Reviewerly/exHarmony huggingface dataset:
Dynamic Network Structure: Doubly Stacking Broad Learning Systems With Residuals and Simpler Linear Model Transmission

While broad learning system (BLS) has demonstrated its distinctive performance with its solid theoretical foundation, strong generalization capability and fast learning speed, a relatively large network structure ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., a large number of enhancement nodes) is often required to assure satisfactory performance especially for challenging datasets, which may inevitably deteriorate its generalization capability due to overfitting phenomenon. In this study, by stacking several broad learning sub-systems, a doubly <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> tacked <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u> road <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> earning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> ystem through <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> esiduals and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> impler linear model <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> ransmission, called RST&BLS, is presented to enhance BLS performance in network size, generalization capability and learning speed. With the use of shared feature nodes and simpler linear models between stacked layers, the design methodology of RST&BLS is motivated by three facets: 1) analogous to human-like neural behaviors that some common neuron blocks are always activated to deal with the correlated problems, an enhanced ensemble of BLS sub-systems is resulted; 2) rather than a complicated model, human prefers a simple model (as a component of the final model); 3) extra overfitting-avoidance capability between shared feature nodes and the remaining hidden nodes from the second layer can be assured in theory. Except for performance advantage over the comparative methods, experimental results on twenty-one classification/regression datasets indicate the superiority of RST&BLS in terms of smaller network structure ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> fewer adjustable parameters), better generalization capability and fewer computational burdens.
Methodology for Complete Decorrelation of Power Supply EM Side-Channel Signal and Sensitive Data

Electro-magnetic (EM) side channel attacks have become a serious threat to security of Internet-of-Things (IoT) devices. Power supply generated by voltage regulators is one of the most common attack targets due to its strong EM emanations. In this brief we derive analytical conditions for complete theoretical decorrelation of the power supply EM side-channel signal and the sensitive data. The output of the power supply converter is modelled as amplitude modulation (AM) of the load signal by the converter capacitance that acts as a carrier. By applying Price theorem (Papoulis and Pillai, 2002), we obtain the exact theoretical conditions that converter capacitance needs to fulfil in order to prevent EM side-channel attacks. The conditions are further adapted for practical implementation. When the proposed methodology is applied to AES measured traces, the correlation coefficient between the leaked signal and the sensitive data is 0.05. Such low correlation indicates the proposed methodology is a promising candidate against the attacks that exploit AM signals to extract sensitive data, such as, TEMPEST and active EM attacks. Test Vector Leakage Assessment (TVLA) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> -test detects no leaky points, thereby confirming circuit protection against differential and correlation EM attacks as well.
Predicting Meeting Success With Nuanced Emotions

While current meeting tools are able to capture key analytics (e.g., transcript and summarization), they do not often capture nuanced emotions (e.g., disappointment and feeling impressed). Given the high number of meetings that were held online during the COVID-19 pandemic, we had an unprecedented opportunity to record extensive meeting data with a newly developed meeting companion application. We analyzed 72 h of conversations from 85 real-world virtual meetings and 256 self-reported meeting success scores. We did so by developing a deep-learning framework that can extract 32 nuanced emotions from meeting transcripts, and by then testing a variety of models predicting meeting success from the extracted emotions. We found that rare emotions (e.g., disappointment and excitement) were generally more predictive of success than more common emotions. This demonstrates the importance of quantifying nuanced emotions to further improve productivity analytics, and, in the long term, employee well-being.
Towards Trace-Based Synchronization of Variability Annotations in Evolving Model-Driven Product Lines

Annotative model-driven product lines allow to derive individual variants from a multi-variant model by exploiting annotations. Those declare the presence of each model element in a specific set of variants via a logical expression over features and may change during evolution. This provokes the risk of introducing conflicts causing logically cohesive elements of different models to appear in diverging sets of variants, which threatens the consistency of the product line. Existing work on propagating annotations across models employs the comparatively simple strategy of either overwriting or manually protecting any changed annotation in the target model but does not consider a backward propagation nor any form of synchronization. Therefore, we contribute a sophisticated method for synchronizing annotations which detects corresponding elements based on model transformation traces and resolves conflicting annotations by preserving syntactically different but semantically equal annotations according to the feature model. We demonstrate challenges and our solution method in a scenario of synchronizing two corresponding evolving multi-variant models.
Investigation of the Utility of Features in a Clinical De-identification Model: A Demonstration Using EHR Pathology Reports for Advanced NSCLC Patients

Electronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (ML) based named entity recognition (NER) model has emerged as a popular technique of automatic de-identification.The performance of a machine learning model highly depends on the selection of appropriate features. The objective of this study was to investigate the usability of multiple features in building a conditional random field (CRF) based clinical de-identification NER model.Using open-source natural language processing (NLP) toolkits, we annotated protected health information (PHI) in 1,500 pathology reports and built supervised NER models using multiple features and their combinations. We further investigated the dependency of a model's performance on the size of training data.Among the 10 feature extractors explored in this study, n-gram, prefix-suffix, word embedding, and word shape performed the best. A model using combination of these four feature sets yielded precision, recall, and F1-score for each PHI as follows: NAME (0.80; 0.79; 0.80), LOCATION (0.85; 0.83; 0.84), DATE (0.86; 0.79; 0.82), HOSPITAL (0.96; 0.93; 0.95), ID (0.99; 0.82; 0.90), and INITIALS (0.97; 0.49; 0.65). We also found that the model's performance becomes saturated when the training data size is beyond 200.Manual de-identification of large-scale data is an impractical procedure since it is time-consuming and subject to human errors. Analysis of the NER model's performance in this study sheds light on a semi-automatic clinical de-identification pipeline for enterprise-wide data warehousing.
Siamese Cross-Domain Tracker Design for Seamless Tracking of Targets in RGB and Thermal Videos

Multimodal (RGB and thermal) applications are swiftly gaining importance in the computer vision community with advancements in self-driving cars, robotics, Internet of Things, and surveillance applications.Both the modalities have complementary performance depending on illumination constraints.Hence, a judicious combination of both modalities will result in robust RGBT systems capable of all-day all-weather applications.Several studies have been proposed in the literature for integrating the multimodal sensor data for object tracking applications.Most of the proposed networks try to delineate the information into modality-specific and modality shared features and attempt to exploit the modality shared features in enhancing the modality specific information.In this work, we propose a novel perspective to this problem using a Siamese inspired network architecture.We design a custom Siamese cross-domain tracker architecture and fuse it with a mean shift tracker to drastically reduce the computational complexity.We also propose a constant false alarm rate inspired coasting architecture to cater for real-time track loss scenarios.The proposed method presents a complete and robust solution for object tracking across domains with seamless track handover for all-day all-weather operation.The algorithm is successfully implemented on a Jetson-Nano, the smallest graphics processing unit (GPU) board offered by NVIDIA Corporation.Impact Statement-Surveillance and tracking systems form an integral part of our society today.However, most of the systems are deployed in a non-real-time fashion where data is saved and processed later.Typical examples include traffic cameras, surveillance systems etc. Deep learning (DL) networks have paved the way to automate this application with minimal manual intervention.They work with RGB and thermal data independently to cater for all-day all-weather operation.However, with the architecture presented in this paper, we have demonstrated that a custom-designed single Siamese network can handle cross-domain data effectively and can operate continuously day and night.With automated day and night detection, the operator can be alerted based on the trained urgency minimizing the reaction time and operational costs.With the proposed network we are looking at a potential overhaul of the existing manual surveillance systems to automated Artificial Intelligence (AI) based systems.
Antenna Optimization Based on Auto-Context Broad Learning System

To enhance the efficiency of antenna optimization, surrogate model methods can usually be used to replace the full-wave electromagnetic simulation software. Broad learning system (BLS), as an emerging network with strong extraction ability and remarkable computational efficiency, has revolutionized the conventional artificial intelligence (AI) methods and overcome the shortcoming of excessive time-consuming training process in deep learning (DL). However, it is difficult to model the regression relationship between input and output variables in the electromagnetic field with the unsatisfactory fitting capability of the original BLS. In order to further improve the performance of the model and speed up the design of microwave components to achieve more accurate prediction of hard-to-measure quality variables through easy-to-measure parameter variables, the conception of auto-context (AC) for the regression scenario is proposed in this paper, using the current BLS training results as the prior knowledge, which are taken as the context information and combined with the original inputs as new inputs for further training. Based on the previous prediction results, AC learns an iterated low-level and context model and then iterates to approach the ground truth, which is very general and easy to implement. Three antenna examples, including rectangular microstrip antenna (RMSA), circular MSA (CMSA), and printed dipole antenna (PDA), and 10 UCI regression datasets are employed to verify the effectiveness of the proposed model.
The benefits and drawbacks of adopting mobile learning as perceived by junior high school teachers in Taiwan

The Taiwan government is trying to explore the benefits of using mobile devices to enhance the effectiveness of student learning. While many plans and projects have been launched, but the results are still meager. This research is thus aimed to discover the benefits and challenges of promoting mobile learning in Taiwan. 120 self-developed questionnaires were delivered to five junior high schools that have participated in governmental mobile learning projects. The results indicated that teachers give higher average scores to the benefits of mobile learning than drawbacks. The major benefits include inspiring students' learning interests, improving students' concentration, enriching the curriculum contents, and enhancing learning conveniences, while school members lacking internet security literacy and lacking adequate competences to effectively apply mobile learning in the classroom are perceived as the major challenges.
Real-time multitask multihuman–robot interaction based on context awareness

Abstract This study presents a novel context awareness multihuman–robot interaction (MHRI) system that allows multiple operators to interact with a robot. In the system, a monocular multihuman 3D pose estimator is first developed with the convolutional neural network. The estimator first regresses a set of 2D joints representations of body parts and then restores the 3D joints positions based on these 2D representations. Further, the 3D joints are assigned to the corresponding individual with a priority–redundancy association algorithm. The whole 3D pose of each person is reconstructed in real time, even in crowded scenes containing both self-occlusion of the body and inter-person occlusion. Then, the identities of multiple persons are recognized with action context and 3D skeleton tracking to improve interactive efficiency. For context-awareness multitask interaction, the robot control strategy is designed based on target goal generation and correction. The generated goal is taken as a reference to the model predictive controller (MPC) to generate motion trajectory. Different interactive requirements are adapted by adjusting the weight parameters of the energy function of the MPC controller. Multihuman–robot interactive experiments, including dynamic obstacle avoidance (human–robot safety) and cooperative handling, demonstrate the feasibility and effectiveness of the MHRI, and the safety and collaborative efficiency of the system are evaluated with HRI metrics.
MisConv: Convolutional Neural Networks for Missing Data

Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image inpainting or autonomous vehicles and robots. While imputation-based techniques are still one of the most popular solutions, they frequently introduce unreliable information to the data and do not take into account the uncertainty of estimation, which may be destructive for a machine learning model. In this paper, we present MisConv, a general mechanism, for adapting various CNN architectures to process incomplete images. By modeling the distribution of missing values by the Mixture of Factor Analyzers, we cover the spectrum of possible replacements and find an analytical formula for the expected value of convolution operator applied to the incomplete image. The whole framework is realized by matrix operations, which makes MisConv extremely efficient in practice. Experiments performed on various image processing tasks demonstrate that MisConv achieves superior or comparable performance to the state-of-the-art methods.
Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap

ABSTRACTABSTRACTFeature selection (FS) is an optimisation problem that reduces the dimension of the dataset and increases the performance of the machine learning algorithms and classification through the selection of the optimal subset features and elimination of the redundant features. However, the huge search space is an important challenge in the FS problem. Due to their satisfactory capabilities to handle high-dimension search spaces, meta-heuristic search algorithms have recently gained much attention and become popular in the FS problem. For these algorithms, choosing a proper fitness function plays an important role. The fitness function orients the searching strategy of the algorithms to obtain best solutions. Appropriate fitness functions will help the algorithms with exploring the search space more effectively and efficiently. In this work, firstly the efficiency of two of the most outstanding and successful heuristic algorithms in the FS domain, namely genetic algorithm (GA) and grey wolf optimiser (GWO), are investigated and analysed with a single-objective fitness function. Secondly, two recent feature selection techniques based on GA and GWO, namely feature selection, weight, and parameter optimisation (FWP) and binary GWO (BGWO) with their fitness function are investigated and analysed. Thirdly, in order to remove the detected drawbacks and weaknesses of the FS algorithms and to enhance their efficiency, a new multi-objective weighted fitness function based on multiple predominant criteria has been presented. The effectiveness of the proposed fitness function on the FS algorithms is evaluated by using SVM and associative classification on 11 different large and small datasets. The experimental results show the superiority of proposed fitness function (where features were reduced and the classification performance has been improved) over single-objective fitness function and other existing fitness functions. Furthermore, another key aim of this study is to present a comprehensive study about the strengths and weaknesses of the FS algorithms which can be used as guidelines for future possible works to more improve the developments of these algorithms.KEYWORDS: ClassificationFeature SelectionMeta-Heuristic AlgorithmsFitness FunctionGenetic AlgorithmGrey Wolf Optimiser Disclosure statementNo potential conflict of interest was reported by the author(s).
DILIE: Deep Internal Learning for Image Enhancement

We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. The methods mostly fall into two categories: training with prior examples methods and training with no-prior examples methods. Recently, Deep Internal Learning solutions to image enhancement in training with no-prior examples setup are gaining attention. We perform image enhancement using a deep internal learning framework. Our Deep Internal Learning for Image Enhancement framework (DILIE) enhances content features and style features and preserves semantics in the enhanced image. To validate the results, we use structure similarity and perceptual error, which is efficient in measuring the unrealistic deformation present in the images. We show that DILIE framework outputs good quality images for hazy and noisy image enhancement tasks.
IoT-based Covid-19 Patient Service Robot Design

Various innovations have emerged since the Covid-19 Pandemic. Innovations that arise are mainly to reduce the rate of disease spread. Studies on the development of robots are still rare when viewed from various studies. This study enriches the conceptual research related to designing a patient care robot that can be controlled remotely. The technology developed is based on the Internet of Things (IoT). This robot can deliver patient needs and communicate with patients through the LCD screen available on the robot. With the design of this Covid-19 patient service robot, patient care can be carried out remotely. This technology has the potential to be developed and optimized according to the needs in the field.
Bag of Tricks for Citation Intent Classification via SciBERT

Citation intent classification task has a long history starting from manual and rule-based methods several decades ago. We follow its variant stated as multiclass classification of sentences with references. Last years with the rise of deep learning models and transformers many solutions of NLP tasks were improved and citation intent classification task was one of them. The big problem of the field is limited availability of labelled data. We focused on two recent and relatively big datasets: ACL-ARC (6 classes, 1941 instances) and SciCite (3 classes, 11020 instances) and tried to apply all modern techniques. It is not so easy to fine-tune transformers on small imbalanced datasets with a large number of classes. Therefore we proposed combination of tricks for SciBERT fine-tuning, which allow us to achieve 0.72 Macro-F1 on ACL-ARC dataset which is slightly better than previously reported results.
Study of NMT: An Explainable AI based approach

Machine translation (MT) which translates source to the target language is a challenging task. It is one of the important area under Natural language Processing (NLP).These days we are having neural based translation system viz. Neural Machine Translation (NMT). In this study we have discussed some of the important architecture of NMT systems and their working in brief. NMT uses deep neural network frame work in its design. Deep neural network based machine learning (ML) approach is entirely black box for end user. NMT requires several hyper parameter tuning during training. We still don't get any satisfactory answer of why we are getting any specific result (output) with specific set of linguistic features and the hyper parameters that are tuned. We have tried to focus on this aspect with very recent concept in AI i.e. explainable AI (XAI). XAI, can interpret and explain the internal behavior of model's architecture which can provide us reasons (explanation) when we get any result (output) from our model. We have discussed some of the techniques such as LIME, SHAPELY, Seq-2-Seq-Vis, LSTM-Vis which are widely used by data scientists and incorporate them in ML pipeline to design an unbiased, robust and accurate model.
Modern Dine-In

&#x201C;Respect for food is a respect for life, for who we are and what we do&#x201D;. - THOMAS KELLER. The Food is an essential and a very important component in day to day life. We had seen food is being wasted in garbage, so we came up with our idea of an app named &#x201C;MODERN DINE-IN&#x201D;. We are creating an android mobile application in which students and staff can order their lunch online in their college canteen. Based on the Pre-orders the lunch will be prepared. Hence there will be time allocation to order the lunch i.e., Today to next day 9 A.M. They will have payment options like pay while receiving the lunch or online payment through e-wallet, etc&#x2026; After that, a QR code would be generated respective to the order. Our main objective is to reduce the wastage of food by using our Android mobile application. We will declare whether the canteen is full or going to fill up, by using the no. of persons count. When the Canteen is fully occupied the students or the staff can wait till the canteen gets vacant or they can use the buffet system. After scanning the QR code in the canteen by the administrator, the lunch will be distributed by the staffs to the respective students.