Reviewerly/exHarmony huggingface dataset:
A particle swarm optimizer with multi-level population sampling and dynamic p-learning mechanisms for large-scale optimization

Large-scale optimization, which has received much attention in recent years, is inherently a challenging problem. This paper proposes a particle swarm optimizer with multi-level population sampling and dynamic p-learning mechanisms to address the problem. The multi-level sampling mechanism in the proposed method is developed for supporting a balanced evolutionary search. The mechanism works by partitioning the particles of swarm into multi-levels based on their fitness before each generation of evolution. A subset of swarm is then dynamically sampled from the particles at various levels for evolution such that encouraging exploration at the beginning of evolution while exploitation towards the end of evolution, thus appropriately searching the space. The dynamic p-learning mechanism, on the other hand, is introduced to allow efficient particle learning while preserving the swarm diversity during evolution. In this mechanism, each particle is devised to learn from one of the top 100p% particles of the sub-swarm and the value of p associated with each particle is dynamically adjusted during evolution. By employing the above two mechanisms, the resulting method aims to appropriate search the solution space of large-scale global optimization problem for identifying the optimal or near-optimal solution. The performance of the proposed method has been evaluated on CEC’2010 and CEC’2013 benchmark suites for large-scale optimization and compared with related methods. Our results confirm the merits of the devised mechanisms in the proposed method. The results also show that our method can achieve a superior performance and outperform related methods.
IoT-Based Human Fall Detection System

Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person’s death. Preventing human falls is a social desire, but it is almost impossible to achieve because it is not possible to ensure full prevention. A possible solution is the detection of human falls in near real-time so that help can quickly be provided. This has the potential to greatly reduce the severity of the fall in long-term health consequences. This work proposes a solution based on the internet of things devices installed in people’s homes. The proposed non-wearable solution is non-intrusive and can be deployed not only in homes but also in hospitals, rehabilitation facilities, and elderly homes. The solution uses a three-layered computation architecture composed of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are used for human fall classification; both approaches are compared. The results showed that the combination of both models is possible and brings benefits to the system, achieving an accuracy of 92.5% without false negatives.
Classification of movie reviews using term frequency-inverse document frequency and optimized machine learning algorithms

The Internet Movie Database (IMDb), being one of the popular online databases for movies and personalities, provides a wide range of movie reviews from millions of users. This provides a diverse and large dataset to analyze users' sentiments about various personalities and movies. Despite being helpful to provide the critique of movies, the reviews on IMDb cannot be read as a whole and requires automated tools to provide insights on the sentiments in such reviews. This study provides the implementation of various machine learning models to measure the polarity of the sentiments presented in user reviews on the IMDb website. For this purpose, the reviews are first preprocessed to remove redundant information and noise, and then various classification models like support vector machines (SVM), Naïve Bayes classifier, random forest, and gradient boosting classifiers are used to predict the sentiment of these reviews. The objective is to find the optimal process and approach to attain the highest accuracy with the best generalization. Various feature engineering approaches such as term frequency-inverse document frequency (TF-IDF), bag of words, global vectors for word representations, and Word2Vec are applied along with the hyperparameter tuning of the classification models to enhance the classification accuracy. Experimental results indicate that the SVM obtains the highest accuracy when used with TF-IDF features and achieves an accuracy of 89.55%. The sentiment classification accuracy of the models is affected due to the contradictions in the user sentiments in the reviews and assigned labels. For tackling this issue, TextBlob is used to assign a sentiment to the dataset containing reviews before it can be used for training. Experimental results on TextBlob assigned sentiments indicate that an accuracy of 92% can be obtained using the proposed model.
Interactive and Interpretable Online Human Activity Recognition

This demo paper puts forward our interactive and inspectable real-time recognition demo for human activity recognition. The demo captures online data from wearable sensors such as inertial measurement units, recognizes the performed human activity using Hidden Markov Models, and displays the search&#x2019;s current state along with the recognition results in real-time. Therefore, allowing users to interact with the system and probe its recognition by altering their actions and comparing their expectations with the demo&#x2019;s performance. Students, Researchers, or the general public can easily extend the detected classes by utilizing the integrated recording, segmentation, and re-training tools &#x2014; all while running on moderate hardware or even IoT devices like a Raspberry Pi. The demo is titled "ASK2.0" and provides an easy-to-use and powerful way to teach and explain real-time human activity recognition and machine learning.
DetectFormer: Category-Assisted Transformer for Traffic Scene Object Detection

Object detection plays a vital role in autonomous driving systems, and the accurate detection of surrounding objects can ensure the safe driving of vehicles. This paper proposes a category-assisted transformer object detector called DetectFormer for autonomous driving. The proposed object detector can achieve better accuracy compared with the baseline. Specifically, ClassDecoder is assisted by proposal categories and global information from the Global Extract Encoder (GEE) to improve the category sensitivity and detection performance. This fits the distribution of object categories in specific scene backgrounds and the connection between objects and the image context. Data augmentation is used to improve robustness and attention mechanism added in backbone network to extract channel-wise spatial features and direction information. The results obtained by benchmark experiment reveal that the proposed method can achieve higher real-time detection performance in traffic scenes compared with RetinaNet and FCOS. The proposed method achieved a detection performance of 97.6% and 91.4% in AP50 and AP75 on the BCTSDB dataset, respectively.
Multi-Behavior Sequential Transformer Recommender

In most real-world recommender systems, users interact with items in a sequential and multi-behavioral manner. Exploring the fine-grained relationship of items behind the users' multi-behavior interactions is critical in improving the performance of recommender systems. Despite the great successes, existing methods seem to have limitations on modelling heterogeneous item-level multi-behavior dependencies, capturing diverse multi-behavior sequential dynamics, or alleviating data sparsity problems. In this paper, we show it is possible to derive a framework to address all the above three limitations. The proposed framework MB-STR, a Multi-Behavior Sequential Transformer Recommender, is equipped with the multi-behavior transformer layer (MB-Trans), the multi-behavior sequential pattern generator (MB-SPG) and the behavior-aware prediction module (BA-Pred). Compared with a typical transformer, we design MB-Trans to capture multi-behavior heterogeneous dependencies as well as behavior-specific semantics, propose MB-SPG to encode the diverse sequential patterns among multiple behaviors, and incorporate BA-Pred to better leverage multi-behavior supervision. Comprehensive experiments on three real-world datasets show the effectiveness of MB-STR by significantly boosting the recommendation performance compared with various competitive baselines. Further ablation studies demonstrate the superiority of different modules of MB-STR.
Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series

Anomaly detection in high-dimensional time series is typically tackled using either reconstruction- or forecasting-based algorithms due to their abilities to learn compressed data representations and model temporal dependencies, respectively. However, most existing methods disregard the relationships between features, information that would be extremely useful when incorporated into a model. In this work, we introduce Fused Sparse Autoencoder and Graph Net (FuSAGNet), which jointly optimizes reconstruction and forecasting while explicitly modeling the relationships within multivariate time series. Our approach combines Sparse Autoencoder and Graph Neural Network, the latter of which predicts future time series behavior from sparse latent representations learned by the former as well as graph structures learned through recurrent feature embedding. Experimenting on three real-world cyber-physical system datasets, we empirically demonstrate that the proposed method enhances the overall anomaly detection performance, outperforming baseline approaches. Moreover, we show that mining sparse latent patterns from high-dimensional time series improves the robustness of the graph-based forecasting model. Lastly, we conduct visual analyses to investigate the interpretability of both recurrent feature embeddings and sparse latent representations.
Hierarchical Frequency-Assisted Interactive Networks for Face Manipulation Detection

Recently, face manipulation techniques have caused increasing trust concerns in our society. Although current face manipulation detection methods achieve impressive performance regarding intra-dataset evaluation, they are struggling to improve the generalization and robustness ability. To address this issue, we propose a novel Hierarchical Frequency-assisted Interactive Networks (HFI-Net) to explore comprehensive frequency-related forgery cues for face manipulation detection. At first, we formulate HFI-Net as a dual-branch network to take full advantage of both CNN and transformer for capturing local details and global context information, respectively. Considering the forged faces are easy to show flaws in the frequency domain, a novel Frequency-based Feature Refinement (FFR) module is proposed to learn frequency-based attention from RGB features. FFR module emphasizes forgery cues and suppresses the pristine semantics information by keeping middle-high frequency features while discarding the low-frequency ones. Based on FFR, we further develop a co-sharing Global-Local Interaction (GLI) module to conduct frequency-assisted interactions while capturing complementarity among dual branches. Lastly, we further implement the GLI module in each stage of the network to effectively explore multi-level frequency artifacts. Extensive experiments are conducted on several popular benchmarks including FaceForensics++, Celeb-DF, DeepFake-TIMIT, DFDC, UADFV, and DeeperForensics-1.0, which shows that our model outperforms the state-of-the-art, especially in unseen datasets, manipulations, and perturbations evaluation.
Sat-NeRF: Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras

We introduce the Satellite Neural Radiance Field (SatNeRF), a new end-to-end model for learning multi-view satellite photogrammetry in the wild. Sat-NeRF combines some of the latest trends in neural rendering with native satellite camera models, represented by rational polynomial coefficient (RPC) functions. The proposed method renders new views and infers surface models of similar quality to those obtained with traditional state-of-the-art stereo pipelines. Multi-date images exhibit significant changes in appearance, mainly due to varying shadows and transient objects (cars, vegetation). Robustness to these challenges is achieved by a shadow-aware irradiance model and uncertainty weighting to deal with transient phenomena that cannot be explained by the position of the sun. We evaluate Sat-NeRF using WorldView-3 images from different locations and stress the advantages of applying a bundle adjustment to the satellite camera models prior to training. This boosts the network performance and can optionally be used to extract additional cues for depth supervision.
NTIRE 2022 Challenge on Night Photography Rendering

This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://nightimaging.org/
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation

Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by modeling how users transit among items. However, the short interaction sequences limit the performance of existing SR. To solve this problem, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to leverage information from other domains to improve the sequential recommendation performance of a single domain. Solving CDSR is challenging. On the one hand, how to retain single domain preferences as well as integrate cross-domain influence remains an essential problem. On the other hand, the data sparsity problem cannot be totally solved by simply utilizing knowledge from other domains, due to the limited length of the merged sequences. To address the challenges, we propose DDGHM, a novel framework for the CDSR problem, which includes two main modules, i.e., dual dynamic graph modeling and hybrid metric training. The former captures intra-domain and inter-domain sequential transitions through dynamically constructing two-level graphs, i.e., the local graphs and the global graphs, and incorporating them with a fuse attentive gating mechanism. The latter enhances user and item representations by employing hybrid metric learning, including collaborative metric for achieving alignment and contrastive metric for preserving uniformity, to further alleviate data sparsity issue and improve prediction accuracy. We conduct experiments on two benchmark datasets and the results demonstrate the effectiveness of DDGHM.
Efficient room-temperature molecular single-photon sources for quantum key distribution

Quantum key distribution (QKD) allows the distribution of cryptographic keys between multiple users in an information-theoretic secure way, exploiting quantum physics. While current QKD systems are mainly based on attenuated laser pulses, deterministic single-photon sources could give concrete advantages in terms of secret key rate (SKR) and security owing to the negligible probability of multi-photon events. Here, we introduce and demonstrate a proof-of-concept QKD system exploiting a molecule-based single-photon source operating at room temperature and emitting at 785 nm. With an estimated maximum SKR of 0.5 Mbps, our solution paves the way for room-temperature single-photon sources for quantum communication protocols.
Reflash Dropout in Image Super-Resolution

Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in lowlevel vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a different behaviour as high-level tasks and is sensitive to the dropout operation. However, in this paper, we show that appropriate usage of dropout benefits SR networks and improves the generalization ability. Specifically, dropout is better embedded at the end of the network and is significantly helpful for the multi-degradation settings. This discovery breaks our common sense and inspires us to explore its working mechanism. We further use two analysis tools - one is from a recent network interpretation work, and the other is specially designed for this task. The analysis results provide side proofs to our experimental findings and show us a new perspective to understand SR networks.
HOW IMPORTANT ARE ACTIVATION FUNCTIONS IN REGRESSION AND CLASSIFICATION? A SURVEY, PERFORMANCE COMPARISON, AND FUTURE DIRECTIONS

Inspired by biological neurons, the activation functions play an essential part in the learning process of any artificial neural network (ANN) commonly used in many real-world problems. Various activation functions have been proposed in the literature for classification as well as regression tasks. In this work, we survey the activation functions that have been employed in the past as well as the current state-of-the-art. In particular, we present various developments in activation functions over the years and the advantages as well as disadvantages or limitations of these activation functions. We also discuss classical (fixed) activation functions, including rectifier units, and adaptive activation functions. In addition to discussing the taxonomy of activation functions based on characterization, a taxonomy of activation functions based on applications is presented. To this end, the systematic comparison of various fixed and adaptive activation functions is performed for classification datasets such as MNIST, CIFAR-10, and CIFAR-100. In recent years, a physics-informed machine learning framework has emerged for solving problems related to scientific computations. For this purpose, we also discuss various requirements for activation functions that have been used in the physics-informed machine learning framework. Furthermore, various comparisons are made among different fixed and adaptive activation functions using various machine learning libraries such as TensorFlow, PyTorch, and JAX. Our findings show that activation functions such as rectified linear unit (ReLU) and its variants, which are currently the state-of-the-art for many classification problems, do not work well in physics-informed machine learning frameworks due to the stringent requirement of the existence of derivatives, whereas other activation functions such as hyperbolic tangent, swish, and sine give better performance, with superior results achieved with adaptive activation functions, especially for multiscale problems.
What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT

Purpose The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model. Design/methodology/approach The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses. Findings The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students. Practical implications This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots. Originality/value Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.
Estimating Ultraviolet Radiation From Global Horizontal Irradiance

Terrestrial ultraviolet radiation (UV) radiation is a primary factor contributing to the degradation of photovoltaic (PV) modules' efficiency and reliability over time. Therefore, accurate knowledge of terrestrial UV incident on the surface of the PV materials is essential to understand the degradation of PV modules and provide reliable assessment of their service life. As PV is deployed in various climate zones, it is crucial that terrestrial UV information is available at various locations. However, the availability of terrestrial UV data-measured or modeled-is extremely limited. On the other hand, total solar irradiance (TS) datasets are relatively abundant. In this study, the National Renewable Energy Laboratory, its industry partners, and ASTM's International Subcommittee on Radiometry and Service Life Prediction are developing a simple method to estimate the clear-sky terrestrial UV irradiance (280-400 nm, 295-400 nm, 285-385 nm, or 295-385 nm) from total irradiance data (280-4000 nm). The goal is to provide reliable estimates of the UV received by samples as a function of location, orientation, tilt, and airmass, thus encompassing a variety of conditions. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) model is used to estimate the UV:TS ratio under various scenarios, and examines the influences of atmospheric constituents, such as aerosols, precipitable water vapor or ozone, and of the local surface characteristics (albedo), on the predicted UV.
Structural Damage Evaluation Method for Metro Shield Tunnel

The reasonable damage evaluation method for tunnel structure can be used to determine the overall safety state of the tunnel structure and can help to establish the maintenance plan of the tunnel structure. As a fuzzy system, the evaluation process of tunnel damage has the characteristics of randomness and fuzziness. Traditional tunnel damage evaluation methodologies do not account for the fuzziness and randomness of the system. Therefore, this study puts forward a visual evaluation method based on cloud theory for evaluating tunnel damage. Damage evaluation for a shield tunnel section of Changsha Metro Line Two in China has been taken as a case study. This method effectively considers the fuzziness and randomness of the evaluation system, improves robustness of the results of the structural damage evaluation, and is a simple and visual method.