Reviewerly/exHarmony huggingface dataset:
PolarSig: An efficient digital signature based on polar codes

Code-based digital signatures suffer from two main drawbacks: large public key size and slow signature generation. Large public key size is inherent in almost all the code-based cryptosystems and other post-quantum alternatives; however, slow signature generation is due to their specific structure. Most of the current code-based signature schemes are constructed based on Courtois, Finiasz, and Sendrier (CFS) signature. CFS uses a counter to produce decodable syndromes or the complete decoding technique that imposes some extra computational cost to the signing algorithm for many choices of codes. In this study, the authors propose an efficient digital signature, PolarSig, which can reduce both public key size and signing time simultaneously. PolarSig uses some specific instances of polar codes that enable us to decode every random syndrome. Moreover, they apply puncturing and randomised omitting of frozen bits to protect the authors’ scheme from commonplace attacks targeting former cryptosystems based on polar codes. Besides, they prove that their signature is existentially unforgeable under a chosen message attack secure in the random oracle model.
Compiling Quantitative Type Theory to Michelson for Compile-Time Verification and Run-time Efficiency in Juvix

Michelson, the stack-based virtual machine of the Tezos blockchain, integrates type-checking for program execution completion but not program correctness. Manual stack tracking is efficient but less ergonomic to write in than a higher-level lambda calculus with variables. Compiling McBride’s Quantitative Type Theory to Michelson allows for compile-time verification of semantic predicates and automatic stack optimisation by virtue of the type-theoretic usage accounting system.
Design and Implementation of an Information Service System for Scientific and Technological Achievements Transformation Based on Spring Boot

Through the mobile cloud service platform of Heyuan University Town, a system is built to improve the level of community informatization of Heyuan University Town, improve everyone’s lifestyle, to facilitate the clothing, food, housing, and transportation of community residents, businesses and college students in the university town. The server side of the platform adopts spring boot technology, the mobile side adopts Android+HTML5 technology, and the front and back end data communication is carried out by using JSON key value to transmit value. The platform mainly provides such functional modules as cardio University Town, University mall, University Town BBS, my university, etc., to meet the information needs of different users in the university town community.
BUDI: Building Urban Designs Interactively Can Spatial-Based Collaboration be Seamless?

BUDI (Building Urban Designs Interactively) is an integrated 3D visualization and remote collaboration platform for complex urban design tasks. Users with different backgrounds can remotely engage in the entire design cycle, improving the quality of the end result. In BUDI, a virtual environment was designed to seamlessly expand beyond a traditional two-dimensional surface into a fully immersive three-dimensional space. Clients on various devices connect with servers for different functionalities tailored for various user groups. A demonstration with a local urban planning use-case shows the costs and benefits of BUDI as a spatial-based collaborative platform. We consider the trade-offs encountered when trying to make the collaboration seamless. Specifically, we introduce the multi-dimensional data visualization and interactions the platform provides, and outline how users can interact with and analyze various aspects of urban design.
Application of locally linear embedding algorithm on hotel data text classification

Abstract As a non-linear dimension reduction method, manifold learning algorithm projects high-dimensional input to a low-dimensional space by maintaining the local structure of the data, and discovers the inherent geometric structure hidden in the data. In this paper, we attempt to apply the manifold learning algorithm to the field of Chinese text classification, and use the locally linear embedding algorithm to reduce the dimension of the ctrip hotel review data set. Then, we utilize extreme gradient boosting (XGBoost) and logistic regression to classify the text. Experimental results show that it is effective and feasible to use manifold learning algorithm for text classification. Moreover, the classification effect of logistic regression is better than XGBoost in the text classification of hotel reviews.
Lexical Repository Development for Bugis, a Minority Language

The Bugis community migrated from Sulawesi, Indonesia and settled in the Malay Peninsula in the seventeenth century as a minority community. As a minority community in a new territory which was dominated by a more powerful community, the use of their mother tongue in communication within a family and other public domain has been non-dominant and has been shifted to Malay. With this regard, this research intends to investigate the factors that have led the Bugis community to shift their language to Malay. Due to language shift, the Bugis language has become non-dominant and is currently facing rapid attrition. Therefore, this research also attempts to contribute knowledge about Bugis language loss by developing a lexical repository for the language. In order to accomplish this motive, observation and interviews were carried out in a number of Bugis villages in Johore and Selangor in Malaysia; to find out the factors which led to the language shift. On the other hand, the development of the repository was done by collecting a large number of words through Rapid Word Collection (RWC) based on the semantic domains. Currently, as many as five thousand words have already been collected and included in the repository. The development of this lexical repository can help in storing as many Bugis lexical items as possible. Storing these items can help in preserving this minority language that is nearly extinct.
Authorship Attribution of Brazilian Literary Texts Through Machine Learning Techniques

Authorship attribution is the process of identifying the author of a particular document. This task has been performed by experts in the field. However, with the advancement of natural language processing tools and machine learning techniques, this activity has also been performed by computer systems. Authorship attribution has applicability from the detection of plagiarism and copyright to the resolution of forensic problems. There are several works on this subject in the English idiom, however those that consider texts in Portuguese are few. Therefore, this paper aims to study authorship attribution of texts of Brazilian literature. We carried out our experiments using Naïve Bayes and Random Forests methods, and for the feature extraction we considered Term Frequency - Inverse Document Frequency and Part of Speech techniques. The results showed that the Random Forests using as input the textual features extracted by Part of Speech presented the best cross-validation accuracy, although not the best runtime.
Usability in Mixed Initiative Systems

As computation becomes ubiquitous, our environments are enriched with new possibilities for communication and interaction. These possibilities involve various systems to aid the user in completing his tasks and one such system is Mixed-initiative system. Any system that interacts with humans can leverage the usability criteria. However, most of the existing work related to mixed-initiative systems discusses the effectiveness of the system instead of overall usability. In this paper, we first discuss the mixed initiative systems and then we consider the usability of mixed-initiative systems. This involves learnability, efficiency, error, memorability, and satisfaction, which are regarded as five facets of usability. This examination can be used as a guideline while designing a mixed-initiative system to improve overall usability of the system.
Adaptiveness and consistency of a class of online ensemble learning algorithms

Summary Expert based ensemble learning algorithms often serve as online learning algorithms for an unknown, possibly time‐varying, probability distribution. Their simplicity allows flexibility in design choices, leading to variations that balance adaptiveness and consistency. This article provides an analytical framework to quantify the adaptiveness and consistency of expert based ensemble learning algorithms. With properly selected states, the algorithms are modeled as a Markov chains. Then quantitative metrics of adaptiveness and consistency can be calculated through mathematical formulas, other than relying on numerical simulations. Results are derived for several popular ensemble learning algorithms. Success of the method has also been demonstrated in both simulation and experimental results.
Sub-aperture optical system testing by using of a modified simulateous fit method

Sub-aperture stitching interferometry can be used for measurement of wavefront of large aperture optical system. A variety of sub-aperture stitching algorithms have been studied to reconstruct the sub-aperture data to obtain full aperture wavefront. The simultaneous fitting method plays an important role among those stitching algorithms which uses a series of global polynomials to accomplish the fitting of the test wavefronts, however, it can only be applied in the case of there have no overlap between each sub-apertures. Therefore, a modified simultaneous fitting method is proposed and is applied to measure the wavefront of large aperture optical system. The proposed algorithm is applicable whether there exists overlap between each sub-aperture or not. The numerical simulation is carried out to evaluate the accuracy of the algorithm. Further, a practical stitching experiment that test an optical system with a diameter of 850mm was implemented to demonstrate the modified algorithm.
Research on Image Processing Technology of Dynamic Face Video Recognition Based on Computer Vision Monitoring

Abstract At present, face recognition methods are mainly used to identify static images. In surveillance video, faces in different video frames are correlated and only part of the face can effectively reflect face information; according to the characteristics of face image changes in surveillance videos, A face recognition method based on surveillance video is proposed; firstly, a video face sequence is obtained by combining face detection and tracking technology, and then a partial face image recognition result in the video face sequence is taken as a guide to select all face sequence images The representative face image in the image is recognized, and finally the face information is comprehensively reflected according to the recognition results of all the selected face images; the experimental results show that this method can effectively improve the face in the surveillance video while ensuring the recognition rate and the false recognition rate. Real-time recognition.
Dynamic Multi-hop Reasoning

Multi-hop reasoning is an essential part of the current reading comprehension and question answering areas. The reasoning methods have been extensively studied, and most of them are generally focused on the pre-retrieval based inference, with the help of a few paragraphs. These methods are fixed and unable to cope with dynamic and complex questions. Here, we propose to utilize the dynamic graph reasoning network for multi-hop reading comprehension question answering.Specifically, the new approach continuously infers the clue entities and candidate answers based on the question and clue paragraphs. The clue entities and candidate answers extracted at each hop are used as new nodes to expand the dynamic graph. Then we iteratively update the semantic representation of the questions via dynamic question memory, and apply the graph attention network to encode the information of inference paths. Extensive experiments on two datasets verify the advantage and improvements of the proposed approach.
Visual Analytics for Production Line Failure Detection

There are several situations where a company has to analyze data gathered from its production line (machines, raw materials, operations, warehouses, ...) to identify the cause of the situation and to react as soon as possible. One of these situations is the detection of production failures after a faulty product has been detected. Failure detection has always been a critical issue in the industrial sector, mainly because measures should be taken both to prevent them happening again, and to fix already finished or sold products. Access to production line data is required to know what has happened. Most companies store this data, at least by using an ERP software. However, it is complicated and human resource intensive to detect a failure just by looking to the raw data provided by an ERP. This paper focuses on this situation, supporting employees visualizing and navigating production line data. An application transforming production line data into a graph structure, and applying Visual Analytics and Big Data approaches, has been designed and developed. This application has been integrated with an existing ERP, Izaro from Group Zucchetti. Results have been positively evaluated, as they greatly improve existing tools to analyze and navigate production line data from the ERP.
Information Extraction from Public Meeting Articles

Public meeting articles are the key to understanding the history of public opinion and public sphere in Australia. Information extraction from public meeting articles can obtain new insights into Australian history. In this paper, we create an information extraction dataset in the public meeting domain. We manually annotate the date and time, place, purpose, people who requested the meeting, people who convened the meeting, and people who were convened of 1258 public meeting articles. We further present an information extraction system, which formulates information extraction from public meeting articles as a machine reading comprehension task. Experiments indicate that our system can achieve an F1 score of 74.98% for information extraction from public meeting articles.
Efficient Watermarking in Color Video Using DWT-DFT and SVD Technique

With the advancement in Internet technology, a digital video can be easily modified, copied, and distributed among a large audience. Copyright protection and security become very essential aspects because of the extensive use of digital multimedia applications. Digital watermarking is being used for copyright protection, data authenticity for multimedia contents such as image, audio, and video. In this paper, a DWT-DFT-SVD-based method is opted to improve robustness and overall computational requirements. The computed PSNR between original video signal and watermarked signal is improved up to 60 db. The normalized correlation value of the original and the extracted watermark image have a high level of imperceptibility. The proposed scheme shows strong robustness against several geometric and non-geometric attacks.
Design of Quantum Key Fusion Model for Power Multi-terminal

With the construction of State Grid informatization, professional data such as operation inspection, marketing, and regulation have gradually shifted from offline to online. In recent years, cyberspace security incidents have occurred frequently, and national and group cybersecurity threats have emerged. As the next-generation communication system, quantum security has to satisfy the security requirements. Also, it is especially important to build the fusion application of energy network quantum private communication technology and conventional network, and to form a safe and reliable quantum-level communication technology solution suitable for the power grid. In this paper, from the perspective of the multi-terminal quantum key application, combined with a mature electricity consumption information collection system, a handheld meter reading solution based on quantum private communication technology is proposed to effectively integrate the two and achieve technological upgrading. First, from the technical theory and application fields, the current situation of quantum private communication technology and its feasibility of combining with classical facilities are introduced and analyzed. Then, the hardware security module and handheld meter reading terminal equipment are taken as typical examples to design and realize quantum key shared storage, business security process application model; finally, based on the overall environment of quantum key distribution, the architecture design of multi-terminal quantum key application verification is implemented to verify the quantum key business application process.
Mirage Net

Benefit from feature presentation with huge parameters and high GPU computing resources, deep convolution neural network has been widely studied in image denoising due to its considerable denoising performance. However, these parameters will consume quantities of memory and computing resources, meanwhile, lots of them are correlated and redundant. We propose a low calculation cost and fast denoising convolution neural network, namely Mirage Net, inspired by the natural phenomenon of mirage. Based on our refraction convolution, which is the combination of depth-wise and point-wise convolution, Mirage Net can reduce parameter redundancy and learn effective presentations from one-layer deeper feature maps by cheap cost linear transformations which will be concatenated with previous feature maps as input of the next convolution layer. We also use alternating training strategy with multi-loss which accelerate the training processing and convergence rate. Our experiments on public datasets show that Mirage Net can achieve higher quality denoised images than DnCNN, and furthermore, the calculation cost is only half of them.
Study on the application of elementary number theory in conflict algorithm

Abstract Analyze the time complexity of the algorithm, for multiple threads to execute multiple transactions at the same time, and map the result of the remainder of the prime number to the hash address. After calculation, the formula is a convergent array. The larger the value of x, the smaller the calculated value, indicating that the algorithm can complete the task in a certain limited time. Applying the probability of random prime numbers to the design of conflict management algorithms can avoid endless waiting for each other between transactions, and also avoid the waste of computing resources caused by cancelling earlier transactions. Compared with the radical algorithm of direct termination, this algorithm gives the transaction a certain waiting and retry time, avoids the long execution of the long transaction, and guarantees the commit time of the transaction.
Tell a Tail 360$$^{\circ }$$: Immersive Storytelling on Animal Welfare

Immersive technologies aligned with storytelling can create novel and powerful tools to inform and ponder on social issues. This demo paper describes a cinematic virtual reality project, Tell a Tail 360 $$^{\circ }$$ , on the rescue of abandoned companion animals. Tell a Tail 360 $$^{\circ }$$ was designed targeting teens (13–19 years old) and its inclusion in a classroom setting. By using 360 $$^{\circ }$$ videos of a rescue kennel, animal hospital and the field work of non-governmental organizations, we intend for the target audience to be exposed to different animal welfare issues relevant to their context.
Operationalization of Information Acquisition Switching Behavior in the Context of Idea Selection

Decision makers ought to adapt their information acquisition (IA) contingent to the task, which has not yet been investigated in the context of idea selection. Therefore, this paper suggests an operationalization of IA switching behavior using eye-tracking data. A first data analysis indicates that raters switch between modes of high and low IA in an idea selection task. These modes of IA could be associated with compensatory and non-compensatory information integration. The extent of switches between IA modes seems to stay stable between the first and the second half of the task with a slight decreasing trend towards the end. Future research will add cognitive load to explain occurring switches between different IA modes and may allow to deduce recommendations for more efficient IT designs, preserving rater’s cognitive resources.
Designing for Social Transformation with Alternative Infrastructures

Emerging "decentralized," peer-to-peer, and offline-first alternatives to existing digital communications have gained attention alongside increased calls to action around the current state of the internet. As new technologies that delegate decision-making and action away from a central authority, these alternatives seek to redistribute power within online communities and radically rethink networks. My dissertation focuses on a selection of decentralization projects in order to understand the potentials for social transformation that value-driven design practices hold. Further, my research provides a rich account of the networked contingency this present wave of decentralization arises out of, along with tracing the way the concept operates as a sense-making frame for those building and using these new infrastructures.
High precision monoscopic fringe reflectometry based on a reference plane

Fringe reflectometry is a favorite method for 3D measurement of specular surfaces with the advantages of simple structure, low cost and high precision. The traditional reflectometry method is based on the telecentric optical path assumption. When the surface has a large diameter or curvature, this assumption is hard to be guaranteed. In addition, the non-sinusoidal distortion of fringes will also introduce significant phase errors and degrade the measurement accuracy. To avoid these problems, a high precision monoscopic fringe reflection measurement model is proposed based on a reference plane. The relative phase change between the measured surface and the reference plane is used to get the accurate corresponding screen points for each point on camera imaging plane. With these correspondence point pairs, the normal vectors and the 3D geometry can be obtained. To validate the effectiveness of the proposed method, a concave spherical mirror was measured. The peak to valley value of the residue error can reach 0.784μm which demonstrates high accuracy of the method.
Salient object detection via multi-spectral co-connectivity and collaborative graph ranking

Salient object detection (SOD) has become an active research direction with extensive applications in computer vision tasks. Although integrating RGB and infrared thermal (RGB-T) data has proven to be effective in adverse environments, it is difficult for RGB-T SOD methods to highlight the salient objects completely when objects cross the image boundary. To address the aforementioned problem, this paper proposes an effective RGB-T SOD algorithm based on multi-spectral co-connectivity (MSCC) and collaborative graph ranking. Specifically, we introduce the multi-spectral weighted color distance to construct an improved undirected weighted graph and compute the MSCC-based saliency map. Simultaneously, the MSCC-based background probability map is also calculated and employed in the following processing of real background seeds selection. Then, we utilize collaborative graph learning (CGL) and calculate the CGL-based saliency map in a two-stage ranking framework. Finally, we integrate these two saliency maps through multiplying or averaging to enhance the final saliency result. The experimental comparison results of 5 quantitative evaluation indicators between the proposed algorithm and 9 state-of-the-art methods on RGB-thermal datasets VT821 and VT1000 datasets demonstrate the robustness and superiority of the proposed work.