Reviewerly/exHarmony huggingface dataset:
La funzione strumentale della “realtà aumentata” nei processi di rappresentazione, trasmissione e costruzione della conoscenza

The paper examines the potential offered by augmented reality in the didactic field, with particular reference to the possibility of creating “augmented” experiences, which make it possible to obtain a high level of interaction with the sources and places that have acted and are acting for conservation and activation of historical memory. The subject of analysis is the psycho-pedagogical value of the information enrichment offered by AR, which allows to support the narrative potential of historical sources through the presentation of documents, interactive objects, 360° videos and 2D and 3D reconstructions. Particular attention is paid to: the possibility of using augmented reality to innovate textbooks and teaching methodologies; the possibility of creating “active” study paths with a high level of interaction and personalization; the recent applications of AR in the museum field; the psycho-pedagogical role of AR in the construction of stories and narratives and the impact that this type of technology can have on the methods of transmission and construction of stories.
Predicting the Future Research Gaps Using Hybrid Approach

Sri Lanka is one of the global biodiversity hotspots that contain a large variety of fauna and flora. But nowadays Sri Lankan wildlife has faced many issues because of poor management and policies to protect wildlife. The lack of technical and research support leads many researchers to retreat to select wildlife as their domain of study. This study demonstrates a novel approach to data mining to find hidden keywords and automated labeling for past research work in this area. Then use those results to predict the trending topics of researches in the field of biodiversity. To model topics and extract the main keywords, the authors used the latent dirichlet allocation (LDA) algorithms. Using the topic modeling performance, an ontology model was also developed to describe the relationships between each keyword. They classified the research papers using the artificial neural network (ANN) using ontology instances to predict the future gaps for wildlife research papers. The automatic classification and labeling will lead many researchers to find their desired research papers accurately and quickly.
Vision-Based Employee Activity Classification

An employee should be competent and expertise in their respective fields. An evaluation is needed to maintain the quality of employee’s performance, one of which can be done by observing their activity during working hours. This research discusses the classification of the employee’s activity in desk work. Classification of employee’s activity is investigated using ResNet and the Cyclical Learning Rate method in a novel dataset, i.e. vision-based employee activity. Classification is done by looking at three types of employee activities: talking on the phone, using a PC, and playing smartphone. The most optimal result of this research is ResNet50 using CLR with image input of 224x224x3 which has an accuracy of 87.01% and 12.99% error rate for talking on the phone, 99.95% accuracy and 0.05% error rate for using a pc, 81.67% accuracy and 18.83% error rate for playing smartphone, and has a decreasing loss value. In addition, this research shows that cyclical learning rate significantly affects the model performance.
Research on a &amp;amp;#x0003c;Computing Basic&amp;amp;#x0003e; Course －Factors Influencing Intention for Higher Level Study and Computing Language Confidence

This study surveyed students who took computer programming (Python) courses established as liberal arts education, and investigated their motivation for taking this class, their intentions of taking advanced courses, and their confidence in the computing language. The purpose of this study was to suggest improvements in education in light of the recent increase of such classes. The collected data was subjected to descriptive statistics analysis, correlation analysis, and regression analysis.&lt;br/&gt;First, as a result of this study we found that the more knowledge the students gained about IT through the class, the more inclined they were to take an advanced course. Therefore, we can predict that the level of academic motivation felt by the students will increase when the students learn not only the technical aspects of programming in Python, but also knowledge related to the application of a programming course in society.&lt;br/&gt;Second, the students said that the more helpful their tutor was, the more confident they became in their studies of Python, and the more their skill improved when it came to programming in this language. Furthermore, considering that tutors can play a key role in helping students to learn, and that their help has a significant influence on the confidence level of students, I strongly recommend that universities introduce a ‘tutoring system’ for increasing the effects of education.
Target Detection Algorithm Based on Unconstrained Environment for Video Human Motion

The existing human target detection algorithm can only detect a human body object that is substantially upright in the image. The human body of sport video is in a variety of ways during the movement, and the current detection algorithm is difficult to achieve the desired detection effect. In order to detect various postures from sport video of human targets. The paper proposed a human target detection algorithm without attitude limitation. Firstly, the algorithm finds the human candidate target by shape context matching. Secondly, the hypothetical target is removed by combining the direction gradient histogram features with the support vector machine. The experimental results show that the proposed algorithm not only can detect human targets in various poses, but also has a much higher detection speed than the direction gradient histogram detection algorithm.
External Labeling: Fundamental Concepts and Algorithmic Techniques

This book focuses on techniques for automating the procedure of creating external labelings, also known as callout labelings. In this labeling type, the features within an illustration are connected by thin leader lines (called leaders) with their labels, which are placed in the empty space surrounding the image.
Does the duration of rapid release cycles affect the bug handling activity?

Software projects are regularly updated with new functionality and bug fixes through so-called releases. In recent years, many software projects have been shifting to shorter release cycles and this can affect the bug handling activity. Past research has focused on the impact of switching from traditional to rapid release cycles with respect to bug handling activity, but the effect of the rapid release cycle duration has not yet been studied. We empirically investigate releases of 420 open source projects with rapid release cycles to understand the effect of variable and rapid release cycle durations on bug handling activity. We group the releases of these projects into five categories of release cycle durations. For each project, we investigate how the sequence of releases is related to bug handling activity metrics and we study the effect of the variability of cycle durations on bug fixing. Our results did not reveal any statistically significant difference for the studied bug handling activity metrics in the presence of variable rapid release cycle durations. This suggests that the duration of fast release cycles does not seem to impact bug handling activity.
Entangled Sensor Networks Empowered by Machine Learning

We propose and experimentally demonstrate a reconfigurable radio-frequency photonic sensor network based on continuous-variable multipartite entanglement. We further show that the entangled sensor network can be trained to undertake quantum-enhanced data classification tasks.
Government information sharing scheme for cross-departmental collaboration

"Internet plus government services" often involves cross-departmental information sharing, among which there are a lot of problems such as data security, data permissions and lack of motivative, these problems make it difficult to share data in the cross-departmental collaboration government businesses. To address this issue, the main mode and related technology application of the current government data sharing and exchange are analyzed, and a decentralized data sharing mode based on block chain technology is proposed. This government information sharing scheme for cross departmental collaboration combines smart contract and data sharding storage technology, it can effectively reduce the cost of cross-departmental data sharing, and has good performance and scalability.
Data generation using geometrical edge probability for one-class support vector machines

In the tasks of data mining and machine learning with unlabeled data, one-class support vector machines (OCSVMs) are one of the most widely used methods for anomaly detection as one class classification. However, the choice of hyperparameters, a critical step for learning effective OCSVM decision boundaries, remains open as a post analysis step and is often undecided. To tackle this issue, this paper proposes a new data-generation method using geometrical edge probability suitable for OCSVM hyperparameter selection. Our method improves the limitations of existing methods in that the geometrical edge probability enables the generation of both pseudo target data and pseudo anomaly data. In addition, the proposed method combined with bootstrapping is able to determine the distribution of anomaly data. We evaluate the proposed method for datasets with 16 different dimensions and demonstrate the performance improvement in the classification of target and anomaly data.
Occupation mechanism for eliminating double-spending attacks on trusted transaction blockchain

Using blockchain technology to build a trusted transaction foundation has become a hot topic of current research and practice. The double-spending phenomenon refers to the bifurcation of the blockchain structure. In the process of trusted transactions, the fork caused by malicious attacks will destroy the correctness, integrity, and consistency of the entire ledger, and causes serious economic losses to users. Therefore, eliminating double-spending attacks is an important issue to ensure the security of the blockchain. The current method of defending against double-spending attacks in trusted transactions mainly revolves around the Bitcoin blockchain system. In the process of researching the Nano blockchain grid project, a high-throughput trusted transaction system, we proposed a double-spending elimination solution for the system: the occupying mechanism. By using Java to reconstruct the Nano project and implement the occupying mechanism, the experimental results verify the effectiveness of the occupying mechanism, and it is superior to the Nano project's double-spending solution based on the traditional voting mechanism in terms of time performance.
Approximate Training of Object Detection on Large-Scale Datasets

Modern deep learning approaches require an enormous amount of data to produce high-quality robust results, but broad availably of large-scale datasets for computer vision tasks introduced new issues – a long-tail problem and terrifically increased training time. These problems are especially tough for the object detection field where researchers required to use computation heavy neural networks that process high-resolution images to produce state-of-the-art results. Recently researchers concentrated more on solving the long-tail problem as a more promising in terms of detection performance pushing the computational bar even higher. In this paper, we introduced a novel approach for approximated training that significantly reduces training time on large-scale datasets while maintaining competitive state-of-the-art results. The proposed approach has a lot of speed-accuracy trade-offs which allow researchers more precisely calibrate their training pipelines depending on the number of available computational power. We assume that this will allow the research community to validate faster their hypotheses on million-scale datasets and incentivize broader pool of researchers to conduct their research on larger datasets. The proposed approach weights each data sample from the dataset based on an “anomaly” score of deep features produced by the network and selects only a fraction of data samples that have the highest score for the training phase. These samples contain underrepresented classes that contribute more to the model’s convergence compare to a randomly picked sample from the dataset. The proposed approach reduces impact of long-tail problem for unbalanced datasets. We validated the proposed approach by incorporating it into the training pipeline of Cascade R-CNN-based object detector on Open Images V5 dataset which reduced training time from three weeks to one with neglectable degradation of Open Images mAP from 0.60 to 0.59.
Research at Rome's Centro Ricerche Musicali on Interactive and Adaptive Installations and on Augmented Instruments

Abstract Established in 1988 by composers Laura Bianchini and Michelangelo Lupone, the Centro Ricerche Musicali (CRM) in Rome was officially recognized in 1990 as a Music Research Center by the Ministry for University Education and Scientific and Technological Research. The Center focuses on musical production in relation to new technologies, in order to create a continual interaction among musical language, scientific thought, and technological resources. The staff at CRM, comprising musicians, technicians, visual artists, architects, information technology specialists, engineers, and researchers, aim to promote study of the aesthetic, analytical, and scientific aspects of music. In the beginning, research at CRM concerned the design and development of specific hardware devices for live electronics and composition, such as the Fly10 (1983–1985) and Fly30 (1990) systems. Subsequent studies on a physical model for the bow-and-string system in 1997 gave rise to the development of virtual musical instruments. From 1999 onwards, other areas of research have included interactivity and adaptivity applied to musical forms, the development of specific technologies for sound art installations and sculptural–musical works, and augmented instruments such as the Feed-Drum, SkinAct, WindBack, and ResoFlute. This article presents a brief history of CRM and some artistic productions by composers working at the Center.
Single Image Region Algorithm Based on Deep Network

This paper designed an image region feature extraction and recognition algorithm based on deep neural network model based on deep network as for the problem that it is hard to extract and recognize the eigenvalue in image region, aiming at accurately extracting image eigenvalue while reducing the consumption of time. Through the experimental test of data set, the rationality and feasibility of the algorithm are confirmed, which can effectively reduce the time consumption and improve the accuracy of image processing.
Successful Adoption of Cloud Computing Security Model for SMEs in Libya

Cloud computing system facilitates the promotion of the convenient on demand networks that include application services, servers, networks, and storage devices. It is becoming significant to have the cloud computing security system as the issues related to the security still appear to be unsolved. Therefore, the utilization of the technological advances is essential for the organizations in Libya. The key purpose of this paper is to incorporate cost effectiveness, IT compliance, and security effectiveness for the successful adoption of the Cloud Computing security system in Libyan companies. At the moment, there is a shortage of the guidelines for the adoption of the Cloud Computing and building the trust. Thus, this study focuses on the examining different theories and identifying the new integrated theoretical framework accordingly for the adoption of the Cloud Computing in general, specifically in Small and Medium Enterprises (SMEs) in Libya.
On the Convergence of Reinforcement Learning in Nonlinear Continuous State Space Problems

We consider the problem of Reinforcement Learning for nonlinear stochastic dynamical systems. We show that in the RL setting, there is an inherent "Curse of Variance" in addition to Bellman’s infamous "Curse of Dimensionality", in particular, we show that the variance in the solution grows factorial-exponentially in the order of the approximation. A fundamental consequence is that this precludes the search for anything other than "local" feedback solutions in RL, in order to control the explosive variance growth, and thus, ensure accuracy. We further show that the deterministic optimal control has a perturbation structure, in that the higher order terms do not affect the calculation of lower order terms, which can be utilized in RL to get accurate local solutions.
Optimal Control for Path Planning on a 2 DOF Robotic Arm with Prismatic and Revolute Elastic Joints

This work presents the mathematical description, simulation and results for a dynamic analysis of a robotic arm with two degrees of freedom, one revolutionary and one a prismatic. The revolute link was considered with an elastic joint and the prismatic link was considered an ideal link. The SDRE technique was used to control the elastic joint to eliminate errors in position, vibration and to guide the arm through a specific path for position and speed. The work still has a wide variety of possibilities to improvement on the dynamic representation of the prismatic joint with some flexibility, a more detailed mathematical representation on the elastic joint control and explore more possibilities beyond SDRE control that can provide more precision on the speed control.
Tuning Image Descriptors and Classifiers: The Case of Emotion Recognition

Greche, Latifa Taamouch, Abdelhak Akil, Mohamed Es-Sbai, NajiaIn general, the recognition involves several steps as follows: data acquisition, pre-processing, segmentation, feature extraction and classification. Automatic facial expression recognition has become a crucial technology in the computer vision field and its applications including identification and security, Medicine and Monitoring. The facial expression recognition system requires an algorithmic pipeline that involves two main blocks: feature extraction and classification. A large experimental session must lead to the adequate algorithmic pipeline, notably for identifying the best methods for feature extraction and classification to achieve robust facial expression recognition with high accuracy. Thus, it is essential to analyse data using multiple methods of feature extraction and classification. In this paper, we propose an approach to automate the analysis of data by repeating tests made to tune and compare feature extraction and classification methods. We evaluate our proposed data analysis approach using video sequences with fundamental emotion states: neutral expression, disgust, fear, happiness, sadness, anger and surprise. To transform the face images into vectors of features, we use shape, texture, and contour descriptors. This enables storing images in a table of vectors. Each table related to every descriptor is analysed with Five classifiers have been used, which are support vector machine, linear discriminant analysis, k-nearest neighbors, naïve Bayes, and binary tree classifiers. The techniques 10-fold and Leave-One-Out Cross-validations and the grid search have been used to tune the hyperparameters methods and compare them, computing the average recognition rate F-measure as evaluation metric. Experimentation on ChonKanade Image database (CK+) show that the proposed data analysis approach can find out the optimal combination to separate the data classes and identify the expression with a an F-score of 96.44%.
Grounding of Concept, Indexical, and Name

The semantics of agent-based DBS is ‘grounded’ in that the Content kinds concept, indexical, and name have their foundation in the agent’s recognition and action, whereby each Content kind has its own computational Mechanism. For a concept it is pattern matching between the type provided by memory and raw data provided by the agent’s interface component. For an indexical it is pointing at a STAR value of the agent’s on-board orientation system (OBOS). For a name it is the address of the ‘named referent’ which is inserted as the core value into a lexical name proplet in an act of baptism. Orthogonal to the Content kinds and their computational Mechanisms are the Semantic kinds referent, property, and relation with their associated Syntactic kind noun, adj and intransitive verb, and transitive verb. It is shown that the Semantic kind of referent is restricted to the Syntactic kind of noun, but utilizes the computational Mechanisms of matching, pointing, and baptism. Furthermore, figurative use is restricted to the computational Mechanism of matching, but uses the Semantic kinds referent, property, and relation.
Search for a New Paradigm of Education and Artificial Intelligence. Place and Role of Artificial Intelligence in the New Education System

Humanity is on the cusp of the fourth industrial revolution. Cardinal changes are expected in all spheres of human activity, including the education system. A global reform of education is expected, which will lead to a change in its very paradigma, the creation of a new teaching methodology, a change in the forms and methods of teaching, and the creation of new teaching methods. But in this direction there are many difficulties and unsolved scientific problems. For example, there is no clear idea of what the “paradigm of education” is, how it is related to the teaching methodology, and the ways of transforming the education system are not defined. The paper considers new models of the use of information technologies in teaching, defines the place and role of artificial intelligence in the new education system.
Autoencoding Features for Aviation Machine Learning Problems

The current practice of manually processing features for high-dimensional and heterogeneous aviation data is labor-intensive, does not scale well to new problems, and is prone to information loss, affecting the effectiveness and maintainability of machine learning (ML) procedures. This research explored an unsupervised learning method, autoencoder, to extract effective features for aviation machine learning problems. The study explored variants of autoencoders with the aim of forcing the learned representations of the input to assume useful properties. A flight track anomaly detection autoencoder was developed to demonstrate the versatility of the technique. The research results show that the autoencoder can not only automatically extract effective features for the flight track data, but also efficiently deep clean data, thereby reducing the workload of data scientists. Moreover, the research leveraged transfer learning to efficiently train models for multiple airports. Transfer learning can reduce model training times from days to hours, as well as improving model performance. The developed applications and techniques are shared with the whole aviation community to improve effectiveness of ongoing and future machine learning studies.