Reviewerly/exHarmony huggingface dataset:
Face Mask Detection System for Smart Door

The World Health Organization declared a new Coronavirus infection (COVID-19) to be a hazardous virus that might easily spread among humans after a rapid outbreak. When it comes to precautions, it's a must to wear a face mask when going outside and meeting new people. Few people, on the other hand, refuse to wear or improperly wear a face mask for a variety of reasons. In this instance, establishing a face mask detector is critical. Using advanced deep learning techniques in particular Mnet and Res-Net, a smart door with an embedded camera is developed that will open if the person in front of the camera is wearing his or her mask appropriately. Using this notion, a variety of Organizations and Institutions might keep track of those who are not wearing their masks or are wearing them wrongly and alert the administration, allowing them to take appropriate action. During the COVID-19 epidemic, when the government has mandated the use of face masks, this approach will be extremely beneficial.
A Study On Meta Learning Optimization Techniques

In the last decade, the domain of Artificial Intelligence (AI) has undergone rapid advancement and has achieved ability to mimic near-human intelligence in multiple aspects. Deep Neural Networks (DNNs), when trained with large datasets and optimal computational resources can achieve great outcomes, but the learning process is exhaustively time consuming. Along with this there still exists several challenges with respect to computational resources constraints, scarcity of data related to specific task and so on. In various situations, where the data is scarce, the algorithms are incapable of learning tasks quickly and accurately similar to that human intelligence. Meanwhile, the problem has been addressed with advancements in deep meta-learning in multiple researches. Meta-Learning has wide range of applications where in the insights from meta-data of the either data, tasks or the previously trained models can be utilised to optimise the learning process. To get an in-depth overview of available meta-learning techniques for DNN model optimization, we have carried-out a survey of optimization-based meta-learning techniques, their advantages, drawbacks and open challenges.
Autonomous AI Chat Bot Therapy for Patient with Insomnia

This paper’s endeavor is concentrated primarily aiding insomniacs. The importance of accomplishing the work is critical since insomnia is a common sleep condition that stops people from sleeping and therefore is common in today’s society. Because of its chronic nature, insomnia is connected to a considerable deterioration in an individual’s quality of life. The system is powered via Artificial Intelligence and Deep Learning. The goal of this system is to increase the number of encounters with these individuals as they get more sad and anxious. So, essentially, a friendly Chat Bot is created to better understand and treat insomniacs. The algorithm, how it works, and why it has to be implemented are briefly discussed in the paper.
ReLis: Let The Book Talk [Digital Library]

The scientific community benefits from digital libraries since they serve as a repository for essential research data, information, and conclusions. Physical records of scientific studies and research have faced a major challenge for a long time: they were destroyed or lost. However, owing to digital libraries, online copies of studies and research may now be stored and gathered, resulting in a virtual information heritage for future generations. Libraries are used to store books, but we need a system to easily navigate through a particular book. Moreover, all book lovers can't visit the library and buy or rent books, so the system being created aims to design and create a smart E-Library system that helps to solve this issue. ReLis is a smart e-library system that is eco-friendly and secure, taking into account the sensitivity of user information and digital data. Web security is vital for business progression and protecting information, users, and businesses from hazards, and the suggested system attempts to adopt preventive measures to secure user data from threats. Keeping in mind the current situation and growth of online learning, this system and its features will prove to be very beneficial to the users.
Radio Frequency Transmitter Identification Based on Fingerprinting and Convolutional Neural Network

The radio frequency (RF) transmitter identification has a wide application prospect in both military and public communications. The traditional RF transmitter identification of technique is mainly based on expert experience, which shows the shortcomings of low recognition accuracy and weak generalization ability. With the fast development in computer vision, deep learning attracts a lot of attention in recent years and is believed to be a promising scheme in RF transmitter identification. In this paper, the RF transmitter identification is studied based on the RF impairment features extracted from the original data. As a typical deep learning scheme, Convolutional Neural Network (CNN) is adopted to train a classifier to distinguish the RF transmitters. The experiment results show that with the proposed classifier, the same-waveform LoRA signals from different transmitters can be identified with very high accuracy.
A Survey: Complex Knowledge Base Question Answering

Knowledge base question answering(KBQA) is a technique that utilizes the rich semantic information in the knowledge base and fully understands the question to obtain the answer. At present, scholars put more energy into solving complex relationship problems. This paper first outlines the background and core challenges of complex KBQA. Second, two mainstream complex KBQA methods are introduced, namely, semantic parsing (SP-based) and information retrieval (IR-based) methods. Finally, future research trends are analyzed.
Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study

The widespread secondary use of electronic medical records (EMRs) promotes health care quality improvement. Representation learning that can automatically extract hidden information from EMR data has gained increasing attention.We aimed to propose a patient representation with more feature associations and task-specific feature importance to improve the outcome prediction performance for inpatients with acute myocardial infarction (AMI).Medical concepts, including patients' age, gender, disease diagnoses, laboratory tests, structured radiological features, procedures, and medications, were first embedded into real-value vectors using the improved skip-gram algorithm, where concepts in the context windows were selected by feature association strengths measured by association rule confidence. Then, each patient was represented as the sum of the feature embeddings weighted by the task-specific feature importance, which was applied to facilitate predictive model prediction from global and local perspectives. We finally applied the proposed patient representation into mortality risk prediction for 3010 and 1671 AMI inpatients from a public data set and a private data set, respectively, and compared it with several reference representation methods in terms of the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1-score.Compared with the reference methods, the proposed embedding-based representation showed consistently superior predictive performance on the 2 data sets, achieving mean AUROCs of 0.878 and 0.973, AUPRCs of 0.220 and 0.505, and F1-scores of 0.376 and 0.674 for the public and private data sets, respectively, while the greatest AUROCs, AUPRCs, and F1-scores among the reference methods were 0.847 and 0.939, 0.196 and 0.283, and 0.344 and 0.361 for the public and private data sets, respectively. Feature importance integrated in patient representation reflected features that were also critical in prediction tasks and clinical practice.The introduction of feature associations and feature importance facilitated an effective patient representation and contributed to prediction performance improvement and model interpretation.
Fast Intra Mode Decision Algorithm of HEVC Based on Convolutional Neural Network

Aiming at the high complexity and large amount of computation of HEVC intra mode decision algorithm, a fast intra mode decision algorithm based on convolutional neural network is proposed. Firstly, different size of prediction units are scaled to the same size through bilinear interpolation, and then sent to convolution neural network to perform convolution pooling and other operations. Finally, the mode decision results are selected by softmax layer. Five standard test sequences with different resolutions are used to form data sets, and the convolutional neural network model is trained and tested. Experimental results show that the proposed algorithm is equivalent to the standard test software HM16.0 in image quality and video bit rate, but the total coding time is improved by about 20.14&#x0025;.
Enhanced Simple Question Answering with Contrastive Learning

Answer natural language questions on knowledge bases ( $$\mathsf {KBQA}$$ ) has attracted wide attention. Several techniques have been developed for answering simple questions. These techniques mostly rely on deep networks to perform classification for relation prediction. Nowadays, contrastive learning has shown its powers in improving performances of classification, while most prior techniques do not gain benefit from this. In light of these, we propose a novel approach to answering simple questions on knowledge bases. Our approach has two key features. (1) It leverages pre-trained transformers to gain better performance on entity linking. (2) It employs a contrastive learning based model for relation prediction. We experimentally verify the performance of our approach, and show that our approach achieves an accuracy of 83.54%, which beats existing state-of-the-art techniques, on a typical benchmark dataset; we also conduct a deep analysis to show advantages of our technique, especially its sub-modules.
Configurable Image Rectification and Disparity Refinement for Stereo Vision

Stereo matching is an essential topic in computer vision. However, the high complexity and massive computation restrict its applicability in real-time. This brief presents a pixel-level pipeline coprocessor for configurable binocular stereo rectification, region-optimized semi-global matching (SGM), left-right consistency checking (LRC), and disparity refinement. The contributions are summarized as follows: 1) Instead of storing the entire image of a large number of lines, the buffer size can be significantly reduced by the adjustment range computed according to the pre-calibrated coefficients. 2) An approximate divider with Newton iterations can reach the precision of 10−7 for high accuracy rectification with low latency. 3) A four-layer parallel two-stage pipeline is leveraged to address the computational bottleneck of the SGM, i.e., cost aggregation. 4) The pseudo-dual-port SRAMs and stacks mechanism solve the problem of inverse pixel flow in hole filling with low resource consumption. Finally, the proposed architecture is implemented on an Intel Stratix-IV FPGA and can process 144 frames ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\mathbf {\times }768$ </tex-math></inline-formula> -pixel) per second at a maximum working frequency of 113.13 MHz.
Rethinking Textual Adversarial Defense for Pre-Trained Language Models

Although pre-trained language models (PrLMs) have achieved significant success, recent studies demonstrate that PrLMs are vulnerable to adversarial attacks. By generating adversarial examples with slight perturbations on different levels (sentence / word / character), adversarial attacks can fool PrLMs to generate incorrect predictions, which questions the robustness of PrLMs. However, we find that most existing textual adversarial examples are unnatural, which can be easily distinguished by both human and machine. Based on a general anomaly detector, we propose a novel metric (Degree of Anomaly) as a constraint to enable current adversarial attack approaches to generate more natural and imperceptible adversarial examples. Under this new constraint, the success rate of existing attacks drastically decreases, which reveals that the robustness of PrLMs is not as fragile as they claimed. In addition, we find that four types of randomization can invalidate a large portion of textual adversarial examples. Based on anomaly detector and randomization, we design a universal defense framework, which is among the first to perform textual adversarial defense without knowing the specific attack. Empirical results show that our universal defense framework achieves comparable or even higher after-attack accuracy with other specific defenses, while preserving higher original accuracy at the same time. Our work discloses the essence of textual adversarial attacks, and indicates that (i) further works of adversarial attacks should focus more on how to overcome the detection and resist the randomization, otherwise their adversarial examples would be easily detected and invalidated; and (ii) compared with the unnatural and perceptible adversarial examples, it is those undetectable adversarial examples that pose real risks for PrLMs and require more attention for future robustness-enhancing strategies.
Adaptive Cloud Bundle Provisioning and Multi-Workflow Scheduling via Coalition Reinforcement Learning

The efficient cloud resource provisioning for the execution of complex workflow applications has always been one of the important research issues. Most of the existing approaches focus on the resource provisioning of single-type virtual machine (VM) instances for the single or multiple workflows, while few consider the situation of provisioning multi-type VM instances simultaneously. As a result, the executing performance of complex workflows degrades. Different from the existing work, this paper proposes an adaptive cloud bundle provisioning and multi-workflow scheduling model to dynamically perform both the horizontal and vertical cloud resource scaling on multi-type VM instances for the execution of complex workflows. Among the model, a depth-first-search coalition reinforcement learning (DFSCRL) provisioning policy is presented to realize the resource scaling, which integrates the physical machine (PM) coalition formation with the Q-learning algorithm, then dynamically generates an optimal multi-type VM instance bundle from the PM coalition, and finally provisions these instances to the concurrent execution of multiple workflows. The theoretical proofs and various experiments with the multifaceted metrics demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art relevant policies.
Improving Dialogue Generation with Commonsense Knowledge Fusion and Selection

Knowledge-aware dialogue generation aims to generate informative and meaningful responses with external knowledge. Existing works are still insufficient to encode retrieved knowledge regardless of the dialogue context, which probably leads to the introduction of irrelevant information. In this paper, we propose a dialogue generation model named CKFS-DG, which filters out context-irrelevant and off-topic knowledge to reduce the influence of redundant knowledge. Specifically, we design a knowledge-enriched encoder and a topic fact predictor to improve the quality of fusion knowledge. For achieve the knowledge-enriched encoder, we put forward a context-knowledge attention mechanism to dynamically filter out irrelevant knowledge conditioned on context. For the topic fact predictor, we utilize the probability distribution on retrieved facts to retain on-topic knowledge. The experimental results on English Reddit and Chinese Weibo dataset demonstrate that CKFS-DG outperforms the state-of-the-art neural generative methods in knowledge utilization, and CKFS-DG could reduce the influence of irrelevant knowledge to generate reasonable responses.
Adversarial Machine Learning in e-Health: Attacking a Smart Prescription System

Machine learning (ML) algorithms are the basis of many services we rely on in our everyday life. For this reason, a new research line has recently emerged with the aim of investigating how ML can be misled by adversarial examples. In this paper we address an e-health scenario in which an automatic system for prescriptions can be deceived by inputs forged to subvert the model's prediction. In particular, we present an algorithm capable of generating a precise sequence of moves that the adversary has to take in order to elude the automatic prescription service. Experimental analyses performed on a real dataset of patients' clinical records show that a minimal alteration of the clinical records can subvert predictions with high probability.
A New K-Multiple-Means Clustering Method

In the field of clustering, non-spherical data clustering is a relatively complex case. To satisfy the practical application, the solution should be able to capture non-convex patterns in data sets with high performance. At present, the multi-prototype method can meet the former requirement, but the time cost is still high. This paper proposes a new multi-prototype extension of the K-multiple-means type algorithm, which aims to further reduce the computation time in processing non-spherical data sets with a concise principle while maintaining close performance. Compared with other methods, the method still adopts the idea of multiple prototypes and uses agglomerative strategies in the phase of class cluster connection. However, to reduce the amount of data involved in the computation and the interference of incorrect partition, the subclass data of the first partition is filtered. In addition, the agglomeration is divided into two stages: the agglomeration between prototypes and the agglomeration between clusters, and two agglomeration modes are provided to deal with different clustering tasks. Before updating the means, the filtered data needs a quadratic partition. Experimental results show that compared with the state-of-the-art approaches, the proposed method is still effective with lower time complexity in both synthetic and real-world data sets.
Aliasing-Free Nonlinear Signal Processing Using Implicitly Defined Functions

Digital signal processing relies on the Nyquist-Shannon sampling theorem that applies to and requires a continuous signal with a limited bandwidth.However, many systems or networks of signal processing involve nonlinear functions, which could generate new frequency components beyond the original bandwidth and lead to aliasing.Indeed, aliasing-induced shift variance has long been a nuisance and unsolved problem in convolutional neural networks, and recently been found to severely impair the performance of machine learning applications.The same problem exists in other fields such as computational lithography.In this paper, a new method and algorithms are introduced to solve the problem of aliasing induced by nonlinear functions involving operations other than linear convolutions and pointwise multiplications.Said new method and algorithms employ implicitly defined functions that are implemented via iterations of polynomial operations, so that aliasing is completely avoided by upsampling signals before polynomial operations and limiting signal spectra before downsampling.Theoretical analyses and exemplary algorithms are presented to implement nonlinear functions commonly used in signal processing networks.In particular, exemplary embodiments and numerical experiments are reported to illustrate and verify aliasing-free operations of Wiener-Padé approximants, which are already universal in their ability to approximate any continuous activation functions to a desired accuracy.
Dynamic Embedding Graph Attention Networks for Temporal Knowledge Graph Completion

Most link prediction algorithms are designed for static knowledge graphs (KGs). Notably, KGs usually evolve with time. Currently, researchers pay more attention to temporal knowledge graph completion (TKGC). However, existing methods are insufficient to utilize the complex structure between entities and their neighbor nodes, and modeling of entities is not comprehensive enough. Therefore, we propose DEGAT (Dynamic Embedding Graph Attention Networks), an attention-based TKGC method. Specifically, we use a generalized graph attention network as an encoder to aggregate the features of neighbor nodes and relations. Thus, the model can learn the features of entities from their neighbors without complicated matrix operations. In decoder, we introduce a diachronic embedding function for entities. We build a novel model DE-ConvKB for TKGC by equipping static models with a dynamic entity embedding function that provides the features of entities at any point time. The results show significant improvements over the state-of-the-art methods on three public datasets.
Sentiment Analysis Based on Deep Learning in E-Commerce

Social media allow businesses to find out what customers are thinking about their products and to participate in the conversation. Companies, therefore, have an interest in using them to market their products, identify new opportunities and improve their reputation. The main objective of our study was to recognize feelings expressed in opinions, ratings, recommendations about a product using a construction based on a corpus of sentiment lexicon with different deep learning algorithms. In this work, we will then analyze an e-commerce platform in order to know the feelings of customers towards the products. This study is conducted based on a static dataset of 41,778 smartphone product reviews in french collected on Amazon.com. For the classification of reviews, we applied the Long short-term memory network (LSTM). The results showed that the LSTM deep learning algorithm yielded a good performance with an accuracy of 95%.
Prompt-Based Self-training Framework for Few-Shot Named Entity Recognition

Exploiting unlabeled data is one of the plausible methods to improve few-shot named entity recognition (few-shot NER), where only a small number of labeled examples are given for each entity type. Existing works focus on learning deep NER models with self-training for few-shot NER. Self-training may induce incomplete and noisy labels which do not necessarily improve or even deteriorate the model performance. To address this challenge, we propose a prompt-based self-training framework. In the first stage, we introduce a self-training approach with prompt tuning to improve the model performance. Specially, we explore several label selection strategies in self-training to mitigate error propagation from noisy pseudo-labels. In the second stage, we fine-tune the BERT model over the high confidence pseudo-labels and original labels. We conduct experiments on two benchmark datasets. The results show that our method outperforms existing few-shot NER models by significant margins, demonstrating its effectiveness for the few-shot setting.