Reviewerly/exHarmony huggingface dataset:
Solid Waste Image Classification Using Deep Convolutional Neural Network

Separating household waste into categories such as organic and recyclable is a critical part of waste management systems to make sure that valuable materials are recycled and utilised. This is beneficial to human health and the environment because less risky treatments are used at landfill and/or incineration, ultimately leading to improved circular economy. Conventional waste separation relies heavily on manual separation of objects by humans, which is inefficient, expensive, time consuming, and prone to subjective errors caused by limited knowledge of waste classification. However, advances in artificial intelligence research has led to the adoption of machine learning algorithms to improve the accuracy of waste classification from images. In this paper, we used a waste classification dataset to evaluate the performance of a bespoke five-layer convolutional neural network when trained with two different image resolutions. The dataset is publicly available and contains 25,077 images categorised into 13,966 organic and 11,111 recyclable waste. Many researchers have used the same dataset to evaluate their proposed methods with varying accuracy results. However, these results are not directly comparable to our approach due to fundamental issues observed in their method and validation approach, including the lack of transparency in the experimental setup, which makes it impossible to replicate results. Another common issue associated with image classification is high computational cost which often results to high development time and prediction model size. Therefore, a lightweight model with high accuracy and a high level of methodology transparency is of particular importance in this domain. To investigate the computational cost issue, we used two image resolution sizes (i.e., 225×264 and 80×45) to explore the performance of our bespoke five-layer convolutional neural network in terms of development time, model size, predictive accuracy, and cross-entropy loss. Our intuition is that smaller image resolution will lead to a lightweight model with relatively high and/or comparable accuracy than the model trained with higher image resolution. In the absence of reliable baseline studies to compare our bespoke convolutional network in terms of accuracy and loss, we trained a random guess classifier to compare our results. The results show that small image resolution leads to a lighter model with less training time and the accuracy produced (80.88%) is better than the 76.19% yielded by the larger model. Both the small and large models performed better than the baseline which produced 50.05% accuracy. To encourage reproducibility of our results, all the experimental artifacts including preprocessed dataset and source code used in our experiments are made available in a public repository.
On Distinctive Image Captioning via Comparing and Reweighting

Recent image captioning models are achieving impressive results based on popular metrics, i.e., BLEU, CIDEr, and SPICE. However, focusing on the most popular metrics that only consider the overlap between the generated captions and human annotation could result in using common words and phrases, which lacks distinctiveness, i.e., many similar images have the same caption. In this paper, we aim to improve the distinctiveness of image captions via comparing and reweighting with a set of similar images. First, we propose a distinctiveness metric-between-set CIDEr (CIDErBtw) to evaluate the distinctiveness of a caption with respect to those of similar images. Our metric reveals that the human annotations of each image in the MSCOCO dataset are not equivalent based on distinctiveness; however, previous works normally treat the human annotations equally during training, which could be a reason for generating less distinctive captions. In contrast, we reweight each ground-truth caption according to its distinctiveness during training. We further integrate a long-tailed weight strategy to highlight the rare words that contain more information, and captions from the similar image set are sampled as negative examples to encourage the generated sentence to be unique. Finally, extensive experiments are conducted, showing that our proposed approach significantly improves both distinctiveness (as measured by CIDErBtw and retrieval metrics) and accuracy (e.g., as measured by CIDEr) for a wide variety of image captioning baselines. These results are further confirmed through a user study.
3D Reconstruction of High Reflective Welding Surface Based on Binocular Structured Light Stereo Vision

The inspection of welding surface quality is an important task for welding work. With the development of product quality inspection technology, automated and machine vision-based inspection have been applied to more industrial application fields because of its non-contact, convenience, and high efficiency. However, challenging material and optical phenomena such as high reflective surface areas often present on welding seams tend to produce artifacts such as holes in the reconstructed model using current visual sensors, hence leading to insufficiency or even errors in the inspection result. This paper presents a 3D reconstruction technique for highly reflective welding surfaces based on binocular style structured light stereo vision. The method starts from capturing a fully lit image for identifying highly reflective regions on a welding surface using conventional computer vision models, including gray-scale, binarization, dilation, and erosion. Then, fringe projection profilometry is used to generate point clouds on the interested area. The mapping and alignment from 2D image to 3D point cloud is then established to highlight features that are vital for eliminating “holes”—large featureless areas—caused by high reflections such as the specular mirroring effect. A two-way slicing method is proposed to operate on the refined point cloud, following the concept of dimensionality reduction to project the sliced point cloud onto different image planes before a Smoothing Spline model is applied to fit the discrete point formed by projection. The 3D coordinate values of points in the “hole” region are estimated according to the fitted curves and appended to the original point cloud using iterative algorithms. Experiment results verify that the proposed method can accurately reconstruct a wide range of welding surfaces with significantly improved precision.
Assessment of Ki-67 proliferation index with deep learning in DCIS (ductal carcinoma in situ)

The proliferation index (PI) is crucial in histopathologic diagnostics, in particular tumors. It is calculated based on Ki-67 protein expression by immunohistochemistry. PI is routinely evaluated by a visual assessment of the sample by a pathologist. However, this approach is far from ideal due to its poor intra- and interobserver variability and time-consuming. These factors force the community to seek out more precise solutions. Virtual pathology as being increasingly popular in diagnostics, armed with artificial intelligence, may potentially address this issue. The proposed solution calculates the Ki-67 proliferation index by utilizing a deep learning model and fuzzy-set interpretations for hot-spots detection. The obtained region-of-interest is then used to segment relevant cells via classical methods of image processing. The index value is approximated by relating the total surface area occupied by immunopositive cells to the total surface area of relevant cells. The achieved results are compared to the manual calculation of the Ki-67 index made by a domain expert. To increase results reliability, we trained several models in a threefold manner and compared the impact of different hyper-parameters. Our best-proposed method estimates PI with 0.024 mean absolute error, which gives a significant advantage over the current state-of-the-art solution.
Symphony: Composing Interactive Interfaces for Machine Learning

Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a framework for composing interactive ML interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards. We developed Symphony through participatory design sessions with 10 teams (n=31), and discuss our findings from deploying Symphony to 3 production ML projects at Apple. Symphony helped ML practitioners discover previously unknown issues like data duplicates and blind spots in models while enabling them to share insights with other stakeholders.
Simulating excited states of the Lipkin model on a quantum computer

We simulate the excited states of the Lipkin model using the recently proposed Quantum Equation of Motion (qEOM) method. The qEOM generalizes the EOM on classical computers and gives access to collective excitations based on quasi-boson operators $\hat{O}^\dagger_n(\alpha)$ of increasing configuration complexity $\alpha$. We show, in particular, that the accuracy strongly depends on the fermion to qubit encoding. Standard encoding leads to large errors, but the use of symmetries and the Gray code reduces the quantum resources and improves significantly the results on current noisy quantum devices. With this encoding scheme, we use IBM quantum machines to compute the energy spectrum for a system of $N=2, 3$ and $4$ particles and compare the accuracy against the exact solution. We found that the results of the approach with $\alpha = 2$, an analog of the second random phase approximation (SRPA), are, in principle, more accurate than with $\alpha = 1$, which corresponds to the random phase approximation (RPA), but the SRPA is more amenable to noise for large coupling strengths. Thus, the proposed scheme shows potential for achieving higher spectroscopic accuracy by implementations with higher configuration complexity, if a proper error mitigation method is applied.
A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators

Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain limitations in its search mechanism, such as local optima stagnation and convergence speed. This is a general problem that faces almost all optimization problems, which can be solved by enhancing the search process of an optimizer using an assistant search tool, such as using hybridizing with another optimizer or applying other search techniques to boost the search capability of an optimizer. Following this concept to address this critical problem, in this paper, we present an alternative version of the AO to alleviate the shortcomings of the traditional one. The main idea of the improved AO (IAO) is to use the search strategy of the Whale Optimization Algorithm (WOA) to boost the search process of the AO. Thus, the IAO benefits from the advantages of the AO and WOA, and it avoids the limitations of the local search as well as losing solutions diversity through the search process. Moreover, we apply the developed IAO optimization algorithm as a feature selection technique using different benchmark functions. More so, it is tested with extensive experimental comparisons to the traditional AO and WOA algorithms, as well as several well-known optimizers used as feature selection techniques, like the particle swarm optimization (PSO), differential evaluation (DE), mouth flame optimizer (MFO), firefly algorithm, and genetic algorithm (GA). The outcomes confirmed that the using of the WOA operators has a significant impact on the AO performance. Thus the combined IAO obtained better results compared to other optimizers.
Sentiment is Not Stance: Target-Aware Opinion Classification for Political Text Analysis

Abstract Sentiment analysis techniques have a long history in natural language processing and have become a standard tool in the analysis of political texts, promising a conceptually straightforward automated method of extracting meaning from textual data by scoring documents on a scale from positive to negative. However, while these kinds of sentiment scores can capture the overall tone of a document, the underlying concept of interest for political analysis is often actually the document’s stance with respect to a given target—how positively or negatively it frames a specific idea, individual, or group—as this reflects the author’s underlying political attitudes. In this paper, we question the validity of approximating author stance through sentiment scoring in the analysis of political texts, and advocate for greater attention to be paid to the conceptual distinction between a document’s sentiment and its stance. Using examples from open-ended survey responses and from political discussions on social media, we demonstrate that in many political text analysis applications, sentiment and stance do not necessarily align, and therefore sentiment analysis methods fail to reliably capture ground-truth document stance, amplifying noise in the data and leading to faulty conclusions.
Glove-Based Hand Gesture Recognition for Diver Communication

We have developed a smart dive glove that recognizes 13 static hand gestures used in diving communication. The smart glove employs five dielectric elastomer sensors to capture finger motion and implements a machine learning classifier in the onboard electronics to recognize gestures. Five basic classification algorithms are trained and assessed: the decision tree, support vector machine (SVM), logistic regression, Gaussian naïve Bayes, and multilayer perceptron. These basic classifiers were selected as they perform well in multiclass classification problems, can be trained using supervised learning, and are model-based algorithms that can be implemented on a microprocessor. The training dataset was collected from 24 participants providing for a range of different hand sizes. After training, the algorithms were evaluated in a dry environment using data collected from ten new participants to test how well they cope with new information. Furthermore, an underwater experiment was conducted to assess any impact of the underwater environment on each algorithm’s classification. The results show all classifiers performed well in a dry environment. The accuracies and F1-scores range between 0.95 and 0.98, where the logistic regressor and SVM have the highest scores for both the accuracy and F1-score (0.98). The underwater results showed that all algorithms work underwater; however, the performance drops when divers must focus on buoyancy control, breathing, and diver trim.
Voxel-RCNN-Complex: An Effective 3-D Point Cloud Object Detector for Complex Traffic Conditions

The complex traffic conditions and high traffic flow are big challenges to the perception of autonomous vehicles. As the basis of environmental perception technology, object detection based on point cloud is of great significance for the normal operations of autonomous vehicles. Considering the complex traffic conditions, in this work, we use the One millioN sCenEs (ONCE) dataset to train an effective 3-D object detector, namely Voxel-region convolution neural network (RCNN)-Complex. This is accomplished by modifying the Voxel RCNN to make it suitable for complex traffic conditions. We add the residual structures in the 3-D backbone and design a heavy 3-D feature extractor, which is conducive to extracting high-dimensional information. We also design a 2-D backbone composed of residual structures, self-calibration convolution, and spatial attention and channel attention mechanism; this expands the receptive field and captures more context information. As compared with the Voxel RCNN, the proposed Voxel-RCNN-Complex significantly improves the detection performance for long-distance and small objects. In order to further increase the robustness of the proposed model and alleviate category imbalance, we use a class-balanced sampling strategy (CBSS). We evaluate the proposed model using the ONCE dataset. The results show that the proposed model achieves an mAP of 65.34% and an inference speed of 13.8 FPS. The experiments show that the proposed model performs better than other methods on the ONCE dataset. This demonstrates the effectiveness of the proposed Voxel-RCNN-Complex. Moreover, we also test the proposed model in an intelligent vehicle platform on real roads.
Temporal Action Proposal Generation with Background Constraint

Temporal action proposal generation (TAPG) is a challenging task that aims to locate action instances in untrimmed videos with temporal boundaries. To evaluate the confidence of proposals, the existing works typically predict action score of proposals that are supervised by the temporal Intersection-over-Union (tIoU) between proposal and the ground-truth. In this paper, we innovatively propose a general auxiliary Background Constraint idea to further suppress low-quality proposals, by utilizing the background prediction score to restrict the confidence of proposals. In this way, the Background Constraint concept can be easily plug-and-played into existing TAPG methods (BMN, GTAD). From this perspective, we propose the Background Constraint Network (BCNet) to further take advantage of the rich information of action and background. Specifically, we introduce an Action-Background Interaction module for reliable confidence evaluation, which models the inconsistency between action and background by attention mechanisms at the frame and clip levels. Extensive experiments are conducted on two popular benchmarks, ActivityNet-1.3 and THUMOS14. The results demonstrate that our method outperforms state-of-the-art methods. Equipped with the existing action classifier, our method also achieves remarkable performance on the temporal action localization task.
The Use of Mobile Gamification Technology for Sustainability Learning in Saudi Higher Education

Mobile game applications are perceived as an appropriate solution for promoting the sustainability of learning among students due to its useful and unique characteristics.Educational mobile games is the use of mobile game applications for enhancing the learning process.Recently, this topic has been getting a lot of attention from many scholars.However, the amount of research undertaken on mobile gamification is still limited.One of the main problems that hinder the success of mobile gamification is sustainability.Designers might overlook some important factors that increase the sustainability of learning due to lack of models that covers all the essential factors to achieve this goal.Therefore, this study proposes a new model that aims to enhance the learning sustainability using mobile gamification applications.This research provides empirical evidence and explains some important factors, such as enjoyment, perceived ease of use, perceived usefulness, behavioural intention to use, efficiency and effectiveness, to facilitate the effective utilization of mobile gamification applications, and thus, will contribute in increasing the sustainability of learning effectively using mobile game applications.
Flipbook as Innovation of Digital Learning Media: Preparing Education for Facing and Facilitating 21st Century Learning

Various competencies that students need to master in an effort to strengthen competitiveness, especially in the field of human resources in the 21st century. Learning media integrated with technology is one of the important learning resources to support the learning process. The lack of innovation in the use of learning media that is integrated with technology makes it less optimal for the achievement of learning objectives and mastery of competencies by students. This study aims to identify the need for opportunities for developing instructional media that are integrated with flipbook-based technology in the hope that it can increase students' opportunities in improving scientific literacy skills. This research is a qualitative research with a survey method. Data collection techniques using observations and needs analysis questionnaires. The quantitative data from the questionnaire were then analyzed using quantitative descriptive techniques using percentages that were displayed in a table. The research subjects took senior high school students in grades one and two, with a total of 70 students. The results of this study indicate that 35.7% of students stated that teachers still use Microsoft Powerpoint media and 27.1% of printed teaching materials. The results also showed that students had positive perceptions of the opportunities for developing and utilizing flipbooks as a medium for digital learning. Through the results of this research, it can become the basis for educators in developing and implementing flipbook-based digital learning media to facilitate the learning process.
Nestor: A Tool for Natural Language Annotation of Short Texts

Nestor is a software tool that annotates natural language CSV (comma-separated variable) files, with a UTF-8 (Unicode Transformation Format – 8-bit) encoding, using a process called tagging [1]. The objective of Nestor is to help analysts make their natural language data, which is often unstructured, filled with technical content, jargon, mispellings, and abbreviations, computable to improve analysis. An example of natural language data that could be input to Nestor and the subsequent output data and the corresponding output is shown in Table 1. The annotated datasets generated by Nestor (as either a CSV or .h5 file) can be used for different analysis techniques, such as failure prediction, problem hot spot identification, and maintenance technician expertise assessment, as shown in [2–10]. Currently, the majority of use cases involve maintenance in the engineering domain (manufacturing, mining, heating ventilation and air conditioning (HVAC)), however, any natural language CSV file with UTF-8 encoding can be input to Nestor.
Sequential/Session-based Recommendations

In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Although SRSs and SBRSs have been extensively studied, there are many inconsistencies in this area caused by the diverse descriptions, settings, assumptions and application domains. There is no work to provide a unified framework and problem statement to remove the commonly existing and various inconsistencies in the area of SR/SBR. There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real- world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area.