Reviewerly/exHarmony huggingface dataset:
The Development of the Light Post-editing Module for English-Kazakh Translation

Applied intelligent systems play an important role in the modern world. One of their tasks is machine translation (MT) from one language into another one. MT allows people to freely communicate despite language barriers. This new technology is a special step in helping to understand what a companion speaks, or writes to you. Automatic post-editing is the task of correcting errors present in texts as a result of machine translation. Since MT cannot always give the desired result, it becomes necessary to edit the translation. The drawbacks of the translation have to be eliminated by post-editing. This need for post-editing is largely determined by the quality of machine translation: low-quality translation leaves a lot of room for post-editing, and high-quality and human translations require minimal text editing. This work describes the development of the light post-editing module for the English-Kazakh language pairs. The neural network model is trained on pairs mt, pe and triplets src, mt, pe using the OpenNMT framework. Then the results of the BLEU metrics mt - pe and mt - ape are compared, and a conclusion about the quality of post-editing is made.
Prioritizing user concerns in app reviews – A study of requests for new features, enhancements and bug fixes

: App developers spend exhaustive manual efforts towards the identification and prioritization of informative end-user reviews. Informative reviews are those that express end-users’ requests for new features, bug fixes and possible enhancements. Problem Statement : While prior studies have proposed approaches to convert app reviews into actionable knowledge , these are limited in utility due to being domain knowledge dependent or manually-based. : In this study, in order to facilitate app maintenance and evolution cycles, we develop two novel automated prioritization techniques to rank informative reviews, and also compare their performances. : We developed the techniques comprising of entropy, frequency, TF-IDF and sentiment scoring methods using reviews from four popular apps comprising more than 1000 informative reviews in each app. Time and accuracy metrics were then used to measure the performance of our techniques. We performed evaluations where the ranking outcomes generated by our techniques were compared to those provided by regular app users and developers using two rounds of evaluations (internal and external evaluations). : Our results show that the time required for prioritization was in the range of 17–25 s and the accuracy of prioritization was in the range of 73–90%. : These are promising outcomes when compared to prior work, where our outcomes were 4% and 185% better in terms of accuracy and time respectively. Thus, it is anticipated that our proposed techniques could support app developers in identifying and prioritizing informative reviews.
Machine Learning Model Validation for Early Stage Studies with Small Sample Sizes

In early stage biomedical studies, small datasets are common due to the high cost and difficulty of sample collection with human subjects. This complicates the validation of machine learning models, which are best suited for large datasets. In this work, we examined feature selection techniques, validation frameworks, and learning curve fitting for small simulated datasets with known underlying discriminability, with the aim of identifying a protocol for estimating and interpreting early stage model performance and for planning future studies. Of a variety of examined validation configurations, a nested cross-validation framework provided the most accurate reflection of the selected features' discriminability, but the relevant features were often not properly identified during the feature selection stage for datasets with small sample sizes. Ultimately, we recommend that: (1) filter-based feature selection methods should be used to minimize overfitting to noise-based features, (2) statistical exploration should be conducted on datasets as a whole to estimate the level of discriminability and the feasibility of the classification problems, and (3) learning curves should be employed using nested cross-validation performance estimates for forecasting accuracy at larger sample sizes and estimating the required number of samples to converge towards best performance. This work should serve as a guideline for researchers incorporating machine learning in small-scale pilot studies.
The growing cost of deep learning for source code

Attempting to mitigate problems associated with the trend toward massive dataset scaling.
An Artificial Neural Network Examination of the Intention to Implement Blockchain in the Supply Chains of SMEs in Tonga

The research intends to examine the impacts of the technology, organization, and environmental factors on the implementation of blockchain in the supply chains of SMEs in the Kingdom of Tonga. These include regulatory support, competitive pressure, cost, upper management support, complexity, and relative advantage. The research uses SEM-PLS to test the hypotheses and the Artificial Neural Network method to analyze and classify survey data from 201 SMEs. Findings show that relative advantage, cost, complexity, and competitive pressure significantly affect implementing blockchain in the supply chains. As SMEs frequently have limited capital to invest in technology but meets the same obligations to streamline business operations to optimize profits, blockchain provides a feasible choice for the firms’ sustainability with its characteristics of security, transparency, and immutability that are prospective to develop SMEs’ performance. Thus, the paper provides novel insight regarding the determinants of SMEs' intention to implement blockchain in their supply chains.
TKN: An efficient approach for discovering top-k high utility itemsets with positive or negative profits

Top-k high utility itemsets (HUIs) mining permits discovering the required number of patterns - k, without having an optimal minimum utility threshold (i.e., minimum profit). Multiple top-k HUIs mining algorithms have been introduced with interesting results. However, these algorithms focus mainly on mining patterns from positive profit datasets, while few preliminary studies can handle datasets with negative profits. Moreover, conventional top-k HUI mining algorithms, that are meant for exploring positive profit datasets, perform poorly when mining top-k HUIs on highly dense and large datasets. In this paper, we propose TKN (efficiently mining Top-K HUIs with positive or Negative profits) which employs generalized and adaptive techniques to mine both positive and negative profit datasets effectively. The proposed approach adopts transactional projection and merging mechanisms to decrease the dataset traversing cost. Furthermore, several pruning and threshold elevating ideas are utilized to significantly narrow the exploration space. To highlight the reliability of the devised TKN, a series of extensive comparisons were conducted using two versions of six real datasets. The obtained results reveal that TKN is clearly superior in finding the required number of patterns, whether on positive or negative profit datasets, compared to the current cutting-edge competitors.
Comparison of XGBoost and the Neural Network model on the class-balanced datasets

The Extreme Gradient Boosting method and Deep learning methods are classical machine learning methods widely used in many fields. However, the advantages and disadvantages of these two methods have been a long-debated topic. This paper evaluates the classification performance of the XGBoost and Multiple-Layer Perceptron Neural Network on the structured data. We compare the classification performance of both methods, using the large scale, public datasets, and show the overall trend with the different percentages of datasets used. The experiment validated the higher accuracy in all datasets obtained through the XGBoost method. We concluded that the XGBoost method overcomes the complex data distribution with the feature space to better classify performance on the structured data.
Factors Influencing Nursing Students’ Immersive Virtual Reality Media Technology-Based Learning

This study aims to identify the effects of cognitive and emotional variables related to immersive virtual reality media technology on learning for nursing students. Methods/Statistical analysis: The subjects of this study were 121 nursing students from a university in area D. After experiential learning with virtual reality from 6-8 June 2019, data was collected through questionnaires. For virtual reality learning, VIVE's hTC VIVE ECO CE model was used. The collected data was analyzed using the IBM SPSS 26.0 program. Multiple Regression Analysis was used to analyze the factors influencing the subject's virtual reality learning effects.The learning effects of the virtual reality medium had a statistically significant positive correlation with the virtual reality technology recognition, sensory immersion, realism, learning satisfaction, learning necessity, and continuous use intention (p < 0.001) scores. In personality traits, only Openness, Extraversion (p < 0.01), and Conscientiousness (p < 0.05) had a statistically significant positive correlation. As a result of regression analysis, the explanatory power of the learning effect of the virtual reality medium was 63.9% (F = 53.61, p < 0.001), with learning satisfaction, sensory immersion, continuous use intention, and Extraversion being significant influencing factors (p < 0.05). Improvements/Applications: This study is meaningful in the sense that it provided strategic implications for the teaching and learning method of virtual reality technology-based learning by considering the insights necessary to develop a learning program using virtual reality technology, according to the characteristics of virtual reality technology, and the learner's cognitive and psychological variables.
Real-time crowd counting via lightweight scale-aware network

Accurate crowd counting in congested scenes is still challenging, and its inference speed is also essential in intelligent edge devices. To tackle this issue, we propose a novel lightweight scale-aware network (LSANet) for high-accuracy real-time crowd counting, which consists of four structures: local extractor (LE), scale feature extraction module (SFEM), scale feature fusion module (SFFM), and density map regressor (DMR). Specifically, we devise the SFEM and SFFM carefully to reinforce the learning capability of scale representation, which significantly improves the counting accuracy. Besides, the multi-level intersection-over-union (MIoU) loss function is proposed to reduce background false recognition and estimation errors. Extensive experiments on four mainstream datasets demonstrate that our method achieves the optimal trade-off between effectiveness and efficiency compared with previous lightweight methods.
A hybrid multi-objective optimization algorithm for software requirement problem

The process of selecting software requirements aims to identify the optimal set of requirements that enhances the value of a software release while keeping costs within the budget. It is referred to as the next release problem (NRP) and is classified as a non-deterministic polynomial (NP) hard problem. Additionally, the addressed requirements are complicated by interconnections and other constraints. In the current paper, the NRP is defined as a multi-objective optimization problem with two conflicting objectives, the satisfaction of customers and cost of development, and three constraints to address two real-world instances of the NRP. A hybrid algorithm combining the multi-objective artificial bee colony and differential evolution named (HABC-DE) is proposed in this work. The proposed approach involves management from the original artificial bee colony (ABC) with operators of the differential evolution (DE) algorithm to balance the optimization process's exploitation and exploration stages. The results demonstrated that the suggested algorithm was capable of efficiently generating high-quality non-dominated solutions with 163.48 ± 4.9295 for mean and standard deviation values which can help decision-makers choose the right set of requirements for a new software release production.
EHTask: Recognizing User Tasks From Eye and Head Movements in Immersive Virtual Reality

Understanding human visual attention in immersive virtual reality (VR) is crucial for many important applications, including gaze prediction, gaze guidance, and gaze-contingent rendering. However, previous works on visual attention analysis typically only explored one specific VR task and paid less attention to the differences between different tasks. Moreover, existing task recognition methods typically focused on 2D viewing conditions and only explored the effectiveness of human eye movements. We first collect eye and head movements of 30 participants performing four tasks, i.e., Free viewing, Visual search, Saliency, and Track, in 15 360-degree VR videos. Using this dataset, we analyze the patterns of human eye and head movements and reveal significant differences across different tasks in terms of fixation duration, saccade amplitude, head rotation velocity, and eye-head coordination. We then propose EHTask - a novel learning-based method that employs eye and head movements to recognize user tasks in VR. We show that our method significantly outperforms the state-of-the-art methods derived from 2D viewing conditions both on our dataset (accuracy of 84.4% versus 62.8%) and on a real-world dataset ( 61.9% versus 44.1%). As such, our work provides meaningful insights into human visual attention under different VR tasks and guides future work on recognizing user tasks in VR.
Multi-label semantic feature fusion for remote sensing image captioning

For remote sensing image (RSI) captioning tasks, two-stage RSI captioning methods have achieved high performance because they introduce the results of other RSI tasks, such as image classification as prior information. However, most previous works treat image classification in the two-stage RSI captioning method as a single-label classification task, which cannot adequately describe the entire contents of complex RSIs and may cause semantic ambiguity. To settle this problem and further refine image feature representation, we introduce multi-label classification into two-stage RSI captioning to provide sufficient and accurate prior semantic information and propose a multi-label semantic feature fusion (MLSFF) framework. Specifically, we design a robust multi-label semantic attribute extractor to extract multi-label semantic attributes of RSIs. To obtain discriminative feature representations for RSI captioning, we propose two cross-modal semantic feature fusion operators that fuse the extracted semantic attributes and the image feature extracted by the convolutional neural network. The results of extensive numerical experiments show that the proposed method can achieve state-of-the-art performance on the UCM-Captions, Sydney-Captions, and RSICD datasets. Specifically, on the UCM-Captions dataset, our method achieves a gain of 8.2% in Sm score over the SAT (LAM) method (Zhang et al., 2019c). On the Sydney-Captions dataset, our method improves the Sm score by 17.4% compared with the TCE loss-based method (Li et al., 2020a). On the RSICD, our method outperforms the multi-level attention method (Li et al., 2020b) by 3.2% in terms of the Sm score. Code is available at https://github.com/xtye5025/MLSFF.
Chronic Patients' Emotions Toward Self-Managing Care IT

Patients’ emotions toward health IT can play an important role in explaining their usage of it. One form of health IT is self-managing care IT, such as activity trackers that can be used by chronic patients to adopt a healthy lifestyle. The goal of this study is to understand the factors that influence the arousal of emotions in chronic patients while using these tools. Past studies, in general, tend to emphasize how IT shapes emotions, underplaying the role of the individual user’s identity and, specifically, how central health is to the user’s self in shaping emotions. In this research, the authors argue that patients’ health identity centrality (i.e., the extent to which they consider health as central to their sense of self) can play an important role in forming their dependence on health IT by affecting their use of it directly and shaping their emotions around it.
Unsupervised learning of depth estimation from imperfect rectified stereo laparoscopic images

Learning-based methods have achieved remarkable performances on depth estimation. However, the premise of most self-learning and unsupervised learning methods is built on rigorous, geometrically-aligned stereo rectification. The performances of these methods degrade when the rectification is not accurate. Therefore, we explore an approach for unsupervised depth estimation from stereo images that can handle imperfect camera parameters. We propose an unsupervised deep convolutional network that takes rectified stereo image pairs as input and outputs corresponding dense disparity maps. First, a new vertical correction module is designed for predicting a correction map to compensate for the imperfect geometry alignment. Second, the left and right images, which are reconstructed based on the input image pair and corresponding disparities as well as the vertical correction maps, are regarded as the outputs of the generative term of the generative adversarial network (GAN). Then, the discriminator term of the GAN is used to distinguish the reconstructed images from the original inputs to force the generator to output increasingly realistic images. In addition, a residual mask is introduced to exclude pixels that conflict with the appearance of the original image in the loss calculation. The proposed model is validated on the publicly available Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED) dataset and the average MAE is 3.054 mm. Our model can effectively handle imperfect rectified stereo images for depth estimation.
3D reconstruction of Borobudur reliefs from 2D monocular photographs based on soft-edge enhanced deep learning

We propose a deep learning-based reconstruction method for three-dimensional (3D) cultural heritage objects, which are destroyed or disappear and thus are unavailable for 3D scanning to create digital archives. The proposed reconstruction method uses a single monocular photo and predicts its depth values based on a monocular depth estimation network. While most recent studies have focused on scenes with sharp (high-curvature) edges and large depth differences, such as indoor and outdoor scenes, the proposed method is designed to exert its performance on scenes with small depth differences and soft (low-curvature) edges. The key idea is to involve the soft edges as guidance information in the depth estimation network. We demonstrate the effectiveness of the proposed method by applying it to the hidden reliefs of the Borobudur temple, a UNESCO world heritage site. The hidden reliefs were buried behind stone walls and are no longer visible. The 3D information of the reliefs can only be estimated from their old monocular photos. We achieved 97% reconstruction accuracy for validation data. We also integrate the reconstructed hidden reliefs with 3D scanned data of the visible regions and execute see-through visualization for the integrated data. This visualization can simultaneously show us both visible regions and invisible regions of the temple.
Mineral prospectivity mapping: a potential technique for sustainable mineral exploration and mining activities – a case study using the copper deposits of the Tagmout basin, Morocco

Mineral prospectivity mapping (MPM) based on the principle of geometric mean was applied to stream sediment geochemical, fault density, and aeromagnetic data from Tagmout basin, Morocco to determine new areas for optimizing copper exploration. The application of a fuzzy operator using stream sediment data, factor analysis, and fault density map, allowed weights to be assigned to these parameters so that the MPM function can process them to indicate the most favorable zones of copper mineralization. The model's accuracy as evaluated using a normalized density index (Nd with value 1.22) shows the reliability of the method. The potential copper concentration areas represent 8.22% of the entire basin of which 30% are concentrated in the western portion of the basin and other significant areas are in the southwest and northeast portions. The results indicate that MPM is a powerful technique for planning exploration programs that aim for sustainable mining activities.
Self-Organizing Migrating Algorithm with narrowing search space strategy for robot path planning

This article introduces a version of the Self-Organizing Migrating Algorithm with a narrowing search space strategy named iSOMA. Compared to the previous two versions, SOMA T3A and Pareto that ranked 3rd and 5th respectively in the IEEE CEC (Congress on Evolutionary Computation) 2019 competition, the iSOMA is equipped with more advanced features with notable improvements including applying jumps in the order, immediate update, narrowing the search space instead of searching on the intersecting edges of hyperplanes, and the partial replacement of individuals in the population when the global best improved no further. Moreover, the proposed algorithm is organized into processes named initialization, self-organizing, migrating, and replacement. We tested the performance of this new version by using three benchmark test suites of IEEE CEC 2013, 2015, and 2017, which, together contain a total of 73 functions. Not only is it superior in performance to other SOMAs, but iSOMA also yields promising results against the representatives of well-known algorithmic families such as Differential Evolution and Particle Swarm Optimization. Moreover, we demonstrate the application of iSOMA for path planning of a drone, while avoiding static obstacles and catching the target.
rethnicity: An R package for predicting ethnicity from names

In this study, a new R package, rethnicity 1 is provided for predicting ethnicity based on names. The Bidirectional Long Short-Term Memory (Bi-LSTM), a recurrent neural network architecture commonly used for natural language processing, was chosen as the model for our study. The Florida Voter Registration was used as the training and testing data. Special care was given for the accuracy of minority groups by adjusting the imbalance in the dataset. The models were trained and exported to C++ and then integrated with R using Rcpp. Additionally, the availability, accuracy, and performance of the package were compared with other solutions.