Reviewerly/exHarmony huggingface dataset:
The role of school mediation in the development of digital skills among Spanish minors

Given the growing importance of digital literacy in children’s education, this article analyzes the role of school mediation in the development of digital competences among Spanish minors. Based on the results obtained from a survey of 2,900 Spanish schoolchildren between the ages of 9 and 17 years conducted at the end of 2018, we explore their attachment to their school, the digital mediation they receive at the center, and their digital competences. The data show that they have a positive feeling of belonging to their school. The most widespread competences among minors are precisely those most related to the playful use of digital devices. Analysis of the association between school mediation and the acquisition of various types of digital skills shows that mediation is effective in the acquisition of most competences among pre-adolescents, confirming the need for early school mediation. There is no doubt that media and digital education contributes to the achievement of more inclusive environments. Educational policies oriented toward safer use of the Internet and a change in the pedagogical paradigm in primary and secondary education should contribute to enhanced development of digital skills during childhood and adolescence.
Advanced Testing of Physical Security Systems through AI/ML

or down for experiment sizes. At the time of the conference we will have completed two full-scale, national, government challenges on this range. These challenges are evaluating the performance and operating costs for AI/ML-based cyber security tools for application into large, government-sized environments. These evaluations will be described, in order to provide motivation and context for various design decisions and adaptations we have made. The first challenge measured end-point security tools against 100K malware samples chosen across a range of types. The second is network detection of attempted penetration and exploitations with varying levels of covertness in a high-volume, business network. The scale of each of these challenges is requiring us to create automation systems to repeat the experiments identically for each tool. Preventing there being easy signs of malicious activity for the AI/ML tools to focus on has been a particularly interesting and challenging aspect of designing and executing these challenge events. After the events, the range continues to be used for other research such as adversarial machine learning where the repeatability, scale, and automation required for the national challenge events become essential elements for research.
IoT Security-Quality-Metrics Method and Its Conformity with Emerging Guidelines

This study proposes a security-quality-metrics method tailored for the Internet of things (IoT) and evaluates conformity of the proposed approach with pertinent cybersecurity regulations and guidelines for IoT. Cybersecurity incidents involving IoT devices have recently come to light; consequently, IoT security correspondence has become a necessity. The ISO 25000 series is used for software; however, the concept of security as a quality factor has not been applied to IoT devices. Because software vulnerabilities were not the device vendors’ responsibility as product liability, most vendors did not consider the security capability of IoT devices as part of their quality control. Furthermore, an appropriate IoT security-quality metric for vendors does not exist; instead, vendors have to set their security standards, which lack consistency and are difficult to justify by themselves. To address this problem, the authors propose a universal method for specifying IoT security-quality metrics on a globally accepted scale, inspired by the goal/question/metric (GQM) method. The method enables vendors to verify their products to conform to the requirements of existing baselines and certification programs and to help vendors to tailor their quality requirements to meet the given security requirements. The IoT users would also be able to use these metrics to verify the security quality of IoT devices.
A Discussion On The Effects Of Blockchain Technology Within The Context Of Sustainable Development

The solutions made possible by blockchain technology, and crypto assets, are changing the dynamics of many industries. Blockchain technology, which is the subject of discussion with the constructive destruction and energy consumption it triggers, offers a global and inclusive vision that serves sustainability. In this article, a discussion is presented on the effects of blockchain technology, on the environment, economy and society, which are the three main areas of sustainable development. It can be said that blockchain technology has positive and negative effects on these three main areas. Providing the opportunity to create collective value and support social impact projects with a focus on sustainable society, this technology also enables the democratic management of data. Blockchain technology, which allows the development of new business models with a focus on sustainable economy, has the potential to increase financial inclusion. While blockchains working with proof-of-work method have the potential to harm the environment with high energy consumption, blockchains working with different methods can reduce energy consumption. At the same time, studies that will increase the use of renewable energy resources in mining continue, and it is foreseen that the negative effects of this technology on the sustainable environment can be eliminated.
Improved YOLOv3 Helmet Detection Algorithm

In order to solve the problem of low detection accuracy and slow speed of helmet wearing detection in video surveillance of construction site, YOLO-Helmet, a helmet wearing detection algorithm based on improved YOLOv3(You Only Look Once V3) is proposed. The network structure is improved on the basis of the YOLOv3 algorithm. Firstly, the size of the input image is increased. Secondly, using depthwise separable convolution replace the traditional convolution in Darknet-53, which can reduce the loss of features, decrease model parameters and increase the detection speed. Thirdly, multi-scale feature fusion structure is used to get more shallow information, so as to improve the accuracy of helmet wearing detection. Experimental results show that compared with YOLOv3, YOLOv3-He1met increases by 52% in FPS (Frame Detection Per Second) and 5.7% in mAP (Mean Average Precision). The algorithm improves the detection accuracy and speed of helmet wearing detection, which has certain practicality for production.
Digital Media Art Communication Based on Wireless Cooperative Routing with Minimum Energy Consumption

Wireless cooperative routing algorithm transmits the data collected in the target area to users, so that users can obtain monitoring information timely and accurately. In the traditional low-power adaptive clustering hierarchical routing protocol, the process of building clusters is random, the resources of nodes are not fully utilized, the node death speed is fast, the network life cycle is short, and the performance is not stable enough. In addition, the route maintenance process is cumbersome and will occupy a lot of bandwidth. In order to solve the problems of real-time transmission of digital media art communication data and network lifetime optimization, a wireless cooperative routing algorithm based on minimum energy consumption is proposed. The facts of transmission strength consumption, node residual strength, and minimal information transmission extension are analyzed, a new weight feature is proposed, and a multipath statistics routing scheme is developed by using the usage of the minimal strength consumption. All digital media art propagation sensor nodes transmit data to sink nodes along multiple transmission paths. Simulation results show that the algorithm can prolong the network lifetime, reduce and balance the node energy consumption, reduce the data transmission delay, reduce the energy consumption of wireless cooperative routing based on the minimum energy consumption by 64.5%, and increase the number of compressed images by 182%.
Digital Currencies—More than a Passing Fad?

The financial system is currently in a period of exceptionally rapid technological and organizational change, with the adoption of cloud computing to store and process financial data, artificial intelligence to analyze it, and blockchain to secure it. It is fashionable to assert that digital currencies will be part of that future. But cryptocurrencies like Bitcoin are too volatile to possess the essential attributes of money. Stablecoins have fragile currency pegs that diminish their utility in transactions. And central bank digital currencies are a solution in search of a problem.
AES Image Encryption (Advanced Encryption Standard)

Abstract: An Image Encryption and Decryption Using AES (Advance Encryption Standard) Algorithm is proposed in the project. Due to increasing use of image in various field, it is very important to protect the confidential image data from unauthorized access. The design uses the iterative approach with block size of 128 bit and key size of 128, 192 or 256 bit. The numbers of round for key size of 256 bits is 14 , for 128 bits is 10 and for 192 bits is 12. As secret key increases the security as well as complexity of the cryptography algorithms. In this paper , an algorithm in which the image is an input to AES Encryption to get the encrypted image and then input it to AES Decryption to get the original image is proposed and explained which will further be implemented by me. The paper shows the study in which a system could be used for effective image data encryption and key generation in diversified application areas, where sensitive and confidential data needs to be transmitted along with the image.
Application of Improved Differential Evolution Algorithm in Solving Equations

Abstract The problem of finding roots of equations has always been an important research problem in the fields of scientific and engineering calculations. For the standard differential evolution algorithm cannot balance the convergence speed and the accuracy of the solution, an improved differential evolution algorithm is proposed. First, the one-half rule is introduced in the mutation process, that is, half of the individuals perform differential evolutionary mutation, and the other half perform evolutionary strategy reorganization, which increases the diversity of the population and avoids premature convergence of the algorithm; Second, set up an adaptive mutation operator and a crossover operator to prevent the algorithm from falling into the local optimum and improve the accuracy of the solution. Finally, classical high-order algebraic equations and nonlinear equations are selected for testing, and compared with other algorithms. The results show that the improved algorithm has higher solution accuracy and robustness, and has a faster convergence speed. It has outstanding effects in finding roots of equations, and provides an effective method for engineering and scientific calculations.
Attacking TrustZone on devices lacking memory protection

Abstract ARM TrustZone offers a Trusted Execution Environment (TEE) embedded into the processor cores. Some vendors offer ARM modules that do not fully comply with TrustZone specifications, which may lead to vulnerabilities in the system. In this paper, we present a DMA attack tutorial from the insecure world onto the secure world, and the design and implementation of this attack in a real insecure hardware.
Development of Children's Ability to Recognize Letters Through Unscrambled Words at BIAS Kindergarten Yogyakarta

The development of children's ability to recognize letters at the beginning of entering the B1 kindergarten class is still low. The purpose of this study was to improve children's ability to recognize letters through word deciphering games. The research method used is classroom action research, with the research subject of the BI group at the Integrated Islamic Kindergarten of Bina Anak Sholeh Yogyakarta. The technique in collecting data is in the form of observation, which is processed by the percentage technique. The results of the research in each cycle have shown an increase in the ability of children to recognize letters from the initial conditions, cycle I to cycle I. Thus it can be concluded that word deciphering games can improve children's ability to recognize letters.
Neural Networks in Engineering Design: Robust Practical Stability Analysis

Abstract In recent years, we are witnessing artificial intelligence being deployed on embedded platforms in our everyday life, including engineering design practice problems starting from early stage design ideas to the final decision. One of the most challenging problems is related to the design and implementation of neural networks in engineering design tasks. The successful design and practical applications of neural network models depend on their qualitative properties. Elaborating efficient stability is known to be of a high importance. Also, different stability notions are applied for differently behaving models. In addition, uncertainties are ubiquitous in neural network systems, and may result in performance degradation, hazards or system damage. Driven by practical needs and theoretical challenges, the rigorous handling of uncertainties in the neural network design stage is an essential research topic. In this research, the concept of robust practical stability is introduced for generalized discrete neural network models under uncertainties applied in engineering design. A robust practical stability analysis is offered using the Lyapunov function method. Since practical stability concept is more appropriate for engineering applications, the obtained results can be of a practical significance to numerous engineering design problems of diverse interest.
Introduction to the Theory of Imprecise Probability

Abstract The theory of imprecise probability is a generalization of classical ‘precise’ probability theory that allows modeling imprecision and indecision. This is a practical advantage in situations where a unique precise uncertainty model cannot be justified. This arises, for example, when there is a relatively small amount of data available to learn the uncertainty model or when the model’s structure cannot be defined uniquely. The tools the theory provides make it possible to draw conclusions and make decisions that correctly reflect the limited information or knowledge available for the uncertainty modeling task. This extra expressivity however often implies a higher computational burden. The goal of this chapter is to primarily give you the necessary knowledge to be able to read literature that makes use of the theory of imprecise probability. A secondary goal is to provide the insight needed to use imprecise probabilities in your own research. To achieve the goals, we present the essential concepts and techniques from the theory, as well as give a less in-depth overview of the various specific uncertainty models used. Throughout, examples are used to make things concrete. We build on the assumed basic knowledge of classical probability theory.
Robotic arm reinforcement learning control method based on autonomous visual perception

The traditional robotic arm control methods are often based on artificially preset fixed trajectories to control them to complete specific tasks, which rely on accurate environmental models, and the control process lacks the ability of self-adaptability. Aiming at the above problems, we proposed an end-to-end robotic arm intelligent control method based on the combination of machine vision and reinforcement learning. The visual perception uses the YOLO algorithm, and the strategy control module uses the DDPG reinforcement learning algorithm, which enables the robotic arm to learn autonomous control strategies in a complex environment. Otherwise, we used imitation learning and hindsight experience replay algorithm during the training process, which accelerated the learning process of the robotic arm. The experimental results show that the algorithm can converge in a shorter time, and it has excellent performance in autonomously perceiving the target position and overall strategy control in the simulation environment.
Application of Artificial Intelligence in Tesla- A Case Study

Background/Purpose: Artificial intelligence algorithms are like humans, performing a task repeatedly, each time changing it slightly to maximize the result. A neural network is made up of several deep layers that allow for learning. Financial services, ICT, life science, oil and gas, retail, automotive, industrial healthcare, and chemicals and manufacturing sectors are among the industries that employ these algorithms. The electric motor is a new concept, and the automobile industry is now undergoing intensive research to determine whether it is practicable and financially viable. There are already some first movers, such as Tesla, who have successfully established their model and are moving forward. Tesla is forcing the auto industry to adapt quickly. Tesla introduced Autopilot driver capability for its Model S vehicle. Tesla Autopilot is a suite of sophisticated driver-assist technologies that include traffic adjustment, congested roads navigation system, autopilot car-parks, computer-controlled road rules, semi-autonomous route planning on major roadways, and the ability to summon the vehicle out of a designated car-park. This article provides a comprehensive analysis of Tesla Company and Innovations of Autopilot Vehicles. Objective: This case study report addresses the growth of Tesla Company in the field of Autonomous Vehicles. Design/Methodology/Approach: The knowledge for this case study of Tesla was gathered from various academic articles, online articles, and the SWOT framework. Findings/Result: Based on the research, this paper discusses the technological histories, Autopilot driving features, safety concerns, financial plans, market challenges, different models, and how Tesla Inc. is accelerating the world's movement in multiple initiatives such as the contribution of the global economic system, study in the Artificial Intelligence and Machine Learning area. Originality/Value: This paper study provides a brief overview of Tesla Inc. given the various data collected, and information about Tesla Autopilot vehicles using Artificial Intelligence based Innovations in Entrepreneurial Oriented Cars. Paper type: A Research Case study paper - focuses on Application of Artificial Intelligence in Tesla Autopilot Vehicles and growth &amp; Journey of the Tesla Inc. Company.
Determinants of Effective Change Management for Software Deployment Projects

Software application deployment change management is one of the emerging research themes that is gaining increased focus day by day. Our study examined the factors that affect software application deployment change management in Agile software development settings. Our study provided a systematic review and synthesized the approaches, practices, and challenges reported for adopting and implementing deployment change management. The prime objective of our study was to systematically synthesize the data extracted and formulate evidence-based practical recommendations that are influential in software deployment change management. Six research themes are proposed to evaluate the rationale of the research question. This qualitative study and systematic review explored the pertinent research articles and key findings from prominent academic databases. Based on the selected criteria, the final screening revealed 25 articles from an immense set of publications. Key findings that emerged from these publications are correlated with the six research themes: (a) timely communication with all stakeholders; (b) the reliance of deployment approaches on past experience; (c) the importance of collaboration among team members having adequate knowledge of DevOps tools; (d) the ramification of the differences among development, test, and production environments; (e) the influential areas that reap the benefits of continuous delivery and deployment; and (f) the challenges of the effective use of containerization. We also found indications of the significance of Lewin’s three-step change process model in the Agile development and deployment environment. Overall, our study deepens understanding of this thriving research area and contributes to the literature on Agile deployment and the software change management process.
MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning

Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives. We present MAGMA - a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. Building on Frozen, we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input. The pretraining is entirely end-to-end using a single language modeling objective, simplifying optimization compared to previous approaches. Importantly, the language model weights remain unchanged during training, allowing for transfer of encyclopedic knowledge and in-context learning abilities from language pretraining. MAGMA outperforms Frozen on open-ended generative tasks, achieving state of the art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks, while pretraining on 0.2% of the number of samples used to train SimVLM.
Self-Supervised Video Pose Representation Learning for Occlusion- Robust Action Recognition

Action recognition based on human pose has witnessed increasing attention due to its robustness to changes in appearances, environments, and view-points. Despite associated progress, one remaining challenge has to do with occlusion in real-world videos that hinders the visibility of all joints. Such occlusion impedes representation of such scenes by models that have been trained on full-body pose data, obtained in laboratory conditions with specific sensors. To address this, as a first contribution, we introduce OR- VPE, a novel video pose embedding network that is streamlined to learn an occlusion-robust representation for pose sequences in videos. In order to enable our embedding network to handle partially visible joints, we propose to incorporate a sub-graph data augmentation mechanism during training, which simulates occlusions, into a video pose encoder based on Graph Convolutional Networks (GCNs). As a second contribution, we apply a contrastive learning module to train the video pose representation in a self-supervised manner without the necessity of action annotations. This is achieved by maximizing the mutual information of the same pose sequence pruned into different spatio-temporal subgraphs. Experimental analyses show that compared to training the same encoder from scratch, our proposed OR-VPE, with pre-training on a large-scale dataset, NTU-RGB+D 120, improves the performance of the downstream action classification on Toyota Smarthome, N-UCLA and Penn Action datasets.