Reviewerly/exHarmony huggingface dataset:
Online Adaptive Integration of Observation and Inpainting for Diminished Reality with Online Surface Reconstruction

Diminished reality (DR) is one of the frameworks in augmented reality, which visually makes obstructive objects transparent and shows their background. The techniques in DR can be divided into observation-based and inpainting-based methods. The observation-based method completes the region of interest (ROI) with the color and shape observed as the camera moves, while the inpainting-based method completes the region by inferring it from the surrounding pixels. In this paper, we propose a method that adaptively integrates the advantages of both methods with online surface reconstruction. The inpainting-based method completes the ROI at the beginning, while the observation-based method gradually completes the re-gion using the reconstruction with RGB-D SLAM as the camera moves. In this way, our proposed method adaptively utilizes both the inpainting-based and observation-based methods according to the camera movement online. The evaluation with various scenes was conducted to demonstrate the effectiveness of our proposed integration method by comparing it with each method.
CleverTrash: an IoT system for waste sorting with deep learning

Waste recycling is a major part of the life cycle of our goods. The principle of recycling is to reuse the waste. A major challenge that waste recycling facilities are facing is the disruptive wastes that are not well sorted. Sorting errors represent additional management costs. One example of sorting problem is differentiating types of plastic. The most known recyclable plastic type is Polyethylene Terephthalate (PET). One way to solve the sorting errors is going to the source of waste: people. By educating people we can achieve fewer errors.CleverTrash deals with a recycle bin enhanced with a waste recognition system that educates users to properly recycle their waste. It also provides statistics on the sorting quality in recycling bins thanks to an embedded system (Raspberry Pi and sensors). These statistics are provided to the manager to improve the waste collection strategy. We explore the performance of Convolutional Neural Networks (CNNs) inside the waste recognition system. In machine learning, CNNs are mostly specialized in image recognition. We explore the performance of CNNs inside the waste recognition system. We started the project from scratch. We gathered our own dataset by setting up a trash can incorporating an embedded video and lighting system. The camera takes pictures of the garbage being thrown away. A machine learning model classifies the images. The main challenge was to develop the best machine learning classifier model from the pictures and evaluate its performance on this use case. We compare seven types of CNN algorithms and produce the results in the present paper.Our results show that CNNs have difficulties distinguishing between PET bottles and glass bottles on a classification "PET versus Other (non-PET)". It suggests that CNNs are strong to classify different shapes but weak when shapes are similar. It led us to try a classification of "Bottles versus Other (non-Bottle)" which performs better than "PET versus Other". The best result of the former classification performed as well as the worst result of the latter classification.
Local overlap reduction procedure for dynamic ensemble selection

Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class. A promising approach among the ensemble-based methods in the context of imbalance learning is Dynamic Selection (DS). DS techniques single out a subset of the classifiers in the ensemble to label each given unknown sample according to their estimated competence in the area surrounding the query. Because only a small region is taken into account in the selection scheme, the global class disproportion may have less impact over the system's performance. However, the presence of local class overlap may severely hinder the DS techniques' performance over imbalanced distributions as it not only exacerbates the effects of the under-representation but also introduces ambiguous and possibly unreliable samples to the competence estimation process. Thus, in this work, we propose a DS technique which attempts to minimize the effects of the local class overlap during the classifier selection procedure. The proposed method iteratively removes from the target region the instance perceived as the hardest to classify until a classifier is deemed competent to label the query sample. The known samples are characterized using instance hardness measures that quantify the local class overlap. Experimental results show that the proposed technique can significantly outperform the baseline as well as several other DS techniques, suggesting its suitability for dealing with class under-representation and overlap. Furthermore, the proposed technique still yielded competitive results when using an under-sampled, less overlapped version of the labelled sets, specially over the problems with a high proportion of minority class samples in overlap areas. Code available at https://github.com/marianaasouza/lords.
Image Sanitization in Online Social Networks: A General Framework for Breaking Robust Information Hiding

With the development of robust information hiding (RIH) approaches, secret messages can be extracted successfully from stego-data after transmission through lossy channels of online social networks (OSNs). To interrupt illegal covert communications in OSNs, some methods sanitize the uploaded images by image processing operations to destroy the hidden data that may exist. However, none of the existing methods takes the RIH methods that can resist scaling into consideration, while scaling is a common operation in OSNs. In this paper, we first propose a general framework for image sanitization in OSN platforms, which serves as a countermeasure against the RIH. By using such a framework, the secret messages embedded in the upload images can be removed and the quality of the sanitized image can be well maintained. Our framework contains two deep neural networks: Scaling-Net and SC-Net. The Scaling-Net is dedicated to the sanitization of oversized images while the SC-Net is designed for other images. To achieve a good image quality, we also propose a discriminator for adversarial training of the Scaling-Net and SC-Net. Experimental results on different datasets demonstrate that our proposed method outperforms the state-of-the-art methods. The source code and pretrained models are available at our code repository ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zyzhu19/Image_Sanitization</uri> ).
SATMask: Spatial Attention Transform Mask for Dense Instance Segmentation

There are often dense objects in the images processed by instance segmentation, but too dense objects will cause the problem that the objects are difficult to segment. Most of the current dense instance segmentation methods are based on dense sliding window such as TensorMask. However, the sliding window has the problems of high computation and difficult design of anchor. In order to solve the above difficulties, we propose an anchor-free and single shot dense image segmentation framework, named SATMask, which adds a Spatial Attention Transform (SAT) mask head on anchor-free one stage object detector (FCOS) to predict high quality instance mask with low complexity, and uses feature-aligned pyramid network to fuse the feature map generated by backbone to obtain rich spatial details and better semantic information. Extensive experiments on the challenging COCO and Cityscapes datasets demonstrate the effectiveness of SATMask. In particular, under the same backbone (ResNet-101), SATMask achieves 39.8% AP on COCO, surpassing the state-of-the-art instance segmentation method Mask R-CNN 1.5% AP.
Multi-objective optimization for cloud provisioning: A case study in large-scale microservice notification applications

An approach to optimizing a large-scale microservice deployment for a critical notification system is presented. This paper addresses optimization for three objectives: cloud service cost, cloud resource utilization of CPU, RAM and storage, and meeting the system notification deadlines. To the best of our knowledge, this is the first work dealing with multi-objective optimization for microservice notification applications where the notification load is variable and depends on other priorly executed microservice in request processes. Furthermore, best of our knowledge, this is the first work that is based on multi-objective optimization, analyzing and proposing a Pareto front and optimal list from preselected VM candidates. First, the paper formulates multi-objective optimization problems for microservice-based large-scale notification system applications and provides the mathematical deployment model. Then, based on the expected workload and required multi-objective criteria, the paper proposes a set of algorithms that provide the Pareto front optimal solution for preselecting a Cloud Service provider’s VM type. In the end, the paper presents a case study that demonstrates the use of the proposed technique.
Did You Use My GAN to Generate Fake? Post-hoc Attribution of GAN Generated Images via Latent Recovery

This study proposes a method that enables attribution of GAN-generated images to the GAN model that generated the images. Existing attribution methods (e.g., model watermark) require preprossessing on the model before model publication to attain high attribution performance. This study proposes a post-hoc attribution method that does not require preprocessing before model publication. Our attribution method is designed based on the fact that latent recovery can attain better image recovery if images to be attributed are generated by the source model. Our experimental evaluation shows that our post-hoc attribution method attains almost the same attribution performance as existing methods that require preprocessing if more than five images are available for attribution.
Geometric Algebra Adaptive Filter Based on Multi-Dimensional Complex-Valued Random Fourier Features

The complex-valued random Fourier geometric algebra mapping (CRFGAM) method can solve the over-coupling issue of the real and imaginary parts for complex-valued signals. In order to improve the accuracy of nonlinear mapping in the CRFGAM method, this paper proposes a multi-dimensional complex-valued random Fourier geometric algebraic mapping (MDCRFGAM) method by expanding the CRFGAM to a multi-dimension mapping space. Therefore, a multi-dimensional complex-valued random Fourier geometric algebra least mean square (MDCRFGALMS) algorithm is proposed based on the MDCRFGAM method. Simulations on nonlinear channel equalization are conducted to validate the performance superiorities of the proposed algorithm from the aspect of filtering accuracy.
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation

Multi-modal video similarity evaluation is important for video recommendation systems such as video de-duplication, relevance matching, ranking, and diversity control. However, there still lacks a benchmark dataset that can support supervised training and accurate evaluation. In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task. The Tencent-MVSE dataset contains video pairs similarity annotations, and diverse metadata including Chinese title, automatic speech recognition (ASR) text, as well as human-annotated categories/tags. We provide a simple baseline with a multi-modal Transformer architecture to perform supervised multi-modal video similarity evaluation. We also explore pre-training strategies to make use of the unpaired data. The whole dataset as well as our baseline will be released to promote the development of the multi-modal video similarity evaluation. The dataset has been released in https://tencent-mvse.github.io/.
MegaLitePT: A Corpus of Literature in Portuguese for NLP

We present the section of the MegaLite corpus based on literary texts in Portuguese. This new section has been developed and adapted to be used for Computational Creativity tasks, such as Natural Language Processing, Automatic Text Generation (ATG), and other similar purposes. We highlight characteristics of the Portuguese section, such as the numbers of documents, authors, sentences and tokens and also how it is structured and formatted. We show how the ATG algorithms, which we have previously developed, behave when trained on this corpus, by using a human evaluation protocol where a mixture of automatically generated and natural texts is classified, using four criteria: grammaticality, coherence, identification of context, and an adapted Turing test.
DISF: Dynamic Instance Segmentation with Semantic Features

In this work, we propose a flexible and efficient instance segmentation framework, termed DISF (Dynamic Instance Segmentation with Semantic Features), which is a more novel two-stage instance segmentation framework. Firstly, we divide the image into multiple regions of the same size and directly classify the pixels in different regions, which converts the instance-level segmentation task into the pixel-level classification task within the region. We make full use of the location and size information of objects to distinguish different instances of the same category and obtain relatively coarse instance segmentation results. Secondly, we decouple the prediction of instance masks into convolution kernel prediction and instance features prediction. The instance masks are dynamically generated by convolution operations between the predicted convolution kernel and instance features. The segmentation results in this way do not contain redundant information. Thirdly, a parallel branch of semantic segmentation is added to refine instance segmentation results further. Semantic features provide global information about the image from a higher level. Semantic features and instance features are sent to the Features Fusion Module (FFM) to optimize the relatively coarse instance segmentation results generated in the previous stage. The experimental results reveal the promising potential of DISF in instance-level recognition.
Design and Analysis of a Scalable and Efficient Quantum Circuit for LWE Matrix Arithmetic

Quantum computing furnishes exponential speed up over classical computing in specific areas. For example, Shor’s algorithm can factor two numbers in a polynomial time complexity. Thus, many encryption algorithms that rely on large number factorization are potentially vulnerable to quantum computers. In order to address this, the National Institute of Standard and Test (NIST) has organized a competition to evaluate several post quantum cryptography (PQC) algorithms, that are secure from the attacks from quantum computers. Several of these lattice-based PQC encryption algorithms are based on Learning With Errors (LWE) computation. Conversely, LWE is the heaviest computation in a classical computer, which incurs significant portion of the latency overhead for the entire encryption algorithm. In this paper, we design an optimized quantum circuit for LWE computation. The proposed quantum circuit does not need any ancillary qubits and scales efficiently and easily if there are more qubits available on a higher qubit quantum computer.
A Multi-Objective Adaptive Upper Threshold Approach for Overloaded Host Detection in Cloud Computing

Cloud data centers (CDC) have become an increasingly critical issue because of their large-scale deployment, which has resulted in increased energy consumption (EC) and SLA. The SLA and EC can be greatly reduced by using an efficient virtual machine consolidation (VMC) approach. This study presents a multi-objective adaptive upper threshold (UTh) technique for identifying overloaded hosts. The dynamic virtual machine consolidation (DVMC) is then obtained by combining a modified overloaded host detection technique with a different VM selection method (i.e., minimum migration time (Mmt) and minimum utilization (Mu)). The simulation results indicate that the modified Interquartile range (Iqr) overloaded host detection algorithm outperforms the existing overloaded host detection algorithms (i.e., InterQuartile range (Iqr), local regression (Lr), and dynamic voltage frequency scale (DVFS) algorithms) in terms of EC, SLA, and the number of virtual machine (VM) migrations.
A Max-Flow Based Approach for Neural Architecture Search

Neural Architecture Search (NAS) aims to automatically produce network architectures suitable to specific tasks on given datasets. Unlike previous NAS strategies based on reinforcement learning, genetic algorithm, Bayesian optimization, and differential programming, we formulate the NAS task as a Max-Flow problem on search space consisting of Directed Acyclic Graph (DAG) and thus propose a novel NAS approach, called MF-NAS, which defines the search space and designs the search strategy in a fully graphic manner. In MF-NAS, parallel edges with capacities are induced by combining different operations, including skip connection, convolutions and pooling, and the weights and capacities of the parallel edges are updated iteratively during the search process. Moreover, we interpret MF-NAS from the perspective of non-parametric density estimation and show the relationship between the flow of a graph and the corresponding classification accuracy of a neural network architecture. We evaluate the competitive efficacy of our proposed MF-NAS across different datasets with different search spaces that are used in DARTS/ENAS and NAS-Bench-201.
Parallel Assisted Learning

In the era of big data, a population's multimodal data are often collected and preserved by different business and government entities. These entities often have their local machine learning data, models, and tasks that they cannot share with others. Meanwhile, an entity often needs to seek assistance from others to enhance its learning quality without sharing proprietary information. How can an entity be assisted while it is assisting others? We develop a general method called parallel assisted learning (PAL) that applies to the context where entities perform supervised learning and can collate their data according to a common data identifier. Under the PAL mechanism, a learning entity that receives assistance is obligated to assist others without the need to reveal any entity's local data, model, and learning objective. Consequently, each entity can significantly improve its particular task. The applicability of the proposed approach is demonstrated by data experiments.
BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit Neural Networks

In this paper, we propose Branch-wise Activation-clipping Search Quantization (BASQ), which is a novel quantization method for low-bit activation. BASQ optimizes clip value in continuous search space while simultaneously searching L2 decay weight factor for updating clip value in discrete search space. We also propose a novel block structure for low precision that works properly on both MobileNet and ResNet structures with branch-wise searching. We evaluate the proposed methods by quantizing both weights and activations to 4-bit or lower. Contrary to the existing methods which are effective only for redundant networks, e.g., ResNet-18, or highly optimized networks, e.g., MobileNet-v2, our proposed method offers constant competitiveness on both types of networks across low precisions from 2 to 4-bits. Specifically, our 2-bit MobileNet-v2 offers top-1 accuracy of 64.71% on ImageNet, outperforming the existing method by a large margin (2.8%), and our 4-bit MobileNet-v2 gives 71.98% which is comparable to the full-precision accuracy 71.88% while our uniform quantization method offers comparable accuracy of 2-bit ResNet-18 to the state-of-the-art non-uniform quantization method. Source code is on https://github.com/HanByulKim/BASQ .
A Secure Comparison Protocol in the Malicious Model

Secure comparison is an important problem in the field of secure computing. A classic example is the Millionaires' problem, which is securely compares the wealth of two millionaires without revealing their wealth. However most traditional secure comparison protocol requires autonomous computation among the participants. It is necessary to ensure that the model of the participants is a semi-honest model, otherwise the protocol will not be able to execute correctly. In this paper, we introduce a semi-honest judge to improve the Damgård-Geisler-KrØigaard(DGK) comparison protocol by using techniques such as zero-knowledge proof and homomorphic encryption. The improved protocol is not only applicable to the malicious participant model, but also the introduced judge would not get any information of data. At last, the security analysis shows that the proposed scheme in this paper can guarantee the data fairness of the comparison protocol while ensuring the privacy security of the participants.
Crowd Counting and Localization Beyond Density Map

Crowd analysis in general and counting in congested scenes, in particular, is an effective and vibrant research domain in computer vision due to its numerous applications.Understanding the risk analysis and safety aspects of crowd dynamics at various vital occasions related to sports, cultural and religious activities, specifically, Hajj and Umrah is essential.Thousands of people gathered in a small area to carry out their rites.Localizing and counting the annotated head points is quite challenging due to occlusion and large-scale variation in the congested environment.To deal with these problems, a small and effective solution is to generate the density maps.However, the significant flaws of the density map have a blurry Gaussian blob which is less effective for counting and localizing head annotations in the congested scene.To overcome these issues, we propose Congested Scene Crowd Counting and Localization Network (CSCCL-Net) with a Focal inverse Distance Transform (FIDT) map that can count and localize the people simultaneously in the highly congested scene.To evaluate the proposed model's efficiency, extensive tests were performed on the ShanghaiTech part A, ShanghaiTech part B, and JHU-CROWD++ datasets.The proposed model outperforms existing state-of-the-art techniques regarding high accuracy and low Mean Absolute Error (MAE) and Mean Square Error (MSE) values.
Digital Literacy Needs for Online Learning Among Peri-Urban, Marginalised Youth in South Africa

As online learning modes become more common, this can exacerbate educational inequalities for learners who do not have the ability to utilise these modes effectively. This has been seen in the COVID-19 crisis where there has been a shift to remote and distance learning modalities despite the limited ability for all learners to benefit equitably. In particular, digital literacy remains a fundamental barrier to benefitting from online and blended learning. This paper reports on a study that investigated the digital literacy needs and preferences of peri-urban, marginalised youth when utilising online and blended learning in South Africa and how online education platforms can be designed to better suit such groups. It is argued that for online courses to truly support marginalised groups, it needs to be ensured that these learners are digitally equipped and digitally literate in terms of accessing, utilising, and benefitting equitably from online learning.