Reviewerly/exHarmony huggingface dataset:
Regulating Social Media Platforms: A Comparative Policy Analysis

High-profile scandals related to electoral interference, fake news and misinformation, violations of data privacy, and suppression of political activism by anti-democratic regimes have cast a cloud over the social media industry in recent years. The question is not if, but when and how, reform will be undertaken and with what consequences. Against this backdrop, the purpose of this article is to conduct a comparative analysis of competing alternatives for social media platform regulation. Its focus is international, encompassing not only proposals advanced by different groups within the United States, but also selected major developments abroad. The goal of such a comparison is to improve understanding of limited as well as more comprehensive strategies of intervention while evaluating their appeal for addressing the controversy that surrounds the social media industry based on policy effectiveness and other technical and normative criteria.
Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data

Accurate forecasting of solar irradiance is a key issue for planning and management of renewable solar energy production technologies. The present paper aims to propose new machine learning forecasting models based on optimized ANNs in order to accurately predict solar irradiance. For this purpose, an evolutionary framework is suggested to generate multiple models for different time horizons up to 6 h ahead by the evolution of the forecasting history and ANN architecture. A dataset of 28 Moroccan cities is used in our experiments in order to explore the performances of the proposed models against different climatic conditions. The proposed framework is then evaluated through a zoning scenario giving the ability to our models to accurately forecast solar irradiance in sites where no such data is available. Two other scenarios are used to assess and compare the resulting performances. For all studied scenarios obtained results show good generalization abilities with NRMSE varying from 7.59% to 12.49% and NMAE from 4.41% to 8.12% as best performances for solar irradiance forecasting from 1 to 6 h ahead respectively. A comparative study is then conducted with three other models (smart persistence, regression trees and random forest), showing better performances of our proposed HAEANN models.
Hidden Two-Stream Collaborative Learning Network for Action Recognition

The two-stream convolutional neural network exhibits excellent performance in the video action recognition. The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames, to train a model each, and to finally integrate the outputs of the two models. Nevertheless, the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition, and the temporal and the spatial streams are just simply fused at the ends, with one stream failing and the other stream succeeding. We propose a novel hidden twostream collaborative (HTSC) learning network that masks the steps of extracting the optical flow in the network and greatly speeds up the action recognition. Based on the two-stream method, the two-stream collaborative learning model captures the interaction of the temporal and spatial features to greatly enhance the accuracy of recognition. Our proposed method is highly capable of achieving the balance of efficiency and precision on large-scale video action recognition datasets.
Up or Down? Adaptive Rounding for Post-Training Quantization

When quantizing neural networks, assigning each floating-point weight to its nearest fixed-point value is the predominant approach. We find that, perhaps surprisingly, this is not the best we can do. In this paper, we propose AdaRound, a better weight-rounding mechanism for post-training quantization that adapts to the data and the task loss. AdaRound is fast, does not require fine-tuning of the network, and only uses a small amount of unlabelled data. We start by theoretically analyzing the rounding problem for a pre-trained neural network. By approximating the task loss with a Taylor series expansion, the rounding task is posed as a quadratic unconstrained binary optimization problem. We simplify this to a layer-wise local loss and propose to optimize this loss with a soft relaxation. AdaRound not only outperforms rounding-to-nearest by a significant margin but also establishes a new state-of-the-art for post-training quantization on several networks and tasks. Without fine-tuning, we can quantize the weights of Resnet18 and Resnet50 to 4 bits while staying within an accuracy loss of 1%.
TransRHS: A Representation Learning Method for Knowledge Graphs with Relation Hierarchical Structure

Representation learning of knowledge graphs aims to project both entities and relations as vectors in a continuous low-dimensional space. Relation Hierarchical Structure (RHS), which is constructed by a generalization relationship named subRelationOf between relations, can improve the overall performance of knowledge representation learning. However, most of the existing methods ignore this critical information, and a straightforward way of considering RHS may have a negative effect on the embeddings and thus reduce the model performance. In this paper, we propose a novel method named TransRHS, which is able to incorporate RHS seamlessly into the embeddings. More specifically, TransRHS encodes each relation as a vector together with a relation-specific sphere in the same space. Our TransRHS employs the relative positions among the vectors and spheres to model the subRelationOf, which embodies the inherent generalization relationships among relations. We evaluate our model on two typical tasks, i.e., link prediction and triple classification. The experimental results show that our TransRHS model significantly outperforms all baselines on both tasks, which verifies that the RHS information is significant to representation learning of knowledge graphs, and TransRHS can effectively and efficiently fuse RHS into knowledge graph embeddings.
Deep Learning Model for Fine-Grained Aspect-Based Opinion Mining

Despite the great manufactures' efforts to achieve customer satisfaction and improve their performance, social media opinion mining is still on the fly a big challenge. Current opinion mining requires sophisticated feature engineering and syntactic word embedding without considering semantic interaction between aspect term and opinionated features, which degrade the performance of most of opinion mining tasks, especially those that are designed for smart manufacturing. Research on intelligent aspect level opinion mining (AOM) follows the fast proliferation of user-generated data through social media for industrial manufacturing purposes. Google's pre-trained language model, Bidirectional Encoder Representations from Transformers (BERT) widely overcomes existing methods in eleven natural language processing (NLP) tasks, which makes it the standard way for semantic text representation. In this paper, we introduce a novel deep learning model for fine-grained aspect-based opinion mining, named as FGAOM. First, we train the BERT model on three specific domain corpora for domain adaption, then use adjusted BERT as embedding layer for concurrent extraction of local and global context features. Then, we propose Multi-head Self-Attention (MSHA) to effectively fuse internal semantic text representation and take advantage of convolutional layers to model aspect term interaction with surrounding sentiment features. Finally, the performance of the proposed model is evaluated via extensive experiments on three public datasets. Results show that performance of the proposed model outperforms performances of recent the-of-the-art models.
A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.
Cyberbullying Detection in Social Networks Using Deep Learning Based Models

Cyberbullying is a disturbing online misbehaviour with troubling consequences. It appears in different forms, and in most of the social networks, it is in textual format. Automatic detection of such incidents requires intelligent systems. Most of the existing studies have approached this problem with conventional machine learning models and the majority of the developed models in these studies are adaptable to a single social network at a time. Recently deep learning based models have been used for similar objectives, claiming that they can overcome the limitations of the conventional models, and improve the detection performance. In this paper, we investigated the findings of a recent literature in this regard and validated their findings using the same datasets as they did. We further expanded the work by applying the developed methods on a new dataset. We aimed to further investigate the performance of the models in new social media platforms. Our findings show that the deep learning based models outperform the machine learning models previously applied to the same dataset. We believe that the deep learning based models can also benefit from integrating other sources of information and looking into the impact of profile information of the users in social networks.
OMNI: A Framework for Integrating Hardware and Software Optimizations for Sparse CNNs

Convolution neural networks (CNNs) as one of today's main flavor of deep learning techniques dominate in various image recognition tasks. As the model size of modern CNNs continues to grow, neural network compression techniques have been proposed to prune the redundant neurons and synapses. However, prior techniques disconnect the software neural networks compression and hardware acceleration, which fail to balance multiple design parameters, including sparsity, performance, hardware area cost, and efficiency. More concretely, prior unstructured pruning techniques achieve high sparsity at the expense of extra performance overhead, while prior structured pruning techniques relying on strict sparse patterns lead to low sparsity and extra hardware cost. In this article, we propose OMNI, a framework for accelerating sparse CNNs on hardware accelerators. The innovation of OMNI stems from that it uses hardware amenable on-chip memory partition patterns to seamlessly engage the software CNN model compression and hardware CNN acceleration. To accelerate the compute-intensive convolution kernel, a promising hardware optimization approach is memory partition, which divides the original weight kernels into several groups so that the different hardware processing elements can simultaneously access the weight. We exploit the memory partition patterns including block, cyclic, or hybrid as a means of CNN compression patterns. Our software CNN model compression balances the sparsity across different groups and our hardware accelerator employs hardware parallelization coordinately with the sparse patterns, leading to a desirable compromise between sparsity and performance. We further develop performance models to help the designers to quickly identify the pattern factors subject to an area constraint. Last, we evaluate our design on application specific integrated circuit (ASIC) and field-programmable gate array (FPGA) platform. Experiments demonstrate that OMNI achieves 3.4×- 6.2× speedup for the modern CNNs, over a comparably ideal dense CNN accelerator. OMNI shows 114.7× energy efficiency improvement compared with GPU platform. OMNI is also evaluated on Xilinx ZC706 and ZCU102 FPGA platforms, achieving 41.5 GOP/s and 125.3 GOP/s, respectively.
Intelligent personal assistants for autonomous second language learning: An investigation of Alexa

The ubiquity of smartphones and the growing popularity of smart speakers have given rise to cloud-based, intelligent personal assistants (IPAs), such as Siri and Google Assistant. However, little is known about the use of IPAs for Autonomous Second Language Learning (ASLL). Thus, the aims of this study were twofold: to assess Japanese English as a Foreign Language (EFL) students’ perceptions towards IPAs, also known as virtual assistants, for ASLL, and to better understand learner behavior of these technologies. A total of 14 Japanese university students were given smart speakers and interacted with a companion IPA, Amazon Alexa, over a two-month period in their homes. Moreover, the participants completed a survey consisting of Likert-scale items and open-ended questions to obtain their views of the IPA for ASLL. While the results indicated that the students had mostly favorable views of Alexa for L2 learning, many of them did not actively engage with the virtual assistant during the data collection period. Furthermore, students tended to give up when faced with communication difficulties with the IPA. These findings highlight the potential of IPAs for ASLL and underscore the gap between what students say, and what they actually do, with language learning technology.
AIM 2020 Challenge on Video Temporal Super-Resolution

Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis. From low-frame-rate (15 fps) videos, the challenge participants are required to submit higher-frame-rate (30 and 60 fps) sequences by estimating temporally intermediate frames. To simulate realistic and challenging dynamics in the real-world, we employ the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. There have been 68 registered participants in the competition, and 5 teams (one withdrawn) have competed in the final testing phase. The winning team proposes the enhanced quadratic video interpolation method and achieves state-of-the-art on the VTSR task.
Transfer learning for nonparametric classification: Minimax rate and adaptive classifier

Human learners have the natural ability to use knowledge gained in one setting for learning in a different but related setting. This ability to transfer knowledge from one task to another is essential for effective learning. In this paper, we study transfer learning in the context of nonparametric classification based on observations from different distributions under the posterior drift model, which is a general framework and arises in many practical problems. We first establish the minimax rate of convergence and construct a rate-optimal two-sample weighted $K$-NN classifier. The results characterize precisely the contribution of the observations from the source distribution to the classification task under the target distribution. A data-driven adaptive classifier is then proposed and is shown to simultaneously attain within a logarithmic factor of the optimal rate over a large collection of parameter spaces. Simulation studies and real data applications are carried out where the numerical results further illustrate the theoretical analysis. Extensions to the case of multiple source distributions are also considered.
Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method

Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.
Path Planning for Unmanned Aerial Vehicle Using a Mix-Strategy-Based Gravitational Search Algorithm

Path planning is a global optimization problem aims to program the optimal flight path for Unmanned Aerial Vehicle (UAV) that has short length and suffers from low threat. In this paper, we present a Mixed-Strategy based Gravitational Search Algorithm (MSGSA) for the path planning. In MSGSA, an adaptive adjustment strategy for the gravitational constant attenuation factor alpha ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha$ </tex-math></inline-formula> ) is presented firstly, in which the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> is adjusted based on the evolutionary state of the particles. This helps to adaptively balance the exploration and exploitation of the algorithm. In addition, to further alleviate the premature convergence problem, a Cauchy mutation strategy is developed for MSGSA. In this strategy, only when the global best particle cannot be further improved for several times the mutation is executed. In the MSGSA based path planning procedure, we construct an objective function using the flight length cost, threat area cost, and turning angle constraint to decrease the flight risk and obtain the smoother path. For performance evaluation, the MSGSA is applied to two typical simulated flight missions with complex flight environments, including user-defined forbidden flying areas, Radar, missile, artillery and anti-aircraft gun. The obtained flight paths are compared with that of the standard Gravitational Search Algorithm (GSA) and two improved variants of GSA, i.e. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gbest</i> -guided GSA (GGSA), and hybrid Particle Swarm Optimization and GSA (PSOGSA). The experimental results demonstrate the superiority of the MSGSA based method in terms of the solution quality, robustness, as well as the constraint-handling ability.
A Hybrid robust watermarking system based on discrete cosine transform, discrete wavelet transform, and singular value decomposition

Digital data sharing is increased day by day due to the rapid access of the Internet by mass people. Multimedia, for example text, image, video, and audio can be easily accessed by unauthorized users. To ensure multimedia security, researchers are focusing on digital image watermarking. A robust watermarking system must satisfy the basic design requirements like imperceptibility, robustness, security, and payload capacity. Watermarking can be embedded into a host image through different transform operations, such as discrete cosine transform (DCT), discrete wavelet transform (DWT), and singular value decomposition (SVD). However, a single transformation does not ensure all the design requirements simultaneously. To fill this gap, a hybrid blind digital image watermarking with a combination of DCT, DWT, and SVD is proposed in this paper. At first, the Arnold map is used to encrypt the watermark image. Then DCT is applied to it and to the host image followed by DWT before SVD. After that, the watermarked image is generated by embedding the watermark image into the host image. The performance is evaluated under various attacks and experimental results show that our algorithm ensures improved robustness while preserving high imperceptibility and enhanced security than the state-of-the-art methods.
Human rights in times of pandemics

Human rights-based governance is crucial in a pandemic, but what does this entail? This chapter presents a health and human rights approach that calls for state actors to respect the principles of necessity and proportionality through governing with solidarity, trust, and transparency, instead of coercion and fear. Policy makers must learn from past pandemics, in particular HIV/AIDS, and protect and respect all rights. Using this approach, the chapter highlights problematic elements of European states’ legislative responses to COVID-19, using restrictions on movement as a case study. First, several examples are explored, such as stay-at-home orders targeting elders, pregnant women, and children, as well as bans on outdoor exercise, where it is suggested that in line with the principle of proportionality, less restrictive measures could be as effective. Second, for the wealthy and those living in safe environments, stay-at-home orders may provide a proportionate response, while for other groups, impacts may be disproportionate and sometimes discriminatory. Third, while scientific certainty is impossible with a novel virus, measures that run contrary to scientific evidence should be avoided. The widespread use of criminal sanctions is in opposition to the human rights approach.