Reviewerly/exHarmony huggingface dataset:
Frequency-Based Motion Representation for Video Generative Adversarial Networks

Videos contain motions of various speeds. For example, the motions of one's head and mouth differ in terms of speed - the head being relatively stable and the mouth moving rapidly as one speaks. Despite its diverse nature, previous video GANs generate video based on a single unified motion representation without considering the aspect of speed. In this paper, we propose a frequency-based motion representation for video GANs to realize the concept of speed in video generation process. In detail, we represent motions as continuous sinusoidal signals of various frequencies by introducing a coordinate-based motion generator. We show, in that case, frequency is highly related to the speed of motion. Based on this observation, we present frequency-aware weight modulation that enables manipulation of motions within a specific range of speed, which could not be achieved with the previous techniques. Extensive experiments validate that the proposed method outperforms state-of-the-art video GANs in terms of generation quality by its capability to model various speed of motions. Furthermore, we also show that our temporally continuous representation enables to further synthesize intermediate and future frames of generated videos.
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization

Recently, tensor singular value decomposition (t-SVD) has emerged as a promising tool for hyperspectral image (HSI) processing. In the t-SVD, there are two key building blocks: (i) the low-rank enhanced transform and (ii) the accompanying low-rank characterization of transformed frontal slices. Previous t-SVD methods mainly focus on the developments of (i), while neglecting the other important aspect, i.e., the exact characterization of transformed frontal slices. In this letter, we exploit the potentiality in both building blocks by leveraging the Hierarchical nonlinear transform and the Hierarchical matrix factorization to establish a new Tensor Factorization (termed as H2TF). Compared to shallow counter partners, e.g., low-rank matrix factorization or its convex surrogates, H2TF can better capture complex structures of transformed frontal slices due to its hierarchical modeling abilities. We then suggest the H2TF-based HSI denoising model and develop an alternating direction method of multipliers-based algorithm to address the resultant model. Extensive experiments validate the superiority of our method over state-of-the-art HSI denoising methods.
Mechanised DPO Theory: Uniqueness of Derivations and Church-Rosser Theorem

We demonstrate how to use the proof assistant Isabelle/HOL to obtain machine-checked proofs of two fundamental theorems in the double-pushout approach to graph transformation: the uniqueness of derivations up to isomorphism and the so-called Church-Rosser theorem. The first result involves proving the uniqueness of pushout complements, first established by Rosen in 1975. The second result formalises Ehrig’s and Kreowski’s proof of 1976 that parallelly independent direct derivations can be interchanged to obtain derivations ending in a common graph. We also show how to overcome Isabelle’s limitation that graphs in locales must have nodes and edges of pre-defined types.
A Monoidal View on Fixpoint Checks

Fixpoints are ubiquitous in computer science as they play a central role in providing a meaning to recursive and cyclic definitions. Bisimilarity, behavioural metrics, termination probabilities for Markov chains and stochastic games are defined in terms of least or greatest fixpoints. Here we show that our recent work which proposes a technique for checking whether the fixpoint of a function is the least (or the largest) admits a natural categorical interpretation in terms of gs-monoidal categories. The technique is based on a construction that maps a function to a suitable approximation and the compositionality properties of this mapping are naturally interpreted as a gs-monoidal functor. This guides the realisation of a tool, called UDEfix that allows to build functions (and their approximations) like a circuit out of basic building blocks and subsequently perform the fixpoints checks. We also show that a slight generalisation of the theory allows one to treat a new relevant case study: coalgebraic behavioural metrics based on Wasserstein liftings.
Earthquake Processes Through the Lenses of Video Cameras or 'A Picture is Worth a Thousand Words'

The widespread introduction of video cameras into our daily lives has allowed us to solve several scientific mysteries and correct the errors that accompany them. For example, the textbook process of sodium explosion in water, filmed with a video camera, which seemed to have been studied "up and down" and familiar to us from the chemistry lessons at school, gave scientists not just a big, but a huge surprise. Video footage of the process showed that since the discovery of this reaction in the 19th century, absolutely all chemists in the world (100%!) were wrong about the nature of the explosion of sodium in water, and the real cause of the explosion was not the rattle gas, which forms on the contact of sodium and water, but the so-called Coulomb explosion. The same thing happened with Mr. Reid's theory of earthquakes Elastic Recoil, which has dominated geophysics as far back as 1910. Numerous CCTV cameras around the world have recorded numerous underground shock processes and have shown that seismic processes do not proceed in the way that modern geophysicists imagine and present us with the Elastic Recoil theory: "Earthquakes occur when tectonic plates slide against each other or along a fault, whose movement is hindered by friction. As a consequence, energy is stored in the rocks in the form of elastic stresses. When the stress reaches the critical point of the ultimate strength of the rocks, there is a sharp rupture of the rocks with their mutual displacement in the form of an earthquake. There is no sharp rupture of rocks, but there is an oscillatory process, in which rocks of the Earth's crust simultaneously vibrate not only in perpendicular directions but also with vertical displacements and often with circular vectors. This cannot happen with the sharp displacement of rocks in one direction, and this century-old mistake of seismologists in the form of the theory of Elastic Recoil did not allow the whole geophysics to develop and led the science into such an impassable jungle, that geophysicists will have to throw off the vines of false knowledge of seismic processes entangling them for a long time yet. This article is intended to show the deepest delusion of modern geophysicists when interpreting seismic processes.
Intellectual Property in the Tech Singularity, the Paradox in an Era of Limitless Innovation - AI’s Disruption Catalyzes of Mainstream Norms Shapes the Way to Technological Breakthrough

The traditional intellectual property system, faces challenges due to the rapid development of technology and the arrival of artificial intelligence (AI). The emergence of AI systems blurs the lines between mortality and machine authorship, and the digital era has witnessed widespread brand infringement, challenging traditional enforcement mechanisms. In response, a legal system with AI protection is explored, combining the efficiency and scalability of AI with the legal framework and guidelines will depend on factors such as citizens' concerns, regulatory criticality, economic and political elements, and international developments.The potential use of AI e.g. in music composition, raises ethical and practical issues that must be addressed to ensure the technology is used immorally and beneficially. The final decision on whether a work qualifies as an intellectual invention rests with the courts and authorities if it has shown originality. The development of applications for strong artificial intelligence is crucial for exploring and expanding current technology capabilities, allowing individuals to collaborate on research projects in various fields and disciplines of science.Artificial intelligence applications are expected to revolutionize various fields and disciplines of science, allowing individuals to collaborate on research projects. The future perspective of AI is crucial, as it aims to reduce development time and cost. However, the lack of regulation and single authority in big tech companies raises concerns about the potential for unpredictable and irreversible technological growth. The world has changed significantly in the last two decades, with inventions such as the wheel, compass, solar energy, printing press, nuclear bomb, internet search engines, mobile phones, and MRNA vaccines. The acceleration of inventions and the rapid pace of modern tools will lead to a saturation point sooner than expected. This will impact not only overall technological progress but also daily life, as history has taught us that humans are predictable in their weaknesses and approaches to technology.Crime, education, and legal systems are crucial in addressing the risks of AI systems. Government participation is necessary to develop regulations and prevent widespread access to these systems. Education is another key aspect to ensure a positive future for AI systems.
Community Building in Change Processes

In November 2020, at the top of the second COVID-19 wave in Portugal, I started the Weekly Reflection Sessions as an experiment with the premise that our days are full of missed learning opportunities and that reflection is one way to make the most of that potential. However, I was not expecting to create a virtual space that would help people connect with their purpose, process the highs and lows in their lives, and feel calmer, relieved, motivated, and less lonely. Change management is above anything else about people: no framework survives the reality and complexity of the human experience. This chapter presents an easily replicable 45-min process that any manager, leader, or team member can facilitate to support and supercharge personal change in a group setting and online. I review how this project went from an invitation posted on social media to a weekly online event attended by people worldwide, where attendees reflect and share their weekly learnings: a way to process the ever-changing reality of being human—individually and as a community.
Mitigating Emotional Harm on Social Media: A Filtering Approach Using Synesketch and Euclidean Distance

Social media has become a prevalent issue that has attracted the attention of both society and researchers. Social media platforms have undergone tremendous expansion over the past ten years, offering users the ability to share personal messages, information, pictures, videos and ideas at an unprecedented pace. The negative impacts of social media usage are especially concerning for the younger generation, who spend a significant amount of time engaging in online social interactions and are thus more susceptible to harm. To address these concerns, we propose a solution that uses Synesketch and the Euclidean distance measure to filter emotionally harmful content in social media. We demonstrated the practical application of our approach on a dataset of tweets obtained through the Twitter API and achieved promising results. Our proposed approach can be valuable in various natural language processing applications, such as sentiment analysis, opinion mining, and social media monitoring.
A GPU-Based Artificial Orca Algorithm for Solving Traveling Salesman Problem

The present paper proposes to solve a complex discrete problem known as Travelling Salesman Problem (TSP) based on a recent swarm intelligence algorithm called Artificial Orca Algorithm (AOA). The AOA is inspired by the lifestyle of orcas and their living behavior. This algorithm proves its efficiency in solving continuous optimization problems. To extend the application of this algorithm, we propose a discrete version of AOA, using the swap operator in the fundamental phases of AOA while respecting its basic concepts. However, the larger the TSP sample size and the population of AOA, the higher the running time. For this reason, a GPU-based AOA is suggested based on the Master-Worker paradigm. To test the efficiency of the proposed approaches, we apply the two versions of adapted AOA on some benchmark instances of TSPLIB. The results show the proposed discrete AOA’s efficiency, especially with integrating the master-worker paradigm through GPU for the gain in running time.
Improving Thermal Object Detection for Optimized Deep Neural Networks on Embedded Devices

Detecting objects during the dark is critical for autonomous driving, Lately, thermal cameras have become popular, yet object detectors show low performance on thermal images. In this paper, we aim to improve the capacity of deep neural networks to detect objects from thermal images and run in a restricted environment. The main contributions of this work are 1) Optimize a famous object detector to run on an embedded device. 2) propose a set of improvements to improve the feature extraction from thermal images. Comprehensive experiments were conducted on Utokyo dataset [1] and USC thermal drone dataset [2]. The proposed approach achieves higher precision and near real-time speed.
Data Analysis and Visualization of INA-CBT Labuan Bajo System

Indonesia Cable-Based Tsunameter (INA-CBT) has been successfully deployed and operating at Labuan Bajo of East Nusa Tenggara Province with one landing station (LS) and two ocean bottom units (OBUs). We present the data flow of the first time ever working CBT system in Indonesia, by dividing it into four major steps, i.e., data acquisition, data processing, data transmission, and data visualization. In data acquisition, the bottom pressure recorder (BPR) and 3-axes accelerometer sensors acquire water level and ground shaking data to be sent to the LS. The data processing step adds a timestamp to each data and processes BPR data with a tsunami detection algorithm (TDA), while data transmission sends the data from LS to the read down station (RDS). Lastly, RDS data are visualized in a web application. The data shows that the system can detect earthquake events with a time difference compared to that of the Meteorological, Climatological, and Geophysical Agency (BMKG). Furthermore, the system can also predict the sea level 15 seconds in advance. However, as no tsunami occurs, the tsunami alert feature cannot be verified yet. At this moment, we are expecting to develop another much more suitable algorithm for the INA-CBT system in the future by utilizing all the data collected.
Object Local Aspect Aided CNN for Recognition

Object recognition is a subfield of computer vision that refers to the ability to identify objects in images. The existing methods in the literature can be classified into two main approaches, the classic approach based on a combination of local visual characteristics and classifiers, and the new approach based on deep learning. Each approach has its advantages and drawbacks. In this paper, we aim to improve the object recognition process in the case of occluded objects, from which both approaches suffer. To do this, we propose a CNN model guided by visual local aspect which describes the appearance of objects in the scene. The proposed model proved its effectiveness in the recognition of objects, especially in the case of occluded objects, and it reaches an acceptable rate of accuracy that outperforms a simple CNN.
Extreme Learning Machines Based on Convolutional Neural Network and Convolutional Autoencoder for Image Classification: Comparative Study

Feature extraction is a critical process for any machine learning model. It provides a representation that contains the most relevant data. Convolutional Neural Network (CNN) and Convolutional Autoencoder (CAE) models have recently successfully been applied as feature extraction techniques. In this work, we combine them with the Extreme Learning Machine (ELM) classifier to study the performance of CNN-ELM and CAE-ELM models. The image classification task is used to evaluate the studied methods. The tests are performed on three benchmark datasets: MNIST, CIFAR10, and CIFAR-100. According to the experimental results, although the CNN-ELM outperforms the CAE-ELM in terms of accuracy, it still suffers from the overfitting problem.
Deep Sparse Depth Completion Using Multi-Affinity Matrix

Image-guided depth completion aims to generate dense depth maps from sparse depth maps guided by their corresponding color (RGB) images. In this paper, we propose deep sparse depth completion using multi-affinity matrix. Recently, spatial propagation networks (SPNs) are used to refine depth maps obtained by initial depth completion. However, they use the same affinity matrix even in multiple iterations that has a limit to improving performance, which is not effective in considering the relationship between adjacent pixels. Thus, we replace it with a multi-affinity matrix to represent the relationship between an output pixel and its neighboring ones. Moreover, deep neural networks can effectively fuse features from two different modalities based on confidence maps. Inspired by dynamic guided filtering, we use a convolutional spatial propagation network (CSPN) to fuse multi-modal features at multiple stages. When training the color branch of the proposed network, we adopt supervised learning that constrains the output features of all layers in the decoder. Since spatially varying features are required for multi-modal feature fusion, the proposed network produces adaptive affinity features using a single decoder. Experimental results on KITTI and NYU-v2 datasets show that the multi-affinity matrix represents the dependency between pixels and predicts accurate depth values by calculating the weights between neighboring pixels. The proposed network based on multi-affinity matrix achieves state-of-the-art performance in terms of root mean square error (RMSE) and mean absolute error (MAE).
Habits and perceptions regarding open science by researchers from Spanish institutions

The article describes the results of the online survey on open science (OS) carried out on researchers affiliated with universities and Spanish research centres and focused on open access to scientific publications, the publication process, the management of research data and the review of open articles. The main objective was to identify the perception and habits of researchers with regard to practices closely linked to open science and the scientific value added is that offers an in-depth picture of researchers as one of the main actors to whom this transformation and implementation of open science will fall. It focuses on the different aspects of OS: open access, open data, publication process and open review in order to identify habits and perceptions. This is to make possible an implementation of the OS movement. The survey was carried out among researchers who had published in the years 2020-2021, according to data obtained from WoS. It was emailed to a total of 8,188 researchers and obtained a total of 666 responses, of which 554 were complete, the rest being forms with some questions unanswered. The main results showed that open access still requires the diffusion of practices and services provided by the institution, as well as training (library or equivalent service) and institutional support from the competent authorities (vice rectors or equivalent) in specific aspects such as data management. In the case of data, around 50% of respondents stated they had stored data in a repository, and of all the options, the most frequently given was that of an institutional repository, followed by a discipline repository. Among the main reasons for doing this, we found transparency, visibility of data and the ability to validate results. For those who stated they had never stored data, the most frequent reasons for not having done so were privacy and confidentiality, the lack of a mandated data policy or a lack of knowledge of how to do it. In terms of open peer review, participants mentioned a certain reticence to the opening of evaluations due to potential conflicts of interest that may arise or because lower-quality content might be accepted in order to avoid conflicts. In addition, the hierarchical structure of senior researcher versus junior researcher might affect reviews. The main conclusions indicate a need for persuasion of OA to take place; APCs are an economic barrier rather than the main criterion for journal selection; OPR practices may seem innovative and emerging; scientific and evaluation policies seem to have a clear effect on the behaviour of researchers; researchers state that they share research data more for reasons of persuasion than out of obligation. Researchers do question the pathways or difficulties that may arise on a day-to-day basis and seem aware that we are undergoing change, where academic evaluation or policies related to open science, its implementation and habits among researchers may change. In this sense, more and better support is needed on the part of institutions and faculty support services.
Improving Medical Vision-Language Contrastive Pretraining With Semantics-Aware Triage

Medical contrastive vision-language pretraining has shown great promise in many downstream tasks, such as data-efficient/zero-shot recognition. Current studies pretrain the network with contrastive loss by treating the paired image-reports as positive samples and the unpaired ones as negative samples. However, unlike natural datasets, many medical images or reports from different cases could have large similarity especially for the normal cases, and treating all the unpaired ones as negative samples could undermine the learned semantic structure and impose an adverse effect on the representations. Therefore, we design a simple yet effective approach for better contrastive learning in medical vision-language field. Specifically, by simplifying the computation of similarity between medical image-report pairs into the calculation of the inter-report similarity, the image-report tuples are divided into positive, negative, and additional neutral groups. With this better categorization of samples, more suitable contrastive loss is constructed. For evaluation, we perform extensive experiments by applying the proposed model-agnostic strategy to two state-of-the-art pretraining frameworks. The consistent improvements on four common downstream tasks, including cross-modal retrieval, zero-shot/data-efficient image classification, and image segmentation, demonstrate the effectiveness of the proposed strategy in medical field.