Reviewerly/exHarmony huggingface dataset:
CONSTRUCTIVE REQUIREMENTS FOR THE IMPLEMENTATION OF INFORMATION AND METHODOLOGICAL SUPPORT IN VOCATIONAL EDUCATION INSTITUTIONS

Educational and methodical materials in modern institutions of secondary vocational education are associated with the use of complex information and technical systems, computing and measuring instruments, and their implementation, using systems for transmission, storage, processing, formalization, and coding of information. At the heart of these processes are intensively implemented information systems that operate on the basis of information and communication technologies. Many studies have shown that the use of the above tools and systems requires the formation of a certain information culture of teachers of secondary vocational education, which they acquire in the process of vocational education. The current state of development and use of information and methodological support, which operates on the basis of information and communication technologies in secondary vocational education institutions, is analyzed. Pedagogical and ergonomic requirements and pedagogical conditions for the electronic educational resource for academic disciplines are substantiated. The conducted pedagogical experiment on the assessment of the level of competence of the teacher of secondary vocational education in the field of creation and use of the electronic educational resource as a part of information and methodical maintenance of educational process is described. In the process of designing information and methodological support of the educational process, significant sources of information were identified: pedagogical and ergonomic quality of the developed electronic educational resources; formation of professionally important qualities in the conditions of use of means of information and communication technologies; formation of the teacher’s readiness to develop and use an electronic educational resource based on the implementation of didactic capabilities of information and communication technologies.&#x0D; Keywords: information and methodological support; institutions of professional (vocational) education; professional education; educational process; electronic educational resource.
Deep Learning-Based Object Recognition and Detection Model

The chapter discusses the design and development of a novel object detection model keeping the motive that anyone and everyone with little to no prior knowledge can easily use it. The object detection is a part of computer vision which deals with detection and localization of objects in images or videos. These systems can be implemented for surveillance, keeping track of goods, robotics, medical field, self-driving cars, and much more. A lot of organizations are using it for various purposes such as it is used by NASA for analysis for deep space analysis, self-driving cars use it for making accurate decisions, and airports use it for security and surveillance. Although it has numerous use cases, but because of complex implementation and need of expertise in the field, it is not being widely used currently. In this chapter, we have made an effort to automate most of the process that a user should do in order to train and use models for object detection. The most complex part of using these technologies is training custom models, and only people having expertise in field are doing it. With the system designed by us, anyone would be able to train custom models on their own and they don’t have to do much for it. Normally, first users have to gather large amount of images and then annotate them 124one by one, which is a tedious and time taking process. One has to do this for thousands of images and each of those images can have multiple instances of various objects to train a model to give a good accuracy. We are automating the whole process with the help of AWS recognition module of AWS. You only look once (YOLO) object detection model is used for automation, as it is fast and accurate at the same time which gives it edge over the other models available for use. This model is based on deep learning to learn through the images provided by user in training phase and then later on can be used to perform detection on images or real-time videos. A graphic user interface (GUI) is also developed, so the users can easily interact with the system and train their own custom models and use as they want it to be used. Data are sent over the AWS servers and retrieve the results using a block of python code, then the response collected from AWS is manipulated and the data are extracted that is required to annotate the images and then processing is done according to the need of our target model. This format can be different for various models which can be used for object detection.
The Extent of the Students' Response to Music Notation Programs in Computer and Music Subject in Art Schools of the Jordanian Universities

This study aims to reveal the extent of the music students response to the computer subject and music for musical notation programs in Arts colleges in the Jordanian University, and the study used the descriptive and analytical approach, by collecting data and information's for this topic and analyzing it. The study consisted of all students of musical arts (BA) Bachelors degree 58 males and females in the second semester of the year 2019/2020 ,and due to the great importance of linking music learning and education in modern technology. These programs are considered among the many supporting factors for education, which don't rely on traditional method of education. This study was conducted on students who are currently studying in different academic levels and from both sexes, males and females. And to achieve this goal, the study sought to answer its questions. The study reached the following conclusions: First: The extent of the applied response of music students at Yarmouk University and the University of Jordan to the computerized music notation program was high. Second: The extent of educational response of music students at Yarmouk University and the University of Jordan to the computerized Sibelius music notation program was high. Third: The extent of the cognitive response of music students at Yarmouk University and the University of Jordan to Computer and Music subject was high
Petrological and Geochemical Characteristics of Al-rich Pelitic Granulites/Paragneiss from Thana, District-Bhilwara Rajasthan: Implication for Its Origin

Al-rich pelitic granulites/paragneiss are frequently observed in the mediumto high-grade metamorphic rocks of Thana. It is dominantly composed of sillimanite-kyanite-garnet-biotite-plagioclase-k-feldspar and a subordinate amount of quartz. Garnet both xenoblastic as well as idioblastic is wrapped round by flaky minerals. These Al-rich pelitic granulites are the result of the metamorphism of pre-existing sedimentary rocks under medium to high grade P-T conditions and consist essentially of sillimanite-kyanite -staurolite-garnet-biotite bearing Al-rich pelitic granulites/paragneiss. The geochemical data reflect that Thana pelitic granulite is of S-type and peraluminous in nature. Geochemically, the protolith for these Al-rich pelitic granulites/paragneiss are shale or greywacke. Paper records the petrography, geochemical characters and a probable origin of these Al-rich pelitic granulites/paragneiss.
Dynamic Smoothing of the Path of a Wheeled Robot with Automatic Fulfillment of Design Restrictions

The main concern when planning the path of an autonomous robot is to ensure the safety of movement. The next problem is to make the path curvature smooth, boundedness and limit its rate of change with design restrictions of the robot. If one tries to solve these problems directly and simultaneously, he will need to perform such cumbersome calculations that cannot be done in real-time mode. We propose to simplify and automate the generation of planar paths for a wheeled robot in this study by splitting the solution into two stages. At the first stage, we construct a primitive path as a planar polyline. Its segments connect the control points and ensure the working scenario executes safely. At the second stage, we solve the problem of smoothing the polyline joints without any geometric calculations. The proposed solution uses a dynamic generator constructed as a copy of the equations describing the movement of the center of mass of the wheeled platform. To imitate the robot’s control actions, we synthesize the generator’s corrective actions using the decomposition and S-shaped feedbacks. This approach ensures that the robot’s design restrictions on velocity, acceleration, and control torques are met. Also, the output variables of the generator simulate the coordinates of the robot’s center of mass. Thus, these variables will generate a naturally smoothed path with an acceptable curvature under tracking the coordinates of the reference non-smooth route.
Adaptive Camouflage for Special Purpose Vehicles

Special cars and vehicles can be used by police and military services. These can be buses, trucks for transporting people or off-road vehicles on wheels or tracks, equipped with armor or various equipment. One of the ways to reduce the visibility of the vehicle is camouflage. Vehicles are currently equipped with conventional camouflage, designed for camouflage using two main elements: color and pattern. The use of technology in city determines the need for continuous changes in camouflage elements to adapt to external conditions. Vehicles equipped with green camouflage will stand out clearly against a white wall. An active camouflage system could constantly update color and pattern, hiding vehicles in their current environment. The current adaptive camouflage systems are quite complex and expensive. The purpose of this study is to develop a system that is inexpensive and simple in design, which does not lose in terms of application efficiency to its analogues. The proposed system uses computer vision and image clustering methods to recognize the background color and determine the dominant color on it. The received value is passed to the RGB LED color panel controller to activate the corresponding color.
A Bi-Objective Learn-and-Deploy Scheduling Method for Bursty and Stochastic Requests on Heterogeneous Cloud Servers

In this article, we consider the dynamic allocation of bursty requests stochastically arriving at heterogeneous servers with uncertain setup times. Lower expected response time and less power consumption are desirable objectives of users and service providers respectively. However, sudden increase and decrease of cloud servers caused by bursty requests are rather challenging to get an appropriate trade-off between the two conflicting objectives which are closely related to the launched servers. The heterogeneity of the cloud servers further makes it more difficult to decide how to switch on and off servers and effectively and efficiently allocate bursty requests with balanced objectives. Based on a Markov decision process, a real-time bilevel decision-making model is constructed for unallocated requests which includes: whether to launch a server and which type of server to launch. A learn-and-deploy algorithm framework is proposed which contains two complementary stages. In the first stage, an effective offline bi-objective optimization algorithm is proposed to learn a set of policies, which provides helpful trade-off information for a decision-maker to choose a preferred policy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> . In terms of the system status, a policy decides whether to launch a server according to a state-action table and which server to launch using a server priority sequence. In the second stage, a computationally efficient policy deployment method is proposed to search the corresponding action in the selected policy based on the current system status and apply it to the real-time system. Experimental studies over a large number of random and real instances have been conducted to validate the effectiveness of the proposed bilevel model and algorithm. Compared to the most recent existing method, the performance of the proposed approach can at most achieve an 80% improvement on power consumption and 20% improvement on response time.
An Online Multiobject Tracking Network for Autonomous Driving in Areas Facing Epidemic

Multi-object tracking is of great importance in autonomous driving. However, with the outbreak of COVID-19, multi-object tracking faces new challenges in areas gripped by epidemics because of complex motion blur, frequent occlusions, and appearance deformations. To reliably improve object trajectory association in epidemic-plagued areas, we propose a temporal-spatial aggregation embedding network (TSAEN) for multi-object tracking. Our embedding network contains a temporal-aware correlation module (TACM) and spatial-aggregate embedding module (SAEM) that can fully obtain and aggregate appearance clues related to moving objects in previous frames. The TACM learns the temporal homogeneity features of the current and previous frames to perceive features with correlated appearance cues. Then, the SAEM adjusts the spatial deformation for each perceived temporal homogeneity feature and aggregates them for re-ID embedding learning. The experimental results demonstrate that our proposed method is able to achieve excellent overall performance.
Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression Data

Recent studies have established the potential of classifiers designed using association rule mining methods. The current study proposes such an associative classifier to efficiently detect dengue fever using gene expression data. Labelled gene expression data has been preprocessed and discretized to mine association rules using well-established rule mining methods. Thereafter, unsupervised clustering methods have been applied to the discretized gene expression data to reduce and select the most promising features. The final feature reduced discretized gene expression data is subsequently used to mine rules in order to classify subjects into ‘Dengue Fever’ or ‘Healthy’. Two well-known association rule mining methods, viz., Apriori and FP-Growth, have been used here along with various types of well established clustering methods. Extensive analysis has been reported with performance parameters in terms of accuracy, precision, recall and false positive rate using 5-fold cross-validation. Furthermore, a separate investigation has been conducted to find the most suitable number of features and confidence of association rule mining methods. The experimental results obtained indicate accurate detection of dengue fever patients at an early stage using the proposed associative classification method.
Artful Material Utterance: A core competence for Participatory Designers

This paper explores the idea of ‘material utterance’ as a way of reflecting on and analysing specific communicative actions in the practice of designing, and in particular for participatory designing. Material utterance extends the notion of utterance as used in linguistic pragmatics. This approach focuses attention on particular, situated communicative actions and draws attention to their materiality and embodiment. Using examples from a single participatory design initiative, the paper illustrates how attending to the specifics of individual material utterances, and relating them to the speaker/maker's intent, provides a useful perspective for reflection on our practice.
Mirrors: Reflecting the PDC Community

Since its first edition in 1990, the Participatory Design Conference (PDC) community has continued to evolve by bringing together researchers and practitioners from diverse disciplinary and cultural backgrounds [1]. A key aspect at the center of this community's practice has been research and design exploration in the context of Information and Communication Technologies [2,3]. Since the 1990s the PDC community has investigated Participatory Design (PD) practice in diverse and multi-faceted directions - from work focused on accountability and user involvement in the design process [4,5] to best processes and tools to enable diverse stakeholders to engage in co-design activities [6,7,8,9], experimental innovation and open-ended processes [10,11,12,13,14], PD practice decolonization [15,16,17] and pluriversality in PD practice [18,19] to name a few examples. Over the past three decades PD practice evolved greatly, as did its community. However, while PD practice and its evolution is documented [20,21,22,23], our understanding and documentation of how the PD community itself evolved over time is limited. This Situated Action focuses on an effort to deepen our understanding of the PD community by leveraging PD practice and actively involving the community in depicting (and, in the process, critiquing) its own profile.
Self-designing protests

This paper explores the notion of participatory design activism to describe design interventions that fall in between participatory design and design activism, arising organically through self-organisation. Illustrated by three examples from the Colombian graphic (design) explosion, which occurred during the mass protests in 2021, a case is made to decentre the designer from the process, arguing that protests are instead (co)designed by a variety of actors. The nature of the interventions described show that participatory design can make a difference through its marriage to design activism. In this context, designers take a step back and allow others to use, appropriate and expand on their creative concepts, effectively self-designing the protest as well as its surrounding environment.
Unleashing Indigenous Iban Values For Collective Technology Design

The digital divide continues to be a problem on a global scale. Since the pandemic began, there has been widespread media coverage of people being unable to work from home or conduct online studies due to the lack of internet access. Many development and design approaches have been used to accomplish specific rural community objectives, including user-centred, participatory and co-design. Each of these strategies is effective at accomplishing certain specific objectives. The purpose of this research is to investigate the cultural and values dimension of the Iban indigenous community in Malaysian Borneo, Sarawak, using a value sensitive design approach. Domestic and cultural probes techniques were used to conduct design workshops with members of the community to explore indigenous communal values and finally, the card sorting method was used to reflect and create meaning from the information gathered. Our processes of engagement and co-design targets establishing a pluriversal design space for building trust, dialogue and co-existence despite the differences between academic and indigenous knowledge systems. In future, the primary discovery regarding collective values will be incorporated into the design and development of Community Networks as a solution bridging the digital divide for the indigenous community in Sarawak.
KN(eat)TING: Sharing the table, crafting knowledge

KN(eat)TING: Sharing the table, crafting knowledge is an invitation to engage in shared practices that focus on the process more than the outcomes. As situated action, this project seeks for participants of the PDC2022 to inhabit a safe space where conversations around the conference topics, together with questions, thoughts and lived experiences can be shared. This rehearsal of tomar once, Chilean tea ritual, explicitly focuses on furthering critical conversations through joint material production under decolonial, queer frameworks. There are two planned outcomes: One is the experience of engaging in a territory that focuses on the process as a site for analysis and crafting of knowledge. The second is the co-creation of knitted pieces, materialising how these dialogues have the potential to craft knowledge. This situated action proposes that design education can benefit from textile practices as means of engaged thinking with the senses, embracing and bolstering the power of territories that deviate from western ontologies.
Relationality, commoning, and designing

This workshop explores and reflects upon both how relational ontologies can support design processes that target commons and commoning as outcomes, and how commons and commoning can work as speculative lenses for the understanding of relationality in Participatory Design. Here, we invite the PD community to engage with questions such as: how do we embrace and rely upon relationality when designing collectively and in a participatory manner within more-than-human ensembles? How do we become commoners and what do we nurture in common? What do we lose and what do we gain by considering commons with a keen eye on relationality? Which kind of relational qualities are essential for commoning design and designing commons? In short, “Relationality, commoning, and designing” aims to be a venue for critically supporting alternative and more sustainable futures for all (not only humans) by means of participatory designing and commoning.
Encryption Algorithms for Cloud Computing and Quantum Blockchain

Cloud computing is the succeeding generation of technology that delivers shared resources on demand, especially data storage. Following the model of pay per use, the cloud provides services as per user requirements. User’s data is stored in cloud storage at remote locations. Cloud storage can be beneficial in terms of accessibility and reliability, backup protection that is robust, and rapid deployment, with the purpose of recovery and lowering the storage cost because the organization does not have to purchase and manage expensive hardware. Cloud storage highlights major concerns related to data protection and data privacy and security. Whereas the issues of security and compliance do occur in cloud storage. Here, encryption techniques can protect data against unauthorized access. For the security, encryption and decryption of data could be done by security algorithms and such algorithms would surely enhance the security framework of any network. This chapter proposes the attribute-based access policy to combine with an identity-based encryption technique for cloud storage after a survey of various encryption algorithms for cloud platforms.
A Novel Knowledge Recommendation Method for Online Arts Education

Recommendation algorithms are often used in shopping, movies, music and other fields because of their excellent performance in solving the information explosion problem. Although it has been around for a long time, its application in education has always been rare and lagging behind. In order to improve the applicability of the recommendation algorithm in the field of online arts education, and to solve the imperfect problem of professional knowledge system construction caused by the lack of paths in the learning process for arts professional knowledge learners, this paper proposes to combine the knowledge graph of the arts field with a novel knowledge graph recommendation method. An algorithm Co-RippleNet is introduced, which overcome RippleNet&#x2019;s lack of explicit interest extraction by combining it with the co-word network. And through experiments compared with other recommended methods, it shows that this method has better performance on the education.