Reviewerly/exHarmony huggingface dataset:
CRC-Based Correction of Multiple Errors Using an Optimized Lookup Table

In this paper, we propose a new approach to perform multiple error correction in wireless communications over error-prone networks.It is based on the cyclic redundancy check syndrome, using an optimized lookup table that avoids performing arithmetic operations.This method is able to achieve the same correction performance as the state-of-the-art approaches while significantly reducing the computational complexity.The table is designed to allow multiple bit error correction simply by navigating within it.Its size is constant when considering more than two errors, which represents a tremendous advantage over earlier lookup table-based approaches.Simulation results of a C implementation performed on a Raspberry Pi 4 show that the proposed method is able to process single and double error corrections of large payloads in 100 ns and 642 µs, respectively, while it would take 300 µs and 1.5 s, respectively, with the state-of-the-art CRC multiple error correction technique.This represents a speedup of nearly 3000× × × for single error and 2300× × × for double error correction, respectively.Compared to table-based approaches, the proposed method offers a speedup of nearly 1200× × × for single error and 2300× × × for double error correction under the same conditions.We also show that when multiple candidate error patterns are present, numerous errors can be corrected by adding a checksum cross-validation step.
The effectiveness of augmented reality for English vocabulary instruction of Greek students with intellectual disability

For students with intellectual disability the need for effective teaching strategies in Foreign Language Learning is imperative. In recent years, the advent of Augmented Reality (AR) in Special Education has shown promising results. In the present research an AR intervention for English vocabulary instruction of three students with intellectual disability (ID) was implemented. A multiple probe across participants design was used to examine the effectiveness of an AR intervention on the students’ responses regarding identified correct vocabulary items in English as a Foreign Language (EFL) in two thematic categories- food and animals. Results showed a functional relationship between the correct responses made during probe sessions and the implementation of the AR intervention for all three students. In addition, a follow up phase was conducted in order to evaluate the maintained outcomes, suggesting that there was a positive maintenance effect. Social validity was also assessed, indicating that the intervention package was practical and useful. Limitations of the present study and suggestions for future research are also discussed.
Age Estimation Based on Facial Images Using Hybrid Features and Particle Swarm Optimization

Face images provide significant biological information. Face age estimation is one of the primary research directions in face images. The appearance of the face changes dynamically, and these changes are influenced by a variety of factors such as light, aging, makeup, beard, etc. The human face has many characteristics, including emotions, sex, race, age, etc. Face age estimation has numerous applications in biometrics, security, commercial, military, interaction with computers, and providing services to the individual. In this paper, the age estimation system is divided into four distinct stages. The first step is to extract local features including Gabor wavelets (GW), Local Binary Pattern (LBP), Local Phase Quantization (LPQ), and Histogram of Oriented Gradients (HOG). These attributes are then combined in the second step as a feature fusion method, which combines four different feature extraction methods. In the following step, Particle Swarm Optimization (PSO) as a meta-heuristic optimization algorithm is used to decrease the size of attributes and find optimal features. Finally, we used classification and regression methods to estimate the age and age groups. Initially, we used support vector machines (SVMs) to determine the age class, and then used support vector regression (SVRs) to estimate the ages within those groups. Finally, our algorithm was examined on two widely used databases, i.e. the FG-NET and the MORPH, to determine its effectiveness regarding aging estimates. In the FGNET dataset, we achieved an MAE of 3.34 years and 75.69 percent classification accuracy, and in the MORPH dataset, we obtained an MAE of 3.21 years and 81.66 percent classification accuracy.
Streaming Variational Monte Carlo

Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. We develop a novel online learning framework, leveraging variational inference and sequential Monte Carlo, which enables flexible and accurate Bayesian joint filtering. Our method provides an approximation of the filtering posterior which can be made arbitrarily close to the true filtering distribution for a wide class of dynamics models and observation models. Specifically, the proposed framework can efficiently approximate a posterior over the dynamics using sparse Gaussian processes, allowing for an interpretable model of the latent dynamics. Constant time complexity per sample makes our approach amenable to online learning scenarios and suitable for real-time applications.
Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on health care. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically provide rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested before for asthma outcome prediction but not for COPD outcome prediction.This study aims to assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations.The patient cohort included all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019. In a secondary analysis of 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model's predictions and suggest tailored interventions.Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months and the predictions for 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months.Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope to use it to facilitate future clinical use of our model.RR2-10.2196/13783.
Viewing Graph Solvability via Cycle Consistency

In structure-from-motion the viewing graph is a graph where vertices correspond to cameras and edges represent fundamental matrices.We provide a new formulation and an algorithm for establishing whether a viewing graph is solvable, i.e. it uniquely determines a set of projective cameras.Known theoretical conditions either do not fully characterize the solvability of all viewing graphs, or are exceedingly hard to compute for they involve solving a system of polynomial equations with a large number of unknowns.The main result of this paper is a method for reducing the number of unknowns by exploiting the cycle consistency.We advance the understanding of the solvability by (i) finishing the classification of all previously undecided minimal graphs up to 9 nodes, (ii) extending the practical solvability testing up to minimal graphs with up to 90 nodes, and (iii) definitely answering an open research question by showing that the finite solvability is not equivalent to the solvability.Finally, we present an experiment on real data showing that unsolvable graphs are appearing in practical situations.
Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Computer vision is one of the hottest research directions in artificial intelligence at present, and its research goal is to give computers the ability to perceive and cognize their surroundings from a single image. Image recognition is an important research direction in the field of computer vision, which has important research significance and application value in industrial applications such as video surveillance, biometric identification, unmanned vehicles, human-computer interaction, and medical image recognition. In this article, we propose an end-to-end, pixel-to-pixel IoT-oriented fuzzy support tensor product adaptive image classification method. Considering the problem that traditional support tensor product classification methods are difficult to directly produce pixel-to-pixel classification results, the research is based on the idea of inverse convolution network design, which directly outputs dense pixel-by-pixel classification results for images to be classified of arbitrary size to achieve true end-to-end and pixel-to-pixel high-score image classification and improve the efficiency of support tensor product models for high-score image classification on a pixel-by-pixel basis. Moreover, considering that network supervised classification training using deep learning requires a large amount of labeled data as true values and obtaining a large number of labeled data sources is a difficult problem in the field of image classification, this article proposes using a large amount of unlabeled high-resolution remote sensing images for learning generic structured features through unsupervised to assist the labeled high-resolution remote sensing images for better-supervised feature extraction and classification training. By finding a balance between generic structural feature learning of images and differentiated feature learning related to the target class, the dependence of supervised classification on the number of labeled samples is reduced, and the network robustness of the support tensor product algorithm is improved under a small number of labeled training samples.
MBA-VO: Motion Blur Aware Visual Odometry

Motion blur is one of the major challenges remaining for visual odometry methods. In low-light conditions where longer exposure times are necessary, motion blur can appear even for relatively slow camera motions. In this paper we present a novel hybrid visual odometry pipeline with direct approach that explicitly models and estimates the camera’s local trajectory within the exposure time. This allows us to actively compensate for any motion blur that occurs due to the camera motion. In addition, we also contribute a novel benchmarking dataset for motion blur aware visual odometry. In experiments we show that by directly modeling the image formation process, we are able to improve robustness of the visual odometry, while keeping comparable accuracy as that for images without motion blur. Both the code and the datasets can be found from https://github.com/ethliup/MBA-VO.
NASPipe: high performance and reproducible pipeline parallel supernet training via causal synchronous parallelism

Supernet training, a prevalent and important paradigm in Neural Architecture Search, embeds the whole DNN architecture search space into one monolithic supernet, iteratively activates a subset of the supernet (i.e., a subnet) for fitting each batch of data, and searches a high-quality subnet which meets specific requirements. Although training subnets in parallel on multiple GPUs is desirable for acceleration, there inherently exists a race hazard that concurrent subnets may access the same DNN layers. Existing systems support neither efficiently parallelizing subnets’ training executions, nor resolving the race hazard deterministically, leading to unreproducible training procedures and potentiallly non-trivial accuracy loss.
The Way to Modern Shutter Speed Measurement Methods: A Historical Overview

Exposure time is a fundamental parameter for the photographer when the photo is composed, and the exact length of the exposure may be an essential determinant of performance in certain camera-based applications, e.g., optical camera communication (OCC) systems. There can be several reasons to measure the shutter speed of a camera: shutter speed may be checked at the time of manufacturing; it may be necessary to recheck in case of an elder camera model; it may be necessary to be measured if its exact value is not provided by the manufacturer; or a precise measurement may be necessary for a demanding application. In this paper various methods for shutter speed measurement are reviewed, presenting and analyzing methods that are still relevant today either for manufacturers, service personnel, amateur photographers, or the developers of camera-based systems. Each presented method is illustrated by real measurement results and the performance properties of the methods are also presented.
New differential cryptanalysis results for the lightweight block cipher BORON

BORON is a 64-bit lightweight block cipher based on the substitution–permutation network that supports an 80-bit (BORON-80) and 128-bit (BORON-128) secret key. In this paper, we revisit the use of differential cryptanalysis on BORON in the single-key model. Using an SAT/SMT approach, we look for differentials that consist of multiple differential characteristics with the same input and output differences. Each characteristic that conforms to a given differential improves its overall probability. We also implemented the same search using Matsui's algorithm for verification and performance comparison purposes. We identified high-probability differentials which were then used in key recovery attacks against BORON-80/128. We used 8-round differentials with a probability of 2−58.16 and 2−62.42 in key recovery attacks against 9 and 10 rounds of BORON-80 and BORON-128 with time/data/memory complexities of 259.18/259.16/224 and 2111.34/263.42/271 respectively. Our key recovery framework provides a more accurate estimate of the attack complexity as compared to previous work. The attacks proposed in this paper are the best differential attacks against BORON-80/128 in the single-key model to date.
Multi-level Convolutional Transformer with Adaptive Ranking for Semi-supervised Crowd Counting

Most existing crowd counting methods have focused on pure convolutional neural network based supervised algorithms. Although these methods have attained good results on some datasets, they still encounter several common problems. The cost of labeling annotations for supervised methods is huge and the shortage of labeled datasets limits the further development of supervised algorithms for crowd counting. Meanwhile, pure CNN-based algorithms have certain limitations in building the connections among these features. To overcome those problems, we proposed a semi-supervised crowd counting algorithm that is a mixture model of CNN and transformer. Specifically, our method consists of two parts Multi-Level Convolutional Transformer (MLCT) and Adaptive Scale Module (ASM). MLCT is the counting branch, with its front end and back end being the CNN and the transformer, respectively. ASM outputs an adaptive scale factor for the unlabeled crowd images. We generate a ranking list based on this factor, which is fed into the MLCT and computes loss by the order of the list. Different from most crowd counting methods, we use a region-level regression target for labeled images, which is a weaker regression approach than the location regression. Furthermore, We train the entire model using a novel loss function that combines L1 loss and ranking loss. Experimental results on the three challenging datasets ShanghaiTech Part A, ShanghaiTech Part B, and UCF-QNRF have all demonstrated the effectiveness of the proposed approach.
Task-driven joint dictionary learning model for multi-view human action recognition

Traditional self-similarity matrices (SSMs) show outstanding performance in multi-view human action recognition except when the view change becomes large enough. To address this problem, a joint dictionary learning (JDL) algorithm based on joint sparse constraint is presented for trading off the contribution to the sparse features from different views. Unfortunately, the dictionaries and classifiers are trained separately. To enhance the performance of the JDL method, the dictionaries and classifiers can be trained simultaneously in this paper. A task-driven joint dictionary learning model (TJDL) is formulated under the joint sparse constraint. In TJDL, the view-shared dictionary, view-specific dictionaries, linear transformation matrices, action classifiers, view-shared sparse codes, and view-specific sparse codes are learned jointly by a coordinate descent algorithm. Finally, the experimental results over three benchmark datasets show that the proposed TJDL algorithm can achieve superior performance, compared to the recent state-of-the-art methods.
Spatial sampling, data models, spatial scale and ontologies: Interpreting spatial statistics and machine learning applied to satellite optical remote sensing

This paper summarizes the development and application of spatial statistical models in satellite optical remote sensing. The paper focuses on the development of a conceptual model that includes the measurement and sampling processes inherent in remote sensing. We organized this paper into five main sections: introducing the basis of remote sensing, including measurement and sampling; spatial variation, including variation through the object-based data model; advances in spatial statistical modelling; machine learning and explainable AI; a hierarchical ontological model of the nature of remotely sensed scenes. The paper finishes with a summary. We conclude that optical remote sensing provides an important source of data and information for the development of spatial statistical techniques that, in turn, serve as powerful tools to obtain important information from the images.
Auto-generation of Smart Contracts from a Domain-Specific XML-Based Language

Smart contracts are a means of facilitating, verifying and enforcing digital agreements. Blockchain technology, which includes an inherent consensus mechanism and programming languages, enables the concept of smart contracts. However, smart contracts written in an existing language, such as Solidity, Vyper, and others, are difficult for domain stakeholders and programmers to understand in order to develop code efficiently and without error, owing to a conceptual gap between the contractual provisions and the respective code. Our study addresses the problem by creating smart legal contract markup language (SLCML), an XML-based smart-contract language with pattern and transformation rules that automatically convert XML code to the Solidity language. In particular, we develop an XML schema (SLCML schema) that is used to instantiate any type of business contract understandable to IT and non-IT practitioners and is processed by computers. To reduce the effort and risk associated with smart contract development, we advocate a pattern for converting SLCML contracts to Solidity smart contracts, a smart contractual oriented computer language. We exemplify and assess our SLCML and transformation approach by defining a dairy supply chain contract based on real-world data.
Context, relevance, and labor

Abstract Since information science concerns the transmission of records, it concerns context. The transmission of documents ensures their arrival in new contexts. Documents and their copies are spread across times and places. The amount of labor required to discover and retrieve relevant documents is also formulated by context. Thus, any serious consideration of communication and of information technologies quickly leads to a concern with context, relevance, and labor. Information scientists have developed many theories of context, relevance, and labor but not a framework for organizing them and describing their relationship with one another. We propose the words context and relevance can be used to articulate a useful framework for considering the diversity of approaches to context and relevance in information science, as well as their relations with each other and with labor.
Financially Backed Covert Security

The security notion of covert security introduced by Aumann and Lindell (TCC'07) allows the adversary to successfully cheat and break security with a fixed probability $$1-\epsilon $$ , while with probability $$\epsilon $$ , honest parties detect the cheating attempt. Asharov and Orlandi (ASIACRYPT'12) extend covert security to enable parties to create publicly verifiable evidence about misbehavior that can be transferred to any third party. This notion is called publicly verifiable covert security (PVC) and has been investigated by multiple works. While these two notions work well in settings with known identities in which parties care about their reputation, they fall short in Internet-like settings where there are only digital identities that can provide some form of anonymity. In this work, we propose the notion of financially backed covert security (FBC), which ensures that the adversary is financially punished if cheating is detected. Next, we present three transformations that turn PVC protocols into FBC protocols. Our protocols provide highly efficient judging, thereby enabling practical judge implementations via smart contracts deployed on a blockchain. In particular, the judge only needs to non-interactively validate a single protocol message while previous PVC protocols required the judge to emulate the whole protocol. Furthermore, by allowing an interactive punishment procedure, we can reduce the amount of validation to a single program instruction, e.g., a gate in a circuit. An interactive punishment, additionally, enables us to create financially backed covert secure protocols without any form of common public transcript, a property that has not been achieved by prior PVC protocols.