Reviewerly/exHarmony huggingface dataset:
Limiting Software Complexity

Measuring Software Complexity Comes with Traps and Pitfalls. What do you have to Consider?
Managing Social Distancing Effectively

Flow management and social distancing are critical when it comes to crowd control. An AI-powered LiDAR solution from Quanergy is attracting real interest.
Improved Image Style Transfer Based on VGG-16 Convolutional Neural Network Model

Abstract In the field of computer vision, image style transfer is an important research direction. With the promotion of artificial intelligence, this technology is becoming more and more popular. Compared with the current image style transfer technology based on artificial intelligence, the traditional technology appears to be complex, time-consuming and inefficient. Migration technology is mainly based on images of the current mainstream style VGG - 16 convolution neural network model, adopts TensorFlow framework, in this article to a picture before the smart migration style photo style, texture features such as the methods of improvement, by modifying the style loss function, makes the image content images can transfer a variety of style, Finally, it is verified by relevant experiments.
A Study on Kaibara Ekiken's Educational Thoughts

The educator and philosopher Kaibara Ekiken, has long been seen as a model for the intellectuals of the Edo period. He is regarded as the progenitor of modern education in Japan and is seen in Japan in a similar light as Aristotle. In this study, the social situation of the Edo period, the life of Ekiken, and his book 『Wazokudozikun』 were comprehensively analyzed to find out about the educational thought process of Ekiken and the resulting social effects. As a result, conclusions were drawn about the educational period, content, and method of education that Ekiken was aiming for. By paying attention to the importance of early childhood in the 'human formation', he tried to awaken young children through a process of 'education without teaching' as well as impacting the beginning and end of things as early as possible. It is speculated that Ekiken's educational ideas influenced not only the ruling class of warriors, but also the education of the merchant class. In particular, it is likely that it had a significant impact on the establishment of Terakoya, (the basic educational institution of merchants), as well as the education without teaching of Detchi Bonggong. This research paper will show that, Ekiken should be evaluated as a “master of the early modern period of education in Japan” rather than as the “beginning of the modern education period in Japan.”
Path Planning of Multiple Automatic Guided Vehicles with Tricycle Kinematics Considering Priorities and Occupancy Time Windows

This paper addresses the problem of path planning for multiple automated guided vehicles that have a kinematic model of a tricycle and drive in a network of one-way roads. The objective of path planning is that all AGVs reach their destination in the shortest possible time without collisions. The proposed path planning algorithm is an extension of the A* algorithm and takes into account the priorities of the AGVs. An occupancy time window approach is used to detect and resolve predicted collisions. The algorithm determines waiting times and waiting locations that allow collision-free driving. The node occupancy algorithm also considers the shape of the vehicle and its speed. The result of the path search algorithm is the path for each vehicle and the waiting times on each road. The resulting paths are converted into corresponding action plans that are sent to all AGVs for execution. The applicability of the proposed path planning algorithm was evaluated in a simulation environment and on real small-scale AGVs.
Improving the Flow in Multi-robot Logistic Systems Through Optimization of Layout Roadmaps

In intralogistic systems, set-up is extremely important and largely determines their performance. In systems that use autonomous guided vehicles (AGVs), planning the roadmaps is a complex problem that is usually solved by human experts. While roadmaps in AGV systems are fixed using magnetic tape, autonomous mobile robots (AMRs) provide additional flexibility because they are able to move around freely. However, to maintain system performance, free movement needs to be constrained, e.g., to avoid deadlocks in narrow aisles. A method is presented that suggests the movement constraints in terms of preferred directions of motion. The free space is modeled as a grid graph with directed, weighted edges. The weights are then optimized by an algorithm inspired by ant colony optimization (ACO) that aims to reduce the amount of conflicting situations, such as collisions, in a given intralogistic problem. The approach is illustrated in a case study which shows that the proposed method can inform a feasible roadmap plan that leads to fewer conflict situations.
Physics-Based Motion Planning of a Fruit Harvesting Manipulator for Pushing Obstacles in a Cluttered Environment

Recently, the agricultural population is decreasing, and the demand for autonomous agricultural robots has increased. For a robot to successfully harvest fruits in diverse environments, various obstacles must be pushed away from its path. During the pushing motion, however, gravity acts on the fruits, causing them to move back and forth and return to their original state after being pushed away. This results in fruit-to-fruit contact, and fruit damage may occur. Therefore, a method to avoid obstacles while reducing the risk of fruit damage is required. However, the direct consideration of fruit damage is difficult because it requires many parameters that are difficult to observe. Herein, we propose a motion planning method by considering the potential energy of fruits as an evaluation index, which could be calculated based on observable parameters; this consideration could also suppress the reciprocating motion of fruits, thus preventing fruit damage. The proposed method was evaluated through simulations, and its effectiveness was verified.
Research on the Construction of “Golden Course” in Cross-regional Collaborative Development Universiti

Abstract : Objective To understand the status of "golden courses" in cross-regional collaborative development of western local universities and to explore the strategies of "golden courses" in cross-regional collaborative development of western local universities. Methods The online survey was conducted by randomly selecting teachers from western local universities through "Questionnaire Star". SPSS25.0 software was used to analyze and process the data. Results 66.9% of western local universities have already started the construction of "Golden Course", 83.2% of teachers think it is necessary to implement it and are willing to participate in the construction of "Golden Course" through cross-regional collaborative development; resource platform, curriculum team and informatization facilities are the most important factors in the construction of "Golden Course". "The lack of curriculum team support, teachers' excessive teaching tasks, limited personal IT skills, and teachers' teaching philosophy and ability to carry out teaching reform and innovation are the most lacking resources in the construction of "golden courses". The main problems of "Golden Course" and cross-regional collaborative development. Conclusion The construction of "golden courses" in western local universities is not optimistic, teachers are not sufficiently aware of "golden courses", information technology teaching facilities are backward, teachers are overloaded with teaching tasks, and there is a lack of resources for the construction of "golden courses" and cross-regional construction of "golden courses". The lack of resources for the construction of "golden courses" and cross-regional construction of "golden courses" can be achieved through policy formulation, incentive measures, strengthening training and learning, improving personal business quality and competence of "golden courses" construction, and sharing high-quality resources to implement cross-regional collaborative development and construction of "golden courses" and
A Bibliometric Review of Vocabulary Learning via Mobile Assisted Language Learning

In recent years, Mobile Assisted Language Learning (MALL) has emerged as a widespread phenomenon in education as well as in EFL/ESL teaching and learning context with its own set of foundations, techniques, and implications.Among the related studies, a continuous growth in the number of MALL vocabulary studies can be witnessed over the last few decades.To this end, the primary purpose of this study is to identify research hotspots and future trends in the available research literature on vocabulary learning using MALL from 2007 to 2022.A total of 229 articles on this topic were selected from the Scopus database and analysed using Vosviewer software.We used bibliometric methods to reveal document types and yearly distribution of the retrieved articles, the most productive countries and journals, the most used keywords, the most used words in title and abstract area, and the most cited publications and authors (citation & co-citation) in the studies over the 16-year period.Findings showed an increased number of studies from 2007 to 2022 have been published in this field but with a slight fluctuation in between 2012 and 2018.China (24 publications) took the top prominent lead among all countries, whilst the most productive journal in this area was Lecture Notes in Computer Science (11 publications) by Springer Publishing company.In terms of the most frequently used keywords, "Vocabulary Learning" (120 occurrence), "Mobile Assisted Language Learning" (114 occurrence) and "App" (40 occurrence) were the top three ranks, meanwhile "Effect" (62 occurrence), "System" (56 occurrence) and "Foreign Language" (46 occurrence) were the most used words in title and abstract field.Based on the citation and co-citation documents and authors network, the most frequently cited document was Chen & Chung (2008) with 271 citations, while Chen, C. M (citation) with 549 citations and Stockwell, G (co-citation) with 104 citations were the most influential authors.Consequently, this study displays the evolution of the literature and may serve as a guide for future research by comparing existing articles and indicating current research interest as well as future research trends on this topic.
Case study on including ethics into introductory data visualization

Given the amount of data created and available to everyone, there is a gap in consumable everyday data analytic tools for anyone to make sense of their data. At anonymous university, we designed an introductory data visualization course to teach college students how to analyze data. Students enrolled in this six-week summer course and used utilize Trifacta \cite{Trifacta}, Tableau \cite{Tableau}, and ObservableHQ \cite{ObservableHQ} on the IEEE VAST Challenge 2022 dataset. The course emphasized the uncertainty and deception involved in data visualization. We structured the course around ethical design choices. In this paper, we describe an overview of the ethical goals of our six-week data visualization summer course, review example student work on the IEEE VAST Challenge, and provide recommendations for ways to add ethical functionality to visualization tools. The goal is for more data visualization tools which are consumable for novice users and support ethical design choices.
San Antonio Research Partnership Portal: Evaluating keyword extraction tools to automate matchmaking for community research partnership

Finding research professionals and collaborators to address community problems continues to be a significant barrier for many local government agencies. Research collaboration between researchers from universities, industries, and local government agencies can be tremendously useful to all organizations. San Antonio Research Partnership Portal is a collaborative initiative to bring researchers and local government agencies in one place to solve community concerns. In this paper, we have investigated the performance of popular keyword extraction tools by measuring the effectiveness of identifying the keywords from research opportunities. The extracted keywords are used in an automated process for San Antonio Research Partnership Portal to match academic researchers with corresponding research opportunities.
FCLWebVis: A flexible cross-language web-based data visualization framework

We present a new web-based, client-server data processing and visualization framework that supports a flexible workflow, enabling the user to customize different data processing and visualization tasks with tools implemented in different programming languages. Our framework supports server-side applications developed with different languages, allowing visualization researchers to easily make their new techniques available to the target users. The client-side of our framework is implemented in the web browser environment with customizable interface and visualizations. We describe the design of the architecture of our framework and the process of adding new user-defined tasks, followed by the demonstration of the proposed framework on a number of data processing and visualization tasks.
Comparative visualization for noise simulation data

Noise, vibration, and harshness (NVH) simulation represents an important step in modern automotive design. It produces large and complex data which is not easy to analyze. The data resides in two domains, the spatial and the frequency domain. In this paper, we extend the current state of the art in visual exploration of such data by supporting comparison tasks. We support the comparison of velocity values of a subset of surface elements for multiple frequency bands. We combine data aggregation on the 3D model with multiple bar charts in a coordinated multiple views system. This new approach allows for an intuitive comparison of multiple velocity values in the context of both domains. We demonstrate the deployment of this approach for an example from the automotive industry, but it can be used with any simulation data that relates to two domains at the same time.
Layered view synthesis for general images

We describe a novel method for monocular view synthesis. The goal of our work is to create a visually pleasing set of horizontally spaced views based on a single image. This can be applied in view synthesis for virtual reality and glasses-free 3D displays. Previous methods produce realistic results on images that show a clear distinction between a foreground object and the background. We aim to create novel views in more general, crowded scenes in which there is no clear distinction. Our main contributions are a computationally efficient method for realistic occlusion inpainting and blending, especially in complex scenes. Our method can be effectively applied to any image, which is shown both qualitatively and quantitatively on a large dataset of stereo images. Our method performs natural disocclusion inpainting and maintains the shape and edge quality of foreground objects.
AInBody: Are you in shape? - An integrated deep learning model that tracks your body measurement

This paper presents AInBody, a novel deep learning-based body shape measurement solution. We have devised a user-centered design that automatically tracks the progress of the body by adequately integrating various methods, including human parsing, instance segmentation, and image matting. Our system guides a user's pose when taking photos by displaying the outline of the latest picture of the user, divides the human body into several parts, and compares before and after photos of the body part level. The parsing performance has been improved through an ensemble approach and a denoising phase in our main module, Advanced Human Parser. In evaluation, the proposed method is 0.1% to 4.8% better than the other best-performing model in average precision in 3 out of 5 parts, and 1.4% and 2.4% superior in mAP and mean IoU, respectively. Furthermore, the inference time of our framework takes approximately three seconds to process one HD image, demonstrating that our structure can be applied to real-time applications.
Evaluating active learning for blind imbalanced domains

Deep learning, which has been very successful in recent years, requires a large amount of data. Active learning has been widely studied and used for decades to reduce annotation costs and now attracts lots of attention in deep learning. Many real-world deep learning applications use active learning to select the informative data to be annotated. In this paper, we first investigate laboratory settings for active learning. We show significant gaps between the results from different laboratory settings and describe our practical laboratory setting that reasonably reflects the active learning use cases in real-world applications. Then, we introduce a problem setting of blind imbalanced domains. Any data set includes multiple domains, e.g., individuals in handwritten character recognition with different social attributes. Major domains have many samples, and minor domains have few samples in the training set. However, we must accurately infer both major and minor domains in the test phase. We experimentally compare different methods of active learning for blind imbalanced domains in our practical laboratory setting. We show that a simple active learning method using softmax margin and a model training method using distance-based sampling with center loss, both working in the deep feature space, perform well.
ILIAC: Efficient classification of degraded images using knowledge distillation with cutout data augmentation

Image classification is extensively used in various applications such as satellite imagery, autonomous driving, smartphones, and healthcare. Most of the images used to train classification models can be considered ideal, i.e., without any degradation either due to corruption of pixels in the camera sensors, sudden shake blur, or the compression of images in a specific format. In this paper, we have proposed a novel CNN-based architecture for image classification of degraded images based on intermediate layer knowledge distillation and data augmentation approach cutout named ILIAC. Our approach achieves 1.1%, and 0.4% mean accuracy improvements for all the degradation levels of JPEG and AWGN, respectively, compared to the current state-of-the-art approach. Furthermore, ILIAC method is efficient in computational capacity, i.e., about half the size of the previous state-of-the-art approach in terms of model parameters and GFlops count. Additionally, we demonstrate that we do not necessarily need a larger teacher network in knowledge distillation to improve the model performance and generalization of a smaller student network for the classification of degraded images.
Investigating pretrained self-supervised vision transformers for reference-based quality assessment

Reference-based image quality assessment techniques use information from an undistorted reference image of the same scene to estimate the quality of a distorted target image. The main challenge in designing algorithms for quality assessment is to incorporate the behavior of the human visual system into the algorithms. The advent of deep learning (DL) techniques has garnered sufficient interest among researchers in the field of image quality assessment. The common limitation of applying deep learning for image quality assessment is its dependence on a large amount of subjective training data. Recent advances in the field of patch-based self-supervised vision transformers have achieved remarkable results for tasks like object segmentation, copy detection, etc. and other downstream computer vision tasks. In this paper, we study how the distance between the pretrained self-supervised vision transformer features applied on pristine and distorted images is related to the human visual system. Experiments carried out on three publicly available image quality databases (namely KADID-10K, TID2013, and MDID2016) have yielded promising results that can be further exploited to design perceptual reference-based image quality assessment methods.
ORCA: An end-to-end video object removal framework with cropping interested region and quality assessment

Recently, various types of Video Inpainting models have been released. Video Inpainting is used to naturally erase the object you want to erase in the video. However, to use inpainting models, we usually need frames extracted from a video and masks and most people make these data manually. We propose a novel End-to-End Video Object Removal framework with Cropping Interested Region and Video Quality Assessment (ORCA). ORCA is built in an end-to-end way by combining the Detection, Segmentation, and Inpainting modules. The characteristics of proposed framework are going through the cropping step before inpainting step. In addition, We propose our own video quality assessment since ORCA use two models for inpainting. Our new metric indicates the higher quality of the results between two models. Experimental results show the superior performance of the proposed methods.
Time Efficient Video Captioning Using GRU, Attention Mechanism and LSTM

People have a number of video-capturing devices, such as smartphones, cameras, etc. Consequently, they started uploading large amounts of video to the internet, requiring file organization and classification. Many scholars are investing a lot of time in automatically generating sentences for images and videos. It has a variety of uses, such as video indexing, video retrieval, etc. Many approaches are being developed to properly analyze the video and automatically construct the sentence, but understanding the relationship between video information and natural language sentences is still a work in progress. We used the gated recurrent unit, attention mechanism, and long short-term memory unit to develop a recurrent neural network based model for video captioning. We tested our model on Microsoft research video description corpus (MSVD) dataset and showed higher accuracy compared to other state-of-the-art models. The proposed model achieved 0.76 Bleu-1 score and 0.673 Rouge score.
Sentiment analysis of facebook posts on Kolkata police page and public sentiment

This paper is an analysis of the comments posted on the facebook page of Kolkata Police through sentiment analysis tools to understand people's attitudes towards Kolkata Police during lockdown I & II in the state of West Bengal in India. These platforms keep people digitally connected. Social media also has given consumers a virtual soapbox, from where they can convey their opinions. Every day huge volumes of text data are getting generated on social platforms. While humans instantly understand the sentiment behind a single comment, how should we cope with the explosion of content shared on social platforms? It is extremely challenging to analyse the massive amount of unstructured text data manually. Here we need tools to help us. AI-powered sentiment analysis tools use natural language processing techniques to analyse the online conversation and underlying context - positive, neutral, or negative. Sentiment analysis tools mimic human brains and allow us to get the sentiment behind online content. In this study, researcher has used rule-based sentiment analysis techniques (TextBlob and Valence Aware Dictionary and Sentiment Reasoner or VADER) to detect the public sentiment towards the service of Kolkata police during COVID lockdown phase I & II (March 22, 2020, to May 3, 2020)