Reviewerly/exHarmony huggingface dataset:
Song Similarity Analysis With Clustering Method On Korean Pop Song

Trend about the growth of music with the genre of pop, which is the most demanding genre by people, with the attraction that comes from the characteristics that make it unique, so that it becomes a factor in the popularity of the song. Since March 22, 2020, Korean songs or K-Pop have become a popular song genre, beating local pop music based on developments from Google Trends, many factors cause the popularity of the song genre, which comes from characteristics of the song. So these factors are sought through the song’s similarity. Due to the limitations of computers in predicting the song’s similarity-based on sound, so the model created to predict the song’s similarity using a grouping algorithm, those are KMeans Clustering and Self-Organized Map, began by learning the K-Pop songs characteristics obtained from Spotify until they were split into several groups. with each character that is suitable for each group, the factors of the characteristics of the K-pop song are obtained from the results of the analysis on each cluster of the K-pop songs. Thus, generally, the majority of K-pop songs are songs that are energetic, loud, and like to be danceable. Based on the results of the analysis by splitting them into 4 groups using K-Means because it is better than SOM, there are 2 pairs of clusters, each of which has similarities to each other in terms of tempo and mood descriptions. Where the factors of k-pop songs are represented by an explanation of the characteristics both in general and based on each cluster.
Research on Carbon Emission Control Method of Construction Based on Whole Life Cycle

The current construction carbon emission control methods mainly focus on the construction stage of the project as the research object, which can not control the carbon emission of each link in the construction, resulting in the carbon emission control effect is not ideal. Therefore, in order to achieve the goal of emission reduction, design the carbon emission control method of construction based on the whole life cycle. According to the life cycle theory, the energy consumption data of construction link is collected, and the carbon emission prediction model of construction is constructed. According to the prediction results, carbon emission control schemes and measures are selected. So far, the design of carbon emission control method based on the whole life cycle of construction is completed. Experiments show that this method can effectively reduce the carbon emissions in construction and promote the green development of construction.
A Minimalist Open Source Behavior Tree Framework in C++

Decision making is one of the most important topics in agent related environments, like video games and robotics competitions. Among many agent behavior architectures, behavior trees became the standard in the scientific and industrial community, due to their modular and extensible properties. In this work, we developed an open source behavior tree framework in C++. This framework benefits from the large experience our robotics team, ITAndroids, has using behavior trees in robot soccer competitions. Its implementation is minimalist, but easily extensible since we needed to accommodate requirements from different projects in our team. We also show tests and application examples of this framework. Despite being developed with robotics competitions in mind, we believe it may be easily integrated into many applications where intelligent agents are needed. We were able to create a simple, intuitive, and efficient structure, which can be easily implemented and extended.
Some geometrical and topological properties of DNNs' decision boundaries

Geometry and topology of decision regions are closely related with classification performance and robustness against adversarial attacks. In this paper, we use differential geometry to theoretically explore the geometrical and topological properties of decision regions produced by deep neural networks (DNNs). The goal is to obtain some geometrical and topological properties of decision boundaries for given DNN models, and provide some principled guidance to design and regularization of DNNs. First, we present the curvatures of decision boundaries in terms of network parameters, and give sufficient conditions on network parameters for producing flat or developable decision boundaries. Based on the Gauss-Bonnet-Chern theorem in differential geometry, we then propose a method to compute the Euler characteristics of compact decision boundaries, and verify it with experiments.
To the Side, to the Side: Academic Libraries and LibGuides Layout Adoption

In 2014, Springshare introduced a new LibGuides layout option. In addition to the traditional horizontally-tabbed layout (top navigation), LibGuides designers now have the option of vertically-arrayed tabs on the upper left-hand side of the page (side navigation). Like the university hosting this study, schools have, to varying degrees, shifted their LibGuides toward side navigation and away from top navigation. To better understand the research behind this migration, researchers at the University of Northern Iowa conducted a thorough literature review on navigation menu location preferences and navigation menu efficacy within the field of librarianship and computer science. This review identified a complex and diverse set of results not often reported in the library literature. To gauge the extent of migration toward side navigation in LibGuides, this study investigated the navigation layout of the ten most visited LibGuides at 462 academic libraries throughout the United States during the summers of 2019 and 2021. While the majority of the LibGuides reviewed at research universities are now in side navigation format, the adoption rate of side navigation in LibGuides differed dramatically by the size and type of academic institution.
Application of Blockchain in the Saffron Industry

In the era of tech-driven globalization, supply chains are becoming increasingly complex, with an increasing number of stakeholders spread across continents. A complex supply chain requires complex management systems. Supply chain management digitalization and information technology have advanced simultaneously in time, but a more technological and connected society calls for more information, uncovering the limits of supply chain management tools available today, which, while providing efficiency, automation, and organizational capabilities, are quite scarce in providing transparency. Consumer awareness on sensible issues such as climate change, human rights, or counterfeiting is increasing.
A Study on Use of Machine Learning to Evaluate the Security of Common Key Cryptosystem

This paper investigates a machine learning based method to classify common keys of Advanced Encryption Standard (AES). Analysis to encryption procedures of AES identifies key sub-procedures that provide security features against machine learning based attack.
Forecasting in Big Data Environments: An Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)

This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors (“big data” environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of a back-propagation algorithm of a neural net with skip-layer connections. We expressly include both linear and nonlinear components. This is a high-dimensional learning approach including both sparsity $$L_1$$ and smoothness $$L_2$$ penalties, allowing high-dimensionality and nonlinearity to be accommodated in one step. This approach selects significant predictors as well as the topology of the neural network. We estimate optimal values of shrinkage hyperparameters by incorporating a gradient-based optimization technique resulting in robust predictions with improved reproducibility. The latter has been an issue in some approaches. This is statistically interpretable and unravels some network structure, commonly left to a black box. An additional advantage is that the nonlinear part tends to get pruned if the underlying process is linear. In an application to forecasting equity returns, the proposed approach captures nonlinear dynamics between equities to enhance forecast performance. It offers an appreciable improvement over current univariate and multivariate models by actual portfolio performance.
Dimension-Based Interactions with Virtual Assistants: A Co-Design Project with Design Fictions

This study presents a co-design project that invites participants with little or no background in artificial intelligence (AI) and machine learning (ML) to design their ideal virtual assistants (VAs) for everyday (/daily) use. VAs are differently designed and function when integrated into people’s daily lives (e.g., voice-controlled VAs are designed to blend in based on their natural qualities). To further understand users’ ideas of their ideal VA designs, participants were invited to generate designs of personal VAs. However, end users may have unrealistic expectations of future technologies. Therefore, design fiction was adopted as a method of guiding the participants’ image of the future and carefully managing their realistic, as well as unrealistic, expectations of future technologies. The result suggests the need for a human–AI relationship based on controls with various dimensions (e.g., vocalness degree and autonomy level) instead of specific features. The design insights are discussed in detail. Additionally, the co-design process offers insights into how users can participate in AI/ML designs.
Semantic Tree-Structured Representation for Visual Question Answering System

Visual question answering (VQA) system is an integrative research problem in the field of artificial intelligence. An image and a textual query are given as input to the VQA system. It tries to find the correct answer by combining the image and deductions collected from input textual queries. It is essential to interpret and retrieve the accurate answers from the visual reasoning queries. Recent studies have made use of parse tree construction on input queries which leads to poor performance due to the lack of semantic interpretation. This work is proposed to achieve comprehensive reasoning by following a semantic representation of the parsed tree construction. The proposed model, semantic tree-based visual question answering system (STVQA) captures the inherent visual evidence of every word parsed from the textual query and combines the visual evidence of its child nodes. The result obtained is transported to the parent nodes in the parse tree. Thus, the STVQA proposed system aims to fulfil global reasoning interpretation from the image and textual query. The VQA system is applicable to various domains such as image retrieval system, surveillance and hence acts as an aid for visually impaired people. The STVQA system is explored on a publicly available benchmark challenging dataset: CLEVR. It is shown that the model is computationally efficient and data-efficient and achieving a new state-of-the-art 90% accuracy.
A Study and Evaluation of Classifiers for Anti-Spam Systems

The volume of e-mails has been increasing in recent years. However, since 2005, at least half of these e-mails have been made up of spam. This massive traffic of unwanted messages causes losses to users, such as the excessive and unnecessary use of the bandwidth of their networks, loss of productivity, exposure of inappropriate content to inappropriate audiences etc. This paper proposes the study and the application of machine learning models to the classification of e-mails in existing anti-spam systems and, in particular, in the new anti-spam system Open-MaLBAS. After carrying out many experiments on different data sets, it was possible both to prove the feasibility of the proposal and to develop a powerful combination of techniques, methods, and models that can be successfully applied to the classification of e-mails in anti-spam systems.
Practical Side-Channel and Fault Attacks on Lattice-Based Cryptography

The impending threat of large-scale quantum computers to classical RSA and ECC-based public-key cryptographic schemes prompted NIST to initiate a global level standardization process for post-quantum cryptography. This process which started in 2017 with 69 submissions is currently in its third and final round with seven main candidates and eight alternate candidates, out of which seven (7) out of the fifteen (15) candidates are schemes based on hard problems over structured lattices, known as lattice-based cryptographic schemes. Among the various parameters such as theoretical post-quantum (PQ) security guarantees, implementation cost and performance, resistance against physical attacks such as Side-Channel Analysis (SCA) and Fault Injection Analysis (FIA) has also emerged as an important criterion for standardization in the final round [1]. This is especially relevant for adoption of PQC in embedded devices, which are most likely used in environments where an attacker can have unimpeded physical access to the device.
Computing normalisers of intransitive groups

The normaliser problem takes as input subgroups G and H of the symmetric group Sn, and asks one to compute NG(H). The fastest known algorithm for this problem is simply exponential, whilst more efficient algorithms are known for restricted classes of groups. In this paper, we will focus on groups with many orbits. We give a new algorithm for the normaliser problem for these groups that performs many orders of magnitude faster than previous implementations in GAP. We also prove that the normaliser problem for the special case G=Sn is at least as hard as computing the group of monomial automorphisms of a linear code over any field of fixed prime order.
Blockchain Technology in Healthcare

Blockchain is an innovation developed to encourage money exchange transactions and take out the requirement for a trusted third party to legally approve and confirm such exchanges to ensure information security and protection. New structures of blockchains have been intended to suit the requirement for this technology in different fields, for example, e-health, the travel industry, and energy. This chapter is concerned about the utilization of blockchains in overseeing and sharing electronic health and medical records to permit patients, emergency clinics, centers, and other stakeholders to share information among themselves and increment interoperability. The choice of the design of the blockchains utilized relies upon the elements chipping in the constructed chain network. In spite of the fact that the utilization of blockchains may decrease redundancy and give guardians reliable records about their patients, it accompanies a few difficulties, which could encroach patients’ security or possibly bargain the entire system of partners. In this chapter, we examine distinctive blockchain structures through various sections like blockchain in healthcare today, how it is changing the future of healthcare, and the benefits of blockchain. The chapter also deals with the implementation and challenges of blockchain in healthcare sector.
FACER-AS: An API Usage-based Code Recommendation Tool for Android Studio

Android developers often need to search for example code to complete their development tasks. While existing code search systems for Android can deliver code against a search query, they do not recommend code for features that a developer might later need to implement. In this paper, we present FACER-AS (FACER for Android Studio); an Android Studio plugin, which uses FACER (Feature-driven API usage-based Code Examples Recommender) as its back-end code search and recommendation engine. FACER provides relevant code against natural language queries (Stage 1) and also recommends code of multiple related features (Stage 2) to facilitate opportunistic code reuse. To evaluate FACER-AS, we perform a user study involving one professional Android developer who uses our tool for the development of their ongoing live Android projects. We analyze the developer's usage of our tool over a span of seven days and find that FACER-AS achieves a 79% success rate for retrieving code against user queries (Stage 1) and a 41% success rate for recommending code for related features (Stage 2). We also observe a 43% reuse rate of Stage 1 recommendations and a 45% reuse rate of Stage 2 recommendations. Our tool's performance analysis and the developer's positive feedback show that FACER-AS can help Android developers with their coding activities. A video demonstration of our tool is available at https://youtu.be/3yN-39wP_FU and the source code of our tool is available at https://doi.org/10.5281/zenodo.5176816.
Continual Learning for Instance Segmentation to Mitigate Catastrophic Forgetting

In recent years, with the development of GPUs, there are various deep learning models that show excellent performance in the field of computer vision, but these models have a fatal limitation of catastrophic forgetting. Continual learning studies for mitigating the catastrophic forgetting have been conducted on classification tasks, but in real-life, object detection and segmentation tasks are more practical than classification tasks. This paper proposes a catastrophic forgetting solution in the instance segmentation network by extending and applying the catastrophic forgetting solution, which was previously limited to classification, to segmentation tasks.
Design and Implementation of Archive Management System Based on Cloud Computing Big Data

research-article Share on Design and Implementation of Archive Management System Based on Cloud Computing Big Data Authors: Zhengnan Wu Wuhan Donghu University, China Wuhan Donghu University, ChinaView Profile , Yuansheng Cheng Middle Changjiang River Bureau of Hydrology and Water Resources Survry, China Middle Changjiang River Bureau of Hydrology and Water Resources Survry, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2115–2118https://doi.org/10.1145/3482632.3484110Online:22 November 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Foundations of Bayesian Learning in Clinical Neuroscience

There is an increasing interest in using prediction models to forecast clinical outcomes within the fields of neurosurgery and clinical neuroscience. The present chapter outlines the foundations of Bayesian learning and introduces Bayes theorem and its use in machine learning methodology. The use of Bayesian networks to structure and define associations between outcome predictors and final outcomes is highlighted and Naïve Bayes classifiers are outlined for use in predicting neurosurgical outcomes, where the understanding of underlying causes is less important. The present work aims to orient researchers in Bayesian machine learning methods and when and how to use them. When used correctly, these tools have the potential to improve the understanding of factors influencing neurosurgical outcomes, aid in structuring the relationships between them, and provide reliable machine learning classification models for predicting neurosurgical outcomes.
Applying the principles of metadesign in the educational platform development

Modern requirements for educational technologies are flexibility and adaptability, with the consideration of the student’s individual characteristics, learning style and basic competencies. The most important resource is time, therefore, shortening the training time and obtaining the most useful and complementary professional competencies and knowledge is an urgent and important task.&#x0D; The article discusses the concept of metadesign as a means for building learning paths, and examines the design and platform application based on its principles. Implementing metadesign principles is demonstrated on the example of the educational trajectory while training software developers. Forming the learning paths within the trajectory using the created educational platform is considered.&#x0D; The functional requirements for the educational platform are stated. The principles of building an educational trajectory within the framework of the platform using modern educational trends and simulators are described.
Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm

Musical choreography is usually completed by professional choreographers, which is very professional and time-consuming. In order to realize the intelligent choreography of musical, based on the mixed density network (MDN), this paper generates the dance matching with the target music through three steps: motion generation, motion screening, and feature matching. The choreography results in this paper have a high degree of matching with music, which makes it possible for the development of motion capture technology and artificial intelligence and computer automatic choreography based on music. In the process of motion generation, the average value of Gaussian model output by MDN is used as the bone position and the consistency of motion is measured according to the change rate of joint velocity in adjacent frames in the process of motion selection. Compared with the existing studies, the dance generated in this paper has improved in motion coherence and realism. In this paper, a multilevel music and action feature matching algorithm combining global feature matching and local feature matching is proposed. The algorithm improves the unity and coherence of music and action. The algorithm proposed in this paper improves the consistency and novelty of movement, the compatibility with music, and the controllability of dance characteristics. Therefore, the algorithm in this paper technically changes the way of artistic creation and provides the possibility for the development of motion capture technology and artificial intelligence.
Hybrid Manual and Gaze-Based Interaction With a Robotic Arm

Robots, very widespread in industrial settings, are now used for many different purposes. Even if several tasks can be fully automated, the presence of a human operator is often needed. However, direct robot control is generally complex, and it is thus important to explore alternative and comfortable ways of communication. In this paper, we propose a hybrid approach to human-robot interaction that exploits both manual and gaze-based input. While simple, our solution goes in the direction of enhancing the control capabilities of robots by human operators.