Reviewerly/exHarmony huggingface dataset:
Towards Flexible Authoring and Personalization of Virtual Reality Applications for Training

Virtual reality applications offer the promise to immerse end users in a synthetic environment where several actions could be observed, simulated, and reproduced, before transferring them to reality, which makes them particularly appropriate for training. Yet, when the training requires complex handling of information, the tasks become cognitively intensive, and developing adequate applications becomes challenging. To address this challenge, we define a method for developing head-mounted-display-based virtual reality applications for modular training tasks, composed of a training model with parameters, a step-wise approach for supporting this development, and a software framework enacting the application of this approach. Authoring such applications is expected to become more flexible and provide personalization facilities. To evaluate the impact of this method, we define a case study concerning an application for training school teachers who deal with a variety of situations in a classroom for an experiment involving N= 7 participants for a set of tasks. Pre-study and post-study acceptances reveal the impact of the software framework and a workload evaluation is conducted using the NASA TLX questionnaire.
Cyber Lessons that the World Can Learn from Lithuania

In an era of rapid technological advancements and increasing online connectivity, the proliferation of cyber threats, including the spread of fake news and disinformation, presents a significant challenge to nations worldwide. Lithuania has emerged as a leading example in addressing these challenges, particularly concerning cyber groups such as Killnet and disinformation / fake news. This paper aims to explore the key cyber lessons that can be learned from Lithuania's proactive approach in dealing with Killnet and combating disinformation / fake news. By analysing Lithuania's cybersecurity strategies and initiatives, this paper identifies crucial lessons that can be applied globally. Firstly, Lithuania recognises the importance of co-ordinated cyber security technologies and national frameworks to counter cyber groups such as Killnet attacks. Secondly, Lithuania has effectively tackled the spread of fake news / disinformation through a comprehensive approach involving legislation, media literacy programs, and strong cooperation between government agencies, civil society organisations, and the private sector. The country's experience underscores the significance of collaborative efforts in combating misinformation, promoting media literacy, and fostering critical thinking skills among the population.
Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement

In the context of public procurement, several indicators called red flags are used to estimate fraud risk. They are computed according to certain contract attributes and are therefore dependent on the proper filling of the contract and award notices. However, these attributes are very often missing in practice, which prohibits red flags computation. Traditional fraud detection approaches focus on tabular data only, considering each contract separately, and are therefore very sensitive to this issue. In this work, we adopt a graph-based method allowing leveraging relations between contracts, to compensate for the missing attributes. We propose PANG (Pattern-Based Anomaly Detection in Graphs), a general supervised framework relying on pattern extraction to detect anomalous graphs in a collection of attributed graphs. Notably, it is able to identify induced subgraphs, a type of pattern widely overlooked in the literature. When benchmarked on standard datasets, its predictive performance is on par with state-of-the-art methods, with the additional advantage of being explainable. These experiments also reveal that induced patterns are more discriminative on certain datasets. When applying PANG to public procurement data, the prediction is superior to other methods, and it identifies subgraph patterns that are characteristic of fraud-prone situations, thereby making it possible to better understand fraudulent behavior.
Research on the reform of marine education in the era of intelligent ship development

Intelligent ships integrate new technologies such as information technology and artificial intelligence, and have significant characteristics such as safety and reliability, energy conservation and environmental protection, and economic efficiency.It is the key direction of the future development of commercial ships.Higher education in navigation under the background of ship intelligence should make full use of the advantages of traditional navigation majors, actively master the core technology of intelligent ship development, and actively carry out the cross-integration of computer science, control theory, communication science, artificial intelligent and other information sciences.High-quality compound "new engineering" navigation talents who master information technology and navigation science are cultivated under the background of intelligent ships.Under the background of intelligent ships, reconstructing the training of professional marine technology professionals in higher vocational colleges is conducive to the transformation and upgrading of traditional navigation majors in higher vocational colleges, and provides cutting-edge exploration for the development of high-quality compound "new engineering" navigation majors.
The strategy of implementing effective scientific research management system in colleges using DEA method under the analysis of multiple cases

As the priority direction of modern university research activities, research management is an important factor that directly affects the ability to stimulate the potential of university research activities and improve its competitiveness in the field of education.Therefore, modern universities must implement advanced scientific research management concepts, innovate, optimize and improve the contents and methods of scientific research management, improve the scientific research management system, provide high-quality services for scientific researchers, and meet the needs of scientific research success.This paper will analyze and study the case analysis and data collection of the innovation and development of university scientific research management, combined with the effectiveness of the case analysis strategy.The results show that the strategy has good effect in scientific research management and has broad application prospects.
Textual Research on the Scientists and Content Background behind the Declaration of 93 People

In July 1914, World War I broke out internationally.Germany invaded neutral Belgium within two months at the beginning of the war.In order to create public opinion for the rationalization of German military operations, 93 people, including outstanding German natural scientists, artists, writers and priests, jointly signed a statement, The Book to the Civilized World, also known as the Declaration of 93 People.This infamous declaration and its influence occupy an important place in the history of human culture and science.In this paper, firstly, the specific contents of the Declaration of 93 People are translated, and then the names of the signatories are listed one by one.Finally, the background and influence of the Declaration are briefly summarized.
Incentive-Aware Decentralized Data Collaboration

Data collaboration enables multiple parties to pool data for deriving meaningful data insights. However, data misuse and unlawful data collection have led to precautionary measures being imposed by individual organizations to guide against data leakage and abuse. As a response, decentralized federated learning (DFL) has emerged as an attractive paradigm to facilitate data collaboration while being amenable to privacy-preserving data and knowledge sharing, cost reduction, and prediction accuracy improvement. Unfortunately, the participating parties in DFL tend to be heterogeneous with skew datasets and uneven capabilities. Inevitably, training and transmission costs, and the presence of free-riders pose challenges to the adoption and participation of DFL. The absence of centralized parameter servers further exacerbates the problem of evaluating the contribution of each individual party. Therefore, an effective incentive mechanism is essential to promote data collaboration. In this paper, we propose a novel Incentive-aware Decentralized fEderated leArning (IDEA) framework for facilitating data collaboration. Specifically, we first design a customizable reward scheme for heterogeneous parties to optimize their respective objectives such as higher model accuracy, communication efficiency, and computational efficiency. To reward fairly to deserving parties while offering flexibility, we propose a novel multi-agent reinforcement learning (MARL) incentive mechanism, which enables heterogeneous parties to learn their own optimal collaboration policy. We then design an efficient decentralized data collaboration algorithm that supports the customizable reward scheme based on individual objective-specific collaboration policy. We theoretically prove that the algorithm achieves a Nash equilibrium, which ensures the fairness of the corresponding rewards for parties. We conduct extensive experiments to evaluate the performance of our proposed framework against four baselines on five real-world datasets. The results show that IDEA outperforms state-of-the-art methods in terms of effectiveness, efficiency, and accumulated reward.
A regularized stochastic configuration network based on weighted mean of vectors for regression

The stochastic configuration network (SCN) randomly configures the input weights and biases of hidden layers under a set of inequality constraints to guarantee its universal approximation property. The SCN has demonstrated great potential for fast and efficient data modeling. However, the prediction accuracy and convergence rate of SCN are frequently impacted by the parameter settings of the model. The weighted mean of vectors (INFO) is an innovative swarm intelligence optimization algorithm, with an optimization procedure consisting of three phases: updating rule, vector combining, and a local search. This article aimed at establishing a new regularized SCN based on the weighted mean of vectors (RSCN-INFO) to optimize its parameter selection and network structure. The regularization term that combines the ridge method with the residual error feedback was introduced into the objective function in order to dynamically adjust the training parameters. Meanwhile, INFO was employed to automatically explore an appropriate four-dimensional parameter vector for RSCN. The selected parameters may lead to a compact network architecture with a faster reduction of the network residual error. Simulation results over some benchmark datasets demonstrated that the proposed RSCN-INFO showed superior performance with respect to parameter setting, fast convergence, and network compactness compared with other contrast algorithms.
Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks

Indigenous African languages are categorized as under-served in Natural Language Processing. They therefore experience poor digital inclusivity and information access. The processing challenge with such languages has been how to use machine learning and deep learning models without the requisite data. The Kencorpus project intends to bridge this gap by collecting and storing text and speech data that is good enough for data-driven solutions in applications such as machine translation, question answering and transcription in multilingual communities. The Kencorpus dataset is a text and speech corpus for three languages predominantly spoken in Kenya: Swahili, Dholuo and Luhya (three dialects of Lumarachi, Lulogooli and Lubukusu). Data collection was done by researchers who were deployed to the various data collection sources such as communities, schools, media, and publishers. The Kencorpus' dataset has a collection of 5,594 items, being 4,442 texts of 5.6 million words and 1,152 speech files worth 177 hours. Based on this data, other datasets were also developed such as Part of Speech tagging sets for Dholuo and the Luhya dialects of 50,000 and 93,000 words tagged respectively. We developed 7,537 Question-Answer pairs from 1,445 Swahili texts and also created a text translation set of 13,400 sentences from Dholuo and Luhya into Swahili. The datasets are useful for downstream machine learning tasks such as model training and translation. Additionally, we developed two proof of concept systems: for Kiswahili speech-to-text and a machine learning system for Question Answering task. These proofs provided results of a performance of 18.87% word error rate for the former, and 80% Exact Match (EM) for the latter system. These initial results give great promise to the usability of Kencorpus to the machine learning community. Kencorpus is one of few public domain corpora for these three low resource languages and forms a basis of learning and sharing experiences for similar works especially for low resource languages. Challenges in developing the corpus included deficiencies in the data sources, data cleaning challenges, relatively short project timelines and the Coronavirus disease (COVID-19) pandemic that restricted movement and hence the ability to get the data in a timely manner.
FIR Security System Using Blockchain Technology

Abstract: FIR (First Information Report) datamanagement is a critical task for law enforcement agencies to maintain law and order. Traditional FIR data management system often plagued by issues of data tampering, unauthorized access, and lack of transparency. In order to overcome this blockchain technology has been implemented which offers a decentralized, secure, and transparent solution to these problems. Storing FIR data on the blockchain can provide several advantages, including increased security, transparency, and immutability. Blockchain technology is a decentralized system that is resistant to modification, making it ideal for storing sensitive data such as FIR data. This system proposes a blockchain-based FIR data management system that utilizes smart contracts to automate the process of registering and accessing FIR data. The immutability of blockchain records can increase the reliability and trustworthiness of FIR data, while the decentralization of the blockchain ensures that there is no single point of failure. The user interface provides a graphical user interface for authorized users to interact with the system, Crime Investigators can view the data from database using their authentication credentials. Thereports, which are prepared by witnesses and police officers, are accessible to the investigator (admin).Investigators have the authority to edit data (i.e., update,remove, and so on), and this data aids investigators in speeding up their investigations and identifying offendersmore quickly. The evidences are secured using Block chain technology with SHA algorithm to create the hash values for each record. Secure cryptography functions to encrypt FIR information before storing on server has beenimplemented. The blockchain technology has the potential to revolutionize FIR data storage and improve the efficiency and effectiveness of law enforcement agencies.
Approximate Logical Dendritic Neuron Model Based on Selection Operator Improved Differential Evolution Algorithm

In order to solve the problem that the classification effect of approximate logic dendritic neuron model is largely limited by the training effect of learning algorithm.The differential evolution algorithm of the population evolution algorithm is selected as the training algorithm of the model.Differential evolution algorithm is a branch of population evolution algorithm, which has the advantages of good robustness and easy implementation.But it also has the disadvantage that it is easy to fall into the stagnation of evolution.In order to solve this key problem, several differential evolution algorithms are investigated.Finally, we noticed that a differential evolution algorithm improved by selection operator can better solve the problem of algorithm evolution stagnation.This paper studies the training of logic dendritic neuron model using the differential evolution algorithm improved by this selection operator.In order to evaluate the performance of the algorithm training model, four representative data sets were used for experiments.When comparing the classification effect, particle swarm optimization algorithm, traditional differential evolution algorithm and genetic algorithm are selected as the comparison experiment.
Independent Evaluation of Learning Outcomes as a Tool of the Internal Quality Assurance System

The article deals with the organization and conduct of the independent evaluation of student learning outcomes in the form of computer testing in 2021-2022. 402 first-year undergraduate students who were divided into 23 groups at 4 faculties took part in the evaluation. The novelty of the research includes the description of the procedure and mechanism of independent evaluation, as well as requirements for the structure of tests, the volume of the test bank, etc. The analysis and generalization of the results indicate the improvement of the performance indicators, quality of knowledge during the second periodic module control.
Creating Trust by Abolishing Hierarchies

Software is going through a trust crisis. Privileged code is no longer trusted and processes insufficiently protect user code from unverified libraries. While usually treated separately, confidential computing and program compartmentalization are both symptoms of the same problem, deeply rooted in hierarchical commodity systems: privileged software's monopoly over isolation.
<scp>B</scp> <sup>3</sup> : Backdoor Attacks against Black-box Machine Learning Models

Backdoor attacks aim to inject backdoors to victim machine learning models during training time, such that the backdoored model maintains the prediction power of the original model towards clean inputs and misbehaves towards backdoored inputs with the trigger. The reason for backdoor attacks is that resource-limited users usually download sophisticated models from model zoos or query the models from MLaaS rather than training a model from scratch, thus a malicious third party has a chance to provide a backdoored model. In general, the more precious the model provided (i.e., models trained on rare datasets), the more popular it is with users. In this article, from a malicious model provider perspective, we propose a black-box backdoor attack, named B 3 , where neither the rare victim model (including the model architecture, parameters, and hyperparameters) nor the training data is available to the adversary. To facilitate backdoor attacks in the black-box scenario, we design a cost-effective model extraction method that leverages a carefully constructed query dataset to steal the functionality of the victim model with a limited budget. As the trigger is key to successful backdoor attacks, we develop a novel trigger generation algorithm that intensifies the bond between the trigger and the targeted misclassification label through the neuron with the highest impact on the targeted label. Extensive experiments have been conducted on various simulated deep learning models and the commercial API of Alibaba Cloud Compute Service. We demonstrate that B 3 has a high attack success rate and maintains high prediction accuracy for benign inputs. It is also shown that B 3 is robust against state-of-the-art defense strategies against backdoor attacks, such as model pruning and NC.
Protecting security of quantum neural network with sampling checks

With the development of quantum computing, the application of quantum neural networks will be more and more extensive, and its security will also face more challenges. Although quantum communication has high security, quantum neural networks may have many internal and external insecure factors in the process of information transmission, such as noise impact during the preparation of input quantum states, privacy disclosure during transmission, and external attacks on the network structure, which may cause major security incidents. Because of the possible insecurity factors of quantum neural networks, this paper proposes a quantum sampling method to detect the state of quantum neural networks at each stage, so as to judge whether there are security risks in quantum neural networks and thus ensure their security. The method also provides a safe basis for further research on the stability and reliability of quantum neural networks.
Research on the Blending Teaching Mode of Probability Theory and Mathematical Statistics Based on OBE Education Philosophy

Based on the OBE education concept, a hybrid teaching model is constructed by combining the characteristics and contents of probability theory and mathematical statistics courses.This paper discusses the teaching mode of probability theory and mathematical statistics from subject-oriented to goal-centered, from teacher-centered to student-centered, from the rational design of online and offline teaching hours, and from quality monitoring to continuous improvement.Student's ability as the output orientation, efficient construction of curriculum teaching reform.
Towards Interpretable Multimodal Predictive Models for Early Mortality Prediction of Hemorrhagic Stroke Patients.

The increasing death rate over the past eight years due to stroke has prompted clinicians to look for data-driven decision aids. Recently, deep-learning-based prediction models trained with fine-grained electronic health record (EHR) data have shown superior promise for health outcome prediction. However, the use of EHR-based deep learning models for hemorrhagic stroke outcome prediction has not been extensively explored. This paper proposes an ensemble deep learning framework to predict early mortality among ICU patients with hemorrhagic stroke. The proposed ensemble model achieved an accuracy of 83%, which was higher than the fusion model and other baseline models (logistic regression, decision tree, random forest, and XGBoost). Moreover, we used SHAP values for interpretation of the ensemble model to identify important features for the prediction. In addition, this paper follows the MINIMAR (MINimum Information for Medical AI Reporting) standard, presenting an important step towards building trust among the AI system and clinicians.
Sentiment Analysis of Social Media Users’ Emotional Response to Sudden Cardiac Arrest During a Football Broadcast

This case series analyzes social media users’ sentiments after successful cardiopulmonary resuscitation of Damar Hamlin following his sudden cardiac arrest on national television.
A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques

Phishing assaults are one of the more prevalent types of cybercrime in the world today. To steal information, users are sent emails and messages. Moreover, websites are used for it. Phishing primarily targets corporate web-sites, such as those for e-commerce, finance, and governmental organizations. In order to obtain sensitive user information, attackers impersonate websites, a phenomenon known as phishing. In addition to exploring the use of machine learning algorithms to identify and stop web phishing assaults, this research suggests utilizing machine learning techniques to detect phish-ing URLs by analysing various aspects of the URLs. The study includes classification models like Logistic Regression, Random Forest, Decision trees, KNN, Naive bayes, SVM and other ensemble learning techniques like Gradient Boosting, XGBoost, Histogram Gradient Boosting, Light Gradient Boosting and AdaBoost were used to detect phishing websites.