Reviewerly/exHarmony huggingface dataset:
<scp>Nonsubsampled</scp> contourlet transform with cross‐guided bilateral filter for despeckling of medical ultrasound images

Abstract This paper aims to enhance the image quality in ultrasound images. The significant difficulties in the ultrasound image are the presence of speckle noise. Speckle is the granular noise, and this kind of noise produces a lot of challenges during medical diagnosis. Also, these kinds of problems degrade image quality. The proposed work overcomes this kind of issue with nonsubsampled contourlet domain‐based cross‐guided bilateral filtering. Initially, additive speckle noise is introduced with the log‐transformed Rayleigh distribution. This additive speckle noise component is to be decomposed through the non‐subsampled contourlet domain transform. Then the decomposed image is despeckled by the cross‐guided bilateral filter, and the cost function of this filter is minimized through adaptive galactic swarm optimization. The proposed filtering technique despeckles the additive noise in the noisy image. Finally, the decomposed image is reconstructed using the inverse transform of the nonsubsampled decomposition. The implementation of the proposed methodology is carried out in MATLAB simulation platform. Also, the performance of the proposed work is measured by few metrics such as structural similarity index matrix, mean structural similarity index matrix, mean square error, peak signal to noise ratio, Bhattacharyya coefficient, and speckle index. The result of cost function optimization provides less distortion when compared to other techniques.
Room Mapping using Ultrasonic Range Sensor on the ATRACBOT (Autonomous Trash Can Robot): A Simulation Approach

ATRACTBOT (Autonomous Trash Can Robot) is a social robot that is equipped with Artificial Intelligence (AI) to carry out its task of collecting waste while still involving humans to raise awareness to dispose of trash in its place. The robot is designed to work indoors, so the ability to map workspaces is needed. In this paper, room mapping is done using eight ultrasonic sensors arranged in such a way that it covers an area of 360 degrees around the robot. The robot moves through the room automatically by using the Braitenberg control method to map the entire room. The experimental results show that the robot succeeded in mapping the room by distinguishing the different color plots for free space and occupied space.
Detection of Listener Uncertainty in Robot-Led Second Language Conversation Practice

Uncertainty is a frequently occurring affective state that learners experience during the acquisition of a second language. This state can constitute both a learning opportunity and a source of learner frustration. An appropriate detection could therefore benefit the learning process by reducing cognitive instability. In this study, we use a dyadic practice conversation between an adult second-language learner and a social robot to elicit events of uncertainty through the manipulation of the robot's spoken utterances (increased lexical complexity or prosody modifications). The characteristics of these events are then used to analyze multi-party practice conversations between a robot and two learners. Classification models are trained with multimodal features from annotated events of listener (un)certainty. We report the performance of our models on different settings, (sub)turn segments and multimodal inputs.
Wearable smart multimeter equipped with AR glasses based on IoT platform

A new digital measurement system is presented in this manuscript. A wearable smart measurement system equipped with monocular augmented reality (AR) glasses and Wi-Fi and Bluetooth modules is designed, which is more suitable for high-altitude measurement occasions than traditional digital multimeters (DMM). The measurement data could be displayed on AR glasses supported by Bluetooth module in real time. Also, the effective management of large-scale industrial measurement data could be implemented through the IoT data interaction platform with high speed communication by employing a Wi-Fi module. In addition, the wire-receiving device is introduced into the system, which effectively solves the problem that occurs when the traditional DMM harness is tangled. Hall sensors are adopted to measure the current and solves the problem that the traditional DMM needs to be connected into an electronic circuit in series.
Examining Bitcoin mempools Resemblance Using Jaccard Similarity Index

In Bitcoin, memory pool is a space of unconfirmed transactions. when a node receives newly generated transactions, the node verifies it and appends it into the local mempool. The transactions are stored in the mempool until they get included in a newly mined block. Since each node has a different capacity for storing unconfirmed transactions; thus, each node has different transactions stored in mempool. In this paper, we examine the mempool similarity using the Jaccard Index among four Bitcoin full nodes.
Image-based Solar Irradiance Forecasting Using Recurrent Neural Networks

The solar power variability is due to the variability of solar irradiance. Several factors are involved in the situation, such as cloud thickness and air pollution. In this paper, we attempt to find a novel way to predict the amount of solar irradiance. A image-based forecasting method is developed, and Long Short-Term Memory (LSTM) neural network is applied for data training. Daily solar irradiance and sky images are record by the record system, and uploaded to the MySQL database for storage. Feature values obtained by analyzing sky images are used as the input of neural network with solar irradiance. After some performance evaluation indicators were demonstrated, we found that the proposed method has good predictive performance with 5 to 60 minutes in present.
A Survey of Event Detection Methods for Eye Movements Classification in Smooth-Pursuit-Based Interactive Applications

Gaze-based interaction is an important research area in the field of human-computer interaction. Gaze-based input gives benefit for motion-impaired people and provides faster and intuitive interaction with an application content. Developing gaze-based interactive applications has to deal with several challenges: data processing, eye movements classification, and action. Smooth pursuit eye movement has been used as one of primary input modalities for gaze-based interactive applications. In developing smooth-pursuit-based interactive applications, challenge in the event classification is important to be addressed. However, there is no survey paper that studies various event detection methods in smooth-pursuit-based interactive applications. To tackle this research gap, we present a brief literature review of some event detection methods that consider smooth pursuit eye movement. This paper also presents a preliminary comparison of event detection methods based on some properties that are needed in smooth-pursuit-based interactive applications. From the preliminary comparison, there are some considerations to be taken in to account in order to develop smooth-pursuit-based interactive applications.
Fixing Bias in Reconstruction-Based Anomaly Detection with Lipschitz Discriminators

Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other errors. We show that this approach exhibits intrinsic biases that lead to undesirable results. Reconstruction-based methods are sensitive to training-data outliers and simple-to-reconstruct points. Instead, we introduce a new unsupervised Lipschitz anomaly discriminator that does not suffer from these biases. Our anomaly discriminator is trained, similar to the ones used in GANs, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These additions allow us to show improved performance on MNIST, CIFAR10, and health record data.
Multi-Frame Super-Resolution Combining Demons Registration and Regularized Bayesian Reconstruction

This letter proposes a multi-frame super-resolution framework by combining Demons registration with Bayesian-based regularized reconstruction. For both of our proposals, D-BTVIR and D-IRWIR, the visual analysis shows improvements in regions where the compared methods produced results with either motion-artifacts or over-smoothed aspect due to misregistration. Quantitative results on simulated deformations show an improvement of 3.5%, on average, in PSNR and 8.0%, in SSIM. Finally, results from the Nemenyi test show that D-IRWIR is statistically superior to the other methods we tested, both considering SSIM and PSNR, and D-BTVIR is statistically equivalent while being 7 times faster than the state-of-the-art Bayesian method.
Reinforcement Learning Toward Decision-Making for Multiple Trusted-Third-Parties in PUF-Cash

Electronic money is the digital representation of physical banknotes enabling offline and online payments. An electronic e-Cash scheme, termed PUF- Cash was proposed in prior work. PUF-Cash preserves user anonymity by leveraging the random and unique statistical properties of physically unclonable functions (PUFs). PUF-Cash is extended meaningfully in this work by the introduction of multiple trusted third parties (TTPs) for token blinding and a fractional scheme to diversify and mask Alice's spending habits from the Bank. A reinforcement learning (RL) framework based on stochastic learning automata (SLA) is proposed to efficiently select a subset of TTPs as well as the fractional amounts for blinding per TTP, based on the set of available TTPs, the computational load per TTP and network conditions. An experimental model was constructed in MATLAB with multiple TTPs to verify the learning framework. Results indicate that the RL approach guarantees fast convergence to an efficient selection of TTPs and allocation of fractional amounts in terms of perceived reward for the end-users.
A Fast Laser Adjustment-Based Laser Triangulation Displacement Sensor for Dynamic Measurement of a Dispensing Robot

Height measurement and location by a laser sensor is a key technology to ensure accurate and stable operation of a dispensing robot. In addition, alternation of dynamic and static working modes of a robot, as well as variation of surface and height of a workpiece put forward strict requirements for both repeatability and respond speed of the location system. On the basis of the principle of laser triangulation, a displacement sensor applied to a dispensing robot was developed, and a fast laser adjustment algorithm was proposed according to the characteristics of static and dynamic actual laser imaging waveforms on different objects. First, the relationship between the centroid position of static waveform and peak intensity for different measured objects was fitted by least square method, and the intersection point of each curve was solved to confirm the ideal peak intensity, and therefore reduce the interference of different measured objects. Secondly, according to the dynamic centroid difference threshold of two adjacent imaging waveforms, the static and dynamic working modes of the sensor were distinguished, and the peak intensity was adjusted to different intervals by linear iteration. Finally, a Z direction reciprocating test, color adaptability test, and step response test were carried out on the dispensing robot platform; the experiments showed that the repeatability accuracy of the sensor was 2.7 um and the dynamic step response delay was 0.5 ms.
Multi-Label Classification of ICD Coding Using Deep Learning

This study uses deep learning approach to tackle the multi-label classification problem in ICD coding. The discharge summaries on MIMIC-III dataset are adopted to explore the training methods of text preprocessing, label preprocessing, and model training. Specifically, the methods of label-to-chapter and common label classification are experimented, hoping to find the best recommendations of ICD codes for each diagnosis by the physicians. The result shows CNN has the best performance 76% by micro F1-measure in label-to-chapter. In addition, it also shows CNN outperforms other methods within top-50 and top-100 common labels.
An Empirical Study on Ensemble Learning of Multimodal Machine Translation

With the increasing availability of images, multimodal machine translation (MMT) is leading a vibrant field. Model structure and multimodal information introduction are the hotspot focused by MMT researchers nowadays. Among the existing models, transformer model has reached the state-of-the-art performance in many translation tasks. However, we observe that the performance of MMT based on transformer is highly unstable since transformer model is sensitive to the fluctuation of hyper-parameters especially the number of layers, the dimension of word embeddings and hidden states, the number of multi-heads. Moreover, different ways of introducing image information also have significant influence on the performance of MMT. In this paper, we exploit some integration strategies which depend on different tasks to make collaborative decisions on the final translation results to enhance the stability of MMT based on transformer. Furthermore, we combine different ways of introducing image information to improve the semantic expression of input. Extensive experiments on Multi30K dataset demonstrate that ensemble learning in MMT which integrates text and image features exactly obtain more stable and better translation performance and the best result yields improvement of 5.12 BLEU points over the strong Transformer baseline set in our experiments.
Web Frameworks for Desktop Apps

Background: Novel frameworks for the development of desktop applications with web technologies have become popular. These desktop web app frameworks allow developers to reuse existing code and knowledge of web applications for the creation of cross-platform apps integrated with native APIs. Aims: To date, desktop web app frameworks have not been studied empirically. In this paper, we aim to fill this gap by characterizing the usage of web frameworks and providing evidence on how beneficial are their pros and how impactful are their cons. Method: We conducted an empirical study, collecting and analyzing 453 desktop web apps publicly available on GitHub. We performed qualitative and quantitative analyses to uncover the traits and issues of desktop web apps. Results: We found that desktop web app frameworks enable the development of cross-platform applications even for teams of limited dimensions, taking advantage of the abundant number of available web libraries. However, at the same time, bugs deriving from platform compatibility issues are common. Conclusions: Our study provides concrete evidence on some disadvantages associated with desktop web app frameworks. Future work is required to assess their impact on the required development and maintenance effort, and to investigate other aspects not considered in this first research.
Detection and localization of scorebox in long duration broadcast sports videos

Many studies have been devoted to sports video summarization and content-based video search. However, the semantic importance of caption box or scorebox (SB) appearing in broadcast sports videos has been almost neglected as SB holds key elements for conducting these research tasks. SB localization is challenging as there exists a huge variety of SBs, and almost every broadcast sports video contains a different SB with unique features such as geometry, font, colors, location, and texture. Every time a new sports series emerges, it contains a new type of scorebox that never resembles any other sports series. One can say that, SBs are evolving with unexpected features and novel challenges. Thus, traditional learning-based methods alone are not suitable for detection. This paper proposes a robust method for detecting and localizing SBs appearing in broadcast sports videos. It automatically learns the template of SB and further utilizes the template, as the SB may translate from the usual location and may disappear for a short time. We performed comprehensive experiments on a real-life dataset SP-1 and comparison with state-of-the-art methods shows that the proposed method achieves better performance.
CDC's Flu on Call Simulation: Testing a National Helpline for Use During an Influenza Pandemic

During an influenza pandemic, healthcare facilities are likely to be filled to capacity, leading to delays in seeing a provider and obtaining treatment. Flu on Call is a collaborative effort between the US Centers for Disease Control and Prevention and partners to develop a toll-free telephone helpline to reduce the burden on healthcare facilities and improve access to antivirals for people who are ill during an influenza pandemic. This study tested the feasibility of Flu on Call during a 1-day simulation using a severe pandemic scenario. Trained volunteer actors placed calls to the helpline using prepared scripts that were precoded for an expected outcome (“disposition”) of the call. Scripts represented callers who were ill, those calling for someone else who was ill, and callers who were only seeking information. Information specialists and medical professionals managed the calls. Results demonstrated that Flu on Call may effectively assist callers during a pandemic, increase access to antiviral prescriptions, and direct patients to the appropriate level of care. Overall, 84% of calls exactly matched the expected call disposition; few calls (2%) were undermanaged (eg, the caller was ill but not transferred to a medical professional or received advice from the medical professional that was less intensive than what was warranted). Callers indicated a high level of satisfaction (83% reported their needs were met). Because of the high volume of calls that may be received during a severe pandemic, the Flu on Call platform should evolve to include additional triage channels (eg, through internet, chat, and/or text access).
Sentiment Analysis of Informal Persian Texts Using Embedding Informal words and Attention-Based LSTM Network

The massive volume of comments on websites and social networks has made it possible to raise awareness of people's beliefs and preferences regarding products and services on a large scale. For this purpose, sentiment analysis, which refers to the determination of the sentiment of texts, has been proposed as an intelligent solution. From a methodological point of view, the recent combination of words embedding and deep neural networks (DNNs) has become an effective approach for sentiment analysis. In Persian studies, formal corpuses such as Wikipedia dumps have been used for word embedding. The fundamental difference between formal and informal texts means that the vectors derived from formal texts in informal contexts such as social networks do not result in desirable accuracy. To overcome this drawback, in this paper, we provide a large integrated text corpus of several different sources of informal comments and we also utilize the Fasttext as the word embedding algorithm. In this research, we use Attention-based LSTM, which has been shown to perform more effectively compared to the similar methods in sentiment analysis for the English language. The proposed method is evaluated on the two Persian “Taaghche” and “Filimo” datasets collected in this paper. The experiments on the two Persian datasets prove that utilizing informal vectors in sentiment analysis and applying the attention model improves the prediction accuracy of the DNN in the sentiment analysis of Persian texts.
Image Colorization Using a Deep Transfer Learning

Over the past decade, the automatic image coloring has been of particular interest in applications such as repairing damaged or old images. One of the problems with the auto-coloring is the ability to predict multiple color results for gray image pixels. In the presence of noise, the problem becomes more complicated. Recently, some researchers employ conventional neural networks (CNN) to the problem of image colorization. Usually, the output of the last layer of CNN is used as a feature representation. However, the information contained in this layer may be too coarse spatially to allow exact localization. Conversely, earlier layers may be precise in localization but will not capture semantics. In this article, we use a concept called hypercolumns to achieve the best in both cases and develop a fully automatic image coloring system. Our approach exploits recent advances in deep neural networks and uses the semantic representation to provide an accurate color prediction. Since many elements of the scene naturally appear by the color distribution, we train our model in such a way to predict the color texture of each pixel. The DIV2K dataset has been used for training, and the obtained results are compared with other methods based on PSNR, which are promising.
Aggressive Language Detection with Joint Text Normalization via Adversarial Multi-task Learning

Aggressive language detection (ALD), detecting the abusive and offensive language in texts, is one of the crucial applications in NLP community. Most existing works treat ALD as regular classification with neural models, while ignoring the inherent conflicts of social media text that they are quite unnormalized and irregular. In this work, we target improving the ALD by jointly performing text normalization (TN), via an adversarial multi-task learning framework. The private encoders for ALD and TN focus on the task-specific features retrieving, respectively, and the shared encoder learns the underlying common features over two tasks. During adversarial training, a task discriminator distinguishes the separate learning of ALD or TN. Experimental results on four ALD datasets show that our model outperforms all baselines under differing settings by large margins, demonstrating the necessity of joint learning the TN with ALD. Further analysis is conducted for a better understanding of our method.
Ranking comment sorting policies in online debates

Online debates typically possess a large number of argumentative comments. Most readers who would like to see which comments are winning arguments often only read a part of the debate. Many platforms that host such debates allow for the comments to be sorted, say from the earliest to latest. How can argumentation theory be used to evaluate the effectiveness of such policies of sorting comments, in terms of the actually winning arguments displayed to a reader who may not have read the whole debate? We devise a pipeline that captures an online debate tree as a bipolar argumentation framework (BAF), which is sorted depending on the policy, giving a sequence of induced sub-BAFs representing how and how much of the debate has been read. Each sub-BAF has its own set of winning arguments, which can be quantitatively compared to the set of winning arguments of the whole BAF. We apply this pipeline to evaluate policies on Kialo debates, where it is shown that reading comments from most to least liked, on average, displays more winners than reading comments earliest first. Therefore, in Kialo, reading comments from most to least liked is on average more effective than reading from the earliest to the most recent.