Reviewerly/exHarmony huggingface dataset:
Quantum Transfer Learning Using the Large-Scale Unsupervised Pre-Trained Model Wavlm-Large for Synthetic Speech Detection

The development of quantum machine learning demonstrates its quantum advantages over traditional deep learning, which promises to discover new patterns on supervised classification datasets. This work proposes a classical-to-quantum transfer learning system based on the large-scale unsupervised pre-trained model to demonstrate the competitive performance of quantum transfer learning for synthetic speech detection. We use the pre-trained model WavLM-Large to extract feature maps from speech signals, obtain low-dimensional embedding vectors through classical network components, and then jointly fine-tune the pre-trained model and classical network components with a variational quantum circuit (VQC). We evaluate our system on the ASVspoof 2021 DF task, and the experiments using quantum circuit simulations show that quantum transfer learning can improve the performance of the classical transfer learning baseline on the task.
PROGRAM “SITOGA” (SING, STORY, AND GAMES): PELATIHAN BAHASA INGGRIS BAGI GURU TK DESA SENAUNG KABUPATEN MUARO JAMBI

The purpose of this Community Service (PKM) activity is an effort to help teachers of the Jauharul Iman Kindergarten Senaung Village, Jambi Outer City District, Muaro Jambi Regency, Jambi Province, to find, create and apply English learning materials according to the age of the children. To achieve this goal, the PKM activity carried out is SITOGA (Sing, Story and Games) for kindergarten teachers. This activity takes place during the month of April to May 2022, where the first month is focused on deepening material on the principles of teaching English for Young Learners, introducing learning resources, and learning methods and techniques. The next second month becomes an opportunity for participants (kindergarten teachers) in making learning resources and implementing or practicing learning. The results of PKM activities include participants being able to understand theories of teaching English in kindergarten, teachers being able to recognize several English songs, combining songs and movements in English, making simple English teaching media, designing learning activities with fun activities, and getting to know and practice&#x0D; different types of games
Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning Using Graph Neural Networks and Transfer Learning

Similarity-based retrieval of semantic graphs is a crucial task of Process-Oriented Case-Based Reasoning (POCBR) that is usually complex and time-consuming, as it requires some kind of inexact graph matching. Previous work tackles this problem by using Graph Neural Networks (GNNs) to learn pairwise graph similarities. In this paper, we present a novel approach that improves on the GNN-based case retrieval with a Transfer Learning (TL) setup, composed of two phases: First, the pretraining phase trains a model for assessing the similarities between graph nodes and edges and their semantic annotations. Second, the pretrained model is then integrated into the GNN model by either using fine-tuning, i.e., the parameters of the pretrained model are further trained, or feature extraction, i.e., the parameters of the pretrained model are converted to constants. The experimental evaluation examines the quality and performance of the models based on TL compared to the GNN models from previous work for three semantic graph domains with various properties. The results show the great potential of the proposed approach for reducing the similarity prediction error and the training time.
Evolutionary Optimization Algorithm with Deep Echo State Network for Anomaly Detection on Secure Cloud Computing Environment

Cloud Computing (CC) is undoubtedly an indispensable technology across the world. It indicates a revolution in collaborative services and data storage. Yet, security problems have increased with the move to CC, which includes intrusion detection systems (IDS). Anomaly detection (AD) is a vital method that ensures the security of CC environments. It identifies unusual behaviour that specifies a security threat. AD is important in a secure CC environment for identifying breaches or attacks and monitoring system performance. The design of potential AD in a secure CC environment needs a combination of machine learning (ML) algorithms, continuous monitoring, and data analysis. Therefore, this article introduces a new Falcon Optimization Algorithm with Deep Echo State Network for Anomaly Detection (FOADESN-AD) technique for a secure CC environment. The presented FOADESN-AD technique exploits the DL model with a metaheuristic optimizer for anomaly or intrusion detection in the cloud platform. To accomplish this, the FOADESN-AD technique initially performs a Z-score normalization process. For anomaly detection, the FOADESN-AD technique uses the DESN classifier, which accurately detects the presence of anomalies inthe cloud environment.Moreover, the FOA is utilized to finetune the hyperparameter values of the DESN model, achieving superior classification results.The performance analysis of the FOADESN-AD method is implemented on the CSE-CICIDS-2018 dataset.The experimental values stated the betterment of the FOADESN-AD method over other existing approaches.
MODERN ISSUES OF THE INTEGRATION OF PEDAGOGY AND PSYCHOLOGY INTO THE EDUCATIONAL PROCESS OF HIGHER EDUCATIONAL INSTITUTIONS

Theoretical and methodological approaches to the concepts of "individual style of activity", "psychological" and "pedagogical culture" are given. The similarities and differences in the consideration of these concepts by psychologists and teachers are shown. An article is being formulated on the differentiation and integration of these approaches. The possibilities of developing the personality culture of the future professional in the educational environment of the university are shown by creating a system of psychological and pedagogical influences in order to optimize the process of its adaptation and successful functioning in the conditions of educational, service and future professional activities. The key link in this process is the individual style of activity.
How Artificial Intelligence can leverage Project Management Information system (PMIS) and data driven decision making in project management

This article focuses on the opportunities of utilizing Artificial intelligence in project management in general. Special emphasis to project management data driven decision making and the relevant tools represented in processes, tools and techniques of PMIS. The data used to support this research is collected from primary and secondary sources to emphasize the research paper results. The approach used in data collection was a qualitative method where interviews conducted. Advanced projects and cases from Dubai and UAE were also investigated. The results showed that with the creation of big data, AI can now be more than just technology and is capable of performing more complex tasks using advanced techniques and algorithms. AI helps more effectively through integrating project management phases and the entire project lifecycle process. Bringing AI to the project management domain across the world can provide a sustainable world for future generations. It also assists in faster and smarter decisions. It is recommended to monitor AI until users are satisfied and can trust the systems outcome. AI will have a tremendous growth as projects in smart cities are growing bigger , complex and demanding.
Photonic-reconfigurable entanglement distribution network based on silicon quantum photonics

The entanglement distribution network connects remote users by sharing entanglement resources, which is essential for realizing quantum internet. We propose a photonic-reconfigurable entanglement distribution network (PR-EDN) based on a silicon quantum photonic chip. The entanglement resources are generated by a quantum light source array based on spontaneous four-wave mixing in silicon waveguides and distributed to different users through time-reversed Hong–Ou–Mandel interference by on-chip Mach–Zehnder interferometers with thermo-optic phase shifters (TOPSs). A chip sample is designed and fabricated, supporting a PR-EDN with 3 subnets and 24 users. The network topology of the PR-EDN could be reconfigured in three network states by controlling the quantum interference through the TOPSs, which is demonstrated experimentally. Furthermore, a reconfigurable entanglement-based quantum key distribution network is realized as an application of the PR-EDN. The reconfigurable network topology makes the PR-EDN suitable for future quantum networks requiring complicated network control and management. Moreover, it is also shown that silicon quantum photonic chips have great potential for large-scale PR-EDN, thanks to their capacities for generating and manipulating plenty of entanglement resources.
Text-To-Image Generation Using AI

This model is suggested to produce the photos that are provided in the text.This has the power to turn abstract images into actual ones.DALL-E is necessary for the conversion.Making artistic, realism-based graphics from the description will be enjoyable.Kotline and Java were used to create this Android application project.For this project, we employed a natural language description prompt.Through prompts, it generates visuals.This will help incorporate our many ideas and thoughts into the diagrammatic presentation.DALLE can be used for business endeavors such as advertising, publishing, selling, etc.It will display images of our choice, resulting in anthropomorphic imagery and the collaboration of unconnected thoughts.It is viable and produces believable items.We can turn our inventive thoughts into reality using this Android application project.It is user-friendly software with no visual problems.And we can't discover this fictitious picture generator on any search engine.This Android application project will generate an image in whatever size you want.And it does not affect image quality.A picture's quality size will be 256*256, 512*12, and 1024*1024.Based on the quality of our network, we may select a quality size.We must first ensure that the network infrastructure is in place before we can use this application.
Readers use word length information to determine word order.

It is assumed by the OB1-reader model that activated words are flexibly associated with spatial locations. Supporting this notion, recent studies show that readers can confuse the order of words. As word position coding is assumed to rely, among other things, on low-level visual cues, OB1 predicts that it must be harder to determine the order of words when these are of equal length, and consequently, that it is more difficult to read uniform word length sentences. Here we review recent evidence, obtained by our peers, in line with this prediction. We additionally report an analysis of eye-movement data from the GECO corpus, replicating the phenomenon in a natural reading setting, and an experiment revealing a negative impact of length uniformity in a grammatical decision task. By virtue of the spatiotopic sentence-level representation, OB1-reader is currently the only model of reading to account for these behaviors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Designing Skill Cards for New Professions: Easy-toread Facilitators and Validators

Easy-to-read (E2R) can be defined broadly as a text simplification method aimed at making information more accessible to people with reading and learning difficulties, promoted intensely in recent years as a top-priority European strategy for social inclusion. Although validation of E2R content is an important step in the process, neither validation nor those involved in it, i.e. validators and facilitators, get official recognition. The present article focuses on the creation of skill cards for E2R validators and facilitators within the Train2Validate European project, in an attempt to promote the professionalization of validation work.
A Model for Quantifying Investment Decision-making Using Deep Reinforcement Learning (PPO Algorithm)

Bitcoin has nowadays shown its importance in finance systems. With great interest to all kinds of investors in the financial sector, it is important to analyse the relationship between Bitcoin and gold. We consider bitcoin and gold as two stocks and calculate the correlation between bitcoin and gold. By introducing the calculation of dynamic penalty coefficient, the problem of dual-stock portfolio investment is transformed into the problem of single-stock purchase investment, which greatly reduces the difficulty of feature engineering and model application. In terms of decision-making model, deep reinforcement learning (PPO algorithm) is used to make quantitative investment decisions. Therefore, we use the expected data in SLTM as the input data of deep reinforcement learning, and combine it with deep reinforcement learning for training. Compared with machine learning to quantify investment decisions, after a period of training, the accuracy rate has improved by 10.038%.
Doing digital childrens geographies, imperfectly: methodological reflections on a child-led digital tour in a slum neighbourhood in the Philippines

This chapter offers a reflection on slum-dwelling children’s participation in digitally-mediated fieldwork in the Philippines. It focuses on the role of digital technologies in how communication and digital identities as researchers and participants are negotiated. This chapter discusses how geographical boundaries played both hindrance and catalyst to researcher-participant interactions and unpacks the implications of this experience for digital geographies in terms of the limitations of doing research via digital platforms and how digitally-mediated research enables children to effectively challenge the research process of using digital technologies itself. This chapter offers an invitation to geographers using digitally-mediated methods toward a research agenda on how digital geographies make salient the role of children in digital geographies. I contribute to the broader discussions within the field of digital geographies by offering a reflexive discussion on the unintended consequences of doing digital fieldwork.
Predicting chaotic dynamics from incomplete input via reservoir computing with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>D</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math> -dimension input and output

Predicting future evolution based on incomplete information of the past is still a challenge even though data-driven machine learning approaches have been successfully applied to forecast complex nonlinear dynamics. The widely adopted reservoir computing (RC) can hardly deal with this since it usually requires complete observations of the past. In this paper, a scheme of RC with $(D+1)$-dimension input and output (I/O) vectors is proposed to solve this problem, i.e., the incomplete input time series or dynamical trajectories of a system, in which certain portion of states are randomly removed. In this scheme, the I/O vectors coupled to the reservoir are changed to $(D+1)$-dimension, where the first $D$ dimensions store the state vector as in the conventional RC, and the additional dimension is the corresponding time interval. We have successfully applied this approach to predict the future evolution of the logistic map and Lorenz, R\"ossler, and Kuramoto-Sivashinsky systems, where the inputs are the dynamical trajectories with missing data. The dropoff rate dependence of the valid prediction time (VPT) is analyzed. The results show that it can make forecasting with much longer VPT when the dropoff rate $\ensuremath{\theta}$ is lower. The reason for the failure at high $\ensuremath{\theta}$ is analyzed. The predictability of our RC is determined by the complexity of the dynamical systems involved. The more complex they are, the more difficult they are to predict. Perfect reconstructions of chaotic attractors are observed. This scheme is a pretty good generalization to RC and can treat input time series with regular and irregular time intervals. It is easy to use since it does not change the basic architecture of conventional RC. Furthermore, it can make multistep-ahead prediction just by changing the time interval in the output vector into a desired value, which is superior to conventional RC that can only do one-step-ahead forecasting based on complete regular input data.
Logic of Nature Seen in Particle Properties, in the Rise of the Universe and Consequences for the Structure of Complex Systems

To push realistic learning processes and simulations to extremes far beyond present artificial intelligence, they must be conform with nature and have to follow mathematical logic. To see in full strength the logic structure of nature, the study of basic systems - as hadrons and leptons - is best suited, because ambiguities due a large complexity are reduced to a minimum. To describe such systems, a new quantum theory has been derived recently, in which in addition to the usual treatment of fermions bosons are considered also to fulfill energy and momentum conservation explicitly. With negative binding energies of fermions and positive ones of bosons, the total energy is zero, giving rise to a quantitative description of hadron and lepton properties with uncertainties &lt; 1/1000. In addition, this formalism provides a mechanism, by which particles could be created out of the vacuum, indicating that the universe emerged out of nothing (the vacuum). This leads finally to a realistic view of the cosmic evolution. Further, the present theory can be taken as basis for the description of complex systems in gravity, heavy atoms, molecules, materials and even biological structures.
A Framework for Cybersecurity Awareness in Saudi Arabia

The rapid advancement in technology has improved people's lives, but it has also increased the risks that come with using the Internet, including cybercrimes. Lately, Saudi Arabia, a booming economy, has become one of the prime targets of cyberattacks. The massive amount of cyberattacks targeting Saudi Arabia can be attributed to the lack of cybersecurity awareness among Saudi people. The objective of this study is to propose methods on the national level to increase the awareness of cybersecurity among Saudi people. We conducted a cybersecurity assessment survey to assess the cybersecurity awareness among Saudi people. The survey result indicated negligent behavior and lack of awareness. To address this issue, we proposed a cybersecurity awareness framework which targets all strata of Saudi Arabia demography. The proposed framework not only emphasized training programs in schools, universities and organizations but also addresses the awareness issue in people from informal backgrounds. The framework also includes the importance of incident response and its role in reducing incidents.
MnTTS2: An Open-Source Multi-speaker Mongolian Text-to-Speech Synthesis Dataset

Text-to-Speech (TTS) synthesis for low-resource languages is an attractive research issue in academia and industry nowadays. Mongolian is the official language of the Inner Mongolia Autonomous Region and a representative low-resource language spoken by over 10 million people worldwide. However, there is a relative lack of open-source datasets for Mongolian TTS. Therefore, we make public an open-source multi-speaker Mongolian TTS dataset, named MnTTS2, for the benefit of related researchers. In this work, we prepare the transcription from various topics and invite three professional Mongolian announcers to form a three-speaker TTS dataset, in which each announcer records 10 h of speeches in Mongolian, resulting 30 h in total. Furthermore, we build the baseline system based on the state-of-the-art FastSpeech2 model and HiFi-GAN vocoder. The experimental results suggest that the constructed MnTTS2 dataset is sufficient to build robust multi-speaker TTS models for real-world applications. The MnTTS2 dataset, training recipe, and pretrained models are released at: https://github.com/ssmlkl/MnTTS2 .
How Many Data Does Machine Learning in Human–Computer Interaction Need?: Re-Estimating the Dataset Size for Convolutional Neural Network-Based Models of Visual Perception

Artificial intelligence (AI)-based user-interface (UI) design and evaluation are currently constrained by the scarcity of human-generated training data. Correspondingly, choosing appropriate neural network (NN) architecture and carefully planning the sample size is essential for building accurate machine learning models. Previously, we have estimated that for a convolutional NN to produce better mean square errors (MSEs) than feature-based models, the required training dataset size should be approximately 3000. Our current validation study with roughly 4000 web UIs and 233 subjects suggests that the estimation should be closer to 17,000. We propose corrected regression models suggesting that the dataset size effect is better described using a logarithmic function. We also report significant differences in MSEs between the employed perception dimensions, with Aesthetics models having an MSE 21.5% worse than Complexity and 12.1% worse than Orderliness.
Broad Learning System for Indoor CSI Fingerprint Localization

With the development of the Internet of Things (IoT), the demand for location-based services in indoor environments proliferates. Channel State Information (CSI) based fingerprint localization has become a research hot spot. However, a compelling method to address this issue does not appear because of the hardship of processing CSI data. Meanwhile, many fingerprint localization algorithms have a time-consuming offline training phase. Therefore, we propose an indoor CSI fingerprint localization system based on the broad learning system (BLS). First, we filter the outliers, generate delegates of CSI by the data nugget algorithm, and use tensor decomposition to reconstruct the CSI delegates. Moreover, we utilize Isometric mapping (Isomap) to extract CSI features to reduce BLS’s complexity. The experimental results show that our scheme outperforms several existing algorithms in two indoor environments.
Efficient Pattern-based Static Analysis Approach via Regular-Expression Rules

Pattern-based static analyzers like SpotBugs use bug patterns (rules) to detect bugs may have several limitations: (1) too slow, (2) do not usually support analysis of partial programs, (3) require parsing code into AST/CFG, and (4) high false positive rate. Each pattern relies on analysis context (e.g., data flow analysis) to improve the accuracy of the analysis. To understand the analysis contexts required by each pattern, we study the design of bug patterns in SpotBugs. Based on our study, we present Codegex, an efficient pattern-based static analysis approach that uses regular expression with several strategies to extract more information from program texts (syntax and type information). It can analyze partial and complete code quickly without parsing code into AST. We evaluate Codegex using two settings. First, we compare the effectiveness and efficiency of Codegex and SpotBugs in analyzing 52 projects. Our results show that Codegex can detect bugs with comparable accuracy as SpotBugs but up to 590X faster, showing the potential of using Codegex as the fast stage of SpotBugs in a two-stage approach for instant feedback. Second, we evaluate Codegex in automated code review by running it on 4256 PRs where it generated 372 review comments and received 116 feedback. Overall, 78.45% of the feedback that we received is positive, indicating the promise of using Codegex for automated code review.