Reviewerly/exHarmony huggingface dataset:
Compressed Video Quality Enhancement with Motion Approximation and Blended Attention

In recent years, various methods have been proposed to tackle the compressed video quality enhancement problem. It aims at restoring the distorted information in low-quality target frames from high-quality reference frames in the compressed video. Most methods for video quality enhancement contain two key stages, i.e., the synchronization and the fusion stages. The synchronization stage synchronizes the input frames by compensating the estimated motion vector to reference frames. The fusion stage reconstructs each frame with the compensated frames. However, the synchronization stage in previous works merely estimates the motion vector between the reference frame and the target frame. Due to the quality fluctuation of frames and region occlusion of objects, the missing detail information cannot be adequately replenished. To make full use of the temporal motion between input frames, we propose a motion approximation scheme to utilize the motion vector between the reference frames. It is able to generate additional compensated frames to further refine the missing details in the target frame. In the fusion stage, we propose a deep neural network to extract frame features with blended attention to the texture details and the quality discrepancy at different times. The experimental results show the effectiveness and robustness of our method.
Contextual Recommender Systems in Business from Models to Experiments

Within the last decade, several researchers have focused on using contextual information to design new systems that generate personalized recommendations matching users specific contexts. In this respect, Recommended Systems (RS) have been used in different domains to assist users decision making by providing item recommendations and thereby improving the quality. Context-Aware Recommender Systems (CARS) go further, taking contexts (e.g., time, location, occasion, etc.) into consideration to suggest items that are appropriate to users specific contextual situations. We give in this chapter an overview of the current state of the art in CARS and decision-making process, associated types, challenges, limitations, and business adoptions. Experimental evaluation processes that can be performed to assess the quality of any contextual recommendation system is discussed. Also, an empirical evaluation between CARS and baseline recommender systems is performed on two benchmark datasets.
MIDE

This paper studies privacy in the context of decision-support queries that classify objects as either true or false based on whether they satisfy the query. Mechanisms to ensure privacy may result in false positives and false negatives. In decision-support applications, often, false negatives have to remain bounded. Existing accuracy-aware privacy preserving techniques cannot directly be used to support such an accuracy requirement and their naive adaptations to support bounded accuracy of false negatives results in significant privacy loss depending upon distribution of data. This paper explores the concept of minimally-invasive data exploration for decision support that attempts to minimize privacy loss while supporting bounded guarantee on false negatives by adaptively adjusting privacy based on data distribution. Our experimental results show that the MIDE algorithms perform well and are robust over variations in data distributions.
Matrix World - A Programmable 3D Multichain Metaverse

Matrix World is a decentralized open virtual world that lets users interact with immersive 3D applications simultaneously running on different blockchains. The world consists of Lands, issued as Non-Fungible Tokens (NFTs), which permanently persist on blockchain networks such as Ethereum and Flow. Thus, Matrix Lands are tradable and transferable via blockchain networks and their owners retain complete control over the creations on their Lands. These Lands can be bound to a sandbox in the Matrix Network which has its own compute and storage resources. These resources allow landowners and creators to govern the visual appearance, inner properties, and lifecycle logic of the Land’s creations, ultimately resulting in the production of an immersive 3D application that can operate in perpetuity in the Matrix World cyberspace. In Matrix World, users can take advantage of standard 3D open-world features such as building 3D architectures, hosting virtual meetings, exhibiting digital assets (e.g., NFTs), etc., along with more advanced functionality such as creating and hosting 3D decentralized applications (DApps) using Matrix’s built-in creator services and computational resources. These DApps include 3D games and 3D marketplaces, among others. Our ultimate goal is to create unlimited possibilities and opportunities in the metaverse by eliminating the boundary between blockchains and building a next generation 3D open-world DApp platform on top of Matrix World.
PPSL: Privacy-Preserving Text Classification for Split Learning

With the blooming of machine learning, Distributed Collaborative Machine Learning (DCML) approaches have been used in various applications to scale up the training process. However, they may have privacy issues that need to be addressed. Split learning is one of the latest DCML approaches that enable the training of the machine learning models without sharing the raw data. In this paper, we study the novel problem of building a privacy-preserving text classifier in the split learning setting. This task, however, is challenging due to the need to maintain the utility of the text data for downstream tasks while protecting privacy by preventing the leakage of private attributes. We address this dilemma of privacy and utility in this work. We propose a text classification for split learning, PPSL, which utilizes the adversarial learning to minimize the private attribute leakage. We study the impacts of increasing the training time and the number of hidden layers on the privacy of split learning. Our experimental results demonstrate the effectiveness of the proposed framework that retains the sentiment meaning and preserves the private attributes while minimizing the leakage.
SynLibras: A Disentangled Deep Generative Model for Brazilian Sign Language Synthesis

Recent advances regarding deep generative models have strengthened a realm of approaches in which discriminative and generative tasks are tackled jointly in an analysis-by-synthesis manner. In this category, variational autoencoders (VAEs) and generative adversarial networks (GANs) aim for learning latent data representations from which sampling of synthetic images may be performed. However, sampling in such models normally does not allow for independent control of diverse factors of variation. Despite general efforts to overcome this issue, deep generative models tailored for sign language with disentangled factors of variation are yet not vastly explored in the literature. In this work, we introduce the SynLibras, a novel model that allows for disentangling appearance and gestural communication (i.e. body, hands and face poses) on image synthesis. Our model is capable of performing cross-language pose-transfer while maintaining the appearance of the source signer. We perform experiments on the RWTH-PHOENIX-Weather dataset and evaluation using the PSNR and the SSIM metrics. To our knowledge, the SynLibras is the first method for Brazilian sign language (Libras) synthesis in images. We compare our model with the EDN, a well-known general pose-transfer method, achieving better results on Libras synthesis. Finally, we also introduce the SynLibras-Pose, a dataset with annotated poses of Libras signers performing single words.
The Basics

Python programs are written in a text editor, such as Notepad, PyCharm, or the code editor in Python’s development environment (IDLE), and saved with a .py file extension.
Breast Cancer Detection Using Convolutional Neural Networks Model

Breast cancer is a significant disease that threatens people's health nowadays. A standard way to detect breast cancer is using radiology images by skilled physicians. However, this method causes problems in locating the cancerous area and intensive work in diagnosing and detecting histopathology images due to technical problems. The research results in the latest years can be divided into two directions, methods relying on machine learning or methods relying on deep learning. Due to the high dependency on the labeled data, we conducted two series of experiments that represent two reforming methods, analyzing a data set of a large number of breast cancer images to deal with these disadvantages. For the first series of experiments, we mutate the size of the training set using different models and compare the performances of the three models. For the second part, we do data augmentation on models to compare them before and after data augmentation and observe whether it makes these they achieve or reach the effect of the qualified model. Moreover, as the result shows, both our methods can reduce the workload in diagnosing breast cancer while maintaining or even improving the test accuracy, which benefits the follow-up work and development of the breast cancer diagnosis field.
Study on Lexical Analysis of Malicious URLs using Machine Learning

The act of phishing has an impact all over the world in the past decade. The numbers of attacks are increasing day-by-day while gaining access over the internet. The attacks are done with minimum efforts and low cost. Due to this, the attackers can do maximum number of phishing attacks in less time. The protection of users connected to the internet is so cumbersome and hard. Detecting malicious URLs is one way to mitigate the act of phishing. A mandatory solution can be performed by using information extracted only from a URL. In this paper, the study consists of three phases: first phase includes lexical features evaluation from existing studies as well as novel features evaluation to find out an optimal feature vector, second phase includes classification algorithm identification which provides a classification model to compared with existing studies and the third phase includes the study of first two phases to produce and evaluate a scalable classification system which evaluates the malicious URLs obtained from various sources.
IEUM: Bridging Transportation to Humans

IEUM, a small data cube, is a fundamental agent of transportation envisioned in a future shared mobility system. By communicating with the user, IEUM understands what its user needs at the moment and acts as a mediating agent among humans, cars, and other traffic infrastructures. While taking you to your personalized route of the day, IEUM will also enhance the traffic and energy efficiency of our transportation systems with other IEUMs out on the road. With your buddy IEUM, moving is full of fun.
Attitude Control for Quadcopters using Reinforcement Learning

In this paper, a novel control strategy based on Reinforcement Learning is presented to achieve better performance of attitude control for quadcopters. By using Proximal Policy Optimization, the agent is trained via a reward function and interaction with the environment. The control algorithm obtained from this training process is simulated and tested against proportional-integral-derivative control, being the most common attitude control algorithm used in drone races. The resulting control policies were comparable to the baseline counterpart and, in some cases, outperformed it in terms of noise rejection and robustness to external disturbances.
Improved Particle Swarm Optimization Algorithm with Fireworks Search

Particle Swarm Optimization (PSO) is an iterative swarm intelligence algorithm, whose main idea is introducing random factors into the evolutionary equation to simulate the foraging behavior of birds. However, this random search model gives rise to the weakened local search ability of PSO, the premature convergence problem and the inability to find the optimal solution. To improve this issue, an Improved Particle Swarm Optimization with Fireworks Search (FS-PSO) is proposed in this paper. In FS-PSO, explosive sparks and gaussian mutation sparks are used to search at the end of the iteration of PSO based on the search mechanism of the Fireworks Algorithm (FWA), which is aiming to increase the population diversity of the particles and enhance the local search ability of PSO. The simulation results show that the improvement of FS- PSO is effective to the premature convergence problem of PSO.
A Robust Image Watermarking Technique Using Multi-DW-DCT Scheme

There is a present need for high-quality, robust, and effective watermarking methods, due to the increasingly rapid utilization and general growth of digital data on the internet. Digital multimedia data such as images, video, and audio can be secured using watermarking techniques. Digital watermarking plays a critical role in defending against multimedia piracy, which can be defined as the illegal copying of such media. The watermark is embedded in a piece of multimedia content, comprises confidential information related to ownership, origin, and copy control, and is responsible for issues related to imperceptibility and robustness. In this paper, we propose an algorithm for digital watermarking, the basis of which is a multi-level numerical analysis technique using discrete wavelets and a discrete cosine transform (DW-DCT) scheme. The variable visibility factor is employed to embed watermarks of the host image with low-frequency components. We also test our watermarking algorithm performance using the Peak-Signal-to-Noise-Ratio (PSNR) parameters as a benchmark. Our results show that with a 0.010 visibility factor, the PSNR is 41.766 dB when using DWT, whereas it reached 44.839 dB with the proposed method.
EdgeViT: Efficient Visual Modeling for Edge Computing

With the rapid growth of edge intelligence, a higher level of deep neural network computing efficiency is required. Visual intelligence, as the core component of artificial intelligence, is particularly worth more exploration. As the cornerstone of modern visual modeling, convolutional neural networks (CNNs) have greatly developed in the past decades. Variants of light-weight CNNs have also been proposed to address the challenge of heavy computing in mobile settings. Though CNNs’ spatial inductive biases allow them to learn representations with fewer parameters across different vision tasks, these models are spatially local. To acquire a next-level model performance, vision transformer (ViT) is now a viable alternative due to the potential of multi-head attention mechanism. In this work, we introduce EdgeViT, an accelerated deep visual modeling method that incorporates the benefits of CNNs and ViTs in a light-weight and edge-friendly manner. Our proposed method can achieve top-1 accuracy of 77.8% using only 2.3 million parameters, 79.2% using 5.6 million parameters on ImageNet-1k dataset. It can achieve mIoU up to 78.3 on PASCAL VOC segmentation while only using 3.1 million parameters which is only half of MobileViT parameter budget.
A K-anonymity Optimization Algorithm Under Attack Model

The current k-anonymity algorithm still suffers from problems such as location information leakage and poor quality of service due to improper selection of k. To tackle the above drawbacks, we propose a personalized k-anonymity optimization algorithm under attack model. Achieving optimal quality of service and personalized privacy protection in a context where attackers launch the strongest attacks based on a priori knowledge. Quantifying location privacy and quality of service based on location entropy and attack models. On this basis, the optimal k is obtained through the game to achieve privacy and service quality optimization. Security analysis and simulation experiments demonstrate the privacy and effectiveness of the algorithm. The experimental results show the specific k for different scenarios and different anonymity mechanisms.
Efficient Implementation of ECDH for Sigfox Communication

In recent years, a variety of IoT devices are connected to the Internet, which allows us to make our lives more convenient. LPWA (Low Power Wide Area) network enables data transmission over long distances with low power consumption. In this research, the authors focused on Sigfox, which is a well-known LPWA. However, since information on the traffic is easily eavesdropped, a countermeasure for this point would not be ignorable. Elliptic curve is suitable for IoT devices with limited computing resources because of its strong cryptographic strength with a short key length. In this research, we conducted experiments to create a shared key using Sigfox communication and elliptic curve cryptography. Also, this paper discusses improve the efficiency of computation on elliptic curves.
On a Weakly Supervised Classification Problem

We consider a weakly supervised classification problem. It is a classification problem where the target variable can be unknown or uncertain for some subset of samples. This problem appears when the labeling is impossible, time-consuming, or expensive. Noisy measurements and lack of data may prevent accurate labeling. Our task is to build an optimal classification function. For this, we construct and minimize a specific objective function, which includes the fitting error on labeled data and a smoothness term. Next, we use covariance and radial basis functions to define the degree of similarity between points. The further process involves the repeated solution of an extensive linear system with the graph Laplacian operator. To speed up this solution process, we introduce low-rank approximation techniques. We call the resulting algorithm WSC-LR. Then we use the WSC-LR algorithm for analysis CT brain scans to recognize ischemic stroke disease. We also compare WSC-LR with other well-known machine learning algorithms.
Epsilon-Differentially Private and Fully Secure Logistic Regression on Vertically Split Data

Collaborative networks of organisations need solutions for the secure federated training of machine learning models in distributed environments and for the privacy preservation of the models' parameters after their publication. We propose, present, and evaluate a two-party fully secure logistic regression on a vertically split dataset using an epsilon-differential privacy (DP) mechanism and secure multi-party computation (MPC) protocols. The idea of combining DP and MPC is not new; however, existing solutions leak more information than the one we propose. We empirically evaluate the performance of the proposed mechanism and comparatively argue its originality and relevance in the light of state-of-the-art related work.
Preliminary Study: Electronic Counting in Indonesian National Election Using Computer Answer Sheets and DMR Software

The 2019 election is the most expensive, complicated, and the worst of the 12 elections in Indonesian history. There are 894 election officers' death due to overworking load and it cost the nation 25 trillion Rupiah or about 1.8 billion USD, involved 16 parties, 192.2 million voters, 7.6 million officers, and 810,329 polling stations spreading in 83,405 villages throughout Indonesia. The election was held simultaneously for selecting legislative members and the president, utilized five ballots, and was held one day, including for polling, and counting, making the 2019 elections the most complicated in the world. The time for processing five ballots for voting took about 5-7 minutes on average and another five minutes for counting. Moreover, the ballot tabulation process from polling stations to the national level was manual and took 36 calendar days, adding complexity to the post-election process. This work aims to propose an approach to improve the efficiency and accountability of the voting and tabulation process. Propose to use a single ballot like a computer answer sheet, marked with a regular marker, optical scanned, and finally processed by Digital Mark Reader (DMR) software. The proposal can save 280.7 million square meters of paper use nationally, shorten 80% of voting time to no more than one minute, increase pooling station capacity by 930%, and 8 working hours for officers in. And in the Kapuk-Jakarta Administration Area alone, it can save a budget of Rp 3.4 billion and 38,556 working hours.
Least-Squares Linear Dilation-Erosion Regressor Trained Using a Convex-Concave Procedure

This paper presents a hybrid morphological neural network for regression tasks called linear dilation-erosion regressor ( $$\displaystyle \ell $$ -DER). An $$\displaystyle \ell $$ -DER is given by a convex combination of the composition of linear and morphological operators. They yield continuous piecewise linear functions and, thus, are universal approximators. Besides introducing the $$\displaystyle \ell $$ -DER model, we formulate their training as a difference of convex (DC) programming problem. Precisely, an $$\displaystyle \ell $$ -DER is trained by minimizing the least-squares using the convex-concave procedure (CCP). Computational experiments using several regression tasks confirm the efficacy of the proposed regressor, outperforming other hybrid morphological models and state-of-the-art approaches such as the multilayer perceptron network and the radial-basis support vector regressor.
Computerized Tool for Students Training in Solar Geometry

Renewable energies have experienced significant growth in recent years. The new installed capacity from renewable sources has surpassed the new installed capacity from conventional sources. Solar energy is the fastest growing and the most pervasive. In this sense, it is essential that the sizing and design of solar energy generation systems (such as photovoltaic systems or solar thermal systems) be carried out considering the solar geometry to ensure that that the location of the generation system allows capturing the greatest amount of solar radiation on any given day or time of the year and, thus, generate more energy. This paper presents the development of a computerized tool, for educational purposes, that allows performing solar geometry calculations to be used in solar energy generation systems. The computerized tool was developed using MATLAB® AppDesigner. The computerized tool facilitates and helps the study of how solar geometry affects the performance of solar energy systems. The computerized tool was tested with students of Energy Engineering Program at U niversidad de Medellín and allowed to verify that the learning process of the subject have improved substantially.
Design of cloud platform for niche monitoring of forest pests based on Internet of Things

In view of the frequent occurrence of dangerous forest diseases and insect pests in China, which have caused varying degrees of damage to forest resources and ecological environment, this study designed a niche monitoring system for forest diseases and insect pests based on STM32 and the Internet of Things. The design uses STM32 as the data acquisition terminal and repeater, and transmits data to the cloud via the Internet through the W5500 module, realizing remote monitoring of the host computer's webpage. Through this monitoring system, users can understand the environmental information and historical conditions that have a greater impact on forest diseases and insect pests in the deployment area of the system at any time, including carbon dioxide concentration, oxygen concentration, soil pH, temperature and humidity, light intensity, etc., collected by the system The terminal can adapt to about 20 kinds of sensors, and can easily and comprehensively monitor and manage different forest niches. This study provides a basis for forest pest monitoring, which can efficiently and timely provide forest ecological environment information and reduce the consumption of human and material resources. It is of great significance to the scientific management of forest workers.