Reviewerly/exHarmony huggingface dataset:
BRIDGE: Building Plan Repository for Image Description Generation, and Evaluation

In this paper, a large scale public dataset containing floor plan images and their annotations is presented. BRIDGE (Building plan Repository for Image Description Generation, and Evaluation) dataset contains more than 13000 images of the floor plan and annotations collected from various websites, as well as publicly available floor plan images in the research domain. The images in BRIDGE also has annotations for symbols, region graphs, and paragraph descriptions. The BRIDGE dataset will be useful for symbol spotting, caption and description generation, scene graph synthesis, retrieval and many other tasks involving building plan parsing. In this paper, we also present an extensive experimental study for tasks like furniture localization in a floor plan, caption and description generation, on the proposed dataset showing the utility of BRIDGE.
Automated Detection and Classification of Liver Cancer from CT Images using HOG-SVM model

Liver cancer patients have a high death rate due to the diagnosis of the disease in the final stages. Computer-aided diagnosis from various medical imaging techniques can assist significantly in detecting liver cancer at a very early stage. This paper presents an automated method of detecting liver cancer in abdominal CT images and classifying them using the histogram of oriented gradient - support vector machine (HOG-SVM) algorithm. The proposed model consists of several stages where the image is first normalized and preprocessed using a Median and Gaussian filter to remove noise in the image. The image segmentation and liver area extraction are executed in the second stage combining thresholding and contouring. We integrated an ROI based histogram oriented gradient (HOG) feature extraction to train the classifier which impels the classification faster than the conventional methods. Finally, liver CT images are classified implementing support vector machine and segmented results are highlighted with different markers. The proposed system is tested on real data of 27 confirmed early-stage liver cancer and the experimental result shows an accuracy of 94% detecting liver cancer.
CNN-Based Cross-Dataset No-Reference Image Quality Assessment

Recent works on no-reference image quality assessment (NR-IQA) have reported good performance for various datasets. However, they suffer from significant performance drops in cross-dataset evaluations which indicates poor generalization power. We propose a Siamese architecture and training procedures for cross-dataset deep NR-IQA that achieves clearly better performance. Moreover, we show that the architecture can be further boosted by i) pre-training with a large aesthetics dataset and ii) adding low-level quality cues, sharpness, tone and colourfulness, as additional features.
Comparison of Machine Learning Classification Method on Text-based Case in Twitter

As Artificial Intelligence (AI) and Machine Learning (ML) gaining momentum on industry and academic field, a deeper understanding for AI and ML are highly required. One of the most popular sub-field in this field is text analysis. This paper will discuss the performance of classification methods for text-based data and give the best choices of classification method in term of accuracy and training time, so that will help ML enthusiast to build ML project that does not require high computational cost. This paper aimed to give recommendation to practitioner and academic about which classifier best for text classification. This paper will limit its study in supervised learning only. The tested algorithm will be Support Vector Machine, Logistic Regression, Naive Bayes, Random Forest, and K-Nearest Neighbor. To simplify the project, text will be labelled into single-label data, not multi-label. The test shows that SVM gives best result, in term of accuracy and training time among other methods.
Applying semantics into Service-oriented IoT Framework

Introducing semantics into the Internet of Things (IoT) has been attracting increasing attention from researchers and industrial practitioners. Semantic technologies have been used to enable interoperability as well as deal with the heterogeneity, massive scale, and dynamic nature of IoT resources. With the emergence of Industry 4.0, semantic technologies arise as a potential approach toward information modeling and dynamic reconfiguration of highly complex automation systems with high diversity of domains, protocols, tools or hardware platforms. Applying semantics into existing IoT frameworks requires a thorough understanding of the framework architectures as well as careful considerations of different semantic technologies. To support this process, we survey the literature on the contributions and usage of semantics in IoT. We find that semantics are mainly used to handle interoperable systems and heterogeneous standards. In this paper, we also propose procedures for applying semantics into IoT frameworks. Further, we present our idea of using semantics to enable dynamical orchestration of services within the Arrowhead Framework - an IoT framework that supports the development of industrial automation systems.
Real-time calibration of coherent-state receivers: Learning by trial and error

The optimal discrimination of coherent states of light with current technology is a key problem in classical and quantum communication, whose solution would enable the realization of efficient receivers for long-distance communications in free-space and optical fiber channels. In this article, we show that reinforcement learning (RL) protocols allow an agent to learn near-optimal coherent-state receivers made of passive linear optics, photodetectors and classical adaptive control. Each agent is trained and tested in real time over several runs of independent discrimination experiments and has no knowledge about the energy of the states nor the receiver setup nor the quantum-mechanical laws governing the experiments. Based exclusively on the observed photodetector outcomes, the agent adaptively chooses among a set of ~3 10^3 possible receiver setups, and obtains a reward at the end of each experiment if its guess is correct. At variance with previous applications of RL in quantum physics, the information gathered in each run is intrinsically stochastic and thus insufficient to evaluate exactly the performance of the chosen receiver. Nevertheless, we present families of agents that: (i) discover a receiver beating the best Gaussian receiver after ~3 10^2 experiments; (ii) surpass the cumulative reward of the best Gaussian receiver after ~10^3 experiments; (iii) simultaneously discover a near-optimal receiver and attain its cumulative reward after ~10^5 experiments. Our results show that RL techniques are suitable for on-line control of quantum receivers and can be employed for long-distance communications over potentially unknown channels.
A Multi-Modal Person Perception Framework for Socially Interactive Mobile Service Robots

In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot’s user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments.
A Framework For Transitioning Of Traditional Software Development Method To Distributed Agile Software Development

Plan driven methods or traditional methods of software development developers know all the requirements in advance because they are stable in nature. In the challenging world of software development where customer opinion and demands are changing on day to day basis, these traditional methods cause project failures due to poor customer communication and rigid time frame structures. Agile Software Development (ASD) is software development methodology that emphasizes on adaptability. Due to which it satisfies customer - centered development that supports quick and flexible response to change. Distributed Agile software development (DASD), is a methodology in software development paradigm where development teams are distributed in different countries across the globe. In this paper we have proposed a framework for transitioning of traditional software development method to Distributed Agile Software Development. The proposed framework addresses time - zone based problems in Distributed Environment.
Theory of two atoms in a chiral waveguide

A theory is presented that describes the atom and field dynamics for two atoms in a chiral waveguide. A source-field approach is used that enables one to identify the various physical processes contributing to these dynamics. Each atom is prepared in an arbitrary state at $t=0$ and the field intensity and correlation functions are calculated, fully accounting for retardation. When the atoms are prepared in identical superposition states, the effects of constructive and destructive interference play a significant role on both the field intensity and second-order correlation function. It is also shown that the results can be taken over to provide a solution for the related problem of a single-photon pulse incident on an atom prepared in an arbitrary initial state.
Versatile Layout Understanding via Conjugate Graph

Recent advances in document understanding, especially text recognition, provide new opportunities to address the page segmentation problem. In this paper, we propose a method to groups text lines into semantic objects. We model a page as a graph where nodes represent text lines and the edges their geometric relations. The logical segmentation task then refers to identify all text lines belonging to some logical sub-division of the page. We model this task as categorizing edges as relevant or not to build the targeted sub-division (sub-graph). This edge categorization is performed using structured machine learning algorithms (graph Conditional Random Field and Edge Convolutional Network). We use a connected components-based approach following the edge classification for aggregating the nodes. This simple approach shows very robust results for various layout and various page sub-division. We experiment on table segmentation into multiple sub-divisions (rows, columns, and cells) and minutes segmentation into resolutions. Our sub-division and page-layout oblivious approach shows near-par performance as compared to task dedicated approaches and even outperforms them in certain setups.
Component Splitting-based Approach for Multivariate Beta Mixture Models Learning

Mixture models have become arguably one of the most widely used statistical approaches to perform inference on various types of data and have been successfully applied in data mining and numerous real world applications. In this work, we focus on the variational learning of finite multivariate Beta mixture models. Component splitting is one of the major assets of our model which prevents over-fitting. Furthermore, the number of components can be estimated automatically and simultaneously along with parameters estimation. The performance and effectiveness of proposed model is verified by experimenting on three real-life applications, namely, cell image categorization, texture classification and object detection.
Elastic Deployment of Software Containers in Geo-Distributed Computing Environments

Software containers are ever more adopted to manage and execute distributed applications. Indeed, they enable to quickly scale the amount of computing resources by means of horizontal and vertical elasticity. Most of the existing works consider the deployment of containers in centralized data centers. However, to exploit the diffused presence of edge/fog computing resources, we need new solutions that deploy containers while also considering their placement on decentralized resources. In this paper, we present a two-step approach that manages the run-time adaptation of container-based applications deployed over geo-distributed virtual machines. In the first step, our approach exploits Reinforcement Learning (RL) solutions to control the horizontal and vertical elasticity of the containers. In the second step, it addresses the container placement by solving a suitable integer linear programming problem or using a network-aware heuristic. A wide set of simulation results shows the benefits and flexibility of the proposed approach, which can satisfy stringent application requirements expressed in terms of response time percentiles.
Task-Oriented Optimal Sequencing of Visualization Charts

A chart sequence is used to describe a series of visualization charts generated in the exploratory analysis by data analysts. It provides information details in each chart as well as a logical relationship among charts. While existing research targets suggesting chart sequences that match human's perceptions, little attention has been paid to formulate task-oriented connections between charts in a chart design space. We present a novel chart sequencing method based on reinforcement learning to capture the connections between charts in the context of three major analysis tasks, including correlation analysis, anomaly detection, and cluster analysis. The proposed method formulates a chart sequencing procedure as an optimization problem, which seeks an optimal policy to sequencing charts for the specific analysis task. In our method, a novel reward function is introduced, which takes both the analysis task and the factor of human cognition into consideration. We conducted one case study and two user studies to evaluate the effectiveness of our method under the application scenarios of visualization recommendation, sequencing charts for reasoning analysis results, and making a chart design choice. The study results showed the power of our method.
Quasi Fourier-Mellin Transform for Affine Invariant Features

Fourier-Mellin transform (FMT) has been widely used for the extraction of rotation- and scale-invariant features. However, affine transform is a more reasonable approximation model for real viewpoint change. Due to shearing, the integral along the angular direction in the calculation of FMT cannot be used to extract the inherent features of an image undergoing affine transform. To eliminate the effect of shearing, whitening transform should be conducted on the integral along the radial direction. FMT can hardly be modified by conventional whitening-based methods with low computational cost due to additional processes. In this paper, two factors are constructed and embedded into FMT. Quasi Fourier-Mellin transform (QFMT) is proposed. The embedding of these factors is equivalent to whitening transform and can eliminate the effect of shearing in the affine transform. In particular, QFMT can also be calculated by integrating along the radial direction followed by integrating along the angular direction, as in FMT. Based on QFMT, the quasi Fourier-Mellin descriptor (QFMD) is constructed for the extraction of affine invariant features. Some experiments have also been conducted to test the performance of the proposed method.
Winograd-Based Real-Time Super-Resolution System on FPGA

With the rapid development of computer vision theory and visual display devices, High Frame Rate (HFR) and Ultra High Definition (UHD) techniques have received increasing attention from academic and industry. As they put high demands on performance and energy-efficiency, efficient customized hardware is required. In this paper, we propose an FPGA-based super-resolution system that enables real-time UHD upscaling in both high image quality and high frame rates. Our system crops each frame into blocks, measures their total variation values, and dispatches them accordingly to a neural network or an interpolation module for upscaling. We also propose a fast transposed convolution algorithm based on Winograd algorithm, which reduces the number of multiplications. Experimental results show that the proposed super-resolution system achieves superior performance in both reconstruction performance and efficiency over previous works.
Contextual Stroke Classification in Online Handwritten Documents with Graph Attention Networks

Classifying strokes into different categories is an essential preprocessing step in the automatic document understanding process. To tackle this task, it is crucial to integrate different types of contextual information. Previous methods which are based on conditional random fields or recurrent neural networks have some limitations in model capacity or computational cost. In this paper, we propose a novel framework based on graph attention networks to solve this problem, which casts the stroke classification problem into the node classification problem in a document graph. In the graph, each node represents a stroke and the edges are built from temporal and spatial interactions between strokes. Combined graph convolution with attention mechanisms to dynamically aggregate features from the neighborhood, our model is very flexible to control the message passing routine between different nodes and therefore has strong capability learning context-aware features. We perform comparison experiments on the IAMonDo dataset and experimental results demonstrate the superiority of our approach.
Cybersecurity Breaches and the Role of Information Technology Governance in Audit Committee Charters

ABSTRACT Cybersecurity is a serious and growing risk for organizations. Firms with board of director involvement in information technology governance (ITG) may be better equipped to deal with this risk. Yet little is known about the audit committee's role in ITG. This study uses efficiency and institutional theories to investigate the influence of security breaches and board-level technology committees on disclosing ITG roles in the audit committee charter. We develop hypotheses and test them using a sample of 189 firms. Results show that firms with a technology committee and a data breach are more likely to disclose ITG roles in the audit committee charter. This suggests that firms experiencing a data breach realize their vulnerability and, by already having oversight at the board level, it is more natural for them to increase oversight by assigning ITG roles to the audit committee. We provide implications and areas for future research.
Short-term PV power forecasting using empirical mode decomposition in integration with back-propagation neural network

AbstractSolar panel photovoltaic (PV), grid-connected and off-grid connected systems are promptly increasing in India, to enrich the solar power generation. Solar power generation is one of the fur...
Monocular Depth Estimation for UAV Obstacle Avoidance

In this paper, we present a novel method for obstacle avoidance of a quadrotor equiped with a single front camera. The proposed method is composed of three parts: depth estimation, obstacle detection, and obstacle avoidance controll. We use convolutional neural networks (CNN) to estimate depth from RGB image. Then the depth image is fed into the obstacle avoidance system, in which proposed control algorithm steers the quadrotor to fly away from obstacles, and after that, continue towards the destination. We conduct a lot of experiments, either in virtual environment with a simulated drone, or in real world with a quadrotor Parrot Bebop2, to verify the effectiveness of our method.
Drug traders on a local dark web marketplace

This article uses the concepts of habitus and way of life to analyze cultural and socioeconomic factors related to the lives of drug users who utilize a Finnish dark web site. It draws on a set of 9300 posts on that site, Sipulitori, to understand the way of life of the site's users. The article analyses usernames and forum posts, qualitatively and quantitatively, to find representations of the users' ways of life, and to understand their relationship to drugs. The article finds several different relationships to drugs, as well as the absence of some recognized drug user types on this particular dark web trading site. Drugs appear to be the defining factor of many users' lives, regardless of how actively they are using and how strongly that aspect dominates said lives. The article concludes with a brief discussions on how its findings can be used as basis for future research on such trading forums.
AugToAct

Human activity recognition (HAR) from wearable sensor data has recently gained widespread adoption in a number of fields. However, recognizing complex human activities, postural and rhythmic body movements (e.g. dance, sports) is challenging due to the lack of domain-specific labeling information, the perpetual variability in human movement kinematics profiles due to age, sex, dexterity and the level of professional training. In this paper, we propose a deep activity recognition model to work with limited labeled data, both for simple and complex human activities. To mitigate the intra and inter-user spatio-temporal variability of movements, we posit novel data augmentation and domain normalization techniques. We depict a semi-supervised technique that learns noise and transformation invariant feature representation from sparsely labeled data to accommodate intra-personal and inter-user variations of human movement kinematics. We also postulate a transfer learning approach to learn domain invariant feature representations by minimizing the feature distribution distance between the source and target domains. We showcase the improved performance of our proposed framework, AugToAct, using a public HAR dataset. We also design our own data collection, annotation and experimental setup on complex dance activity recognition steps and kinematics movements where we achieved higher performance metrics with limited label data compared to simple activity recognition tasks.