Reviewerly/exHarmony huggingface dataset:
BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters

With a resolution improvement, the size of modern remote sensing images increases. This makes it desirable to compress them, mostly by using lossy compression techniques. Often the images to be compressed (or some component images of multichannel remote sensing data) are noisy. The lossy compression of such images has several peculiarities dealing with specific noise filtering effects and evaluation of the compression technique’s performance. In particular, an optimal operation point (OOP) may exist where quality of a compressed image is closer to the corresponding noise-free (true) image than the uncompressed (original, noisy) image quality, according to certain criterion (metrics). In such a case, it is reasonable to automatically compress an image under interest in the OOP neighborhood, but without having the true image at disposal in practice, it is impossible to accurately determine if the OOP does exist. Here we show that, by a simple and fast preliminary analysis and pre-training, it is possible to predict the OOPs existence and the metric values in it with appropriate accuracy. The study is carried out for a better portable graphics (BPG) coder for additive white Gaussian noise, focusing mainly on one-component (grayscale) images. The results allow for concluding that prediction is possible for an improvement (reduction) in the quality metrics of PSNR and PSNR-HVS-M. In turn, this allows for decision-making about the existence or absence of an OOP. If an OOP is absent, a more “careful” compression is recommended. Having such rules, it then becomes possible to carry out the compression automatically. Additionally, possible modifications for the cases of signal-dependent noise and the joint compression of three-component images are considered and the possible existence of an OOP for these cases is demonstrated.
Ensemble-Based Deep Learning Model for Network Traffic Classification

Network Traffic Classification enables a number of practical applications ranging from network monitoring to resource management, with security implications as well. Nowadays, traffic classification has become a challenging task in order to distinguish among a variety of applications due to the huge amount of generated traffic. Therefore, developing Machine Learning (ML) models, which can successfully identify network applications, is one of the most important tasks. However, among the ML models applied to network traffic classification so far, no model outperforms all the others. To solve these issues, this paper proposes a novel Deep Learning (DL)-based approach that incorporates multiple Decision Tree based models. This approach employs a non-linear blending ensemble method by combining tree-based classifiers through DL in order to maximize generalization accuracy. This ensemble consists of two levels called base classifiers and meta-classifiers. In the first level, Decision Tree-based models are used as the base classifiers while in the second level, DL is used as a meta-model to combine the outputs of the base classifiers. Using two publicly available datasets, we show that our proposed ensemble is suitable for network traffic classification and outperforms the linear blending (using logistic regression as meta-model) as well as several well-known ML models, which are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), AdaBoost, K-Nearest Neighbors (KNN), LightGBM, Catboost, and XGBoost.
Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming Over HTTP (DASH)

Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.
Self-Alignment for Black-Box Domain Adaptation of Image Classification

Recently, black-box domain adaptation attracts a lot of attention, which is a new concept to realize domain adaptation with only a cloud API service instead of the source data or well-trained source model, reflecting the focus on development of cloud services and concerns about data security. However, the existing black-box domain adaptation methods always only use high-confidence samples which limits their performance. We propose a self-alignment approach based on statistic moment matching to realize black-box domain adaptation. We construct a model for target domain in the initial stage of our work. Then, we put target data into source model API to obtain the pseudo-labels and divide the target data into high-confidence and low-confidence parts according to their pseudo-labels confidence. By matching the data distributions between these two parts and self-supervised learning on high-confidence part, the performance on both parts samples can be boosted respectively. Information maximization is also applied to the target data to further improve their classification performance. Experiment results confirm that our method achieves state-of-the-art performance.
Radio Frequency Fingerprint Identification Based on Logarithmic Power Cosine Spectrum

With the development of new Internet of Things (IoT) applications, such as unmanned ground vehicle (UGV), unmanned aerial vehicle (UAV), and unmanned surface vessel (USV), the security of wireless device identification is getting more and more attention.The radio frequency fingerprint (RFF) technology utilizes the unique hardware characteristics of the device for identity authentication, which overcomes the security risks of traditional authentication schemes based on distributable keys.In this paper, we propose an RFF identification scheme based on logarithmic power cosine spectrum for transient signals.We use the sliding window variance trajectory-based posterior probability density detection algorithm to improve the efficiency of transient signal detection, then adopt the discrete Fourier transform to extract the logarithmic envelope power spectrum of the transient signal, after that utilize the cosine transform to compresses the frequency domain features, and finally construct the 7-dimensional RFF feature vector.For the application scenario of USV, we acquire the radio frequency signals of 7 marine automatic identification systems (AIS) as the test dataset.The experimental results show that the proposed scheme can achieve ideal recognition accuracy using a Support Vector Machine as the classifier.Meanwhile, we use Principal Component Analysis to verify that the 7-dimensional RFF feature vector has good distinguishability for different wireless devices.
Consistent, Continuous, and Customizable Mid-Air Gesture Interaction for Browsing Multimedia Objects on Large Displays

Browsing multimedia objects, such as photos, videos, documents, and maps represents a frequent activity in a context of use where an end-user interacts on a large vertical display close to bystanders, such as a meeting in a corporate environment or a family display at home. In these contexts, mid-air gesture interaction is suitable for a large variety of end-users, provided that gestures are consistently mapped to similar functions across media types. We present Lui (Large User Interface), a ready-to-deploy and to-use application for browsing multimedia objects by consistent mid-air gesture interaction on a large display that is customizable by mapping new gesture classes to functions in real-time. The method followed to design the gesture interaction and to develop the application consists of four stages: (1) a contextual gesture elicitation study (23 participants × 18 referents = 414 proposed gestures) is conducted with the various media types to determine a consensus set satisfying consistency, (2) the continuous integration of this consensus set with gesture recognizers into a pipeline software architecture, (3) a comparative testing of these recognizers on the consensus set to configure the pipeline with the most efficient ones, and (4) an evaluation of the interface regarding its global quality and specific to the implemented gestures.
The Application of Multimedia Information Technology in the Moral Education Teaching System of Colleges and Universities

The rapid development of multimedia technology provides technical support for higher education and makes higher education move towards automation and informatization. Multimedia technology has been widely used in the teaching of colleges and universities in China, and the traditional teaching mode will be gradually replaced. Multimedia education is the product of the combination of multimedia technology and modern educational technology. It can create a broader space for the future of education and help education achieve diversified development. This technology can not only improve the teaching efficiency but also further complete the comprehensive training of students’ quality. The popularization of multimedia information technology in the classroom is also a main direction of today’s education reform. The application of multimedia technology in moral education teaching system is highly valued by teachers. Students have a strong autonomy to understand and accept the network education mode. Therefore, network multimedia information technology can be widely used. In particular, for moral education, network education is more feasible and scientific. It can widely cover every student through the construction of network platform and integrate moral education teaching into campus culture for gradual development. This paper discusses multimedia information technology. This paper discusses the design of moral education teaching system under the environment of multimedia information technology, including the design of various functions, in order to improve the integrity of moral education teaching in colleges and universities.
Utilizing Human Feedback in Autonomous Driving: Discrete vs. Continuous

Deep reinforcement learning (Deep RL) algorithms are defined with fully continuous or discrete action spaces. Among DRL algorithms, soft actor–critic (SAC) is a powerful method capable of handling complex and continuous state–action spaces. However, a long training time and data efficiency are the main drawbacks of this algorithm, even though SAC is robust for complex and dynamic environments. One of the proposed solutions to overcome this issue is to utilize human feedback. In this paper, we investigate different forms of human feedback: head direction vs. steering and discrete vs. continuous feedback. To this end, a real-time human demonstration from steer and human head direction with discrete or continuous actions were employed as human feedback in an autonomous driving task in the CARLA simulator. We used alternating actions from a human expert and SAC to have a real-time human demonstration. Furthermore, to test the method without potential individual differences in human performance, we tested the discrete vs. continuous feedback in an inverted pendulum task, with an ideal controller to stand in for the human expert. The results for both the CARLA and the inverted pendulum tasks showed a significant reduction in the training time and a significant increase in gained rewards with discrete feedback, as opposed to continuous feedback, while the action space remained continuous. It was also shown that head direction feedback can be almost as good as steering feedback. We expect our findings to provide a simple yet efficient training method for Deep RL for autonomous driving, utilizing multiple sources of human feedback.
LDA-GAN: Lightweight domain-attention GAN for unpaired image-to-image translation

Recently, image-to-image translation has attracted the interest of researchers, which purpose is to learn a mapping between two image domains. However, image translation will become an intrinsically ill-posed problem when given unpaired training data, that is, there are infinite mappings between two domains. Existing methods usually fail to learn a relatively accurate mapping, leading to poor quality of generated results. We believe that if the framework can focus more on the translation of important object regions instead of irrelevant information, such as background, then the difficulty of mapping learning will be reduced. In this paper, we propose a lightweight domain-attention generative adversarial network (LDA-GAN) for unpaired image-to-image translation, which has fewer parameters and lower memory usage. An improved domain-attention module (DAM) is introduced to establish a long-range dependency between two domains. Thus, the generator can focus more on the relevant regions to generate more realistic images. Furthermore, a novel separable-residual block (SRB) is designed to retain depth and spatial information during the translation with a lower computational cost. Extensive experiments show the effectiveness of our model on various image translation tasks according to qualitative and quantitative evaluation.
A weakly-supervised graph-based joint sentiment topic model for multi-topic sentiment analysis

• A novel graph-based joint sentiment topic model, with an importance sampling-based training scheme and self-training approached, is proposed, that integrates an edge-gated graph convolutional network to better learn the representation of topics and works. Multi-topic sentiment analysis, which aims to identify the topics and classify their corresponding sentiment, is of great value in understanding consumers’ behaviour and improving services. Because of the high cost of manual annotation of the datasets, topic model-based approaches that model the joint distributions of both topics and sentiments have been studied previously. Some studies proposed models that leverage the prior knowledge derived from the pre-trained word embeddings and have proven effective. However, most of the existing models are based on the assumption that words and topics are conditionally independent, ignoring the dependency relations among them. Additionally, the fine-tuning of the pre-trained word embeddings to incorporate the contextual information is also neglected in these models. This could result in the ambiguous representations of topics. In this paper, we propose a novel weakly-supervised graph-based joint sentiment topic model (W-GJST) that integrates an edge-gated graph convolutional network (E-GCN) into a joint sentiment-topic model. An importance sampling-based training method is proposed to learn the contextual representations of topics and words efficiently. Additionally, a self-training multi-topic classifier is designed for the multi-label topic identification. Experiments on two benchmark datasets demonstrate the superiority of the proposed W-GJST compared to the baseline models in terms of topic modelling, topic identification and topic-sentiment identification.
Sparse-Based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolution is important for the quantification and analysis of retinal vasculature. However, the resolution of OCTA images is inversely proportional to the field of view at the same sampling frequency, which is not conducive to clinicians for analyzing larger vascular areas. In this paper, we propose a novel Sparse-based domain Adaptation Super-Resolution network (SASR) for the reconstruction of realistic [Formula: see text]/low-resolution (LR) OCTA images to high-resolution (HR) representations. To be more specific, we first perform a simple degradation of the [Formula: see text]/high-resolution (HR) image to obtain the synthetic LR image. An efficient registration method is then employed to register the synthetic LR with its corresponding [Formula: see text] image region within the [Formula: see text] image to obtain the cropped realistic LR image. We then propose a multi-level super-resolution model for the fully-supervised reconstruction of the synthetic data, guiding the reconstruction of the realistic LR images through a generative-adversarial strategy that allows the synthetic and realistic LR images to be unified in the feature domain. Finally, a novel sparse edge-aware loss is designed to dynamically optimize the vessel edge structure. Extensive experiments on two OCTA sets have shown that our method performs better than state-of-the-art super-resolution reconstruction methods. In addition, we have investigated the performance of the reconstruction results on retina structure segmentations, which further validate the effectiveness of our approach.
Usability Evaluation of the SmartWheeler through Qualitative and Quantitative Studies

Intelligent powered wheelchairs remain a popular research topic that can improve users' quality of life. Although our multidisciplinary research team has put a lot of effort into adding features based on end-users needs and impairments since 2006, there are still open issues regarding the usability and functionalities of an intelligent powered wheelchair (IPW).For this reason, this research presents an experience with our IPW followed by a study in two parts: a quantitative one based on the System Usability Scale (SUS) questionnaire and a qualitative one through open questions regarding IPW functionalities with novice users, e.g., IPW non-users. These users never used an IPW before, but are users and aware of the impacts of the technology used in our IPW, being undergraduate to postdoctoral students and staff (faculty, lecturers, research engineers) at the Faculty of Engineering of Université de Sherbrooke.The qualitative analyses identified different behaviours among the novice users. The quantitative analysis via SUS questionnaire done with novice users reports an "okay" rating (equivalent with a C grade or 68 SUS Score) for our IPW's usability. Moreover, advantages and disadvantages opinions were gathered on the IPW as well as comments which can be used to improve the system.The results reported in these studies show that the system, e.g., IPW, was judged to be sufficiently usable and robust by novice users, with and without experience with the software used in developing the IPW.
Graph-Based Text Summarization and Its Application on COVID-19 Twitter Data

Large volumes of structured and semi-structured data are being generated every day. Processing this large amount of data and extracting important information is a challenging task. The goal of an automatic text summarization is to preserve the key information and the overall meaning of the article to be summarized. In this paper, a graph-based approach is followed to generate an extractive summary, where sentences of the article are considered as vertices, and weighted edges are introduced based on the cosine similarities among the vertices. A possible subset of maximal independent sets of vertices of the graph is identified with the assumption that adjacent vertices provide sentences with similar information. The degree centrality and clustering coefficient of the vertices are used to compute the score of each of the maximal independent sets. The set with the highest score provides the final summary of the article. The proposed method is evaluated using the benchmark BBC News data to demonstrate its effectiveness and is applied to the COVID-19 Twitter data to express its applicability in topic modeling. Both the application and comparative study with other methods illustrate the efficacy of the proposed methodology.
Selecting data to clean for fact checking

We study the optimization problem of selecting numerical quantities to clean in order to fact-check claims based on such data. Oftentimes, such claims are technically correct, but they can still mislead for two reasons. First, data may contain uncertainty and errors. Second, data can be "fished" to advance particular positions. In practice, fact-checkers cannot afford to clean all data and must choose to clean what "matters the most" to checking a claim. We explore alternative definitions of what "matters the most": one is to ascertain claim qualities (by minimizing uncertainty in these measures), while an alternative is just to counter the claim (by maximizing the probability of finding a counterargument). We show whether the two objectives align with each other, with important implications on when fact-checkers should exercise care in selective data cleaning, to avoid potential bias introduced by their desire to counter claims. We develop efficient algorithms for solving the various variants of the optimization problem, showing significant improvements over naive solutions. The problem is particularly challenging because the objectives in the fact-checking context are complex, non-linear functions over data. We obtain results that generalize to a large class of functions, with potential applications beyond fact-checking.
Mobile-Based Augmented Reality Application in Pharmacy Schools Implemented in Pharmaceutical Compounding Laboratories: Students’ Benefits and Reception

Augmented reality (AR) is a technological approach which combines virtual objects such as text, pictures or videos with physical objects (real-world). The study aimed to design, implement and validate a mobile-based AR application, as a self-paced, interactive, student-centered learning tool be used in the pharmaceutical compounding laboratory course for first year pharmacy students.A mobile-based AR application (Amplified Rx app; HeyPayLess Inc) compatible with iOS and android operating system was developed. A cross-over study design was conducted where alternatively, one group was subjected to ARx app implementation in 8 formulations and the other group served as control. The reception and benefits to students were assessed via a 10 questions survey. In this case, 69 (2019) and 55 (2020) students participated in the study.Students' use of ARx app was increased in 2020 which indicates its usefulness. For acceptability, leaners enjoyed interactive materials and tutorial videos were the most used and appealing item. Learners described the installation, scanning and operation to be very easy in both years. 86.95% of learners were confident conducting the experiments with the assistance of ARx app in 2019 and increased to 92.73% in 2020. 33.33% considered ARx app to be the most helpful resource in 2019, and the percent was significantly increased to 76.36% in 2020.AR technology implementation in pharmaceutical education could create student-centered engaging and interactive learning experience in fundamental areas such as pharmaceutical compounding laboratories.