Reviewerly/exHarmony huggingface dataset:
The Farther the Better: Balanced Stereo Matching via Depth-Based Sampling and Adaptive Feature Refinement

Existing stereo matching methods achieve satisfactory average accuracy on a whole predicted disparity map under common global metrics, but ignore the fine-grained performance at region level, especially for far regions in the scene, which is more crucial in actual auto-driving scenarios. There are two factors accounting for this problem: 1) Depth resolution. Existing methods use disparity-based sampling to extract matching candidates uniformly according to the disparity range, but leads to sparser sampling density at far regions than that of close regions in terms of depth range, resulting in low depth resolution in far regions. 2) Feature discriminability. Limited image resolution and inferior feature extraction at far regions result in the obtained features with low discrimination, which influences the subsequent matching process between stereo images. To improve the estimation accuracy of far regions and thus achieve a balanced performance at region level, we design a novel two-stage Balanced Stereo Matching Network (BSMNet) to address the above problems. The coarse stage of BSMNet introduces a direct depth-based sampling strategy, which generates matching candidates according to scene depth instead of disparity, thus improving the depth resolution and obtaining initial depth map with more balanced accuracy. Then, a depth refinement stage is proposed to solve the problem of low feature discriminability and further optimizes the initial depth map obtained from the coarse stage. It selects matching candidates and computes their similarity scores from a carefully designed adaptive feature volume guided by a learnable scale map, thus making the final estimation more accurate. Different from the existing methods that construct stereo matching based on disparity prediction, our proposed pipeline is to directly optimize on depth information. Experiments show that our BSMNet can obtain an obvious performance improvement at far regions without discarding that at close regions, so as to largely outperform existing state-of-the-art methods.
Alignment-Free Video Compression Artifact Reduction

The past few years have witnessed the great success of using multi-frame information to enhance the quality of compressed video. Most existing methods do frame or feature alignments to bring similar information from neighborhood frames much closer to enhance frame quality. However, inaccurate motion estimation will bring new artifacts. In this paper, we propose a new approach without alignment, which takes each non-Peak Quality Frame (non-PQF) and its two adjacent Peak Quality Frames (PQFs) as input. A Pre-processing module based on multi-scale feature extraction strategy is used to broaden receptive field of the network. Then, an enhancement module uses a two-stream feature extraction architecture to combine deep architecture and attention mechanism to further gather similar information. In this module, the lost high-frequency and similar information can be further retrieved from the adjacent PQFs. The proposed network is trained in an end-to-end manner. Compared with the alignment based methods, the competitive results can be obtained. A large number of qualitative and quantitative experimental results demonstrate the robustness and effectiveness of the proposed method.
A survey on Optimisation-based Semi-supervised Clustering Methods

Clustering methods are developed for categorizing data points into different groups so that data points within each group have high similarities. Classic clustering algorithms are unsupervised, meaning that there is not any kind of complementary information to be utilized for attaining better clustering results. However, in some clustering problems, one may have supplementary information which can be employed for guiding the clustering process. In the presence of such information, the problem is semi-supervised clustering. In some articles, the problem of semi-supervised clustering is modeled as an optimization problem. In this research, optimization-based semi-supervised clustering papers from 2013 to 2020 are reviewed. This review is conducted based on a four-step procedure. It is attempted to explore objective functions and optimization algorithms used in these articles, as well as application domain and types of supervised information.
Detecting adversarial examples by positive and negative representations

Deep neural networks (DNNs) have been successfully applied in various fields. However, it has been demonstrated that a well-designed and quasi-imperceptible perturbation can confuse the targeted DNNs classifier with high confidence and lead to misclassification. Examples with such perturbations are called adversarial examples, and it is a challenging task to detect them. In this paper, we propose a positive–negative detector (PNDetector) to detect adversarial examples. The PNDetector is based on a positive–negative classifier (PNClassifier), which is trained by both the original examples (called positive representations) and their negative representations with the same structural and semantic features. The principle of the PNDetector is that the feature space of the positive and negative representations of adversarial examples under the PNClassifier has a high probability of belonging to different categories, while its performance on clean examples is not reduced by adding negative example representations into the train set. We test the PNDetector with adversarial examples generated by eight typical attack methods on four typical datasets. The experimental results demonstrate that the proposed detector is efficient in all datasets and under all attack types. Furthermore, its detection performance is comparable to that of state-of-the-art methods.
Neural Network Algorithm for English Teaching Evaluation

Aiming at the problem that the accuracy of English teaching quality evaluation is not high at present, this paper proposes a teaching quality evaluation method based on genetic algorithm (GA) and RBF neural network, GA is used to optimize the initial weights of RBF neural network. The experimental results show that the method can effectively evaluate the quality of English teaching, and has high accuracy and real-time performance.
Privacy-Preserving Human Action Recognition with a Many-Objective Evolutionary Algorithm

Wrist-worn devices equipped with accelerometers constitute a non-intrusive way to achieve active and assisted living (AAL) goals, such as automatic journaling for self-reflection, i.e., lifelogging, as well as to provide other services, such as general health and wellbeing monitoring, personal autonomy assessment, among others. Human action recognition (HAR), and in particular, the recognition of activities of daily living (ADLs), can be used for these types of assessment or journaling. In this paper, a many-objective evolutionary algorithm (MaOEA) is used in order to maximise action recognition from individuals while concealing (minimising recognition of) gender and age. To validate the proposed method, the PAAL accelerometer signal ADL dataset (v2.0) is used, which includes data from 52 participants (26 men and 26 women) and 24 activity class labels. The results show a drop in gender and age recognition to 58% (from 89%, a 31% drop), and to 39% (from 83%, a 44% drop), respectively; while action recognition stays closer to the initial value of 68% (from: 87%, i.e., 19% down).
RHPTree—Risk Hierarchical Pattern Tree for Scalable Long Pattern Mining

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems.
Ubiquitous learning a new challenge of ubiquitous computing: state of the art

New technological developments made interaction possible with computer systems and applications anywhere and anytime. These applications must be capable to be adapted to the user as a person and his current situation. In 2021, following the pandemic COVID-19, learners across the country have had the opportunity to experience online modes of learning. Therefore, in the current study, an attempt has been carried out to develop a new paradigm of learning, known as ubiquitous learning or U-learning (UL), which is supported by ubiquitous computing technologies. Ubiquitous learning has become an economic and scientific issue, especially with the COVID-19 pandemic. Furthermore, the paper also aims to provide fundamental information related to U-learning as the definition and characteristics of U-learning and the areas of the most common applications in our daily lives. Finally, U-learning applications in the different domains are explained and compared to further improve the understanding of the concept of U-learning and the importance of using UL in our lives.
An Adaptive Level-Based Learning Swarm Optimizer for Large-Scale Optimization

This paper proposes an adaptive version of an existing promising large-scale optimizer named level-based learning swarm optimizer (LLSO). Though such an optimizer has shown promising performance in dealing with large-scale optimization, it is much sensitive to its two introduced parameters. To alleviate this dilemma, this paper devises two simple yet effective adaptive adjustment strategies for the two parameters, leading to an adaptive LLSO(ALLSO). Specifically, this paper first defines a novel aggregation indicator based on the difference between the global best fitness and the averaged fitness of the swarm, to roughly evaluate the evolution state of the swarm. Then, based on this indicator, two adaptive adjustment strategies are devised to dynamically determine the values of the two parameters during the evolution. With these two strategies, the swarm is expected to maintain a potentially good balance between intensification and diversification. Extensive experiments conducted on two widely used large- scale benchmark sets demonstrate that the two adaptive strategies effectively improve the performance of LLSO.
TVS: a trusted verification scheme for office documents based on blockchain

Abstract To realize the encryption of document information, authority authentication, and traceability of historical records, we propose a trusted verification scheme (TVS) for office documents to ensure security. Specifically, the scheme is realized by timestamps, smart contracts (or chaincode), and other blockchain technologies. It is based on the features of blockchain, such as security, credibility, immutability, and traceability of network behavior. And the TVS stores users and documents information through blockchain; it can monitor the state changes of office documents in real time by setting the trigger conditions of smart contracts. The experiment indicates that we have realized the real-time monitoring of data and the traceability of historical records. Moreover, we have achieved the purpose of document encryption and authority authentication, ensuring the authenticity and objectivity of data, avoiding the illegal tampering of malicious users to realize the trusted verification for documents.
Keep it Simple: Handcrafting Feature and Tuning Random Forests and XGBoost to face the Affective Movement Recognition Challenge 2021

In this paper, we face the Affective Movement Recognition Challenge 2021 which is based on 3 naturalistic datasets on body movement, which is a fundamental component of everyday living both in the execution of the actions that make up physical functioning as well as in rich expression of affect, cognition, and intent. The datasets were built on deep understanding of the requirements of automatic detection technology for chronic pain physical rehabilitation, maths problem solving, and interactive dance contexts respectively. In particular, we will rely on a single, simple yet effective, approach able to be competitive with state-of-the-art results in the literature on all of the 3 datasets. Our approach is based on a two step procedure: first we will carefully handcraft features able to fully and synthetically represent the raw data and then we will apply Random Forest and XGBoost, carefully tuned with rigorous statistical procedures, on top of it to deliver the predictions. As requested by the challenge, we will report results in terms of three different metrics: accuracy, F1-score, and Matthew Correlation Coefficient.
Learn to overfit better: finding the important parameters for learned image compression

For most machine learning systems, overfitting is an undesired behavior. However, overfitting a model to a test image or a video at inference time is a favorable and effective technique to improve the coding efficiency of learning-based image and video codecs. At the encoding stage, one or more neural networks that are part of the codec are finetuned using the input image or video to achieve a better coding performance. The encoder en-codes the input content into a content bitstream. If the finetuned neural network is part (also) of the decoder, the encoder signals the weight update of the finetuned model to the decoder along with the content bitstream. At the decoding stage, the decoder first updates its neural network model according to the received weight update, and then proceeds with decoding the content bitstream. Since a neural network contains a large number of parameters, compressing the weight update is critical to reducing bitrate overhead. In this paper, we propose learning-based methods to find the important parameters to be overfitted, in terms of rate-distortion performance. Based on simple distribution models for variables in the weight update, we derive two objective functions. By optimizing the proposed objective functions, the importance scores of the parameters can be calculated and the important parameters can be determined. Our experiments on lossless image compression codec show that the proposed method significantly outperforms a prior-art method where overfitted parameters were selected based on heuristics. Furthermore, our technique improved the compression performance of the state-of-the-art lossless image compression codec by 0.1 bit per pixel.
Named Entity Recognition and Relation Extraction for COVID-19: Explainable Active Learning with Word2vec Embeddings and Transformer-Based BERT Models

Deep learning for natural language processing acquires dense vector representations for n-grams from large-scale unstructured corpora. Converting static embeddings of n-grams into a dataset of interlinked concepts with explicit contextual semantic dependencies provides the foundation to acquire reusable knowledge. However, the validation of this knowledge requires cross-checking with ground-truths that may be unavailable in an actionable or computable form. This paper presents a novel approach from the new field of explainable active learning that combines methods for learning static embeddings (word2vec models) with methods for learning dynamic contextual embeddings (transformer-based BERT models). We created a dataset for named entity recognition (NER) and relation extraction (REX) for the Coronavirus Disease 2019 (COVID-19). The COVID-19 dataset has 2,212 associations captured by 11 word2vec models with additional examples of use from the biomedical literature. We propose interpreting the NER and REX tasks for COVID-19 as Question Answering (QA) incorporating general medical knowledge within the question, e.g. "does 'cough' (n-gram) belong to 'clinical presentation/symptoms' for COVID-19?". We evaluated biomedical-specific pre-trained language models (BioBERT, SciBERT, ClinicalBERT, BlueBERT, and PubMedBERT) versus general-domain pre-trained language models (BERT, and RoBERTa) for transfer learning with COVID-19 dataset, i.e. task-specific fine-tuning considering NER as a sequence-level task. Using 2,060 QA for training (associations from 10 word2vec models) and 152 QA for validation (associations from 1 word2vec model), BERT obtained an F-measure of 87.38%, with precision = 93.75% and recall = 81.82%. SciBERT achieved the highest F-measure of 94.34%, with precision = 98.04% and recall = 90.91%.
SMP-Graph: Structure-Enhanced Unsupervised Semantic Graph Representation for Precise Medical Procedure Coding on EMRs

Automatic ICD coding, as a fundamental task in the field of healthcare management, has been paid much attention by researchers. However, the current deep learning-based ICD coding research mostly focus on the introduction of external diagnostic description text or the imposition of rules, while ignoring the structured features of the coding text itself. Especially for short texts of medical procedure codes, it is much more important to mine the information value in the texts. In this paper, we propose a structure-enhanced unsupervised semantic graph representation for precise medical procedure coding (SMP-Graph). The SMP-Graph representation method constructs each medical procedure text with an inductive heterogeneous graph and particularly enhances the kernel knowledge by extracting the axis words and chapter title and allocating them with distinct node representations. Both the nodes and edges are generated by the unsupervised pretrained model and then interact information in a bidirectionally weighted graph structure. Therefore, the SMP-Graph really realizes the intra-integration of unsupervised contextualized information and graph-based global information from the medical procedure code. Experiments conducted on the Chinese ICD-9-CM-3 procedure text dataset we collected from EMRs demonstrate that the SMP-Graph is a better representation method that outperforms other representative methods for medical procedure coding. Characteristic analysis is also conducted to prove the interpretability and adaptability of the SMP-Graph on the medical procedure coding task.
Text Encryption Using Pell Sequence and Elliptic Curves with Provable Security

The demand for data security schemes has increased with the significant advancement in the field of computation and communication networks. We propose a novel three-step text encryption scheme that has provable security against computation attacks such as key attack and statistical attack. The proposed scheme is based on the Pell sequence and elliptic curves, where at the first step the plain text is diffused to get a meaningless plain text by applying a cyclic shift on the symbol set. In the second step, we hide the elements of the diffused plain text from the attackers. For this purpose, we use the Pell sequence, a weight function, and a binary sequence to encode each element of the diffused plain text into real numbers. The encoded diffused plain text is then confused by generating permutations over elliptic curves in the third step. We show that the proposed scheme has provable security against key sensitivity attack and statistical attacks. Furthermore, the proposed scheme is secure against key spacing attack, ciphertext only attack, and known-plaintext attack. Compared to some of the existing text encryption schemes, the proposed scheme is highly secure against modern cryptanalysis.
Driver Distracted Behavior Detection Technology with YOLO-Based Deep Learning Networks

In order to develop a non-contact driving behavior detection system for the improvement of driving safety, in this study, the YOLO-based deep learning technology is utilized by setting up a webcam on the dashboard to detect the driver's behaviors. By RGB-channel images as inputs, the YOLO-based deep learning models, including YOLOv3-tiny, YOLOv3-tiny-3l, YOLO-fastest, YOLO-fastest-xl are adopted and trained as the candidate detectors. The detected behaviors involve normal driving, distracted head turning, drowsiness, eating, talking on the phone, etc. The experimental results show that when the same parameters are set, the YOLO-fastest-xl has the best performance with multi-category datasets, and its F1-score, false negative rate (FNR), and mAP are 91.84%, 6.94%, and 95.81%, respectively. By the software implementation, the proposed design performs 30 frames per second (FPS) on the NVIDIA GPU-based embedded platform.
Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Local discriminative regions play important roles in fine-grained image analysis tasks. How to locate local discriminative regions with only category label and learn discriminative representation from these regions have been hot spots. In our work, we propose Searching Discriminative Regions (SDR) and Learning Discriminative Regions (LDR) method to search and learn local discriminative regions in images. The SDR method adopts attention mechanism to iteratively search for high-response regions in images, and uses this as a clue to locate local discriminative regions. Moreover, the LDR method is proposed to learn compact within category and sparse between categories representation from the raw image and local images. Experimental results show that our proposed approach achieves excellent performance in both fine-grained image retrieval and classification tasks, which demonstrates its effectiveness.