Reviewerly/exHarmony huggingface dataset:
Contact Tracing, Testing, and Control of COVID-19—Learning From Taiwan

Taiwan is a country of about 24 million people, 81 miles off the coast of mainland China. As of late April 2020, Taiwan had about 330 confirmed cases of coronavirus disease 2019 (COVID-19) and 6 deaths. By comparison, the US had about 1 million confirmed cases of COVID-19, and 60 000 deaths.
PMHLD: Patch Map-Based Hybrid Learning DehazeNet for Single Image Haze Removal

Images captured in a hazy environment usually suffer from bad visibility and missing information. Over many years, learning-based and handcrafted prior-based dehazing algorithms have been rigorously developed. However, both algorithms exhibit some weaknesses in terms of haze removal performance. Therefore, in this work, we have proposed the patch-map-based hybrid learning DehazeNet, which integrates these two strategies by using a hybrid learning technique involving the patch map and a bi-attentive generative adversarial network. In this method, the reasons limiting the performance of the dark channel prior (DCP) have been analyzed. A new feature called the patch map has been defined for selecting the patch size adaptively. Using this map, the limitations of the DCP (e.g., color distortion and failure to recover images involving white scenes) can be addressed efficiently. In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously. A combination of traditional and learning-based methods can efficiently improve the haze removal performance of the network. Experimental results show that the proposed method can achieve better reconstruction results compared to other state-of-the-art haze removal algorithms.
On the Usage of the Simulator Sickness Questionnaire for Virtual Reality Research

Originally developed for military flight simulators in the 1990s, the Simulator Sickness Questionnaire (SSQ) has been widely adopted to quantify sickness elicited by modern virtual reality systems. We illustrate the inherent challenges of applying the SSQ in virtual reality research and highlight large differences that can be found in related work. Based on our observations, we conclude by providing suggestions on how to improve the expressiveness of SSQ results in future studies and encourage researchers to consider simpler measurement methods if their research questions allow.
Triple color image encryption based on 2D multiple parameter fractional discrete Fourier transform and 3D Arnold transform

This paper proposes a new encryption and decryption method for triple color images using 2D multiple parameter fractional discrete Fourier transform (MPFrDFT) and 3D Arnold transform (AT). The proposed method converts three color images into Bayer images, which are considered as the three components of a color image. These three Bayer images are combined vertically and then baker chaotic map is applied to permute rows and columns and a similar process is applied in the horizontal combination, the image then obtained is considered as the complex valued image (CVI). Then apply 2D MPFrDFT on this CVI. The output of 2D MPFrDFT is separated into three components. Apply 3D AT into the three components and the output is considered as the three color components of the encrypted image. The experimental results and the security analysis of the proposed method are given to validate the feasibility and robustness of the method. The statistical analyses like histogram, correlation and entropy confirm the robustness of the proposed method against statistical attacks and experimental results show that the method is resistant to occlusion attack. The mathematical analysis shows that the brute force attack is not possible in proposed method.
Energy-Efficient Drone Coverage Path Planning using Genetic Algorithm

Unmanned Aerial Vehicles (UAVs) have been increasingly used in environmental sensing and surveying applications. Coverage path planning to survey an area while following a set of waypoints is required to complete a task. Due to the battery capacity, the UAV flight time is often limited. In this paper, we formulate the UAV path planning problem as a traveling salesman problem in order to optimize UAV energy. We propose a genetic algorithm to solve the optimization problem i.e. to minimize the energy consumption for the UAV to complete a task. We also consider reducing the number of turns to allow the UAV to optimize the flight path and to minimize its energy consumption. We compare the energy consumption of the proposed genetic algorithm to the greedy algorithm with different number of waypoints. Results show that our proposed algorithm consumes 2-5 times less energy than that of the greedy algorithm by reducing the number of turns while covering all the waypoints.
Review and Classification of Bio-inspired Algorithms and Their Applications

Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: evolutionary based bio-inspired algorithms, swarm intelligence-based bio-inspired algorithms, ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms, swarm intelligence based bio-inspired algorithms, ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms.
FedSteg: A Federated Transfer Learning Framework for Secure Image Steganalysis

The protection of user private data has long been the focus of AI security. We know that training machine learning models rely on large amounts of user data. However, user data often exists in the form of isolated islands that can not be integrated under many secure and legal constraints. The large-scale application of image steganalysis algorithms in real life is still not satisfactory due to the following challenges. First, it is difficult to aggregate all of the scattered steganographic images to train a robust classifier. Second, even if the images are encrypted, participants do not want irrelevant people to peek into the hidden information, resulting in the disclosure of private data. Finally, it is often impossible for different participants to train their tailored models. In this paper, we introduce a novel framework, referred to as FedSteg, to train a secure, personalized distributed model through federated transfer learning to fulfill secure image steganalysis. Extensive experiments on detecting several state-of-the-art steganographic methods i.e., WOW, S-UNIWARD, and HILL, validate that FedSteg achieves certain improvements compared to traditional non-federated steganalysis approaches. In addition, FedSteg is highly extensible and can be easily employed to various large-scale secure steganographic recognition tasks.
Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence

Increasingly, interventions aimed at improving care are likely to use such technologies as machine learning and artificial intelligence. However, health care has been relatively late to adopt them. This article provides clinical examples in which machine learning and artificial intelligence are already in use in health care and appear to deliver benefit. Three key bottlenecks toward increasing the pace of diffusion and adoption are methodological issues in evaluation of artificial intelligence-based interventions, reporting standards to enable assessment of model performance, and issues that need to be addressed for an institution to adopt these interventions. Methodological best practices will include external validation, ideally at a different site; use of proactive learning algorithms to correct for site-specific biases and increase robustness as algorithms are deployed across multiple sites; addressing subgroup performance; and communicating to providers the uncertainty of predictions. Regarding reporting, especially important issues are the extent to which implementing standardized approaches for introducing clinical decision support has been followed, describing the data sources, reporting on data assumptions, and addressing biases. Although most health care organizations in the United States have adopted electronic health records, they may be ill prepared to adopt machine learning and artificial intelligence. Several steps can enable this: preparing data, developing tools to get suggestions to clinicians in useful ways, and getting clinicians engaged in the process. Open challenges and the role of regulation in this area are briefly discussed. Although these techniques have enormous potential to improve care and personalize recommendations for individuals, the hype regarding them is tremendous. Organizations will need to approach this domain carefully with knowledgeable partners to obtain the hoped-for benefits and avoid failures.
Social Acceptability in HCI: A Survey of Methods, Measures, and Design Strategies

With the increasing ubiquity of personal devices, social acceptability of human-machine interactions has gained relevance and growing interest from the HCI community. Yet, there are no best practices or established methods for evaluating social acceptability. Design strategies for increasing social acceptability have been described and employed, but so far not been holistically appraised and evaluated. We offer a systematic literature analysis (N=69) of social acceptability in HCI and contribute a better understanding of current research practices, namely, methods employed, measures and design strategies. Our review identified an unbalanced distribution of study approaches, shortcomings in employed measures, and a lack of interweaving between empirical and artifact-creating approaches. The latter causes a discrepancy between design recommendations based on user research, and design strategies employed in artifact creation. Our survey lays the groundwork for a more nuanced evaluation of social acceptability, the development of best practices, and a future research agenda.
Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector

In this paper we propose object detection technique to detect objects in real time on any device running the model and in any environment. Object detection and training is a vast, vibrant and yet inconclusive and complex area of the computer vision. In this proposed work, convolutional neural network are used to develop a model which is composed of multiple layers to classify the given objects into any of the defined classes. The proposed schemes then use multiple images to detect the objects and label them with their respective class label. These objects are detected by making use of higher resolution feature maps. This is possible because of the recent advancement in deep learning with image processing. These images can be from the video frames which are fed into the model. Our scheme uses separate filters with different default boxes to tackle the difference in aspect ratio and also used multi-scale feature maps for object detection. The training of the model takes place until the error rate is less. The trained model is used to test some sample images. To speed up the computational performance of object detection technique we have use single shot multi-box detector algorithm along with the help of architecture of faster region convolutional neural network. The accuracy in detecting the objects is checked by the different parameters like loss function (LP), mean average precision (mAP), frames per second (FPS).
Modeling Personalized Item Frequency Information for Next-basket Recommendation

Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items. Recurrent neural network (RNN) has proved to be very effective for sequential modeling, and thus been adapted for NBR. However, we argue that existing RNNs cannot directly capture item frequency information in the recommendation scenario.
Learning to Forget for Meta-Learning

Few-shot learning is a challenging problem where the goal is to achieve generalization from only few examples. Model-agnostic meta-learning (MAML) tackles the problem by formulating prior knowledge as a common initialization across tasks, which is then used to quickly adapt to unseen tasks. However, forcibly sharing an initialization can lead to conflicts among tasks and the compromised (undesired by tasks) location on optimization landscape, thereby hindering the task adaptation. Further, we observe that the degree of conflict differs among not only tasks but also layers of a neural network. Thus, we propose task-and-layer-wise attenuation on the compromised initialization to reduce its influence. As the attenuation dynamically controls (or selectively forgets) the influence of prior knowledge for a given task and each layer, we name our method as L2F (Learn to Forget). The experimental results demonstrate that the proposed method provides faster adaptation and greatly improves the performance. Furthermore, L2F can be easily applied and improve other state-of-the-art MAML-based frameworks, illustrating its simplicity and generalizability.
A Novel Multi-Perspective Benchmarking Framework for Selecting Image Dehazing Intelligent Algorithms Based on BWM and Group VIKOR Techniques

The increasing demand for image dehazing-based applications has raised the value of efficient evaluation and benchmarking for image dehazing algorithms. Several perspectives, such as inhomogeneous foggy, homogenous foggy, and dark foggy scenes, have been considered in multi-criteria evaluation. The benchmarking for the selection of the best image dehazing intelligent algorithm based on multi-criteria perspectives is a challenging task owing to (a) multiple evaluation criteria, (b) criteria importance, (c) data variation, (d) criteria conflict, and (e) criteria tradeoff. A generally accepted framework for benchmarking image dehazing performance is unavailable in the existing literature. This study proposes a novel multi-perspective (i.e., an inhomogeneous foggy scene, a homogenous foggy scene, and a dark foggy scene) benchmarking framework for the selection of the best image dehazing intelligent algorithm based on multi-criteria analysis. Experiments were conducted in three stages. First was an evaluation experiment with five algorithms as part of matrix data. Second was a crossover between image dehazing intelligent algorithms and a set of target evaluation criteria to obtain matrix data. Third was the ranking of the image dehazing intelligent algorithms through integrated best–worst and VIseKriterijumska Optimizacija I Kompromisno Resenje methods. Individual and group decision-making contexts were applied to demonstrate the efficiency of the proposed framework. The mean was used to objectively validate the ranks given by group decision-making contexts. Checklist and benchmarking scenarios were provided to compare the proposed framework with an existing benchmark study. The proposed framework achieved a significant result in terms of selecting the best image dehazing algorithm.
Attention-Aware Multi-View Stereo

Multi-view stereo is a crucial task in computer vision, that requires accurate and robust photo-consistency among input images for depth estimation. Recent studies have shown that learning-based feature matching and confidence regularization can play a vital role in this task. Nevertheless, how to design good matching confidence volumes as well as effective regularizers for them are still under in-depth study. In this paper, we propose an attention-aware deep neural network “AttMVS” for learning multi-view stereo. In particular, we propose a novel attention-enhanced matching confidence volume, that combines the raw pixel-wise matching confidence from the extracted perceptual features with the contextual information of local scenes, to improve the matching robustness. Furthermore, we develop an attention-guided regularization module, which consists of multilevel ray fusion modules, to hierarchically aggregate and regularize the matching confidence volume into a latent depth probability volume.Experimental results show that our approach achieves the best overall performance on the DTU dataset and the intermediate sequences of Tanks & Temples benchmark over many state-of-the-art MVS algorithms.
Future Video Synthesis With Object Motion Prediction

We present an approach to predict future video frames given a sequence of continuous video frames in the past. Instead of synthesizing images directly, our approach is designed to understand the complex scene dynamics by decoupling the background scene and moving objects. The appearance of the scene components in the future is predicted by non-rigid deformation of the background and affine transformation of moving objects. The anticipated appearances are combined to create a reasonable video in the future. With this procedure, our method exhibits much less tearing or distortion artifact compared to other approaches. Experimental results on the Cityscapes and KITTI datasets show that our model outperforms the state-of-the-art in terms of visual quality and accuracy.
Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches

Recent advances in machine learning and computer graphics have made it easier to convincingly manipulate video and audio. These so-called deep-fake videos range from complete full-face synthesis and replacement (face-swap), to complete mouth and audio synthesis and replacement (lip-sync), and partial word-based audio and mouth synthesis and replacement. Detection of deep fakes with only a small spatial and temporal manipulation is particularly challenging. We describe a technique to detect such manipulated videos by exploiting the fact that the dynamics of the mouth shape - visemes - are occasionally inconsistent with a spoken phoneme. We focus on the visemes associated with words having the sound M (mama), B (baba), or P (papa) in which the mouth must completely close in order to pronounce these phonemes. We observe that this is not the case in many deep-fake videos. Such phoneme-viseme mismatches can, therefore, be used to detect even spatially small and temporally localized manipulations. We demonstrate the efficacy and robustness of this approach to detect different types of deep-fake videos, including in-the-wild deep fakes.
Classification of Cancer Types Using Graph Convolutional Neural Networks

Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently.In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 markers genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific.Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.
The Technological Challenge Facing Higher Education Professors: Perceptions of ICT Tools for Developing 21st Century Skills

To consider that some quality teaching-learning processes in the Higher Education is practically unthinkable without the use of technology, especially because of its impact in developing the necessary skills and abilities for the 21st century. (1) Background: One of the initial steps to successfully approach this challenge is to analyze how university professors consider that ICTs can contribute to developing skills and abilities in their students. In this regard, their perceptions are one of the factors that will limit the use they make of these tools. (2) Method: For this reason, a quantitative research has been designed in which 345 university professors have participated for different branches of knowledge of one Spanish university. (3) Results: Among the most relevant results, we can highlight that the professors recognize the potentials of the ICTs and consider they have a positive effect on learning and development of 21st century skills in their students. These benefits are seen in different fields like communication, collaboration, and critical thinking, among others. Likewise, the importance of the technology culture teaching role is outstanding. (4) Conclusions: Finally, the need for integration of ICTs in a pedagogical model in which professor training in digital skills acquires relevance is shown.