Reviewerly/exHarmony huggingface dataset:
A Semantic Approach for Domain-Specific Design Patterns Recommendations in CMS-based Web Development

Today's web designers are encountering multiple issues regarding website development within specific application domains. These issues can be successfully addressed using Domain-Specific Design Patterns, enhancing the final deliverable quality for the domain under consideration. Having in mind the benefits of using web design patterns, domain ontologies can also complement their identification within the context of a specific application domain. They define the underlying semantic context of a domain, by encapsulating the domain knowledge. To this end, an approach is proposed to support the automated recommendation of candidate web design patterns relying on the respective domain ontology. The website designs, occurring in a particular application domain, are automatically analyzed so that all recurrent patterns among them can be detected. These patterns are validated and matched against domain's ontology, and subsequently evaluated and categorized. Finally, the best-rated design solutions are recommended as candidate design patterns for addressing typical domain design problems. Ultimately, it becomes easier for domain designers to recognize best practices among them and thus identify domain-specific design patterns.
Recursive Neural Text Classification Using Discourse Tree Structure for Argumentation Mining and Sentiment Analysis Tasks

This paper considers sentiment classification of movie reviews and two argument mining tasks: verification of political statements and categorization of quotes from an Internet forum corresponding to argumentation (factual or emotional). In the case of the fact-checking problem, justifications can be used additionally in one of its sub-tasks. A strong model for solving these and similar problems still does not exist. It requires the style-based approach to achieve the best results. The proposed model effectively encodes parsed discourse trees due to the recursive neural network. The novel siamese model based on it is suggested to analyze discourse structures for the pairs of texts. In the paper, the comparison with state-of-the-art methods is given. Experiments illustrate that the proposed models are effective and reach the best results in the assigned tasks. The evaluation also demonstrates that discourse analysis improves quality for the classification of longer texts.
An Effective Knowledge-Based Modeling Approach towards a “Smart-School Care Coordination System” for Children and Young People with Special Educational Needs and Disabilities

There is a significant need for a computer-aided modeling, effective information analysis and ontology knowledge base models to support both special needs children and care providers. As this research work correlated to the symmetry scope, it proposes an innovative generic smart knowledge-based “School Care Coordination System” (SCCS), which is established on a novel holistic six-layered data management model. The development of the Smart-SCCS adopts a methodology of ontology engineering to transform the given theoretical unstructured special educational needs and disabilities (SEND) code of practice into a comprehensive knowledge representation and reasoning system. The intended purpose is to deliver a system that can coordinate and bring together education, health and social care services into a single application to meet the needs of children and young people (CYP) with SEND. Moreover, it enables coordination, integration and monitoring of education, health and social care activities between different actors (formal, informal and CYP in the education sector) involved in the school care process network to provide personalized care interventions based on a predefined care plan. The developed ontology knowledge-based model has been proven efficient and solved the enormous difficulties faced by schools and local authorities on a daily basis. It enabled the coordination of care and integration of information for CYP from different departments in health, social care and education. The developed model has received significant attention with great feedback from all the schools and the local authorities involved, showing its efficiency and robustness.
A Generic Construction of Predicate Proxy Key Re-encapsulation Mechanism

Proxy re-encryption (PRE), formalized by Blaze et al. in 1998, allows a proxy entity to delegate the decryption right of a ciphertext from one party to another without obtaining the information of the plaintext. In recent years, many studies have explored how to construct PRE schemes that support fine-grained access control for complex application scenarios, such as identity-based PRE and attribute-based PRE. Besides, in order to achieve more flexible access control, the predicate proxy re-encryption (PPRE) is further studied. However, existing PPRE is restricted with the inner product predicate function. Therefore, how to realize the PPRE of arbitrary predicate function is still a problem to be solved. In this manuscript, we propose a secure generic construction of predicate proxy key re-encapsulation mechanism built from a “linear” predicate key encapsulation mechanism. Since the secure key encapsulation mechanism can be used as a building block to construct public key encryption, we can obtain a PPRE from our construction. As a result, the results open up new avenues for building more flexible and fine-grained PPRE.
A Discover of Class and Image Level Variance Between Different Pruning Methods on Convolutional Neural Networks

Neural network pruning techniques have been widely used due to their little deterioration to test set accuracy while removing a great amount of weights in a network. Recent research [1] has shown that pruning impacts classification of classes and images differently even in one task. In this paper, we dive more along this line and find that different kinds of pruning methods will have different influences on classes and images, but pruning methods belonging to the same family will have a similar influence. Specifically, using iterative L1 unstructured pruning gets the least deviation for classes' accuracy from the overall accuracy and structured pruning is more likely to lead to high deviation. These findings show that choice of pruning methods can be quite nuanced and should be treated cautiously before it is used in sensitive domains.
PZXG: A Genealogy Data Service Platform for Kinship Management and Application

Genealogy is one of the three kinds of historical literatures which are valuable to historical research and humanities research. However, traditionally, it is hard to build and revise a genealogy. And it is even impossible to manage and query the genealogy efficiently. In this paper, we proposed PZXG, which is a genealogy data service platform, to solve these issues. Firstly, for structured and unstructured genealogy data, we employ relational database, graph database and distributed file system to store them respectively. Secondly, depending on the hybrid data model of genealogy data, various management features like data acquisition, kin seeking, genealogy exhibition and automatic typesetting are provided. Then in order to extract knowledge from genealogy data, the features of routine statistics and query, root tracing, correlation analysis and data visualization in genealogical data are discussed, and several methods and ideas of text mining for genealogical text are given. Finally, two more urgent research points in the genealogical data management platform are suggested: extracting structural information from unstructured genealogy data and construction of genealogy-specific text language model.
Approximate Matching of Spatiotemporal RDF Data by Path

Due to an ever-increasing number of RDF data with time features and space features, it is an important task to query efficiently spatiotemporal RDF data over RDF datasets. In this paper, the spatiotemporal RDF data contains time features, space features and text features, which are processed separately to facilitate query. Meanwhile the decomposition graph algorithm and the combination query paths algorithm are designed. The query graph with spatiotemporal features is split into multiple paths, and then every path in the query graph is used to search for the best matching path in the path sets contained in the data graph. Due to the existence of inaccurate matchings, approximate matchings are performed according to the evaluation function to find the best matching path. Finally, all the best paths are combined to generate a matching result graph. Our approach is evaluated from approximate performances and query performances. The experimental results show that the effectiveness and efficiency of our method.
Parameter identification of a nonlinear radial basis function‐based state‐dependent autoregressive network with autoregressive noise via the filtering technique and the multiinnovation theory

Summary This article studies the parameter estimation problems of radial basis function‐based state‐dependent autoregressive models with autoregressive noises (RBF‐ARAR models). To reduce the effect of the colored noise to parameter estimation, the data filtering technique is applied and a filtering based generalized stochastic gradient algorithm is derived for the RBF‐ARAR models. In order to achieve more accurate parameter estimates, a filtering based multiinnovation generalized stochastic gradient (F‐MI‐GSG) algorithm is proposed by utilizing the current and past innovations. Introducing two forgetting factors, a filtering based multiinnovation generalized forgetting gradient algorithm is developed to improve the transient performance of the F‐MI‐GSG algorithm. The effectiveness of the proposed algorithms is verified through the simulation examples.
3D face modeling from single image based on discrete shape space

Abstract In this article, we propose a novel 3D face modeling method which constructs a new 3D face model from a low‐dimensional feature space consisted of a large set of blend shapes based on the discrete shape space theory. The details of original face features are completely retained during the modeling process and a large number of new natural faces are constructed by several face samples. The optimization process of our method is independently decoupled for different facial attributes (identity, expression, and head pose), which improves the application flexibility and reduces the probability of it falling into a local optimal situation. The new facial data with new attributes are constructed based on the geodesic path search in discrete shape space with sufficient freedom and accuracy. In experiments and applications based on public databases (Helen, LFW, and CUFS), the modeling results show our method can provide high‐quality 3D face model, with enough freedom for face expression editing and natural facial expression animation from a small facial sample set.
Neural Restaurant-aware Dish Recommendation

Food is the first necessity of the people. Due to the fast-paced modern life, people usually choose to dine out for convenience. While existing methods have paid efforts for the food recommendation, they are mainly limited in inferring users' personal preferences for online recipes, and ignore the dish ordering process in dine-out scenarios. Given the same recipe, different restaurants may produce various tastes due to food cuisines or chefs' cooking habits. In the current restaurant, users' general favored dish may have bad word-of-mouth. Thus, apart from their personal taste preferences, users also turn to restaurant specialties to guarantee the dish quality. As such, the restaurant-related dish quality and users' personal taste should be considered simultaneously. To address this task, we propose a neural restaurant-aware dish recommender to infer users' preferences for dishes in a specific restaurant. Given a dish in the current restaurant, whether to order it or not is mainly decided by two factors: users' personal taste and the dish quality in this restaurant. Our proposed model can: 1) capture users' personal diet preferences by the strong expressiveness of neural networks; 2) evaluate how good the current restaurant is at cooking certain dishes. To show the effectiveness of our proposed model, we conduct extensive experiments on a real dataset, demonstrating significant improvements over the several competing models, such as NCF with an average improvement of 36%, and PITF with 3.4%.
Mapping BPMN2 Service Choreographies to Colored Petri Nets

Nowadays, software systems are often built by reusing and integrating existing services distributed over the Internet. Service choreography is a service engineering approach to compose together and coordinate services by specifying their external interactions in terms of flows of peer-to-peer message exchanges, given from a global perspective. BPMN2 offers a dedicated notation, called Choreography Diagrams, to specify service choreographies. However, BPMN2 specifications lack formal semantics causing some misinterpretations by practitioners and researchers. Colored Petri Net (CPN) is a formally proved notation with mathematical semantics and tool support for different analysis techniques. In this paper, we present a mapping to transform BPMN2 Choreography Diagrams into Colored Choreography Nets (CCNs). The latter is a CPN for enabling simulation and analysis of servicechoreographies.
Blockchain Based Full Privacy Preserving Public Procurement

Public procurement is one of the government activities most prone to corruption, and e-procurement systems have been recommended to increase transparency, outreach, and competition. Other benefits include ease of access to public tenders and easier detection of irregularities. One of the main challenges with the existing e-procurement/auction systems is ensuring bid privacy of the losing bidders and collusion between bidders and auctioneer. Most of the auction systems, proposed, depend either upon auctioneer(s) or on the trusted third party, which, according to us, is the biggest problem for addressing corruption. We propose a blockchain based solution for Public Procurement, which eliminates auctioneers/third-parties using secure multi-party computation (MPC). Our solution fully preserves bid privacy and is secure against malicious bidders.
Continuous-discrete trajectory PHD and CPHD filters

This paper presents the continuous-discrete trajectory probability hypothesis density (CD-TPHD) and the continuous-discrete trajectory cardinality PHD (CD-TCPHD) filter. We consider continuous-time models for target appearance, dynamics and disappearance, which are discretised to obtain the corresponding continuous-discrete models. The CD-TPHD filter propagates a Poisson point process approximation to the posterior (multi-trajectory) density over the set of alive trajectories sampled at the time instants when the measurements have been taken. The CD-TCPHD filter proceeds analogously but propagating a density on an independent and identically distributed (IID) cluster process. An important, novel feature of these filters is that they can infer the time of appearance and the state at appearance time of the trajectories in continuous time, not being constrained to discretised time steps.
Salient object detection via light-weight multi-path cascaded networks

Recently, deep learning-based saliency detection has achieved fantastic performance over conventional works. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. It is difficult for a network to learn saliency at the boundaries of salient regions. In this paper, a Light-Weight Multi-Path Cascaded Network (LMCN) is proposed for image saliency detection – an encoder-decoder architecture that explicitly exploits all the information available along the down-sampling process to enable full-resolution prediction using long-range residual connections. The Res2Net is adopted as a backbone network to better extract multi-level and multi-scale feature maps by constructing hierarchical residual-like connections within one single residual block. Also, the network is lightweighted by the bottleneck structure and depth-wise convolution, which ultimately reduces the number of parameters by more than 50% while maintaining the same performance. Comprehensive experiments are carried out and new state-of-the-art results are set in eight public datasets. Experiment results show that the proposed approach substantially improves previous state-of-the-art performances.
Exploring the Effects of Role Design on Agent Learning

In multiagent organizations, the coordination of problem-solving capabilities builds the foundation for processing complex tasks. Roles provide a structured approach to consolidate task-processing responsibilities. However, designing roles remains a challenge since role configurations affect individual and team performance. On the one hand, roles can be specialized on certain tasks to allow for efficient problem solving. On the other hand, this reduces task processing flexibility in case of disturbances. As agents gain experience knowledge by enacting certain roles, switching roles becomes difficult and requires training. Hence, this paper explores the effects of different role designs on learning agents at runtime. We utilize an adaptive Belief-Desire-Intention agent architecture combined with a reinforcement learning approach to model experience knowledge, task-processing improvement, and decision-making in a stochastic environment. The model is evaluated using an emergency response simulation in which agents manage fire departments for which they configure and control emergency operations. The results show that specialized agents learn to process their assigned tasks more efficient than generalized agents.
Research on Video Violence Detection Technology of UAV on Cloud Platform

Violence is the main cause of crime, affecting people’s security and social stability directly. Video surveillance, as an important means of security, plays an increasingly important role in the fight against crime. The traditional video monitoring system records and stores the video only, and the monitoring range is affected by the location, and it can only rely on human analysis to monitor the video information and find the criminals after the violence. UAV (Unmanned Aerial Vehicle) is regarded as a camera that can move freely. UAV cameras are used to monitor public places, shoot videos and send them to the cloud platform to detect the violence in the videos, which can discover the violence in public places in time and give early warning to prevent criminal incidents.
Research on Video Face Retrieval Method Based on Deep Learning and Key Frame

In order to improve the accuracy and speed of face retrieval in video, this paper proposes a video face retrieval method based on deep learning and key frames. The method firstly uses the inter-frame difference method to extract the key frames of the retrieved video, and then uses MTCNN's face detection and alignment and FaceNet's face recognition function to perform face recognition on key frames to determine whether the retrieved person is included. At the same time, according to the characteristics of video continuity, the retrieval results are specially processed. The experimental results show that the accuracy of the proposed method reaches 99.65%, and the retrieval speed can reach 68FPS.
Convex graph invariant relaxations for graph edit distance

The edit distance between two graphs is a widely used measure of similarity that evaluates the smallest number of vertex and edge deletions/insertions required to transform one graph to another. It is NP-hard to compute in general, and a large number of heuristics have been proposed for approximating this quantity. With few exceptions, these methods generally provide upper bounds on the edit distance between two graphs. In this paper, we propose a new family of computationally tractable convex relaxations for obtaining lower bounds on graph edit distance. These relaxations can be tailored to the structural properties of the particular graphs via convex graph invariants. Specific examples that we highlight in this paper include constraints on the graph spectrum as well as (tractable approximations of) the stability number and the maximum-cut values of graphs. We prove under suitable conditions that our relaxations are tight (i.e., exactly compute the graph edit distance) when one of the graphs consists of few eigenvalues. We also validate the utility of our framework on synthetic problems as well as real applications involving molecular structure comparison problems in chemistry.
Enhancing Second Language Listening Skills Through Smartphones: A Case Study

The ubiquitous growth of smartphones with uninterrupted connectivity offers immense scope for delivering quality second language education in developing countries, especially in India. Though mobile learning has huge potential in India, there is little research on Mobile Assisted Language Learning (MALL). The present paper studies the role of MALL in enhancing English language skills among engineering students and examines a) effectiveness and viability of smartphones for second language listening b) issues and challenges faced by learners and teachers, and c) perceptions of stakeholders. A quasi experimental study for over a semester was conducted with an experimental group (n = 59) who brought their own devices (BYOD) and a control group (n = 60) who used desktop computers in the language lab. The participants were undergraduate students of engineering belonging to a college affiliated to Jawaharlal Nehru Technological University Kakinada in India. The analysis of the scores revealed a statistically significant difference in the performance of the experimental group. The perceptions of stakeholders were collected through questionnaire and focus group discussions. The study witnessed a positive impact on the participants in enhancing participants’ L2 listening skills and improved motivation towards learning L2 listening. The results of the study have implications for teachers and practitioners of mobile assisted language learning.
Exact Algorithm for the One-Dimensional Quadratic Euclidean Cardinality-Weighted 2-Clustering with Given Center Problem

We consider a strongly NP-hard problem of clustering a finite set of points in Euclidean space into two clusters. In this problem, we find a partition of the input set minimizing the sum over both clusters of the weighted intracluster sums of the squared distances between the elements of the clusters and their centers. The weight factors are the cardinalities of the corresponding clusters and the centers are defined as follows. The center of the first cluster is unknown and determined as the centroid, while the center of the other one is given as input (is the origin without loss of generality). In this paper, we present a polynomial-time exact algorithm for the one-dimensional case of the problem.
Assessing the standardisation of Egyptian shabti manufacture via morphology and elemental analyses

Ancient Egyptian shabtis have been described as mass-produced, mould-made objects. We analyse the morphology and elemental composition of three sets of seven shabtis using 3D scanning, X-ray fluorescence spectrometry and principal component analysis clustering. The morphology and elemental composition of the shabtis allow us to conclude that these object types were made with different degrees of morphological and compositional standardisation indicating different manufacturing methods. We suggest that mass-production is an oversimplified label to describe shabti manufacturing as our findings demonstrate evidence of batch-processing methods.