Reviewerly/exHarmony huggingface dataset:
Handwritten Style Recognition for Chinese Characters on HCL2020 Dataset

Structural features of Chinese characters provide abundant style information for handwritten style recognition, while prior work on this task has few senses of using structural information. Meanwhile, based on current handwritten Chinese character datasets, it is hard to obtain a good generalization model only by character category and writer information. Therefore, we add the structural information known as morpheme which is the smallest and unique structure in Chinese character into the large handwritten dataset HCL2000 and update it to HCL2020. We also present a deep fusion network (Morpheme-based Handwritten Style Recognition Network, M-HSRNet), capturing both overall layout characteristics and detail structural features of characters to recognize handwritten style. The evaluation results of the proposed model on HCL2020 are observed to prove the effectiveness of morpheme. Together with the proposed Morpheme Encoder module, our approach achieves an accuracy of 78.06% in handwritten style recognition, which is 3 points higher than the result without morpheme information.
The distortion correction algorithm applied on wide field of view space plane camera and the verification test

In the laboratory verification test of space plane array camera with wide field of view, the image distortion caused by camera distortion will reduce the ranging accuracy for space camera with on orbit ranging function. In addition to verifying the performance of space camera, the distortion correction in laboratory stage can also verify the validity of distortion calibration results. First of all, a nonlinear distortion mathematical model including radial distortion and tangential distortion is established based on the principle of optical lens distortion. Distortion correction is generally divided into two steps. Firstly, the coordinate conversion relationship between each pixel in the ideal image and the corresponding pixel in the original image with distortion need to be established. The purpose of it is to make the pixel of original fall on the right position. Secondly, assign the gray value of the original image to the corresponding coordinate in the ideal image, and re-determine the gray value of the new pixel. By the bilinear interpolation method, the gray value of the original image with distortion is assigned to the undistorted image. Finally, the undistorted image can be obtained. The distortion correction algorithm is realized by MATLAB and verified on a space plane array camera. The corrected image distortion is less than 0.1 pixel
Coreveillance—Making Our World a “SAFER” Place

The culture in our civic society has undergone an abrupt change due to the extensive use of CCTV surveillance. Currently, our urban society is exclusively dependent upon CCTV footage for divulging any abnormal situation. This scenario has given rise to new openings for the researchers to judiciously utilize video analytics techniques to focus on many sensitive issues in society such as contravention of human rights (HR). The purpose of this paper is to design a surveillance platform equipped with situation intelligence to aid government and non-government working departments in taking corrective action against unfavorable intruders and destructive mishaps.
Sharing Electricity in Brazil: a crypto-currency for micro/mini-grid transactive energy

The growing presence of Distributed Generation has kept alive the discussions for the active integration of medium/low-voltage power consumers into the energy market. Besides technical and economic difficulties faced so far, another point of view emerges from the use of blockchain technologies to make the Transactive Energy approach a reality. The current article presents a distributed application model for energy management and trade between members of a shareable consumption group, a kind of Distributed Generation regulated by the Brazilian Government. It also showcases the methodology of the crypto-currency Sharing Electricity in Brazil that can be used to allow power exchange operations at the scope of micro/mini-grids.
A Resource Trusted Model Based on Game Theory in Cloud Computing

Abstract With the development of cloud computing, task scheduling has been an important problem in cloud computing. We should consider not only the performance of resources, but also the reliability of resources and users for resource allocation and scheduling. We proposed a trusted model based on game theory to evaluate the trusted scores of cloud participants. Simulation results show cloud users and resource tend to send feedback information to agents in order to increase their scores. Also trusted score is an important factor to affect the decision of resource allocation. Results show our model improved resource utilization and load balance.
Cipher Block Parallel Mode

Most of the current symmetric cryptographic algorithms are deterministic algorithms, i.e., for a given plain text and key mix, the encryption dependably results in the same cipher-text. The attacker can misuse this deterministic nature of symmetric block encryption algorithms by carrying cryptanalysis. Different block cipher modes are utilized as a part of an extension to these algorithms to make the resulting Cipher probabilistic in nature. Secure Cipher Block Modes make symmetrical algorithms to execute serially. The proposed cipher block Parallel (CPB) Mode is secure and brings parallelism in the execution of symmetric algorithms. Thereby enables faster encoding of the information blocks on a Multi-processor system framework simultaneously. This paper discusses the design and implementation of cipher block parallel mode in detail, including its security and performance.
An FFM-based Model for Students' In-class Learning Postures Estimation

In the recent years, education quality monitoring has been attached great importance by Chinese government, and with the promulgation of the development plan for the new generation of artificial intelligence, the application of this technology in education quality monitoring fields has attracted wide attention of scholars. How to apply relevant algorithms of machine vision to classroom quality assessment is the research focus of smart education. In this paper, considering the timeliness of feedback, the stability of algorithms, the accuracy rate and speed in classroom quality assessment, we propose a model called Face Features Matching (FFM), based on face detection networks and feature points matching methods, which innovatively combines machine learning with parameter estimation and numerical methods. Our model is divided into two sub-modules: face feature extraction and optimal parameter estimation. It can be applied successfully to classroom scenarios and become an important part of automated classroom quality assessment.
Pre- and Post-processing on Generative Adversarial Networks for Old Photos Restoration: A Case Study

Old historical images are an invaluable source of knowledge that allows people to learn about past events and, in general, the form of the world in the past. In the case of townscapes, the photos may depict specific details as building appearance prior to their reconstruction, enlargement or demolition, or even former appearance of cities (buildings, inhabitants, transportation, among others). In this sense, more and better details of the image lead to an exact representation of a city in a given time. Generative Adversarial Networks (GANs) are a category of deep artificial neural networks (DANNs) that show great success in generating realistic characteristics into image, video and voice data. This work explores how the pre- and post-processing techniques influence the overall effectiveness of GANs-based techniques for restoring and coloring old photos. Pre- and post-processing based on traditional image processing methods preserve and enhance the information contained in old photographs; however, their effectiveness is limited by the amount of information retained in the original photograph. On the other hand, GANs-based techniques offer the ability to increase the amount of information and thus boost the effectiveness of traditional methods. Experiments are performed referring to the old photos of Quito's city. The preliminary results show that pre- and post-processing algorithms are essential even in artificial intelligence approaches, eliminating undesirables artifacts and increasing visual quality.
IIMR: A Framework for Intangible Mid-Air Interactions in a Mixed Reality Environment

Augmented Reality (AR) has been getting increased traction lately with a plethora of AR-enabled devices on the market ranging from smartphones to commercial Head-Mounted Displays (HMDs). With the rise in AR, a need exists for there to be a more natural and intuitive form of interaction with virtual content that does not interfere with the user experience. Moreover, advanced and sophisticated hardware such as the Leap Motion Controller makes this possible by employing sensors that can track hands accurately in real-time. This paper presents a framework, IIMR (Intangible Interactions in Mixed Reality), that operates over a network and allows for mid-air interactions using just the bare-hands. The aim is to provide researchers and developers with an inexpensive and accessible solution for quickly developing and prototyping hand interactions in a mixed reality environment. The system consists of three modules, namely a Smartphone, a Google cardboard-like HMD, and a Leap Motion Controller. Diverse applications are developed to evaluate the framework, and the results from qualitative usability studies are outlined.
<sup>1-1</sup> Forefront of Silicon Quantum Computing

Forefront of the silicon quantum computer development is described.
Probabilistic Reachability for Uncertain Stochastic Hybrid Systems via Gaussian Processes

Cyber-physical system models often feature stochastic behaviour that itself depends on uncertain parameters (e.g., transition rates). For these systems, verifying reachability amounts to computing a range of probabilities depending on how uncertainty is resolved. In general, this is a hard problem for which rigorous solutions suffer from the well-known curse of dimensionality. In this paper we focus on hybrid systems with random parameters whose distribution is subject to nondeterministic uncertainty. We show that for these systems the reachability probability is a smooth function of the nondeterministic parameters, and thus Gaussian processes can be used to approximate the reachability probability function itself very efficiently over its entire domain. Furthermore, we introduce a novel approach that exploits rigorous probability enclosures for training Gaussian processes. We apply our approaches to non-trivial hybrid systems case studies, and we empirically demonstrate their advantages with respect to standard statistical model checking.
Beyond the screen’s edge: eye and head movements while looking at rotated scenes in VR

We examined the extent to which image shape (square vs. circle), image rotation, and image content (landscapes vs. fractal images) influenced eye and head movements. Both the eyes and head were tracked while observers looked at natural scenes in a virtual reality (VR) environment. In line with previous work, we found a bias for saccades in line with the image horizon for landscape images, and unusually, for fractal images as well. Interestingly, when viewing landscapes, but not fractals, observers rotated their head in line with the image rotation, thereby enabling saccades to be made in cardinal, rather than oblique directions. This clear distinction between how the eyes and head respond to image content suggests that they may be subserved by different control strategies. We discuss our findings in relation to current theories of attentional control, and how insights from VR might inform past and future eye-tracking studies.
Characterizing Triviality of the Exponent Lattice of a Polynomial Through Galois and Galois-Like Groups

The problem of computing the exponent lattice which consists of all the multiplicative relations between the roots of a univariate polynomial has drawn much attention in the field of computer algebra. As is known, almost all irreducible polynomials with integer coefficients have only trivial exponent lattices. However, the algorithms in the literature have difficulty in proving such triviality for a generic polynomial. In this paper, the relations between the Galois group (respectively, the Galois-like groups) and the triviality of the exponent lattice of a polynomial are investigated. The \(\mathbb {Q}\)-trivial pairs, which are at the heart of the relations between the Galois group and the triviality of the exponent lattice of a polynomial, are characterized. An effective algorithm is developed to recognize these pairs. Based on this, a new algorithm is designed and implemented to prove the triviality of the exponent lattice of a generic irreducible polynomial, which considerably improves a state-of-the-art implementation of an algorithm of the same type when the polynomial degree becomes larger. In addition, the concept of the Galois-like groups of a polynomial is introduced. Some properties of the Galois-like groups are proved and, more importantly, a sufficient and necessary condition is given for a polynomial (which is not necessarily irreducible) to have trivial exponent lattice.
Pengembangan Media Pembelajaran Game Education pada Mata Pelajaran Bahasa Inggris Materi My Body

The media game of education in this study referred to the overview of the basic body along with pictures to attract students' attention. The type of research used in this study was Research and Development (R&amp;D). R&amp;D is a research strategy or method that is powerful enough to improve practice. The data were collected through questionnaires, interviews, and documentation. The object was to measure the success of the learning process as evidenced by the student's scores. The variable used as a measurement was the feasibility of the product. The results showed that the need for game education in learning was an important medium to make the teachers easier delivering the learning material, attracting students, especially for grade 1 students at Madrasah Ibtidaiyah, students were also easier to master the topic about my body. The development of learning media for game education can be used by students not only when learning at school. The effectiveness test using the SPSS program also showed the value of Sig. (2-tiled) p = 0.000 &lt;0.05, which means that game media education was effective to improve English learning outcomes about my body.
The Intelligent Nursing House for the Elderly Based on the IoT System

Abstract As the problem of the aging population is gradually intensifying, the current nursing house for the elderly cannot meet their living needs. Based on this, this article builds an intelligent nursing house system based on IOT. This article first puts forward the problems existing in today’s elderly care system, and gives detailed solutions from three aspects: health, diet and maintenance. We combine the Indoor health care and auxiliary module and Dining module into the main system network, and list home appliance maintenance solutions, which organically integrate nursing house and the IOT and cater to the intelligent background of the IOT. Finally, we can achieve the goal of intelligent nursing house.
Features of the design of animation graphics of educational multimedia edition on the discipline “Draft Geometry”

Abstract The article describes the theoretical foundations and practical methods of designing animation graphics for educational multimedia publications on the subject of descriptive geometry. The use of animated graphics as an effective means of visualizing volumetric-spatial models is considered. The effectiveness of the educational multimedia edition with elements of animated graphics in the educational process is investigated, a comparison is made with the traditional methods of teaching the discipline.
Generation and Use of Hints and Feedback in a Hilbert-Style Axiomatic Proof Tutor

Abstract This paper describes logax , an interactive tutoring tool that gives hints and feedback to a student who stepwise constructs a Hilbert-style axiomatic proof in propositional logic. logax generates proofs to calculate hints and feedback. We compare these generated proofs with expert proofs and student solutions, and conclude that the quality of the generated proofs is comparable to that of expert proofs. logax recognizes most steps that students take when constructing a proof. Even if a student diverges from the generated solution, logax still provides hints, including next steps or reachable subgoals, and feedback. With a few improvements in the design of the set of buggy rules, logax will cover about 80% of the mistakes made by students by buggy rules. The hints help students to complete the exercises.
Visual Tracking Using Wang–Landau Reinforcement Sampler

In this study, we present a novel tracking system, in which the tracking accuracy can be considerably enhanced by state prediction. Accordingly, we present a new Q-learning-based reinforcement method, augmented by Wang–Landau sampling. In the proposed method, reinforcement learning is used to predict a target configuration for the subsequent frame, while Wang–Landau sampler balances the exploitation and exploration degrees of the prediction. Our method can adapt to control the randomness of policy, using statistics on the number of visits in a particular state. Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance. Numerical results demonstrate that our method substantially outperforms other state-of-the-art visual trackers and runs in realtime because our method contains no complicated deep neural network architectures.
A Hybrid Algorithm to Solve Multi-model Optimization Problems Based on the Particle Swarm Optimization with a Modified Firefly Algorithm

Multi-model optimization brings out the concept of finding all or most of the multiple solutions of a problem, as opposed to a single best solution. Recently many researches have focused on finding the capability of swarm intelligence and evolutionary algorithms in solving such mathematical problems. This paper proposes a hybrid algorithm combining the particle swarm optimization algorithm (PSO) with a new modified firefly algorithm, which has been originally modified for the root finding purpose of nonlinear systems of equations (MODFA) to be used in multi-model optimization. The concept of the hybrid algorithm divides the solution into two parts where PSO is used to find one global optimum and with that the MODFA finds other optimal solutions as much as possible. Benchmark multi-model optimization problems with different dimensions have been used on the new algorithm to test its capability. Results obtained for the evaluation criteria demonstrate the suitability of the method. The results compared with several state-of-the-art multi-model optimization algorithms showed that the proposed hybrid algorithm performs competitively with these algorithms.
Tectonic‐magmatic framework identified by remote sensing analyses and geochronology in West Junggar, Central Asian Orogenic Belt

The temporal and spatial evolutions of the Darbut Fault deformation patterns were analysed for multiple deformation stages in this study by using a regional geological survey, quantitative remote sensing image analyses, and structural analyses. Remote sensing images indicating that the Karamay granitoid batholith is greenish‐grey, the Hongshan granitoid batholith is yellowish‐green along the Darbut Fault, and the Miaoergou, Akebasitao, and Hatu granitoid batholiths are round in shape and luminous yellow colour. The remote sensing images of the Darbut Fault, Hongshan granitoid batholith, and data from previous research suggest that the upright folds (F 1 ) in the Carboniferous strata near the Darbut Fault and northeast‐southwest (NE‐SW) striking sub‐vertical dextral shear zones in the mélanges represent the regional dextral strike‐slip shear zones. The NE‐SW striking transcurrent sinistral strike‐slip faults and ‘S‐shaped’ drag folds (F 2 ) overprinted the dextral shear zones. The Hongshan batholith is a complex pluton, which was composed of alkaline feldspar granite, syenite granite, and monzogranite. The LA‐ICP‐MS U–Pb 311–307 Ma of zircons from the ductilely deformed Hongshan alkaline feldspar granite and monzogranites are formed during the dextral shear zone of the Darbut Fault. The 302 Ma Hongshan alkaline feldspar granite is contemporarily formed during the sinistral strike‐slip stage of the Darbut Fault. The Hongshan complex pluton has a prolonged formation age and witnesses the whole process of ocean‐land conversion from the capture of island arc‐related rocks, granite formed in the post‐collision stage, and dyke swarms formed in the intracontinental extension stage.
MISMIS: Misinformation and Miscommunication in social media: aggregating information and analysing language

The general objectives of the project are to address and monitor misinformation (biased and fake news) and miscommunication (aggressive language and hate speech) in social media, as well as to establish a high quality methodological standard for the whole research community (i) by developing rich annotated datasets, a data repository and online evaluation services; (ii) by proposing suitable evaluation metrics; and (iii) by organizing evaluation campaigns to foster research on the above issues.
Fine-Grained Evaluation of Knowledge Graph Embedding Models in Downstream Tasks

Knowledge graph (KG) embedding models are proposed to encode entities and relations into a low-dimensional vector space, in turn, can support various machine learning models on KG completion with good performance and robustness. However, the current entity ranking protocol about KG completion cannot adequately evaluate the impacts of KG embedding models in real-world applications. However, KG embeddings is not widely used as word embeddings. An asserted powerful KG embedding model may not be effective in downstream tasks. So in this paper, we commit to finding the answers by using downstream tasks instead of entity ranking protocol to evaluate the effectiveness of KG embeddings. Specifically, we conduct comprehensive experiments on different KG embedding models in KG based recommendation and question answering tasks. Our findings indicate that: 1) Modifying embeddings by considering more complex KG structural information may not achieve improvements in practical applications, such as updating TransE to TransR. 2) Modeling KG embeddings in non-euclidean space can effectively improve the performance of downstream tasks.
Mining Latent Features of Knowledge Graphs for Predicting Missing Relations

Knowledge Graphs (KGs) model statements as head-relation-tail triples. Intrinsically, KGs are assumed incomplete especially when knowledge is represented under the Open World Assumption. The problem of KG completeness aims at identifying missing values. While some approaches focus on predicting relations between pairs of known nodes in a graph, other solutions have studied the problem of predicting missing entity properties or relations even in the presence of unknown tails. In this work, we address the latter research problem: for a given head entity in a KG, obtain the set of relations which are missing for the entity. To tackle this problem, we present an approach that mines latent information about head entities and their relations in KGs. Our solution combines in a novel way, state-of-the-art techniques from association rule learning and community detection to discover latent groups of relations in KGs. These latent groups are used for predicting missing relations of head entities in a KG. Our results on ten KGs show that our approach is complementary state-of-the-art solutions.