Reviewerly/exHarmony huggingface dataset:
Automatically Combining Conceptual Models Using Semantic and Structural Information

A conceptual model is a necessary precursor to a simulation model. It defines the problem space by listing relevant concepts and it identifies salient mechanisms by specifying which concepts are related. In this paper, we focus on conceptual models expressed as causal or ‘cognitive’ maps, in which concepts form nodes and their relationships are directed, weighted edges specifying causal strengths. When modeling complex social systems, participants may provide their perspectives through individual causal maps. To create a simulation model, these maps need to be aggregated into a coherent conceptual model. Two challenges arise: nodes may have different names although participants ascribed the same meanings (i.e., linguistic variability) and disagreements on causal strengths need to be reconciled. Although other fields have long proposed algorithms to aggregate knowledge bases (e.g., ontology matching), there is a paucity of solutions for causal maps. In this paper, we propose a solution for causal maps that leverages recent advances in graph matching. We demonstrate the feasibility and potential of our approach on a case study with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=22$</tex> maps.
On Time-Lock Cryptographic Assumptions in Abelian Hidden-Order Groups

In this paper we study cryptographic finite abelian groups of unknown order and hardness assumptions in these groups. Abelian groups necessitate multiple group generators, which may be chosen at random. We formalize this setting and hardness assumptions therein. Furthermore, we generalize the algebraic group model and strong algebraic group model from cyclic groups to arbitrary finite abelian groups of unknown order. Building on these formalizations, we present techniques to deal with this new setting, and prove new reductions. These results are relevant for class groups of imaginary quadratic number fields and time-lock cryptography build upon them.
TF-Blender: Temporal Feature Blender for Video Object Detection

Video objection detection is a challenging task because isolated video frames may encounter appearance deterioration, which introduces great confusion for detection. One of the popular solutions is to exploit the temporal information and enhance per-frame representation through aggregating features from neighboring frames. Despite achieving improvements in detection, existing methods focus on the selection of higher-level video frames for aggregation rather than modeling lower-level temporal relations to increase the feature representation. To address this limitation, we propose a novel solution named TF-Blender,which includes three modules: 1) Temporal relation mod-els the relations between the current frame and its neighboring frames to preserve spatial information. 2). Feature adjustment enriches the representation of every neigh-boring feature map; 3) Feature blender combines outputs from the first two modules and produces stronger features for the later detection tasks. For its simplicity, TF-Blender can be effortlessly plugged into any detection network to improve detection behavior. Extensive evaluations on ImageNet VID and YouTube-VIS benchmarks indicate the performance guarantees of using TF-Blender on recent state-of-the-art methods.
Exploring the Limits of Large Scale Pre-training

Recent developments in large-scale machine learning suggest that by scaling up data, model size and training time properly, one might observe that improvements in pre-training would transfer favorably to most downstream tasks. In this work, we systematically study this phenomena and establish that, as we increase the upstream accuracy, the performance of downstream tasks saturates. In particular, we investigate more than 4800 experiments on Vision Transformers, MLP-Mixers and ResNets with number of parameters ranging from ten million to ten billion, trained on the largest scale of available image data (JFT, ImageNet21K) and evaluated on more than 20 downstream image recognition tasks. We propose a model for downstream performance that reflects the saturation phenomena and captures the nonlinear relationship in performance of upstream and downstream tasks. Delving deeper to understand the reasons that give rise to these phenomena, we show that the saturation behavior we observe is closely related to the way that representations evolve through the layers of the models. We showcase an even more extreme scenario where performance on upstream and downstream are at odds with each other. That is, to have a better downstream performance, we need to hurt upstream accuracy.
Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.
SurRF: Unsupervised Multi-View Stereopsis by Learning Surface Radiance Field

The recent success in supervised multi-view stereopsis (MVS) relies on the onerously collected real-world 3D data. While the latest differentiable rendering techniques enable unsupervised MVS, they are restricted to discretized (e.g., point cloud) or implicit geometric representation, suffering from either low integrity for a textureless region or less geometric details for complex scenes. In this paper, we propose SurRF, an unsupervised MVS pipeline by learning Surface Radiance Field, i.e., a radiance field defined on a continuous and explicit 2D surface. Our key insight is that, in a local region, the explicit surface can be gradually deformed from a continuous initialization along view-dependent camera rays by differentiable rendering. That enables us to define the radiance field only on a 2D deformable surface rather than in a dense volume of 3D space, leading to compact representation while maintaining complete shape and realistic texture for large-scale complex scenes. We experimentally demonstrate that the proposed SurRF produces competitive results over the-state-of-the-art on various real-world challenging scenes, without any 3D supervision. Moreover, SurRF shows great potential in owning the joint advantages of mesh (scene manipulation), continuous surface (high geometric resolution), and radiance field (realistic rendering).
Pyramid Co-Attention Compare Network for Few-Shot Segmentation

Few-shot segmentation(FSS), which aims to extract never learned classes of objects from query images with a few annotated support samples, is a challenging problem especially in the cases that the appearance of objects in the support and the query images is significant different. Therefore, we propose a deep network called Pyramid Co-Attention Compare Network (PCCNet) to narrow the gap between them by introducing a Pyramid Co-attention Module (PCAM). PCAM acts as a task-specific transformer to transform the features of corresponding objects in query and support images into a space in which they are much closer by taking advantage of the underlying relation between query and support images. We also introduce a Prototypical Guide Module (PGM) which uses non-parametric metric learning to guide parametric metric learning so as to combine the advantages of them. In addition, a Superpixel Refine Module(SRM) is proposed to optimize the final output segmentation masks. Experiments conducted on Pascal-5i shows that our PCCNet achieves a mean Intersection-over-Union(mIoU) score of 63.01% for 1-shot segmentation and 64.57% for 5-shot segmentation, outperforming state-of-the-art methods by margin of 2.2% and 1.6%, respectively.
Semi-paired Image-to-Image Translation using Neighbor-based Generative Adversarial Networks

Image-to-image translation aims at learning the mapping between an input image and an output image using a training set of aligned image pairs. In reality, obtaining paired images is difficult and expensive. Generally, the data often exist in the form of partial pairing, that is, a small number of images are paired and most of the images are not paired. In this paper, we present a semi-paired image-to-image translation approach using neighbor-based generative adversarial networks. Our goal is to break the restriction that training images must be paired, and meanwhile guarantee the quality of image translation. For the unpaired images, we introduce an inverse mapping and cycle consistency loss to enforce the image reconstruction; for the paired images, we make full use of the one-to-one strong correlation to guide the image translation. To further take advantage of the paired images, our approach employs neighbor images to further expand the paired information and establishes the neighbor-based cycle consistency. Our method is characterized by flexibility and adaptability under various scenarios, such as target deformation, day-night transformation, etc. Compared with the previous methods, the experimental results prove the superiority of our method.
Interoperability of electronic health records using Semantic Knowledge Graphs. A use case applied at the UTPL University Hospital

Patient medical information is diverse, extensiveand of high value in supporting informed medical decision-making.This information is highly complex, is distributed among differentsystems, presents high heterogeneity, is stored in different formats,and has different structuring levels. The management of thisinformation poses interoperability challenges in tasks related to dataintegration and reuse. In this paper, an alternative is presented toface these challenges using semantic technologies. We propose totransform this heterogeneous, distributed, and unstructuredinformation in a way that ensures high interoperability, reuse, anddirect processing by machine agents. The pilot of this proposal wasdeveloped at the UTPL Hospital.
Study of the Utility of Text Classification Based Software Architecture Recovery Method RELAX for Maintenance

Background. The software architecture recovery method RELAX produces a concern-based architectural view of a software system graphically and textually from that system's source code. The method has been implemented in software which can recover the architecture of systems whose source code is written in Java. Aims. Our aim was to find out whether the availability of architectural views produced by RELAX can help maintainers who are new to a project in becoming productive with development tasks sooner, and how they felt about working in such an environment. Method. We conducted a user study with nine participants. They were subjected to a controlled experiment in which maintenance success and speed with and without access to RELAX recovery results were compared to each other. Results. We have observed that employing architecture views produced by RELAX helped participants reduce time to get started on maintenance tasks by a factor of 5.38 or more. While most participants were unable to finish their tasks within the allotted time when they did not have recovery results available, all of them finished them successfully when they did. Additionally, participants reported that these views were easy to understand, helped them to learn the system's structure and enabled them to compare different versions of the system. Conclusions. Through the speedup to the start of maintenance experienced by the participants as well as in their formed opinions, RELAX has shown itself to be a valuable help that could provide the basis of further tools that specifically support the development process with a focus on maintenance.
D-Net: A Generalised and Optimised Deep Network for Monocular Depth Estimation

Depth estimation is an essential component in computer vision systems for achieving 3D scene understanding. Efficient and accurate depth map estimation has numerous applications including self-driving vehicles and virtual reality. This paper presents a new deep network, called D-Net, for depth estimation from a single RGB image. The proposed network is designed as an efficient, accurate and universal model that can adopt a wide range of encoder backbones. Our approach gathers strong global and local contextual features at multiple resolutions and transfers these to high resolutions for clearer depth maps. For the encoder backbone we adopt state-of-the-art models including EfficientNet [1], HRNet[2] and Swin Transformer [3] to obtain densely labelled depth maps. The proposed D-net can be trained end-to-end and is designed to have minimal parameters and a reduced computational complexity. Extensive evaluations on the NYUv2 [4] and KITTI [5] benchmark datasets show that our model is highly accurate across multiple backbones and achieves state-of-the-art performance on both benchmark datasets when combined with the Swin Transformer and HRNets.
Validating a two-qubit nonstoquastic Hamiltonian in quantum annealing

We propose a two qubit experiment for validating tunable antiferromagnetic $XX$ interactions in quantum annealing. Such interactions allow the time-dependent Hamiltonian to be non-stoquastic, and the instantaneous ground state can have negative amplitudes in the computational basis. Our construction relies on how the degeneracy of the Ising Hamiltonian's ground states is broken away from the end point of the anneal: above a certain value of the antiferromagnetic $XX$ interaction strength, the perturbative ground state at the end of the anneal changes from a symmetric to an antisymmetric state. This change is associated with a suppression of one of the Ising ground states, which can then be detected using solely computational basis measurements. We show that a semiclassical approximation of the annealing protocol fails to reproduce this feature, making it a candidate `quantum signature' of the evolution.
The Effectiveness of Image Preprocessing on Digital Handwritten Scripts Recognition with The Implementation of OCR Tesseract

Optical Character Recognition (OCR) has been widely discussed in various topics in the rise of robotics, artificial intelligence and computer vision. OCR has become a solution in extracting characters from the image into machine-encoded text. This research aims to discuss character recognition from digital handwritten image. However, characters recognition problems using OCR has been more or less solved. OCR mainly implemented in reading characters from scanned of printed documents. In this research, image preprocessing including convert to grayscale, morphological operations and noise removal has been successfully boost the accuracy score of OCR performance. The average success outcome resulted to 79.26% in reading characters from the image.
Desafío TEP – Positive Educational Trajectories. A Public-Private Alliance to Strengthen Public Education During the Pandemic

Abstract This case study describes the Desafío TEP project, which arose from a public-private alliance between the Arauco Educational Foundation, the Center for Advanced Research in Education (CIAE) from the University of Chile, and the Andalién Sur Local Public Education Service (SLEP). The goal of the project is to develop a model for the prevention of school exclusion (resulting from repetition and dropout) in public schools. Although the initiative was in consideration to be suspended due to the closure of schools across the country caused by COVID-19, instead the proposal was completely redesigned, recognizing that the pandemic would increase the problems of school exclusion. The innovations generated in this redesign are anticipated to lead to long-term sustainability and scalability in the region through cost reductions and the promotion of remote interactions between different establishments working in networks, such as the SLEP, the schools, and the teams from the CIAE and Arauco Educational Foundation. This network is further supported by the actors in the educational system of the territory.
Automatic Podcast Generation

A massive leap forward in the field of Human Computer Interaction in living memory has been achieved by the Google Duo system to sustain a natural sounding and coherent phone call with a human being without them being able to tell the difference. The computer system capitalized on recent developments in the field of Synthetic voice generation along with real time processing and response generation. The aim of this work is to replicate the success of that presentation as well as to build upon that body of work and generate useful content summaries which can be converted into high quality podcasts. In particular, our approach first comprises of extracting text data from web pages using various Natural Language Processing (NLP) tools as well as deep neural networks. After that it summarises text into byte sized chunks using extractive summarisation. Then, in the end it generates clear, high quality audio podcasts from the produced summaries using recently developed text to speech engines.
Chaotic Image Cryptography Systems: A Review

In recent decades, image encryption has been a popular and important field of research. The image encryption techniques have been studied thoroughly to ensure the safety of digital images on transmission through the networks. A large range of algorithms for chaotic-based cryptographic systems has been suggested and submitted to enhance the efficiency of the encryption methods. The chaotic map is one technique to guarantee security. The benefits of chaotic image encryption include the fact that it is simple to implement; it has a faster encryption speed, and it is powerful against attacks. Due to their extreme sensitivity to initial conditions, unpredictability, and random-like behaviours, many image encryption systems using chaotic maps have been proposed. This study paper presents a scientific review of many types of researches during the (2014-2020) years that used chaotic with its various types (one-dimensional, multi-dimensional, or hyper-chaotic) to process the digital images in the encryption stage or the scrambling phase. Furthermore, it presents a future reading of researches that has a wider role in developing the cryptography field by improving the efficiency of Algorithms where using a chaotic map with other methods gives better results than using chaotic alone in scrambling and encryption methods.
Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Current semantic segmentation methods focus only on mining "local" context, i.e., dependencies between pixels within individual images, by context-aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization criteria (e.g., IoU-like loss). However, they ignore "global" context of the training data, i.e., rich semantic relations between pixels across different images. Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting. The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes. It raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely explored before. Our method can be effortlessly incorporated into existing segmentation frameworks without extra overhead during testing. We experimentally show that, with famous segmentation models (i.e., DeepLabV3, HRNet, OCR) and backbones (i.e., ResNet, HR-Net), our method brings consistent performance improvements across diverse datasets (i.e., Cityscapes, PASCAL-Context, COCO-Stuff, CamVid). We expect this work will encourage our community to rethink the current de facto training paradigm in fully supervised semantic segmentation.
Brain Tumors Classification System Using Convolutional Recurrent Neural Network

The brain is a body organ that controls exercise of the relative multitude of parts of the body. Conceding robotized mind tumors in MRI (Magnetic Reverberation Imaging) is a confounded assignment given size and area variety. This strategy decides a wide range of malignancies in the body. Past techniques devour additional time with less accuracy. A manual assessment can be mistaken because of the degree of intricacies engaged with cerebrum tumors and their properties. However, the above proposition isn’t appropriate for mind tumors because of colossal varieties in size and shape. Our proposed strategy to magnify arrangement performance. First, the expanded tumor district using picture enlargement is utilized to return for capital invested rather than the unique tumor area since it can give hints for tumor types. Second, expanded tumor locale split into progressively refined ring structure subregions. With three-component extraction approaches, employing photographs for information augmentation and rotating photographs at various angles, evaluate the performance of the suggested strategy on a large dataset. Utilizing Convolutional Recurrent Neural Network (CRNN), grouping of the tumor into three categories and thus give a virtual portrayal of exact value.
Towards Sustainability of Systematic Literature Reviews

Background: The software engineering community has increasingly conducted systematic literature reviews (SLR) as a means to summarize evidence from different studies and bring to light the state of the art of a given research topic. While SLR provide many benefits, they also present several problems with punctual solutions for some of them. However, two main problems still remain: the high time-/effort-consumption nature of SLR and the lack of an effective impact of SLR results in the industry, as initially expected for SLR. Aims: The main goal of this paper is to introduce a new view --- which we name Sustainability of SLR --- on how to deal with SLR aiming at reducing those problems. Method: We analyzed six reference studies published in the last decade to identify, group, and analyze the SLR problems and their interconnections. Based on such analysis, we proposed the view of Sustainability of SLR that intends to address these problems. Results: The proposed view encompasses three dimensions (social, economic, and technical) that could become SLR more sustainable in the sense that the four major problems and 31 barriers (i.e., possible causes for those problems) that we identified could be mitigated. Conclusions: The view of Sustainability of SLR intends to change the researchers' mindset to mitigate the inherent SLR problems and, as a consequence, achieve sustainable SLR, i.e., those that consume less time/effort to be conducted and updated with useful results for the industry.