Reviewerly/exHarmony huggingface dataset:
ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation

Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of video analysis and understanding.Despite the great achievement in TAPG, most existing works ignore the human perception of interaction between agents and the surrounding environment by applying a deep learning model as a black-box to the untrimmed videos to extract video visual representation.Therefore, it is beneficial and potentially improve the performance of TAPG if we can capture these interactions between agents and the environment.In this paper, we propose a novel framework named Agent-Aware Boundary Network (ABN), which consists of two sub-networks (i) an Agent-Aware Representation Network to obtain both agent-agent and agents-environment relationships in the video representation, and (ii) a Boundary Generation Network to estimate the confidence score of temporal intervals.In the Agent-Aware Representation Network, the interactions between agents are expressed through local pathway, which operates at a local level to focus on the motions of agents whereas the overall perception of the surroundings are expressed through global pathway, which operates at a global level to perceive the effects of agents-environment.Comprehensive evaluations on 20-action THUMOS-14 and 200-action ActivityNet-1.3 datasets with different backbone networks (i.e C3D, SlowFast and Two-Stream) show that our proposed ABN robustly outperforms state-ofthe-art methods regardless of the employed backbone network on TAPG.We further examine the proposal quality by leveraging proposals generated by our method onto temporal action detection (TAD) frameworks and evaluate their detection performances.The source code will be uploaded to github and publicly available upon the acceptance a .
Comparing Deep Reinforcement Learning Algorithms’ Ability to Safely Navigate Challenging Waters

Reinforcement Learning (RL) controllers have proved to effectively tackle the dual objectives of path following and collision avoidance. However, finding which RL algorithm setup optimally trades off these two tasks is not necessarily easy. This work proposes a methodology to explore this that leverages analyzing the performance and task-specific behavioral characteristics for a range of RL algorithms applied to path-following and collision-avoidance for underactuated surface vehicles in environments of increasing complexity. Compared to the introduced RL algorithms, the results show that the Proximal Policy Optimization (PPO) algorithm exhibits superior robustness to changes in the environment complexity, the reward function, and when generalized to environments with a considerable domain gap from the training environment. Whereas the proposed reward function significantly improves the competing algorithms’ ability to solve the training environment, an unexpected consequence of the dimensionality reduction in the sensor suite, combined with the domain gap, is identified as the source of their impaired generalization performance.
Prediction of Solar Power Using Near-Real Time Satellite Data

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by &gt;50% of the days with errors &lt;10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.
Image Classification Using Transfer Learning and Deep Learning

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used. This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features

We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g.Random Forest, using handcrafted features) can combine with transformers (e.g.RoBERTa) to augment model performance.First, we explore suitable transformers and traditional ML models.Then, we extract 255 handcrafted linguistic features using self-developed extraction software.Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment.The use of handcrafted features help model performance on smaller datasets.Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.Table 11: Entity Density Features (EnDF).idx Code Definition 55 ra_SSToT_C ratio of SS transitions : total, count from Entity Grid 56 ra_SOToT_C ratio of SO transitions : total, count from Entity Grid 57 ra_SXToT_C ratio of SX transitions : total, count from Entity Grid 58 ra_SNToT_C ratio of SN transitions : total, count from Entity Grid 59 ra_OSToT_C ratio of OS transitions : total, count from Entity Grid 60 ra_OOToT_C ratio of OO transitions : total, count from Entity Grid 61 ra_OXToT_C ratio of OX transitions : total, count from Entity Grid 62 ra_ONToT_C ratio of ON transitions : total, count from Entity Grid 63 ra_XSToT_C ratio of XS transitions : total, count from Entity Grid 64 ra_XOToT_C ratio of XO transitions : total, count from Entity Grid 65 ra_XXToT_C ratio of XX transitions : total, count from Entity Grid 66 ra_XNToT_C ratio of XN transitions : total, count from Entity Grid 67 ra_NSToT_C ratio of NS transitions : total, count from Entity Grid 68 ra_NOToT_C ratio of NO transitions : total, count from Entity Grid 69 ra_NXToT_C ratio of NX transitions : total, count from Entity Grid 70 ra_NNToT_C ratio of NN transitions : total, count from Entity Grid Table 12: Entity Grid Features (EnDF) Part 1.
Improved heuristic job scheduling method to enhance throughput for big data analytics

Data-parallel computing platforms, such as Hadoop and Spark, are deployed in computing clusters for big data analytics. There is a general tendency that multiple users share the same computing cluster. The schedule of multiple jobs becomes a serious challenge. Over a long period in the past, the Shortest-Job-First (SJF) method has been considered as the optimal solution to minimize the average job completion time. However, the SJF method leads to a low system throughput in the case where a small number of short jobs consume a large amount of resources. This factor prolongs the average job completion time. We propose an improved heuristic job scheduling method, called the Densest-Job-Set-First (DJSF) method. The DJSF method schedules jobs by maximizing the number of completed jobs per unit time, aiming to decrease the average Job Completion Time (JCT) and improve the system throughput. We perform extensive simulations based on Google cluster data. Compared with the SJF method, the DJSF method decreases the average JCT by 23.19% and enhances the system throughput by 42.19%. Compared with Tetris, the job packing method improves the job completion efficiency by 55.4%, so that the computing platforms complete more jobs in a short time span.
A Multi-Layer Network for Aspect-Based Cross-Lingual Sentiment Classification

In the recent era, the advancement of communication technologies provides a valuable interaction source between people of different regions. Nowadays, many organizations adopt the latest approaches, i.e., sentiment analysis and aspect-oriented sentiment classification, to evaluate user reviews to improve the quality of their products. The processing of multi-lingual user reviews is a key challenge in Natural Language Processing (NLP). This paper proposes a multi-layer network with divided attention to perform aspect-based sentiment classification for cross-lingual data. It extracts the Part-of-Speech (POS) tagging information of the given reviews, preprocesses them, and converts them into tokens. Furthermore, bi-lingual dictionaries are leveraged to map the converted tokens from one language to another. Given the preprocessed and mapped reviews, vectors are generated by leveraging the multi-lingual BERT and passed to the proposed deep learning classifier. The 10351 restaurant reviews from SemEval-2016 Task 5 dataset are exploited for the prediction of aspect-based sentiment. The results of cross-lingual validation suggest that the proposed approach significantly outperforms the state-of-the-art approaches and improves the precision, recall, and F1 by more than 23%, 20%, and 22%, respectively.
The Convergence of Deep Learning and Computer Vision: Smart City Applications and Research Challenges

In recent years, deep learning strategies started to outshine traditional machine learning methods in a few fields, with Computer Vision being one of the most noticeable ones.The Computer Vision is becoming more suitable nowadays at identifying patterns from images than the human visual cognitive system.It ranges from raw information recording to methods and ideas that span digital image processing, machine learning, and computer graphics.The wide utilization of Computer Vision has attracted many researchers to incorporate their ideas with different fields and disciplines.The era of smart cities has emerged to meet the recent demands of citizens using information and communication technology.This paper reviews research efforts that utilize Deep Learning Frameworks and Computer Vision Applications in support of smart city applications like smart healthcare, smart transportation, smart agriculture, etc.Furthermore, the paper identified key research challenges that emanate from the use of deep learning and computer vision in support of smart city services.
An Unsupervised Approach for Content-Based Clustering of Emails Into Spam and Ham Through Multiangular Feature Formulation

The rapid growth of spam email attacks and the inherent malicious dynamism within those attacks on a range of social, personal and business activities warrants an intelligent and automated anti-spam framework. Attempts like malware propagation, identity theft, sensitive data pilfering, monetary as well as reputational damage are sharply increasing, endangering the privacy of the victim. Current solutions that are rather incomplete when the multidimensional feature range of email, is taken into account. We believe a methodology based on Artificial Intelligence, especially unsupervised machine learning is the way forward. This research attempts to investigating the application of unsupervised learning for the clustering of Spam and Ham emails. The overall goal of the research is to develop an unsupervised framework that solely depends on unsupervised methodologies through a clustering approach that includes multiple algorithms, primarily using the email content (body) and the subject header. The clustering has been done on a novel binary dataset of 22,000 entries of ham and spam emails, composed of ten features (reduced from eleven to ten after the feature reduction). Seven out of these ten features are unique to this study, engineered to represent impactful analytical email characteristics from a multiangular point of view. Out of five different clustering algorithms investigated in this work, OPTICS produced the optimum clustering demonstrating a 0.26% higher average efficacy than its nearest performer DBSCAN. The average balanced accuracy for OPTICS and DBSCAN was found to be ≈75.76%.
On the Adversarial Robustness of LASSO Based Feature Selection

In this paper, we investigate the adversarial robustness of feature selection based on the $\ell_1$ regularized linear regression model, namely LASSO. In the considered model, there is a malicious adversary who can observe the whole dataset, and then will carefully modify the response values or the feature matrix in order to manipulate the selected features. We formulate the modification strategy of the adversary as a bi-level optimization problem. Due to the difficulty of the non-differentiability of the $\ell_1$ norm at the zero point, we reformulate the $\ell_1$ norm regularizer as linear inequality constraints. We employ the interior-point method to solve this reformulated LASSO problem and obtain the gradient information. Then we use the projected gradient descent method to design the modification strategy. In addition, We demonstrate that this method can be extended to other $\ell_1$ based feature selection methods, such as group LASSO and sparse group LASSO. Numerical examples with synthetic and real data illustrate that our method is efficient and effective.
A Framework for Personalized Fully Immersive Virtual Reality Learning Environments with Gamified Design in Education

Traditional learning methods frequently fail to provoke students’ interest, stimulate their enjoyment and encourage them to participate in learning activities, resulting in discomfort, distractions, and disengagement, if not quitting. Education’s goal is to improve the quality and effectiveness of teaching and learning methods. This paper aims to present a framework based on Virtual Reality (VR) technology and contemporary Head Mounted Displays, that incorporates game-based techniques and adaptive design according to the student’s profile. As a result, this paper analyzes the relevant literature, the VR apparatus, the importance of VR, as well as gamification, personalization and adaptive design in education, which are the learning foundations on which the framework is based. Finally, the framework’s modules and structure are presented, taking into account all of the previously mentioned parameters. This novel framework aspires to serve as a basis for educational applications that use immersive Virtual Reality technologies to transform learning procedures into entertaining, engaging, enjoyable, and effective experiences.
Power-optimal, stabilized entangling gate between trapped-ion qubits

Abstract To achieve scalable quantum computing, improving entangling-gate fidelity and its implementation efficiency are of utmost importance. We present here a linear method to construct provably power-optimal entangling gates on an arbitrary pair of qubits on a trapped-ion quantum computer. This method leverages simultaneous modulation of amplitude, frequency, and phase of the beams that illuminate the ions and, unlike the state of the art, does not require any search in the parameter space. The linear method is extensible, enabling stabilization against external parameter fluctuations to an arbitrary order at a cost linear in the order. We implement and demonstrate the power-optimal, stabilized gate on a trapped-ion quantum computer.
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities.However, existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks.In this study, we explore the effectiveness of leveraging entity representations for downstream cross-lingual tasks.We train a multilingual language model with 24 languages with entity representations and show the model consistently outperforms word-based pretrained models in various crosslingual transfer tasks.We also analyze the model and the key insight is that incorporating entity representations into the input allows us to extract more language-agnostic features.We also evaluate the model with a multilingual cloze prompt task with the mLAMA dataset.We show that entity-based prompt elicits correct factual knowledge more likely than using only word representations.Our source code and pretrained models are available at https: //github.com/studio-ousia/luke.
Voice-Activated Virtual Home Assistant Use and Social Isolation and Loneliness Among Older Adults: Mini Review

A lack of social connectedness is common among older adults due to living alone, loss of loved ones, reduced mobility, and, more recently, social distancing created by the global Covid-19 pandemic. Older adults are vulnerable to social isolation and loneliness, which pose significant health risks comparable to those of smoking, obesity, physical inactivity, and high blood pressure. A lack of social connectedness is also correlated with higher mortality rates even when controlling for other factors such as age and comorbid conditions. The purpose of this mini review was to explore the emerging concepts of older adults' use of commercially available artificial intelligent virtual home assistants (VHAs; e.g., Amazon Echo, Google Nest), and its relationship to social isolation and loneliness. A secondary purpose was to identify potential areas for further research. Results suggest that VHAs are perceived by many older adult users as "companions" and improve social connectedness and reduce loneliness. Available studies are exploratory and descriptive and have limited generalizability due to small sample sizes, however, similar results were reported across several studies conducted in differing countries. Privacy concerns and other ethical issues and costs associated with VHA use were identified as potential risks to older adults' VHA adoption and use. Older adults who were using VHAs expressed the need and desire for more structured training on device use. Future research with stronger methods, including prospective, longitudinal, and randomized study designs are needed. Public education, industry standards, and regulatory oversight is required to mitigate potential risks associated with VHA use.
Enhancing stimulated Raman excitation and two-photon absorption using entangled states of light

We find that stimulated Raman excitation of an atom by a two-photon pulse can be enhanced by orders of magnitude if the photons are simultaneously frequency correlated and spatially anticorrelated. That is, a correlated photon pair must have an inherent time delay between its constituent photons. This counterintuitive feature is a manifestation of the uncertainty principle, which yields that frequency-correlated photons cannot be time (spatially) correlated. This is opposite to two-photon absorption by a three-level atom, for which the enhancement occurs if photons in the pulse are frequency anticorrelated and spatially correlated, that is, photons in the pair simultaneously interact with the atom. Our findings can be useful for imaging and spectroscopy of biological samples which demand low illumination intensity.
Boosting the Transferability of Video Adversarial Examples via Temporal Translation

Although deep-learning based video recognition models have achieved remarkable success, they are vulnerable to adversarial examples that are generated by adding human-imperceptible perturbations on clean video samples. As indicated in recent studies, adversarial examples are transferable, which makes it feasible for black-box attacks in real-world applications. Nevertheless, most existing adversarial attack methods have poor transferability when attacking other video models and transfer-based attacks on video models are still unexplored. To this end, we propose to boost the transferability of video adversarial examples for black-box attacks on video recognition models. Through extensive analysis, we discover that different video recognition models rely on different discriminative temporal patterns, leading to the poor transferability of video adversarial examples. This motivates us to introduce a temporal translation attack method, which optimizes the adversarial perturbations over a set of temporal translated video clips. By generating adversarial examples over translated videos, the resulting adversarial examples are less sensitive to temporal patterns existed in the white-box model being attacked and thus can be better transferred. Extensive experiments on the Kinetics-400 dataset and the UCF-101 dataset demonstrate that our method can significantly boost the transferability of video adversarial examples. For transfer-based attack against video recognition models, it achieves a 61.56% average attack success rate on the Kinetics-400 and 48.60% on the UCF-101.