Reviewerly/exHarmony huggingface dataset:
Apprentices’ Opportunities and Criticalities in Adopting Affordable User Interface Development Tools

Day by day, more and more people get in touch with user interface (UI) development matters, since user experience (UX) and, as a direct consequence, user-centered design (UCD) gain importance in establishing the success of products on the market or, simply, because users feel the need to interact with products easier and more directly. In the last years, affordable UI development tools have appeared on the market, freeing apprentices from owning specific skill and knowledge and boosting interactive activities by allowing different competencies to work in synergy. This research exploits the experience where university students - the apprentices - use several affordable tools to develop the UI of a refrigerator. Pointing out the opportunities and criticalities encountered during the experience allows highlighting those variables to consider in selecting the most suitable tools already on the market as well as in developing new ones, all of this aiming at a sort of “discount UI development”.
Interpreting Offers with Attributes

This chapter continues the work of the previous one in creating a DSL for expressing offers for the Time Machine Store. As you may remember, deep embedding was chosen as the way to model the offers language in Haskell. A deeply embedded language is divided between its syntax, expressed as a set of Haskell data types, and its interpretation, which is responsible for assigning a meaning to each value in the language. In this example, the interpretation of an offer could be a function that, given a list of products and prices, applies the offers and discounts and returns the new price.
Posture Interactive Self Evaluation Algorithm Based on Computer Vision

Many workers and citizens have been forced to make a lifestyle change in the past two years due to the pandemic emergency. In order to keep a high level of personal health, the doctors suggest to do fitness exercises. Before the pandemic it was possible to do these exercises at the gym or during dedicated session in the office supervised by professional trainers. During the pandemic emergency the gyms were closed, the workers were forced to stay home and the people started to do gym exercises by themselves without the control of a professional figure. This situation could lead to several diseases associated to musculoskeletal disorders if the exercises are performed incorrectly. In this work, an approach based on the pose-estimator application OpenPose is developed. The reference exercise is an isometric squat performed by a professional trainer. During the exercise, thanks to a deep neural network, the pose-estimator gets a series of key-points and vectors which represent the user’s pose. A dataset of videos (for both the correct and incorrect postures) has been used to train several machine learning algorithms. The result is an automatic tool that recognizes incorrect poses during the exercise and helps the performer to correct it.
An Augmented Reality Framework for Remote Factory Acceptance Test: An Industrial Case Study

Factory acceptance test is the inspection of equipment and components at the supplier’s premises before delivery or final inspection. However, this control can represent a considerable cost for the customer, especially when the manufacturer’s company is geographically far from the customer one and inspections must be frequent. In this paper, the authors present a framework to support the factory acceptance test based on augmented reality (AR) techniques and model-based definition aimed at dimensional checks that does not require the physical presence of the customer at the supplier's premises. The supplier must be previously equipped with an automatic measuring machine. Once the component under inspection is placed inside the machine, this reads the type and the position of the features to be measured along with the related specification limits directly from the annotated 3D model of the component. The results are automatically transmitted to the customer’s site. Through a tablet, the supplier, guided by the customer, reads the results of the measures directly on the measured object through augmented or mixed reality techniques. Any out-of-specification dimension can be remeasured in real time with the customer’s remote assistance using traditional measurement techniques. The proposed architecture, at an advanced stage of experimentation, is discussed with reference to an industrial case study proposed and using an entry level commercial 3D scanner.
Design of XR Applications for Museums, Including Technology Maximising Visitors’ Experience

eXtended Reality (XR) technology can enhance the visitors’ experience of museums. Due to the variety of XR technologies available that differ in performance, quality of the experience they provide, and cost, it is helpful to refer to the evaluation of the various technologies performed through user studies to select the most suitable ones. This paper presents a set of empirical studies on XR application for museums to select the appropriate technologies to meet visitors’ expectations and maximise the willingness of repeating and recommending the experience. They provide valuable insights for developing Virtual Museum applications increasing the level of presence and experience economy.
Performance Evaluation of an Immersive Measurement Instrument for Automotive Field Applications

The development of cutting-edge technologies in the industrial sector has led to the demand for increasingly specific tools in the optimization of efficiency problems. The automotive sector is the one that makes the most use of these technologies, such as, among all, Virtual Reality (VR) and Augmented Reality (AR). A virtual reality tool is inserted as a guide tool for the user in drawing as quickly as possible the key information for optimizing the process. This research work fits into this context, the goal of which was the implementation of an immersive platform for carrying out accurate measurements within an entirely virtual automotive environment. A VR system of this type allows to check the dimensional and shape tolerances of car components, performing measurement, with high precision and in real time, in a custom-made virtual environment, in which it is possible to simulate the presence of a myriad of components and test their mutual interaction. A comparative test was carried out, obtained by varying the graphical and geometrical model in the VR settings, in order to evaluate the level of usability and the degree of efficiency of a tool for measuring distances between objects in a virtual reality environment, depending on the system parameters. The validation of a measurement instrument in VR is part of an increasingly current technological context in which the need to optimize the time-cost curve embraces the need for increasingly accurate results.
Preliminary Validation of a Virtual Environment for Simulation and Recognition of Human Gestures

Humans are able to communicate by a wide variety of means. Gestures often play an important role in this multimodal communication. In order to also ensure robust interaction between humans and machines, it is important that machines are able to recognize human gestures. This typically requires time-consuming subject tests that limit the number of conditions that can be tested. However, by moving these tests from the physical to a virtual environment, each test condition can be evaluated quickly, eliminating the need for numerous repetitions. The purpose of this work was to validate the use of a virtual test environment in comparison to physical testing. This was done by conducting a subject test and developing a virtual model of the human upper limb. The motion profile of the subject performing a simple gesture was recorded with a visual optical motion capture system and used as input for the newly developed virtual model. Acceleration signals captured with an IMU attached to the subject's right wrist were used as a reference signal and compared to signals simulated by a digital twin of the sensor. The pilot study proved the capabilities of the proposed approach and showed some of its limitations.
Energy Efficient Trajectory Planning in Robotic Cells via Virtual Prototyping Tools

The constant growth in global energy demand, and corresponding prices rise, is soaring new engineering methods for reducing energy consumption in manufacturing processes. For decades, industrial robotics have been enabling quality enhancement of end-products by using flexible manufacturing processes, without much concern to energy cost, but now a makeover is happening. Calls for sustainable and green manufacturing processes are being promoted across the globe with the aim to produce more goods and with less consumption. In this paper, a new method is presented focusing on the optimization of energy intake by industrial robots, without the need to change their hardware set and just modifying the trajectory planning of the end-effector. A test case scenario consisting of a robotic cell with 4 pick-and-place manipulators has been set to validate the method. Starting from a pre-scheduled trajectory, robots are moved at the highest speed and acceleration and, by performing the sequenced operations, the optimal trajectories are defined. The goal is to find a trajectory that minimizes the time cycle and the total energy consumption, while avoiding collisions between the robots’ links: comparing the results thereof to those of the pre-scheduled trajectory, noticeable energy saving has been obtained along with possible decrease of the cycle time.
More Monads: Now for Recommendations

The previous chapter introduced monads. In particular, the tasks of handling errors using Maybe, constants using Reader, logging using Writer, and state were presented through the same point of view, making clear how monad functions form the scaffolding that combines code for all of them.
A Mixed Reality Application for Collaborative and Interactive Design Review and Usability Studies

In recent days product design and review cycles are considerably held to many constraints and requirements. One of the main requirements regards the usability of solutions and the impact that design choices have on final users. Therefore, it is becoming crucial to anticipate human factors concerns in the preliminary phases of product development process. Also, since human factors affect the users subconsciously and influence the decision making in a significant way, they need to be enabled to observe the users while interacting with the product in real-time. In this framework, the platform that the human factors expert and the users use plays a vital role in influencing the human factor assessments and evaluations. Latest developments in Extended Reality opened the way toward the possibility to conceive new simulation platforms that allow experts to deeply explore the products in real-time through collaborative and interactive environments before the physical mockup of the product. The purpose of this paper is to outline a Mixed Reality (MR) tool in the field of aircraft interior design to demonstrate it as a potential co-creative platform for involving human factor experts in the loop while the task is ongoing. An MR multi-user, co-located, collaborative and interactive environment of an aircraft galley is developed where the HF specialist and flight crew member can co-exist to visualize the real scale model of the galley and perform an operational task. Such co-creative tool is foreseen to execute usability tests during design review phase and reduce time, costs of product development cycle while meeting the user requirements.
A Novel Approach to Use Marker-Less Mixed Reality Applications with In-Motion Systems

Extended Reality (XR) technologies can be a valid tool for supporting operators with assembly instructions in real-time, directly superimposed on the product. A strategy to face dynamic effects, such as sudden environmental changes and objects in movement, still needs to be defined to implement effective XR applications in moving production lines. The physical environment must be tracked and recognized to determine the position and the orientation of digital content in space. This paper aims to address these open issues by proposing a Mixed Reality (MR) application to support workers in the wire harness process for the tractor’s drivelines production, which requires the product to be in motion. For this aim, the Microsoft Azure Spatial Anchors with Microsoft HoloLens 2 were adopted. The designed and developed MR application allows the operator to display, step-by-step, consecutive work instructions provided through textual indications, pictures, videos, and animations. Through the Azure Spatial Anchors, the digital content can be anchored over the driveline parts. By knowing the speed of the production line and the duration of each wiring task, a time-controlled application has been developed, to assure the visualization of the instruction in correspondence of the relative driveline elements, in the right time. The application has been tested on-field with expert operators. Despite the presence of some drawbacks related both to the driveline motion and technology, the operators’ feedback pointed out satisfactory and promising results since the application allowed them to reduce errors and forgetfulness.
Intelligent Action Recognition and Dance Motion Optimization Based on Multi-Threshold Image Segmentation

In order to improve the recognition and optimization effect of dance movements, this paper combines multi-threshold image segmentation technology to perform intelligent recognition of dance movements. Moreover, this paper analyzes the window focus calibration process in the cascade multi-threshold expansion method and focuses the window on the object body to expand from small to large. Then, due to the local optimum problem of single-layer expansion, this paper proposes a cascade expansion method, and comparative experiments analyze the effect of this method on window optimization. In addition, this paper combines the actual situation of dance movement training to construct an intelligent action recognition and dance movement optimization system based on multi-threshold image segmentation. The research shows that the intelligent action recognition and dance motion optimization method based on multi-threshold image segmentation proposed in this paper has a good effect.
Design and Implementation of Education and Training Management System Based on Blockchain

Ensuring the quality of education and training has always been the focus of social concern. Traditionally, many chain education and training institutions have adopted a centralized management platform, which will lead to a lack of effective supervision among institutions. If the training records of trainees have problems such as data falsification/tampering, it is not easy to trace back, so that the security and credibility of the training data cannot be guaranteed. Moreover, due to the influence of geographical, economic and other factors, there is a lack of unified assessment standards in various regions, which makes it difficult to guarantee the training quality of trainees. In this context, this paper proposes an education and training management system (ETS) based on Hyperledger Fabric blockchain technology to provide a safe and reliable traceability solution for education and training management. In this system, we define some rules through smart contracts to implement different business logic. Using the characteristics of decentralization and high credibility of the blockchain, it solves the problems of uneven training quality and untraceable training records in the traditional education and training process. The system we built enables reliable sharing and privacy protection of training data. In addition, this paper provides an effective network configuration idea to obtain the best performance of the blockchain system. The performance of the proposed system is evaluated by experiments.
At Design-Time Approach for Supervisory Control of Opacity

Opacity is a property of information flow that characterizes the ability of a system to keep a secret information hidden from a malicious external entity, called an attacker. Given a critical system that may leak confidential information, an attacker with partial observation of the system and a subset of controllable actions, we propose an approach to synthesize a controller that enforces the system’s opacity. This controller is designed as a function that applies, at run time, to the current execution to disable any controllable action that eventually leads to the violation of the opacity of the system. The supervision function is built at design time based on a new version of the symbolic observation graph that represents a reduced abstraction of the state space graph of the system preserving the observation of both the attacker and the controller. The language induced by this function is proven to be controllable, observable and supremal no matter the relation that exists between the observations of the attacker and the controller.
PK-Graph: Partitioned $$k^2$$-Trees to Enable Compact and Dynamic Graphs in Spark GraphX

Graphs are becoming increasingly larger, with datasets having millions of vertices and billions (or even trillions) of edges. As a result, the ability to fit the entire graph into the main memory of a single machine faces challenges in common hardware, even more so in edge/IoT-like devices (i.e., more energy efficient but also more resource constrained). Reading the graph from secondary storage may pose in itself significant overhead, negatively impacting query performance and storage requirements. It thus becomes relevant to explore techniques to optimize the storage of graphs, specially in memory, in a way that circumvents space limitations, while avoiding compromising the performance of processing. We observe that current graph storage systems manage the graph representation by storing graphs in an uncompressed format, either: i) in a shared architecture which leads to a higher space overhead and the inability to represent the graph entirely in main memory, or ii) in a distributed architecture, where the graph dataset is partitioned over a cluster of machines with each one storing in main memory only a fragment (shard) of the (uncompressed) graph. We present PK-Graph, our proposal which extends a distributed graph processing system, highly used in academia and industry (Spark GraphX), in order to deploy the use of a compressed graph representation, with added support for dynamic updatable graphs (not currently supported in GraphX). Our experimental results show that PK-Graph can achieve up to 50% lower graph memory usage, while maintaining competitive performance in executing typical graph operations used in common applications.
Bi2E: Bidirectional Knowledge Graph Embeddings Based on Subject-Object Feature Spaces

In high connectivity knowledge graph, distance based knowledge graph embedding methods show promising performance on link prediction task, and are capable of encoding complex relations and key relation patterns. However, the existing methods fail to achieve excellent results in knowledge graph with poor context structure information. To mitigate this problem, we propose Bi2E, a bidirectional model based on subject-object feature spaces. To enhance the efficiency of data utilization and perceive more potential semantic links, we utilize the bidirectionality of relation to model from both forward and reverse directions. And Bi2E represents triples in the subject-object feature spaces, which enables it to capture richer feature information from rare data. In addition, Bi2E employs adaptive margin $$\gamma $$ which makes embedded representation more flexible by only using a small amount of feature information. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of Bi2E. Moreover, we set a new state-of-the-art on two low connectivity knowledge graph benchmarks.
Distance learning in the system of higher education in Kazakhstan (based on main platforms and services)

In this article, the author considers distance learning as a new stage in the development of education. The author focuses on the latest achievements in the educational process, as well as the impact of the self-isolation process on the introduction of distance learning. The main advantages and disadvantages of distance learning technology are determined. The basic principles that a teacher needs to master for conducting distance learning of students are considered. The main educational platforms and services are presented, such as Microsoft Teams, Moodle, Zoom, Cisco Webex, Google Classroom. The main features of each platform and service for distance learning are highlighted. The strengths and weaknesses of each of the above platforms and services are considered.
Assessing Software Fault Risk with Machine Learning

This paper presents a data-driven solution for assessing fault (defect) risk in software code. Our particular interest is in code bases of large software projects (including open-source projects) that involve multiple developers, and where the project code base evolves over an extended period of time (multiple years). Our goal is to predict fault risk at the level of code check-in episodes, where individual commits are assessed for risk. Our solution predicts code risk by analyzing a rich variety of data that has been aggregated about software engineering development behavior. It is powered primarily by machine learning. A central aspect of this work is on determining the most effective machine learning algorithm(s) for this problem, after a comprehensive investigation of a wide variety of algorithms and frameworks. The resulting system is effective in predicting risk code commits in repositories with a very sparse code fault density (1–3%). We are able to achieve an average of tenfold improvement in the precision (Precision@100) of identifying faulty commits, and an as high as 20-fold improvement in some cases. The improved efficiency in identifying risky code translates directly to expected improvement in efficiency (reduction of time and effort) for code review.
Overview of ILSAs and Aspects of Data Reuse

The landscape of international large-scale assessments (ILSAs) in education has constantly grown since the implementation of the first study in the early 1960s. This chapter provides an overview of recent school-related ILSAs, i.e., ICCS, ICILS, PASEC, PIRLS, PISA, SACMEQ, TALIS, TERCE, TIMSS, and TIMSS Advanced, including a description of common features and major differences, and addresses the reuse of ILSA data in order to conduct secondary analyses. Topics include objectives of ILSAs, domains of investigation, study sizes in terms of participating entities (countries, economies, regions), target populations and samples, techniques and levels of data collection, the construction and contents of the international databases, and methods of data analysis, with a focus on those relevant for data reuse. Time issues are also addressed, including first implementation, frequency of data collection, and number of cycles. The chapter suggests various ways of classifying ILSAs, using different tables to facilitate fast insights. The initiators, responsible for the development and conduct of these ILSAs, i.e., the CONFEMEN, IEA, OECD, SEACMEQ, and UNESCO, are briefly described and the measures they take for supporting secondary analyses. In this context, the first centralized platform, the ILSA Gateway, is presented as a source that facilitates access to information and materials from all ILSAs and has the potential to encourage and inspire knowledge exchange, discussions, and future research. The chapter concludes with a brief outline of some remaining gaps and challenges.