Reviewerly/exHarmony huggingface dataset:
Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment

In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture best-exposed images, which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric that captures complementary quality attributes and ensures light-weight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows the preservation of sharp edges and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates the higher qualitative and quantitative performance compared with conventional approaches.
Review of Digital Wallet Requirements

The financial industry has proven to be an important factor in our daily lives through the adaption of new technology. Fintech companies are driven towards enhancing financial services currently being provided by consecutive financial institutions. Digital wallet is the latest invention of finance technology, which is a great tool for making our payment transaction very easily and fast. Many digital wallet applications have already been developed and implemented in payment transactions. An effective number of cyber threats targetting the monetary system have made security an imperative component of the banking system. This paper outlines the digital wallets' threats and also provides the requirements of digital wallets that technically try to address the fear of security customers as usual providers,,while assisting in the successful implementation of digital wallets.
Spatiotemporal Representation Learning for Video Anomaly Detection

Video-based anomalous human behavior detection is widely studied in many fields such as security, medical care, education, and energy. However, there are still some open problems in anomalous behavior detection, such as the large and complicated model is difficult to train, the accuracy of anomalous behavior detection is not high enough and the speed is not fast enough. A spatiotemporal representation learning model is proposed in this paper. Firstly, the spatial-temporal features of the video are extracted by the constructed multi-scale 3D convolutional neural network. Then the scene background is modeled by the high-dimensional mixed Gaussian model and used for anomaly detection. Finally, the accurate position of anomalous behavior in the video data is achieved by calculating the position of the last output feature, that is, the position of the receptive field. The proposed model does not require specific training. Moreover, the proposed method has the advantages of high versatility, fast calculation speed and high detection accuracy. We validated the proposed algorithm on two representative surveillance scene datasets, the Subway and the UCSDSped2. Results show that proposed algorithm has achieved the detection rate of 18 FPS under the condition of common computing resources, and meet the real-time requirements. Moreover, compared the similar methods, the proposed method has achieved the competitive results in both frame-level accuracy and pixel-level accuracy.
Evaluating the Research Performance of Islamabad-Based Higher Education Institutes

Research discovers new knowledge, ideas, and technologies essential in driving the future of society and humanity. Without research, a relevant and modern country cannot exist. Similarly, the research output of universities and degree-awarding institutes (DAIs) plays a significant role in higher education and development of any country. Research fosters professional excellence in faculty, important for delivering outstanding student education and training. Therefore, this research is an evaluation of the research output of higher education DAIs in the capital of Pakistan. This research was conducted in four steps: (a) data collection: where the data were extracted from Scopus for the years 2008–2017; (b) data cleansing and labeling: which included removal of nonrequired contents and labeling of nonnumeric data to meaningful classes; (c) feature selection: useful features according to proposed research questions were selected; and (d) data analysis: data were analyzed according to research questions and results obtained from experiments with the help of statistical tools. It is concluded that with only 1% of country’s population and around 11% of Higher Education Commission’s (HEC) recognized universities geographically located at Islamabad Capital Territory, Islamabad-based universities and DAIs contributed 34% of the total publications of Pakistan produced during the study time period. This shows Islamabad-based universities and DAIs standing at the national level. The public sector universities shared the major portion of total publications of the studied universities. Private sector universities and DAIs shared 8% of the total publications.
EgoVQA - An Egocentric Video Question Answering Benchmark Dataset

Recently, much effort and attention has been devoted to Visual Question Answering (VQA) on static images and Video Question Answering (VideoQA) on third-person videos. In the meantime, first-person question answering has more natural use cases while this topic remains seldom studied. A typical meaningful scenario is an intelligent agent provides assistance to handicapped people to perceive the environment by the queries, localize objects and persons based on descriptions, and identify intentions of surrounding people to guide their reactions (e.g., shake hands or avoid punches). However, due to the lack of first-person video datasets, seldom study had been carried on first-person VideoQA task. To address this issue, we collected a novel egocentric VideoQA dataset called EgoVQA with 600 question-answer pairs with visual contents across 5,000 frames from 16 first-person videos. Various types of queries such as "Who", "What", "How many" are provided to form a semantically rich corpus. We use this database to evaluate the performance of four mainstream third-person VideoQA methods to illustrate their performance gap between first-person related questions and third-person related questions. We believe that EgoVQA dataset will facilitate future research on the imperative task of first-person VideoQA.
Mining Audio, Text and Visual Information for Talking Face Generation

Providing methods to support audio-visual interaction with growing volumes of video data is an increasingly important challenge for data mining. To this end, there has been some success in speech-driven lip motion generation or talking face generation. Among them, talking face generation aims to generate realistic talking heads synchronized with the audio or text input. This task requires mining the relationship between audio signal/text and lip-sync video frames and ensures the temporal continuity between frames. Due to the issues such as polysemy, ambiguity, and fuzziness of sentences, creating visual images with lip synchronization is still challenging. To overcome the problems above, we present a data-mining framework to learn the synchronous pattern between different channels from large recorded audio/text dataset and visual dataset, and apply it to generate realistic talking face animations. Specifically, we decompose this task into two steps: mouth landmarks prediction and video synthesis. First, a multimodal learning method is proposed to generate accurate mouth landmarks with multimedia inputs (both text and audio). Second, a network named Face2Vid is proposed to generate video frames conditioned on the predicted mouth landmarks. In Face2Vid, optical flow is employed to model the temporal dependency between frames, meanwhile, a self-attention mechanism is introduced to model the spatial dependency across image regions. Extensive experiments demonstrate that our method can generate realistic videos with background, and exhibit the superiorities on accurate synchronization of lip movements and smooth transition of facial movements.
Integrated Planning and Control for Collision-free Trajectory Generation in 3D Environment with Obstacles

Control of autonomous robots (ARs) is a hot topic over the past three decades, and motion planning is becoming one of the major challenges in AR control. Substantial works have been done for motion planning in 2-dimensional (2D) environments, but planning in 3D complex environments is still an open problem to be investigated. In this paper, we propose a trajectory planning method to plan a high-quality and safe trajectory in 3-dimensional (3D) environments with multiple obstacles. The proposed method is based on the integration of a global optimal 3D path planner with a local planner. First, a hierarchical path planning algorithm is used as a global path planner to compute a near-optimal path formed by a set of waypoints. Second, a local path trajectory planner is designed to track the path computed by the global path planner with multiple constraints of dynamics and environment satisfied. Finally, simulations are conducted to validate the effectiveness of the proposed trajectory generation approach subject to the constraints of robot physical limits and environmental requirements.
Handwriting Recognition Based on Temporal Order Restored by the End-to-End System

In this paper, we present an original framework for offline handwriting recognition. Our developed recognition system is based on Sequence to Sequence model employing the encoder decoder LSTM, for recovering temporal order from offline handwriting. Handwriting temporal recovery consists of two parts which are respectively extracting features using a Convolution Neural Network (CNN) followed by an LSTM layer and decoding the encoded vectors to generate temporal information using BLSTM. To produce a human-like velocity, we make a Sampling operation by the consideration of trajectory curvatures. Our work is validated by the LSTM recognition system based on Beta Elliptic model that is applied on Arabic and Latin On/Off dual handwriting character database.
A Cognitive Model for Emotion Awareness in Industrial Chatbots

Industrial applications are increasingly adopting conversational agents (chatbots) for tasks ranging from primitive conversation interfaces to intelligent human assistants. In this emerging field of research, there has been a strong drive towards modelling human-like characteristics and behaviors in chatbots. However, only a limited number of research endeavors have focused on a chatbot for automatic characterization of end-user emotions. In order to address this limitation, we propose an artificial intelligence based cognitive model for emotion awareness in chatbots using Markov chains, word embedding, and Natural Language Processing. The proposed model is able to extract emotions from conversations, detect emotion transitions over time, predict real-time emotions and intelligently profile human participants based on their distinct emotional characteristics. We conducted experiments using a real-world end-user dataset to demonstrate the functionality of the proposed model. Results from experiments confirm the plausibility of this model for emotion awareness in industrial conversational agents.
Constrained Heterogeneous Vehicle Path Planning for Large-area Coverage

There is a strong demand for covering a large area autonomously by multiple UAVs (Unmanned Aerial Vehicles) supported by a ground vehicle. Limited by UAVs' battery life and communication distance, complete coverage of large areas typically involves multiple take-offs and landings to recharge batteries, and the transportation of UAVs between operation areas by a ground vehicle. In this paper, we introduce a novel large-area-coverage planning framework which collectively optimizes the paths for aerial and ground vehicles. Our method first partitions a large area into sub-areas, each of which a given fleet of UAVs can cover without recharging batteries. UAV operation routes, or trails, are then generated for each sub-area. Next, the assignment of trials to different UAVs and the order in which UAVs visit their assigned trails are simultaneously optimized to minimize the total UAV flight distance. Finally, a ground vehicle transportation path which visits all sub-areas is found by solving an asymmetric traveling salesman problem (ATSP). Although finding the globally optimal trail assignment and transition paths can be formulated as a Mixed Integer Quadratic Program (MIQP), the MIQP is intractable even for small problems. We show that the solution time can be reduced to close-to-real-time levels by first finding a feasible solution using a Random Key Genetic Algorithm (RKGA), which is then locally optimized by solving a much smaller MIQP.
Adaptive Neural Network for Node Classification in Dynamic Networks

Given a network with the labels for a subset of nodes, transductive node classification targets to predict the labels for the remaining nodes in the network. This technique has been used in a variety of applications such as voxel functionality detection in brain network and group label prediction in social network. Most existing node classification approaches are performed in static networks. However, many real-world networks are dynamic and evolve over time. The dynamics of both node attributes and network topology jointly determine the node labels. In this paper, we study the problem of classifying the nodes in dynamic networks. The task is challenging for three reasons. First, it is hard to effectively learn the spatial and temporal information simultaneously. Second, the network evolution is complex. The evolving patterns lie in both node attributes and network topology. Third, for different networks or even different nodes in the same network, the node attributes, the neighborhood node representations and the network topology usually affect the node labels differently, it is desirable to assess the relative importance of different factors over evolutionary time scales. To address the challenges, we propose AdaNN, an adaptive neural network for transductive node classification. AdaNN learns node attribute information by aggregating the node and its neighbors, and extracts network topology information with a random walk strategy. The attribute information and topology information are further fed into two connected gated recurrent units to learn the spatio-temporal contextual information. Additionally, a triple attention module is designed to automatically model the different factors that influence the node representations. AdaNN is the first node classification model that is adaptive to different kinds of dynamic networks. Extensive experiments on real datasets demonstrate the effectiveness of AdaNN.
Improving 3D Object Detection for Pedestrians with Virtual Multi-View Synthesis Orientation Estimation

Accurately estimating the orientation of pedestrians is an important and challenging task for autonomous driving because this information is essential for tracking and predicting pedestrian behavior. This paper presents a flexible Virtual Multi-View Synthesis module that can be adopted into 3D object detection methods to improve orientation estimation. The module uses a multi-step process to acquire the fine-grained semantic information required for accurate orientation estimation. First, the scene's point cloud is densified using a structure preserving depth completion algorithm and each point is colorized using its corresponding RGB pixel. Next, virtual cameras are placed around each object in the densified point cloud to generate novel viewpoints, which preserve the object's appearance. We show that this module greatly improves the orientation estimation on the challenging pedestrian class on the KITTI benchmark. When used with the open-source 3D detector AVOD-FPN, we outperform all other published methods on the pedestrian Orientation, 3D, and Bird's Eye View benchmarks.
Combining VR and Biofeedback

Abstract. This study explored the effects of combining virtual reality (VR) and biofeedback on the restorativeness of the created experience as judged by the user and the user’s sense of presence. In a between-subjects experiment, we disentangled the effects of display type (VR vs. computer screen) and biofeedback (electrodermal activity biofeedback vs. no biofeedback) in the context of immersive simulated relaxation environments. After a stress induction, 94 healthy participants were randomly assigned to one of four relaxing treatments. There were no treatment-specific differences in subjective stress or physiological arousal. However, VR compared with computer screen increased the sense of presence and, partly, perceived restorativeness. When combined with VR, biofeedback increased physical presence and, in part, perceived restorativeness. The study offers insight that allows for the identification of future research avenues.
Visual Social Relationship Recognition

Social relationships form the basis of social structure of humans. Developing computational models to understand social relationships from visual data is essential for building intelligent machines that can better interact with humans in a social environment. In this work, we study the problem of visual social relationship recognition in images. We propose a dual-glance model for social relationship recognition, where the first glance fixates at the person of interest and the second glance deploys attention mechanism to exploit contextual cues. To enable this study, we curated a large scale People in Social Context dataset, which comprises of 23,311 images and 79,244 person pairs with annotated social relationships. Since visually identifying social relationship bears certain degree of uncertainty, we further propose an adaptive focal loss to leverage the ambiguous annotations for more effective learning. We conduct extensive experiments to quantitatively and qualitatively demonstrate the efficacy of our proposed method, which yields state-of-the-art performance on social relationship recognition.
VA-StarGAN: Continuous Affect Generation

Recent advances in Generative Adversarial Networks have shown impressive results for the task of facial affect synthesis. The most successful architecture is StarGAN, which is effective, but can only generate a discrete number of expressions. However, dimensional emotion representations, usually valence (indicating how positive or negative an emotional state is) and arousal (measuring the power of the emotion activation), are more appropriate to represent subtle emotions appearing in everyday human computer interactions. In this paper, we adapt StarGAN for continuous emotion synthesis and propose VA-StarGAN; we use a correlation-based loss instead of the usual MSE; we adapt the discriminator network to account for continuous output; we exploit and utilize the in-the-wild Aff-Wild and AffectNet databases; we propose a trick for generating the target domain when training the generator. Qualitative experiments illustrate the generation of realistic images, whilst comparison with state-of-the-art approaches shows the superiority of our method. Quantitative experiments (in which the synthesized images are used for data augmentation in training Deep Neural Networks) further validate our development.
Towards producing mineral resource-potential maps within a mineral systems framework, with emphasis on Australian orogenic gold systems

Discovery of new deposits has slowed dramatically since the beginning of the 21st century despite increased expenditure. Targeting below deep, complex cover is recognised as key to opening up new search-spaces and re-invigorating exploration success. Existing techniques and technologies are reaching maturity as direct-detection exploration methods are costly and have limited success when targeting beneath cover. Thus, the application of a predictive, conceptual-based approach to exploration targeting is required. The mineral systems concept provides a framework within which the critical elements of a system can be defined and incorporated into a predictive exploration targeting model. However, modelling of entire mineral systems remains in its infancy. As such, the effective translation of the mineral systems concept into robust targeting models is defined as a significant research challenge by several authors. A conceptual whole-system modelling approach is proposed in light of the critical elements of an orogenic gold mineralising system and a review of existing exploration tools. The approach combines probabilistic sub-models for predicting total mineral endowment with localised mineral distribution. Linking resource endowment assessments (total mineral endowment), with fluid dynamics and fluid-rock interaction modelling (local mineral distribution), provides an opportunity to develop resource-potential maps that define the approximate cost-benefit of exploring any given location. Further research is required for such modelling methods to become feasible. Although hydrodynamic and hydrogeologic modelling has advanced in other research fields, methods for modelling auriferous fluid flow through the crust remain under-developed. This, in part, is due to detailed 3D geological data being unavailable or prohibitively expensive to acquire. Potential solutions to limited data availability include the development of more advanced interpolation and geophysical inversion methodologies, based on data from well-understood geological provinces, resulting in improved 3D geological interpretations. Collaboration between academia and the minerals industry, to develop an integrated approach to entire mineral system modelling, may lead to successful predictive exploration, by providing a meaningful output to guide decision-making during the exploration targeting process.
Requirement change management challenges in GSD: An analytical hierarchy process approach

Abstract Majority of software development firms are adopting the concepts of global software development (GSD) in order to develop high‐quality and low‐cost products. However, the requirements change management (RCM) becomes a significant challenge in the GSD environment because of the unavailability of proper RCM framework and taxonomy. The objective of this study is to develop a taxonomy of the challenging factors of the RCM process in GSD. The taxonomy is developed based on the results of the data collected during the survey study and the implementation of the analytical hierarchy process (AHP). Total 25 challenging factors are identified and mapped into four core categories, ie, “organizational management,” “team,” “technology,” and “process.” Moreover, the AHP analysis is performed to prioritize the challenging factors and their categories. The prioritization process highlight that “process” is the most significant category of the RCM challenging factors. The taxonomy developed in this study provides a robust framework to tackle problems associated with RCM activities in GSD environment, which is significant to the success and progression of GSD firms.