Reviewerly/exHarmony huggingface dataset:
Voxel-Based Immersive Mixed Reality: A Framework for Ad Hoc Immersive Storytelling

Abstract Volumetric video recordings of storytellers, when experienced in immersive virtual reality, can elicit a sense of copresence between the user and the storyteller. Combining a volumetric storyteller with an appropriate virtual environment presents a compelling experience that can convey the story with a depth that is hard to achieve with traditional forms of media. Volumetric video production remains difficult, time-consuming, and expensive, often excluding cultural groups who would benefit most. The difficulty is partly due to ever-increasing levels of visual detail in computer graphics, and resulting hardware and software requirements. A high level of detail is not a requirement for convincing immersive experiences, and by reducing the level of detail, experiences can be produced and delivered using readily available, nonspecialized equipment. By reducing computational requirements in this way, storytelling scenes can be created ad hoc and experienced immediately—this is what we are addressing with our approach. We present our portable real-time volumetric capture system, and our framework for using it to produce immersive storytelling experiences. The real-time capability of the system, and the low data rates resulting from lower levels of visual detail, allow us to stream volumetric video in real time to enrich experiences with embodiment (seeing oneself) and with copresence (seeing others). Our system has supported collaborative research with Māori partners with the aim of reconnecting the dispersed Māori population in Aotearoa, New Zealand to their ancestral land through immersive storytelling. We present our system in the context of this collaborative work.
Multi-Objective Binary Whale Optimization-Based Virtual Machine Allocation in Cloud Environments

With the rising demands for the services provided by cloud computing, virtual machine allocation (VMA) has become a tedious task due to the dynamic nature of the cloud. Millions of virtual machines (VMs) are allocated and de-allocated at every instant, so an efficient VMA has been a significant concern to enhance resource utilization and depreciate its wastage. Encouraged by the prodigious performance of the nature-inspired algorithm, the binary whale optimization approach has been eventuated to get to grips with the VMA issue with the focus on minimizing the resource waste and volume of servers working actively. The deliberate approach's accomplishment is assessed against the literature's well-known algorithms for VMA issues. The comparison results showed that the least resource wastage fitness of 15.68, minimum active servers of 216, and effective CPU and memory utilization of 88.31% and 88.79%, respectively, have been achieved.
Online evaluation of the Kolmogorov–Smirnov test on arbitrarily large samples

This paper presents an approximative online algorithm to perform the Kolmogorov–Smirnov test. There is a ubiquitous need for evaluating the fitness between statistical distributions and data samples, which this test conveniently meets. Taking some inspiration from the challenges of detecting concept drifts in data streams, our methodology shows how this goodness-of-fit statistical test can be used to detect such events, taking advantage of the fact that it is non-parametric and could be adapted to handle streams while keeping its original relatively small algorithmic complexity. The presented work focused on the one-sample test, which evaluates the hypothesis that a given univariate sample follows some reference distribution, for assessing an input stream with high precision in a time- and space-efficient fashion. The performance of our algorithm and some of the state-of-the-art methods were compared using synthetic and real data. We evaluated the accuracy, effectiveness and efficiency of these methods by making extensive experiments in multiple scenarios: varying reference distribution and its parameters, sample size, available memory, drift point and query interval. The results showed that our algorithm is advantageous in most cases, even with substantial restrictions of computational resources.
Learning rotation equivalent scene representation from instance-level semantics: A novel top-down perspective

This paper focuses on rotation variant scene recognition. Different from existing rotation invariant recognition approaches which learn from either rotated images or rotated convolutional filters in a bottom-up manner, a new top-down perspective by learning is explored from instance-level semantic representation. The goal is to eliminate the convolutional feature differences in bottom-up feature propagation caused by the rotation sensitive nature of convolution operation. Our rotation equivalent convolutional neural network (RE-CNN) scheme consists of three components. Firstly, our key instance selection module highlights the instances strongly related to the scene scheme regardless of their orientation. Secondly, our key instance aggregation module builds a scene representation invariant to the position change of each instance caused by rotation. Finally, our semantic fusion module allows the framework to be organized as a whole and implements rotation regularization. Notably, our RE-CNN scheme can be adapted to existing CNNs in a plug-in-and-play manner. Extensive experiments on rotation variant scene recognition benchmarks from four domains demonstrate the state-of-the-art performance and generalization capability of the proposed RE-CNN.
Age detection from handwriting using different feature classification models

Digitized handwritten documents have been used for various purposes, including age detection, a crucial area of research in fields like forensic investigation and medical diagnosis. Automated age recognition is deemed to be a difficult task, due to the great degree of similarity and overlap across people’s handwriting. Consequently, the efficiency of the classification system is determined by extracting pertinent features from handwritten documents. This research proposes a set of age-related features suggested by a graphologist to classify handwritten documents into two age groups: youth adult and mature adult. The extracted features are: slant irregularity (SI), pen pressure irregularity (PPI), text lines irregularity (TLI) and the percentage of black and white pixels (PWB). Support Vector Machines (SVM) and Neural Network (NN) classifiers have been used to train, validate and test the proposed approach using two different datasets: the FSHS and the Khatt datasets. When applied to the FSHS dtaset using SVM and NN approaches, the proposed method resulted in a classification accuracy of 71% and 63.5%, respectively. Meanwhile, when applied to the Khatt dataset, our method outperformed state-of-the-art methods with a classification accuracy of 65.2% and 67% utilising SVM and NN classifiers, respectively. These are the best rates available right now in this field.
Autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithms

Abstract The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the configuration of its hyperparameters is among the most complicated tasks while applying ML to new problems. It necessitates well awareness and knowledge of ML algorithms. The algorithm selection problem (ASP) is defined as the process of identifying the algorithm (s) that can deliver top performance for a particular problem, task, and evaluation measure. In this context, meta-learning is one of the approaches to achieve this objective by using prior learning experiences to assist the learning process on unseen problems and tasks. As a data-driven approach, appropriate data characterization is of vital importance for the meta-learning. Nonetheless, the recent literature witness a variety of data characterization techniques including simple, statistical and information theory based measures. However, their quality still needs to be improved. In this paper, a new Autoencoder-kNN (AeKNN) based meta-model with built-in latent features extraction is proposed. The approach is aimed to extract new characterizations of the data, with lower dimensionality but more significant and meaningful features. AeKNN internally uses a deep autoencoder as a latent features extractor from a set of existing meta-features induced from the dataset. From this new features vectors the computed distances are more significant, thus providing a way to accurately recommending top-performing pipelines for previously unseen datasets. In an application on a large-scale hyperparameters optimization task for 400 real world datasets with varying schemas as a meta-learning task, we show that AeKNN offers considerable improvements of the classical kNN as well as traditional meta-models in terms of performance.
MetaWCE: Learning to Weight for Weighted Cluster Ensemble

Cluster ensemble (CE) integrates multiple clustering solutions to effectively improve the accuracy and robustness of unsupervised clustering. To reduce the impacts of low-quality solutions, existing CE methods often design heuristic criteria to appraise these clustering solutions and allocate weights for them. However, such heuristic-based weighting methods rely on human experience and lack knowledge of the relation between weights and data characteristics, failing to adaptively adjust weights for various datasets. To address this, we propose Meta-learning-based Weighted Cluster Ensemble (MetaWCE), which learns the weights-data relation automatically and sets adaptive CE weights. Specifically, metadata is employed to describe data characteristics at a dataset level. To bridge metadata and weights, a meta-learning strategy is introduced to simulate the weighting process to ensure that relation between weights and metadata can be learned to directly optimize the ensemble performance in an end-to-end manner. Experiments on three datasets indicate that MetaWCE significantly improves ensemble performance and achieves obvious improvements over strong baseline methods.
Towards well-generalizing meta-learning via adversarial task augmentation

Meta-learning aims to use the knowledge from previous tasks to facilitate the learning of novel tasks. Many meta-learning models elaborately design various task-shared inductive bias, and learn it from a large number of tasks, so the generalization capability of the learned inductive bias depends on the diversity of the training tasks. A common assumption in meta-learning is that the training tasks and the test tasks come from the same or similar task distributions. However, this is usually not strictly satisfied in practice, so meta-learning models need to cope with various novel in-domain or cross-domain tasks. To this end, we propose to use task augmentation to increase the diversity of training tasks, thereby improving the generalization capability of meta-learning models. Concretely, we consider the worst-case problem around the base task distribution, and derive the adversarial task augmentation method which can generate inductive bias-adaptive 'challenging' tasks. Our method can be used as a simple plug-and-play module for various meta-learning models, and improve their generalization capability. We conduct extensive experiments under in-domain and cross-domain few-shot learning and unsupervised few-shot learning settings, and evaluate our method on different types of data (images and text). Experimental results show that our method can effectively improve the generalization capability of various meta-learning models under different settings.
Forensic Analysis and Detection of Spoofing Based Email Attack Using Memory Forensics and Machine Learning

Emails encounter many types of cyber-attacks and email spoofing is one of the most common and challenging investigation problems. This paper identifies spoofing-based email attacks in an organization by analyzing received and replied emails. The detection works by capturing the email traces via memory forensics. Unlike the traditional approaches of capturing the entire physical memory, we only capture the memory of relevant processes for email header extraction. It significantly reduces the size of the memory dump and makes detection faster. We suggest a novel mechanism called URL extractor, which uses seven novel features from URL to identify the live running email message process by applying ML that traces received emails and captures their header fields for analysis. The authentication header fields of SPF, DKIM, DMARC, and ARC are examined closely to develop a detection algorithm for received emails. Similarly, novel header fields of Reference along with MX record are applied for the detection of replied emails. The MX record is fetched to verify the domain name by sending a forward ns-lookup query to DNS. It also includes an email attack alert mechanism for intimating IT admins of an organization regarding suspected attacks. The results thus obtained show that email detection takes 35 secs (apprx.) to complete with high accuracy and low false positives.
TERSE: Tiny Encryptions and Really Speedy Execution for Post-Quantum Private Stream Aggregation

The massive scale and performance demands of privacy-preserving data aggregation make integration of security and privacy difficult. Traditional tools in private computing are not well-suited to handle these challenges, especially for more limited client devices. Efficient primitives and protocols for secure and private data aggregation are a promising approach for private data analytics with resource-constrained devices. However, even such efficient primitives may be much slower than computation with plain data (i.e., without security/privacy guarantees). In this paper, we present TERSE, a new Private Stream Aggregation (PSA) protocol for quantum-secure time-series additive data aggregation. Due to its simplicity, low latency, and low communication overhead, TERSE is uniquely well-suited for real-world deployment. In our implementation, TERSE shows very low latency for both clients and servers, achieving encryption latency on a smartphone of 0.0003 ms and aggregation latency of 0.0067 ms for 1000 users. TERSE also shows significant improvements in latency over other state-of-the-art quantum-secure PSA, achieving improvements of 1796 $$\times $$ to 12406 $$\times $$ for encryption at the client’s end and 848 $$\times $$ to 5433 $$\times $$ for aggregation and decryption at the server’s end.
Robust phase unwrapping via non-local regularization

Phase unwrapping is an indispensable step in recovering the true phase from a modulo-2π phase. Conventional phase unwrapping methods suffer from error propagation under severe noise. In this Letter, we propose an iterative framework for robust phase unwrapping with high fidelity. The proposed method utilizes the transport-of-intensity equation to solve the phase unwrapping problem with high computational efficiency. To further improve reconstruction accuracy, we take advantage of non-local structural similarity using low-rank regularization. Meanwhile, we use an adaptive iteration strategy that dynamically and automatically updates the denoising parameter to avoid over-smoothing and preserve image details. A set of simulation and experimental results validates the proposed method, which can provide satisfying results under severe noise conditions, and outperform existing state-of-the-art phase unwrapping methods with at least 6 dB higher peak SNR (PSNR).
Training Multilayer Neural Network Based on Optimal Control Theory for Limited Computational Resources

Backpropagation (BP)-based gradient descent is the general approach to train a neural network with a multilayer perceptron. However, BP is inherently slow in learning, and it sometimes traps at local minima, mainly due to a constant learning rate. This pre-fixed learning rate regularly leads the BP network towards an unsuccessful stochastic steepest descent. Therefore, to overcome the limitation of BP, this work addresses an improved method of training the neural network based on optimal control (OC) theory. State equations in optimal control represent the BP neural network’s weights and biases. Meanwhile, the learning rate is treated as the input control that adapts during the neural training process. The effectiveness of the proposed algorithm is evaluated on several logic gates models such as XOR, AND, and OR, as well as the full adder model. Simulation results demonstrate that the proposed algorithm outperforms the conventional method in terms of improved accuracy in output with a shorter time in training. The training via OC also reduces the local minima trap. The proposed algorithm is almost 40% faster than the steepest descent method, with a marginally improved accuracy of approximately 60%. Consequently, the proposed algorithm is suitable to be applied on devices with limited computation resources, since the proposed algorithm is less complex, thus lowering the circuit’s power consumption.
Using Ontologies to Create Machine-Actionable Datasets: Two Case Studies

Achieving the highest levels of compliance with the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship requires machine-actionable semantic representations of data and metadata. Human and machine interpretation and reuse of measurement datasets rely on metrological information that is often specified inconsistently or cannot be inferred automatically, while several ontologies to capture the metrological information are available, practical implementation examples are few. This work aims to close this gap by discussing how standardised measurement data and metadata could be presented using semantic web technologies. The examples provided in this paper are machine-actionable descriptions of Earth observation and bathymetry measurement datasets, based on two ontologies of quantities and units of measurement selected for their prominence in the semantic web. The selected ontologies demonstrated a good coverage of the concepts related to quantities, dimensions, and individual units as well as systems of units, but showed variations and gaps in the coverage, completeness and traceability of other metrology concept representations such as standard uncertainty, expanded uncertainty, combined uncertainty, coverage factor, probability distribution, etc. These results highlight the need for both (I) user-friendly tools for semantic representations of measurement datasets and (II) the establishment of good practices within each scientific community. Further work will consequently investigate how to support ontology modelling for measurement uncertainty and associated concepts.
Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction

Underwater images often suffer from low contrast, low visibility, and color deviation. In this work, we propose a hybrid underwater enhancement method consisting of addressing an inverse problem with novel Retinex transmission map estimation and adaptive color correction. Retinex transmission map estimation does not rely on channel priors and aims to decouple from the unknown background light, thus avoiding error accumulation problem. To this end, global white balance is performed before estimating the transmission map using multi-scale Retinex. To further improve the enhancement performance, we design the adaptive color correction which cleverly chooses between two color correction procedures and prevents channel stretching imbalance. Quantitative and qualitative comparisons of our method and state-of-the-art underwater image enhancement methods demonstrate superiority of the proposed method. It achieves the best performance in terms of full-reference image quality assessment. In addition, it also achieves superior performance in the non-reference evaluation.
Deep Learning for Drawing Numbering in Engineering Drawing Management: A Case Study for Refrigerated Compartment Product

Engineering drawing numbering (DN) is one of the most essential procedures for seamless platform integration towards intelligent manufacturing. In spite of this, it is difficult to handle the numbering work in an appropriate and effective manner. This is due to the unpredictability of the names of the manufactured parts and the ineffable relationship between the number and the shape of the parts. This paper proposes a method for numbering items based on historical numbering records based on deep learning. First, name-number (NN) duplexes are generated by retrieving the records. [Formula: see text]-means[Formula: see text] is then used to cluster these NN duplexes. Second, it involves looking up the names of the newly designed items using KNN in order to generate an initial numbering system. Third, a modified multi-view convolutional neural network (MVCNN) is utilized for numbering in situations where the same name is different from the previous number (SNDN). Finally, the most recent sequence numbers are appended to complete the numbering. When the system based on the proposed scheme for authentic engineering application is implemented on a refrigerated compartment, the correctness obtained is over 95%, and the efficiency is increased by 5–6 times.
A Comprehensive Review of Tunnel Detection on Multilayer Protocols: From Traditional to Machine Learning Approaches

Tunnels, a key technology of traffic obfuscation, are increasingly being used to evade censorship. While providing convenience to users, tunnel technology poses a hidden danger to cybersecurity due to its concealment and camouflage capabilities. In contrast to previous studies of encrypted traffic detection, we perform the first measurement study of tunnel traffic and its unique characteristics and focus on the challenges and solutions in detecting tunnel traffic among traditional and machine learning techniques. This study covers an almost twenty-year research period from 2003 to 2022. First, we present the concepts of two types of tunnels, broad and narrow tunnels, respectively, as well as a framework for major tunnel applications, such as Tor (the second-generation onion router), proxy, VPN, and their relationships. Second, we analyze state-of-the-art methods from traditional to machine learning applications to systematize tunnel traffic detection, including HTTP, HTTPS, DNS, SSH, TCP, ICMP and IPSec. A quantitative evaluation is presented with five crucial indicators applied to the detection methods and reviews. We further discuss the research work based on datasets, feature engineering, and challenges that have are solved, partly solved and unsolved. Finally, by providing open questions and the potential directions, we hope to inspire future work in this area.
Privacy in Blockchain-based Smart Grids

Blockchain technologies offer an interesting opportunity for Smart Grids, by introducing accountability transparency and trust without requiring any trusted central authority. However, due to the transparency of data and operations, and the “store forever” policy, they also offer important privacy challenges, only partially taken up by the default features provided by the conventional blockchain. In this work we analyse the literature studies on privacy solutions for blockchain-based smart grid application. We provide a taxonomy for the different solutions and discussing the new research opportunities that are emerging.