Reviewerly/exHarmony huggingface dataset:
OGATE: A framework for autonomous controllers assessment

Autonomous robots can face a variety of applications integrating Artificial Intelligence (AI) techniques, for instance Planning & Scheduling (P&S). While robotics and planning systems are commonly well assessed through test benchs and metrics, in autonomous robots literature it is usual to present isolated case studies for evaluating such works. For instance, experiments are presented in very specific circumstances and often the data provided is not enough to enable a characterization of the autonomous features performance, providing only a demonstration of effectiveness. The main issue is the absence of a framework to assess autonomous controllers from a general perspective. We propose a research focused on a set of general applicable metrics to enable assessment of autonomous controllers. In this way, our objective is to analyse the deliberation and reaction capabilities of an autonomous robot in real operative scenarios. Such metrics are implemented in OGATE, a domain independent tool that automatically carries on with controllers’ testing, generating objective and reproducible performance assessments. To test this framework we have used two autonomous controllers that rely on different technologies for P&S. Results show that we are able to obtain relevant data, enabling the characterization of different P&S integration in robotics.
GhostCount: A lightweight convolution network based on high‐altitude video for vehicle instantaneous counting in dense traffic scenes

Abstract Instantaneous vehicle counting of traffic scenes based on high‐altitude video is an important way for real‐time traffic information collection in intelligent transportation systems (ITS). However, vehicle counts based on high‐altitude video are susceptible to problems such as denseness, occlusion and small size. The mainstream method is to use a Convolutional Neural Network (CNN) to output density maps and obtain vehicle count results. However, most CNNs are computationally expensive and have poor real‐time performance. Therefore, we propose a lightweight CNN named GhostCount, specially designed for high‐accuracy vehicle counts on edge devices. First, we combine ResNet‐18 and Lightweight RefineNet to build an encoder–decoder network architecture to effectively extract vehicle features in complex traffic scenes. Next, we replace the ordinary convolutional layers in ResNet‐18 with Ghost modules to lighten the network. Finally, a binary cross‐entropy loss function is introduced to suppress background noise. We demonstrate GhostCount on public datasets (TRANCOS, CARPK, PUCPR+) and our self‐built dataset (CSCAR). Results show that GhostCount can perform instantaneous vehicle counting with higher accuracy and faster inference speed than other representative lightweight CNNs. The method we propose would provide new solutions and ideas for ITS applications such as traffic information collection and smart parking management.
Marginal log-linear models and mediation analysis

After reviewing some not well known results about marginal log-linear models, the paper derives some new ones and shows how they might be relevant in mediation analysis when all variables are categorical. By focusing on the interactions between treatment and response, both marginally and jointly with respect to the mediating variables, a new relation between these parameters is derived. In addition, the paper describes a new class of models in which linear constraints may be imposed simultaneously on these two sets of interaction parameters. An application to education transmission from parents to their children is used as an illustration.
CLP-GCN: Confidence and label propagation applied to Graph Convolutional Networks

Node classification is an important task in many graph-related applications. Recently, graph neural networks like Graph Convolutional Networks (GCNs) have provided low-dimensional informative representations of the nodes of the graphs. However, there are some issues with the application of GCNs in real-world problems. First, information sharing among nodes is limited to directly connected nodes at each layer of GCNs and the contribution of all neighbors of each node is the same in feature aggregation. Second, learning a new representation and training a classifier over this representation require a considerable amount of labeled nodes while there are few labeled nodes in many real world node classification tasks. To compensate for the lack of enough labeled data, in this paper, we propose a novel method for label propagation for GCNs. The main idea of this method, named CLP-GCN, is to propagate the confidence scores of the labels along with the pseudo-labels assigned to the nodes. Two closeness criteria are used to estimate the similarity of each pair of nodes and this similarity measure governs the label propagation from one node of the graph to its neighbors. The confidence of each pseudo-label is determined based on the confidences of the adjacent nodes with the same label. Moreover, the adjacency matrix of GCN is replaced with a more informative matrix representing the structural closeness of the nodes resulting in a more selective feature smoothing in this network. Comprehensive experiments on popular datasets like Citation, Co-authorship, Co-purchase, and knowledge graph datasets demonstrate the superiority of CLP-GCN over state-of-the-art methods.
Blockchain state channels with compact states through the use of RSA accumulators

One of the major concerns regarding currently proposed public blockchain systems relates to the feasible transaction processing rate. It is common for such systems to limit this rate to maintain the required levels of security and decentralisation. State channels are an approach to overcome this limitation, as they aim to decrease the required on-chain transactions for a given application and thus indirectly increase the capacity (in terms of applications) of public blockchain systems. In the present paper, we propose a state channel design that, through the use of RSA accumulators, operates on a compact state structure. This scheme is optimal for applications whose state includes large sets of elements. The novel state channel design is presented by analysing all state channel operations and how they have to be revised. The security of the design is discussed, while a practical use case scenario regarding the use of the design for an on-chain asset (e.g., non-fungible tokens) exchange application is also analysed.
An AI-Powered Teacher Assistant for Student Problem Behavior Diagnosis

Abstract Student problem behavior refers to the students’ undesirable conducts and actions in schools. These behaviors severely affect students’ growth and development. In many cases, student problem behaviors are caused by the unsatisfied psychological needs, which are defined as the need deficiency. Hence, diagnosing students’ need deficiencies and regulating their problem behaviors are important educational tasks. The previous psychological studies have analyzed how distinct factors might affect student’s problem behaviors. However, the school teachers without such expertise may still encounter difficulties in applying these findings. Hence, this chapter describes the process of designing and implementing an intelligent teacher assistant, which could advise teachers and help them to handle the student problem behavior. Technically, it utilizes a task-oriented dialogue system to help identify the underlying reasons (i.e., the student need deficiency) behind their problem behaviors, and accordingly relies on a community question answering system to provide the advice. It also employs the semantic search technology to find the similar cases that have been well resolved by the experienced teachers.
Thermal Biometric Features for Drunk Person Identification Using Multi-Frame Imagery

In this work, multi-frame thermal imagery of the face of a person is employed for drunk identification. Regions with almost constant temperature on the face of sober and drunk persons are thoroughly examined for their capability to discriminate intoxication. Novel image processing approaches as well as feature extraction techniques are developed to support the drunk identification procedure. These techniques constitute novel ideas in the theory of image analysis and algorithm development. Nonlinear anisotropic diffusion is employed for a light smoothing on the images before feature extraction. Feature vector extraction is based on morphological operations performed on the isothermal regions on the face. The classifier chosen to verify the drunk person discrimination capabilities of the procedure is a Support Vector Machine (SVM). Obviously, the isothermal regions on the face change their shape and size with alcohol consumption. Consequently, intoxication identification can be carried out based only on the thermal signatures of the drunk person, while the signature of the corresponding sober person is not needed. A sample of 41 participants who drank in a controlled alcohol consumption procedure was employed for creating the database, which contains 4100 thermal images. The proposed method for intoxication identification achieves a success rate of over 86% and constitutes a fast non-invasive test that can replace existing breathalyzer check up.
Construction of a Character Dataset for Historical Uchen Tibetan Documents under Low-Resource Conditions

The construction of a character dataset is an important part of the research on document analysis and recognition of historical Tibetan documents. The results of character segmentation research in the previous stage are presented by coloring the characters with different color values. On this basis, the characters are annotated, and the character images corresponding to the annotation are extracted to construct a character dataset. The construction of a character dataset is carried out as follows: (1) text annotation of segmented characters is performed; (2) the character image is extracted from the character block based on the real position information; (3) according to the class of annotated text, the extracted character images are classified to construct a preliminary character dataset; (4) data augmentation is used to solve the imbalance of classes and samples in the preliminary dataset; (5) research on character recognition based on the constructed dataset is performed. The experimental results show that under low-resource conditions, this paper solves the challenges in the construction of a historical Uchen Tibetan document character dataset and constructs a 610-class character dataset. This dataset lays the foundation for the character recognition of historical Tibetan documents and provides a reference for the construction of relevant document datasets.
Exploiting Dual-Attention Networks for Explainable Recommendation in Heterogeneous Information Networks

The aim of explainable recommendation is not only to provide recommended items to users, but also to make users aware of why these items are recommended. Traditional recommendation methods infer user preferences for items using user-item rating information. However, the expressive power of latent representations of users and items is relatively limited due to the sparseness of the user-item rating matrix. Heterogeneous information networks (HIN) provide contextual information for improving recommendation performance and interpreting the interactions between users and items. However, due to the heterogeneity and complexity of context information in HIN, it is still a challenge to integrate this contextual information into explainable recommendation systems effectively. In this paper, we propose a novel framework-the dual-attention networks for explainable recommendation (DANER) in HINs. We first used multiple meta-paths to capture high-order semantic relations between users and items in HIN for generating similarity matrices, and then utilized matrix decomposition on similarity matrices to obtain low-dimensional sparse representations of users and items. Secondly, we introduced two-level attention networks, namely a local attention network and a global attention network, to integrate the representations of users and items from different meta-paths for obtaining high-quality representations. Finally, we use a standard multi-layer perceptron to model the interactions between users and items, which predict users' ratings of items. Furthermore, the dual-attention mechanism also contributes to identifying critical meta-paths to generate relevant explanations for users. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of DANER on recommendation performance as compared with the state-of-the-art methods. A case study illustrates the interpretability of DANER.
Multiple vehicles tracking and detection using weight high order singular value decomposition dimensionality reduction and double classifiers

Controlling and managing city traffic is one of them. In order to use image processing to prevent accidents on the road, vehicle tracking and detection are essential. By following moving objects, surveillance video monitoring and human activity recording are carried out. By taking this into account, a useful technique for image processing that detects automobiles from the image is suggested. For numerous vehicle tracking and detection systems, the ECNN-SVM (Enhanced Convolution Neural Network with Support Vector Machine) has just been introduced. However, the larger dimensional data space and inaccurate edge recognition make this system’s performance difficult. The WHOSVD (Weight High Order Singular Value Decomposition) approach, which reduces the dimension and breaks up the positive and negative training picture samples, is established to improve training speed and visual vehicle recognition. To effectively identify the edges at corners, improved canny edge detection is used for edge detection. Mean Kernel Fuzzy C Means (MKFCM) clustering algorithm-based three-dimensional bounding box estimation is used to identify the vehicle items. By merging the feature value of samples with their class labels, the Speed Factor Based Cuckoo Search Algorithm (SFCSA) is introduced for feature selection. The WHOSVD algorithm was used as the input for the enhanced convolutional neural network (ECNN), which is introduced for low-dimensional space and is used for vehicle detection and tracking. Occlusion problems are resolved and target features are further identified using a machine learning classifier. For common algorithms like CNN+SVM, Support Vector Machine (SVM), and the proposed technique, experimentation is done in regards to the metrics of accuracy, f-measure, precision, and recall for performance evaluation.
A novel transfer learning model on complex fuzzy inference system

Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer’s disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system’s increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model.
A mutation-based approach for the formal and automated analysis of security ceremonies

There is an increasing number of cyber-systems (e.g., systems for payment, transportation, voting, critical infrastructures) whose security depends intrinsically on human users. In this paper, we introduce a novel approach for the formal and automated analysis of security ceremonies. A security ceremony expands a security protocol to include human nodes alongside computer nodes, with communication links that comprise user interfaces, human-to-human communication and transfers of physical objects that carry data, and thus a ceremony’s security analysis should include, in particular, the mistakes that human users might make when participating actively in the ceremony. Our approach defines mutation rules that model possible behaviors of a human user, automatically generates mutations in the behavior of the other agents of the ceremony to match the human-induced mutations, and automatically propagates these mutations through the whole ceremony. This allows for the analysis of the original ceremony specification and its possible mutations, which may include the way in which the ceremony has actually been implemented or could be implemented. To automate our approach, we have developed the tool X-Men, which is a prototype that builds on top of Tamarin, one of the most common tools for the automatic unbounded verification of security protocols. As a proof of concept, we have applied our approach to three real-life case studies, uncovering a number of concrete vulnerabilities. Some of these vulnerabilities were so far unknown, whereas others had so far been discovered only by empirical observation of the actual ceremony execution or by directly formalizing alternative models of the ceremony by hand, but X-Men instead allowed us to find them automatically.
Teacher as a Subject of the Digital Educational Ecosystem

The study aims to provide a scientific substantiation for the characteristics of the digital educational ecosystem from the perspective of assessing its potential for a teacher to achieve the position of a subject and to identify the minimum list of necessary professional competencies of a teacher. The paper describes the features of the digital educational ecosystem as a new environment for a teacher in the context of digital transformation of education; defines the basic requirements for teacher qualifications in the aspect of the digital component; identifies the list of a teacher’s professional competencies for productive pedagogical activities as a part of a digital educational ecosystem. Scientific novelty lies in determining the main characteristics of the digital educational ecosystem, its digital technological capabilities in providing favourable conditions for continuous learning, professional and personal development of a person, assessing the qualification requirements of a modern teacher as a subject of the digital educational ecosystem. The main results are represented by the list of the necessary composition of a teacher’s professional competencies in the context of the features of pedagogical activity organisation in the digital educational ecosystem.
DESIGN BASED RESEARCH OF A SENSOR BASED MOBILE LEARNING COMPANION

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 8117-8124ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.2093Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
OPEN-SOURCE AR/VR LEARNING APPLICATIONS FOR PHOTONIC QUANTUM EXPERIMENTS

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 7726-7731ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1991Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Agricultural Export, Growth and the Poor in Africa: A Meta Analysis

Over the past decade, a growing number of studies have examined the role of agricultural export in economic growth in Africa. The literature, however, provides conflicting results about the agricultural export-led growth hypothesis. In this study, we aim to re-examine the impact of agricultural export on economic growth by performing a meta-analysis. There are two questions of interest, namely, (a) whether publication bias is present in the agricultural export-growth literature in Africa, and (b) if there is an overall effect of agricultural export on economic growth. Our meta-analysis finds significant negative publication bias in the literature. Moreover, after correction for publication bias, the results show that agricultural export leads to growth in low-income and lower middle-income African countries. These results are consistent with the agricultural export-led growth hypothesis. By this, we provide empirical basis that may enhance policy decisions on resource allocation to areas of comparative advantage. Additionally, the existence of an income differential effect on the agricultural export-led growth nexus implies that agricultural export-growth strategy has a pronounced effect on the poor in Africa. The result suggests that the poor in Africa could follow an agricultural growth strategy because it is a promising means to increase per capita wealth. JEL Classification: C10; C40; I30; N50; O55
DEVELOPING PROFESSIONAL IDENTITY IN FIRST YEAR OF UNIVERSITY

Appears in: ICERI2022 Proceedings Publication year: 2022Page: 171 (abstract only)ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0070Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Non-interactive Boolean Searchable Asymmetric Encryption With Bilateral Access Control

Abstract Searchable asymmetric encryption (SAE) enables a client to search over a data owner’s encrypted data. Nevertheless, state-of-the-art SAE schemes allow a data owner to specify access control policy for a client, while they have not considered the threat case of a malicious data owner. To address the problem, this work presents a non-interactive SAE scheme with bilateral access control: (i) allowing data owner and client to both specify policies toward the other party; (ii) allowing client to perform arbitrary boolean queries with sub-linear search complexity. Technically, we extend Cash et al.’s highly scalable SSE into an asymmetric setting and introduce the property of data owner authenticity. By refining identity-based matchmaking encryption, we formalize the syntax and security definition of our SAE with identity-based bilateral access control. Moreover, the security of the proposed SAE can be reduced to discrete logistic assumption and decisional bilinear Diffie–Hellman assumption. As an enhanced extension, we present a non-interactive multi-client SAE scheme with fuzzy identity-based bilateral access control. In addition, we implement the proposed schemes in real cloud platform and evaluate their performance on a real-world dataset. The result confirms that our SAE schemes achieve bilateral access control for both data owner and client with highly acceptable efficiency.