Reviewerly/exHarmony huggingface dataset:
FakeTagger

In recent years, DeepFake is becoming a common threat to our society, due to the remarkable progress of generative adversarial networks (GAN) in image synthesis. Unfortunately, existing studies that propose various approaches, in fighting against DeepFake and determining if the facial image is real or fake, is still at an early stage. Obviously, the current DeepFake detection method struggles to catch the rapid progress of GANs, especially in the adversarial scenarios where attackers can evade the detection intentionally, such as adding perturbations to fool the DNN-based detectors. While passive detection simply tells whether the image is fake or real, DeepFake provenance, on the other hand, provides clues for tracking the sources in DeepFake forensics. Thus, the tracked fake images could be blocked immediately by administrators and avoid further spread in social networks.
Impact of Virtual Reality on Student Motivation in a High School Science Course

It is essential to expose students to real situations in science courses, to experience how classroom concepts are reflected in the real world. However, the materials and methods available are not always very adequate; for example, chemistry courses involve the supervision of reagents to avoid risky situations, in addition to the costs, logistics of preparing materials, and possible adverse environmental factors. As an alternative solution, the following experience was carried out using virtual reality (VR) equipment, with very realistic applications that allowed 304 fourth semester high school students to have an immersive, interactive, and contextualized experience of the disciplinary contents. The students were asked about their perception regarding the motivation and acceptance of the use of virtual reality. The results were 72% positive for attention, 61% positive for relevance, 64% positive for trust, and 71% positive for satisfaction. Also, they mentioned their intention to continue using this resource and create lines of research to study the different aspects that could form a disciplinary proposal for an entire course based on virtual reality.
College English Smart Classroom Teaching Model Based on Artificial Intelligence Technology in Mobile Information Systems

With the development of mobile information, artificial intelligence technology has developed rapidly, which is changing everything in our life. Meanwhile, smart education appeared and attracted much attention; however, there is still a lack of systematic discussion on how to take the road of artificial intelligence plus smart classroom. In order to promote the intelligent development of education, this article mainly talks about the application of artificial intelligence technology to college English smart classrooms. In the process of teaching, a new teaching model of college English smart classroom was designed, and the teaching model was carried out through the experimental class and the survey results of the students. The survey results show that students’ satisfaction with this teaching model is as high as 80%.
Designing an alternate reality board game with augmented reality and multi-dimensional scaffolding for promoting spatial and logical ability

Logical thinking and reasoning of geometric space are among the key abilities of science and technology education. This research developed a framework of an alternate reality-based board game incorporating augmented reality (AR) exploration with multi-dimensional scaffolding, including cognitive scaffolding, metacognitive scaffolding, and peer scaffolding. Features included alternate reality games, board games, and AR technology. An educational alternate reality game Lost in Space based on the above framework was developed, and scaffolding was designed to help players focus on spatial and logical thinking. Participants were 127 tenth-grade students from northern Taiwan. Empirical analysis was conducted with the aim of exploring the learners’ learning outcomes, flow state, and perceptions of the multi-dimensional scaffolding as well as its usefulness. The results showed that there was no significant difference between the learning achievement of the students in the experimental group (using game learning) and the control group (using general teaching materials). However, students in the experimental group showed significant improvement in learning achievement after the game, while students in the control group showed no significant improvement in learning achievement. Moreover, experimental group learners could sense most of the cognitive scaffolding, metacognitive scaffolding, and peer scaffolding.
FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation

Dense optical flow estimation plays a key role in many robotic vision tasks. In the past few years, with the advent of deep learning, we have witnessed great progress in optical flow estimation. However, current networks often consist of a large number of parameters and require heavy computation costs, largely hindering its application on low power-consumption devices such as mobile phones. In this paper, we tackle this challenge and design a lightweight model for fast and accurate optical flow prediction. Our proposed FastFlowNet follows the widely-used coarse-to-fine paradigm with following innovations. First, a new head enhanced pooling pyramid (HEPP) feature extractor is employed to intensify high-resolution pyramid features while reducing parameters. Second, we introduce a new center dense dilated correlation (CDDC) layer for constructing compact cost volume that can keep large search radius with reduced computation burden. Third, an efficient shuffle block decoder (SBD) is implanted into each pyramid level to accelerate flow estimation with marginal drops in accuracy. Experiments on both synthetic Sintel data and real-world KITTI datasets demonstrate the effectiveness of the proposed approach, which needs only <sup>1</sup>/10 computation of comparable networks to achieve on par accuracy. In particular, FastFlowNet only contains 1.37M parameters; and can execute at 90 FPS (with a single GTX 1080Ti) or 5.7 FPS (embedded Jetson TX2 GPU) on a pair of Sintel images of resolution 1024 &#x00D7; 436.Code is available at: https://git.io/fastflow
DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction

In this work, we propose a new framework, called Document Image Transformer (DocTr), to address the issue of geometry and illumination distortion of the document images. Specifically, DocTr consists of a geometric unwarping transformer and an illumination correction transformer. By setting a set of learned query embedding, the geometric unwarping transformer captures the global context of the document image by self-attention mechanism and decodes the pixel-wise displacement solution to correct the geometric distortion. After geometric unwarping, our illumination correction transformer further removes the shading artifacts to improve the visual quality and OCR accuracy. Extensive evaluations are conducted on several datasets, and superior results are reported against the state-of-the-art methods. Remarkably, our DocTr achieves $20.02%$ Character Error Rate (CER), a $15%$ absolute improvement over the state-of-the-art methods. Moreover, it also shows high efficiency on running time and parameter count.
Towards responsible media recommendation

Abstract Reading or viewing recommendations are a common feature on modern media sites. What is shown to consumers as recommendations is nowadays often automatically determined by AI algorithms, typically with the goal of helping consumers discover relevant content more easily. However, the highlighting or filtering of information that comes with such recommendations may lead to undesired effects on consumers or even society, for example, when an algorithm leads to the creation of filter bubbles or amplifies the spread of misinformation. These well-documented phenomena create a need for improved mechanisms for responsible media recommendation, which avoid such negative effects of recommender systems. In this research note, we review the threats and challenges that may result from the use of automated media recommendation technology, and we outline possible steps to mitigate such undesired societal effects in the future.
Zero Trust: The What, How, Why, and When

Trust is a critical characteristic of computer systems, but the traditional approach of evaluating systems has failed to deliver the required levels of confidence. We review the emerging zero trust paradigm and propose a new set of zero trust tenets and an enhanced zero trust model.
Differential evolution with rankings-based fitness function for constrained optimization problems

When evolutionary algorithms are employed to solve constrained optimization problems (COPs), how to efficiently make use of the information of some promising infeasible solutions is very important in the process of searching for the optimal feasible solution. In this paper, for selecting and making full use of some better infeasible solutions, a rankings-based fitness function method is designed. Specifically, the final fitness function of each individual is obtained by weighting two rankings, which are got after sorting the population based on the ɛ constraint technique and only based on the objective function, respectively. Furthermore, the weight is dynamically adjusted by considering the proportion of feasible solutions and generation information. By doing this, the tradeoff in constraints and objective can be addressed. Moreover, the promising offspring are generated by three differential evolution strategies with distinct characters to balance diversity and convergence. In addition, 116 benchmark problems from three test suites are used to evaluate the performance of the proposed method. Nine commonly used practical problems are selected to test the potential of the algorithm to solve real-world problems. Experimental results indicate that the proposed method shows superior or competitive to other state-of-the-art methods tailored for COPs. Moreover, the effectiveness of each introduced component in the proposed algorithm is investigated by the ablation study.
Echo state network activation function based on bistable stochastic resonance

Stochastic resonance (SR) is a phenomenon wherein an information-carrying signal is enhanced via noise in a nonlinear system. This phenomenon enables living beings to adapt to noisy environments and use environmental noise to obtain useful information. A novel activation function of the echo state network (ESN) based on bistable SR is proposed in this study. Instead of using the tanh activation function—which is representative of the traditional threshold activation function—the bistable SR activation function is used to improve the noise adaptability of the ESN. Further, the proposed activation function provides a short-term memory (STM) ability that is not provided by the widely used threshold activation function, and thus, a physical reservoir can be designed using the proposed function. An STM task and a parity check task are used to verify the short-term memory and nonlinear ability of the bistable SR activation function. Further, two different prediction benchmarks prove that the proposed activation function can improve the noise adaptability of ESN. Finally, a visual recognition task is performed to demonstrate the potential of the SR activation function for physical reservoir computing.
Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning

Sampling-based path planning is a popular methodology for robot path planning. With a uniform sampling strategy to explore the state space, a feasible path can be found without the complex geometric modeling of the configuration space. However, the quality of the initial solution is not guaranteed, and the convergence speed to the optimal solution is slow. In this paper, we present a novel image-based path planning algorithm to overcome these limitations. Specifically, a generative adversarial network (GAN) is designed to take the environment map (denoted as RGB image) as the input without other preprocessing works. The output is also an RGB image where the promising region (where a feasible path probably exists) is segmented. This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner. We conduct a number of simulation experiments to validate the effectiveness of the proposed method, and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution. Furthermore, apart from the environments similar to the training set, our method also works well on the environments which are very different from the training set.
An Autonomous UAV-Assisted Distance-Aware Crowd Sensing Platform Using Deep ShuffleNet Transfer Learning

Autonomous unmanned aerial vehicles (UAVs) are essential for detecting and tracking specific events, such as automatic navigation. The intelligent monitoring of people’s social distances in crowds is one of the most significant events caused by the coronavirus. The virus is spreading more quickly among the crowds, and the disease cycle continues in congested areas. Due to the error that occurs when humans monitor their activity, an automated model is required to alert to social distance violations in crowds. As a result, this article proposes a two-step framework based on autonomous UAV videos, including human tracking and deep learning-based recognition of the crowd’s social distance. The deep architecture is a modified-fast and lightweight ShuffleNet learning structure. First, the Kalman filter is used to determine the positions of individuals, and then the modified ShuffleNet is used to refine the bounding boxes obtained and determine the social distance. The social distance is calculated using the initial refinement of the bounding box obtained during the tracking step and the scale in frames of the human body. The observed average accuracy, average processing time (APT), and processed frame per second (FPS) for three congestion datasets were 97.5%, 84 milliseconds, and 11.5 FPS, respectively. Real-time decision-making was achieved by reducing the size and resolution of the frames. Additionally, the frames were re-labeled to reduce the computational complexity associated with detecting social distancing. The experimental results demonstrated that the proposed method could operate more quickly and accurately on various resolution frames of UAV videos with difficult conditions.
Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization

Abstract White blood cells, WBCs for short, are an essential component of the human immune system. These cells are our body's first line of defense against infections and diseases caused by bacteria, viruses, and fungi, as well as abnormal and external substances that may enter the bloodstream. A wrong WBC count can signify dangerous viral infections, autoimmune disorders, cancer, sarcoidosis, aplastic anemia, leukemia, tuberculosis, etc. A lot of these diseases and disorders can be extremely painful and often result in death. Leukemia is among the more common types of blood cancer and when left undetected leads to death. An early diagnosis is necessary which is possible by looking at the shapes and determining the numbers of young and immature WBCs to see if they are normal or not. Performing this task manually is a cumbersome, expensive, and time-consuming process for hematologists, and therefore computer-aided systems have been developed to help with this problem. This paper proposes an improved method of classification of WBCs utilizing a combination of preprocessing, convolutional neural networks (CNNs), feature selection algorithms, and classifiers. In preprocessing, contrast-limited adaptive histogram equalization (CLAHE) is applied to the input images. A CNN is designed and trained to be used for feature extraction along with ResNet50 and EfficientNetB0 networks. Ant colony optimization is used to select the best features which are then serially fused and passed onto classifiers such as support vector machine (SVM) and quadratic discriminant analysis (QDA) for classification. The classification accuracy achieved on the Blood Cell Images dataset is 98.44%, which shows the robustness of the proposed work.
Requirements Engineering for Machine Learning: A Systematic Mapping Study

Machine learning (ML) has become a core feature for today’s real-world applications, making it a trending topic for the software engineering community. Requirements Engineering (RE) is no stranger to this and its main conferences have included workshops aiming at discussing RE in the context of ML. However, current research on the intersection between RE and ML mainly focuses on using ML techniques to support RE activities rather than on exploring how RE can improve the development of ML-based systems. This paper concerns a systematic mapping study aiming at characterizing the publication landscape of RE for ML-based systems, outlining research contributions and contemporary gaps for future research. In total, we identified 35 studies that met our inclusion criteria. We found several different types of contributions, in the form of analyses, approaches, checklists and guidelines, quality models, and taxonomies. We discuss gaps by mapping these contributions against the RE topics to which they were contributing and their type of empirical evaluation. We also identified quality characteristics that are particularly relevant for the ML context (e.g., data quality, explainability, fairness, safety, and transparency). Main reported challenges are related to the lack of validated RE techniques, the fragmented and incomplete understanding of NFRs for ML, and difficulties in handling customer expectations. There is a need for future research on the topic to reveal best practices and to propose and investigate approaches that are suitable to be used in practice.
Chaotic Behavior Analysis of a New Incommensurate Fractional-Order Hopfield Neural Network System

This study intends to examine different dynamics of the chaotic incommensurate fractional-order Hopfield neural network model. The stability of the proposed incommensurate-order model is analyzed numerically by continuously varying the values of the fractional-order derivative and the values of the system parameters. It turned out that the formulated system using the Caputo differential operator exhibits many rich complex dynamics, including symmetry, bistability, and coexisting chaotic attractors. On the other hand, it has been detected that by adapting the corresponding controlled constants, such systems possess the so-called offset boosting of three variables. Besides, the resultant periodic and chaotic attractors can be scattered in several forms, including 1D line, 2D lattice, and 3D grid, and even in an arbitrary location of the phase space. Several numerical simulations are implemented, and the obtained findings are illustrated through constructing bifurcation diagrams, computing Lyapunov exponents, calculating Lyapunov dimensions, and sketching the phase portraits in 2D and 3D projections.
MCRNet: Multi-level context refinement network for semantic segmentation in breast ultrasound imaging

Automated semantic segmentation in breast ultrasound imaging remains a challenging task due to the adverse impacts of poor contrast, indistinct target boundaries, and a large number of shadows. Recently, convolutional neural networks (CNN) with U-shape have demonstrated considerable performance in medical image segmentation. However, classic U-shaped networks suffer from the potential semantic gaps due to the incompatibility of encoder and decoder features, thereby resulting in sub-optimal semantic segmentation performance in ultrasound imaging. In this work, we focus on improving the U-shaped CNN through adaptively reducing semantic gaps and enhancing contextual relationships between encoder and decoder features. Specifically, we propose two lightweight yet effective context refinement blocks including inverted residual pyramid block (IRPB) and context-aware fusion block (CFB). The former can selectively extract multi-scale semantic representations according to input features, aiming to adaptively reduce semantic gaps between encoder and decoder features. The latter can exploit semantic interactions of inter-features to enhance contextual correlations between the encoder and the decoder, aiming at improving the feature fusion scheme of low- and high-level features. Further, we develop a novel multi-level context refinement network (MCRNet) by seamlessly plugging these two context refinement blocks into an encoder-decoder architecture according to the multi-level manner, thereby achieving fully automated semantic segmentation in ultrasound imaging. In order to objectively validate the proposed method, we carry out extensive qualitative and quantitative analyses based on two publicly available breast ultrasound databases including BUSI and UDIAT. The experimental results greatly reflect the efficacy of our proposed method. Meanwhile, compared with nine state-of-the-art semantic segmentation methods, our proposed MCRNet also achieves superior performance while persevering fine computational efficiency.
Subspace alignment based on an extreme learning machine for electronic nose drift compensation

The drift caused by gas sensors has always been a bottleneck in the development of electronic nose (E-nose) systems. Traditional drift compensation methods directly correct the drift components, making such approaches time-consuming and laborious. In the field of E-nose drift compensation, cross-domain adaption learning is an efficient technique. In this paper, we propose a novel subspace alignment extreme learning machine (SAELM) that considers multiple criteria to construct a unified extreme learning machine (ELM)-based feature representation space and thus achieve domain alignment. First, the method minimizes both the geometric and statistical distributions between different domains. Second, the dependence between features and labels is enhanced using the Hilbert–Schmidt independence criterion (HSIC) to alleviate the blurring of the correspondence between the two caused by drift. Third, to improve the feature extraction ability of the subspace learning method, the l2,1 norm is leveraged to constrain the output weights of the ELM. The aim of this method is to learn a robust subspace to increase the consistency between domains and enhance the feature–label​ dependency of the source domain while preserving the intrinsic information of both domains. Extensive experiments on sensor drift data are conducted, and the proposed SAELM method yields the greatest improvements on E-nose drift datasets.
Physical reservoir computing with FORCE learning in a living neuronal culture

Rich dynamics in a living neuronal system can be considered as a computational resource for physical reservoir computing (PRC). However, PRC that generates a coherent signal output from a spontaneously active neuronal system is still challenging. To overcome this difficulty, we here constructed a closed-loop experimental setup for PRC of a living neuronal culture, where neural activities were recorded with a microelectrode array and stimulated optically using caged compounds. The system was equipped with first-order reduced and controlled error learning to generate a coherent signal output from a living neuronal culture. Our embodiment experiments with a vehicle robot demonstrated that the coherent output served as a homeostasis-like property of the embodied system from which a maze-solving ability could be generated. Such a homeostatic property generated from the internal feedback loop in a system can play an important role in task solving in biological systems and enable the use of computational resources without any additional learning.