Reviewerly/exHarmony huggingface dataset:
Introduction to Deep-Learning Concepts and TensorFlow

Deep learning evolved from artificial neural networks, which have existed since the 1940s. Neural networks are interconnected networks of processing units called artificial neurons that loosely mimic the axons found in a biological brain. In a biological neuron, dendrites receive input signals from various neighboring neurons, typically more than 1000 of them. These modified signals are then passed on to the cell body or soma of the neuron, where these signals are summed together and then passed on to the axon of the neuron. If the received input signal is more than a specified threshold, the axon will release a signal, which will be passed on to the neighboring dendrites of other neurons. Figure 2-1 depicts the structure of a biological neuron for reference.
Working with Intelligent Narrative Technologies

Thue, DavidArtificial IntelligenceArtificial Intelligence (AI) systems have been used to generate narrative structuresNarrative structure and simulate virtual story charactersCharacter at a variety of different scales, across both academia and industryIndustry (industrial). Such systems are often built from specialized components known as intelligent narrative technologies. The goal of this chapter is to highlight some of the challenges that can arise when such technologies are used as part of authoring or executing an interactive story. Authoring in a way that works with these technologies often requires a host of technical skills, such as writing computer codeCode, building mathematical models, or predicting the effect of a simple changeChange on a large, complex systemComplex system. In addition to explaining why these skills are needed and the problems that they help to solve, this chapter will highlight recent and ongoing efforts to make authoring for intelligent narrative technologies more accessibleAccessible (accessibility) to those with fewer technical skills.
An Analysis of the Dark Web Challenges to Digital Policing

The Dark Web is the hidden group of Internet sites that can only be accessed through specific software. The Dark Web enables private computer networks to communicate anonymously without revealing identifying information. Keeping Internet activity anonymous and private can be beneficial for both legal and illegal applications. Although it is used to evade government censorship, it is also deployed for highly illegal activity. The aim of this paper is to provide a critical analysis of the technical, legal, and ethical challenges to policing the Dark Web. The most significant recommendation identified in this paper is the need for stronger national cyber security strategies, increased awareness and use of the UN Cybercrime Repository, and greater support from intergovernmental organisations. This would help to contribute towards addressing many of the technical, legal and ethical challenges concerning the multi-jurisdictional nature of Dark Web investigations and lack of reliable data and resources while ensuring transparency and accountability. The recommendations proposed in this paper are restricted by certain limitations, therefore, further research is recommended into the field of digital policing and the Dark Web.
Source-free semi-supervised domain adaptation via progressive Mixup

Existing domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data. However, the source data are not always available due to the privacy preserving consideration or bandwidth limitations. To address this issue, source-free domain adaptation is proposed to perform domain adaptation without accessing the source data. Recently, the adaptation paradigm is attracting increasing attention, and multiple works have been proposed for unsupervised source-free domain adaptation. However, without utilizing any supervised signal and source data at the adaptation stage, the optimization of the target model is unstable and fragile. To alleviate the problem, we focus on utilizing a few labeled target data to guide the adaptation, which forms our method into semi-supervised domain adaptation under a source-free setting. We propose a progressive data interpolation strategy including progressive anchor selection and dynamic interpolation rate to reduce the intra-domain discrepancy and inter-domain representation gap. Extensive experiments on three public datasets demonstrate the effectiveness as well as the better scalability of our method.
Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition

Systems that analyse faces have seen significant improvements in recent years and are today used in numerous application scenarios. However, these systems have been found to be negatively affected by facial alterations such as tattoos. To better understand and mitigate the effect of facial tattoos in facial analysis systems, large datasets of images of individuals with and without tattoos are needed. To this end, we propose a generator for automatically adding realistic tattoos to facial images. Moreover, we demonstrate the feasibility of the generation by using a deep learning-based model for removing tattoos from face images. The experimental results show that it is possible to remove facial tattoos from real images without degrading the quality of the image. Additionally, we show that it is possible to improve face recognition accuracy by using the proposed deep learning-based tattoo removal before extracting and comparing facial features.
Domain Constraints-Driven Automatic Service Composition for Online Land Cover Geoprocessing

With the rapid development of web service technology, automatic land cover web service composition has become one of the key challenges in solving complex geoprocessing tasks of land cover. Service composition requires the creation of service chains based on semantic information about the services and all the constraints that should be respected. Artificial intelligence (AI) planning algorithms have recently significantly progressed in solving web service composition problems. However, the current approaches lack effective constraints to guarantee the accuracy of automatic land cover service composition. To address this challenge, the paper proposes a domain constraints-driven automatic service composition approach for online land cover geoprocessing. First, a land cover service ontology was built to semantically describe land cover tasks, data, and services, which assist in constructing domain constraints. Then, a constraint-aware GraphPlan algorithm was proposed, which constructs a service planning graph and searches services based on the domain constraints for generating optimal web service composition solutions. In this paper, the above method was integrated into a web prototype system and a case study for the online change detection automatic geoprocessing was implemented to test the accuracy of the method. The experimental results show that with this method, a land cover service chain can generate automatically by user desire objective and domain constraints, and the service chain execution result is more accurate.
Self-balancing Robot Using Arduino and PID Controller

Robots are programmable machines built to mimic human actions. One such action is locomotion of robots which is the recent area of research. Two-wheeled robots which diligently stabilize it may contribute to the locomotion of robots in upcoming decades. In this paper, the PID controller and Arduino are utilized to design the self-balancing robot. This paper focuses primarily on developing a controller that will aid the robot and test against several parameters such as position, balance along vertical axis and signals for controlling. The accelerometer and gyroscope sensor values are used to determine the precise position of the robot in 3 dimensional space. The sensor values are sent to the controller which controls the rotation of wheels thus aiding in balancing the robot. The two-wheeled robot turns precisely while navigating through different obstacles as against four-wheeled robots.
An Enhanced Optimized Abstractive Text Summarization Traditional Approach Employing Multi-layered Attentional Stacked LSTM with the Attention RNN

Progress in technology is occurring at an escalating speed all through this world. The human concentration stretch remains continually lessening, and the time span an individual needs to put in reading is decreasing at a rapid pace. Due to the development of technology, a wide amount of text data are produced daily through social networking services, business firms, medical information services, and news. In fact, it remains a complicated job to excerpt fascinating designs out of the text data like views, summaries, and actuality particulars possessing variable length. Due to the issues of the length of text data and the complication of feature value excerption in news, this study proffers a hybrid feature excerption out of the multi-layered attentional StackedLSTM (long short-term memory) (MASta-LSTM-RNN) alongside the Attention RNN network which automatically produces a summary out of a lengthy text. In this study, an input of the StackedLSTM module and the correlation betwixt words and classes are portrayed by a discrete vector as an input of the MLP module for the sake of attaining exhaustive learning of spatial attribute data, time-series attribute data, and correlation betwixt words and classes of news text. The correlation between words is portrayed by the word vector. For inspecting the steadiness and execution of the proffered methodology, several experiments were executed. The outcomes of these experiments exhibit that the proffered methodology resolves the issues of text length, the complication of attribute excerption in the news text, and categorization of news text effectually and has acquired finer results.
Development Research of English Speaking Program using Metaverse

Objectives The purpose of this study is to develop an English speaking program using metaverse for language learners to core skills required in 21st century.&#x0D; Methods Nine Korean EFL students at age 12-13 who completed the national curriculum of grade 6 were recruited as the participants of the pilot study to confirm the effects of the program. The effects of the designed lessons were examined through the questionnaire-based tests, learning progress monitoring, and interviews. The variables of the tests included digital literacy, and L2 motivational experience.&#x0D; Results The results of pre- and post-test comparison showed improvements in digital literacy, and motivation with regard to English learning experience. According to the interviews, the learners and the instructor were positive on the whole about the use of metaverse in English classrooms based on the lesson design proposed in this study. The learners showed great interests in the metaverse-based English lessons in general, and reported higher immersion into classes than previous school classes due to diverse activities and affordance of metaverse. The instructor who delivered the lesson emphasized the importance of teacher training for the success of the future metaverse-mediated language learning. The progress monitoring and interviews also indicated a demand for dealing technical issues.&#x0D; Conclusions The implications of this study and suggestions for future research are as follows. This study confirmed the effects of metaverse-mediated language learning on high immersion by transcending temporal and spacial borders, leading to authentic learning and L2 speaking experience. Moreover, with appropriate task designs, metaverse helped learners foster not only oral communication skills but also digital literacy in English classes. Lastly, this study suggests developing a detailed guideline for potential problems regarding manipulation and technical errors for successful application of metaverse in educational programs in future research and classrooms.
Adaptive Synapse Adjustment for Multivariate Cortical Learning Algorithm

This paper proposes an adaptive synapse adjustment method in the cortical learning algorithm (CLA), which simultaneously predicts multiple time-series data. The cortical learning algorithm is a promising time-series data prediction algorithm. A way to predict multiple time-series data simultaneously is to prepare multiple CLA predictors. Although one CLA predictor handles one time-series data, CLA predictors are associated with each other by using synapses. However, the synapse relation between multiple CLA predictors harms the multivariate prediction when the multiple time-series data are lowly related. The proposed method evaluates a partial prediction accuracy for each synapse segment. The proposed method adaptively adds and deletes synapses based on the calculated partial prediction accuracy. This suppresses the synapses crossing multiple CLA predictors, which harms the simultaneous predictions of multiple time-series data. Experiments using artificial and real-world meteorological data show that the proposed method achieves higher prediction accuracy than the conventional method by suppressing the harmful effects caused by synapses crossing multiple predictors.
3D human pose detection using nano sensor and multi-agent deep reinforcement learning

Due to the complexity of three-dimensional (3D) human pose, it is difficult for ordinary sensors to capture subtle changes in pose, resulting in a decrease in the accuracy of 3D human pose detection. A novel 3D human motion pose detection method is designed by combining Nano sensors and multi-agent deep reinforcement learning technology. First, Nano sensors are placed in key parts of the human to collect human electromyogram (EMG) signals. Second, after de-noising the EMG signal by blind source separation technology, the time-domain and frequency-domain features of the surface EMG signal are extracted. Finally, in the multi-agent environment, the deep reinforcement learning network is introduced to build the multi-agent deep reinforcement learning pose detection model, and the 3D local pose of the human is output according to the features of the EMG signal. The fusion and pose calculation of the multi-sensor pose detection results are performed to obtain the 3D human pose detection results. The results show that the proposed method has high accuracy for detecting various human poses, and the accuracy, precision, recall and specificity of 3D human pose detection results are 0.97, 0.98, 0.95 and 0.98, respectively. Compared with other methods, the detection results in this paper are more accurate, and can be widely used in medicine, film, sports and other fields.
Comparative Analysis of Encryption Algorithms

Purpose: Encryption algorithm allows users to extend the assurance found in the physical world to the electronic world in the carrying out of our day-to-day activities. This research attempts to make a comparative evaluation of two encryption algorithms (Advanced Encryption Standard (AES) and Rivest Shamir Algorithm (RSA) in order to ascertain the most reliable in terms of their encryption time, decryption time, Key length, and cipher length.&#x0D; Methodology: Java programming was used for the development and Mongo Db to generate data parsing.&#x0D; Findings: The results obtained from the development revealed that AES is considered more efficient because it uses lesser time for encryption and decryption, reduces cipher and key length as compared with RSA which consumes longer encryption and decryption time, and increases cipher and key length. Excel package was also used to display the pictorial representation of the comparison between these algorithms.&#x0D; Recommendation: Advanced encryption standard is recommended for security-based application developers because of the small time of encryption and decryption.
Improved gauge-unfixing formalism through a prototypical second-class system

Abstract We contextualize the improved gauge-unfixing (GU) formalism within a rather general prototypical second-class system, obtaining a corresponding first-class equivalent description enjoying gauge invariance which can be applied to several situations. The prototypical system is chosen to represent a considerable class of relevant models in field theory. By considering the improved version of the GU formalism, we show that any gauge-invariant function can be obtained in terms of a specific deformation in phase space, benefiting thus from the fact that no auxiliary variables are needed in the process. In this way, the resulting converted first-class system is constructed out of the same original canonical variables, preserving the number of degrees of freedom. We illustrate the technique with an application to the nonlinear sigma model.
The role of supply chain alignment in coping with resource dependency in blockchain adoption: empirical evidence from China

Purpose The deployment of blockchain technology (BT) throughout the supply chain is usually led by large firms that dominate the supply chain. Leading firms can encourage other resource-constrained partners to get on board by providing technical and financial support. However, due to the uncertain consequences of relying on leading firms, these partners may still be reluctant to adopt BT. Drawing on resource dependence theory, this study aims to investigate whether and when supply chain alignment can be used as a dependency coping strategy to increase the willingness of resource-constrained partners to adopt BT. Moreover, it aims to examine the motivators for supply chain alignment. Design/methodology/approach This study adopted a survey research design and collected data from 364 small and medium-sized enterprises in China. Findings Supply chain alignment positively affects BT adoption. The effect of supply chain alignment on BT adoption is contingent on guanxi (a Chinese cultural tradition of interpersonal connections that facilitate a mutual exchange of favors). Relative advantage, technology complexity, organizational readiness and cost are motivators for supply chain alignment. Supply chain alignment mediates the effect of cost, technology complexity and relative advantage on BT adoption. Originality/value This research addresses the problem of resource dependency in the context of BT adoption which has been overlooked by previous research. Moreover, this paper enriches the BT literature by identifying supply chain alignment as an important channel for technology–organization–environment factors to influence BT adoption.
AI and Co-Simulation Driven Resource Management in Fog Computing Environments

Research Summary: In the past decade, the evolution of our digital lives has accelerated across multiple facets, including efficient computation, communication and transportation, making our lives simpler and more convenient. This evolution has been driven by several factors such as the rising concern for climate change and sustainable computing, the expected end of Moore's law for silicon-based compute systems and the recent lifestyle-changing pandemics. With changing user demands and application scenarios, novel techniques are required to fuel further growth for high-fidelity and scalable computation.
Information and communication technologies’ (ICTs) use among farmers in Qena Governorate of Upper Egypt

Purpose The key purpose of this study is to investigate the use of information and communication technologies (ICTs) among farmers in Qena Governorate of Upper Egypt farmers' use of ICTs in Qena Governorate, one of Upper Egypt's largest agricultural governorates. Design/methodology/approach A semi-structured questionnaire survey, as well as some personal interviews, was used to collect detailed information about farmers' use or non-use of ICTs. The study's participants included 131 farmers. Findings Respondents were mostly males who were married and had an average level of education and age. The majority of respondents had 26–30 years of work experience. The majority of them used ICTs for 4–6 h per day. All respondents used ICTs to learn about new agricultural products, increase land productivity, gather market information and learn about livestock. Mobile phones, social media and the internet were identified as the most useful and simple-to-use technologies by all respondents. Illiteracy, a lack of knowledge and awareness and the cost of CITs were discovered to have an impact on respondents' use of information. Originality/value The significance of this study stems from the hotly debated topic of the need to use technology tools in agriculture, whether for agricultural work or communication with others. ICTs provide a variety of new ways to communicate and exchange information and knowledge.
No pain, no gain? The effects of adding a pain stimulus in virtual training for police officers

Virtual training systems provide highly realistic training environments for police. This study assesses whether a pain stimulus can enhance the training responses and sense of the presence of these systems. Police officers (n = 219) were trained either with or without a pain stimulus in a 2D simulator (VirTra V-300) and a 3D virtual reality (VR) system. Two (training simulator) × 2 (pain stimulus) ANOVAs revealed a significant interaction effect for perceived stress (p = .010, ηp2 = .039). Post-hoc pairwise comparisons showed that VR provokes significantly higher levels of perceived stress compared to VirTra when no pain stimulus is used (p = .009). With a pain stimulus, VirTra training provokes significantly higher levels of perceived stress compared to VirTra training without a pain stimulus (p < .001). Sense of presence was unaffected by the pain stimulus in both training systems. Our results indicate that VR training appears sufficiently realistic without adding a pain stimulus. Practitioner summary: Virtual police training benefits from highly realistic training environments. This study found that adding a pain stimulus heightened perceived stress in a 2D simulator, whereas it influenced neither training responses nor sense of presence in a VR system. VR training appears sufficiently realistic without adding a pain stimulus.
MSWasm: Soundly Enforcing Memory-Safe Execution of Unsafe Code

Most programs compiled to WebAssembly (Wasm) today are written in unsafe languages like C and C++. Unfortunately, memory-unsafe C code remains unsafe when compiled to Wasm—and attackers can exploit buffer overflows and use-after-frees in Wasm almost as easily as they can on native platforms. Memory- Safe WebAssembly (MSWasm) proposes to extend Wasm with language-level memory-safety abstractions to precisely address this problem. In this paper, we build on the original MSWasm position paper to realize this vision. We give a precise and formal semantics of MSWasm, and prove that well-typed MSWasm programs are, by construction, robustly memory safe. To this end, we develop a novel, language-independent memory-safety property based on colored memory locations and pointers. This property also lets us reason about the security guarantees of a formal C-to-MSWasm compiler—and prove that it always produces memory-safe programs (and preserves the semantics of safe programs). We use these formal results to then guide several implementations: Two compilers of MSWasm to native code, and a C-to-MSWasm compiler (that extends Clang). Our MSWasm compilers support different enforcement mechanisms, allowing developers to make security-performance trade-offs according to their needs. Our evaluation shows that on the PolyBenchC suite, the overhead of enforcing memory safety in software ranges from 22% (enforcing spatial safety alone) to 198% (enforcing full memory safety), and 51.7% when using hardware memory capabilities for spatial safety and pointer integrity. More importantly, MSWasm’s design makes it easy to swap between enforcement mechanisms; as fast (especially hardware-based) enforcement techniques become available, MSWasm will be able to take advantage of these advances almost for free.