Reviewerly/exHarmony huggingface dataset:
Person classification from aerial imagery using local convolutional neural network features

The need for search and rescue is not one that will go away. Extending beyond the concerns of hikers, natural disasters have brought the necessity for search and rescue in the heart of civilization. The advent of new technologies, such as unmanned aerial vehicles, can help decrease the cost of search and rescue operations and increase survival rates by finding the lost individuals in a faster manner. Visual object recognition is key to successful drone application for assisting search and rescue activities, and it is critical to develop a fully autonomous system. In recent years, deep convolutional neural networks have proven themselves as a powerful class of models and have become de facto a standard in a computer vision community. Moreover, a number of studies have shown that intermediate activations extracted from a deep convolutional neural network, pre-trained on large image dataset, can be adopted as universal image representation and transferred to other image classification tasks, leading to striking performances. In this paper, we investigate the effectiveness of different layer activations on the performance of convolutional features for binary person classification from UAV imagery, using various deep network architectures. Experiments have demonstrated that convolutional network generated features can deliver extremely competitive results, comparable to human-level performance. Furthermore, they reveal that in said context fully connected layer activations generalize well and are more expressive than the mid- or higher-level layers activations. Our work provides guidance for transferring pre-trained deep convolutional neural networks to address remote person classification tasks.
Real-Time FPGA Implementation of Connected Component Labelling for a 4K Video Stream

We present a hardware implementation in reconfigurable logic of a single-pass connected component labelling (CCL) and connected component analysis (CCA) module. The design supports a video stream in 4 pixel per clock format (4 ppc) and allows real-time processing of 4K/UHD video stream (3840 $$\,\times \,$$ 2160 pixels) at 60 frames per second. We discuss the applied modification and simplifications and their impact on the algorithm’s performance. We verified the proposed module in an exemplary application – skin colour areas segmentation – on the ZCU 102 evaluation board with Xilinx Zynq UltraScale+ MPSoC device.
Aesthetic Knowledge Diagrams: Bridging Understanding and Communication

Knowledge diagrams represent all substantial aspects of information involved in designing, codifying and representing company or domain knowledge assets. The chapter considers not only design but also the use of such maps including but not limited to facilitation of learning; eliciting, capturing, archiving and using expert knowledge; planning instruction; assessment of deep understandings; research planning; collaborative knowledge modelling; creation of knowledge portfolios; curriculum design; e-learning; and administrative and strategic planning and monitoring. Knowledge diagrams belong to the multidisciplinary fields of knowledge engineering (KE) and knowledge management (KM), bringing in concepts and methods from several computer science domains such as artificial intelligence, databases, expert systems, decision support systems and information systems. KE is strongly related to cognitive and social sciences and socio-cognitive engineering, where knowledge is considered to be produced by humans and structured according to mutual understanding of how human reasoning and logic work. Currently, KE is related to the construction of shared conceptual frameworks, often presented visually as knowledge diagrams. The chapter describes cognitive aspects of knowledge diagram design, using ideas coined by Gestalt psychology, involving good form and aesthetic perception. It is aimed at all researchers and practitioners interested in the use of knowledge diagrams, as outlined above.
Mining Cluster Patterns in XML Corpora via Latent Topic Models of Content and Structure

We present two innovative machine-learning approaches to topic model clustering for the XML domain. The first approach consists in exploiting consolidated clustering techniques, in order to partition the input XML documents by their meaning. This is captured through a new Bayesian probabilistic topic model, whose novelty is the incorporation of Dirichlet-multinomial distributions for both content and structure. In the second approach, a novel Bayesian probabilistic generative model of XML corpora seamlessly integrates the foresaid topic model with clustering. Both are conceived as interacting latent factors, that govern the wording of the input XML documents. Experiments over real-world benchmark XML corpora reveal the overcoming effectiveness of the devised approaches in comparison to several state-of-the-art competitors.
A Critical Review of Object Detection using Convolution Neural Network

To recognize an object, for a human, is an easy task but for machines, to perform the same task with same efficiency is a complex task. For computer systems, images are sets of numeric values that have no meaning in itself. To make these numbers useful, diverse techniques have been proposed. Comparative to others, deep learning approaches achieved state-of-the-art performance in many computer vision applications, such as object detection, image classification, image retrieval, human pose estimation. To detect object of interest, Convolutional Neural Network (CNN) has been observed widely successful method. Few factors are there to get better accuracy and performance for instance efficient model, larger datasets and hardware support. This study aims to review CNN methods for object detection by highlighting the contribution and challenges from few recent research papers. Also how well to use these CNN techniques in combination to other methods for best suited results with other. Better performance such as increased accuracy, fast processing reduce error rates also introduced few new concerns and issues in parallel regarding the discussed methods such as time consumption, anonymous behavior of Neural Network. To address these issues a conceptual model is presented using CNN and Lease Square Support Vector Machine (LS-SVM).
Annotation cost-sensitive active learning by tree sampling

Active learning is an important machine learning setup for reducing the labelling effort of humans. Although most existing works are based on a simple assumption that each labelling query has the same annotation cost, the assumption may not be realistic. That is, the annotation costs may actually vary between data instances. In addition, the costs may be unknown before making the query. Traditional active learning algorithms cannot deal with such a realistic scenario. In this work, we study annotation cost-sensitive active learning algorithms, which need to estimate the utility and cost of each query simultaneously. We propose a novel algorithm, the cost-sensitive tree sampling algorithm, that conducts the two estimation tasks together and solve it with a tree-structured model motivated from hierarchical sampling, a famous algorithm for traditional active learning. Extensive experimental results using datasets with simulated and true annotation costs validate that the proposed method is generally superior to other annotation cost-sensitive algorithms.
Remote Supervision of an Unmanned Surface Vessel - A Comparison of Interfaces

We compared three different Graphical User Interfaces (GUI) that we have designed and implemented to enable human supervision of an unmanned ship. Our findings indicate that a 3D GUI presented either on a screen or in a Virtual Reality (VR) setting provides several objective and subjective benefits compared to a Baseline GUI representing traditional tools.
Time and ontology for resource recommendation system

Nowadays social tagging systems have considerable growth. These systems help users to find their favorite resources among the large volume of information. Many ways have been proposed for recommending. Since the tags are connotations of users’ interests and also the time of assigning tags indicates the current interests of users, so the combination of semantic influence of tags and time of tag assignment information can effect on the accuracy of recommendations. In this paper, an item recommendation system is proposed that by using important available information in tagging systems, e.g. time, and also using available ontology, the accuracy of recommended results has been improved. The evaluation of the proposed system is performed on movielens.org dataset. The results in comparing with other methods demonstrated the improving quality of the proposed system.
Network traffic anomaly detection algorithm using mahout classifier

Now, the classifier network anomaly traffic detection method is generally considered to be a general method of good detection effect and high detection precision. In order to detect abnormal network traffic more efficiently, so as to ensure the security of Internet users, a network traffic anomaly detection algorithm based on Mahout Classifier is studied. Aiming at the problem of time correlation and the influence of abnormal samples on accuracy of detection statistics, and the first detection point is eliminated and the applicable detection point is added. The BP neural network algorithm and Bayesian network model are used to predict the abnormal probability of the anomaly node, the detection precision is optimized, and the exception points of the training set are reorganized. In view of the anomalies detected by anomaly detection models, an emergency response method is proposed, which not only detects anomalies, but also handles anomalies.
Low-Overhead Adaptive Brightness Scaling for Energy Reduction in OLED Displays

Organic Light Emitting Diode (OLED) is rapidly emerging as the mainstream mobile display technology. This is posing new challenges on the design of energy-saving solutions for OLED displays, specifically intended for interactive devices such as smartphones, smartwatches and tablets. To this date, the standard solution is brightness scaling. However, the amount of the scaling is typically set statically (either by the user, through a setting knob, or by the system in response to predefined events such as low-battery status) and independently of the displayed image. In this work we describe a smart computing technique called Low-Overhead Adaptive Brightness Scaling (LABS), that overcomes these limitations. In LABS, the optimal content-dependent brightness scaling factor is determined automatically for each displayed image, on a frame-by-frame basis, with a low computational cost that allows real-time usage. The basic form of LABS achieves more than 35 percent power reduction on average, when applied to different image datasets, while maintaining the Mean Structural Similarity Index (MSSIM) between the original and transformed images above 97 percent.
A Machine Learning Approach for the Automatic Classification of Schizophrenic Discourse

Schizophrenia is a chronic neurobiological disorder whose early detection has attracted significant attention from the clinical, psychiatric, and also artificial intelligence communities. This latter approach has been mainly focused on the analysis of neuroimaging and genetic data. A less explored strategy consists in exploiting the power of natural language processing (NLP) algorithms applied over narrative texts produced by schizophrenic subjects. In this paper, a novel dataset collected from a proper field study is presented. Also, grammatical traits discovered in narrative documents are used to build computational representations of texts, allowing an automatic classification of discourses generated by schizophrenic and non-schizophrenic subjects. The attained results showed that the use of the proposed computational representations along with machine learning techniques enables a novel and precise strategy to automatically detect texts produced by schizophrenic subjects.
Simultaneous service selection for multiple composite service requests: A combinatorial auction approach

Emerging developments in service-based systems have contributed to a range of proposals for the rapid development of complex applications by composing existing web services through standards-based interfaces. With the availability of a large number of such services offering identical functionality at varying levels of price and other Quality-of-Service (QoS) parameters, selecting the best web services for composition is critical– the problem referred to as composite service selection (CSS). Extant CSS approaches have tended to offer solutions to the problem for a single composite service. We extend CSS to address the setting involving multiple, simultaneous composite service requests. We propose a service selection mechanism based on combinatorial auctions to solve this problem by simultaneously matching the web service offers and requests and performing extensive evaluations in four market sectors with varying economic sizes and complexity of requests. The results indicate that the proposed simultaneous mechanism achieves a higher success rate in allocating services to requesters compared to sequential matching mechanisms. While the average cost difference in procuring composite services under the two mechanisms is not statistically significant, the simultaneous mechanism achieves more stable and homogenous costs over time.
Evolutionary Heuristic A* Search: Pathfinding Algorithm with Self-Designed and Optimized Heuristic Function

The performance and efficiency of A* search algorithm heavily depends on the quality of the heuristic function. Therefore, designing an optimal heuristic function becomes the primary goal of developing a search algorithm for specific domains in artificial intelligence. However, it is difficult to design a well-constructed heuristic function without careful consideration and trial-and-error, especially for complex pathfinding problems. The complexity of a heuristic function increases and becomes unmanageable to design when an increasing number of parameters are involved. Existing approaches often avoid complex heuristic function design: they either trade-off the accuracy for faster computation or taking advantage of the parallelism for better performance. The objective of this paper is to reduce the difficulty of complex heuristic function design for A* search algorithm. We aim to design an algorithm that can be automatically optimized to achieve rapid search with high accuracy and low computational cost. In this paper, we present a novel design and optimization method for a Multi-Weighted-Heuristics function (MWH) named Evolutionary Heuristic A* search (EHA*) to: (1) minimize the effort on heuristic function design via Genetic Algorithm (GA), (2) optimize the performance of A* search and its variants including but not limited to WA* and MHA*, and (3) guarantee the completeness and optimality. EHA* algorithm enables high performance searches and significantly simplifies the processing of heuristic design. We apply EHA* to multiple grid-based pathfinding benchmarks to evaluate the performance. Our experiment result shows that EHA* (1) is capable of choosing an accurate heuristic function that provides an optimal solution, (2) can identify and eliminate inefficient heuristics, (3) is able to automatically design multi-heuristics function, and (4) minimizes both the time and space complexity.
Joint Attentive Spatial-Temporal Feature Aggregation for Video-Based Person Re-Identification

Video-based person re-identification (Re-ID) remains to be a promising but challenging computer vision task, suffering from a lack of discriminative features that better aggregate both the spatial and temporal information.In this paper, we propose a joint attentive spatial-temporal feature aggregation network (JAFN) for the video-based person Re-ID, simultaneously learning the quality-and frame-aware model to obtain attention-based spatial-temporal feature aggregation.Specifically, we utilize CNN to learn the spatial features, while introducing the LSTM to separately learn the temporal features.For the feature aggregation, we introduce two attention mechanisms respectively for generating the quality and frame significance score, where the quality score measures the quality of the images for attentive spatial feature aggregation, and the frame score measures the significance of the image frames contributing to the temporal feature.Then, we utilize the set-pooling for both the quality-aware spatial feature and the frame-aware temporal feature aggregation based on the attentive scores.The residual learning is also introduced to play between the LSTM and the CNN for adaptive spatial-temporal feature fusion.Furthermore, we adopt the data balance to alleviate the data disproportions existing in datasets of the video-based Re-ID.The extensive experimental results conducted on the PRID2011, i-LIDS-VID, and MARS datasets demonstrate the effectiveness of the proposed JAFN.Furthermore, comparison results conducted on different modules and features in the JAFN show that our approach is of favorable generalization ability on attentively aggregating both the spatial and temporal features.
Depth Map Upsampling via Multi-Modal Generative Adversarial Network

Autonomous robots for smart homes and smart cities mostly require depth perception in order to interact with their environments. However, depth maps are usually captured in a lower resolution as compared to RGB color images due to the inherent limitations of the sensors. Naively increasing its resolution often leads to loss of sharpness and incorrect estimates, especially in the regions with depth discontinuities or depth boundaries. In this paper, we propose a novel Generative Adversarial Network (GAN)-based framework for depth map super-resolution that is able to preserve the smooth areas, as well as the sharp edges at the boundaries of the depth map. Our proposed model is trained on two different modalities, namely color images and depth maps. However, at test time, our model only requires the depth map in order to produce a higher resolution version. We evaluated our model both quantitatively and qualitatively, and our experiments show that our method performs better than existing state-of-the-art models.
Scenery-Based Fashion Recommendation with Cross-Domain Geneartive Adverserial Networks

To build an effective fashion recommendation system is a still challenging issue due to its high complexity. Previous research works generally have focused on how to provide fashion items visually similar to the user's current fashion taste. However, a scenery (natural landscape) around users is also an important affective factor in recommending fashions. This paper presents a novel system to recommend fashion designs that fit target sceneries. To address this, the exemplar photos regarding the target landscape are first collected from the database. Afterwards, a cross-domain generative adversarial network (GAN) is applied to generate fashion designs from the sceneries. The experimental results demonstrate the feasibility of the proposed system and imply further research directions.
Computational Simulation and Prediction on Electrical Conductivity of Oxide-Based Melts by Big Data Mining

Electrical conductivity is one of the most basic physical⁻chemical properties of oxide-based melts and plays an important role in the materials and metallurgical industries. Especially with the metallurgical melt, molten slag, existing research studies related to slag conductivity mainly used traditional experimental measurement approaches. Meanwhile, the idea of data-driven decision making has been widely used in many fields instead of expert experience. Therefore, this study proposed an innovative approach based on big data mining methods to investigate the computational simulation and prediction of electrical conductivity. Specific mechanisms are discussed to explain the findings of our proposed approach. Experimental results show slag conductivity can be predicted through constructing predictive models, and the Gradient Boosting Decision Tree (GBDT) model is the best prediction model with 90% accuracy and more than 88% sensitivity. The robustness result of the GBDT model demonstrates the reliability of prediction outcomes. It is concluded that the conductivity of slag systems is mainly affected by TiO₂, FeO, SiO₂, and CaO. TiO₂ and FeO are positively correlated with conductivity, while SiO₂ and CaO have negative correlations with conductivity.
Attitude Control of Quad-copter using Deterministic Policy Gradient Algorithms (DPGA)

In aerial robotics, intelligent control has been a buzz for the past few years. Extensive research efforts can be witnessed to produce control algorithms for stable flight operation of aerial robots using machine learning. Supervised learning has the tendency but training an agent using supervised learning can be a tedious task. Moreover, the data gathering could be expensive and always prone to inaccuracies due to parametric variations and system dynamics. An alternative approach is to ensure the stability of the aerial robots with the help of Deep Re-inforcement Learning (DRL). This paper deals with the intelligent control of quad-copter using deterministic policy gradient algorithms. In this research, state of the art Deep Deterministic Policy Gradient (DDPG) and Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithms are employed for attitude control of quad-copter. An open source simulation environment GymFC is used for training of quad-copter. The results for comparative analysis of DDPG & D4PG algorithms are also presented, highlighting the attitude control performance.
Keeping Master Green at Scale

Giant monolithic source-code repositories are one of the fundamental pillars of the back end infrastructure in large and fast-paced software companies. The sheer volume of everyday code changes demands a reliable and efficient change management system with three uncompromisable key requirements --- always green master, high throughput, and low commit turnaround time. Green refers to a master branch that always successfully compiles and passes all build steps, the opposite being red. A broken master (red) leads to delayed feature rollouts because a faulty code commit needs to be detected and rolled backed. Additionally, a red master has a cascading effect that hampers developer productivity--- developers might face local test/build failures, or might end up working on a codebase that will eventually be rolled back.