Reviewerly/exHarmony huggingface dataset:
Escapeling: A Gamified, AI-Supported Chatbot for Collaborative Language Practice

Escapeling is a Telegram bot aimed at improving English skills outside of a classroom setting. Intermediate language learners can collaboratively practice in a gamified escape-room scenario wherein they must complete several tasks. The three currently available tasks cover some of the most important language learning aspects: improving grammar knowledge via sentence correction, enhancing vocabulary via word guessing and encouraging writing practice in a discussion task. To complete the tasks, the users are required to cooperate by reaching common decisions or prompting their peers. Engagement with the bot is stimulated by in-game achievements and narrative components, supporting the long-term goal of alien ship escape. The bot's adaptive AI-based module tracks individual user performance and changes the task difficulty accordingly. Initial user acceptance tests suggest the efficiency of AI-assisted adaptive language learning as an aid to traditional learning approaches. Work on Escapeling is still in progress.
Primary Node Selection Algorithm of PBFT Based on Anomaly Detection and Reputation Model

The combination of reputation model and PBFT consensus algorithm is a competitive solution to improve consortium blockchains’ TPS. However, there are still many problems for PBFT with reputation model such as detecting malicious nodes and centralization problem. In this paper, an anomaly detection algorithm base on for machine learning is adopted to detect the malicious nodes in consortium blockchains using PBFT consensus mechanism. And then, the results of anomaly detection are applied to evaluate the reputation value of the node. Besides, random number is used to ensure the randomness and fairness of the PBFT primary node selection in the reputation model. The experimental test shows the anomaly detection model can improve significantly the consensus efficiency of PBFT. The test also shows that the use of random number can effectively avoid the centralization caused by the imbalance of the reputation value growth speed between nodes.
An efficient multimedia compression-encryption scheme using latin squares for securing Internet-of-things networks

The security of multimedia content and its applications have become a a very important task in digital communication technology. The extensive use of multimedia content over wireless networks makes the network applications exposed to different types of attacks. Cryptography is one of the main technologies that provide security and confidentiality to prevent unauthorised data access. A suitable design of an encryption system must be chosen to fully secure and maintain the privacy of data. On the other hand, the large size of data will be the prominent issue. In order to maintain a better performance of Internet-of-Things (IoT) applications, an appropriate compression-encryption scheme has to be used to ensure security and high speed without increasing the cryptosystem complexity in order to achieve a large communication capacity. Therefore, this paper presents a hybrid scheme of H.264/MVC compression and encryption process based on Latin Square Cipher (LSC) using symmetric keys. The Latin Square Whitening and Latin Square Substitution and Permutation operations are included in the encryption algorithm. The simulation results and analysis on different RGB video frames using MATLAB show that the proposed scheme achieves high protection, integrity, and security performance for different transmitted video frames. In addition, high robustness against different network attacks is achieved compared to the state-of-the-art algorithms.
Linguistic Data Summarization: A Systematic Review

This paper constitutes a systematic review of Linguistic Data Summarization theory and its trends. Authors analyzed the advantages and limitations of different techniques to generate linguistic summaries. In this investigation, authors discuss about the high variability of protoforms used by researches in several domains. This review exposes the strategy of validation of researches reported in bibliography. We identify the approaches in data summarization followed by the main authors. As conclusions, of this investigation, authors identify the following opportunities for improvement in this research area. One opportunity deal with the use of linguistic summaries not only for description, but also for prediction in decision-making problems. Other is about the necessity of new algorithms to facilitate the generation of linguistic summaries and its applications in several domains.
Einstein-Podolsky-Rosen steering and entanglement based on two-photon correlation for bipartite Gaussian states

We propose and demonstrate a standard form of Einstein-Podolsky-Rosen (EPR) steering for an arbitrary bipartite Gaussian state under which a simple EPR steering criterion for two-photon correlation can be achieved. In terms of a beam splitter preparing three different steerable states, we further test that the simplified criterion is reliable and efficient when it is used to determine whether an arbitrary bipartite Gaussian state is steerable or not. Based on the same theoretical framework, the standard form and criterion of entanglement are reconstructed and discussed. Furthermore, it is found that EPR steering requires stronger two-photon correlation than entanglement, while its direction is strictly dependent on the mean photon number of two light modes. Moreover, the inherent asymmetric feature can be intuitively embodied in the corresponding criteria, and the essential difference and inherent relation between two standard forms is revealed. We hope the present results could be helpful for potential applications in quantum information processing.
Data Obfuscation Technique in Cloud Security

Cloud storage, in general, is a collection of Computer Technology resources provided to consumers over the internet on a leased basis. Cloud storage has several advantages, including simplicity, reliability, scalability, convergence, and cost savings. One of the most significant impediments to cloud computing's growth is security. This paper proposes a security approach based on cloud security. Cloud security now plays a critical part in everyone's life. Due to security concerns, data is shared between cloud service providers and other users. In order to protect the data from unwanted access, the Security Service Algorithm (SSA), which is called as MONcrypt is used to secure the information. This methodology is established on the obfuscation of data techniques. The MONcrypt SSA is a Security as a Service (SaaS) product. When compared to current obfuscation strategies, the proposed methodology offers a better efficiency and smart protection. In contrast to the current method, MONcrypt eliminates the different dimensions of information that are uploaded to cloud storage. The proposed approach not only preserves the data's secrecy but also decreases the size of the plaintext. The exi sting method does not reduce the size of data until it has been obfuscated. The findings show that the recommended MONcrypt offers optimal protection for the data stored in the cloud within the shortest amount of time. The proposed protocol ensures the confidentiality of the information while reducing the plaintext size. Current techniques should not reduce the size of evidence once it has been muddled. Based on the findings, it is clear that the proposed MONcrypt provides the highest level of protection in the shortest amount of time for rethought data.
AFL: An Adaptively Federated Multitask Learning for Model Sharing in Industrial IoT

In the Industrial Internet of Things (IIoT), model and computing power sharing among devices can improve resource utilization and work efficiency. However, data privacy and security issues hinder the sharing process. Besides, in the process of model sharing, due to the customization of industrial equipment functions and the high separation of model and task types between devices, it is difficult to share model and optimize models among devices with different task requirements. In this article, we propose an adaptively federated multitask learning (AFL) for IIoT devices efficiently model sharing. Inspired by the parameter sharing mechanism, AFL builds a sparse sharing structure by designing an iterative pruning network and generating subnets for each task. Moreover, for better share relevant information, we further propose tailored task mask layers for effectively training specialized subnets, and an adaptive loss function to dynamically adjust the priority between tasks. Extensive experiments show that AFL can successfully fit hundreds of tasks from different devices into one model, which preserves both high accuracy and system scalability, and outperforms other related approaches that naively combine federated learning with multitask learning.
A content-independent method for LFM signal source identification

Linear frequency modulation (LFM) is a spread spectrum modulation technique that does not require pseudo-random coding sequences, and it is widely used in radar and sonar technology. In many applications, it is required to distinguish the source of the signal. The radio frequency fingerprint (RFF) is one of the most promising methods to identify the signal sources because of its uniqueness. In this paper, a novel method of feature extraction is proposed to identify the RFF of LFM signal. The proposed method can provide a new kind of feature of RFF for the recognition with high accuracy. The feature can be extracted from the whole signal without separating transient signals and steady-state signals, and it independents of the content of the signal. The proposed method can provide the recognition accuracy as high as 93.68%.
Realizing dynamic resource orchestration on cloud systems in the cloud-to-edge continuum

Cloud computing has been widely utilized to handle the huge volume of data from many cutting-edge research areas such as Big Data and Internet of Things (IoT). The fast growing of edge devices makes it difficult for cloud systems to process all data and jobs originating from edge devices, which leads to the development of edge computing by completing jobs on edges instead of clouds. Unfortunately, edge devices generally possess only limited computing power. Therefore, jobs demanding heavy computation under strict time constraints could have more difficulties to successfully complete their work on edges than on clouds in the Cloud-to-Edge continuum. If cloud systems could dynamically orchestrate cloud resources to expedite the execution of those jobs, not only their timely execution could be assured, also the loading of edge devices could be reduced. The Apache Hadoop is considered one of the most popular cloud systems in industry and academia. However, it does not support dynamic resource allocation. Previously we proposed and implemented a new model which can dynamically adjust the computing resources assigned to given jobs in the Hadoop cloud system to speed up their execution. Like other computer software, cloud systems completely rely on their underlying operating systems to access hardware components such as CPUs and hard drives. In this paper, we report our efforts to improve our model to collaborate with the Linux operating system to accelerate the execution of jobs with high priority to a greater extent. Compared with what our original model achieved, experiments show that our ameliorated model could further quicken the execution of prioritized jobs in Hadoop by up to around 21%. As a result, jobs from edges that require substantial computing resources promptly could have better chances to get accomplished on cloud systems.
Artificial Intelligence Based Predictive Threat Hunting In The Field of Cyber Security

Artificial intelligence (AI) is a broad field of computer science that focuses on designing smart machines capable of performing tasks typically requiring human intelligence. Despite the fact that security solutions are growing progressively modern and stable, cyberattacks are still evolving and are at their extreme. The main reason is that conventional methods of malware detection fail. Cyber attackers are actively developing new ways to prevent defence programmes from infecting malware networks and servers. Most anti-malware and antivirus applications currently use signature-based detection to identify attacks, which is unsuccessful in detecting new threats. This is where Artificial Intelligence is most handy. The standardised models for threatened hunting and performance quantification from the start of hazard hunting to the end still allow methodological rigour and completeness to be studied remain undefined. The organised practise of hazard hunts seeks to disclose the presence of TTP in the field of detection that has not already been detected. In this study, a realistic and comprehensive model is outlined to detect attackers in six stages: aim, scale, equipment, planning, execution and input. This study describes Threat Hunting in an ecosystem as the constructive, analyst-driven scanning mechanism for attackers TTP. The model has been checked for real-world data sets using a variety of threats. The effectiveness and practicality of this research have been shown with and without a blueprint through danger hunts. In addition, the article presents an analysis of the concept of threat hunting based on data from Ukrainian electricity grid attacks in an online environment to highlight the effects of this model on threat hunting in a simulated environment. The findings of this analysis include an effective and repetitive way to search for and quantify honesty, coverage and rigour.
An Extended Type-1 Generalized Feistel Networks: Lightweight Block Cipher for IoT

In the last few years, a large amount of private and sensitive data generated by Internet of Things (IoT) devices need to be transmitted, stored, and processed in security, which become a serve challenge because these resource-constrained IoT devices cannot satisfy the requirements of conventional encryption ciphers. In this article, we propose the extended Type-1 generalized Feistel networks (Type-1 EGFNs) to improve the diffusion speed of Type-1 generalized Feistel networks (Type-1 GFNs). We instantiate Type-1 EGFNs into a lightweight block cipher that is named ALLPC, and analyze the performance of the cipher in terms of software implementation, hardware implementation, and security. The Type-1 EGFNs overcome the shortcoming of slow diffusion of Type-1 GFNs, and the ALLPC cipher is quite suitable for the resource-constrained IoT environment. Compared with other existing lightweight block ciphers, the ALLPC cipher can achieve compact hardware and low-cost software (e.g., low memory) while having low execution time, high throughput, and low energy consumption on the 32-bit ARM-Cortex M3 STM32F103 platform. In our security analysis, the ALLPC cipher can attain sufficient security degree against differential cryptanalysis, zero-correlation attack, and linear cryptanalysis, and owns a meaningful avalanche effect. Experimental results shows that our ALLPC cipher outperforms other existing lightweight ciphers in throughput, execution time and energy consumption.
Fusion of Camera-based Vessel Detection and AIS for Maritime Surveillance

Camera-based vessel identification is one of the essential maritime surveillance measures. The existing deep learning (DL)-based vessel classification methods have challenges in achieving the detailed vessel information (e.g., vessel identity) under poor visual conditions. In some applications, they cannot meet the safe scheduling and abnormal activity identification purposes in the camera surveillance system. This paper proposed a fusion framework, which takes the visual detection and the automatic identification system (AIS) as inputs to obtain accurate and robust vessel identification to address these challenges. Visual detection uses an innovative method to estimate the distance of the identified vessel target from the camera and the azimuth angle relative to the camera based on the images from the monocular camera. The distance and azimuth angle were further fused with AIS data to obtain the fused output. The test results based on 3976 images show that the mean absolute error of the visual-based distance estimation method is 0.72km. The success rate of visual detection and AIS data association is 75.7%.
Analysis of cybersecurity competencies: Recommendations for telecommunications policy

The paper aims to analyse and assess cybersecurity competencies and define the recommended solutions to improve the human factor in cybersecurity. The article presents the results of theoretical and empirical research that were carried out in 2019–2021. The research subject constitutes one of the priorities of many countries and international organisations. Cybersecurity is one of the essential foundations for implementing the idea of sustainable development. A cybersecurity analysis using a layered structure was proposed in the theoretical part. Empirical research was conducted using a diagnostic poll method based on a survey. The presentation of the research results includes an analysis of statistical dependencies. The paper presents the research results on cybersecurity competencies in the field of threats to state cyberspace and methods of securing and protecting data. An important element of the research was to identify forms of education that can be used to achieve greater efficiency in increasing cybersecurity competencies. The result of the implementation of the research objectives was the development of recommended solutions facilitating the improvement of the human factor in the field of cybersecurity.
The moderating role of ICT diffusion between financial development and economic growth: a bootstrap ARDL approach in Saudi Arabia

Several studies have proven a positive impact of information and communication technologies (ICT) on economic growth. Nevertheless, some studies have suggested a limited effect, while others have found no statistically significant effect. Faced with this problem, we conducted a study with the aim to assess the role of moderation of ICT diffusion between financial development and economic expansion in Saudi Arabia from 1990 to 2019. Using the bootstrap approach for the ARDL model, the results prove that financial development as well as ICT diffusion affect negatively (positively) economic development. The financial development interaction term with ICT diffusion has a positive and statistically significant effect on economic growth. The results suggest that ICT diffusion does not only directly impact economic growth but also increase the indirect impact of financial development on economic growth. This result indicates that ICT diffusion boosts the role of financial development in economic development. This means that financial development can only boost the Saudi economy when ICTs are well developed.
DIALED: Data Integrity Attestation for Low-end Embedded Devices

Verifying integrity of software execution in low-end microcontroller units (MCUs) is a well-known open problem. The central challenge is how to securely detect software exploits with minimal overhead, since these MCUs are designed for low cost, low energy and small size. Some recent work yielded inexpensive hardware/software co-designs for remotely verifying code and execution integrity. In particular, a means of detecting unauthorized code modifications and control-flow attacks were proposed, referred to as Remote Attestation (ℛA) and Control-Flow Attestation (CFA), respectively. Despite this progress, detection of data-only attacks remains elusive. Such attacks exploit software vulnerabilities to corrupt intermediate computation results stored in data memory, changing neither the program code nor its control flow. Motivated by lack of any current techniques (for low-end MCUs) that detect these attacks, in this paper we propose, implement and evaluate DIALED, the first Data-Flow Attestation (CFA) technique applicable to the most resource-constrained embedded devices (e.g., TI MSP430). DIALED works in tandem with a companion CFA scheme to detect all (currently known) types of runtime software exploits at fairly low cost.
Non-diacritized Arabic speech recognition based on CNN-LSTM and attention-based models

Arabic language has a set of sound letters called diacritics, these diacritics play an essential role in the meaning of words and their articulations. The change in some diacritics leads to a change in the context of the sentence. However, the existence of these letters in the corpus transcription affects the accuracy of speech recognition. In this paper, we investigate the effect of diactrics on the Arabic speech recognition based end-to-end deep learning. The applied end-to-end approach includes CNN-LSTM and attention-based technique presented in the state-of-the-art framework namely, Espresso using Pytorch. In addition, and to the best of our knowledge, the approach of CNN-LSTM with attention-based has not been used in the task of Arabic Automatic speech recognition (ASR). To fill this gap, this paper proposes a new approach based on CNN-LSTM with attention based method for Arabic ASR. The language model in this approach is trained using RNN-LM and LSTM-LM and based on nondiacritized transcription of the speech corpus. The Standard Arabic Single Speaker Corpus (SASSC), after omitting the diacritics, is used to train and test the deep learning model. Experimental results show that the removal of diacritics decreased out-of-vocabulary and perplexity of the language model. In addition, the word error rate (WER) is significantly improved when compared to diacritized data. The achieved average reduction in WER is 13.52%.
Measuring attitudes as a complex system

We test a method for applying a network-based approach to the study of political attitudes. We use cognitive-affective mapping, an approach that visually represents attitudes as networks of concepts that an individual associates with a given issue. Using a software tool called Valence, we asked a sample of Canadians (n = 111) to draw a cognitive-affective map (CAM) of their views on the carbon tax. We treat these networks as a series of undirected graphs and examine the extent to which support for the tax can be predicted based on each graph's emotional and structural properties. We find evidence that the emotional but not the structural properties significantly predict individuals' attitudes toward the carbon tax. We also find associations between CAMs' structural properties (density and centrality) and several measures of political interest. Our results provide preliminary evidence for the efficacy of CAMs as a tool for studying political attitudes. The study data are available at https://osf.io/qwpvd/?view_only=6834a1c442224e72bf45e7641880a17f.
Mining Outlying Aspects on Healthcare Data

Machine learning and artificial intelligence have a wide range of applications in medical domain, such as detecting anomalous reading, anomalous patient health condition, etc. Many algorithms have been developed to solve this problem. However, they fail to answer why those entries are considered as an outlier. This research gap leads to outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present an interesting application of outlying aspect mining in the medical domain. This paper aims to effectively and efficiently identify outlying aspects using different outlying aspect mining algorithms and evaluate their performance on different real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, have outstanding performance on this task and have promising results.