Reviewerly/exHarmony huggingface dataset:
MANAJEMEN STRATEGI DALAM MENGEMBANGKAN KEUNGGULAN DAYA SAING PERPUSTAKAAN UNIVERSITAS NEGERI MALANG

Human resource problems, vision and mission that are not according to needs, work programs and budgets must be immediately responded to by the leadership of the UM Library so that he has a competitive advantage in carrying out his duties and functions properly. This study aims to obtain a method of how the UM Library implements strategic management by describing and analyzing the environmental analysis process, strategy formulation, strategy implementation, and strategy evaluation carried out by library leaders in developing the competitive advantage of UM libraries. This study explores data and information based on direct interviews with UM Library leaders and documentation related to strategic management in developing the advantages of UM Libraries. At the stage of environmental analysis with structuration theory, developing multi-directional communication, resources exceeding national standards, realizing an eco campus, observing user behavior and collaborating with other organizations. The strategy formulation stage, by formulating a contextual and flexible vision and mission, making goals in line with the goals of institutions, national libraries and ministries, implementing coopetition strategies, and making institutional and non-institutional policies. UM Library Leaders in implementing the strategy by running programs both written and intended, optimizing capital and making easy and simple work procedures. At the strategy evaluation stage, the UM library leadership conducts internal, external, formal and informal evaluations.
An Infinite Dimensional Analysis of Kernel Principal Components

We study non-linear data-dimension reduction. We are motivated by the classical linear framework of Principal Component Analysis. In nonlinear case, we introduce instead a new kernel-Principal Component Analysis, manifold and feature space transforms. Our results extend earlier work for probabilistic Karhunen-Lo\`eve transforms on compression of wavelet images. Our object is algorithms for optimization, selection of efficient bases, or components, which serve to minimize entropy and error; and hence to improve digital representation of images, and hence of optimal storage, and transmission. We prove several new theorems for data-dimension reduction. Moreover, with the use of frames in Hilbert space, and a new Hilbert-Schmidt analysis, we identify when a choice of Gaussian kernel is optimal.
PENINGKATAN KETERAMPILAN DAN HASIL BELAJAR TEMATIK PADA MATERI PECAHAN MELALUI PENERAPAN PENDEKATAN CTL (CONTEXTUAL TEACHING AND LEARNING) SISWA KELAS III SD NEGERI PERUMNAS PEUNYARENG TAHUN AJARAN 2021/2022

Abstract: Improving Skills and Thematic Learning Outcomes in Fractional Materials Through the Application of the Ctl (Contextual Teaching and Learning) Approach for Class III Students of Public Elementary School Peunyareng, Academic Year 2021/2022. This study aims to improve the Skills and Learning Outcomes of Fractions in Thematic Learning of Grade III students of SD Negeri Perumnas Peunyareng, Academic Year 2021/2022. The model used in this classroom action research is the CTL (Contextual Teaching and Learning) approach. The subjects of this classroom action research are students III of SD Negeri Perumnas Peunyareng, Academic Year 2021/2022. The number of students is 15 students with 10 male students and 5 female students. This classroom action research is carried out in the 2021/2022 Academic Year. This classroom action research is carried out over a period of 3 months, from February 2022 to April 2022 in the even semester. The methodology of this research is classroom action research consisting of two cycles and each cycle consists of two meetings. Each cycle consists of planning, implementing, observing and reflecting. The research procedure consisted of pre research, planning cycle one, implementing cycle one action, observing cycle one, reflecting on cycle one, planning cycle two, implementing cycle two, observing cycle two and reflecting on cycle two. The data collection technique is collecting test scores which are carried out at the end of each learning cycle in each cycle using a question instrument (written test). Observation data is done by looking at the students' skills in the learning process. Data were analyzed by means of percentage statistics. The results showed that there was an increase in the mastery of student learning outcomes from 34.78% in the pre-study to 60.86% in the first cycle and increased to 78.26% in the second cycle. Student learning skills have increased from moderate to good category and good category to very good category. The CTL (Contextual Teaching and Learning) approach can improve skills and thematic learning outcomes in fractions of third grade students at SD Negeri Perumnas Peunyareng, Academic Year 2021/2022
Learning to Generate Realistic LiDAR Point Clouds

We present LiDARGen, a novel, effective, and controllable generative model that produces realistic LiDAR point cloud sensory readings. Our method leverages the powerful score-matching energy-based model and formulates the point cloud generation process as a stochastic denoising process in the equirectangular view. This model allows us to sample diverse and high-quality point cloud samples with guaranteed physical feasibility and controllability. We validate the effectiveness of our method on the challenging KITTI-360 and NuScenes datasets. The quantitative and qualitative results show that our approach produces more realistic samples than other generative models. Furthermore, LiDARGen can sample point clouds conditioned on inputs without retraining. We demonstrate that our proposed generative model could be directly used to densify LiDAR point clouds. Our code is available at: https://www.zyrianov.org/lidargen/
Text-Free Learning of a Natural Language Interface for Pretrained Face Generators

We propose Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis. Leveraging the recent advances in Contrastive Language-Image Pre-training (CLIP), no text data is required during training. Fast text2StyleGAN is formulated as a conditional variational autoencoder (CVAE) that provides extra control and diversity to the generated images at test time. Our model does not require re-training or fine-tuning of the GANs or CLIP when encountering new text prompts. In contrast to prior work, we do not rely on optimization at test time, making our method orders of magnitude faster than prior work. Empirically, on FFHQ dataset, our method offers faster and more accurate generation of images from natural language descriptions with varying levels of detail compared to prior work.
Trustless Cross-chain Communication for Zendoo Sidechains

In the Zendoo white paper, we introduced a novel sidechain construction for Bitcoin-like blockchains, which allows a mainchain to create and communicate with sidechains of different types without knowing their internal structure. In this paper, we take a step further by introducing a comprehensive method for sidechains to communicate amongst each other. We will also discuss the details of a cross-chain token transfer protocol that extends the generic communication mechanism. With the cross-chain token transfer protocol, it can enable a broad range of new applications, such as an exchange platform, that allows the ability to trade tokens issued from different sidechains.
PixTrack: Precise 6DoF Object Pose Tracking using NeRF Templates and Feature-metric Alignment

We present PixTrack, a vision based object pose tracking framework using novel view synthesis and deep feature-metric alignment. We follow an SfM-based relocalization paradigm where we use a Neural Radiance Field to canonically represent the tracked object. Our evaluations demonstrate that our method produces highly accurate, robust, and jitter-free 6DoF pose estimates of objects in both monocular RGB images and RGB-D images without the need of any data annotation or trajectory smoothing. Our method is also computationally efficient making it easy to have multi-object tracking with no alteration to our algorithm through simple CPU multiprocessing. Our code is available at: https://github.com/GiantAI/pixtrack
Students Perception of ICT for Specific Purpose in The Logistics Industry

In today's dynamic and competitive world, logistics knowledge has become more complicated and knowledge intensive. Regarding the topic of TESOL in the logistic sector, which is the use of English as a foreign language and a current economic necessity, the use of English as a foreign language is one of the economic sources for boosting the employability of workers through superior communication skills. This study tries to answer the question of how students in the logistics industry perceive the use of ICT in English language learning. This research chapter addresses the importance of logistics education from the perspective of undergraduates. Using a quantitative methodology and survey design, the current study examined the frequency of ICT usage for purposes. The majority of students said that the usage of ICT makes it easier and more interesting for them to study English, is a good way for instructors to distribute learning materials, improves their speaking and listening abilities, and expands their vocabularies. Students and instructors can communicate, share, and discuss course materials and assignments effectively through the use of ICT and the logistics theme
Collaborative 3D face alignment and head pose estimation with frontal face constraint based on RGB and sparse depth

A collaborative multi-task learning framework is proposed for integrating 3D face alignment and head pose estimation using RGB and sparse depth with a frontal face constraint. Most previous works involve using dense depth images to exploit spatial information, but the acquisition of high-resolution dense depth images is not always available due to the limitation of the image capture device. Besides, the existing multi-task learning methods have the problem of insufficient learning since they have not bridged the semantic gap between both tasks. In this letter, integrating 3D face alignment and head pose estimation into a collaborative multi-task learning framework is proposed, which is supervised by a frontal face constraint. In addition, sparse depth is incorporated into the network to provide additional facial geometrical information. A UV position map is generated to conduct face alignment, and a head pose vector is output to predict the head orientation. The performance on AFLW2000-3D dataset is evaluated, and the minimum quantization errors are achieved for both tasks when compared with state-of-the-art methods, demonstrating the superiority of the proposed method.
Special Issue on the Application of Machine Learning and Artificial Intelligence in Fault Diagnostics

The rapid development of data science and associated artificial intelligence (AI) methods has seen a substantial increase in interest in their application to anomaly detection, fault diagnostic, and prognostic challenges across a wide range of industrial and civil applications. Such approaches may well be the complement that is sought for conventional physical model-based and statistical approaches which often struggle to achieve the desired performance when dealing with complex engineering systems. Researchers have started to apply a range of machine learning and AI-based methods to the large-scale, multi-dimensional data that is often associated with large-scale sensor systems, particularly those which involve IoT devices. There is clear scope for the further development of such approaches, such as deep learning, transfer learning methods, and AI models, to enhance the performance of condition monitoring and associated technologies, and this is the key motivation for this Special Issue. This Special Issue on the Application of Machine Learning and Artificial Intelligence in Fault Diagnostics contains 6 papers.
DOP: toward an open-source and informative alternative to the DOI linking system

Digital object identifier (DOI) links in scientific publications are not informative. DOIs are not standardized to provide useful information about the published articles, authors’ names, journals’ names, and dates of publication (look at the DOI of this manuscript, it does not tell much about it). Besides, so far there is no free and open-source alternative to the current DOI system. Here, I suggest a new open and free linking and indexation system called DOP (for “Date Of Publication”) based on four main components of any scientific publication: 1) the main author’s name, 2) the journal’s name, 3) the date of publication (hence the denomination “DOP”), and 4) the time of publication. By using these four elements, DOP links are candidly informative, comparable and even memorable compared with the current links of DOI system. For example, a paper published by authors X in journal Y, on date D and time T can have a DOP link identifier as follows: DOP: authorX/JournalY/D/T where D refers to the date of publication per year, month and day, and T refers to the time of publication per hour, minute, and second. By using the date and time of publication per hour, minute and second, DOP links will be easily standardizable and always specific and unique among journals and authors and between them. Subsequently, a large bibliographic database can also be established based on DOP identification links for the indexation and archiving of scientific publications in the various scientific fields.
Physical Movement and Golden Metaphors in to the Amida-dō: A Comparison of the Merit of the Fortune by Gaining Discipline （&lt;i&gt;kaifuku&lt;/i&gt;）, Incense, and Zendou’s &lt;i&gt;kangyo-no-sho&lt;/i&gt;

At Amida-dō, I found ornamental objects which seem to have been gilded on ironware. Of course, it is impossible to alloy iron and gold. Until now, it had been thought that the iron objects, which must be load bearing, received only applications such as lacquer for rust-proofed. The discovery of this gilt object from the ancient Amida-dō poses a major scientific and technological problem, and at the same time it is necessary to discuss its meaning in the context of the Amida-dō. What is more, in a symbolic place in the same Amida-dō I found a Buddha statue with a slightly open mouth.
Mine Personnel Positioning System Based on Dual-Wavelength Phase Unwrapping

During the construction of coal mine laneway, due to the particularity of the geographical location and structure of the laneway, its accidents often occur, causing great trouble to the personal and property safety of the construction personnel. Therefore, according to the environmental characteristics of the coal mine tunnel and the positive correlation between the phase envelope range and the wavelength, this paper proposes a structural design of the personnel positioning system of the coal mine tunnel based on the dual wavelength phase unwrapping, which combines the coherent detection structure with the dual wavelength phase measurement method in digital holography. In order to reduce false positives in the position information, the synthetic wavelength is used to increase the spread range of the phase. The experiment shows that this structure can reduce the false positioning without reducing the positioning accuracy, and the average double wavelength phase demodulation can reduce the positioning false alarm by 82.5%, which greatly improves the accuracy of the prediction of the tunnel construction safety, brings great protection to the personal and property safety of the tunnel construction personnel, and improves the mining efficiency of the coal mine.
Misunderstanding and Offense in the Digital World as Impacts of Community Low Level of Language Literacy and Communication Skills

AbstractCommunication is part of social interaction that acts as a means of exchanging information, meeting individual needs and development. However, such social interaction activities may fail and create interaction problems, including misunderstanding and offense. Misunderstanding occurs when the listener thinks he or she has understood or misinterpreted what the speaker meant while offense occurs when the listener is unable to accept, is annoyed and disturbed by what the speaker is saying. Since entering the digital era which began about 10 years ago, misunderstandings and offenses have become increasingly common. The rapid flow of information exchange where everyone is almost free to speak and respond to speech makes these two issues even more unstoppable. Responding to these, as the main concern of this paper, the author invites the readers to discuss about misunderstanding and offense as results of individual low level of literacy and communication skills.Keywords: Misunderstanding and Offense, Communication, Language Literacy
Interruption in Interaction of Research Presentation

The present study highlights the communication which is concerned spoken discourse that occurs in an interaction in research presentations. The goals of this study are to explore how the interruptions in interaction are used in EFL (English a Foreign Language) students’ research presentation and what sorts and intentions on it. Thus, this study scrutinizes EFL students who presented research proposals and lecturers who supervised and examined them at the postgraduate program of State University of Makassar in 2017. The number of participants in this study was 20 academicians who were taken as a sample purposively. This study was navigated by descriptive qualitative method. To collect data, the writer employed discourse analysis approach by using video recorder to probe the spoken of speaker in doing conversation. Data recording was transformed into transcript. The transcription was coded to obtain the excerpt to be interpreted to find out the sorts of interruption in interaction of research presentation. Findings exposed that interruption are delivered in a competitive and cooperative manner. The two forms of interruption, there are two sorts with several intentions, including alert that aims to remind, inform, and correct the previous speaker. In addition, there is also a met comment that aims to questioning, suggesting, and jesting.
The Effectiveness of Social Media Facebook IAIN Parepare in Providing Information to Prospective New Students at Sman 3 Parepare

Social media is online media that makes it easy for audiences to provide and get information, now social media has many platforms such as Facebook. Facebook has many users who can update daily and others. The purpose of this study was to determine the effectiveness of IAIN Parepare's Facebook social media in providing information to prospective new students at SMAN 3 Parepare by covering three indicators namely frequency or intensity, duration, and activity. The method used in this research is descriptive quantitative research instrument in the form of a questionnaire given to 55 research samples (respondents) 3rd grade students of SMAN 3 Parepare. The results of this study indicate the ineffectiveness of IAIN Parepare's Facebook social media in providing information for several reasons, one of which is not using Facebook to find information regarding prospective new students. It can be concluded in this study that IAIN Parepare Facebook is not effective in providing information to prospective new students at SMAN 3 Parepare.
MHDNNL

Task optimization scheduling is one of the key concerns of both cloud service providers (CSPs) and cloud users. The CSPs hope to reduce the energy consumption of executing tasks to save costs, while the users are more concerned about shorter task completion time. In cloud computing, multi-queue and multi-cluster (MQMC) is a common resource configuration mode, and batch is a common task commission mode. The task scheduling (TS) in these modes is a multi-objective optimization (MOO) problem, and it is difficult to get the optimal solution. Therefore, the authors proposed a MOO scheduling algorithm for this model based on multiple heterogeneous deep neural networks learning (MHDNNL). The proposed algorithm adopts a collaborative exploration mechanism to generate the samples and use the memory replay mechanism to train. Experimental results show that the proposed algorithm outperforms the benchmark algorithms in minimizing energy consumption and task latency.
Fake Face Detection Using CNN

Abstract: Real and Fake face recognition using CNN and deep learning is presented in the paper. Searching for the authenticity of an image with the naked eye becomes a complicated task in detecting image forgeries. The goal of this study is to evaluate how well different deep learning approaches perform. The initial stage of the proposed strategy is to train several pre-trained deep learning models on the image dataset for recognizing real and fake images to identify fake faces. In order to assess the effectiveness of these models, we consider how well they separate two classes - false and true. Regarding the models tested so far, the VGG models have the best training accuracy (86%) on VGG-16, while VGG-16 shows an excellent test set. accuracy with 10 epochs or less, which is competitively better than all other methods. The outputs of these models were examined to find out exactly
I2PA : An Efficient ABC for IoT

Internet of Things (IoT) is very attractive because of its promises. However, it brings many challenges, mainly issues about privacy preserving and lightweight cryptography. Many schemes have been designed so far but none of them simultaneously takes into account these aspects. In this paper, we propose an efficient ABC scheme for IoT devices. We use ECC without pairing, blind signing and zero knowledge proof. Our scheme supports block signing, selective disclosure and randomization. It provides data minimization and transactions' unlinkability. Our construction is efficient since smaller key size can be used and computing time can be reduced. As a result, it is a suitable solution for IoT devices characterized by three major constraints namely low energy power, small storage capacity and low computing power.
Weeding of Printed Books at Rev B.M Pugh Library of Assam Agricultural University, Jorhat

Weeding is a process of removing obsolete collection of books from the stacks of library. The paper emphasizes on weeding of printed books at Rev BM Pugh library. The books which were not being issued and outmoded were weeded out to maintain the space in the stacks for new arrivals. The paper also discusses the procedure and policy adopted in weeding out of superseded books. The superseded editions, unissued and torn out books were identified through the copies available on the stacks and Koha LMS. Subsequently, the weeded-out books were freely distributed to public by organizing a Book Day. The freely distributed books were recorded in the Accession Register. The weeding procedure makes the collection active and creates space for new arrivals.