Reviewerly/exHarmony huggingface dataset:
Study on Distortion Compensation of Underwater Archaeological Images Acquired through a Fisheye Lens and Practical Suggestions for Underwater Photography - A Case of Taean Mado Shipwreck No. 1 and No. 2 -

Underwater archaeology relies heavily on photography and video image recording during surveillances and excavations like ordinary archaeological studies on land. All underwater images suffer poor image quality and distortions due to poor visibility, low contrast and blur, caused by differences in refractive indices of water and air, properties of selected lenses and shapes of viewports. In the Yellow Sea (between mainland China and the Korean peninsula), the visibility underwater is far less than 1 m, typically in the range of 30 cm to 50 cm, on even a clear day, due to very high turbidity. For photographing 1 m x 1 m grids underwater, a very wide view angle (180o) fisheye lens with an 8 mm focal length is intentionally used despite unwanted severe barrel-shaped image distortion, even with a dome port camera housing. It is very difficult to map wide underwater archaeological excavation sites by combining severely distorted images. Development of practical compensation methods for distorted underwater images acquired through the fisheye lens is strongly desired. In this study, the source of image distortion in underwater photography is investigated. We have identified the source of image distortion as the mismatching, in optical axis and focal points, between dome port housing and fisheye lens. A practical image distortion compensation method, using customized image processing software, was explored and verified using archived underwater excavation images for effectiveness in underwater archaeological applications. To minimize unusable area due to severe distortion after distortion compensation, practical underwater photography guidelines are suggested.
An adaptive scale estimation target tracking algorithm based on UAV

Uav(Unmanned aerial vehicle) has been widely used in military and civil fields. Object tracking is one of the key technologies in uav application. Mean-shift tracking algorithm is suitable for UA V target tracking due to its small computation, fast speed and good robustness. There are some problems with mean-shift tracking based on uav: (1) The fast moving of the target and uav leads to the target easily being lost. We use the deep learning network pretrained in the dataset to predict the target location in the ground station to solve this problem. (2) From the perspective of UAV, the image size of the tracked target is easy to change, and the background is more complex. In view of this, we introduce the neighborhood background information of the target into the similarity measurement between the target and the candidate, propose a scale-estimation mechanism which relies solely on the mean-shift procedure for the Hellinger distance, and detect the size estimation to reduce the influence of background clutter.
A Proposal for Discreet Auxiliary Figures for Reducing VR Sickness and for Not Obstructing FOV

We aim to reduce the virtual reality (VR) sickness by superimposing discreet auxiliary figures: “gazing point” on the center of the user’s field of view, “dots” on the four corners of the field of view, “user’s horizontal line” links a line to the user’s head movement, and “real world’s horizontal line” links to the real-world horizontal line. We conducted an experiment to evaluate the degree to which these figures could reduce VR sickness in a VR environment on a head-mounted display (HMD) by using Simulator Sickness Questionnaire (SSQ) and skin conductance (SC) on the user’s palm. The results show that the VR sickness tended to reduce by the superimposition of “dots.”
A Game-Based Fault-Tolerant Path Planning Algorithm For Space Manipulator

This paper studies the fault-tolerant path planning for the space manipulator with single joint locked failure. To search a reasonable path in both Cartesian and joint spaces, a Game-based RRT* algorithm is proposed by integrating information, including both minimum path and minimum singular value, from two spaces. First, the degraded working space for single joint locked failure is established by constructing orientation reachability to the reachable position space. Secondly, to improve the convergence rate of RRT*, the rasterized grid of the degraded working space is used to construct the path tree. Then, integrating the minimum singular value and minimum path to each grid, a numerical steer mechanism based on game theory is designed to find the new grid for a tree. In this mechanism, game theory is introduced to avoid the single parameter dominant caused by different parameter dimensions. Finally, the fault-tolerant path is planned by this improved RRT* algorithm. Simulation results demonstrate that the proposed Game-based RRT* algorithm can search a path that satisfies the requirements of singularity avoidance and path minimum simultaneously.
Helping People on the Fly: Ad Hoc Teamwork for Human-Robot Teams

We present the Bayesian Online Prediction for Ad hoc teamwork (BOPA), a novel algorithm for ad hoc teamwork which enables a robot to collaborate, on the fly, with human teammates without any pre-coordination protocol. Unlike previous works, BOPA relies only on state observations/transitions of the environment in order to identify the task being performed by a given teammate (without observing the teammate’s actions and environment’s reward signals). We evaluate BOPA in two distinct settings, namely (i) an empirical evaluation in a simulated environment with three different types of teammates, and (ii) an experimental evaluation in a real-world environment, deploying BOPA into an ad hoc robot with the goal of assisting a human teammate in completing a given task. Our results show that BOPA is effective at correctly identifying the target task, efficient at solving the correct task in optimal and near-optimal times, scalable by adapting to different problem sizes, and robust to non-optimal teammates, such as humans.
Low-altitude UAV Recognition and Classification Algorithm Based on Machine Learning

Aiming at the difficulty of target location and classification in low altitude surveillance and anti Unmanned Aerial Vehicle(UAV) system. This paper mainly studies the recognition algorithm of low-altitude UAV. First, the UAV image is preprocessed, and candidate partial images of different sizes and positions are generated through a sliding window, and the moment invariant features of the image are extracted. Then, the neural network is used to train the image, and the support vector machine classifier is used to classify the aircraft, and then the recognition and classification algorithm of the aircraft target in the low-altitude airspace are finished. Based on the theoretical algorithm research, this paper uses MATLAB software to simulate and analyze the aircraft recognition algorithm, and the accuracy is more than 90%. The results show that the research algorithm can be used for UAV recognition and low-altitude aircraft classification.
On Bayesian model selection for INGARCH models viatrans-dimensional Markov chain Monte Carlo methods

There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends on its past mean values and on past observations. We consider both linear and log-linear models. Our purpose is to select the most appropriate order of such models, using a trans-dimensional Bayesian approach that allows jumps between competing models. A small simulation experiment supports the usage of the method. We apply the methodology to real datasets to illustrate the potential of the approach.
Context Aware Generation of Cuneiform Signs

With the advent of deep learning in many research areas, the need for very large datasets is emerging. Especially in more specific domains like cuneiform script, annotated data is scarce and costly to obtain by experts only. Therefore, approaches for automatically generating labeled training data are of high interest. In this paper, we present an approach for generating cuneiform signs in larger images of cuneiform tablets. The proposed method allows to control the class of the generated sample as well as the visual appearance by considering context information from surrounding pixels. We evaluate our method on different numbers of cuneiform tablets for training and examine methods for determining the number of training iterations. Besides generating images of promising visual quality, we are able to improve classification performance by augmenting original data with generated samples. Additionally, we demonstrate that our approach is applicable to other domains as well, like digit generation in house number signs.
A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods

The spatial calculation of vector data is crucial for geochemical analysis in geological big data. However, large volumes of geochemical data make for inefficient management. Therefore, this study proposed a shapefile storage method based on MongoDB in GeoJSON form (SSMG) and a shapefile storage method based on PostgreSQL with open location code (OLC) geocoding (SSPOG) to solve the problem of low efficiency of electronic form management. The SSMG method consists of a JSONification tier and a cloud storage tier, while the SSPOG method consists of a geocoding tier, an extension tier, and a storage tier. Using MongoDB and PostgreSQL as databases, this study achieved two different types of high-throughput and high-efficiency methods for geochemical data storage and retrieval. Xinjiang, the largest province in China, was selected as the study area in which to test the proposed methods. Using geochemical data from shapefile as a data source, several experiments were performed to improve geochemical data storage efficiency and achieve efficient retrieval. The SSMG and SSPOG methods can be applied to improve geochemical data storage using different architectures, so as to achieve management of geochemical data organization in an efficient way, through time consumed and data compression ratio (DCR), in order to better support geological big data. The purpose of this study was to find ways to build a storage method that can improve the speed of geochemical data insertion and retrieval by using excellent big data technology to help us efficiently solve problem of geochemical data preprocessing and provide support for geochemical analysis.
Predictive models with end user preference

Abstract Classical machine learning models typically try to optimize the model based on the most discriminatory features of the data; however, they do not usually account for end user preferences. In certain applications, this can be a serious issue as models not aware of user preferences could become costly, untrustworthy, or privacy‐intrusive to use, thus becoming irrelevant and/or uninterpretable. Ideally, end users with domain knowledge could propose preferable features that the predictive model could then take into account. In this paper, we propose a generic modeling method that respects end user preferences via a relative ranking system to express multi‐criteria preferences and a regularization term in the model's objective function to incorporate the ranked preferences. In a more generic perspective, this method is able to plug user preferences into existing predictive models without creating completely new ones. We implement this method in the context of decision trees and are able to achieve a comparable classification accuracy while reducing the use of undesirable features.
Memory Hardware Quality Requirements for Hybrid Multi Cloud Computing

In this era where data has been growing exponentially due to the explosion of mobile applications, social media and Internet of Things (IOT), it is inevitable that the organization's Information Technology (IT) infrastructure is driven to be transformed from the traditional infrastructure to the cloud infrastructure. The hybrid multi cloud infrastructure enables organization to manage their data, workload and processes across multiple on premises, private and public cloud services. Memory devices such as DRAM, flash and Storage Class Memory (SCM) are key technologies in enabling the hybrid multi cloud infrastructure. Hence, the quality of these memory devices used in the hardware subsystems is critical to ensure reliable and resilient hybrid multi cloud infrastructure.
Assessing the Sense of Presence to Evaluate the Effectiveness of Virtual Reality Wildfire Training

Virtual Reality is beneficial for high-risk training such as firefighting, extreme weather, and police due to cost and risk concerns. A fundamental characteristic of VR is creating the sense of being there and train participants to make the right decisions under high pressure. This research presents an experimental study that aims to evaluate the effectiveness of a Virtual Environment to train wildfire fighting by assessing the sense of presence. We have 10 participants who experienced the same Virtual Environment in two VR conditions represented by HTC Vive and Google Cardboard. The two devices represent two immersion levels. To evaluate the effectiveness, we used the Igroup Presence Questionnaire (IPQ) for participants to rate the subjective opinions of spatial presence, the real-time recorded ECG signal to indicate the stress level during the experiment, and skin temperature to show the excitement level.
Filtering conversations through dialogue acts labels for improving corpus-based convergence studies

Cognitive models of conversation and research on user-adaptation in dialogue systems involves a better understanding of speakers convergence in conversation. Convergence effects have been established on controlled data sets, for various acoustic and linguistic variables. Tracking interpersonal dynamics on generic corpora has provided positive but more contrasted outcomes. We propose here to enrich large conversational corpora with dialogue act (DA) information. We use DA-labels as filters in order to create data sub sets featuring homogeneous conversational activity. Those data sets allow a more precise comparison between speakers’ speech variables. Our experiences consist of comparing convergence on low level variables (Energy, Pitch, Speech Rate) measured on raw data sets, with human and automatically DA-labelled data sets. We found that such filtering does help in observing convergence suggesting that studies on interpersonal dynamics should consider such high level dialogue activity types and their related NLP topics as important ingredients of their toolboxes.
InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images

In this paper, we present InSeGAN, an unsupervised 3D generative adversarial network (GAN) for segmenting (nearly) identical instances of rigid objects in depth images. Using an analysis-by-synthesis approach, we design a novel GAN architecture to synthesize a multiple-instance depth image with independent control over each instance. InSeGAN takes in a set of code vectors (e.g., random noise vectors), each encoding the 3D pose of an object that is represented by a learned implicit object template. The generator has two distinct modules. The first module, the instance feature generator, uses each encoded pose to transform the implicit template into a feature map representation of each object instance. The second module, the depth image renderer, aggregates all of the single-instance feature maps output by the first module and generates a multiple-instance depth image. A discriminator distinguishes the generated multiple-instance depth images from the distribution of true depth images. To use our model for instance segmentation, we propose an instance pose encoder that learns to take in a generated depth image and reproduce the pose code vectors for all of the object instances. To evaluate our approach, we introduce a new synthetic dataset, "Insta-10," consisting of 100,000 depth images, each with 5 instances of an object from one of 10 classes. Our experiments on Insta-10, as well as on real-world noisy depth images, show that InSeGAN achieves state-of-the-art performance, often outperforming prior methods by large margins.
Research on Human Action Recognition Based on Global-Local Features of Video

In the research of human behavior recognition, the two-stream network structure shows excellent results. Aiming at the branch feature of two-stream networks, this paper proposes a two-stream human behavior research method based on global-local features. This method first uses a mixture of Gaussian background modeling methods to extract silhouette features as global contour features, and then uses an end-to-end learnable unsupervised network TV-Net to generate optical flow motion features, which are used as the network input, and the Xception network is used as The feature generation network which does not change the model scale while improving the accuracy, and performs fusion classification on the output of the two-stream branch network to obtain the behavior recognition result. This method refines the motion information contained in the global and local features for classification, reduces the computational complexity, and shows a good level of recognition on both public and internal data sets.
Regional Continuing Education Model for Teachers in “Industrial Arts” Subject Area

The purpose of the paper is to design a Continuing Education Model for Industrial Arts teachers of Irkutsk Oblast. The article considers the need to develop a regional Continuing Education Model for teachers in the “Industrial Arts” subject area, describes positive experience of teaching personnel development in the region. The authors have presented a project of the structure of the Continuing Education Model for teachers in the “Industrial Arts” subject area of Irkutsk Oblast that includes target, technological and result-evaluating components. Special place in the Model is given to the technological component, which is revealed through the personalised approach to development and improvement of professional competencies of teachers in the “Industrial Arts” subject area. The study is novel in that it develops ways of organising continuing education reflected in the developed Model and aimed at teachers in the “Industrial Arts” subject area of Irkutsk Oblast. As a result of analysing regulatory documents and requests of teachers in the “Industrial Arts” subject area for continuing education, the authors have proposed ways of organising acquisition of new professional competencies and/or their improvement, which will be implemented through the technological component of the designed Continuing Education Model for Industrial Arts teachers.
Recognizing Handwritten Chinese Texts with Insertion and Swapping Using a Structural Attention Network

It happens in handwritten documents that text lines distort beyond sequential structure because of in-writing editions such as insertion and swapping of text. This kind of irregularity can not be handled using existing text line recognition methods that assume regular character sequences. In this paper, we regard this irregular text recognition as a two-dimensional (2D) problem and propose a structural attention network (SAN) for recognizing texts with insertion and swapping. Particularly, we present a novel structural representation to help SAN learn these irregular structures. With the guidance of the structural representation, SAN can correctly recognize texts with insertion and swapping. To validate the effectiveness of our method, we chose the public SCUT-EPT dataset which contains some samples of text with insertion and swapping. Due to the scarcity of text images with text insertion and swapping, we generate a specialized dataset which only consists of these irregular texts. Experiments show that SAN promises the recognition of inserted and swapped texts and achieves state-of-the-art performance on the SCUT-EPT dataset.
Research and Industry Partnerships in Cybersecurity and Privacy Research: New Frontiers or Fueling the Tech Sector?

Presents the introductory editorial for this issue of the publication.
ShadowDGA: Toward Evading DGA Detectors with GANs

Domain generation algorithms (DGAs) are widely used in modern botnets to generate a large number of domain names through which bots can communicate with their command and control (C & C) servers. In recent years, many machine learning based approaches have been proposed to automatically detect algorithmically generated domains in real time and have achieved success in traditional DGAs. Nevertheless, they are somewhat unavailable for adversarial domains. In this paper, we develop a more threatening DGA called ShadowDGA that utilizes generative adversarial networks (GANs) to simulate the distribution of benign domains without any knowledge about the DGA detector to evade detection. Experimental results demonstrate that the domains generated by ShadowDGA are the most difficult to detect compared to existing DGA families. We also present an effective defense method for adversarial domains without retraining. These findings indicate that detectors that rely solely on features extracted from the domain name are vulnerable, while a robust DGA detector should contain additional contextual information.
A Study on Evaluation Model of VR Shopping Experience based on User-Centered Approach

VR shopping could become a new purchasing channel that combines the advantages of offline shopping and online shopping to provide a new immersive experience for users. According to the changes of shopping environment, this paper proposes the necessity of studying shopping experience in VR, aiming to give comprehensive evaluation indexes for VR shopping experience.Firstly, we reviewed the research related on online and offline shopping experiences, VR shopping, and the characteristics of virtual reality. Also, through some case studies, we have an understanding of the current development progress of VR shopping platform. In VR shopping environment, a comprehensive understanding of shopping experience is needed. Therefore, we adopt the user-centered design method combined with context mapping and correspondence analysis to integrate online and offline shopping experiences, also analyze the relationship between user experience and interaction in the shopping process. On the basis of integrated shopping experience, we finally proposed an evaluation model of VR shopping experience combines 1) Integrated Shopping Experience, 2) Usability of VR Environment and System, 3) Features of VR, and then revised and supplemented through expert evaluation, result to a total of 33 evaluation items. It is expected that it can be used to construct and evaluate the new VR shopping environment and provide reference for the development of VR shopping.