Reviewerly/exHarmony huggingface dataset:
A Sketch of a Theory and Modelling Notation for Elucidating the Structure of Representations

A structural theory of visual representations and an accompanying modelling notation are outlined. The theory identifies three types of fundamental representational components, specified as cognitive schemas, that span internal mental and external physical aspects of representations. The structure of diverse and complex example representations are analyzed. Twenty-three requirements that a general theory of representations must address are presented. The new theory satisfies the large majority of them. The theory and notation may provide a basis for future methods to predict the efficacy of representations.
Temporal Enrichment and Querying of Ontology-Compliant Data

We consider the problem of answering temporal queries on RDF stores, in the presence of time-agnostic RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source into the target ontology. Our proposed solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches the data and the ontology with temporal information from the sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology, using SPARQL supplemented with time annotations. The algorithm translates the queries into the standard SPARQL form that respects the structure of the temporal RDF information while preserving the question semantics. We present the algorithms, report on their implementation and experimental results for two application domains, and discuss future.
Retrieval of Facial Sketches Using Linguistic Descriptors: An Approach Based on Hierarchical Classification of Facial Attributes

The facial sketch of the offender may be one of the essential evidences in arresting the offender. Usually, sketch artist or software packages are used to generate the facial sketch on the basis of the eyewitness’s description, which includes discrete areas of the face such as nose, mouth and eyebrows. Most of the time, eyewitness cannot describe all the facial cues of a criminal in terms of length and width. So it may be a time-consuming process for both the artist and the eyewitness. To overcome this difficulty, our work aims to make the job of the eyewitness easier by describing the criminal’s facial parts in terms of natural language such as ‘long nose’ and ‘big eyes’. To address this, we have adopted standard linguistic descriptors in English language which provides an easy human perception of facial regions. In this paper, we have trained more than 200 sketches to detect the faces and extract the specific corner points. Then, they are classified hierarchically in terms of linguistic hedges based on the Euclidean distance. Finally, the sketches are retrieved based on the score. Experimental results are obtained by using the benchmark datasets like PRIP_HDC and CUHK.
Sequential Inverse Covariance Intersection Fusion Kalman Predictor for Multi-sensor Systems with Packet Dropouts and Correlated Noise

This paper focuses on the problem of the multi-sensor state estimation for the networked system with packet dropouts and linear correlated additive white noises. By the state augmented method and the fictitious noise technology, the original system is transformed into a standard system with white noises, and then applying the local Kalman predictor, the SICI fusion estimator is designed by the inverse covariance intersection fusion algorithm. Compared with the CI fusion algorithm, the SICI fusion algorithm presented in this paper has a smaller computational burden. Compared with the SCI fusion algorithm, the SICI fusion algorithm has a lower degree of conservativeness and provides more accurate fusion results. It is proved that the estimation accuracy of the SICI fusion algorithm is higher than that of SCI fusion, and the superiority of the algorithm is verified by a simulation example.
API Design and Development for Developer Payroll in Startups Based on Agile Productivity Factors

Software startups are growing rapidly now. One of the important challenges faced by startups is how to formulate and calculate payroll for developers appropriately and easily that can satisfy both developers and startups. To help startups in calculating the developer payroll formulation appropriately and easily, a payroll application is required. However, developing a payroll application is not too easy and takes a lot of effort for startups. Especially for new startups that still do not have more resources in developing a payroll application. In this paper, Application Programming Interface (API) for developer payroll is designed and implemented based on agile projects. The API is designed based on the payroll calculation method that considers developer productivity parameters, i.e developer's years of experience, type of roles, bug, and timeliness. API for developer payroll is expected to facilitate and ease the development of developer payroll information system by startups in need. This study designed and developed 7 Payroll REST API resources using ASP.NET Web API. The API testing is conducted using POSTMAN with 100% successful test results.
Learning Individual Features to Decompose State Space for Robotic Skill Learning

Due to suffering from the diversity and complexity of robotic tasks in continuous domains, robotic skill learning is the most challenging issue in this area, especially for robots with high-dimensional state spaces. To learn structured policies for continuous control, the graph neural networks (GNN) was previously applied to incorporate explicitly the robot structure into the policy network. In this work, we tackle the problem of robotic skill learning in high-dimensional state space with the help of graph neural networks. Instead of utilizing a general purpose multi-layer perceptron (MLP) as a unified controller to output actions for all joints of the robot, we construct a separate controller for each joint of the robot by using the individual features that have been extracted by GNN model. Empirical results on simulated continuous systems, including applications to PR2 task and Centipede task, demonstrate that the proposed framework can achieve satisfactory learning performance, and more importantly, it significantly reduces the parameters of the policy network.
Sequential Inverse Covariance Intersection Fusion Kalman Filter for Networked Systems with Multiplicative Noises

This paper mainly studies the fusion estimation problem of the networked multi-sensor systems with multiplicative noises. Firstly, the state space model is transformed into a new system with fictitious noises to obtain the local Kalman filter. Secondly, applying the Sequential Inverse Covariance Intersection (SICI) fusion algorithm, the SICI fusion estimator is presented, which avoids the computational burden of the cross-covariance among local estimators. Compared with the Sequential Covariance Intersection (SCI) fusion algorithm, the SICI fusion algorithm has lower conservativeness, and is proved that its estimation accuracy is higher than those of the local filters and SCI fusion estimator. A simulation example shows the effectiveness and consistency of the presented fusion estimators.
On professional and pedagogical competence development of technical university teaching staff

Abstract The fundamental task of engineering education lies in interdisciplinary integration of content and teaching methods through the development of multimedia and virtual technologies. This leads to new demands on the professional knowledge, abilities, skills, and qualities of a teaching staff member. The main objective is to test the effectiveness of methods of formation and development of professional and pedagogical competence. The main results of the research allow identifying methods of development of professional and pedagogical competence (collaborative, personalized; training, consultations) and evaluation criteria, characterizing a public recognition of the scientific and educational activity of teachers. These methods allow developing a program for the training of faculty members, including participation in the scientific and teaching activity, organization of personal exhibitions, the development of individual projects to optimize higher education. The criteria identified allow to include characteristics of actual and potential activities of a teacher.
A Bayesian mixture of experts approach to covariate misclassification

Abstract This article considers misclassification of categorical covariates in the context of regression analysis; if unaccounted for, such errors usually result in mis‐estimation of model parameters. With the presence of additional covariates, we exploit the fact that explicitly modelling non‐differential misclassification with respect to the response leads to a mixture regression representation. Under the framework of mixture of experts, we enable the reclassification probabilities to vary with other covariates, a situation commonly caused by misclassification that is differential on certain covariates and/or by dependence between the misclassified and additional covariates. Using Bayesian inference, the mixture approach combines learning from data with external information on the magnitude of errors when it is available. In addition to proving the theoretical identifiability of the mixture of experts approach, we study the amount of efficiency loss resulting from covariate misclassification and the usefulness of external information in mitigating such loss. The method is applied to adjust for misclassification on self‐reported cocaine use in the Longitudinal Studies of HIV‐Associated Lung Infections and Complications.
Ontology-Based Full-Text Searching Using Named Entity Recognition

Travelling to different places depends on lots of factors such as hotels, restaurants, nearby hospitals, places to visit in cities, etc. All this information is available on different websites in an unstructured manner thus people do not get information as per their queries in organized format. People search for these factors on search engines which use keyword matching mechanism. Therefore, this paper presents full-text queries searching mechanism which gives precise results in a structured format. Here, our system scraps data from websites to collect information about cities, hotels and hospitals. Concepts of linked data using ontology are implied which has the capability to relate multiple sources of data available on different websites and infer new knowledge from it. Natural Language processing methods such as co-reference resolution is used, which forms a relationship between sentences scrapped from web, which helps to perform better search query without losing meaning of sentences during the processing. In our work, we have also used the Named entity recognition mechanism which applies tags on words with the real-world concepts that they represent. These tags are further utilized by Python library named RDFLib to match the tags which form a relationship between classes within ontology. This relationship between classes and tags are further used to insert and extract data from ontology.
Upper Bound on the Size of FP-Tree

Efficient tree structures known as FP-tree [3] are used to store a database in memory for mining frequent patterns. However, there has been no discussion on tight upper bound for the number of nodes in this tree. Instead, a very loose upper bound of $$2^n$$ (where n is the number of distinct items in the database) is used. In this paper, we provide a tighter upper bound for the number of nodes in a closed form solution. This result is illustrated in the context of various examples both in graphical and mathematical forms.
Evaluating Author Attribution on Emirati Tweets

Author Attribution (AA) is a critical stylometry problem that tries to deduce the identity of the authors of electronic texts (e-texts) by only examining the texts. AA is essential for enhancing various application domains, such as recommender systems and forensics. Nevertheless, existing techniques in AA have not been assessed with Emirati social media e-texts. The reason is that no suitable dataset exists for evaluating AA techniques in this context. This paper introduces the Khonji-Iraqi Emirati Tweets Author Identification (AID) dataset with 30 authors (KIT-30), and detailed evaluations. Compound grams, a new definition of grams, are introduced, which allows us to achieve higher classification accuracy. Also, when the number of suspect authors increases, the classification accuracy degradation is not as severe as previously reported, when using suitable data representation. Furthermore, in order to work towards addressing the lack of conveniently-available implementations of stylometry methods, we have developed an extensive e-text feature extraction library, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fextractor</i> , with a highly intuitive API. The library generalizes all existing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -gram-based feature extraction methods under the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">at least</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-frequent,</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\texttt {dir}$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-directed,</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-skipped</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-grams</i> , and allows grams to be diversely defined, including definitions that are based on high-level grammatical aspects, such as Part of Speech (POS) tags, as well as lower-level ones, such as the distribution of function words and word shapes.
CNN-Self-Attention-DNN Architecture For Mandarin Recognition

Connectionist temporal classification (CTC) is a frequently used approach for end-to-end speech recognition. It can be used to calculate CTC loss with artificial neural network such as recurrent neural network (RNN) and convolutional neural network (CNN). Recently, the self-attention architecture has been proposed to replace RNN due to its parallelism in NLP. In this paper, we propose CNN-SELF-ATTENTION-DNN CTC architecture which use self-attention to replace RNN and combine with CNN and deep neural network (DNN). We evaluate it with an about 170 hours Mandarin speech dataset and achieved 7% absolute character error rate (CER) reduction compared to DEEP-CNN with less training time than Bi-LSTM. Finally, we do an analysis on the test results to find the factors that affect CER.
Analysing the Practicality of Drawing Inferences in Automation of Commonsense Reasoning

Commonsense reasoning is the simulation of human ability to make decisions during the situations that we encounter every day. It has been several decades since the introduction of this subfield of artificial intelligence but it has barely made some significant progress. The modern computing aids also have remained impotent in this regard due to the absence of a strong methodology towards commonsense reasoning development. Among several accountable reasons for the lack of progress, drawing inference out of commonsense knowledge base stands out. The study presented here emphasizes a detailed analysis of representation of reasoning uncertainties and feasible prospects of existing inference methods for reasoning. Also, the difficulties in deducing and systematizing commonsense reasoning and the substantial progress made in reasoning that influences the study have been discussed. Additionally, the paper discusses the possible impacts of an effective inference technique in commonsense reasoning.
An Evolutionary Game Tree Search Algorithm of Military Chess Game Based on Neural Value Network

Incomplete information game is an important research direction in artificial intelligence. Military chess is a typical incomplete information game. Since a player does not know the opponent's chess type in the whole game, military chess has high requirements for the design of the situation evaluation algorithm and the search algorithm. Most of the current military chess AI models are based on human experience. However, such models have disadvantages, such as uncertainty and the difficulty in adjusting parameters. In this work, a three-level decision model for incomplete situation is presented. Opponent imitation and feature extraction of the situation are promoted based on game rules while the neural network and the situation evaluation model are combined to help searching the game tree. Chess manual and self-play games are used to train the model. Our proposed method is verified with experiments of playing against the AI model from the National Computer Game Competition.
Enrichment characteristics of Cu and Co displayed by low-density geochemical mapping in Zambia

The Sino–Zambian Cooperation project provides national catchment sediment geochemical data for 58 elements including Cu and Co from 746 sediment samples at 736 sampling sites, corresponding to a sampling density of about 1 site per 1000 km2. The Cu and Co were determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) under strict quality control using field duplicates, standard reference materials and analytical replicate samples. The 95% ranges (2.5%–97.5%) of Cu and Co concentrations were from 1.8 to 57.81 × 10−6 and from 0.54 to 22.83 × 10−6, respectively, reflecting locally unevenness distributions. The median values of Cu and Co are 5.77 × 10−6 and 2.98 × 10−6, respectively. The most noticeable anomalous patterns of Cu and Co are distributed between Lusaka and Solwezi (Lufilian Arc Belt) with the trend of increasing concentrations from southeast to northwest. The all spatial distribution patterns of Cu and Co in tectonic units, drainage basins and geomorphological landscapes show good relations with parent rocks, climate, Cu (Co) deposits and smelting plants in Lufilian Arc Belt. The Cu and Co concentrations show metallogenic belts in Mkushi area, between Lusaka and Solwezi areas, and in Livingstone area.
Tracking of drone flight by neural network Siamese-RPN

Recently deep learning has made a great progress in the image classification and video object tracking. However the speed of traditional region proposal algorithms is too slow and become a bottleneck. Researchers have proposed a lot of neural networks for the improvements. With the region proposal network proposed, neural network can finish the computation enabling cost-free region proposals, which make it possible to do real-time detection and tracking. This paper will set up a system which can detect and track drone by using neural networks such as Faster R-CNN and Siamese-RPN.
Task Allocation of Multi-AUV Based on Multi-objective Discrete Particle Swarm Optimization and Simulated Annealing

An multi-objective discrete particle swarm optimization and simulated annealing (MODPSO-SA) algorithm is proposed to solve the cooperative task allocation for multiple autonomous underwater vehicle (AUV). Through analyzing the task priority sequence constraints and energy consumption, the mathematical model of heterogeneous multi-AUV cooperative task allocation is set up. Aiming at the problem that particle swarm optimization is easy to fall into local extremum, a new multi-objective local search strategy is formed by combining the improved simulated annealing (SA) and shift strategy, which balances the global development ability and local mining ability. The simulation results show that the MODPSO-SA algorithm can get reasonable solution set, which can solve the task assignment of multi-AUV effectively.
Basic Requirements to Designing Collaborative Augmented Reality

Augmented Reality (AR) has become a ubiquitous technology in daily working, learning, and spare life. However, AR still lacks research in terms of productivity and design for being effective on users and the situations it is used in. Particularly in collaborative and also educational settings, the technology has to be adapted in order to be effective for all users concerned. Moreover, the design of the scenario the technology is realised in has to be adapted to the needs and requirements of a productive digital learning environment. In this context, the question still needs investigation how these applications affected e.g. the learning process. As a basis, this paper examines the state of the art of the design process in terms of developing collaborative AR applications and their respective use cases. Besides technical requirements, this paper also focuses on user-centered criteria in terms of usability and effectiveness for collaborative processes in order to lay the groundwork for designing a didactical concept. In order to develop a list of requirements of design criteria for both the didactic scenario and the collaborative AR application, a literature-based requirements analysis has been conducted. Thus, a set of criteria for designing collaborative AR has been deduced. This set is categorised in order to give a detailed description for developing AR applications for collaborative settings on different levels and constraints, e.g. regarding didactic methods, implementation process, usability and user experience design, and necessary resources (for both developing and using collaborative AR). Also, the catalogue presented in this paper will not be finalised but iteratively adjusted to the findings of the empirical study.
Intention Multiple-Representation Model for Logistics Intelligent Customer Service

With the development of the Internet, more and more people express their ideas on the internet in the form of short text. And a question with the same intention can be expressed in different ways. So it is necessary and important to understand the meaning of short text when we want to provide intelligent service to customer. Many studies have focused on the short texts based on public data sets. However, little studies have been carried out or can be effectively used in some specific fields. Taking logistics Intelligent Customer Service (ICS) as an example, the short texts has the above characteristics. To solve this issue about intention multiple-representation in logistics, a self-attention-based model, that is, One question to Many question (O2M) is proposed. On the basis of classification task, the model can learn the mapping relation from customer questions to standard questions. And it consists of three parts: standard questions domain, customer questions domain and selector. For the two domains, they learn semantic patterns of their own questions. And the selector becomes the bridge between them. Extensive experiments were carried out on logistics corpus. And the results show that the model is effective and the accuracy of the model is higher than that of traditional neural network models.