Reviewerly/exHarmony huggingface dataset:
Effects of Multisensory Integration through Spherical Video-Based Immersive Virtual Reality on Students’ Learning Performances in a Landscape Architecture Conservation Course

Many courses are transitioning from offline to online instruction in the wake of the COVID-19 pandemic. Landscape architecture conservation courses face problems such as reduced interest in learning, poor learning attitudes and low learning efficiency among students. At the same time, due to the nature of landscape architecture conservation courses, students need more experience to learn well, and many landscape architecture courses do not meet this requirement. Online education also lacks the necessary education scenarios and is not very immersive, making it difficult to meet students’ learning needs. Continued advances in technology have provided new ways for people to connect with nature, increasing awareness and adoption of sustainable landscape architecture practices. To solve the above problems, this study uses multisensory spherical video-based immersive virtual reality technology to develop a VR learning system for landscape architecture conservation courses based on the senses of sight, sound and smell. This system is simple to operate, but interactive and immersive. A quasi-experimental study was also conducted to test the effectiveness of the system. Analyzing the results of the study, students in the experimental group outperformed students in the control group in terms of learning achievements, learning model satisfaction, technology acceptance, flow experience and learning attitudes, which suggests that the use of multisensory spherical video-based immersive virtual reality technology in a landscape architecture conservation course is effective in improving students’ learning performances, and that the study can provide input for the development of other courses.
Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks

Environmental scientists often use birds to understand ecosystems because they are sensitive to environmental changes, but few experts are available. To make it easier to recognize bird species, an automatic system that can classify bird species is needed. There are lots of models to choose from, but some models require very high computational data when training data, reducing training time can result in less wasted electrical energy so that it can have a good effect on the environment. For this reason, it is necessary to test a model that has a small complexity in training time, whether it can produce good performance. Based on the number of neural network models available, this study will classify using the EfficientNet, EfficientNetV2, MobileNet, MobileNetV2, and NasnetMobile models to determine whether these models can have good performance. From the research results, all the models tested have good performance with an accuracy between 95% - 97%. The MobileNetV2 model has the less efficiency with the smallest training time while maintaining good performance.
Handling Constrained Optimization in Factor Graphs for Autonomous Navigation

Factor graphs are graphical models used to represent a wide variety of problems across robotics, such as Structure from Motion (SfM), Simultaneous Localization and Mapping (SLAM) and calibration. Typically, at their core, they have an optimization problem whose terms only depend on a small subset of variables. Factor graph solvers exploit the locality of problems to drastically reduce the computational time of the Iterative Least-Squares (ILS) methodology. Although extremely powerful, their application is usually limited to unconstrained problems. In this letter, we model constraints over variables within factor graphs by introducing a factor graph version of the Augmented Lagrangian (AL) method. We show the potential of our method by presenting a full navigation stack based on factor graphs. Differently from standard navigation stacks, we can model both optimal control for local planning and localization with factor graphs, and solve the two problems using the standard ILS methodology. We validate our approach in real-world autonomous navigation scenarios, comparing it with the de facto standard navigation stack implemented in ROS. Comparative experiments show that for the application at hand our system outperforms the standard nonlinear programming solver Interior-Point Optimizer (IPOPT) in runtime, while achieving similar solutions.
Convolutional Feature Descriptor Selection for Mammogram Classification

Breast cancer was the most commonly diagnosed cancer among women worldwide in 2020. Recently, several deep learning-based classification approaches have been proposed to screen breast cancer in mammograms. However, most of these approaches require additional detection or segmentation annotations. Meanwhile, some other image-level label-based methods often pay insufficient attention to lesion areas, which are critical for diagnosis. This study designs a novel deep-learning method for automatically diagnosing breast cancer in mammography, which focuses on the local lesion areas and only utilizes image-level classification labels. In this study, we propose to select discriminative feature descriptors from feature maps instead of identifying lesion areas using precise annotations. And we design a novel adaptive convolutional feature descriptor selection (AFDS) structure based on the distribution of the deep activation map. Specifically, we adopt the triangle threshold strategy to calculate a specific threshold for guiding the activation map to determine which feature descriptors (local areas) are discriminative. Ablation experiments and visualization analysis indicate that the AFDS structure makes the model easier to learn the difference between malignant and benign/normal lesions. Furthermore, since the AFDS structure can be regarded as a highly efficient pooling structure, it can be easily plugged into most existing convolutional neural networks with negligible effort and time consumption. Experimental results on two publicly available INbreast and CBIS-DDSM datasets indicate that the proposed method performs satisfactorily compared with state-of-the-art methods.
Analyzing Social Media Messaging on Masks and Vaccines: A Case Study on Misinformation During the COVID-19 Pandemic

Abstract Misinformation and disinformation during infectious disease outbreaks can hinder public health responses. This analysis examines comments about masks and COVID-19 vaccines on Twitter during the first six months of the COVID-19 pandemic. We conducted a content analysis of 6,600 randomly selected English-language tweets, examining tweets for health, political, of societal frames; inclusion of true information, false information, partially true/misleading information, and/or opinion; political components; risk frames; and use of specific types of rumor. We found false and partially false information in 22% of tweets in which we were able to assess veracity. Tweets with misinformation were more likely to mention vaccines, be political in nature, and promote risk elevating messages (p&lt;0.5). We also found false information about vaccines as early as January 2020, nearly a year before COVID-19 vaccines became widely available. These findings highlight a need for new policies and strategies aimed to counter harmful and misleading messaging.
Expanding on the use of YouMiMe as technology instructional design in learning

Incorporating YouMiMe or Youtube and MindMeister applications as pedagogical innovations is investigated in this study, which aims to acquire students' English skills for the first semester of academic year 2021 in higher education.The quantitative study method was employed in this study.The group includes 57 students from the computer science faculty.Students were required to watch the teaching materials through video via You Tube.The students pay attention to pronunciation, vocabulary, listening and reading carefully.Then the students discuss in groups to write sentences to paragraph, which they insert into the MindMeister application.So, the students share their knowledge and record the presentation.The final step is that the team submits their video presentation to the You Tube apps as part of the application process.Course outcomes were linked to video content on the You Tube channel.Videos on You Tube, participant responses, and achievement tests were used to gather the data.Descriptive statistics and a questionnaire were used to examine the data.Students' grades had risen significantly on the posttest, according to the results.In addition, it was discovered that the students used their online smartphones to analyses, organize, and evaluate the information they were given, as well as the tools and equipment they already had in their labs, and that they did so collaboratively.This authentic experience, according to the students, also allowed them to develop ICT skills in the context of their learning.The t-test analysis yielded the value t = -10.184with sig (2-tailed) = 0,000, which is smaller than the significance level of 0.05, according to the findings.According to the results, using YouMiMe as a technology-based instructional method has a significant impact on students' English skills.
Deeply Explain CNN Via Hierarchical Decomposition

In computer vision, some attribution methods for explaining CNNs attempt to study how the intermediate features affect network prediction. However, they usually ignore the feature hierarchies among the intermediate features. This paper introduces a hierarchical decomposition framework to explain CNN’s decision-making process in a top-down manner. Specifically, we propose a gradient-based activation propagation (gAP) module that can decompose any intermediate CNN decision to its lower layers and find the supporting features. Then we utilize the gAP module to iteratively decompose the network decision to the supporting evidence from different CNN layers. The proposed framework can generate a deep hierarchy of strongly associated supporting evidence for the network decision, which provides insight into the decision-making process. Moreover, gAP is effort-free for understanding CNN-based models without network architecture modification and extra training processes. Experiments show the effectiveness of the proposed method. The data and source code will be publicly available at https://mmcheng.net/hdecomp/ .
Digital Twin-Driven Computing Resource Management for Vehicular Networks

This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twins of edge servers. Second, computing resources of the edge servers are allocated in real time to individual vehicles via low-complexity matching-based allocation that complies with the reference policies. By leveraging digital twins, the proposed scheme can adapt to dynamic service demands and vehicle mobility in a scalable manner. Simulation results demonstrate that the proposed digital twin-driven scheme enables the vehicular network to support more computing tasks than benchmark schemes.
Automated Foreign Object Debris Detection System based on UAV

The presence of Foreign Object Debris (FOD) on airport platforms constitutes a big risk, both for aircraft and for personnel. This debris, whatever its nature or size, whether it's a private effect, a tool, a component from an aircraft, or any object, As soon because it isn't observed and removed, it's liable becoming a FOD within the moving area. FOD can even be violently projected by jet blast, which might cause damage to other aircraft and injure personnel on the bottom, This paper discuss briefly FOD detection systems and the use of unmanned aerial systems for an automated FOD detection system on runways, which involves taking images of the runway with an Unmanned Aerial Vehicle (UAV), which could be detected and identified using artificial intelligence techniques. The method for determining an exact FOD position from aerial data is described in this study using a perspective projection transformation is used to determine the object's location in the field. For accurate findings, a strong object detection is essential, which is why the cutting-edge deep neural network YOLOV5 is used with both DeepSort Object tracking method. The paper represent an Automated UAV Navigation with PID control based for path tracking. A GUI that has been developed alow the operator to select the runway's intended path to be scanned and visualize the tracked FOD that has been found and its position in order to send a report that the operator can erase from the runway. The proposed system was assessed in real-time testing and a built-in Simulation under GAZEBO using the commercial quad copter Bebop connected to a base station operating under the Robot Operating System (ROS). our approach successfully identified several FODs using a combination of YOLOv5 and deepsort with an inference speed of 30 fps with a high accuarcy over 80%. The advantages of this system is the fulfilment of the FAA performance criteria of an AFDS, it facilitate the FOD scanning operation by using a graphical user interface that allow the operator to start the FOD scanning operation by selecting only the interested area in the runway, drone navigation tests with a 10 m/s wind speed were satisfactory, as well as it's ability to locate and send report of the detected FODs with small distance error less than 40 cm while a drone navigate with a 5m/s speed.
LZ77 via Prefix-Free Parsing

Previous chapter Next chapter Full AccessProceedings 2023 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX)LZ77 via Prefix-Free ParsingAaron Hong, Massimiliano Rossi, and Christina BoucherAaron Hong, Massimiliano Rossi, and Christina Boucherpp.123 - 134Chapter DOI:https://doi.org/10.1137/1.9781611977561.ch11PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper, we present an algorithm for constructing the Lempel-Ziv 77 (LZ77) factorization using prefix-free parsing, an algorithm that was first developed as preprocessing algorithm for constructing the Burrows-Wheeler transform and the suffix array for input text. It was then demonstrated that the output of prefix-free parsing can be an effective data structure that emulates a compressed suffix tree (CST). PFP-LZ77 marries this data structure with the algorithm proposed by Karkkäinen et al. [CPM 2013]. In particular, we implemented a modification of the primitives used to support CST operations to enable previous smaller value and next smaller value queries on the suffix array of the text. This allow PFP-LZ77 to exploit the repetitiveness of the text, while building the LZ77 parse. We show that PFP-LZ77, SE-KKP, and ReLZ were the only methods capable of scaling to large datasets (e.g., 1024 copies of chromosome 19). And although SE-KKP and ReLZ performed well, SE-KKP was between 2.5 and 5 times slower, and used between 3 and 11 times more memory than PFP-LZ77 for reasonably large datasets (i.e., more than 16 copies of chromosome 19), and ReLZ only generates an LZ77 approximation. Our implementation is available at https://github.com/AaronHong1024/PFP_LZ77. Previous chapter Next chapter RelatedDetails Published:2023eISBN:978-1-61197-756-1 https://doi.org/10.1137/1.9781611977561Book Series Name:ProceedingsBook Code:PRAL23Book Pages:iii-213
Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center

Abstract The medical specialty of critical care, or intensive care, provides emergency medical care to patients suffering from life-threatening complications and injuries. The medical specialty is featured by the generation of a huge amount of high-granularity data in routine practice. Currently, these data are well archived in the hospital information system for the primary purpose of routine clinical practice. However, data scientists have noticed that in-depth mining of such big data may provide insights into the pathophysiology of underlying diseases and healthcare practices. There have been several openly accessible critical care databases being established, which have generated hundreds of scientific outputs published in scientific journals. However, such work is still in its infancy in China. China is a large country with a huge patient population, contributing to the generation of large healthcare databases in hospitals. In this data descriptor article, we report the establishment of an openly accessible critical care database generated from the hospital information system.
Explainable AI in Cybersecurity Operations: Lessons Learned from xAI Tool Deployment

MiTechnological advances relating to artificial intelligence (AI) and explainable AI (xAI) techniques are at a stage of development that requires better understanding of operational context.AI tools are primarily viewed as black boxes and some hesitation exists in employing them due to lack of trust and transparency.xAI technologies largely aim to overcome these issues to improve operational efficiency and effectiveness of operators, speeding up the process and allowing for more consistent and informed decision making from AI outputs.Such efforts require not only robust and reliable models but also relevant and understandable explanations to end users to successfully assist in achieving user goals, reducing bias, and improving trust in AI models.Cybersecurity operations settings represent one such context in which automation is vital for maintaining cyber defenses.AI models and xAI techniques were developed to aid analysts in identifying events and making decisions about flagged events (e.g.network attack).We instrumented the tools used for cybersecurity operations to unobtrusively collect data and evaluate the effectiveness of xAI tools.During a pilot study for deployment, we found that xAI tools, while intended to increase trust and improve efficiency, were not utilized heavily, nor did they improve analyst decision accuracy.Critical lessons were learned that impact the utility and adoptability of the technology, including consideration of end users, their workflows, their environments, and their propensity to trust xAI outputs.
A ROS-based hybrid algorithm for the UAV path planning problem

Purpose It is possible with classical path planning algorithms to plan a path in a static environment if the instant position of the vehicle is known and the target and obstacle positions are constant. In a dynamic case, these methods used for the static environment are insufficient. The purpose of this study is to find a new method that can provide a solution to the four-rotor unmanned aerial vehicle (UAV) path planning problem in static and dynamic environments. Design/methodology/approach As a solution to the problem within the scope of this study, there is a new hybrid method in which the global A* algorithm and local the VFH+ algorithm are combined. Findings The performance of the designed algorithm was tested in different environments using the Gazebo model of a real quadrotor and the robot operating system (ROS), which is the widely used platform for robotic applications. Navigation stacks developed for mobile robots on the ROS platform were also used for the UAV, and performance benchmarks were carried out. From the proposed hybrid algorithm, remarkable results were obtained in terms of both planning and implementation time compared to ROS navigation stacks. Originality/value This study proposes a new hybrid approach to the path planning problem for UAVs operating in both static and dynamic environments.
Additive-Homomorphic Functional Commitments and Applications to Homomorphic Signatures

Functional Commitments (FC) allow one to reveal functions of committed data in a succinct and verifiable way. In this paper we put forward the notion of additive-homomorphic FC and show two efficient, pairing-based, realizations of this primitive supporting multivariate polynomials of constant degree and monotone span programs, respectively. We also show applications of the new primitive in the contexts of homomorphic signatures: we show that additive-homomorphic FCs can be used to realize homomorphic signatures (supporting the same class of functionalities as the underlying FC) in a simple and elegant way. Using our new FCs as underlying building blocks, this leads to the (seemingly) first expressive realizations of multi-input homomorphic signatures not relying on lattices or multilinear maps.
SPIQ: Data-Free Per-Channel Static Input Quantization

Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community. Examples of such solutions comprise quantization, i.e. converting the processing values (weights and inputs) from floating point into integers e.g. int8 or int4. Concurrently, the rise of privacy concerns motivated the study of less invasive acceleration methods, such as data-free quantization of pre-trained models weights and activations. Previous approaches either exploit statistical information to deduce scalar ranges and scaling factors for the activations in a static manner, or dynamically adapt this range on-the-fly for each input of each layer (also referred to as activations): the latter generally being more accurate at the expense of significantly slower inference. In this work, we argue that static input quantization can reach the accuracy levels of dynamic methods by means of a per-channel input quantization scheme that allows one to more finely preserve cross-channel dynamics. We show through a thorough empirical evaluation on multiple computer vision problems (e.g. ImageNet classification, Pascal VOC object detection as well as CityScapes semantic segmentation) that the proposed method, dubbed SPIQ, achieves accuracies rivalling dynamic approaches with static-level inference speed, significantly outperforming state-of-the-art quantization methods on every benchmark.
A Blueprint for Collaborative Cybersecurity Operations Centres with Capacity for Shared Situational Awareness, Coordinated Response, and Joint Preparedness

With digital technologies now being part of the fabric of our societies, identifying and managing cybersecurity threats becomes imperative. Within the European Union, several initiatives are underway, aiming to motivate, regulate and eventually orchestrate the establishment of capacity and enhancement of situational awareness, incident response, and preparedness capabilities, with an expected emphasis on operators of essential services and state actors entrusted with cybersecurity. In this context, the institution of cooperation and information exchange channels to allow for coordinated cross-border responses to large-scale incidents is particularly prioritized. Motivated by the above, this work presents a conceptual blueprint in support of architecting and establishing interoperable Cyber Security Operations Centres that combine capacity for situational awareness, incident response, and preparedness, also benefiting from the interplay between them, ultimately enhancing national cybersecurity capabilities, cross-border collaboration, and national supervision of their critical sectors, in line with current and upcoming regulatory requirements and the ever-increasing need for national and international cooperation.