Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Healthcare and Medicine Data Sharing and Service System

As the remarkable development of blockchain technology, it attracts a considerable attentions and has a significant impact on various scientific domains, such as healthcare and medicine industry. However, in real-world scenarios of healthcare and medicine applications, data sharing and service are confronted with some challenges because the data has the characteristics of multi-source, heterogeneity, large-scale, etc. Moreover, security and management issues during the stages of data extraction, storage, transfer, access, etc., should be taken into account for healthcare and medicine data sharing and service. To provide a more flexible, reliable, and convenient service for healthcare and medicine, this paper proposes a data sharing and service system, termed HMChain, developed on blockchain technology. This system consists of three layers referring to a data extraction and storage layer for multi-source data integration and distributed data storage, a data management layer for data secure transfer and access, and a data application layer for various user-oriented services. Furthermore, several healthcare and medicine data sharing and service scenarios have been depicted in detail. Overall, this system can provide convenient services for healthcare management, clinical research, medicine traceability, neuroscience research, etc.
Good Morning Chatbot, Do I Have Any Meetings Today? Investigating Trust in AI Chatbots in a Digital Workplace

The adoption and implementation of Conversational AI applications such as AI chatbots in the workplace is rapidly growing. The success of integrating AI chatbots into organisations critically depends on employees’ use since AI technology evolves through machine learning and analysis of use data. Since trust is a key aspect that determines technology continuous use, this research questions: How employees experience trust in Conversational AI and how it impacts its continuous use? To answer the research question, we conducted qualitative inductive research using rich empirical data from a large international organisation. The findings highlight that employees developed three forms of trust, namely, emotional, cognitive and organisational trust. The theoretical and practical implications of the findings are discussed.
Bibrecord-Based Literature Management with Interactive Latent Space Learning

Every researcher must conduct a literature review, and the document management needs of researchers working on various research topics vary. However, there are two significant challenges today. First, traditional methods like the tree hierarchy of document folders and tag-based management are no longer effective with the enormous volume of publications. Second, although their bib information is available to everyone, many papers can be accessed only through paid services. This study attempts to develop an interactive tool for personal literature management solely based on their bibliographic records. To make such a tool possible, we developed a principled "human-in-the-loop latent space learning" method that estimates the management criteria of each researcher based on his or her feedback to calculate the positions of documents in a two-dimensional space on the screen. Since a set of bibliographic records forms a graph, our model is naturally designed as a graph-based encoder-decoder model that connects the graph and the space. The experiments with ten researchers from humanities, science, and engineering domains show that the proposed framework gives much superior results to a typical graph convolutional encoder-decoder model.
ICPR 2022 Challenge on Multi-Modal Subtitle Recognition

Video subtitle recognition, as one of the basic elements of video editing, has received increasing attention recently. However, the misaligned between audio and subtitles as well as the costly manual annotation remain a demanding issue toward subsequent intelligent processing. In this paper, we introduce a multi-modal subtitle recognition challenge for ICPR 2022, in which we present a large-scale video dataset (215 hours in total for visual and audio annotations), and 3 tracks including: 1) extracting subtitles in visual modality with audio annotation (ESV); 2) extracting subtitles in audio modality with visual annotations (ESA); and 3) extracting subtitles with both visual and audio annotation (ESVA). The challenge attracts 376 participants, among which the methods of top 3 teams on each track have been elaborated.
Weapon operating pose detection and suspicious human activity classification using skeleton graphs

Spurt upsurge in violent protest and armed conflict in populous, civil areas has upstretched momentous concern worldwide. The unrelenting strategy of the law enforcement agencies focuses on thwarting the conspicuous impact of violent events. Increased surveillance using a widespread visual network supports the state actors in maintaining vigilance. Minute, simultaneous monitoring of numerous surveillance feeds is a workforce-intensive, idiosyncratic, and otiose method. Significant advancements in Machine Learning (ML) show potential in realizing precise models to detect suspicious activities in the mob. Existing pose estimation techniques have privations in detecting weapon operation activity. The paper proposes a comprehensive, customized human activity recognition approach using human body skeleton graphs. The VGG-19 backbone extracted 6600 body coordinates from the customized dataset. The methodology categorizes human activities into eight classes experienced during violent clashes. It facilitates alarm triggers in a specific activity, i.e., stone pelting or weapon handling while walking, standing, and kneeling is considered a regular activity. The end-to-end pipeline presents a robust model for multiple human tracking, mapping a skeleton graph for each person in consecutive surveillance video frames with the improved categorization of suspicious human activities, realizing effective crowd management. LSTM-RNN Network, trained on a customized dataset superimposed with Kalman filter, attained 89.09% accuracy for real-time pose identification.
Continual Learning with Adversarial Training to Enhance Robustness of Image Recognition Models

In recent years, deep learning has been widely adopted in many image recognition tasks with great success. However, this method is vulnerable against well-designed attacks, causing serious safety and security problems in life- critical applications. To overcome the issue of continuously evolving attacks, in this work, we develop a new defensive approach that integrates continual learning and adversarial training to improve both corruption robustness and structure compactness of the defensive model. Our approach adopts the structure of progressive neural model to establish a robust model over time. It includes adaptive phases of model growing and pruning in continual learning, and performs adversarial training iteratively during the learning process. To evaluate the performance of the proposed approach, we conduct a series of experiments to compare our approach with others in defending the current well-known adversarial attacks. The results that our model can obtain best performance and provide an effective approach in cybersecurity.
DNNCloak: Secure DNN Models Against Memory Side-channel Based Reverse Engineering Attacks

As deep neural networks (DNN) expand their attention into various domains and the high cost of training a model, the structure of a DNN model has become a valuable intellectual property and needs to be protected. However, reversing DNN models by exploiting side-channel leakage has been demonstrated in various ways. Even if the model is encrypted and the processing hardware units are trusted, the attacker can still extract the model’s structure and critical parameters through side channels, potentially posing significant commercial risks. In this paper, we begin by analyzing representative memory side-channel attacks on DNN models and identifying the primary causes of leakage. We also find that the full encryption used to protect model parameters could add extensive overhead. Based on our observations, we propose DNNCloak, a lightweight and secure framework aiming at mitigating reverse engineering attacks on common DNN architectures. DNNCloak includes a set of obfuscation schemes that increase the difficulty of reverse-engineering the DNN structure. Additionally, DNNCloak reduces the overhead of full weights encryption with an efficient matrix permutation scheme, resulting in reduced memory access time and enhanced security against retraining attacks on the model parameters. At last, we show how DNNCloak can defend DNN models from side-channel attacks effectively, with minimal performance overhead.
Behavior Control of Cooperative Vehicle Infrastructure System in Container Terminals Based on Q-learning

Aiming at the problem of low intelligence of horizontal transport vehicles in container terminal, the research on cooperative vehicle infrastructure system is carried out combined with the characteristics of closure and low-speed for container terminals. The model for the behavior control and optimization of unmanned vehicles is designed based on Q-Learning, and the cooperative vehicle infrastructure system of container terminal is developed. A verification experiment is designed. Through sensor information recognition and behavior control training, the accuracy rate of driving behavior control of the system is more than 90%, which can effectively command vehicles, ensure traffic safety and improve traffic efficiency.
Oscar: A Semantic-based Data Binning Approach

Binning is applied to categorize data values or to see distributions of data. Existing binning algorithms often rely on statistical properties of data. However, there are semantic considerations for selecting appropriate binning schemes. Surveys, for instance, gather respon-dent data for demographic-related questions such as age, salary, number of employees, etc., that are bucketed into defined semantic categories. In this paper, we leverage common semantic categories from survey data and Tableau Public visualizations to identify a set of semantic binning categories. We employ these semantic binning categories in Oscar: a method for automatically selecting bins based on the inferred semantic type of the field. We conducted a crowdsourced study with 120 participants to better understand user preferences for bins generated by Oscar vs. binning provided in Tableau. We find that maps and histograms using binned values generated by Oscar are preferred by users as compared to binning schemes based purely on the statistical properties of the data.
Unsupervised Unpaired Super-Resolution Using an Active Sampling Strategy Based on Edge Detection

Most existing super-resolution (SR) methods rely on pairs of low resolution (LR) and high resolution (HR) images and predetermined degradation operations (e.g., bicubic), usually trained by supervised learning. However, they often fail in real-world scenarios because of the occurrence of noise and blur. The key reason is that the degradation process is unknown, and no HR-LR pairs can be obtained directly. To address the above issues, this paper explores the optimization of an unsupervised unpaired SR method inspired by generative models such as Generative Adversarial Networks (GAN) and Cycle-Consistent Adversarial Networks (CycleGAN). We propose an active sampling strategy based on edge detection, and introduce a denoising network to construct a novel unsupervised unpaired SR framework. The active sampling strategy can perform image-level feature alignment by sampling image patches actively, thus optimizing the learning direction of the generator. At the same time, the denoising network can reduce the learning difficulty of the generator by preprocessing real-world LR images. Extensive experiments indicate that our method obtains better performance over other existing solutions to the unsupervised unpaired SR challenge,
Researching basic searchable encryption schemes in databases that support SQL

Currently, many users prefer to outsource data to third-party cloud servers in order to mitigate the load of local storage. However, storing sensitive data on remote servers creates security challenges and is a source of concern for data owners. With ever-growing security and privacy concerns, it is becoming increasingly important to encrypt data stored remotely. However, the use of traditional encryption prevents the search operation in the encrypted data. One approach to solving this problem is searchable encryption. Solutions for search in secure databases cover a wide range of cryptographic techniques, although there is still no dominant solution. Designing secure search systems is a balance between security, functionality, performance, and usability. Therefore, this paper provides an overview of some of the important current secure search solutions. The main searchable encryption systems of databases that support SQL are considered. The strengths and weaknesses of the analyzed systems and the techniques implemented in them are highlighted. A comparative analysis of some characteristics of the compared systems is given. Attention is drawn to the fact that the ability to perform search operations in encrypted data leads to a complication of systems, an increase in the amount of required memory and query execution time. All this indicates the openness of the protected search problem and the need for further research in this direction to ensure secure work with remote databases and data warehouses.
Learning Real-Time Dynamic Responsive Gap-Traversing Policy for Quadrotors With Safety-Aware Exploration

In this paper, we propose a dynamic responsive policy for a quadrotor to traverse through narrow gaps based on deep reinforcement learning (RL). The policy provides agile and dynamically adjusted gap-traversing actions for the quadrotor in real-time by directly mapping the observed states of the quadrotor and gaps to motor thrusts. In contrast to existing optimization-based methods, our RL-based policy couples trajectory planning and control modules, which are strictly independent and computationally complex in previous works. Moreover, a safety-aware exploration is presented to reduce the collision risk and improve the safety of the policy, which also facilitates the transfer of the policy to real-world environments. Specifically, we formulate a safety reward on the state spaces of both position and orientation to inspire the quadrotor to traverse through the narrow gap at an appropriate attitude closer to the center of the gap. With the learned gap-traversing policy, we implement extensive simulations and real-world experiments to evaluate its performance and compare it with the related approaches. Our implementation includes several gap-traversing tasks with random positions and orientations, even if never trained specially before. All the performances indicate that our RL-based gap-traversing policy is transferable and more efficient in terms of real-time dynamic response, agility, time-consuming, and generalization.
The Lossless Compression Algorithm of Hyperspectral Aerospace Images With Correlation and Bands Grouping

The existing solutions of the problem of lossless compression for hyperspectral aerospace images are analyzed. Based on them, a compression algorithm is proposed taking into account interbands correlation, which is characterized by data transformation with a decrease in the range of values of the initial values by forming a set of groups of channels with a high intra-group correlation of the corresponding pairs with the selection of optimal parameters. The results of comparative experiments of the proposed compression algorithm on AVIRIS remote sensing system data are presented in comparison with the most common archivers Winrar, 7Z, JPEG Lossless with varying compression ratio, image geometric size, number of channels, number of channel groups. At the same time, their parameters are found that allow obtaining the best results in the degree of compression.
Self-adaptive Multi-scale Aggregation Network for Stereo Matching

Nowadays stereo matching architectures based on convolutional neural network has achieved remarkable performance. However the existing methods still lack the capability to find the correspondence in ill-posed regions. In this paper we present Self-adaptive Multi-scale Aggregation Network (SMA-Net) for stereo matching. First of all, we construct the cost volume through multi-channels group-wise correlation, which divided the features into groups with different number of channels to enhance the ability of measuring the similarities of features from stereo images. Secondly the self-adaptive cost aggregation is used to regularize the two scale cost volumes from different aggregation branches with intermediate supervise. We conduct comprehensive experiments on SceneFlow, KITTI2012, and KITTI2015 datasets. The competitive results prove that the approach in this paper outperforms many other stereo matching algorithms especially in ill-posed regions.
Design and simulation of a bio-inspired neural network for the motor control of a mobile automaton

In this work we propose a complex biologically inspired decision-making neural network in order to trigger motor behaviors in a mobile robotic automaton. Such behaviors are fleeing, exploring, and waiting and are chosen after balancing appetitive/desirable stimuli versus aversive/undesirable stimuli present at the input, from the environment in which the automaton is located. In addition, it is sought that the automaton obtains historical learning from the decisions that it has previously made.
UOHTD: Urdu Offline Handwritten Text Dataset

We present our Offline Urdu Handwritten Text Dataset (UOHTD) in this paper by collecting 800 Urdu handwritten samples written by 800 native language writers. It consists of images in the form of a dataset containing written text samples scanned with multiple spatial resolutions. 8000 text lines and 40000 words as patches have been extracted from sample pages and checked manually and formally using a ground truth database. Machine Learning Tools have been utilized to extract sample pages and segment them into lines and words. Initial trials on demographic (gender and age group) classification of Urdu writers with samples of Offline Urdu Handwritten Text Dataset (UOHTD) has produced promising results (85% for gender and 79% for age group classification) using CNNs. The database would be made available to the researcher worldwide for study into various handwritten-related topics including text recognition, identification of the writer's age, ethnicity, demographics, gender, and handedness, as well as verification.
Provable Security of $$\textrm{HADES}$$ Structure

The $$\textrm{HADES}$$ design strategy combines the classical $$\textrm{SPN}$$ construction with the Partial $$\textrm{SPN}$$ ( $$\textrm{PSPN}$$ ) construction, in which at every encryption round, the non-linear layer is applied to only a part of the state. In a $$\textrm{HADES}$$ design, a middle layer that consists of $$\textrm{PSPN}$$ rounds is surrounded by outer layers of $$\textrm{SPN}$$ rounds. The security arguments of $$\textrm{HADES}$$ with respect to statistical attacks usually use only the $$\textrm{SPN}$$ rounds, disregarding the $$\textrm{PSPN}$$ rounds. There are few results about provable security of $$\textrm{HADES}$$ and the research on $$\textrm{HADES}$$ focuses on its designs (the $$\textrm{MDS}$$ matrix) and attacks (differential and linear). So in this paper, we show that the four-round $$\textrm{HADES}$$ with the middle two rounds that consist of partial $$S\text {-boxes}$$ surrounded by outer two rounds of $$\textrm{SPN}$$ , on condition that the linear layer has to be $$\textrm{MDS}$$ matrix, with independent $$S\text {-boxes}$$ and independent round keys, is birthday bound security.
LMS: Benchmarking ATutor, Moodle and Docebo

The society in which we live is in constant technological evolution. Technologies are conquering the market, the combination of content delivery and communication allow the creation of interactive processes. Each new generation grows with the support of devices that provide a constant flow of information wherever you are. Didactic has also suffered the effects of this revolution through the development of systems dedicated to e-learning. This study aimed to investigate a specific area of e-learning, the technological platforms that allow the creation and management of a distance learning path. In fact, it is possible to find a wide range on the market, but it is not easy to choose the one that best suit your need. For this reason, we focused on the evaluation methods of three of the most popular platforms, to analyze in detail the learning management tools.
Deep Models for Mispronounce Prediction for Vietnamese Learners of English

Second language learners’ correct and exact pronunciation is one of the important factors that help improve their own communication skills. Therefore, a system for predicting mispronunciation or assessing pronunciation accuracy for second language learners has been proposed and studied for decades. However, the results obtained are still very limited. In this paper, we present two popular deep learning models including Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to solve the problem of predicting incorrect pronunciation for Vietnamese learners of English. This has great significance in building systems to help Vietnamese people during their English acquisition, specifically to improve their correct pronunciation of English. The experiment results on the L2-ARCTIC dataset have shown that both models achieve state-of-the-art performance. In addition, we also found that the LSTM model outperforms the CNN model by 6.3% in terms of accuracy due to the memory mechanism at each unit. The source code of our approach can be found at https://github.com/vdquang1991/Mispronounce_Prediction .
Joint Few-Shot Text Classification Aided by Label Semantic and Sentence-Aware Interaction

Meta-learning has achieved remarkable performance to address few-shot text classification. However, previous studies fail to attach importance to the label semantic completely and overlook the implicit interaction between lexical features of sentences. In this paper, we explore three enhancement components of the meta-learner aided by the label semantic and sentence-aware interaction, e.g., the label-augmented encoder, the interaction extractor, and the label semantic discriminator. Significantly, these modules are agnostic to the choice of few-shot text classification methods and can be easily incorporated into various existing meta-learning frameworks to improve the classification performance and adaptive ability of the model. We conduct extensive experiments on five benchmark datasets. The results demonstrate that meta-learning approaches upgraded by the above three enhancement components obtain considerable superiority over state-of-the-art models in all datasets. In particular, the average accuracy of 1-shot classification and 5-shot classification is increased by 3.60% and 2.28 %, respectively.