Reviewerly/exHarmony huggingface dataset:
SOGNet: Scene Overlap Graph Network for Panoptic Segmentation

The panoptic segmentation task requires a unified result from semantic and instance segmentation outputs that may contain overlaps. However, current studies widely ignore modeling overlaps. In this study, we aim to model overlap relations among instances and resolve them for panoptic segmentation. Inspired by scene graph representation, we formulate the overlapping problem as a simplified case, named scene overlap graph. We leverage each object's category, geometry and appearance features to perform relational embedding, and output a relation matrix that encodes overlap relations. In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances. The mask logits after removing overlaps are fed into per-pixel instance id classification, which leverages the panoptic supervision to assist in the modeling of overlap relations. Besides, we generate an approximate ground truth of overlap relations as the weak supervision, to quantify the accuracy of overlap relations predicted by our method. Experiments on COCO and Cityscapes demonstrate that our method is able to accurately predict overlap relations, and outperform the state-of-the-art performance for panoptic segmentation. Our method also won the Innovation Award in COCO 2019 challenge.
Unified Graph and Low-Rank Tensor Learning for Multi-View Clustering

Multi-view clustering aims to take advantage of multiple views information to improve the performance of clustering. Many existing methods compute the affinity matrix by low-rank representation (LRR) and pairwise investigate the relationship between views. However, LRR suffers from the high computational cost in self-representation optimization. Besides, compared with pairwise views, tensor form of all views' representation is more suitable for capturing the high-order correlations among all views. Towards these two issues, in this paper, we propose the unified graph and low-rank tensor learning (UGLTL) for multi-view clustering. Specifically, on the one hand, we learn the view-specific affinity matrix based on projected graph learning. On the other hand, we reorganize the affinity matrices into tensor form and learn its intrinsic tensor based on low-rank tensor approximation. Finally, we unify these two terms together and jointly learn the optimal projection matrices, affinity matrices and intrinsic low-rank tensor. We also propose an efficient algorithm to iteratively optimize the proposed model. To evaluate the performance of the proposed method, we conduct extensive experiments on multiple benchmarks across different scenarios and sizes. Compared with the state-of-the-art approaches, our method achieves much better performance.
MIPaaL: Mixed Integer Program as a Layer

Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures average accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a mixed integer linear program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, an algorithm that iteratively tightens the continuous relaxation by adding constraints removing fractional solutions. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and optimization separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP. Lastly, we demonstrate generalization performance in several transfer learning tasks.
Arabic Sentiment Analysis: A Systematic Literature Review

With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.
Using Feature Fusion Strategies in Continuous Authentication on Smartphones

With the increasing prevalence of mobile devices, people prefer to use smartphones to make payments, take photos, and collect personal vital information. Due to the high possibility of smartphone illegal access, the security and privacy of the devices become more important and critical. In this article, we present FusionAuth, a sensor-based continuous authentication system leveraging the accelerometer, gyroscope, and magnetometer on smartphones to capture users’ behavioral patterns. In order to improve the authentication performance and enhance system reliability, we are among the first to utilize two feature fusion strategies of serial feature fusion and parallel feature fusion to combine the designed features from the three sensors in the feature extraction module. Based on the trained one-class support vector domain description classifier, we evaluate the authentication performance of FusionAuth in terms of impact of window size and user size, and accuracy on different users. The experimental results demonstrate that FusionAuth reaches 1.47% mean balanced error rate with the serial fusion and achieves 1.79% mean BER with the parallel fusion.
LSTM Multi-modal UNet for Brain Tumor Segmentation

Deep learning models such as convolutional neural network has been widely used in 3D biomedical image segmentation. However, most of them neither consider the correlations between different modalities, nor fully exploit depth information. To better leverage the multi-modalities and depth information, we proposed an architecture for brain tumor segmentation in multi- modal magnetic resonance images (MRI), named LSTM multi- modal UNet. Experiments results on BRATS-2015 show that our method outperforms the state-of-the-art biomedical segmentation approaches.
MoVi: A large multi-purpose human motion and video dataset

Large high-quality datasets of human body shape and kinematics lay the foundation for modelling and simulation approaches in computer vision, computer graphics, and biomechanics. Creating datasets that combine naturalistic recordings with high-accuracy data about ground truth body shape and pose is challenging because different motion recording systems are either optimized for one or the other. We address this issue in our dataset by using different hardware systems to record partially overlapping information and synchronized data that lend themselves to transfer learning. This multimodal dataset contains 9 hours of optical motion capture data, 17 hours of video data from 4 different points of view recorded by stationary and hand-held cameras, and 6.6 hours of inertial measurement units data recorded from 60 female and 30 male actors performing a collection of 21 everyday actions and sports movements. The processed motion capture data is also available as realistic 3D human meshes. We anticipate use of this dataset for research on human pose estimation, action recognition, motion modelling, gait analysis, and body shape reconstruction.
Speech Synthesis Evaluation — State-of-the-Art Assessment and Suggestion for a Novel Research Program

Speech Synthesis Evaluation : State-of-the-Art Assessment and Suggestion for a Novel Research Program
Virtual Reality Games and the Role of Body Involvement in Enhancing Positive Emotions and Decreasing Anxiety: Within-Subjects Pilot Study

In the last few years, the introduction of immersive technologies, especially virtual reality, into the gaming market has dramatically altered the traditional concept of video games. Given the unique features of virtual reality in terms of interaction and its ability to completely immerse the individual into the game, this technology should increase the propensity for video games to effectively elicit positive emotions and decrease negative emotions and anxiety in the players. However, to date, few studies have investigated the ability of virtual reality games to induce positive emotions, and the possible effect of this new type of video game in diminishing negative emotions and anxiety has not yet been tested. Furthermore, given the critical role of body movement in individuals' well-being and in emotional responses to video games, it seems critical to investigate how body involvement can be exploited to modulate the psychological benefits of virtual reality games in terms of enhancing players' positive emotions and decreasing negative emotions and anxiety.This within-subjects study aimed to explore the ability of commercial virtual reality games to induce positive emotions and diminish negative emotions and state anxiety of the players, investigating the effects of the level of body involvement requested by the game (ie, high vs low).A total of 36 young adults played a low body-involvement (ie, Fruit Ninja VR) and a high body-involvement (ie, Audioshield) video game in virtual reality. The Visual Analogue Scale (VAS) and the State-Trait Anxiety Inventory, Form-Y1 (STAI-Y1) were used to assess positive and negative emotions and state anxiety.Results of the generalized linear model (GLM) for repeated-measures multivariate analysis of variance (MANOVA) revealed a statistically significant increase in the intensity of happiness (P<.001) and surprise (P=.003) and, in parallel, a significant decrease in fear (P=.01) and sadness (P<.001) reported by the users. Regarding the ability to improve anxiety in the players, the results showed a significant decrease in perceived state anxiety after game play, assessed with both the STAI-Y1 (P=.003) and the VAS-anxiety (P=.002). Finally, the results of the GLM MANOVA showed a greater efficacy of the high body-involvement game (ie, Audioshield) compared to the low body-involvement game (ie, Fruit Ninja VR), both for eliciting positive emotions (happiness, P<.001; and surprise, P=.01) and in reducing negative emotions (fear, P=.05; and sadness, P=.05) and state anxiety, as measured by the STAI-Y1 (P=.05).The two main principal findings of this study are as follows: (1) virtual reality video games appear to be effective tools to elicit positive emotions and to decrease negative emotions and state anxiety in individuals and (2) the level of body involvement of the virtual video game has an important effect in determining the ability of the game to improve positive emotions and decrease negative emotions and state anxiety of the players.
Using YouTube to Learn Anatomy: Perspectives of Jordanian Medical Students

The study is aimed at exploring the popularity, impact, and usefulness of using YouTube in learning anatomy as perceived by Jordanian medical students studying at Jordan University of Science and Technology.The present work is a cross-sectional questionnaire-based study. First-, second-, and third-year medical students were invited to complete an anonymous questionnaire. Students' responses were numerically coded, and the results were analyzed to reveal any statistically significant differences related to gender or level of study.The results showed that 96.4% of the students used YouTube in general, 91.2% used it as a source of information, and 83.9% used YouTube as a learning tool in medical school. Further, YouTube was used by 79.1% of the students as an anatomy-learning tool. Most of these students used this platform in learning gross anatomy. The study also revealed that dissection videos were the most viewed anatomy-related content. Regarding the perceived value of YouTube as an anatomy-learning tool, the majority of the students reported that YouTube offered them useful anatomical information and enhanced their understanding, memorization, and recall of anatomical information. In addition, most of them recommended using YouTube as an anatomy-learning tool. Statistical analysis of the results revealed the presence of gender-related significant differences in students' perspectives. Such differences were also found among students of different levels of study.Medical students have positive attitudes toward using YouTube in augmenting their anatomy learning. For this, educators are encouraged to adopt YouTube as an educational tool in their anatomy instruction and to create new anatomy-related YouTube videos to enhance their students' learning.
The Effect of Augmented Reality on Students’ Learning Performance in Stem Education

The effect of one of the most popular 3D visualization and modelling technologies with haptic and touch feedback possibilities—augmented reality (AR)—is analysed herein. That includes a specific solution, incorporating augmented reality. A case study for delivering STEM (science, technology, engineering, and mathematics) content using this tool at one secondary school in Sofia is presented. The experience gained in one school year of using facilities for a STEM enrichment program has been examined.
Advanced Equivalence Checking for Quantum Circuits

In the not-so-distant future, quantum computing will change the way we tackle certain problems.It promises to dramatically speed-up many chemical, financial, cryptographical, and machine-learning applications.However, in order to capitalize on those promises, complex design flows composed of steps such as compilation, decomposition, mapping, or transpilation need to be employed before being able to execute a conceptual quantum algorithm on an actual device.This results in many descriptions at various levels of abstraction which may significantly differ from each other.The complexity of the underlying design problems makes it ever more important to not only provide efficient solutions for the single steps, but also to verify that the originally intended functionality is preserved throughout all levels of abstraction.This motivates methods for equivalence checking of quantum circuits.However, most existing methods for this are inspired by equivalence checking in the classical realm and have merely been extended to support quantum circuits (i.e., circuits which do not only rely on 0's and 1's, but also employ superposition and entanglement).In this work, we propose an advanced methodology which takes the different paradigms of quantum circuits not only as a burden, but as an opportunity.In fact, the proposed methodology explicitly utilizes characteristics unique to quantum computing in order to overcome the shortcomings of existing approaches.We show that, by exploiting the reversibility of quantum circuits, complexity can be kept feasible in many cases.Moreover, we show that, in contrast to the classical realm, simulation is very powerful in verifying quantum circuits.Experimental evaluations confirm that the resulting methodology allows one to conduct equivalence checking dramatically faster than ever before-in many cases just a single simulation run is sufficient.An implementation of the proposed equivalence checking flow is publicly available at https://iic.jku.
Lifelong Zero-Shot Learning

Zero-Shot Learning (ZSL) handles the problem that some testing classes never appear in training set. Existing ZSL methods are designed for learning from a fixed training set, which do not have the ability to capture and accumulate the knowledge of multiple training sets, causing them infeasible to many real-world applications. In this paper, we propose a new ZSL setting, named as Lifelong Zero-Shot Learning (LZSL), which aims to accumulate the knowledge during the learning from multiple datasets and recognize unseen classes of all trained datasets. Besides, a novel method is conducted to realize LZSL, which effectively alleviates the Catastrophic Forgetting in the continuous training process. Specifically, considering those datasets containing different semantic embeddings, we utilize Variational Auto-Encoder to obtain unified semantic representations. Then, we leverage selective retraining strategy to preserve the trained weights of previous tasks and avoid negative transfer when fine-tuning the entire model. Finally, knowledge distillation is employed to transfer knowledge from previous training stages to current stage. We also design the LZSL evaluation protocol and the challenging benchmarks. Extensive experiments on these benchmarks indicate that our method tackles LZSL problem effectively, while existing ZSL methods fail.
Special Issue on Ensemble Learning and Applications

During the last decades, in the area of machine learning and data mining, the development of ensemble methods has gained a significant attention from the scientific community. Machine learning ensemble methods combine multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Combining multiple learning models has been theoretically and experimentally shown to provide significantly better performance than their single base learners. In the literature, ensemble learning algorithms constitute a dominant and state-of-the-art approach for obtaining maximum performance, thus they have been applied in a variety of real-world problems ranging from face and emotion recognition through text classification and medical diagnosis to financial forecasting.
Should You Fine-Tune BERT for Automated Essay Scoring?

Most natural language processing research now recommends large Transformer-based models with fine-tuning for supervised classification tasks; older strategies like bag-ofwords features and linear models have fallen out of favor.Here we investigate whether, in automated essay scoring (AES) research, deep neural models are an appropriate technological choice.We find that fine-tuning BERT produces similar performance to classical models at significant additional cost.We argue that while state-of-the-art strategies do match existing best results, they come with opportunity costs in computational resources.We conclude with a review of promising areas for research on student essays where the unique characteristics of Transformers may provide benefits over classical methods to justify the costs.
An Efficient and Privacy-Preserving Outsourced Support Vector Machine Training for Internet of Medical Things

As the use of machine learning in the Internet-of-Medical Things (IoMT) settings increases, so do the data privacy concerns. Therefore, in this article, we propose an efficient privacy-preserving outsourced support vector machine scheme (EPoSVM), designed for IoMT deployment. To securely train the support vector machine (SVM), we design eight secure computation protocols to allow the cloud server to efficiently execute basic integer and floating-point computations. The proposed scheme protects training data privacy and guarantees the security of the trained SVM model. The security analysis proves that our proposed protocols and EPoSVM satisfy both security and privacy protection requirements. Findings from the performance evaluation using two real-world disease data sets also demonstrate the efficiency and effectiveness of EPoSVM in achieving the same classification accuracy as a general SVM.
SolarGAN: Multivariate Solar Data Imputation Using Generative Adversarial Network

Photovoltaic (PV) is receiving increasing attention due to its sustainability and low carbon footprint. However, the penetration level of PV is still relatively low because of its intermittency. This uncertainty can be handled by accurate PV forecasting, which requires high-quality solar data. Nevertheless, up to 40% of solar data can be found missing, which significantly worsens the quality of solar data. This letter proposes a novel solarGAN method for multivariate solar data imputation, in which necessary modifications are made on the input of generative adversarial network (GAN) to effectively tackle the relatively independent solar time series data. Case studies on a public dataset show that the proposed solarGAN outperforms several commonly-used machine learning and GAN based data imputation methods with at least 23.9% reduction of mean squared error.
Angry by design: toxic communication and technical architectures

Abstract Hate speech and toxic communication online is on the rise. Responses to this issue tend to offer technical (automated) or non-technical (human content moderation) solutions, or see hate speech as a natural product of hateful people. In contrast, this article begins by recognizing platforms as designed environments that support particular practices while discouraging others. In what ways might these design architectures be contributing to polarizing, impulsive, or antagonistic behaviors? Two platforms are examined: Facebook and YouTube. Based on engagement, Facebook’s Feed drives views but also privileges incendiary content, setting up a stimulus–response loop that promotes outrage expression. YouTube’s recommendation system is a key interface for content consumption, yet this same design has been criticized for leading users towards more extreme content. Across both platforms, design is central and influential, proving to be a productive lens for understanding toxic communication.
Sistematik Review: Dampak Perkuliahan Daring Saat Pandemi COVID-19 Terhadap Mahasiswa Indonesia

&lt;p&gt;&lt;strong&gt;Background&lt;/strong&gt;: Learning online and working from home for educators is a change that must be made by lecturers to continue teaching students. Distance education has the aim of improving the quality of education and the relevance of education as well as increasing the equitable access and expansion of education. Distance Education that is carried out with good quality assurance and in accordance with the needs of stakeholders is one mechanism for expanding access to quality higher education that is expected.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method&lt;/strong&gt;: Systematic review using a database: Google Scholar. Search results that fulfill the criteria are then analyzed by article.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results&lt;/strong&gt;: Online learning has several impacts on students, namely (1) Online Learning Still Confuses Students (2) Students become passive, less creative and productive, (3) The accumulation of information / concepts on students is less useful, (4) Students Experiencing Stress, (5) Improvement of Student Language Literacy Ability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion&lt;/strong&gt;: At present, the spread of Covid-19 still shows an upward trend so that education practitioners and stakeholders can continue to survive and innovate in implementing learning. This is because good learning is able to invite students' interest and attention.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;