Reviewerly/exHarmony huggingface dataset:
FashionVQA: A Domain-Specific Visual Question Answering System

Humans apprehend the world through various sensory modalities, yet language is their predominant communication channel. Machine learning systems need to draw on the same multimodal richness to have informed discourses with humans in natural language; this is particularly true for systems specialized in visually-dense information, such as dialogue, recommendation, and search engines for clothing. To this end, we train a visual question answering (VQA) system to answer complex natural language questions about apparel in fashion photoshoot images. The key to the successful training of our VQA model is the automatic creation of a visual question-answering dataset with 168 million samples from item attributes of 207 thousand images using diverse templates. The sample generation employs a strategy that considers the difficulty of the question-answer pairs to emphasize challenging concepts. Contrary to the recent trends in using several datasets for pretraining the visual question answering models, we focused on keeping the dataset fixed while training various models from scratch to isolate the improvements from model architecture changes. We see that using the same transformer for encoding the question and decoding the answer, as in language models, achieves maximum accuracy, showing that visual language models (VLMs) make the best visual question answering systems for our dataset. The accuracy of the best model surpasses the human expert level, even when answering human-generated questions that are not confined to the template formats. Our approach for generating a large-scale multimodal domain-specific dataset provides a path for training specialized models capable of communicating in natural language. The training of such domain-expert models, e.g., our fashion VLM model, cannot rely solely on the large-scale general-purpose datasets collected from the web.
THE IMPLEMENTATION OF CLT (COMMUNICATIVE LANGUAGE TEACHING) TO IMPROVE PUPILS’ SPEAKING ABILITY

The purpose of this research was to determine wheter using CLT (Communicative Language Teaching) can improve pupils’ speaking ability at Prateepsat Ismail Memorial School or not. This research used a quasi-experimental research that required a pre-test and a post-test for both groups. The research population was pupils in grade 2, where class 2/2 as an experimental class consisiting of 36 pupils and class 2/1 as a control class consisting of 36 pupils. In collecting data, reseracers used test. The test consisted of a pre-test and post-test. Data were analyzed statistically. After being analyzed, the result of t-counted was 11,977 with 70 degrees of freedom and a significance level of 0,05, the table is 1,666. This research used a significance level of 0,05 because it has moderate accuracy. Therefore the result of t-counted is higher than t-table. It means that the hypothesis of this research is accepted. In conclusion, there was a significant effect in using CLT (Communicative Language Teaching) to improve pupils’ speaking ability of the second grade at Prateepsat Ismail Memorial School, Thailand.
Abstract database "Ukrainika scientific": editorial preparation of the electronic resource

The role of electronic resources as a means of scientific communication in modern globalized society is considered. The main scientific electronic resources of the leading library institution of Ukraine — the V. I. Vernadsky National library of Ukraine are given. The features of some of the most popular resources among the visitors of the Library are described. The place of the national abstract database "Ukrainika scientific" among the important resources of the Library is determined. The statistical data of visits to information resources of the V. I. Vernadsky National library of Ukraine for a separate period of 2020 are given. Their types are distinguished on the basis of periodicity and type of information intended for perception. Attention is paid to the use of computer technology in the process of creating and editing electronic resources. The general requirements for the preparation and editing of the information array "Ukrainika scientific" are outlined. The specifics of editing scientific abstracts are described and recommendations for improving this process as a whole are offered. The expediency of editorial processing of abstract texts in several stages is substantiated. Emphasis is placed on a thorough check of the sources of the information provided. Particular attention is paid to detecting and correcting distortions in texts after scanning. The peculiarities of the design of formulaic and special expressions in the scientific texts of "Ukrainika scientific" are considered. It is noted that when using formulas, it is necessary to follow certain rules and be sure to check the correctness of their reproduction in html format. It is stated that professional content and editing of the information array of the national abstract database "Ukrainika scientific" will increase the informational and scientific value of the resource, will promote the scientific achievements of Ukrainian scientists and the fullest satisfaction of readers' requests. Emphasis is placed on the professional skills of employees who participate in the quality content and editorial processing of "Ukrainika scientific".
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability

Time series anomaly detection is extensively studied in statistics, economics, and computer science. Over the years, numerous methods have been proposed for time series anomaly detection using deep learning-based methods. Many of these methods demonstrate state-of-the-art performance on benchmark datasets, giving the false impression that these systems are robust and deployable in many practical and industrial real-world scenarios. In this paper, we demonstrate that the performance of state-of-the-art anomaly detection methods is degraded substantially by adding only small adversarial perturbations to the sensor data. We use different scoring metrics such as prediction errors, anomaly, and classification scores over several public and private datasets ranging from aerospace applications, server machines, to cyber-physical systems in power plants. Under well-known adversarial attacks from Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) methods, we demonstrate that state-of-the-art deep neural networks (DNNs) and graph neural networks (GNNs) methods, which claim to be robust against anomalies and have been possibly integrated in real-life systems, have their performance drop to as low as 0%. To the best of our understanding, we demonstrate, for the first time, the vulnerabilities of anomaly detection systems against adversarial attacks. The overarching goal of this research is to raise awareness towards the adversarial vulnerabilities of time series anomaly detectors.
Comparison of Object Detection Algorithms for Street-level Objects

Object detection for street-level objects can be applied to various use cases, from car and traffic detection to the self-driving car system. Therefore, finding the best object detection algorithm is essential to apply it effectively. Many object detection algorithms have been released, and many have compared object detection algorithms, but few have compared the latest algorithms, such as YOLOv5, primarily which focus on street-level objects. This paper compares various one-stage detector algorithms; SSD MobileNetv2 FPN-lite 320x320, YOLOv3, YOLOv4, YOLOv5l, and YOLOv5s for street-level object detection within real-time images. The experiment utilizes a modified Udacity Self Driving Car Dataset with 3,169 images. Dataset is split into train, validation, and test; Then, it is preprocessed and augmented using rescaling, hue shifting, and noise. Each algorithm is then trained and evaluated. Based on the experiments, the algorithms have produced decent results according to the inference time and the values of their precision, recall, F1-Score, and Mean Average Precision (mAP). The results also shows that YOLOv5l outperforms the other algorithms in terms of accuracy with a mAP@.5 of 0.593, MobileNetv2 FPN-lite has the fastest inference time among the others with only 3.20ms inference time. It is also found that YOLOv5s is the most efficient, with it having a YOLOv5l accuracy and a speed almost as quick as the MobileNetv2 FPN-lite. This shows that various algorithm are suitable for street-level object detection and viable enough to be used in self-driving car.
Visual Subtitle Feature Enhanced Video Outline Generation

With the tremendously increasing number of videos, there is a great demand for techniques that help people quickly navigate to the video segments they are interested in. However, current works on video understanding mainly focus on video content summarization, while little effort has been made to explore the structure of a video. Inspired by textual outline generation, we introduce a novel video understanding task, namely video outline generation (VOG). This task is defined to contain two sub-tasks: (1) first segmenting the video according to the content structure and then (2) generating a heading for each segment. To learn and evaluate VOG, we annotate a 10k+ dataset, called DuVOG. Specifically, we use OCR tools to recognize subtitles of videos. Then annotators are asked to divide subtitles into chapters and title each chapter. In videos, highlighted text tends to be the headline since it is more likely to attract attention. Therefore we propose a Visual Subtitle feature Enhanced video outline generation model (VSENet) which takes as input the textual subtitles together with their visual font sizes and positions. We consider the VOG task as a sequence tagging problem that extracts spans where the headings are located and then rewrites them to form the final outlines. Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model. Experiments on DuVOG show that our model largely outperforms other baseline methods, achieving 77.1 of F1-score for the video segmentation level and 85.0 of ROUGE-L_F0.5 for the headline generation level.
Your Blockchain Needn't Care How the Message is Spread

In a blockchain system, nodes regularly distribute data to other nodes. The ideal perspective taken in the scientific literature is that data is broadcast to all nodes directly, while in practice data is distributed by repeated multicast. Since correctness and security typically have been established for the ideal setting only, it is vital to show that these properties carry over to real-world implementations. This can be done by proving that the ideal and the real behavior are equivalent. In the work described in this paper, we take an important step towards such a proof by proving a simpler variant of the above equivalence statement. The simplification is that we consider only a concrete pair of network topologies, which nevertheless illustrates important phenomena encountered with arbitrary topologies. For describing systems that distribute data, we use a domain-specific language of processes that is embedded in a general-purpose process calculus. This allows us to leverage the rich theory of process calculi in our proof, which is machine-checked using the Isabelle proof assistant.
Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learning

Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured federated learning (QKD-FL), in which data owners connected via quantum channels can train an FL global model collaboratively without exposing their local datasets. To facilitate QKD-FL, the architectural design and routing management framework are essential. However, effective implementation is still lacking. To this end, we propose a hierarchical architecture for QKD-FL systems in which QKD resources (i.e., wavelengths) and routing are jointly optimized for FL applications. In particular, we focus on adaptive QKD resource allocation and routing for FL workers to minimize the deployment cost of QKD nodes under various uncertainties, including security requirements. The experimental results show that the proposed architecture and the resource allocation and routing model can reduce the deployment cost by 7.72\% compared to the CO-QBN algorithm.
Exact Penalty Method for Federated Learning

Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stochastic) gradient descent methods or the alternating direction method of multipliers. In this paper, we deploy an exact penalty method to deal with federated learning and propose an algorithm, FedEPM, that enables to tackle four critical issues in federated learning: communication efficiency, computational complexity, stragglers' effect, and data privacy. Moreover, it is proven to be convergent and testified to have high numerical performance.
PENGARUH REKONSTRUKSI PROFIL CAMSHAFT MESIN ASTRO 108 CC TERHADAP TORSI DAN DAYA

Nowadays motorcycle is the most used transportation in Indonesia and in overseas. Sometime the engine performance that is used by the rider is not maximal with the standard. Some sectors are re-modified to increase motorcycle’s engine performance. One o part that is modified is Camshaft to change the height of valve lift. The research purpose is to acknowledge the impact of the height of valve lift, power and torque. From the experiment of dynotest can be known that standard camshaft produces maximum power about 6,9 N.m at speed 4916 Rpm and the maximum torque is 7,76N.m at speed 7969 Rpm. The modified camshaft 1 produces power about 7,9 HP at speed 8481 Rpm and maximum torque is 7,28 N.m at speed 6844 Rpm. The modified Camshaft 2 produces power about 8,1 HP at speed 8538 Rpm and the maximum torque is about 7,29 N.m at speed 6973 Rpm. The power of modified camshaft 3 is about 8,4HP at speed 8872 rpm and the torque is about 7,23 N.m at 7085 rpm. The best power is on modified camshaft 3 with 8,4 HP and the best torque is in Standard Camshaft with 7,76 N.m.
Dealing with Imbalanceness in Hierarchical Classification Problems Through Data Resampling

Many important classification problems are imbalanced. Although resampling approaches are a common solution for different types of classification problems, they were still not defined for hierarchical classification problems. The objective of this work is to propose novel resampling approaches to handle the class imbalanceness issue in hierarchical classification problems. Four directions were investigated: (i) The use of classic resampling methods; (ii) A label path conversion strategy; (iii) The design of schemas to use resampling algorithms with local approaches; (iv) The proposal of global resampling algorithms. To show the impacts of the contribution of this work, we have investigated the imbalanceness issue in the COVID-19 identification in chest x-ray images.
The Use of an Augmented Reality Application to improve User Experience in Museums in São Luís - Maranhão

Museums are often acknowledged as a place to get involved into education, history and the world of art, serving large communities with the access to their exhibits as well as being seen as a knowledge source provided by their guides, collections, archives and others. Whilst visitors' experience in such given contexts may have been almost the same for a long time in which the interaction with the displayed artifacts and the experience within the context is restrained due to impositions to prevent visitors from handle assets and collections -, in many museums, gradually, the approach towards their visitors has changed as an attempt to improve the quality of experience offered. One strategy applied to improve visitors' experience in an exhibition environment is to use Augmented Reality (AR) as a manner to bring the user into a more active role and witness exhibitions at their own pace. This strategy consists in the employment of Augmented Reality in order to supply the visitor with a more detailed and interactive perspective of the assets presented, extending the comprehension of the surrounding space and adding an extra informative and interactive real time layer. This project's main goal is to provide an augmented reality application to be used by São Luís museums' visitors, in order to make exhibitions more attractive and educative to a large percentage of the population, as long as they have access to smartphones and mobile devices. The developed application was done focusing on fast development and recognition quality. Also, experiments using 2D and 3D shapes were conducted in order to prepare the system for future realistic fiducial markers and real life objects. The results satisfied the usage concepts, although latency and performance are still a concern when regarding multi-targets and connecting to a remote web service. The construction of an AR system has enough support to deliver both tracking and display virtual objects with no major performance loss, hence identifying one target at time and generating virtual content during usage can be done in a scenario such as the museum metaphor, accomplishing the conceptual goals aimed prior to the developed app.
Evaluating Loss Functions for Illustration Super-Resolution Neural Networks

As display technologies evolve and high-resolution screens become more available, the desirability of images and videos with high perceptual quality grows in order to properly utilize such advances. At the same time, the market for illustrated mediums, such animations and comics, has been in steady growth over the past years. Based on these observations, we were motivated to explore the super-resolution task in the niche of drawings. In absence of original high-resolution imagery, it is necessary to use approximate methods, such as interpolation algorithms, to enhance low-resolution media. Such methods, however, can produce undesirable artifacts in the reconstruct images, such as blurring and edge distortions. Recent works have successfully applied deep learning to this task, but such efforts are often aimed at real-world images and do not take in account the specifics of illustrations, which emphasize lines and employ simplified patterns rather than complex textures, which in turn makes visual artifacts introduced by algorithms easier to spot. With these differences in mind, we evaluated the effects of the choice of loss functions in order to obtain accurate and perceptually pleasing results in the super-resolution task for comics, cartoons, and other illustrations. Experimental evaluations have shown that a loss function based on edge detection performs best in this context among the evaluated functions, though still showing room for further improvements.
Partial Least Squares: A Deep Space Odyssey

Modern visual pattern recognition models are based on deep convolutional networks. Such models are computationally expensive, hindering applicability on resource-constrained devices. To handle this problem, we propose three strategies. The first removes unimportant structures (neurons or layers) of convolutional networks, reducing their computational cost. The second inserts structures to design architectures automatically, enabling us to build high-performance networks. The third combines multiple layers of convolutional networks, enhancing data representation at negligible additional cost. These strategies are based on Partial Least Squares (PLS) which, despite promising results, is infeasible on large datasets due to memory constraints. To address this issue, we also propose a discriminative and lowcomplexity incremental PLS that learns a compact representation of the data using a single sample at a time, thus enabling applicability on large datasets. We assess the effectiveness of our approaches on several convolutional architectures and computer vision tasks, which include image classification, face verification and activity recognition. Our approaches reduce the resource overhead of both convolutional networks and Partial Least Squares, promoting energyand hardware-friendly models for the academy and industry scenarios. Compared to state-of-theart methods for the same purpose, we obtain one of the best trade-offs between predictive ability and computational cost.
DaVInt - Data Visualization and Interaction Lab

A multidisciplinary research project between Communication and Computer Science researchers gave rise to the Data Visualization and Interaction Lab, known as DaVInt lab. Officially created in 2017, DaVInt comprises students and researchers from Communication, and Computer Science and related areas. DaVInt's main research topics are related to visual analytics of social media, narrative visualization, and data science. However, computer vision projects and other individual initiatives are also developed. Its main contributions are divided into three main areas: scientific, through our publications; technological, making the results of our researches available to the community; and student education, through which we disseminate the area beyond academia.
Improved Smart Eye Tracker Communicator Using Low Resolution Webcam

Eye tracking is a tool presented in many applications ranging from scientific research to commercial applications. One of them is assistive technologies that aim to help people with some disabilities, including communication. However, the applications usually require specific hardware components or a high computational cost. This work proposes the Smart Eye Communicator II (SEC-II), an evolution of a previously presented algorithm to detect the pupil center and the user&amp;apos;s gaze direction in real-time, using a low-resolution webcam and a conventional computer without a need for calibration. In SEC-II, a face aligner, which gets a canonical face alignment based on translation, scale, and rotation, has been added to the system. Likewise, strategies using eye coordinates were implemented to find the dominant the algorithm eye. By implementing these new approaches, achieved 86% accuracy, even under variable and non-uniform environmental conditions. Moreover, a graphical interface was implemented connecting the SEC-II to the internet and allowing users to express their desires and watch online videos chosen by themselves.
Data augmentation on graphs for table type classification

Tables are widely used in documents because of their compact and structured representation of information. In particular, in scientific papers, tables can sum up novel discoveries and summarize experimental results, making the research comparable and easily understandable by scholars. Since the layout of tables is highly variable, it would be useful to interpret their content and classify them into categories. This could be helpful to directly extract information from scientific papers, for instance comparing performance of some models given their paper result tables. In this work, we address the classification of tables using a Graph Neural Network, exploiting the table structure for the message passing algorithm in use. We evaluate our model on a subset of the Tab2Know dataset. Since it contains few examples manually annotated, we propose data augmentation techniques directly on the table graph structures. We achieve promising preliminary results, proposing a data augmentation method suitable for graph-based table representation.