Reviewerly/exHarmony huggingface dataset:
Forcing-Seq2Seq Model: An Automatic Model of Title Generation for Natural Text Using Deep Learning

With the rapid development of social media channels, many popular users have posted several high-quality writings as casual blogs, notes or reviews. Some of them are even selected by editors to be published in professional venues. However, the original posts often come without titles, which are needed to be manually added by the editing teams. This task would be done automatically, with the recent advancement of AI techniques, especially deep learning. Even though auto-title can be considered as a specific case of text summarization, this job poses some major different requirements. Basically, a title is generally short but it needs to capture major content while still maintaining the writing style of the original document. To fulfill those constraints, we introduce Forcing-Seq2Seq Model, an enhanced Seq2Seq architecture, in which the classical TF-IDF scores are incorporated with Named Entity Recognition method to identify the major keywords of the original texts. To enforce the appearance of those keywords in the generated titles, the specific Teacher Forcing mechanism combined with the language model technique is employed. We have tested our approach with real datasets and obtained promising initial results, on both metrics of machine and human perspectives.
Gamma Correction Method Based on Phase-shifting Keying

Phase Measurement Profilometry (PMP) plays an important role in reconstructing three-dimensional surface of objects. However, the reconstructed profile error is always resulted from gamma distortion. In this paper, a simple and efficient method is proposed to solve the gamma distortion. Firstly, the fringe mapping is analyzed via Gamma distortion model. Then, based on the phase-shifting keying (PSK), two sets of phase-shifting patterns of different phase are generated separately. It shows that the effect of the second harmonic can be eliminated by adding the corresponding captured fringe patterns. Finally, the accurate wrapped phase can be obtained. The proposed method does not require the phase error compensation and the pre-calibrated procedure. Experiments show that the proposed method has good performance in terms of accuracy and correction effect.
CLOTON: A GAN based approach for Clothing Try-On

Cloton is a framework that produces a picture of a person wearing the target clothing while taking into account arbitrary body shapes and poses. As the generation of an individual image necessitates the extraction of body shape and pose from one image and target clothing from another, the architecture is divided into a number of tasks. With these two images as input, the system generates a new picture of the user wearing the target clothing using a generative network. The output is this generated image, which is then sent back to the user. By using encoder-decoder networks as the generator model and training it using the generative adversarial approach used by GANs, Cloton manages to generate visually appealing garment swapped images of the user. Cloton also has good results in the quantitative analysis of the system as compared to previous works.
Practically Efficient Attribute-Based Encryption for Compartmented and Multilevel Access Structures

Compartmented access structures (CASs) regulate the access control by requesting the consent of various compartments. Thus, they are particularly useful to the Internet of Things or Wireless Sensor Networks applications with cloud support. The construction of practically efficient attribute-based encryption (ABE) schemes for CASs is faced with the fact that these access structures cannot be represented by Boolean formulas. The use of multilinear map based ABE schemes for general Boolean circuits is not only impractical but also suffers from the lack of secure multilinear map candidates. Also, the schemes based on lattice cryptography, even if they are secure, are highly inefficient in practice. We show in this paper that for CASs we can construct practically efficient ABE schemes based on secret sharing and just one bilinear map. The construction can also be applied to multilevel access structures. The security proof is performed in a general context that can apply to other similar access structures.
AO-AutoTrack: Anti-occlusion Real-Time UAV Tracking Based on Spatio-temporal Context

Correlation filter (CF)-based methods have shown extraordinary performance in visual object tracking for unmanned aerial vehicle (UAV) applications, but target occlusion and loss is still an urgent problem to be solved. Aiming at this problem, most of the existing discriminant correlation filter (DCF)-based trackers try to combine with object detection, thereby significantly improving the anti-occlusion performance of the visual object tracking algorithm. However, the significantly increases the computational complexity and limits the speed of the algorithm. This work proposes a novel anti-occlusion target tracking algorithm using the target's historical information, i.e., the AO-AutoTrack tracker. Specifically, after the target is occluded or lost, the target information contained in the spatio-temporal context is used to predict the subsequent position of the target, and the historical filter is employed to re-detect the target. Extensive experiments on two UAV benchmarks have proven the superiority of our method and its excellent anti-occlusion performance.
Interactive Theorem Proving for Logic and Information

Automated reasoning is the study of computer programs that can build proofs of theorems in a logic. Such programs can be either automatic theorem provers or interactive theorem provers. The latter are also called proof assistants because the user constructs the proofs with the help of the system. We focus on the Isabelle proof assistant. The system ensures that the proofs are correct, in contrast to pen-and-paper proofs which must be checked manually. We present applications to logical systems and models of information, in particular selected modal logics extending classical propositional logic. Epistemic logic allows intelligent systems to reason about the knowledge of agents. Public announcements can change the knowledge of the system and its agents. In order to account for this, epistemic logic can be extended to public announcement logic. An axiomatic system consists of axioms and rules of inference for deriving statements in a logic. Sound systems can only derive valid statements and complete systems can derive all valid statements. We describe formalizations of sound and complete axiomatic systems for epistemic logic and public announcement logic, thereby strengthening the foundations of automated reasoning for logic and information.
The German EU Council Presidency Translator

Abstract This contribution describes the German EU Council Presidency Translator (EUC PT), a machine translation service created for the German EU Council Presidency in the second half of 2020, which is open to the general public. Following a series of earlier presidency translators, the German version exhibits important extensions and improvements. The German EUC PT is the first to integrate systems from commercial vendors, public services, and a research center, using a mix of custom and generic translation engines, and to introduce a new webpage translation widget. A further important feature is the close collaboration with human translators from the German ministries, who were provided with computer-assisted translation tool plugins integrating machine translation services into their daily work environments. Uptake by the public reflects a huge interest in the service, showing the need for breaking language barriers.
Coupling Smart Contracts: A Comparative Case Study

When software systems become more complex, it can be advantageous to partition their code into multiple, separate components. In this work, we examine how multiple smart contracts can be coupled to work together. When coupling smart contracts, different design approaches are possible with their own advantages and disadvantages. As an example, we couple two smart contract applications on the Ethereum blockchain: Palinodia and DecentID. Palinodia can be used to ensure the integrity of downloaded executable binaries by checking their hashes against the hashes stored in the blockchain. To make sure that not everyone can modify the data stored on the blockchain, an identity management system is required. This task is fulfilled by DecentID, which provides decentralized identities stored as smart contracts on the blockchain. We evaluate approaches of coupling these two applications and discuss their benefits and drawbacks for this use case.
A new approach to probabilistic classification based on Gaussian process and support vector machine

This paper proposes three algorithms that combine the Support Vector Machine (SVM) and Gaussian Process (GP) in a unified framework to classify large datasets efficiently and obtain probabilistic information on the classification results. The first algorithm works in two steps. In Step 1, the algorithm judiciously selects a sample of size m from the training dataset of size n, where m ≪ n. Step 2 then runs a Gaussian Process Classification (GPC) on the selected sample. The second algorithm is based on the first one, where instead of a standard GPC, a sparse GPC (a low-cost variant of standard GPC) is run in Step 2. The third algorithm is based on manipulating the Gaussian Process Regression (GPR) to classify the data. Unlike GPC, GPR uses an exact inference method that greatly reduces the computational complexity. We have experimented with seven datasets to compare the performance of the proposed algorithms with the existing state-of-the-art methodologies. In addition to providing probabilistic information, the proposed algorithms have proved to be computationally efficient, especially in the training phase, where they consistently deliver faster results than the existing algorithms. The accuracies provided by the proposed algorithms are satisfactory, and at times even surpass the accuracy obtained by the existing algorithms.
A Survey on Statistical Approaches for Abstractive Summarization of Low Resource Language Documents

Text summarization is an important application of natural language processing (NLP). A huge amount of data is generated everyday through the internet, newspapers, etc. Quick understanding of the documents helps reader to save time, retains interest in the reading, and provides the clarity of the content. Text summarization facilitates this by two approaches—Extractive and Abstractive. Where extractive approach retains the key phrases and key sentences in the document, abstractive approach focuses on generation of new summary sentences by understanding the crux of document. Summary generation becomes more challenging in case of low resource language documents, as low resource documents lack the large corpora. This paper intends to analyze and compare the techniques used for the abstractive summarization of low resource languages.
Parallel Peeling of Bipartite Networks for Hierarchical Dense Subgraph Discovery

Wing and Tip decomposition are motif-based analytics for bipartite graphs that construct a hierarchy of butterfly (2,2-biclique) dense edge and vertex induced subgraphs, respectively. They have applications in several domains, including e-commerce, recommendation systems, document analysis, and others. Existing decomposition algorithms use a bottom-up approach that constructs the hierarchy in an increasing order of the subgraph density. They iteratively select the edges or vertices with minimum butterfly count peel , i.e., remove them along with their butterflies. The amount of butterflies in real-world bipartite graphs makes bottom-up peeling computationally demanding. Furthermore, the strict order of peeling entities results in a large number of sequentially dependent iterations. Consequently, parallel algorithms based on bottom up peeling incur heavy synchronization and poor scalability. In this article, we propose a novel Parallel Bipartite Network peelinG (PBNG) framework that adopts a two-phased peeling approach to relax the order of peeling, and in turn, dramatically reduce synchronization. The first phase divides the decomposition hierarchy into few partitions and requires little synchronization. The second phase concurrently processes all partitions to generate individual levels of the hierarchy and requires no global synchronization. The two-phased peeling further enables batching optimizations that dramatically improve the computational efficiency of PBNG. We empirically evaluate PBNG using several real-world bipartite graphs and demonstrate radical improvements over the existing approaches. On a shared-memory 36 core server, PBNG achieves up to 19.7× self-relative parallel speedup. Compared to the state-of-the-art parallel framework P ar B utterfly , PBNG reduces synchronization by up to 15,260× and execution time by up to 295×. Furthermore, it achieves up to 38.5× speedup over state-of-the-art algorithms specifically tuned for wing decomposition. Our source code is made available at https://github.com/kartiklakhotia/RECEIPT .
Research of Robust Video Object Tracking Algorithm Based on Jetson Nano Embedded Platform

The majority of recent work has tended to improve the overall tracking capability of trackers. Despite the success of these methods, their inherent complexity has limited their scope of application. In contrast, it is more practical to improve the performance on embedded platforms without increasing the complexity of tracking algorithms. Limited by the computing power of the embedded system, this paper proposed an interesting tracker based on the Kernel correlation filter (KCF). To tackle the occlusion and disappearance problem during object tracking, we suggest a lightweight object detection network to relocate the target by using color features and then reinitialize the tracker. In addition, we avoid the problem of scale variation in object tracking by adaptively cross-linking the KCF algorithm with the embedded platform. The proposed method improves the performance of KCF tracker on embedded platform without increasing its complexity and achieves robust tracking of video targets. We have captured a lot of videos and done a lot of experiments to prove the effectiveness of our method.
Development of Pop-up Book Based on QR Code Solid Figure Material with Character Strengthening Curiosity in 5th-Grade Elementary School

This study produced a pop-up book based on QR code solid figure material with character strengthening curiosity in 5 elementary schools. The ADDIE model is used in this research and development. All grade 5<sup>th</sup> students of SD Negeri 2 Mojorembun, Nganjuk Regency were subjects of this study. The material experts, media experts and conduct validity tests by achieving very valid category results. At the same time, the practical test obtains a very high practical value and reaches the convenient category. The product&#x0027;s development results received a good response and enthusiasm from students in the learning activities. The results of the response questionnaire showed that the product developed practically used for learning activities and strengthened the character of curiosity based on the observation attitudes of students in the teaching and learning activities with excellent criteria. In addition, the use of product development can improve students&#x0027; learning outcomes and provide an understanding of the material of solid figures. It is known from the value they get when working on exercises and getting an average score above the minimum completion criteria to learn the material of solid figures well and maximally.
A Scalable Architecture for Electronic Payments

We present a scalable architecture for electronic retail payments via central bank digital currency and offer a solution to the perceived conflict between robust regulatory oversight and consumer affordances such as privacy and control. Our architecture combines existing work in payment systems and digital currency with a new approach to digital asset design for managing unforgeable, stateful, and oblivious assets without relying on either a central authority or a monolithic consensus system. Regulated financial institutions have a role in every transaction, and the consumer affordances are achieved through the use of non-custodial wallets that unlink the sender from the recipient in the transaction channel. This approach is fully compatible with the existing two-tiered banking system and can complement and extend the roles of existing money services businesses and asset custodians.
Fundamentals of metric theory of real numbers in their $\overline{Q_3}$-representation

In the paper we were studied encoding of fractional part of a real number with an infinite alphabet (set of digits) coinciding with the set of non-negative integers. The geometry of this encoding is generated by $Q_3$-representation of real numbers, which is a generalization of the classical ternary representation. The new representation has infinite alphabet, zero surfeit and can be efficiently used for specifying mathematical objects with fractal properties.&#x0D; We have been studied the functions that store the "tails" of $\overline{Q_3}$-representation of numbers and the set of such functions,some metric problems and some problems of probability theory are connected with $\overline{Q_3}$-representation.
Re-Identify Deformable Targets for Visual Tracking

Visual tracking is an important topic in computer vision, where the methods extracting features from the target's appearance have made significant progress. However, deformation causes an entirely different appearance from the original target, which reduces the precision of target's position. In this paper, we propose a novel method, named re-identify deformable targets for visual tracking (ReDT), to localize the target with a reliable appearance. Specially, we utilize a detector to achieve the candidates in the keyframes, and these candidates have the possibility to contain the reliable appearance of the deformable target. After that, we propose a similarity estimation to evaluate the candidates and determine a new target appearance, which fuses appearance with spatial location and category information to improve both inter-class and within-class discrimination. Extensive experiments and results demonstrate the effectiveness and advancement of the proposed method on two long-term tracking datasets, LaSOT and VOT-2018 LTB35, and three routine challenging tracking datasets, GOT-10k, OTB2015, and UAV123.
Residual Multi-resolution Network for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is an important tool to improve the quality of HSIs for subsequent tasks. In this paper, we propose a novel method based on Residual Multiresolution Network (RMRNet) for HSI denoising, which exploits multiscale information better from multiresolution versions of HSIs produced by pixelshuffle operation. The convolutional neural network (CNN) is used for extracting the spatial information among different resolution HSI, respectively. Enhanced representation will be obtained by fusing these multiresolution features. Wide receptive fields are provided by dilated convolution. Spectral information is also considered in the proposed network. To ease the flow of low-frequency information, we use a residual structure in our method. In addition, the experiment results on the simulated dataset demonstrate the superiority of our RMRNet.
Automatic Analysis of RPA in Digital Service Testing of Grid System

As science and technology in China has continuously developed, more and more programs and technologies are moving towards automation in power companies, especially in power digital service systems. The use of automated programs such as artificial intelligence can effectively improve the service capabilities of grid companies and meet the daily needs of enterprises and provide better services to users. The function of RPA is important in the digital service of grid system. With the use of function of RPA, the digital power service can be fully realized, grid enterprises can be effectively assisted in power system maintenance, power dispatching, real-time power grid monitoring, fault alarm processing and digital service functions It has completely changed the traditional power grid service system by combining with grid system and is an important reform of grid system. Therefore, this paper mainly elaborates the concept, development status and development prospects of RPA, and optimizes the difficulties of RPA during the testing to make it run stably.
Application of Optimization and Machine Learning for Sentiment Analysis

Sentiment analysis is called detecting emotions extracted from text features and is known as one of the most important parts of opinion extraction. Through this process, we can determine if a script is positive, negative or neutral. In this research, sentiment analysis is performed with textual data. A text feeling analyzer combines natural language processing (NLP) and machine learning techniques to assign weighted assessment scores to entities, subjects, subjects, and categories within a sentence or phrase. In expressing mood, the polarity of text reviews could be graded on a negative to positive scale using a learning algorithm.&#x0D; The current decade has seen significant developments in artificial intelligence, and the machine learning revolution has changed the entire AI industry. After all, machine learning techniques have become an integral part of any model in today's computing world. However, the ensemble to learning techniques is promise a high level of automation with the extraction of generalized rules for text and sentiment classification activities. This thesis aims to design and implement an optimized functionality matrix using to the ensemble learning for the sentiment classification and its applications.
Improved non-autoregressive dialog state tracking model

Dialogue systems, a powerful tool of human-machine interaction, are widely applied in e-commerce, online education, and cellphone assistant, etc. Dialogue state tracking (DST), updating the state of user goals during dialogue, is a core part of task-oriented dialogue systems. Recent research has made progress in low-latency and good-performance DST neural network models, i.e., non-autoregressive dialogue state tracking model (NADST). However, there are still some rooms for improvement in dialogue state tracking. In this paper, we propose following ways to improve the efficiency of NADST: (1) adding shrinkage residual network into fertility prediction; (2) constructing residual connection between different hierarchical attentions; (3) inserting a relative position encoding into state decoder for improving the performance of state prediction. The results of analysis and experiments indicate that the proposed model is the SOTA non-autoregressive method of dialog state tracking.