Reviewerly/exHarmony huggingface dataset:
Efficient Public Verification of Confidential Supply-Chain Transactions

Ensuring sustainable sourcing of crude materials and production of goods is a pressing problem in consideration of the growing world population and rapid climate change. Supply-chain traceability systems based on distributed ledgers can help to enforce sustainability policies like production limits. We propose two mutually independent distributed-ledger-based protocols that enable public verifiability of policy compliance. They are designed for different supply-chain scenarios and use different privacy-enhancing technologies in order to protect confidential supply-chain data: secret sharing and homomorphic encryption. The protocols can be added to existing supply-chain traceability solutions with minor effort. They ensure confidentiality of transaction details and offer public verifiability of producers' compliance, enabling institutions and even end consumers to evaluate sustainability of supply chains. Through extensive theoretical and empirical evaluation, we show that both protocols perform verification for lifelike supply-chain scenarios in perfectly practical time.
Factorization Machine-based Unsupervised Model Selection Method

Machine learning is broadly used in many intelligent cybernetic systems. With the burgeoning of the communities of AI, the number of machine learning-based models is rapidly increasing, but picking a suitable and optimal (or relatively good) model from overwhelming options has become a conundrum when deploying a new system. Therefore, we are motivated by an intriguing question: Can we automatically select a proper model for new data? However, unsupervised model selection poses two main challenges: (i) Evaluation and comparison of candidate models on the new data are infeasible due to the lack of labels; and (ii) It is non-trivial to build relationships between model performance and data characteristics when the interaction between these characteristics should be considered. In light of these limitations, this paper proposes a factorization machine-based unsupervised model selection method. Following mainstream model selection protocols, we also leverage model performance on prior known datasets. Differently, we learn higher-order complex relationships between model performance and dataset characteristics. Specifically, our method transfers the historical performance into a second-order function of meta-features and embedding weights by harnessing the power of factorization machine. This function can be subsequently used to select a proper model when given a new dataset. Extensive experiments show that our method obtains more superior model selection performance than five state-of-the-art approaches, and our method executes faster than its competitors by approximate three magnitudes.
Image Saliency Prediction in Novel Production Scenarios

Predicting image saliency has many potential useful applications across several industries, including film production, creative marketing content, product design, and quality control in manufacturing. Although image saliency prediction, in general, has made substantial progress with the advent of deep learning on benchmark datasets, it still has room to improve when applied to novel scenarios. This paper presents our experimental findings in dataset building and model development for predicting saliency of cinematic images in a real-world context. To evaluate the proposed model, we conducted experiments on both benchmark datasets (i.e., SALICON, MIT1003, MIT300, CAT2000) as well as a private dataset. The results demonstrate the superior performance of our proposed method. We highlight a film production use case, but the model and methods explored here may also generalize to other areas relevant to image saliency.
Document-Level Joint Biomedical Event Extraction Model Using Hypergraph Convolutional Networks

Biomedical event extraction is a fundamental information extraction task, which aims to identify biomedical event triggers and parameters in the text. Although various deep learning and Graph Convolutional Network (GCN) models have been proposed for this task, these models are insufficient to acquire enough local and global context information of documents. To effectively extract joint local and global context information, we propose a joint biomedical event extraction model named BGHGCN, which consists of Bi-directional Long Short-Term Memory (BiLSTM), improved BiAffine Graph Parser (IBGP), GCN and hypergraph convolutional networks (HGCN). Our model employs BiLSTM to learn word sequence features and uses improved BiAffine Graph Parser to enrich dependent syntax features. Afterwards we use GCN to extract local features from IBGP and BiLSTM. Specifically, we introduce HGCN to jointly extract local and global context information with a new fusion mechanism of local feature and incidence matrix, which can effectively extract structural features of hypergraph including node and hyperedge features. Finally, we evaluated our model on two biomedical event datasets MLEE and GE to compare with other baseline models.
Vietnamese Text Detection, Recognition and Classification in Images

Detecting and recognizing text in images is a task that has received a lot of attention recently due to its high applicability in many fields such as digitization, storage, lookup, authentication However, most current research works and products are focusing on detecting and extracting text from images but not paying very much attention to analyzing and exploiting semantics and nuances of those extracted texts. In this study, we propose a three-in-one system to detect, recognize and classify Vietnamese text in images collected from social media to help authorities in monitoring tasks. The system receives as input images containing Vietnamese text, uses the Character-Region Awareness For Text detection (CRAFT) model to perform background processing to produce areas containing text in the image; these text containers will then be rearranged in the same order as in the original image, and the text in the image will also be extracted out according to the text container. Next, we use VietOCR model to convert these text images into text fragments. Finally, these texts will be classified using an ensemble of machine learning models. Preliminary results show that the proposed model has an accuracy of up to 88.0% in detecting and recognizing text and 94% in classifying text nuances on the collected data set.
Learning Control with Evolution Strategy for Inhomogeneous Open Quantum Ensembles

This paper investigates the application of an evolutionary algorithm, evolution strategy (ES)($\mu+\lambda$) to the control design in several inhomogeneous open quantum ensembles. We apply the ES ($\mu+\lambda$) to assist the sampling-based learning control (SLC) technique, by which a set of control signals is designed to drive the inhomogeneous open quantum ensemble to a given target state. We illustrate our algorithm in two-level and four-level inhomogeneous open quantum ensembles. Numerical results show the effectiveness of the proposed control algorithm. The comparison with other evolutionary algorithms such as differential evolution (DE) and genetic algorithm (GA) shows the superiority of our ES ($\mu+\lambda$) both in average fidelity and stability. In a four-level open quantum ensemble, for example, the fitness error after optimization using the ES ($\mu+\lambda$) is decreased by around 59% compared to DE, and the standard deviation is lowered by about 47%.
A Cell Diagnosis Support System with Providing the Reason for Prediction

In this paper we propose a cellular diagnostic support system for cervical cancer from WSI which presents the reason for diagnosis. First, cytoplasmic and nuclear regions are extracted from cell images using U-2-Net. For the extracted regions, 29 image features were calculated, and machine learning is applied to perform cell classification based on the Bethesda system. The results showed an accuracy of 68.5% in the 6-class classification of cervical cancer.
Project Features That Make Machine-Learning Based Fault Proneness Analysis Successful

Over the past years, we have witnessed the extensive use of various software fault proneness prediction techniques utilizing machine learning. These techniques use data from multiple sources representing various facets of the software systems being investigated. In spite of the complexity and performance of all such techniques and approaches proposed by the research community, we cannot yet expertly reason on the features which may render a software system a good or bad candidate for their application. In this paper, we build on the corpus of established machine learning approaches, and we perform an evaluation of system-wide process metrics versus the results acquired by the indiscriminate application of a published best set of classifiers. More specifically, we analyze the fault proneness prediction results obtained by applying a combination of the best classifiers and file features to 207 open source projects in order to identify which project features make a system suitable for Machine Learning based fault proneness analysis or not. Based on this analysis, we propose a meta-evaluator of the overall nature of a system that can be used to gauge in advance the performance that can be expected when applying the selected technique in terms of the key performance measures namely: Accuracy, Fl-measure, Precision, Recall and ROC-AUC.
Supporting Law-Enforcement to Cope with Blacklisted Websites: Framework and Case Study

Cyber attackers have long abused web domains and URLs to carry out various attacks such as Phishing, web scamming, and malware attacks. In order to defend against these attacks, URL blacklisting has been widely used. However, this approach has significant weaknesses, especially from a law-enforcement point of view. In particular, the law-enforcement does not know what to do with a blacklist because it is unclear what needs to be done (e.g., shutting down a host or domain) due to the subtleties associated with the problem. In order to help the law-enforcement in dealing with blacklisted URLs, we propose a novel framework based on Machine Learning (ML) while providing the law-enforcement with probabilistic classification and interpretability of the predictions made by the interpretable model. Our probabilistic classification and interpretability measures provide a basis for law-enforcement trustworthy decision-making and remove the black-box nature of traditional ML-based approaches. Experimental results show that the framework is practical and has further potential to tackle website maliciousness.
Agent-Level Differentially Private Federated Learning via Compressed Model Perturbation

Federated learning (FL) involves a network of distributed agents that collaboratively learn a common model without sharing their raw data. Privacy and communication are two critical concerns of FL, but they are often treated separately in the literature. While random noise can be added during the FL process to defend against privacy inference attacks, its magnitude is linearly proportional to the model size, which can be very large for modern deep neural networks and lead to severe degradation in model accuracy. On the other hand, various compression techniques have been proposed to improve the communication efficiency of federated learning, but their interplay with privacy protection is largely ignored. Motivated by the observation that privacy protection and communication reduction are closely related in the context of FL, we propose a new federated learning scheme called CMP-Fed that achieves agent-level differential privacy with high model accuracy by leveraging the communication compression techniques in FL with large model sizes. The key component of CMP-Fed is compressed model perturbation (CMP), which first compresses the shared model updates before perturbing them with random noise at each communication round of federated learning. Experimental results based on Fashion-MNIST dataset show that CMP-Fed can largely outperform the existing differentially private federated learning schemes in terms of model accuracy under the same privacy guarantee while still enjoying the communication benefit of model compression.
LG-Hand: Advancing 3D Hand Pose Estimation with Locally and Globally Kinematic Knowledge

3D hand pose estimation from RGB images suffers from the difficulty of obtaining the depth information. Therefore, a great deal of attention has been spent on estimating 3D hand pose from 2D hand joints. In this paper, we leverage the advantage of spatial-temporal Graph Convolutional Neural Networks and propose LG-Hand, a powerful method for 3D hand pose estimation. Our method incorporates both spatial and temporal dependencies into a single process. We argue that kinematic information plays an important role, contributing to the performance of 3D hand pose estimation. We thereby introduce two new objective functions, Angle and Direction loss, to take the hand structure into account. While Angle loss covers locally kinematic information, Direction loss handles globally kinematic one. Our LG-Hand achieves promising results on the First-Person Hand Action Benchmark (FPHAB) dataset. We also perform an ablation study to show the efficacy of the two proposed objective functions.
English-Vietnamese Cross-lingual Semantic Textual Similarity using Sentence Transformer model

Cross-lingual Semantic Textual Similarity (STS) is a challenging problem in Natural Language Understanding tasks, especially for low-resource languages like Vietnamese. Currently, one of the state-of-the-art approaches for this problem is to use distilled multilingual Sentence Transformer model. However, there are few studies on how these models work for English-Vietnamese language pairs. In this paper, we aim to inspect the performance of these models in the English-Vietnamese STS tasks. From our findings, we will propose possible improvements for this approach in the future.
Improved rank discrepancy-aware sampling based on bidirectional distillation for top-K recommender system

Nowadays, the model compression method of knowledge distillation has drawn great attentions in Recommender systems (RS). The strategy of bidirectional distillation performs the bidirectional learning for both the teacher and the student models such that these two models can collaboratively improve with each other. However, this strategy cannot effectively exploit representation capabilities of each item and lack of the interpretability for the importance of items. Thus, how to develop an effective sampling scheme is still valuable for us to further study and explore. In this paper, we propose an improved rank discrepancy-aware item sampling strategy to enhance the performance of bidirectional distillation learning. Specifically, by employing the distillation loss, we train the teacher and student models to reflect the fact that a user has partiality for the unobserved items. Then, we propose the improved rank discrepancy-aware sampling strategy based on feedback learning mechanism to transfer just the useful information which can effectively enhance each other. The key part of the multiple distillation training aims to select valuable items which can be re-distilled in the network for training. The proposed technique can effectively solve the problem of high ambiguity in nature for recommender system. Experimental results on several real-world recommender system datasets well demonstrate that the improved bidirectional distillation strategy shows better performance.
Adaptive Weights and Sample’s Distribution for Few Shot Classification

In recent years, few-shot classification algorithms have been developing. But many few-shot classification algorithms are facing their bottlenecks. After our study and research of the Prototypical Network, we found that the prototype calculation method and loss function are relatively simple, which can be improved better. We present our few-shot classification algorithm in this paper, which is inspired by Prototypical Network and center loss function. Based on the Prototypical Network, we propose our Adaptive Weights Model(AWM) to give each sample a better weight parameter, so that we can get a more reasonable prototype. Based on this model, bad samples(Contain a lot of noise) will get a smaller weight, while good samples(Contain very little noise) will get a larger weight. Based on the center loss function, we propose our Adaptive Sample’s Distribution Model(ASDM), which enables us to optimize the distribution of samples. Then, we did a lot of experiments based on our model. The results show that the our model is effective. Few-shot learning is becoming more and more important in machine learning. However, most few-shot learning algorithms only rely on deep neural networks to process samples. Here, we offer a different idea to give more reasonable weights to the samples.
Learning Hierarchical Traversability Representations for Efficient Multi-Resolution Path Planning

Path planning on grid-based obstacle maps is an essential and much-studied problem with applications in robotics and autonomy. Traditionally, in the AI community, heuristic search methods (e.g., based on Dijkstra, $\text{A}^{*}$, or random trees) are used to solve this problem. This search, however, incurs a high computational cost that grows with the size and resolution of the obstacle grid and has to be mitigated with effective heuristics to allow path planning in real-time. This work introduces a learning framework using a deep neural network with a stackable convolution kernel to establish a hierarchy of directional traversability representations with decreasing resolution that can serve as an efficient heuristic to guide a multi-resolution path planner. This path planner finds paths efficiently, starting on the lowest resolution traversability representation and then refining the path incrementally through the hierarchy until it addresses the original obstacle constraints. We demonstrate the benefits and applicability of this approach on datasets of maps created to represent both indoor and outdoor environments to represent different real-world applications. The conducted experiments show that our method can accelerate path planning by 40% in indoor environments and 65% in outdoor environments compared to the same heuristic search method applied to the original obstacle map, which demonstrates the effectiveness of this method.
Pattern Spotting and Image Retrieval in Historical Documents using Deep Hashing

This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents. First, a region proposal algorithm detects object candidates in the document page images. Next, deep learning models are used for feature extraction, considering two distinct variants, which provide either real-valued or binary code representations. Finally, candidate images are ranked by computing the feature similarity with a given input query. A robust experimental protocol evaluates the proposed approach considering each representation scheme (real-valued and binary code) on the DocExplore image database. The experimental results show that the proposed deep models compare favorably to the state-of-the-art image retrieval approaches for images of historical documents, outperforming other deep models by 2.56 percentage points using the same techniques for pattern spotting. Besides, the proposed approach also reduces the search time up to 200$\times$, and the storage cost up to 6,000$\times$ when compared to related works based on real-valued representations.
Knowledge-based Problem Solving and Reasoning methods

Intelligent Problem Solver (IPS) is an intelligent system for solving practical problems in the determined domain by using human knowledge. Thus, designing of the knowledge base and the inference engine of IPS systems are important. This study proposed a general model for knowledge representation by using a kernel ontology combining other knowledge components, called Integ-Ontology Based on this model, the model of problems is presented. The reasoning method is also proposed. This method includes the inference processing and techniques of heuristic rules, sample problems and pattern to speed up the problem solving. The Integ-Ontology and its reasoning method are applied to design practical IPS in solid geometry and Direct Current (DC) Electrical Circuits.
Towards An Accurate and Effective Printed Document Reader for Visually Impaired People

This paper introduces a solution to assist visually impaired or blind (VIB) people in independently accessing printed and electronic documents. The highlight of the solution is the cost-effectiveness and accuracy. Extracting texts and reading out to users are performed by a pure smartphone application. To be usable by VIB people, advanced technologies in image and speech processing are leveraged to enhance the user experience and accuracy in converting images to texts. To build accurate optical character recognition (OCR) models with low-quality images, we combine different solutions includings 1) generating a large and balanced dataset with various backgrounds, 2) correcting the distortion and direction, and 3) applying the sequence to sequence model with transformers as the encoder. For ease of use, the text to speech (TTS) model generates voice instructions at every interaction, and the interface is designed and adjusted according to user feedback. A test on a scanned document set has showed the high accuracy of the OCR model with 98,6% by characters, and the fluency of the TTS model. As being indicated in a trial with VIB people, our application can help them read printed documents conveniently, and it is an affordable solution since the popularity of smartphones.
HM-MDS: A Human-machine Collaboration based Online Medical Diagnosis System

Online medical diagnosis refers to diagnosing diseases and providing treatment suggestions on the websites. It develops rapidly and has become a new choice for patients to seek medical treatment. Although manual online medical diagnosis is reliable, it has problems such as low efficiency, heavy burden on doctors, and long waiting time for patients. Relying on machines for automatic disease diagnosis is highly efficient, which, however, has low accuracy and reliability. In general, online medical diagnosis usually has two stages: inquiry and diagnosis. Inquiry stage refers to asking about the patient’s physiological, where the questions are usually streamlined, and thus can be handled by the machine. Diagnosis stage is to diagnose the disease and provide medical recommendations, which has strict requirements for accuracy and safety, and thus should be handled by the human. Inspired by this, in the paper we propose a human-machine collaboration based online medical diagnosis system, i.e., HM-MDS. In inquiry stage, the system employs the machine. It uses the BERT+CRF to identify symptoms in the patient’s dialogue and uses a DQN-based method to ask about symptoms. In diagnosis stage, the system employs both the machine and the human. The machine generates a pre-diagnosis result by calculating disease probability. Then the human doctor gives the final diagnosis result by checking the pre-diagnosis result and revising it if necessary. Obviously, HM-MDS can effectively save human doctor’s time as well as patient’s time, while ensure the accuracy of the diagnosis result. We conduct experiments on a real-world dataset. The results show our approach improves the online medical diagnosis’s reliability as well as patient satisfaction, and ensures diagnosis accuracy. The time cost for a reliable medical diagnosis is reduced to 36% compared with pure manual work.