Reviewerly/exHarmony huggingface dataset:
Accuracy Comparison of Different Machine Learning Models in Phishing Detection

The constantly evolving phishing attacks have forced scientists to counter such an attack. Most phishing attacks come with dangerous links or URLs that can potentially lead to data or information leaking to third parties. Machine Learning has been proven great in many cases of both known and unknown types of analysis. Its ability to both give reasoning and “self-taught” in addition with easier duplication makes it great to counter phishing attacks. This paper compares different machine learning algorithms to detect whether a URL is a legitimate URL or a phishing URL with a certain feature using a Web page phishing detection dataset. The machine learning algorithms that were compared are Naive Bayes, K-Nearest Neighbor, Random Forest, Decision Tree, Support Vector Machine, and Logistic Regression. The models were trained using a phishing dataset that has been passed through a preprocessing and encoding layer. The model resulting accuracy, along with other evaluation metrics are recorded and compared to each other. The results are pretty close with the highest accuracy claimed by the Random Forest algorithm which is 98,04% out of 11429 URLs.
Shift Happens: Human Factors Considerations for Handoffs in Cyber-Security Operations Centers

Cybersecurity breaches have consequences not only at a technical level, but also at economic, legal, individual levels. Organizations depend heavily on the internet to conduct daily operations and activities. As cyber threats increase with frequency and severity, organizations and the cybersecurity experts they employ are facing an uphill battle. To our knowledge, very little research has been done to investigate the handoff processes that exist in Security Operations Centers (SOCs). Researchers and practitioners within the cybersecurity community would benefit from a better understanding of how handoff mechanisms influence SOC team processes and performance. To help attain this goal, this paper aims to explore handoff processes within other domains and illuminate what aspects of handoffs may be extended to SOCs and in what ways handoffs performed in SOCs may need special consideration.
Cross-Domain Meta-Learner for Cold-Start Recommendation

The cold-start problem is a major factor that limits the effectiveness of recommendation systems. Having too few available interaction records brings a series of challenges when predicting user preferences. At present, there are two main kinds of strategies for solving this problem from different perspectives. One is cross-domain recommendation (CDR), which introduces additional information by domain knowledge propagation with transfer learning. However, CDR methods follow traditional training processes in machine learning and cannot solve this typical few-shot problem from the perspective of optimization. The other type of methods that has recently emerged is based on meta-learning. Most of these approaches focus only on generating a meta-model to perform better on new tasks and ignore improvements based on cross-domain information. Therefore, it is necessary to design a novel approach to solve this problem with both domain knowledge and meta-optimization. To achieve this goal, a novel cross-domain meta-learner for cold-start recommendation (MetaCDR) is proposed. In MetaCDR, we design a domain knowledge meta-transfer module to connect different domain networks. In addition, we introduce a pretraining strategy to ensure its efficiency. The experimental results show that MetaCDR performs significantly better than state-of-the-art models in a variety of scenarios.
Face Expression Recognition Based on Lightweight Fused Attention Mechanism

To address the problems of redundant model counts, large computational effort, poor targeting of effective feature extraction and easy loss of large amount of information in traditional convolutional neural networks for expression recognition, a lightweight fused attention mechanism approach for face expression recognition is proposed. The method is based on ResNet convolutional neural network, and the depthwise separable convolutional module is added in the feature extraction stage to reduce the number of parameters, and then the attention mechanism of the fusion channel is used to improve the extraction and representation ability of the model for important feature information. The PReLU is used to replace the ReLU to prevent Dying ReLU problems. The model has been simulated on the public RAF-DB dataset. The results show that the accuracy of facial expression recognition reached 85.53%, while the number of parameters and computational effort are kept at low levels. The results verify the effectiveness and superiority of the improved model.
Autonomous Control of a Quadcopter using Machine Learning Algorithm

During the recent years, there has been an increase in the potential use of UAVs all engineering domains such as commercial photography, aerial reconnaissance, payload delivery, etc. UAVs/Quadcopters are generally designed to operate in known and stable environmental conditions where environment dynamics are well known or can be easily linearized. But most practical problems contain unknown or non-linear dynamics of the system or the environment. Machine Learning (ML) provides the techniques for using intelligent control systems that can perform desired tasks in such unknown conditions. This paper provides a framework using a Machine Learning algorithm to enable UAV navigation in such environments through the implementation of an intelligent reinforcement learning controller. Research started with detailed mathematical modelling of a quadcopter, based on the Newton-Euler equations of forces and moments, later quadcopter model was employed with PID controller as well as ML controller. A conventional PID controller was used to find the linearized response of the quadcopter. The results obtained by both controllers were then compared using 6 DoF simulations. Furthermore, the quadcopter is made to follow certain trajectories to determine the accuracy of the ML controller.
Methods of crypto-stable symmetric encryption in the residual number system

This paper proposes new symmetric cryptography algorithms of Residue Number System and its Modified Perfect Form. According to the first method, ciphertext is regarded as a set of residues to the corresponding sets of modules (keys) and decryption or decimal number recovery from its residues takes place according to the Chinese Remainder Theorem. To simplify the calculations, it is proposed to use a Modified Perfect Form of Residue Number System, which leads to a decrease in the number of arithmetic operations (in particular, finding the inverse and multiplying by it) during the decryption process. Another method of symmetric encryption based on the Chinese Remainder Theorem can be applied when fast decryption is required. In this algorithm, the plaintext block is divided into sub-blocks that are smaller than the corresponding module and serve as remainders on dividing some number, which is a ciphertext, by these modules. Plaintext recovery is based on finding the ciphertext remainders to the corresponding modules. Examples of cryptography algorithms implementation and their encryption schemes are given. The security of the proposed methods is estimated on the basis of the Prime number theorem and the Euler function. It is investigated which bitness and a number of modules are required for the developed symmetric security systems to ensure the same security level as the largest length key of the AES algorithm does. It is found that as the number of modules increases, their bitness decreases. Graphical dependencies of cryptanalysis complexity on bitness and a number of modules are built. It is shown that with the increase of specified parameters, the security of the developed methods also increases.
A Graph Neural Network Based Model for IoT Binary Components Similarity Detection

IoT binary similarity detection is a way to determine whether two IoT components have a homology relationship. It is used to address security concerns arising from the reuse of open source components in the IoT software supply chain. In order to solve the problems caused by different architectures and different optimization levels during compilation, we propose a graph neural network based similarity assessment model for IoT binary components. We introduce an attention mechanism to get graph-level embedding based on GraphSAGE node-level embedding extraction, witch considers the importance of function nodes. Through comparative experiments with other models, this model shows better performance with 96.96% accuracy.
Blockchain Policy Tool Selection in CHina’s Blockchain Industry Clustering Areas

According to the data from Cyberspace Administration of China, the formation time of China blockchian industry clustering coincided with the release time of related policies that support the development of blockchian industry. To detect the relationship between regional blockchain industry clustering and the policy tools used when different regions release blockchain related policies, we conduct text analysis on regional blockchian industry indicators and blockchain related policies using K-means clustering method. The results show that in provinces which have higher blockchain industry clustering, regions with better economy and technology pay more attention to innovative R&D support in environment-side policy tools, while less developed regions pay more attention to supply-side and demand-side policy tools. Besides, provinces with lower blockchain industry clustering mainly focus on strategic measure or target planning in environment-side policy tools.
Using Social Media for Dynamic Information Dissemination in the 21st Century

The implementation and usage of information and communication technology (ICT) in library functions and facilities has revolutionized the way people use information and librarians perform their work. As a result of the advent of social media, the world's knowledge outlook has changed significantly, resulting in the sharing of thoughts, emotions, images, and videos as resources. A library is worth considering; it is a key to learning, a foundation for long-term mastery of information, and it promotes independent decision-making. The use of social media in library activities has enticed a significant number of users, but it has also challenged libraries to modernize their service delivery. Facebook, Twitter, Wikis, WhatsApp, MySpace, and LinkedIn facilitate community courses, collaboration, and information sharing. As a consequence, it is vital for libraries to consider and prioritize their users' needs.
DETA: A Point-Based Tracker With Deformable Transformer and Task-Aligned Learning

Current point-based trackers are usually implemented by the following two branches: a classification branch for predicting the target candidate locations and a regression branch for regressing the tracking box, which may lead to a spatial misalignment between the two tasks. Meanwhile, they ignore a meaningful exploration on how to define positive and negative samples during training and explicit border information for accurate box prediction. In this research, we investigate the key issues of point-based trackers and unlock their key limitations. First, we design a novel task-aligned component and a new loss function, named task-aligned loss, to learn the alignment of the classification and regression tasks. Second, we introduce a border alignment (BorderAlign) component in both the classification and regression branches to effectively exploit the border features of a tracking target. Third, we develop an adaptive training sample assignment (ATSA) to adaptively divide the positive and negative samples based on the statistical characteristics of the tracking object. Finally, a deformable transformer is developed to enhance the representations of search features and explore rich temporal contexts among video frames. Extensive experimental results demonstrate that the proposed tracker achieves state-of-the-art performance on six tracking benchmark datasets.
Ordering the Boolean Cube Vectors by Their Weights and with Minimal Change

The topic of generating all subsets of a given set occupies an important place in the books on combinatorial algorithms. The considered algorithms, like many other generating algorithms, are of two main types: for generating in lexicographic order or in Gray code order. Both use binary representation of integers (i.e., binary vectors) as characteristic vectors of the subsets. Ordering the vectors of the Boolean cube according to their weights is applied in solving some problems—for example, in computing the algebraic degree of Boolean functions, which is an important cryptographic parameter. Among the numerous orderings of Boolean cubic vectors by their (Hamming) weights, two are most important. The weight ordering, where the second criterion for sorting the vectors of equal weights, is the so-called weight-lexicographic order. It is considered in detail in [2]. Considered here is the second weight ordering, where the vectors of equal weights are arranged so that every two consecutive vectors differ in exactly two coordinates, i.e., they are ordered by minimal change. The properties of this ordering are derived. Based on these, an algorithm was developed that generates the vectors of the Boolean cube in this ordering. It uses a binary representation of integers and only performs additions of integers instead of operations on binary vectors. Its time and space complexities are of a linear type with respect to the number of vectors generated. The algorithm was used in the creation of sequence A351939 in OEIS [8].
Better security assessment communication

Security of complex process plants, such as nuclear power plants, requires assessing the information technology process and the operation technology process. When business processes and humans are included, it becomes challenging to identify all possible consequences arising from different business and technical threats and communicate them, when the case involves different languages, cultures, and competence backgrounds. This paper presents a four-step concept for supporting an ISO27002 security assessment using a unified model language (UML) sequence diagram capable of modeling actors and systems objects and their messages. First, we used the ISO27001 and RASCI table to identify the different levels of information security responsibility within a complex process plant. Secondly, together with the stakeholders of the plant, we built scenarios by making a reference architecture including the connection between IT and OT systems. Thirdly, we mapped the ISO 27002 security controls to the scenarios exemplified with the UML sequence diagram. Lastly, we assessed the maturity level. We tested the method together with the ICT team of a Ukrainian NPP on four different scenarios. The reference architecture included the plant data storage system that is making the plant data accessible for users on the enterprise network. The results indicate that communicating security controls by using UML sequence diagram scenarios displaying the bridge between IT and OT, ease the stakeholder's understanding of a complex process. Visualizing sequence diagrams and security controls in a combined way is an enabler for better communication during a security assessment.
Enhancing Human Robot Communication by Generating Spatial Information Using Uncertain Terms

Human interactions are not always specific and literal. In day-to-day scenarios, humans interact with each other in the manner that a lot of those involve uncertainties. When the robot has to interact with the user regarding an instance in a navigation scenario, the human user would effectively understand and react quickly if the robot interacts using the same uncertain concepts. However, the interactions, perception of situations, awareness of environmental parameters, and interpretations of those parameters should be accurate and human-like at the same time. Therefore, this paper proposes an intelligent system that has the capability to interpret spatial parameters and navigation parameters associated with navigation in a dynamic environment in a human-like manner. The proposed system considers the navigation of an intelligent wheelchair in a domestic environment. Parameters such as the velocity of the obstacles, distances to particular obstacles, and the size of the obstacles are perceived and interpreted according to the uncertainties attached to them. Understanding of the situation is enhanced by considering the fuzzy interpretations of numerical values of each parameter and the mutual effect of other parameters on these fuzzy interpretations of a particular parameter.
UAV Detection Method Using Convolution Neural Networks Optimized By Hyper Parameters Tuning

Recently, a great advantageous of autonomous unmanned air vehicles (UAVs) have increased widely due to device low cost and smaller size. Unmanned aerial vehicles (UAV) are commonly exploited to provide abundant helpfully services such as package delivery and surveillance systems. However, the possible threat to public security and personals live occasioned by drones intrusion should be addressed as a critical issue needs to resolve. In this paper, a new deep learning model for UAV detection using a customized convolution neural network CNN named IC-CNN is designed and introduced. The main objective of this research is to detect the drone object appearing at variant locations in the acquired images with different resolutions. Meanwhile, birds’ objects may possibly present in the image foreground. An optimal configuration of hyper-parameters adopted in this work is conducted to get higher accuracy of drones’ detection. The proposed deep learning model adopts forward and backward training workflow with customized layers for detecting drone object in the acquired images and distinguishing drone object from bird object. An optimization process is achieved to select accurate hyper-parameters that effected mainly in the classification accuracy. IC-CNN deep learning model is constructed by stacking sequence of convolution; maximum pooling and fully connected layers integrated in light weight network architecture. The proposed deep learning model based UAV detection system is evaluated using three types of drones’ image datasets along with common evaluation metrics. The obtained results achieved 88%, 100% and 100% detection accuracy on the three datasets respectively.
A Study on Vestibular Illusion of Flying Cadets Based on CSA-ELM Method

Vestibule is one of the sensory organs of pilots for spatial orientation, and vestibular illusion is an important factor affecting flight safety. Therefore, it is of great significance to study the vestibular illusion such as anti- rotation illusion of flying cadets under different rotational speeds and head movements. In this study, through the vestibular function training experiment of flying cadets, the anti-rotation illusion of flying students at 60°/s, 90°/s and 120°/s are simulated. Among them, the steering is divided into forward and reverse, and the ECG characteristic data of participants are obtained in real time. Then, the crow search algorithm (CSA) is combined with the extreme learning machine (ELM) to build the CSA-ELM identification model. Based on CSA-ELM model, the pilot's anti-rotation illusion is identified. The results show that the method is better than other methods in meeting the accuracy requirements. The above shows that the research results in this paper can provide a theoretical basis support for the assessment and evaluation of flight cadet training microscopic behavior.
Learner-Initiated Language Learning Through Social Media Sites (SMSs)

Social media sites have become an essential part of communication and interaction all over the globe. They have also offered numerous opportunities to language learners across geographic borders, paralleled by a new research interest in their potential. The present study joins this relatively new line of research as it adds data from a sample of Yemeni English language learners about their uses and perceived benefits of using social media sites in English beyond formal education. The study came up with a conclusion that Yemeni EFL learners were actively participating in social media sites and were aware of their language-related benefits. The participants reported that social media sites were helpful for building various aspects of their English proficiency but found them most useful for their writing and reading skills, expanding their vocabulary, having access to authentic materials, and communicating with English speaking friends, both native and non-native speakers. They ranked their usefulness in the following order: Facebook, WhatsApp, and Twitter.
Data is about detail

Businesses continue to operate under increasingly complex demands such as ever-evolving regulatory landscape, personalization requirements from software apps, and stricter governance with respect to security and privacy. In response to these challenges, large enterprises have been emphasizing automation across a wide range, starting with business processes all the way to customer experience. As AI continues to be a core component of software systems being developed, data assumes a predominant role. AI-centric software systems of industrial scale need large amounts of training data, that in our experience, has introduced several challenges. In this paper, through an empirical study based on interviews with AI practitioners, we present current challenges that need to be addressed in 'data requirements' of Software Systems with NLP at the Core (SSNLPCore). We further discuss the impact of the challenges and techniques currently employed by practitioners for addressing them. Our findings reveal that a focus on details pertaining to data is required early into the project lifecycle, which include aspects such as how we may select, process, and annotate data. This can ensure that the AI component is effective in meeting business goals of software systems.
Robot Localisation and Uncertainty Control with Intermittent Range-only Measurements

In this paper, we address the mobile robot localisation problem by combining relative measurements with intermittent measurements collected from range sensors (e.g., Ultra-Wide Band transmitters) with measurements on the relative motion of the robot. We present some basic results on the possibility of reconstructing the state using a very small number of measurements from two different anchors by setting up a system of linear equations. Next, we study how the observability of a trajectory can be quantified by the condition number of the system matrix, and hence related to the manoeuvres executed by the robot and to sampling time used to collect the measurements. As discussed in this paper, this result can be used as a basis to define control strategies aimed to maximise observability.