Reviewerly/exHarmony huggingface dataset:
Pose Mask: A Model-Based Augmentation Method for 2D Pose Estimation in Classroom Scenes Using Surveillance Images

Solid developments have been seen in deep-learning-based pose estimation, but few works have explored performance in dense crowds, such as a classroom scene; furthermore, no specific knowledge is considered in the design of image augmentation for pose estimation. A masked autoencoder was shown to have a non-negligible capability in image reconstruction, where the masking mechanism that randomly drops patches forces the model to build unknown pixels from known pixels. Inspired by this self-supervised learning method, where the restoration of the feature loss induced by the mask is consistent with tackling the occlusion problem in classroom scenarios, we discovered that the transfer performance of the pre-trained weights could be used as a model-based augmentation to overcome the intractable occlusion in classroom pose estimation. In this study, we proposed a top-down pose estimation method that utilized the natural reconstruction capability of missing information of the MAE as an effective occluded image augmentation in a pose estimation task. The difference with the original MAE was that instead of using a 75% random mask ratio, we regarded the keypoint distribution probabilistic heatmap as a reference for masking, which we named Pose Mask. To test the performance of our method in heavily occluded classroom scenes, we collected a new dataset for pose estimation in classroom scenes named Class Pose and conducted many experiments, the results of which showed promising performance.
Few-Shot Classification with Dual-Model Deep Feature Extraction and Similarity Measurement

From traditional machine learning to the latest deep learning classifiers, most models require a large amount of labeled data to perform optimal training and obtain the best performance. Yet, when limited training samples are available or when accompanied by noisy labels, severe degradation in accuracy can arise. The proposed work mainly focusses on these practical issues. Herein, standard datasets, i.e., Mini-ImageNet, CIFAR-FS, and CUB 200, are considered, which also have similar issues. The main goal is to utilize a few labeled data in the training stage, extracting image features and then performing feature similarity analysis across all samples. The highlighted aspects of the proposed method are as follows. (1) The main self-supervised learning strategies and augmentation techniques are exploited to obtain the best pretrained model. (2) An improved dual-model mechanism is proposed to train the support and query datasets with multiple training configurations. As examined in the experiments, the dual-model approach obtains superior performance of few-shot classification compared with all of the state-of-the-art methods.
Novel Low-Power Construction of Chaotic S-Box in Multilayer Perceptron

Multilayer perceptron is composed of massive distributed neural processors interconnected. The nonlinear dynamic components in these processors expand the input data into a linear combination of synapses. However, the nonlinear mapping ability of original multilayer perceptron is limited when processing high complexity information. The introduction of more powerful nonlinear components (e.g., S-box) to multilayer perceptron can not only reinforce its information processing ability, but also enhance the overall security. Therefore, we combine the methods of cryptography and information theory to design a low-power chaotic S-box (LPC S-box) with entropy coding in the hidden layer to make the multilayer perceptron process information more efficiently and safely. In the performance test, our S-box architecture has good properties, which can effectively resist main known attacks (e.g., Berlekamp Massey-attack and Ronjom-Helleseth attack). This interdisciplinary work can attract more attention from academia and industry to the security of multilayer perceptron.
A Data Preprocessing Based on Cluster and Testing of Parameter Identification Method in Power Distribution Network

We present a data prepossessing method for parameter identification based on clustering and hypothesis testing in a power distribution network to successfully achieve a more accurate result. This method considers the similarities of data in both spatial relationship and statistical theory, then builds a sophisticated data processing method to improve the performance of dynamic model-based parameter identification models, i.e., Markov chain Monte Carlo and sequential model-based global optimization. We applied this data processing method to the actual feeder data with no adjustment of the other condition. The experiment shows that our method achieves a 4.8% improvement in accuracy at most.
SUM-GAN-GEA: Video Summarization Using GAN with Gaussian Distribution and External Attention

Video summarization aims to generate a sparse subset that is more concise and less redundant than the original video while containing the most informative parts of the video. However, previous works ignore the prior knowledge of the distribution of interestingness of video frames, making it hard for the network to learn the importance of different frames. Furthermore, traditional models alone (such as RNN and LSTM) are not robust enough in capturing global features of the video sequence since the video frames are more in line with non-Euclidean data structure. To this end, we propose a new summarization method based on the graph model concept to learn the feature relationship connections between video frames, which can guide the summary generator to generate a robust global feature representation. Specifically, we propose to use adversarial learning to integrate Gaussian distribution and external attention mechanism (SUM-GAN-GEA). The Gaussian function is a priori mapping function that considers the distribution of the interestingness of actual video frames and the external attention can reduce the inference time of the model. Experimental results on two popular video abstraction datasets (SumMe and TVSum) demonstrate the high superiority and competitiveness of our method in robustness and fast convergence.
Arabic Syntactic Diacritics Restoration Using BERT Models

The Arabic syntactic diacritics restoration problem is often solved using long short-term memory (LSTM) networks. Handcrafted features are used to augment these LSTM networks or taggers to improve performance. A transformer-based machine learning technique known as bidirectional encoder representations from transformers (BERT) has become the state-of-the-art method for natural language understanding in recent years. In this paper, we present a novel tagger based on BERT models to restore Arabic syntactic diacritics. We formulated the syntactic diacritics restoration as a token sequence classification task similar to named-entity recognition (NER). Using the Arabic TreeBank (ATB) corpus, the developed BERT tagger achieves a 1.36% absolute case-ending error rate (CEER) over other systems.
A novel motion planning algorithm for a three DoF foldable parallel compensation platform based on prediction and B-spline

Foldable parallel mechanisms are suitable to compensate for the motion of vessels due to their compact structure, multidimensional motion, and large carrying capacity. However, the vessel sways violently under harsh sea conditions, challenging the rational utilization of the mechanism workspace. This work proposes a novel motion planning algorithm based on motion prediction and the B-spline for a three-degrees-of-freedom foldable parallel compensation platform. First, for harsh sea conditions, the motion law of a guardian vessel is studied, and the encoder-decoder model is used to predict the roll, pitch, and heave motions of the vessel. Second, we propose a multidimensional motion compensation strategy under the limits of the mechanism dimensions. Then, a backward kinematics model is developed. Its workspace under the structural and actuator limitations is proposed. Finally, based on the theory of the nonuniform rational B-spline curve and target motion prediction, a dynamic motion planning algorithm is proposed, and experiments are conducted. The motion planning algorithm can provide smooth motion commands under the mechanism workspace, velocity, acceleration constraints. It can be realized using asynchronous calculation, which meets real-time motion control requirements.
‘Personas for lighting’. Three methods to develop personas for the indoor lighting environment

The objective of this research is to describe and compare three different methods of generating ‘persona for lighting’ to envision users’ behaviour within the lighting environment. ‘Personas’ are used to represent typical users, highlighting their needs, perspectives, and expectations to aid user-centric design approaches. The researchers looked for the most useful method of shaping ‘personas for lighting’ to learn about users’ satisfaction with the various lighting conditions to identify their needs. Method one of lighting persona development, was based on interviews with 87 users of five buildings of four different types: an office, a primary school, two university buildings, and a factory. The lighting conditions were observed and measured in all the buildings. As a result, 22 personas for lighting were created. In method two personas were generated based on pre-interviews, workshops on lighting and post-interviews with ten users along with the onsite lighting measurements. Later, due to the Covid-19 pandemic’s lockdowns, an online survey on the visual lighting environment in home offices was carried out among 694 students and professionals from seven countries to create two more personas for lighting (method three). All 26 ‘personas for lighting’ were generated in relation to observed lighting conditions, based on the satisfaction, preferences and needs of the users working within variously lit indoor environments. All the tested methods can be used for nearly any type of building and room, but the resulting personas are different due to the specific limitations of the methods. The created personas may help to identify future users’ lighting preferences, needs and requirements and assist designers. However, to fully understand their impact on the lighting research practice they should be tested in real projects.
Collaborative Decision Support with Ontology-Based Neuro-Symbolic Artificial Intelligence: Challenges and Conceptual Model

The development of artificial intelligence technologies and the growing complexity of decision-making in the management of modern production systems necessitate the collaboration of humans and AI, including teams of heterogeneous participants (for example, experts and agents operating on the basis of artificial intelligence). The paper discusses the tasks that have to be resolved to enable collaborative human-machine decision support systems and the main problems that arise during the creation of such systems. The analysis of modern results in the field of ontology-oriented neuro-symbolic artificial intelligence is carried out, primarily aimed at explaining neural network models using ontologies and at using symbolic knowledge to improve the efficiency of neural network models. A conceptual model of a collaborative human-machine decision support based on ontology-oriented neuro-symbolic artificial intelligence is proposed.
Draft-Based Learning Model to Discover Data from a Cognitive Causal Model

In this research, we present a novel learning model using the draft causal model (DCML); this model is based on a generative approach that uses the spectral properties of the causal model graph. This article addresses the problem of cognitive causal model (CM) learning in complex systems (with nonnumeric and unobservable variables), which is represented by a directed weighted signed graph. The challenge is to find a way to generate training data that expresses specific spectral representative features of the model, i.e., its image, and then, to train the model using this image and obtain an adjacency matrix that satisfies the requirements of the modeled system. The learning criterion is the receipt of the first (or any other) rank in the response vector by the target vertex, which according to the decision-maker, personifies the directionality of the modeled system under the constraints that ensure the adequacy of the model. The presented DCML model implements semi-supervised learning with a generative instantiation mechanism and represents a new application of generative learning for a class of CMs in the absence of observable variables.
An Approach to Fusing Strategic Objectives into Agent-Level Decision Making in Load Balancing

Apart from the increased amount of data being transferred, modern networks are characterized by high complexity of their topology. This is one of the reasons why conventional architectural approaches and technologies are now constantly subjected to series of modifications the most crucial of which is caused by the necessity for decentralized operation. There exists a good portion of algorithms which enable handle this problem. However, only a few of those optimize against multiple objectives, much less enable subjecting a network to coordination. In this paper, we propose an approach that enables setting of multiple objectives for load balancing process in a way so it only requires a coordinating entity to operate with strategic-level terms. Decisions made on this level fuse into local-level decision making performed by individual agents (network nodes), thus enabling separation of responsibilities in terms of scope thus allowing to fit the process of load balancing taking states of the network’s particular parts into consideration.
The Learning of Fuzzy Models Based on the Fuzzy Bayesian Approach

A fuzzy Bayesian approach for learning fuzzy rule-based models is proposed. The proposed approach consists in the preliminary designing of a fuzzy Bayesian network (learning model), the parameters of which correspond to the parameters of the fuzzy rule-based model being learned. When new data is received, the individual parameters of the fuzzy Bayesian network are changed. This allows to localize the variable parameters of the fuzzy rule-based model and to perform point correction of the fuzzy model without relearning it. An example of using the proposed approach for analyzing the impact of climate phenomena on the vulnerability of the natural environment is presented.
Assessing the Impact of Transformations on Physical Adversarial Attacks

The decision of neural networks is easily shifted at an attacker's will by so-called adversarial attacks. Initially only successful when directly applied to the input, recent advances allow attacks to breach the digital realm, leading to over-the-air physical adversarial attacks. During training, some physical phenomena are simulated through equivalent transformations to increase the attack's success. In our work, we evaluate the impact of the selected transformations on the performance of physical adversarial attacks. We quantify their performance across diverse attack scenarios, e.g., multiple distances and angles. Our evaluation motivates that some transformations are indeed essential for successful attacks, no matter the target class. These also appear to be responsible for creating shapes within the attacks, which are semantically related to the target class. However, they do not ensure physical robustness alone. The choice of the remaining transformations appears to be context-dependent, e.g., some being more advantageous for long-range attacks, but not for close-range ones. With our findings, we not only provide useful information on generating physical adversarial attacks, but also help research on defenses to understand their weaknesses.
Forgeability and Membership Inference Attacks

A membership inference (MI) attack predicts whether a data point was used for training a machine learning (ML) model. MI attacks are currently the most widely deployed attack for auditing privacy of a ML model. A recent work by Thudi et. al. [18] show that approximate machine unlearning is ill-defined. For this, they introduce the notion of forgeability where using forged datasets, one could unlearn without modifying the model at all. In this paper, we show a connection between machine unlearning and membership inferencing. Specifically, we study how to leverage forgeability to repudiate claims on membership inferencing. We show that the ability to forge enables the dataset owner to construct a Proof-of-Repudiation (PoR) which empowers the dataset owner to plausibly repudiate the predictions of an MI attack. This casts a doubt on the reliability of MI attacks in practice. Our empirical evaluations show that it is possible to construct PoRs efficiently.
Proactive Detection of Query-based Adversarial Scenarios in NLP Systems

Adversarial attacks can mislead a Deep Learning (DL) algorithm into generating erroneous predictions via feeding maliciously-disturbed inputs called adversarial examples. DL-based Natural Language Processing (NLP) algorithms are severely threatened by adversarial attacks. In real-world, black-box adversarial attacks, the adversary needs to submit many highly-similar queries before drafting an adversarial example. Due to this long process, in-progress attack detection can play a significant role in adversarial defense in DL-based NLP algorithms. Although there are several approaches for detecting adversarial attacks in NLP, these approaches are reactive in the sense that they can detect adversarial examples only when they are fabricated and fed into the algorithm. In this study, we take one step towards proactive detection of adversarial attacks in NLP systems by proposing a robust, history-based model named Stateful Query Analysis (SQA) to identify suspiciously-similar sequences of queries capable of generating textual adversarial examples to which we refer by adversarial scenarios. The model exhibits a detection rate of over 99.9% in our extensive experimental tests against several state-of-the-art black-box adversarial attack methods.
Optimising Vulnerability Triage in DAST with Deep Learning

False positives generated by vulnerability scanners are an industry-wide challenge in web application security. Accordingly, this paper presents a novel multi-view deep learning architecture to optimise Dynamic Application Security Testing (DAST) vulnerability triage, with task-specific design decisions exploiting the structure of traffic exchanges between our rules-based DAST scanner and a given web app. Leveraging convolutional neural networks, natural language processing and word embeddings, our model learns separate yet complementary internal feature representations of these exchanges before fusing them together to make a prediction of a verified vulnerability or a false positive. Given the amount of time and cognitive effort required to constantly manually review high volumes of DAST results correctly, the addition of this deep learning capability to a rules-based scanner creates a hybrid system that enables expert analysts to rank scan results, deprioritise false positives and concentrate on likely real vulnerabilities. This improves productivity and reduces remediation time, resulting in stronger security postures. Evaluations are conducted on a real-world dataset containing 91,324 findings of 74 different vulnerability types curated from DAST scans on nineteen organisations. Results show our multi-view architecture significantly reduces both the false positive rate by 20% and the false negative rate by 40% on average across all organisations compared to the single-view approach.
Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy

The stochastic partial differential equation approach to Gaussian processes (GPs) represents Matérn GP priors in terms of finite element basis functions and Gaussian coefficients with a sparse precision matrix. Such representations enhance the scalability of GP regression and classification to datasets of large size by setting and exploiting sparsity. In this paper we reconsider the standard choice through an analysis of the estimation performance. Our theory implies that, under certain smoothness assumptions, one can reduce the computation and memory cost without hindering the estimation accuracy by setting in the large asymptotics. Numerical experiments illustrate the applicability of our theory and the effect of the prior lengthscale in the preasymptotic regime.
Compression for Bayer CFA Images: Review and Performance Comparison

Bayer color filter array (CFA) images are captured by a single-chip image sensor covered with a Bayer CFA pattern which has been widely used in modern digital cameras. In the past two decades, many compression methods have been proposed to compress Bayer CFA images. These compression methods can be roughly divided into the compression-first-based (CF-based) scheme and the demosaicing-first-based (DF-based) scheme. However, in the literature, no review article for the two compression schemes and their compression performance is reported. In this article, the related CF-based and DF-based compression works are reviewed first. Then, the testing Bayer CFA images created from the Kodak, IMAX, screen content images, videos, and classical image datasets are compressed on the Joint Photographic Experts Group-2000 (JPEG-2000) and the newly released Versatile Video Coding (VVC) platform VTM-16.2. In terms of the commonly used objective quality, perceptual quality metrics, the perceptual effect, and the quality–bitrate tradeoff metric, the compression performance comparison of the CF-based compression methods, in particular the reversible color transform-based compression methods and the DF-based compression methods, is reported and discussed.
Perceptions of digital competence in learning and teaching English in the context of online education

Digital competence has become a crucial capability in the learning process, in the working place, and in personal communication. The aim of the article is to explore master students’ perceptions of their digital competence by identifying frequency, expertise, and satisfaction in using information communication technologies (ICT) in their learning process when studying at university and teaching English as a foreign language (EFL) in the context of online education. The sample comprised 49 master students who participated in distant evening classes at a private university in Kazakhstan while working simultaneously as teachers of English in various educational institutions during the day. Master students’ perceptions of digital competence is one of the approaches to reveal the gaps in students’ digital competence development necessary for learning and teaching EFL online. The main research tool for data collection was the online survey allowing the master students to measure their level of digital competence. Evident from the results, the majority of respondents are digitally competent; yet there is a cohort of master students who scored low on ICT skills needed for teaching and admitted the necessity of improvement. Also, according to the findings, the respondents’ frequency, expertise, and satisfaction level by their digital competence needed in the learning is higher than that in teaching. This can also imply that even though the frequency and expertise in using digital technologies for learning contributes to the development of the expertise in using them for conducting their own classes, master students need special training on the use of ICT for pedagogical purposes.