Reviewerly/exHarmony huggingface dataset:
Doing Collaborative Research on Symphonic Orchestra Audiences: Interventionist Ethnography of Music Practices

In this chapter, we will focus on research in the inter- and transdisciplinary collaboration between an orchestra, a higher arts education institute, and a university situated in the South of the Netherlands. Starting from the shared problem of how symphonic orchestras can shape new futures through innovating their practices, one project focused on studying and experimenting with artistically relevant forms of audience participation. It involved the design and organization of experimental concerts. Drawing on the work by Richard Sennett (Together: The rituals, pleasures and politics of cooperation. Allen Lane, 2012) on cooperation, and his distinction between dialectic and dialogic conversations, we present fieldwork on one of these concerts. We show how the practical work to make an event happen can be traced through the many conversations that shaped the collaborative process. We conclude that cooperation is a skill that needs care, imagination, but also the open-endedness of continued learning from each other.
Augmented Reality in Sports and Physical Education

Physical education (PE) courses involve the provision of sports knowledge and the engagement in motor skill practices. Instruction based on augmented reality (AR) has been rarely applied in the hands-on teaching of these courses. Currently, PE teaching is mostly aided by video-assisted instructions. However, such instruction does not provide interactive experience in practices and fails to integrate textbook-based static learning with dynamic learning based on motor skill demonstrations. Because AR can overlay virtual information on a real object, this technology enables learners to, for example, manipulate an interactive three-dimensional human character model overlayed on a textbook while reading the textbook. Thus, AR can overcome the disadvantage of video-assisted instruction. To investigate the effects of AR-assisted instruction on learning outcomes, motor skills of various difficulty levels, and learning motivation in students, two experiments were conducted using different teaching materials; specifically, instructions on basic running drills and Mach drills were offered to students. A quasi-experimental design was adopted. The results indicated that AR-based teaching materials outperformed video-based ones, particularly in the learning outcomes of difficult motor skills.
Augmented Reality for Cognitive Impairments

Augmented reality (AR) is a rapidly developing technology that has introduced new approaches for the design of human technology interaction in various fields. However, application of AR in the area of assistive systems for cognitively impaired people is not deeply studied yet. In this chapter, we investigate the state of the art of AR in assistive systems for the cognitively impaired. Specifically, we will investigate the role of AR during the design, implementation, and performance assessment of perception and memory augmentation assistive systems. We start with a summary of various technologies utilized for the development of AR systems, including sensor and camera technology, data visualization methods, computing paradigms, and intelligent data processing algorithms. Then, we discuss the fundamental mechanisms behind human memory system and look at the examples of the first technology-based human memory and intelligence augmentation systems. We overview various methods for estimation of the human cognitive state and mental workload utilized during the evaluations of such assistive systems in human involved experimental studies. Our main objective in this work is to find out the current status, challenges, and future perspectives of AR in the research of human memory and perception augmentation systems for the people with cognitive impairments.
Decision Support System For Submitting Work Leave Using The Vikor Method

Human resources have an important role in an office, namely as the spearhead for carrying out the organization's activities, because after all the progress and success of a company cannot be separated from the role and capabilities of good human resources. This study aims to determine the effect of work motivation (X1) and work ability (X2) variables partially and simultaneously on employee performance (Y). This type of research is explanatory research with a quantitative approach and is carried out using the genetic method. Genetics is a search technique that in computer science to find approximate solutions for optimization and Genetic search problems are particularly applied as computer simulations in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimization problem develops into a solution. - a better solution. This research was conducted on 40 permanent employees of the Binjai City Regional Civil Service Agency.
Decision Supporting System For Providing Free Accommodation To Foreign Tourists In North Sumatera Using Smart Method

A program for providing free accommodation to foreign tourists in North Sumatra has been designed using the SMART method. This program uses a system that functions as a ranking. This program uses a ranking system that aims to select on desktop devices such as computers and laptops to be carried out in a systematic assessment of the person concerned, this program uses the MySQL system to do programming, this program is intended for agencies whose function is to select foreign tourists. The Program for Providing Free Accommodation to Foreign Tourists in Sumatra uses XAMPP which can be quite easy to use, with this program it can help and facilitate several educational institutions who want to provide free accommodation in exchange for promotional services as well as teaching at their institutions.
On the Software Projects’ Duration Estimation Using Support Vector Regression

Estimating the project’s duration is one of the critical steps in helping to ensure project success. It helps to allocate resources and personnel appropriately during project development. This study aims to look for a more suitable algorithm between two selected algorithms for estimating project duration. Two machine learning algorithms, Multiple Linear Regression and Support Vector Regression, were used to estimate the project’s duration. The data used here is an ISBSG dataset with intelligent preprocessing to give an ideal fit to the algorithm used. The dependent variables used in the test are project size, maximum team size, and resource level. With the two algorithms selected, the estimated value of the project's duration is relatively close to the actual duration of the project. Through the six evaluation criteria, R-square, MAE, MAPE, RMSE, MBRE, MIBRE and the pair-wise t-test statistical method, the Support Vector Regression algorithm gives a much better estimate of the project's duration than the Multiple Linear Regression algorithm.
Hiding Simple Text Files in Multimedia Files: A New Method and Its Implementation

Specific data, due to their nature, must be adequately secured. It can be done, among others, by the use of steganographic methods, whose purpose is to hide information by embedding hidden information in a selected object (carrier). The most common way of hiding text in a carrier is the Least Significant Bit (LSB) method. Nevertheless, this method requires a sufficiently large medium to host hidden information. This article proposes a new method to hide simple text files. Firstly, secret data is converted with a simple affine cipher (which can be replaced with another form of encryption). Then the content is transformed directly into the output file without using a carrier. The output file can be a graphic file with an RGB structure or a sound file. The method is discussed in the example of selected test files. As the analysis shows, the resulting data obtained occupy less disk space than those obtained using the LSB method. Additionally, the implementation of the method in the form of a Python script is presented. Finally, the modifications and limitations of the proposed method of hiding the text are discussed.
An Efficient Hybrid Particle Swarm and Teaching-Learning-Based Optimization for Arch-Dam Shape Design

Particle swarm optimization is a popular meta-heuristic with highly explorative features; however, in its standard form it suffers from a poor convergence rate and weak search refinement on multi-dimensional problems. The present work improves the conventional particle swarm optimizer in three ways: adding a greedy selection for better intensification; embedding an extra movement borrowed from teacher–learner-based optimization; and utilizing a neighborhood strategy by averaging over a random half of the swarm. The performance of the proposed method is subsequently evaluated on three sets of problems. The first set includes uni-modal, multi-model, separable and non-separable test functions. The proposed method is compared with a standard particle swarm optimizer and its variants as well as other meta-heuristic algorithms. Engineering benchmark problems including the optimal design of a tubular column, a coiled spring, a pressure vessel and a cantilever beam constitute the second set. The third set includes constrained sizing design of a 120-bar dome truss and the optimal shape design of the Morrow Point double-arch concrete dam as a practical case study. Numerical results reveal considerable enhancement of the standard particle swarm via the proposed method to exhibit competitive performance with the other studied meta-heuristics. In the optimal design of Morrow Point Dam, the proposed method resulted in a material consumption 21 times smaller than the best of the initial population and 26% better than a recommended practical design.
Liubai Technique in the Real-time Electroacoustic Chinese Art Song ‘Lang Tao Sha’ (浪淘沙)

Liubai (留白, literally ‘leaving blankness’) is a unique method of expression in Chinese classical paintings. The core spirit of the technique is inextricably linked to ancient Chinese Taoism and traditional aesthetics, which are prominently featured by simplicity and nationality. This article, taking the real-time interactive electroacoustic Chinese art song ‘Lang Tao Sha’ (浪淘沙), explores the use of the artistic technique of liubai from the perspective of a soprano singer by focusing on the three aspects of lyrics, vocal music and electroacoustic music creation, and singing performance.
La lingua del<i>Tractatus de Locis Sanctis</i>di Francesco Pipino da Bologna Volgarizzamento veneziano del XV secolo

This article presents the linguistic description of the Venetian version of the Tractatus de locis terre sancte, the Latin travel chronicle to the Holy Land written by Francesco Pipino from Bologna (1270-1330 ca.). The description we propose is divided into phonetics and morphology, then followed by some general considerations about the described features and an index with the attested lexical and onomastic entries.
A fast two-level grid index algorithm for common edge extraction in vector data compression

In this paper, a fast common edge extraction method based on a two-level grid indexing strategy was proposed for eliminating the common edge ‘cracks’. A coarse-grained first-level grid index was firstly built which maps each polygon with its intersecting polygons in the graphics set; secondly, a fine-grained second-level grid index is established linking each grid cell with the vertices falling into the cell, which can be used to quickly find the common points shared by intersecting polygons. The common edge ‘cracks’ problem can be solved if the deleted common points are consistent on two adjacent graphics during compression.
A State-of-the-Art Association Rule Mining Survey and its Rare Application, Challenges, Progress

In recent years, association rule mining is exploring its popularity in the rule mining research communities. In this paper, we present the taxonomy of association patterns analysis approaches in depth. The association patterns analysis is based on support constraints, interestingness, and patterns dimension from different levels of abstraction. An in-depth research statistical view is presented in each category of the association patterns analysis research. Among the identified association rule mining research categories, we have selected rare pattern mining for further exploration of our study. Analysis of rare patterns is very much helpful in determining the rare relationships between patterns. Also, we will identify novel rare data patterns analysis techniques to solve the various problems in real-life applications. The overall research impact in the association rule mining research domain can be found in this review article. It will focus on various research scopes and the future directions of rule mining as well as rare pattern mining research.
An Enhanced Spotted Hyena Optimization Algorithm and Its Application to Engineering Design Scenario

The Spotted Hyena Optimization (SHO) algorithm is inspired by simulating the predatory behavior of spotted hyenas. While the mathematical model of the SHO algorithm is simple and optimal, it is easy to fall into local optimization and causes premature convergence compared to some metaheuristic algorithms. To the end, we propose an enhanced Spotted Hyena Optimization algorithm, a hybrid SHO algorithm using Elite Opposition-Based Learning coupled with the Simplex Method called EOBL-SM-SHO. The EOBL-SM-SHO algorithm combines the characteristics of the simplex method’s geometric transformations (reflection, inside contraction, expansion, and outside contraction) with more practical information on elite opposition-based learning strategy. They can significantly strengthen the SHO algorithm’s search range and augment the hyena population’s diversity. Furthermore, we employ eleven benchmark functions and three engineering design issues to gauge the effectiveness of the EOBL-SM-SHO algorithm. Our extensive experimental results unveil that EOBL-SM-SHO achieves better accuracy and convergence rate than the state-of-the-art algorithms (e.g., Artificial Gorilla Troops Optimizer (GTO), Cuckoo Search (CS), Farmland Fertility Algorithm (FFA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Spotted Hyena Optimizer (SHO)).
Crossover‐SGD: A gossip‐based communication in distributed deep learning for alleviating large mini‐batch problem and enhancing scalability

Summary Distributed deep learning is an effective way to reduce the training time for large datasets as well as complex models. However, the limited scalability caused by network‐overheads makes it difficult to synchronize the parameters of all workers and gossip‐based methods that demonstrate stable scalability regardless of the number of workers have been proposed. However, to use gossip‐based methods in general cases, the validation accuracy for a large mini‐batch needs to be verified. For this, we first empirically study the characteristics of gossip methods in a large mini‐batch problem and observe that gossip methods preserve higher validation accuracy than AllReduce‐SGD (stochastic gradient descent) when the number of batch sizes is increased, and the number of workers is fixed. However, the delayed parameter propagation of the gossip‐based models decreases validation accuracy in large node scales. To cope with this problem, we propose Crossover‐SGD that alleviates the delay propagation of weight parameters via segment‐wise communication and random network topology with fair peer selection. We also adapt hierarchical communication to limit the number of workers in gossip‐based communication methods. To validate the effectiveness of our method, we conduct empirical experiments and observe that our Crossover‐SGD shows higher node scalability than stochastic gradient push.
One-Stage Visual Relationship Referring With Transformers and Adaptive Message Passing

There exist a variety of visual relationships among entities in an image. Given a relationship query ⟨subject, predicate, object⟩, the task of visual relationship referring (VRR) aims to disambiguate instances of the same entity category and simultaneously localize the subject and object entities in an image. Previous works of VRR can be generally categorized into one-stage and multi-stage methods. The former ones directly localize a pair of entities from the image but they suffer from low prediction accuracy, while the latter ones perform better but they are indirect to localize only a couple of entities by pre-generating a rich amount of candidate proposals. In this paper, we formulate the task of VRR as an end-to-end bounding box regression problem and propose a novel one-stage approach, called VRR-TAMP, by effectively integrating Transformers and an adaptive message passing mechanism. First, visual relationship queries and images are respectively encoded to generate the basic modality-specific embeddings, which are then fed into a cross-modal Transformer encoder to produce the joint representation. Second, to obtain the specific representation of each entity, we introduce an adaptive message passing mechanism and design an entity-specific information distiller SR-GMP, which refers to a gated message passing (GMP) module that works on the joint representation learned from a single learnable token. The GMP module adaptively distills the final representation of an entity by incorporating the contextual cues regarding the predicate and the other entity. Experiments on VRD and Visual Genome datasets demonstrate that our approach significantly outperforms its one-stage competitors and achieves competitive results with the state-of-the-art multi-stage methods.
Cryptanalysis of multivariate threshold ring signature schemes

Petzoldt et al. proposed the first multivariate threshold ring signature scheme extending the idea of a ring identification scheme in 2013. They claimed that their t-out of n threshold ring signature satisfies the security properties of unforgeability and source anonymity. Later, Zhang-Zhao in 2014 and Duong et al. in 2021 proposed other multivariate threshold ring signature schemes using different tools with the same technique. In this article, we cryptanalyze all these schemes and give an attack on their source anonymity which shows how the verifier can find the actual set of signers after some finite number of trials, viz., with at most N=(nn−t)+(nn−t−1)+…+(n1)+(n0) in t-out of n threshold ring.
China's new data capabilities will outlive zero-COVID

Significance These were used to monitor the exposure risk of individuals and provide a basis to mandate quarantine or refuse access to public venues. Impacts The phase-out of COVID-19 apps will differ on a regional basis, creating uncertainty and divergence in the short term. The apps give the government a useful case study of the way in which existing data can be reconfigured for novel purposes. The phase-out of the apps provides insights into the cost-benefit analyses of Chinese security services.
Construction of a Compact and High-Precision Classifier in the Inductive Learning Method for Prediction and Diagnostic Problems

The study is dictated by the need to make reasonable decisions in the classification of observations, for example, in the problems of medical prediction and diagnostics. Today, as part of the digitalization in healthcare, decision-making by a doctor is carried out using intelligent information systems. The introduction of such systems contributes to the implementation of policies aimed at ensuring sustainable development in the health sector. The paper discusses the method of inductive learning, which can be the algorithmic basis of such systems. In order to build a compact and high-precision classifier for the studied method, it is necessary to obtain a set of informative patterns and to create a method for building a classifier with high generalizing ability from this set of patterns. Three optimization models for the building of informative patterns have been developed, which are based on different concepts. Additionally, two algorithmic procedures have been developed that are used to obtain a compact and high-precision classifier. Experimental studies were carried out on the problems of medical prediction and diagnostics, aimed at finding the best optimization model for the building of informative pattern and at proving the effectiveness of the developed algorithmic procedures.
Ontology-Based Metrics Computation for System Security Assurance Evaluation

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Self-Writer: Clusterable Embedding Based Self-Supervised Writer Recognition from Unlabeled Data

Writer recognition based on a small amount of handwritten text is one of the most challenging deep learning problems because of the implicit characteristics of handwriting styles. In a deep convolutional neural network, writer recognition based on supervised learning has shown great success. These supervised methods typically require a lot of annotated data. However, collecting annotated data is expensive. Although unsupervised writer recognition methods may address data annotation issues significantly, they often fail to capture sufficient feature relationships and usually perform less efficiently than supervised learning methods. Self-supervised learning may solve the unlabeled dataset issue and train the unsupervised datasets in a supervised manner. This paper introduces Self-Writer, a self-supervised writer recognition approach dealing with unlabeled data. The proposed scheme generates clusterable embeddings from a small fixed-length image frame such as a text block. The training strategy presumes that a small image frame of handwritten text should include the writer’s handwriting characteristics. We construct pairwise constraints and nongenerative augmentation to train Siamese architecture to generate embeddings depending on such an assumption. Self-Writer is evaluated on the two most widely used datasets, IAM and CVL, on pairwise and triplet architecture. We find Self-Writer to be convincing in achieving satisfactory performance using pairwise architectures.
A CNN-BiGRU Text Classification Model Merging Positional Encoding and Attention Mechanism

Aiming at the problems that Convolution Neural Network (CNN) and Bi-directional Gated Recurrent Unit (BiGRU) have insufficient ability to extract text features and cannot obtain the word weight that has a great impact on classification in text, a text classification model based on positional encoding and attention mechanism is proposed.Firstly, positional encoding is introduced before CNN input to obtain the word vector containing text position information, which improves the limited ability of convolution sliding window to extract word association information.At the same time, multichannel CNN and BiGRU are used to obtain local important features and global context information respectively.Finally, the attention mechanism is introduced to fuse the important output features of each channel, and the classification result is obtained by Softmax.The experimental results of two public data sets show that the model can effectively extract text semantic features and improve the accuracy of text classification.
Comparative analysis of web application interfaces with buttons in graphical and text form for universal design

The purpose of this article is to compare application interfaces with buttons in graphical and text form. For the needs of the research, a web application was prepared with the functionality to change the form of buttons. The analysis of the availability of the application was performed using the WAVE tool. A research group of 10 students was prepared for the eyetracking experiment and form. The surveys conducted on the eyetracker allowed to assess which version of the interface enables faster and correct execution tasks. LUT’s list was used to test the quality of the designed interface.