Reviewerly/exHarmony huggingface dataset:
Fast video‐based queue length detection approach for self‐organising traffic control

With the development of traffic control theory, self-organising control has become one of the most promising techniques for traffic control. Among the various parameters of self-organising control, the queue length (number of vehicles) is a key one. Compared to the approaches using other detectors, vision-based approaches have a greater potential in queue length detection. However, the complicated traffic conditions (such as the changeable weather and the vehicle occlusion) and the large calculation of image processing have posed great challenges to the real-time application. Here, a fast video-based queue length detection approach is proposed. For each lane, according to the level of traffic congestion evaluated by the foreground area ratio, the corresponding vehicle counting method is adopted. When the traffic is determined congested, a fast area-based method for estimating the number of vehicles is adopted. Otherwise, a fast vehicle detection and counting method combining background difference and Adaboost classifier is adopted. The experiment results demonstrate that the proposed approach can handle actual traffic environment and has great potential for real-time application.
Robust multi‐view videos face recognition based on particle filter with immune genetic algorithm

The task of face recognition in real-world scenarios is still a challenging one. There exist many techniques for the recognition of faces in videos. The recognition task may be computationally easier, but is susceptible to pose variations, lighting conditions etc. This paper focuses on recognition of faces from multi-view videos using the combination of particle filtering with Immune Genetic Algorithm (IGA) and HSH, which is insensitive to pose variations. Particle filtering along with the IGA efficiently track the target using immune system mechanism and then the recognition phases are carried out using HSH. For recognition of video, the ensemble feature similarity calculated which can be measured with the limiting Bhattacharyya distance of features in the Reproducing Kernel Hilbert space. The proposed system with HSH provides better performance than using Spherical Harmonics (SH) for recognising the face of the target and the performance are analysed with existing techniques for recognition of face.
Facial expression recognition of 3D image using facial action coding system (FACS)

Facial expression or mimic is one of the results of muscle motion on the face. In a large Indonesian dictionary, the expression is a disclosure or process of declaring, i.e. showing or expressing intentions, ideas of feelings and so on. Facial expression is affected by the cranial nerve VII or Nervus Facialis. In research conducted Paul Ekman got a standardization of expression in the format of a movement called the Facial Action Coding System (FACS). In his research, Paul Ekman said six basic expressions of happiness, sadness, shock, fear, anger, and disgust. In muscle anatomy, that every moving muscle must be contraction, and in the event of contraction, the muscle will expand or swell. Muscles are divided into three parts of origo and insersio as the tip of muscle and belli as the midpoint of the muscle, so any movement occurs then the muscle part belli will expand or swell. Data retrieval technique that is by recording data in 3D, any contraction occurs then the belli part of the muscle will swell and this data will be processed and compared. From this data processing will be obtained the maximum strength of contraction that will be used as a reference for the magnitude of expression made by the model. In the detection of expression is ecluidience distance by comparing the initial data with movement data. The result of this research is a detection of expression and the amount of expression that occurs. A conclusion of this research, we can reconstruction of facial expression detection using FACS, for the example the happiness expression using AU 6 and AU 12 and in this research AU 6 and AU 12 in area 1 and area 4, and in this area it so higher than the other.
Minimum entropy and histogram-pair based JPEG image reversible data hiding

In this paper, based on the block entropy in the spatial domain obtained by summing the squares of all AC coefficients of each 8 × 8 block, a reversible data hiding (RDH) scheme for JPEG images is presented which selects the DCT blocks with minimum entropy for RDH. This scheme applies Parseval theorem and entropy principle to achieve the minimum entropy in the spatial domain. It embeds data into the DCT coefficients based on a histogram–pair RDH scheme using histogram shifting. Moreover, four thresholds including the embedding amplitude threshold, the fluctuation threshold, the lower frequency threshold and the higher frequency threshold, are utilized and adjusted to achieve optimized embedding performance. In particular, the proposed scheme does not embed data into zero-valued DCT coefficients, and can obtain optimal PSNR with relatively small file-size increase. Experimental results have demonstrated that, compared with the prior arts, the quality of proposed marked image is higher, and the file-size increase after RDH is less as well.
InferPy: Probabilistic modeling with Tensorflow made easy

InferPy is a high-level Python API for probabilistic modeling built on top of Edward and Tensorflow. InferPy, which is strongly inspired by Keras, focuses on being user-friendly by using an intuitive set of abstractions that make easy to deal with complex probabilistic models. It should be seen as an interface rather than a standalone machine-learning framework. In general, InferPy has the focus on enabling flexible data processing, easy-to-code probabilistic modeling, scalable inference and robust model validation.
Designing a financial management smartphone app for users with mixed literacies

India is experiencing a radical technology transformation as smartphone ownership and data usage has skyrocketed, with penetration even into low-income areas in urban India. This provides an opportunity to design more inclusive services on the smartphone. This paper describes the design of SalaPrayana, a financial management application for end-users with a range of literacies. Designing apps for a target audience with a range of literacies introduces tensions between visual interface design and the complexity of information conveyed. We present our designs and discuss some of these challenges. We found we could communicate familiar information, such as payment history, easily to all users, but other more complex information such as 'Loan Status' remained challenging.
But what do participants want? Comment on the “Data Sharing in Psychology” special section (2018).

This commentary addresses a recent special section on data sharing (i.e., open data) in the February-March 2018 American Psychologist. In 4 articles, the authors outline how open data can positively impact psychology and provide guidelines for adopting open data practices, which we believe is to be commended. However, this special issue has not acknowledged a crucial concern in the open data debate: the views and desires of participants. Participants are the backbone of psychological research and an important stakeholder in open data issues. We review research that has studied participants' opinions of open data and outline concerns regarding open data raised by some groups of participants. We conclude with recommendations, including a call to psychological researchers to move beyond opinion and instead to empirically examine the impact of open data. We believe psychology is a discipline uniquely poised to execute these recommendations and guide researchers' understandings of how to appropriately and ethically implement open data practices across multiple disciplines. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Determination of Ship Collision Avoidance Path using Deep Deterministic Policy Gradient Algorithm

The stability, reliability and efficiency of a smart ship are important issues as the interest in an autonomous ship has recently been high. An automatic collision avoidance system is an essential function of an autonomous ship. This system detects the possibility of collision and automatically takes avoidance actions in consideration of economy and safety. In order to construct an automatic collision avoidance system using reinforcement learning, in this work, the sequential decision problem of ship collision is mathematically formulated through a Markov Decision Process (MDP). A reinforcement learning environment is constructed based on the ship maneuvering equations, and then the three key components (state, action, and reward) of MDP are defined. The state uses parameters of the relationship between own-ship and target-ship, the action is the vertical distance away from the target course, and the reward is defined as a function considering safety and economics. In order to solve the sequential decision problem, the Deep Deterministic Policy Gradient (DDPG) algorithm which can express continuous action space and search an optimal action policy is utilized. The collision avoidance system is then tested assuming the 90°intersection encounter situation and yields a satisfactory result.
Advanced Internet of Things and Big Data Technology for Smart Human-Care Services

Recently, the range of Internet of things (IoT) technology could be utilized in the real world by the rapid spread of sensor devices.Various sensed information such as temperature, illumination, pressure, and object detection has become a basic point for solving solutions to complex problems that have been difficult to solve by convergence with big data.Particularly, not only the detection of human health and changes in the natural environment but also the rapid development of the analysis and application technology is continuing.As a result, it is expected to dramatically improve the prediction of disaster and quality of life.In order to implement IoT-based human-care technology for improving the quality of life, we aim to deal with sensor technology and communication technologies as well as big data analytics and its applications.This includes a wide range of application technologies that can be realized in real life as well as theoretical problem solving in relation to the IoT.In this special issue, the latest research efforts on new theories, technologies, and research are related to IoT and big data technology for smart human-care services and to improve advanced technology.25 research papers are published in this issue, covering a wide variety of topics including advanced sensor technology for smart human-care services, management issues of social network big data for smart human-care services, data and knowledge mining for smart human-care services, emerging IoT applications and systems for smart human-care services, and IoT solutions
Information-Theoretic Transfer Learning Framework for Bayesian Optimisation

Transfer learning in Bayesian optimisation is a popular way to alleviate “cold start” issue. However, most of the existing transfer learning algorithms use overall function space similarity, not a more aligned similarity measure for Bayesian optimisation based on the location of the optima. That makes these algorithms fragile to noisy perturbations, and even simple scaling of function values. In this paper, we propose a robust transfer learning based approach that transfer knowledge of the optima using a consistent probabilistic framework. From the finite samples for both source and target, a distribution on the optima is computed and then divergence between these distributions are used to compute similarities. Based on the similarities a mixture distribution is constructed, which is then used to build a new information-theoretic acquisition function in a manner similar to Predictive Entropy Search (PES). The proposed approach also offers desirable “no bias” transfer learning in the limit. Experiments on both synthetic functions and a set of hyperparameter tuning tests clearly demonstrate the effectiveness of our approach compared to the existing transfer learning methods. Code related to this paper is available at: https://github.com/AnilRamachandran/ITTLBO.git and Data related to this paper is available at: https://doi.org/10.7910/DVN/LRNLZV .
Quality of experience and quality of service‐aware handover for video transmission in heterogeneous networks

Summary Heterogeneous networks offer a wide range of multimedia services, such as entertainment, advertising, and video conferences. In this multimedia scenario, users can access video content via heterogeneous wireless networks, such as LTE macro and small cells. Users also expect to receive real‐time videos with Quality of Experience (QoE) support, which is a challenging task due to the great diversity of radio base stations in such heterogeneous environments. In this article, we introduce a Quality of Service (QoS‐)/QoE‐ and Radio‐aware (SER) handover management algorithm for heterogeneous networks to provide video dissemination with QoS/QoE support. SER algorithm considers the analytic hierarchy process (AHP) to adjust the degree of importance of each criterion in order to select the appropriate radio base station that the mobile node must connect, allowing efficient handover decision making for video transmission with high user experience. Simulation results show that the SER algorithm delivered videos with significant improvement on QoE than existing handover algorithms.
HexaGAN: Generative Adversarial Nets for Real World Classification

Most deep learning classification studies assume clean data. However, when dealing with the real world data, we encounter three problems such as 1) missing data, 2) class imbalance, and 3) missing label problems. These problems undermine the performance of a classifier. Various preprocessing techniques have been proposed to mitigate one of these problems, but an algorithm that assumes and resolves all three problems together has not been proposed yet. In this paper, we propose HexaGAN, a generative adversarial network framework that shows promising classification performance for all three problems. We interpret the three problems from a single perspective to solve them jointly. To enable this, the framework consists of six components, which interact with each other. We also devise novel loss functions corresponding to the architecture. The designed loss functions allow us to achieve state-of-the-art imputation performance, with up to a 14% improvement, and to generate high-quality class-conditional data. We evaluate the classification performance (F1-score) of the proposed method with 20% missingness and confirm up to a 5% improvement in comparison with the performance of combinations of state-of-the-art methods.
Prospectivity mapping for high sulfidation epithermal porphyry deposits using an integrated compositional and topographic remote sensing dataset

The targeting and discovery of epithermal porphyry mineral deposits can be enhanced using a structured quantitative methodology to analyse the distribution of ore deposits and model their spatial association with exploration evidence providing improved understanding on the controls of ore deposition. A novel exploration tool integrating field and ASTER SWIR and TIR satellite imagery has been developed which provides an enhanced means of resolving surface expressions of the residual silica core of the lithocap. The alteration zones were clearly resolved by the remote sensing data and an intimate spatial relationship between high-grade altered rocks and topographic highs was identified at a number of locations. A Mineral Prospectivity Modelling (MPM) approach, parameterized by the results of the remote sensing study, using a GIS-based weighted linear combination implementation of a Multi-Criteria Evaluation approach and utilising a fuzzy Analytical Hierarchy Process to elucidate expert knowledge has been implemented to target high sulfidation epithermal porphyry deposits on the Island of Lesvos, Greece. The results from this integrated altitudinal-compositional modelling approach closely matched the hydrothermal alteration mapped in the field supporting the accuracy of this MPM approach.
Explore Deep Neural Network and Reinforcement Learning to Large-scale Tasks Processing in Big Data

Large-scale tasks processing based on cloud computing has become crucial to big data analysis and disposal in recent years. Most previous work, generally, utilize the conventional methods and architectures for general scale tasks to achieve tons of tasks disposing, which is limited by the issues of computing capability, data transmission, etc. Based on this argument, a fat-tree structure-based approach called LTDR (Large-scale Tasks processing using Deep network model and Reinforcement learning) has been proposed in this work. Aiming at exploring the optimal task allocation scheme, a virtual network mapping algorithm based on deep convolutional neural network and [Formula: see text]-learning is presented herein. After feature extraction, we design and implement a policy network to make node mapping decisions. The link mapping scheme can be attained by the designed distributed value-function based reinforcement learning model. Eventually, tasks are allocated onto proper physical nodes and processed efficiently. Experimental results show that LTDR can significantly improve the utilization of physical resources and long-term revenue while satisfying task requirements in big data.
Log Data Analysis with ANFIS: A Fuzzy Neural Network Approach

This study explores the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS), a neuro-fuzzy approach, to analyze the log data of technology-based assessments to extract relevant features of student problem-solving processes, and develop and refine a set of fuzzy logic rules that could be used to interpret student performance. The log data that record student response processes while solving a science simulation task were analyzed with ANFIS. Results indicate the ANFIS analysis could generate and refine a set of fuzzy rules that shed lights on the process of how students solve the simulation task. We conclude the article by discussing the advantages of combining human judgments with the learning capacity of ANFIS for log data analysis and outlining the limitations of the current study and areas of future research.
Injecting Domain Knowledge in Electronic Medical Records to Improve Hospitalization Prediction

Electronic medical records (EMR) contain key information about the different symptomatic episodes that a patient went through. They carry a great potential in order to improve the well-being of patients and therefore represent a very valuable input for artificial intelligence approaches. However, the explicit knowledge directly available through these records remains limited, the extracted features to be used by machine learning algorithms do not contain all the implicit knowledge of medical expert. In order to evaluate the impact of domain knowledge when processing EMRs, we augment the features extracted from EMRs with ontological resources before turning them into vectors used by machine learning algorithms. We evaluate these augmentations with several machine learning algorithms to predict hospitalization. Our approach was experimented on data from the PRIMEGE PACA database that contains more than 350,000 consultations carried out by 16 general practitioners (GPs).
Egocentric Point of Interest Recognition in Cultural Sites

We consider the problem of the detection and recognition of points of interest in cultural sites. We observe that a “point of interest” in a cultural site may be either an object or an environment and highlight that the use of an object detector is beneficial to recognize points of interest which occupy a small part of the frame. To study the role of objects in the recognition of points of interest, we augment the labelling of the UNICT-VEDI dataset to include bounding box annotations for 57 points of interest. We hence compare two approaches to perform the recognition of points of interest. The first method is based on the processing of the whole frame during recognition. The second method employs a YOLO object detector and a selection procedure to determine the currently observed point of interest. Our experiments suggest that further improvements on point of interest recognition can be achieved fusing the two methodologies. Indeed, the results show the complementarity of the two approaches on the UNICT-VEDI dataset.
An Effective Multi-UAVs-Based Evacuation Guidance Support for Disaster Risk Reduction

In recent years, various natural disasters have occurred in various regions of the world. Especially, tsunamis by a big earthquake cause great damage. We have been tackling an evacuation guidance support system by agent-based multi-UAVs for disaster risk reduction. Specifically, software agent generate evacuation guidance plans for UAV, and select the safest plan as the evacuation guidance route considering the disaster situations and the regional characteristics etc. In addition, UAVs provide efficient evacuation guidance support by cooperation of other UAVs. In this paper, we describe the function of selecting the route for UAV and evacuation guidance support by cooperative multi-UAVs. We performed simulation experiments on evacuation guidance based on the safe route selection, and evacuation guidance support by multi-UAVs. Additionally, we also performed an experiment on autonomous flight based on the route selection in real world.
Improving the management efficiency of GPU workloads in data centers through GPU virtualization

Summary Graphics processing units (GPUs) are currently used in data centers to reduce the execution time of compute‐intensive applications. However, the use of GPUs presents several side effects, such as increased acquisition costs and larger space requirements. Furthermore, GPUs require a nonnegligible amount of energy even while idle. Additionally, GPU utilization is usually low for most applications. In a similar way to the use of virtual machines, using virtual GPUs may address the concerns associated with the use of these devices. In this regard, the remote GPU virtualization mechanism could be leveraged to share the GPUs present in the computing facility among the nodes of the cluster. This would increase overall GPU utilization, thus reducing the negative impact of the increased costs mentioned before. Reducing the amount of GPUs installed in the cluster could also be possible. However, in the same way as job schedulers map GPU resources to applications, virtual GPUs should also be scheduled before job execution. Nevertheless, current job schedulers are not able to deal with virtual GPUs. In this paper, we analyze the performance attained by a cluster using the remote Compute Unified Device Architecture middleware and a modified version of the Slurm scheduler, which is now able to assign remote GPUs to jobs. Results show that cluster throughput, measured as jobs completed per time unit, is doubled at the same time that the total energy consumption is reduced up to 40%. GPU utilization is also increased.