Reviewerly/exHarmony huggingface dataset:
Cryptography and Blockchain Solutions for Security Protection of Internet of Things Applications

In recent decades, the industrial applications of the internet of things (IoT) have been attracting massive motivation for research and improvement of industrial operations. The IoT technology integrates various smart objects (or things) to form a network, share data among the connected objects, store data, and process data to support business applications. It is challenging to find a univocal architecture as a reference for different business applications, which can relate to many sensors, intelligence devices, networks, and protocols for operations. Moreover, some of the IoT infrastructural components are a shortage of computational processing power, locally saving ability, and data communication capacity, and these components are very vulnerable to privacy and security attacks. This chapter presents an overview of different IoT-based architectures and security-related issues. Finally, the chapter discusses the challenges of cryptography and blockchain-based solutions after reviewing the threats of IoT-based industry-specific business cases.
Blockchain Imaginaries and Their Metaphors: Organising Principles in Decentralised Digital Technologies

Heralded as revolutionary in their potential to improve efficiency, transparency, and sustainability, blockchain technologies promise new forms of large-scale coordination between actors that do not necessarily trust each other. This paper examines blockchain imaginaries and associated metaphors. Our analysis focuses on bitcoin and ethereum, today’s most prominent blockchains that use the proof-of-work consensus mechanism. We identify three principles that organise blockchain imaginaries: substantial, morphological, and structural. These principles position blockchain as an enabler of economic, political and epistemological practices, respectively. Blockchain infrastructure and protocols rely on substantial metaphors (e.g. gold, gas) to govern resource allocation, morphological metaphors (e.g. work, trust) to generate consensus and structural metaphors (e.g. chain, transaction) to establish shared knowledge. Those imaginaries rely on metaphorical displacements of meaning that make blockchain technology relevant and intelligible while simultaneously shaping the direction of technological development and positing these technologies as new forms of economic, political and epistemological organisation. They are not merely descriptive but performative. We conclude by showing how these principles partially overlap with three symbolically generalized media: money, power and truth. Money organises scarcity within the economic system, power organises consensus within the political system, and truth organises knowledge within the science system.
Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns

The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.
The Importance of the Job Role in Social Media Cybersecurity Training

Social media has become embedded in our everyday lives, personal activities and the workplace. Thus, educating users on emerging cybersecurity challenges for social media has become imperative. As such, we have investigated the feasibility of an awareness-raising and adaptive cybersecurity training system. Our investigation is aided by a questionnaire, which was administered online using Google Forms. We collected answers from 641 employees from a variety of sectors: education, healthcare, leadership and management, arts, entertainment, police and the military. We found that a one-size-fits-all training approach is highly ineffective, as people's understanding and knowledge can vary greatly. Thus, we have proceeded to identify the factors that influence the success of any given approach. Information such as gender, age, education level, job roles, and training preferences seem essential considerations for developing a robust training strategy. Our investigation concludes that “job role” is the most significant factor associated with people's preferences and perceptions in cybersecurity training. Also, people appear to be in favour of adaptive training. Moreover, a mixed delivery approach is likely to be welcomed.
Real-time Macedonian Sign Language Recognition System by using Transfer Learning

The objective for developing a real-time sign language recognition system is twofold: improving inter-personal communication and supporting inclusive human-computer interaction with hearing-impaired population using a particular sign language. This study describes the design and implementation of a system for real-time Macedonian Sign Language recognition in images and videos. A robust and lightweight model was proposed based on transfer learning of suitable pretrained architectures, namely, Single Shot Detector (SSD) MobileNetV2 and SSD MobileNetV2 FPNLite. The proposed models were fine-tuned and extensively evaluated in a number of diverse scenarios to account for the inherent difficulties in recognizing particular letters. In the absence of publicly available dataset, we have created a dataset consisting of two-handed images of 28 out of 31 letters of the Macedonian alphabet; the three letters expressed by dynamic gestures were excluded from the study. The results point out to a state-of-the-art prediction accuracy on the classification task of Macedonian sign language alphabet.
Overview On Hardware Characteristics Of Virtual Reality Systems

The scope of this paper is to present an overview of the different types of virtual reality (VR) systems from the hardware point of view. To conduct this survey first brief history of VR and classification of VR systems is presented. Next different types of input and output devices, necessary for the composing of a VR system, are described. After that, a comparison between the most common models head mounted displays is provided. As a result, common hardware characteristics of VR systems are summarized. The aim of the conducted analyses is to be in help of the users, so they easily can choose which type of VR system is suitable for their needs and then select the appropriate devices for their particular purpose. In addition, the use of VR technologies in education has been presented in order to outline the importance of implementation of virtual reality to enhance the conventional education.
Grammatically Derived Factual Relation Augmented Neural Machine Translation

Transformer-based neural machine translation (NMT) has achieved state-of-the-art performance in the NMT paradigm. This method assumes that the model can automatically learn linguistic knowledge (e.g., grammar and syntax) from the parallel corpus via an attention network. However, the attention network cannot capture the deep internal structure of a sentence. Therefore, it is natural to introduce some prior knowledge to guide the model. In this paper, factual relation information is introduced into NMT as prior knowledge, and a novel approach named Factual Relation Augmented (FRA) is proposed to guide the decoder in Transformer-based NMT. In the encoding procedure, a factual relation mask matrix is constructed to generate the factual relation representation for the source sentence, while in the decoding procedure an effective method is proposed to incorporate the factual relation representation and the original representation of the source sentence into the decoder. Positive results obtained in several different translation tasks indicate the effectiveness of the proposed approach.
Harris Hawks Optimization Method based on Convolutional Neural Network for Face Recognition Systems

This paper discusses the momentum gradient dependent on the convolutional neural organization's strong point. It is a new methodology introduced to detect evenness in the data set of faces. The proposed face recognition framework was created for various purposes. Through Gabor wavelet change, facial evenness was extracted from the face-preparing information. After that, we applied a profound learning process to carry out verification. After applying the proposed method to YALE and ORL data sets, we simulated them using MATLAB 2021a. Before this, similar trials were directly applied through Harris Hawks Optimization (HHO) for including the determination approach. The extraction process was conducted with many picture tests to execute the Gabor wavelet method, which proved more viable than other strategies applied in our examination. When we applied the HHO on the ORL dataset, the acknowledgment rate was 93.63%. It was 94.26% when the three techniques were applied to the YALE dataset. It shows that the HHO calculation improved the exactness rate to 96.44% in the case of the YALE dataset and 95.88% in the ORL dataset.
Examining the trends and operations of modern Dark-Web marketplaces

Currently, the Dark Web is one key platform for the online trading of illegal products and services. Analysing the .onion sites hosting marketplaces is of interest for law enforcement and security researchers. This paper presents a study on 123k listings obtained from 6 different Dark Web markets. While most of current works leverage existing datasets, these are outdated and might not contain new products, e.g., those related to the 2020 COVID pandemic. Thus, we build a custom focused crawler to collect the data. Being able to conduct analyses on current data is of considerable importance as these marketplaces continue to change and grow, both in terms of products offered and users. Also, there are several anti-crawling mechanisms being improved, making this task more difficult and, consequently, reducing the amount of data obtained in recent years on these marketplaces. We conduct a data analysis evaluating multiple characteristics regarding the products, sellers, and markets. These characteristics include, among others, the number of sales, existing categories in the markets, the origin of the products and the sellers. Our study sheds light on the products and services being offered in these markets nowadays. Moreover, we have conducted a case study on one particular productive and dynamic drug market, i.e., Cannazon. Our initial goal was to understand its evolution over time, analyzing the variation of products in stock and their price longitudinally. We realized, though, that during the period of study the market suffered a DDoS attack which damaged its reputation and affected users' trust on it, which was a potential reason which lead to the subsequent closure of the market by its operators. Consequently, our study provides insights regarding the last days of operation of such a productive market, and showcases the effectiveness of a potential intervention approach by means of disrupting the service and fostering mistrust.
Multi-Label Classification of Hate Speech Severity on Social Media using BERT Model

Detection of offensive and hate speeches on social media using multi-label classification technique is a relatively new fine-grained solution to classification problems. This paper investigates intelligent learning models based on the BERT model for multi-label classification of hate speech. The approach utilized a semi-supervised pseudo-labeling technique to automatically label a newly created multi-social media data which was then augmented and balanced using AugLy and GPT-2 libraries before being used to train the BERT model. Alpha evaluation of the model returned a score of 0.948695 for toxic, 0.946662 for severe toxic, 0.944483 for obscene, 0.946159 for threat, 0.909272 for insult and 0.734659 for identity hate respectively. Examples were ranked and one among such ranked examples gave a probability score of 96%, 89.91% and 80.21% for the top three likely labels. The results compared well with that of the human-annotated severity ranking.
Research on VR/AR integration in education

Virtual reality (VR) and augmented reality (AR) are taking off in almost every part of our living. Lately VR/AR technologies are increasingly present in education as well. Virtual experiences offer a great opportunity to involve and motivate students to learn through experience. Accordingly, the data and the research topics and publications exploring VR/AR implementation in the field of education are also evolving rapidly. To investigate the research papers and trends, in this study we used the metadata from IEEE database for the research publications in this field starting from 1990 till 2021. The study presents our major findings regarding the research on application of virtual and augmentative reality technologies in education. It includes several quantitative as well as qualitative results from the analysis of the data gathered from over 700 publications. The research showed that the main increase in the research publications that study the use of AR/VR in education is in the last decade which corresponds with the release of the first headsets. Also, AR research papers are entering the scene later but show a more continuous trend till the present time. In addition to this, in terms of authors of the papers and their countries, it was found that the more developed countries lead the game with a larger number of authors while the non-developed countries remain with no authors of AR/VR publications.
Using compositionality to understand parts in whole objects

A fundamental question for any visual system is whether its image representation can be understood in terms of its components. Decomposing any image into components is challenging because there are many possible decompositions with no common dictionary, and enumerating the components leads to a combinatorial explosion. Even in perception, many objects are readily seen as containing parts, but there are many exceptions. These exceptions include objects that are not perceived as containing parts, properties like symmetry that cannot be localized to any single part and special categories like words and faces whose perception is widely believed to be holistic. Here, I describe a novel approach we have used to address these issues and evaluate compositionality at the behavioural and neural levels. The key design principle is to create a large number of objects by combining a small number of pre-defined components in all possible ways. This allows for building component-based models that explain neural and behavioural responses to whole objects using a combination of these components. Importantly, any systematic error in model fits can be used to detect the presence of emergent or holistic properties. Using this approach, we have found that whole object representations are surprisingly predictable from their components, that some components are preferred to others in perception and that emergent properties can be discovered or explained using compositional models. Thus, compositionality is a powerful approach for understanding how whole objects relate to their parts.
An end-to-end framework for flight trajectory data analysis based on deep autoencoder network

In order to jointly solve the tasks of abnormal trajectory detection and flow pattern recognition for flight trajectory data analysis, an end-to-end framework based on deep autoencoder network is proposed in this paper. Considering the coupling relationship between the two tasks, a structured sparsity-inducing norm is introduced into the reconstruction-based loss function to separate abnormal trajectories from the whole and extract low-dimensional and outliers-free representations from the remaining normal trajectories. On this basis, cluster assignment hardening is applied to further learn cluster-friendly representations as well as cluster assignment for each trajectory. The effectiveness and efficiency of the framework are validated on flight trajectories arriving at Hong Kong International Airport. Experimental results show that the proposed framework not only detects typical spatial anomalies, including holding patterns and rerouting patterns, but also identifies fine-grained cluster structures. Furthermore, it surpasses current state-of-the-art methods in terms of anomaly detection performance and cluster quality, with an improvement of 13.56% and 22.82%, respectively. With parallel computing, its time cost can be reduced to less than 1 second, which helps to perceive traffic situations and monitor abnormal behaviors in real time.
Challenges of protecting confidentiality in social media data and their ethical import

This article discusses the challenges of pseudonymizing unstructured, noisy social media data for cybersecurity research purposes and presents an open-source package developed to pseudonymize personal and confidential information (i.e., personal names, companies, and locations) contained in such data. Its goal is to facilitate compliance with EU data protection obligations and the upholding of research ethics principles like the respect for the autonomy, privacy and dignity of research participants, the social responsibility of researchers, and scientific integrity. We discuss the limitations of the pseudonymizer package, their ethical import, and the additional security measures that should be adopted to protect the confidentiality of the data.
A Multimodel-Based Deep Learning Framework for Short Text Multiclass Classification with the Imbalanced and Extremely Small Data Set

Text classification plays an important role in many practical applications. In the real world, there are extremely small datasets. Most existing methods adopt pretrained neural network models to handle this kind of dataset. However, these methods are either difficult to deploy on mobile devices because of their large output size or cannot fully extract the deep semantic information between phrases and clauses. This paper proposes a multimodel-based deep learning framework for short-text multiclass classification with an imbalanced and extremely small dataset. Our framework mainly includes five layers: the encoder layer, the word-level LSTM network layer, the sentence-level LSTM network layer, the max-pooling layer, and the SoftMax layer. The encoder layer uses DistilBERT to obtain context-sensitive dynamic word vectors that are difficult to represent in traditional feature engineering methods. Since the transformer part of this layer is distilled, our framework is compressed. Then, we use the next two layers to extract deep semantic information. The output of the encoder layer is sent to a bidirectional LSTM network, and the feature matrix is extracted hierarchically through the LSTM at the word and sentence level to obtain the fine-grained semantic representation. After that, the max-pooling layer converts the feature matrix into a lower-dimensional matrix, preserving only the obvious features. Finally, the feature matrix is taken as the input of a fully connected SoftMax layer, which contains a function that can convert the predicted linear vector into the output value as the probability of the text in each classification. Extensive experiments on two public benchmarks demonstrate the effectiveness of our proposed approach on an extremely small dataset. It retains the state-of-the-art baseline performance in terms of precision, recall, accuracy, and F1 score, and through the model size, training time, and convergence epoch, we can conclude that our method can be deployed faster and lighter on mobile devices.
Knowledge driven indoor object‐goal navigation aid for visually impaired people

Aiming to help improve quality of life of the visually impaired people, this paper presents a novel wearable aid in the shape of a helmet for helping them find objects in indoor scenes. An object-goal navigation system based on a wearable device is developed, which consists of four modules: object relation prior knowledge (ORPK), perception, decision and feedback. To make the aid also work well in unfamiliar environment, ORPK is used for sub-goal inference to help the user find the target goal. And a method that learns the ORPK from unlabelled images by utilising a scene graph and knowledge graph is proposed. The effectiveness of the aid is demonstrated in real world experiments.
Quantum Computing Approaches for Mission Covering Optimization

Quantum computing has the potential to revolutionize the way hard computational problems are solved in terms of speed and accuracy. Quantum hardware is an active area of research and different hardware platforms are being developed. Quantum algorithms target each hardware implementation and bring advantages to specific applications. The focus of this paper is to compare how well quantum annealing techniques and the QAOA models constrained optimization problems. As a use case, a constrained optimization problem called mission covering optimization is used. Quantum annealing is implemented in adiabatic hardware such as D-Wave, and QAOA is implemented in gate-based hardware such as IBM. This effort provides results in terms of cost, timing, constraints held, and qubits used.
Tool-supported teaching of UML diagrams in software engineering education - A systematic literature review

There is hardly a university that does not offer a course in software engineering for computer scientists. Due to the expanding complexity of software systems and rapidly changing requirements, it has become increasingly difficult to teach students all the content they need for their professional careers in the industry or academia. Additionally, teaching modelling with modelling languages like UML is a sophisticated task for educators. Student-generated solutions may be visually different from a sample solution and still be correct. Regarding large software engineering courses, individual feedback for students is usually not possible or comes with a time delay. However, it would contribute to their learning success. Therefore, a rising number of software tools can be found to support this area of teaching. In this paper, a systematic literature review is presented. It follows the methodology of Kitchenham and provides an overview about the tools, that are used to support teaching of modelling in higher software engineering education. Alongside the functionalities and their differences, this literature review summarizes the difficulties, that motivated educators to develop these tools.