Reviewerly/exHarmony huggingface dataset:
Towards a cyber-physical system for sustainable and smart building: a use case for optimising water consumption on a SmartCampus

Abstract In recent years, the joint advance of the Internet of Things and Artificial Intelligence is enabling challenging developments for Smart Cities and Communities (S&amp;CC). In particular, the SmartCampus, as an essential part of S&amp;CC, acquires a transverse protagonism. On the one hand, SmartCampuses are a realistic representation of more complex systems (i.e., intelligent cities or territories) where to deploy sensors and plan specific goals. Nevertheless, on the other hand, Smartcampuses allow the coexistence of different technologies and networks of experts that facilitate the development, testing, and evolution of technologies. This paper describes the Cyber-Physical System SmartPoliTech, an Internet of Things Framework, as part of a future smart campus. SmartPoliTech develops an innovative framework that facilitates communication between different systems, data visualization, consumption modeling, alert generation, and the awareness of sustainability and environmental issues. This framework is based on a Service-Oriented Architecture to control all processes, from hardware to decision-making systems. This paper provides a sustainable and intelligent water management system to predict water consumption using Gaussian Mixture Models as day-, month- and even hour-dependent functions based on this Cyber-Physical System. The proposed solution can be used in any facility, with significant benefits being foreseen in metrics such as the minimization of water wastage.
Point-of-interest recommendation model considering strength of user relationship for location-based social networks

Point of interest (POI) recommendation systems have drawn the attention of researchers in multiple domains, particularly location-based social networks (LBSNs). However, owing to barriers in data collection and information classification, most existing systems lack adaptability for users with varied relationship circles, which leads to unsatisfactory recommendation results. In this study, a model that considers user relationship strength is provided based on a data-driven method for improving the POI recommendations. It defines the user relationship according to an analysis of the user’s check-in behavior. The user’s social links are then embedded in the spatiotemporal model for POI recommendations. The effectiveness of this dynamic recommendation model is demonstrated by comparing six state-of-art POI recommendation techniques on three real-world datasets. The experiment results found significant correlations between the user relationship strength and check-in locations, which improved the model performance. Conceptually, this study supports the hypothesis that user relationship traits help explain personal preferences in LBSN usage and places visited. This study provides an intelligent social network system that provides real-time, location-aware recommendations for retailers.
Underwater Biological Detection Based on YOLOv4 Combined with Channel Attention

Due to the influence of underwater visual characteristics on the observation of underwater creatures, the traditional object detection algorithm is ineffective. In order to improve the robustness of underwater biological detection, based on the YOLOv4 detector, this paper proposes an underwater biological detection algorithm combined with the channel attention mechanism. Firstly, the backbone feature extraction network CSPDarknet53 of YOLOv4 was improved, and a residual block combined with the channel attention mechanism was proposed to extract the weighted multi-scale effective features. Secondly, the weighted features were repeatedly extracted through the feature pyramid to separate the most significant weighted features. Finally, the most salient weighted multi-scale features were used for underwater biological detection. The experimental results show that, compared with YOLOv4, the proposed algorithm improved the average accuracy of the Brackish underwater creature dataset detection by 5.03%, and can reach a detection rate of 15fps for underwater creature video clips. Therefore, it is feasible to apply this method to the accurate and real-time detection of underwater creatures. This research can provide technical reference for the exploration of marine ecosystems and the development of underwater robots.
Obstacle Avoidance With Dynamic Avoidance Risk Region for Mobile Robots in Dynamic Environments

Uncertainty in dynamic environment increases complexity and difficulty in obstacle avoidance of robots. An avoidance method with dynamic obstacle avoidance risk region in a dynamic environment is proposed in this study. First, using the state estimation of the extended kalman filter, a dynamic obstacle avoidance risk region is constructed along the direction of the obstacle movement. Second, the robot's safe obstacle avoidance operation against dynamic obstacles combined with nonlinear model predictive control is realized. Finally, a series of experiments are used to verify the effectiveness of the proposed method. The experimental results showed that the robot can easily control its movement and a flat obstacle avoidance trajectory is obtained via the proposed method.
Energy optimised D* AUV path planning with obstacle avoidance and ocean current environment

Abstract For the path planning of autonomous underwater vehicles (AUVs) in the ocean environment, in addition to the planned path length and safe obstacle avoidance, it is also necessary to pay attention to the impact of ocean currents on the planned path. Therefore, this paper improves the original D* algorithm, and adds the obstacle cost item and the steering angle cost item as constraints on the basis of the original cost function, thus ensuring the navigation safety of the AUV. Considering that ocean currents have a greater impact on the energy consumption of AUVs, this paper establishes a cost model for the impact of ocean currents on AUV energy consumption and applies it to the D* path planning algorithm, so that AUVs can use ocean currents to reduce energy consumption, which can be seen through simulation experiments. The simulation results show that the improvement of the algorithm can plan an optimal energy consumption path.
Innovation of engineering teaching methods based on multimedia assisted technology

Presently, computer network technology and application have penetrated all fields of society, particularly towards advanced primary education in Chinese universities and engineering colleges. They are hardly used to improve the standard of advanced teaching using multimedia assistive instruction. This paper examines the teaching mode of physical education based on Advanced Multimedia Technology (AMT) for Teaching Evaluation in engineering education to improve the current standard of education. Education in physical activity has a distinct advantage in terms of its instructional role and services in the network environment, which offers a functional training atmosphere for the learners. According to a questionnaire survey, the students said the impact of digital teaching became substantially higher than the conventional teaching in physical education and hence agree that computer-aided instruction would increase learning interest than traditional mode. Instantly, the difficulties of physical education teaching provide some suggestions based on the student evaluation of teaching in multimedia-assisted technology, which has become a successful approach to evaluating teaching faculties. The experimental research indicates that the suggested methods can precisely classify student evaluation tasks. Mutual trust and cooperation between teachers and students are strengthened by the implementation of multimedia-assisted teaching and other techniques with a positive impact, and the practical capacity of students is greatly improved.
Scarcity-aware spam detection technique for big data ecosystem

To expand their business, companies in the industry use the big data ecosystem for handling enormous amounts of information. For this purpose, text data must be analyzed while ensuring data security and organizing authenticated and valuable data using spam filters. Several methods are available such as Word2Vec, bag-of-words, BERT, and term frequency-inverse document frequency (TF-IDF). However, none of these resolve the data scarcity issue that may result in the presence of incomplete information in collected documents. A technique that groups each document by subject and applies approximation using statistical methods is required to effectively solve this problem. This study proposes a natural language processing-based technique for spam detection that alters topics using a least-squares model and uses gradient-descent and altering-least-squares (AMALS) models to estimate missing data through TF-IDF and uniform-distribution. A performance evaluation demonstrates that the proposed technique outperforms 98% than the existing industrial TF-IDF model in predicting spam in big data ecosystems.
DualMMP-GAN: Dual-scale multi-modality perceptual generative adversarial network for medical image segmentation

Multi-modality magnetic resonance imaging (MRI) can reveal distinct patterns of tissue in the human body and is crucial to clinical diagnosis. But it still remains a challenge to obtain diverse and plausible multi-modality MR images due to expense, noise, and artifacts. For the same lesion, different modalities of MRI have big differences in context information, coarse location, and fine structure. In order to achieve better generation and segmentation performance, a dual-scale multi-modality perceptual generative adversarial network (DualMMP-GAN) is proposed based on cycle-consistent generative adversarial networks (CycleGAN). Dilated residual blocks are introduced to increase the receptive field, preserving structure and context information of images. A dual-scale discriminator is constructed. The generator is optimized by discriminating patches to represent lesions with different sizes. The perceptual consistency loss is introduced to learn the mapping between the generated and target modality at different semantic levels. Moreover, generative multi-modality segmentation (GMMS) combining given modalities with generated modalities is proposed for brain tumor segmentation. Experimental results show that the DualMMP-GAN outperforms the CycleGAN and some state-of-the-art methods in terms of PSNR, SSMI, and RMSE in most tasks. In addition, dice, sensitivity, specificity, and Hausdorff95 obtained from segmentation by GMMS are all higher than those from a single modality. The objective index obtained by the proposed methods are close to upper bounds obtained from real multiple modalities, indicating that GMMS can achieve similar effects as multi-modality. Overall, the proposed methods can serve as an effective method in clinical brain tumor diagnosis with promising application potential.
Study on Machine Learning Applications in Ideological and Political Education under the Background of Big Data

With the development of big data and data mining technology, machine learning has been applied in many fields. However, there are a large number of difficulties for students who majored in ideological and political education. It is very necessary for those students to integrate machine learning technology into ideological and political education courses. In this paper, we introduced how to integrate machine learning into ideological and political education courses in class. Firstly, we explained what teachers should do before/in/after class for teaching machine learning courses and what students should prepare. Secondly, we took the introduction section of machine learning courses as an example to connect each content with ideological and political education and illustrate them in the way of ideological and political education. Thirdly, we took the decision tree algorithm that belongs to machine learning as an example to explore the ideological and political education philosophy in the decision tree algorithm. Finally, we make a questionnaire from the perspective of learning attitude, learning influence, and learning effect to investigate the outcomes of students with our teaching way. Our results presented valuable meaningful information for students who majored in not only computer science but also ideological and political education, thus promoting the progress of interdisciplinary and making machine learning courses understood more easily in the class of ideological and political education.
Concept Bottleneck Model With Additional Unsupervised Concepts

With the increasing demands for accountability, interpretability is becoming an essential capability for real-world AI applications. However, most methods utilize post-hoc approaches rather than training the interpretable model. In this article, we propose a novel interpretable model based on the concept bottleneck model (CBM). CBM uses concept labels to train an intermediate layer as the additional visible layer. However, because the number of concept labels restricts the dimension of this layer, it is difficult to obtain high accuracy with a small number of labels. To address this issue, we integrate supervised concepts with unsupervised ones trained with self-explaining neural networks (SENNs). By seamlessly training these two types of concepts while reducing the amount of computation, we can obtain both supervised and unsupervised concepts simultaneously, even for large-sized images. We refer to the proposed model as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">concept bottleneck model with additional unsupervised concepts</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CBM-AUC</i> ). We experimentally confirmed that the proposed model outperformed CBM and SENN. We also visualized the saliency map of each concept and confirmed that it was consistent with the semantic meanings.
DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices

As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col-laboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3 x speedup compared to state-of-the-art methods.
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning

Model-agnostic meta-learning (MAML) and its variants have become popular approaches for few-shot learning. However, due to the non-convexity of deep neural nets (DNNs) and the bi-level formulation of MAML, the theoretical properties of MAML with DNNs remain largely unknown. In this paper, we first prove that MAML with over-parameterized DNNs is guaranteed to converge to global optima at a linear rate. Our convergence analysis indicates that MAML with over-parameterized DNNs is equivalent to kernel regression with a novel class of kernels, which we name as Meta Neural Tangent Kernels (MetaNTK). Then, we propose MetaNTK-NAS, a new training-free neural architecture search (NAS) method for few-shot learning that uses MetaNTK to rank and select architectures. Empirically, we compare our MetaNTK-NAS with previous NAS methods on two popular few-shot learning benchmarks, miniImageNet, and tieredImageNet. We show that the performance of MetaNTK-NAS is comparable or better than the state-of-the-art NAS method designed for few-shot learning while enjoying more than 100x speedup. We believe the efficiency of MetaNTK-NAS makes itself more practical for many real-world tasks. Our code is released at github.com/YiteWang/MetaNTK-NAS.
Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning

In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets perform multiple local iterations in each communication round and then concurrently transmit their updated gradients to an edge server over the same radio channel for global model aggregation using over-the-air computation (AirComp). We derive the upper bound of the time-average norm of the gradients to characterize the convergence of AirComp-assisted FL, which reveals the impact of the model aggregation errors accumulated over all communication rounds on convergence. Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the transceiver design for AirComp-assisted FL. As the alternating optimization algorithm suffers from high computation complexity, we further develop a knowledge-guided learning algorithm that exploits the structure of the analytic expression of the transmit power to achieve computation-efficient transceiver design. Simulation results demonstrate that the proposed knowledge-guided learning algorithm achieves a comparable performance as the alternating optimization algorithm, but with a much lower computation complexity. Moreover, both proposed algorithms outperform the baseline methods in terms of convergence speed and test accuracy.
nnSpeech: Speaker-Guided Conditional Variational Autoencoder for Zero-Shot Multi-speaker text-to-speech

Multi-speaker text-to-speech (TTS) using a few adaption data is a challenge in practical applications. To address that, we propose a zero-shot multi-speaker TTS, named nnSpeech, that could synthesis a new speaker voice without fine-tuning and using only one adaption utterance. Compared with using a speaker representation module to extract the characteristics of new speakers, our method bases on a speaker-guided conditional variational autoencoder and can generate a variable Z, which contains both speaker characteristics and content information. The latent variable Z distribution is approximated by another variable conditioned on reference mel-spectrogram and phoneme. Experiments on the English corpus, Mandarin corpus, and cross-dataset proves that our model could generate natural and similar speech with only one adaption speech.
Reasoning on the Air: An Implicit Semantic Communication Architecture

Semantic communication is a novel communication paradigm which draws inspiration from human communication focusing on the delivery of the meaning of a message to the intended users. It has attracted significant interest recently due to its potential to improve efficiency and reliability of communication, enhance users' quality-of-experience (QoE), and achieve smoother cross-protocol/domain communication. Most existing works in semantic communication focus on identifying and transmitting explicit semantic meaning, e.g., labels of objects, that can be directly identified from the source signal. This paper investigates implicit semantic communication in which the hidden information, e.g., implicit causality and reasoning mechanisms of users, that cannot be directly observed from the source signal needs to be transported and delivered to the intended users. We propose a novel implicit semantic communication (iSC) architecture for representing, communicating, and interpreting the implicit semantic meaning. In particular, we first propose a graph-inspired structure to represent implicit meaning of message based on three key components: entity, relation, and reasoning mechanism. We then propose a generative adversarial imitation learning-based reasoning mechanism learning (GAML) solution for the destination user to learn and imitate the reasoning process of the source user. We prove that, by applying GAML, the destination user can accurately imitate the reasoning process of the users to generate reasoning paths that follow the same probability distribution as the expert paths. Numerical results suggest that our proposed architecture can achieve accurate implicit meaning interpretation at the destination user.
Shadow Distillation: Quantum Error Mitigation with Classical Shadows for Near-Term Quantum Processors

A method for mitigating errors in near-term quantum computers utilizing randomized measurements explores the trade-off between the number of measurement settings and the number of repetitions of each measurement.
The optimization landscape of hybrid quantum–classical algorithms: From quantum control to NISQ applications

This review investigates the landscapes of hybrid quantum–classical optimization algorithms that are prevalent in many rapidly developing quantum technologies, where the objective function is computed by either a natural quantum system or an engineered quantum ansatz, but the optimizer is classical. In any particular case, the nature of the underlying control landscape is fundamentally important for systematic optimization of the objective. In early studies on the optimal control of few-body dynamics, the optimizer could take full control of the relatively low-dimensional quantum systems to be manipulated. Stepping into the noisy intermediate-scale quantum (NISQ) era, the experimentally growing computational power of the ansatz expressed as quantum hardware may bring quantum advantage over classical computers, but the classical optimizer is often limited by the available control resources. Across these different scales, we will show that the landscape’s geometry experiences morphological changes from favorable trap-free landscapes to easily trapping rugged landscapes, and eventually to barren-plateau landscapes on which the optimizer can hardly move. This unified view provides the basis for understanding classes of systems that may be readily controlled out to those with special consideration, including the difficulties and potential advantages of NISQ technologies, as well as seeking possible ways to escape traps or plateaus, in particular circumstances.
TransCAM: Transformer attention-based CAM refinement for Weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) with only image-level supervision is a challenging task. Most existing methods exploit Class Activation Maps (CAM) to generate pixel-level pseudo labels for supervised training. However, due to the local receptive field of Convolution Neural Networks (CNN), CAM applied to CNNs often suffers from partial activation -- highlighting the most discriminative part instead of the entire object area. In order to capture both local features and global representations, the Conformer has been proposed to combine a visual transformer branch with a CNN branch. In this paper, we propose TransCAM, a Conformer-based solution to WSSS that explicitly leverages the attention weights from the transformer branch of the Conformer to refine the CAM generated from the CNN branch. TransCAM is motivated by our observation that attention weights from shallow transformer blocks are able to capture low-level spatial feature similarities while attention weights from deep transformer blocks capture high-level semantic context. Despite its simplicity, TransCAM achieves a new state-of-the-art performance of 69.3% and 69.6% on the respective PASCAL VOC 2012 validation and test sets, showing the effectiveness of transformer attention-based refinement of CAM for WSSS.