Reviewerly/exHarmony huggingface dataset:
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey

Deep learning has successfully solved a wide range of tasks in 2D vision as a dominant AI technique. Recently, deep learning on 3D point clouds has become increasingly popular for addressing various tasks in this field. Despite remarkable achievements, deep learning algorithms are vulnerable to adversarial attacks. These attacks are imperceptible to the human eye, but can easily fool deep neural networks in the testing and deployment stage. To encourage future research, this survey summarizes the current progress on adversarial attack and defense techniques on point-cloud classification. This paper first introduces the principles and characteristics of adversarial attacks and summarizes and analyzes adversarial example generation methods in recent years. Additionally, it provides an overview of defense strategies, organized into data-focused and model-focused methods. Finally, it presents several current challenges and potential future research directions in this domain.
A Low-Complexity Human Head Detection Network Based on YOLOv6

Indoor and outdoor surveillance have become essential aspects of public safety. In recent years, intelligent surveillance has made a lot of progress, with human head detection becoming a common technique in this field. However, there are some challenges in human head detection such as failure to detect targets, lighting conditions, and crowded areas. In this paper, we proposed an improved head network that decreases the number of YOLOv6-n model parameters while simultaneously increasing detection accuracy. Additionally, to accelerate model convergence, we designed a box regression loss function, QIoU, which converges in terms of the bounding box's width and height, as well as its X and Y axes center positions. Our improved algorithm achieves 91.6% AP and 52.2% AR on SCUT-HEAD dataset, respectively, which are higher than the baseline with SIoU loss function. Finally, our method also has better accuracy performance on small targets (i.e., 47.6 %).
Comparison of Platelet Counts Estimated by Peripheral Blood Smear Examination and Automated Haematology Analyzer

Introduction: Platelet count estimation is one of the common as well as important investigations in clinical practice to diagnose many diseases like dengue, malaria, etc. Different methods for platelet estimations are available. These methods are manual counting semi-automated and automated hematology analyzer counting, platelet count estimation by peripheral blood smear (PBS) method, etc. Semi-automated or automated analyzers may produce erroneous results in the presence of particles or light scatter like giant platelets, fragmented red blood cells, and platelet clumps, so alternative methods like PBS examination can be used for validation. Aim: To compare platelet count estimation performed by the automated cell counter method and the PBS examination method. Objective: Peripheral Blood smear examination acts as a good quality control tool to validate the results produced by the automated cell counter. Materials and Methods: The present study was carried out in the Department of Pathology, Akshaya Health Centre, Urban Bangalore. The study included 100 random blood samples collected into ethylenediaminetetraacetic acid (EDTA) vacutainers over 3 months. These were analyzed by both peripheral blood smear and automated cell counter for platelet estimation. The statistical analysis was done for test performances and their comparisons by using the coefficient of variation (CV), linear regression, and mean differences with SPSS software. Results: No significant difference (p = 0.06635) was observed between the manual peripheral blood smear (PBS) method (platelets average per 100x, multiplied by 15000/ µl) of platelet estimation (207.13 ±15.898 x 1000/ µl) and that of automated cell counter platelet value (206.53 ±16.278 x 1000/ µl). A significant positive correlation was observed between the results of both methods (r=0.9995, p &lt; 0.001) when analyzed by the Pearson correlation test. Conclusion: The peripheral blood smear platelet estimation results are comparable with automated analyzer results. Hence, the PBS examination serves as a quality control tool in assessing the results of the automated cell counters.
Strategi Pengembangan Sumber Daya Perpustakaan Pusat Universitas Pendidikan Indonesia Melalui Kerja Sama Perpustakaan IAIN Salatiga

&lt;p&gt;Problems in libraries can be overcome one way by collaborating between libraries. Collaboration can be carried out by any type of library, including state university libraries. College libraries have the same functions as libraries in general and the functions included in the Tri Dharma of Higher Education include education, research and community service. The development of library resources at the Central Library of the Indonesian Education University is carried out in collaboration with the Salatiga State Islamic Institute Library. This research aims to find out what cooperation looks like, how cooperation is carried out, and what are the challenges in carrying out collaboration between the two libraries. The research method used is a qualitative research method and data collection techniques in the form of interviews with the Head of the Library Services Division of the UPI Central Library. The research results show that the cooperation carried out is cooperation in the field of information services and is bound by a memorandum of agreement with six scopes of cooperation. Several challenges were found, namely in terms of busyness of library managers, permission from the head of the library, and budget in carrying out collaboration. There are several scopes that cannot be realized in the implementation of cooperation. This cannot be used as a barrier to working together, but is a motivation for both parties because the priority is service to users.&lt;/p&gt;
Improving Fluent Reading Skills in Learning English Using the Demonstration Method for Grade III Students at Talitakum Private Elementary School

This study aims to improve the ability to read fluently in learning English, especially aspects of reading skills using the demonstration method for third grade students at Talitakum Private Elementary School. This type of research is pre-experimental research which only involves one class and is carried out in 2 meetings. Experimental units are conducted total sample which involved all grade III students. The results showed that: (1) student learning outcomes before being given treatment based on an average score reaching 33.3% were in the low category. Being in the high category, this can be concluded after being given treatment and obtaining a score of 83.3%. (2) Then, a figure of 76.9% is obtained for the average student involvement, which shows that students are active in their education. (3) The use of the demonstration model in this class is approved; this is shown by the positive response of students of 77.2%. Furthermore, it was found that the calculated t value was 4.5 with a frequency (df) of 22-1=21, this shows a significance level of 5%. This is in accordance with the t test procedure for carrying out inferential statistical analysis, as shown in t table 2. The null hypothesis (Ho) is rejected, and the alternative hypothesis (Hi) is accepted because t count &gt; t table at the 5% significance level. Talitakum Private Elementary School has an increasing influence on the ability to read fluently in learning English using the demonstration method.
DEVELOPMENT OF DIGITAL LITERACY OF TEACHERS

The purpose of this article was to study the level of development of the main areas of digital literacy of a modern teacher and determine the requirements for the development of key digital skills in the context of an active transition to digital education. The formation of digital literacy of teachers is a hot topic in modern education. With the development of technology and the transition to an online learning format, teachers must have knowledge and skills in the use of digital technologies and electronic resources. The main competencies that they should have are considered. The article also discusses various methods and tools for the formation of digital literacy of teachers, including online courses and trainings, self-education and exchange of experience with colleagues, the use of digital platforms and applications in teaching. The results of the formation of digital literacy of teachers can be extremely useful for improving the quality of education, increasing the effectiveness of teaching and creating more flexible and adaptive learning models.
Automating Test-Specific Refactoring Mining: A Mixed-Method Investigation

Refactoring is a practice commonly used by developers to restructure the source code without changing its external behavior. Over the last decades, the software engineering research community has been making use of mining software repository techniques to investigate refactoring under multiple perspectives, identifying properties and impact of this practice on source code quality, other than using refactoring data coming from software repositories to build automated recommendation systems. While the current state of the art proposes various automated tools to mine refactoring data, there is still a lack of instruments that may help researchers when mining test-specific refactoring data. The availability of those instruments may enable additional, specialized techniques to support developers while refactoring test code. In this paper, we introduce an approach that extends REFACTORINGMINER-a well-established refactoring mining tool having high precision and recall scores- and is able to detect seven test-specific refactoring operations. We perform mixed-method research to assess capabilities and usefulness of the approach. First, we compare the test-specific refactoring data extracted by the approach against an oracle of 375 test-specific refactorings. Second, we engage with 15 software engineering researchers and apply a technology acceptance model to investigate how they would benefit from our approach. The key results of the study show that our approach reaches 100% and 92.5% of precision and recall scores, respectively. In addition, the approach is considered useful and suitable for various research tasks, including the definition of novel learning models able to recommend test-specific refactoring actions.
Enabling Multimodal User Interactions for Genomics Visualization Creation

Visualization plays an important role in extracting insights from complex and large-scale genomics data. Traditional graphical user interfaces (GUIs) offer limited flexibility for custom visualizations. Our prior work, Gosling, enables expressive visualization creation using a grammar-based approach, but beginners may face challenges in constructing complex visualizations. To address this, we explore multimodal interactions, including sketches, example images, and natural language inputs, to streamline visualization creation. Specifically, we customize two deep learning models (YOLO v7 and GPT3.5) to interpret user interactions and convert them into Gosling specifications. A workflow is proposed to progressively introduce and integrate multimodal interactions. We then present use cases demonstrating their effectiveness and identify challenges and opportunities for future research.
AOSR-Net: All-in-One Sandstorm Removal Network

Most existing sandstorm image enhancement methods are based on traditional theory and prior knowledge, which often restrict their applicability in real-world scenarios. In addition, these approaches often adopt a strategy of color correction followed by dust removal, which makes the algorithm structure too complex. To solve the issue, we introduce a novel image restoration model, named all-in-one sandstorm removal network (AOSR-Net). This model is developed based on a re-formulated sandstorm scattering model, which directly establishes the image mapping relationship by integrating intermediate parameters. Such integration scheme effectively addresses the problems of over-enhancement and weak generalization in the field of sand dust image enhancement. Experimental results on synthetic and real-world sandstorm images demonstrate the superiority of the proposed AOSR-Net over state-of-the-art (SOTA) algorithms.
Gridded Glyphmaps for Supporting Spatial COVID-19 Modelling

We describe our use of gridded glyphmaps to support development of a repurposed COVID-19 infection model during the height of the pandemic. We found that gridded glyphmaps’ ability to interactive summarise multivariate model input, intermediate results and outputs across multiple scales supported our model development tasks in ways that the modellers had not previously seen. We recount our experiences, reflect on potential to support more spatial model development more generally and suggest areas of further work.
SwinMotionSeg: A Point Cloud Moving Object Segmentation Model Based on Swin-Transformer

Autonomous robots and self-driving cars equipped with LiDAR sensors must accurately detect and segment moving objects. Traditional methods of segmenting moving objects directly from point cloud data or converting them into voxels have issues with cumbersome operations and low robustness. Moreover, CNNs are generally more challenging to establish global feature associations compared to the Transformer architecture. This paper proposes a new moving object segmentation model called SwinMotionSeg, which is based on the Swin-Transformer and designed for point clouds. The model avoids the direct processing of complex point cloud data by extracting features from range images and their residual image. Additionally, it incorporates a Meta-Kernel module, promoting the accuracy of 3D position information in the range image. It also introduces KNN processing after back-projection to enhance the segmentation capability of the moving object point cloud. The Multi-scale feature fusion module is added to enhance the segmentation capability of multi-scale moving objects. This paper explores the IoU metrics of moving objects on the validation set of SemanticKITTI using a model based on the Transformer architecture, achieving point cloud moving object segmentation, going beyond the partially convolutional neural network-based approach.
How Connectivism is Applied to Corporate Training - Practice from China Construction Bank

Connectivism is a learning theory for the increasingly open and complex digital age. Although corporate training should be an important field of practice of this theory, there is a lack of systematic cases. China Construction Bank has carried out an innovative practice based on connectivism, considering the future development of corporate training and the need to support achieving strategic goals. This paper systematically traces this process and answers the question “how connectivism is applied to corporate training” based on a case study. The process, results and business values of the program are systematically presented to provide references for related practices.
Synthetic data generation based on grid deformation for waste recycling applications

Today, waste recycling is supported by intelligent robots that use machine learning to identify and sort recyclables. The development of computer vision applications based on machine learning relies heavily on large datasets that are used to train deep neural network models. In recent years, methods that allow the creation of large training datasets from a limited initial set of images have been investigated. This paper describes a method in which segmented images of real recyclables (polyethylene terephthalate, PETE) are artificially deformed using mesh transformation to create new instances of the recyclable objects. The new instances are placed on real backgrounds to create synthetic images. This process allows the generation of large artificial datasets used for training neural networks. We evaluate the usability of these datasets by studying the extent to which they can improve the performance of trained models when applied in real and challenging industrial images. In particular, we consider the main metrics used to evaluate the performance of classification models, namely Accuracy, Precision and Recall. The results obtained show that including even small-scale object deformations in the artificial datasets can slightly improve the Accuracy and significantly improve the model Recall, while Precision of the model remains unchanged.
[DEMO] ABE to the Rescue: Efficient Encrypted Communications for Disaster Management

Efficient and secure message dissemination plays an important role during a disaster environment. Name-based publish/subscribe systems, especially role-based names, using principles of Information-Centricity provide an efficient frame-work for communications among first responders. However, a challenge is maintaining confidentiality during communication. We have developed an encryption framework that leverages graph-based naming systems which provides role-based communication among first responders. Our framework is built on top of the dynamic role-based names and can be implemented using attribute-based encryption (ABE) or public key encryption (PKE). In this demo, we show the operations of our framework in a typical scenario of first responders using the application.
MedCT-BERT: Multimodal Mortality Prediction using Medical ConvTransformer-BERT Model

In the Intensive Care Unit (ICU), mortality prediction tasks primarily rely on clinical records consisting of patients’ clinical time series data and physicians’ diagnostic opinions. However, due to the irregularity of time series data and clinical records, existing medical multimodal models focus on generating complete and regular data to address this issue, neglecting the impact of generated modality data on multimodal feature fusion. Inaccurate generation of modality data may lead to noise. To overcome this problem, we propose a novel medical multimodal model named MedCT-BERT in this paper. Specifically, the existing models can address the irregularities in time series data but are limited to handling fixed time series data. We optimize their hyperparameters for better performance. By doing so, our model can process and generate complete regular imputed values in parallel for time series data with varying missing values and sequence lengths. To iteratively refine the generated imputed values, we introduce a time series feature correlation information to reduce noise in multimodal data fusion. We conduct experiments on the MIMIC-III dataset with MedCT-BERT and validate the effectiveness of the model in mortality prediction tasks.
Depth Estimation and Validation of Plenoptic Light Field Camera

A light field (LF) camera can record both the spatial and directional information of light rays in a three-dimensional (3D) scene. This is different from traditional photography, which sums up all the light rays to record their intensity and discard the directional information. Accurate depth estimation is crucial for 3D sense reconstruction which can be achieved by the directional information provided by the LF camera. In this study, a refocus model has been examined to accurately estimate the metric depth of a 3D scene. A range of camera calibrations has been conducted to determine the refocus model parameters and thus to achieve accurate depth estimation. Ground truth data from a stereo camera is utilized to validate the calibrations. The results indicate that the root-mean-square error is approximately 5cm when the depth range is 23.5cm to 65.5cm. Therefore, it is demonstrated that the proposed refocused model is feasible to estimate the depth information of the 3D scene accurately.
HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects of Cloud Properties on Climate Patterns

Clouds have a significant impact on the Earth’s climate system. They play a vital role in modulating Earth’s radiation budget and driving regional changes in temperature and precipitation. This makes clouds ideal for climate intervention techniques like Marine Cloud Brightening (MCB) which refers to modification in cloud reflectivity, thereby cooling the surrounding region. However, to avoid unintended effects of MCB, we need a better understanding of the complex cloud to climate response function. Designing and testing such interventions scenarios with conventional Earth System Models is computationally expensive. Therefore, we propose a hybrid AI-assisted visual analysis framework to drive such scientific studies and facilitate interactive “what-if” investigation of different MCB intervention scenarios to assess their intended and unintended impacts on climate patterns. We work with a team of climate scientists to develop a suite of hybrid AI models emulating cloud-climate response function and design a tightly coupled frontend interactive visual analysis system to perform different MCB intervention experiments.
THE USE OF NEW EDUCATIONAL TECHNOLOGIES FOR PREPARING STUDENTS IN ENGINEERING : An innovative perspective on entrepreneurship and competitiveness

This paper aims to explore the use of new educational technologies for preparing students in engineering, with a focus on innovative approaches to entrepreneurship and competitiveness. The study was conducted using mixed-methods approach, including a literature review and a survey of high school students in Bucharest, using a questionnaire as the research instrument. The implications of this study are significant for both academics and practitioners in the field of engineering education. By highlighting the perspectives and needs of high schools students, this paper provides insights into the potential of new technologies to enhance the quality of education and prepare students for future careers in engineering. These findings can inform the development of new research and practice methodologies in the field. The key contribution of this paper is to demonstrate the unique value of new educational technologies for preparing students in engineering, as well as the potential of survey research in understanding student perspectives and needs. By highlighting the importance of innovative approaches to entrepreneurship and competitiveness, this paper offers an original perspective on the potential of these technologies to enhance the quality of engineering education and prepare students for future success.
CORONAVIRUS 2 - SEVERE ACUTE RESPIRATORY SYNDROME (SARS-COV-2) – THE CRISIS THAT DETERMINED THE PRISON ADMINISTRATION SYSTEM TO REALLY STEP TOWARDS DIGITALIZATION

Objectives: this study aims to present the difficulties that the coronavirus outbreak has brought to the recruitment and staffing process, the solutions identified at that time, as well as the direction in which they can be developed in the future, given that a large part of them were systemic problems, which made it difficult for the system to respond promptly to the crisis situation, but not only. The case study of this paper will be related to the use of digital solutions in the recruitment of staff within the prison administration system, respectively the implications for the resilience of these organizations. Implications: This study is addressed to the human resources workers in the units that make up the prison administration system, but also to the workers with management positions, as all of them will have to use new work tools and knowledge. They will have to improve certain working procedures or even develop new ones. All these things take into account the new risks related to each situation, which will have to be managed and harmonized within the prison administration system’s management plan. Originality Value: through this paper, we will make an inventory of the difficulties that were encountered, the solutions identified at that time, the analysis of the results obtained through those solutions, and finally, we will draw conclusions regarding their keeping, their development in one direction or another, or the abandonment of them, where the situation requires it.