Reviewerly/exHarmony huggingface dataset:
Endoscopic Ultrasound Image Synthesis Using a Cycle-Consistent Adversarial Network

Endoscopic ultrasound (EUS) is a challenging procedure that requires skill, both in endoscopy and ultrasound image interpretation. Classification of key anatomical landmarks visible on EUS images can assist the gastroenterologist during navigation. Current applications of deep learning have shown the ability to automatically classify ultrasound images with high accuracy. However, these techniques require a large amount of labelled data which is time consuming to obtain, and in the case of EUS, is also a difficult task to perform retrospectively due to the lack of 3D context. In this paper, we propose the use of an image-to-image translation method to create synthetic EUS (sEUS) images from CT data, that can be used as a data augmentation strategy when EUS data is scarce. We train a cycle-consistent adversarial network with unpaired EUS images and CT slices extracted in a manner such that they mimic plausible EUS views, to generate sEUS images from the pancreas, aorta and liver. We quantitatively evaluate the use of sEUS images in a classification sub-task and assess the Fréchet Inception Distance. We show that synthetic data, obtained from CT data, imposes only a minor classification accuracy penalty and may help generalization to new unseen patients. The code and a dataset containing generated sEUS images are available at: https://ebonmati.github.io.
Traffic Sign Detection and Recognition for Hazy Images: ADAS

Self driving cars are picking up pace and thus serves as a challenging research domain. One of the most crucial functions in an autonomous vehicle is accurately detecting and recognising the traffic signs. Traffic sign detection is a crucial task in traffic sign recognition systems. Deep neural networks are proven powerful in traffic sign classification. As traffic sign violation leads to law and order disruption, posing a threat to human life, there is a need for robust algorithms with efficient actuation to perform the delegated task. Thus, this paper proposes a robust algorithm which addresses challenges like haze, fog, unclear images due to improper condition of roads to efficiently detect and recognize traffic signs using Image manipulation, Optical Character Recognition (OCR) algorithm and You Only Look Once (YOLOv3) detection algorithm. The proposed algorithm in this paper for hazy images that aids in ADAS applications has an accuracy of 87.29%. This contributes to the emerging research domains of autonomous vehicles and self-driving cars.
On the Use of Causal Models to Build Better Datasets

In recent years, Machine Learning and Deep Learning communities have devoted many efforts to studying ever better models and more efficient training strategies. Nonetheless, the fundamental role played by dataset bias in the final behaviour of the trained models calls for strong and principled methods to collect, structure and curate datasets prior to training. In this paper we provide an overview on the use of causal models to achieve a deeper understanding of the underlying structure beneath datasets and mitigate biases, supported by several real-life use cases from the medical and industrial domains.
DyGA: A Hardware-Efficient Accelerator With Traffic-Aware Dynamic Scheduling for Graph Convolutional Networks

With the growing applications of Graph Convolutional Networks (GCN), there is also an increasing demand for its efficient hardware acceleration. Compared with CNN tasks, GCN tasks have new challenges such as randomness, sparsity, and nonuniformity, which will lead to poor performance of previous AI accelerators. In this paper, we propose DyGA, a hardware-efficient GCN accelerator, which is featured by strategies of graph partitioning, customized storage policy, traffic-aware dynamic scheduling, and out-of-order execution. Synthesized and evaluated under TSMC 28-nm, the accelerator achieves an average throughput of over 95% of its peak performance with full utilization of hardware on representative graph data sets. Having a high area-efficiency with 0.217 GOPS/K-logic-gates and 8.06 GOPS/KB-PE-buffer, and thus an energy-efficiency of 384GOPS/W, the proposed accelerator outperforms previous state-of-the-art works in the sparse data processing.
A VR-Based Simulator Using Motion Feedback of a Real Powered Wheelchair for Evaluation of Autonomous Navigation Systems

Autonomous navigation systems for powered wheelchairs are drawing attention to support older adults in moving outdoors. It is important to evaluate how the users behave and feel when they experience autonomous navigation for their development. VR-based simulators are used to evaluate user's behavior and subjective assessment in various scenarios without putting anybody at risk. However, it is difficult to reproduce realistic motion feedback in the simulator using motion platforms. Moreover, this physical feedback influences user's perception of self-motion, which affects their behavior and feeling. In this paper, we proposed a novel simulator that gives real motion feedback while simulating various scenarios by combining a virtual reality headset and a real powered wheelchair. Experiment results showed that subjective assessment of comfort during autonomous navigation differed between the proposed simulator and a simulator without motion feedback, indicating that perception of the wheelchair's behavior was enhanced by real motion feedback.
Methods for Using Class Based N-gram Language Models in the Kaldi Toolkit

This paper explains in detail several methods for utilization of class based n-gram language models for automatic speech recognition, within the Kaldi speech recognition framework. It reexamines an existing implementation for word-level grammars, and then presents two methods of converting ARPA-format language models into a corresponding weighted finite state transducer which is then used for decoding in Kaldi in a usual way. One of the methods relies on implicit expansion of class n-grams within the language model during the conversion into the transducer, and the other one on explicit expansion of the ARPA file by changing class n-gram entries into an array of regular class instance n-grams, while the conversion procedure remains unchanged. Some limitations and optional restrictions for the procedures are discussed as well. The proposed methods are tested on a model trained on a Serbian judicial corpus which includes classes for all types of personal nouns (first names, last names, place names, street names and organization names).
Behaviour-Conditioned Policies for Cooperative Reinforcement Learning Tasks

The cooperation among AI systems, and between AI systems and humans is becoming increasingly important. In various real-world tasks, an agent needs to cooperate with unknown partner agent types. This requires the agent to assess the behaviour of the partner agent during a cooperative task and to adjust its own policy to support the cooperation. Deep reinforcement learning models can be trained to deliver the required functionality but are known to suffer from sample inefficiency and slow learning. However, adapting to a partner agent behaviour during the ongoing task requires ability to assess the partner agent type quickly. We suggest a method, where we synthetically produce populations of agents with different behavioural patterns together with ground truth data of their behaviour, and use this data for training a meta-learner. We additionally suggest an agent architecture, which can efficiently use the generated data and gain the meta-learning capability. When an agent is equipped with such a meta-learner, it is capable of quickly adapting to cooperation with unknown partner agent types in new situations. This method can be used to automatically form a task distribution for meta-training from emerging behaviours that arise, for example, through self-play.
Spatially Aggregated Photovoltaic Power Prediction Using Wavelet and Convolutional Neural Networks

Forecasting the power generation from intermittent renewable energy sources, such as Photovoltaic (PV) systems, is crucial for the reliable operations of power systems. In this paper, we consider the task of spatially aggregated PV power generation from large-scale, grid-connected and geographically dispersed PV sites. PV power generation data is highly uncertain, non-linear and non-stationary, making accurate forecasting very challenging. We present a new approach, Wavelet Convolutional Neural Networks (WCNNs), by combining Wavelet Transformation (WT) with Convolutional Neural Networks (CNNs). The WCNNs approach first applies time-invariant WT to decompose the highly fluctuating PV power time series into multiple components. It then predicts the approximation (i.e., low frequency smoothed time series) and details (i.e., high frequency random noise) using CNNs and linear regression, respectively. Extensive evaluation using a real dataset from the Australian Energy Market Operator (AEMO) shows that WCNNs is an effective approach and outperforms the state-of-the-art machine learning models both with and without WT.
Cascaded Hierarchical Context-Aware Vehicle Re-Identification

Vehicle Re-Identification (Re-ID) is a challenging task, which aims to match the surveillance images containing the same vehicle. Since vehicles of the same type tend to share very similar appearance, slight difference in local areas are usually critical in the vehicle Re-ID task. Recently, some fine-grained Re-ID algorithms have achieved superior performance by modeling the key areas with specific semantics such as windows, lights, car orientation, etc. However, such methods are labor-intensive to label the key areas for object detection. This work proposes a Cascaded Hierarchical Context-Aware scheme namely CHCA, which is free of fine-grained labeling, to adaptively extract the visual features of discriminative local areas based on surrounding hierarchical context information with a specially designed recursive cross-level attention mechanism. It does not require any additional supervision and is easy to be embedded in existing networks. Extensive experiments on three popular vehicle Re-ID benchmarks demonstrate the effectiveness of CHCA, which has competitive results with existing state-of-the-art methods applying fine-grained labels.
LiteGT

Transformers have shown great potential for modeling long-term dependencies for natural language processing and computer vision. However, little study has applied transformers to graphs, which is challenging due to the poor scalability of the attention mechanism and the under-exploration of graph inductive bias. To bridge this gap, we propose a Lite Graph Transformer (LiteGT) that learns on arbitrary graphs efficiently. First, a node sampling strategy is proposed to sparsify the considered nodes in self-attention with only O (Nlog N) time. Second, we devise two kernelization approaches to form two-branch attention blocks, which not only leverage graph-specific topology information, but also reduce computation further to O (1 over 2 Nlog N). Third, the nodes are updated with different attention schemes during training, thus largely mitigating over-smoothing problems when the model layers deepen. Extensive experiments demonstrate that LiteGT achieves competitive performance on both node classification and link prediction on datasets with millions of nodes. Specifically, Jaccard + Sampling + Dim. reducing setting reduces more than 100x computation and halves the model size without performance degradation.
An Effective Similarity Measure for Improving Performance of User Based Collaborative Filtering

Collaborating filtering (CF) has become one of the most powerful approaches in the recommender system. Neighborhood-based CF uses a similarity measure to identity neighbors of an active user, and these neighbors play an essential role in the personalized recommendation. Recently introduced new heuristic similarity measure (NHSM) based CF is found to be performing well compared to the CF approaches, which use traditional measures like Pearson correlation coefficient (PCC), proximity impact popularity (PIP), etc. However, NHSM is not appropriately normalized, and it may mislead in finding neighbors in specific scenarios. In this paper, we propose an improved NHSM similarity measure to excel in the recommendation by overcoming the shortfall of NHSM. We propose to utilize hyperbolic trigonometric function for the normalization of each component of NHSM. Relative difference (RD) is exploited to address the misleading problem of NSHM. Experimental results demonstrate that our improved NHSM (i-NHSM) based CF outperforms NHSM based CF.
Dynamics of field observables in quantum channels

Abstract We consider the time evolution of the mean values of the quadrature operators’ observables and the dynamics of the mean photon number variance for an arbitrary quantum state in a single mode transmitted through an optical fiber channel. Utilizing the density matrix formalism and the quantum optics theory we derive expressions for the dynamics of mentioned field observables for such prominent quantum states as squeezed vacuum state, squeezed coherent state and superposition of coherent states.
Blockchain during COVID-19: The Technology to Help Society

At the end of 2019, a new coronavirus was reported in the form of unknown pneumonia: It was the beginning of the SARS-CoV-2 pandemic. It is crucial to develop the strategies to manage this new pandemic in order to improve their effectiveness, as opposed to the ones adopted in 2003 during the SARS pandemic. In 17 years, new digital technologies and tools have been created; therefore, we can take advantage of them in the social management of the pandemic. The aim of our paper is to evaluate how blockchain technology can be used and what advantages it brings in managing an emergency situation such as of the COVID-19 pandemic. In particular, the effects of the adoption of this technology will be assessed when applied both to the management of the information flow between health infrastructures, and to track monetary, technical, and medical supply donations to hospital structures. We therefore propose a high-level, decentralized architecture that assists in administrating large-scale information and stores the gathered data in a blockchain supervised by the authorities. The distrust suffered by institutions today, the fear experienced in the last year due to the pandemic, and the birth of a technology that allows for the creation of reliable networks, pose thoughtful challenges to be faced to recover trust and hope for the future again.
All-optical Bell states through a multi-poled, integrated waveguide device

In this paper, we propose the generation of all four polarization-entangled Bell states in a potassium titanyl phosphate waveguide device. This is achieved through non-degenerate spontaneous parametric downconversion and quasi-phase-matching with special non-uniform poling in different lengths. These biphoton states consist of the signal being emitted in a telecommunication wavelength. Through their joint spectral amplitude, we study the frequency correlation properties of the photon pairs. Polarization-entangled photon pairs are used as a resource of entanglement in various quantum optics experiments and there is a demand for generating all four photonic Bell states in a single waveguide-based device in quantum information applications.
Research on Recognition Method of Basketball Goals Based on Image Analysis of Computer Vision

Moving target detection is involved in many engineering applications, but basketball has some difficulties because of the time-varying speed and uncertain path. The purpose of this paper is to use computer vision image analysis to identify the path and speed of a basketball goal, so as to meet the needs of recognition and achieve trajectory prediction. This research mainly discusses the basketball goal recognition method based on computer vision. In the research process, Kalman filter is used to improve the KCF tracking algorithm to track the basketball path. The algorithm of this research is based on MATLAB, so it can avoid the mixed programming of MATLAB and other languages and reduce the difficulty of interface design software. In the aspect of data acquisition, the extended EPROM is used to store user programs, and parallel interface chips (such as 8255A) can be configured in the system to output switch control signals and display and print operations. The automatic basketball bowling counter based on 8031 microprocessor is used as the host computer. After the level conversion by MAX232, it is connected with the RS232C serial port of PC, and the collected data is sent to the workstation recording the results. In order to consider the convenience of user operation, the GUI design of MATLAB is used to facilitate the exchange of information between users and computers so that users can see the competition results intuitively. The processing frame rate of the tested video image can reach 60 frames/second, more than 25 frames/second, which meet the real-time requirements of the system. The results show that the basketball goal recognition method used in this study has strong anti-interference ability and stable performance.
Fruit Ordering System through Fruity Healthy Mobile Application

Mobile Application is the software that is used for portable devices such as smartphones and tablet computers. It is compatible with Android, iOS, Harmony OS, and Windows phones. People nowadays prefer to order food via existing applications or platforms. Especially during movement control orders (MCO) of the year 2020 in Malaysia as the epidemic spreads globally. In this paper, the functionality of Fruity Healthy as fruit ordering application that allows customers to purchase fruits with only one click will be discussed. Users can shop from an online store rather than going to the physical store. Moreover, to assist farmers to expand their market sales, a fruit ordering application will help to promote their fruits through an online platform. The objectives of this project are to help farmers increase their sales market, to develop a mobile application that is used for ordering fruit and compare the scalability of the new application with the existing applications. Android Studio is the software that has been chosen to propose the application and will be written in the Java programming language. At the end of this project, users able to utilize the application for ordering and promoting fruit. It makes it easier for farmers to earn their expected salary by using the internet as a secondary platform. Customers’ information will be recorded in the database so that sellers can choose who to target and how to enhance sales in the future.
Probing Filters to Interpret CNN Semantic Configurations by Occlusion

Deep neural networks has been widely used in many fields, but there are growing concerns about its black-box nature. Previous interpretability studies provide four types of explanations including logical rules, revealing hidden semantics, sensitivity analysis, and providing examples as prototypes. In this paper, an interpretability method is proposed for revealing semantic representations at hidden layers of CNNs through lightweight annotation by occluding. First, visual semantic configurations are defined for a certain class. Then candidate filters whose activations are related to these specified visual semantics are probed by occluding. Finally, lightweight occlusion annotation and a scoring mechanism is used to screen out the filters that recognize these semantics. The method is applied to the datasets of mechanical equipment, animals and clothing images. The proposed method performs well in the experiments assessing interpretability qualitatively and quantitatively.
Online regularized pairwise learning with non-i.i.d. observations

In this paper, we consider the online regularized pairwise learning (ORPL) algorithm with least squares loss function for non-independently and identically distribution (non-i.i.d.) observations. We first establish new Bennett’s inequalities for [Formula: see text]-mixing sequence, geometrically [Formula: see text]-mixing sequence, [Formula: see text]-geometrically ergodic Markov chain and uniformly ergodic Markov chain. Then we establish the convergence rates for the last iterate of the ORPL algorithm with the polynomially decaying step sizes and varying regularization parameters for non-i.i.d. observations. These established results in this paper extend the previously known results of ORPL from i.i.d. observations to the case of non-i.i.d. observations, and the established result of ORPL for [Formula: see text]-mixing can be nearly optimal rate of ORPL for i.i.d. observations with [Formula: see text]-norm.
Vehicle Tire Text Reader: Text Spotting and Rectifying for Small, Curved, and Rotated Characters

Tire burst of vehicles is one of the main causes of highway accidents. Furthermore, tire aging is the most important cause of tire burst. It will be of great significance to traffic safety, through identifying the tire text code (TTC) to determine whether or not a tire is over the service life. In this paper, a coarse-to-fine method of detection and rectification for TTC is proposed. First, text regions are located by using deep convolutional neural network (DCNN) model. Second, through fitting the tire circle curve, close-up image of TTC is rotated to a positive direction. Thirdly, a text boundary points generation strategy is proposed, which is based on the rotating bounding box of each character. Fourth, the curved text is rectified and input to the DCNN recognition model. On the tire image dataset, the detection accuracy of TTC and characters is 94% and 96.5% respectively, and the recognition accuracy is 94.5%. Through additional experimental results, it is proved that the proposed method has strong robustness to illumination and camera shooting angle, which can meet the real-time detection and recognition requirements as well.
TV Remote Control and Older Adults: A Systematic Literature Review

The remote control is a device normally used in conjunction with the TV-set, being this the preferred via of interaction. However, users like older adults frequently have difficulties using this device, due to the decrease in fine motor skills such as coordination of movements, grabbing objects, and also to memory and visual impairments. In this context, the present study presents a review of articles from the last five years, making it possible to understand in detail the difficulties of older adults in using the TV remote control, as well as to understand what solutions were found to overcome these difficulties. This review also made possible to understand how the physical interface of the remote control is composed and what operations and functions it is normally used for. The paper ends with a discussion that lists the general findings, also allowing to answer the objective imposed in this study. From the findings of the review, it is possible to understand the specific characteristics of older adults that emerge with aging, as well as the potential to improve the interface of the TV remote controls according to these characteristics, from simplifying the device interface, the redesign of its buttons, until the implementation of different input modalities such as voice commands and gestures.