Reviewerly/exHarmony huggingface dataset:
Self-Supervised Cross-Video Temporal Learning for Unsupervised Video Domain Adaptation

We address the task of unsupervised domain adaptation (UDA) for videos with self-supervised learning. While UDA for images is a widely studied problem, UDA for videos is relatively unexplored. In this paper, we propose a novel self-supervised loss for the task of video UDA. The method is motivated by inverted reasoning. Many works on video classification have shown success with representations based on events in videos, e.g., ‘reaching’, ‘picking’, and ‘drinking’ events for ‘drinking coffee’. We argue that if we have event-based representations, we should be able to predict the relative distances between clips in videos. Inverting that, we propose a self-supervised task to predict the difference of the distance between two clips from the source video and the distance between two clips from the target video. We hope that such a task would encourage learning event-based representations of the videos, which is known to be beneficial for classification. Since we predict the difference of clip distances between clips from source videos and target videos, we ‘tie’ the two domains and expect to achieve well-adapted representations. We combine this purely self-supervised loss and the source classification loss to learn the model parameters. We give extensive empirical results on challenging video UDA benchmarks, i.e., UCF-HMDB and EPIC-Kitchens. The presented qualitative and quantitative results support our motivations and method.
A 40-nm 91-mW, 90-fps Learning-Based Full HD Super-Resolution Accelerator

Super-resolution has been utilized in a plenty of applications to provide better visual experience. To meet the high-throughput and low-power needs, some dedicated accelerators for super-resolution have been proposed. Neural-network (NN)-based super-resolution accelerators achieve impressive restoration performance, but the high-computational complexity does not allow a high throughput for video streaming. This work presents a super-resolution accelerator that implements the rapid and accurate image super-resolution (RAISR) algorithm for reconstructing super-resolution images. The utilization of the low-resolution (LR) upscaler is increased by 50% by the proposed memory scheduling scheme. Kernel compression is utilized to reduce the overall on-chip memory by 72%. A patch reuse scheme achieves a 91% reduction in external memory access times compared to the direct-mapped design. The architecture is flexible to reconstruct full HD images with a variety of upscaling factors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> ). Fabricated in a 40-nm CMOS technology, the proposed super-resolution accelerator integrates 3.11-M gates in a core area of 3.33 mm2. The chip is able to deliver a throughput of 90 frame/s (fps) for all supported upscaling factors and dissipates 91 mW at 200 MHz. Compared with the state-of-the-art designs, this work achieves a 5.4-to- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$28.4\times $ </tex-math></inline-formula> higher normalized throughput with 5.1-to- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$36\times $ </tex-math></inline-formula> lower normalized energy dissipation.
HRI Framework for Continual Learning in Face Recognition

Recognizing human partners is an essential social skill for building personalized and long-term human-robot interactions. However, robots deployed in complex, real-world environments have to face several challenges, such as managing unstructured interactions with multiple users, limited computational resources, and intrinsic and continuous variability of their sensory evidence. To cope with these challenges, we propose a framework to perform autonomous incremental learning for open-set face recognition suitable for unconstrained HRI scenarios. We validated the proposed framework in a real-world experiment, demonstrating its suitability to let the robot autonomously interact with multiple people while creating a labeled database of their faces across various encounters. Furthermore, we evaluated how an off-the-shelf model performed with data gathered from the HRI setting and proposed a fine-tuned model obtained with a transfer learning technique. Analyses about automatic threshold determination and rehearsal methods for memory sampling were also proposed. Our preliminary results suggest that exploiting the first-hand robot's experience could be crucial to ensure better models' performance and, therefore, could be advantageous for the acceptance and effectiveness of social robots in the long run. With this work, we aim to provide insights on continual learning approaches in the HRI field to promote autonomous and personalized solutions meaningful for real-world applications.
System Supporting Poetry Generation Using Text Generation and Style Transfer Methods

The paper presents the Bairon system that supports the automatic generation of poetry. The proposed system allows generating a poem in the literary style of the selected writer using the user's input as the first line, and translating the given text into Shakespearean English. To accomplish that, GPT-2 and T5 language models were fine-tuned. We also propose easy to understand metrics to evaluate the quality of the generated poems and their similarity to the corresponding poet's original work, and to present the results. Additionally, the Poetry Turing Test with human participants was conducted to get another measure of quality of the generated poetry.
Teaching-Learning-Based Optimization Algorithm Based on Cauchy Reverse and Cross Selection

Teaching-learning-based optimization (TLBO) algorithm is a heuristic algorithm for solving optimization problems in various fields. TLBO algorithm imitates the classroom teaching process. In this paper, the basic TLBO algorithm is briefly introduced. Then, to solve the problem that the TLBO algorithm is not accurate and easy to fall into local optimum, a teaching-learning-based optimization algorithm on cauchy reverse and cross selection (TLBO - CRCS) is proposed. First, cauchy reverse learning is used to speed up the convergence of the algorithm. Secondly, the basic teaching-learning-based optimization algorithm is improved, the strategy of randomly selecting the “teaching” stage or “learning” stage is proposed, and improved crossover and selection strategies are introduced in the teaching and learning stage. Finally, 28 IEEE CEC2013 test set functions are selected and compared with the other four algorithms. Experimental results show that the proposed algorithm has the best overall performance.
Machine Learning For Detecting Twitter Bot

Machine learning for detecting twitter bot is a web-based application that allows the user to find the account is a legitimate account or twitter bot account. Previously, only the overview of the bots are found for the Twitter and Facebook. So it just talks about the overview of the site with various other machine learning algorithms. To enhance this, in this paper a website is created to find particularly in Twitter. To achieve the outcome only decision tree classifier is used in the project. Here classifications like followers count, following count etc., is used to find whether the account is legitimate account or illegitimate account.
One-hour Ahead Spatio-Temporal Solar GHI Forecasting Using Long Short-term Memory

Using time-series information from surrounding sites, this paper explores the short-term forecasting of solar global horizontal irradiance (GHI) at a single target site. Long Short-term Memory (LSTM) is presented to forecast GHI, based on the integration of spatial and temporal information. Particle Swarm Optimization (PSO) is used to optimize the hyperparameter and time step in LSTM. The simulation results demonstrate that the proposed method is more accurate than existing methods for 1-hour ahead GHI forecasting.
Analysis of the application of data mining clustering model in the selection of the best study program based on student interests at the University of Aceh Province

Limited student capacity at State Universities (PTN) in Aceh provides opportunities for Private Universities (PTS) in Aceh to hold lectures at universities. It provides opportunities for private universities, institutes, colleges, academies, and other polytechnics in student admissions. Marketing strategies in doing promotions are needed to be able to attract students to enroll in the college. Instead, the promotion strategy must be on target effectively and efficiently. Therefore, a model is needed in looking at the grouping of students who are most widely accepted in the study program and the origin of the school. This is to facilitate the campus in carrying out promotional strategies to schools that contribute the most students to private universities in Aceh such as al-Muslim and uniki universities. The existence of this problem requires a system in looking at the grouping patterns that are most widely accepted to private universities based on schools and study program interests. Furthermore, a model is needed to make the student's interest strategy in looking at the study program in demand so that private universities can prepare strategies in conducting promotions going forward and the funds allocated for promotion can be used appropriately. Data taken at the time of the study can be seen from each school that contributed as much students to historical data starting from 2015, 2016, 2017, 2018, 2019 2020, which then the data was taken from each college. The results of this study can also look at the grouping of schools that contribute a lot of students to each school and the pattern of grouping students most widely accepted by school and historical in previous years using the optimal pattern of number of clusters with the average silhoutewidth method in looking at the distribution of students who have been in college The results of the k-means research analysis of the application of the Data Mining Clustering Model in the Selection of the Best Study Programs Based on Student Interests at the University, the results of cluster 1: the minimum average in the number of students spending per year is 7 people, max 12 people, cluster 2: per year is 2 people, max 5 people, cluster 3: 26 people per year, max 31 people, cluster 4: minimum average of 15 people, max 20 people.
Pattern-free Plenoptic 2.0 Camera Calibration

The plenoptic 2.0 camera is a light field acquisition system consisting of a main lens and a micro-lens array (MLA) at a non-focal distance of the main lens. While it allows to retrieve the geometry of the scene, the distances between the main lens, the MLA and the sensor are usually unknown. Therefore, the use cases for plenoptic cameras stay limited while they have more potential applications such as virtual reality, provided that their camera parameters are precisely known. In this paper, we present a pattern-free calibration method to retrieve the plenoptic camera's intrinsic parameters from the rendered subaperture images, i.e. the distance from main lens to the MLA and sensor, the focal length and the principal point. The proposed method utilises the relation between scene's distances, i.e. depth, and the subaperture images' micro-disparities, which contains information about the camera parameters. To the best of our knowledge, it is the first pattern-free calibration method for plenoptic 2.0 cameras. We compare the parameters obtained using our method applied to subaperture images rendered from three different software tools (Plenoptic Toolbox, Reference Lenslet content Convertor, and Lenslet to Multiview) with an open-source pattern-based method (Compote). The proposed pattern-free calibration method has consistent camera parameters with the traditional pattern-based calibration method. We therefore reliably obtain the intrinsic parameters of any plenoptic camera from its captured images, even in the absence of any calibration pattern.
DIRECTIONS FOR IMPROVING THE DISTANCE FORMAT OF PRE-SCHOOL EDUCATION BACHELORS TRAINING

The article presents the experience of teaching the practical discipline «Artistic needlework» for students of the specialty 012 «Pre-school education» in a distance format. Changes in the organization of the educational process took place in connection with the 2020-2022 global pandemic of COVID-19, which forced the change of face-to-face learning format to distance education. Teachers of practical disciplines faced significant difficulties: working out technical skills of making products, mastering the methods of educational interaction and communication in the process of subject-transformative and artistic activities with preschool children. The transition to a distance learning format required the development of electronic educational content and the search for effective tools for educational interaction. The conducted experimental research made it possible to find out the attitude of students to the form of mastering the educational material and to study the effectiveness of the use of digital ICT services in ensuring the educational process. The advantages and disadvantages of distance learning of the practically oriented discipline «Artistic needlework» according to the opinion of the students of education were highlighted. It has been found that the most effective way to master the discipline is the organization of online classes, which allows the teacher and students to communicate, show the main technical techniques of making products in real time, give methodological recommendations for completing tasks, and discuss educational tasks. Various services are offered for the organization of educational interaction while studying the discipline «Artistic needlework».
Applicative Research of Data-mining Algorithm in Electronic Medical Record System

Based on the analysis of various data-mining algorithms and the cases of illness from one community hospital, we study the application of the data-mining algorithm to electronic medical records. Combining two common medical records-hypertension and hysteromyoma, a comparative analysis result of different data-mining models is summarized for the characteristics and applicable scope of various algorithms to establish a mining model. The result shows that the model is suitable for the above common diseases and available to medical personnel. It also helps to improve residents' prevention ability, assist doctors' diagnosis, and improve hospital management.
Viewport-Dependent Delivery for Conversational Immersive Video

Real-time immersive video has been an area of interest for the research and standardization community over the past few years. It has gained particular importance in the era of 5G services. 3GPP (Third Generation Partnership Project) Release 17 includes Immersive teleconferencing for Multimedia Telephony services over IP Multimedia System. Viewport-dependent delivery (VDD) is a mechanism for bandwidth savings by assigning more bits to the part of the 360-degree video currently in the viewport and fewer or no bits to the areas of the 360-degree video that a user does not currently view. The technique has been extensively studied in the context of adaptive HTTP streaming of immersive video, where the 360-degree video is pre-encoded in different versions, and the appropriate version is selected and requested by the viewer’s client application, depending on their current viewport orientation. However, conversational video has more stringent latency requirements than HTTP streaming; the time from capture to rendering is in the order of a few milliseconds for a good user experience. Conversational immersive video also allows adapting the encoding during the media delivery session according to the user viewport and network bandwidth characteristics. This paper explores tile-based and viewport region encoding for VDD and how they apply to conversational video. In particular, we explore the challenges related to the absence of periodic intra-coded frames in conversational video. Based on simulation results, we show that VDD methods may not always be a performant alternative to viewport-independent delivery in conversational use cases. We also discuss possible encoder optimization that would improve the VDD performance.
Customizable FPGA-based Accelerator for Binarized Graph Neural Networks

Graph convolutional networks (GCNs) have demon-strated their excellent algorithmic performance in various graph-based learning applications. Nevertheless, the massive amount of computation required by analyzing graph data structures puts a heavy burden on the hardware performance, limiting their deployment in real-life scienarios. To address this issue, we propose a customizable FPGA-based design to accelerate binarized GCNs (BiGCNs). The proposed accelerator is parameterized by different loop unrolling and memory partition factors, which can be reconfigured to fulfill different user needs. To ease the bandwidth requirement of BiGCNs, our design overlaps the data transfer with computation. We also adopt COO (Coordinate) format storage for the adjacency matrix to skip the redundant computation to improve hardware performance. Our experimental results demonstrate that the proposed FPGA-based BiGCN design achieves 202× and 10.6× speedup than CPU and GPU implementations on the Flickr dataset.
Discussion on Fault Analysis for High-capacity and Multi Column Parallel Arrester

The operation data show that the failure rate of high-capacity and multi column parallel arrester, such as the neutral bus arrester, the series compensation arrester and so on, is relatively high, and the cause of most faults is not clear. In order to improve the reliability of multi column parallel arrester, several possible factors are studied in this paper, such as the volt ampere characteristics of arrester under different temperature, the current sharing performance under different temperature or different current waveforms, and the energy absorption capacity under different current waveforms and so on. It is pointed out that the high failure rate of high capacity and multi column parallel arrester is mainly due to the large dispersion of energy absorption capacity and inevitable “short resistor”. It is proposed that the fundamental way to solve the high failure rate of multi column parallel arrester is to reduce the “short resistor” probability, optimize the structure and improve the withstand capacity with “short resistor”.
A Survey: Vehicle Detection and Counting

Controlling the flow of traffic requires an advance traffic management to know what is happening on the busiest urban junctions. As there are billions of traffic cameras around the world that act as sensors, it is quite easy to collect real-time data about traffic flows, but then using that data for processing and controlling traffic flows is another challenge. For the detection of vehicles, there are several outdated techniques, such as the use of an inductive loop detector, infrared, laser sensors, etc. Numerous deep learning techniques and computer vision techniques are currently being experimented with worldwide by researchers. The purpose of this paper is to review and compare deep learning- and computer vision-based systems for detecting and counting vehicles for various video-based surveillance applications. In reviewing the works of various researchers, we considered the approaches, accuracy, and results upward, as well as the future difficulties of this field of research.
MultiPoseSeg: Feedback Knowledge Transfer for Multi-Person Pose Estimation and Instance Segmentation

Multi-person pose estimation and instance segmentation suffer large performance loss when images are with an increasing number of people and their uncontrolled complex appearance. Yet, existing models cannot efficiently leverage unbalanced training images, i.e., few of them are with multi-person, and most are with single-person, making them ineffective for challenging multi-person scenarios. To tackle multi-person cases with a limited portion of them, we propose MultiPoseSeg, a data preparation and feedback knowledge transfer system designed for multi-person pose estimation and instance segmentation. First, MultiPoseSeg categorically performs random data reduction to reduce the single-person bias from the train dataset. Second, MultiPoseSeg employs the knowledge transfer from ancestor models to converge the model learning with a limited amount of data and time. This way, our model learns and train on human pose and instance segmentation to advance the training and testing accuracy. Finally, MultiPoseSeg proposes keypoint maps to identify the keypoint coordinates for soft and hard keypoints and segmentation maps to assign centroid to each human instance, which helps to cluster the pixels to a particular instance. We have evaluated MultiPoseSeg using COCO and OCHuman challenging datasets and demonstrated MultiPoseSeg outperforms state-of-the-art bottom-up models in terms of both accuracy and runtime performance, achieving 0.728 mAP for pose and 0.445 mAP for segmentation on COCO dataset. All the unbiased data and code has been made available at: https://github.com/RaiseLab/MultiPoseSeg
Learning Multi-Granularity Features for Re-Identifying Figures in Portrait Thangka Images

Re-identifying the figures in portrait Thangka images is of great significance to the protection and dissemination of Thangka. Existing portrait Thangka image retrieval methods are limited in extracting features of multiple granularities, and consequently can not distinguish between Thangka figures which have different identities but subtle visual differences. To cope with this challenging task, we propose a Multi-Granularity Feature Learning (MGFL) framework consisting of part and global branches. The part branch splits feature maps along the vertical dimension with inspiration by the composition rules of portrait Thangka images. The global branch integrates inception feature pyramid, channel and spatial attention mechanisms to capture discriminative features of various granularities. To combine the multi-granularity features, we propose to use step-wise graph convolution to better exploit the relationships among different composition elements in portrait Thangka images. Extensive qualitative and quantitative experiments on a dataset with 6,208 portrait Thangka images of 145 identities collected by ourselves demonstrate the superiority of our method. We will release the dataset and code to the public for research purpose.
Constraint-based Task Specification and Trajectory Optimization for Sequential Manipulation

To economically deploy robotic manipulators the programming and execution of robot motions must be swift. To this end, we propose a novel, constraint-based method to intuitively specify sequential manipulation tasks and to compute time-optimal robot motions for such a task specification. Our approach follows the ideas of constraint-based task specification by aiming for a minimal and object-centric task description that is largely independent of the underlying robot kinematics. We transform this task description into a non-linear optimization problem. By solving this problem we obtain a (locally) time-optimal robot motion, not just for a single motion, but for an entire manipulation sequence. We demonstrate the capabilities of our approach in a series of experiments involving five distinct robot models, including a highly redundant mobile manipulator.