Reviewerly/exHarmony huggingface dataset:
Lightweight Privacy-preserving Medical Diagnosis in Edge Computing

In the era of machine learning, mobile users are able to submit their symptoms to doctors at any time, anywhere for personal diagnosis. It is prevalent to exploit edge computing for real-time diagnosis services in order to reduce transmission latency. Although data-driven machine learning is powerful, it inevitably compromises privacy by relying on vast amounts of medical data to build a diagnostic model. Therefore, it is necessary to protect data privacy without accessing local data. However, the blossom has also been accompanied by various problems, i.e., the limitation of training data, vulnerabilities, and privacy concern. As a solution to these above challenges, in this paper, we design a lightweight privacy-preserving medical diagnosis mechanism on edge. Our method redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to reduce amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edges. Additionally, the proposed scheme is able to provide a secure diagnosis on edge while maintaining privacy to ensure an accurate and timely diagnosis. The proposed system with secure computation could securely construct the XGBoost model with lightweight overhead, and efficiently provide a medical diagnosis without privacy leakage. Our security analysis and experimental evaluation indicate the security, effectiveness, and efficiency of the proposed system.
Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Although convolutional neural network based stereo matching architectures have made impressive achievements, there are still some limitations: 1) Convolutional Feature (CF) tends to capture appearance information, which is inadequate for accurate matching. 2) Due to the static filters, current convolution based disparity refinement modules often produce over-smooth results. In this paper, we present two schemes to address these issues, where some traditional wisdoms are integrated. Firstly, we introduce a pairwise feature for deep stereo matching networks, named LSP (Local Similarity Pattern). Through explicitly revealing the neighbor relationships, LSP contains rich structural information, which can be leveraged to aid CF for more discriminative feature description. Secondly, we design a dynamic self-reassembling refinement strategy and apply it to the cost distribution and the disparity map respectively. The former could be equipped with the unimodal distribution constraint to alleviate the over-smoothing problem, and the latter is more practical. The effectiveness of the proposed methods is demonstrated via incorporating them into two well-known basic architectures, GwcNet and GANet-deep. Experimental results on the SceneFlow and KITTI benchmarks show that our modules significantly improve the performance of the model. Code is available at https://github.com/SpadeLiu/Lac-GwcNet.
Thirty Years in Silicon Photonics: A Personal View

Silicon Photonics, the technology where optical devices are fabricated by the mainstream microelectronic processing technology, was proposed almost 30 years ago. I joined this research field at its start. Initially, I concentrated on the main issue of the lack of a silicon laser. Room temperature visible emission from porous silicon first, and from silicon nanocrystals then, showed that optical gain is possible in low-dimensional silicon, but it is severely counterbalanced by nonlinear losses due to free carriers. Then, most of my research focus was on systems where photons show novel features such as Zener tunneling or Anderson localization. Here, the game was to engineer suitable dielectric environments (e.g., one-dimensional photonic crystals or waveguide-based microring resonators) to control photon propagation. Applications of low-dimensional silicon raised up in sensing (e.g., gas-sensing or bio-sensing) and photovoltaics. Interestingly, microring resonators emerged as the fundamental device for integrated photonic circuit since they allow studying the hermitian and non-hermitian physics of light propagation as well as demonstrating on-chip heavily integrated optical networks for reconfigurable switching applications or neural networks for optical signal processing. Finally, I witnessed the emergence of quantum photonic devices, where linear and nonlinear optical effects generate quantum states of light. Here, quantum random number generators or heralded single-photon sources are enabled by silicon photonics. All these developments are discussed in this review by following my own research path.
PLUMENet: Efficient 3D Object Detection from Stereo Images

3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accurate 3D estimates, methods that exploit stereo cameras have recently shown promising results at a lower cost. Existing approaches tackle this problem in two steps: first depth estimation from stereo images is performed to produce a pseudo LiDAR point cloud, which is then used as input to a 3D object detector. However, this approach is suboptimal due to the representation mismatch, as the two tasks are optimized in two different metric spaces. In this paper we propose a model that unifies these two tasks and performs them in the same metric space. Specifically, we directly construct a pseudo LiDAR feature volume (PLUME) in 3D space, which is then used to solve both depth estimation and object detection tasks. Our approach achieves state-of-the-art performance with much faster inference times when compared to existing methods on the challenging KITTI benchmark [1].
Towards Sustainable Use of Machine Translation: Usability and Perceived Quality from the End-User Perspective

Artificial intelligence-grounded machine translation has fundamentally changed public awareness and attitudes towards multilingual communication. In some language pairs, the accuracy, quality and efficiency of machine-translated texts of certain types can be quite high. Hence, the end-user acceptability and reliance on machine-translated content could be justified. However, machine translation in small and/or low-resource languages might yield significantly lower quality, which in turn may lead to potentially negative consequences and risks if machine translation is used in high-risk contexts without awareness of the drawbacks, critical assessment and modifications to the raw output. The current study, which is part of a more extensive project focusing on the societal impact of machine translation, is aimed at revealing the attitudes towards usability and quality as perceived from the end-user perspective. The research questions addressed revolve around the machine translation types used, purposes of using machine translation, perceived quality of the generated output, and actions taken to improve the quality by users with various backgrounds. The research findings rely on a survey of the population (N = 402) conducted in 2021 in Lithuania. The study reveals the frequent use of machine translation for a diversity of purposes. The most common uses include work, research and studies, and household environments. A higher level of education correlates with user dissatisfaction with the generated quality and actions taken to improve it. The findings also reveal that age correlates with the use of machine translation. Sustainable measures to reduce machine translation related risks have to be established based on the perceptions of different social groups in different societies and cultures.
A Hyperledger Fabric-Based System Framework for Healthcare Data Management

This study examined the requirements for privacy-preserving and interoperability in healthcare data sharing and proposed a blockchain-based solution. The Hyperledger Fabric framework was adopted due to its enterprise-grade data processing capabilities and enhanced privacy protection functions. In addition to the Fabric’s built-in privacy-preserving functions, healthcare data-specific smart contracts with hierarchical access control were developed to strengthen privacy protection in data sharing. The proposed healthcare data-sharing framework is based on Australian medical practices with the aim to upgrade, rather than to replace, the existing data management models. The outcome of this study demonstrates the feasibility of applying blockchain technology to improve privacy-preservation while enhancing interoperability in healthcare data management.
Impact of Artificial Intelligence on HR practices in the UAE

Abstract Social trends and information technology are creating pressure, resulting in organizations being forced to update and recreate themselves. Against this backdrop there is an increasing trend to adopt artificial intelligence technologies. This study aims to explore and investigate the effect of Artificial Intelligence (AI) on Human Resources (HR) practices in UAE companies with the help of mixed-method design. A mixed-method design was applied to explore and test the research questions. Semi-structured interviews and survey were conducted, respectively for both the study designs. AI and HR personnel were approached as sample participants for this study. Thematic analysis and PLS-SEM (Partial least squares path modeling) were used to analyze the data, respectively. Positive and significant effects of planned training and development process (0.231→0.021), tactical performance appraisal and integration of AI (0.719→0.000), and integration of AI and efficient HR practices (0.204→0.131) were shown. The moderating role of ease of use on integration of AI and efficient HR practices was negative and insignificant. AI plays an essential role in shifting the HR functions to enter the digitization era. The types of businesses and abilities require some activities such as training and development due to the increased use of technology.
A Novel Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for Bird Monitoring and Repelling

Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A novel path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this paper. This path planning optimization algorithm aims to manage the drone’s distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm’s performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The field tests were also essential to understand the algorithm’s behavior of the path planning algorithm in the UAV, showing that there is less efficiency with fewer points of interest, but this does not correlate with the flight time. In addition, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted. Thus, the proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time, being suited for this and other applications.
Adaptive Poincaré Point to Set Distance for Few-Shot Classification

Learning and generalizing from limited examples, i.e., few-shot learning, is of core importance to many real-world vision applications. A principal way of achieving few-shot learning is to realize an embedding where samples from different classes are distinctive. Recent studies suggest that embedding via hyperbolic geometry enjoys low distortion for hierarchical and structured data, making it suitable for few-shot learning. In this paper, we propose to learn a context-aware hyperbolic metric to characterize the distance between a point and a set associated with a learned set to set distance. To this end, we formulate the metric as a weighted sum on the tangent bundle of the hyperbolic space and develop a mechanism to obtain the weights adaptively, based on the constellation of the points. This not only makes the metric local but also dependent on the task in hand, meaning that the metric will adapt depending on the samples that it compares. We empirically show that such metric yields robustness in the presence of outliers and achieves a tangible improvement over baseline models. This includes the state-of-the-art results on five popular few-shot classification benchmarks, namely mini-ImageNet, tiered-ImageNet, Caltech-UCSD Birds-200-2011(CUB), CIFAR-FS, and FC100.
Reliable Propagation-Correction Modulation for Video Object Segmentation

Error propagation is a general but crucial problem in online semi-supervised video object segmentation. We aim to suppress error propagation through a correction mechanism with high reliability. The key insight is to disentangle the correction from the conventional mask propagation process with reliable cues. We introduce two modulators, propagation and correction modulators, to separately perform channel-wise recalibration on the target frame embeddings according to local temporal correlations and reliable references respectively. Specifically, we assemble the modulators with a cascaded propagation-correction scheme. This avoids overriding the effects of the reliable correction modulator by the propagation modulator. Although the reference frame with the ground truth label provides reliable cues, it could be very different from the target frame and introduce uncertain or incomplete correlations. We augment the reference cues by supplementing reliable feature patches to a maintained pool, thus offering more comprehensive and expressive object representations to the modulators. In addition, a reliability filter is designed to retrieve reliable patches and pass them in subsequent frames. Our model achieves state-of-the-art performance on YouTube-VOS18, YouTube-VOS19 and DAVIS17-Val/Test benchmarks. Extensive experiments demonstrate that the correction mechanism provides considerable performance gain by fully utilizing reliable guidance.
SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

Makeup transfer is not only to extract the makeup style of the reference image, but also to render the makeup style to the semantic corresponding position of the target image. However, most existing methods focus on the former and ignore the latter, resulting in a failure to achieve desired results. To solve the above problems, we propose a unified Symmetric Semantic-Aware Transformer (SSAT) network, which incorporates semantic correspondence learning to realize makeup transfer and removal simultaneously. In SSAT, a novel Symmetric Semantic Corresponding Feature Transfer (SSCFT) module and a weakly supervised semantic loss are proposed to model and facilitate the establishment of accurate semantic correspondence. In the generation process, the extracted makeup features are spatially distorted by SSCFT to achieve semantic alignment with the target image, then the distorted makeup features are combined with unmodified makeup irrelevant features to produce the final result. Experiments show that our method obtains more visually accurate makeup transfer results, and user study in comparison with other state-of-the-art makeup transfer methods reflects the superiority of our method. Besides, we verify the robustness of the proposed method in the difference of expression and pose, object occlusion scenes, and extend it to video makeup transfer.
Privacy-Preserving Threshold-Based Image Retrieval in Cloud-Assisted Internet of Things

Encrypted image retrieval is a promising technique for achieving data confidentiality and searchability the in cloud-assisted Internet of Things (IoT) environment. However, most of the existing top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> ranked image retrieval solutions have low retrieval efficiency and may leak the values and orders of similarity scores to the cloud server. Hence, if a malicious server learns user background information through some improper means, then the malicious server can potentially infer user preferences and guess the most similar image content according to similarity scores. To solve the above challenges, we propose a privacy-preserving threshold-based image retrieval scheme using the convolutional neural network (CNN) model and a secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor (kNN) algorithm, which improves the retrieval efficiency and prevents the cloud server from learning the values and orders of similarity scores. Formal security analysis shows that our proposed scheme can resist both ciphertext-only attack (COA) and chosen-plaintext attack (CPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible for real-world data sets.
Experimental evaluation of autonomous map-based Spot navigation in confined environments

In this article, we address the task of experimental evaluation of autonomous map-based navigation of a Boston Dynamics Spot quadruped robot in confined environments equipped with the developed autonomy package that incorporates a 3D lidar, an IMU, and an onboard computer. For that, we propose an integrated software and hardware system that is considered as an enabler for robot localization and risk-aware path planning, based on the known map. The system design itself is modular and incorporates the perception capabilities required for autonomous navigation. The Spot robot first is utilized to build the offline map of the environment by utilizing the Google Cartographer simultaneous localization and mapping (SLAM) package. During the next step, the online environmental information from the autonomy package sensors and the offline map are provided to the onboard computer to localize the robot on the known map by utilizing means provided by Cartographer. Finally, the occupancy information is provided to the online grid-based path planner that generates risk-aware paths. The extensive experimental evaluation of the proposed system is performed in corridors and SubT environments.
Deep Neural Networks Using Residual Fast-Slow Refined Highway and Global Atomic Spatial Attention for Action Recognition and Detection

In this work, we propose two Deep Neural Networks, DNN-1 and DNN-2, based on Fast-Slow Refined Highway (FSRH) and Global Atomic Spatial Attention (GASA) to effectively recognize and detect actions. The proposed DNN-1 includes a 3D Convolutional Neural Network (3DCNN), Residual FSRH (R_FSRH), reduction layer, and classification layer for action recognition. In action detection of subject-region extraction and classification, the proposed DNN-2 consists of a 3DCNN, region proposal network, R_FSRH, GASA, and classification-localization layer. The 3DCNN takes the front layer to the “Mixed-3c” layer of the pre-trained Inflated 3D (I3D) network as the backbone structure. FSRH is composed of two Refined Highway (RH) units to extract a pair of features from fast and slow actions, where RH has temporal attention from a non-local 3D convolution and an affine transform by a temporal bilinear inception. In R_FSRH, multiple cascaded FSRHs with different residual connections were investigated to determine an effective one. GASA sequentially computes and concatenates the correlation features of an atomic subject and other subjects to effectively discover high-level semantic information. In ablation studies, extensive experiments were conducted to demonstrate the superior performance of the proposed DNN-1 and DNN-2 on five challenging video datasets of JHMDB-21, UCF101-24, traffic police, Charades, and AVA. Notably, the proposed DNN-1 shows state-of-the-art performance on UCF101-24 and TP, and DNN-2 exhibits state-of-the-art performance on AVA, and the second best on Charades, to the best of our knowledge. Therefore, the DNN-1 and DNN-2 proposed herein can be outstanding context-aware engines for various video understanding applications.
To be Closer: Learning to Link up Aspects with Opinions

Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA) (Huang and Carley, 2019).However, the trees obtained from offthe-shelf dependency parsers are static, and could be sub-optimal in ABSA.This is because the syntactic trees are not designed for capturing the interactions between opinion words and aspect words.In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure.The aspect and opinion words are expected to be closer along such tree structure compared to the standard dependency parse tree.The learning process allows the tree structure to adaptively correlate the aspect and opinion words, enabling us to better identify the polarity in the ABSA task.We conduct experiments on five aspectbased sentiment datasets, and the proposed model significantly outperforms recent strong baselines.Furthermore, our thorough analysis demonstrates the average distance between aspect and opinion words are shortened by at least 19% on the standard SemEval Restau-rant14 (Pontiki et al., 2014) dataset 1 .