Reviewerly/exHarmony huggingface dataset:
Blind image quality assessment with channel attention based deep residual network and extended LargeVis dimensionality reduction

Image Quality Assessment (IQA) is one of the fundamental problems in the fields of image processing, image/video coding and transmission, and so on. In this paper, a Blind Image Quality Assessment (BIQA) approach with channel attention based deep Residual Network (ResNet)and extended LargeVis dimensionality reduction is proposed. Firstly, ResNet50 with channel attention mechanism is used as the backbone network to extract the deep features from the image. In order to reduce the dimensionality of the deep features, LargeVis, which is originally designed for the visualization of large scale high-dimensional data, is extended by using Support Vector Regression (SVR) to perform on a single feature vector data. The extended LargeVis can remove the redundant information of the deep features so as to obtain a low-dimensional and discriminative feature representation. Finally, the quality prediction model is established by using SVR as the fitting method. The low-dimensional feature representation and quality score of the image form the pair-wise data samples to train the fitting model. Experimental results on authentic distortions datasets and synthetic distortions datasets show that our proposed method can achieve superior performance compared with the state-of-the-art methods.
Graph Learning From Noisy and Incomplete Signals on Graphs

Learning the graph structure underlying observed graph signals is important in many graph signal processing (GSP) applications. This problem has been extensively addressed as graph Laplacian learning with the constraint that the graph signals have smooth variations on the resulting topology. The current approaches focus primarily on the case that the signals are observed across all nodes and possibly corrupted by additive Gaussian noise. In this paper, we propose a general framework for graph learning where the graph signal is partially observed and corrupted by sparse outliers in addition to Gaussian noise. We present a general optimization framework that addresses this problem and show how this formulation encapsulates a variety of problems in GSP including Laplacian learning and graph regularized low-rank matrix completion. The proposed optimization is solved with ADMM and the resulting algorithms are evaluated on both simulated graphs with different topology and real world graph-based data clustering.
Design Guidelines and a Prototype Implementation for Cyber-Resiliency in IT/OT Scenarios Based on Blockchain and Edge Computing

The advent of the Internet of Things (IoT) and its spread in industrial environments has changed production lines by dramatically fostering the dynamicity of data sharing and the connectivity of machines. However, such increased flexibility (also pushed by the adoption of edge devices) must not negatively affect the security and safety of industrial environments. The proposed solution adopts the blockchain to securely store in distributed ledgers topology information and access rules, maximizing the cyber resiliency of industrial networks. Topology information and access rules are stored and queried in a completely distributed manner, ensuring data availability even in case a centralized controller is temporarily down or the network partitioned. Moreover, blockchain consensus algorithms foster a participative validation of topology information, to ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a nonrepudiable manner. Finally, the adoption of configurable edge gateways allows to take prompt countermeasures in case potential threats are identified, by activating access rules stored in ledgers in a secure and distributed manner. In addition to solution design guidelines and architectural considerations, the article also presents performance results achieved with our CyberChain working prototype, with the goal of not only demonstrating the feasibility of the proposed solution but also its suitability in industrial environments.
Multi-Task Occlusion Learning for Real-Time Visual Object Tracking

Occlusion handling is one of the important challenges in the field of visual tracking, especially for real-time applications, where further processing for occlusion reasoning may not always be possible. In this paper, an occlusion-aware real-time object tracker is proposed, which enhances the baseline SiamRPN model with an additional branch that directly predicts the occlusion level of the object. Experimental results on GOT-10k and VOT benchmarks show that learning to predict occlusion levels end-to-end in this multi-task learning framework helps improve tracking accuracy, especially on frames that contain occlusions. Up to 7% improvement on EAO scores can be observed for occluded frames, which are only 11% of the data. The performance results over all frames also indicate the model does favorably compared to the other trackers.
Cell Multi-Bernoulli (Cell-MB) Sensor Control for Multi-Object Search-While-Tracking (SWT)

Information-driven control can be used to develop intelligent sensors that can optimize their measurement value based on environmental feedback. In object tracking applications, sensor actions are chosen based on the expected reduction in uncertainty also known as information gain. Random finite set (RFS) theory provides a formalism for quantifying and estimating information gain in multi-object tracking problems. However, estimating information gain in these applications remains computationally challenging. This paper presents a new tractable approximation of the RFS expected information gain applicable to sensor control for multi-object search and tracking. Unlike existing RFS approaches, the information gain approximation presented in this paper considers the contributions of non-ideal noisy measurements, missed detections, false alarms, and object appearance/disappearance. The effectiveness of the information-driven sensor control is demonstrated through two multi-vehicle search-while-tracking experiments using real video data from remote terrestrial and satellite sensors.
A Reasonable Data Pricing Mechanism for Personal Data Transactions with Privacy Concern

In the past few years, more and more data marketplaces for personal data transactions sprung up. However, it is still very challenging to estimate the value of privacy contained in the personal data. Especially when the buyer already has some related datasets, he is able to obtain more privacy by combining and analyzing the bought data and the data he already has. The main research motivation of this work is to reasonably price the data with privacy concern. We propose a reasonable data pricing mechanism which prices the personal privacy data from three aspects and is different from the existing work, we propose a new concept named 'privacy cost' to quantitatively measure the privacy information increment after a data transaction rather than directly measuring the privacy information contained in a single dataset. In addition, we use the information entropy as an important index to measure the information content of data. And we conduct a set of experiments on our personal data pricing method, and the results show that our pricing method performs better than the alternatives.
Hand Gesture Recognition on a Resource-Limited Interactive Wristband

Most of the reported hand gesture recognition algorithms require high computational resources, i.e., fast MCU frequency and significant memory, which are highly inapplicable to the cost-effectiveness of consumer electronics products. This paper proposes a hand gesture recognition algorithm running on an interactive wristband, with computational resource requirements as low as Flash &lt; 5 KB, RAM &lt; 1 KB. Firstly, we calculated the three-axis linear acceleration by fusing accelerometer and gyroscope data with a complementary filter. Then, by recording the order of acceleration vectors crossing axes in the world coordinate frame, we defined a new feature code named axis-crossing code. Finally, we set templates for eight hand gestures to recognize new samples. We compared this algorithm’s performance with the widely used dynamic time warping (DTW) algorithm and recurrent neural network (BiLSTM and GRU). The results show that the accuracies of the proposed algorithm and RNNs are higher than DTW and that the time cost of the proposed algorithm is much less than those of DTW and RNNs. The average recognition accuracy is 99.8% on the collected dataset and 97.1% in the actual user-independent case. In general, the proposed algorithm is suitable and competitive in consumer electronics. This work has been volume-produced and patent-granted.
Longitudinal First-Person HCI Research Methods

In this chapter, we focus on longitudinal first-person research methods in HCI. First-person research involves data collection and experiences from the researcher themselves, as opposed to external users (or participants). We present three projects where longitudinal ‘auto-approaches’ to research and design in HCI were applied, namely one auto-ethnography and two autobiographical designs. These projects help illustrate the benefits and challenges of using these first-person research methods in longitudinal HCI and interaction design research. We conclude the chapter by reflecting on themes and lessons that resonate across the three projects (i.e., range of participation, data collection, time to reflect, concluding).
An Artificial Bee Colony Algorithm Based on Improved Search Strategy

Aiming at the shortcomings of the original ABC algorithm, such as initial randomness, prematurity and slow convergence speed, this paper proposes an artificial bee colony algorithm with improved search strategy. In order to increase initial population quality and enhance the overall search ability, it firstly initializes the swarm by using chaotic mapping and reverse learning method. Secondly, the position updating formula based on local better and global best is used to improve the efficiency of the iterative optimization process. Finally, the proposed algorithm is contrasted to PSO algorithm, artificial bee colony algorithm and other improved artificial bee colony optimization algorithms, and the standard test function is used for simulation experiments.
Optimal Hedging with Margin Constraints and Default Aversion and its Application to Bitcoin Perpetual Futures

We consider the hedging problem where a futures position can be automatically liquidated by theexchange without notice. We derive a semi-closed form for an optimal hedging strategy with dualobjectives -- to minimise both the variance of the hedged portfolio and the probability of liquidationsdue to insufficient collateral. The optimal solution depends on the statistical characteristics of the spot and futures extreme returns and parameters that characterise the hedger by loss aversion, choice of leverage and collateral management. An empirical analysis of bitcoin shows that the optimal strategy combines superior hedge effectiveness with a reduction in the probability of liquidation. We compare the performance of seven major direct and inverse hedging instruments traded on five different exchanges, based on minute-level data. We also link this performance to novel speculative trading metrics, which differ markedly between venues.
Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment

A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.
FAST and Efficient Microlens-Based Motion Search for Plenoptic Video Coding

The motion estimation which plays an important role in video coding requires much computation for encoding. In this paper, from the ray motion characteristics in the lenslet plenoptic video, we derive a new motion search model and propose a fast and efficient microlens-based motion search method. Theoretical analysis and experimental results have verified the new model and demonstrated its efficiency in search. Under the HEVC random-access configuration, we achieve not only substantial encoding time reduction (56.7%), but also bitrate saving of 1.3% on average compared to relevant existing works. Under the low delay configuration, the performances are 23.3% and 2.3%, respectively for encoding time reduction and bitrate saving.
The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing

Transfer learning is a growing field that can address the variability of activity recognition problems by reusing the knowledge from previous experiences to recognise activities from different conditions, resulting in the leveraging of resources such as training and labelling efforts. Although integrating ubiquitous sensing technology and transfer learning seem promising, there are some research opportunities that, if addressed, could accelerate the development of activity recognition. This paper presents TL-FmRADLs; a framework that converges the feature fusion strategy with a teacher/learner approach over the active learning technique to automatise the self-training process of the learner models. Evaluation TL-FmRADLs is conducted over InSync; an open access dataset introduced for the first time in this paper. Results show promising effects towards mitigating the insufficiency of labelled data available by enabling the learner model to outperform the teacher’s performance.
An Improved Knowledge Representation Model Based on TransH

In the process of training the knowledge representation model, numerous negative samples are needed. Considering the fact that samples obtained from traditional negative sampling are of low quality and will contribute little towards the training, we propose an improved knowledge representation model based on the translation model TransH (adv-TransH). The model adopts the self-adversarial negative sampling method, which generates negative samples according to the current entity and relation embeddings and gives different weights to negative samples with different scores. Experimental results show that the proposed method makes training more efficient and improves the performance of knowledge representation.
A Facial Landmark Detection Method Based on Deep Knowledge Transfer

Facial landmark detection is a crucial preprocessing step in many applications that process facial images. Deep-learning-based methods have become mainstream and achieved outstanding performance in facial landmark detection. However, accurate models typically have a large number of parameters, which results in high computational complexity and execution time. A simple but effective facial landmark detection model that achieves a balance between accuracy and speed is crucial. To achieve this, a lightweight, efficient, and effective model is proposed called the efficient face alignment network (EfficientFAN) in this article. EfficientFAN adopts the encoder-decoder structure, with a simple backbone EfficientNet-B0 as the encoder and three upsampling layers and convolutional layers as the decoder. Moreover, deep dark knowledge is extracted through feature-aligned distillation and patch similarity distillation on the teacher network, which contains pixel distribution information in the feature space and multiscale structural information in the affinity space of feature maps. The accuracy of EfficientFAN is further improved after it absorbs dark knowledge. Extensive experimental results on public datasets, including 300 Faces in the Wild (300W), Wider Facial Landmarks in the Wild (WFLW), and Caltech Occluded Faces in the Wild (COFW), demonstrate the superiority of EfficientFAN over state-of-the-art methods.
A Field Dependence-Independence Perspective on Eye Gaze Behavior within Affective Activities

Evidence suggests that human cognitive differences affect users’ visual behavior within various tasks and activities. However, a human cognitive processing perspective on the interplay between visual and affective aspects remains up-to-date understudied. In this paper, we aim to investigate this relationship by adopting an accredited cognitive style framework (Field Dependence-Independence – FD-I) and provide empirical evidence on main interaction effects between human cognition and emotional processing towards eye gaze behavior. For doing so, we designed and implemented an eye tracking study (n = 22) in which participants were initially classified according to their FD-I cognitive processing characteristics, and were further exposed to a series of images, which triggered specific emotional valence. Analysis of results yield that affective images had a different effect on FD and FI users in terms of visual information exploration time and comprehension, which was reflected on eye gaze metrics. Findings highlight a hidden and rather unexplored effect between human cognition and emotions towards eye gaze behavior, which could lead to a more holistic and comprehensive approach in affective computing.
Twitter Sentiment Analysis for COVID-19

With the current onset of the Coronavirus pandemic social interaction has been deeply affected. However, social media and micro-blogging platforms like Twitter are becoming more of relevance to the masses to express their feelings, opinions, concerns and problems. A large amount of data is generated in this process which may be highly valuable in deriving deep insights about the effects of the pandemic on the people. With modern technological tools like data mining, data processing and APIs, harnessing this sea of data has become more of a possibility. Moreover, with the application of advanced machine learning tools like sentiment analysis, understanding the people's mindset behind the tweet has also become a possibility. The study oversees the development of a web based interface for connecting two sections of the society: one which is in need of help and the other with the desire to help. In this study, we aim to utilise data analysis and advanced machine learning techniques like RoBERTa(Robustly Optimised BERT pre-training Approach) and CNN-RoBERTa Sentiment Extraction to incorporate methods like sentiment analysis, frequency distribution and comparative analysis on data found from social media for efficient comparison on the effects of COVID-19 in India while also developing a web-based interface for effective exposition and insights over the crisis.
A Real-Time Deep Learning Approach for Real-World Video Anomaly Detection

Anomaly detection in video streams with imbalanced data and real-time constraints is a challenging task of computer vision. This paper proposes a novel real-time approach for real-world video anomaly detection exploiting a supervised learning methodology. In particular, we present a deep learning architecture based on the analysis of contextual, spatial, and motion information extracted from the video. A data balancing strategy based on hard-mining and adaptive framerate is used to avoid overfitting and increase detection accuracy. The approach defines an extended taxonomy by differentiating anomalies in ”soft” and ”hard”. A novel anomaly detection score based on a sigmoidal function has been introduced to reduce false positive rate while maintaining a high level of true positive rate. The proposed methodology has been validated with a set of experiments on a well-known video anomaly dataset: UCF-CRIME. The experiments on the testbed demonstrate the impact of the contextual information and data balancing on the classification performances, considering only ”hard” anomalies during training and that the proposed model can achieve state-of-the-art performances while minimizing resource consumption.
English Grammar Detection Based on LSTM-CRF Machine Learning Model

Deep learning and neural network have been widely used in the field of speech, vocabulary, text, pictures, and other information processing fields, which has achieved excellent research results. Neural network algorithm and prediction model were used in this paper for the study and exploration of English grammar. Aiming at the application requirements of English grammar accuracy and standardization, we proposed a machine learning model based on LSTM-CRF to detect and analyze English grammar. This paper briefly summarized the development trend of deep learning and neural network algorithm and designed the structure pattern of radial basis function neural network in grammar semantic detection and analysis on the basis of deep learning artificial neural network theory. Based on the morphological features of English grammar, a grammar database was established according to the rules of English word segmentation. In this paper, we proposed an improved conditional random field CRF (Conditional Random Field) network model based on LSTM (Long Short-Term Memory) neural network. It can improve the problem that the traditional machine learning model relies on feature point selection in English grammar detection. The machine learning model based on LSTM-CRF was used to recognize English grammar text entities. The results show that the English grammar detection system based on the LSTM-CRF model can simplify the process structure in the recognition process, reduce the unnecessary operation cycle, and improve the overall detection accuracy.
An Overview of Crossover Techniques in Genetic Algorithm

Pachuau, Joseph L. Roy, Arnab Kumar Saha, AnishArtificial Genetic Algorithm is proposed to mimic the natural selection process. It provides an elegant and relatively simple way to solve non-polynomial problems. The crossover, one of the basic step of GA, is an imitation of reproduction in biological beings. Crossover exchanges information between different individuals to generate offspring with the hope of obtaining better genes. The basic aim of crossover is the same, but various approaches are proposed depending on the problem. This paper presents a review on the crossover, basics and some problem-specific techniques.