Reviewerly/exHarmony huggingface dataset:
Stacking approach for accurate Invasive Ductal Carcinoma classification

The accurate diagnosis of Breast cancer (BC) requires adequately exploiting Artificial intelligence (AI)-based methods in the diagnosing process. To tackle the issue of accurate BC diagnosis, we have proposed a deep learning-based stacking method (StackBC). In particular, we have incorporated deep learning models including Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, Transfer Learning (TL) and Data Augmentation (DA) approaches have been incorporated to balance the dataset and adequately train the model. To further improve the predictive outputs of the model, we used the stacking technique. Among the three individual base classifiers , the performance of the GRU model was better. Hence, we selected the GRU as a meta classifier to distinguish between Non-IDC and IDC breast images. The experimental results confirmed that the StackBC method outperformed state-of-the-art methods. • Breast cancer detection and diagnostic method called Stack Breast Cancer is proposed. • Our CNN, LSTM and GRU models were evaluated using a breast histology image dataset. • Transfer learning and data augmentation were used to improve the models’ performances. • Stacking the models produced an improved model with a GRU meta predictor and performance of Stack BC model were higher compared to existing models.
Power quality events recognition using enhanced empirical mode decomposition and optimized extreme learning machine

In this paper, a novel approach based on Empirical Mode Decomposition (EMD) and an Extreme Learning Machine (ELM) for the detection and classification of Power Quality Events (PQEs) is proposed. The EMD technique is used for computing the prominent features required to characterize the PQE signals. A down-sampled Kriging Interpolation (KI) based EMD is suggested to enhance the performance of the EMD operation in terms of accuracy and speed. The ELM is applied for the classification of Power Quality Disturbances (PQDs), considering all the derived features through the KI-EMD approach. Symbiotic Organism Search (SOS) optimization technique is applied to enhance the performance and robustness of ELM by optimally computing the values of the system parameters. The performance of the proposed approach is justified with test cases under diverse noise conditions. Comparative results and analysis are provided to show an improvement of 2-5% in terms of accuracy, speed, and robustness compared to other conventional methods. Experimental results validate the efficacy of the proposed approach under real-time conditions.
LiTMNet: A deep CNN for efficient HDR image reconstruction from a single LDR image

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Enhanced Digital Image and Text Data Security Using Hybrid Model of LSB Steganography and AES Cryptography Technique

In the present innovation, for the trading of information, the internet is the most well-known and significant medium. With the progression of the web and data innovation, computerized media has become perhaps the most famous and notable data transfer tools. This advanced information incorporates text, pictures, sound, video etc moved over the public organization. The majority of these advanced media appear as pictures and are a significant part in different applications, for example, chat, talk, news, website, web-based business, email, and digital books. The content is still facing various challenges in which including the issues of protection of copyright, modification, authentication. Cryptography, steganography, embedding techniques is widely used to secure the digital data. In this present the hybrid model of LSB steganography and Advanced Encryption Standard (AES) cryptography techniques to enhanced the security of the digital image and text that is undeniably challenging to break by the unapproved person. The security level of the secret information is estimated in the term of MSE and PSNR for better hiding required the low MSE and high PSNR values.
Virtual sensor-based imputed graph attention network for anomaly detection of equipment with incomplete data

For the safe operation of complex equipment, it is essential to implement accurate anomaly detection on the key parts of equipment. However, due to the extreme conditions of the complex equipment, some sensors sometimes fail to collect signals, which makes existing methods unable to take full advantage of these incomplete multi-source data. To better detect anomalies by incomplete data, we proposed a virtual sensor-based imputed graph attention network, which generates signals to impute the time of sensor record failure by generative adversarial network (GAN) and extracts the features of complete signals mixed with real signals and generated signals by graph attention network (GAT). Creatively, “virtual sensor", a sensor representation is introduced into GAN as part of the input to make generated signal have the characteristic of its respective channel information. Through it, the signals of different channels as recorded by real sensors can be obtained. Additionally, the graph structure of multi-source signal is obtained by learning with no prior knowledge. Compared to existing methods with complete data, the proposed method is able to have better performances even without complete data. Furthermore, to demonstrate the ability for missing data imputation, we discuss the generation effect and downstream task performance of the proposed model and its variants in ablation experiments.
Strategic analysis for adopting blockchain technology under supply chain competition

This paper studies the strategy of adopting blockchain technology (BT) in two supply chains, each consisting of one manufacturer and one retailer. We find that the adoption of BT in supply chains is related to consumers’ traceability awareness and the blockchain-based traceability cost-sharing between the manufacturer and retailer. Specifically, when both consumers’ traceability awareness and the proportion of the blockchain-based traceability cost borne by each retailer are low or moderate, the equilibrium strategy could be that one or both supply chains adopt BT. Moreover, when one supply chain adopts BT, the other may be a free rider. When both supply chains adopt BT, one supply chain can gain a competitive advantage when its retailer shares more of the blockchain-based traceability cost than the rival. Counterintuitively, when traceability awareness is high, neither of the two supply chains should adopt BT.
Delineating privacy aspects of COVID tracing applications embedded with proximity measurement technologies &amp; digital technologies

As the COVID-19 pandemic expanded over the globe, governments implemented a series of technological measures to prevent the disease's spread. The development of the COVID Tracing Application (CTA) was one of these measures. In this study, we employed bibliometric and topic-based content analysis to determine the most significant entities and research topics. Additionally, we identified significant privacy concerns posed by CTAs, which gather, store, and analyze data in partnership with large technology corporations using proximity measurement technologies, artificial intelligence, and blockchain. We examined a series of key privacy threats identified in our study. These privacy risks include anti-democratic and discriminatory behaviors, politicization of care, derogation of human rights, techno governance, citizen distrust and refusal to adopt, citizen surveillance, and mandatory legislation of the apps' installation. Finally, sixteen research gaps were identified. Then, based on the identified theoretical gaps, we recommended fourteen prospective study strands. Theoretically, this study contributes to the growing body of knowledge about the privacy of mobile health applications that are embedded with cutting-edge technologies and are employed during global pandemics.
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions, standard FL methods are unstable and require intensive hyperparameter tuning to achieve optimal performance. Conventional hyperparameter optimization algorithms are impractical in real-world FL applications as they involve numerous training trials, which are often not affordable with limited compute budgets. In this work, we propose an efficient reinforcement learning (RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL, in which an online RL agent can dynamically adjust hyperparameters of each client based on the current training progress. Extensive experiments are conducted to investigate different search strategies and RL agents. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset as well as two real-world medical image segmentation datasets for COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT.
Non-Gaussian Risk Bounded Trajectory Optimization for Stochastic Nonlinear Systems in Uncertain Environments

We address the risk bounded trajectory optimization problem of stochastic nonlinear robotic systems. More precisely, we consider the motion planning problem in which the robot has stochastic nonlinear dynamics and uncertain initial locations, and the environment contains multiple dynamic uncertain obstacles with arbitrary probabilistic distributions. The goal is to plan a sequence of control inputs for the robot to navigate to the target while bounding the probability of colliding with obstacles. Existing approaches to address risk bounded trajectory optimization problems are limited to particular classes of models and uncertainties such as Gaussian linear problems. In this paper, we deal with stochastic nonlinear models, nonlinear safety constraints, and arbitrary probabilistic uncertainties, the most general setting ever considered. To address the risk bounded trajectory optimization problem, we first formulate the problem as an optimization problem with stochastic dynamics equations and chance constraints. We then convert probabilistic constraints and stochastic dynamics constraints on random variables into a set of deterministic constraints on the moments of state probability distributions. Finally, we solve the resulting deterministic optimization prob-lem using nonlinear optimization solvers and get a sequence of control inputs. To our best knowledge, it is the first time that the motion planning problem to such a general extent is considered and solved. To illustrate the performance of the proposed method, we provide several robotics examples.
RCL: Recurrent Continuous Localization for Temporal Action Detection

Temporal representation is the cornerstone of modern action detection techniques. State-of-the-art methods mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the temporal domain with a dis-cretized grid, and then regress the accurate boundaries. In this paper, we revisit this foundational stage and introduce Recurrent Continuous Localization (RCL), which learns a fully continuous anchoring representation. Specifically, the proposed representation builds upon an explicit model con-ditioned with video embeddings and temporal coordinates, which ensure the capability of detecting segments with arbi-trary length. To optimize the continuous representation, we develop an effective scale-invariant sampling strategy and recurrently refine the prediction in subsequent iterations. Our continuous anchoring scheme is fully differentiable, al-lowing to be seamlessly integrated into existing detectors, e.g., BMN [20] and G-TAD [41]. Extensive experiments on two benchmarks demonstrate that our continuous represen-tation steadily surpasses other discretized counterparts by ~2% mAP. As a result, RCL achieves 52.92% mAP@0.5 on THUMOS14 and 37.65% mAP on ActivtiyNet vl.3, outper-forming all existing single-model detectors.
Short range correlation transformer for occluded person re-identification

Occluded person re-identification is one of the challenging areas of computer vision, which faces problems such as inefficient feature representation and low recognition accuracy. Recently, vision transformer is introduced into the field of re-identification and achieved state-of-the-art results by constructing global feature relationships between patch sequences. However, vision transformer is not good at capturing short-range correlations of patch sequence and exploiting spatial correlation in patch sequence, which leads to a decrease in the accuracy and robustness of the network in the face of occluded person re-identification. Therefore, to address the above problems, we design a partial feature transformer-based occluded person re-identification framework named PFT. The proposed PFT utilizes three modules to enhance the efficiency of vision transformer. (1) Patch full dimension enhancement module. We design a learnable tensor with the same size as patch sequences, which is full-dimensional and deeply embedded in patch sequences to enrich the diversity of training samples. (2) Fusion and reconstruction module. We extract the less important part of obtained patch sequences, and fuse them with original patch sequence to reconstruct the original patch sequences. (3) Spatial Slicing Module. We slice and group patch sequences from spatial direction, which can effectively improve the short-range correlation of patch sequences. Experimental results over occluded and holistic re-identification datasets demonstrate that the proposed PFT network achieves superior performance consistently and outperforms the state-of-the-art methods.
Acceptance by athletes of a virtual reality head-mounted display intended to enhance sport performance

While a growing number of studies have highlighted the potential of virtual reality (VR) to improve athletes’ skills, no research has yet focused on acceptance of a VR head-mounted display (VR-HMD) designed to increase sport performance. However, even if technological devices could potentially lead to performance improvement, athletes may not always accept them. To investigate this issue, the Technology Acceptance Model (TAM) examines if perceived usefulness, perceived ease of use, perceived enjoyment, and subjective norms (i.e., social influence) are positive predictors of intention to use a specific technology. The aims of the present study were to test with competitive athletes the validity of the TAM before a first use of a VR-HMD intended to enhance sport performance and to examine to what extent the level of practice and the type of sport practiced have an influence on the previous variables of the TAM. The study sample comprised 1162 French athletes (472 women, 690 men, Mage = 24.50 ± 8.51 years) who usually practiced a sport in competition (from recreational to international level). After reading a short text presenting the VR-HMD and its interests for sport performance, the participants filled out an online questionnaire assessing their acceptance of this technological device before a first use. The results of the structural equation modeling analysis revealed that perceived usefulness, perceived ease of use, perceived enjoyment, and subjective norms were positive predictors of intention to use this VR-HMD, validating the suitability of the TAM for investigating the acceptance by athletes of a VR-HMD designed to increase their sport performance. The results also showed that athletes of all sport levels (a) had a significant intention to use VR, (b) found it quite useful (except for recreational athletes), quite easy to use, and quite pleasant to use, even if their entourage would not encourage them to use it (except for international athletes), and (c) found the VR-HMD easy and pleasant to use whatever the sport practiced. Notably some athletes (e.g., triathletes, swimmers, cyclists) did not find the VR-HMD significantly useful and did not have significant intention to use it to increase their performance. Identifying acceptance by athletes of such a device may increase the likelihood that it will be used by athletes of different levels and from different sports, so that they can benefit from all its advantages related to the improvement of their sport performance. Needs-based targeted interventions may also be conducted toward athletes who might be reluctant to integrate this type of device into their training.
Adaptive feedback from artificial neural networks facilitates pre-service teachers’ diagnostic reasoning in simulation-based learning

In simulations, pre-service teachers need sophisticated feedback to develop complex skills such as diagnostic reasoning. In an experimental study with N = 178 pre-service teachers about simulated pupils with learning difficulties, we investigated the effects of automatic adaptive feedback, which is based on artificial neural networks, on pre-service teachers' diagnostic reasoning. Diagnostic reasoning was operationalised as diagnostic accuracy and the quality of justifications. We compared automatic adaptive feedback with static feedback, which we provided in form of an expert solution. Further, we experimentally manipulated whether the learners worked individually or in dyads on the computer lab-based simulations. Results show that adaptive feedback facilitates pre-service teachers’ quality of justifications in written assignments, but not their diagnostic accuracy. Further, static feedback even had detrimental effects on the learning process in dyads. Automatic adaptive feedback in simulations offers scalable, elaborate, process-oriented feedback in real-time to high numbers of students in higher education.
Augmented Reality in the Construction Industry: Use-Cases, Benefits, Obstacles, and Future Trends

Information is the lifeblood of modern construction. Advances in Information and Communication Technology have been and are continuing to progress at rapid rates. Construction companies that are successfully able to adopt and integrate new technologies will gain a competitive edge. One emerging technology that has great potential to transform the construction industry is Augmented Reality (AR). While AR has been of interest to researchers for some time, no single research effort has yet comprehensively investigated the opportunities, benefits, challenges, and future paths toward implementing AR in modern construction. The main objective of this research is to investigate the potential of AR throughout the lifecycle of a construction project from the perspective of the construction industry. Responses from 93 industry practitioners were collected to investigate use-cases of AR throughout the project lifecycle, highlight potential benefits, and identify obstacles to entry that have slowed the implementation of AR thus far. Cluster analysis was employed to determine AR use-cases with the highest usage potential and identify the highest perceived benefits and obstacles of AR. Finally, the future of AR in construction is forecast through a series of statements that describe potential trends of AR in the construction industry. This study contributes to the existing body of knowledge by exploring the potential of AR as perceived by construction practitioners.
Towards Open-Set Object Detection and Discovery

With the human pursuit of knowledge, open-set object detection (OSOD) has been designed to identify unknown objects in a dynamic world. However, an issue with the current setting is that all the predicted unknown objects share the same category as "unknown", which require incremental learning via a human-in-the-loop approach to label novel classes. In order to address this problem, we present a new task, namely Open-Set Object Detection and Discovery (OSODD). This new task aims to extend the ability of open-set object detectors to further discover the categories of unknown objects based on their visual appearance without human effort. We propose a two-stage method that first uses an open-set object detector to predict both known and unknown objects. Then, we study the representation of predicted objects in an unsupervised manner and discover new categories from the set of unknown objects. With this method, a detector is able to detect objects belonging to known classes and define novel categories for objects of unknown classes with minimal supervision. We show the performance of our model on the MS-COCO dataset under a thorough evaluation protocol. We hope that our work will promote further research towards a more robust real-world detection system.
Deep learning-based anomaly detection from ultrasonic images

Non-destructive testing is a group of methods for evaluating the integrity of components. Among them, ultrasonic inspection stands out due to its ability to visualize both shallow and deep sections of the material in the search for flaws. Testing of the critical components can be a tiring and time-consuming task. Therefore, human experts in analyzing inspection data could use a hand in discarding anomaly-free data and reviewing only suspicious data. Using such a tool, errors would be less common, inspection times would shorten and non-destructive testing would be more efficient. In this work, we evaluate multiple state-of-the-art deep-learning anomaly detection methods on the ultrasonic non-destructive testing dataset. We achieved an average performance of almost 82% of ROC AUC. We discuss in detail the advantages and disadvantages of the presented methods.