Reviewerly/exHarmony huggingface dataset:
A Multifactor Combined Data Sharing Scheme for Vehicular Fog Computing Using Blockchain

Vehicular fog computing (VFC), as an extended model of fog computing, combines fog computing with traditional in-vehicle networks to provide real-time response services for users. However, in such a dynamic system architecture, achieving secure and efficient data sharing is an enormous challenge. Ciphertext-policy attribute-based encryption (CP-ABE) is widely regarded as an excellent way of achieving one-to-many data sharing. Nevertheless, several practical challenges hinder its widespread application in VFC, such as inefficient attribute revocation, single-factor access control, and centralized data storage. For this purpose, we design a multifactor combined data sharing scheme for VFC with CP-ABE and blockchain (MC-DS-VFC, in short). We first propose an efficient attribute revocation mechanism that does not require complex key update operations. We then embed time, user attributes, and access interests into data sharing for more fine-grain access control, which enables users with sufficient attributes to efficiently access real-time shared data according to their access interests. Finally, we combine the interplanetary file system (IPFS) and the blockchain maintained by roadside units (RSUs) to achieve distributed collaborative storage. Furthermore, our mechanism also supports user traceability, attribute joining, online/offline encryption, and verifiable outsourced decryption. Our proposal is shown to satisfy the indistinguishability under chosen plaintext attack (IND-CPA) in the standard model. Theoretical analysis and simulation experiments indicate that the MC-DS-VFC scheme is efficient and practical for VFC.
Zero-Shot and Few-Shot Learning With Knowledge Graphs: A Comprehensive Survey

Machine learning (ML), especially deep neural networks, has achieved great success, but many of them often rely on a number of labeled samples for supervision. As sufficient labeled training data are not always ready due to, e.g., continuously emerging prediction targets and costly sample annotation in real-world applications, ML with sample shortage is now being widely investigated. Among all these studies, many prefer to utilize auxiliary information including those in the form of knowledge graph (KG) to reduce the reliance on labeled samples. In this survey, we have comprehensively reviewed over 90 articles about KG-aware research for two major sample shortage settings—zero-shot learning (ZSL) where some classes to be predicted have no labeled samples and few-shot learning (FSL) where some classes to be predicted have only a small number of labeled samples that are available. We first introduce KGs used in ZSL and FSL as well as their construction methods and then systematically categorize and summarize KG-aware ZSL and FSL methods, dividing them into different paradigms, such as the mapping-based, the data augmentation, the propagation-based, and the optimization-based. We next present different applications, including not only KG augmented prediction tasks such as image classification, question answering, text classification, and knowledge extraction but also KG completion tasks and some typical evaluation resources for each task. We eventually discuss some challenges and open problems from different perspectives.
The Python programming language as an alternative tool for solving tasks of the Unified State Exam in informatics

Programming is the basis of the content line "Algorithms and programming", which is studied throughout the entire school course of informatics, therefore programming tasks are one of the thematic blocks of the Unified State Exam (USE) in informatics. In particular, one of the educational languages used in solving tasks of the USE in informatics is the Python programming language. This language can be used both for solving the problems of the "Programming" section, and for solving other tasks of the Unified State Exam, simplifying the way to solve them if the students have the skills to write programs in this programming language using the main algorithmic structures (sequencing, selection, and iteration) and data structures (arrays, strings). The article describes the possibilities of using the Python programming language as an alternative tool for solving tasks No. 2, No. 5, No. 15 and No. 16 of the USE in informatics using the example of tasks from the demo version of the USE 2023 control measuring materials, as well as tasks from the open bank of USE tasks.
Multi-Class Double-Transformation Network for SAR Image Registration

In SAR image registration, most existing methods consider the image registration as a two-classification problem to construct the pair training samples for training the deep model. However, it is difficult to obtain a mass of given matched-points directly from SAR images as the training samples. Based on this, we propose a multi-class double-transformation network for SAR image registration based on Swin-Transformer. Different from existing methods, the proposed method directly considers each key point as an independent category to construct the multi-classification model for SAR image registration. Then, based on the key points from the reference and sensed images, respectively, a double-transformation network with two branches is designed to search for matched-point pairs. In particular, to weaken the inherent diversity between two SAR images, key points from one image are transformed to the other image, and the transformed image is used as the basic image to capture sub-images corresponding to all key points as the training and testing samples. Moreover, a precise-matching module is designed to increase the reliability of the obtained matched-points by eliminating the inconsistent matched-point pairs given by two branches. Finally, a series of experiments illustrate that the proposed method can achieve higher registration performance compared to existing methods.
A New Frontier: AI and Ancient Language Pedagogy

Abstract In November 2022, ChatGPT 3.5 was released on a public research preview, gaining notoriety for its ability to pull from a vast body of information to create coherent and digestible bodies of text that accurately respond to queries (OpenAI, 2022). It is able to recognise the grammar and vocabulary of ancient languages, translate passages, and compose texts at an alarmingly accurate and rapid rate. For teachers, this AI has had mixed reviews. Some fear its ability to produce well-written work effortlessly, while others are excited by its abilities to push the boundaries of current teaching practices. This paper explores how well ChatGPT explains grammatical concepts, parses inflected forms, and translates Classical Latin, Ancient Greek, and Classical Sanskrit. Overall, ChatGPT is rather good at working with Classical Latin and Sanskrit, but its abilities with Ancient Greek are deeply problematic. Although it is quite flawed at this time, ChatGPT, when used properly, could become a useful a tool for ancient language study. With proper guiding phrases, students could use this AI to practise vocabulary, check their translations, and rephrase grammatical concepts.
A novel sparrow search algorithm with integrates spawning strategy

Sparrow Search Algorithm (SSA) has recently received more attention in the intelligent optimization group. However, the high randomness and local optimal problems of the algorithm limit the application of the algorithm. In this paper, a novel sparrow search algorithm (NSSA) combined with spawning technology is proposed, which effectively improves the problem of the algorithm falling into local optimization. First of all, it is proposed to replace the traditional stochastic method with the good point set theory to find the initial individual, so that the initial population is more evenly distributed in the search space and improve the quality of the initial solution. Secondly, the spawning strategy of the cuckoo algorithm is integrated into the discoverer stage, which improves the search method of the discoverer and enhances the global search ability, which makes the algorithm avoids the sudden decline of population diversity and premature convergence. Finally, Levy flight and Brownian motion are used to disturb the position of the sparrow dimension by dimension to improve the ability of the algorithm to jump out of the local optimal value in subsequent iterations. By comparing and analyzing NSSA and SSA, DE, ALO, GOA, DOA, HSSA and XSSA algorithms in 12 common evaluation functions and CEC-2017 test functions, and apply NSSA to the TSP problem. The simulation results show the effectiveness and superiority of the proposed scheme.
Predicting the future of experiential and adventurous learning in the metaverse

This essay defines the metaverse and its many uses. It then draws parallels to adventure and examines the elements that make adventure a unique learning medium. Next, it predicts the future based on the present and the past and discusses the drawbacks of this process and its outcomes. Finally, it concludes with a few observations and expectations around adventure not being easily replaced by the metaverse in the near future, but long term advances might tell a different story.
Software fault prediction using deep learning techniques

Software fault prediction (SFP) techniques identify faults at the early stages of the software development life cycle (SDLC). We find machine learning techniques commonly used for SFP compared to deep learning methods, which can produce more accurate results. Deep learning offers exceptional results in various domains, such as computer vision, natural language processing, and speech recognition. In this study, we use three deep learning methods, namely, long short-term memory (LSTM), bidirectional LSTM (BILSTM), and radial basis function network (RBFN) to predict software faults and compare our results with existing models to show how our results are more accurate. Our study uses Chidamber and Kemerer (CK) metrics-based datasets to conduct experiments and test our proposed algorithm. We conclude that LSTM and BILSTM perform better, whereas RBFN is faster in producing the required results. We use k-fold cross-validation to do the model evaluation. Our proposed models provide software developers with a more accurate and efficient SFP mechanism.
On the spectral zeta function of second order semiregular non-commutative harmonic oscillators

In this paper we give a meromorphic continuation of the spectral zeta function for second order semiregular Non-Commutative Harmonic Oscillators (NCHO). By "semiregular systems" we mean systems with terms with degree of homogeneity scaling by 1 in their asymptotic expansion. As an application of our results, we first compute the meromorphic continuation of the Jaynes-Cummings (JC) model spectral zeta function. Then we compute the spectral zeta function of the JC generalization to a 3-level atom in a cavity. For both of them we show that it has only one pole in 1.
Improving Data Access for Researchers in the Digital Services Act

Joint submission in response to the Call for Evidence on the Delegated Regulation on data access provided for in the Digital Services Act (DSA). Article 40 DSA is a crucial provision to operationalize the regulation’s risk mitigation provisions vis-a-vis very large online platforms (VLOPs) and very large search engines (VLOSEs). In essence, Article 40 DSA enables data access to Digital Services Coordinators (DSCs) or the Commission, “vetted researchers” and other researchers, provided certain conditions are met. Our submission is predominantly concerned with the data access for vetted researchers and researchers in relation to VLOPs.
Set-theoretic Types for Erlang

Erlang is a functional programming language with dynamic typing. The language offers great flexibility for destructing values through pattern matching and dynamic type tests. Erlang also comes with a type language supporting parametric polymorphism, equi-recursive types, as well as union and a limited form of intersection types. However, type signatures only serve as documentation; there is no check that a function body conforms to its signature.
Chinese mathematics teachers’ TPACK and attitudes toward ICT integration in the post-pandemic era

This study investigated primary mathematics teachers’ technological, pedagogical, and content knowledge (TPACK) and attitudes towards information and communication technology (ICT) integration in mainland China during the post-pandemic period. Quantitative data were collected through a web-based self-determined questionnaire. Statistical analysis was performed using independent sample t-tests, and Mann-Whitney U tests to assess gender and teaching experience in years. Spearman’s correlation test also examined the relationship between TPACK and teachers’ attitudes towards ICT integration. The findings revealed that most mathematics teachers had adequate non-technology-related knowledge but lacked technology-related knowledge. However, they were willing to incorporate digital technologies in their teaching after experiencing large-scale online teaching. There was no significant gender or teaching grade differences in TPACK, but teaching experience positively correlated with TPACK. The study emphasizes the importance of assessing teachers’ practical circumstances before introducing TPACK development programs. Finally, the implications for teachers, educators, and policymakers were discussed at the end of this paper.
LeL-GNN: Learnable Edge Sampling and Line Based Graph Neural Network for Link Prediction

Graph neural networks lose a lot of their computing power when more network layers are added. As a result, the majority of existing graph neural networks have a shallow depth of learning. Over-smoothing and information loss are two of the key issues that restrict graph neural networks from going deeper. As network depth goes up, the embeddings of all the nodes eventually converge on the same value, which separates output representations from input vectors and causes over-smoothing. Moreover, layers of graph pooling are required in a deep learning model to retrieve specified features for prediction, which results in some degree of information loss. In this research, we present a new and multi-scale approach for overcoming these constraints by using concepts from graph theory, namely learnable edge sampling and line graphs. An edge-sampling mechanism that selects a particular number of edges through a learning parameter before training reduces oversmoothing, and the issue of information loss is alleviated using a line graph technique that converts the original graph into a similar line graph. Our method of edge sampling preserves the core spectral features of the graph without affecting its fundamental structure. Our suggested technique outperforms state-of-the-art models on publicly available datasets of diverse applications while having minimal constraints and great training skills.
Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach

Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them. Recent advancements in interpretable machine learning tools allow us to look inside the black box of advanced prediction methods to extract interpretable models while maintaining similar prediction accuracy, but few studies have investigated the specific hospital readmission prediction problem with this spirit.Our goal is to develop a machine-learning (ML) algorithm that can predict 30- and 90- day hospital readmissions as accurately as black box algorithms while providing medically interpretable insights into readmission risk factors. Leveraging a state-of-art interpretable ML model, we use a two-step Extracted Regression Tree approach to achieve this goal. In the first step, we train a black box prediction algorithm. In the second step, we extract a regression tree from the output of the black box algorithm that allows direct interpretation of medically relevant risk factors. We use data from a large teaching hospital in Asia to learn the ML model and verify our two-step approach.The two-step method can obtain similar prediction performance as the best black box model, such as Neural Networks, measured by three metrics: accuracy, the Area Under the Curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC), while maintaining interpretability. Further, to examine whether the prediction results match the known medical insights (i.e., the model is truly interpretable and produces reasonable results), we show that key readmission risk factors extracted by the two-step approach are consistent with those found in the medical literature.The proposed two-step approach yields meaningful prediction results that are both accurate and interpretable. This study suggests a viable means to improve the trust of machine learning based models in clinical practice for predicting readmissions through the two-step approach.
The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn><mml:mi>T</mml:mi></mml:math>-qutrit, a two-mode bosonic qutrit

Quantum computers often manipulate physical qubits encoded on two-level quantum systems. Bosonic qubit codes depart from this idea by encoding information in a well-chosen subspace of an infinite-dimensional Fock space. This larger physical space provides a natural protection against experimental imperfections and allows bosonic codes to circumvent no-go results that apply to states constrained by a 2-dimensional Hilbert space. A bosonic qubit is usually defined in a single bosonic mode but it makes sense to look for multimode versions that could exhibit better performance. In this work, building on the observation that the cat code lives in the span of coherent states indexed by a finite subgroup of the complex numbers, we consider a two-mode generalisation living in the span of 24 coherent states indexed by the binary tetrahedral group $2T$ of the quaternions. The resulting $2T$-qutrit naturally inherits the algebraic properties of the group $2T$ and appears to be quite robust in the low-loss regime. We initiate its study and identify stabilisers as well as some logical operators for this bosonic code.
Recommender Systems in Cybersecurity

Abstract With the growth of CyberTerrorism, enterprises worldwide have been struggling to stop intruders from obtaining private data. Despite the efforts made by Cybersecurity experts, the shortage of skillful security teams and the usage of intelligent attacks have slowed down the enhancement of defense mechanisms. Furthermore, the pandemic in 2020 forced organizations to work in remote environments with poor security, leading to increased cyberattacks. One possible solution for these problems is the implementation of Recommender Systems to assist Cybersecurity human operators. Our goal is to survey the application of Recommender Systems in Cybersecurity architectures. These decision-support tools deal with information overload through filtering and prioritization methods, allowing businesses to increase revenue, achieve better user satisfaction, and make faster and more efficient decisions in various domains (e-commerce, healthcare, finance, and other fields). Several reports demonstrate the potential of using these recommendation structures to enhance the detection and prevention of cyberattacks and aid Cybersecurity experts in treating client incidents. This survey discusses several studies where Recommender Systems are implemented in Cybersecurity with encouraging results. One promising direction explored by the community is using Recommender Systems as attack predictors and navigation assistance tools. As contributions, we show the recent efforts in this area and summarize them in a table. Furthermore, we provide an in-depth analysis of potential research lines. For example, the inclusion of Recommender Systems in security information event management systems and security orchestration, automation, and response applications could decrease their complexity and information overload.
Hand Gesture Recognition and Conversion to Speech for Speech Impaired

The primary method of interaction between individuals is communication. In the case of speech-impaired people, the communication between them and normal people is not understandable by the latter as the former communicates through gestures and sign language. In the case of speech-impaired. Today, the Internet of Things (IoT) can be used for gesture recognition and to convert gestures into audible speech. In this project, a prototype is created such that the gesture or the sign language that is communicated is recognized by the flex sensors connected to the fingertips of a plain glove. The ESP32 microcontroller recognizes the gesture and converts it into the text format and this text format is updated onto the Google Firebase and is converted to speech form thus producing the text onto the OLED display sensor and the converted speech for communication in the mobile application Kodular Companion.
A Multi-View Face Expression Recognition Method Based on DenseNet and GAN

Facial expression recognition (FER) techniques can be widely used in human-computer interaction, intelligent robots, intelligent monitoring, and other domains. Currently, FER methods based on deep learning have become the mainstream schemes. However, these methods have some problems, such as a large number of parameters, difficulty in being applied to embedded processors, and the fact that recognition accuracy is affected by facial deflection. To solve the problem of a large number of parameters, we propose a DSC-DenseNet model, which improves the standard convolution in DenseNet to depthwise separable convolution (DSC). To solve the problem wherein face deflection affects the recognition effect, we propose a posture normalization model based on GAN: a GAN with two local discriminators (LD-GAN) that strengthen the discriminatory abilities of the expression-related local parts, such as the parts related to the eyes, eyebrows, mouth, and nose. These discriminators improve the model’s ability to retain facial expressions and evidently benefits FER. Quantitative and qualitative experimental results on the Fer2013 and KDEF datasets have consistently shown the superiority of our FER method when working with multi-pose face images.
Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms

One of the main functions of NLP (Natural Language Processing) is to analyze a sentiment or opinion of the text considered.In this research the objective is to analyze the sentiment in the form of tweets towards the Covid-19 vaccination.In this study, the collected tweets are in the form of a dataset from Kaggle that have been categorized into positive and negative depending on the polarity of the sentiment in that tweet, to visualize the overall situation.The reviews are translated into vector representations using various techniques, including Bag-Of-Words and TF-IDF to ensure the best result.Machine learning algorithms like Logistic Regression, Naïve Bayes, Support Vector Machine (SVM) and others, and Deep Learning algorithms like LSTM and Bert were used to train the predictive models.The performance metrics used to test the performance of the models show that Support Vector Machine (SVM) achieved the highest accuracy of 88.7989% among the machine learning models.Compared to the related research papers the highest accuracy obtained using LSTM is 90.59 % and our model has predicted with the highest accuracy of 90.42% using BERT techniques.