Reviewerly/exHarmony huggingface dataset:
AvatarMe: Realistically Renderable 3D Facial Reconstruction “In-the-Wild”

Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image. Nevertheless, to the best of our knowledge, there is no method which can produce high-resolution photorealistic 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data. In this paper, we introduce AvatarMe, the first method that is able to reconstruct photorealistic 3D faces from a single "in-the-wild" image with an increasing level of detail. To achieve this, we capture a large dataset of facial shape and reflectance and build on a state-of-the-art 3D texture and shape reconstruction method and successively refine its results, while generating the per-pixel diffuse and specular components that are required for realistic rendering. As we demonstrate in a series of qualitative and quantitative experiments, AvatarMe outperforms the existing arts by a significant margin and reconstructs authentic, 4K by 6K-resolution 3D faces from a single low-resolution image that, for the first time, bridges the uncanny valley.
Developing a short assessment instrument for Technological Pedagogical Content Knowledge (TPACK.xs) and comparing the factor structure of an integrative and a transformative model

Technological Pedagogical Content Knowledge (TPACK) is regarded as one of the most important models describing teachers' competencies for successfully teaching with technology. TPACK is most frequently assessed by means of self-report questionnaires, which beside their inherent methodological limitations present constraints related either to the validity, reliability, or practical applicability of existing instruments. Furthermore, the internal structure of the TPACK framework is a topic of debate. The two goals of this study were (1) to develop a valid and reliable short questionnaire for measuring TPACK (TPACK.xs), and (2) to use this instrument to investigate TPACK's internal relations, assessing whether the framework reflects an integrative or a transformative view regarding how the TPACK knowledge domains interact. An initial questionnaire of 42 items was administered to 117 pre-service upper secondary school teachers. Reliability analysis and confirmatory factor analysis were used to reduce the number of items per subscale and fit the model. Structural equation modelling investigated the internal relations between components. Results show that the final TPACK.xs questionnaire, consisting of 28 items, can be considered a valid and reliable instrument for assessing pre-service teachers' TPACK. Furthermore, the internal relations of knowledge components support a transformative view of the TPACK model.
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

Next Point-of-Interest (POI) recommendation which is of great value to both users and POI holders is a challenging task since complex sequential patterns and rich contexts are contained in extremely sparse user check-in data. Recently proposed embedding techniques have shown promising results in alleviating the data sparsity issue by modeling context information, and Recurrent Neural Network (RNN) has been proved effective in the sequential prediction. However, existing next POI recommendation approaches train the embedding and network model separately, which cannot fully leverage rich contexts. In this paper, we propose a novel unified neural network framework, named NeuNext, which leverages POI context prediction to assist next POI recommendation by joint learning. Specifically, the Spatio-Temporal Gated Network (STGN) is proposed to model personalized sequential patterns for users’ long and short term preferences in the next POI recommendation. In the POI context prediction, rich contexts on POI sides are used to construct graph, and enforce the smoothness among neighboring POIs. Finally, we jointly train the POI context prediction and the next POI recommendation to fully leverage labeled and unlabeled data. Extensive experiments on real-world datasets show that our method outperforms other approaches for next POI recommendation in terms of Accuracy and MAP.
Robustness of LSTM neural networks for multi-step forecasting of chaotic time series

Recurrent neurons (and in particular LSTM cells) demonstrated to be efficient when used as basic blocks to build sequence to sequence architectures, which represent the state-of-the-art approach in many sequential tasks related to natural language processing. In this work, these architectures are proposed as general purposes, multi-step predictors for nonlinear time series. We analyze artificial, noise-free data generated by chaotic oscillators and compare LSTM nets with the benchmarks set by feed-forward, one-step-recursive and multi-output predictors. We focus on two different training methods for LSTM nets. The traditional one makes use of the so-called teacher forcing, i.e. the ground truth data are used as input for each time step ahead, rather than the outputs predicted for the previous steps. Conversely, the second feeds the previous predictions back into the recurrent neurons, as it happens when the network is used in forecasting. LSTM predictors robustly show the strengths of the two benchmark competitors, i.e., the good short-term performance of one-step-recursive predictors and greatly improved mid-long-term predictions with respect to feed-forward, multi-output predictors. Training LSTM predictors without teacher forcing is recommended to improve accuracy and robustness, and ensures a more uniform distribution of the predictive power within the chaotic attractor. We also show that LSTM architectures maintain good performances when the number of time lags included in the input differs from the actual embedding dimension of the dataset, a feature that is very important when working on real data.
Pandemic designs for the future: perspectives of technology education teachers during COVID-19

Purpose The disruption caused by the pandemic declaration and subsequent public health measures put in place have had a substantial effect on teachers’ abilities to support student engagement in technology education (TE). The purpose of this paper is to explore the following research question: How do TE teachers see emergency remote teaching (ERT) transitions to blended learning into the next academic year affecting their profession? Design/methodology/approach A snowball and convenience sampling design was used to recruit specialist teachers in TE through their professional organization and were asked to respond to the question: What are your concerns about the future of teaching TE remotely? The qualitative data collected from the participants (N = 42) was analyzed thematically (Braun and Clarke, 2006). Findings The analysis revealed that the switch to ERT impacted the teachers’ ability to support hands-on competency development owing to inequitable student access to tools, materials and resources, all of which affected student motivation and engagement. As a result, teachers raised questions about the overall effectiveness of online learning approaches and TE’s future and sustainability if offered completely online. Originality/value This research is the first of its kind exploring the experiences of TE teachers during the COVID-19 pandemic. In answer to the challenges identified by teachers, the authors offer a blended learning design framework informed by pandemic transformed pedagogy that can serve as a model for educators to use when designing blended instruction.
Analyzing the Impact of COVID-19 on Education Professionals. Toward a Paradigm Shift: ICT and Neuroeducation as a Binomial of Action

This study analyzed the current situation of education in the context of the pandemic caused by COVID-19. The worldwide health emergency situation has caused the confinement of people and with it, the closure of centers and the transfer of face-to-face education to online education. Faced with these facts, teachers have had to adapt at a dizzying pace not only to new methodological approaches, but also to their own confinement, presenting high levels of stress. The purpose of this study is to offer a proposal that optimizes the work of education professionals in the current context of a pandemic through the use of Information and Communication Technologies (ICT) under the novel approach of the contributions of neuroeducation in the field of managing emotions and motivational processes, contributing to meaningful learning in students. The symbiosis of ICT and neuroeducation can make a great contribution to the paradigm shift that is taking place today.
Federated Variance-Reduced Stochastic Gradient Descent With Robustness to Byzantine Attacks

This paper deals with distributed finite-sum optimization for learning over multiple workers in the presence of malicious Byzantine attacks. Most resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced stochastic gradient noise challenges discerning malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the `honest' workers. This motivates reducing the variance of stochastic gradients as a means of robustifying SGD. To this end, a novel Byzantine attack resilient distributed (Byrd-) SAGA approach is introduced for federated learning tasks involving multiple workers. Rather than the mean employed by distributed SAGA, the novel Byrd-SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, Byrd-SAGA attains provably linear convergence to a neighborhood of the optimal solution, with the asymptotic learning error determined by the number of Byzantine workers. Numerical tests corroborate the robustness to various Byzantine attacks, as well as the merits of Byrd-SAGA over Byzantine attack resilient distributed SGD.
Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry

The release of openly available, robust natural language generation algorithms (NLG) has spurred much public attention and debate. One reason lies in the algorithms' purported ability to generate humanlike text across various domains. Empirical evidence using incentivized tasks to assess whether people (a) can distinguish and (b) prefer algorithm-generated versus human-written text is lacking. We conducted two experiments assessing behavioral reactions to the state-of-the-art Natural Language Generation algorithm GPT-2 (Ntotal = 830). Using the identical starting lines of human poems, GPT-2 produced samples of poems. From these samples, either a random poem was chosen (Human-out-of-theloop) or the best one was selected (Human-in-the-loop) and in turn matched with a human-written poem. In a new incentivized version of the Turing Test, participants failed to reliably detect the algorithmicallygenerated poems in the Human-in-the-loop treatment, yet succeeded in the Human-out-of-the-loop treatment. Further, people reveal a slight aversion to algorithm-generated poetry, independent on whether participants were informed about the algorithmic origin of the poem (Transparency) or not (Opacity). We discuss what these results convey about the performance of NLG algorithms to produce human-like text and propose methodologies to study such learning algorithms in human-agent experimental settings.
One-Class Convolutional Neural Network

We present a novel Convolutional Neural Network (CNN) based approach for one class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the cross-entropy loss to learn a good representation as well as the decision boundary for the given class. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. The proposed One Class CNN (OC-CNN) is evaluated on the UMDAA-02 Face, Abnormality-1001, FounderType-200 datasets. These datasets are related to a variety of one class application problems such as user authentication, abnormality detection and novelty detection. Extensive experiments demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. The source code is available at : github.com/otkupjnoz/oc-cnn.
A Novel Fast Single Image Dehazing Algorithm Based on Artificial Multiexposure Image Fusion

Poor weather conditions, such as fog, haze, and mist, cause visibility degradation in captured images. Existing imaging devices lack the ability to effectively and efficiently mitigate the visibility degradation caused by poor weather conditions in real time. Image depth information is used to eliminate hazy effects by using existing physical model-based approaches. However, the imprecise depth information always affects dehazing performance. This article proposes an image fusion-based algorithm to enhance the performance and robustness of image dehazing. Based on a set of gamma-corrected underexposed images, pixelwise weight maps are constructed by analyzing both global and local exposedness to guide the fusion process. The spatial-dependence of luminance of the fused image is reduced, and its color saturation is balanced in the dehazing process. The performance of the proposed solution is confirmed in both theoretical analysis and comparative experiments.
A comprehensive review on deep learning-based methods for video anomaly detection

Video surveillance systems are popular and used in public places such as market places, shopping malls, hospitals, banks, streets, education institutions, city administrative offices, and smart cities to enhance the safety of public lives and assets. Most of the time, the timely and accurate detection of video anomalies is the main objective of security applications. The video anomalies such as anomalous activities and anomalous entities are defined as the abnormal or irregular patterns present in the video that do not conform to the normal trained patterns. Anomalous activities such as fighting, riots, traffic rule violations, and stampede as well as anomalous entities such as weapons at the sensitive place and abandoned luggage should be detected automatically in time. However, the detection of video anomalies is challenging due to the ambiguous nature of the anomaly, various environmental conditions, the complex nature of human behaviors, and the lack of proper datasets. There are only a few dedicated surveys related to deep learning-based video anomaly detection as the research domain is in its early stages. However, state of the art lacks a review that provides a comprehensive study covering all the aspects such as definitions, classifications, modelings, performance evaluation methodologies, open and trending research challenges of video anomaly detection. Hence, in this survey, we present a comprehensive study of the deep learning-based methods reported in state of the art to detect the video anomalies. Further, we discuss the comparative analysis of the state of the art methods in terms of datasets, computational infrastructure, and performance metrics for both quantitative and qualitative analyses. Finally, we outline the challenges and promising directions for further research.
Instance Adaptive Self-training for Unsupervised Domain Adaptation

The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such a problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing scalability and performance. In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. Besides, we propose the region-guided regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. Our method is so concise and efficient that it is easy to be generalized to other unsupervised domain adaptation methods. Experiments on ‘GTA5 to Cityscapes’ and ‘SYNTHIA to Cityscapes’ demonstrate the superior performance of our approach compared with the state-of-the-art methods.
Learning Delicate Local Representations for Multi-person Pose Estimation

In this paper, we propose a novel method called Residual Steps Network (RSN). RSN aggregates features with the same spatial size (Intra-level features) efficiently to obtain delicate local representations, which retain rich low-level spatial information and result in precise keypoint localization. Additionally, we observe the output features contribute differently to final performance. To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations. Our approach won the 1st place of COCO Keypoint Challenge 2019 and achieves state-of-the-art results on both COCO and MPII benchmarks, without using extra training data and pretrained model. Our single model achieves 78.6 on COCO test-dev, 93.0 on MPII test dataset. Ensembled models achieve 79.2 on COCO test-dev, 77.1 on COCO test-challenge dataset. The source code is publicly available for further research at https://github.com/caiyuanhao1998/RSN/ .
SP2F: A secured privacy-preserving framework for smart agricultural Unmanned Aerial Vehicles

The current advancement in Unmanned Aerial Vehicles (UAVs) and the proliferation of the Internet of Things (IoT) devices is revolutionizing conventional farming operations into precision agriculture. The agricultural UAVs combined with IoT use an open channel i.e., the Internet to assist cultivators with data collection, processing, monitoring, and making correct decisions on the farm. However, the use of the Internet opens up a wide range of challenges such as security (e.g., performing cyber-attacks), risk of data privacy (e.g., data poisoning and inference attacks), etc. The usage of current conventional centralized security measures has limitations in terms of a single point of failure, verifiability, traceability, and scalability. Motivated from the aforementioned challenges, we propose a Secured Privacy-Preserving Framework (SP2F) for smart agricultural UAVs. The proposed SP2F framework has two main engines, a two-level privacy engine, and a deep learning-based anomaly detection engine. In the two-level privacy engine, a blockchain, and smart contract-based enhanced Proof of Work (ePoW) is designed for data authentication, and to mitigate data poisoning attacks. A Sparse AutoEncoder (SAE) is applied for transforming data into a new encoded format for preventing inference attacks. In the anomaly detection engine, a Stacked Long-Short-Term Memory (SLSTM) is used to train and evaluate the results of the proposed two-level privacy engine using two publicly accessible IoT-based datasets, namely ToN-IoT and IoT Botnet. Finally, based on thorough analysis, and comparison, we identify that the SP2F framework outperforms several state-of-the-art techniques in both non-blockchain and blockchain frameworks.
WITHDRAWN: Effectiveness of artificial intelligence techniques against cyber security risks apply of IT industry

The aim of the researcher was to determine the effectiveness of artificial intelligence techniques against cyber security risks particularly in case of Iraq, Researcher has opted for quantitative method of research design along with primary data. The researcher collected the data from employees working in this IT industry. The sample size for this study was 468 and confirmatory factor analysis, discriminant validity, basic analysis of model and lastly, hypothesis assessment was carried out. The P-values of all variables were obtained as significant apart from expert system which had no significant relation with artificial intelligence and cyber security. Geographical area, sample size, less variables and accessibility was the main issue.
RetinexDIP: A Unified Deep Framework for Low-Light Image Enhancement

Low-light images suffer from low contrast and unclear details, which not only reduces the available information for humans but limits the application of computer vision algorithms. Among the existing enhancement techniques, Retinex-based and learning-based methods are under the spotlight today. In this paper, we bridge the gap between the two methods. First, we propose a novel &#x201C;generative&#x201D; strategy for Retinex decomposition, by which the decomposition is cast as a generative problem. Second, based on the strategy, a unified deep framework is proposed to estimate the latent components and perform low-light image enhancement. Third, our method can weaken the coupling relationship between the two components while performing Retinex decomposition. Finally, the RetinexDIP performs Retinex decomposition without any external images, and the estimated illumination can be easily adjusted and is used to perform enhancement. The proposed method is compared with ten state-of-the-art algorithms on seven public datasets, and the experimental results demonstrate the superiority of our method. Code is available at: <uri>https://github.com/zhaozunjin/RetinexDIP</uri>.
ACTION-Net: Multipath Excitation for Action Recognition

Spatial-temporal, channel-wise, and motion patterns are three complementary and crucial types of information for video action recognition. Conventional 2D CNNs are computationally cheap but cannot catch temporal relationships; 3D CNNs can achieve good performance but are computationally intensive. In this work, we tackle this dilemma by designing a generic and effective module that can be embedded into 2D CNNs. To this end, we propose a spAtio-temporal, Channel and moTion excitatION (ACTION) module consisting of three paths: Spatio-Temporal Excitation (STE) path, Channel Excitation (CE) path, and Motion Excitation (ME) path. The STE path employs one channel 3D convolution to characterize spatio-temporal representation. The CE path adaptively recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels in terms of the temporal aspect. The ME path calculates feature-level temporal differences, which is then utilized to excite motion-sensitive channels. We equip 2D CNNs with the proposed ACTION module to form a simple yet effective ACTION-Net with very limited extra computational cost. ACTION-Net is demonstrated by consistently outperforming 2D CNN counterparts on three backbones (i.e., ResNet-50, MobileNet V2 and BNInception) employing three datasets (i.e., Something-Something V2, Jester, and EgoGesture). Code is provided at https://github.com/V-Sense/ACTION-Net.