-Aggregation Scheme with Deep Learning Security for Smart Grid

Various smart meter data aggregation protocols have been developed in the literature to address the rising privacy threats against customers' energy consumption data. However, most of these protocols require a smart meter (installed at the consumer's end) to either maintain a secret key or to run an authenticated key establishment scheme for interacting with the aggregator. Both of these approaches create additional requirements for the system. To address this issue, this article first proposes a machine-learning-based ultra-light-weight data aggregation scheme for smart grids that does not require a secret key to be maintained for communicating with the aggregator. In particular, unlike existing data aggregation schemes, in the proposed data aggregation scheme, neither the server nor the smart meter needs to store any secret. Instead, for every round of data aggregation, each smart meter uses an embedded PUF for generating a unique random response for a given challenge. On the other hand, the server maintains a PUF model for each smart meter for producing the same random response. This unique secret key is used to ensure the privacy of the metering data. Next, we propose an optimized data aggregation scheme using collaborative learning to enhance the performance of the proposed scheme.
Research on Hyper-Parameter Optimization of Activity Recognition Algorithm Based on Improved Cuckoo Search

Activity recognition methods often include some hyper-parameters based on experience, which greatly affects their effectiveness in activity recognition. However, the existing hyper-parameter optimization algorithms are mostly for continuous hyper-parameters, and rarely for the optimization of integer hyper-parameters and mixed hyper-parameters. To solve the problem, this paper improved the traditional cuckoo algorithm. The improved algorithm can optimize not only continuous hyper-parameters, but also integer hyper-parameters and mixed hyper-parameters. This paper validated the proposed method with the hyper-parameters in Least Squares Support Vector Machine (LS-SVM) and Long-Short-Term Memory (LSTM), and compared the activity recognition effects before and after optimization on the smart home activity recognition data set. The results show that the improved cuckoo algorithm can effectively improve the performance of the model in activity recognition.
Decentralization and web3 technologies

Today, the majority of the web's content and user data is controlled by a few large tech companies. There is a growing movement to devolve this control evenly across the entire internet, representing the transition to Web3. In order for this movement to be successful, technologies and protocols must be developed to enable web users to use the web securely without trusting any other user. That is, today's web is structured so that users must trust these companies, so trustless alternatives haven't already been developed. Broadly, this movement emphasizes developing peer-to-peer networks, blockchains, and distributed storage systems. These systems make use of cryptographic primitives to guarantee security.&lt;br&gt;
A Novel Assistive Glove to Convert Arabic Sign Language into Speech

People with speech disorders often communicate through special gestures and sign language gestures. However, other people around them might not understand the meaning of those gestures. The research described in this article is aimed at providing an assistive device to help those people communicate with others by translating their gestures into a spoken voice that others can understand. The proposed device includes an electronic glove that is worn on the hand. It employs an MPU6050 accelerometer/gyro with 6 degrees of freedom to continuously monitor hand orientation and movement, plus a potentiometer for each finger, to monitor changes in finger posture. The signals from the MPU6050 and the potentiometers are routed to an Arduino board, where they are processed to determine the meaning of each gesture, which is then voiced using the audio streams stored in an SD memory card. The audio output drives a speaker, allowing the listener to understand the meaning of each gesture. We built a database with the help of 10 deaf people who cannot speak. We asked them to wear the glove while performing a set of 40 Arabic sign language words and recorded the resulting data stream from the glove. That data was then used to train seven different learning algorithms. The results showed that the Decision Tree learning algorithm achieved the highest accuracy of 98%. A usability study was then conducted to determine the usefulness of the assistive device in real-time.
An Ontology – based Contextual Approach for Cross-domain Applications in Internet of Things

The Internet of Things is an ecosystem which enables objects and devices such as sensors or actuators to communicate and exchange information with each other without human intervention. One of the main challenges in the Internet of Things is the lack of semantic interoperability; devices cannot understand the meaning of raw data, due to the diversity and heterogeneity in data formats from different sources. In order to deal with semantic interoperability, the ontologies are the one way to integrate semantics to raw data; they describe an IoT system and represent the data in a standardized way. The IoT devices provide a great deal of IoT data, mainly used for specific IoT applications such as smart home, smart farming, smart cities or healthcare. Therefore, existing applications became isolated in vertical silos, each one of them use independently their own model (i.e. ontology), which makes this ontologies also limited to a specific domain. Our approach has the goal of breaking down these vertical silos and achieves a semantic interoperability across IoT domains in cross-domain applications. In this paper, we have proposed a development of a single cross-domain ontology named CDOnto, it is considered to be a generic across different IoT domains, which can be extended by domain-specific ontologies. The proposed model follows a contextual approach to organize and distinguish the combined domains (i.e. contexts) representations. In addition, the ontology allows reasoning across overlapping domains and infers a complementary and new knowledge required in cross-domain applications.
Converting of Boolean Expression to Linear Equations, Inequalities and QUBO Penalties for Cryptanalysis

There exists a wide range of constraint programming (CP) problems defined on Boolean functions depending on binary variables. One of the approaches to solving CP problems is using specific appropriate solvers, e.g., SAT solvers. An alternative is using the generic solvers for mixed-integer linear programming problems (MILP), but they require transforming expressions with Boolean functions to linear equations or inequalities. Here, we present two methods of such a transformation which applies to any Boolean function defined by explicit rules giving values of the Boolean function for all combinations of its Boolean variables. The first method represents the Boolean function as a linear equation in the original binary variables and, possibly, binary ancillaries, which become additional variables of the MILP problem being composed. The second method represents the Boolean function as a set of linear inequalities in the original binary variables and one additional continuous variable (representing the value of the function). The choice between the first or second method is a trade-off between the number of binary variables and number of linear constraints in the emerging MP problem. The advantage of the proposed approach is that both methods reduce important cryptanalysis problems, such as the preimaging of hash functions or breaking symmetric ciphers as the MILP problems, which are solved by the generic MILP solvers. Furthermore, the first method enables to reduce the binary linear equations to quadratic unconstrained binary optimization (QUBO), by the quantum annealer, e.g., D-Wave.
A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units

This paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and validation dataset included pneumonia, sepsis, congestive heart failure, hypotension, chest pain, coronary artery disease, fever, respiratory failure, acute coronary syndrome, shortness of breath, seizure and transient ischemic attack, and aortic stenosis patients’ recorded data. Then we tested the models on the unseen data of patients diagnosed with coronary artery disease, congestive heart failure or acute coronary syndrome. We included the admission characteristics, clinical prescriptions, physiological measurements, and discharge characteristics of those patients. We assessed the models’ performance using mean residuals and running times as metrics. We ran multiple experiments to study the data partition’s impact on the learning phase. The total running time of our best-evaluated model is 123,450.9 mS. The best model gives an average accuracy of 98%, highlighting the location of discharge, initial diagnosis, location of admission, drug therapy, length of stay and internal transfers as the most influencing patterns to decide a patient’s readiness for discharge.
SECTIONS Model Analysis for Pre-service English Teachers' Media Selection in Pandemic Covid 19

The development of technology-based learning cannot replace the teacher's role as an educator, but the teacher who does not want to learn technology, the teacher will be replaced.During the COVID-19 pandemic and the digital era of technology, it provides opportunities for teachers to develop creative ideas in the use of online learning media.This article aims to analyze the selection of elearning media using the SECTIONS model for pre-service teachers.The Structural Equation Model (SEM) approach was used to analyze the relationship between components of SECTIONS model and media selection variable.The number of participants in this study were 200 pre-service English teachers in the Institut Agama Islam Negeri Syekh Nurjati, Indonesia.The results showed that there was a significant effect between components of SECTIONS model and selection of e-learning media.The SECTIONS model is effective and efficient for selecting English e-learning media.Through the SECTIONS model, it can minimize the negative impacts caused by incorrectly determining e-learning media.
Autonomous 6-DOF Manipulator Operation for Moving Target by a Capture and Placement Control System

The robot control technology combined with a machine vision system provides a feasible method for the autonomous operation of moving target. However, designing an effective visual servo control system is a great challenge. For the autonomous operation of the objects moving on the pipeline, this article is dedicated to developing a capture and placement control system for the six degrees of freedom (6-DOF) manipulator equipped with an eye-in-hand camera. Firstly, a path planning strategy of online capture and offline placement is proposed for real-time capture and efficient placement. Subsequently, to achieve the fast, stable, and robust capture for a moving target, a position-based visual servo (PBVS) controller is developed by combining estimated velocity feedforward and refined PID control. Feedforward control is designed using the estimated velocity by a proposed motion estimation method for high response speed. PID control is refined by dead zone constraint to reduce the manipulator's jitter caused by the frequent adjustment of manipulator control system. Besides, the proportional, integral, and differential coefficients of PID controller are adaptively tuned by fuzzy control to reject the noise, disturbance, and dynamic variation in the capture process. Finally, validation experiments are performed on the constructed ROS-Gazebo simulation platform, demonstrating the effectiveness of the developed control system.
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection

Cyber intrusion attacks that compromise the users' critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily life and the Internet. The large volume and high complexity of such intrusion attacks have impeded the effectiveness of most traditional defence techniques. While at the same time, the remarkable performance of the machine learning methods, especially deep learning, in computer vision, had garnered research interests from the cyber security community to further enhance and automate intrusion detections. However, the expensive data labeling and limitation of anomalous data make it challenging to train an intrusion detector in a fully supervised manner. Therefore, intrusion detection based on unsupervised anomaly detection is an important feature too. In this paper, we propose a three-stage deep learning anomaly detection based network intrusion attack detection framework. The framework comprises an integration of unsupervised (K-means clustering), semi-supervised (GANomaly) and supervised learning (CNN) algorithms. We then evaluated and showed the performance of our implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and TON_IoT.
Factors to consider when choosing data collection methods

Novice and experienced researchers may encounter numerous methodological challenges when it comes to data collection. Data have to be collected from the right sources using appropriate data collection tools to make research studies impactful. There are numerous data collection methods that researchers may use but not all methods are appropriate for all research studies. Choosing inappropriate research methods may result in invalid research findings and lead to erroneous conclusions. This means researchers have to carefully choose data collection methods to make their studies achieve their respective goals. This study intended to identify factors that researchers have to consider in choosing data collection methods. A systematic literature review was used to analyze 28 journal articles published between 2018 and 2022. From the analysis the study identified ten (10) factors that researchers have to consider in choosing data collection methods. These factors are; the research goal, the scope of the study, sample size, type of data, time and user-friendliness to the subjects. Other factors are research approach, safety and security of a researcher, the need for triangulation and theoretical framework. Before choosing methods for data collection, it is important to put into consideration these factors to increase the chances of achieving research objectives and answering research questions of research projects.
Beni Başka Âlemlere Götür! Kullanıcı Temelli Metaverse Etkinlik Deneyimi

Metaverese can be considered as one of the most talked about virtual reality channels in today's world, and it is also widely used as an entertainment tool. In this study, this virtual medium or realm was investigated as an experience tool among university students participating in metaverse leisure activities. Within the scope of the research, semi-structured interviews were conducted with the participants who experienced metaverse activities. The results of this study revealed eight themes titled as illusory presence, FOMO-based non-French, escape, flow, instant gratification, horizon line, spirit realm and virtual symptomatic. The findings show that metaverse channels, which are an indispensable part of today's life, are also widely used as a means of experience, entertainment and pleasure. As a result of the metaverse literature review and interviews with the event users of the metaverse, the researchers concluded that this new virtual environment is widely used for purposes such as getting rid of boredom, having new experiences, having fun, and establishing social interaction.
Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.
Chaotic based multimedia encryption: a survey for network and internet security

Nowadays the security of multimedia data storage and transfer is becoming a major concern. The traditional encryption methods such as DES, AES, 3-DES, and RSA cannot be utilized for multimedia data encryption since multimedia data include an enormous quantity of redundant data, a very large size, and a high correlation of data elements. Chaos-based approaches have the necessary characteristics for dynamic multimedia data encryption. In the context of dynamical systems, chaos is extremely dependent on the initial conditions, non-convergence, non-periodicity, and exhibits a semblance of randomness. Randomness created from completely deterministic systems is a particularly appealing quality in the field of cryptography and information security. Since its inception in the early '90s, chaotic cryptography has seen a number of noteworthy changes. Throughout these years, several scientific breakthroughs have been made. This paper will give an overview of chaos-based cryptography and its most recent advances.