Reviewerly/exHarmony huggingface dataset:
Location-Based Augmented Reality for Cultural Heritage Education: Creating Educational, Gamified Location-Based AR Applications for the Prehistoric Lake Settlement of Dispilio

Today, augmented reality (AR) applications are being used in many fields, such as advertising, entertainment, tourism, and education. Location-based augmented reality (AR) is a technology where interactive digital content is associated with real-world locations and their geo-based markers. The merging of the real-world environment with the digital content occurs when the user reaches these locations. Location-based AR applications are typically experienced using mobile devices with the ability to report the user’s location via GPS. These applications are increasingly used in education, since it has been shown that they positively affect student satisfaction and engagement. Furthermore, it is known in the literature that learner satisfaction and engagement increase when gamification and storytelling techniques are incorporated into the educational process. The aim of the study is to present two location-based, educational augmented-reality applications that utilize gamification and storytelling to provide cultural heritage knowledge about a prehistoric lake settlement. The study also aims to provide ideas and guidance to educators who wish to create applications that transform educational visits to archeological sites and museums into engaging augmented-reality experiences. Both applications underwent a preliminary evaluation using a sample of 71 higher-education students and a sample of 58 school students. The findings showed that the applications scored well in aspects such as ease of use, student satisfaction, and perceived educational usefulness.
A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch

Large-scale grids have gradually become the dominant trend in power systems, which has increased the importance of solving the challenges associated with large-scale economic dispatch (LED). An increase in the number of decision variables enlarges the search-space scale in LED. In addition to increasing the difficulty of solving algorithms, huge amounts of computing resources are consumed. To overcome this problem, we proposed a surrogate-assisted adaptive bat algorithm (GARCBA). On the one hand, to reduce the execution time of LED problems, we proposed a generalized regression neural network surrogate model based on a self-adaptive “minimizing the predictor” sampling strategy, which replaces the original fuel cost functions with a shorter computing time. On the other hand, we also proposed an improved hybrid bat algorithm (RCBA) named GARCBA to execute LED optimization problems. Specifically, we developed an evolutionary state evaluation (ESE) method to increase the performance of the original RCBA. Moreover, we introduced the ESE to analyze the population distribution, fitness, and effective radius of the random black hole in the original RCBA. We achieved a substantial improvement in computational time, accuracy, and convergence when using the GARCBA to solve LED problems, and we demonstrated this method’s effectiveness with three sets of simulations.
Improved Bidirectional RRT* Algorithm for Robot Path Planning

In order to address the shortcomings of the traditional bidirectional RRT* algorithm, such as its high degree of randomness, low search efficiency, and the many inflection points in the planned path, we institute improvements in the following directions. Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles.
Illumination estimation for nature preserving low-light image enhancement

In retinex model, images are considered as a combination of two components: illumination and reflectance. However, decomposing an image into the illumination and reflectance is an ill-posed problem. This paper presents a new approach to estimate the illumination for low-light image enhancement. This work contains three major tasks: estimation of structure-aware initial illumination, refinement of the estimated illumination, and the final correction of lightness in refined illumination. We have proposed a novel approach for structure-aware initial illumination estimation leveraging a new multi-scale guided filtering approach. The algorithm refines proposed initial estimation by formulating a new multi-objective function for optimization. Further, we proposed a new adaptive illumination adjustment for correction of lightness using the estimated illumination. The qualitative and quantitative analysis on low-light images with varying illumination shows that the proposed algorithm performs image enhancement with color constancy and preserves the natural details. The performance comparison with state-of-the-art algorithms shows the superiority of the proposed algorithm.
A Modified RNN-Based Deep Learning Method for Prediction of Atmospheric Visibility

Accurate atmospheric visibility prediction is of great significance to public transport safety. However, since it is affected by multiple factors, there still remains difficulties in predicting its heterogenous spatial distribution and rapid temporal variation. In this paper, a recursive neural network (RNN) prediction model modified with the frame-hopping transmission gate (FHTG), feature fusion module (FFM) and reverse scheduled sampling (RSS), named SwiftRNN, is developed. The new FHTG is used to accelerate training, the FFM is used for extraction and fusion of global and local features, and the RSS is employed to learn spatial details and improve prediction accuracy. Based on the ground-based monitoring data of atmospheric visibility from the China Meteorological Information Center during 1 January 2018 to 31 December 2020, the SwiftRNN model and two traditional ConvLSTM and PredRNN models are performed to predict hourly atmospheric visibility in central and eastern China at a lead of 12 h. The results show that the SwiftRNN model has better performance in the skill scores of visibility prediction than those of the ConvLSTM and PredRNN model. The averaged structural similarity (SSIM) of predictions at a lead up to 12 h is 0.444, 0.425 and 0.399 for the SwiftRNN, PredRNN and ConvLSTM model, respectively, and the averaged image perception similarity (LPIPS) is 0.289, 0.315 and 0.328, respectively. The averaged critical success index (CSI) of predictions over 1000 m fog area is 0.221, 0.205 and 0.194, respectively. Moreover, the training speed of the SwiftRNN model is 14.3% faster than the PredRNN model. It is also found that the prediction effect of the SwiftRNN model over 1000 m medium grade fog area is significantly improved along with lead times compared with the ConvLSTM and PredRNN model. All above results demonstrate the SwiftRNN model is a powerful tool in predicting atmospheric visibility.
Hierarchical-Based Binary Moth Flame Optimization for Feature Extraction in Biomedical Application

Feature extraction is a key challenging task to find the optimal features by alleviating irrelevant features to improve the classification accuracy. The brute force methods yield complete feature space and employ exhaustive search that makes the feature selection a non-deterministic polynomial problem. The meta-heuristic algorithm offers a better optimal solution through random search rather than a complete search. However, the MFO falls with local optima and poor convergence. In this paper, a novel methodology based on the Hierarchical Binary Moth Flame Optimization (HBMFO) with a K-Nearest Neighbor (KNN) classifier is proposed for feature extraction. The motive of this work is to reduce high dimensionality for large datasets through feature extraction. Here, the adaptive hierarchical method has been proposed to update the moth position towards the optimal solution pertaining to searching the space. The simulation results are accomplished on UCI Repository datasets to validate the superiority of the suggested optimization algorithm over existing techniques and feature selection can be carried out with respect to improving stability and accuracy.
Computation-Efficient Knowledge Distillation via Uncertainty-Aware Mixup

Knowledge distillation (KD) has emerged as an essential technique not only for model compression, but also other learning tasks such as continual learning. Given the richer application spectrum and potential online usage of KD, knowledge distillation efficiency becomes a pivotal component. In this work, we study this little-explored but important topic. Unlike previous works that focus solely on the accuracy of student network, we attempt to achieve a harder goal – to obtain a performance comparable to conventional KD with a lower computation cost during the transfer. To this end, we present UNcertainty-aware mIXup (UNIX), an effective approach that can reduce transfer cost by 20% to 30% and yet maintain comparable or achieve even better student performance than conventional KD. This is made possible via effective uncertainty sampling and a novel adaptive mixup approach that select informative samples dynamically over ample data and compact knowledge in these samples. We show that our approach inherently performs hard sample mining. We demonstrate the applicability of our approach to improve various existing KD approaches by reducing their queries to a teacher network. Extensive experiments are performed on CIFAR100 and ImageNet. Code and model are available at https://github.com/xuguodong03/UNIXKD.
Dialogue Robot Competition for the Development of an Android Robot with Hospitality

To promote the research and development of an android robot with hospitality, we organized a dialogue robot competition in which the task is to serve a customer in a travel destination recommendation task. The robot acts as a salesperson at a travel agency and needs to help customers choose their desired tourist destinations. This paper describes the task setting, software distributed for the competition, evaluation procedure, and results of the preliminary and final rounds of the competition.
Toward Interpretability in Fault Diagnosis for Autonomous Vehicles: Interpretation of Sensor Data Anomalies

To guarantee the safety and reliability of autonomous driving applications, it is indispensable to construct a proper fault diagnosis framework tailored to autonomous vehicles. Fault diagnosis aims to provide essential information about the system operational status and its interpretation facilitates decision-making and mitigates the potential operation risks. In the present work, interpretability issue in fault diagnosis for autonomous vehicles is discussed from the sensor data analytics perspective. Environmental impact is first evaluated using the noise energy as a measure to interpret the impact on sensor data caused by the environment. A signal quality indicator is proposed and Savitzky–Golay filters are applied for online denoising, acting as a countermeasure to mitigate the impact and to enhance the data quality. Then, the adversarial learned denoising shrinkage autoencoder (ALDSAE), an adversarial learning neural network, is constructed for sensor data anomaly detection. It considers an adversarial training technique to improve the performance of the anomaly detector. A residual explainer specific to the ALDSAE model is employed to calculate the contribution of each input feature to the anomaly score in order to interpret the anomaly detection results. Several experiments with the collected data from an autonomous vehicle in a real test field are implemented to validate the effectiveness of the proposed approaches. Results show that the area under the ROC curve (AUC_ROC) of the proposed ALDSAE is over 20% higher in average than several traditional anomaly detectors, and the mean explanation accuracy of the residual explainer achieves similar performance with the widely employed kernel Shapley additive explanation (SHAP) with more than 99% reduction in mean response time.
Image Watermarking Using Least Significant Bit and Canny Edge Detection

With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today's world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB's high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks.
Empirical mode decomposition and power spectrum filtering for detection of frequency channels related to multi-scale geochemical anomalies: Metal exploration targeting in Moalleman district, NE Iran

Geochemical anomaly patterns for metal exploration targeting usually reveal non-stationarity due mainly to the non-linear synthesis of ore-forming processes. The spectrum–area (S–A) multifractal filtering and the bidimensional empirical mode decomposition (BEMD) are robust methodologies for detecting geochemical anomalies with different scaling properties. The former portrays the generalized scale invariance characteristics of a geochemical image in the two-dimensional Fourier frequency domain. It relies on estimating scaling exponents of power-law functions in the spectral energy density plane to design filter functions that amplify a geochemical signal in specific frequency channels. The BEMD decomposes a geochemical image into a number of bidimensional intrinsic mode functions (BIMFs). It represents the spatial–frequency–energy distribution of a given image that suits non-stationary geochemical signals arising from non-linear Earth systems. This paper gauges the relevance of BEMD and S–A in signalizing frequency channels that correspond to regional- and local-scale geochemical anomalies through a comparative experiment in the Moalleman district, NE Iran. The BEMD filtered the multi-element anomaly map adaptively into a set of BIMFs (BIMF1, BIMF2, and BIMF3) and a residual signal, in order of decreasing frequency, yielding geochemical patterns with scaling characteristics from local to regional. The S–A model filtered the multi-element anomaly map into very high, high, and low frequency channels that characterize noise signatures and geochemical anomalies associated with local and regional ore-forming processes, respectively. The results indicate that the high frequency channel derived from the BEMD filtering (i.e., BIMF1) can portray more effectively local-scale metal-vectoring anomalies distributed at/around ore-controlling faults/fractures. However, the low frequency channel yielded by S–A filtering has enhanced effectively geochemical anomaly architectures that were developed through regional metallogenesis. Therefore, we considered the local-scale geochemical anomalies (i.e., BIMF1) to delimit metal-vectoring targets, whereby four exploration priorities were defined as new areas for further metal prospecting within the study area.
Beyond OCR + VQA: Towards end-to-end reading and reasoning for robust and accurate textvqa

Text-based visual question answering (TextVQA), which answers a visual question by considering both visual contents and scene texts, has attracted increasing attention recently. Most existing methods employ an optical character recognition (OCR) module as a pre-processor to read texts, then combine it with a visual question answering (VQA) framework. However, inaccurate OCR results may lead to cumulative error propagation, and the correlation between text reading and text-based reasoning is not fully exploited. In this work, we integrate OCR into the flow of TextVQA, targeting the mutual reinforcement of OCR and VQA tasks. Specifically, a visually enhanced text embedding module is proposed to predict semantic features from the visual information of texts, by which texts can be reasonably understood even without accurate recognition. Further, two elaborate schemes are developed to leverage contextual information in VQA to modify OCR results. The first scheme is a reading modification module that adaptively selects the answer results according to the contexts. Second, we propose an efficient end-to-end text reading and reasoning network, where the downstream VQA signal contributes to the optimization of text reading. Extensive experiments show that our method outperforms existing alternatives in terms of accuracy and robustness, whether ground truth OCR annotations are used or not.
When do robo-advisors make us better investors? The impact of social design elements on investor behavior

Investors increasingly can obtain advice from “robo-advisors", artificial intelligence–enabled digitalized service agents. We study whether and why the provision of investment advice from a robo-advisor improves individuals’ investment decisions. In two consequential induced-value experiments, we analyze the well-documented disposition effect, which reflects investors’ greater propensity to realize past gains than past losses. We find that the availability of a robo-advisor reduces (i.e., mitigates) investors’ disposition effect (Study 1). Moreover, imbuing the robo-advisor with social design elements (e.g., a name and the ability to communicate using natural language) negatively affects investment behavior (i.e., increases the disposition effect). The extent to which investors seek advice mediates this effect, i.e., investors ask for advice to a lesser extent from a robo-advisor with, compared to without, social design elements (Study 2). Our findings advance our understanding of the benefits of artificial intelligence-enabled advisors for improving decision making. However, our results also imply increased psychological hurdles of asking for advice from human-like robo-advisors and highlight potential risks of imbuing them with social design elements, which has become a widespread practice to give robo-advisors a human touch.
Robust representation learning for heterogeneous attributed networks

The aim of heterogeneous attributed network embedding is mapping network into low-dimensional representations while preserving topological structure and attributed content. However, when the content similarity of two closely related nodes is small in the heterogeneous attributed network, we find that the embedding vectors obtained by combining network structure information and content information are bad. To tackle this problem, we propose a new robust representation learning model for Heterogeneous Attributed Networks Embedding with Dual-Space (HANE-DS), whose idea is to embed heterogeneous attributed network into dual spaces. Firstly, a heterogeneous attributed network is converted to a heterogeneous structure network and a heterogeneous content network. Secondly, the network nodes of the heterogeneous structure network are embedded into the structure space by using our proposed the structure embedding model, while the network nodes of the heterogeneous content network are embedded into the content space by applying our proposed the content embedding model. Finally, by utilizing the learning embedding vectors for downstream tasks, our approach can capture more comprehensive, more prosperous, and more reasonable information. Up to present, this is the first paper that focuses on the heterogeneous attributed network embedding based on Dual-Space. Three real-world networks and five synthetic networks to evaluate the HANE-DS model, and experimental results show that the HANE-DS can outperform baselines. For classification and clustering, the HANE-DS is superior to the baseline methods. For link prediction, the HANE-DS is in the top three of the baseline methods. For recommendation, the HANE-DS is also ahead of the baseline methods in terms of HR@K. On the synthetic networks, it is verified that the baseline models are sensitive to the content similarity of two closely related nodes. In comparison, our model is insensitive to that situation. In summary, the HANE-DS is a robust model for the heterogeneous attributed network embedding.
CUSS-Net: A Cascaded Unsupervised-Based Strategy and Supervised Network for Biomedical Image Diagnosis and Segmentation

Biomedical image segmentation and classification are critical components in a computer-aided diagnosis system. However, various deep convolutional neural networks are trained by a single task, ignoring the potential contribution of mutually performing multiple tasks. In this paper, we propose a cascaded unsupervised-based strategy to boost the supervised CNN framework for automated white blood cell (WBC) and skin lesion segmentation and classification, called CUSS-Net. Our proposed CUSS-Net consists of an unsupervised-based strategy (US) module, an enhanced segmentation network named E-SegNet, and a mask-guided classification network called MG-ClsNet. On the one hand, the proposed US module produces coarse masks that provide a prior localization map for the proposed E-SegNet to enhance it in locating and segmenting a target object accurately. On the other hand, the enhanced coarse masks predicted by the proposed E-SegNet are then fed into the proposed MG-ClsNet for accurate classification. Moreover, a novel cascaded dense inception module is presented to capture more high-level information. Meanwhile, we adopt a hybrid loss by combining a dice loss and a cross-entropy loss to alleviate the imbalance training problem. We evaluate our proposed CUSS-Net on three public medical image datasets. Experiments show that our proposed CUSS-Net outperforms representative state-of-the-art approaches.