Reviewerly/exHarmony huggingface dataset:
Matrix Factorization in Recommender Systems: Algorithms, Applications, and Peculiar Challenges

Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that has been successfully exploited in various applications, but sometimes they fail to provide accurate recommendations because they depend majorly on the rating matrix, which is always scanty and of very high dimension. Matrix factorization (MF) algorithms are variants of latent factor models, which are easy, fast, and efficient. This article reviews the related research and advances in the application of matrix factorization techniques in recommender systems. Popular matrix factorization algorithms utilized in recommender systems were reviewed. The peculiar challenges of using matrix factorization in recommender systems were also enumerated and discussed with the goal of identifying the different problems solved with the use of matrix factorization techniques as applied in recommender systems.
Implementation of YOLOv4 Algorithm for Multiple Object Detection in Image and Video Dataset using Deep Learning and Artificial Intelligence for Urban Traffic Video Surveillance Application

Artificial intelligence and machine learning have great ability to solve real-time problems related to various fields and have many applications. One such subfield is deep learning, deep learning has many architectures like DNN, CNN and RNN. One of the major applications of deep learning is object detection. YOLO a SOTA object detector considered to be a smart convolutional neural network having capability of detecting objects in the image, classifying them accordingly and localizing the object perfectly with annotations. YOLOv4 is employed in this work for multiple object detection in image and video for traffic surveillance applications trained using a custom dataset created with Indian road traffic images. 9 different classes namely Car, Bus, Van, Truck, Two-wheeler, Auto, Person, Bicycle and Mini truck are considered in creating custom dataset. Proposed custom model trained for 500 epochs with a custom image dataset has achieved a training mAP of 92% and a training loss of 0.001. Achieved an accuracy of 99.28%, sensitivity of 94.44% and MCC of 96.78% on unseen or test dataset. Proposed model predicted all the classes with an average precision of 98.32%. This proves that our model is robust and proficient in detecting road traffic classes and has scope in developing traffic surveillance systems.
Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models

The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’ location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’ tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.
Experimental quantum enhanced optical interferometry

Optical quantum interferometry represents the oldest example of quantum metrology, and it is at the source of quantum technologies. The original squeezed state scheme is now a significant element of the last version of gravitational wave detectors, and various additional uses have been proposed. Further quantum-enhanced schemes, from the SU(1,1) interferometer to twin beam correlation interferometry, have also reached the stage of proof of principle experiments, thus enlarging the field of experimental quantum interferometry and paving the way to several additional applications, from Planck scale signals search to small effect detection. In this review, I will describe these experimental achievements, and I will focus on their schemes, advantages, applications, and possible further developments.
Coarse-to-Fine Joint Distribution Alignment for Cross-Domain Hyperspectral Image Classification

Domain adaptation (DA) aims to enhance the feature transferability of a model across different domains with feature distribution differences, which has been widely explored in many computer vision tasks such as semantic segmentation and object detection, but has not been fully studied in hyperspectral image (HSI) classification task. Compared with the natural image-based DA, HSI-based DA still faces two main challenges: First, due to the strong spectral variability of HSIs, it is difficult to extract discriminative and domain-invariant features from different domains, resulting in the misalignment of cross-domain features; Second, class-wise (or fine-grained) spectral feature inconsistency between domains also inevitably degrades the classification accuracy. To address these issues, in this article, we propose a novel coarse-to-fine joint distribution alignment (JDA) framework for cross-domain classification of HSIs. Specifically, the training samples from source and target domains are first fed into a coupled variational autoencoders (VAE) module, which is composed of two well-designed VAEs equipped with mutual information metric to learn high-level domain-invariant representations in a shared latent space, so that the network can learn a coarse-grained source-target feature consistency. Furthermore, to alleviate the class-wise inter-domain feature inconsistency, a JDA module is constructed to perform a fine-grained cross-domain alignment by matching the joint probability distributions between the source and target domains through adversarial learning. Extensive experiments on both simulated and real hyperspectral datasets demonstrate the superiority of the proposed method in comparison with several conventional and state-of-the-art methods.
Efficient product formulas for commutators and applications to quantum simulation

We construct product formulas for exponentials of commutators and explore their applications. First, we directly construct a third-order product formula with six exponentials by solving polynomial equations obtained using the operator differential method. We then derive higher-order product formulas recursively from the third-order formula. We improve over previous recursive constructions, reducing the number of gates required to achieve the same accuracy. In addition, we demonstrate that the constituent linear terms in the commutator can be included at no extra cost. As an application, we show how to use the product formulas in a digital protocol for counterdiabatic driving, which increases the fidelity for quantum state preparation. We also discuss applications to quantum simulation of one-dimensional fermion chains with nearest- and next-nearest-neighbor hopping terms, and two-dimensional fractional quantum Hall phases.
Evaluation of HMDs by QFD for Augmented Reality Applications in the Maxillofacial Surgery Domain

Today, surgical operations are less invasive than they were a few decades ago and, in medicine, there is a growing trend towards precision surgery. Among many technological advancements, augmented reality (AR) can be a powerful tool for improving the surgery practice through its ability to superimpose the 3D geometrical information of the pre-planned operation over the surgical field as well as medical and instrumental information gathered from operating room equipment. AR is fundamental to reach new standards in maxillofacial surgery. The surgeons will be able to not shift their focus from the patients while looking to the monitors. Osteotomies will not require physical tools to be fixed on patient bones as guides to make resections. Handling grafts and 3D models directly in the operating room will permit a fine tuning of the procedure before harvesting the implant. This article aims to study the application of AR head-mounted displays (HMD) in three operative scenarios (oncological and reconstructive surgery, orthognathic surgery, and maxillofacial trauma surgery) by the means of quantitative logic using the Quality Function Deployment (QFD) tool to determine their requirements. The article provides an evaluation of the readiness degree of HMD currently on market and highlights the lacking features.
Repo2Vec: A Comprehensive Embedding Approach for Determining Repository Similarity

How can we identify similar repositories and clusters among a large online archive, such as GitHub? Determining repository similarity is an essential building block in studying the dynamics and the evolution of such software ecosystems. The key challenge is to determine the right representation for the diverse repository features in a way that: (a) it captures all aspects of the available information, and (b) it is readily usable by ML algorithms. We propose Repo2Vec, a comprehensive embedding approach to represent a repository as a distributed vector by combining features from three types of information sources. As our key novelty, we consider three types of information: (a) metadata, (b) the structure of the repository, and (c) the source code. We also introduce a series of embedding approaches to represent and combine these information types into a single embedding. We evaluate our method with two real datasets from GitHub for a combined 1013 repositories. First, we show that our method outperforms previous methods in terms of precision (93 % vs 78 %), with nearly twice as many Strongly Similar repositories and 30 % fewer False Positives. Second, we show how Repo2Vec provides a solid basis for: (a) distinguishing between malware and benign repositories, and (b) identifying a meaningful hierarchical clustering. For example, we achieve 98 % precision, and 96 % recall in distinguishing malware and benign repositories. Overall, our work is a fundamental building block for enabling many repository analysis functions such as repository categorization by target platform or intention, detecting code-reuse and clones, and identifying lineage and evolution.
The Watchlist Imbalance Effect in Biometric Face Identification: Comparing Theoretical Estimates and Empiric Measurements

Recently, different research groups have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision. This negative effect has been referred to as “watchlist imbalance effect” by the researchers and exhibits high relevance in real applications of biometrics, most prominently in identification searches against criminal databases and blacklists.In this work, we conduct a detailed analysis of said effect. In particular, we compare empiric observations with theoretical estimates, based on the verification performance across demographic groups and the composition of the used gallery. The experimental evaluations are conducted by systematically varying the size and demographic composition of a cleaned subset of the academic MORPH database and utilising the state-of-the-art open-source ArcFace face recognition system.
Online safety awareness and human factors: An application of the theory of human ecology

Efforts have been made on large and small scales to reduce cybersecurity threats around the world, including in Malaysia. However, scholars have argued that, in spite of the technological preparations countries can take to shield themselves from attack, human factors may be the key reason behind increasing breaches in cybersafety in recent years. In this review, developed in a social sciences context, we argue that intra- and interpersonal human factors, such as biological, psychological, and cultural factors, must be considered holistically to effectively increase awareness of the importance of online safety. We also argue that these personal and psychological factors can be framed using Urie Bronfenbrenner's theory of human ecology, which, along with previous studies, points to the importance of examining the various layers of environmental factors that affect human behavior. Lastly, this paper also considers what types of education and training can effectively utilize the knowledge of human factors to increase online safety awareness.
<scp>BlockchainBus</scp>: A lightweight framework for secure virtual machine migration in cloud federations using blockchain

Abstract Cloud federations offer numerous advantages such as improved resilience, improved performance, and the prevention of vendor lock‐in. In order to function efficiently, however, they must be supported by automated solutions to deploy cloud resources securely and efficiently. In particular, a system which allows virtual machine (VM) migration from one cloud to another while recording this for charging and security purposes is particularly useful. This paper presents a lightweight framework called BlockchainBus which offers secure VM migration in cloud federations using blockchain. This framework is a VM migration Blockchain ledger which can be used to record these migrations. BlockchainBus framework is implemented using the HyperLedger solution and deployed on the Microsoft Azure cloud platform. The overhead of this system is then evaluated. The performance of the BlockchainBus framework was compared with the overall VM migration time and was determined as 2.36 seconds. This result is shorter than the overall VM migration time of 5 seconds and indicates that the BlockchainBus gives better performance.
Supervised dimensionality reduction technology of generalized discriminant component analysis and its kernelization forms

Supervised subspace projection technology is a major method for dimensionality reduction in pattern recognition. At present, most supervised subspace projection algorithms are derived from the multi-dimensional extended version of Fisher linear discriminant analysis (FDA), also known as Multi-dimensional Fisher discriminant analysis (MD-FDA). However, MD-FDA needs to be improved further because the projection vectors in the noise-subspace cannot be sorted and the ill-condition of the within-class scatter matrix may cause severe numerical instabilities. Generalized discriminant component analysis (GDCA), the generalization of MD-FDA, together with its kernelization forms are proposed and correspondingly rigorous mathematical proofs are detailed in this paper. By virtue of 5 validation data sets derived from UCI Machine Learning Repository and our laboratory, the theoretical validity and technical advantages of GDCA as well as its kernelization forms are verified, and the effectiveness of the newly proposed method is demonstrated in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms.
Modeling sentimental bias and temporal dynamics for adaptive deep recommendation system

Recommendation systems rely on the historic data of users' purchases and their feedbacks to profile their preferences and make future recommendations. Most of these systems usually employ Collaborative Filtering (CF) models to analyze users’ ratings and infer the latent factors which represent the user and item features in k-dimensional latent space. However, the historical rating data used for recommendations are usually sparsed and unbalanced. Various approaches have been used to resolve these issues by combining the user’s ratings and reviews to better capture the user’s sentiments and make accurate recommendations. Other challenges comprise changes in users’ preferences and items’ perceptions over time. Therefore, this paper presents a new Sentiment Scoring Model (SSM) based on Long-/Short-Term Memory and a combination function that catches the sentiment bias between user rating and review to relieve the sparsity and unbalanced dataset. Next, we proposed an Adaptive LSTM (ALSTM) method that can model the drifting of user and item features to improve the recommendation accuracy. We show the performance of our model on the three real-world rating datasets from Amazon reviews, which comprises Fine Food, Baby, and Cell-phone & Accessories categories. The result shows the superiority of our proposed model over the existing static and dynamic models. The statistical test shows that all the performance gains are significant at p < 0.05.
SPAN: A self-paced association augmentation and node embedding-based model for software bug classification and assignment

Effective bug classification and assignment to relevant developers improves the efficiency of software management. However, textually dependent approaches produce inconsistent results on varying datasets, while approaches that depend upon multi-source data can produce dataset conflicts and inaccuracy. Accordingly, we introduce a model based on Self-Paced Association augmentation and Node embedding (SPAN), which uses an effective combination of textually dependent and independent bug categorization to produce consistent results, followed by a bug assignment mechanism to prevent conflicts. To this end, we present a novel unified classifier and assignment model that exploits the connections between nodes in the Software Bug Report Network (SBRNet) to identify the target features. The model is capable of accurately categorizing bugs in a self-paced manner with association augmentation. Finally, we present an approach that assigns the most appropriate developer for bug resolution through SBRNet node information embedding. Our deep two-step self-paced solution is capable of categorizing software bugs with improved accuracy, while still utilizing fewer features. Results reveal that our model is more effective (up to 98% classification accuracy and 96% for bug assignment) when compared to its counterparts.
A Blockchain-Enabled Incentivised Framework for Cyber Threat Intelligence Sharing in ICS

Industrial Control Systems (ICS) have been targeted increasingly by sophisticated cyberattacks. Improving ICS security has drawn significant attention in the literature that emphasises the importance of Cyber Threat Intelligence (CTI) sharing in accelerating detection, mitigation, and prevention of cyberattacks. However, organisations are reluctant to exchange CTI due to fear of exposure, reputational damage, and lack of incentives. Furthermore, there has been limited discussion about the factors influencing participation in sharing CTI about ICS. The existing CTI-sharing platforms rely on centralised trusted architectures that suffer from a single point of failure and risk companies&#x2019; privacy as the central node maintains CTI details. In this paper, we address the needs of organisations involved in the management and protection of ICS and present a novel framework that facilitates secure, private, and incentivised exchange of CTI related to ICS using blockchain. We propose a blockchain-enabled framework that facilitates the secure dissemination of CTI data among multiple stakeholders in ICS. We provide the framework design, development and evaluate the framework&#x2019;s feasibility in a real-world application environment using practical use-case scenarios. Our proposed design shows a more practical and efficient framework for a CTI sharing network for ICS, including the bestowal and acknowledgment of data privacy, trust barriers, and security issues ingrained in this domain.
Full-field deformation measurements in the transmission electron microscope using digital image correlation and particle tracking

Determining displacement and/or strain fields at the nanoscale during material deformation can be instrumental in developing a multiscale understanding of material response. Full-field quantitative kinematic measurements based on transmission electron microscopy (TEM) are lagging behind other microscopy techniques. Here, we develop an experimental approach combining digital image correlation (DIC) and particle tracking (PT) for characterizing in situ microscale deformation of amorphous SiO2 in the TEM. Gold nanoparticles deposited on SiO2 provide both the speckle pattern required by DIC when averaged over a subset region and target particles for PT. To demonstrate and validate the feasibility of using DIC and PT in the TEM, micron sized SiO2 beam samples are machined using a focused ion beam (FIB) and loaded in the TEM via indentation. DIC and PT are then applied to measure in situ displacements from a sequence of TEM images taken during loading and creep of the beam. Results of the two measurement methods agree well with each other and with the applied displacement measurements, thus demonstrating their effectiveness in determining local displacements from TEM experiments. Sources of noise resulting from sample drift and image intensity variations are discussed.
Facial landmark points detection using knowledge distillation-based neural networks

Facial landmark detection is a vital step for numerous facial image analysis applications. Although some deep learning-based methods have achieved good performances in this task, they are often not suitable for running on mobile devices. Such methods rely on networks with many parameters, which makes the training and inference time-consuming. Training lightweight neural networks such as MobileNets are often challenging, and the models might have low accuracy. Inspired by knowledge distillation (KD), this paper presents a novel loss function to train a lightweight Student network (e.g., MobileNetV2) for facial landmark detection. We use two Teacher networks, a Tolerant-Teacher and a Tough-Teacher in conjunction with the Student network. The Tolerant-Teacher is trained using Soft-landmarks created by active shape models, while the Tough-Teacher is trained using the ground truth (aka Hard-landmarks) landmark points. To utilize the facial landmark points predicted by the Teacher networks, we define an Assistive Loss (ALoss) for each Teacher network. Moreover, we define a loss function called KD-Loss that utilizes the facial landmark points predicted by the two pre-trained Teacher networks (EfficientNet-b3) to guide the lightweight Student network towards predicting the Hard-landmarks. Our experimental results on three challenging facial datasets show that the proposed architecture will result in a better-trained Student network that can extract facial landmark points with high accuracy.