Reviewerly/exHarmony huggingface dataset:
Key determinants and strategies for cybersecurity education in Yemen

The availability of an education system capable of developing skills, and thus a trained workforce with cyber-competence, is a prerequisite for avoiding cyber-attacks on a country’s critical infrastructure. Hiring foreign workers or outsourcing some operations is possible, but this is not a long-term solution and poses other problems. The available literature provides recommendations for creating a nationwide development, but little is known about the variables affecting the growth of &lt;b&gt;&lt;i&gt;cybersecurity education&lt;/i&gt;&lt;/b&gt; in developing countries like Yemen. Using data from 19 semi-structured meetings with academic officials from seven universities and academic entities, and then qualitative analysis these data, this article discusses the &lt;b&gt;&lt;i&gt;determinants&lt;/i&gt; &lt;/b&gt;of &lt;b&gt;&lt;i&gt;cybersecurity education&lt;/i&gt;&lt;/b&gt;, and approaches to improve &lt;b&gt;&lt;i&gt;cybersecurity education&lt;/i&gt;&lt;/b&gt;. To enhance current preparation, there is an urgent need for a national &lt;b&gt;&lt;i&gt;cybersecurity education&lt;/i&gt;&lt;/b&gt; strategy, which supports a number of initiatives and creates a multi-stakeholder space, where government, &lt;b&gt;&lt;i&gt;labor market&lt;/i&gt;&lt;/b&gt;, and academia can actively work together to meet national &lt;b&gt;&lt;i&gt;cybersecurity education&lt;/i&gt;&lt;/b&gt; requirements. Also, there is needed to complete the preparations to promote research and development skills and cybersecurity &lt;b&gt;&lt;i&gt;awareness&lt;/i&gt;&lt;/b&gt; while enhancing educator training and academic cybersecurity programse.
Comparing the Performance of Different Supervised Learning Algorithms

In recent years, machine learning has grown in popularity, with applications in a wide range of industries. This study discusses the fundamentals of machine learning and its various approaches, such as supervised classifier, unsupervised classifier and reinforcement learning. Moreover, the drawbacks of machine learning, such as the requirement for a lot of labelled data, the possibility of bias in the training process etc. are studied. Finally, some of the field's potential future developments, such as the use of machine learning in areas like healthcare and finance, have been elaborated. Furthermore, two algorithms of machine learning such as Decision Tree and Naive Bayes algorithms are compared. Overall, this work provides a thorough overview of machine learning's current state and potential future impact.
Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction

Survival analysis is widely applied for assessing the expected duration of patient status towards event occurrences such as mortality in healthcare domain, which is generally considered as a time-to-event problem. Patients with multiple complications have high mortality risks and oftentimes require specific intensive care and clinical treatments. The progression of complications is time-varying according to disease development and intrinsic interactions between complications with respect to mortality are uncertain. Classical methods for mortality prediction and survival analysis in critical care, such as risk scoring systems and cause-specific survival models, were not designed for this multi-event survival analysis problem and able to measure the competing risks of death for mutually exclusive events. In addition, multivariate temporal information of complications is not taken into consideration while estimating differentiated mortality risks in the early stage.In this paper, we propose a novel multi-event survival analysis solution using a tree-based autoregressive survival model of multi-modal electronic health record data. Specifically, we focus on modeling the temporal trajectory of complications and estimating the mortality risk associated with multiple potential complications simultaneously. In dynamic modeling, no assumptions are made for the relationships between time-dependent variables and risk transition over time.Validated with the eICU database, our model achieves a better prediction performance with C-index ranging in 74-80%, compared to state-of-the-art machine learning methods in the literature, for the complications of acute respiratory distress syndrome and cardiovascular disease cases.Our model provides the distinguishable mortality risk curves over time for specific complications and the track of risk development that could potentially support the ICU resource reallocation.
Is linguistic decision-making constrained by the same cognitive factors in student and in professional translation?

Abstract This article analyses the extent to which four well-known general cognitive constraints – syntactic priming, cognitive routinisation, markedness of coding and structural integration – impact the linguistic output of translation students and professional translators similarly. It takes subject placement variation in Dutch as a test case to gauge the effect of the four constraints and relies on a controlled corpus of student and professional French-to-Dutch L1 news translations, from which all declarative main clauses with either a preverbal or a postverbal subject were extracted. All corpus instances were annotated for four random variables, the fixed variable expertise and ten other fixed variables, which were considered good proxies for the cognitive constraints. A mixed-effects regression analysis reveals that by and large the cognitive constraints have an identical effect on student and professional translators’ output, with priming and structural integration having the strongest impact on subject placement. However, students diverge from professionals when translating French clauses with a left-dislocated adjunct into Dutch, which is interpreted as an indication of a difference in automatisation when dealing with specific French-Dutch cross-linguistic differences.
Group target tracking via jointly optimizing group partition and association

This paper considers the problem of indistinguishable group target tracking, where the individual targets within a group are closely spaced and resolvable, move in a coordinated manner, and the group splitting and merging may occur. In such a case, the performance of the commonly used multi-target tracking methods may be degraded. We focus on three aspects of the problem: deal with the group association, group splitting and merging, and derive the state estimates of the individual targets. First, in order to deal with the group partition and the group association globally, we propose a novel group target tracking method within the multiple hypothesis tracking (MHT) framework by extending the target partition and the measurement partition as parts of global hypotheses. An efficient algorithm is given to find the M-best group partition and association simultaneously. Second, we use the undirected graph to represent the interaction between groups and then obtain an identification method of the group splitting and merging. Third, in response to the state estimate of the group target, a group-individual Bayesian filter is designed to estimate the states of the virtual leader and the individual targets. Finally, numerical experiments are presented to illustrate the effectiveness of the proposed method.
Blockchain Technology

Blockchain is an emerging technology and has gained trust in the world of tech in no time. Data is the most important asset for any organization or institute as it holds the base of all information. Every now and then, cases of data leaking, piracy, and stealing could be found in many companies, businesses, industries, and markets ruining the database and causing hindrance to growth. This is where blockchain acts as a savior for these business and industrial organizations by protecting their data and transactions. Blockchain is a method of recording and handling data that makes it tough for the system to be altered, hacked, or manipulated by illegal sources. “Block” of public or the “chain” is the assembly that keeps transactional records which is connected by peer-to-peer nodes. This storage is also known as a “digital ledger.” It can be defined as a distributed ledger that replicates and allocates the transactions across the network of computers contributing to the blockchain.
Transformation of the Telecom Industry as a Result of Blockchain Technology in India

The study analyses the transformation of the telecom industry through blockchain technology as instructed by TRAI. India is a developing country where many sectors depend on telecommunication, which made the telecom industry adopt blockchain technology to eliminate various shortcomings and provide security, transparency, immutability, and control from the point of transaction and have an immediate and tangible value to roaming and settlements, identity management, mobile number portability, SLA monitoring, prevention of phone theft, and by creating a single source of truth of networks. Other use cases emerge including 5G participants with blockchain, telecom infrastructure marketplace, payments through mobile wallets, device identity, security management to elucidate timeworn industry disputes, unveiling of latest products and services with security, and drive growth to the new world of digital transformation.
MULTI-PURPOSE PASSWORD DATASET GENERATION AND ITS APPLICATION IN DECISION MAKING FOR PASSWORD CRACKING THROUGH MACHINE LEARNING

This article proposes a method for multi-purpose password dataset generation suitable for use in further machine learning and other research related, directly or indirectly, to passwords. Currently, password datasets are not suitable for machine learning or decision-driven password cracking. Most password datasets are just any old password dictionaries that contain only leaked and common passwords and no other information. Other password datasets are small and include only weak passwords that have previously been leaked. The literature is rich in terms of methods used for password cracking based on password datasets. Those methods are mainly focused on generating more password candidates like the ones included in the training dataset. The proposed method exploits statistical analysis of leaked passwords and randomness to ensure diversity in the dataset. An experiment with the generated dataset has shown significant improvement in time when performing dictionary attack but not when performing brute-force attack.
Password-authenticated proofs of retrievability for multiple devices checking cloud data

Traditional privately-verifiable proofs of retrievability (PoR) require users to securely generate and store secret keys for data storage and retrieval. This incurs significant inconveniences for users who multiple devices to store and retrieve their data, as secret keys need to be installed on all of these devices. In this paper, we propose a cryptographic primitive called password-authenticated proofs of retrievability (PAPoR), where users are able to use memorable passwords to outsource their data to a cloud and retrieve their data for integrity check. In a PAPoR scheme, users are required to register their passwords with the cloud server before issuing storage and retrieval requests. During the storage and retrieval phases, users communicate with the cloud server to recover secrets tied to the passwords. These secrets are used to complete the storage and retrieval tasks. After describing a formal security definition for the PAPoR framework, we give a concrete construction and the security analysis, and discuss several variants to improve its security. In addition, we implement the given PAPoR construction to evaluate its performance.
Adaptive pedagogical strategies responding to emergency remote teaching – immediate responses of Hungarian primary school teachers

Digital disruption is not a new phenomenon in education; however, it has become more prominent during the COVID-19 pandemic due to school closures and the related emergency remote teaching (ERT) period. Our study aims to explore the different pedagogical strategies that primary school teachers adopted during this period and determine how successful these strategies were in involving and engaging students. Altogether, 4028 teachers from 343 primary schools answered our online survey from all the regions of Hungary. The sample adequately represents the Hungarian primary school teacher population in terms of gender and age. We used cluster analysis and identified four clusters of pedagogical strategies; then, we used analysis of variance to explore how teachers’ digital competence and their ability to involve students in online learning varied across different clusters. Our analysis grasps the complexity of the issue, as it shows that two rather distinct strategies were both successful in involving students, and thus, there is no single solution best suited to digital learning. Overall, digitally competent teachers loosened the originally strict structure of education and provided more feedback, which proved to be an important element in successfully involving students in digital learning during ERT. The framework validated in our research can be used by policymakers and school administrators in different national and educational contexts, enabling them to understand the complexity of online teaching and learning. Furthermore, our results can offer some practical pointers for school teachers on how to combine different pedagogical strategies.
Intelligent Video Surveillance using Deep Learning

In the current era, the majority of public places such as supermarket, public garden, mall, university campus, etc. are under video surveillance. There is a need to provide essential security and monitor unusual anomaly activities at such places. The major drawback in the traditional approach, that there is a need to perform manual operation for 24 ∗ 7 and also there are possibilities of human errors. This paper focuses on anomaly detection and activity recognition of humans in the videos. The anomaly detection system uses principal component analysis network (PCANet) and Convolutional Neural Network (CNN) to solve the problems of manual operation such as the false alarms, missing of anomalous events and locating the position of an anomaly in the video. The frames wise abnormal event is detected using principal component analysis and Support Vector Machines (SVM) classifier. The location of the abnormality in a frame is detected using Convolutional Neural Network.
Applying Address Encryption and Timing Noise to Enhance the Security of Caches

Encrypting the mapping relationship between physical and cache addresses has been a promising technique to prevent conflict-based cache side-channel attacks. However, this method is not foolproof and the attackers can still build a side-channel despite the increased difficulty of finding the minimal eviction set. To address this issue, we propose a new protection method that integrates both address encryption and timing noise extension mechanisms. By adding the timing noise extension mechanism to the address encryption method, we can randomly generate cache misses that prevent the attackers from pruning the eviction set. Our analysis shows that the timing noise extension mechanism can cause the attackers to fail in obtaining accurate timing information for accessing memory. Furthermore, our proposal reduces the timing noise generating rate, minimizing performance overhead. Our experiments on SPEC CPU 2017 show that the integrated mechanism only resulted in a tiny performance overhead of 2.9%.
Communicating Actor Automata - Modelling Erlang Processes as Communicating Machines

Brand and Zafiropulo's notion of Communicating Finite-State Machines (CFSMs) provides a succinct and powerful model of message-passing concurrency, based around channels. However, a major variant of message-passing concurrency is not readily captured by CFSMs: the actor model. In this work, we define a variant of CFSMs, called Communicating Actor Automata, to capture the actor model of concurrency as provided by Erlang: with mailboxes, from which messages are received according to repeated application of pattern matching. Furthermore, this variant of CFSMs supports dynamic process topologies, capturing common programming idioms in the context of actor-based message-passing concurrency. This gives a new basis for modelling, specifying, and verifying Erlang programs. We also consider a class of CAAs that give rise to freedom from race conditions.
Leveraging Blockchain Technology in the Construction Industry

Taking into account the significance and hurdles of blockchain technology in construction work, the current study examines the potential of blockchain technology in the construction industry and how this disruptive technology could revamp construction projects. Using blockchain to simplify the contractual processes and paperwork associated with these complex projects could save money, free up valuable resources, and accelerate project completion. The blockchain records are tamper-proof, which will help enforce work compliance and discourage potential record fabrication. As a result, it makes sense to begin focusing on blockchain convergence efforts in these sectors and then extend them as the technology improves.
A Critical Glance on the Use of Blockchain in Business and Its Futuristic Approach

This chapter's goal is to assess blockchain technology's potential, current condition, and how some aspects of this revolutionary creation could change how “business as usual” is done across various industries. This study unifies the theoretical background of a profusion of research articles published in highly regarded scientific journals over the past 10 years, as well as some findings, in order to streamline the review and cover the rapidly growing blockchain domain. The researcher suggests a thorough classification of blockchain-enabled applications covering a range of industries, including supply chain, business, health, IoT, privacy, data management, and others based on a structured, systematic review and thematic content analysis of the current literature. It also draws attention to important themes, patterns, and fresh study opportunities along with the defects in the previously disclosed information, namely the limits of the blockchain technology and how these constraints affect businesses as well as other sectors of the economy.
Augmenting healthy brain magnetic resonance images using generative adversarial networks

Machine learning applications in the medical sector face a lack of medical data due to privacy issues. For instance, brain tumor image-based classification suffers from the lack of brain images. The lack of such images produces some classification problems, i.e., class imbalance issues which can cause a bias toward one class over the others. This study aims to solve the imbalance problem of the "no tumor" class in the publicly available brain magnetic resonance imaging (MRI) dataset. Generative adversarial network (GAN)-based augmentation techniques were used to solve the imbalance classification problem. Specifically, deep convolutional GAN (DCGAN) and single GAN (SinGAN). Moreover, the traditional-based augmentation techniques were implemented using the rotation method. Thus, several VGG16 classification experiments were conducted, including (i) the original dataset, (ii) the DCGAN-based dataset, (iii) the SinGAN-based dataset, (iv) a combination of the DCGAN and SinGAN dataset, and (v) the rotation-based dataset. However, the results show that the original dataset achieved the highest accuracy, 73%. Additionally, SinGAN outperformed DCGAN by a significant margin of 4%. In contrast, experimenting with the non-augmented original dataset resulted in the highest classification loss value, which explains the effect of the imbalance issue. These results provide a general view of the effect of different image augmentation techniques on enlarging the healthy brain dataset.
Intelligent English Language Translation And Grammar Learning Based On Internet Of Things Technology

research-article Free Access Share on Intelligent English Language Translation And Grammar Learning Based On Internet Of Things TechnologyJust Accepted Author: Yuanyuan Chen Office of Science and Technology Administration, Zhengzhou University of Economics and Business, Zhengzhou, He Nan, 451191, China Intelligent English Translation; Grammar Learning; Internet of Things Technology; Artificial Intelligence Office of Science and Technology Administration, Zhengzhou University of Economics and Business, Zhengzhou, He Nan, 451191, China Intelligent English Translation; Grammar Learning; Internet of Things Technology; Artificial Intelligence 0009-0003-1237-4982Search about this author Authors Info & Claims ACM Transactions on Asian and Low-Resource Language Information ProcessingAccepted on April 2023https://doi.org/10.1145/3588769Published:11 April 2023Publication History 0citation50DownloadsMetricsTotal Citations0Total Downloads50Last 12 Months50Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Medical Health Record using Block Chain

The future of healthcare systems is being shaped by incorporating emerged technological innovations to drive new models for patient care. By acquiring, integrating, analyzing, and exchanging medical data at different system levels, new practices can be introduced, offering a radical improvement to healthcare services. We present a novel smart and secure Medical health record system which, leverages blockchain technologies, permits patient centric medical records transactions .Office of the National Coordinator (ONC) for Health Information Technology is seeking patient-centric MHR designs that shift data ownership from providers to patients. There are multiple barriers to patient-centric MHR in the current system, such as security and privacy concerns, data inconsistency, timely access to the right records across multiple healthcare facilities. After investigating the current workflow of MHR, our system provides a feasible solution to these challenges by utilizing the unique features of blockchain. In particular, we develop a blockchain-based architecture and enable a flexible configuration thereof, which optimize medical data sharing between different health entities and fulfill the diverse levels of Quality of Service (QoS) that MHR may require. Finally, we highlight the benefits of the proposed MHR system and possible directions for future research.
Transitioning Business Practices in the Retail Industry Through Blockchain Technology

Blockchain is a new technology that allows for the decentralized and transactional sharing of data across a large network of untrusted participants. It allows for new types of distributed software architectures. Although the technology was initially used primarily in digital currency, it is a promising technology in other areas as well. This chapter provides a simplified introduction to blockchain technology. It also discusses how blockchain technology can be used in some business processes in the retail sector to greatly benefit both customers and retailers. This chapter specifically discusses issues of retail supply chain management (SCM) and how blockchain technology is transitioning the retail supply chain management. The chapter discusses the market trend in blockchain adoption as well as some of the challenges.