Reviewerly/exHarmony huggingface dataset:
Quantum-Inspired Moth-Flame Optimizer With Enhanced Local Search Strategy for Cluster Analysis

Clustering is an unsupervised learning technique widely used in the field of data mining and analysis. Clustering encompasses many specific methods, among which the K-means algorithm maintains the predominance of popularity with respect to its simplicity and efficiency. However, its efficiency is significantly influenced by the initial solution and it is susceptible to being stuck in a local optimum. To eliminate these deficiencies of K-means, this paper proposes a quantum-inspired moth-flame optimizer with an enhanced local search strategy (QLSMFO). Firstly, quantum double-chain encoding and quantum revolving gates are introduced in the initial phase of the algorithm, which can enrich the population diversity and efficiently improve the exploration ability. Second, an improved local search strategy on the basis of the Shuffled Frog Leaping Algorithm (SFLA) is implemented to boost the exploitation capability of the standard MFO. Finally, the poor solutions are updated using Levy flight to obtain a faster convergence rate. Ten well-known UCI benchmark test datasets dedicated to clustering are selected for testing the efficiency of QLSMFO algorithms and compared with the K-means and ten currently popular swarm intelligence algorithms. Meanwhile, the Wilcoxon rank-sum test and Friedman test are utilized to evaluate the effect of QLSMFO. The simulation experimental results demonstrate that QLSMFO significantly outperforms other algorithms with respect to precision, convergence speed, and stability.
HOIG: End-to-End Human-Object Interactions Grounding with Transformers

Visual grounding is a crucial and challenging problem in many applications. While it has been extensively investigated over the past years, human-centric grounding with multiple instances is still an open problem. In this paper, we introduce a new task of Human-Object Interactions (HOI) Grounding to localize all the referring human-object pair instances in an image with a given ⟨human, interaction, object⟩ phrase. We design an encoder-decoder architecture to model the task as a set prediction problem based on transformers. A vision-language alignment module and a grounding decoder are designed to learn accurate cross-modal contexts and interactions. Our model accomplishes alignment and prediction in an end-to-end manner without pre-trained detectors or post-processing. Experiments on two challenging datasets prove the strength of our model.
Maxim: DRL-Based Cross-Camera Streaming Configuration for Real-Time Video Analytics

Real-time video analytics (VA) with requirement of high accuracy necessitates intensive bandwidth resource consumption, calling for an adaptive streaming configuration strategy to strike a balance in VA pipelines. Existing works however suffer from a key limitation: the profiling-based strategy not only wastes unnecessary resources with the golden configuration transmission but also is trapped into only coarse-grained adaptation due the contradiction between profiling granularity and the profiling cost. In this paper, we for the first time reveal the correlation between the video dynamics and motion degree and highlight the limitation of traditional profiling-based strategy. We then propose Maxim, the first learning-based framework that solves the fine-grained VA configuration adaptation problem employing a novel deep reinforcement learning-based methodology without using any golden configuration in inference stage. Maxim could optimize the trade-off between resources cost and inference accuracy. Besides, Maxim also employs an enhanced cross-camera collaboration based on spatial and temporal correlation among cameras, which further improves robustness and performance in a large camera network. Extensive experiments confirm the superiority of our work compared with SOTA works, with a 76.6% improvement in comprehensive.
X-HRNet: Towards Lightweight Human Pose Estimation with Spatially Unidimensional Self-Attention

High-resolution representation is necessary for human pose estimation to achieve high performance, and the ensuing problem is high computational complexity. In particular, predominant pose estimation methods estimate human joints by 2D single-peak heatmaps. Each 2D heatmap can be hori-zontally and vertically projected to and reconstructed by a pair of 1D heat vectors. Inspired by this observation, we introduce a lightweight and powerful alternative, Spatially Unidimensional Self-Attention (SUSA), to the pointwise (1 x 1) convolution that is the main computational bottleneck in the depthwise separable 3 x 3 convolution. Our SUSA reduces the computational complexity of the pointwise (1 x 1) convolution by 96% without sacrificing accuracy. Furthermore, we use the SUSA as the main module to build our lightweight pose estimation backbone X-HRNet, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> represents the estimated cross-shape attention vectors. Extensive experiments on the COCO benchmark demonstrate the superiority of our X-HRNet, and comprehensive ablation studies show the effectiveness of the SUSA modules. The code is publicly available at https://github.com/cool-xuan/x-hrnet.
TCA-VAD: Temporal Context Alignment Network for Weakly Supervised Video Anomly Detection

Video Anomaly detection (VAD) with weakly supervised is usually formulated as a multiple instance learning (MIL) problem. Although the current MIL-based methods have achieved promising detection performance, the temporal dependencies in videos are not well exploited. There may multiple abnormal clips in a given anomaly video, while the previous work only focused on the most abnormal one. To address above issues, a temporal context alignment (TCA) network for video anomaly detection is proposed in this work. Its merits are three-fold, 1) a sparse continuous sampling strategy is proposed to adapt the varying length of untrimmed videos; 2) a multi-scale attention module is used to establish the video temporal dependencies; 3) a top-k loss strategy is used to enlarge the distance between the top-k normal and abnormal clips. Extensive experiments demonstrate the noticeable anomaly discriminability of the proposed network on two public datasets (ShanghaiTech and UCF-Crime).
Improving GAN-Generated Image Detection Generalization Using Unsupervised Domain Adaptation

In recent years, with the significant improvement of Gener-ative Adversarial Networks (GANs), fake images generated by GAN become hardly distinguishable from real ones, thus threatening the authentication of digital images. To resolve this issue, several fake image detectors based on supervised binary classification have been designed. However, current methods remain vulnerable when testing samples are gener-ated by an unknown GAN model. In this work, an unsuper-vised domain adaptation strategy is introduced to improve the performance in the generalization of GAN-generated image detection by using a small number of unlabeled images from the target domain. Self-Attention block and novel loss function have been constructed to optimize the domain adaptation process, thus getting a better generalization. Experimental results demonstrate that the proposed scheme achieves high detection accuracy with few unlabeled images in the target domain, which shows that unsupervised methods can be used for the detection of GAN-generated images.
Spatio-Temporal Self-Supervision Enhanced Transformer Networks for Action Recognition

With the development of deep neural networks, video action recognition has gradually become a research hotspot in recent years. However, the additional temporal dimension in video makes this task very challenging. In this paper, we propose a novel Spatio-Temporal Self-Supervision enhanced Transformer Networks (STTNet) for video action recognition, which mainly consists of Self-Supervised SpatioTemporal Representation Learning module and Transformer based Spatio-Temporal Aggregator module. Concretely, our STTNet can adaptively encode the spatial and temporal enhanced key features, which are respectively learned through the Temporal and Spatial Self-Supervised sub-module using the unlabeled video data, in a nonlinear and nonlocal manner via the Transformer based SpatioTemporal Aggrega-tor. The extensive experiments on three widely used datasets (HMDB51, UCF101 and Something-Something V1) demon-strate that our proposed STTNet can achieve the state-of-the-art performance. Code is available at https://github.com/ICME2022/STTNet.
Attention Interactive Graph Convolutional Network for Skeleton-Based Human Interaction Recognition

Skeleton-based Human interaction action recognition is a great challenge in the field of human activity analysis. The existing graph convolutional networks (GCNs) based recognition methods ignore the spatial-temporal interactive features between two persons. Therefore, we propose Attention Inter-active Graph Convolutional Network(AIGCN) to explore hu-man interaction. First, we design an Interactive Attention En-coding GCN(IAE-GCN) module to extract interactive spatial structures, reflecting their influence on each other by regarding joint position encoding as semantic information. Second, we propose Interactive-Attention Mask TCN (IAM-TCN) that extracts temporal interactive features, representing temporal attentional excitation signal among the different joints of dif-ferent people along the time. Finally, we verify the effectiveness of our method on classic human interaction datasets SBU and interactive action sub-datasets of NTU-RGB+D and NTU-RGB+D 120, and experiment shows that our models achieve state-of-the-art performance.
Video Super-Resolution with Spatial-Temporal Transformer Encoder

The challenge of Video super-resolution (VSR) is how to make full use of the spatial-temporal coherence among neigh-bouring LR frames to generate high-resolution (HR) prediction. In this study, we propose to use transformer on VSR to capture long-range temporal dependencies. Specifically, we first spatially divide LR images into patches and split each patch into sub-patches. Transformer encoders are applied to both the patches and sub-patches, such that the self-attention modules can extract both global and local correlations. To accelerate the training process and filter out irrelevant features, we only select top-k similar features for the attention scheme. We then feed the extracted long-range correlations into a temporal, spatial and channel attention fusion mod-ule’ which enhances the useful information along all three di-mensions' respectively. Extensive experiments on benchmark datasets show that the proposed model outperforms state-of-the-art VSR methods in terms of PSNR/SSIM values and vi-sual qualities.
Review of augmented reality in academic and research libraries

Purpose The paper aims to provide substantive information regarding augmented reality (AR) and its usage in academic and research libraries. Design/methodology/approach Qualitative research based on the content analysis method was used for this study. Articles from top databases such as Science Direct, Emerald and Taylor &amp; Francis were consulted, and most cited articles were extracted from those searches for this study. Findings The research reveals that most libraries in developed countries are executing this technology to give maximal information to their patrons in the augmented world. The study also shows that advanced technologies are needed these days, and without adopting these technologies, no advancement in the library sector can be made. Originality/value This paper sheds light on AR conversation and, by extension, implementation; it will further boost the morale of academic and research librarians. Additionally, it sets the framework for researchers and practitioners that the application service is lacking despite the rise of technological advancements.
Innovative Approaches Used to Prepare Pre-service Teachers to Activate Learning with Digital Technologies

This chapter explores the innovative approaches employed within core subjects of two initial teacher education degrees, where the emphasis has been on the connection of pedagogy, digital technologies, collaborative learning, team teaching and learning spaces to activate pre-service teacher learning. Whilst the specific focus of the subjects is to prepare pre-service teachers to be educators who embrace digital technologies as a tool to support learners and enhance learning, it is the informal reflexivity espoused within new team teaching approaches that cater to novel ways of engaging with the challenges associated with digital pedagogies. The chapter will draw upon several years of research, and the experiences of teacher educators in the field of digital pedagogies, whilst highlighting how an approach that embodies creative inquiry has enabled pre-service teachers to connect with their prior learning experiences to form new understandings of the role of digital technology in their future classrooms. It is the agentic actions of the authors that drive the innovative approaches in learning design and pedagogical practices associated with these subjects.
Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis

Mortality is a major primary endpoint for long-term hemodialysis (HD) patients. The clinical status of HD patients generally relies on longitudinal clinical observations such as monthly laboratory examinations and physical examinations.A total of 829 HD patients who met the inclusion criteria were analyzed. All patients were tracked from January 2009 to December 2013. Taken together, this study performed full-adjusted-Cox proportional hazards (CoxPH), stepwise-CoxPH, random survival forest (RSF)-CoxPH, and whale optimization algorithm (WOA)-CoxPH model for the all-cause mortality risk assessment in HD patients. The model performance between proposed selections of CoxPH models were evaluated using concordance index.The WOA-CoxPH model obtained the highest concordance index compared with RSF-CoxPH and typical selection CoxPH model. The eight significant parameters obtained from the WOA-CoxPH model, including age, diabetes mellitus (DM), hemoglobin (Hb), albumin, creatinine (Cr), potassium (K), Kt/V, and cardiothoracic ratio, have also showed significant survival difference between low- and high-risk characteristics in single-factor analysis. By integrating the risk characteristics of each single factor, patients who obtained seven or more risk characteristics of eight selected parameters were dichotomized as high-risk subgroup, and remaining is considered as low-risk subgroup. The integrated low- and high-risk subgroup showed greater discrepancy compared with each single risk factor selected by WOA-CoxPH model.The study findings revealed WOA-CoxPH model could provide better risk assessment performance compared with RSF-CoxPH and typical selection CoxPH model in the HD patients. In summary, patients who had seven or more risk characteristics of eight selected parameters were at potentially increased risk of all-cause mortality in HD population.
Cloud-based bug tracking software defects analysis using deep learning

Abstract Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.
MAFF: Self-adaptive Memory Optimization for Serverless Functions

Function-as-a-Service (FaaS), a key enabler of serverless computing, has been proliferating, as it offers a cheap alternative for application development and deployment. However, while offering many advantages, FaaS also poses new challenges. In particular, most commercial FaaS providers still require users to manually configure the memory allocated to the FaaS functions based on their experience and knowledge. This often leads to suboptimal function performance and higher execution costs. In this paper, we present a framework called MAFF that automatically finds the optimal memory configurations for the FaaS functions based on two optimization objectives: cost-only and balanced (balance between cost and execution duration). Furthermore, MAFF self-adapts the memory configurations for the FaaS functions based on the changing function inputs or other requirements, such as an increase in the number of requests. Moreover, we propose and implement different optimization algorithms for different objectives. We demonstrate the functionality of MAFF on AWS Lambda by testing on four different categories of FaaS functions. Our results show that the suggested memory configurations with the Linear algorithm achieve 90% accuracy with a speedup of 2x compared to the other algorithms. Finally, we compare MAFF with two popular memory optimization tools provided by AWS, i.e., AWS Compute Optimizer and AWS Lambda Power Tuning, and demonstrate how our framework overcomes their limitations.
Automated Topic Categorisation of Citizens’ Contributions: Reducing Manual Labelling Efforts Through Active Learning

Abstract Political authorities in democratic countries regularly consult the public on specific issues but subsequently evaluating the contributions requires substantial human resources, often leading to inefficiencies and delays in the decision-making process. Among the solutions proposed is to support human analysts by thematically grouping the contributions through automated means. While supervised machine learning would naturally lend itself to the task of classifying citizens’ proposal according to certain predefined topics, the amount of training data required is often prohibitive given the idiosyncratic nature of most public participation processes. One potential solution to minimise the amount of training data is the use of active learning. While this semi-supervised procedure has proliferated in recent years, these promising approaches have never been applied to the evaluation of participation contributions. Therefore we utilise data from online participation processes in three German cities, provide classification baselines and subsequently assess how different active learning strategies can reduce manual labelling efforts while maintaining a good model performance. Our results show not only that supervised machine learning models can reliably classify topic categories for public participation contributions, but that active learning significantly reduces the amount of training data required. This has important implications for the practice of public participation because it dramatically cuts the time required for evaluation from which in particular processes with a larger number of contributions benefit.
Circuit-level convergence of electronics and photonics: basic concepts and recent advances

Integrated photonics is widely regarded as an important post-Moore's law research direction. However, it suffers from intrinsic limitations, such as lack of control and satisfactory photonic memory, that cannot be solved in the optical domain and must be combined with electronics for practical use. Inevitably, electronics and photonics will converge. The photonic fabrication and integration technology is gradually maturing and electronics-photonics convergence (EPC) is experiencing a transition from device integration to circuit design. We derive a conceptual framework consisting of regulator, oscillator, and memory for scalable integrated circuits based on the fundamental concepts of purposeful behavior in cybernetics, entropy in information theory, and symmetry breaking in physics. Leveraging this framework and emulating the successes experienced by electronic integrated circuits, we identify the key building blocks for the integrated circuits for EPC and review the recent advances.
Proactive Chatbot Framework Based on the PS2CLH Model: An AI-Deep Learning Chatbot Assistant for Students

Nowadays, universities are using more technologies dealing with students’ interactions. The chatbot supported with Artificial Intelligence (AI) – Deep Learning (DL) technology exhibited a better ability and efficiency in various assistant situations. However, the effectiveness of the education chatbot is still not satisfactory. This paper proposes a new chatbot framework that integrated students’ learning profiles and enhanced chatbot components to improve student interaction. The new chatbot framework uses knowledge from the PS2CLH model and AI - DL to build a proactive chatbot for assisting students’ learning on their academic subjects and controllable learning factors. One of the principal novelties of the chatbot framework lies in the student-lecturer/assistant facilitator. The proactive chatbot applies multimodality to students’ learning process to retain students’ attention and explain the content in different ways using text, image, video, and audio to assist students and improve their learning experience effectively. Furthermore, the chatbot proactively suggests controllable learning factors for students to work on, improving their academic performance. The testing results demonstrated that the proactive chatbot offered sound accuracy and more effective learning support than other chatbots.