Reviewerly/exHarmony huggingface dataset:
Inpresso AR: a generic Augmented Book

Augmented Reality (AR) is a technology that enables to show virtual information superimposed on the real world and interact with it. In recent years, this technology has been heavily explored in various contexts, with a focus on enriching the experience provided by smartphones and wearables. AR books techniques consist of adding interactive and immersive properties to physical books, transforming the reading activity into a unique experience. In this paper, it is proposed a generic augmented book that is capable of downloading and playing different AR experiences for any existing book. A hybrid experiences specification schema was defined to characterize the AR experiences. The proposed augmented book application recognizes a book, downloads from the server the experiences specification file associated with it, builds the necessary AR experiences assets and plays them. A prototype was built and some preliminary tests were conducted. The approach described here allows physical books to be connected with remote servers, transforming them into dynamic sources of information and into products much more immersive, interactive and attractive.
Application of Artificial Intelligence Technology in Future Equipment Control System

With the continuous progress of intelligent and unmanned weapons and equipment, the battlefield is becoming increasingly complex and the application of armored equipment is becoming more diversified, which requires the equipment systems to update and recombine with the changes of the battlefield. Based on the existing technology, this paper prospects the future development trend of intelligent fire control system.
Basi empiriche per una didattica delle lingue slave basata sull'intercomprensione

The present paper aims to verify to what extent the L2 Russian skills of Italian university students facilitate the comprehension of written Polish, Croatian and Ukrainian. The analysis attempts to identify which lexical and grammatical elements appear to be transparent and which ones present greater difficulty and should be taught explicitly. Verbal morphology seems to be easily identified and interpreted based on parallel texts. Concerning the lexicon, the results suggest that while most lexical items appear to be readily recognisable, a short list of common, non-transparent lexical items should be included in the perspective syllabus. The study concludes that while the spontaneous intercomprehension skills of L2 Russian learners are not sufficient for adequate comprehension, they can be significantly improved through language instruction with relatively little effort.
SMT-LIB Theory of Nominative Data

In this article, we describe the theory of nominative data, formulate the basic principles of the composition-nominative approach, define the class of nominative data and functions, and describe a calculus for the Theory of Nominative Data. By using nominative data, we can increase the adequacy level of representation of data structures, functions, and compositions that are used in programming languages. Thus, in terms of composition-nominative approach, we can build program verification system based on a unified conceptual basis. Computer-aided verification of computer programs often uses SMT (satisfiability modulo theories) solvers. A common technique is to translate preconditions, postconditions, and assertions into SMT formulas in order to determine whether required properties can hold. The SMT-LIB Standard was created for forming a common standard and library for solving SMT problems. Now, it is one of the most used libraries for SMT systems. Formulas in SMT-LIB format are accepted by the great majority of current SMT solvers. The theory of nominative data is of interest for software modelling and verification, but currently lacks support in the SMT-LIB format. In the article, we propose the declaration for the theory of nominative data for the SMT-LIB Standard 2.6.
Non-blind image blur removal method based on a Bayesian hierarchical model with hyperparameter priors

In many image blur removal schemes, a proper point spread function is usually estimated in advance from the blurry image, then the latent image comes out by using existing non-blind techniques. However, some of the techniques suffer from strong artifacts. Therefore, an efficient non-blind method plays an important role in image restoration issues. In most models, image priors act as the regularization terms that hold image details and suppress noises. This paper introduces a new image prior based on a parameterized scaled Gaussian model and a gamma distribution, with hyperparameters based on the statistical properties of tens of thousands of images. Our regularized cost function is then formed via a Bayesian hierarchical approach. It consists of a data fidelity term and a series of constraints on image gradients in multiple orientations. The former is used to assure the best approximation of the original image, and the latter is for preserving sharp edges. The optimization problem is solved by an effective tail-recursive algorithm based on the conjugate descent technique. Experimental results show that our model can both deal with simulated data and real scenes. The comparisons show our method outperforms others and achieves promising results.
Applying Point Spectroscopy Data to Characterize Sand Properties

Sand is a grouping of unconsolidated rock and mineral particles, typically sourced from the weathering of a host rock. Quartz is most resistant to the forces of erosion and abrasion in coastal environments, meaning that heavy mineral is more commonly found in sands, and depending on chemical composition, typically will give sand lighter coloration where it appears in sufficient quantities. This chapter aims to evaluate whether spectroscopy techniques can be used to characterize sand properties using qualitative and quantitative methods by combining the geological/microscopic and remote sensing spectroscopy techniques. Geologists normally collect sand information using the microscopic and sieving techniques in which a set of sand attributes. Sand composition is not independent from its color. Sand color generally is determined by its composition and particularly mineral content. A spectroscopic technique is an attractive tool compared to or used in tangent with traditional sand analysis methods.
A combined CNN and LSH for fast plant species classification

The number of plants in the world is very important, to distinguish between them in a reasonable time can be problematic. Recent works has shown that convolutional neural network (CNN) models have great potential in the field of classification. The advantage of using this type of architecture, besides being robust, is that the network learns the characteristic vectors automatically thanks to the convolution layers. Due to his effectiveness, Locality Sensitive Hashing (LSH) becomes a popular method for large scale image classifcation. In this work, we propose a CNN-LSH method for fast plant species classification. Under the same conditions, our evaluation shows that our proposed method can reduce the time classification by 60 % compared to a CNN.
Manifold Alignment with Multi-graph Embedding

In this paper, a novel manifold alignment approach via multi-graph embedding (MA-MGE) is proposed. Different from the traditional manifold alignment algorithms that use a single graph to describe the latent manifold structure of each dataset, our approach utilizes multiple graphs for modeling multiple local manifolds in multi-view data alignment. Therefore a composite manifold representation with complete and more useful information is obtained from each dataset through a dynamic reconstruction of multiple graphs. Experimental results on Protein and Face-10 datasets demonstrate that the mapping coordinates of the proposed method provide better alignment performance compared to the state-of-the-art methods, such as semi-supervised manifold alignment (SS-MA), manifold alignment using Procrustes analysis (PAMA) and manifold alignment without correspondence (UNMA).
Supersensitive Optical Phase Measurement using Deterministically Generated Multiphoton Entangled State

We use a multiphoton Greenberger-Horne-Zeilinger state to demonstrate optical phase measurement with sensitivity that beats the standard quantum limit. The state is produced deterministically at rates which exceed 300 MHz from a single point source.
Digital Library for Thai Astronomical History Study on French Document Resource

Thai history in the era of Ayutthaya Kingdom was mostly documented by French missionaries during the 17th-18th centuries. Huge amount of resources in form of manuscripts, books, microfilms are preserved and provided by several institutions such as Bibliotèque National de France, etc. Nowadays, the advance of digital technology allows us to access these resources publicly. Many resources were digitized in form of scanned images. This work aims to establish our own specific digital library for Thai astronomical history study. Document management system was developed. It includes data acquisition and collection management. To be able to access knowledge behind the texts, the scanned images were transformed into machine-readable format by optical characters recognition (OCR). Search engine was implemented to allow historians to find pieces of reverent information from keywords. In our circumstance, Thai historians may not have French reading skill. We integrated an automatic French to English language translation by using machine translation technique. Our system provides the historians the e-books of the French historical original documents in English. To automatically extract knowledge from texts, we perform the natural language processing to identify name-entities, such as name of person, places, events, etc., from texts. This enables the historian to explore some meaningful concepts via the indices of the texts. The indices were also automatically linked to Wikipedia as an existing knowledge pool. There are still some limitations of our project including the processes of OCR, language machine translation, name-entity recognition which remain challenged in computer science research.
Determining NOx And SOx Emissions by Soft Sensor

The NOx and SOx output of a combined heat and power plant is studied with the aim of replacing the physical sensor array with a mathematical formula that can compute the emissions rather than measure them. The model is determined using machine learning from historical measurements and uses neural networks. As the model can be cheaply deployed, will not drift, and will not malfunction or fail, the model has significant added value over a physical sensor. We find that the accuracy of the model is comparable to the accuracy of the measurement and is thus suitable for a full replacement.
Recognizing and Grading 3D Modeling Objects Using YOLO Based Deep Learning Network

This study proposes a novel approach using YOLO based deep learning network to help the teacher grading 3D modeling objects created by the learners automatically. The training dataset is the collections of rendering outputs from the teacher's 3D modeling object. The testing data is the rendering outputs of the learners' projects. The grading will rely on the testing results of recognition confidences. This is an initial study from draft inspiration by the deep learning network on object detections and recognitions. More applications and modifications are to be discussed, designed and examined in further studies.
Amazighe POS tagging using Long Short Term Memory Networks

Morphosyntactic processing of natural languages is mainly restricted by the lack of labelled data sets. Deep Learning methods proved their efficiency in domains such as imaging or acoustic process. Part-of-speech tagging is an important preprocessing step in many natural language processing applications.
Block-regressed face alignment algorithm based on supervised descent method

In recent years, the accuracy of face alignment has been improved a lot. However, the landmarks of each component in face are trained together in most algorithms, which ignore their dedicated characteristic and limit the further accuracy improvement. In this paper, we propose a new approach to improve the localization performance of facial landmarks by taking each component as independent task instead of the whole face. Namely, independent regressors are learned for each component using the gradient descent method. If only considering independent regressors for component, the inherent correlation between the components may be neglected. This paper proposed a strategy to effectively combine the results of the whole face regression and the independent components regressions. In this way, the effect of holistic and independent results are all taken into consideration, which can further enhance the alignment accuracy. A large number of experiments show that our method is better than the single loss function in both detection accuracy and reliability.
Recognition of Facial Expression Based on the Position of Hands Surrounding the Face Through Median Filter

There is an ultimate goal to design a system where machine can recognize natural interaction between humans and machines. It’s crucial to design such system based on the HCI systems. This field is widely studied by many Computer Vision (CV) and Machine Learning researchers. Even many systems, based on the study has been designed and implemented on the basis of its popularity such as Gesture recognition based HCI application, FER systems and the combination of these two to get better output in a human emotion recognition system. In the visual representation of gesture recognition such as body, hand, face, sign, emotions, expressions, movements of body parts are used to build such interactive system. Many challenges occurred during the design such as lightning condition, background, head orientation, hand orientation, location, position of body gesture, static and dynamic movements and, so on. FER based on the hand gesture is one of the challenging and main research highlights in the field of HCI. Through facial expressions, we can convey the useful, natural, intension to the other person during the communication. Also, with the hand gesture and its position during the communication, gives more accuracy in the recognition process.
Embedded Authentication and Blockchain in Micro-course

The decentralization and consensus of blockchain provide the network trust and data certification of a low cost. In addition to financial technology, it has also attracted widespread attention in many applications. In this paper, we discuss on the design of the micro-course certification in university. The proposed architecture is suitable for the integration of school information systems and enhances the trust and micro-course elective. Unlike general blockchain applications, micro-course certification must be provided by a university-approved teacher or staff, thus it is important for their authentication. In this application, the password authentication is not secure enough, and the cost of public key certification is too high. Therefore, we use the authenticated public key technology of the RSA system technology, the authenticated data is the predetermined portion and embedded to RSA public key, such that the authenticated certifier and effective period can be directly provided by RSA public key. In comparison with traditional RSA, we only modified the key generation and the other part of RSA cryptosystem are all the same. This combination of blockchain and public key authentication can achieve a secure and easy-to-use micro-course certification system.
New Features of Editing Role Positioning Under the Background of Artificial Intelligence

As the development and progress of the science and technology, artificial intelligence has been a breakthrough in the field of editing in which editing machine is one of good example. With the continuous development of artificial intelligent on editing industry, human-computer conflict has become one of the hot points in the academic circle, which has brought many discussions at current stage. The purpose of this paper is to explore how to get the positioning for editors and coordinate the relationships between instrumental rationality and humanistic spirit under certain circumstances of artificial intelligence shuffle on the editing industry. The study of this paper is based on the theory of artificial intelligence algorithms to study the evolutionary algorithms for data sources of evidence, which is based on the emergency of artificial intelligence under the background of machine editor and also for the traditional editing on the information production, distribution and management mechanism of the industry as well as editing practitioners professional role with function achieved, connotation of the attributes and skills of editing practitioners to work put forward on new requirements. In this paper, the research results show that the widespread using of artificial intelligence, which does not mean that the editor practitioners will be replaced by editing machine of artificial intelligence because of the inadequacy of the technique on artificial intelligence algorithms to be always unable to break through the symbol manipulation level that is not like human consciousness which is equipped with a self-consciousness that can produce complex concepts, consciousness and meaningful system.
Financial Application: Predicting the Wealth Management Products Buying Behaviour of Listed Companies

The behaviors of business entities affect the entire business world in a variety of ways. Based on the prediction of these behaviors, we could make appropriate business plans in advance to achieve profitable investment, precision marketing and risk management. But factors affecting these business issues are complex and diverse, making financial analysis invalid. Big data technology based on machine learning brings us the dawn to business intelligence applications. In this paper, we will predict the wealth management products buying behaviour of Chinese listed companies by reasonably defining the capability range of machine learning algorithms to solve business problems. Our data comes from the financial database of the Wind, which provides strong support for real-time applications. We not only use the model to backtest historical data, but also deploy the model online to predict the behavior of 2019. In addition, we obtain the importance scores of factors that influence these predictions, providing valuable explanatory bases for business applications.
Investigation of Automatic Video Summarization using Viewer’s Physiological, Facial and Attentional Features

Video summarization aims at the selection of a concise and representative set of keyframes or video segments that allows the identification of the video content. In either cases, traditional summarization techniques usually work by segmenting the video into shots, representing video frames as feature vectors of color, texture, audio, among other features, clustering frames with similar features and selecting most representative keyframes or segments, sometimes guided by a video-to-summary ratio target. The resulting summaries are typically subject independent and do not take into account specific viewer's behavior. Instead of using intrinsic features extracted from the video for summarization, in this article we study whether personalized (subject dependent) video summaries can be obtained from physiological, facial, and attentional data captured from the viewers. More specifically, we study the relationship between personalized video summaries reported by viewers and their data captured during the display of different video genres. A dataset of fifteen videos was used in the experiments. During the exhibition of the videos, the viewer's physiological, facial, and attentional data were recorded, analyzed and synchronized. Several machine learning models were trained to test our hypothesis. We obtained k-fold cross validation accuracies that were above the chance for the best learned models. As a result of this study, we conclude that it is possible to train a learning machine that can produce customized summaries that are closer to user preferences compared to randomly produced summaries.
Multi-class Vehicle Detection Using Multi-scale Hard Negative Mining

The performance capabilities of object detection processes have been greatly improved due to the development of deep learning methods. As the performance of object detection methods improves, studies of problems that remained unsolved are now becoming more common. In CCTV technology, such as tracking technology, it has become easier to resolve the matching issue as the performance of object detection methods has improved. A network such as YOLOv3, a single stage multi scale based object detection method, robustly detects objects of various sizes while maintaining real-time performance. Object detection methods for multi scale structures are associated with the problem of an imbalance between a positive box and a negative box on each feature scale. In the CCTV environment, the object detection performance can be degraded due to this ‘unbalance’ problem because the number of objects corresponding to the positive box is relatively small. The learning time is also important because re-training is required for new environments that are constantly being added. In order to solve this problem, we propose a method that solves the unbalance problem through multi scale hard negative mining and that improves the object detection performance while also reducing the learning time.
Differentially Private Graph Clustering Algorithm Based on Structure Similarity

With the widespread use of new information systems such as social networks, recommendation systems as well as location-based services, graph data has become a very common and important data type. It has been shown that, from these collected graph data, some special substructures can be found through clustering analysis, and can further support the intelligent decision. However, directly publishing or using clustering results on these graph data would disclose the privacy information of system users. To this end, based a classical structural clustering algorithm for networks (SCAN) and the technology of differential privacy, we propose a differentially private graph clustering algorithm named DP-SCAN. Specifically, we first reasonably calibrate the global sensitivity of the function of computing structure similarity between nodes in the graph, and thus specify parameters of the Laplace mechanism for capturing differential privacy. Then, we provide details of the DP-SCAN algorithm. The theoretical analysis indicates that DPSCAN algorithm satisfies ε-differential privacy, without trading off the clustering efficiency. The experimental results show that, when compared with the original SCAN clustering algorithm, DP-SCAN clustering algorithm can maintain the validity of clustering under the premise of satisfying differential privacy.