Reviewerly/exHarmony huggingface dataset:
Fast image quantization with efficient color clustering

Color image quantization has been widely used as an important task in graphics manipulation and image processing. The key to color image quantization is to generate an efficient color palette. At present, there are many color image quantization methods that have been presented, which are fundamentally clustering-based algorithms. As an illustration, the K-means clustering algorithm is quite popular. However, the K-means algorithm has not been given sufficient focus in the field of color quantization due to its high computational effort caused by multiple iterations and its very susceptibility to initialization. This paper presented an efficient color clustering method to implement fast color quantization. This method mainly addresses the drawbacks of the conventional K-means clustering algorithm, which involves reducing the data samples and making use of triangular inequalities to accelerate the nearest neighbor search. The method mainly contains two stages. During the first phase, an initial palette is generated. In the second phase, quantized images are generated by a modified K-means method. Major modifications include data sampling and mean sorting, avoiding traversal of all cluster centers, and speeding up the time to search the palette. The experimental results illustrate that this presented method is quite competitive with previously presented color quantization algorithms both in the matter of efficiency and effectiveness.
THE USE OF WATCHING YOUTUBE VIDEOS FOR ACQUIRING STUDENTS’ LISTENING COMPREHENSION

YouTube offers plenty of authentic material created by people worldwide which is frequently used as learning media. Numerous studies on YouTube have been carried out, some of which examined the effect of YouTube on listening ability, and the use of YouTube as learning media. However, only a few studies talk about the use of YouTube on students’ listening comprehension particularly to monitor self-improvement for getting English listening proficiency. Therefore, anchored in Anderson’s (1995) theory and filling the void of previous studies, this research was conducted to describe the use of watching YouTube videos for acquiring students’ listening comprehension. This research applied a qualitative research approach using a structured questionnaire and semi-structured interview as the instruments to collect the data. The subjects of this research were 4 students of the English Language Education Study Program of Lambung Mangkurat University. Findings showed that the use of YouTube videos by the respondents means they use extensive listening to improve their listening comprehension. The participants portrayed using YouTube as a way of self-improvement. The improvement they felt after watching YouTube videos was that they are able to easily find the main idea and general information while listening. It can be concluded that watching YouTube videos frequently is useful enough to improve students' listening comprehension.
Human Fall Detection for Smart Home Caring using Yolo Networks

In order to help the elderly and limit the incidence of falls that result in injuries, effective fall detection in smart home applications is a challenging topic.Many techniques have been created employing both vision and non-vision-based technologies.Many researchers have been drawn to the visionbased technique amongst them because of its viability and application.However, there is still room for improvement in the effectiveness of fall detection given the poor accuracy rate and high computational cost issues with current vision-based techniques.This study introduces a new dataset for posture and fall detection, whose photo images were gathered from Internet resources and data augmentation.It employs YOLO networks for fall detection purpose.Furthermore, different YOLO networks are implemented on our dataset to address the most accurate and effective model.Based on assessment parameters including accuracy, F1 score, recall, and mAP, the performance of the various YOLOv5n, s and YOLOv6s versions are compared.As experimental results showed, the YOLOv5s performed better than other.
Online learning resource recommendation method based on multi-similarity metric optimization under the COVID-19 epidemic

With the continuous COVID-19 pneumonia epidemic, online learning has become a normal choice for many learners. However, the problems of information overload and knowledge maze have been aggravated in the process of online learning. A learning resource recommendation method based on multi similarity measure optimization is proposed in this paper. We optimize the user score similarity by introducing information entropy, and use particle swarm optimization algorithm to determine the comprehensive similarity weight, and determine the nearest neighbor user with both score similarity and interest similarity through secondary screening in this method. The ultimate goal is to improve the accuracy of recommendation results, and help learners learn more effectively. We conduct experiments on public data sets. The experimental results show that the algorithm in this paper can significantly improve the recommendation accuracy on the basis of maintaining a stable recommendation coverage.
The Impact of Information Sharing and Inventory Management Practices on Firms’ Performance in Supply Chain Practices

This study’s aim is to conduct an empirical investigation of the impact of supply chain practices on firm performance. The prime objective of the paper is to study the impact of information-sharing practices and inventory management on firm performance. To realize the specified objective of the study, data were gathered from 170 individuals who are familiar with the supply chain practices of the companies and analyzed using structural equation modeling (SEM). The result shows a significant direct and indirect effect of information sharing and inventory management practices on firm performance. Generally, the results show that higher information-sharing practices and better inventory management practices lead to greater firm performance; and higher information-sharing leads to improved inventory management, which in turn leads to greater firm performance. Theoretically, the result provides evidence of the effects of information sharing and inventory management on the supply chain practices’ performance in a firm. The managerial implications of the results are enhancing information sharing and inventory management practices by adopting, internalizing, and building information technology into all the business operations to enhance information sharing and inventory management practices to enhance firm performance.
The PSG challenge: towards comprehensive scene understanding

The Panoptic Scene Graph Generation (PSG) challenge evaluates computer vision models to identify relations in images beyond object classification and localization, enabling a deeper understanding of scenes for real-world AI applications.
Participatory Design and Prototyping

Participatory design actively involves users throughout the design process, from initial discovery of their needs to the final assessment of the system. As in all forms of interaction design, this requires two complementary processes: generating new ideas that expand the design space and selecting specific ideas, thus contracting the design space. Interaction designers create prototypes that help them generate and explore this space of possibilities, ideally with the ongoing participation of users. Successful prototypes represent different aspects of the system that highlight specific design questions. We classify prototypes based on: representation, which refers to their physical form, ranging from paper, cardboard, or foam mock-ups to complete software or hardware simulations; precision, which refers to the level of detail, ranging from simple hand-drawn sketches or physical mock-ups to polished computer-generated images or 3D printed objects; interactivity, which refers to the level of interaction possible, ranging from no interaction, such as a video scenario, to partial interaction, when a designer “walks through” a scenario, to fully interactive, either simulating interaction with the Wizard of Oz method or trying an operational software or hardware prototype; life cycle, which refers to the expected evolution of the prototype, from “throw-away” prototypes in the earliest design phase to successively more developed prototypes in later design phases, to actual components of the final system; and scope, which refers to the aspects of the final system covered by the prototype, including breadth-first horizontal prototypes, depth-first vertical prototypes, or path-based story prototypes. This chapter explains how to create and use these different kinds of prototypes, with examples selected from key phases of the interaction design process.
URL based Phishing Detection using Machine Learning

Phishing is a malicious act involving combination of both social engineering and technical deception to steal an individual’s personal identity data and financial account credentials. Sophisticated phishing attacks are increasing day by day resulting in financial and other data loss to industries and user community. Phishers use malicious URLs as one of the way to cause harm and steal information. A plethora of work has been reported to detect the phishing attacks using different machine learning techniques which indicative to achieving higher accuracy in detecting malicious URLs. This paper investigates the different techniques, which researchers had applied for accurate detection of phishing and brings forth the current challenges. The paper also proposes a phishing detection model that implements machine-learning technique to detect phishing attack.
DIGITAL LITERACY AND COMPETENCE FOR EDUCATORS

Over the last few decades concept of digital literacy and digital competence have been widely used in public discourse. These are the latest concepts, skills necessary for modern life, concerned with the scientific and technical implementation of new emerging technology in various aspects of life such as education, economy, politics and production. The development of these digital concepts depends upon both traditional and diverse media. Digital literacy is a skill that provides plenty of opportunities to the person in current digital environment, so that it can be called a new modern skill. Digitally literate teachers can facilitate and equip their students with modern skills in the competitive world. Use of digital devices and Information and communication technology exposes the students to a new approach in teaching and learning. The paper discusses the significance of teacher’s awareness to digital literacy and competence vis a vis the use of digital devices to create the digital environment in the classroom.
LQR controller design for stabilization of non-linear DIP system based on ABC algorithm

Inverted pendulum systems, such as double or single, rotational or translational inverted pendulums are non-linear and unstable, which have been the most dominant approaches for control systems. The double inverted pendulum is one kind of a non-linear, unstable system, multivariable, and strong coupling with a wide range of control methods. To model these types of systems, many techniques have been proposed so that motivating researchers to come up with new innovative solutions. The Linear Quadratic Regulator (LQR) controller has been a common controller used in this field. Meanwhile, the Artificial Bee Colony (ABC) technique has become an alternative solution for employing Bee Swarm Intelligence algorithms. The research solutions of the artificial bee colony algorithm in the literature can be beneficial, however, the utilization of discovered sources of food is ineffective. Thus, in this paper, we aim to provide a double inverted pendulum system for stabilization by selecting linear quadratic regulator parameters using a bio-inspired optimization methodology of artificial bee colony and weight matrices Q and R. The results show that when the artificial bee colony algorithm is applied to a linear quadratic regulator controller, it gains the capacity to autonomously tune itself in an online process. To further demonstrate the efficiency and viability of the suggested methodology, simulations have been performed and compared to conventional linear quadratic regulator controllers. The obtained results demonstrate that employing artificial intelligence (AI) together with the proposed controller outperforms the conventional linear quadratic regulator controllers by more than 50 % in transient response and improved time response and stability performance
Disentangled Training with Adversarial Examples for Robust Small-Footprint Keyword Spotting

A keyword spotting (KWS) engine continuously running on the device is exposed to various speech signals that are usually unseen beforehand. It is a challenging problem to build a small-footprint and high-performing KWS model with robustness under different acoustic environments. In this paper, we explore how to effectively apply adversarial examples to improve KWS robustness. We propose datasource-aware disentangled learning with adversarial examples to reduce the mismatch between the original and adversarial data as well as the mismatch across original training datasources. The KWS model architecture is based on depth-wise separable convolution and a simple attention module. Experimental results demonstrate that the proposed learning strategy improves false reject rate by 40.31% at 1% false accept rate on the internal dataset, compared to the strongest baseline without adversarial examples. Our best-performing system achieves 98.06% accuracy on the Google Speech Commands V1 dataset.
Implementation of photon partial distinguishability in a quantum optical circuit simulation

We are concerned with numerical simulations of quantum optical circuits under certain realistic conditions, specifically that photon quantum states are not perfectly indistinguishable. The partial photon distinguishability presents a serious limitation in implementation of optical quantum information processing. In order to properly assess its effect on quantum information protocols, accurate numerical simulations, which closely emulate quantum circuit operations, are essential. Our specific objective is to provide a computer implementation of the partial photon distinguishability which is in principle applicable to existing simulation techniques used for ideal quantum circuits and which avoids a need for their significant modification. Our approach is based on the Gram-Schmidt orthonormalization process, which is well suited for our purpose. Photonic quantum states are represented by wavepackets which contain information on their time and frequency distributions. In order to account for the partial photon distinguishability, we expand the number of degrees of freedom associated with the circuit operation extending the definition of the photon channels to incorporate wavepacket degrees of freedom. This strategy allows to define delay operations in the same footing as the linear optical elements.
Soft Skills Development for Trust as the Next Frontier in the Construction of the Community with a Shared Future for Mankind

Confidence enables, precedes, and sustains all. This ancient gem from Chinese political philosophy brings to mind a timeless and universal social rule.
Towards Scale Adaptive Underwater Detection Through Refined Pyramid Grid

Most object detection methods have achieved impressive performance on several public benchmarks, instead, facing underwater detection tasks, it is challenging to detect marine targets because of the inherent illumination inhomogeneity in underwater images. Moreover, the imbalanced foreground-background proposals further aggravate the situation of capturing marine organisms. To address the problems, we analyze the deficiency of existing feature pyramid structures and propose a multi-depth and multi-breadth pyramid architecture named Refined Pyramid Grid (RPG). A Harmonizing Focal Loss (HFL) is then proposed to generalize the discrete labels in focal loss to the continuous version to improve the optimization. Experimental results on the real-world datasets have demonstrated the efficiency and reliability of the proposed framework regarding underwater object detection tasks.
Nephele: Extending Virtualization Environments for Cloning Unikernel-based VMs

Unikernels gained an increasing interest in the recent years because they provide efficient resource allocation and high performance for cloud services by bundling the application with a minimal set of OS services in a guest VM. Although a unikernel is by design small and lightweight, fleets of unikernels based on the same image are not necessarily more efficient than containers because the latter can rely upon OS primitives for sharing memory. Futhermore, porting POSIX applications on top of unikernels brings a new challenge: what does fork() mean in the world of unikernels where there is memory isolation within a VM? Lacking fork() support significantly reduces the applicability of unikernels in popular cloud applications.
Period VITS: Variational Inference with Explicit Pitch Modeling for End-To-End Emotional Speech Synthesis

Several fully end-to-end text-to-speech (TTS) models have been proposed that have shown better performance compared to cascade models (i.e., training acoustic and vocoder models separately). However, they often generate unstable pitch contour with audible artifacts when the dataset contains emotional attributes, i.e., large diversity of pronunciation and prosody. To address this problem, we propose Period VITS, a novel end-to-end TTS model that incorporates an explicit periodicity generator. In the proposed method, we introduce a frame pitch predictor that predicts prosodic features, such as pitch and voicing flags, from the input text. From these features, the proposed periodicity generator produces a sample-level sinusoidal source that enables the waveform decoder to accurately reproduce the pitch. Finally, the entire model is jointly optimized in an end-to-end manner with variational inference and adversarial objectives. As a result, the decoder becomes capable of generating more stable, expressive, and natural output waveforms. The experimental results showed that the proposed model significantly outperforms baseline models in terms of naturalness, with improved pitch stability in the generated samples.
Decoupled Visual Causality for Robust Detection

The existing empirical risk minimization algorithms learn the association between inputs and labels, and face substantial difficulties when apply to different distributions because of various confounders. Causal intervention becomes a solid solution to this issue by analyzing the visual causality, instead, those approaches fail at disentangling the confounders and mediators within the causality, and bring negative effects to the prediction. In this paper, we propose a disentangled visual causal model to eliminate the effects of confounders while reserving the corresponding mediators. Specifically, confounders are considered as different objects on the image, while mediators are formulated as some critical components of the targets that contribute to a distinctive identification. Extensive experiments on coco datasets have demonstrated the superiority of our model over other state-of-the-art baselines.
Graphit: Iterative Reweighted ℓ<sub>1</sub>Algorithm for Sparse Graph Inference in State-Space Models

State-space models (SSMs) are a common tool for modeling multi-variate discrete-time signals. The linear-Gaussian (LG) SSM is widely applied as it allows for a closed-form solution at inference, if the model parameters are known. However, they are rarely available in real-world problems and must be estimated. Promoting sparsity of these parameters favours both interpretability and tractable inference. In this work, we propose GraphIT, a majorization-minimization (MM) algorithm for estimating the linear operator in the state equation of an LG-SSM under sparse prior. A versatile family of non-convex regularization potentials is proposed. The MM method relies on tools inherited from the expectation-maximization methodology and the iterated reweighted-l1 approach. In particular, we derive a suitable convex upper bound for the objective function, that we then minimize using a proximal splitting algorithm. Numerical experiments illustrate the benefits of the proposed inference technique.
Analisis Faktor Demotivasi Belajar Siswa Pasca Pandemi COVID-19

&#x0D; One of the factors that influence the success of student learning is motivation. Motivation is one of the impulses contained in humans to want to do an act in order to achieve certain goals. In practice, distance or indoor (online) learning causes a lot of loss of motivation or demotivation of students as educational objects. According to the findings, prior to the pandemic, the most important characteristics that contributed to a lack of intrinsic motivation in students were low test scores, poor school facilities, learning content and resources, teacher competence and teaching style, and low test scores. On the other side, once the pandemic happened, the most notable variables that were demotivating people were poor test results, insufficient teacher competency and teaching style, inadequate school facilities, learning content and resources, and a lack of intrinsic desire.&#x0D; Keywords: Analition, Demotivation, Pandemi Covid-19
Interpretability in the Context of Sequential Cost-Sensitive Feature Acquisition

Despite the popularity of complex machine learning models, domain experts often struggle to understand and are reluctant to trust them due to lack of intuition and explanation of their predictions. Moreover, these cannot be used in many real–world applications, where features are not readily available but acquired at a cost. To address the latter challenge, dynamic instance–wise joint feature selection and classification selects both the order and the number of features to individually classify each data instance when features are sequentially acquired one at a time. Herein, its model–based and post hoc interpretability is demonstrated validating its utility in high–stakes applications. As a case study, predicting the credit risk of an individual based on financial and other data is considered. Experimental results show that the proposed method is indeed interpretable without sacrificing prediction accuracy.
Children’s Magazine as an Interactive Project, or the Mailbox in Mauritius Wolff’s “Zadushevnoye Slovo”

The article provides a thematic, structural and comparative analysis of the section “Mailbox” of the children’s magazine “Zadushevnoye slovo” (Sincere Word) (1876—1918, publisher M. Wolff). The texts reviewed represent an important and informative source on the history of everyday life and the history of childhood. The paper identifies the following forms of interaction between the editorial board and its readers: publication of letters with stories about family, favorite toys, travels, etc.; children’s questions and answers addressed to each other; entertaining tasks and their solutions; “prize” competitions (drawings, opinion essays), followed by sending out prize books and publication of winners’ names; questionnaires; personal appeals. The editorial board declared “developing the ability of children to express their thoughts and feelings correctly and coherently” as the aim of the column. The “answers and advice” section helped children to improve their writing skills; the framework provided a certain canon for communication. Being interactive, “Mailbox” stimulated interest in the magazine, attracted readers and increased the number of regular subscribers. However, its evaluation by literary critics and educators was and often remains negative.