Reviewerly/exHarmony huggingface dataset:
Autonomous Navigation and Real Time Mapping Using Ultrasonic Sensors in NAO Humanoid Robot

Mapping is an essential and basic requirement for a mobile robot in order to be able to navigate autonomously. This paper proposes a solution for autonomous navigation and real-time mapping using the virtual humanoid robot called NAO. The robot navigates through its environment using ultrasonic sensors only and develops a 2-D map of the environment. For implementation and testing, the Webots simulator was used. It provides real-time values, modification and designing of the 3-D world arena, and plugins for other parameters control. We test autonomous navigation in differently shaped environments. The proposed mathematical algorithm allows the autonomous navigation of the robot and calculates the position of the robot and the obstacles (if any). The results indicate that the algorithm can localize the robot within the environment whereas the accuracy in localization can be increased by adding a control constant to the orientation of the robot. The results demonstrate that the algorithm is more effective in the rectangular arena than in the triangular and pentagon arenas.
Modeling hierarchical attention interaction between contexts and triple-channel encoding networks for document-grounded dialog generation

Dialog systems have attracted attention as they are promising in many intelligent applications. Generating fluent and informative responses is of critical importance for dialog systems. Some recent studies introduce documents as extra knowledge to improve the performance of dialog generation. However, it is hard to understand the unstructured document and extract crucial information related to dialog history and current utterance. This leads to uninformative and inflexible responses in existing studies. To address this issue, we propose a generative model of a neural network with an attention mechanism for document-grounded multi-turn dialog. This model encodes the context of utterances that contains the given document, dialog history, and the last utterance into distributed representations via a triple-channel. Then, it introduces a hierarchical attention interaction between dialog contexts and previously generated utterances into the decoder for generating an appropriate response. We compare our model with various baselines on dataset CMU_DoG in terms of the evaluation criteria. The experimental results demonstrate the state-of-the-art performance of our model as compared to previous studies. Furthermore, the results of ablation experiments show the effectiveness of the hierarchical attention interaction and the triple channel for encoding. We also conduct human judgment to evaluate the informativeness of responses and the consistency of responses with dialog history.
An efficient VoLTE covert timing channel for 5 G networks: RDCTC

Covert Channels are necessary to protect the privacy of individuals as well as to obtain confidentiality to governmental and military agencies. A novel covert timing channel is proposed with three main improvements over the existing ones: Reorder Density (RD) metric-based packet reordering that reduces exposure, introducing Modified Turbo Code (MTC) for efficient encoding, and Discrete Haar Wavelet Transform (DHWT) that increases channel reliability. A threat model is proposed and shown to be inefficient to effectively meet covert channel properties against network jitter, noise, and curious attackers that try to identify a channel exists. Run and test is applied to investigate randomness of the proposed covert channel. The proposed covert channel is adoptable to 5 G VoLTE networks. These proposed strategies significantly improve the robustness while also minimizing the detectability of covert channels in VoLTE.
Corridors-based navigation for automated vehicles convoy in off-road environments*

Most of the studies found in the literature for autonomous driving solutions applied to vehicles convoying are related to highway platooning applications. However, in recent years both the agriculture and military fields seem to have an increased interest in such solutions for off-road applications. This work presents a corridor-based strategy for motion planning of automated vehicles convoys in off-road environments. From the wake generated by the leading vehicle, the follower vehicle estimates the boundaries of a navigable corridor encompassing the leader's path. Using these boundaries and the outputs from the on-board navigation subsystem, the vehicle plans the most suitable trajectory to follow the leader while avoiding obstacles. In order to keep the vehicles within safety distance thresholds, an inter-distance model is applied to the speed profile generation. The proposed strategy has been implemented and validated with real vehicles, showing promising results in fully operational off-road environments.
Classifying Speech into Offensive and Hate Categories along with Targeted Communities using Machine Learning

In the modern-day age, the problem of hate speech and offensive speech has increased due to the internet being widely used and technical resources being available to most people. If not moderated, it could lead to severe riots and hate-mongering against minorities. Such behavior is difficult to moderate so an efficient approach with be to filter out hate and offensive speech. Therefore, using one vs rest classification this research study has introduced a system to classify a comment as being normal, hateful, or offensive, and the communities targeted by it; totally 18 labels are used, one for the classification of comment and the other 17 being the target communities. In addition to the global accuracy, this research study has also provided individual accuracy for each community being targeted in the One Vs Rest model. The proposed research study exhibits the following global test accuracies for the different models for comparative analysis - One vs Rest - 90.97%, Binary Relevance - 28.7%, Label Powerset - 39.1%, Multi KNN - 19.6%.
Using Thematic English Learning and Augmented Reality to Enhance Vocabulary Learning Motivation and Enjoyment of Elementary School Students

Nowadays, vocabulary learning for second-grade students can be enhanced with appropriate teaching techniques, which can also improve their learning motivation and enjoyment at the same time. The use of Augmented Reality- Assisted Language Learning (ARALL) has been proven to be an innovative way to enhance the learning achievements of lower graders. Therefore, this study aimed to find out whether applying ARALL can improve the ability of lower-grade students in English vocabulary learning while also enhancing their learning motivation and enjoyment. Twenty-three lower-grade students participated in this study, and a mixed research method was adopted for data analysis. The results showed that the students did improve their vocabulary, and they enjoyed this ARALL instruction. Furthermore, through AR thematic English teaching methods, the students could remember the vocabulary rapidly and enhance their own confidence and enjoyment in learning English.
Cybersecurity in the Digital Classroom: Implications for Emerging Policy, Pedagogy and Practice

Recent cybersecurity education literature has focused on developments in cybersecurity curricula, qualifications and accreditation, pedagogy and practice to increase the number of cybersecurity professionals, in both the UK and internationally. There has been little research published to date on the online learning, teaching and assessment environment as a cyber target in its own right. This chapter appraised and discussed the dangers in, and emerging threats to, using online environments. It proposes a set of steps and mitigation measures that can be taken to make it more difficult for cybercriminals to attack educational institutions.
Aspect-Based Sentiment Analysis of Online Reviews for Business Intelligence

COVID-19 pandemic has affected the patients to a large extent who are in immediate need of medical care. Now-a-days people rely on online reviews shared on review websites to gather information about hospitals like the availability of beds, availability of ventilators etc. However, these reviews are large in number and unstructured which makes it difficult to interpret them. Hence, the authors have proposed a methodology that will apply an aspect based sentiment analysis on the reviews to gain meaningful insights of the hospital based on different aspects like doctor, staff, facilities etc. For the purpose of empirical validation, a total of 26,071 reviews pertaining to 325 hospitals were scrapped. The authors concluded that the study can be useful for patients as it helps them to select the hospital which best suits them. The study also helps hospital administration to improve the current services according to needs of the patients.
SIM-PIPE DryRunner: An approach for testing container-based big data pipelines and generating simulation data

Big data pipelines are becoming increasingly vital in a wide range of data intensive application domains such as digital healthcare, telecommunication, and manufacturing for efficiently processing data. Data pipelines in such domains are complex and dynamic and involve a number of data processing steps that are deployed on heterogeneous computing resources under the realm of the Edge-Cloud paradigm. The processes of testing and simulating big data pipelines on heterogeneous resources need to be able to accurately represent this complexity. However, since big data processing is heavily resource-intensive, it makes testing and simulation based on historical execution data impractical. In this paper, we introduce the SIM - PIPE Dry Runner approach - a dry run approach that deploys a big data pipeline step by step in an isolated environment and executes it with sample data; this approach could be used for testing big data pipelines and realising practical simulations using existing simulators.
Image Data Augmentation Method based on Style Transfer

Because there aren't enough accessible images of military vehicles, overfitting is a common occurrence when using a detection model in the military sector. Besides, low-contrast military vehicles are more difficult to be spotted in the field. Therefore, we create a dataset of military vehicles that consists of a training set and two different test sets, and we suggest an efficient method for image data augmentation that is mostly based on style transfer. Specifically, the process of data augmentation contains targets mask generation, style transfer, and details addition, and doesn't need extra annotation work. In the experimental part, YOLO v5s is applied to verify the efficacy of our method. Our method enables us to improve the precisions by 0.101 and 0.134 in the high-contrast situation, and achieve the precisions of 0.729 and 0.515 in the low-contrast situation when using single-style stylized images dataset and multi-style stylized images dataset respectively, in experiments. The results suggest that our method can reduce overfitting and show a rather satisfactory performance on our self-made dataset.
A Blockchain Approach to IoT Security and Reliability Analysis

An investigation area of interest including the use of blockchain technology to address Internet of Things security concerns (IoT). Despite the fact that numerous relevant ideas have been offered, there are few published works that provide hypothetical and information backing. The examination of model building and execution assessment is the focus of this research. First, an IoT security paradigm is outlined, using blockchain and the Inter-Planetary-File-System (IPFS) in a view. Many of the security risks associated with traditional IoT designs can be avoided with this paradigm, and framework execution is entirely focused on distributed large limit stockpiling, simultaneity, and query. In addition, the suggested model's display is evaluated using normal idleness and throughput, which are important for further investigation and enhancement of this course. The blockchain-based security model's feasibility has been demonstrated via analysis and experiments.
Online Harassment Detection using Machine Learning

Online communication has become more accessible with the recent digitalization techniques. This advancement also comes with its own advantages and disadvantages. The major disadvantage is the occurrence of bullying and online harassment in cyberspace, which is referred to as cyberbullying. A lot of effort has been taken to deal with this problem. The sufferers will only know the extent of their psychological and mental consequences. Several research studies have proposed different techniques for identifying cyberbullying, however the majority of them focus on a specific job, such as text recognition systems by using Machine Learning (ML) techniques or voice recognition systems using Natural Language Processing (NLP) techniques. To overcome this drawback, the proposed research study offers a multitasking approach, which can work with both text and audio data.
EVRQ: Achieving Efficient and Verifiable Range Query over Encrypted Traffic Data

As a promising solution in boosting the efficiency of urban traffic, intelligent transportation systems (ITS) have been increasingly deployed. Traffic message dissemination, connecting various services and entities, has been regarded as an essential service in ITS, and range query service has also been frequently used for on-demand traffic message retrieval. Meanwhile, as the dataset grows, the service provider tends to outsource the range query services to fog nodes. However, as the fog nodes are not fully trustable, directly outsourcing the services to them may invoke security concerns: on the one hand, the private information, including the traffic messages, query requests, and results should not be revealed to the fog nodes. On the other hand, the fog nodes may return incorrect and/or incomplete query results. Although many schemes have been proposed to achieve privacy-preserving range queries, few of them can support verifiability over dynamic datasets. Therefore, in this paper, we propose an efficient and verifiable range query (EVRQ) scheme over encrypted traffic messages. Specifically, we first build two techniques to respectively enable the fog nodes to efficiently determine and prove whether two rectangles intersect. Then, based on the two approaches and R-tree technique, we create a Merkle R-tree, which we use to build our EVRQ scheme. Security analysis shows that our proposed scheme is really privacy-preserving and can achieve verifiability of the query results. In addition, extensive experiments are conducted, and the results demonstrate that our proposed scheme is indeed efficient.
SRRNet: A Semantic Representation Refinement Network for Image Segmentation

Semantic context has raised concerns in semantic segmentation. In most cases, it is applied to guide feature learning. Instead, this paper applies it to extract the semantic representation, which records the global feature information of each category with a memory tensor. Specifically, we propose a novel semantic representation (SR) module, which consists of semantic embedding (SE) and semantic attention (SA) blocks. The SE block adaptively embeds features into the semantic representation by calculating the memory similarity, and the SA block aggregates the embedded features with semantic attention. The main advantages of the SR module lie in three aspects: i) it enhances the representation ability of semantic context by employing global (cross-image) semantic information; ii) it improves the consistency of intraclass features by aggregating global features of the same categories; and iii) it can be extended to build a semantic representation refinement network (SRRNet) by iteratively applying the SR module across multiple scales, shrinking the semantic gap and enhancing the structural reasoning of the model. Extensive experiments demonstrate that our method significantly improves the segmentation results and achieves superior performance on the PASCAL VOC 2012, Cityscapes, and PASCAL Context datasets.
A security protocol for D2D communications in 5G networks using elliptic curve cryptography

Advancements in the field of wireless and mobile communication motivate academia, industry, and standardisation organisations to explore new technologies to improve overall efficiency, spectrum utilisation, power consumption, and security. One of the most essential technologies proposed for short-range communications, which will play a key role in 5G wireless networks, is device-to-device (D2D) communications. D2D communication enables direct communication between two devices when they are in close proximity without requiring the help of a fixed infrastructure like base stations. Despite the significant benefits provided by D2D communications, this new networking communication paradigm introduces unique security threats to D2D services that should be solved in order to limit any malicious attacks. Towards this end, in this paper, we focus on secure D2D communications and propose a protocol for establishing secure D2D communication using elliptic curve cryptography. The protocol utilises: (i) specific Mobile Identifiers acquired from the hardware of the mobile phone (i.e. IMEI) and the SIM card (i.e. IMSI, MSISDN) provided by the mobile operator and (ii) the SIM storage for saving private keys or signature data. The proposed security protocol is verified and validated with the help of the Scyther tool, and the performance is evaluated in terms of resiliency analysis.
Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

The traditional Infrastructure as a Service (IaaS) cloud platform tends to realize high data availability by introducing dedicated storage devices. However, this heterogeneous architecture has high maintenance cost and might reduce the performance of virtual machines. In homogeneous IaaS cloud platform, servers in the platform would uniformly provide computing resources and storage resources, which effectively solve the above problems, although corresponding mechanisms need to be introduced to improve data availability. Efficient storage resource availability management is one of the key methods to improve data availability. As mechanical hard disk is the main way to realize data storage in IaaS cloud platform at present, timely and accurate prediction of mechanical hard disk failure and active data backup and migration before mechanical hard disk failure would effectively improve the data availability of IaaS cloud platform. In this paper, we propose an improved algorithm for early warning of mechanical hard disk failures. We first use the Relief feature selection algorithm to perform parameter selection. Then, we use the zero-sum game idea of Generative Adversarial Networks (GAN) to generate fewer category samples to achieve a balance of sample data. Finally, an improved Long Short-Term Memory (LSTM) model called Convolution-LSTM (C-LSTM) is used to complete accurate detection of hard disk failures and achieve fault warning. We evaluate several models using precision, recall, and Area Under Curve (AUC) value, and extensive experiments show that our proposed algorithm outperforms other algorithms for mechanical hard disk warning.
Computational metrics of an injection-locked large area semiconductor laser for neural network computing (Conference Presentation)

Artificial neural networks (ANNs) have become a staple computing technique in many fields. Yet, they differ from classical computing hardware by taking a connectionist and parallel approach to computing and information processing. Here, we present a high performance, scalable, fully parallel, and autonomous PNN based on a large area vertical-cavity surface-emitting laser (LA-VCSEL). We implement 300+ hardware nodes and train the network to perform up to 6-bit header recognition, XOR classification and digital to analog conversion. Moreover, we investigate the impact of different physical parameters, namely, injection wavelength, injection power, and bias current on performance, and link these parameters to the general computational measures of consistency and dimensionality.
Cross-Domain Trust Architecture: A Federated Blockchain Approach

Abstraet- This paper proposes a federated hierarchical trust interaction (FHTI) architecture for the cross-domain loT. FHTI combines federated learning with blockchain to propose a credible evaluation model which ensures the safety and dependability of blockchain users.
A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection

As a result of rapid digitisation, online transactions using credit cards have become popular. With this, fraudulent activities have also increased considerably. Although many supervised and unsupervised machine learning techniques were proposed in past research for identifying fraudulent transactions, they do not fully utilize the tabular and hierarchical structure present in transaction datasets. Recently, the TabBERT neural network model was proposed to calculate row-wise embeddings that capture both inter and intra dependencies between transactions in tabular time series data. In this research, we present a systematic experimental framework to assess the effectiveness of applying the embeddings calculated using the TabBERT model for credit card fraud detection. We employ the calculated row embeddings for fraud detection using three unsupervised machine learning algorithms and two supervised machine learning algorithms. We perform our experiments on a synthetic dataset that has been generated using the TabGPT model. Overall, TabBERT-based embeddings increase the performance of the supervised learning models with the extreme gradient boosting model achieving a precision of 99% and an F1 score of 98%, and the multilayer neural network model achieving a precision of 97% and an F1 score of 95%. For unsupervised learning, the use of TabBERT embeddings increases the recall rate of K-means clustering algorithm by 0.19%.
Evaluating the Factors that Affect the Adoption of Blockchain Technology in the Pharmaceutical Supply Chain - A Case Study from Sri Lanka

One of the significant causes of medicine counterfeiting is the pharmaceutical industry’s inadequate supply chain system, which makes it hard to keep track of it. This study aims to identify the factors affecting the adoption of Blockchain in the pharmaceutical supply chain in Sri Lanka. The study’s conceptual framework is developed through a thorough literature review and structured interviews. Sample data is acquired from supply chain practitioners, pharmaceutical manufacturers, Medical Supply Division, and National Medicine Regulatory Authority to validate the conceptual model. The Partial Least Squares, Structural Equation Modelling (PLS-SEM) technique was used to investigate the effect of factors on the adoption of Blockchain. Based on a thorough examination of the literature, the suggested conceptual model incorporates the complex relationships between eight significant factors, namely1) Relative advantage, 2) Upper management support, 3) Human resources, 4) Compatibility, 5) Cost, 6) Complexity, and 7) Technological Infrastructure and 8) Architecture. Academics can use the proposed framework to design and review blockchain-based research as a starting point for implementing blockchain applications in the pharmaceutical supply chain.