Reviewerly/exHarmony huggingface dataset:
Immersive virtual reality increases liking but not learning with a science simulation and generative learning strategies promote learning in immersive virtual reality.

We investigated the instructional effectiveness of using an interactive and immersive virtual reality (IVR) simulation versus a video for teaching scientific knowledge in 2 between-subjects experiments. In Experiment 1, 131 high school students (84 females) used a science simulation that involved forensic analysis of a collected DNA sample in a virtual laboratory environment rendered in IVR or as a video covering the same material. In Experiment 2, 165 high school students (111 females) replicated the experiment with approximately half of each group being asked to engage in the generative learning strategy of enactment after the lesson—that is, carrying out the learned procedures with concrete manipulatives. Across both experiments, the IVR groups reported significantly higher perceived enjoyment and presence than the video group. However, no significant differences were found between media for procedural knowledge in Experiment 1 and 2, or transfer in Experiment 2. Also, there was no difference in declarative knowledge across media in Experiment 1, and there was a media effect favoring video in Experiment 2 (ηp² = 0.028). Enactment lead to significantly better procedural knowledge (ηp² = 0.144) and transfer (ηp² = 0.088) in the IVR group but not in the video group. In conclusion, learning in IVR is not more effective than learning with video but incorporating generative learning strategies is specifically effective when learning through IVR. The results suggest that the value of IVR for learning science depends on how it is integrated into a classroom lesson. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
There's No Place Like Home: Continuing Design in Use

Publisher Summary Design is a process that is tightly coupled to use and that continues during the use of a system. This chapter discusses what may be involved in continuing design in use, and how one, in the initial design process, may create systems that are tailorable. Ideal tailorable systems are those in which there are means for the users, or supporters near the users, to make them fit different work situations. Tailoring a system, continuing designing in use, is an activity different from initial design. The activity is related to specific use situations and the result is not a new system, but a modified system, that is, a system with a history that relates it to the earlier version and problems with its use. The chapter also discusses various activities involved in modifying computer systems.
Volatility co-movement between Bitcoin and Ether

Using a bivariate Diagonal BEKK model, this paper investigates the volatility dynamics of the two major cryptocurrencies, namely Bitcoin and Ether. We find evidence of interdependencies in the cryptocurrency market, while it is shown that the two cryptocurrencies' conditional volatility and correlation are responsive to major news. In addition, we show that Ether can be an effective hedge against Bitcoin, while the analysis of optimal portfolio weights indicates that Bitcoin should outweigh Ether. Understanding volatility movements and interdependencies in cryptocurrency markets is important for appropriate investment management, and our study can thus assist cryptocurrency users in making more informed decisions.
Supply-Demand-Based Optimization: A Novel Economics-Inspired Algorithm for Global Optimization

A novel metaheuristic optimization algorithm, named supply-demand-based optimization (SDO), is presented in this paper. SDO is a swarm-based optimizer motivated by the supply-demand mechanism in economics. This algorithm mimics both the demand relation of consumers and supply relation of producers. The proposed algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and six engineering optimization problems. The results on the unconstrained test functions prove that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance, and convergence rate. The results on the constrained engineering problems suggest that SDO is considerately competitive in terms of computational expense, convergence rate, and solution accuracy. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/71764-supply-demand-based-optimization.
3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization

Temporal action localization is a challenging computer vision problem with numerous real-world applications. Most existing methods require laborious frame-level supervision to train action localization models. In this work, we propose a framework, called 3C-Net, which only requires video-level supervision (weak supervision) in the form of action category labels and the corresponding count. We introduce a novel formulation to learn discriminative action features with enhanced localization capabilities. Our joint formulation has three terms: a classification term to ensure the separability of learned action features, an adapted multi-label center loss term to enhance the action feature discriminability and a counting loss term to delineate adjacent action sequences, leading to improved localization. Comprehensive experiments are performed on two challenging benchmarks: THUMOS14 and ActivityNet 1.2. Our approach sets a new state-of-the-art for weakly-supervised temporal action localization on both datasets. On the THUMOS14 dataset, the proposed method achieves an absolute gain of 4.6% in terms of mean average precision (mAP), compared to the state-of-the-art. Source code is available at https://github.com/naraysa/3c-net.
Blockchain technology for a sustainable agri-food supply chain

Purpose The present paper is aimed at 1) performing a systematic literature review (SLR) on applications in the perspective of sustainable agri-food supply chain (SC) of blockchain technology (BCT); 2) analyzing the selected literature, focusing on the advantages of the sustainable uses of the blockchain of the aforementioned SC and 3) presenting an outlook and research directions capable of addressing unresolved problems. Design/methodology/approach The SLR was conducted using detailed criteria to identify academic articles. Moreover, specific keywords and databases were used. The time frame considered included the years 2010–2020. Findings The review analysis indicates that the use of BCT or BCT supported by ICT/IoT contributes to sustainability of agri-food production. However, this technology can lead to several challenges such as scalability, privacy leakage, high cost and connectivity problems. Research limitations/implications The paper demonstrates that BCT can widely use agri-food supply chain due to its intrinsic characteristics. However, it is not excluded that the criteria chosen may not have identified important articles regarding BCT, the agri-food sector and sustainability. Originality/value Although the body of academic literature published on this topic is expansive, the effect of BCT on the agri-food SC's sustainability aspects has not yet been adequately analyzed. Thus, the article is aimed at investigating how BCT is used in the SC. In particular, the article is intended to update information about BCT and its impact on sustainability.
Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism

In this paper, a parameter adaptation-based ant colony optimization (ACO) algorithm based on particle swarm optimization (PSO) algorithm with the global optimization ability, fuzzy system with the fuzzy reasoning ability and 3-Opt algorithm with local search ability, namely PF3SACO is proposed to improve the optimization ability and convergence, avoid to fall into local optimum. In the PF3SACO, a new dynamic parameter adjustment mechanism by the PSO and the fuzzy system is designed to adaptively adjust the pheromone importance factor α, pheromone volatilization coefficient ρ and the heuristic function importance factor β to accelerate the convergence, improve the search ability, enhance the local search ability and avoid premature. This is achievable by parameter adaptation to reflect the dynamic search characteristic by exploring and exploiting in the search process for the parameter values to be close to the optimal values. In addition, 3-Opt algorithm is applied to optimize the generated path to eliminate the cross path, obtain the optimal path and avoid to fall into local optimum. The optimization performance of the PF3SACO is investigated on fifteen travelling salesman problems (TSPs) with the scales from 42 to 783 cities. The experiment results show that the PF3SACO has better optimization performance by comparing with ABC, NACO, HYBRID, ACO-3Opt, PACO-3Opt, PSO-ACO-3Opt and some other well-known algorithms in most TSP in term of the solution quality, robustness and space distribution. It provides a reference to solve the large-scale TSP for obtaining better path length.
Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks

Deeper and wider convolutional neural networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such overparameterized neural network has received increased attention. A typical pruning algorithm is a three-stage pipeline, i.e., training, pruning, and retraining. Prevailing approaches fix the pruned filters to zero during retraining and, thus, significantly reduce the optimization space. Besides, they directly prune a large number of filters at first, which would cause unrecoverable information loss. To solve these problems, we propose an asymptotic soft filter pruning (ASFP) method to accelerate the inference procedure of the deep neural networks. First, we update the pruned filters during the retraining stage. As a result, the optimization space of the pruned model would not be reduced but be the same as that of the original model. In this way, the model has enough capacity to learn from the training data. Second, we prune the network asymptotically. We prune few filters at first and asymptotically prune more filters during the training procedure. With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable. The experiments show the effectiveness of our ASFP on image classification benchmarks. Notably, on ILSVRC-2012, our ASFP reduces more than 40% FLOPs on ResNet-50 with only 0.14% top-5 accuracy degradation, which is higher than the soft filter pruning by 8%.
DCSR: Dilated Convolutions for Single Image Super-Resolution

Dilated convolutions support expanding receptive field without parameter exploration or resolution loss, which turn out to be suitable for pixel-level prediction problems. In this paper, we propose multiscale single image super-resolution (SR) based on dilated convolutions. We adopt dilated convolutions to expand the receptive field size without incurring additional computational complexity. We mix standard convolutions and dilated convolutions in each layer, called mixed convolutions, i.e., in the mixed convolutional layer, and the feature extracted by dilated convolutions and standard convolutions are concatenated. We theoretically analyze the receptive field and intensity of mixed convolutions to discover their role in SR. Mixed convolutions remove blind spots and capture the correlation between low-resolution (LR) and high-resolution (HR) image pairs successfully, thus achieving good generalization ability. We verify those properties of mixed convolutions by training 5-layer and 10-layer networks. We also train a 20-layer deep network to compare the performance of the proposed method with those of the state-of-the-art ones. Moreover, we jointly learn maps with different scales from a LR image to its HR one in a single network. Experimental results demonstrate that the proposed method outperforms the state-of-the-art ones in terms of PSNR and SSIM, especially for a large-scale factor.
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input's length, they are prohibitively slow for very long sequences. To address this limitation, we express the self-attention as a linear dot-product of kernel feature maps and make use of the associativity property of matrix products to reduce the complexity from $\mathcal{O}\left(N^2\right)$ to $\mathcal{O}\left(N\right)$, where $N$ is the sequence length. We show that this formulation permits an iterative implementation that dramatically accelerates autoregressive transformers and reveals their relationship to recurrent neural networks. Our linear transformers achieve similar performance to vanilla transformers and they are up to 4000x faster on autoregressive prediction of very long sequences.
Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language

We present the pilot edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. It comprises two tasks, a coarse-grained binary classification task and a fine-grained multi-class classification task. The shared task had 20 participants submitting 51 runs for the coarse-grained task and 25 runs for the fine-grained task. Since this is a pilot task, we describe the process of extracting the raw-data for the data collection and the annotation schema. We evaluate the results of the systems submitted to the shared task. The shared task homepage can be found at https://projects.cai. fbi.h-da.de/iggsa/
A comprehensive survey on recent metaheuristics for feature selection

Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.
Verizon: 2019 Data Breach Investigations Report

Verizon's annual Data Breach Investigations Report (DBIR), compiled with the assistance of a large number of security firms, law enforcement agencies and government bodies, is considered by many to be a key indicator of cyber security trends and developments.
Lookahead Optimizer: k steps forward, 1 step back

The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms. Recent attempts to improve SGD can be broadly categorized into two approaches: (1) adaptive learning rate schemes, such as AdaGrad and Adam and (2) accelerated schemes, such as heavy-ball and Nesterov momentum. In this paper, we propose a new optimization algorithm, Lookahead, that is orthogonal to these previous approaches and iteratively updates two sets of weights. Intuitively, the algorithm chooses a search direction by looking ahead at the sequence of ``fast weights generated by another optimizer. We show that Lookahead improves the learning stability and lowers the variance of its inner optimizer with negligible computation and memory cost. We empirically demonstrate Lookahead can significantly improve the performance of SGD and Adam, even with their default hyperparameter settings on ImageNet, CIFAR-10/100, neural machine translation, and Penn Treebank.
An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

Feature selection, an optimization problem, becomes an important pre-process tool in data mining, which simultaneously aims at minimizing feature-size and maximizing model generalization. Because of large search space, conventional optimization methods often fail to generate global optimum solution. A variety of hybrid techniques merging different search strategies have been proposed in feature selection literature, but mostly deal with low dimensional datasets. In this paper, a hybrid optimization method is proposed for numerical optimization and feature selection, which integrates sine-cosine algorithm (SCA) in Harris hawks optimization (HHO). The goal of SCA integration is to cater ineffective exploration in HHO, moreover exploitation is enhanced by dynamically adjusting candidate solutions for avoiding solution stagnancy in HHO. The proposed method, namely SCHHO, is evaluated by employing CEC’17 test suite for numerical optimization and sixteen datasets with low and high-dimensions exceeding 15000 attributes, and compared with original SCA and HHO, as well as, other well-known optimization methods like dragonfly algorithm (DA), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), Grey wolf optimization (GWO), and salp swarm algorithm (SSA); in addition to state-of-the-art methods. Performance of the proposed method is also validated against hybrid methods proposed in recent related literature. The extensive experimental and statistical analyses suggest that the proposed hybrid variant of HHO is able to produce efficient search results without additional computational cost. With increased convergence speed, SCHHO reduced feature-size up to 87% and achieved accuracy up to 92%. Motivated from the findings of this study, various potential future directions are also highlighted.
A Feature-Based Learning System for Internet of Things Applications

In many applications of Internet of Things (IoT), the huge amount of data are generated by sensor nodes and processing them are complex. Offloading data classification and anomaly event detection tasks to sink nodes in sensor networks can reduce the computing complexity, lower remote communication loads, and improve the response time for the delay-sensitive IoT applications. Many existing classification and anomaly detection methods cannot be directly applied to these IoT applications, because the computing and energy resources of sensors are limited. In this paper, a new feature-based learning system for IoT applications is proposed to effectively classify data and detect anomaly event. Especially, based on the theory of distributed compression, the sparsity and relativity of the data are exploited to obtain the classification features, which can reduce the computation overhead and energy consumption. Further, an RBF-BP hybrid neural network is employed to detect the anomaly event based on the classification results, by which the training time of neural network can be significantly reduced and the accuracy can be improved for users' decisions.
Framework of Affordances for Virtual Reality and Augmented Reality

Virtual reality (VR) and augmented reality (AR) technologies continue to grow and present possibilities to change the ways we learn, accomplish tasks, and interact with the world. However, widespread adoption has continually languished below purported potential. We suggest that a more complete understanding of the underlying motives driving users to take advantage of VR and AR would aid researchers by consolidating fragmented knowledge across domains and by identifying paths for additional inquiry. Additionally, practitioners could identify areas of unmet motives for using VR and AR. To examine the motives for virtualization, we draw upon Gibson’s seminal work on affordances to create a framework of generalized affordances for virtually assisted activities relative to the affordances of physical reality. This framework facilitates comparison of virtualized activities to non-virtualized activities, comparison of similar activities across VR and AR, and delineates areas of inquiry for future research. The validity of the framework was explored through two quantitative studies and one qualitative study of a wide variety of professionals. We found that participants perceive a significant difference between physical reality and both VR and AR for all proposed affordances, and that for many affordances, users perceive a difference in the ability of AR and VR to enact them. The qualitative study confirmed the general structure of the framework, while also revealing additional sub-affordances to explore. Theoretically, this suggests that examining the affordances that differentiate these technologies from physical reality may be a valid approach to understanding why users adopt these technologies. Practitioners may find success by focusing development on the specific affordances that VR or AR is best equipped to enact.
Training Generative Adversarial Networks with Limited Data

Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset. We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images. We expect this to open up new application domains for GANs. We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5.59 to 2.42.
Precision Measurement of the Optical Conductivity of Atomically Thin Crystals via the Photonic Spin Hall Effect

How to measure the optical conductivity of atomically thin crystals is an important but challenging issue due to the weak light-matter interaction at the atomic scale. The photonic spin Hall effect, as a fundamental physical effect in light-matter interaction, is extremely sensitive to the optical conductivity of atomically thin crystals. Here we report a precision measurement of the optical conductivity of graphene, where the photonic spin Hall effect acts as a measurement pointer. By use of the weak-value-amplification technique, the optical conductivity of monolayer graphene taken as a universal constant of $(0.993\ifmmode\pm\else\textpm\fi{}0.005){\ensuremath{\sigma}}_{0}$ is detected, and a high measurement resolution of $1.5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}8}\phantom{\rule{0.2em}{0ex}}{\mathrm{\ensuremath{\Omega}}}^{\ensuremath{-}1}$ is obtained. For few-layer graphene without a twist, we find that the conductivities increase linearly with the number of layers. Our idea could provide an important measurement technique for probing other parameters of atomically thin crystals, such as the magneto-optical constant, circular dichroism, and optical nonlinear coefficient.