Reviewerly/exHarmony huggingface dataset:
Class Design for Junior High School Students Using Pattern Language: A Case Study of the Practice at Nichihara Junior High School

The purpose of this paper is to report on how to design a lesson for junior high school students using the pattern language [1] of human actions as a case study of the Period of Integrated Study. This practice was conducted for 27 second-year students of Nichihara Junior High School in Tsuwano Town, Shimane Prefecture, Japan, as pre-post studying for their school trip in November 2021. The school trip involves learning about local history and culture in groups, and then actually explore the area. The lessons were held in five times, using pattern language to realize proactive, interactive and authentic learning which is emphasized in the new curriculum [2]. In the first lesson, the students worked to think of solutions to problems that might occur during the school trip, using patterns. The pattern selection activity here is designed using the pattern language remix method proposed by Douglas Schuler [3, 4]. In the second and third lessons, students thought of eight patterns that they would like to practice during the school trip, and then discussed and decided on eight patterns that they would like to cherish as a team. In the fourth lesson, after the school trip, the students reflected on their behavior during the school trip, using mock-up papers, sticky notes, and the patterns they had chosen in their prior study. In the fifth lesson, the whole class reviewed their behavior and chose patterns that they would like to be aware of in their daily school life. This case shows that the use of pattern language in the Period of Integrated Study can be one approach to achieving proactive, interactive and authentic learning: express what the students value in the process of choosing patterns, exchange ideas among classmates and reflect on their actions and learnings during the school trip. From student and teacher feedback, the use of pattern cards is effective in making it easier for students who are not good at speaking to participate in dialogue activities while including playful elements in the learning process and using visual information such as illustrations. In addition, when using pattern language for the first time, there is a need to get used to it by using the pattern cards several times in several ways.
Cyber exclusions: An investigation into the cyber insurance coverage gap

The importance of cyber insurance as a tool for financial resilience to mitigate the accelerating corporate losses caused by cybercrime is growing. However, there exists a lack of standardization and mutual understanding in cyber insurance policies. With less than a third of cyber insurance claims paid in 2017 in the U.S., there exists a significant gap between the cyber risks businesses need to cover and those actually covered through their cyber insurance policies. This research uses inductive qualitative content analysis to examine the existing exclusions in the terms and conditions of 40 German cyber insurers and compares the summarized results with existing cyber risk events. We posit that the lack of understanding of cyber policy wordings related to cyber risks is a significant problem for companies that could suffer significant losses. The resulting categorization of 15 exclusions and interrelationships with cyber risk events will support businesses, the insurance industry, and researchers in their efforts to understand, measure, and manage cyber risk.
FPGA Implementation of Hybrid Karatsuba Multiplications for NIST Post-Quantum Cryptographic Hardware Primitives

Modern cryptographic algorithms demand the use and necessity for large integer polynomial multiplications. However for large input operand size of multipliers, the complexity of the hardware design arises in terms of space and time. Thus, in this paper efforts has been made to design an efficient polynomial multiplier by implementing a hybrid Karatsuba multiplication algorithm. The overall performance of the proposed design is measured using Area-Time-Product (ATP). Hardware implementation of the proposed architecture is done using Virtex-7 FPGA device in Xilinx ISE platform.
Clustering in Globally Coupled Chaotic Circuits with Changing Weights

In this study, we focus on clustering phenomena in a network composed of coupled chaotic circuits. In this circuit network, the coupling strength is reflected by the distance information when the chaotic circuits are placed in a two-dimensional space. We propose a method that applies Hebbian rule to synchronization. Namely, the coupling weights are changed depending on synchronization states. By using the computer simulations, it is confirmed that the proposed methods is more effective than the standard method for clustering.
Mobile Ad-hoc Networks Topic Modelling and Dataset Querying

Topic Modelling is an unsupervised machine learning technique that’s capable of scanning a set of documents, detecting word and phrase patterns within them, and automatically clustering word groups and similar expressions that best characterize a set of documents. It is considered an unsupervised machine learning technique as it does not require a predefined list of tags or training data that’s been previously classified by humans. The research conducted in this paper can be viewed as supplemental to the qualitative analysis conducted in the sphere of MANETS and the current applications of machine learning, intrusion detection and risk calculation. This is accomplished via the use of Topic Modelling. The dissection, review and discussion of the empirical data is some of the key processes which stem from the outputs of utilizing topic modelling. The produced empirical data of this paper is intrinsic to the further understanding of the scope of studies done particularly in the realm of Risk Calculation and contemporary Machine Learning and Intrusion Detection research related to MANETS. This paper seeks to provide guiding indicators that direct the literary review of existing bodies of work.
Hardware Design of Optimized Large Integer Schoolbook Polynomial Multiplications on FPGA

The requirements of hardware design for large integer polynomial multiplications is the need of the hour in various cryptographic fields involving large computational complexities. Schoolbook multiplication, being a common alternative is presented in this paper for implementation. A highly optimized Schoolbook multiplier is proposed, which is much faster than the traditional ones. The overall performance of the algorithm is evaluated using Area-Time-Product (ATP). Hardware implementation of the proposed schoolbook multiplication architecture is done using Virtex-7 FPGA device in Xilinx ISE platform.
An Intelligent System for Semantic Information Extraction and Knowledge Graph Construction from Multi-Type Data Sources

Knowledge graph technology has been widely used in various fields and numerous platforms provides the knowledge-graph-based applications. Nevertheless, most of the applications require users to master expertise and programming ability. In this paper, a knowledge graph construction and application platform for general fields is completed in the web application form, which is user-friendly and interactive. Primarily, it can construct knowledge graphs of various fields automatically or semi-automatically from multiple types of data sources, including structured and unstructured data. Additionally, it can store, manage and update multiple versions of knowledge graphs by graph database Neo4j, and visualize specific information about each node and relationship of the knowledge graph. Finally, the system establishes a semantic parsing-based knowledge base Question Answering(KBQA) system based on the user-built knowledge graph, assisting users to realize the graph-related applications based on their own data.
Edge-Cloud Hybrid Tiny Data Reduction Model for Anomaly Detection

With the development of network technology, image processing has been combined with the depth of the Internet of things to learn, and other technology plays an important role in anomaly detection. But ordinary visual sensor features are simple, mostly to transmit the image to the cloud, not only can't found the abnormal situation in time, and because the data for long-distance transmission and increasing network bandwidth leads to network delay Disasters and other situations are destructive and sudden. If not discovered in time, irreparable losses will be caused. Therefore, how to reduce the amount of data transmission and network delay in abnormal detection has become a big challenge In this paper, an anomaly detection model based on edge-cloud is proposed. Firstly, the anomaly is detected on the edge end, the anomaly image is extracted and compressed, and the anomaly is classified in the cloud. To achieve this, first of all, it is proposed to build an anomaly primary detection model on the edge end, and the data collected by the vision sensor is detected as abnormal. Then a data reduction method on the edge end is proposed, and the abnormal image is feature-extracted to reduce data transmission. Finally, the features of the transmission are fine-classified using a detailed classification of an anomaly on the cloud. A large number of experimental results show that this method can realize the anomaly detection technology without affecting the recognition accuracy and reducing the data transmission.
An Efficient Fashion Recommendation System using a Deep CNN Model

The primary goal of the recommender system is to make suggestions for products that are comparable to the given query image. It can be difficult to separate related objects from a vast data set. Systems for online purchasing are looking into how to make product recommendations based on the user's interests. In the past, different statistical techniques and similarity measures were employed to gather comparable items, which resulted in less accurate and precise product recommendations. An efficient Deep CNN model is proposed for classifying the given product. The proposed model is evaluated using fashion products data set, and the results are pleasing. This makes it possible to reliably and precisely recommend the products with an accuracy percentage of 89.02%. The proposed model outperforms other existing models in terms of classification metrics
Privacy Leakage in GAN Enabled Load Profile Synthesis

Load profile synthesis is a commonly used technique for preserving smart meter data privacy. Recent efforts have successfully integrated advanced generative models, such as the Generative Adversarial Networks (GAN), to synthesize high-quality load profiles. Such methods are becoming increasingly popular for conducting privacy-preserving load data analytics. It is commonly believed that performing analyses on synthetic data can ensure certain privacy.In this paper, we examine this common belief. Specifically, we reveal the privacy leakage issue in load profile synthesis enabled by GAN. We first point out that the synthesis process cannot provide any provable privacy guarantee, highlighting that directly conducting load data analytics based on such data is extremely dangerous. The sample re-appearance risk is then presented under different volumes of training data, which indicates that the original load data could be directly leaked by GAN without any intentional effort from adversaries. Furthermore, we discuss potential approaches that might address this privacy leakage issue.
Multi-Scaled Attentive Knowledge Tracing

Learning is a dynamic, complex, and time-series process. Knowledge tracing (KT) aims to simulate learners' learning process by using learners' behavioral performance in past learning activities. In recent years, self-attentive mechanisms have been widely used in KT model. The literature shows that attention-based KT models generally outperform traditional deep knowledge tracing models. In order to simulate the learning process of learners more effectively we propose a new multi-scale attentive knowledge tracing model for KT. Specifically, the model uses multi-scale multi-head attention to capture learner features at different time scales and use them to model learners' learning behaviors. We also use relative position encoding to maintain the consistency of location information across multiple scales of attention. Experiments on real datasets show that our model outperforms state-of-the-art KT methods.
Kyber Accelerator on FPGA Using Energy-Efficient LUT-Based Barrett Reduction

Contemporary cryptographic algorithms such as RSA and ECC can collapse due to the development of quantum computing. NIST has announced the first standardized post-quantum cryptography algorithms that are resilient to the quantum computer's attack, among which CRYSTALS-KYBER is the only algorithm for public-key encryption. This algorithm uses the number theoretic transform (NTT) to reduce the time complexity of the polynomial multiplication. However, NTT itself is still a big computational bottleneck of the entire algorithm. Also, modulo reduction consumes almost half of the energy of the butterfly unit for processing NTT. This paper presents a Kyber accelerator using LUT-based Barrett reduction, which reduces the energy for modulo operation by 40.6% from the original Barrett reduction. The test system is implemented on the Xilinx Zynq UltraScale+ ZCU104 and shows 113.3x speedup for NTT and 1.78x speedup for Kyber compared to the original CPU.
Blockchain based Healthcare Data Management

Block Chain is an emerging technology which includes a number of features by default such as, distributed ledger, decentralised storage, authentication, security, and traceability. In health sector, patient data are sensitive and it is very essential to be secured, which can be done using blockchain technology. Exchange of healthcare data between hospitals is limited by privacy and dependency on centralized data management systems. Such a centralised storage can be a concern since it can lead to data leakage, data manipulation, mistrust, and single point of failure. Blockchain offers a decentralised computing and storage solution that can help with these issues. It includes smart contracts, identity verification and more. Integrating block chain technology with identity management will be the solution for some issues, such as centralized governing of identities. This system proposes Ethereum based blockchain data management for healthcare application, to store and review the patient record by web application allowing only identity verified users like patient, doctors, family members and hospital staff to have the secure access to health information. Ethereum currently uses a proof-of-work consensus mechanism. Solidity is the popular language for writing Ethereum smart contracts. The data on Ethereum blockchain is stored using tire data structures to manage temporary and permanent data. To protect data integrity, ownership, and permissions, smart contracts are created. Since the business can be handled by smart contracts, there won't be a need for centralised authority to oversee and authorise it, which will cut costs.
The Critical Success Factors for Security Education, Training and Awareness (SETA) Programmes

This study explores the Critical Success Factors (CSFs) for Security Education, Training and Awareness (SETA) programmes. Data is gathered from 20 key informants (using semi-structured interviews) from various geographic locations including the Gulf nations, Middle East, USA, UK, and Ireland. The analysis of these key informant interviews produces eleven CSFs for SETA programmes. These CSFs are mapped along the phases of a SETA programme lifecycle (design, development, implementation, and evaluation).
Comparative Analysis of Autonomous Vehicle in Computer Vision

The Autonomous driving is the area of interest to many researchers and much has been accomplished in this area, a detailed chronology of which is provided in this paper. Autonomous cars are vehicles that are powered without any human interference using digital technologies. By sensing the environmental effects, they are able to drive themselves on the highways. They are built to take up less room on the road to prevent traffic jams and reduce the possibility of accidents. But due to many constraints like safety, unpredictable scenarios, still lack the ability to make fully automated vehicles successful. In this work we aim to describe the dataset available and every move as well as the research done so far in the autonomous driving vehicle
Beware of Titles: Analysing Media Reporting of Cybercrime in UK and UAE

In this paper, media reporting of cybercrime victims (with a specific focus on organisations) was examined. For this purpose, data were collected from media outlets in the UK and UAE. A basic premise of this paper is that media has the ability to restrict readers to either ‘opposing’ or ‘favouring’ views about the subject of the news. Research findings demonstrated that victim organisations were mostly shaped within a negative media frame in a given time period. Comparative analysis between UK and UAE news media showed the usage of similar language in portraying victim organisations.
Enhanced Deep Autoencoder based Recommender System

With the colossal volume of information on the Internet, it is becoming more and more difficult for users to find the desired information quickly and easily. To surmount this problem, recommender systems have been developed as an effective solution. However, the main challenges faced by these systems are data scarcity and their constantly increasing dimensions. Recently, several researchers have used deep learning to solve these problems. In this paper, we first propose a movie recommendation system based on collaborative filtering using the auto-encoder. The role of this approach is to overcome the data scarcity problem and therefore improve accuracy. To enhance our approach we use pre-trained RBM (Restricted Boltzmann Machine) models to build our second Deep Auto-Encoder. To validate our approach, we conduct experiments on a set of real data, and we compare the two systems using evaluation metrics.
Evolution of Virtual and Augmented Reality Technologies in Historical and Archaeological Research

History of the emergence and development of virtual reality technologies is analyzed, with an emphasis on applications in historical and archaeological research since the early 1990s to the present day. These technologies are considered both in the context of projects for the three-dimensional reconstruction of industrial and cultural heritage and for educational purposes (to create immersive effects). The first part contains very brief description of the most valuable achievements in the field of Virtual and Augmented Reality technologies development. Implementation of computer-based visualization is one of the most important issues of the paper in the context of discussed research projects.
Image Inpainting for Fingerprint Analysis

Biometric frameworks are broadly utilized for checking a singular personality because of its different benefits. Unique mark examination has been utilized for ID of suspects in numerous violations in recent years. Unique mark matching is one of the promising techniques introduced in different associations. The image inpainting procedure is utilized in unique mark recovery works based on the exhibition of finger impression confirmation/matching framework. This research work implements a novel image inpainting method to perform unique finger impression recovery. The outcomes show that the image inpainting approach can be utilized to recreate the damaged piece of unique finger impression, which assists the working process of finger impression check/matching framework.
ApproxTorch: An Approximate Multiplier Evaluation Environment for CNNs based on Pytorch

Recently, approximate multipliers for CNNs have been studied hard, but evaluation of CNNs with approximate multipliers is always slow and requires many coding efforts. To solve this problem, we present ApproxTorch, an evaluation environment to simulate CNN models with 8-bit approximate multipliers. ApproxTorch provides Python classes for approximate convolution layers and fully-connected layers just like Pytorch classes which makes model transformation much easier. The behavior of an approximate multiplier is represented as a look-up-table and implemented as memory access. By exploiting the powerful Pytorch library for GPU, ApproxTorch can run CNNs with approximate multipliers much faster than traditional methods on CPU.
Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo State Networks as Implicit Machine Learning

In this paper, we propose the use of neural network surrogates of stiff QsP models, which reduces and accelerates QsP models by training ML approximations on simulations. We describe how common neural network methodologies, such as residual neural networks, recurrent neural networks, and physics/biologically-informed neural networks, are fundamentally related to explicit solvers of ordinary differential equations (ODEs), and thus are ill-equipped to deal with stiffness. To address this issue, we showcase methods from scientific machine learning (SciML) which combine techniques from mechanistic modeling with traditional deep learning. We describe the continuous-time echo state network (CTESN) as the implicit analogue of ML architectures. We demonstrate the CTESN's ability to surrogatize a production QsP model, a >1,000 ODE chemical reaction system from the SBML Biomodels repository, and a reaction-diffusion partial differential equation. We showcase the ability to accelerate QsP simulations by up to 5.2x against the optimized Differential Equations. jl solvers while achieving <5% relative error. This shows how incorporating the numerical properties of QsP methods into ML can improve the intersection, and thus presents a potential method for accelerating repeated calculations such as global sensitivity analysis and virtual populations.