Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Dance Online Teaching System Based on Optimized Load Balancing Algorithm

With the continuous improvement of the network hardware environment, people turn the demand target to the network application environment and the construction of information resources. How to build a network teaching platform for general undergraduate teaching to ensure the stability of the system and high-quality services during operation especially large-scale concurrent access will inevitably lead to the increase in the business volume of each core part of the network, the number of visits, and data traffic. With the growth of the network, the corresponding processing power and computing intensity also increase rapidly, which causes problems such as overloading of core network equipment, network bottlenecks, and network congestion. Simply pursuing high-performance hardware to solve problems will undoubtedly result in high cost investment; moreover, equipment with excellent performance cannot meet the needs of the current rapidly growing business volume. According to the design goal of the dance online teaching platform, to meet the online teaching load requirements of many people at the same time, the pressure of the web server cluster must be great. Because many people in online at the same time put too much pressure on the web server, this part of the network cannot be processed in time, which leads to the phenomenon that the performance of this part and even the whole network is degraded. In severe cases, it will even cause network communication services to come to a standstill, that is, the so-called deadlock phenomenon. If the protocol software cannot detect congestion and reduce the packet sending rate, the network will be paralyzed due to congestion. This situation will cause the problem of movement delay for online dance teaching, which will seriously affect the quality of teaching. Therefore, the dance online course system should be continuously improved, the quality of online courses should be continuously improved, and the study of the practical application of load balancing technology in the network teaching environment has become an important means to solve the relationship between supply and demand of network teaching. According to the experimental analysis, when the number of Worker' actuators is fixed, the execution time span of MakeSpan increases with the increase of tasks, while the time required by the optimized load balancing algorithm proposed in this paper increases by 1.32 s on average with the increase of tasks, and the time required by heuristic algorithm and bee colony algorithm increases by 3.68 s and 3.45 s on average with the increase of tasks. On the whole, the optimized load balancing algorithm proposed in this paper has obvious advantages.
Learning to automatically spectate games for Esports using object detection mechanism

Human game observers, who control in-game cameras and provide viewers with an engaging experience, are a vital part of electronic sports (Esports) which has emerged as a rapidly growing industry in recent years. However, such a professional human observer poses several problems. For example, they are prone to missing events occurring concurrently across the map. Further, human game observers are difficult to afford when only a small number of spectators are spectating the game. Consequently, various methods to create automatic observers have been explored, and these methods are based on defining in-game events and focus on defined events. However, these event-based methods necessitate detailed predefined events, demanding high domain knowledge when developing. Additionally, these methods cannot show scenes that contain undefined events. In this paper, we propose a method to overcome these problems by utilizing multiple human observational data and an object detection method, Mask R-CNN, in a real-time strategy game (e.g., StarCraft). By learning from human observational data, our method can observe scenes that are not defined as events in advance. The proposed model utilizes an object detection mechanism to find the area where the human spectator is interested. We consider the pattern of the two-dimensional spatial area that the spectator is looking at as the object to find, rather than treating a single unit or building as an object which is conventionally done in object detection. Consequently, we show that our automatic observer outperforms both current rule-based methods and human observers. The game observation video that compares our method and the rule-based method is available at https://www.youtube.com/watch?v=61JIfSrLHVk.
Robustness Learning via Inference-Softmax Cross Entropy in Misaligned Distribution of Image

Adversarial examples easily mislead vision systems based on deep neural networks (DNNs) trained with softmax cross entropy (SCE) loss. The vulnerability of DNN comes from the fact that SCE drives DNNs to fit on the training examples, whereas the resultant feature distributions between the training and adversarial examples are unfortunately misaligned. Several state-of-the-art methods start from improving the inter-class separability of training examples by modifying loss functions, where we argue that the adversarial examples are ignored, thus resulting in a limited robustness to adversarial attacks. In this paper, we exploited the inference region, which inspired us to apply margin-like inference information to SCE, resulting in a novel inference-softmax cross entropy (I-SCE) loss, which is intuitively appealing and interpretable. The inference information guarantees that it is difficult for neural networks to cross the decision boundary under an adversarial attack, and guarantees both the inter-class separability and the improved generalization to adversarial examples, which was further demonstrated and proved under the min-max framework. Extensive experiments show that the DNN models trained with the proposed I-SCE loss achieve a superior performance and robustness over the state-of-the-arts under different prevalent adversarial attacks; for example, the accuracy of I-SCE is 63% higher than SCE under the PGD50un attack on the MNIST dataset. These experiments also show that the inference region can effectively solve the misaligned distribution.
ChebyLighter: Optimal Curve Estimation for Low-light Image Enhancement

Low-light enhancement aims to recover a high contrast normal light image from a low-light image with bad exposure and low contrast. Inspired by curve adjustment in photo editing software and Chebyshev approximation, this paper presents a novel model for brightening low-light images. The proposed model, ChebyLighter, learns to estimate pixel-wise adjustment curves for a low-light image recurrently to reconstruct an enhanced output. In ChebyLighter, Chebyshev image series are first generated. Then pixel-wise coefficient matrices are estimated with Triple Coefficient Estimation (TCE) modules and the final enhanced image is recurrently reconstructed by Chebyshev Attention Weighted Summation (CAWS). The TCE module is specifically designed based on dual attention mechanism with three necessary inputs. Our method can achieve ideal performance because adjustment curves can be obtained with numerical approximation by our model. With extensive quantitative and qualitative experiments on diverse test images, we demonstrate that the proposed method performs favorably against state-of-the-art low-light image enhancement algorithms.
Evaluating contrastive explanations for AI planning with non-experts: a smart home battery scenario

Smart home systems with AI planning functionality have the potential to improve the lives of users. However, there is an emerging expectation that users should better understand and trust the decision-making of AI systems. In this paper, a smart home battery system is developed with a supplementary explanation module that allows non-expert users to intuitively visualise the planning process and to better understand its recommendations. The module relies on a notion of contrastive explanations, related to iterative planning, allowing users to ask contrastive questions based on state- and action-constraints that may or may not be satisfiable. The system is intended for an experimental study where participants interact with the planning system and complete a questionnaire, with the research objective being to evaluate the usefulness of the explanation module.
ReFu: Refine and Fuse the Unobserved View for Detail-Preserving Single-Image 3D Human Reconstruction

Single-image 3D human reconstruction aims to reconstruct the 3D textured surface of the human body given a single image. While implicit function-based methods recently achieved reasonable reconstruction performance, they still bear limitations showing degraded quality in both surface geometry and texture from an unobserved view. In response, to generate a realistic textured surface, we propose ReFu, a coarse-to-fine approach that refines the projected backside view image and fuses the refined image to predict the final human body. To suppress the diffused occupancy that causes noise in projection images and reconstructed meshes, we propose to train occupancy probability by simultaneously utilizing 2D and 3D supervisions with occupancy-based volume rendering. We also introduce a refinement architecture that generates detail-preserving backside-view images with front-to-back warping. Extensive experiments demonstrate that our method achieves state-of-the-art performance in 3D human reconstruction from a single image, showing enhanced geometry and texture quality from an unobserved view.
Explainable AI for Non-Experts: Energy Tariff Forecasting

Non-expert users are increasingly affected by the decisions of systems that rely on machine learning (ML), yet it is often difficult for these users to understand the predictions of ML models. In this paper, we propose a web-based platform to evaluate explainable AI (XAI) for non-experts in the context of time series forecasting, focusing on energy price predictions as an exemplary use case. The XAI methods we consider include local feature importance and counterfactual explanations. The platform relies on gamification to encourage user engagement. Our research objective is to evaluate the effectiveness of these different approaches from the perspective of non-expert understanding of machine learning models.
Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning

Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, state-of-the-art DRL algorithms still struggle to learn long-horizon, multi-step and sparse reward tasks, such as stacking several blocks given only a task-completion reward signal. To improve learning efficiency for such tasks, this paper proposes a DRL exploration technique, termed A^2, which integrates two components inspired by human experiences: Abstract demonstrations and Adaptive exploration. A^2 starts by decomposing a complex task into subtasks, and then provides the correct orders of subtasks to learn. During training, the agent explores the environment adaptively, acting more deterministically for well-mastered subtasks and more stochastically for ill-learnt subtasks. Ablation and comparative experiments are conducted on several grid-world tasks and three robotic manipulation tasks. We demonstrate that A^2 can aid popular DRL algorithms (DQN, DDPG, and SAC) to learn more efficiently and stably in these environments.
An Explainable Regression Framework for Predicting Remaining Useful Life of Machines

Prediction of a machine’s Remaining Useful Life (RUL) is one of the key tasks in predictive maintenance. The task is treated as a regression problem where Machine Learning (ML) algorithms are used to predict the RUL of machine components. These ML algorithms are generally used as a black box with a total focus on the performance without identifying the potential causes behind the algorithms’ decisions and their working mechanism. We believe, the performance (in terms of Mean Squared Error (MSE), etc.,) alone is not enough to build the stakeholders’ trust in ML prediction rather more insights on the causes behind the predictions are needed. To this aim, in this paper, we explore the potential of Explainable AI (XAI) techniques by proposing an explainable regression framework for the prediction of machines’ RUL. We also evaluate several ML algorithms including classical and Neural Networks (NNs) based solutions for the task. For the explanations, we rely on two model agnostic XAI methods namely Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). We believe, this work will provide a baseline for future research in the domain.
A Unified System for Encryption and Multi-Secret Image Sharing Using S-box and CRT

Multi-Secret Image Sharing (MSIS) is used when multiple images need to be shared to multiple participants, but the images can not be recovered without the presence of all shares. In this paper, a unified system for performing encryption and (n,n)-MSIS is proposed. While MSIS is based on the XOR operation, encryption combines the utilization of Chinese Remainder Theorem (CRT), SHA-256, and S-box for improved security. The same designed system is used for the generation of secret shares and the recovery of secret images. In addition, a sensitive system key is designed where three pairwise relatively prime subkeys are automatically generated for utilization in the CRT. The resulting secret shares pass statistical evaluation criteria such as RMSE, correlation, and entropy, and give good results for differential attack measures, and runtime. In addition, the proposed system succeeds in passing the NIST SP-800-22 statistical test suite and key sensitivity measures.
Enhancing Semi-Supervised Learning with Cross-Modal Knowledge

Semi-supervised learning (SSL), which leverages a small number of labeled data that rely on expert knowledge and a large number of easily accessible unlabeled data, has made rapid progress recently. However, the information comes from a single modality and the corresponding labels are in form of one-hot in pre-existing SSL approaches, which can easily lead to deficiency supervision, omission of information and unsatisfactory results, especially when more categories and less labeled samples are covered. In this paper, we propose a novel method to further enhance SSL by introducing semantic modal knowledge, which contains the word embeddings of class labels and the semantic hierarchy structure among classes. The former helps retain more potential information and almost quantitatively reflects the similarities and differences between categories. The later encourages the model to construct the classification edge from simple to complex, and thus improves the generalization ability of the model. Comprehensive experiments and ablation studies are conducted on commonly-used datasets to demonstrate the effectiveness of our method.
Static and Dynamic Dependency Visualization in a Layered Software City

Abstract A Software City is an established way to visualize metrics such as the test coverage or complexity. As current layouting algorithms are mainly based on the static code structure, dependencies that are orthogonal to this structure often clutter the visualization and are hard to grasp. This paper applies layered graph drawing to layout a Software City in 3D. The proposed layout takes both the dependencies and the static code structure into account. While having the static dependencies encoded in the layout, we can additionally display dynamic dependencies as arcs atop the city in the night view of the Layered Software City. By applying a trace clustering technique we can further reduce the number of shown arcs. We evaluate the advantages of our layout over a classic layouting algorithm in a controlled study on a real-world project and also report on a short study that evaluates the visualization of dynamic dependencies. The source code of the layouting algorithm and the raw data of the quantitative evaluations are available from https://github.com/qaware/holoware-software-city .
Learning Shared Representations for Recommendation with Dynamic Heterogeneous Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have been widely used for collaborative filtering, due to their effectiveness in exploiting high-order collaborative signals. However, two issues have not been well addressed by existing studies. First, usually only one kind of information is utilized, i.e., user preference in user-item graphs or item dependency in item-item graphs. Second, they usually adopt static graphs, which cannot retain the temporal evolution of the information. These can limit the recommendation quality. To address these limitations, we propose to mine three kinds of information (user preference, item dependency, and user behavior similarity) and their temporal evolution by constructing multiple discrete dynamic heterogeneous graphs (i.e., a user-item dynamic graph, an item-item dynamic graph, and a user-subseq dynamic graph) from interaction data. A novel network (PDGCN) is proposed to learn the representations of users and items in these dynamic graphs. Moreover, we designed a structural neighbor aggregation module with novel pooling and convolution operations to aggregate the features of structural neighbors. We also design a temporal neighbor aggregation module based on self-attention mechanism to aggregate the features of temporal neighbors. We conduct extensive experiments on four real-world datasets. The results indicate that our approach outperforms several competing methods in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Dynamic graphs are also shown to be effective in improving recommendation performance.
Tackling the Accuracy-Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal

Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows extending the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this article, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning, which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.
Rapid prediction of essential oils major components by Vis/NIRS models using compositional methods

Essential oils (EOs) have often a major compound, which content is important for the industry. Thus, rapid techniques suitable to analyze the EOs composition are of great interest. Near infrared spectroscopy (NIRS) has proven suitable solutions for many similar targets. NIRS depends on multivariate correlation methods, which were designed for the real space, with values comprised between - ∞ and + ∞. However, essential oils compounds are compositional data (CoDa), restricted to a simplex space. These are co-dependent data, therefore their multivariate correlation with the spectral variables needs specific methods. This study evaluates predictive models of the contents of cinnamaldehyde, eugenol methyl-ether and thymol, respectively in EOs of cinnamon, clove and thyme, built by using CoDa method. This last was the isometric log-ratio transformation (ILR). Besides, the calibrations set without CoDa methods, were assessed. The results showed clearly the best prediction in external validation exercises was by using ILR. This predictive exercise pointed out the compositional method provided better model robustness than the calibration without this.
Pedestrian Detection Using MobileNetV2 Based Mask R-CNN

Pedestrian detection system is one of the recent technological innovations to save human lives on the roadways. According to WHO, road accidents highly contribute to the increasing mortality. These traffic accidents can be avoided by utilizing an autonomous vehicle equipped with AI algorithms to identify the pedestrians efficiently. Advances in computer vision and deep learning techniques open up new research possibilities. This research study has introduced novel algorithms to combine two of the most efficient deep learning models. It is well-known that the Mask R-CNN is the most efficient deep learning model used for performing object detection in two stages. This process leverages high accuracy but it also include limitations such as low detection speed and high computational cost. To resolve this problem a lighter version of Mask R-CNN with MobileNetV2 architecture has been developed. In order to make Mask R-CNN light, some modifications have been done in Region Proposal Network (RPN) like a Convolution Operation is replaced by the Depthwise Separable Convolution Operation. To further speed up the process, MobileNetV2 architecture is used in the place of ResNet-101. MobileNetV2 uses inverted residual block and linear bottleneck to generate less number of parameters and reduced network computation to save time and speed up the process. The main goal of the proposed model is to detect the pedestrian with high accuracy and at high speed by consuming less computational cost without compromising robustness of the system. Further, the experimentation has been carried out with INRIA dataset and recorded a 98.9% detection rate, 0.87 mAP and 0.85 mIoU, which is far better than the standard Mask R-CNN with ResNet-101 architecture. The proposed model's weight is also 65% lighter than the conventional model, allowing it to operate faster and spend less time in interpretation. The performance of ResNet-101 based Mask R-CNN and MobileNetV2 based Mask R-CNN were compared in this study. This model can be expanded in future to yield more accurate findings and improve the ability to deal with emerging challenges in smart vehicles.