Reviewerly/exHarmony huggingface dataset:
Unmanned aerial vehicles and machine learning for detecting objects in real time

An unmanned aerial vehicle (UAV) image recognition system in real-time is proposed in this study. To begin, the you only look once (YOLO) detector has been retrained to better recognize objects in UAV photographs. The trained YOLO detector makes a trade-off between speed and precision in object recognition and localization to account for four typical moving entities caught by UAVs (cars, buses, trucks, and people). An additional 1500 UAV photographs captured by the embedded UAV camera are fed into the YOLO, which uses those probabilities to estimate the bounding box for the entire image. When it comes to object detection, the YOLO competes with other deep-learning frameworks such as the faster region convolutional neural network. The proposed system is tested on a wild test set of 1500 UAV photographs with graphics processing unit GPU acceleration, proving that it can distinguish objects in UAV images effectively and consistently in real-time at a detection speed of 60 frames per second.
Image scrambler based on novel 4-D hyperchaotic system and magic square with fast Walsh–Hadamard transform

A novel 4-D hyperchaotic system that have seven positive parameters in third order with thirteen terms is proposed,in this paper, the proposed chaotic behavior is proved by analysis of the Lyapunov's exponent, fractional dimension, zero-one test, sensitivity dependent on initial condition (SDIC), phase portraits, and waveform analysis, this study offers an innovative designed image encryption algorithm depending on a 4-D chaotic system using fast discrete Walsh-Hadamard transform and magic matrix that is both effective and simple for image encryption and gives it a higher level of security. This new 4-D hyperchaotic system is used to produce a random key in this algorithm. The implemented and simulated results using mathematica programs and MATLAB programs were supplied qualitatively and in figures. The proposed system is hyperchaotic, according to testing results, because it possesses two Lyapunov positive exponents.
Utilizing 3D Technology to Support Language Learning

Problems that often arise in the teaching and learning process are inadequate and communicative learning media, limited space and time, and inappropriate learning methods.To minimize these bottlenecks, software should be used.This paper aims to use the software as a medium to support language learning.The software used has been produced and released on the Playstore to be used in language learning, increase vocabulary, and improve foreign language skills.The research method used is the descriptive content analysis study method.The aim is to solve and answer existing problems, and the main goal is to objectively describe the conditions and issues that occur in the description.The analysis is carried out on international, national and similar sources related to 3D Technology.
Sampling-based Gaussian Mixture Regression for Big Data

This paper proposes a nonuniform subsampling method for finite mixtures of regression models to reduce large data computational tasks. A general estimator based on a subsample is investigated, and its asymptotic normality is established. We assign optimal subsampling probabilities to data points that minimize the asymptotic mean squared errors of the general estimator and linearly transformed estimators. Since the proposed probabilities depend on unknown parameters, an implementable algorithm is developed. We first approximate the optimal subsampling probabilities using a pilot sample. After that, we select a subsample using the approximated subsampling probabilities and compute estimates using the subsample. We evaluate the proposed method in a simulation study and present a real data example using appliance energy data.
Congestion Attacks in Payment Channel Networks

Payment channel networks provide a fast and scalable solution to relay funds, acting as a second layer to slower and less scalable blockchain protocols. In this paper, we present an accessible, low-cost attack in which the attacker paralyzes multiple payment network channels for several days. The attack is based on overloading channels with requests that are kept unresolved until their expiration time. Reaching the maximum allowed unresolved requests (HTLCs) locks the channel for new payments. The attack is in fact inherent to the way off-chain networks are constructed, since limits on the number of unresolved payments are derived from limits on the blockchain. We consider three main versions of the attack: one in which the attacker attempts to block as many high liquidity channels as possible, one in which it disconnects as many pairs of nodes as it can, and one in which it tries to isolate individual nodes from the network. We evaluate the costs of these attacks on Bitcoin's Lightning Network and compare how changes in the network have affected the cost of attack. Specifically, we consider how recent changes to default parameters in each of the main Lightning implementations contribute to the attacks. As we evaluate the attacks, we also look at statistics on parameters in the Lightning Network, which are of independent interest and compare the various implementations of Lightning nodes. Finally, we suggest mitigation techniques that make these attacks much harder to carry out.
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation

Self-supervised vision-and-language pretraining (VLP) aims to learn transferable multi-modal representations from large-scale image-text data and to achieve strong performances on a broad scope of vision-language tasks after finetuning. Previous mainstream VLP approaches typically adopt a two-step strategy relying on external object detectors to encode images in a multi-modal Transformer framework, which suffer from restrictive object concept space, limited image context and inefficient computation. In this paper, we propose an object-aware end-to-end VLP framework, which directly feeds image grid features from CNNs into the Transformer and learns the multi-modal representations jointly. More importantly, we propose to perform object knowledge distillation to facilitate learning cross-modal alignment at different semantic levels. To achieve that, we design two novel pretext tasks by taking object features and their semantic labels from external detectors as supervision: 1.) Object-guided masked vision modeling task focuses on enforcing object-aware representation learning in the multi-modal Transformer; 2.) Phrase-region alignment task aims to improve cross-modal alignment by utilizing the similarities between noun phrases and object labels in the linguistic space. Extensive experiments on a wide range of vision-language tasks demonstrate the efficacy of our proposed framework, and we achieve competitive or superior performances over the existing pretraining strategies.
A Quasi-Newton Trust-Region Method for Optimization Under Uncertainty Using Stochastic Simplex Approximate Gradients

Summary The goal of field-development optimization is maximizing the expected value of an objective function, e.g., net present value for a producing oil field or amount of CO2 stored in a subsurface formation, over an ensemble of models that describe the uncertainty range. A single evaluation of the objective function requires solving a system of partial differential equations, which can be computationally costly. Hence, it is most desirable for an optimization algorithm to reduce the number of objective-function evaluations while delivering high convergence rate. Here, we develop a quasi-Newton method that builds on approximate evaluations of objective-function gradients and takes more effective iterative steps using a trust-region approach compared to line search. We implement three gradient formulations: ensemble optimization (EnOpt) gradient, and two variants of the stochastic simplex approximate gradient (StoSAG), all computed using perturbations around the point of interest. We modify the formulations to enable exploiting the objective-function structure. Instead of returning a single value for the gradient, the reformulation breaks up the objective function into its sub-components and returns a set of sub-gradients. We then can utilize our prior problem-specific knowledge through passing a ‘weight’ matrix to act on the sub-gradients. Two quasi-Newton updating algorithms are implemented: Broyden–Fletcher–Goldfarb–Shanno and the symmetric rank 1. We first evaluate the variants of our method on test challenging functions (e.g., stochastic variants of Rosenbrock and Chebyquad). Then, we present an application of well-control optimization for a realistic synthetic case. Our results confirm that StoSAG gradients are significantly more effective than EnOpt gradients for accelerating convergence. An important challenge to stochastic gradients is determining a-priori the adequate number of perturbations. We report that the optimal number of perturbations depends on both the number of decision variables and the size of uncertainty ensemble and provide practical guidelines for its selection. We show on the test functions that imposing our prior knowledge on the problem structure can improve the gradient quality and significantly accelerate convergence. In many instances, the quasi-Newton algorithms deliver superior performance compared to the steepest-descent algorithm, especially during the early iterations. Given the computational cost involved in typical applications, rapid and noteworthy improvements at early iterations is greatly desirable for accelerated project delivery. Furthermore, our method is robust, exploits parallel processing, and can be readily applied in a generic fashion for a variety of problems where the true gradient is difficult to compute or simply not available.
Deep Regression Ensembles

We introduce a methodology for designing and training deep neural networks (DNN) that we call "Deep Regression Ensembles" (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE has two components, randomly drawn input weights and output weights trained myopically (as if the final output layer) using linear ridge regression. Within a layer, each neuron uses a different subset of inputs and a different ridge penalty, constituting an ensemble of random feature ridge regressions. Our experiments show that a single DRE architecture is at par with or exceeds state-of-the-art DNN in many data sets. Yet, because DRE neural weights are either known in closed-form or randomly drawn, its computational cost is orders of magnitude smaller than DNN.
Sensor Cloud-Based Theoretical Machine Learning Models for Predicting Pandemic Diseases

Many pandemic diseases spread across the country or the whole world and affect the people as well as take their lives, for example, Cholera, severe acute respiratory syndrome, H1N1, Ebola, and Coronavirus disease 2019, etc. Research shows that the risk of pandemics has expanded during the past few decades owing to enhanced world travel and migration, urbanization, land use modifications, and greater natural environmental destruction. The increasing possibility of such a pandemic combined with the threat’s predicted spread and death tolls increases the need to avoid this global epidemic before it happens. The only strategy to prevent a large-scale pandemic is to avoid the transmission of such an epidemic to and across living organisms. Nevertheless, mitigation allows the public health community to reliably anticipate what the suspect pathogen is to have the appropriate solutions to either deter the transmission of the novel infection to humans’ altogether or control the disease as a community epidemic. This chapter presents the theoretical models to predict the pandemic diseases before they spread in the community or the whole world. Sensor cloud paradigm is used to continuously monitor the patient activity and sensed information is saved into a cloud server where various machine learning (ML) algorithms are used to find the type of disease and provide the appropriate medication to the affected person. Advanced theoretical ML models are proposed to get the information about the infection caused to the human body before it becomes a pandemic to the county or world.
Cognitive assessment of motivation to perform classroom or online math tasks among engineering students

&lt;span lang="EN-US"&gt;This study explored the cognitive algebra mechanism underlying mathematical motivation in 672 engineering students. The experimental design included the combination of four factors (task modality versus task difficulty versus task structure versus task relevance) to compose 36 written experimental scenarios. Each one described a hypothetical situation about assigned activities in math class. The participant's task was to read each scenario and estimate how much motivation they would experience if performing the assigned math activity. The results indicated five cognitive motivational patterns among the participants. All the clusters considered the task's relevance as an essential factor in judging their mathematical motivation. Besides this, Clusters 1, 2, 3, and 5 considered the assigned task's difficulty and structure in judging their degree of motivation, but they evaluated the factors differently. The low math motivation cluster integrated the factors according to a summative cognitive rule. Clusters 2, 3, and 5 used a multiplicative rule to integrate the information, and Cluster 4 did not show an information integration systematic mechanism. These findings pointed to the diversity of motivational cognitive profiles among students. This type of cognitive characterization can help design programs that encourage students to learn and enjoy science subjects that will impact their professional development and daily life.&lt;/span&gt;
Efficient Non-Parametric Optimizer Search for Diverse Tasks

Efficient and automated design of optimizers plays a crucial role in full-stack AutoML systems. However, prior methods in optimizer search are often limited by their scalability, generability, or sample efficiency. With the goal of democratizing research and application of optimizer search, we present the first efficient, scalable and generalizable framework that can directly search on the tasks of interest. We first observe that optimizer updates are fundamentally mathematical expressions applied to the gradient. Inspired by the innate tree structure of the underlying math expressions, we re-arrange the space of optimizers into a super-tree, where each path encodes an optimizer. This way, optimizer search can be naturally formulated as a path-finding problem, allowing a variety of well-established tree traversal methods to be used as the search algorithm. We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent-form detection that leverage the characteristics of optimizer update rules to further boost the sample efficiency. We provide a diverse set of tasks to benchmark our algorithm and demonstrate that, with only 128 evaluations, the proposed framework can discover optimizers that surpass both human-designed counterparts and prior optimizer search methods.
SGM-Net: Semantic Guided Matting Net

Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hat, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting methods need the help of additional inputs (such as trimap, background image, etc.), or the model with high computational cost and complex network structure, which brings great difficulties to the application of human matting in practice. To alleviate such problems, most existing methods (such as MODNet) use multi-branches to pave the way for matting through segmentation, but these methods do not make full use of the image features and only utilize the prediction results of the network as guidance information. Therefore, we propose a module to generate foreground probability map and add it to MODNet to obtain Semantic Guided Matting Net (SGM-Net). Under the condition of only one image, we can realize the human matting task. We verify our method on the P3M-10k dataset. Compared with the benchmark, our method has significantly improved in various evaluation indicators.
Efficient On-Device Session-Based Recommendation

On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing promising recommendation performance. To fit the powerful neural session-based recommendation models in resource-constrained mobile devices, tensor-train decomposition and its variants have been widely applied to reduce memory footprint by decomposing the embedding table into smaller tensors, showing great potential in compressing recommendation models. However, these model compression techniques significantly increase the local inference time due to the complex process of generating index lists and a series of tensor multiplications to form item embeddings, and the resultant on-device recommender fails to provide real-time response and recommendation. To improve the online recommendation efficiency, we propose to learn compositional encoding-based compact item representations. Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item. Then the composition of the codeword embedding vectors from different embedding matrices (i.e., codebooks) forms the item embedding. Since the size of codebooks can be extremely small, the recommender model is thus able to fit in resource-constrained devices and meanwhile can save the codebooks for fast local inference.Besides, to prevent the loss of model capacity caused by compression, we propose a bidirectional self-supervised knowledge distillation framework. Extensive experimental results on two benchmark datasets demonstrate that compared with existing methods, the proposed on-device recommender not only achieves an 8x inference speedup with a large compression ratio but also shows superior recommendation performance.
On the Adversarial Convex Body Chasing Problem

In this work, we extend the convex bodies chasing problem (CBC) to an adversarial setting, where an agent (the Player) is tasked with chasing a sequence of convex bodies generated adversarially by another agent (the Opponent). The Player aims to minimize the total cost associated with its own movements, while the Opponent tries to maximize the same cost. The set of feasible convex bodies is finite and known to both agents, which allows us to provide performance guarantees with max-min optimality. Under certain assumptions, we show the continuity of the optimal value function, and propose an algorithm to numerically approximate the optimal policies for both the Player and the Opponent within a guaranteed tolerance. Finally, the theoretical results are verified through numerical examples.
On the Choice of Databases in Differential Privacy Composition

Differential privacy (DP) is a widely applied paradigm for releasing data while maintaining user privacy. Its success is to a large part due to its composition property that guarantees privacy even in the case of multiple data releases. Consequently, composition has received a lot of attention from the research community: there exist several composition theorems for adversaries with different amounts of flexibility in their choice of mechanisms. But apart from mechanisms, the adversary can also choose the databases on which these mechanisms are invoked. The classic tool for analyzing the composition of DP mechanisms, the so-called composition experiment, neither allows for incorporating constraints on databases nor for different assumptions on the adversary's prior knowledge about database membership. We therefore propose a generalized composition experiment (GCE), which has this flexibility. We show that composition theorems that hold with respect to the classic composition experiment also hold with respect to the worst case of the GCE. This implies that existing composition theorems give a privacy guarantee for more cases than are explicitly covered by the classic composition experiment. Beyond these theoretical insights, we demonstrate two practical applications of the GCE: the first application is to give better privacy bounds in the presence of restrictions on the choice of databases; the second application is to reason about how the adversary's prior knowledge influences the privacy leakage. In this context, we show a connection between adversaries with an uninformative prior and subsampling, an important primitive in DP. To the best of our knowledge, this paper is the first to analyze the interplay between the databases in DP composition, and thereby gives both a better understanding of composition and practical tools for obtaining better composition bounds.
An Overview of the Data-Loader Landscape: Comparative Performance Analysis

Dataloaders, in charge of moving data from storage into GPUs while training machine learning models, might hold the key to drastically improving the performance of training jobs. Recent advances have shown promise not only by considerably decreasing training time but also by offering new features such as loading data from remote storage like S3. In this paper, we are the first to distinguish the dataloader as a separate component in the Deep Learning (DL) workflow and to outline its structure and features. Finally, we offer a comprehensive comparison of the different dataloading libraries available, their trade-offs in terms of functionality, usability, and performance and the insights derived from them.
Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge

Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph. In this paper, we show that it can also be used to explain data differentials, for example in the context of Explainable AI (XAI), and we show that it can in fact be done in a way that is meaningful to a human observer. Our approach utilizes a large class hierarchy, curated from the Wikipedia category hierarchy, as background knowledge.
mRobust04: A Multilingual Version of the TREC Robust 2004 Benchmark

Robust 2004 is an information retrieval benchmark whose large number of judgments per query make it a reliable evaluation dataset. In this paper, we present mRobust04, a multilingual version of Robust04 that was translated to 8 languages using Google Translate. We also provide results of three different multilingual retrievers on this dataset. The dataset is available at https://huggingface.co/datasets/unicamp-dl/mrobust