Reviewerly/exHarmony huggingface dataset:
A multidimensional representation model of interpersonal collaboration: Under the perspective of cognitive representation

Joint action is a common form of interpersonal interaction in our daily life.Successful joint action depends on effective collaboration between individuals, and the co-representation ability is the basis of such interpersonal collaboration.However, how humans represent behaviors of self and others and act during joint action, is still vague.From the perspective of cognitive representation, research concerning the representation process and mechanism of joint action and related theories were systematically reviewed, and based on this, a multidimensional representation model of joint action was proposed.The model explored the process and content of collaboration between individuals in joint actions from three dimensions: spatial dimension, embodied dimension and social dimension.The implication of this model for intelligent human-computer interaction was also discussed.
Quantum digital signature with unidimensional continuous-variable against the measurement angular error

The continuous-variable quantum digital signature (CV-QDS) scheme relies on the components of quantum key generation protocol (KGP) to negotiate classical signature, which is more compatible with optical fibers. Nevertheless, the measurement angular error of heterodyne detection or homodyne detection will cause security issues when performing KGP in the distribution stage. For that, we propose to utilize unidimensional modulation in KGP components, which only requires to modulate single quadrature and without the process of basis choice. Numerical simulation results show that the security under collective attack, repudiation attack and forgery attack can be guaranteed. We expect that the unidimensional modulation of KGP components could further simplify the implementation of CV-QDS and circumvent the security issues caused by the measurement angular error.
Modeling mental peculiarities of a decision maker by a Fourier-holography technique

A task of modeling individual mental features of a decision-maker using a Fourier holography setup is considered. The problem is considered for a situation when current conditions of decision-making contradict to the previously learned rule of decision-making logic modeled by the non-cooperative game "Prisoner's Dilemma". The approach to the problem is based on a hypothesis of the correlation between mental features and the properties of the neural network as a material carrier of intelligence. The 6f Fourier holography scheme of the resonant architecture is considered as a three-layer neural network implementing a neuro-physiologically motivated concept of the "excitation ring" proposed by A.M. Ivanitsky. We analytically assess the dependence of the validity limits of the classical total probability formula for a disjunction of incompatible events on the characteristics of low-frequency filters in holograms and the correlation radii of the training image of the basic decision rule. Analytical results are confirmed by results of the numerical simulation.
Sign Language Recognition for Bangla Alphabets Using Deep Learning Methods

Language is an essential aspect of communication. We can understand and communicate each other’s feelings through language. However, certain members of our society cannot talk or usually listen, leaving them with only sign language as a means of communication. Although researchers put a lot of time and effort into deciphering sign languages, most of their efforts have been focused on sign digits, and some are limited to simple samples. To address these prevalent concerns in earlier research, we created a new dataset of Bangla alphabets consisting of 2340 samples with different backgrounds. We also proposed a custom CNN architecture and compared its performance with other state-of-the-art models like ResNet, EfficientNet InceptionV3, and VGG19. All state-of-the-art models were trained and evaluated with custom dataset weights and ImageNet weights, and the best results were compared to our custom CNN. Our custom CNN did better than all the state-of-the-art models on our dataset with 92% accuracy.
Use of a Knowledge Network for Management and Sharing of Teaching Resources for Building Construction Simulations

A Knowledge network can well describe the knowledge development progress during teaching process and the relations of teaching resources. Managing the virtual simulation teaching resources based on knowledge network technology and arranging the scattered resources around each teaching point are very important for teachers to form a complete teaching system. However, there are many problems pending for solutions in research fields such as the construction of Chinese knowledge network and the text management based on knowledge network technology. For this reason, this paper aims to study the management and sharing of building construction virtual simulation teaching resources based on knowledge network analysis. At first, this paper elaborated on the construction process of the knowledge network of teaching resources, and introduced in detail a few steps during the construction process, including the entity/attribute extraction, coreference resolution, and relation extraction, etc. Then, this paper proposed an improved spectral clustering algorithm for the management and sharing of the said teaching resources, and realized the resource management and sharing works on a platform. At last, experimental results verified the validity of the proposed algorithm and the constructed model.
A Counterfactual Collaborative Session-based Recommender System

Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user’s selection of items. However, these causes widely exist in the real world, and few studies have investigated their role in SBRSs. In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. We find that the OSCs are essentially the confounders in SBRSs, which leads to spurious correlations in the data used to train SBRS models. To address this problem, we propose a novel SBRS framework named COCO-SBRS (COunterfactual COllaborative Session-Based Recommender Systems) to learn the causality between OSCs and user-item interactions in SBRSs. COCO-SBRS first adopts a self-supervised approach to pre-train a recommendation model by designing pseudo-labels of causes for each user’s selection of the item in data to guide the training process. Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem. As a result, COCO-SBRS can learn the causalities in data, preventing the model from learning spurious correlations. The experimental results of our extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed framework over ten representative SBRSs.
Fashion CUT: Unsupervised Domain Adaptation for Visual Pattern Classification in Clothes Using Synthetic Data and Pseudo-labels

Accurate product information is critical for e-commerce stores to allow customers to browse, filter, and search for products. Product data quality is affected by missing or incorrect information resulting in poor customer experience. While machine learning can be used to correct inaccurate or missing information, achieving high performance on fashion image classification tasks requires large amounts of annotated data, but it is expensive to generate due to labeling costs. One solution can be to generate synthetic data which requires no manual labeling. However, training a model with a dataset of solely synthetic images can lead to poor generalization when performing inference on real-world data because of the domain shift. We introduce a new unsupervised domain adaptation technique that converts images from the synthetic domain into the real-world domain. Our approach combines a generative neural network and a classifier that are jointly trained to produce realistic images while preserving the synthetic label information. We found that using real-world pseudo-labels during training helps the classifier to generalize in the real-world domain, reducing the synthetic bias. We successfully train a visual pattern classification model in the fashion domain without real-world annotations. Experiments show that our method outperforms other unsupervised domain adaptation algorithms.
A Reflection on the Use of Systemic Thinking in Software Development

The research examines the value and potential usefulness of using systemic thinking, which looks at the interconnectedness of things, to comprehend the complexities of software development projects and the technical and human factors involved.It considers two different aspects of systemic thinking -psychological and sociological -and posits that these can assist in understanding how software teams function and attain their objectives, as well as the goals of the entities for which they work.Our research aims to provide a novel contribution to the field by investigating the use of systemic thinking in software development teams and organizations.We evaluate the reliability and validity of the survey applied to different groups of relevant participants, relate our findings to existing literature, and identify the most representative factors of systemic thinking.Despite the popularity of various factors that fall under the umbrella of 'systems thinking', there is limited understanding of their effectiveness in improving organizational performance or productivity, particularly when it comes to psychological and sociological systemic factors.The relationship between the use of systems thinking and organizational performance is often based on anecdotal evidence, rather than the identification and application of specific factors.Our work emphasizes the importance of understanding and applying such factors in order to build a solid foundation for the effective use of system dynamics and systems thinking tools, which is crucial for software development teams.
A novel SGD-DLSTM-based efficient model for solar power generation forecasting system

Abstract Globally, Solar Power (SP) is generated by employing Photovoltaic (PV) systems. Accurate forecasting of PV power is a critical issue in ensuring secure operation along with economic incorporation of PV in smart grids. For providing an accurate forecasting model, various prevailing methodologies have been developed even then, there requires a huge enhancement. Thus, for Solar Power Generation (SPG) forecasting with deviation analysis, a novel Strengthen Gaussian Distribution-centric Deep Long Short Term Memory (SGD-DLSTM) methodology has been proposed here. Firstly, the PV modelling is formulated. After that, as of the PV, the data is gathered; likewise, for the deviation analysis, the historical data is gathered. Next, the pre-processing is performed; this stage undergoes two steps namely the Missing Value (MV) imputation and the scaling process. Afterwards, the features pertinent to the weather condition along with SP are extracted. After that, by utilizing the Intensive Exploitation-centric Shell Game Optimizer (IESGO) algorithm, the significant features are selected as of the features extracted. Then, the SPG is predicted by inputting the selected features into the SGD-DLSTM classifier. Next, by computing the Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE) measures, the predicted outcome’s deviation is assessed. In the experimental evaluation, by means of these measures, the proposed system’s performance is contrasted with the conventional techniques. Therefore, from the experimental assessment, it was established that the proposed model exhibits better performance than the prevailing research works. When analogized to the prevailing methodologies, a better accuracy of 97.25% was attained by the proposed system.
Color Image Encryption using Chaotic Algorithm and 2D Sin-Cos Henon Map for High Security

In every form of electronic communication, data security must be an absolute top priority. As the prevalence of Internet and other forms of electronic communication continues to expand, so too does the need for visual content. There are numerous options for protecting transmitted data. It's important that the transmission of hidden messages in images remain unnoticed to avoid raising any red flags. In this paper, we propose a new deep learning-based image encryption algorithm for safe image retrieval. The proposed algorithm employs a deep artificial neural network model to extract features via sample training, allowing for more secure image network transmission. The algorithm is incorporated into a deep learning-based image retrieval process with Convolution Neural Networks(CNN), improving the efficiency of retrieval while also guaranteeing the security of ciphertext images. Experiments conducted on five different datasets demonstrate that the proposed algorithm vastly improves retrieval efficiency and strengthens data security. Also hypothesised a 2D Sin-Cos-Henon (2D-SCH)-based encryption algorithm for highly secure colour images. We demonstrate that this algorithm is secure against a variety of attacks and that it can encrypt all three colour channels of an image simultaneously.
A Study on the Calculation of the Maximum Tolerable Period of Disruption for Business Continuity Management of National Critical Infrastructure Systems: A Case Study in the Field of Residential Water Supply

In this study, we tried to suggest a method for calculating the maximum tolerable period of disruption (MTPD) in water supply facilities among national core infrastructure facilities. Currently, the method for estimating the MTPD of national core infrastructure includes the amount of resources available and the recovery time of functions, but there is no specific standard or methodology. By analyzing previous studies and case studies, the framework for determining MTPD using added value among economic factors was confirmed and verified. Based on the framework, it was possible to determine the applicability by deriving estimates in Gongchon. Water Purification Plant in Incheon among domestic water supply facilities. In future research, we intend to conduct verification research on the possibility of actual utilization by identifying the reduction rate of added value targeting national core infrastructure in other fields and applying the framework derived through case study.
Comparing the effects of target and masker voice familiarity on children’s speech-in-speech recognition

Familiarity with a target voice has been shown to aid spoken word recognition for both children and adults. Familiarity effects of a target voice are not well understood, and even less is known about the impacts of masker voice familiarity. The present study investigated long-term target and masker voice familiarity effects on children’s speech-in-speech recognition by using the child’s own mother’s voice as stimuli. Open-set sentence recognition thresholds (SRTs) were measured adaptively in a two-talker speech masker. Twenty children were tested in three conditions, hearing their mother’s voice and two unfamiliar female voices: (1) Familiar Target/Unfamiliar Masker; (2) Familiar Masker/Unfamiliar Target; and (3) Unfamiliar Target/Unfamiliar Masker. Condition 1 measured effects of target familiarity; Condition 2 examined effects of masker familiarity; Condition 3 served as baseline. Results showed SRTs were significantly better when target speech was spoken by a familiar talker (the mother). When background speech was spoken by the familiar talker, performance was worse relative to an unfamiliar masker. This suggests that children do benefit from long-term voice familiarity with the target but not the masker speech. Thus, voice familiarity may impact children’s ability to attend to the target speech but does not always improve segregation.
Análisis de riesgos y vulnerabilidades en la educación 4.0 del proceso de enseñanza – aprendizaje.

This article presents an analysis of the risks and vulnerabilities presented by Education 4.0 of the teaching-learning process, in the digital educational society, in which the citizen was subjected through the global contingency that we live in today. For this purpose, we focused on a qualitative research that considers the conditions and characteristics of education 4.0; in addition to the probabilities of security incidents, materialization of threats regarding the media that occur in the teaching-learning process.&#x0D; However, we carried out the analysis method, oriented in Open Web Security Project (OWASP), which focuses on identifying risks and providing information on the probability and technical impact, using OWASP-Top-10-2017 scheme; on the other hand, it is specified in Table 2, the proactive risk control and in Table 3, the requirements and download software against the OWASP risk assessment framework, which gave us support to establish relationship of risks and vulnerabilities in Education 4. 0, and conclude with security recommendations on the protection of relevant information of the actors involved in this process, and that they are not exposed in unscrupulous hands of cybercriminals who take advantage to carry out criminal acts.
A modeling and calibrating method of FBG sensors for wing deformation displacement measurement

The airborne distributed Position and Orientation System (POS) is a key piece of equipment for providing high-precision motion parameters for aerial remote sensing systems. However, wing deformation degrades the performance of distributed POS, thus, it is urgent to obtain high-precision deformation information to assist distributed POS. In this study, a modeling and calibrating method of fiber Bragg grating (FBG) sensors for wing deformation displacement measurement is proposed. First, based on the cantilever beam theory and piecewise superposition, a modeling and calibrating method for wing deformation displacement measurement is established. The wing is then placed under different deformation conditions, and the changes in the wing deformation displacement and corresponding wavelength variations of the pasted FBG sensors are obtained using theodolite coordinate measurement system and FBG demodulator, respectively. Subsequently, linear least square fitting is deployed to develop the relationship model between the wavelength variations of the FBG sensors and wing deformation displacement. Finally, the wing deformation displacement at the measuring point in the temporal and spatial dimensions is obtained by fitting and interpolation. An experiment is conducted, and the results show that the accuracy of the proposed method can reach 0.721 mm with a wing length of 3 m, which can be used in the motion compensation of an airborne distributed POS.
Intent-based Web Page Summarization with Structure-Aware Chunking and Generative Language Models

This paper introduces a structure-aware method to segment web pages into chunks based on their web structures. We utilize large language models to select chunks correspond to a given intent and generate the abstractive summary. Experiments on a food pantry dataset developed for mitigating food insecurity show that the proposed framework is promising.
Fast trajectory planning for Dubins vehicles under cumulative probability of radar detection

This paper studies a minimum-time trajectory planning problem under radar detection, where a Dubins vehicle aims to approach a target under a limited probability of being detected. Since the probability is accumulated along the vehicle’s trajectory in an integral form, we have to address a non-convex constrained functional optimization problem. To this end, Pontryagin’s minimum principle is adopted to derive the optimality conditions, based on which we obtain a set of parameterized trajectories that contain all optimal ones. By leveraging the design of intermediate points, fast algorithms are proposed to approximately compute a minimum-time trajectory among the set. Simulations are performed to validate the effectiveness and efficiency of the proposed algorithms.
Artificial Intelligence, Augmented Reality and Education

This chapter proposes several tools based on Artificial Intelligence (AI) and Augmented Reality (AR) to promote the transmission of knowledge in the educational context. AI is already used to provide assistance as part of educational processes (Squirrel AI, Google Classroom) and can generate effective methodological and assessment resources (Alexa, SIRI, NVIDIA Canvas, WriteToLearn, Lingvist, Duolingo, Gradescope, Smartick). In addition, the combination of AI and AR facilitates the learning of scientific, geographical, mathematical and social content through the use of interactive scenarios (Metaverse, Munzee, Goosechase, Blippar, PlugXr or ActionBound, Bodyplanet, Arloon Geometry, Chromville Science, Explore the World). The objectives of this chapter are to analyse the functionality, advantages and limitations of the use AI and AR in an educational environment, to illustrate its practical applicability and to create a table/guide with didactic examples.
Performance Comparison between Coherent Ising Machines and Quantum Annealer

NTT has been developing a coherent Ising machine (CIM), which efficiently finds solutions to groundstate-search problems of the Ising model using a network of optical parametric oscillators.We introduce our recent experiment that compared the computational performance of CIMs (the one developed by NTT and another developed by Stanford University) with a quantum annealer, which solves the Isingmodel problems using superconducting devices.
Research on The Construction of Digital Campus for Vocational Colleges

&lt;p&gt;According to the relevant requirements of the Code for Digital Campus of Vocational Colleges of the Ministry of Education, combined with the characteristics of teaching and education of vocational colleges and the needs of information construction, this paper analyzes the new requirements and characteristics of digital campus construction of vocational colleges, and puts forward the connotation and construction principles of digital campus. On this basis, the overall construction framework and construction contents of digital campus of vocational colleges are given. Finally, according to the characteristics of vocational college informatization construction, this paper puts forward some suggestions to promote the implementation of vocational college digital campus construction, which can provide reference for promoting the modernization of vocational education with the help of informatization.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
SIMPEL (Sistem Informasi Manajemen Pelatihan) Internal BRI Menggunakan Metode Agile dengan Model Extreme Programming dan Algoritma Brute Force

This research develops SIMPEL (Training Management Information System) Internal BRI using Agile Methods, especially the Extreme Programming (XP) Method, and the Brute force Algorithm as a tool. XP is used because it is known as a method that is fast and flexible in accommodating changing user needs. The Brute force algorithm is implemented to help users search for training or similar information. White Box Testing is carried out to test the algorithm and shows that the time needed to find a participant data value is 0.43 ms. The results of this study indicate that the use of Agile Methods and Brute force Algorithms can increase efficiency and flexibility in software development.