Reviewerly/exHarmony huggingface dataset:
Study on UX design in enhancing student motivations in mobile language learning

This research investigates ways to increase students' motivations in engaging with an m-learning app used in a blended learning environment for learning basic level Chinese. The app, K0T0T0M0 Plus, has been praised by the students, but the current learning patterns show low retention rates. In this study we propose a set of design strategies that facilitates the design of new user interface features, meeting the students' learning goals and experiential goals, in order to create deeper engagement and motivation. These strategies, that satisfy both user experience and learning design requirements, will provide us a more holistic view into the students' overall learning experience, potentially informing us how motivation can be sustained long-term in a learning setting.
Unsupervised Mutual Mean Teaching with Heterogeneous Models for Person Re-Identification

Abstract Person re-identification (Re-ID) aims at matching images of the same pedestrian under different cameras. State-of-the-art unsupervised domain adaptation (UDA) methods for person Re-ID transferred the learned knowledge from the source domain and further finetuned with pseudo-label target domain via clustering. To reduce the influence of noisy pseudo labels, the Mutual Mean-Teaching (MMT) framework was proposed by conducting pseudo label refinery to better model inter-sample relations in the target domain. Currently, MMT requires the use of the two same deep models for cooperative correction of noisy labels. In this paper, we extend the MMT framework to use two heterogeneous models for cooperative error correction. Experiments show that the proposed heterogeneous MMT framework can work well with two different deep models and its performance, however, is largely limited by the weaker model.
Infographics of Wisdom

The work presented in this paper is part of the research project "Wisdom Transfer (WT): Towards the scientific inscription of individual legacies in contexts of retirement from art and design higher education and research”. Considering that, in Portugal, art and design research has only recently been validated as a scientific discipline, it can be argued that the available scientific heritage precedes the formalization of these disciplines - and, consequently, resides in an older generation of researchers who led a first moment of transition from practice to academia. Due to the advanced age of these scholars, all this wisdom and experience is in danger of being lost. The WT project aims to investigate, register and legitimize this academic, cultural and artistic heritage. In the first phase of the work, a group of 30 art and design retired academics graduated from the School of Fine Arts of Porto were selected using snowball sampling to be individually interviewed, to collect testimonies and life stories within the academy, both as students and lecturers. This collection was done along with visual analysis of data gathered both at their studios and from their work. Considering the results of these primary sources, the purpose of this study is to analyze all the information, synthesize it and present it in an infographic structure. Subsequently, it is intended to compile these infographics and present it on a website, in order to facilitate access to all interested parties, especially current art and design students.
A perceptive watermarking approach applied to COVID-19 imaging data

This work presents a watermarking algorithm applied to medical images of COVID-19 patients, intending to preserve its diagnose and that it not be modified when watermark will be inserted. Besides, we tried to protect the information of the patient using an imperceptible watermarking. Our technique is based on a perceptive approach to insert the watermark by decomposing the medical image using the Hermite transform. We use as watermark two image logos, including text strings to demonstrate that the watermark can contain relevant information of the patient. Some metrics were applied to evaluate the performance of the algorithm. Finally, we present some results about robustness with some attacks applied to watermark images.
Multi-windowed vertex-frequency analysis for signals on undirected graphs

The recent emerging graph signal processing technologies have been widely applied to analyze signals defined on irregular domains, e.g., data collected from social networks, sensor networks, or transportation systems. Vertex frequency analysis, especially the windowed graph Fourier transform, is one of the most important tools for graph signal analysis and representations. Nevertheless, with a selected window function, it is rather challenging to construct tight frames via the windowed graph Fourier transform. To facilitate the construction of tight frames, in this paper, we consider multi-windowed graph Fourier transforms to develop novel vertex frequency analysis methods. Firstly, under the multi-windowed setting, tight graph Fourier frames are elaborately constructed to fulfill technical demands in different application scenarios. The canonical dual frames of the multi-windowed graph Fourier frames are investigated to establish the reconstruction formulas of graph signals. Additionally, we propose shift multi-windowed graph Fourier frames by directly using the shift operators, e.g., the adjacency matrix. The related tight frames, dual frames and their constructions are also discussed. Experimental results show that the proposed two types of frames can efficiently extract vertex-frequency features of synthetic graph signals. Furthermore, anomaly data can also be detected by these frames.
Maximizing Image Information Using Multi-Chimera Transform Applied on Face Biometric Modality

With the development of mobile technology, the usage of media data has increased dramatically. Therefore, data reduction represents a research field to maintain valuable information. In this paper, a new scheme called Multi Chimera Transform (MCT) based on data reduction with high information preservation, which aims to improve the reconstructed data by producing three parameters from each 16×16 block of data, is proposed. MCT is a 2D transform that depends on constructing a codebook of 256 picked blocks from some selected images which have a low similarity. The proposed transformation was applied on solid and soft biometric modalities of AR database, giving high information preservation with small resulted file size. The proposed method produced outstanding performance compared with KLT and WT in terms of SSIM and PSNR. The highest SSIM was 0.87 for the proposed scheme MCT of the full image of AR database, while the existed method KLT and WT had 0.81 and 0.68, respectively. In addition, the highest PSNR was 27.23 dB for the proposed scheme on warp facial image of AR database, while the existed methods KLT and WT had 24.70 dB and 21.79 dB, respectively.
Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection

There is a growing trend in using machine learning techniques for detecting environmental context in communication networks. Machine learning is one of the promising candidate areas where quantum computing can show a quantum advantage over their classical algorithmic counterpart on near term Noisy Intermediate-Scale Quantum (NISQ) devices. The goal of this paper is to give a practical overview of (supervised) quantum machine learning techniques to be used for indoor-outdoor detection. Due to the small number of qubits in current quantum hardware, real application is not yet feasible. Our work is intended to be a starting point for further explorations of quantum machine learning techniques for indoor-outdoor detection.
Raspberry Pi based Multi-optional Wireless Wheelchair Control and Gesture Recognized Home Assist System

Abstract Smart Wheelchair is a controlled device designed with the help of the user command to have self-mobility. It reduces the human effort of the user and encourages wheelchair riding. In fact, it also provides an opportunity for physically impaired people to move from one location to another. The main work of this paper is to gain more information, to construct a wheelchair using Raspberry Pi and RF controller to solve the difficulties faced by the physically challenged person. The device is fitted with the RF module, GSM and GPS module, servo motor, wheelchair and the whole module is controlled by wheelchair. To achieve a desire movement, the user will provide input commands to control the wheelchair and it is interfaced by RF communication and voice command. Depending upon the user convenience either RF communication or voice command can be used. Solution has also been proposed during an emergency by using an alert system. The movements received and the hand gesture shown by the visually impaired are recognizedandcontrastedwiththemodelsstoredonthereceiver.Ifthetemplates suit the templates stored then the home appliances are managedaccordingly.
On the Origin of Kerberos

Reports on the special features and applications supported by Kerberos, a cryptographic authentication and key distribution system developed by MIT Project Athena with the goal that a single login provide access to many different computing services. Kerberos is distributed as a component of most major operating systems, including Microsoft Windows, Apple OS/X and IOS, IBM z/OS, and many versions of Unix.
A Text Line Extraction Method for Archival Document Transcription

In order to reinforce the enrichment and exploitation of archival collections, a growing need for computer-aided tools able to assist researchers, historians and archivists in historical document image transcription has been recently highlighted. However, to ensure an efficient text transcription from archival handwritten and printed document images, a robust text line segmentation task is required. Thus, in this paper we propose a method able to extract whole text lines from archival document images. The proposed method is firstly based on our previous work reported at ICDAR 2019, which focused on extracting only the main area covering the text core. A post-processing step is introduced in this paper to extract whole text lines (including the ascender and descender components). The post-processing step is based on topological structural analysis of binary images. To illustrate the effectiveness of the proposed method, we have conducted experiments on archival document images collected from the Tunisian national archives. Qualitative and quantitative results are reported and discussed.
Unsupervised spatial-awareness attention-based and multi-scale domain adaption network for point cloud classification

Domain adaption is a special transfer learning method, whose source domain and target domain generally have different data distribution, but need to complete the same task. There have been many significant types of research on domain adaptation in 2D images, but in 3D data processing, domain adaptation is still in its infancy. Therefore, we design a novel domain adaptive network to complete the unsupervised point cloud classification task. Specifically, we propose a multi-scale transform module to improve the feature extractor. Besides, a spatial-awareness attention module combined with channel attention to assign weights to each node is designed to represent hierarchically scaled features. We have validated the proposed method on the PointDA-10 dataset for domain adaption classification tasks. Empirically, it shows strong performance on par or even better than state-of-the-art.
Toward a One-interaction Data-driven Guide: Putting Co-speech Gesture Evidence to Work for Ambiguous Route Instructions

While recent work on gesture synthesis in agent and robot literature has treated gesture as co-speech and thus dependent on verbal utterances, we present evidence that gesture may leverage model context (i.e. the navigational task) and is not solely dependent on verbal utterance. This effect is particularly evident within ambiguous verbal utterances. Decoupling this dependency may allow future systems to synthesize clarifying gestures that clarify the ambiguous verbal utterance while enabling research in better understanding the semantics of the gesture. We bring together evidence from our own experiences in this domain that allow us to see for the first time what kind of end-to-end concerns models need to be developed to synthesize gesture for one-shot interactions while still preserving user outcomes and allowing for ambiguous utterances by the robot. We discuss these issues within the context of "cardinal direction gesture plans" which represent instructions that refer to the actions the human must follow in the future.
Type inhabitation of atomic polymorphism is undecidable

Abstract Atomic polymorphism $\mathbf{F_{at}}$ is a restriction of Girard and Reynold’s system $\mathbf{F} $(or $\lambda 2$) which was first introduced in Ferreira [ 2] in the context of a philosophical commentary on predicativism. $\lambda 2$ is a well-known and powerful formal tool for studying polymorphic functional programming languages and formal methods in program specification and development, but its computational power far exceeds the recursive level of interest in applications. Hence, the interest of studying subsystems of $\lambda 2$ with weaker computational power. $\mathbf{F_{at}}$ is defined by restricting instantiation to atomic variables only. It turns out that the type system is still sufficiently powerful to possess embeddings of full intuitionistic propositional calculus [ 3, 4], and since the calculus has fewer connectives and strong normalizability is simple to prove [ 3], this result allows us to circumvent many of the extra computational complexities present when dealing with the proof theory of IPC. It is natural to inquire whether type inhabitation, i.e. provability in the corresponding fragment of second-order intuitionistic propositional logic, is decidable or not and in general to see whether the negative results involving the undecidability of type inhabitation, typability and type-checking for $\mathbf{F}$ still hold in this fragment. A further theme would be to study the result of adding type constructors, recursors or even dependent types to $\mathbf{F_{at}}$. In this paper, we show that type inhabitation for $\mathbf{F_{at}}$ is undecidable by codifying within it an undecidable fragment of first-order intuitionistic predicate calculus, adapting and modifying the technique of Urzyczyn’s [ 1, 7] purely syntactic proof of the undecidability of type inhabitation for $\mathbf{F}$.
IQ-VQA: Intelligent Visual Question Answering

Despite tremendous progress in the field of Visual Question Answering, models today still tend to be inconsistent and brittle. Thus, we propose a model-independent cyclic framework which increases consistency and robustness of any VQA architecture. We train our models to answer the original question, generate an implication based on the answer and then learn to answer the generated implication correctly. As part of the cyclic framework, we propose a novel implication generator which generates implied questions from any question-answer pair. As a baseline for future works on consistency, we provide a new human-annotated VQA-Implications dataset. The dataset consists of 30k implications of 3 types - Logical Equivalence, Necessary Condition and Mutual Exclusion - made from the VQA validation dataset. We show that our framework improves consistency of VQA models by Open image in new window on the rule-based dataset, Open image in new window on VQA-Implications dataset and robustness by Open image in new window , without degrading their performance.
Adaptive Future Frame Prediction with Ensemble Network

Future frame prediction in videos is a challenging problem because videos include complicated movements and large appearance changes. Learning-based future frame prediction approaches have been proposed in kinds of literature. A common limitation of the existing learning-based approaches is a mismatch of training data and test data. In the future frame prediction task, we can obtain the ground truth data by just waiting for a few frames. It means we can update the prediction model online in the test phase. Then, we propose an adaptive update framework for the future frame prediction task. The proposed adaptive updating framework consists of a pre-trained prediction network, a continuous-updating prediction network, and a weight estimation network. We also show that our pre-trained prediction model achieves comparable performance to the existing state-of-the-art approaches. We demonstrate that our approach outperforms existing methods especially for dynamically changing scenes.
Assessment of Stress and Strain Distribution at Crack Tip Using Digital Image Correlation

Abstract DIC program developed to obtain the full-field measurements of displacement, strain, and stress around the crack/defect. In the present work, the tensile test is conducted for two different flat specimens, which were painted by speckle pattern to allow using features of DIC system for strain measurements. Two defected/cracked copper and aluminum samples have been investigated. The experimental rig was especially designed and manufactured to perform the tensile test. A comparison between the results of the technique investigated with the results obtained from open source software (Ncorr &amp; GOM), results obtained from the finite element analysis using (ANSYS) software using, and the exact and analytical solutions. The results achieved by the proposed DIC technique have agreement with other methods. The experiments conducted for two flat Aluminum and Copper plates. The DIP percentage of accuracy varies from (97) % to (99) % with the actual physical measurements. The obtained DIC percentage of accuracy as minimum and maximum were (88) % and (95) % with ANSYS software.
GAN-Based Image Compression Using Mutual Information for Optimizing Subjective Image Similarity

Recently, image compression systems based on convolutional neural networks that use flexible nonlinear analysis and synthesis transformations have been developed to improve the restoration accuracy of decoded images. Although these methods that use objective metric such as peak signal-to-noise ratio and multi-scale structural similarity for optimization attain high objective results, such metric may not reflect human visual characteristics and thus degrade subjective image quality. A method using a framework called a generative adversarial network (GAN) has been reported as one of the methods aiming to improve the subjective image quality. It optimizes the distribution of restored images to be close to that of natural images; thus it suppresses visual artifacts such as blurring, ringing, and blocking. However, since methods of this type are optimized to focus on whether the restored image is subjectively natural or not, components that are not correlated with the original image are mixed into the restored image during the decoding process. Thus, even though the appearance looks natural, subjective similarity may be degraded. In this paper, we investigated why the conventional GAN-based compression techniques degrade subjective similarity, then tackled this problem by rethinking how to handle image generation in the GAN framework between image sources with different probability distributions. The paper describes a method to maximize mutual information between the coding features and the restored images. Experimental results show that the proposed mutual information amount is clearly correlated with subjective similarity and the method makes it possible to develop image compression systems with high subjective similarity.
Densely Annotated Photorealistic Virtual Dataset Generation for Abnormal Event Detection

Many timely computer vision problems, such as crowd event detection, individual or crowd activity recognition, person detection and re-identification, tracking, pose estimation, segmentation, require pixel-level annotations. This involves significant manual effort, and is likely to face challenges related to the privacy of individuals, due to the intrinsic nature of these problems, requiring in-depth identifying information. To cover the gap in the field and address these issues, we introduce and make publicly available a photorealistic, synthetically generated dataset, with detailed dense annotations. We also publish the tool we developed to generate it, that will allow users to not only use our dataset, but expand upon it by building their own densely annotated videos for many other computer vision problems. We demonstrate the usefulness of the dataset with experiments on unsupervised crowd anomaly detection in various scenarios, environments, lighting, weather conditions. Our dataset and the annotations provided with it allow its use in numerous other computer vision problems, such as pose estimation, person detection, segmentation, re-identification and tracking, individual and crowd activity recognition, and abnormal event detection. We present the dataset as is, along with the source code and tool to generate it, so any modification can be made and new data can be created. To our knowledge, there is currently no other photorealistic, densely annotated, realistic, synthetically generated dataset for abnormal crowd event detection, nor one that allows for flexibility of use by allowing the creation of new data with annotations for many other computer vision problems. Dataset and source code available: https://github.com/RicoMontulet/GTA5Event .
PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models

Datasets displaying temporal dependencies abound in science and engineering applications, with Markov models representing a simplified and popular view of the temporal dependence structure. In this paper, we consider Bayesian settings that place prior distributions over the parameters of the transition kernel of a Markov model, and seek to characterize the resulting, typically intractable, posterior distributions. We present a Probably Approximately Correct (PAC)-Bayesian analysis of variational Bayes (VB) approximations to tempered Bayesian posterior distributions, bounding the model risk of the VB approximations. Tempered posteriors are known to be robust to model misspecification, and their variational approximations do not suffer the usual problems of over confident approximations. Our results tie the risk bounds to the mixing and ergodic properties of the Markov data generating model. We illustrate the PAC-Bayes bounds through a number of example Markov models, and also consider the situation where the Markov model is misspecified.
An Empirical Study for Class Imbalance in Extreme Multi-label Text Classification

Extreme multi-label text classification (XMTC) is the problem of finding the most relevant multi-labels from a text corpus with millions of labels. One of the key challenges in XMTC is that most labels appear only a few times, i.e., the class imbalance issue. To overcome the class imbalance problem, existing studies suggested various methods using different loss functions (i.e., focal loss function) and data augmentation (i.e., mix-up). In this paper, we investigate the effectiveness of two main approaches over the RNN-based and transformer-based deep XMTC models. In experimental results, we found that some improvement can be achieved when focal loss and mix-up are applied for deep XMTC models on various datasets.