Reviewerly/exHarmony huggingface dataset:
Virtual Reality-Based Parallel Coordinates Plots Enhanced with Explainable AI and Data-Science Analytics for Decision-Making Processes

We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algorithms including a novel SuMC (Subspace Memory Clustering) solution. These analytical methods were applied to both the main visualizations and supporting cross-dimensional scatter plots. They automate part of the analytical work that in the previous version of IPCP had to be done by an expert. We test the refined system with two sample datasets that represent the optimum solutions of two different multi-objective optimization studies in turbomachinery. The first one describes 54 data items with 29 dimensions (DS1), and the second 166 data items with 39 dimensions (DS2). We include the details of these methods as well as the reasoning behind selecting some methods over others.
Fast Algorithms for Deep Octonion Networks

This brief presents the results of a study of the possibilities of reducing the arithmetic complexity of computing basic operations in octonionic neural networks and also proposes new algorithmic solutions for efficiently performing these operations. Here, we primarily mean the operation of multiplying octonions, the operation of computing the dot product of two octonion-valued vectors, and the operation of multiple multiplications of an octonion by several other octonions. In order to reduce the computational complexity of these operations, it is proposed to use the fast Walsh-Hadamard transform, which is well known in digital signal processing. Using this transform reduces the number of multiplications and additions of real numbers required to perform computations. Thus, the use of the proposed algorithms will speed up computations in octonion-valued neural networks.
Design of key-dependent bijective S-Boxes for color image cryptosystem

The S-Boxes with high nonlinearity and low differential uniformity are highly desirable for designing a secure image encryption scheme to ensure secure communication of digital images over the Internet. This paper presents a novel image cryptosystem using key-dependent bijective S-Boxes based on the Henon map. In this paper, the 2D Henon-map is used to generate key-dependent bijective S-Boxes (for confusion) and pseudo-random sequence (for diffusion) to design a color image cryptosystem with high confusion and diffusion capability. We perform the security analysis of the proposed encryption scheme and the experimental results shows that the proposed image encryption scheme is able to resist the statistical attack (entropy and NIST randomness test), differential attack (NPCR and UACI), correlation and chosen-plaintext attack.
Cervical Cancer Risk Prediction Using XGboost Classifier

Machine learning is now the most recurrently used computational technique for clinical diagnosis. Many algorithms have been developed for the prediction and identification of various diseases at an early stage, which benefits in decreasing the mortality rate. Several studies indicate that even after the development of a vaccine, cervical cancer remains the prime cause of death in women when compared with other cancer types. Cancer risk predictions have been helpful for targeted screening and maximizing treatment gains. In this paper, a scalable tree boosting method, known as Extreme Gradient Boosting (XGBoost) has been used to achieve cervical cancer risk prediction. The tuned model uses regularization and handles missing data with its in-built sparse-aware and quantile algorithms. It also outperforms the available state-of-the-art machine learning models, through its parallel computational capabilities and distribution across clusters. The results enhance the chances of applying machine learning to identify the relevant disease predictors and thus become an important tool for early detection of cervical cancer.
Entropic gradient descent algorithms and wide flat minima*

Abstract The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: the local entropy , which is useful for analysis and algorithm development, and the local energy , which is easier to compute and was shown empirically in extensive tests on state-of-the-art networks to be the best predictor of generalization capabilities. We show semi-analytically in simple controlled scenarios that these two measures correlate strongly with each other and with generalization. Then, we extend the analysis to the deep learning scenario by extensive numerical validations. We study two algorithms, entropy-stochastic gradient descent and replicated-stochastic gradient descent, that explicitly include the local entropy in the optimization objective. We devise a training schedule by which we consistently find flatter minima (using both flatness measures), and improve the generalization error for common architectures (e.g. ResNet, EfficientNet).
Multistage classification of oral histopathological images using improved residual network

Oral cancer is a prevalent disease happening in the head and neck region. Due to the high occurrence rate and serious consequences of oral cancer, an accurate diagnosis of malignant oral tumors is a major priority. Thus, early diagnosis is very effective to give the patient a prompt response to treatment. The most efficient way for diagnosing oral cancer is from histopathological imaging, which provides a detailed view of inside cells. Accurate and automatic classification of oral histopathological images remains a difficult task due to the complex nature of cell images, staining methods, and imaging conditions. The use of deep learning in imaging techniques and computational diagnostics can assist doctors and physicians in automatically analysing Oral Squamous Cell Carcinoma biopsy images in a timely and efficient manner. Thus, it reduces the operational workload of the pathologist and enhance patient management. Training deeper neural networks takes considerable time and requires a lot of computing resources, due to the complexity of the network and the gradient diffusion problem. With this motivation and inspired by ResNet's significant successes to handle the gradient diffusion problem, in this study we suggest the novel improved ResNet-based model for the automated multistage classification of oral histopathology images. Three prospective candidate model blocks are presented, analyzed, and the best candidate model is chosen as the optimal one which can efficiently classify the oral lesions into well-differentiated, moderately-differentiated and poorly-differentiated in significantly reduced time, with 97.59% accuracy.
TSUNAMI: Triple Sparsity-Aware Ultra Energy-Efficient Neural Network Training Accelerator With Multi-Modal Iterative Pruning

This article proposes the TSUNAMI, which supports an energy-efficient deep-neural-network training. The TSUNAMI supports multi-modal iterative pruning to generate zeros in activation and weight. Tile-based dynamic activation pruning unit and weight memory shared pruning unit eliminate additional memory access. Coarse-zero skipping controller skips multiple unnecessary multiply-and-accumulation (MAC) operations at once, and fine-zero skipping controller skips randomly located unnecessary MAC operations. Weight sparsity balancer solves a utilization degradation caused by weight sparsity imbalance, and the workload of each convolution core is allocated by a random channel allocator. The TSUNAMI achieves an energy efficiency of 3.42 TFLOPS/W at 0.78V and 50MHz with floating-point 8-bit activation and weight. Also, it achieves an energy efficiency of 405.96 TFLOPS/W at 90% sparsity condition.
The impact of control and mitigation strategies during the second wave of coronavirus infections in Spain and Italy

European countries struggled to fight against the second and the third waves of the COVID-19 pandemic, as the Test-Trace-Isolate (TTI) strategy widely adopted over the summer and early fall 2020 failed to contain the spread of the disease effectively. This paper sheds light on the effectiveness of such a strategy in two European countries (Spain and Italy) by analysing data from June to December 2020, collected via a large-scale online citizen survey with 95,251 and 43,393 answers in Spain and Italy, respectively. Our analysis describes several weaknesses in each of the three pillars of the TTI strategy: Test, Trace, and Isolate. We find that 40% of respondents had to wait more than 48 hours to obtain coronavirus tests results, while literature has shown that a delay of more than one day might make tracing all cases inefficient. We also identify limitations in the manual contact tracing capabilities in both countries, as only 29% of respondents in close contact with a confirmed infected individual reported having been contact traced. Moreover, our analysis shows that more than 45% of respondents report being unable to self-isolate if needed. We also analyse the mitigation strategies deployed to contain the second wave of coronavirus. We find that these interventions were particularly effective in Italy, where close contacts were reduced by more than 20% in the general population. Finally, we analyse the participants' perceptions about the coronavirus risk associated with different daily activities. We observe that they are often gender- and age-dependent, and not aligned with the actual risk identified by the literature. This finding emphasises the importance of deploying public-health communication campaigns to debunk misconceptions about SARS-CoV-2. Overall, our work illustrates the value of online citizen surveys to quickly and efficiently collect large-scale population data to support and evaluate policy decisions to combat the spread of infectious diseases, such as coronavirus.
Multi-View Evolutionary Training for Unsupervised Domain Adaptive Person Re-Identification

Clustering-based approaches have been successfully applied to unsupervised domain adaptation (UDA) tasks for person re-identification (Re-ID), where no annotations are provided in target domain. However, the clustering process is sensitive to noises, leading to imperfect pseudo labels that could damage the training performance. In this work, we propose a Multi-view Evolutionary Training (MET) method to effectively reduce noises in clustering results from two dimensions. First, to improve the clustering accuracy at each time frame (i.e. snapshot quality), a Multi-view Diffusion (MvD) module is proposed. Through capturing data relationships from multiple viewpoints and aggregating their information, noises and bias from each individual viewpoint can be eliminated, and more reliable similarity matrix can be produced for clustering. Second, to improve the temporal consistency between clustering at different iterations, i.e. temporal consistency, we propose an Evolutionary Local Refinement (ELR) module, which utilizes the previous clustering results to guide and improve current results, and further make the training process more stable and robust. Extensive experiments demonstrate that our method can provide clustering results with high quality, and achieve state-of-the-art performance on UDA Re-ID.
Assessing Users’ Perception on the Current and Potential Educational Value of an Electrical Engineering YouTube Channel

Full comprehension of abstract concepts present in engineering education has been usually considered challenging. Engaging multimedia resources have proven to be useful pedagogical aids to increase students&#x2019; motivation. In fact, already existing dissemination videos might be suitable to fulfill this objective. This research aims to contribute assessing video implications in the enhancement of engineering education quality through the evaluation of the current pedagogical use of a specific electrical engineering YouTube channel. To meet this objective, we characterize the use of such channel through a quantitative methodology based on a 5-point Likert scale survey (Cronbach&#x2019;s alpha &#x003D; 0.76). Sample data were collected from 912 respondents, evaluating users&#x2019; perception on the channel&#x2019;s content and format adequacy, their preferences, and their perceptions on video integration in educational contexts. Results show (3.98 over 5) that there is currently a far-reaching educational use of the channel, and a general perception that its contents and audiovisual format are adequate for such purpose. Most users agree (4.74 over 5) that this kind of pedagogical resource could enhance education quality. As limitations, an underrepresentation of teachers in the sample could be highlighted, though student community is well represented. Overall findings suggest that the format and cognitive load in scientific dissemination YouTube channels might be perceived as suitable for pedagogical use, as means to improve education experience. This complementary use unveils the need to implement technology integration models to facilitate their pedagogical insertion, which will be addressed as future works along with more evaluations of similar dissemination channels.
FEVA: A Federated Video Analytics Architecture for Networked Smart Cameras

Video analytics using networked smart cameras has become a core function for many applications including surveillance, object detection, AR/VR, and so on. In recent years, a number of architectures have been proposed to organize the computing and networking resources of cloud and edge cameras to collectively complete an analytics task (e.g., 3D reconstruction, multi-view re-identification). Unfortunately, in many applications, image sharing can lead to privacy con-cerns. One example is the high definition map (HD map) for autonomous driving. An HD map has a highly dynamic layer of real-time objects. Vehicles can collectively contribute videos from their onboard cameras to construct such a layer, but the video images can contain private information (e.g., the license plate numbers of front cars). In this article, we propose FEVA, a new federat-ed video analytics architecture. Intrinsically, FEVA keeps the video image data local to the edge for analytics and transmits the analytics results to the cloud for aggregation. FEVA partitions the video analytics computing tasks in a way that is priva-cy-preserving and maximizes the overall analytics accuracy under the computing and communication resource constraints of the edge devices. We show how FEVA can be used in practice by a case study using FEVA to support a video analyt-ics application on multi-view vehicles 3D reconstruction. We implement FEVA by extending the open source platform TensorFlow Federated from Google. We deploy our case in an environment with four Amazon OeepLens cameras. Our eval-uation shows that FEVA can protect privacy while effectively increasing the accuracy of the video analytics application.
Assessing the effectiveness of a three-way decision-making framework with multiple features in simulating human judgement of opinion classification

Three-way opinion classification (3WOC) models are based on a human perspective of opinion classification and offer human-like decision-making capabilities. The purpose of this study was to determine the effectiveness of a three-way decision-making framework with multiple features (fuzzy features and semantic features) in simulating human judgement of opinions. This was an quantitative study. A simple prototype of the three-way decision model was run against the Amazon Musical Instrument dataset to evaluate the model. The data used to verify the results were collected from 125 respondents via an online survey. The participants tested the model in context, then immediately filled in the online questionnaire. Results show that the statistical correlation between semantic features and fuzzy feature is low. Therefore, classification coverage and accuracy can be increased when both types of features are used together rather than using one type of feature alone. With the integration of semantic features and fuzzy features, we found that our three-way decision model performs better than a two-way classification model. Furthermore, the 3WOC model is a simulation of human judgements executed when people make decisions. Finally, we offer usability recommendations based on our analysis. A three-way decision-making framework is a better solution to simulate human judgement of opinion classification than a two-way decision model. The research outcomes will help in the development of better opinion classification systems that can support businesses and organisations to make strategic plans to improve their products or services based on customer preference patterns.
Multi-Size Object Detection in Large Scene Remote Sensing Images Under Dual Attention Mechanism

The remote sensing images in large scenes have a complex background, and the types, sizes, and postures of the targets are different, making object detection in remote sensing images difficult. To solve this problem, an end-to-end multi-size object detection method based on a dual attention mechanism is proposed in this paper. First, the MobileNets backbone network is used to extract multi-layer features of remote sensing images as the input of MFCA, a multi-size feature concentration attention module. MFCA employs an attention mechanism to suppress noise, enhance effective feature reuse, and improve the adaptability of the network to multi-size target features through multi-layer convolution operation. Then, TSDFF (two-stage deep feature fusion module)deeply fuses the feature maps output by MFCA to maximize the correlation between the feature sets and especially improve the feature expression of small targets. Next, the GLCNet (global-local context network) and the SSA (significant simple attention module) distinguish the fused features and screen out useful channel information, which makes the detected features more representative. Finally, the loss function is improved to truly reflect the difference between the candidate frames and the real frames, enhancing the network’s ability to predict complex samples. The performance of our proposed method is compared with other advanced algorithms on NWPU VHR-10, DOTA, RSOD open datasets. Experimental results show that our proposed method achieves the best AP (average precision) and mAP (mean average precision), indicating that the method can accurately detect multi-type, multi-size, and multi-posture targets with high adaptability.
Exploring lived experiences of agile developers with daily stand-up meetings: a phenomenological study

Daily stand-up meetings (DSMs) are the most popular technique in agile methodologies and are deemed crucial for communication among the individual agile software developers to perform. Only a handful of studies in the past have shown the effect of the characteristics of DSMs on the attitude of agile developers. Little is known about how agile developers experience DSMs in their roles as junior/senior developers, and what feelings evoke these experiences. The purpose of this study was to describe and interpret agile developers’ lived experiences with DSMs in their diverse roles. We conducted a hermeneutic phenomenological study with 19 professional agile developers. The lived experiences across the interviews revealed as an interaction between four categories: rationalising irrelevancy of DSMs, experiencing challenges with DSMs, conflicted opinion on the advantages of DSMs, and finding solutions. Developers experienced DSMs were too short to facilitate clear problem identification, or solve problems, or have a meaningful outcome. Senior developers experienced DSMs differently than junior developers in terms of sharing information, interest in other’s work, monitoring progress, and facilitating decision making. Based on these findings, we discuss the theoretical contributions of our study, and offer recommendations for practitioners.
An onboard-eye-to-hand visual servo and task coordination control for aerial manipulator based on a spherical model

Compared with the unmanned aerial vehicle (UAV), the aerial manipulator extends the ability to interact with the environment by using the onboard manipulator. The complex dynamic characteristics of the combined system increase the challenges of accurately control the aerial manipulator to approximate the target object. In this paper, an onboard-eye-to-hand visual servo and task coordination control strategy is proposed for the aerial manipulator to enhance the accurate manipulation and flight stability. By establishing a new spherical coordinate error equation, the flying platform (quadrotor) and onboard manipulator can be controlled simultaneously. Meanwhile, the multi-task coordinated control scheme is adopted to achieve precise positioning and grasping of the target object, including estimating the object pose, dynamically compensating the change of the center of gravity of the aerial manipulator, and avoiding the onboard manipulator joint limitations. The stability of the closed-loop system is analyzed by the Lyapunov method. Finally, the feasibility of the proposed visual servo method is verified in simulations and real-world outdoor experiments, respectively. The strengths of the proposed method are demonstrated by comparing it with the conventional visual servo method.
Learning When to Quit: Meta-Reasoning for Motion Planning

Anytime motion planners are widely used in robotics. However, the relationship between their solution quality and computation time is not well understood, and thus, determining when to quit planning and start execution is unclear. In this paper, we address the problem of deciding when to stop deliberation under bounded computational capacity, so called meta-reasoning, for anytime motion planning. We propose data-driven learning methods, model-based and model-free meta-reasoning, that are applicable to different environment distributions and agnostic to the choice of anytime motion planners. As a part of the framework, we design a convolutional neural network-based optimal solution predictor that predicts the optimal path length from a given 2D workspace image. We empirically evaluate the performance of the proposed methods in simulation in comparison with baselines.