Reviewerly/exHarmony huggingface dataset:
A model for information security culture with creativity and innovation as enablers – refined with an expert panel

Purpose This study aims to elicit an understanding of creativity and innovation to enable a totally aligned information security culture. A model is proposed to encourage creativity and innovation as part of the information security culture. Design/methodology/approach The study first applied a theoretical approach with a scoping literature review using the preferred reporting items for systematic reviews and meta-analyses method to propose a conceptual model for engendering employee creativity and innovation as part of the information security culture. A qualitative research method was further applied with expert interviews and qualitative data analysis in Atlas.ti to validate and refine the conceptual model. Findings A refined and validated information security culture model enabled through creativity and innovation is presented. The input from the expert panel was used to extend the model by 18 elements highlighting that the risk appetite of an organisation defines how much creativity and innovation can be tolerated to reach a balance with the potential risks it might introduce. Embedding creativity and innovation as part of the organisational culture to facilitate it further as part of the information security culture can aid in combating cyber threats and incidents; however, it should be managed through a decision-making process while governed within policies that define the boundaries of creativity and innovation in information security. Research limitations/implications The research serves as a point of reference for further research about the influence of creativity and innovation in information security culture which can be investigated through structural equation modelling. Practical implications This study offers novel insights for managerial practice to encourage creativity and innovation as part of information security. Originality/value The research proposes a novel concept of introducing creativity and innovation as part of the information security culture and presents a novel model to facilitate this.
A survey of radio frequency fingerprint feature extraction and recognition based on deep learning

Unique structures in the electromagnetic waves emitted by the transmitter are called Radio Frequency Fingerprinting (RFF). By extracting the unique radio frequency fingerprints of wireless devices at the physical layer, it can be used to identify specific individuals and prevent malicious attacks, ultimately improving network security. The method of identifying transmitters through unique machine fingerprints is called specific emitter identification. At present, RFF is mainly used in two parts, military and civilian. The military field uses RFF to identify strategic equipment such as radar, and the civilian field uses RFF to improve network security. First, the collected signals are extracted with features, and then the extracted features are classified and identified, so as to match specific emitters. This paper first starts from the generation of RFF, introduces the traditional extraction and identification methods, and then combines the current development of deep learning, summarizes the RFF extraction and identification based on deep learning, analyzes the advantages of deep learning methods compared with traditional methods.Finally, an outlook is made on the future development prospects of RF fingerprints, and opinions on the future development of RF fingerprints are put forward.
English writing retrieval system based on machine learning algorithm

When non-native English speakers write in English, they often encounter situations where they do not know how to say a sentence or how to express a meaning. The current machine translation system is difficult to be practically applied because the accuracy rate is too low and the translation results cannot meet the requirements. Because the online dictionary system can only search by keywords, it is not helpful for the search of the whole sentence. This paper designs an English writing retrieval system to help people write smoothly. The system designed in this paper takes the WEB server as the frame, and establishes the system structure of the retrieval processing module, the query matching module and other functional modules that connect the corpus ontology database. The application experiments of the two machine learning algorithms proposed in this paper, neural network and decision tree, in the retrieval system show that the retrieval effect of the neural network algorithm is better than that of the decision tree algorithm.
Multi-objective Global Path Planning for UAV-assisted Sensor Data Collection Using DRL and Transformer

Unmanned-Aerial-Vehicles’ (UAVs) inherent features such as high dynamicity, quick deployment, and line of sight communication have motivated the research of UAV-assisted IoT networks. In such networks, one critical issue is path planing scheduling, which unfortunately is a complex multi-objective optimization problem (MOP). Although there exist extensive traditional MOP algorithms, the efficiency is unacceptable due to the resource constrains and they are unscalable for dynamic scenarios. In order to achieve a more efficient yet scalable multi-objective path planing algorithm, we innovatively propose a framework integrating deep reinforcement learning (DRL) and transformer. We firstly decompose the MOP problem into a series of sequencing subproblems with weighted objectives, and then we present a modified transformer network to solve each sequencing subproblem and further a DRL algorithm to facilitate the subproblem network training. Experimental results demonstrate that the proposed algorithm is superior to NSGA-II, MOEA/D and pointer network in terms of robustness, convergence, diversity of solutions, and temporal complexity.
Probing the Impacts of Visual Context in Multimodal Entity Alignment

Multimodal entity alignment (MMEA) aims to identify equivalent entities across different multimodal knowledge graphs (KGs), and this topic has drawn increasing attention in recent years. Although the benefits of multimodal information have been observed, its negative impacts are non-negligible as injecting images without constraints brings much noise. It also remains unknown to what extent or under what circumstances visual context is truly helpful to the task. In this work, we employ graph structures and visual context to align entities in different multimodal KGs and propose to selectively combine feature similarities between cross-KG entities of these two aspects when making alignment decision. Specifically, we exploit image classification techniques and entity types to remove potentially un-useful images (visual noises) via generating entity mask vectors in the learning and inference processes. The extensive experiments have validated that the incorporation of selected visual context can substantially improve the MMEA. We also provide a thorough analysis about the impacts of the visual modality and discuss a few cases where injecting entity images induces misalignment.
Identification of the Similarity of Bangla Words Using Different Word Embedding Techniques

From the browser search bar to the voice command in a computer system, everywhere Natural Language Processing (NLP) is broadly used nowadays. NLP is mainly used for the purpose of converting user data into compatible search materials. Some specific applications of NLP are sentiment analysis, text mining, news classification and many more. Those concepts are also very important in case of Bangla language processing. A number of researches have been conducted to process Bangla language to produce potential output from the user data of Bangla-speaking people. Word embedding is a major part of NLP and bears a vast importance in processing languages. In this work the word embedding for Bangla language is mainly focused. In doing the embedding words in Bangla language we have used Word2Vec model and FastText model with Gensim library. The Word2Vec model produces vector of words and similarly does the FastText model while FastText breaks the words into small blocks to train into machine. There are a very countable number of researches regarding the word embedding in Bangla language. In the proposed word FastText produces promising result over the Word2Vec model although no numerical conclusion was possible to derive at this phase of proposed implementation.
Explainable Recommender Systems: From Theory to Practice

Recommender systems support users’ decision-making, and they are key for helping them discover resources or relevant items in an information-overloaded environment such as the web. Like other Artificial Intelligence-based applications, these systems suffer from the problem of lack of interpretability and explanation of their results. Enriching or augmenting the system output with explanations increases the users’ trustworthiness and reliability regarding the system decisions. Therefore, it is important not only to measure the performance of automatic models but also to measure the explainability of the system. In this paper, we present research related to explainable recommender systems and a demonstrative case. To illustrate how explainable recommendations can be generated, we present two scenarios based on the Tripadvisor dataset.
Personalized Exergaming for the Elderly Through an Adaptive Exergame Platform

Exergaming technologies have rejuvenated the perspective of using game environments as facilitators of exercising adherence for health promotion. The entertaining character of exergames and the high accessibility of modern pervasive computing technologies have enabled a more sustained embracement of this niche technology, even by elderly adults. In spite of this view, personalization remains an open research issue with anemic solutions even though it represents a critical aspect to enhance exergaming usefulness. This paper explores notions for building an adaptive exergame platform that employs off-the-shelf body tracking sensors to collect movement data during gameplay and creates real-time game adaptations based on performance metrics and machine learning models. The adaptation layer of the platform provides the system intelligence for data analysis capable of adapting the gameplay in terms of adjustments of game parameters to match the skills of each player, thus resulting in a personalized game experience. The importance of an adaptive platform in the exergaming domain of the elderly is discussed and methods for its implementation are suggested.
A Blockchain-Based Model for OAuth Authorization via Third-Party Web Application

Authentication and authorization are critical factors for network security. In this context, three problems were identified. First, password fatigue, the difficulty of remembering a password when Internet service usage increases. Second, services centralization; that is, the dependence on a single entity for the operations management, which can infringe the privacy of users. Third, lack of service availability; it means, the permanent, or temporary suspension of the service. Denial-of-Service attacks denials cause delays in response times, and severe interruptions to centralized schemes. Faced with these challenges, this research proposes a blockchain-based model for OAuth authorization via third-party web application. For this, the versatilities that both technologies provide to solve the aforementioned issues. Then, it explains how together blockchain and OAuth 2.0 grant access to a web resource via a third-party web application. Consequently, a model is developed and addressed using smart contracts and web forms. To demonstrate the validity of our model, we propose a study case for a user to access a web resource using a third-party web application. Finally, conclusions and future work are described at the end of the paper.
Evaluating Classification Model Against Bayes Error Rate

For a classification task, we usually select an appropriate classifier via model selection. How to evaluate whether the chosen classifier is optimal? One can answer this question via Bayes error rate (BER). Unfortunately, estimating BER is a fundamental conundrum. Most existing BER estimators focus on giving the upper and lower bounds of the BER. However, evaluating whether the selected classifier is optimal based on these bounds is hard. In this article, we aim to learn the exact BER instead of bounds on BER. The core of our method is to transform the BER calculation problem into a noise recognition problem. Specifically, we define a type of noise called Bayes noise and prove that the proportion of Bayes noisy samples in a data set is statistically consistent with the BER of the data set. To recognize the Bayes noisy samples, we present a method consisting of two parts: selecting reliable samples based on percolation theory and then employing a label propagation algorithm to recognize the Bayes noisy samples based on the selected reliable samples. The superiority of the proposed method compared to the existing BER estimators is verified on extensive synthetic, benchmark, and image data sets.
Self-Aligned Spatial Feature Extraction Network for UAV Vehicle Reidentification

Compared with existing vehicle reidentification (VeID) tasks conducted with datasets collected by fixed surveillance cameras, VeID for an unmanned aerial vehicle (UAV) is still under-explored and could be more challenging. Vehicles with the same color and type show extremely similar appearances from the UAV’s perspective so that mining fine-grained characteristics becomes necessary. Recent works tend to extract distinguishing information by regional features and component features. The former requires input images to be aligned and the latter entails detailed annotations, both of which are difficult to meet in UAV application. To extract efficient fine-grained features and avoid tedious annotating work, this letter develops an unsupervised self-aligned network consisting of three branches. The network introduced a self-alignment module to convert the input images with variable orientations to a uniform orientation, which is implemented under the constraint of a triple loss function designed with spatial features. On this basis, spatial features, obtained by vertical and horizontal segmentation methods, and global features are integrated to improve the representation ability in embedded space. Extensive experiments are conducted on UAV-VeID dataset, and our method achieves the best performance compared with recent reidentification (ReID) works.
Properties of Standard and Sketched Kernel Fisher Discriminant

Kernel Fisher discriminant (KFD) is a popular tool as a nonlinear extension of Fisher's linear discriminant, based on the use of the kernel trick. However, its asymptotic properties are still rarely studied. We first present an operator-theoretical formulation of KFD which elucidates the population target of the estimation problem. Convergence of the KFD solution to its population target is then established. However, the complexity of finding the solution poses significant challenges when <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is large and we further propose a sketched estimation approach based on a <inline-formula><tex-math notation="LaTeX">$m\times n$</tex-math></inline-formula> sketching matrix which possesses the same asymptotic properties (in terms of convergence rate) even when <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is much smaller than <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> . Some numerical results are presented to illustrate the performances of the sketched estimator.
Public Opinion Analysis for the Covid-19 Pandemic Based on Sina Weibo Data

The outbreak of Covid-19 has been continuously affecting human lives and communities around the world in many ways. In order to effectively prevent and control the Covid-19 pandemic, public opinion is analyzed based on Sina Weibo data in this paper. Firstly the Weibo data was crawled from Sina website to be the experimental dataset. After preprocessing operations of data cleaning, word segmentation and stop words removal, Term Frequency Inverse Document Frequency (TF-IDF) method was used to perform feature extraction and vectorization. Then public opinion for the Covid-19 pandemic was analyzed, which included word cloud analysis based on text visualization, topic mining based on Latent Dirichlet Allocation (LDA) and sentiment analysis based on Naïve Bayes. The experimental results show that public opinion analysis based on Sina Weibo data can provide effective data support for prevention and control of the Covid-19 pandemic.
Image Interpolation Algorithm Based on Texture Complexity and Gradient Optimization

A novel image interpolation algorithm based on texture complexity and edge structure enhancement is proposed to solve the problems of blurring and edge jagging caused by traditional image enlargement. This interpolation method is useful for magnifying a single image. Firstly, polynomial model is used in complex areas, and then the horizontal, vertical and diagonal gradients of other regions are extracted. According to the gradient, the image is divided into large gradient pixels, medium gradient pixels and flat areas, and the corresponding interpolation method is adopted for the pixels with different features. This method only relies on a single original image to achieve the amplification effect, the interpolation amplification effect of low bit rate video decoded image is better, and it is convenient for real-time image amplification, so it has practical significance. Compared with other algorithms, the algorithm has great advantages in subjective and objective effects.
Binocular Stereo Vision

The human visual system is a natural stereoscopic vision system that acquires 3-D information through binocular imaging. In computer vision, stereo vision mainly studies how to use (multi-image) imaging technology to obtain distance (depth) information of objects in a scene from (multiple) images. This chapter will introduce the workflow of stereo vision, and analyzes the six functional modules involved in the process of stereo vision one by one. This chapter will discuss the method of matching binocular images based on regions. First, the principle of mask matching is introduced, and then the focus is on the detailed analysis of various constraints in stereo matching. This chapter will discuss the method of matching binocular images based on features. Based on the introduction of the basic steps and methods, the widely used Scale Invariant Feature Transformation (SIFT) is described in detail, and dynamic programming based on ordering constraints is also discussed. This chapter will introduce a method for detecting and correcting the errors of the parallax map/image, which is characterized by being more versatile and fast.
Image Compression and Meaningful Ciphertext Encryption Based on Histogram Shift Embedding

In this paper, a meaningful ciphertext compression and encryption method is proposed. Firstly, the image is sparsely transformed based on wavelet transform, scrambled and compressed to get the Gaussian noise-like ciphertext. Then, the distribution of noise-like ciphertext is changed by a histogram shift, and the meaningful ciphertext is obtained by the least significant bit (LSB) XOR. Finally, the ciphertext is modified by $$2^K$$ correction, and the ciphertext with better quality is obtained. In order to ensure the plaintext sensitivity and the ability to resist the attack of selecting plain ciphertext, the plaintext of SHA-256 is used to generate dynamic keys so that the whole process can achieve the effect of one-image-one-key. Experimental results show that this meaningful ciphertext encryption method has excellent quality of ciphertext and ensures visual security as well as. At the same time, the algorithm has a good ability to resist conventional attacks and has certain practicability.
CCRL: Contrastive Cell Representation Learning

Cell identification within the H &E slides is an essential prerequisite that can pave the way towards further pathology analyses including tissue classification, cancer grading, and phenotype prediction. However, performing such a task using deep learning techniques requires a large cell-level annotated dataset. Although previous studies have investigated the performance of contrastive self-supervised methods in tissue classification, the utility of this class of algorithms in cell identification and clustering is still unknown. In this work, we investigated the utility of Self-Supervised Learning (SSL) in cell clustering by proposing the Contrastive Cell Representation Learning (CCRL) model. Through comprehensive comparisons, we show that this model can outperform all currently available cell clustering models by a large margin across two datasets from different tissue types. More interestingly, the results show that our proposed model worked well with a few number of cell categories while the utility of SSL models has been mainly shown in the context of natural image datasets with large numbers of classes (e.g., ImageNet). The unsupervised representation learning approach proposed in this research eliminates the time-consuming step of data annotation in cell classification tasks, which enables us to train our model on a much larger dataset compared to previous methods. Therefore, considering the promising outcome, this approach can open a new avenue to automatic cell representation learning.
P2P-The Key Behind Regulatory Framework of DeFi Services

Decentralized Finance (DeFi) is an emerging financial technology that functions on the similar mechanism underlying cryptocurrencies. In DeFi the transactions will happen over a secure distributed ledger system which maintains anonymity of the person who carries out that transaction. DeFi completely removes the control that banks have on the customers transactions thereby making it safe and secured. Peer-to-Peer (P2P) is one amongst the premises on which DeFi operates. This chapter makes an attempt to study the role of P2P in DeFi Financial Transactions (DFFT).
Data Pipeline of Efficient Stream Data Ingestion for Game Analytics

This paper explores approaches to optimize micro-services that manage game analytics in terms of stream analysis. Typically, the micro-services parameters should be adjusted to suit the streaming data efficiently. We focus on the important data pipeline’s issues, i.e. the throughput and velocity of the data stream generated by extraction information modules. We investigate the existing technologies employed by the empirical studies as well as the architecture of the micro-services that make up the end-to-end data pipeline. The findings are reported as conclusion.
An Algorithm of Complete Coverage Path Planning Based on Improved DQN

Complete coverage path planning (CCPP) is an important and fundamental issue in robotics. In order to design an efficient CCPP, a new CCPP algorithm is proposed in this paper for robot to complete CCPP tasks. The improved DQN use the matrix of the map as the input and use convolutional layers to extract the feature. When the network is trained, the trained data were chosen by rank-based prioritization instead of random sampling. And use the dynamic reward to reduce the bad effect which due to the lack of the positive samples. At last, the improved DQN divide the network into two parts which are value function and advantage function to emphasize the value of the state which is very important for CCPP. The simulation studies show that the improved DQN proposed in this paper is capable to complete CCPP task more effective in complex environment.