Reviewerly/exHarmony huggingface dataset:
Attentive Deep Stitching and Quality Assessment for 360$^{\circ }$ Omnidirectional Images

360° omnidirectional images are very helpful in creating immersive multimedia contents, which enables a huge demand in their efficient generation and effective assessment. In this paper, we leverage an attentive idea to meet this demand by addressing two concerns: how to generate a good omnidirectional image in a fast and robust way and what is a good omnidirectional image for human. To this end, we propose an attentive deep stitching approach to facilitate the efficient generation of omnidirectional images, which is composed of two modules. The low-resolution deformation module aims to learn the deformation rules from dual-fisheye to omnidirectional images with joint implicit and explicit attention mechanisms, while the high-resolution recurrence module enhances the resolution of stitching results with the high-resolution guidance in a recurrent manner. In this way, the stitching approach can efficiently generate high-resolution omnidirectional images that are highly consistent with human immersive experiences. Beyond the efficient generation, we further present an attention-driven omnidirectional image quality assessment (IQA) method which uses joint evaluation with both global and local metrics. Especially, the local metric mainly focuses on the stitching region and attention region that mostly affect the Mean Opinion Score (MOS), leading to a consistent evaluation of human perception. To verify the effectiveness of our proposed assessment and stitching approaches, we construct a hybrid benchmark evaluation with 7 stitching models and 8 IQA metrics. Qualitative and quantitative experiments show our stitching approach generate preferable results with the state-of-the-art models at a 6× faster speed and the proposed quality assessment approach surpasses other methods by a large margin and is highly consistent with human subjective evaluations.
Artificial Intelligence: State of the Art

Artificial Intelligence (AI) is the most fascinating and discussed technology in the current decade for its nature of mimic human intelligence. As John McCarthy defines it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. AI simply means the study of building machines with human like sense (perceiving), analysis or understand and response. Precisely, it’s the Weak AI, the AI systems are capable to do a specific kind of job for which it is trained. Even, the journey of AI was started back in 1950s, it become popular and started using in recent years for three reasons. First, the availability of big data; the gigantic amount of data generated by the e-commerce, social networks and businesses, second the machine learning algorithms are improved and more reliable, third the cloud and high-performance computer systems become cheap. The AI is changing the personal, social, and business landscape with every new day. It is used to develop products ranging from general to specific, such as playing music, playing chess, Painting, self-driving cars, proving theorems, etc. AI is widely used in automobile, logistic, healthcare, stock-trading, robotics, finance, transport, education like industries. This chapter starts with defining AI and its relationship with machine learning and deep learning followed by a brief time-line of the evaluation of AI, advantages and challenges of AI in today’s world. Then discuss about the three fundamental techniques problem solving, knowledge and reasoning, and learning, artificial neural networks and natural language processing (NLP) are presented.
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing

A conventional camera performs various signal processing steps sequentially to reconstruct an image from a raw Bayer image. When performing these processing in multiple stages the residual error from each stage accumulates in the image and degrades the quality of the final reconstructed image. In this paper, we present a fully convolutional neural network (CNN) to perform defect pixel correction, denoising, white balancing, exposure correction, demosaicing, color transform, and gamma encoding. To our knowledge, this is the first CNN trained end-to-end to perform the entire image signal processing pipeline in a camera. Through extensive experiments, we show that the proposed CNN based image signal processing system performs better than the conventional signal processing pipelines that perform the processing sequentially.
Digital image forgery detection using deep learning approach

Abstract This paper presents an algorithm for detecting one of the most commonly used types of digital image forgeries - splicing. The algorithm is based on the use of the VGG-16 convolutional neural network. The proposed network architecture takes image patches as input and obtains classification results for a patch: original or forgery. On the training stage we select patches from original image regions and on the borders of embedded splicing. The obtained results demonstrate high classification accuracy (97.8% accuracy for fine-tuned model and 96.4% accuracy for the zero-stage trained) for a set of images containing artificial distortions in comparison with existing solutions. Experimental research was conducted using CASIA dataset.
Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network

The accurate forecasting of photovoltaic (PV) power generation is critical for smart grids and the renewable energy market. In this paper, we propose a novel short-term PV forecasting technique called the space-time convolutional neural network (STCNN) that exploits the location information of multiple PV sites and historical PV generation data. The proposed structure is simple but effective for multi-site PV forecasting. In doing this, we propose a greedy adjoining algorithm to preprocess PV data into a space-time matrix that captures spatio-temporal correlation, which is learned by a convolutional neural network. Extensive experiments with multi-site PV generation from three typical states in the US (California, New York, and Alabama) show that the proposed STCNN outperforms the conventional methods by up to 33% and achieves fairly accurate PV forecasting, e.g., 4.6–5.3% of the mean absolute percentage error for a 6 h forecasting horizon. We also investigate the effect of PV sites aggregation for virtual power plants where errors from some sites can be compensated by other sites. The proposed STCNN shows substantial error reduction by up to 40% when multiple PV sites are aggregated.
Augmented Reality Interface for Smart Home Control using SSVEP-BCI and Eye Gaze

We investigate the integration of eye-tracking and a Brain-Computer Interface into an Augmented Reality system to control a smart home environment. Through a head-mounted display, we present context-dependent control elements which the user selects by directing attention towards them. We show that the combination of both modalities leads to the most robust detection of selections and an interface which is accepted by its users.
Deterministic Promotion Reinforcement Learning Applied to Longitudinal Velocity Control for Automated Vehicles

Reinforcement learning is regarded as a potential method to be applied in automated vehicles, but the stability and efficiency of algorithms are concerns. To improve them, the deterministic promotion reinforcement learning method is put forward, which can promote the policy determinately. Correspondingly, the policy evaluation in critic and the exploration in actor are improved, which combines a normalization-based evaluation and a model-free search guide. The aim is finding the right action exploration direction by critic, then the direction is used to update and guide action exploration in actor. The modified method decreases the dependencies of exploring a good action for promotional updating and only makes deterministic promotion in policy. Consequently, the efficiency of the algorithm is improved without loss in stability. More notably, it can relieve the cold-start and circumvent the limitations in learning with constrained physical systems. To verify the proposed method, the longitudinal velocity control problem for automated vehicles is considered, which contains car-following and non-car-following conditions in a unitized form. The learning system is established in Carsim. Furthermore, some different reinforcement learning technologies are used to accelerate learning. Real-vehicle experiments for validation are also given. The results indicate that the proposed method can achieve permissible learning performance in the longitudinal velocity continuous control problem.
Convolution Neural Network for Text Mining and Natural Language Processing

Abstract The objective of this study is to get an overview of the improvements applied in a number of studies and problems that have not been resolved. We have surveyed more than 30 scientific articles obtained from scientific article portals such as Science Direct, IEEE explore, Arxiv, and Google Scholar. Based on this abstract, we obtain similarities and differences based on the problem solved, the pre-processing method for data input, and the approach taken to achieve the goal. The results show that some problems have not been resolved by CNN in the text mining domain and NLP. This happens because CNN is used to solve problems in each case such as sentiment analysis, classification of documents or NLP cases such as entities and their relationships, or semantic representation. CNN that is proficient in image classification has proven its ability to process text. Appropriate data representations and methods have brought that success. However, a number of studies only convey the results they are working on. No one has specifically discussed high computing problems on CNN with consistent and measurable parameters. Thus there are still many studies that use CNN for mining text and NLP are still open to completion
Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network

A piping and instrumentation diagram (P&amp;ID) is a key drawing widely used in the energy industry. In a digital P&amp;ID, all included objects are classified and made amenable to computerized data management. However, despite being widespread, a large number of P&amp;IDs in the image format still in use throughout the process (plant design, procurement, construction, and commissioning) are hampered by difficulties associated with contractual relationships and software systems. In this study, we propose a method that uses deep learning techniques to recognize and extract important information from the objects in the image-format P&amp;IDs. We define the training data structure required for developing a deep learning model for the P&amp;ID recognition. The proposed method consists of preprocessing and recognition stages. In the preprocessing stage, diagram alignment, outer border removal, and title box removal are performed. In the recognition stage, symbols, characters, lines, and tables are detected. The objects for recognition are symbols, characters, lines, and tables in P&amp;ID drawings. A new deep learning model for symbol detection is defined using AlexNet. We also employ the connectionist text proposal network (CTPN) for character detection, and traditional image processing techniques for P&amp;ID line and table detection. In the experiments where two test P&amp;IDs were recognized according to the proposed method, recognition accuracies for symbol, characters, and lines were found to be 91.6%, 83.1%, and 90.6% on average, respectively.
Improving Term Weighting Schemes for Short Text Classification in Vector Space Model

Short text is one of the predominant forms of communication with unique characteristics such as short length, high sparsity, and lack of shared context and word co-occurrence.These characteristics distinguish short text from general text and make short text classification a challenging task.Term weighting is an important pre-processing step for text classification in the vector space model.In this paper, we propose three modifications to existing state-of-the-art term weighting schemes: ifn-tp-icf, RFR and modOR and a new term weighting scheme: ifn-modRF.We compare the proposed schemes with ten existing unsupervised and supervised schemes using three datasets of informally written short text: a self-labelled dataset of real-world events from Twitter, a Yahoo!questions dataset and a dataset of product reviews.Based on the experimental results using three popular classifiers, we observe that the proposed scheme ifn-modRF achieves the best F1-scores on the Twitter dataset, while the proposed modification modOR is a consistent performer with the best scores in most of the experiments.The proposed modification ifn-tp-icf also outperform the original scheme in most experiments.
Recognition Bangla Sign Language using Convolutional Neural Network

The sign language, considered as the main language for deaf and hard of hearing, uses manual communication and body language to convey expressions and plays a major role in developing an identity. Nowadays, sign language recognition is an emerging field of research to improve interaction with the deaf community. The automatic recognition of American, British, and French sign languages with high accuracy has been reported in the literature. Even though, Bangla is one of the mostly spoken languages in the world, no significant research on Bangla sign language recognition can be found in the literature. The main reason for this lagging might be due to the unavailability of a Bangla sign language dataset. In this study, we have presented a large dataset of Bangla sign language consisting of both alphabets and numerals. The dataset was composed of 7052 samples representing 10 numerals and 23864 samples correspond to the 35 basic characters of the alphabet. Finally, the performance of a convolutional neural network in the recognition of numerals and alphabet separately, and in mixing of them, has been evaluated on the developed dataset using 10-fold cross-validation. The proposed method provided an average recognition accuracy of 99.83%, 100%, and 99.80%, respectively for basic characters, numerals, and for their combined usage.
Multi-camera calibration method based on a multi-plane stereo target

Machine vision techniques, including camera calibration methods, are of great importance for the development of vision-based measurements. However, in multi-camera calibration methods, rapidly constructing accurate geometric relationships among different coordinates is very difficult. Herein, we present a multi-camera calibration method capable of calibrating the intrinsic and extrinsic parameters of four cameras using only a single captured image per camera. Unlike Zhang's method, which relies on multiple captured images to calibrate the cameras, the method uses a multi-plane stereo target containing multiple fixed planes to which coded patterns are attached. This target greatly reduces the time required for calibration and improves calibration robustness. The proposed method was experimentally compared with traditional camera calibration. The problem affecting the calibration accuracy in single calibration of multiple cameras is that the feature points on the captured images produce occlusion or different degrees of blurring; in the calibration of multiple cameras multiple times, the error accumulation caused by the calibration of two adjacent cameras is solved. This demonstration of a multi-camera calibration method improves camera calibration and provides a new design philosophy, to the best of our knowledge, for machine vision and vision-based measurement.
Data mattering and self-tracking: what can personal data do?

Self-tracking practices have attracted burgeoning academic interest in recent years. This article draws on interviews with Australians who identify as regular self-trackers, examining the ways in which they describe their encounters with and enactments of their personal data. Vital materialism perspectives are employed to analyse and interpret the participants' accounts of what they make of their self-tracked data and how they incorporate this information into their lives and relationships. Examples of participants' accounts of their data are presented under different headings using the 'data can … ' format to illustrate what data can do. For these self-trackers, data can: tell you the truth, empower you, make you feel in control, provide tangible results, help you be organized, motivate you, make you change, be combined to generate new insights, surprise you, be overwhelming, challenge denial, provide a benchmark, monitor progress, supplement memory, be shared, generate intimacy, make you feel good, make you feel bad, be inaccurate and supplement expert advice. This analytical approach identified the complexities and ambivalences of the affordances, relational connections, affective forces and agential capacities that make personal data matter.
RSS++

While the current literature typically focuses on load-balancing among multiple servers, in this paper, we demonstrate the importance of load-balancing within a single machine (potentially with hundreds of CPU cores). In this context, we propose a new load-balancing technique (RSS++) that dynamically modifies the receive side scaling (RSS) indirection table to spread the load across the CPU cores in a more optimal way. RSS++ incurs up to 14x lower 95th percentile tail latency and orders of magnitude fewer packet drops compared to RSS under high CPU utilization. RSS++ allows higher CPU utilization and dynamic scaling of the number of allocated CPU cores to accommodate the input load while avoiding the typical 25% over-provisioning.
Understanding freehand gestures: a study of freehand gestural interaction for immersive VR shopping applications

Abstract Unlike retail stores, in which the user is forced to be physically present and active during restricted opening hours, online shops may be more convenient, functional and efficient. However, traditional online shops often have a narrow bandwidth for product visualizations and interactive techniques and lack a compelling shopping context. In this paper, we report a study on eliciting user-defined gestures for shopping tasks in an immersive VR (virtual reality) environment. We made a methodological contribution by providing a varied practice for producing more usable freehand gestures than traditional elicitation studies. Using our method, we developed a gesture taxonomy and generated a user-defined gesture set. To validate the usability of the derived gesture set, we conducted a comparative study and answered questions related to the performance, error count, user preference and effort required from end-users to use freehand gestures compared with traditional immersive VR interaction techniques, such as the virtual handle controller and ray - casting techniques. Experimental results show that the freehand-gesture-based interaction technique was rated to be the best in terms of task load, user experience, and presence without the loss of performance (i.e., speed and error count). Based on our findings, we also developed several design guidelines for gestural interaction.
Properly-Weighted Graph Laplacian for Semi-supervised Learning

The performance of traditional graph Laplacian methods for semi-supervised learning degrades substantially as the ratio of labeled to unlabeled data decreases, due to a degeneracy in the graph Laplacian. Several approaches have been proposed recently to address this, however we show that some of them remain ill-posed in the large-data limit. In this paper, we show a way to correctly set the weights in Laplacian regularization so that the estimator remains well posed and stable in the large-sample limit. We prove that our semi-supervised learning algorithm converges, in the infinite sample size limit, to the smooth solution of a continuum variational problem that attains the labeled values continuously. Our method is fast and easy to implement.
Adaptive embedding gate for attention-based scene text recognition

Scene text recognition has attracted particular research interest because it is a very challenging problem and has various applications. The most cutting-edge methods are attentional encoder-decoder frameworks that learn the alignment between the input image and output sequences. In particular, the decoder recurrently outputs predictions, using the prediction of the previous step as a guidance for every time step. In this study, we point out that the inappropriate use of previous predictions in existing attentional decoders restricts the recognition performance and brings instability. To handle this problem, we propose a novel module, namely adaptive embedding gate (AEG). The proposed AEG focuses on introducing high-order character language models to attentional decoders by controlling the information transmission between adjacent characters. AEG is a flexible module and can be easily integrated into the state-of-the-art attentional decoders for scene text recognition. We evaluate its effectiveness as well as robustness on a number of standard benchmarks, including the IIIT5K, SVT, SVT-P, CUTE80, and ICDAR datasets. Experimental results demonstrate that AEG can significantly boost recognition performance and bring better robustness.
Chinese System Usability Scale: Translation, Revision, Psychological Measurement

The Chinese version of the system usability scale (SUS) was re-translated in this study by the addition of an interview process plus the modification and selection of strict translation results. The revised translation is in close accordance with the linguistic usage of Chinese native speakers without any ambiguity. The revised Chinese version of the psychometric measurement is shown to be reliable, effective, and sensitive. We also conducted a comparative study within one group to confirm that the reliability of the cross-cultural adaptation version is higher than that of the original version. The questionnaire provides a tested tool for Chinese language users to help practitioners complete usability assessments.
Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network

Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal. The proposed network, which is constructed based on dense blocks and attention blocks, contains an encoder-decoder architecture, which enables it to directly learn the mapping between the input images and the corresponding haze-free image, without being dependent on the traditional atmospheric scattering model (ASM). To better handle non-uniform hazy remote sensing images, we propose to combine a haze density prior with deep learning, where an initial haze density map (HDM) is firstly extracted from the original hazy image, and is subsequently utilized as the input of the network, together with the original hazy image. Meanwhile, a large-scale hazy remote sensing dataset is created for training and testing of the proposed method, which contains both uniform and non-uniform, synthetic and real hazy remote sensing images. Experimental results on the created dataset illustrate that the developed dehazing method obtains significant progresses over the state-of-the-art methods.