Reviewerly/exHarmony huggingface dataset:
Information Bottlenecked Variational Autoencoder for Disentangled 3D Facial Expression Modelling

Learning a disentangled representation is essential to build 3D face models that accurately capture identity and expression. We propose a novel variational autoencoder (VAE) framework to disentangle identity and expression from 3D input faces that have a wide variety of expressions. Specifically, we design a system that has two decoders: one for neutral-expression faces (i.e. identity-only faces) and one for the original (expressive) input faces respectively. Crucially, we have an additional mutual-information regulariser applied on the identity part to solve the issue of imbalanced information over the expressive input faces and the reconstructed neutral faces. Our evaluations on two public datasets (CoMA and BU-3DFE) show that this model achieves competitive results on the 3D face reconstruction task and state-of-the-art results on identity-expression disentanglement. We also show that by updating to a conditional VAE, we have a system that generates different levels of expressions from semantically meaningful variables.
SPNet: A deep network for broadcast sports video highlight generation

Professionally broadcasted sports videos usually have long durations but contain only a few exciting events. In general, professional bodies and amateur content creators spend thousands of man-hours to manually crop the exciting video segments from these long-duration videos and generate handcrafted highlights. Sports enthusiasts keep them updated with the latest happening based on such highlights. There exists a need for a method that accurately and automatically recognizes the exciting activities in a sports game. To address this issue, we present a deep learning-based network SPNet that recognizes exciting sports activities by exploiting high-level visual feature sequences and automatically generates highlights. The proposed SPNet utilizes the strength of 3D convolution networks and Inception blocks for accurate activity recognition. We divide the sports video excitement into views, actions, and situations. Moreover, we provide 156 new annotations for about twenty-three thousand videos of the SP-2 dataset. Extensive experiments are conducted using two datasets SP-2 and C-sports, and the results demonstrate the superiority of the proposed SPNet. Our proposed method achieves the highest performance for views, action, and situation activities with an average accuracy of 76% on the SP-2 dataset and 82% on the C-sports dataset.
Polarization Sensitive Imaging with Qubits

We compare reconstructed quantum state images of a birefringent sample using direct quantum state tomography and inverse numerical optimization technique. Qubits are used to characterize birefringence in a flat transparent plastic sample by means of polarization sensitive measurement using density matrices of two-level quantum entangled photons. Pairs of entangled photons are generated in a type-II nonlinear crystal. About half of the generated photons interact with a birefringent sample, and coincidence counts are recorded. Coincidence rates of entangled photons are measured for a set of sixteen polarization states. Tomographic and inverse numerical techniques are used to reconstruct the density matrix, the degree of entanglement, and concurrence for each pixel of the investigated sample. An inverse numerical optimization technique is used to obtain a density matrix from measured coincidence counts with the maximum probability. Presented results highlight the experimental noise reduction, greater density matrix estimation, and overall image enhancement. The outcome of the entanglement distillation through projective measurements is a superposition of Bell states with different amplitudes. These changes are used to characterize the birefringence of a 3M tape. Well-defined concurrence and entanglement images of the birefringence are presented. Our results show that inverse numerical techniques improve overall image quality and detail resolution. The technique described in this work has many potential applications.
GNNs for Node Clustering in Signed and Directed Networks

With an increasing number of applications where data can be represented as graphs, graph neural networks are a useful tool to apply deep learning to graph data. In particular, node clustering is an important problem in network analysis. Signed and directed networks are important types of networks that are linked to many real-world problems; their asymmetry provides a challenge for many clustering methods.
Towards Concept Change Detection in Marine Ecosystems

The research presented in this paper aims to accelerate the natural science research process by partially automating the execution of experiments using AI-assisted Concept-Change Detection (C-CD), e.g., for monitoring systems and studying biodiversity and ecosystem functions. The purpose of C-CD is to detect concept changes, also known as concept drift, that may be relevant to the study or ecosystem state. For example, in intertidal marine ecosystems, the event of sudden flooding can lead to dramatic changes in biodiversity. It could also be of scientific interest to take sensor samples more frequently in the period leading up to such events. The paper proposes an architecture for C-CD to customize AI-based analysis of sensor data streams. Furthermore, the paper implements portions of the architecture and is applied on sensor data from the Spiekeroog Coastal Observatory (SCO) as a feasibility study. The study demonstrates C-CD’s ability to detect anomalies that are either of scientific or technical interest to the operation and exploration activities of SCO.
Robust Linearly Constrained Square-Root Cubature Kalman Filter for Mismatched Nonlinear Dynamic Systems

This letter addresses the robust state estimation problem for mismatched nonlinear dynamic systems. In this context, we are interested in nonlinear filters able to provide consistent estimates while mitigating the impact of possible modeling errors. We focus on the cubature Kalman filter (CKF) and its square-root form (SCKF), being the most promising nonlinear Gaussian filters, the latter providing improved numerical stability. We leverage on the recently introduced linearly constraint Kalman filter, and derive its sigma-point-based counterparts, the linearly constrained CKF (LC-CKF) and SCKF (LC-SCKF). Such formulation can be exploited, using the correct constraints, to mitigate a large number of model misspecifications. To assess the validity of this approach, we consider an illustrative vehicle navigation problem. Results are provided to support the discussion and show the good performance of the new filters with respect to the state-of-the-art, which are robust to different types of mismatches between the assumed dynamic system model and the true one.
Visualizing Paired Image Similarity in Transformer Networks

Transformer architectures have shown promise for a wide range of computer vision tasks, including image embedding. As was the case with convolutional neural networks and other models, explainability of the predictions is a key concern, but visualization approaches tend to be architecture-specific. In this paper, we introduce a new method for producing interpretable visualizations that, given a pair of images encoded with a Transformer, show which regions contributed to their similarity. Additionally, for the task of image retrieval, we compare the performance of Transformer and ResNet models of similar capacity and show that while they have similar performance in aggregate, the retrieved results and the visual explanations for those results are quite different. Code is available at https://github.com/vidarlab/xformer-paired-viz.
Poster: Accelerate Cross-Device Federated Learning With Semi-Reliable Model Multicast Over The Air

To achieve efficient model multicast for cross-device Federated Learning (FL) over shared wireless channels, we propose SRMP, a transport protocol that performs semi-reliable model multicast over the air by leveraging existing PHY-aided wireless multicast techniques. The preliminary study shows that, with novel designs, SRMP could reduce the communication time involved in each round of training significantly.
Phonemes Recognition Using Formant Analysis in the Case of Consonant Vowel Transition Case “Amazigh Language”

Several studies in the development of Automatic Speech Recognition (ASR) systems have been yet presented and well studied for many languages. However, the particularities of the Amazigh Language due to its specific acoustic features such as the emphasis on Amazigh consonants pose many problems for developing its automatic speech recognition system. In this paper, we deal with the spoken recognition’s problem in Amazigh Language. We consider here a particular case of the problem, focusing on the vowel-consonant transitions, by treating the recognition of phoneme level. We propose a derivative algorithm allowing the phoneme separation based on the difference between the energies of both consonants and vowels.
Learn from Each Other: A Peer-Teaching Model

The aim of the study was to examine the degree of competence of the teaching-learning goals as formulated by the elementary-school staff members and the expression of these goals in science classes, in which peer teaching takes place, from the students' point of view. As part of this peer teaching, fellow teachers who were assigned from among the students were in charge of teaching the rest of the class that had been divided into groups of four to five. The research adapted mix-methods, which helped to answer the following questions: a). What are the teaching-learning goals of the school staff at Tzemach School that engages in the peer teaching approach combined with traditional teaching? b). How are teaching-learning goals formulated by the peer-teaching school staff in science classes described from the students' point of view? The research tools used included interviews with teachers, interviews with group leaders, focus groups, observations, and drawing analyses. The findings indicate that there is a gap between teachers' perceptions of peer teaching and students' perceptions of peer teaching. Teachers placed greater emphasis on the cognitive aspects, while the students paid more attention to the social-emotional and behavioral aspects. Despite these gaps, students are interested in continuing to study through peer teaching, provided the presence of the accompanying teacher is more significant. Based on the existing model for peer teaching at Tzemach School and according to the findings of the study, a desirable model for integrating peer teaching was developed, which can aid in implementation of the approach for any age and in any another scholastic subject.
Multimodal Attention Network for Violence Detection

Violence detection task is an important branch of the field of computer vision, and multimodal method is currently the main method in the violence detection task. Appearance, optical flow, and audio information are commonly used violence feature information. Appearance and optical flow information reflect visual information, and audio information serves as an auxiliary of visual information. At present, appearance and audio information are the commonly used information in violent video detection. However, few studies focused on motion information. Moreover, there are also few effective methods for multimodal violence feature fusion. In this paper, we propose a lightweight feature fusion network based on the multimodal attention model. It can extract the multi-scale motion feature through both appearance feature and optical flow feature, with audio feature serve as auxiliary features. To demonstrate the effect of our model, we performed experiments on the publicly available dataset VSD2015 and the self-built dataset Violence Correspondence Detection (VCD), where VSD2015 achieves an AP of 0.45 and outperforms state-of-the-art performance.
Text Mining in 19th-Century Essays for Investigating a Possible Collaborative Authorship Problem: John Stuart Mill and Harriet Taylor Mill

In this work, we use machine learning techniques to address a research question regarding the authorship of two famous essays in the nineteenth century. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">On Liberty</i> (1859) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The Subjection of Women</i> (1869) were published under John Stuart Mill’s name, a widely studied nineteenth-century British philosopher. Mill himself attributed them to collaboration with his wife and partner, Harriet Taylor Mill. More than 150 years later, the question remains whether the author of these two canonical texts in the history of political thought was solely John Stuart Mill. Experts are divided on taking John Stuart Mill’s attribution at face value, since Harriet Taylor Mill had died in 1858. Addressing this question, we use a dataset consisted in essays of both authors, to train three state-of-the-art classifiers that are able to learn and distinguish the writing style of each author. Then, we use the models built to attribute the two famous essays of disputed authorship to one of the two. From the results, we conclude that the classifiers are able to learn the two classes very well, and they return high accuracies on the validation set. Regarding the test set, most of the models attribute the two essays to John Stuart Mill, however, the contribution of Harriet Taylor Mill is shown for some chunks of text of both essays. These results, we conclude, explain why experts are divided on this particular research question.
Intelligent Camera Selection Decisions for Target Tracking in a Camera Network

Camera Selection Decisions (CSD) are highly useful for several applications in a multi-camera network. For example, CSD benefit multi-camera target tracking by reducing the number of candidate cameras to look for the target&#x2019;s next location. The correct candidate cameras, decreases the number of false Re-ID queries as well as the computation time. Also, in multi-camera trajectory forecasting (MCTF) to predict where a person will re-appear in the camera network along with the transition time. These applications require a large amount of annotated data for training. In this paper, we use state-representation learning with a reinforcement learning based policy to effectively and efficiently make camera selection decisions. We further demonstrate that by using learned state representations, as opposed to hand-crafted state variables, we are able to achieve state-of-the-art results on camera selection, while reducing the training time for the RL policy. Along with this, we use a reward function that helps to reduce the amount of supervision in training the policy in a semi-supervised way. We report our results on four datasets: NLPR_MCT, DukeMTMC, CityFlow, and WNMF dataset. We show that an RL policy reduces unnecessary Re-ID queries and therefore the false alarms, scales well to larger camera networks, and is target-agnostic.
Trustworthy Machine Learning: Fairness and Robustness

In recent years, machine learning (ML) technologies have experienced swift developments and attracted extensive attention from both academia and industry. The applications of ML are extended to multiple domains, from computer vision, text processing, to recommendations, etc. However, recent studies have uncovered the untrustworthy side of ML applications. For example, ML algorithms could show human-like discrimination against certain individuals or groups, or make unreliable decisions in safety-critical scenarios, which implies the absence of fairness and robustness, respectively. Consequently, building trustworthy machine learning systems has become an urgent need. My research strives to help meet this demand. In particular, my research focuses on designing trustworthy ML models and spans across three main areas: (1) fairness in ML, where we aim to detect, eliminate bias and ensure fairness in various ML applications; (2) robustness in ML, where we seek to ensure the robustness of certain ML applications towards adversarial attacks; (3) specific applications of ML, where my research involves the development of ML-based natural language processing (NLP) models and recommendation systems.
Methods for Effective Speed Control of DC Shunt Motor-Part 2: Fuzzy Logic-Based PID Controller Tuning Method

PID controller is used in applications like motor speed control, liquid level control, temperature control, etc. It works well in both closed-loop and open-loop system configurations. To set gain parameters in the PID controller, the main issue is with the computation of gain parameters in offline mode. So, the controller may not be adjusted itself when deployed online. The disturbance that occurs in a system is spontaneous, thereby the PID gain parameters should be adjusted in tune with the disturbance online. So, the online mode of controller tuning has become popular. Artificial intelligence (AI)-based logical techniques like fuzzy logic, neural networks, and genetic algorithm-based methods have gained prominence in the process of tuning the gain parameters of a PID controller. There is a provision to train the controller in presence of disturbance and hence a better disturbance rejection can be obtained. In the present work, the fuzzy logic is used for tuning the gain parameters of the PID controller adaptively to control a DC shunt motor. From the time domain characteristics, it can be concluded that the controller tuned using fuzzy logic which performs better when compared to a controller tuned using conventional tuning methods.
Deep mutual GANs: representation learning from multiple experts

Representation learning is one of the canonical objectives of most deep learning models. However, the learning of real-world clinical data is often compromised by their inherently imbalanced or long-tailed distribution wherein a few classes have significantly larger numbers of training instances than do the other classes. In this study, we investigated the representation learning of such long-tailed data distributions by the use of a deep mutual ensemble generative adversarial network. Our proposed framework consists of multiple powerful pre-trained discriminator networks that transfer knowledge to multiple individual untrained generator networks. During the training process, each generator learns to collaborate with the other generators. Additionally, each generator receives feedback from the individual discriminators in an adversarial manner. Especially, we explored the use of mutual information shared between the independent generators that makes our framework robust against misclassification of long-tailed data distributions in medical image analysis. We evaluated our proposed framework on four public datasets that represented different medical imaging modalities and imbalance ratios. Our experimental results show that our proposed framework benefits from ensemble learning and shared mutual learning, and achieves compelling results on several medical imaging benchmarks. Thus, our approach offers potential advantages over traditional deep learning in real-world applications.
The Government Support in Distance Education: Case of Bahrain

The COVID-19 pandemic has thrown the global economy into a tailspin and has had a massive impact on higher education. Face-to-face courses have moved to online learning platforms because of the abrupt closing of campuses as a social distancing measure to discourage group dissemination. In literature, the effectiveness of virtual delivery in the basic schooling and higher education sectors is well researched and heavily debated amongst scholars and practitioners, especially during this pandemic. The purpose of the research was to gauge the impact of the government support on the effectiveness of distance education provision on three main areas: platforms, content delivery and the end-user (the student) perspective. This research focused on Kingdom of Bahrain as a case study after exploring number of international examples of government support to distance education during Covid 19 pandemic. This study targeted students in Bahraini universities including public and private universities. According to statistics for higher education in Bahrain, the number of students in Bahraini universities is 38,113 students. This study has used 5% margin error and 95% confidence level to come up with 381 sample size as the minimum responses required. However, we received 486 responses which are above the minimum size of sample. All survey responses are valid for analysis and no exclusion has been made. The research involved a quantitative questionnaire that was answered by 486 respondents amongst students who were enrolled in various academic programmes, post COVID-19, in virtual mode. The questionnaire items were purposely designed to measure the students’ satisfaction as a measure of effectiveness and quality of the virtual training environment. The questionnaire was distributed and administered electronically via a mass emailing campaign. Upon collecting the responses, the data was quantitatively analyzed using descriptive quantitative analysis.
Supporting early career teachers’ digital agency: a role for mentorship?

(2022). Supporting early career teachers' digital agency: a role for mentorship? Irish Educational Studies: Vol. 41, Digital Education Futures: Design for Doing Education Differently, Guest Editors: Tony Hall, Rupert Wegerif, Suzanna Loper, Déirdre Ní Chróinín and Emma O'Brien, pp. 201-211.
Digital Electronic Voting Machine Using Raspberry Pi and Touchscreen Display

India is one of the democratic countries; the two major elections in India are General elections and Assembly elections. These elections are taken with the help of an Electronic Voting Machine (EVM). It is necessary to be present in the home constituency during elections, one cannot vote from another constituency to the candidate of his/her constituency. As a result, lots of people are not able to cast their votes in elections. To solve this issue there is a necessity of such a system that enables voters to vote in their respective constituency from another constituency.Digital Electronic Voting Machine (DEVM) allows voters to vote who are not available in their constituency during elections because of their employment, education, business, etc. This proposed system should be implemented in the area where the migration of people from another constituency is more. There should be a special polling booth for migrant people, where those people can cast their vote on DEVM. People of that constituency can vote in an ordinary way using EVM. This system is implemented using Raspberry Pi 4 and Touchscreen Display. The Graphical User Interface is created using Python that gives voters an option to select their home constituency to cast their vote. The system includes the candidate data of all 543 constituencies. The result of the votes is stored in the memory of Raspberry Pi with respect to the selected constituency. As a result, the voting percentage will increase which is a good sign for democracy.
Research on multiplexed colour QR code with direct readability

With the rapid popularization of QR code technology, colour QR code is an important direction of its development, making it of great value to research a colour QR code that can be read directly while improving the data capacity. This letter proposes a novel composite colour QR code by combining colour coding and multiplexing technology. Based on the actual demands of high-capacity, this code presents the public identification information in the form of the plain code and conceals the secret information (e.g., authentication information such as traceability, blockchain) as the hidden code. The reading conditions of colour QR codes are discussed, a colour coding model is established and the corresponding algorithm to optimize its design is given. Through code scanning tests, it is verified that this kind of code has significant value in the field of anti-counterfeiting, commodity marketing etc.