Reviewerly/exHarmony huggingface dataset:
Towards ‘serendipity engineering for seductive hypermedia’ and ‘user analysis using socialnomics’: The role of ecological cognition

This paper investigates the long-established ecological cognition framework and updates it to better account for the advances in computational science computational intelligence. To do this, two concepts are explored. The first, &#x2018;serendipity engineering for seductive hypermedia,&#x2019; looks at how to design information systems to account for the pleasant occurrences that happen in offline environments studied by those in sales and marketing where beneficial outcomes often occur by chance encounters. The second, &#x2018;user analysis using socialnomics&#x2019; looks at how a parametric user model based on the ecological framework can be used to understand users of information systems from the point of view of supporting a digital economy of users. A number of additional equations are developed using socialnomics that can be applied to digital transformation based on the parametric user model, including to calculate probability of seduction and probability of serendipity in an information system. The parametric model presented has great applicability for information and communications technology solution providers.
Adaptive Convolutional Neural Network Structure for Network Traffic Classification

Network traffic classification has been highly concerned by academia and industry for decades. In recent years, deep learning has attracted many scholars to use it in network traffic classification due to its excellent performance in the fields of computer vision and natural language processing. However, the performance of the neural network depends on its structure in the same dataset. When looking for the neural network to classify network traffic, it is necessary to constantly adjust the structure of the neural network to achieve better results, which is very time-consuming and experience-dependent. To solve the above problem, this paper proposes an Adaptive Convolutional Neural Network Structure for Network Traffic Classification (ACNNS-NTC) algorithm. The proposed algorithm first pre-processes the network traffic data used for training and testing, and then uses particle swarm optimization algorithm to optimize the network structure of the convolutional neural network, to generate convolutional neural network structure for network traffic classification, and verify the classification results. Experimental results show that the accuracies of the ACNNS-NTC algorithm on public datasets (ISCX-IDS2012, USTC-TFC2016, CIC-IDS2017) are above 99%. At the same time, the generated convolutional neural network has a more succinct structure and fewer model parameters compared with the existing methods.
Post-Quantum User Authentication and Key Exchange Based on Consortium Blockchain

Consortium blockchain has been widely used in many application scenarios, where there is the demand for a universal user authentication and key exchange mechanism for all the application users in the system like Know Your Customer. Since current solutions heavily rely on traditional public-key cryptosystems that are vulnerable to attacks from quantum computers, we design and implement the first post-quantum (PQ) user authentication and key exchange system for consortium blockchain, which is integrated with all the PQ public-key (i.e., signature and encryption/KEM) algorithms in the current round of NIST call for national standard. Furthermore, we also provide chaincodes, related APIs together with client codes for further development. Last but not least, we perform a systematic evaluation on the performance of the system including the consumed time of chaincodes execution and the needed on-chain storage space. Based on the experiment results, we discuss the implications of our findings, which are helpful for the PQ blockchain-based application developers, the undergoing NIST call and the developers of the PQ algorithms.
Towards a Framework to compare Blockchain Platforms for Smart Contracts

Blockchain technology represents an innovation from both a structural and an application point of view. Since its inception, it has undergone a strong and fast development and, thanks to the implementation of smart contracts, it has been applied in different application contexts, such as business, healthcare, manufacturing, IoT applications and much more. The interest around blockchain has brought to the definition of several blockchain platforms facilitating their development and application. Thus, to choose the blockchain platform that is more suitable to support a specific business need is a strategic problem. This paper proposes an evaluation framework, i.e. a set of quality attributes, to characterize and compare the different blockchain platforms helping to identify the most suitable one.
Leveraging Information Security Continuous Monitoring to Enhance Cybersecurity

Cyber attacks against U.S. Federal information systems are relentless and increasingly sophisticated. The probability for grave damage continues to escalate despite the efforts and significant resources expended. The detection, analysis, and prioritization of cybersecurity vulnerabilities, threats, and the status of the effectiveness of cybersecurity protection measures is needed within minutes to reduce or eliminate compromise and the associated debilitating consequences. Information Security Continuous Monitoring (ISCM) leverages technology to evolve from compliance-focused cybersecurity to data-driven risk management. ISCM enables real-time or near-real-time cyber situational awareness to be responsive to the explosive rates of vulnerabilities, persistent threats, and determined enemies. Despite the promises of ISCM, Federal government agencies continue to face challenges in achieving effective ISCM. Therefore, as part of our ongoing research, in this paper we discuss the need for ISCM and its current status, analyze its challenges for cybersecurity, and propose the future strategies to leverage ISCM for cybersecurity.
A User-related Semantic Location Privacy Protection Method In Location-based Service

With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.
Transparency-privacy Trade-off in Blockchain-Based Supply Chain in Industrial Internet of Things

The integration of blockchain and Industrial Internet of Things (IIoT) results in a new model of supply chain in which blockchain provides common platform to record the data whereas IIoT facilitates connectivity of the devices. The temper-proof record, traceability, and verification of each step (transparency) from manufacturing to final availability of the product is made possible through blockchain which results in a transparent supply chain. However, the high-level transparency also increases the risk of disclosure of business-related and personal information (minimum privacy) such as business secrets, intellectual property, and special incentives to adversaries. Consequently, a trade-off between transparency and privacy is established. Therefore, this work proposes a blockchain-based supply chain management in the context of IIoT known as SM-IIoT which discusses this transparency-privacy trade-off. Furthermore, the trade-off problem is turned into a Game in which the participants act as players whereas the transparency and privacy leakage are the payoff and loss, respectively. Consequently, the players compete to maximize their payoffs but minimize their individual losses due to privacy leakage. Nash equilibrium gives the solution for the Game which optimizes the payoffs of the players (e.g., maximum transparency and minimum privacy leakage). The scenario is implemented through Hyperledger fabric (HF) blockchain. Moreover, the results suggested that the proposed SM-IIoT outperforms the game theory based privacy preserving analysis approach in big data (GB-PPABD) in terms of transparency. Furthermore, the comparison with other similar works revealed that SM-IIoT owns the key attributes of the supply chain including transparency-privacy trade-off, fairness, and competitive behavior modeling of the participants.
RTPoW: A Proof-of-Work Consensus Scheme with Real-Time Difficulty Adjustment Algorithm

Bitcoin, the first decentralized cryptocurrency system, uses a simple but effective difficulty adjustment algorithm to stabilize its average time of the block creation at 10 minutes. Over time, the volatility of the Bitcoin price has become higher and higher, and it causes the total hashrate (the hash power of the entire network) constantly fluctuating. Both facts and our experimental results prove that Bitcoin's difficulty adjustment algorithm cannot respond in time while the total hashrate is constantly fluctuating. Hence, we propose a consensus protocol with a real-time difficulty adjustment algorithm, RTPoW. RTPoW allows the blockchain to adjust the difficulty target of each block by predicting the real-time total hashrate, so the block time can remain stable even if the total hashrate is wildly fluctuating. To evaluate the effect of RTPoW, we implemented a simulator of an experimental environment and tested our algorithm. The results obtained have confirmed its effectiveness and stability.
A conceptual framework for a Blockchain-based Tax payment financial service

Blockchain promotes secure and transparent data flow, and its distributed nature makes it ideal for conducting transactions visible to the public. Tax payments by citizens are the primary source of funds for governments across the globe. However, the system is overburdened and plagued by problems making tax recovery a lengthy and expensive process. Blockchain based applications and tokens are long thought to be ideal for conducting financial operations. This study proposes a conceptual blockchain-based framework that aims to resolve issues in the current tax collection and recovery system deployed by governments across the globe. Blockchain has provided the public with new and alternative ways to invest and hold wealth and the creation of an integrated taxation service based on decentralized records and reverse token payments can keep outdated financial services stay at par.
GMAF: A Novel Gradient-Based Model with ArcFace for Network Traffic Classification

As the Internet rapidly develops, network applications emerge everywhere and every time on the Internet. Therefore, to effectively regulate the network environment and maintain network security, it is significant to recognize and classify the network traffic correctly. The existing Deep Learning methods are based on a strong assumption&#x2014;the training set contains all the classes of applications. However, different from the natural and complex network environment, new applications emerge every time, or existing applications generate new behaviors, bringing numerous unknown traffic. Unknown traffic significantly reduces classification accuracy so that it will not only threaten network security but also bring new challenges to supervise the network. The paper proposes &#x201C;GMAF&#x201D;, a novel gradient-based model with Additive Angular Margin loss (ArcFace) for open-world network traffic classification. Specifically, the gradient of ArcFace loss layer&#x0027;s weights through the first backpropagation is adopted to identify known traffic and unknown traffic, since its weights have carried the critical information of known traffic after training. Moreover, ArcFace contributes to obtaining highly discriminative features because an additive angular margin penalty is added to the angular between the features and weights. In addition, we adopt class-balanced focal loss to assign more weight for the hard samples and the minority classes, mitigating the effects of class imbalance in the real world. The evaluation results demonstrate that the proposed method can accurately reject the unknown traffic while misidentifies known traffic as little as possible under different scenarios, better than existing advanced research methods. Furthermore, GMAF still works well as the number of unknown classes increases.
Human Activity Classification Using Basic Machine Learning Models

Human activity recognition (HAR) is the object of interest for many researchers in machine learning. In principle, providing accurate and reasonable information on an individual&#x2019;s activities and movements for pervasive computing is a very challenging problem. Recent advances in HAR have led to advanced tracking of highly complex human behaviors. This is progressively driving humans and computers to become seamlessly integrated through devices and software. The impact of this type of research has numerous applications in different sectors. This paper presents our initial experiments on evaluating the performance of popular machine learning algorithms in predicting human behaviors accurately. Our experiments suggest that some models can accomplish high recognition accuracy and low computational cost.
Fast Synchronization of Model Updates for Collaborative Learning in Micro-Clouds

Micro-clouds are increasingly used to carry out collaborative distributed machine learning, avoiding the cost, performance, and privacy issues of traditional cloud-centric learning due to the centralization of large amounts of data to the cloud through wide-area networks (WANs). Synchronizing model updates plays an important role in such a global collaborative learning system. However, due to the lack of flexibility, existing solutions may result in slow completion of data transmission for model updates synchronization among mirco-clouds. This paper proposes an efficient solution (Amusync) to achieve fast model updates synchronization via flexible hierarchical aggregation for model updates and adaptive routing for model data transfers. Amusync optimizes the selection for aggregators and routing paths according to network condition and traffic loads of model updates synchronization and uses an optimization-based algorithm to quickly find solutions. We conduct extensive simulations on real-world network topologies, and the results show that Amusync can speed up the parameter aggregation and distribution by reducing by as much as around 90&#x0025;.
Automated Human Activity Recognition by Colliding Bodies Optimization (CBO) -based Optimal Feature Selection with RNN

In an intelligent healthcare system, Human Activity Recognition (HAR) is considered an efficient approach in pervasive computing from activity sensor readings. The Ambient Assisted Living (AAL) in the home or community helps people provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications, such as content-based video search, sports play analysis, crowd behavior prediction systems, patient monitoring systems, and surveillance systems. This study implements the HAR system using a popular deep learning algorithm, namely Recurrent Neural Network (RNN) with the activity data collected from intelligent activity sensors over time. The activity data is available in the UC Irvine Machine Learning Repository (UCI). The proposed model involves three processes: (1) data collection, (b) optimal feature learning, and (c) activity recognition. The data gathered from the benchmark repository was initially subjected to optimal feature selection that helped to select the most significant features. The proposed optimal feature selection method is based on a new meta-heuristic algorithm called Colliding Bodies Optimization (CBO). An objective function derived from the recognition accuracy has been used for accomplishing the optimal feature selection. The proposed model on the concerned benchmark dataset outperformed the conventional models with enhanced performance.
Applied Blockchain Technology in Saudi Arabia Electronic Health Records

One of the major problems facing the healthcare system in the kingdom of Saudi Arabia is the difficulty in sharing medical data between hospitals. Therefore, the patient may suffer when he/she moves from one hospital to another which leads to waste of time, money, and resources. Another problem facing the healthcare system is data loss which could be caused by attackers, corruption, or viruses. Accordingly, this project aims to solve these problems using blockchain technology and creates a peer-to-peer network for healthcare systems in the kingdom of Saudi Arabia to store uniform electronic health records. Moreover, this project allows sharing and exchanging electronic health records between hospitals, provides high security and synchronization, and allows access to health information at any time in a faster and easier way.
Modeling SQL Statement Correctness with Attention-Based Convolutional Neural Networks

Automated grading of SQL statements is a topic of interest for instructors and students alike. It can give teachers additional time to be more effective in identifying issues quickly, and it can give students a preview of the grade they may receive. Existing attempts in the literature create models based on a variety of methodologies that exploit the structure of SQL statements and model answers; however, very few have leveraged the recent advances in deep learning. This paper employs a convolutional self-attention mechanism to learn complex contextual and grammatical dependencies directly from data of labeled SQL statements. Our experiments suggest that the proposed parameter-sharing strategy can adequately model the problem of detecting the correctness of an SQL statement with a balanced accuracy of up to 81.2% and an AUC of 0.87 in cross-validation.
Spring Buddy: A Self-Adaptive Elastic Memory Management Scheme for Efficient Concurrent Allocation/Deallocation in Cloud Computing Systems

Within the cloud computing scenario, each server usually carries multiple service processes, which intensifies the concurrency pressure of the system. As a result, the process of memory management during page allocation and deallocation becomes a significant bottleneck. Although several methods such as Buddy System and Inverse Buddy System (iBuddy) have been proposed to improve the performance of memory management, they cannot adapt to the highly concurrent environment of cloud computing, because they either force the memory allocation/deallocation requests to be serialized or bring extra fragmentation. To address the above problem, we propose Spring Buddy, which improves the concurrency of both memory allocation and deallocation and avoids unnecessary fragmentation. It can detect the changes of system- and process-level memory request patterns and dynamically adjust the organization of page frames. Inventively, Spring Buddy uses the spring core layer to provide both concurrent response and resource aggregation capability which is adapted to the system's concurrency pressure, and also uses the spring lazy layer to further mitigate the system resource contention through process behavior prediction. To demonstrate the effectiveness of Spring Buddy, we implement it in the Linux kernel. The results demonstrate that Spring Buddy can reduce memory allocation latency by 71.47 % and deallocation latency by 93.20% on average compared to the existing methods.
Cryptocurrency bounced back based on cryptography technology during the COVID-19 pandemic

We all realized that the Corona pandemic created a shift in the daily life style of individuals and organizations based on few rules like Touch-less, contact-less and spacing between individuals which all prevent the spread of this new virus. We also noticed that digital technologies were the valuable tool to cope with this new life style. Teaching remotely, working from home, examining patients using mobile applications, and paying the bills from your PC are all examples of mobilization of digital technologies to respond to the current crisis. Of course, the extensive use of remote applications requires as much confidentiality and security as possible due to the privacy related information, or due to the financial transactions. Cryptography and its applications seemed to be the adequate solution to the security issue. And one of the hot cryptography applications are BlockChains which were the heart of cryptocurrency appearance as an alternative for digitized money. This paper discusses the cryptocurrency and its mechanisms as ways to support the online transactions within the new corona lifestyle. The aim of this paper is to describe how cryptography reinforced the use of cryptocurrency application based on blockchain technology. We emphasize on the fact that asymmetric cryptography-based security represents a powerful tool for these applications.
A Blockchain-based Approach for Educators' Profile Management and Reward system

Blockchain technology, because of its widespread usage in research, and its multifarious and superior properties has also reached the education sector. While most of the work is related to the record-keeping of students&#x0027; data, the work proposed in this paper is teacher-centric. In this paper, a framework for educators has been created that will facilitate their future employability. A supervisory entity has been established to provide feedback to educators as they carry out various teaching activities. The feedback and performance of educators would be uploaded to the blockchain ledger through a smart contract by the supervisor, who would create a work profile for the prospective employer to access. A token system for reward has also been introduced in the framework. Because blockchain data is immutable, secure, and distributed, this research could give educators, educational institutions, and employers an advantage in managing faculty and employee records.