Reviewerly/exHarmony huggingface dataset:
Super-resolution reconstruction of remote sensing images based on convolutional neural network

A method of super-resolution reconstruction of remote sensing images based on convolutional neural network is proposed to address the problems of low-resolution and poor visual quality of remote sensing images. In this method, a sample database with high-resolution (HR) and low-resolution (LR) remote sensing images was constructed. A convolutional neural network was then obtained by determining the intrinsic relationship between HR and LR remote sensing images in the sample database. Multiple pairs of HR and LR images were selected from the sample database and sent into a six-layer convolutional neural network. The experimental results showed that compared with other learning-based methods, such as the fast super-resolution convolutional neural network (FSRCNN), the image quality obtained by our method is improved both subjectively and objectively. Moreover, the training time was ∼17  %   less than in the FSRCNN method.
Light Field Synthesis by Training Deep Network in the Refocused Image Domain

Light field imaging, which captures spatio-angular information of incident light on image sensor, enables many interesting applications like image refocusing and augmented reality. However, due to the limited sensor resolution, a trade-off exists between the spatial and angular resolution. To increase the angular resolution, view synthesis techniques have been adopted to generate new views from existing views. However, traditional learning-based view synthesis mainly considers the image quality of each view of the light field and neglects the quality of the refocused images. In this paper, we propose a new loss function called refocused image error (RIE) to address the issue. The main idea is that the image quality of the synthesized light field should be optimized in the refocused image domain because it is where the light field is perceived. We analyze the behavior of RIL in the spectral domain and test the performance of our approach against previous approaches on both real and software-rendered light field datasets using objective assessment metrics such as MSE, MAE, PSNR, SSIM, and GMSD. Experimental results show that the light field generated by our method results in better refocused images than previous methods.
Measurement of harvesting width of intelligent combine harvester by improved probabilistic Hough transform algorithm

Aiming at the problem that the harvesting width measurement of rice or wheat is susceptible to interference and poor real-time performance, an improved probabilistic Hough transform (PHT) width measurement algorithm based on morphological vertical line extraction is proposed. Inverse perspective mapping (IPM) restores the parallel relationship of the vertical line of the three-dimensional world. Image enhancement of the object region is used to highlight differences between the harvested and unharvested region. The median filter of large size filter window eliminates the interference of local noise. Improved PHT based on the vertical line extraction of the threshold segmentation realizes the real-time accurate measurement of the harvesting width during the operation of the intelligent combine harvester. Experiments demonstrate that the improved PHT can accurately obtain the harvesting width error of not more than 64 mm at an average success rate of 96.025%, and can measure the harvesting width in real time at 77.675 ms/frame.
A Pilot Study on Detecting Violence in Videos Fusing Proxy Models

We propose an approach to detect violence in CCTV feeds that is robust to new datasets and situations. This approach breaks with the traditional assumption of having large amounts of training data that are representative samples. Detecting violence in CCTV feeds is an objectively hard problem that is of paramount importance to solve for effective situational understanding. Violence comprises a large spectrum of activities that can go from abuse, to fighting, to road accidents, that can therefore take place in completely different environments, from public buildings, to underground stations, to roads during the day or the night. This large spectrum of activities and environments makes this a hard classification task for machines. We show that there are specific, detectable, and measurable features of video feeds that correlate with&#x2014;among other things&#x2014;violence and, by fusing such features with semantic knowledge, we can in principle provide estimates of sequences of videos that correlate with violence.
Analysis of Authentication System Based on Keystroke Dynamics

Currently, almost any kind of data is stored on the Internet, which makes security issues of the private data very crucial. The main objective of this paper is to analyze multimodal authentication security systems that are combined with keystroke dynamics (KSD). The keystroke dynamics are one of the behavioral biometrics which describes the typing rhythm of the individual. The proposed work aimed to design a multimodal authentication system using a machine learning/deep learning algorithm for user classification based on traditional and touchscreen features of keystroke dynamics. The results have shown that XGBoost has a higher overall classification accuracy (90.91%) in comparison with the previous works (85.90%) on the same dataset. Confusion Matrix and Receiver Operating Characteristics (ROC) curves were derived for further performance evaluation of the system. Area Under the Curve (AUC) values for each of the classes and for the whole classifier were also reported.
Application of Cognitive Diagnostic Models to Learning and Assessment Systems

Over the past few decades, cognitive diagnostic models have generated a lot of interest due in large part to the call made by the No Child Left Behind Act of 2001 (No Child Left Behind, Act of 2001 Public Law No. 107–110, § 115. Stat, 1425, 2002) for more formative assessments in learning systems. In this chapter, we provide an overview of learning and assessment systems, including the rise in popularity of online and personalized learning systems; we contrast the role of summative and formative assessments in learning systems; and we provide a review of cognitive diagnostic models and the challenges of retrofitting models to data not designed for cognitive diagnostic models.
Smart Phone Anti-counterfeiting System Using a Decentralized Identity Management Framework

The effect of counterfeiting on smart phone sales worldwide is estimated at 184 million units, valued at 45.3 billion EUR or 12.9 % of total sales. The mobile phone counterfeiting, in addition to its economic impact, has serious security, privacy, and even general safety concerns. The proliferation of Smart Phones devices is on the rise, where the number of smart phone devices shipped in 2017 has surpassed 1.5 billion devices and it is has reached around 1.2 billion devices by end of Q3 2018. Most of those devices are attached to different mobile networks operated around the globe, and the challenges arising is how those devices' identities are maintained and verified in addition to how the supply chain actors in smart phones industry can ensure the access to device identity throughout the device life cycle with less control from third parties. Blockchain as a distributed ledger technology positions itself as a suitable candidate to address this challenge. That is mainly attributed to Blockchain's use of cryptographic identifiers, records immutability, and provenance. These features, together, provide a platform to implement the functions of smart phone identity management functions in a global and decentralized environment. This paper presents a the use of a decentralized identity management framework to implement a system for Smart Phone Anti-Counterfeiting that eliminates the need for a central authority and provides the features of identity creation and transfer of ownership, along with the capability of fast and secure reporting of stolen and lost devices that takes effect in the shortest time. The work is implemented using a set of solidity of smart contracts deployed on a private Ethereum Blockchain.
Face Recognition for Attendance Management System Using Multiple Sensors

Abstract The problem about checking attendant is the main problem of teacher in nowadays. In order to solve this problem, Many systems have been completely changed due to this evolve to achieve more accurate results. However, in my study, these study still lack of the efficiency about correct the face and students cannot verify or pose to edit the data when there is error in class. With this reason, this research aims to develop the facing attendant system to be more effective and the mechanic of the system which students can easily verify. The experiment of this research is to find the way to recognize the face by using the technique of Android Face Recognition with Deep Learning which can correctly recognize up to 97%. The database is connected to Attendance Management System web server by using cloud storage. The result on screen in real time on the application so that students can verify and check data.
The Effects of Customisation on Player Experiences and Motivation in a Virtual Reality Game

There is a high demand for physical rehabilitation therapies: More than 475,000 Australian citizens are living with the effects of stroke and are engaged in physical therapies. Developing effective physical rehabilitation programs for these individuals is important both for their health outcomes and for the medical field. Virtual Reality (VR) technologies provide opportunities for improvements in this area. One of the opportunities is to create motivational rehabilitation processes for patients' persistence with their therapies. Using the theoretical framework of Self Determination Theory, this paper reports results from an experimental study (N=60) that tested a motivational design principle (i.e., functional and aesthetic customisation) on participants' player experiences in a VR game. The VR game has simple mechanics that share similarities with a type of upper body rehabilitation exercise; which has been validated as effective for the optimal restoration of scapular muscle imbalances. Findings from the study showed that customisation has a positive effect on players' experiences of autonomy need satisfaction and enjoyment in the VR game setting. These findings have implications that aid designers when designing motivational VR physical rehabilitation applications.
Dual Model Medical Invoices Recognition

Hospitals need to invest a lot of manpower to manually input the contents of medical invoices (nearly 300,000,000 medical invoices a year) into the medical system. In order to help the hospital save money and stabilize work efficiency, this paper designed a system to complete the complicated work using a Gaussian blur and smoothing-convolutional neural network combined with a recurrent neural network (GBS-CR) method. Gaussian blur and smoothing (GBS) is a novel preprocessing method that can fix the breakpoint font in medical invoices. The combination of convolutional neural network (CNN) and recurrent neural network (RNN) was used to raise the recognition rate of the breakpoint font in medical invoices. RNN was designed to be the semantic revision module. In the aspect of image preprocessing, Gaussian blur and smoothing were used to fix the breakpoint font. In the period of making the self-built dataset, a certain proportion of the breakpoint font (the font of breakpoint is 3, the original font is 7) was added, in this paper, so as to optimize the Alexnet-Adam-CNN (AA-CNN) model, which is more suitable for the recognition of the breakpoint font than the traditional CNN model. In terms of the identification methods, we not only adopted the optimized AA-CNN for identification, but also combined RNN to carry out the semantic revisions of the identified results of CNN, meanwhile further improving the recognition rate of the medical invoices. The experimental results show that compared with the state-of-art invoice recognition method, the method presented in this paper has an average increase of 10 to 15 percentage points in recognition rate.
Excitation modelling using epoch features for statistical parametric speech synthesis

In this paper, a novel excitation modelling method is proposed for improving the naturalness of statistical parametric speech synthesis (SPSS). In the proposed approach, the excitation or residual signal is parameterized by using features extracted from the epochs. The epoch parameters used in this work are epoch strength and sharpness. These features are modeled in the statistical framework along with other parameters. During synthesis, the excitation signal is constructed by imposing the generated epoch parameters on the natural instances of excitation signal. The effectiveness of the proposed method is evaluated in the framework of hidden Markov model (HMM)-based and deep neural network (DNN)-based SPSS. Evaluation results have shown that the SPSS systems developed using the proposed excitation model are capable of synthesizing more natural sounding speech compared to the ones based on two state-of-the-art excitation modelling approaches.
Interactive Deep Editing Framework for Medical Image Segmentation

Deep neural networks exhibit superior performance in dealing with segmentation of 3D medical images. However, the accuracy of segmentation results produced by fully automatic algorithms is insufficiently high due to the complexity of medical images; as such, further manual editing is required. To solve this problem, this paper proposes an interactive editing method combined with 3D end-to-end segmentation network. In the training stage, we simulate the user interactions, which are used as training data, by comparing the segmentation automatically generated by convolutional neural network with the ground truth. User interactions are fed into the network along with the images, allowing the network to adjust the segmentation results based on user edits. Our system provides three editing tools for smartly fixing segmentation errors, which cover most commonly used editing styles in medical image segmentation. With the high-level semantic information in the network, our method can efficiently and accurately edit the 3D segmentation. The interactive editing experiments on the BraTS dataset show that our method can significantly improve the segmentation accuracy with a small number of interactions only. The proposed method presents potential for clinical applications.
Hate speech detection using brazilian imageboards

With the changes in human interaction prompted by the development of communications platforms over the internet, hate speech and offensive language emerged as a contemporary problem. Social networks allow users with different opinions and backgrounds to interact without direct eye-to-eye contact. It brings a sense of safety to promote hate speech, which is even more significant in anonymous environments. There are sites called imageboards, composed of different boards aggregating different topics. On some boards, anonymous users widely promote hate speech. However, only a few works in literature have focused on hate speech in imageboards content. This work aims to classify Brazilian Portuguese texts to detect hate speech, using data from the Brazilian 55chan imageboard to build a dataset with hate speech content. Three classifiers were trained to hate speech binary classification. The Linear Support Vector Classifier achieved the best result with 0.955 of F1-score.
Implementation and Comparison of different Data Hiding Techniques in Image Steganography

The daily use of internet for data transmission is increasing dramatically, day by day. So the security of the data being transferred have also become imperative. There are variety of methods used to ensure the security of this data transfer like Cryptography, Steganography and Watermarking. Steganography is the technique which enables the sender to hide the existence of a message so that it is transmitted undetected. Steganography allows the sender to hide the message within a cover message. There are various techniques existing for image steganography (using image as cover). The main idea of this project is to implement three of the most commonly used image steganography techniques to hide text on image and then compare their efficiency and performance in terms of Mean Squared Error (MSE) and Peak Signal to Noise ratio (PSNR). The existing algorithms used for comparison are LSB technique, Pixel Value Differencing technique [4] and LSB Substitution based on similarity of bit pairs. LSB technique is the simplest method where the secret message is encoded into the LSB of each value in the pixel matrix. In Pixel Value Differencing, two or more successive pixel values are selected and then embedded with data. LSB Substitution embeds information by matching message bit blocks with pixel bit blocks.
Coverage Path Planning for Mine Countermeasures: Adapting Track Orientation

Capturing high-quality sonar survey data from autonomous underwater vehicles (AUVs) in complex environments, with little to no a priori knowledge, requires built-in adaptations. In particular, in order to locate objects that sit proud on the sea-floor in areas of sand ripples, special trajectory adaptation is required to mitigate natural shadow zones. Adaptive orientation and track spacing are key capabilities, enabling effective search strategies on the sea bottom. We present two methods for adaptive AUV track orientation: the first approach improves mission efficiency (in a resource-utilisation sense) by minimising the number of AUV turns, whereas the second enhances the data collection quality by adapting to sand ripples. A sensitivity analysis compares the two methods and shows that changing the track orientation during a mission is costly in terms of mission efficiency. Results from our study suggest that the adaptation should be applied once there is a considerable ripple field found on the seabed, and should avoid changing the course multiple times during the mission.
A New Multi-Agent Reinforcement Learning Method Based on Evolving Dynamic Correlation Matrix

Multi-agent reinforcement learning approaches can be roughly classified into two categories.One is the agent-based approach which can be implemented in real distributed systems, though most approaches of this type cannot provide meaningful theoretical verifications.The other can be seen as the more formalized approach, which can provide theoretical results.However, most of current algorithms usually require unrealistic global communication, which makes them impractical for real distributed systems.In this article, we propose a dynamic correlation matrix based multi-agent reinforcement learning approach where the meta-parameters are evolved using an evolutionary algorithm.We believe that our approach is able to fill the gap between the two kinds of traditional multi-agent reinforcement learning approaches by providing both agent-level implementation and system-level convergence verification.The basic idea of this approach is that agents learn not only from local environmental feedback, i.e., their own experiences and rewards, but also from other agents' experiences.In this way, the agents' learning speed can be increased significantly.The performance of the proposed algorithm is demonstrated on a number of application scenarios, including blackjack games, urban traffic control systems and multi-robot foraging.
Sparklify: A Scalable Software Component for Efficient Evaluation of SPARQL Queries over Distributed RDF Datasets

One of the key traits of Big Data is its complexity in terms of representation, structure, or formats. One existing way to deal with it is offered by Semantic Web standards. Among them, RDF – which proposes to model data with triples representing edges in a graph – has received a large success and the semantically annotated data has grown steadily towards a massive scale. Therefore, there is a need for scalable and efficient query engines capable of retrieving such information. In this paper, we propose Sparklify: a scalable software component for efficient evaluation of SPARQL queries over distributed RDF datasets. It uses Sparqlify as a SPARQL-to-SQL rewriter for translating SPARQL queries into Spark executable code. Our preliminary results demonstrate that our approach is more extensible, efficient, and scalable as compared to state-of-the-art approaches. Sparklify is integrated into a larger SANSA framework and it serves as a default query engine and has been used by at least three external use scenarios. Resource type Software Framework Website http://sansa-stack.net/sparklify/ Permanent URL https://doi.org/10.6084/m9.figshare.7963193
DeepReviewer: Collaborative Grammar and Innovation Neural Network for Automatic Paper Review

Nowadays, there are more and more papers submitted to various periodicals and conferences. Typically, reviewers need to read through the paper and give a review comment and score to it based on somehow certain criterion. This review process is labor intensive and time-consuming. Recently, AI technology is widely used to alleviate human labor burden. Can machine learn from human to review papers automatically? In this paper, we propose a collaborative grammar and innovation model - DeepReviewer to achieve automatic paper review. This model learning the semantic, grammar and innovative features of an article by three main well-designed components simultaneously. Moreover, these three factors are integrated by an attention layer to get the final review score of the paper. We crawled paper review data from Openreview and built a real data set. Experimental results demonstrate that our model exceeds many baselines.
Detection of Geothermal Potential Zones Using Remote Sensing Techniques

The transition towards a new sustainable energy model—replacing fossil fuels with renewable sources—presents a multidisciplinary challenge. One of the major decarbonization issues is the question of to optimize energy transport networks for renewable energy sources. Within the range of renewable energies, the location and evaluation of geothermal energy is associated with costly processes, such as drilling, which limit its use. Therefore, the present research is aimed at applying different geomatic techniques for the detection of geothermal resources. The workflow is based on free/open access geospatial data. More specifically, remote sensing information (Sentinel-2A and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)), geological information, distribution of gravimetric anomalies, and geographic information systems have been used to detect areas of shallow geothermal potential in the northwest of the province of Orense, Spain. Due to the variety of parameters involved, and the complexity of the classification, a random forest classifier was employed, since this algorithm works well with large sets of data and can be used with categorical and numerical data. The results obtained allowed identifying a susceptible area to be operated on with a geothermal potential of 80 W·m−1 or higher.