Reviewerly/exHarmony huggingface dataset:
Marker-Based 3D Position-Prediction Algorithm of Mobile Vertiport for Cabin-Delivery Mechanism of Dual-Mode Flying Car

This paper presents an image-processing technique for cabin delivery employing local localization and docking in a mobile station, which is a mobile vertiport for the use of dual-mode flying cars. A dual-mode flying automobile with an aerial electric vehicle (AEV), a ground electric vehicle (GEV), and a cabin is a future method of transportation that can be used in both the air and on the ground. To enable AEVs to land in a specific position, a landing site is necessary. The proposed AEV uses vertical take-off and landing, and a vertiport landing site is required. As vertical take-off and landing sites require a lot of space and are challenging to operate in multiple positions, we suggest a mobile vertiport that can fit into a small space. A mobile station is appropriate for dual-mode flying cars since it includes critical activities such as transporting AEVs from the ground and charging as well as a cabin-delivery system. The mobile station can generate a path to the AEV by calculating the relative position using the markers attached to the AEV and estimating the position of the landing AEV. The mobile station detects a marker for precise positioning correction, followed by exact position correction for cabin delivery, to travel to the accurate position of the AEV. To increase the success rate of cabin delivery, docking markers are identified and the angle position error between the mobile station and cabin is computed and corrected to rectify the position between the cabin and the mobile station for cabin delivery. In addition, the experimental results revealed a mechanically correctable error range that encompassed all experimental values. Consequently, this study showed that image processing may be used to create a mobile station for dual-mode flying automobiles.
Augmentation of Electronic Medical Record Data for Deep Learning

Data imbalance is a well-known challenge in the development of machine learning models. This is particularly relevant when the minority class is the class of interest, which is frequently the case in models that predict mortality, specific diagnoses or other important clinical end-points. Typical methods of dealing with this include over- or under-sampling training data, or weighting the loss function in order to boost the signal from the minority class. Data augmentation is another frequently employed method — particularly for models that use images as input data. For discrete time-series data, however, there is no consensus method of data augmentation. We propose a simple data augmentation strategy that can be applied to discrete time-series data from the EMR. This strategy is then demonstrated using a publicly available data-set, in order to provide proof of concept for the work undertaken in [1], where data is unable to be made open.
The Diagnostics of the Development of Cooperative Skills of Future Primary School Teachers

The study has focused on the importance of implementing the main provisions of the NUS concept, which produce the educational process based on subject-subject interaction. It has been emphasised that the new paradigm of future primary school teacher training generates new ideas into educational practice based on dialogue, cooperation, co-creation, collective action, respect for each individual, the need to understand another position, etc., which are formed in the process of cooperative learning. Therefore, pedagogical higher educational establishments should provide conditions in the system of professional training for the development of students’ cooperative skills. The purpose of the study is to describe the diagnosis of the readiness of future teachers to improve cooperative skills with the help of interactive technologies in the educational process of primary school. The study applied the following methods: “Value orientations” (M. Rokych) and “Map of research of the motivational component of readiness”, questionnaires, “Map of research of the communicative component of readiness”. The authors have described the results of the study on motivational and value, cognitive, activity and communicative components of the readiness of future primary school teachers to form cooperative skills in younger students. It has been found that applicants for higher education in the second year of study are dominated by the average level of development of cooperative skills on all criteria, which aims to further improve the educational process in the lens of research. It has been emphasised that interactive technologies involve the organisation of cooperative learning, when individual tasks grow into a group, where each member of the group contributes to joint activities. The recommendations have been developed that are useful to take into account when organising the process of cooperative learning in classes at higher educational establishments
Image Recognition about Stability of Soft Surrounding Rock in Tunnel Based on ILBP Algorithm

With China’s fast expansion of urban traffic and railway engineering, a great number of subway and railway lines have been erected in subterranean tunnels in recent years. When the tunnel is in the process of construction, it will encounter the weak fracture zone, and the surrounding rock of this part of the tunnel is often poor stability and deformation is difficult to control, which will lead to the smooth progress of the project, delay and other problems. Therefore, to facilitate the timely treatment of tunnel soft surrounding rock, the study of image recognition of its stability is particularly important. Based on this, an improved local binary mode algorithm (ILBP) is proposed in this paper. By adjusting the weight of binary polynomial in local binary mode (LBP) operator, the target of ILBP is to extract the characteristics of weak surrounding rock of tunnel at a specific location, hoping to provide reference for the follow-up study of tunnel engineering.
Skeleton-based Action Recognition via Temporal-Channel Aggregation

Skeleton-based action recognition methods are limited by the semantic extraction of spatio-temporal skeletal maps. However, current methods have difficulty in effectively combining features from both temporal and spatial graph dimensions and tend to be thick on one side and thin on the other. In this paper, we propose a Temporal-Channel Aggregation Graph Convolutional Networks (TCA-GCN) to learn spatial and temporal topologies dynamically and efficiently aggregate topological features in different temporal and channel dimensions for skeleton-based action recognition. We use the Temporal Aggregation module to learn temporal dimensional features and the Channel Aggregation module to efficiently combine spatial dynamic channel-wise topological features with temporal dynamic topological features. In addition, we extract multi-scale skeletal features on temporal modeling and fuse them with an attention mechanism. Extensive experiments show that our model results outperform state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets.
Effects of Learning Accessibility as a Mediator between Learning Styles and Blended Learning in Higher Education Institutions during the Covid-19 Pandemic

: Technology has been rapidly implemented in the learning process during the COVID-19 pandemic. It gives rise to new challenges, especially in higher learning institutions, in planning and mobilizing a sustainable learning environment. Initiatives have been taken to introduce different learning methods using the Learning Management System (LMS), that provide students with a more convenient and flexible way of learning. Thus, this study aims to determine the effects of learning accessibility as a mediator between learning styles and blended learning among students of higher education during the COVID-19 pandemic. A quantitative method using instruments from the Visual, Aural, Read and Kinaesthetic sensory (VARK) model has been selected. A total of 208 students from Universiti Putra Malaysia (UPM) have been randomly chosen as respondents who have fully utilized the LMS during the pandemic. Findings reveal that learning accessibility poses a partial mediation effect between learning styles and blended learning among students of higher education. This finding is significant and has achieved the Goodness-of-fit index (GFI). Therefore, it is proven that there are positive effects among students of higher learning institutions in Malaysia with a learning accessibility lean toward the LMS platform that increases blended learning and adaptability, prompted by the changes in the learning environment during the COVID-19 pandemic.
C$^3$Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy

3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined surfaces remains an unsolved problem. The challenges arise from the nature of optical colonoscopy data, characterized by highly reflective low-texture surfaces, drastic illumination changes and frequent tracking loss. Recent methods demonstrate compelling results, but suffer from: (1) frangible frame-to-frame (or frame-to-model) pose estimation resulting in many tracking failures; or (2) rely on point-based representations at the cost of scan quality. In this paper, we propose a novel reconstruction framework that addresses these issues end to end, which result in both quantitatively and qualitatively accurate and robust 3D colon reconstruction. Our SLAM approach, which employs correspondences based on contrastive deep features, and deep consistent depth maps, estimates globally optimized poses, is able to recover from frequent tracking failures, and estimates a global consistent 3D model; all within a single framework. We perform an extensive experimental evaluation on multiple synthetic and real colonoscopy videos, showing high-quality results and comparisons against relevant baselines.
Relationships Organize Information in Mind and Nature: Empirical Findings of Action–Reaction Relationships (R) in Cognitive and Material Complexity

Diverse phenomena such as feedback, interconnectedness, causality, network dynamics, and complexity are all born from Relationships. They are fundamentally important, as they are transdisciplinary and synonymous with connections, links, edges, and interconnections. The foundation of systems thinking and systems themselves consists of four universals, one of which is action–reaction Relationships. They are also foundational to the consilience of knowledge. This publication gives a formal description of and predictions of action–reaction Relationships (R) or “R-rule”. There are seven original empirical studies presented in this paper. For these seven studies, experiments for the subjects were created on software (unless otherwise noted). The experiments had the subjects complete a task and/or answer a question. The samples are generalizable to a normal distribution of the US population and they vary for each study (ranging from N = 407 to N = 34,398). With high statistical significance the studies support the predictions made by DSRP Theory regarding action–reaction Relationships including its universality as an observable phenomenon in both nature (ontological complexity) and mind (cognitive complexity); mutual dependencies on other universals (i.e., Distinctions, Systems, and Perspectives); role in structural predictions; internal structures and dynamics; efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, parallelism (between cognitive and ontological complexity), universality, and efficacy of action–reaction Relationships (R).
MONOCULAR DEPTH PREDICTION IN PHOTOGRAMMETRIC APPLICATIONS

Abstract. Despite the recent success of learning-based monocular depth estimation algorithms and the release of large-scale datasets for training, the methods are limited to depth map prediction and still struggle to yield reliable results in the 3D space without additional scene cues. Indeed, although state-of-the-art approaches produce quality depth maps, they generally fail to recover the 3D structure of the scene robustly. This work explores supervised CNN architectures for monocular depth estimation and evaluates their potential in 3D reconstruction. Since most available datasets for training are not designed toward this goal and are limited to specific indoor scenarios, a new metric, large-scale synthetic benchmark (ArchDepth) is introduced that renders near real-world scenarios of outdoor scenes. A encoder-decoder architecture is used for training, and the generalization of the approach is evaluated via depth inference in unseen views in synthetic and real-world scenarios. The depth map predictions are also projected in the 3D space using a separate module. Results are qualitatively and quantitatively evaluated and compared with state-of-the-art algorithms for single image 3D scene recovery.
Tracing COVID-19 Infection Chains Within Healthcare Institutions – Another Brick in the Wall Against SARS-CoV-2

Early anticipation of COVID-19 infection chains within hospitals is of high importance for initiating suitable measures at the right time. Infection control specialists can be supported by application systems able of consolidating and analyzing heterogeneous, up-to-now non-standardized and distributed data needed for tracking COVID-19 infections and infected patients' hospital contacts. We developed a system, Co-Surv-SmICS, assisting in infection chain detection, in an open and standards-based way to ensure reusability of the system across institutions. Data is modelled in alignment to various national modelling initiatives and consensus data definitions, queried in a standardized way by the use of OpenEHR as information modelling standard and its associated model-based query language, analyzed and interactively visualized in the application. A first version has been published and will be enhanced with further features and evaluated in detail with regard to its potentials to support specialists during their work against SARS-CoV-2.
The Evaluation of Time-Dependent Initialization Vector Advanced Encryption Standard Algorithm for Image Encryption

Sensitive data has to be protected from the public.• A strong encryption algorithm is required for protecting images.• Computing speed is growing, making algorithms vulnerable to attacks.• An enhanced AES-CBC algorithm is evaluated for the encryption proposed.• The algorithm satisfied the required standards and passed all tests.The Advanced Encryption Standard (AES) has become an attractive encryption method for high security and fast implementation.The encryption algorithm is approved as a standard for widely-used communication and data processing units.However, the advance in technology and the introduction of quantum computers made the encryption scheme vulnerable to attack.Different attack procedures are continuously developed to attack end decrypted important and sensitive data.This paper evaluated an enhanced Advanced Encryption System operating in Cipher Block Chaining mode, suggesting a promising solution for resisting future attacks.The approach depends on a time-dependent initialization vector that produces the initialization vector block depending on the epoch time without sharing any encryption key.The evaluation process includes correlation analysis, global and local Shannon entropy analysis, chi-square analysis, histogram analysis, and differential analysis.The results showed that the enhanced encryption scheme is reliable and can resist most cyber-attacks without exposing any encrypted data to the public.The results were compared with previously published and tested algorithms and found to satisfy and exceed the minimum requirement.So, the encryption method can be implemented safely in future communication channels or used in the file encryption process.
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with camera features. However, the camera-to-LiDAR projection throws away the semantic density of camera features, hindering the effectiveness of such methods, especially for semantic-oriented tasks (such as 3D scene segmentation). In this paper, we break this deeply-rooted convention with BEVFusion, an efficient and generic multi-task multi-sensor fusion framework. It unifies multi-modal features in the shared bird's-eye view (BEV) representation space, which nicely preserves both geometric and semantic information. To achieve this, we diagnose and lift key efficiency bottlenecks in the view transformation with optimized BEV pooling, reducing latency by more than 40x. BEVFusion is fundamentally task-agnostic and seamlessly supports different 3D perception tasks with almost no architectural changes. It establishes the new state of the art on nuScenes, achieving 1.3% higher mAP and NDS on 3D object detection and 13.6% higher mIoU on BEV map segmentation, with 1.9x lower computation cost. Code to reproduce our results is available at https://github.com/mit-han-lab/bevfusion.
Handwritten Letter Recognition using Artificial Intelligence

Abstract: Images are easily processed and analysed by the human brain. When the eye sees a particular image, the brain is able to instantly segment it and recognize its numerous aspects. This project proposes the Deep Learning conceptual models based on Convolutional Neural Network (CNN). A comparison between the algorithms reveals that the handwritten alphabets, classified based on CNNs outperforms other algorithms in terms of accuracy. In this project, different architectures of CNN algorithm are used: Manual Net, Alex Net, LeNet Architecture. These architectures contain a convolution layer, max pooling, flatten, feature selection, Rectifier Linear Unit and fully connected softmax layer respectively. The image dataset with 530 number of training images and 2756 numbers of testing images are used to experiment the proposed network. The best accuracy and loss efficient model will be deployed in the Django framework in order to create a user interface for giving the character to be identified and receiving the output result of identified character. Keywords: Handwritten Recognition, Alphabets, Deep Learning, TensorFlow, CNN, Django.
Analisis Faktor Penyebab Kurangnya Minat Remaja Terhadap Pendidikan Perguruan Tinggi

This research is expected to find out: 1) the inhibiting variables that cause the lack of attention of the youth of Jambula Village to study in additional schools. 2) financial/economic elements that make the younger generation of Jambula Village ignore further teaching. This kind of investigation is an undeniable abstract judgment. The number of respondents was 15. The certainty of the method in this study used purposive review/sampling, with the younger generation in Jambula Village as the main witnesses and the mentors/caregivers of teenagers as resource persons. Data collection techniques used are deepening/perception, meetings and documentation. The data checking methodology uses data reduction, data display and conclusion drawing. The results of this study indicate that: 1) the internal components that make the youth of Jambula City further foster youth are: a) lack of inspiration or desire to continue to college, b) wanting to be independent by looking for work after continuing high school. 2) External factors that cause it are the financial or economic limitations of the school are very large and the social environment factor itself.
Trilogy: A New Paradigm of Consciousness

Consciousness is usually interpreted as a state of being aware of one&amp;rsquo;s environment as well as self, while awareness is understood as knowledge of something. Despite their semantic differences, in philosophy, these terms are often used interchangeably, as is the case of the hard problem of consciousness proposed by Chalmers, which in fact is the hard problem of awareness. Trilogy paradigm of consciousness (or simply &amp;ldquo;trilogy&amp;rdquo;) offers a new paradigm where consciousness is the result of a unique interaction between awareness and the decision-making process. By conferring the input of awareness to the decision-making process, a new mental function of awareness-based choice selection (ABCS) or true free will emerges. Likewise, application of the power of decision-making to the process of awareness gives rise to discretionary selection of information for awareness (DSIA) or intentional attention. The intertwined actions of ABCS and DSIA comprise &amp;ldquo;I&amp;rdquo; which is the faculty of our consciousness and is what sets natural intelligence (NI) apart from artificial intelligence (AI). Based on trilogy, mind is an unconscious compilation of all mental function excluding ABCS and DSIA that are the essence of consciousness. As humans, we are a union of &amp;ldquo;I,&amp;rdquo; our minds, and our bodies.
A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem

Abstract In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles’ textual content to produce recommendations. However, CBF techniques are prone to the well-known cold-start problem. On the other hand, deep learning has shown its effectiveness in understanding the semantics of the text. The present paper proposes a citation recommendation system using deep learning models to classify rhetorical zones of the research articles and compute similarity using rhetorical zone embeddings that overcome the cold-start problem. Rhetorical zones are the predefined linguistic categories having some common characteristics about the text. A deep learning model is trained using ART and CORE datasets with an accuracy of 76 per cent. The final ranked lists of the recommendations have an average of 0.704 normalized discounted cumulative gain (nDCG) score involving ten domain experts. The proposed system is applicable for both local and global context-aware recommendations.
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting

The availability of large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a training-from-scratch to a fine-tuning paradigm. While in some applications the goal is to "nudge" the pre-trained distribution towards preferred outputs, in others it is to steer it towards a different distribution over the sample space. Two main paradigms have emerged to tackle this challenge: Reward Maximization (RM) and, more recently, Distribution Matching (DM). RM applies standard Reinforcement Learning (RL) techniques, such as Policy Gradients, to gradually increase the reward signal. DM prescribes to first make explicit the target distribution that the model is fine-tuned to approximate. Here we explore the theoretical connections between the two paradigms, and show that methods such as KL-control developed for RM can also be construed as belonging to DM. We further observe that while DM differs from RM, it can suffer from similar training difficulties, such as high gradient variance. We leverage connections between the two paradigms to import the concept of baseline into DM methods. We empirically validate the benefits of adding a baseline on an array of controllable language generation tasks such as constraining topic, sentiment, and gender distributions in texts sampled from a language model. We observe superior performance in terms of constraint satisfaction, stability and sample efficiency.