Reviewerly/exHarmony huggingface dataset:
Human Synthesis and Scene Compositing

Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important for a variety of tasks ranging from photo editing, fashion virtual try-on, to special effects and image compression. In this paper, we propose a HUSC (HUman Synthesis and Scene Compositing) framework for the realistic synthesis of humans with different appearance, in novel poses and scenes. Central to our formulation is 3d reasoning for both people and scenes, in order to produce realistic collages, by correctly modeling perspective effects and occlusion, by taking into account scene semantics and by adequately handling relative scales. Conceptually our framework consists of three components: (1) a human image synthesis model with controllable pose and appearance, based on a parametric representation, (2) a person insertion procedure that leverages the geometry and semantics of the 3d scene, and (3) an appearance compositing process to create a seamless blending between the colors of the scene and the generated human image, and avoid visual artifacts. The performance of our framework is supported by both qualitative and quantitative results, in particular state-of-the art synthesis scores for the DeepFashion dataset.
Weighted-Sampling Audio Adversarial Example Attack

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose weighted-sampling audio adversarial examples, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level 1.
Screen content image quality assessment based on convolutional neural networks

Screen content image (SCI) is a composite image including textual and pictorial regions resulting in many difficulties in image quality assessment (IQA). Large SCIs are divided into image patches to increase training samples for CNN training of IQA model, and this brings two problems: (1) local quality of each image patch is not equal to subjective differential mean opinion score (DMOS) of an entire image; (2) importance of different image patches is not same for quality assessment. In this paper, we propose a novel no-reference (NR) IQA model based on the convolutional neural network (CNN) for assessing the perceptual quality of SCIs. Our model conducts two designs solving problems which benefits from two strategies. For the first strategy, to imitate full-reference (FR) CNN-based model behavior, a CNN-based model is designed for both FR and NR IQA, and performance of NR-IQA part improves when the image patch scores predicted by FR-IQA part are adopted as the ground-truth to train NR-IQA part. For the second strategy, image patch qualities of one entire SCI are fused to obtain the SCI quality with an adaptive weighting method taking account the effect of the different image patch contents. Experimental results verify that our model outperforms all test NR IQA methods and most FR IQA methods on the screen content image quality assessment database (SIQAD). On the cross-database evaluation, the proposed method outperforms the existing NR IQA method in terms of at least 2.4 percent in PLCC and 2.8 percent in SRCC, which shows high generalization ability and high effectiveness of our model.
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

Textual entailment is a fundamental task in natural language processing. Most approaches for solving this problem use only the textual content present in training data. A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task. However, the proposed models do not fully exploit the information in the usually large and noisy KGs, and it is not clear how it can be effectively encoded to be useful for entailment. We present an approach that complements text-based entailment models with information from KGs by (1) using Personalized PageRank to generate contextual subgraphs with reduced noise and (2) encoding these subgraphs using graph convolutional networks to capture the structural and semantic information in KGs. We evaluate our approach on multiple textual entailment datasets and show that the use of external knowledge helps the model to be robust and improves prediction accuracy. This is particularly evident in the challenging BreakingNLI dataset, where we see an absolute improvement of 5-20% over multiple text-based entailment models.
Gesture Recognition Intermediary Robot for Abnormality Detection in Human Activities

The world ageing population is increasing, giving rise to research targeted towards improving the quality of life and promoting the independent living of older adults. Detecting abnormalities in the daily activities of the older adults is relevant since abnormalities can be an early sign of health decline, prompting for the need for intervention. Current approaches to abnormality detection involve modelling the usual behavioural routine of the individual as a baseline and comparing subsequent behaviour to the baseline to detect abnormalities. This approach is prone to errors and not flexible since it does not take into account changes in human behavioural routine. Training is usually performed on pre-existing data making the abnormality detection model non-adaptive to new incoming data. An intermediary can be incorporated to enable model predictions to be communicated to humans for verification of the detected anomalies. This paper proposes a gesture recognition approach for facilitating interaction between humans and a robot intermediary. A model capable of recognising hand gestures corresponding to affirmations and denials is implemented. Preliminary evaluation shows that the proposed gesture recognition approach has the potential of being utilised in an assistive robot intermediary.
A Systematic Review on Fusion Techniques and Approaches Used in Applications

Fusion technologies have rapidly evolved. These technologies are normally customized according to the needs of domains. Despite a large number of publications on intelligence fusion applications for various domains, they are scattered. The aim of this review is to present the state of the art for intelligence fusion applications within a specific domain. We identified three major domains for the purpose, namely robotics, military, and healthcare, during the initial process of the systematic review. These three domains are always in need of superior intelligence. Articles were searched mainly in IEEE Xplore. We limit the range of publications to the year 2014 to 2019, to focus on the most recent publications. We adopt the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to screen, filter and evaluate qualities of each retrieved article. As a result, we retrieved 675 articles at the initial stage of the search, we conducted screening and filtering process and reviewed 153 articles potential articles, and finally, we excluded 36 articles as they do not comply with our quality assessment criteria. Only 117 articles are included. The results of this study are a list of classified applications within the domains and a number of relevant techniques or approaches used in each classified application. The finding of this review showed that the most published works for the use of intelligence fusion are mainly applications in the robotics domain, where mostly used techniques are Kalman Filter and its variants. Outcomes of this study can be a guideline or an insight for researchers to further develop and implement in this field.
A Model-Driven Mobile HMI Framework (MMHF) for Industrial Control Systems

With the advent of software technologies, over a period of time, the Industrial Control Systems (ICSs) have grown exponentially. Whereas, almost all ICSs comprise Human Machine Interfaces (HMIs), which are the key component for monitoring and controlling complex industrial systems. For decades, traditional HMIs with simple User Interfaces (UIs) remained operational to minimize the complexities and resulting operational costs. However, due to the emergence of smartphone technologies, the perception about user interfaces has been transformed significantly and users now demand same sort of experience with industrial HMIs, as well. There are few industrial solutions, like, ICONICS GraphWorX to support the development of mobile HMI screens. However, such proprietary solutions are quite expensive. Furthermore, the underlying development approaches and source codes are not accessible in public domain. On the other hand, the state-of-the-art approaches for the development of native mobile HMI screens are hard to find in the literature. Consequently, there is dire need of a cost-effective, easy to use, open source framework for the development of native mobile HMI screens. In order to achieve this goal, here we propose, a Model-driven Mobile HMI Framework (MMHF). MMHF comprises, a Unified Modeling Language (UML) Profile for Mobile HMI (UMLPMH) for modeling of HMI screens. MMHF also includes, an open source transformation engine and a Model Driven Mobile-based HMI Code Generator (MDMHCG) to automatically transform UMLPMH models into target native mobile HMI implementations. Consequently, MMHF enables simpler way to design the HMI screens using UMLPMH and generates native Mobile HMI Screen implementations automatically using MDMHCG. The empirical evidence of MMHF is demonstrated through three (3) benchmark case studies, which prove that the MMHF is a feasible, cost effective and scalable solution to develop native HMI screens for wide-ranging ICSs.
Cross multi-scale locally encoded gradient patterns for off-line text-independent writer identification

Writer identification is experiencing a revival of activity in recent years and continues to attract great deal of attention as a challenging and important area of research in the field of forensic and authentication. In this work, we introduce a reliable off-line system for text-independent writer identification of handwritten documents. Feature engineering is an essential component of a pattern recognition system, which can enhance or decrease the classification performance. A well-designed and defined feature extraction method improves the classification task. This paper proposes, for feature extraction, an effective, yet high-quality and conceptually simple feature image descriptor referred to as Cross multi-scale Locally encoded Gradient Patterns (CLGP). The proposed CLGP feature extraction method, which is expected to better represent salient local writing structure, operates at small observation regions (i.e., connected component sub-images) of the writing sample. CLGP histogram feature vectors computed from all these observation regions in all writing samples are considered as classification inputs to identify query writers using the Nearest Neighbor Classifier (1-NN). Our system is evaluated on six standard databases (IFN/ENIT, AHTID/MW, CVL, IAM, Firemaker, and ICDAR2011) including handwritten samples in Arabic, English, French, Greek, German, and Dutch languages. Comparing the identification performance with old and recent state-of-the-art methods, the proposed system achieves the highest performance on IFN/ENIT, AHTID/MW, and ICDAR2011 databases, and demonstrates competitive performance on IAM, CVL, and Firemaker databases.
AIM 2019 Challenge on Video Extreme Super-Resolution: Methods and Results

This paper reviews the extreme video super-resolution challenge from the AIM 2019 workshop, with emphasis on submitted solutions and results. Video extreme super-resolution x16 is a highly challenging problem, because 256 pixels need to be estimated for each single pixel in the low-resolution (LR) input. Contrary to single image super-resolution (SISR), video provides temporal information, which can be additionally leveraged to restore the heavily downscaled videos and is imperative for any video super-resolution (VSR) method. The challenge is composed of two tracks, to find the best performing method for fully supervised VSR (track 1) and to find the solution which generates the perceptually best looking outputs (track 2). A new video dataset, called Vid3oC, is introduced together with the challenge.
Confronting the Challenges of MALL: Distraction, Cheating, and Teacher Readiness

Mobile technologies appear to be gaining in popularity regarding foreign language instruction they have recently proliferated in academic environments. M-learning opens up new possibilities and opportunities for both teachers and students, and it is inevitable that methodological approaches to pedagogy and ways of teaching foreign languages are accordingly adjusted and reevaluated. Despite the numerous significant advantages mobile technologies offer, scholars and educators have to take into consideration some barriers and limitations in this respect, and respond to both existing and future challenges. Moreover, the studies on MALL (mobile-assisted language learning) seem to be predominantly focused on benefits of specific technological advances when the advantages of MALL are discussed. This article attempts to present a critical review of the literature which deals with MALL as further research and exploration in this area is necessary, especially with regard to challenges and barriers language teachers face when m-technologies are employed. Three major drawbacks related to using mobile technologies in teaching are predominantly discussed, namely learner distraction, cheating, and teacher perception and readiness. Moreover, proposing feasible solutions to the challenges and barriers is also discussed in the article.
A language and framework for dynamic component ensembles in smart systems

Abstract Smart system applications (SSAs)—a heterogeneous landscape of applications of Internet of things, cyber-physical systems, and smart sensing systems—are composed of autonomous yet inherently cooperating components. An important problem in this area is how to hoist the cooperation of software components forming dynamic groups—ensembles—at the architectural level of an SSA. This is hard since ensembles can overlap, be nested, and be dynamically formed and dismantled based on several criteria. A related problem is how to combine component and ensemble specification with a well-established language supported on multiple platforms. To target these problems, we propose a specification and implementation language Trait-based COmponent Ensemble Language (TCOEL) based on Scala internal DSL, to describe both the architecture and formation of dynamic ensembles of components and their functional internals. To raise the level of expressivity, we introduce the concept of domain-specific extensions (traits) to the TCOEL core to reflect different paradigms’ concerns—such as movement in a 2D map, state-space modeling of physical processes, and statistical reasoning about uncertainty. This allows for configuring TCOEL for the needs of a specific SSA use case and, at the same time, facilitates reuse. To evaluate TCOEL, we show how it can be beneficially used in addressing the coordination of agents in a RoboCup Rescue Simulation application.
Exploiting constrained IoT devices in a trustless blockchain-based water management system

We propose a technological framework based on the combination of the Internet of Things (IoT) and Blockchains aiming at incentivizing and rewarding more sustainable water management practices in agriculture. In this context, current IoT-based precision agriculture deployments prefer energy efficiency, which generally translates into power-and-resource-constrained sensing devices. For this reason, often, system integrators of this sector feel the need to interpose third-party hardware intermediaries (e.g., IoT gateways) between sensing devices and blockchain endpoints, so augmenting infrastructural costs and reducing the trustworthiness of the data acquired from the field. In this paper, we present a software architecture specifically designed for a trustless water management system where constrained IoT devices can directly transact sensed data on a public blockchain network. We deploy the proposed solution on off-the-shelf hardware devices and undertake a thorough benchmarking in terms of memory, program size, communication overheads and power consumption. Our results show that, in general, typical IoT devices can be used to directly interact with a blockchain, without severe burden. More specifically, these devices only incur an additional 6% of the energy consumed for their typical interactions with a gateway.
Interpol review of questioned documents 2016–2019

This review paper covers the forensic-relevant literature in questioned documents from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol Review Papers 2019.pdf.
Observational Study on the Impact of Large-Scale Photovoltaic Development in Deserts on Local Air Temperature and Humidity

As an important form of clean energy, photovoltaic (PV) power generation is entering a rapid development phase. Qinghai, China is located on the Qinghai-Tibet Plateau. It has sufficient sunlight and rich heat and light resources, includes a large area of the Gobi Desert, and has become China’s largest base for PV power generation. However, large-scale PV development in deserts changes the local surface energy distribution and impacts local microclimates. This study considered the Gonghe PV Power Plant in Qinghai as an example. Three monitoring stations were set up in the PV power plant, transition, and reference areas, and the influence of large-scale PV developments on the local air temperature and humidity was studied based on long-term, multi-point field observation data. The results showed that the overall daytime air temperature in the PV power plant had changed slightly (increased and decreased), while the night-time temperature dropped significantly. Specifically, in spring and summer, the daytime temperature increased slightly, with a maximum increase of 0.34 °C; in autumn and winter, the daytime temperature decreased slightly, with a maximum decrease of 0.26 °C; in all seasons, the night-time temperature decreased, with a maximum decrease of 1.82 °C during the winter night. The relative humidity in the PV power plant generally increased; except for a slight decrease in summer, the daytime and night-time relative humidity in spring, autumn, and winter always increased. The humidification in winter was the most significant, with increases of 5.00% and 4.76% for the transition and reference areas, respectively. The diurnal air temperature and relative humidity ranges in the PV power plant were greater than those outside the PV power plant. The results obtained in this field observation study could serve as a basis for quantitative evaluation of the microclimate effects of large-scale PV development in deserts and provide technical support for guiding the future planning and development of the PV industry.
Best Frame Selection in a Short Video

People usually take short videos to record meaningful moments in their lives. However, selecting the most representative frame, which not only has high image visual quality but also captures video content, from a short video to share or keep is a time-consuming process for one may need to manually go through all the frames in a video to make a decision. In this paper, we introduce the problem of the best frame selection in a short video and aim to solve it automatically. Towards this end, we collect and will release a diverse large-scale short video dataset that includes 11, 000 videos shoot in our daily life. All videos are assumed to be short (e.g., a few seconds) and each video has human-annotated of the best frame. Then we introduce a deep convolutional neural network (CNN) based approach with ranking objective to automatically pick the best frame from frame sequences extracted via short videos. Additionally, we propose new evaluation metrics, especially for the best frame selection. In experiments, we show our approach outperforms various other methods significantly.
Covid-19: Digital Signature Impact on Higher Education Motivation Performance

At present, the process of validating documents for certain purposes cannot be done face-to-face because of the Covid-19 pandemic. Therefore, this research aims to maximize the existence of smart digital signature technology that guarantees its safety and validity without having to meet face to face. Encrypted digital signatures with RSA-SHA256 with cloud storage features that can share documents. The waterfall method for building systems, the collection of data generated for analysis by observation, and online questionnaires using Google Form. Based on the characteristics of the system, the satisfaction factor analysis of the system with the Slovin formula processed by the SUS score resulted in a score of 95 &gt; 70. The final result of this study is that the digital signature system has a significant impact on increasing motivation to facilitate authorization and secure documents.
Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter Images

This paper presents an approach for leveraging the abundance of images posted on social media like Twitter for large scale 3D reconstruction of cultural heritage landmarks. Twitter allows users to post short messages, including photos, describing a plethora of activities or events, e.g., tweets are used by travelers on vacation, capturing images from various cultural heritage assets. As such, a great number of images are available online, able to drive a successful 3D reconstruction process. However, reconstruction of any asset, based on images mined from Twitter, presents several challenges. There are three main steps that have to be considered: (i) tweets’ content identification, (ii) image retrieval and filtering, and (iii) 3D reconstruction. The proposed approach first extracts key events from unstructured tweet messages and then identifies cultural activities and landmarks. The second stage is the application of a content-based filtering method so that only a small but representative portion of cultural images are selected to support fast 3D reconstruction. The proposed methods are experimentally evaluated using real-world data and comparisons verify the effectiveness of the proposed scheme.