Reviewerly/exHarmony huggingface dataset:
Data Augmentation based Cross-Lingual Multi-Speaker TTS using DL with Sentiment Analysis

Text to S peech (TTS) algorithms have made tremendous strides in recent years in terms of their ability to generate speech in a single language that sounds as natural as possible. However, because to a lack of available training data, the synthesis of speech from the same person in various languages continues to be difficult. It might be challenging to locate people who are proficient in numerous languages at a level equivalent to native speakers. Voice conversion is one method that may be used to create a polyglot corpus, which can then be used to solve this problem. This entails making use of a voice representation model that has been trained on 53 different languages through the application of hybrid deep learning in order to capture speaker-invariant qualities. In this study, we present a novel approach for the conversion of voices across different languages by employing Generated Adversarial Networks (GANs) to train a multilingual TTS system. The concept of individual likeness loss in order to address the special difficulty of maintaining one's individual speaking identity during the training process can be offered. This work is focused to provide the impression that voice data coming from a variety of languages and speakers was produced by the same individual, and one way to do so is by using this word. In order to determine the extent to which our model is useful, two experiments that compared it against benchmarks that made use of varying degrees of parameter sharing between languages are carried out. The purpose of these experiments was to evaluate the accuracy of pronunciation as well as the caliber of the synthetic voice during transitions between different languages.
LBFF: Load-Balancing First Fit Algorithm for Tenant Placement Problem

To meet the prevalence of cloud services, it is of great significance for operators to design a cost-effective tenant placement strategy on physical machines while the services are still guaranteed. Moreover, workload balancing between physical machines is critical for high performance. We characterize the problem of tenant placement as a mixed-integer programming problem that efficiently minimizes the number of active physical machines and balances the workloads among them. To address the NP-hardness of the optimization problem, we first investigate the global lower bound and other inherent properties of the proposed model, and then design two efficient heuristic algorithms. Through numerous simulations at different scales and settings, we demonstrate the superiority of our proposed algorithms over state-of-the-art works in terms of the objective function values and computational time.
Understanding the Scalability Problem of RNIC Cache at the Micro-architecture Level

High-performance Remote Direct Memory Access (RDMA) has been applied in High-Performance Computing (HPC) clusters and large-scale data centers as InfiniBand(IB) or RDMA over Converged Ethernet (RoCE). As the number of connections and the data space accessed by the network increases, the scalability problem of the RDMA Network Interface Card (RNIC) is getting worse because of cache misses on the RNIC. However, the RNIC is a black box for users. To understand the scalability problem, we give a detailed analysis of RNIC communication behavior based on the commercial RNIC driver and PCIe logic analyzer. We design a set of Cache Tests (CacheT) and a universal test methodology to profile the micro-architecture of the cache on the RNIC. We get statistical data by software tests and PCIe logic analyzer and obtain the cache micro-architecture parameters of commercial RNIC. Finally, we present an accurate performance model with 98% accuracy.
User and Interaction Both Matter: Social Relationship Mining Via Interaction Graph Propagating

Social relationship mining benefits many applications such as leadership analysis and advisor recommendation. Existing methods focus on mining user relationships only from the perspective of user-level. To our knowledge, from this perspective, representing the user interactions by edges is not sufficient for the complex information about interactions between users. In addition, mining users' relationship independently ignores the propagation of social interaction across networks. In this paper, we investigate social relationship mining from a new perspective of interaction-level. We propose an Interaction Graph Propagating(IGP) model which constructs an interaction graph. It not only captures the user interaction information as the union but also exploits the propagation between user interactions. In particular, we utilize the graph attention mechanism to distinguish the contributions of each neighbor union. Experimental results on several public datasets demonstrate that IGP achieves significant improvements over state-of-the-art methods.
JUSTIFICATION OF FAST CLASSIFICATION ALGORITHMS ON BIG DATA SETS WITH RELIABILITY AND PERFORMANCE CRITERIA

Classification methods are among the simplest and "oldest" methods of artificial intelligence. This article discusses fast algorithms that can be used to solve Big Data problems. Big Data is a case when the methods and tools used do not allow solving the problem in a pleasant time. Therefore, when solving this type of problem, an important criterion is the performance of classification algorithms. Productivity in this sense refers to potential decision-making time. This time depends on the constructive dimension of the algorithm - the number of typical operations for making a decision. In addition, the execution time of the program depends on the typical operations of the algorithm. The article considers productive algorithms based on M-means principles. At the same time, all possible representatives of classes are replaced by a small number of characteristics of these classes. In the simplest form, these algorithms boil down to the fact that classes are replaced by class centers based on the results of training. Such centers are defined as vectors of average values over all one-dimensional projections of the factor space. Distance measures from these centers in Euclidean and other metric spaces are used to classify unknown objects. Methods of rapid adaptation of these characteristics in learning algorithms are considered. The theoretical justification of the algorithms of adaptive metrics and adaptive rules is given. It is shown that M-means algorithms can be effective in the case that the number of class representatives is extremely large, and the number of classes and the dimension of the factor space are relatively small. The advantages and disadvantages of the considered algorithms are noted. The scope of their practical application is outlined. The principles of spreading these algorithms to a wide range of practical problems are also shown. An example of a comparison of a number of classification algorithms based on reliability and performance criteria is given. It was concluded that the algorithm of adaptive rules is the most effective for a significant number of problems.
Not from Concentrate – An Exploration of the Minds of Consumers by Combining Experimental Design of Ideas (Mind Genomics) with Artificial Intelligence (AI)

The origin of this specific study can be traced to the International Citrus & Beverage Conference, held in September 2023, in Clearwater, Florida.The conference brings together the various individuals involved in the world of citrus and allied products and services.The specific origin of the study was the conference presentation given by author Moskowitz to demonstrate the Mind Genomics method as a new technology.Discussions with authors Plotto and Sims about the best way to talk about Mind Genomics devolved into the notion that one could best explain the method by a live demonstration, from start to (almost) finish, in the allotted time of 50 minutes.Author Sims suggested the topic of 'NFC' , and acted as the technical expert, to introduce the problem.Author Schneider, in turn, ran the computer, typed in the ideas or selected them when the ideas were presented as a group, from which one was to select messages (elements, ideas) to be tested.The output of the demonstration comprised both a book of information about NFC from different points of view (Idea book, produced by the embedded AI, called Idea Coach), as well as the results from the Mind Genomics 'experiment' , after the 100 respondents participated. Background to NFCIn the mid 1940s, in order to get a natural source of vitamin C to those in war-torn Europe, frozen concentrated orange juice was
Systematic Review: Trends in Intelligent Tutoring Systems in Mathematics Teaching and Learning

The use of intelligent tutoring systems (ITSs) is growing rapidly in the field of education. In mathematics, adaptive and personalized scenarios mediated by these systems have been implemented to aid concept comprehension and skill development. This study presents a systematic review on the current status of the use of ITSs in mathematics education in higher education institutions (HEI). More specifically, it inquires about elements rarely addressed in reviews, such as the purposes, advantages, effectiveness, limitations, characteristics, and development and research methodologies in ITSs used for mathematics education. We analyzed 43 documents in 15 specialized and indexed journals in the years 2012-2022. In addition to providing a global overview of the implementation of ITSs in mathematics teaching and learning, we also present trends and opportunities for future research on building educational environments guided by these technologies.
Research on Promoting Professional Development of Foreign Language Teachers in Higher Education Based on Cross-cultural Competence in the Context of Deep Learning

Abstract This paper introduces support vector machines in deep learning algorithms that can model and analyze cross-cultural teaching and knowledge bases. Wavelet functions are used instead of traditional functions to perform deep learning on training samples. Finally, the learning ability of the support vector machine is improved by the wavelet kernel function to complete the effective instruction of higher education teachers to develop students’ intercultural competence. The results show that the accuracy rate of the intercultural teaching level of the deep learning algorithm proposed in this paper reaches up to 99.25%, and the results of the intercultural ability performance of the higher vocational students are excellent, which shows that the method of this paper can improve the intercultural teaching ability of foreign language teachers and strengthen the intercultural knowledge training of students in higher vocational institutions.
Evaluasi Pemanfaatan Teknologi Informasi dan Komunikasi dalam Pembelajaran Jarak Jauh di Kelas 5 SDIT AL-Haraki Depok

This study aims to determine the use of ICT in distance learning in grade 5 SDIT Al-Haraki Depok and evaluate the benefits of information and communication technology in distance learning in grade 5 SDIT Al-Haraki Depok. This research is an evaluative qualitative descriptive study with the aim of examining the use of information and communication technology in distance learning in grade 5 SDIT Al-Haraki Depok. The subjects in this study were school principals, 5th grade homeroom teachers, and 5th grade students for the 2020/2021 academic year. The results of this study indicate that the overall use of ICT in distance learning has been going well and can be maintained. However, there are several main obstacles, namely an unstable network, psychosocial conditions of parents, students and teachers who are already saturated with the distance learning process, and also the lack of variety in the use of learning applications, so that some students are still less enthusiastic in learning and this affects the learning process. student learning outcomes.
A Study on the Application of Charts in Orff Music Activities in Kindergartens

In recent years, Orff music education activities have taken an increasingly important place in kindergarten education and teaching.Nowadays, one of the three most influential and famous music systems in the world is the Orff music education system.In the implementation of Orff music activities, the use of "charts" is essential for young children.However, nowadays, when kindergartens carry out Orff teaching, the focus is more on games or manipulation of musical instruments, and the use of charts to play or perform is less practiced.Therefore, the study of Orff charts should first start from the Orff pedagogy, and analyse the application of Orff charts in the music classroom, which is conducive to teachers to better carry out music activities, the classroom should be the main body of the students, so the study of Orff charts should be combined with the development of the students' thinking, and should be linked to practice.In the Orff music pedagogy, the diagrams are presented more intuitively, make the important and difficult points more simple, and make the children more autonomous in the Orff music activities, and the use of diagrams can guide the children to perceive the mood and emotion of the music.
An Image Processing-Driven System for Fake Currency Detection

Fake currency is a critical factor affecting economies worldwide, including India. In this paper, a novel and structurally efficient approach for detecting and identifying duplication in currency notes is presented using Discrete Wavelet Transform (DWT). The system employs image processing algorithms to extract essential features such as security thread, intaglio printing (RBI logo), and identification mark, which serve as security measures for Indian currency. To identify fake portions in the currency notes and make informed decisions about their authenticity, the matching scores from all fake detection modules are fused together. A crucial aspect of the work lies in comparing the extracted features from various currency notes, enabling us to differentiate between fake and genuine notes effectively. To assess the performance, mean square error is employed as a metric for comparison between two images. A database is build containing authentic Indian notes of different denominations, extract their features, convert them into binary equivalents, and then calculate their mean square error. The proposed Fake Note Detection System takes a test currency note image, performs preprocessing operations to eliminate noise and negative artifacts, and then proceeds with the detection process. The system offers a promising solution to combat counterfeit currency issues and safeguard the integrity of the Indian economy.
Perception of Line Attributes for Visualization

Line attributes such as width and dashing are commonly used to encode information. However, many questions on the perception of line attributes remain, such as how many levels of attribute variation can be distinguished or which line attributes are the preferred choices for which tasks. We conducted three studies to develop guidelines for using stylized lines to encode scalar data. In our first study, participants drew stylized lines to encode uncertainty information. Uncertainty is usually visualized alongside other data. Therefore, alternative visual channels are important for the visualization of uncertainty. Additionally, uncertainty-e.g., in weather forecasts-is a familiar topic to most people. Thus, we picked it for our visualization scenarios in study 1. We used the results of our study to determine the most common line attributes for drawing uncertainty: Dashing, luminance, wave amplitude, and width. While those line attributes were especially common for drawing uncertainty, they are also commonly used in other areas. In studies 2 and 3, we investigated the discriminability of the line attributes determined in study 1. Studies 2 and 3 did not require specific application areas; thus, their results apply to visualizing any scalar data in line attributes. We evaluated the just-noticeable differences (JND) and derived recommendations for perceptually distinct line levels. We found that participants could discriminate considerably more levels for the line attribute width than for wave amplitude, dashing, or luminance.
Deep Image Matting with Sparse User Interactions

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous. Recently, some hint-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, some hints for removing semantic ambiguity and improving matting quality are essential. Apparently, there is a trade-off between interaction cost and matting quality. To balance performance and user-friendliness, we propose an improved deep image matting framework which is trimap-free and only needs sparse user click or scribble interaction to minimize the needed auxiliary constraints while still allowing interactivity. Moreover, we introduce uncertainty estimation that predicts which parts need polishing and conduct uncertainty-guided refinement. To trade off runtime against refinement quality, users can also choose different refinement modes. Experimental results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort. Finally, we demonstrate the extensibility of our framework to video human matting without any structure modification, by adding optical flow-based sparse hint propagation and temporal consistency regularization imposed on the single frame.
State Augmentation via Self-Supervision in Offline Multi-Agent Reinforcement Learning

The utilization of pre-collected offline datasets for learning in the absence of environmental interaction has enabled reinforcement learning (RL) to make significant strides in real-world circumstances. This approach is also attractive for multi-agent reinforcement learning (MARL) tasks, given the complex interactions that occur between agents and the environment. However, when compared to the single-agent approach, offline multi-agent RL faces more challenges due to the larger state and action space, particularly with regard to poor out-of-distribution generalization to the environment. The present study demonstrates the ineffectiveness of directly transferring conservative offline RL algorithms from single-agent settings to multi-agent environments, which is due to the accumulating extrapolation errors that increase in proportion to the number of agents. In this paper, we explore the efficacy of three types of data augmentation techniques that can be applied to the state representation in the context of MARL. By combining the proposed data augmentation techniques with a state-of-the-art offline multi-agent algorithm, we improve the function-approximation of centralized Q-networks. The experimental results conducted on StarCraft II strongly support the effectiveness of the data augmentation techniques in enhancing the performance of offline multi-agent reinforcement learning in the state space.
FINANCIAL AID FRAUD IN A NONPROFIT UNIVERSITY: WHERE WERE THE INTERNAL CONTROLS?

The primary subject matter of this case concerns ethical decision making.The case has a difficulty level of four, appropriate for senior level.The case is designed to be taught in one class period of approximately 50 -75 minutes and is expected to require 2-3 hours of outside preparation by students. CASE SYNOPSISInternal control failures within a nonprofit university's financial aid office resulted in the misappropriation of approximately $1 million in grants over a ten-year period.These grants were earmarked for needs-based students but were diverted by office employees to their own accounts and used for personal expenses.Although the problem was identified and reported to the university's president, no action or public disclosure was made for at least two years.A whistleblower notified an online newspaper of the fraud, and it was subsequently reported to university constituents.The university created an action plan to prevent such an incident from occurring in the future and terminated all employees involved in the fraud.The events described in this case are based on a real-world situation as reported in various newspaper and online sources.
Analysis of the characteristics of skill-based street dance movements based on the improved K-means algorithm

Abstract This paper first analyzes the K-mean algorithm from the core idea, algorithm process and advantages and disadvantages, then further improves the K-mean algorithm by using Gaussian mixture distribution and constructs the skill-based street dance movement recognition model based on the improved algorithm. Finally, the street dance teaching video is used as an example for dance movement acquisition and data pre-processing, and the recognition accuracy analysis of the street dance movement dataset is conducted based on the improved K-mean algorithm. The average recognition rates of the recognition model in the four data sets of the data set were 72.34%, 74.65%, 73.15% and 86.70%, respectively. This shows that analyzing the characteristics of street dance movements using the improved K-mean algorithm is beneficial for optimizing and improving existing street dance movements.
Differences in the Digital Divide, Educational Level, and Gender in Turkey During the COVID-19 Pandemic: A Comparison of Turkish Tertiary-Level Sophomore and Junior Students' Views on Online Learning

The outbreak of the COVID-19 pandemic forced many educational institutions to move face-to-face education to entirely online education. This digital transformation has, in turn, reinforced the digital divide between learners who have easy access to digital devices and the Internet and other learners who do not have those. In addition to the digital divide, the differences in their genders and educational levels potentially impact leveling up learning. Therefore, the study in the present chapter has aimed to examine university-level sophomore and junior students’ differences in learning online during the COVID-19 pandemic in terms of gender, educational level, and the digital divide. Data from a survey indicate that both females and males, and sophomores and juniors had different attitudes toward using digital devices, attending virtual classes and accessing online courses through the mobile phone or the home Internet. Based on the findings on their differences, the chapter makes some implications and recommendations for policymakers, course designers and teachers in the course design, especially in the emergence.
Ordinal Characterization of Similarity Judgments.

Characterizing judgments of similarity within a perceptual or semantic domain, and making inferences about the underlying structure of this domain from these judgments, has an increasingly important role in cognitive and systems neuroscience. We present a new framework for this purpose that makes very limited assumptions about how perceptual distances are converted into similarity judgments. The approach starts from a dataset of empirical judgments of relative similarities: the fraction of times that a subject chooses one of two comparison stimuli to be more similar to a reference stimulus. These empirical judgments provide Bayesian estimates of underling choice probabilities. From these estimates, we derive three indices that characterize the set of judgments, measuring consistency with a symmetric dis-similarity, consistency with an ultrametric space, and consistency with an additive tree. We illustrate this approach with example psychophysical datasets of dis-similarity judgments in several visual domains and provide code that implements the analyses.