Reviewerly/exHarmony huggingface dataset:
The Challenges of Teaching and Assessing Technical Translation in an Era of Neural Machine Translation

Teaching translation in higher education has undeniably been impacted by the innovations brought about by machine translation (MT), more particularly neural machine translation (NMT). This influence has become significantly more noticeable in recent years, as NMT technology progresses hand in hand with artificial intelligence. A case study supported by a questionnaire conducted among translation students (bachelor’s and master’s programmes at ISCAP) probed the degree of student satisfaction with CAT tools and revealed that they favour the use of MT in their translation practices, focusing their work on post-editing tasks rather than exploring other translation strategies and complementary resources. Although MT cannot be disregarded in translation programmes, as machine-generated translations make up an increasingly larger amount of a professional translator’s output, the widespread use of MT by students poses new challenges to translators’ training, since it becomes more difficult to assess students’ level of proficiency. Translation teachers must not only adapt their classroom strategies to accommodate these current translation strategies (NMT) but also, as intended by this study, find new, adequate methods of training and assessing students that go beyond regular translation assignments while still ensuring that students acquire the proper translation competence. Thus, as the use of NMT makes it considerably more challenging to assess a student’s level of translation competence, it is necessary to introduce other activities that not only allow students to acquire and develop their translation competence as defined in the EMT (European Masters in Translation) framework but also enable teachers to assess students more objectively. Hence, this article foregrounds a set of activities usually regarded as “indirect tasks” for technical translation courses that hopefully results in the development of student translation skills and competence, as well as provides more insights for teachers on how to more objectively assess students. It is possible, then, to conclude that these activities, such as different types of paraphrasing and error-detection tasks, may have the potential to encourage creative thinking and problem-solving strategies, giving teachers more resources to assess students’ level of translation competence.
Small Training Datasets for Deep Learning Based Medical Diagnosis

In a computer-aided diagnosis framework binary, patch-based classification of malaria from thick blood smear images is investigated. Following a state-of-the-art study on malaria, deep learning and transfer learning, classical networks like ResNet are being adapted and optimized for training by the public dataset of the Automated Laboratory Diagnostics project of John A. Quinn et al. at Makerere University, Uganda. The dataset comprises 2,703 thick blood smear images treated with Fields stain. Furthermore models are cross-validated on other thick and thin blood smear datasets treated with Giemsa stain. Data augmentation with zoomed-in versions of the image tiles in the training dataset and the use of shallow models with relatively few network parameters is evaluated as a solution to the problem of small training databases. Using grid search, optimum hyperparameters for learning rate scheduling are found empirically for each network architecture. The self-developed deep learning models achieve a TP (True-Positive) rate of 98% at a FP (False-Positive) rate of 0.9% on the thick blood smear images and a TP rate of 90% at a FP rate of 8% on the thin blood smear images. The maximum validation accuracy over the set of models and hyperparameters is 99.3%.
Microlearning with Chatbots

This chapter introduces and defines learning chatbots and explores the role they play in language teaching and learning. It includes microlearning activities making use of chatbots that practitioners can incorporate into their classes.
Convolution Filter Equivariance/Invariance in Convolutional Neural Networks: A Survey

Models parameterized by Convolutional Neural Networks (CNNs), reportedly, have garnered a commanding position in learning based multidimensional signal processing. This feat is achieved to a large extent through the translation equivariance property of the convolutional filters employed. While popular, the required optimum level of model parameters, the limited availability of signal datasets, robustness of the model to input signal transformations, and generalization to out of distribution signal sets are some of the challenges of CNN models. The incorporation of equivariance or consistency to symmetry transformations in which invariance is a special case, have been suggested to not only alleviate these challenges but also bring about model interpretability. This work presents a systematic survey of the equivariant convolutional filter design for convolutional neural networks in image processing applications with the aim of bringing forth design methods in terms of group theory, discrete Fourier transform, discrete cosine transform, and wavelet transform. The theoretical foundations for translation, rotation, scale and affine equivariance under nonlinear transform, their efficient implementations especially in natural image processing applications has been thoroughly discussed.
Enhancing Single-Image Super-Resolution using Patch-Mosaic Data Augmentation on Lightweight Bimodal Network

With the advancement of deep learning, single-image super-resolution (SISR) has made significant strides. However, most current SISR methods are challenging to employ in real-world applications because they are doubtlessly employed by substantial computational and memory costs caused by complex operations. Furthermore, an efficient dataset is a key factor for bettering model training. The hybrid models of CNN and Vision Transformer can be more efficient in the SISR task. Nevertheless, they require substantial or extremely high-quality datasets for training that could be unavailable from time to time. To tackle these issues, a solution combined by applying a Lightweight Bimodal Network (LBNet) and Patch-Mosaic data augmentation method which is the enhancement of CutMix and YOCO is proposed in this research. With patch-oriented Mosaic data augmentation, an efficient Symmetric CNN is utilized for local feature extraction and coarse image restoration. Plus, a Recursive Transformer aids in fully grasping the long-term dependence of images, enabling the global information to be fully used to refine texture details. Extensive experiments have shown that LBNet with the proposed data augmentation with zero-free additional parameters method outperforms the original LBNet and other state-of-the-art techniques in which image-level data augmentation is applied.
Privacy Preserving Queries of Shortest Path Distances

Consider a user with a very limited hardware and internet connection who wants to query a shortest path distance from a web service, but doesn’t want to reveal the source and destination to the server. Using state-of-the-art methods, we show that we can privately query shortest path distances in this case, if we are allowed to use three non-cooperating servers of moderate compute and communication power. We argue that this is not possible with classical shortest path algorithms. Finally, we give some experiments showing the feasibility of the approach.
Capturing Image–Text Modal Relations in Mobile Instant Messaging Through WhatsApp Diaries

Polymedia chat applications such as WhatsApp are facilitating ‘mixed-media’ relationships (Parks, 2017, Communication Research, 4, 505) as they have penetrated our everyday mediated interactions. This has led to calls for a deeper probe into the interconnections of the varied modes. After defining its vital term ‘mode’, the current study focuses on two widely used modes—image and text—in everyday mediated interactions via chat applications. In order to study their interconnections, the study adopts Martinec’s (2005) image–text relation systems. The findings indicate two extreme range points of image–text interconnections in mediated chat app-based interactions. At one extreme point, images and texts seem to be repeating each other’s messages, and at another point, they seem to complement one another. The point where images and texts complement one another highlights the role of images, and the point seems to play evocative and facilitative roles.
MAGMA: Proposing a Massive Historical Graph Management System

In recent years, maintaining the history of graphs has become more and more imperative due to the emergence of related applications in a number of fields like health services, social interactions, and map guidance. Historical graphs focus on being able to store and query the whole evolution of the graph and not just the latest instance. In this paper we have two goals: 1) provide a concise survey of the state-of-art with respect to systems in historical graph management since no such comprehensive discussion exists and 2) propose an architecture for a distributed historical graph management system (named MAGMA - MAssive Graph MAnagement) based on previous research work of the authors.
Simulation Generation Algorithm for Foggy Images in Natural Scenes

The foggy environment seriously affects the automatic inspection or cruise monitoring of outdoor equipment in the power system. Aiming at the lack of fog image data sets, a fog simulation image generation algorithm based on depth estimation was proposed. First of all, by unsupervised depth estimation model building the depth map of an outdoor clear picture. Then using feature fusion to refine the depth chart details. By setting the atmospheric extinction coefficient for transmittance figure. Dark channel method is used to estimate the atmospheric light value of the image. Finally, the fog simulation images are obtained based on the atmospheric scattering model. The experimental results show that the method improves the depth map effectively, and the generated fog simulation image is reliable. The average error rate of fog simulation is 6.2%, which solves the problem of excessive uneven fog edge, and the fog simulation image effect is very good in low visibility. Generating fog images with different visibility labels can solve the problem of lack of fog datasets.
Classification and Detection of Prohibited Objects in X-Ray Baggage Security Images

Airport security is strengthened by baggage screening to prevent potential threats and ensure safety. Researchers have been striving to create more precise models for baggage screening through the use of techniques such as fusion, denoising, and histogram equalization. While object detection in X-ray baggage images mostly relies on the Bag of Visual Words (BoVW) model, Convolutional Neural Networks (CNN) have also demonstrated potential. In recent studies, image projection and transfer learning in deep neural networks have been utilized to increase the accuracy of detecting potentially dangerous items. One study specifically improved the detection of obscured prohibited items by incorporating a De-occlusion Attention Module into a custom dataset of X-ray scanned images. In this research, the focus is on exploring the potential of computer vision for prohibited item detection and classification through image enhancement using a custom dataset resembling X-ray scanned images, as the main contribution. X-Ray machines are commonly used at high-security locations such as airports to scan baggage for hidden prohibited objects, like guns, knives, and razor blades. The research team added new layers to a pre-trained model using transfer learning, manually labeled the images, and enhanced the contrast. The YOLOv2, YOLOv3 and YOLOV5 models were employed to classify X-ray baggage images and detect obscured prohibited items, resulting in a mean average precision (mAP@0.50) of 59.44%, 98.61% and 84.1% respectively. These outcomes indicate the practical applicability of the suggested strategy for real-time baggage screening systems.
Dynamics of Delayed Neuroendocrine Systems and Their Reconstructions Using Sparse Identification and Reservoir Computing

Neuroendocrine system mainly consists of hypothalamus, anterior pituitary, and target organ. In this paper, a three-state-variable delayed Goodwin model with two Hill functions is considered, where the Hill functions with delays denote the hormonal feedback suppressions from target organ to hypothalamus and to anterior in the reproductive hormonal axis. The existence of Hopf bifurcation shows the circadian rhythms of neuroendocrine system. The direction and stability of Hopf bifurcation are also analyzed using the normal form theory and the center manifold theorem for functional differential equations. Furthermore, based on the sparse identification algorithm, it is verified that the transient time series generated from the delayed Goodwin model cannot be equivalently presented by ordinary differential equations from the viewpoint of data when considering that a library of candidates are at most cubic terms. The reason is because the solution space of delayed differential equations is of infinite dimensions. Finally, we report that reservoir computing can predict the periodic behaviors of the delayed Goodwin model accurately if the size of reservoir and the length of data used for training are large enough. The predicting performances are evaluated by the mean squared errors between the trajectories generated from the numerical simulations and the reservoir computing.
Morphosyntactic probing of multilingual BERT models

We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a sentence with a target word and a morphological tag as the desired label, derived from the Universal Dependencies treebanks. We find that pre-trained Transformer models (mBERT and XLM-RoBERTa) learn features that attain strong performance across these tasks. We then apply two methods to locate, for each probing task, where the disambiguating information resides in the input. The first is a new perturbation method that masks various parts of context; the second is the classical method of Shapley values. The most intriguing finding that emerges is a strong tendency for the preceding context to hold more information relevant to the prediction than the following context.
A More Efficient and Practical Modified Nyström Method

In this paper, we propose an efficient Nyström method with theoretical and empirical guarantees. In parallel computing environments and for sparse input kernel matrices, our algorithm can have computation efficiency comparable to the conventional Nyström method, theoretically. Additionally, we derive an important theoretical result with a compacter sketching matrix and faster speed, at the cost of some accuracy loss compared to the existing state-of-the-art results. Faster randomized SVD and more efficient adaptive sampling methods are also proposed, which have wide application in many machine-learning and data-mining tasks.
Advances on External Machine Computing Power Focusing on Internal Personal Value: A Case Study on the New Digital Currency

As global inflation escalates and geopolitical conflicts exacerbate, the world’s economy confronts an intensified degree of instability. In this volatile environment, blockchain currencies emerge as a potential bulwark, offering both value preservation and liquidity benefits. However, the conventional “mining” process introduces significant challenges, such as high energy consumption, data security risks, and detachment from the real economy, which potentially facilitate financial capital manipulation. This research endeavors to mitigate these issues, constructing an innovative blockchain cryptocurrency framework that integrates mining and distribution with intelligent big data. It also incorporates social contributions from individuals in domains such as health, knowledge, and ecological conservation. Consequently, the efficiency of cryptocurrency production and distribution correlates with the individual’s societal contribution. The more substantial the contribution, the higher the intrinsic value of the individual and the more efficient the access. Utilizing a comprehensive framework of mathematical modeling, computer numerical simulation, and fuzzy integrated evaluation, we propose a novel endogenous-value blockchain cryptocurrency system. We quantify and optimize variables such as individual intrinsic value, community efficiency, redistribution weights, and total monetary potential. We introduce an innovative method for accumulating time-decaying values such as knowledge contribution and establish an anti-cheating framework. Our results indicate that this pioneering approach can significantly enhance mining efficiency and optimize cryptocurrency distribution. This counters traditional criticisms of blockchain currencies and paves the way for a more sustainable, fair, and efficient model for future blockchain currency systems.
k-NN Query Optimization for High-Dimensional Index Using Machine Learning

In this study, we propose three k-nearest neighbor (k-NN) optimization techniques for a distributed, in-memory-based, high-dimensional indexing method to speed up content-based image retrieval. The proposed techniques perform distributed, in-memory, high-dimensional indexing-based k-NN query optimization: a density-based optimization technique that performs k-NN optimization using data distribution; a cost-based optimization technique using query processing cost statistics; and a learning-based optimization technique using a deep learning model, based on query logs. The proposed techniques were implemented on Spark, which supports a master/slave model for large-scale distributed processing. We showed the superiority and validity of the proposed techniques through various performance evaluations, based on high-dimensional data.
Efficient Discretization of Optimal Transport

Obtaining solutions to optimal transportation (OT) problems is typically intractable when marginal spaces are continuous. Recent research has focused on approximating continuous solutions with discretization methods based on i.i.d. sampling, and this has shown convergence as the sample size increases. However, obtaining OT solutions with large sample sizes requires intensive computation effort, which can be prohibitive in practice. In this paper, we propose an algorithm for calculating discretizations with a given number of weighted points for marginal distributions by minimizing the (entropy-regularized) Wasserstein distance and providing bounds on the performance. The results suggest that our plans are comparable to those obtained with much larger numbers of i.i.d. samples and are more efficient than existing alternatives. Moreover, we propose a local, parallelizable version of such discretizations for applications, which we demonstrate by approximating adorable images.
GFAM: A Gender-Preserving Face Aging Model for Age Imbalance Data

The objective of face aging is to generate facial images that present the effects of aging. The existing one-hot encoding method for aging and/or rejuvenation patterns overlooks the personalized patterns for different genders and races, causing errors such as a male beard appearing on an aged female face. A gender-preserving face aging model is proposed to address these issues, termed GFAM. GFAM employs a generative adversarial network and includes several subnetworks that simulate the aging process between two adjacent age groups to learn specific aging effects. Specifically, the proposed model introduces a gender classifier and gender loss function that uses gender information as a self-guiding mechanism for maintaining gender attributes. To maintain the identity information of synthetic faces, the proposed model also introduces an identity-preserving module. Additionally, age balance loss is used to mitigate the impact of imbalanced age distribution and enhance the accuracy of aging predictions. Moreover, we construct a dataset with balanced age distribution for the task of face age progression, referred to as Age_FR. This dataset is expected to facilitate current research efforts. Ablation studies have been conducted to extensively evaluate the performance improvements achieved by our method. We obtained relative improvements of 3.75% higher than the model without the gender preserving module. The experimental results provide evidence of the effectiveness of the proposed method, both through qualitative and quantitative analyses. Notably, the mean face verification accuracy for the age-progressed groups (0–20, 31–40, 41–50, and 51–60) was found to be 100%, 99.83%, 99.79%, and 99.11%, respectively, highlighting the robustness of our approach across various age ranges.
Relevance Feature Discovery for Text Mining

Due to large size words also data patterns, it is difficult to ensure the quality of relevant characteristics that are found in text documents that describe user preferences. Most widely used text mining and classification techniques now in use have embraced term-based strategies. However, polysemy and synonymy issues have affected them all. The theory that pattern-based approaches should outperform term-based ones in performance in expressing user preferences has been often held throughout the years, however text mining still struggles with how to employ large-scale patterns successfully. This research introduces a novel methodology for relevance feature discovery to address this hard problem. It finds higher level features in text texts that are both positive and negative patterns and uses them instead of low-level features (terms). Additionally, it organised terms into categories and updates term weights according to the patterns and specificity of those distributions. Significant tests employing this model on the datasets RCV1, TREC themes, and Reuters-21578 reveal that it performs noticeably better than both the most advanced term-based approaches and pattern-based methods.
Calibration of GFS Solar Irradiation Forecasts: A Case Study in Romania

Models based on Numerical Weather Prediction (NWP) are widely used for the day-ahead forecast of solar resources. This study is focused on the calibration of the hourly global solar irradiation forecasts provided by the Global Forecast System (GFS), a model from the NWP class. Since the evaluation of GFS raw forecasts sometimes shows a high degree of uncertainty (the relative error exceeding 100%), a procedure for reducing the errors is needed as a prerequisite for engineering applications. In this study, a deep analysis of the error sources in relation to the state of the atmosphere is reported. Of special note is the use of sky imagery in the identification process. Generally, it has been found that the largest errors are determined by the underestimation of cloud coverage. For calibration, a new ensemble forecast is proposed. It combines two machine learning approaches, Support Vector Regression and Multi-Layer Perceptron. In contrast to a typical calibration, the objective function is constructed based on the absolute error instead of the traditional root mean squared error. In terms of normalized root mean squared error, the calibration roughly reduces the uncertainty in hourly global solar irradiation by 16%. The study was conducted with high-quality ground-measured data from the Solar Platform of the West University of Timisoara, Romania. To ensure high accessibility, all the parameters required to run the proposed calibration procedures are provided.