Reviewerly/exHarmony huggingface dataset:
Learning Stage-Wise GANs for Whistle Extraction in Time-Frequency Spectrograms

Whistle contour extraction aims to derive animal whistles from time-frequency spectrograms as polylines. For toothed whales, whistle extraction results can serve as the basis for analyzing animal abundance, species identity, and social activities. During the last few decades, as long-term recording systems have become affordable, automated whistle extraction algorithms were proposed to process large volumes of recording data. Recently, a deep learning-based method demonstrated superior performance in extracting whistles under varying noise conditions. However, training such networks requires a large amount of labor-intensive annotation, which is not available for many species. To overcome this limitation, we present a framework of stage-wise generative adversarial networks (GANs), which compile new whistle data suitable for deep model training via three stages: generation of background noise in the spectrogram, generation of whistle contours, and generation of whistle signals. By separating the generation of different components in the samples, our framework composes visually promising whistle data and labels even when few expert annotated data are available. Regardless of the amount of human-annotated data, the proposed data augmentation framework leads to a consistent improvement in performance of the whistle extraction model, with a maximum increase of 1.69 in the whistle extraction mean F1-score. Our stage-wise GAN also surpasses one single GAN in improving whistle extraction models with augmented data. The data and code will be available at https://github.com/Paul-LiPu/CompositeGAN\_WhistleAugment.
Effect of decoherence for gate operations on a superconducting bosonic qubit

Abstract High-quality-factor 3D cavities in superconducting circuits are ideal candidates for bosonic logical qubits as their fidelity is limited only by the low photon loss rate. However, the transmon qubits that are used to manipulate bosonic qubits result in the emergence of additional relaxation and dephasing channels. In this work, a numerical study is performed to elucidate the effect of the various loss channels on the performance of logical gates on a bosonic qubit. A gate error model is developed that encapsulates the loss mechanisms for arbitrary gate operations and predicts experimentally achievable gate errors for bosonic qubits. The insights gleaned from this study into loss mechanisms suggest more efficient optimization algorithms that could reduce gate errors on bosonic qubits.
Factors Affecting Customer Satisfaction with The Telecommunication Industry in Saudi Arabia

Telecommunications is a customer-oriented industry in which client satisfaction is crucial for an organization's survival. Social media plays a vital role in customer decisions, acting as both a search tool and a communication channel. On social media platforms, customers can air their grievances, and a company can use these complaints to improve its products and services. During the first quarter of 2022, sentiment analysis was conducted to evaluate customer satisfaction with telecom services in Saudi Arabia. With a machine-learning approach, more than 90K comments were recorded and categorized as positive, negative, or neutral. For the classification, we utilised a support vector machine (SVM) model with an average accuracy of 88%. After that, We utilised thematic analysis of social engagement opinions. We identified seven themes among the comments related to factors affecting efficiency and satisfaction with telecommunications services: product, package, price, promotion, place, people, and public relations. In conclusion, we recommend some solutions to improve efficiency and increase customer satisfaction in the telecom sector.
The Detection of Indonesian Hoax Content about COVID-19 Vaccine using Naive Bayes Multinomial Method

One media currently famously used in all worlds is twitter. The ease of dissemination and the exchange of information is accelerating. Every day, millions of tweets exist using various information, such as politics, technology, sports, academics, and others. The information that is widely found is about COVID-19-19 nowadays. The information on Twitter is not entirely accurate or according to facts and needs to be proven true. Therefore, this study aims to try to detect the information contained in Indonesia using methods of Naive Bayes Multinomial by using the Information Gain feature selection. The classification process is carried out by crawling tweets, preprocessing, then using feature selection, namely Information Gain, and classification using the Multinomial Naive Bayes method. Meanwhile, the validation needs in this study use k-fold cross-validation where the existing dataset is divided into training and testing data that will be tested with a confusion matrix. Researchers have carried out the confusion matrix testing process using 720 datasets divided as train data &amp; the test data received an average accuracy value of 81.39%, precision of 80.36%, and recall of 79.73%. The highest accuracy is using k-fold two. The accuracy value reaches 88.8%, the precision value is 79.1%, and the recall value is 86.3%. The lowest accuracy was obtained on the 8th k-fold with an accuracy value of 73.6%, precision 75.4%, and recall 86.9%.
A NEW APPROACH TO EGOROV&amp;rsquo;S THEOREM BY MEANS OF 𝛼𝛽&amp;minus;STATISTICAL IDEAL CONVERGENCE

In this work, we introduce the -statistical pointwise ideal convergence, -statistical uniform ideal convergence, and -equi-statistical ideal convergence for sequences of fuzzy-valued functions.With the help of some examples, we present the relationship between these convergence concepts.Moreover, we give the -statistical ideal version of Egorov's theorem for the sequences of fuzzy valued measurable functions.
Cost polarization by dequantizing for JPEG steganography

In this article, we study a recently proposed method for improving empirical security of steganography in JPEG images in which the sender starts with an additive embedding scheme with symmetrical costs of ±1 changes and then decreases the cost of one of these changes based on an image obtained by applying a deblocking (JPEG dequantization) algorithm to the cover JPEG.This approach provides rather significant gains in security at negligible embedding complexity overhead for a wide range of quality factors and across various embedding schemes.Challenging the original explanation of the inventors of this idea, which is based on interpreting the dequantized image as an estimate of the precover (uncompressed) image, we provide alternative arguments.The key observation and the main reason why this approach works is how the polarizations of individual DCT coefficients work together.By using a MiPOD model of content complexity of the uncompressed cover image, we show that the cost polarization technique decreases the chances of "bad" combinations of embedding changes that would likely be introduced by the original scheme with symmetric costs.This statement is quantified by computing the likelihood of the stego image w.r.t. the multivariate Gaussian precover distribution in DCT domain.Furthermore, it is shown that the cost polarization decreases spatial discontinuities between blocks (blockiness) in the stego image and enforces desirable correlations of embedding changes across blocks.To further prove the point, it is shown that in a source that adheres to the precover model, a simple Wiener filter can serve equally well as a deep-learning based deblocker.1 This observation was already made in [19].
Determination of Illegal Signs of Coal Mining Expansion in Thai Nguyen Province, Vietnam from a Combination of Radar and Optical Imagery

Vietnam has diverse and rich natural resources with over 5000 mines and 60 different types of minerals. Mineral activities in Vietnam are licensed by the Government and must ensure to protect the environment and daily activities. However, due to the high profits generated from mining, there have been many illegal mining activities taking place all over Vietnam. One of the causes leading to this situation is the insufficiency in monitoring and managing resources and minerals. Additionally, many mines are located in the forest which is difficult to detect. Therefore, in this study, we adopt Sentinel-1 radar satellite image and Sentinel-2 multi-temporal satellite image to determine illegal coal mining activities at the Minh Tien coal mine, Thai Nguyen province, Vietnam. The research method includes applying Google Earth Engine (GEE) for processing the Sentinel-1 images in both ascending and descending directions and Sentinel-2 optical images to calculate the Normalized Difference Vegetation Index (NDVI) series to control the different trends from 2016 to 2021. NDVI is then employed to mask the areas with vegetation that witness no abrupt changes in the land cover. This NDVI also acts as the basis for collecting samples for Random Forest classification of the Sentinel-2 images. The results from Sentinel-1 and Sentinel-2 combined with MASK from NDVI have determined the expansion area of the Minh Tien coal mine. The results are compared with those published on the website of Thai Nguyen Portal and have a significant similarity.
Evaluating Dimensionality Reduction Approaches on Erstwhile Hyperion and Newly Launched PRISMA Datasets

Hyperspectral images have rich feature information, plagued by redundancy and noise. Dimensionality reduction approaches can aid in extracting meaningful information from such voluminous data. Three such approaches have been tested on Hyperion and PRISMA data of Ahmedabad, India. The eigenvalues and visual inspection of the components chosen from the scree plots, show that the kernel principal component analysis (k-PCA) outperforms both standardized and unstandardized principal component analyses (PCA) and minimum noise transform (MNF) in both datasets. Stacked k-PCA components 3, 2 and 1 in Hyperion and 4, 2 and 1 in PRISMA differentiate natural and built-up land cover classes, thereby choosing them for further image processing tasks.
Pedestrian attribute recognition based on dual self-attention mechanism

Recognizing pedestrian attributes has recently obtained increasing attention due to its great potential in person re-identification, recommendation system, and other applications. Existing methods have achieved good results, but these methods do not fully utilize region information and the correlation between attributes. This paper aims at proposing a robust pedestrian attribute recognition framework. Specifically, we first propose an end-to-end framework for attribute recognition. Secondly, spatial and semantic self-attention mechanism is used for key points localization and bounding boxes generation. Finally, a hierarchical recognition strategy is proposed, the whole region is used for the global attribute recognition, and the relevant regions are used for the local attribute recognition. Experimental results on two pedestrian attribute datasets PETA and RAP show that the mean recognition accuracy reaches 84.63% and 82.70%. The heatmap analysis shows that our method can effectively improve the spatial and the semantic correlation between attributes. Compared with existing methods, it can achieve better recognition effect.
Strategi Manajemen Perpustakaan Digital untuk Meningkatkan Kualitas Akademik

As a supporting unit for achieving higher education quality, for many years the university library has been part of the scientific information ecosystem, and the final form of the university library has turned into a digital library. The purpose of this study is to reveal library management in supporting the quality of academic development in tertiary institutions, as well as the advantages and disadvantages of implementing library management which has been carried out so far in the digitalization era. This study uses a qualitative method. The main subjects of this study were library staff, students, and lecturers. Data collection techniques are through interviews and document analysis, while data validity is carried out through triangulation techniques. The results of the study show that college libraries have changed a lot, and the use of digital technology has become an option for the convenience of academic services. However, the management of digital libraries is still faced with the problem of the limited ability of managers in the field of information technology, low budget allocations, and decreased number of visitors. On the other hand, library operations have also not been able to guarantee digital data security.
A Review on Patients Health Monitoring using IoT and Cloud Technologies

At present, the population is increasing day by day. Our traditional methods and human efforts are insufficient to meet the needs of every patient. As every patient needs continuous monitoring of their health status. At present, the observation of a patient’s health is very important, and this monitored data is connected to a cloud and can alert the doctor by forwarding a message. Physicians can monitor patients' or elderly people’s health conditions from a remote area by using the Internet of Things (IoT). And also, medical services can be provided to sick people or patients from remote areas based on their daily health status. With the wide range of sensors like pulses, heartbeat, blood pressure, ECG, and temperature sensors along with IoT, such system provides a sophisticated control for continuous monitoring of the health conditions of patients. The goal of this study is to provide a detailed investigation of the IoT health monitoring system that are in the literature. The various existing system is compared and contrasted based on the efficiency and its accuracy. The advantages and disadvantages of the system along with the experimental aspects are available in the comparative results.
Multimedia Positive Computing Through Transition in Affective and Impression Spaces

This paper focuses on positive computing using multimedia data called multimedia positive computing. The impression of multimedia data is used to change a person’s mood. Presenting a piece of multimedia data induces a person to have a mood corresponding to its impression. This paper proposes a method of the transition of the impression of multimedia data in the impression space. According to the impression transition of multimedia data, a person’s mood is looking forward to being transitted. For a gradual change of the impression of multimedia data, the proposed method decides intermediate points between the points corresponding to the initial and the destination moods. The feasibility of this transition method is shown by demonstrating some transitions of the impressions of pictures.
A method for joint detection and re-identification in multi-object tracking

In order to better balance the detection accuracy and tracking speed, we propose an online balanced multi-object tracking method (BalMOT), which integrates object detection and appearance extraction into a single network, and can simultaneously output detection and appearance embedding. We also model the training of classification, regression, and embedding features as a multi-task training problem and each part is weighted based on the task-independent uncertainty method. In addition, we introduce the transition layer to optimize the repeated gradient information in the network and reduce the training cost. Through the training, our BalMOT system reaches 71.9% multiple object tracking accuracy (MOTA) on the MOT17 challenge dataset, and the speed fluctuates between 17.4 ~ 22.3 frames per second (FPS) according to the size of the input image.
Development of Gamification Based Supplementary Listening Materials to Improve Students’ High Order Thinking Skills

This research aims to develop supplementary listening materials using gamification in the Listening Class.There is one research problem asked, what does a set if supplementary listening materials using gamification for listening class in english education program look like?The purpose of this research is to produces a gamification listening comprehension based of technology E-module.The subject of this research were students of English language Programme in Universitas negeri Malang.The approach in this development adopts the ADDIE.With the idea put on this research subject can experience an increase in listening skills
Kesalahan Afiksasi dalam Caption Instagram @Infojember Edisi Oktober 2022

This study aims to describe affixation errors in the October 2022 edition of the Instagram @infojember social media caption. Qualitative research is the type of this research that uses a descriptive approach. The October 2022 edition of Instagram @infojember social media is the research data source. The data was used in the form of affixation errors that exist in the October 2022 edition of the @infojember caption. The reading and note-taking techniques were used in this research, namely by reading each caption critically; then, if it saw symptoms of affixation errors, it was recorded and grouped according to the indicators that had been made previously. The data analysis techniques by reducing data, presenting data, and drawing conclusions. The study's results found that there are still affixation errors in the October 2022 edition of Instagram @infojember social media, namely 15 errors, with details of 7 errors in prefix errors, 3 errors in suffix errors, and 5 errors in confix errors. The data found have the same types of errors, namely in the form of prefix omission, suffix omission, and confix omission.
BIDIRECTIONAL MOTION LEARNING USING TRANSFORMER BASED SIAMESE NETWORK FOR VIDEO ANOMALY DETECTION

&lt;p&gt;Video anomaly detection aims to identify unusual activity in videos. Recently, reconstruction and future frame prediction-based approaches have been frequently used to detect anomalies. However, due to the high generalization capability of deep neural networks, the reconstruction-based algorithms recreate the abnormal pixels with the normal ones. Therefore, we have developed a novel framework based on future prediction, capable of learning the video data’s bidirectional motion with the help of a transformer-based siamese network. During the forward pass, the siamese network predicts the future sequence and learns the forward motion. In contrast, the backward movement is realized by predicting the inverse sequence. The model has trained on normal video data adversarially. In addition to adversarial loss, inverse loss and soft-DTW loss have been incorporated to make the model more objective. Extensive experiments have been conducted on the benchmark datasets to validate the efficacy of model.&lt;/p&gt;
Harnessing the Digital Nature of Micro-Credentials in Higher Education

This review paper delves into using micro-credentials in higher education ecosystems as a digital enablers. Micro-credentials, which are digital credentials that attest to a learner&amp;rsquo;s mastery of a specific skill or knowledge area, are becoming more popular in higher education. The paper examines the successful implementation of micro-credential frameworks in higher education, using case studies to demonstrate the advantages of micro-credentials. The review emphasizes the agility and flexibility of microcredentials, which enable learners to acquire new skills quickly and respond to changes in the job market. In addition, the paper discusses the digital nature of micro-credentials and how they allow institutionsto provide targeted, skills-based training that isrelevant to employers. It also explores how micro-credentials are delivered through online platforms, making them convenient and easily accessible for learners. The review underscores the significance of digital infrastructure, connectivity, and public utility for promoting micro-credentials. The paper argues that micro-credentials function as a digital enabler for higher edu- cation ecosystems, allowing learners to acquire targeted training and enabling institutions to expand their offerings and reach more students. The paper concludes by highlighting the potential for micro-credentials to help bridge the skills gap and equip learners with the skills necessary to succeed in today&amp;rsquo;s digital economy.
Multi-View Stereo Vision Patchmatch Algorithm Based on Data Augmentation

In this paper, a multi-view stereo vision patchmatch algorithm based on data augmentation is proposed. Compared to other works, this algorithm can reduce runtime and save computational memory through efficient cascading of modules; therefore, it can process higher-resolution images. Compared with algorithms utilizing 3D cost volume regularization, this algorithm can be applied on resource-constrained platforms. This paper applies the data augmentation module to an end-to-end multi-scale patchmatch algorithm and adopts adaptive evaluation propagation, avoiding the substantial memory resource consumption characterizing traditional region matching algorithms. Extensive experiments on the DTU and Tanks and Temples datasets show that our algorithm is very competitive in completeness, speed and memory.
Mediating Effect of Perceived Organizational Support on the Relationship between Organizational Silence and Organizational Commitment

This study aims at examining the mediating effect of perceived organizational support on the relationship between organizational silence and organizational commitment using quantitative research that employs a descriptive survey design. The population for the study consists of 452 employees of Seplat Petroleum Development Company Plc, a Nigeria-based company. Using Taro-Yemani’s formula, 212 employees were randomly selected and participated in the study. Path coefficients obtained from structural equation modeling were used to test the hypotheses. The findings indicate that there is a negative and insignificant relationship between organizational silence and organizational commitment. Whereas the association between organizational silence and perceived organizational support is positive and significant, the relationship between perceived organizational support and organizational commitment is significantly negative. Furthermore, findings affirm that perceived organizational support fully mediates the relationship between organizational silence and organizational commitment. It is recommended that the management of Seplat Petroleum Development Company Plc establish measures to reduce organizational silence, thus increasing employee voice behavior, which will eventually improve organizational commitment and enhance organizational performance. Finally, factors that will improve employees’ perception of organizational support should be enhanced, as this will make employees feel important as well as part of the organization. Keywords: Organizational silence, perceived organizational support, organizational commitment, structural equation modeling DOI: 10.7176/EJBM/15-4-02 Publication date: February 28 th 2023