Reviewerly/exHarmony huggingface dataset:
DPReLU: Dynamic Parametric Rectified Linear Unit and Its Proper Weight Initialization Method

Abstract Activation functions are essential in deep learning, and the rectified linear unit (ReLU) is the most widely used activation function to solve the vanishing gradient problem. However, owing to the dying ReLU problem and bias shift effect, deep learning models using ReLU cannot exploit the potential benefits of negative values. Numerous ReLU variants have been proposed to address this issue. In this study, we propose Dynamic Parametric ReLU (DPReLU), which can dynamically control the overall functional shape of ReLU with four learnable parameters. The parameters of DPReLU are determined by training rather than by humans, thereby making the formulation more suitable and flexible for each model and dataset. Furthermore, we propose an appropriate and robust weight initialization method for DPReLU. To evaluate DPReLU and its weight initialization method, we performed two experiments on various image datasets: one using an autoencoder for image generation and the other using the ResNet50 for image classification. The results show that DPReLU and our weight initialization method provide faster convergence and better accuracy than the original ReLU and the previous ReLU variants.
LSTM-DGWO-Based Sentiment Analysis Framework for Analyzing Online Customer Reviews

Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue. This study combines the LSTM with differential grey wolf optimization (LSTM-DGWO) deep learning model. The app review dataset is processed using the bidirectional encoder representations from transformers (BERT) framework for efficient word embeddings. Then, review features are extracted by the genetic algorithm (GA), and the optimal review feature set is extracted using the firefly algorithm (FA). Finally, the LSTM-DGWO model categorizes app reviews, and the DGWO algorithm optimizes the hyperparameters of the LSTM model. The proposed model outperformed conventional methods with a greater accuracy of 98.89%. The findings demonstrate that sentiment analysis can be practically applied to understand the customer's perception of enhancing products from a business perspective.
Parameter optimization for point clouds denoising based on no-reference quality assessment

Almost all point clouds denoising methods contain various parameters, which need to be set carefully to acquire desired results. In this paper, we introduce an evolutionary optimization algorithm based framework to obtain the parameter configuration of point clouds denoising methods automatically. New no-reference quality assessment metrics are proposed as objective functions to quantitatively evaluate the point clouds during the optimization process. The proposed metrics infer the quality of point clouds in terms of both smoothness and density. The ideas of manifold dimension and holes detection are combined to get the smoothness evaluation results. Simplified local outlier factor is further exploited for the density evaluation. Using public dataset and real-world scanned data, experimental results prove that the automatic tuning parameters provide a significant boost in performance compared with the manual tuning parameters. Furthermore, the results acquired by the proposed metrics achieve better or equivalent performance than the state-of-the-art metrics.
On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness

We study the impact of different loss functions on lesion segmentation from medical images. Although the Cross-Entropy (CE) loss is the most popular option when dealing with natural images, for biomedical image segmentation the soft Dice loss is often preferred due to its ability to handle imbalanced scenarios. On the other hand, the combination of both functions has also been successfully applied in these types of tasks. A much less studied problem is the generalization ability of all these losses in the presence of Out-of-Distribution (OoD) data. This refers to samples appearing in test time that are drawn from a different distribution than training images. In our case, we train our models on images that always contain lesions, but in test time we also have lesion-free samples. We analyze the impact of the minimization of different loss functions on in-distribution performance, but also its ability to generalize to OoD data, via comprehensive experiments on polyp segmentation from endoscopic images and ulcer segmentation from diabetic feet images. Our findings are surprising: CE-Dice loss combinations that excel in segmenting in-distribution images have a poor performance when dealing with OoD data, which leads us to recommend the adoption of the CE loss for these types of problems, due to its robustness and ability to generalize to OoD samples. Code associated to our experiments can be found at https://github.com/agaldran/lesion_losses_ood .
Toward a Generative Pipeline for an AR Tour of Contested Heritage Sites

This paper envisions a pipeline for automating the generation of augmented reality tours of contested heritage sites while employing a critical approach toward the representation of history. Through the design of a generative pipeline, the paper identifies and discusses the potential and pitfalls associated with extracting spatial features from archival manuscripts and presenting them using an augmented reality application. The paper proposes a number of design approaches that assist in automating the transformation of manuscripts into interactive tours while taking into consideration historical, narrative, and technical challenges.
Prototyping a Spatial Skills AR Authoring Tool for Partially Sighted, Blind, and Sighted Individuals

Spatial skills are critical for understanding the relations among objects and people, playing an important role in how we interact with the world. Spatial relationships are built through interactions with physical objects; however, in computational/online environments, these change to bi-dimensional media and computer-assisted design comprised of 3D representations viewable through a flat screen. Due to spatial immersion and interaction limitations, a traditional 2D and 3D approach presents challenges to partially sighted, blind, and sighted individuals. This paper presents the prototyping of a co-design Augmented Reality (AR) authoring tool by recruiting inclusive emerging affordances of consumer-level AR technologies within the context of current e-learning provisions in subject matters, including inclusive design, engineering design, game hardware design, and health sciences. This work has been inspired by the COVID-19 pandemic that has shown the need to level the field in inclusive design for teaching a subject typically oriented to the sighted. Our prototype allows users to create e-learning content for visualization, interaction, collaboration, and inclusive learning. Future work will investigate our tool's impact on skills development and content creation.
Co-located Mixed Reality for Teaching Equine Radiology Techniques to Veterinary Students

The limited laboratory equipment and access to large animals hinder training of students in diagnostic imaging and radiology. Virtual Reality (VR) has been utilized to overcome these problems. However, those systems are designed for human radiology; hence, they are not designed for co-located multi-user collaboration that is essential when dealing with large animals. We propose a collaborative co-located Mixed Reality (MR) system for training students in radiology techniques by utilizing passthrough cameras in VR head-mounted display. It will synchronize the lecturer and the students, limit their exposure to X-rays, and allow staff to guide the students in a real-time MR environment. The hand-tracking touchless interaction provides a more instinctive and hazard-free learning experience.
Comparison of Automatic Question Generation Techniques

Automatic question generation is a technique that generates a question from various sources like structured or unstructured content. Even if, each method has its own shortcoming and strength, we didn’t find any study that did an experimental comparison of the current automatic question generation techniques using a similar dataset. In this study, we analyzed the different state-of-the-art research works, and we identified significant challenges of each model. In addition, we have used BLEU, METEOR, and ROUGE automatic evaluation metrics, to evaluate the experimental test result. From our evaluation result, we observed that most of the tested techniques score below 0.5 in all automated evaluation metrics. Out of tested techniques T5-transformer-based, scores the maximum result. This result point out this research area still needs further investigation and preparing standardized training.
Transparent Slide Detection and Gripper Design for Slide Transport by Robotic Arm

Transparent slide detection, pick and place automation are obtained in order to boost speed, accuracy and smooth workflow between slide coverslipping and digital scanning operation in digital pathology. Fully automatic, precise, robust solution has been developed by robotic arm using computer vision and image processing techniques. Magazine (slide holder) and gripper design, camera selection and mapping to robot base coordinate system are all guided. Python based software algorithms for slide detection and 3D position determination applied in dynamically changeable environment are also explained step by step. Complete automatic process control method is implemented and tested for different types of slide.
Improved RRT* path planning algorithm based on artificial potential field method

Aiming at the problem of water environmental garbage cleaning, this paper based on RRT* planning algorithm, combined with artificial potential field method to improve the path planning algorithm, to achieve the obstacle avoidance path planning of cleaning equipment. In the process of water area garbage cleaning, the cleaning equipment not only needs to avoid obstacles, but also is affected by current resistance in the process of moving. Therefore, the resistance during the moving process of the cleaning device is denoted as the potential field partial repulsion force, and the potential field function is introduced into the algorithm sampling process, so as to solve the problems of blind node expansion and slow convergence rate of RRT* algorithm itself. In addition, the fixed step size in the sampling process is changed to the dynamic step size, which makes the path obtained by RRT* algorithm smoother. Finally, the simulation based on MA TLAB verifies the feasibility and effectiveness of the improved algorithm.
Development of USV Used in Underground Floors Surveying of the Contaminated Buildings at Fukushima Daiichi NPS

A vast amount of radioactive water was generated as a result of the nuclear incident at the Fukushima Daiichi Nuclear Power Station. Underground floors of the radioactive waste management facility were utilized as a temporary storage for contaminated water, and hundreds of sandbags containing zeolite particles were placed onto the floors to remove radioactivity from the water. As the decommissioning work has been ongoing for over a decade now, all zeolite sandbags must be removed, and the floors dried up. A survey was conducted in this highly irradiated environment to investigate the unknown recent conditions of the zeolite and floor structures. A remotely operated USV (Unmanned Surface Vehicle) named “ROV-boat”, modified from a reliable commercial off-the-shelf ROV (Remotely Operated Vehicle), was developed for this purpose. The ROV-boat was equipped with two cameras and a dosimeter installed on the body, with an external float for increased buoyancy, allowing it to move on the water surface to conduct a detailed survey of the environment. The prototype was evaluated and improved iteratively following an agile development approach. The ROV-boat was successfully developed by TEPCO (Tokyo Electric Power Company) several times in the summer of 2021, attaining valuable data for the future dry-up process.
Optimizing Cybersecurity Budgets with AttackSimulation

Modern organizations need effective ways to assess cybersecurity risk. Successful cyber attacks can result in data breaches, which may inflict significant loss of money, time, and public trust. Small businesses and non-profit organizations have limited resources to invest in cybersecurity controls and often do not have the in-house expertise to assess their risk. Cyber threat actors also vary in sophistication, motivation, and effectiveness. This paper builds on the previous work of Lerums et al., who presented an AnyLogic model for simulating aspects of a cyber attack and the efficacy of controls in a generic enterprise network. This paper argues that their model is an effective quantitative means of measuring the probability of success of a threat actor and implements two primary changes to increase the model's accuracy. First, the authors modified the model's inputs, allowing users to select threat actors based on the organization's specific threat model. Threat actor effectiveness is evaluated based on publicly available breach data (in addition to security control efficacy), resulting in further refined attack success probabilities. Second, all three elements - threat effectiveness, control efficacy, and model variance - are computed and evaluated at each node to increase the estimation fidelity in place of pooled variance calculations. Visualization graphs, multiple simulation runs (up to 1 million), attack path customization, and code efficiency changes are also implemented. The result is a simulation tool that provides valuable insight to decision-makers and practitioners about where to most efficiently invest resources in their computing environment to increase cybersecurity posture. AttackSimulation and its source code are freely available on GitHub.
Turbo

Edge computing is being widely used for video analytics. To alleviate the inherent tension between accuracy and cost, various video analytics pipelines have been proposed to optimize the usage of GPU on edge nodes. Nonetheless, we find that GPU compute resources provisioned for edge nodes are commonly under-utilized due to video content variations, subsampling and filtering at different places of a video analytics pipeline. As opposed to model and pipeline optimization, in this work, we study the problem of opportunistic data enhancement using the non-deterministic and fragmented idle GPU resources. In specific, we propose a task-specific discrimination and enhancement module, and a model-aware adversarial training mechanism, providing a way to exploit idle resources to identify and transform pipeline-specific, low-quality images in an accurate and efficient manner. A multi-exit enhancement model structure and a resource-aware scheduler is further developed to make online enhancement decisions and fine-grained inference execution under latency and GPU resource constraints. Experiments across multiple video analytics pipelines and datasets reveal that our system boosts DNN object detection accuracy by 7.27 -- 11.34% by judiciously allocating 15.81 -- 37.67% idle resources on frames that tend to yield greater marginal benefits from enhancement.
Organizing Empirical Studies as Learning Iterations in Design Science Research Projects

Software Quality is a relevant topic that interests both Academy and Industry. Hence, research on this topic should be aligned with the Industry needs, which demands the adoption of research approaches that enable closer interaction between researchers and practitioners. In this context, Design Science Research (DSR) stands out as a way to reduce the gap between theory and practice. DSR is a methodological approach to building innovative artifacts to solve real-world problems and, at the same time, making a scientific contribution. As a problem-oriented research method, DSR seeks to understand the problem, build and evaluate artifacts that allow transforming situations, changing their conditions to better or desirable states. In DSR projects, empirical studies have been usually applied to evaluate the proposed artifact. However, they can also be used to support other activities. Over the last eight years, we have successfully used empirical studies with different purposes in DSR projects. We organized the studies as learning iterations that provide useful knowledge to understand the problem, ground the artifact, develop, evaluate and improve it. As a result, we have experienced a more fluid DSR process and the proposed artifacts have been better grounded and suitable for solving the aimed problem. In this paper, we share our experience by discussing how we have used empirical studies as learning iterations in DSR projects, presenting our approach to organizing empirical studies in a DSR project according to the study purpose and the knowledge it intends to capture, and summarizing two DSR projects that address software quality issues and were developed by using such approach.
Alice and the Caterpillar: A More Descriptive Null Model for Assessing Data Mining Results

“One side will make you grow taller, and the other side will make you grow shorter – The Caterpillar, Alice in Wonderland We introduce a novel null model for assessing the results obtained by analyzing an observed transactional dataset (e.g., significant frequent itemsets) using statistical hypothesis testing. Our null model maintains more properties of the observed dataset than existing models. Specifically, we preserve the Bipartite Joint Degree Matrix of the bipartite graph corresponding to the dataset, which ensures that the number of caterpillars, i.e., paths of length three, is preserved, in addition to the item supports and the transaction lengths, which are the properties considered by previous works. We describe ALICE, a suite of two Markov-Chain Monte-Carlo algorithms for sampling datasets from our null model, based on a carefully defined set of states and efficient operations to move between them. The results of our experimental evaluation show that ALICE mixes fast and scales well, and that our null model finds different significant results than ones previously considered in the literature.
Deep Clustering With Consensus Representations

The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clustering method. Most existing deep clustering methods are designed for a single clustering method, e.g., k-means, spectral clustering, or Gaussian mixture models, but it is well known that no clustering algorithm works best in all circumstances. Consensus clustering tries to alleviate the individual weaknesses of clustering algorithms by building a consensus between members of a clustering ensemble. Currently, there is no deep clustering method that can include multiple heterogeneous clustering algorithms in an ensemble to update representations and clusterings together. To close this gap, we introduce the idea of a consensus representation that maximizes the agreement between ensemble members. Further, we propose DECCS (Deep Embedded Clustering with Consensus representationS), a deep consensus clustering method that learns a consensus representation by enhancing the embedded space to such a degree that all ensemble members agree on a common clustering result. Our contributions are the following: (1) We introduce the idea of learning consensus representations for heterogeneous clusterings, a novel notion to approach consensus clustering. (2) We propose DECCS, the first deep clustering method that jointly improves the representation and clustering results of multiple heterogeneous clustering algorithms. (3) We show in experiments that learning a consensus representation with DECCS is outperforming several relevant baselines from deep clustering and consensus clustering. Our code can be found at https://gitlab.cs.univie.ac.at/lukas/deccs
GraphBERT: Bridging Graph and Text for Malicious Behavior Detection on Social Media

The development of social media (e.g., Twitter) allows users to make speeches with low cost and broad influence. Thus, social media has become a perfect place for users’ malicious behaviors like committing hate crimes, spreading toxic information, abetting crimes, etc. Malicious behaviors are covert and widespread, with potential relevance regarding topic, person, place, and so on. Therefore, it is necessary to develop novel techniques to detect and disrupt malicious behavior on social media effectively. Previous research has shown promising results in extracting semantic text (speech) representation using natural language processing methods. Yet the latent relation between speeches and the connection between users behind speeches is rarely explored. In light of this, we propose a holistic model named Graph adaption BERT (GraphBERT) to detect malicious behaviors on Twitter with both semantic and relational information. Specifically, we first present a novel and a large-scale corpus of tweet data to benefit both graph-based and language-based malicious behavior detection research. Then, we design a novel model GraphBERT to learn comprehensive tweet and user representation with the integration of both semantic information encoded by transformers (i.e., BERT) and relational information encoded by graph neural network. GraphBERT further leverages a weight adaption BERT module implemented between transformer layers to refine tweet embedding using relational information for malicious tweet classification. Finally, the adapted tweet embedding is used with the initial tweet representation to generate user embedding for malicious user detection. The extensive experiments on the collected Twitter data show that our model outperforms the state-of-the-art baseline methods for both tasks (i.e., malicious tweet classification and malicious user detection).