Reviewerly/exHarmony huggingface dataset:
A novel technique for the detection of Covid-19 patients with the applications of three-way decisions using variance-based criterion

Everyone is making constant efforts to establish an effective diagnostic approach, therapy and control of the spread of the pandemic. Due to a flexible formulation, the parameters prior to the normal distributions and explicitly formulate assumptions on the transition probabilities between these categories over time. The spread of the COVID-19 pandemic represents a serious threat for scientists and academics, health professionals and even governments today. The Hospital wards are classified into Intensive Care Unit (ICU), Regular Wards (RW) with Recovered (R) and Deceased (D).. The formulation may be truncated to include particular hypotheses with an epidemiological interpretation. The principles of Three-Way Decision Theory could be used to anticipate and diagnose COVID-19 patients were classified into one of three zones based on their symptoms: Positive, Negative, or Boundary, and treatment are recommended if necessary. The thresholds that distinguish the three zones are determined using a variance-based criterion. Examine the impact of nonpharmaceutical interventions and the findings from data gathered during the second wave of the pandemic in Trivandrum, India.The Three-Way Decision Theory model has a good fit and gives good predictive performance, especially for RW and ICU patients, according to suitable discrepancy metrics that were created to assess and compare models. 95 percent accuracy increased and calculated values for 10 days to demonstrate the temporal aspects of the expected daily reproduction number R.
Plastic gating network: Adapting to personal development and individual differences in knowledge tracing

Knowledge tracing (KT) refers to the issue of predicting learners’ knowledge states based on their learning history and is the core technology for computer-assisted adaptive learning. The latest KT research has improved prediction performance by exploring the relationship between concepts and questions. However, these models have their parameters fixed after training. Without fully or partially re-training, they can only predict all learners’ performance at all time steps using the same set of parameters, making them challenging to adapt to learners’ personal development and individual differences. To address these problems, we take inspiration from synaptic plasticity, the primary neural mechanism conferring biological brains with lifelong learning capabilities, and propose the plastic gating network (PGN) to adapt to variation in learners’ cognition over time and across individuals. At the core of the proposed model are plastic weights, which can constantly evolve after training according to the ongoing inputs and outputs. We adopt plastic weights in calculating gates and cell input in recurrent units, thus allowing the model to develop time- and individual-specific parameters that adapt to learners’ personal development and individual differences after training. Experiments on real-world datasets show that the proposed PGN outperforms baseline methods in predicting the future performance of learners. Crucially, the PGN exhibits effective adaptation to learners’ personal development and individual differences.
Feature enhancement network for stereo matching

In general stereo matching methods, there are mainly two kinds of weaknesses: the interior of large objects with unclear texture; object boundaries and small objects. One of the reasons for the first case is the underuse of inter-feature and intra-feature contexts, leading to the lack of discriminability of feature representations. And the second case could be caused result from the loss of fine-grained structural information in the features. In this work, we present a novel stereo matching network to enhance the feature representations, which contains two prominent components: pyramid context enhancement module and cross-modal enhancement module. Pyramid context enhancement module first constructs multi-scale pyramid features to promote global representation, and then exploits the inter-feature and intra-feature contextual information to improve the discriminability of features. Cross-modal enhancement module proactively degrades features of RGB image and disparity map to implicitly extract structures, and fuses both structures to recover the modal consistent structural information of features. Moreover, we design a disparity weight loss function which can calculates the adaptive weight according to prediction error to balance the loss response. Extensive experiments demonstrate that our method boosts the performance, and achieves better results on five challenging datasets than other typical methods. Code is available at: https://github.com/csl1994/FENet.
A sparse attack method on skeleton-based human action recognition for intelligent metaverse application

Graph convolutional networks (GCNs) based skeleton human action recognition has been one of the hottest techniques in intelligent Metaverse system applications in recent years. However, the robustness of GCN model based skeleton action recognition, has not been widely discussed due to the complex spatio-temporal nature. Base on solving this problem, we propose a sparse attack method with dynamic attention which can generate adversarial samples with lower deviation under the condition of keeping attack success rate in this paper. Firstly, we develop spatial temporal consistency loss which can not only preserve spatial integrity between reference sample and adversarial sample but also keep temporal coherence between consecutive frames across adversarial sample. Then, we develop an interaction-based perturbation contribution analyze method to discard unnecessary perturbations. Besides that, we design a dynamic attention approach to tilt the perturbations towards some joints that have higher dynamics which can decrease overall deviations. Finally, we conduct extensive experiments to evaluate our proposal and the experimental results show that our method contributes to discover potential causes of model fragility and provides material in adversarial training which will increase the robustness of GCN based skeleton human action recognition models.
Black-box attacks on image classification model with advantage actor-critic algorithm in latent space

The Internet of Things (IoT) ecosystem that integrates a wide variety of intelligent multimedia applications and services has undergone a tremendous transformation over the years. As an essential approach for securing IoT-based multimedia services, Artificial Intelligence (AI) has been in innovation at a rapid pace. However, many machine learning systems are vulnerable to adversarial examples, including advanced deep neural networks. By making imperceptible modifications to real examples, the prediction will deviate far from the correct value. A deep reinforcement learning-based black-box attacker on the image classification model is introduced in our research. Different from the existing black-box attacks, which require massive queries and trials in the pixel space, the proposed method compresses the images into latent space through variational inference, querying the optimal examples efficiently with actor-critic networks. Rather than patch-to-patch translation with generative adversarial networks in related works, the fake examples are generated by gradually superimposing the perturbations into the latent space at each step through the Markov decision process (MDP) to high stability and good astringency of the attacker. Experiments evaluated on the ImageNet dataset demonstrate that the proposed attacker can generate adversarial images for most samples in limited steps, greatly reducing the accuracy of the model.
Reinforcement learning based optimization algorithm for maintenance tasks scheduling in coalbed methane gas field

The Coalbed Methane (CBM) well maintenance tasks scheduling optimization is of importance for improving the CBM production efficiency. Traditionally, this problem is addressed using mathematical model based classical optimization method or some meta-heuristic algorithms. However, due to the large-scale nature of this problem, these trials often fail in practical use. Therefore, the Q-learning algorithm based solving method is proposed in this paper. An interactive environment for reinforcement learning is constructed. To validate the effectiveness of proposed method, scenarios with different scales are provided. For the cases with 10, 14, 20, 30, 47, 60, 80 and 100 maintenance tasks respectively, the required solution time is 3.66 s, 4.94 s, 7.66 s, 12.48 s, 21.82 s, 30.82 s, 48.33 s, 73.17 s respectively. The proposed Q-learning algorithm is insensitive with the problem scale, which is promising. Moreover, the Q-learning based algorithm is more efficient than the traditional algorithm along with the increase of the number of tasks.
Locally differentially private high-dimensional data synthesis

In local differential privacy (LDP), a challenging problem is the ability to generate high-dimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents SamPrivSyn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation module. The marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible, the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that SamPrivSyn can not only protect privacy but also retain the correlation information between the attributes.
ELSA: Ensemble learning based sentiment analysis for diversified text

Internet-based media and online business make people use it a lot, especially the high use of cell phones everywhere in the world. These encourage organizations to gain insights from people far away. This creates a tremendous growth of information on the Internet. Concepts are important implications for practically all human practices and practices. It looks for feelings before making a decision. People share their opinions, thoughts, ideas, attitudes, feelings etc. with known languages on social media platform. Most people express their opinions in mixed languages (English with their mother tongue). Analyzing diverse texts is a very challenging area for practitioners and researchers. In this research work, it presents an ensemble technique by collaborating Generative Adversarial network (GAN) and Self-Attention Network (SAN) to analyze the tweets that contains diversified languages. The GAN layer helps to arrange the preprocessed tweets into positive and negative. The SAN layer helps to identify the neutral tweets. The proposed technique is compared along with the existing Concurrent Neural Network (CNN) along with Self-Attention Network (SAN) on Tanglish tweets. The proposed work produced better accuracy than the existing work.
Automated measures of speech content and speech organization in schizophrenia: Test-retest reliability and generalizability across demographic variables

Technological advances in artificial intelligence and natural language processing have increased efficiency of assessing speech content and speech organization in schizophrenia. Despite these developments, there has been little focus on the psychometrics of these approaches. Using two common assessments, the current study addressed this gap by: 1) measuring test-retest reliability; and 2) assessing whether speech content and/or speech organization generalize across demographics. To test these aims, we examined psychometric properties of the Linguistic Inquiry Word Count (LIWC), a speech content measure, and the Coh-Metrix, a speech organization measure. Across baseline to six month (n = 101) and baseline to one year (n = 47) narrative speech samples, we generally observed fair reliability for speech content measures and fair to good reliability for speech organization measures. Regarding demographics, multiple speech indices varied by race, income, and education. The lack of excellent reliability scores for speech indices holds important implications for examining speech variables in clinical trials and highlights the dynamic nature of speech. This work illustrates the importance of designing speech content and speech organization measures with external validity across demographic factors. Future studies examining speech in schizophrenia should account for potential biases against demographic groups introduced by linguistic analysis tools.
PSTile: Perception-Sensitivity-Based 360$^\circ$ Tiled Video Streaming for Industrial Surveillance

360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> video becomes increasingly attractive in smart factories due to its immersive experience for industrial surveillance. However, transmitting this kind of video requires a fairly large demand on the bandwidth due to its high-resolution and panoramic view. Moreover, low-latency responses of the video to users' head movements are required. This leads to the tradeoff between high video quality and low-latency response under limited bandwidth in factories. This article proposes a perception-sensitivity (PS)-based 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> tiled video streaming method called PSTile for industrial surveillance. Specifically, a PS tiling strategy is constructed for valid tile grouping based on the designed PS index. Then, a tile bitrate adaptation problem is formulated to allocate bitrates to valid tiles. It jointly optimizes surveillance accuracy, end-to-end latency, and quality-of-experience of users under accuracy, latency, and bandwidth constraints. Simulations demonstrate that PSTile achieves at least 32.6% lower end-to-end latency and 19.3% higher average video bitrate than grid-tiling and clus-tiling methods.
Norm-Based Adaptive Coefficient ZNN for Solving the Time-Dependent Algebraic Riccati Equation

The time-dependent algebraic Riccati equation (TDARE) problem is applied to many optimal control industrial applications. It is susceptible to interference from measurement noises in the virtual environment, which current methods cannot effectively address. A norm-based adaptive coefficient zeroing neural network (NACZNN) model to solve the TDARE problem is proposed, with an adaptive scale coefficient based on the residual error norm to accelerate convergence speed to the theoretical solution. Momentum enhancement terms enable NACZNN to effectively solve the TDARE problem in real time when perturbed by measurement noise. Simulation experiments were designed and executed, and results confirm the NACZNN model's superior robustness and accuracy when solving the TDARE problem disturbed by noises in real time.
GenPADS: Reinforcing politeness in an end-to-end dialogue system

In a task-oriented dialogue setting, user’s mood and demands can change in an ongoing dialogue, which may lead to a non-informative conversation or may result in conversation drop-off. To rectify such scenarios, a conversational agent should be able to learn the user’s behaviour online, and form informative, empathetic and interactive responses. To incorporate these three aspects, we propose a novel end-to-end dialogue system GenPADS . First, we build and train two models, viz. a politeness classifier to extract polite information present in user’s and agent’s utterances and a generation model (G) to generate varying but semantically correct responses. We then incorporate both of these models in a reinforcement learning (RL) setting using two different politeness oriented reward algorithms to adapt and generate polite responses. To train our politeness classifier, we annotate recently released Taskmaster dataset into four fine-grained classes depicting politeness and impoliteness. Further, to train our generator model, we prepare a GenDD dataset using the same Taskmaster dataset. Lastly, we train GenPADS and perform automatic and human evaluation by building seven different user simulators. Detailed analysis reveals that GenPADS performs better than the two considered baselines, viz. a transformer based seq2seq generator model for user’s and agent’s utterance and a retrieval based politeness adaptive dialogue system (PADS).
Addressing Noise and Class Imbalance Problems in Heterogeneous Cross-Project Defect Prediction

When a software project either lacks adequate historical data to build a defect prediction (DP) model or is in the initial phases of development, the DP model based on related source project's defect data might be used. This kind of SDP is categorized as heterogeneous cross-project defect prediction (HCPDP). According to a comprehensive literature review, no research has been done in the field of CPDP to deal with noise and class imbalance problem (CIP) at the same time. In this paper, the impact of noise and imbalanced data on the efficiency of the HCPDP and with-in project defect prediction (WPDP) model is examined empirically and conceptually using four different classification algorithms. In addition, CIP is handled using a novel technique known as chunk balancing algorithm (CBA). Ten prediction combinations from three open-source projects are used in the experimental investigation. The findings show that noise in an imbalanced dataset has a significant impact on defect prediction accuracy.
ST$^{2}$: Spatial-Temporal State Transformer for Crowd-Aware Autonomous Navigation

Empowering an intelligent agent with the ability of autonomous navigation in complex and dynamic environments is an important and active research topic in embodied artificial intelligence. In this letter, we address this challenging task from the view of exploiting both the spatial and temporal states of a mobile robot interacting with the crowded environment. Specifically, we propose a Spatial-Temporal State Transformer (ST <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> ) to encode the states while leveraging the deep reinforcement learning method to find the optimal navigation policy accordingly. Technically, the proposed ST <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> model consists of a global spatial state encoder and a temporal state encoder, which are built upon the Transformer structure. The spatial state encoder is devised to extract the global spatial features and capture the spatial interaction between pedestrians and the robot. The temporal state encoder is designed to model the temporal correlation among consecutive frames and infer the dynamic relationship of the spatial position transformation. Based on the comprehensive spatial-temporal state representation, the value-based reinforcement learning method is leveraged to obtain the optimal navigation policy. Extensive experiments demonstrate the superiority of the proposed ST <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> over representative state-of-the-art methods. The source code will be made publicly available.
Consensus based target tracking against deception jamming in distributed radar networks

Abstract In this study, an anti‐deception jamming target tracking algorithm based on the data‐level fusion is proposed in a distributed radar network. First, the consensus covariance intersection (CCI) algorithm is proposed to improve tracking performance by introducing the consensus theory to the field of covariance intersection fusion. Second, unscented Kalman filter is utilised to implement the preprocessing of distributed filtering. Then, two distributed target discrimination schemes are developed for non‐collaborative and collaborative false targets respectively. For non‐collaborative false targets, according to the characteristics that estimated spatial locations of the false target from different radars are relatively scattered, a false target elimination method based on nearest neighbour algorithm is proposed. For collaborative false targets, after equipped with passive radars that are less vulnerable to interference, the consensus algorithm is introduced to convey the real target information to the unconnected active radars, thus achieving an effect of global anti‐deception jamming. Finally, the CCI algorithm is deployed for distributed fusion of physical target tracking, so as to improve the overall estimation performance.
Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning

As the technology for offshore wireless transmission and collaborative innovation in unmanned ships continues to mature, research has been gradually carried out in various countries on methods of compressing and transmitting perceptual video while driving ships remotely. High Efficiency Video Coding (H.265/HEVC) has played an extremely important role in the field of Unmanned Aerial Vehicle (UAV) and autopilot, and as one of the most advanced coding schemes, its performance in compressing visual sensor video is excellent. According to the characteristics of shipborne vision sensor video (SVSV), optimizing the coding aspects with high computational complexity is one of the important methods to improve the video compression performance. Therefore, an efficient video coding technique is proposed to improve the efficiency of SVSV compression. In order to optimize the compression performance of SVSV, an intra-frame coding delay optimization algorithm that works in the intra-frame predictive coding (PC) session by predicting the Coding Unit (CU) division structure in advance is proposed in combination with deep learning methods. The experimental results show that the total compression time of the algorithm is reduced by about 45.49% on average compared with the official testbed HM16.17 for efficient video coding, while the Bjøntegaard Delta Bit Rate (BD-BR) increased by an average of 1.92%, and the Peak Signal-to-Noise Ratio (BD-PSNR) decreased by an average of 0.14 dB.
Generative AI: Here to stay, but for good?

ChatGPT has taken the world by storm. Academic’s ethical and practical concerns aside, the street has most certainly found its use for generative artificial intelligence (AI), and there is no longer much point in discussing whether generative AI will be influential. It will, and what remains to be discussed it how influential it will be, and what potential harms arise when we use AI to generate text and other forms of content.