Reviewerly/exHarmony huggingface dataset:
Contact analysis on COVID-19 using a campus network

In 2020, the COVID-19 pandemic is causing significant problems for our society. Although contact tracing is an essential countermeasure for minimizing exposure, its applications are not widely used in Japan. We have developed a Wi-fi based contact tracing system for use in our campus network to protect students and staff. Although the area covered by Wi-fi technology is too wide to accurately analyze contacts, it can still be used for comparative analysis. We conducted preliminary experiments as an example to compare the effects of countermeasures. Our results show that by making 30% of classrooms safe, we can reduce the number of potential exposures by over 90%. This paper summarizes 1) the system design to acquire necessary information while maintaining the privacy of students, and 2) the results of preliminary experiments that compare the effect of countermeasures.
Training Deep Neural Networks with Synthetic Data for Off-Road Vehicle Detection

In tandem with growing deep learning technology, vehicle detection using convolutional neural network is now become a mainstream in the field of autonomous driving and ADAS. Taking advantage of this, lots of real image datasets have been produced in spite of the painstaking work of data collection and ground truth annotation. As an alternative, virtually generated images are introduced. This makes data collection and annotation much easier, but a different kind of problem called `domain gap' is announced. For instance, in off-road vehicle detection, there is a difficulty in producing off-road image dataset not only by collecting real images, but also by synthesizing images sidestepping the domain gap. In this paper, focusing on the off-road army tank detection, we introduce a synthetic image generator using domain randomization on off-road scene context. We train a deep learning model on synthetic dataset using low level features form feature extractor pre-trained on real common object dataset. With proposed method, we improve the model accuracy to 0.86 AP@0.5IOU, outperforming naïve domain randomization approach.
Low-Light Color Imaging via Dual Camera Acquisition

As existing low-light color imaging suffers from the unrealistic color representation or blurry texture with a single camera setup, we are motivated to devise a dual camera system using a high spatial resolution (HSR) monochrome camera and another low spatial resolution (LSR) color camera for synthesizing the high-quality color image under low-light illumination conditions. The key problem is how to efficiently learn and fuse cross-camera information for improved presentation in such heterogeneous setup with domain gaps (e.g., color vs. monochrome, HSR vs. LSR). We have divided the end-to-end pipeline into three consecutive modularized sub-tasks, including the reference-based exposure compensation (RefEC), reference-based colorization (RefColor) and reference-based super-resolution (RefSR), to alleviate domain gaps and capture inter-camera dynamics between hybrid inputs. In each step, we leverage the powerful deep neural network (DNN) to respectively transfer and enhance the illuminative, spectral and spatial granularity in a data-driven way. Each module is first trained separately, and then jointly fine-tuned for robust and reliable performance. Experimental results have shown that our work provides the leading performance in synthetic content from popular test datasets when compared to existing algorithms, and offers appealing color reconstruction using real captured scenes from an industrial monochrome and a smartphone RGB cameras, in low-light color imaging application.
Innovations in Education, or How to Separate the Wheat from the Chaff?

The article focuses on the criteria of pedagogical innovation. Along with such criteria as an obvious novelty and effectiveness, the author dwells on the adequacy of the considered pedagogical experience to ensure the advanced scientific and technological challenges of the time, as well as the solution of priority social problems. This implies that the tasks are reflected primarily in the conceptual documents of the country’s development. This criterion corresponds to a narrow understanding of innovation, which is associated with the system of «scientific novelty – technological (social) project – development – implementation». The broad understanding of the category «innovation» is determined by the development and preservation of the anthropological potential of society and education in particular. The relevance of the proposed basic criterion is revealed as a result of a critical analysis of a scientific publication devoted to the pedagogical experience within the system of military education. The article justifies the real anti-innovative systemic effects of the so called «Innovative approach». In the context of the growing pace of the scientific and technological revolution, the concept of «Asymmetric response» to the innovative challenges of the time is introduced. As a result of the analysis of the achievements of domestic and foreign scientists, a set of «Asymmetric measures» is proposed. They stemmed from the development of universal basic information, creative competencies, and polytechnic skills, as well as humanitarian and economic orientation of training, the development of primarily STEM-areas in school education, simulation and distance learning tools, innovative pedagogical technologies.
A Generative Approach for Android Sensor-based Applications

As smartphones increased in popularity, mobile devices became a crucial technological element of our time. Beyond the end-user platform, smartphones have several sensors to capture and characterise the user's context. Current Context-Aware Mobile (CAM) applications use this data to improve user experience by filtering data, services, and, even, adapting their behaviour. The development of CAM applications can be challenging for mobile developers, due to the diversity of mobile hardware ecosystem and sensor APIs' complexity. This paper presents EasyContext, a visual tool for designing and generating contextual rules for mobile devices, using the Google Awareness API as its contextual information provider. Mobile developers specify what to capture and when to react to context-changes in the visual tool and these contextual rules are exported to Android projects. We evaluated our approach with developers, which were selected to participate in a quasi-experiment. The experiment involves developing two mobile applications using both EasyContext and Google Awareness API. Preliminary results show that it is possible to build applications with fewer lines of code compared to the Google Awareness API. According to the developers, the proposed approach has better readability and makes it easier to design contextual rules.
Inference Graphs for CNN Interpretation

Convolutional neural networks (CNNs) have achieved superior accuracy in many visual related tasks. However, the inference process through intermediate layers is opaque, making it difficult to interpret such networks or develop trust in their operation. We propose to model the network hidden layers activity using probabilistic models. The activity patterns in layers of interest are modeled as Gaussian mixture models, and transition probabilities between clusters in consecutive modeled layers are estimated. Based on maximum-likelihood considerations, nodes and paths relevant for network prediction are chosen, connected, and visualized as an inference graph. We show that such graphs are useful for understanding the general inference process of a class, as well as explaining decisions the network makes regarding specific images.
Detection of Mammographic Masses using FRFCM Optimized by PSO

Since early detection of breast cancer can effectively reduce the mortality rate, hence, in an attempt, mass, a symptom of breast cancer which is difficult to identify due to its subtle nature, is targeted to locate it efficiently with the proposed detection scheme. This paper introduces FRFCM-PSO, a hybrid model of fast and robust fuzzy c-means clustering (FRFCM) and particle swarm optimization (PSO), for the localization of mammographic masses. FRFCM is an improvised version of FCM by employing morphological reconstruction and member-ship filters which alleviates the necessity of additional local spatial information which burdens the method with computational complexity. Moreover, the general limitation of clustering technique of initializing the center point has been mitigated by incorporating optimization method- PSO. Hence, the combinational approach yields a sensitivity of 96.6 % with 2.29 as false positives per image (FPs/I) when evaluated on the mini-MIAS dataset. Further, the FPs are reduced using feature extraction (LBP) and classification (Ensemble classifier) technique where an A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> value of 0.846 is observed with an improvement of 74 % in FPs/I which is further compared with the similar competing scheme.
Selection-Channel-Aware Deep Neural Network to Detect Motion Vector Embedding of HEVC Videos

It is well established that using the selection channel, the probabilities with which the elements in cover are modified during message embedding, would improve the performance of steganalysis. Most video steganographical algorithms embed secret messages in the compressed domain by modifying the motion vectors having less impact on video visual quality, which can be considered as a form of selection channel. Recently, deep neural networks have been rapidly developed for multimedia steganalysis. Although there have been some selection-channel-aware networks for image steganalysis, they cannot be simply extended to video steganalysis because there are great differences between image and video steganographic modification. To our best knowledge, there have been no selection-channel-aware networks for video steganalysis in literature. In this article, we propose a selection-channel-aware deep neural network for video steganalysis. Considering that video structure is quite different from that of image, we focus on the construction of input data matrix for deep convolutional neural network, the definition of probability for motion vector modification, and the network structure of using the selection channel knowledge. Experimental results have demonstrated that the proposed method benefits from selection channel and has satisfactory performance on testing HEVC videos.
Sensor Fusion Based Implicit Authentication for Smartphones

Implicit authentication, as a novel identity authentication mechanism, has received widespread attention. However, the performance of implicit authentication still needs to be improved. In this paper, we propose a sensor fusion based implicit authentication system to enhance the protection level of the identity authentication mechanism for smartphone. First, the sensor used to characterize user behavior is determined by analyzing the authentication performance of each sensor. Then, considering the practicability of the system, one-class classification algorithm One-Class Support Vector Machine (OC-SVM) is used to train the authentication model. Finally, the decision function of each sensor is weighted and fused to deliver a result. Based on the actual data set of 7,500 samples collected from 75 participants, the effectiveness of the system is verified in different operating environments and varied passwords. The results show that the proposed system can improve the accuracy of identity authentication effectively.
Situation Assessment Based on Multi-Entity Bayesian Network

The research on knowledge representation and reasoning methods of battlefield situation assessment is of great military value. The Bayesian network describes causalities among variables by graph model, which provides an effective reasoning method for uncertain information based on Bayesian probability theory. The battlefield environment contains a lot of uncertainties. Different battlefield environments may contain different numbers and types of combat entities. Therefore, it is impossible to determine a Bayesian network that satisfies all conditions in advance. The normal Bayesian network model lacks scalability and reusability. Aiming at the battlefield situation assessment problem, this paper proposes a multi-entity Bayesian network modeling method, which divides the problems into target recognition and intention recognition problems, and considers the target radiation source characteristics in the target recognition problem to improve the model's feature fusion ability. The relationships between different entities are described as Bayesian network segments, and the Bayesian network segments are combined to form a multi-entity Bayesian network. So the modeling process is simplified by using the good scalability of multi-entity Bayesian networks. At last, the feasibility and effectiveness of the proposed method are verified by a situational simulation modeling example.
Keeping Models Consistent between Pretraining and Translation for Low-Resource Neural Machine Translation

Recently, the pretraining of models has been successfully applied to unsupervised and semi-supervised neural machine translation. A cross-lingual language model uses a pretrained masked language model to initialize the encoder and decoder of the translation model, which greatly improves the translation quality. However, because of a mismatch in the number of layers, the pretrained model can only initialize part of the decoder’s parameters. In this paper, we use a layer-wise coordination transformer and a consistent pretraining translation transformer instead of a vanilla transformer as the translation model. The former has only an encoder, and the latter has an encoder and a decoder, but the encoder and decoder have exactly the same parameters. Both models can guarantee that all parameters in the translation model can be initialized by the pretrained model. Experiments on the Chinese–English and English–German datasets show that compared with the vanilla transformer baseline, our models achieve better performance with fewer parameters when the parallel corpus is small.
2.5D dust &amp; magnet visualization for large multivariate data

In this paper, we present a 2.5D visualization technique based on the dust & magnet metaphor, which generally allows for interactively exploring and analyzing large multivariate data sets. In addition to position and color, we introduce height as additional visual variable for particles to encode extra data attributes in the 2.5D visualization, thus increasing the potential for identifying correlations between attributes. Further, we have developed a real-time collision detection algorithm as part of the particle simulation that ensures overlap-free particle positioning, thereby enabling the continuous perception of patterns, clusters, and outliers. These extensions facilitate on-the-fly validation of hypotheses through the highly dynamic configuration of magnets and visual attribute encoding, which also allows for a better integration of the user's domain knowledge.
Segmentation of cervix using minimum spanning superpixel tree detector

Abstract Cervical cancer occurs in cervix region of women which is life threatening and is a key research in the field of medical diagnosis. Cervical cancer can be prevented if precancerous changes is diagnosed earlier and cured properly. But finding cancer at the earlier stage is a tedious process. Cervical cancer screening is crucial since utmost of the screening procedures are invasive in nature. The objective is to automatically extract the area where in the cervical cancer starts to occur. In order to diagnose cervical cancer and distinguish the malignant and benign tissues, a cytology image obtained by cervicographic or colposcopy device is used. A robust segmentation algorithm is proposed that uses superpixel for the image segmentation method. This method is regularized by the combination of structural properties such as color, texture, and spatial data present in the superpixel graph.
Image registration and selection for unmanned aerial vehicle image stitching

In this study, a general transformation-based framework for unmanned aerial vehicle image stitching is proposed that can resist global distortion while improving the local registration accuracy. In the first step, with tie points as constraints, the global transformation function of each image is obtained in an optimization manner and no reference image is needed. In the second step, to reduce data redundancy, an image selection algorithm based on information entropy is proposed. The optimal image combination covering the entire scene is selected from the original image set. In the third step, a local optimization algorithm based on mesh deformation is proposed to further optimize the registration accuracy of the optimal image combination. Finally, all of the images are combined to obtain a high-resolution panorama. On challenging datasets, the proposed algorithm can not only reduce the global distortions caused by the accumulation of errors during image stitching, but can also reduce the redundant data, which will benefit the postprocessing. The local mesh optimization can greatly improve the registration accuracy and eliminate the obvious misalignment problems. Tested on a large number of challenging datasets, the proposed method substantially outperforms several state-of-the-art image-stitching methods and commercial software.
OmniPhotos: Casual 360° VR Photography with Motion Parallax

Until now, immersive 360° VR panoramas could not be captured casually and reliably at the same time as state-of-the-art approaches involve time-consuming or expensive capture processes that prevent the casual capture of real-world VR environments. Existing approaches are also often limited in their supported range of head motion. We introduce OmniPhotos, a novel approach for casually and reliably capturing high-quality 360° VR panoramas. Our approach only requires a single sweep of a consumer 360° video camera as input, which takes less than 3 seconds with a rotating selfie stick. The captured video is transformed into a hybrid scene representation consisting of a coarse scene-specific proxy geometry and optical flow between consecutive video frames, enabling 5-DoF real-world VR experiences. The large capture radius and 360° field of view significantly expand the range of head motion compared to previous approaches. Among all competing methods, ours is the simplest, and fastest by an order of magnitude. We have captured more than 50 OmniPhotos and show video results for a large variety of scenes. We will make our code and datasets publicly available.
Efficient Frequent Subgraph Mining in Transactional Databases

Frequent connected subgraph mining (FCSM) has been an active area of research over the last twenty years. This review shall focus on the practical and theoretical issues arising in the transactional setting, where we are given a finite list of small to medium sized graphs and must find all graphs that are subgraph isomorphic to some user-defined number of graphs in the list. In particular, we present the generic approach to FCSM and investigate sufficient conditions for its computational tractability and intractability. Interestingly, it turns out that both depend on the complexity of the Hamiltonian Path problem. This implies that FCSM is computationally tractable only for very restricted transaction graph classes. We subsequently review existing exact FCSM algorithms with a focus on applicability to arbitrary graph databases and present recent approximative FCSM algorithms that remain computationally tractable for all transactional databases.
Multi-Object Tracking Algorithm for RGB-D Images Based on Asymmetric Dual Siamese Networks

Currently, intelligent security systems are widely deployed in indoor buildings to ensure the safety of people in shopping malls, banks, train stations, and other indoor buildings. Multi-Object Tracking (MOT), as an important component of intelligent security systems, has received much attention from many researchers in recent years. However, existing multi-objective tracking algorithms still suffer from trajectory drift and interruption problems in crowded scenes, which cannot provide valuable data for managers. In order to solve the above problems, this paper proposes a Multi-Object Tracking algorithm for RGB-D images based on Asymmetric Dual Siamese networks (ADSiamMOT-RGBD). This algorithm combines appearance information from RGB images and target contour information from depth images. Furthermore, the attention module is applied to repress the redundant information in the combined features to overcome the trajectory drift problem. We also propose a trajectory analysis module, which analyzes whether the head movement trajectory is correct in combination with time-context information. It reduces the number of human error trajectories. The experimental results show that the proposed method in this paper has better tracking quality on the MICC, EPFL, and UMdatasets than the previous work.
Neural Networks for Regression

So far, we looked at the classification model in deep learning. Can we apply whatever techniques you learned so far as a part of deep learning to a regression problem, which is considered probably the simplest in data analytics? Is it worth even attempting to use deep learning in the areas of regression, considering the overheads of deep learning? Is there an advantage in using deep learning over the traditional statistical techniques – especially in the case of regression modeling? You will find answers to these and similar questions in this chapter.
Real-time embedded TSR using MDEffNet: An efficient compact DNN on varied multi-resolution images across multiple databases

Deep Learning in Traffic Sign Recognition (TSR) System is rapidly developing. Still, not much work has been reported in the literature on architectures that can work efficiently on multiple (more than two) databases across different countries. Each of these databases incorporates images of varied resolutions. Here, a Deep Neural Network called Multiple Database Efficient Network (MDEffNet) has been proposed for TSR. Batch normalization layers have been introduced to reduce the internal co-variant shift, and further, data augmentation has been carried out to achieve high efficiency with reasonable training time. Results at each step of the proposed architecture have been provided. The proposed model is compared with some state-of-the-art methods based on single or two databases, and solo works done in multiple (three) databases originating from different countries. The comparisons prove the high effectiveness of the proposed model. The achieved efficiency is much higher than all of them, including the average human ability. Deep learning techniques often suffer from increased memory and computational requirements. MDEffNet has only 1.36 million parameters ([Formula: see text] 26 times lower than the state-of-art methods) with 130 s per epoch, which is relatively low. Hence, it is also suitable for real-time embedded TSR systems.
Implicit Posterior Sampling Reinforcement Learning for Continuous Control

Value function approximation has achieved notable success in reinforcement learning. Many popular algorithms (e.g. Deep Q Network) maintain a point estimation of the parameters in the value network or policy network. However, the frequentist perspective is prone to overfitting and lacks uncertainty representation. In this paper, we perform Bayesian analysis on the value function. Following the principle "optimism in the face of uncertainty", we conduct a posterior sampling of the value or policy network which implicitly captures the posterior distribution via a Bayesian hypernetwork. Experimental results show that the implicit posterior distribution for modeling the structural dependencies between parameters can better balance exploration and exploitation, and it is competitive to state-of-the-art methods on MuJoCo continuous benchmark.