Reviewerly/exHarmony huggingface dataset:
Instance-based Deep Transfer Learning on Cross-domain Image Captioning

Image captioning task achieves imposing result in generating text description of image by training on the large image and sentence pairs dataset (e.g., MSCOCO). Applied the large publicly available datasets to the specific new task will lead to the problem known as domain shifting due to the different probability distribution. In this research, we propose a Multimodal Instance-based Deep Transfer Learning (MIBTL) for cross-domain image captioning. Generally, transferring knowledge from the source domain into the target domain is pertinent due to the limited number of the dataset from the target domain. The limited number of the dataset also lead to a problem called overfitting. The instance-based strategy intuitively takes into account the influence of data by selecting the most representative data from source domain as the supplement for training on the target domain. We employ deep hash binary code representation of image and text pair to determine the distance between two data points. The experiment conducted by two types of transfer condition includes a slight shift (from MSCOCO to Flickr30k) and significant shift (from MSCOCO to CUB-200 and Oxford-102). The result shows that MIBTL can outperform other baselines to achieve state-of-the-art performance in cross-domain image captioning transfer learning.
Vehicle model recognition using SRGAN for low-resolution vehicle images

An enhanced vehicle model recognizer for low-resolution images is proposed in where SRGAN (Super Resolution Generative Adversarial Network) is used to enhance the image quality and CNN (Convolutional Neural Network) is used to classify the vehicle model from the enhanced images. Many previous vehicle model classifiers trained with only the high-resolution front-images of vehicles have low accuracy against the low-quality images captured by CCTV cameras in real environments. To correctly classify the vehicle model from the low-quality images of arbitrary directions, SRGAN is first used to transform the low-resolution image into the corresponding high-resolution image. Then the direction of the vehicle in the image is determined and the vehicle model is recognized based on the pre-determined direction. The accuracy of the proposed vehicle model classifier is evaluated as 78%, higher than that of the classification without SRGAN.
Intelligent Identification of Ornamental Devanagari Characters Inspired by Visual Fixations

This paper is concerned with the development of techniques for the recognition of ornamental characters motivated by the perceptual processes involved in humans. To understand the perceptual process, we have performed the eye-tracking experiment to recognize the special set of characters, with artistic variations in character structure and form. The novelty of this paper is the use of human visual fixations to supervise the intermediate layers of the convolutional neural network. From the results obtained, we found that the network performs better when trained with the assistance of fixation information compared to the network trained without eye fixations.
Freeform testability considerations for subaperture stitching interferometry

Fizeau interferometry is a flexible tool for optical surface metrology. Different transmission spheres (TSs) enable testing most spherical surfaces, and selecting a TS to measure form irregularity of a given surface is straightforward. New applications, however, have increased the variety of surfaces to test beyond spheres. Aspheres and freeforms are particularly challenging, as interferometers only resolve small deviations from a sphere without additional corrective optics. Furthermore, the surface irregularity specification may be accompanied by tolerances related to mid-spatial frequencies (MSFs), such as power-spectral density (PSD) or local slope. These MSF specifications may require spatial resolution beyond what a typical full aperture test provides. Subaperture stitching interferometry is particularly well-suited to measuring MSFs, and can also significantly increase non-null aspheric and freeform measurement capability. Selecting the most appropriate TS for a given surface, however, becomes more complicated. We explore how the surface specification interacts with the interferometer’s slope capture limit to determine an “optimal magnification” for that surface. We show how to select the most appropriate TS for the surface, given the optimal magnification and other interferometer constraints (e.g. cavity length, focusing range). We demonstrate this TS selection process for a toroid measurement (with 400 micrometers departure from best-fit sphere). We conclude with guidance for designing aspheres and freeforms that can be measured more easily with stitching interferometry.
Research on the parallelization of image quality analysis algorithm based on deep learning

Image quality assessment is an indispensable in computer vision applications, such as image classification, image parsing. With the development of Internet, image data acquisition becomes more conveniently. However, image distortion is inevitable due to imperfect image acquisition system, image transmission medium and image recording equipment. Traditional image quality assessment algorithms only focus on low-level visual features such as color or texture, which could not encode high-level features effectively. CNN-based methods have shown satisfactory results in image quality assessment. However, existing methods have problems such as incomplete feature extraction, partial image block distortion, and inability to determine scores. So in this paper, we propose a novel framework for image quality assessment based on deep learning. We incorporate both low-level visual features and high-level semantic features to better describe images. And image quality is analyzed in a parallel processing mode. Experiments are conducted on LIVE and TID2008 datasets demonstrate the proposed model can predict the quality of the distorted image well, and both SROCC and PLCC can reach 0.92 or higher.
Semantic 3D Reconstruction Using Multi-View High-Resolution Satellite Images Based on U-Net and Image-Guided Depth Fusion

This paper reports our workflow for multi-view semantic stereo reconstruction in the IEEE Data Fusion Contest (DFC) of 2019. The workflow mainly consists of two parts: semantic classification and multi-view stereo digital surface model (DSM) generation. Our strategy involves the interaction of these two components for better classification and DSM results. We firstly classify semantic objects using only original image information and then project them to the generated DSM for voting and post-refinement. An example-based pair selection strategy is used to pre-selected pairs for dense matching, and the fused DSM is performed through an image-guided median filter. Since the adjusted rational polynomial coefficient (RPC) parameters are not accurate in the dataset, these DSMs are co-registered after production. In our workflow, we only consider non-vegetation and non-water area for registration to obtain more robust co-registration result. These co-registered DSMs are fused and refined using the result from the semantic classification. Our method achieved 73.00% of mIoU-3 in the testing data in the contest.
A Robust and Blind 3D Mesh Watermarking Approach Based on Particle Swarm Optimization

This article presents a robust 3D mesh watermarking approach, which adopts an optimization method of selecting watermark vertices for 3D mesh models. The proposed approach can enhance the imperceptibility of the watermarked model without affecting the robustness and capacity factors. The proposed watermark approach depends on an embedding algorithm that use a clustering strategy, based on K−means clustering algorithm in conjunction with the particle swarm optimization to divide the mesh model vertices into groups. Points of interest set (POIs) are selected from these clustered groups and mark it as watermark vertices where the (POIs) are invariant to most of the geometrical and connectivity attacks. Then, the proposed approach inserts the watermark bit stream in the decimal part of spherical coordinates for these selected watermark vertices. The experimental results confirm that the proposed approach proves its superiority compared with state-of-the-art techniques with respect to imperceptibility and robustness.
TEEMo

Recent trends like edge computing move metro and core network elements from access restricted back offices to data centers where their attack surface is exposed to a larger audience. These trends increase the need for means of monitoring these network elements' peripherals in a secure and untampered way.
Comparison of discrete cosine transform and dual-tree complex wavelet transform based on arithmetic coding in medical image compression

Abstract Medical image contains very important information in the medical world used to diagnose diseases by doctors. Storage for storing medical imagery requires large storage media. The difficulty of sending and storing files, hence the process of compression image that can shrink the size of medical image. This research uses the Discrete Cosine Transform and Dual-Tree Complex Wavelet Transform-based Arithmetic Coding. DCT is used in the process of compression of imagery files using the cosine value approach. In DCT, there is a quantization process that causes image quality to decline, as it eliminates some information on imagery. DTCWT uses 2 trees, where tree 1 is for real value and tree 2 for imaginary value. Arithmetic Coding is one of entropy encoding that uses the range and probability values in the calculation. The value of decoding results in Arithmetic Coding has the same value as the original image. DCT produce a higher compression ratio than DTCWT, while DTCWT get higher PSNR than DCT.
Workflow-based Setup of Smart Devices in Mixed Reality

New types of smart devices are emerging every day providing end-users with new ways of interacting with the IoT. Setup and configuration of these devices are, however, rather complex and require technical expertise. Especially in smart homes users often lack these skills and need assistance with setting up devices, which makes them hesitant to adopt new technologies. We present a mixed reality application to support users with the setup of IoT devices. The setup tasks are modelled and executed as formal interactive workflows. An intuitive wizard guides users and presents additional task-related multimedia information in mixed reality. A semantically enriched IoT middleware provides data and control of the IoT devices. We conducted a brief user study to evaluate the application's usability.
Study of alternative building for tsunami evacuation in Kuta Alam sub-district Banda Aceh

Abstract Kuta Alam Sub District has many potential hazards, especially from the sea. The location of the area that is directly adjacent to the sea causes it to be very vulnerable to tsunami. Its topography is lowland less than 1 meter above the sea-level. Tsunami in 2004 had taken a great number of people lives in the area about 14.95 % of the total number of inhabitants which is 55,030 people that time. However, there is still no any evacuation building available in Kuta Alam to escape from Tsunami. We know tsunami which might be occurred anytime unpredictable. In order to save people’s lives, it is needed to conduct a study to find alternatives buildings to be used for evacuation and rescue in the area. The aim of this study to identify the locations and number of alternative buildings that are potential as the tsunami evacuation building in Kuta Alam Sub-district. The study used the survey system toward the buildings with qualitative and quantitative approaches. Data analyzing is conducted spatially through geographic information systems. Based on observation results toward evacuation building in Kuta Alam Sub-district, there are some buildings renovated in order to be functioned as tsunami evacuation buildings. The renovation should be done on roof, school, and ladder evacuation of the mosque that are selected as evacuation buildings. Until today, those buildings have not been renovated to be eligible as evacuation places. The result found that there are 45 public buildings that can be used for alternative evacuation buildings in Kuta Alam Sub-district. That can accommodate 94.06 % of the total number of people this time which is 49,011 people.
Interactive Curation of Datasets for Training and Refining Generative Models

Abstract We present a novel interactive learning‐based method for curating datasets using user‐defined criteria for training and refining Generative Adversarial Networks. We employ a novel batch‐mode active learning strategy to progressively select small batches of candidate exemplars for which the user is asked to indicate whether they match the, possibly subjective, selection criteria. After each batch, a classifier that models the user's intent is refined and subsequently used to select the next batch of candidates. After the selection process ends, the final classifier, trained with limited but adaptively selected training data, is used to sift through the large collection of input exemplars to extract a sufficiently large subset for training or refining the generative model that matches the user's selection criteria. A key distinguishing feature of our system is that we do not assume that the user can always make a firm binary decision (i.e., “meets” or “does not meet” the selection criteria) for each candidate exemplar, and we allow the user to label an exemplar as “undecided”. We rely on a non‐binary query‐by‐committee strategy to distinguish between the user's uncertainty and the trained classifier's uncertainty, and develop a novel disagreement distance metric to encourage a diverse candidate set. In addition, a number of optimization strategies are employed to achieve an interactive experience. We demonstrate our interactive curation system on several applications related to training or refining generative models: training a Generative Adversarial Network that meets a user‐defined criteria, adjusting the output distribution of an existing generative model, and removing unwanted samples from a generative model.
Linearity and tone in the unfolding of information

In this article, we show how speakers manage information flow in real time and signal their interactional expectations. Speech unfolds temporally, not only as a series of lexical items which are grouped into grammatical units, but also as a sequence of tone groups which have the potential to achieve an act of telling. While there is a general belief that speakers commence their discourse with information that is shared prior to telling information that updates the common ground, our analysis of a corpus of monologue and dialogue shows that matters are not so simple. Speakers’ informational needs are balanced moment by moment within and between increments which are themselves shaped by the interlocutors’ shifting apprehensions of communicative purpose and the extent of presumed shared information. In our analysis we combine (i) a speech functional analysis, (ii) a description of a hierarchy of informational foci and (iii) prosody in order to develop a detailed description of how speakers manage information flow in real time. This enables us to show how speakers simultaneously balance informational flow while signalling their interactional expectations. Our conclusion is that speakers manage information flow by balancing textual, interpersonal and ideational choices.
Artificial Intelligence for Dramatic Performance

In the last years, digital media have challenged traditional narrative models with an increasing request for interaction with the user, leading to novel paradigms of storytelling and performance. While most research in interactive storytelling and drama has addressed the role of automation in the story generation process, in this paper we present a framework that relies on Artificial Intelligence techniques to augment the performer–audience interaction in a storytelling setting. Step after step, the emotional response of the audience is automatically detected, the performer decides about her/his attitude towards the audience, and the story is composed and delivered to the audience by the joint systemperformer initiative on an augmented stage. Initially designed as training tool for interactive story editing, the system has been deployed to create a public performance in February 2019.
Dynamic QBF Dependencies in Reduction and Expansion

We provide the first proof complexity results for QBF dependency calculi. By showing that the reflexive resolution path dependency scheme admits exponentially shorter Q-resolution proofs on a known family of instances, we answer a question first posed by Slivovsky and Szeider in 2014 [37]. Further, we conceive a method of QBF solving in which dependency recomputation is utilised as a form of inprocessing. Formalising this notion, we introduce a new version of Q-resolution in which a dependency scheme is applied dynamically. We demonstrate the further potential of this approach beyond that of the existing static system with an exponential separation. Last, we show that the same picture emerges in an analogous approach to the universal expansion paradigm.
Adaptive Unified Data Embedding and Scrambling for Three-Dimensional Mesh Models

Traditionally, unified data embedding and scrambling techniques have been designed for grayscale images, which cannot be applied directly to a three-dimensional (3D) mesh. Recently, the universal use of 3D technology inspired us to innovate in this field. In this paper, an adaptive unified data embedding and scrambling technique for 3D mesh models (3D-AUES) is proposed, which can embed external data and scramble 3D mesh simultaneously. First, a vertex coordinate prediction method called cross prediction is adopted to accurately predict half of the vertices from the other half. The predicted vertices are used to embed external data. We further increase the embedding rate by bit replacement embedding. Then, to improve security, we propose an adaptive threshold to select vertices for embedding. To ensure lossless scrambling, the thresholds and prediction errors are embedded as side information with secret information into the vertices. By adopting an adaptive threshold and multilayer embedding, scalable scrambling quality can be achieved. On the decoder side, with the help of losslessly embedded side information, external data can be successfully extracted, and the original mesh can be restored to predetermined distortion levels, from lossless recovery to partial recovery. Experiments show that 3D-AUES has a high embedding rate, scalable scrambling quality and scalable recovery quality.
Meta-learning Based Evolutionary Clustering Algorithm

In this work, we address the hard clustering problem. We present a new clustering algorithm based on evolutionary computation searching a best partition with respect to a given quality measure. We present 32 partition transformation that are used as mutation operators. The algorithm is a $$(1+1)$$ evolutionary strategy that selects a random mutation on each step from a subset of preselected mutation operators. Such selection is performed with a classifier trained to predict usefulness of each mutation for a given dataset. Comparison with state-of-the-art approach for automated clustering algorithm and hyperparameter selection shows the superiority of the proposed algorithm.
Engaging Mobile-Assisted Learning Activities Using Multiple Mobile Apps for Foreign Language Practice

Mobile applications have been used to support engaging and interactive learning over the past several years. However, most mobile apps for language education were developed for merely vocabulary learning and simple grammar exercises. Hence, it is important for instructors to encourage and guide students to participate in more diverse learning activities using the mobile apps to learn and practice the target language in their daily lives. This chapter attempts to introduce a sample of engaging mobile-assisted learning activities toward the use of multiple mobile apps like TIMeS (Taylor's Integrated Moodle e-Learning System), Naver Blog, and Quizlet. A survey was conducted to gather student responses and feedback regarding this app-based learning, and 266 students responded over four semesters in 2018 and 2019. The chapter examines how these app-based learning activities are advantageous to the students' learning outside of the classroom in terms of enhancing engagement and flexibly.
Advantages of Having Users' Trust and Reputation Values on Data Sharing Process in Online Social Networks

Online Social Networks (OSNs) are quite popular platforms that have an environment where a person can contact to another person without restricting their locations. Users get in contact with each other via sharing contents of data such as videos, photos, messages, and events. Shared contents do not include always only the user's information, who uploads the content to OSNs, but also include other users' information. This type of data sharing might cause serious problems on other people's lives, the type of content is called co-owned data. Current OSNs do not have systems in which there is an enforcement system to make a balance between co-owned data sharing and protection of users' privacy. One way to make the balance is to use reputation systems on co-owned data sharing process in which OSNs' users could be punished or awarded based on their own actions on the co-owned data sharing process. In this work, we propose a reputation system where trust values, which exist between OSNs' users, are used as feedback values. The beta reputation system is used as a groundwork to develop the proposed work's models.
Security Implications of Implementing Multistate Distance-Bounding Protocols

A number of distance-bounding protocols have proposed multistate exchanges. In theory this can potentially improve the security of the protocol when using the same number of exchange rounds, i.e. as there are more than two states the probability of an attacker guessing a response correctly is lower. Similarly, for the same security probability, multistate distance-bounding protocols require less rounds. On the other hand, transmission errors during the timed exchange phase of the distance-bounding protocol also impact on the security of the protocol, as the verifier needs to allow a certain acceptance threshold of incorrect responses. This threshold causes False Acceptance and False Rejection in protocols and further influences the security of the protocol. In this paper, we investigate the security implications of multistate channel implementation and symbol energy considering their effect on the acceptance threshold. We show that implementing multistate responses using simple m- ary modulation methods found in contactless devices, does not necessarily provide the expected security improvement when devices have finite transmission energy to spend on challenge-response exchanges.