Reviewerly/exHarmony huggingface dataset:
IDEAS OF DIDACTIC DESIGN IN THE GLOBAL PRACTICE OF PROFESSIONAL EDUCATION

The article discusses approaches to the use of didactic design ideas in the practiceof professional education. Didactic design is defined as an approach to creatingeducational materials and courses that arose in the middle of the 20th century aspart of research in the field of pedagogy and psychology of learning. An overviewof the emergence of ideas of didactic design through the personalities of famousteachers and psychologists who contributed to the formation and development ofthis direction in the world theory and practice of education is given. It is indicatedthat the didactic design is intended for the creation of a didactic modelingenvironment that supports the most important and time-consuming educational andcognitive actions, and is implemented on the basis of the anthropological principlesof multidimensionality and instrumentality. Its objects, on the one hand, refer to thesocial sphere (educational systems and processes, didactic objects, etc.), on theother hand, these objects can include elements of graphic design, environmentaldesign and interior elements, for example, educational environment; developmentof illustrative and graphic or text support for any educational discipline; projects ofeducational or methodical manuals; development of design learning technologies.It is noted that the functional properties of didactic design are dictated by thespecifics of the subject area and consist in the special organization of educationalmaterial and its visually convenient presentation, as well as programming andsupport of the necessary educational actions with it. An overview of the firstpublications, which became classics, is presented, as well as the topics of the latestpublications of 2022-2023 are analyzed.
Explainable artificial intelligence: tools, platforms, and new taxonomies

Recent advances in machine learning (ML) strategies have introduced several artificial intelligence (AI)-based systems. These AI systems have the capability to perceive, learn, smartly decide, and act quickly on the given situation. Apparently, this is the requirement from such systems but after witnessing their performance, it has been noticed that these systems are unable to explain their actuation to users (humans). This constraint has been taken into consideration by several researchers later, after all this is the main thing required to make our autonomous systems more intelligent and robust. At this instant, researchers felt the need for explainable AI (XAI) that may make the verifiability of taken decision essential. This will increase the demand for an ability to question, understand, and above all generate a trust level over artificial intelligence systems. There are several models but still there is no consensus on the assessment of explainability. Thus, this chapter presents a comprehensive review of current state-of-the-art over the XAI that have a societal impact. In addition to this, one may find the drivers and tools for XAI. Last but certainly not the least is the complete literature review that provides the future research directions for researchers in this area.
A Parallel Peak Filter with Time-Delay Compensation for Multi-Frequency Disturbance Rejections Beyond Control Bandwidth

This paper proposes parallel peak filters to reject multi-frequency disturbances beyond the baseline feedback control bandwidth, which is restricted by time delays of the sample-data control system. Supplementing the baseline feedback control with parallel peak filters has an extremely simple control structure. In addition, a time-delay compensation method is proposed to eliminate the adverse effect of time delay on the designing parallel peak filters. Such deign break through the bandwidth limitation of baseline feedback control and provide a new idea for disturbance rejection beyond the bandwidth. Furthermore, multiple parallel peak filters with time-delay compensation provide multi-frequency disturbance rejection, and each parallel peak filter is independent of each other. Experiments are conducted in an image-based stabilization system, and the experimental results demonstrate that the proposed method can enhance multi-frequency disturbance rejection beyond the baseline feedback control bandwidth in time-delay systems.
Depth-Data-Based Object Cluster Tracking and Velocity Estimation in Robot Workspace

Depth-data-based sensor systems, such as depth cameras or LiDAR systems, are gaining popularity in the field of robotics, especially in human-robot collaboration. To avoid collisions with humans or external objects, object detection and tracking in the workspace is needed. This paper presents an integrated object cluster tracking and velocity estimation method that is purely based on depth data. Therefore, a tracking heuristic based on similarity and the velocity of the object is used to enable the tracking of external objects. To obtain the velocity, a Kalman filter utilizing a constant velocity model is implemented. For experimental verification, a case study comprising two objects moving within the robot workspace is designed. The experimental setup allows for the initial tracking a maximal trackable object velocity of 9 m s −1, and for already tracked objects a velocity deviation of 3.4 m s-1 to correctly track both repetitive and arbitrary motions of the test objects, and thus constitutes the proposed integrated object cluster tracking approach as a foundation for collision avoidance strategies in robotic tasks.
Multi-view Inspection of Flare Stacks Operation Using a Vision-controlled Autonomous UAV

Flare stacks are crucial safety control components in petrochemical plants that required efficient monitoring and inspection. In this work, an Unmanned Aerial Vehicle (UAV)-based multi-view operation inspection system for monitoring and assessing the operation of flare stacks is proposed. Image-Based Visual Servoing (IBVS) control is used to guide the autonomous UAV for multi-view visual data collection. Afterwards, the collected visual data is analyzed using a new Multi-View Convolutional Neural Network (MV-CNN) deep learning model to obtain useful conclusions on the system's operation and classify the current state of the observed system. The proposed system's performance was validated in a simulated petrochemical plant environment with operational flare stacks and the results showed superior performance of the proposed MV-CNN model compared to a conventional single-view CNN model.
Semantics-Empowered Communications: A Tutorial-cum-Survey

Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the “what” and “why” questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.
SOIF-DN: Preserving Small Object Information Flow With Improved Deep Learning Model

Object detection is a constantly evolving field of interest in the field of computer vision. As image-processing technologies continue to advance, detecting small objects has emerged as a critical challenge. However, currently available algorithms still struggle with this task owing to the small flaws in detection and complex backgrounds. This paper proposes a real-time detection method that specializes in recognizing small defects in images using an improved deep learning approach based on a feature pyramid network (FPN). The proposed method uses the small-object information flow (SOIF) module proposed by the backbone network to learn various features in images and quickly extract feature maps with varying gradient information. In addition, high-level feature maps are extracted from the FPN to maximize the information about small objects. These methods maximize the feature map properties of the existing layers and provide an opportunity to explore different features. The proposed deep learning model called SOIF-DN(SOIF-deep neural network) was evaluated on a public benchmark cell dataset and two private datasets, such as printed circuit boards (PCB) and cell images. Upon reviewing the results, our method improves mAP@50 by 12.5% over YOLOv5 in terms of accuracy for small-object detection on PCBs. The speed performance (parameters/runtime) was 1.83 times better than YOLOv5 and 1.56 times better than YOLOv8. Additionally, our proposed SOIF-DN method for a different cell dataset enhances mAP@.5 by 3.8% over YOLOv5 for small object detection. The speed performance (parameters/runtime) was found to be 1.15 times better than YOLOv5 and 1.28 times better than YOLOv8.
Security of elliptic curve cryptosystems over Z_n

Elliptic curves over Galois fields are widely used in modern cryptography. Cryptosystems based on elliptic curves are commonly deemed more secure than RSA for a given key size. However, with the rapid progress of quantum computing, the security of this traditional systems faces unprecedented challenge. To address this concern, this paper explores the resilience of a generalization of traditional elliptic curve cryptography. That is, we explore elliptic curves over non-prime rings (Zn), instead of fields. Elliptic curves over Zn for a composite integer n has been considered by researchers on information security. However, it is unclear how they stand against the unparalleled power of quantum computers. This article investigates quantum attacks on cryptosystems based on this new paradigm. The conclusion sheds light on the pressing and important task of searching for post-quantum cryptographic systems. In particular, the effectiveness of Shors algorithm (or its variation) on such systems is analyzed.
SecureQNN: Introducing a Privacy-Preserving Framework for QNNs at the Deep Edge

Recent concerns about real-time inference and data privacy are making Machine Learning (ML) shift to the edge. However, training efficient ML models require large-scale datasets not available for typical ML clients. Consequently, the training is usually delegated to specific Service Providers (SP), which are now worried to deploy proprietary ML models on untrusted edge devices. A natural solution to increase the privacy and integrity of ML models comes from Trusted Execution Environments (TEEs), which provide hardware-based security. However, their integration with heavy ML computation remains a challenge. This perspective paper explores the feasibility of leveraging a state-of-the-art TEE technology widely available in modern MCUs (TrustZone-M) to protect the privacy of Quantized Neural Networks (QNNs). We propose a novel framework that traverses the model layer-by-layer and evaluates the number of epochs an attacker requires to build a model with the same accuracy as the target with the information disclosed. The set of layers whose information makes the attacker spend less training effort than the owner training from scratch is protected in an isolated environment, i.e., the secure-world. Our framework will be evaluated in terms of latency and memory footprint for two ANNs built for the CIFAR-10 and Visual Wake Words (VWW) datasets. In this perspective paper, we establish a baseline reference for the results.
DeepMind AI predicts the weather

Spotting patterns in data lets Google DeepMind beat the best weather forecasts – sort of
Distributed Computing and Inference for Big Data

Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that have good inference or predictive accuracy while remaining free of individual data or obeying policies and regulations to protect privacy. In this article, we introduce the basic idea of distributed learning and conduct a selected review on various distributed learning methods, which are categorized by their statistical accuracy, computational efficiency, heterogeneity, and privacy. This categorization can help evaluate newly proposed methods from different aspects. Moreover, we provide up-to-date descriptions of the existing theoretical results that cover statistical equivalency and computational efficiency under different statistical learning frameworks. Finally, we provide existing software implementations and benchmark datasets, and we discuss future research opportunities. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
IMPACT OF WORLD BANK PROJECTS ON HUMAN CAPITAL AND IMPLEMENTATION MECHANISMS

The World Bank, established in 1944, has long been recognized as a beacon of international cooperation and development
Unseen Obstacle Detection via Monocular Camera Against Speed Change and Background Noise

This paper proposes a novel obstacle detection system optimized for mobile platforms. The system uses a long-short step Recurrent Neural Network (RNN) for optical flow estimation in various speed scenarios, combined with a global direction filter that filters out background noise, resulting in more robust and accurate obstacle detection. The system is designed to maximize processing speed and resource efficiency on mobile platforms. Performance is demonstrated on own-collected YouTube videos, achieving a precision rate of 95.2%, recall rate of 94.3%, and a frame rate of 75 FPS, surpassing state-of-the-art optical flow techniques. The proposed method is also evaluated on a real robot platform, demonstrating robust performance in detecting and avoiding obstacles of varying sizes and speeds under different lighting and noise conditions. Overall, the proposed system offers a reliable and efficient solution for obstacle detection and avoidance on mobile platforms, with high confidence in obstacle detection and avoidance in various real-world scenarios.
Integration and Application of Information Technology to Explore “Four-Self” Smart Teaching and “Five-Beauty” Smart Evaluation

Changtangli Primary School adheres to the school philosophy of “Let children meet a better future”, explores innovative integration and application of education informatization for 15 years, and forms a distinct model of “Four-Self and Five-Beauty” (“Four-Self” smart teaching model, “Five-Beauty” smart evaluation model). They are developed in inheritance, move forward in innovation, focus on the innovative integration and application of education informatization, empower comprehensive education, and help teachers and students meet a better future.
Drive the High-Quality Development of Education with Data, and Accelerate the Construction of a New Ecology of Smart Education

In recent years, Hexi District has fully implemented the Education Informatization 2.0 Action Plan, and completed three systematic projects: the modernization project of compulsory education schools, the upgrading project of compulsory education schools, and the construction of basic education informatization ‘three links and two platforms’.
Research on Big Data Helps the Physical Development of Rural Students

Focusing on how big data can help small-scale rural schools carry out off-campus sports activities, 122 students from Zhenrenqiao Primary School in Changsha High-tech Zone were used as research objects to carry out a long winter and summer vacation 78 days of follow-up research found that with the support of intelligent technology such as big data, the development and supervision of off-campus sports activities of rural students were solved.
Data-Driven Large-Scale Teaching Model Construction and Implementation

With the core goal of “data-driven large-scale teaching according to aptitude”, Bengbu City has built a data-driven large-scale teaching model implementation framework through UGBS collaborative innovation and comprehensive use of theoretical deduction and practical induction, and has produced good results and influence in local promotion and practice.
Exploring Accelerometer Sensors for Optimized Smart Shoe-Based Fall Detection

The need of immediate fall detection, particularly within the geriatric population, is emphasized by its pivotal role in curtailing consequences post-incident. In this paper we introduce an algorithm that harnesses the subject’s acceleration, angular velocity, and orientation (pitch and roll) to ascertain fall incidents. The evaluation is based on systematic extraction and comparison of features delineated over varied temporal intervals. The presented algorithm uses data extracted from a singular sensor, amalgamating a triaxial accelerometer and a gyroscope, embedded in footwear possessing integrated computational modules. The resilience of the algorithm and efficacy are empirically validated through rigorous assessments, involving five healthy participants simulating 160 fall events and 650 activities indicative of routine locomotion.
Initial Development and Performance Evaluation of a Bengali Voice-Operated Virtual Assistant for Personal Computer Control

This paper presents the preliminary development and performance evaluation of a personal computer assistant designed for voice-operated interaction in Bengali. A bespoke phonetic grammar has been devised to map Bengali phonemes onto English representations, and an algorithm has been developed to mitigate the confusion of the machine in recognizing the same type of phonetics in the language, which helps to enhance the precision in comprehending Bengali commands. The system integrates the Microsoft speech synthesizer to articulate speech in Bengali, which facilitates the expansive human-computer interaction capabilities of the system, particularly for visually impaired individuals who face challenges in text-based computer interactions. The applications of this system extend among Bengali speakers and hold potential utility within rural regions of Bangladesh. The system also aims to replace the traditional mouse and keyboard for users with their own voice commands in their native language. Upon successful implementation of the system, it achieved 92.5% accuracy in the noise-free environment and 76.4% accuracy in the noisy environment with the help of 10 Bangladeshi individuals, including both males and females.
Transhumeral Sensory System to Control Robotic Arm

Humanoid behavior of robots has resulted in significant development in many fields, including medical science and automation. Understanding Human motions and movements plays a crucial role in designing such humanoid robots. Technological research development can apply human actions in significant fields like humanoid robots, medical analysis and assistance systems, and sports analytics. This paper presents a sensory system that interacts and reads the movement and orientation of individual arms and is used to control robotic arms. The proposed system is a 7 Degrees of Freedom (DoF) sensory system with a 7 DoF robotic arm miming the human transhumeral arm. A wearable sensor plays a vital role as the data collected can be used in numerous ways, from training robots and other modules, to analyzing the performance of sports personnel. The sensory band collects data to provide each joint's rotation angle. The sensory system was built using IMU sensors, flex sensors, and a potentiometer with Arduino Uno as the primary controller. The acquired sensory data was communicated to a robotic arm using an RF transceiver setup. This system can be inexpensive and easily used in various fields, including sports analytics in rural areas. The experimental results are promising, providing a linear connection between the sensory system and the robotic arm with average error for the joints below 2%. Incorporating higher-range sensors can improve the performance of the proposed model.
Deep learning architecture towards consumer buying behaviour prediction using multitask learning paradigm

A recommendation system serves as a distributed information filter, predicting customer preferences in reviews, ratings, and comments. Analysing customer behaviour aids in understanding needs and predicting intentions. E-commerce tracks product usage and sentiment to provide a personalized network based on consumer preference modelling. The challenge lies in optimizing item selection for suitable consumers to enhance performance. To address this, an imperative is the item recommendation approach for modelling future consumer behaviour. However, traditional machine learning methods often overlook dynamic product recommendations due to evolving user interests and changes in preferences reflected in customer ratings, causing cold-start issues. To overcome these challenges, a comprehensive deep learning approach is introduced. This approach incorporates a deep neural network for consumer preference prediction, utilizing a multi-task learning paradigm to accommodate variations in consumer ratings. The research contribution lies in applying this network to predict consumer preference scores based on latent multimodal information and item characteristics. Initially, the architecture manages changing consumer aspects and preferences by extracting features and latent factors from customer review rating data. These latent factors include customer demographic information and other concealed features that signify preferences based on experiences and behaviours. Extracted latent features are processed using a sentiment analysis model to generate embedding latent features. A finely-tuned deep neural network with hyper-parameter adjustments serves as a prediction network, forming a customer performance-oriented recommendation system. It processes embedded latent features along with associated sentiments to achieve high prediction accuracy, reliability, and latency. The deep learning architecture, enriched with consumer-specific discriminative information, generates an objective function for item recommendations with minimal error, significantly enhancing predictive performance. Empirical experiments on Amazon review datasets validate the proposed model’s performance, showcasing its enhanced effectiveness and scalability in handling substantial data volumes.