Reviewerly/exHarmony huggingface dataset:
Video-guided machine translation via dual-level back-translation

Video-guided machine translation aims to translate a source language description into a target language using the video information as additional spatio-temporal context. Existing methods focus on making full use of videos as auxiliary material, while ignoring the semantic consistency and reducibility between the source language and the target language. In addition, the visual concept is helpful for improving the alignment and translation of different languages but is rarely considered. Toward this end, we contribute a novel solution to thoroughly investigate the video-guided machine translation issue via dual-level back-translation. Specifically, we first exploit a sentence-level back-translation to obtain the coarse-grained semantics. Thereafter, a video concept-level back-translation module is proposed to explore the fine-grained semantic consistency and reducibility. Lastly, a multi-pattern joint learning approach is utilized to boost the translation performance. By experimenting on two real-world datasets, we demonstrate the effectiveness and rationality of our proposed solution.
The impact of heterogeneous distance functions on missing data imputation and classification performance

This work performs an in-depth study of the impact of distance functions on K-Nearest Neighbours imputation of heterogeneous datasets. Missing data is generated at several percentages, on a large benchmark of 150 datasets (50 continuous, 50 categorical and 50 heterogeneous datasets) and data imputation is performed using different distance functions (HEOM, HEOM-R, HVDM, HVDM-R, HVDM-S, MDE and SIMDIST) and k values (1, 3, 5 and 7). The impact of distance functions on kNN imputation is then evaluated in terms of classification performance, through the analysis of a classifier learned from the imputed data, and in terms of imputation quality, where the quality of the reconstruction of the original values is assessed. By analysing the properties of heterogeneous distance functions over continuous and categorical datasets individually, we then study their behaviour over heterogeneous data. We discuss whether datasets with different natures may benefit from different distance functions and to what extent the component of a distance function that deals with missing values influences such choice. Our experiments show that missing data has a significant impact on distance computation and the obtained results provide guidelines on how to choose appropriate distance functions depending on data characteristics (continuous, categorical or heterogeneous datasets) and the objective of the study (classification or imputation tasks).
An opinion piece on the dos and don’ts of artificial intelligence in civil engineering and charting a path from data-driven analysis to causal knowledge discovery

Artificial intelligence (AI) has been established as a universal language for solving science and engineering problems. Despite the rise of big data, the success of AI in parallel fields, and exciting works published at this frontier, some in the civil engineering community tie AI to a mystique stigma. And yet, there is also ever-growing inertia to embrace AI fully. The mystique of AI arises because (1) AI is not typically taught in a traditional civil engineering curriculum, (2) the majority of civil engineers remain appliers (as opposed to creators) of AI, and (3) commonly adopted AI algorithms leverage blackbox methods – the opposite to that commonly accepted in the civil engineering domain. We write this opinion piece with the aim of presenting a holistic look into the dos and don’ts of adopting AI into civil engineering.
Active Learning for Reducing Labeling Effort in Text Classification Tasks

Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on AL in a text classification setting and next to none has involved the more recent, state-of-the-art Natural Language Processing (NLP) models. Here, we present an empirical study that compares different uncertainty-based algorithms with BERT $$_{base}$$ as the used classifier. We evaluate the algorithms on two NLP classification datasets: Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore heuristics that aim to solve presupposed problems of uncertainty-based AL; namely, that it is unscalable and that it is prone to selecting outliers. Furthermore, we explore the influence of the query-pool size on the performance of AL. Whereas it was found that the proposed heuristics for AL did not improve performance of AL; our results show that using uncertainty-based AL with BERT $$_{base}$$ outperforms random sampling of data. This difference in performance can decrease as the query-pool size gets larger.
High-Resolution Representations Network for Single Image Dehazing

Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose an image dehazing network based on the high-resolution network, called DeHRNet. The high-resolution network originally used for human pose estimation. In this paper, we make a simple yet effective modification to the network and apply it to image dehazing. We add a new stage to the original network to make it better for image dehazing. The newly added stage collects the feature map representations of all branches of the network by up-sampling to enhance the high-resolution representations instead of only taking the feature maps of the high-resolution branches, which makes the restored clean images more natural. The final experimental results show that DeHRNet achieves superior performance over existing dehazing methods in synthesized and natural hazy images.
A state-of-the-art review: Solar trackers

Solar energy is the most widely used renewable energy source because of its versatility and its presence worldwide. Although, these systems have many promising features, they have numerous challenges to be addressed. This paper aims to present a compressive literature survey on the advancement of photovoltaic (PV) systems in terms of materials used, various module geometries, and tracking systems that are used to improve the efficiency of the overall PV solar systems. A particular attention is given on the factors affecting the efficiency of the PV system and efficiency improvement using solar tracking systems. To this end, various tracking systems namely, single axis and dual axis tracking systems mechanisms and their advantages and disadvantages are elaborated. Numerous periodicals related to solar tracking systems have been underlined and performance assessment of different tracking systems has been discussed. Moreover, some of the future trends in PV technologies such as bifacial PV systems are stated.
An English Teaching Ability Evaluation Model Based on Edge Computing

One of the main basic components of the future generation of intelligent combat equipment is the edge computing platform. Evaluate the strategy and describe the test methods and key performance indicators. The method attempts will help to combine big data, intelligent computing models with typical military application scenarios, and conduct targeted research on the capability evaluation methods of embedded edge computing platforms. Aiming at the traditional evaluation method that uses manual evaluation, there is a great subjective consciousness in the evaluation process, which causes the problem of low accuracy of evaluation. A feedback model-based English teaching ability evaluation method is proposed. The use of a feedback mechanism in the English classroom can significantly increase the comparison of ability parameters. The open-minded calculation of the feedback evaluation is used to evaluate the various abilities in the English teaching process. The indications are presented as numbers, and an evaluation feedback model is built to verify the evaluation’s completeness and accuracy. In order to ensure the accuracy of the designed evaluation method of English teaching ability based on the feedback model, the simulated classroom teaching environment is simulated for comparison experiments, and the effectiveness of the designed evaluation method can be verified through the effective analysis of experimental data.
Non-Maximum Suppression Performs Later in Multi-Object Tracking

Multi-object tracking aims to assign a uniform ID for the same target in continuous frames, which is widely used in autonomous driving, security monitoring, etc. In the previous work, the low-scoring box, which inevitably contained occluded target, was filtered by Non-Maximum Suppression (NMS) in a detection stage with a confidence threshold. In order to track occluded target effectively, in this paper, we propose a method of NMS performing later. The NMS works in tracking rather than the detection stage. More candidate boxes that contain the occluded target are reserved for trajectory matching. In addition, unrelated boxes are discarded according to the Intersection over Union (IOU) between the predicted and detected box. Furthermore, an unsupervised pre-trained person re-identification (ReID) model is applied to improve the domain adaptability. In addition, the bicubic interpolation is used to increase the resolution of low-scoring boxes. Extensive experiments on the MOT17 and MOT20 datasets have proven the effectiveness of tracking occluded targets of the proposed method, which achieves an MOTA of 78.3%.
A systematic method of constructing weightwise almost perfectly balanced Boolean functions on an arbitrary number of variables

Boolean functions satisfying good cryptographic criteria when restricted to the set of vectors with constant Hamming weight play an important role in the well-known FLIP stream cipher. In this paper, we present a systematic method of constructing weightwise almost perfectly balanced Boolean functions on an arbitrary number of variables, which equal the direct sum of several known weightwise perfectly balanced Boolean functions. At the same time, we show two concrete constructions of weightwise almost perfectly balanced Boolean functions, whose k-weight nonlinearities and algebraic immunities are discussed at the end of this paper.
A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning

As an emerging artificial intelligence technology, federated learning plays a significant role in privacy preservation in machine learning, although its main objective is to prevent peers from peeping data. However, attackers from the outside can steal metadata in transit and through data reconstruction or other techniques to obtain the original data, which poses a great threat to the security of the federated learning system. In this paper, we propose a differential privacy strategy including encryption and decryption methods based on local features of non-Gaussian noise, which aggregates the noisy metadata through a sequential Kalman filter in federated learning scenarios to increase the reliability of the federated learning method. We name the local features of non-Gaussian noise as the non-Gaussian noise fragments. Compared with the traditional methods, the proposed method shows stronger security performance for two reasons. Firstly, non-Gaussian noise fragments contain more complex statistics, making them more difficult for attackers to identify. Secondly, in order to obtain accurate statistical features, attackers must aggregate all of the noise fragments, which is very difficult due to the increasing number of clients. We conduct experiments that demonstrate that the proposed method can greatly enhanced the system's security.
Systematic Application of Extreme-User Experiences: Impact on the Outcomes of an Undergraduate Medical Device Design Module

Abstract Extreme-user experiences refer to experiences that simulate the extremes of user abilities like reduced or no visual attention or auditory attention. Inspired by the needs experienced by the users who experience physical or cognitive challenges, extreme-user perspectives can make designers understand their designs from an inclusive design perspective and address the latent needs experienced by their users. Yet, they are seldom leveraged in mainstream designs that may or may not be used by extreme users, for example, medical devices. This study applies the extreme-user experiences along the initial stages of the design process to help design students identify the latent needs of six different medical devices. Students used Activity Diagrams for a systematic application of extreme-user experiences. Six teams with a total of 25 students took part in the study. We apply a latency metric to validate the identified needs and discuss the metric's impact in evaluating the latency of design needs. Outcomes support the potential of extreme-user experiences in capturing the latent design needs in medical device design and demonstrate the potential of extreme-user experiences in changing designers' perceptions over their design solutions. The proposed approach aims to help students and medical device design professionals to have a first-person experience on the nuances of user needs that get missed in the current design process. We believe this could lead to future works that focus on designs that reduce the mishaps associated with medical device design.
Fair Sampling Error Analysis on NISQ Devices

We study the status of fair sampling on Noisy Intermediate Scale Quantum (NISQ) devices, in particular the IBM Q family of backends. Using the recently introduced Grover Mixer-QAOA algorithm for discrete optimization, we generate fair sampling circuits to solve six problems of varying difficulty, each with several optimal solutions, which we then run on twenty backends across the IBM Q system. For a given circuit evaluated on a specific set of qubits, we evaluate: how frequently the qubits return an optimal solution to the problem, the fairness with which the qubits sample from all optimal solutions, and the reported hardware error rate of the qubits. To quantify fairness, we define a novel metric based on Pearson's $\chi^2$ test. We find that fairness is relatively high for circuits with small and large error rates, but drops for circuits with medium error rates. This indicates that structured errors dominate in this regime, while unstructured errors, which are random and thus inherently fair, dominate in noisier qubits and longer circuits. Our results show that fairness can be a powerful tool for understanding the intricate web of errors affecting current NISQ hardware.
Multi-user Security of the Elephant v2 Authenticated Encryption Mode

One of the finalists in the NIST Lightweight Cryptography competition is $$\mathsf {Elephant}$$ v2, a parallelizable, permutation-based authenticated encryption scheme. The original first/second-round submission $$\mathsf {Elephant}$$ v1/v1.1 was proven secure against nonce-respecting adversaries in the single-user setting. For the final round, the mode has undergone certain subtle modifications, the most important one being a change in the authentication portion of the mode. These changes require a new dedicated security proof. In this work, we prove the security of the $$\mathsf {Elephant}$$ v2 mode. First of all, our proof shows that $$\mathsf {Elephant}$$ v2 is indeed a secure authenticated encryption scheme and that its security against nonce-respecting adversaries is on par with that of $$\mathsf {Elephant}$$ v1/v1.1. In addition, our security analysis is in the multi-user setting and demonstrates that $$\mathsf {Elephant}$$ v2 fares well if multiple devices use $$\mathsf {Elephant}$$ v2 with independent keys. Moreover, our proof shows that $$\mathsf {Elephant}$$ v2 even ensures authenticity under nonce misuse.
Savitzky Golay and KPCA based Optimal Discrete Wavelet Transform Architecture for Image Compression

• In pre-processing, Savitzky Golay filtering technique (SGFT) is utilized to eliminate the noise signals thereby generating accurate results. • Utilizing DWT along with Kernel principal component analysis and Iterative artificial ecosystem optimization algorithm for post-processing thus obtaining an optimal reconstructed image with high quality. • Comparing our proposed approach with various other techniques to determine the effectiveness of the system. In recent years, the DWT acts as an effective tool in image compression and decomposition. There exist numerous transform techniques for compressing images but there occurs information loss and low picture quality. To overcome such drawbacks this paper aims in developing an optimal reconstructed image with high quality. The proposed approach comprises two significant phases namely the pre-processing and post-processing. During pre-processing, the multispectral images are enhanced by removing the noise signals from the input image. In this paper, a Savitzky Golay filter is employed to eliminate noise signals and smoothing the multi-spectral image for the generation of more accurate results. The pre-processed image is then subjected to post-processing. In post-processing, the DWT along with Kernel principal component analysis and Iterative artificial ecosystem optimization algorithm is employed to obtain an optimal reconstructed image with high quality and without losing the information. In addition to this, the proposed model is trained using aerial images obtained from the SIPI image database. The simulation metrics are evaluated for various approaches and the results reveal that the proposed approach outperforms other approaches.
Consistent fusion method with uncertainty elimination for distributed multi-sensor systems

In this study, the elimination of correlated errors with an unknown correlation in distributed fusion is investigated, and a consistent fusion method for distributed multi-sensor systems is proposed. Unlike most existing fusion methods, the proposed method guarantees the consistency of fusion results without requiring system model parameters or adopting conservative strategies. First, a universal bijection is used to quantify the uncertainty in the estimates to be fused based on the entropy of the independent scalars. Second, the correlated errors caused by unknown mutual information and common process noise are treated as avoidable uncertainties. The avoidable uncertainty is then estimated by using a similarity function based on the Kullback–Leibler divergence. Finally, the avoidable uncertainty is separated from the fusion results by employing a conditional probability model to avoid correlated errors. This method is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method and the inverse covariance intersection method. The simulation results further verify the superiority of the proposed method in terms of the consistency, accuracy, and ability to limit cumulative errors in sequential fusion processes.
Character sequence prediction method for training data creation in the task of text recognition

For text line recognition, much attention is paid to augmentation of the training images. Yet the inner structure of the textual information in the images also affects the accuracy of the resulting model. In this paper, we propose an ANNbased method for textual data generation for printing in images with a background of a synthetic training sample. In our method we avoid the usage of completely random sequences as well as the dictionary-based ones. As a result, we gain the data that saves the basic properties of the target language model, such as the balance of vowels and consonants, but avoid the lexicon-based properties, like the prevalence of the specific characters. Moreover, as our method focuses only on high-levels features and does not try to generate the real words, we can use a small training sample and light-weight ANN for text generation. To check our method, we train three ANNs with same architecture, but with different training samples. We choose machine readable zones as a target field because of their structure that does not correspond with the ordinary lexicon. The results of the experiments on three public datasets of identity documents demonstrate the effectiveness of our method and allows to enhance the state-of-the art results for the target field.
Security methods for AI based COVID-19 analysis system : A survey

Rapid progress and widespread outbreak of COVID-19 have caused devastating influence on the health systems all around the world. The importance of countermeasures to tackle this problem lead to widespread use of Computer Aided Diagnosis (CADs) applications using deep neural networks. The unprecedented success of machine learning techniques, especially deep learning networks in medical images, have led to their recent prominence in improving efficient diagnosis of COVID-19 with increased detection accuracy. However, recent studies in the field of security of AI-based systems revealed that these deep learning models are vulnerable to adversarial attacks. Adversarial examples generated by attack algorithms are not recognizable by the human eye and can easily deceive the state-of-the-art deep learning models, therefore they threaten security-critical learning applications. In this paper, the methodology, results and concerns of recent works on robustness of AI based COVID-19 systems are summarized and discussed. We explore important security concerns related to deep neural networks and review current state-of-the-art defense methods to prevent performance degradation.
Additive Complementary Dual Codes From Group Characters

Additive codes have become an increasingly important topic in algebraic coding theory due to their applications in quantum error-correction and quantum computing. Linear Complementary Dual (LCD) codes play an important role for improving the security of information against certain attacks. Motivated by these facts, we define additive complementary dual codes (ACD for short) over a finite abelian group in terms of an arbitrary duality on the ambient space and examine their properties. We show that the best minimum weight of ACD codes is always greater than or equal to the best minimum weight of LCD codes of the same size and that this inequality is often strict. We give some matrix constructions for quaternary ACD codes from a given quaternary ACD code and also from a given binary self-orthogonal code. Moreover, we construct an algorithm to determine if a given quaternary additive code is an ACD code with respect to all possible symmetric dualities. We also determine the largest minimum distance of quaternary ACD codes for lengths <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n \leq 10$ </tex-math></inline-formula> . The obtained codes are either optimal or near optimal according to Bierbrauer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> +. (2009).
Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images

This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values.