Reviewerly/exHarmony huggingface dataset:
6.7ms on Mobile with over 78% ImageNet Accuracy: Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration.

With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed independently and do not fully consider compiler-level optimizations which is a must-do for mobile acceleration. In this work, we first propose (i) a general category of fine-grained structured pruning applicable to various DNN layers, and (ii) a comprehensive, compiler automatic code generation framework supporting different DNNs and different pruning schemes, which bridge the gap of model compression and NAS. We further propose NPAS, a compiler-aware unified network pruning, and architecture search. To deal with large search space, we propose a meta-modeling procedure based on reinforcement learning with fast evaluation and Bayesian optimization, ensuring the total number of training epochs comparable with representative NAS frameworks. Our framework achieves 6.7ms, 5.9ms, 3.9ms ImageNet inference times with 78.2%, 75% (MobileNet-V3 level), and 71% (MobileNet-V2 level) Top-1 accuracy respectively on an off-the-shelf mobile phone, consistently outperforming prior work.
Improving cluster recovery with feature rescaling factors

The data preprocessing stage is crucial in clustering. Features may describe entities using different scales. To rectify this, one usually applies feature normalisation aiming at rescaling features so that none of them overpowers the others in the objective function of the selected clustering algorithm. In this paper, we argue that the rescaling procedure should not treat all features identically. Instead, it should favour the features that are more meaningful for clustering. With this in mind, we introduce a feature rescaling method that takes into account the within-cluster degree of relevance of each feature. Our comprehensive simulation study, carried out on real and synthetic data, with and without noise features, clearly demonstrates that clustering methods that use the proposed data normalization strategy clearly outperform those that use traditional data normalization.
THE IMPACT OF UNDERSTANDING AND ORGANISATIONAL COMMITMENT ON JOB INVOLVEMENT

All companies rely on committed and loyal employees to reach their goals.Thus far, we know little about the specific effects of the mechanisms of leadership influence on the job involvement of employees.This paper focuses on the specific effect of the use of the "understanding" mechanism of leadership influence on job involvement in environments with both low and high organisational commitment.The paper focuses on two interrelated research questions.First, how high is the impact of the variables of understanding and organisational commitment on the expression of the dependent variable of job involvement?Second, how do the variables of understanding and organisational commitment interact to influence the expression of job involvement?In addition, the question of the direct relevance of the found findings to the quality of leadership is answered.A comprehensive dataset of 218 survey participants was used, to find significant positive effects of the use of "understanding" on job involvement, and a moderating effect of "organisational commitment" in the effect of understanding on job involvement at certain values was discovered.
Creating a Web-based Course-book on Revitalization of the Sampyong for University Students

This study aimed to find out the students" needs for a web-based course-book, describe the validation result from the experts regarding the course book being developed, figure out the practicality aspect of the course book, and describe the effectiveness of course book.The study employed a mixed version of Plomp"s and Tessmer"s research and development (R&D) major procedures.The R&D procedure involved analyzing students" need, designing and producing a prototype of a web-based course-book, and evaluating the prototype including expert validation, one-to-one evaluation, small group evaluation, and field testing.Questionnaire was administered to investigate the students" needs.One-to-one evaluation and small group evaluation were conducted to examine the practicality aspect of the course book being developed.Test was administered to assess the effectiveness of the course-book.Results of the analysis have shown that the students needed a web-based course-book.The course-book consisted of materials, comprehension questions, bibliography, glossary, author profiles, the Sampyong videos and photos, and logout menus.The expert validation average score was 4.27 (out of 5), belonging to category "good".Based on one-to-one and small group evaluation, the web-based course-book was considered practical for university students.The objective test administered revealed that the students" achievement significantly increased from the average scores 66 (pre-test) to 79 (post-test).
Aplikasi Edukasi Pengenalan Profesi Pemadam Kebakaran Berbasis Augmented Reality

In this era, science and technology continue to develop significantly. One technology that is developing rapidly is information technology. This technological development can be used by the community and developers to help their work. One of the benefits of using this information technology is that it can help in the field of education. In addition, there are lots of cutting-edge technologies that are already sophisticated, namely Augmented Reality (AR) technology. In providing education to the younger generation, there are many insights that must be taught in addition to compulsory lessons in school. One of them is an insight into the use of internal fire extinguishers. Until now people tend to lack understanding of how this tool works because it is considered insignificant. Even though the fact is this is important to equip someone when one day there will be a fire. So that in this study proposes the development of an interactive learning application regarding fire extinguishers. This research has several stages including the identification of educational materials, the design and development of educational media, and testing of user convenience for early childhood where they are the target users of this application. This learning application built is a learning platform for children based on Augmented Reality. The test results show that this application gets positive feedback from child users where on average it gives a value of 9. So it can be concluded that child users can learn how the firefighting profession works and children can immediately practice it interactively through this learning application.
Driveable Area Detection Using Semantic Segmentation Deep Neural Network

Autonomous vehicles use road images to detect roads, identify lanes, objects around the vehicle and other important pieces of information. This information retrieved from the road data helps in making appropriate driving decisions for autonomous vehicles. Road segmentation is such a technique that segments the road from the image. Many deep learning networks developed for semantic segmentation can be fine-tuned for road segmentation. The paper presents details of the segmentation of the driveable area from the road image using a semantic segmentation network. The semantic segmentation network used segments road into the driveable and alternate area separately. Driveable area and alternately driveable area on a road are semantically different, but it is a difficult computer vision task to differentiate between them since they are similar in texture, color, and other important features. However, due to the development of advanced Deep Convolutional Neural Networks and road datasets, the differentiation was possible. A result achieved in detecting the driveable area using a semantic segmentation network, DeepLab, on the Berkley Deep Drive dataset is reported.
Long Short-Term Memory Approach for Predicting Air Temperature In Indonesia

Air temperature is one of the main factors for describing the weather behaviour in the earth. Since Indonesia is located on and near equator, then monitoring the air temperature is needed to determine either global climate change occurs or not. Climate change can have an impact on biological growth in various fields. For instance, climate change can affect the quality of production and growth of animal and plants. Therefore, air temperature prediction is important to meteorologists and Indonesian government to provide information in many sectors. Various prediction algorithms have been used to predict temperature and produce different accuracy. In this study, the deep learning method with Long Short-Term Memory (LSTM) model is used to predict air temperature. Here, the results show that LSTM model with one layer and Adaptive Moment Estimation (ADAM) optimizer produce accuracy which is 32% of , 0.068 of MAE and 0.99 of RMSE. Moreover, here, ADAM optimizer is found better than Stochastic Gradient Descent (SGD) optimizer.
Big Cross-Modal Social Media Data Analytics With Deep Intelligence

The thirteen papers in this special section focus on social media data analytics with deep intelligence. Big cross-model social media data analytics with deep intelligence aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data. The addressed topic span from the range of human action recognition to affective computing, disaster detection, classification, retrieval, clustering, vehicle reidentification, and data security.
PENGARUH POTENSI DIRI, LINGKUNGAN SEKOLAH DAN TINGKAT PENDIDIKAN ORANG TUA TERHADAP MINAT SISWA MELANJUTKAN STUDI KE PERGURUAN TINGGI DI MAN 1 BANYUMAS

The purpose of the study was to analyze the influence of self potential, school environment, and parents' education of the interest of the students to continue their studies to university students of class XI MAN 1 Banyumas.This research is quantitative descriptive.The population in this study were students of class XI MAN 1 Banyumas.The hypothesis was analyzed by using multiple linear regression analysis.The conclusions were: 1.The self potential has a positive effect of the interest of the students to continue their studies to university; 2. The school environment has a positive effect of the interest of the students to continue their studies to university; 3. The parents' education no effect of the interest of the students to continue their studies to university.
Pengembangan Media Pembelajaran Matematika Berbasis YouTube Pada Materi Perbandingan Trigonometri

The use of technology in education is becoming increasingly common. So that in its development, educators must be able to take advantage of information and communication technology in the learning process. Efforts to use it are to increase the attractiveness of a learning process, one of which is by using the help of learning media. Because of this, this study aims to produce YouTube-based learning media on trigonometric comparison material, find out expert opinions and student responses to YouTube-based mathematics learning media on trigonometric comparison material from an angle. This research is a research development or Research and Development (R &amp; D) using the Sugiyono procedure. This research was conducted at MAS Nurul Haq Semurup with the research subjects being students of class X. Data collection was carried out using interviews and questionnaires. The instrument used was a questionnaire using a Likert scale. Based on the results of the validation carried out by material experts and media experts, the percentage results were respectively 71% and 77% with valid criteria. Then the learning media was tested through 2 stages, namely small group trials and field trials. The average results obtained were 87.22% for small group trials and 88.16% for field trials with very practical interpretation criteria, so it can be concluded that YouTube-based mathematics learning media on trigonometric comparison material from an angle is valid and practical to use in learning. Overall the results of the study indicate that the learning media developed are suitable for use by students.
Ideas for Developing Augmented Reality in Books As an Effort to Increase Student Reading Interest (Adaptation to Literacy Acceleration from South Korea)

Many learning media models of Augmented Reality were developed in South Korea. Because such learning media are able to support students' understanding more than just reading texts. In conditions of low literacy levels in Indonesia while the trend of technological and information advancements in Indonesia is so rapid, especially those that lead to the industrial revolution 4.0, it is necessary to propose the application of Augmented Reality to reading books with the aim of becoming an effort to increase students' reading interest in Indonesia and especially in the school where the author teaches. The stages in the development of this AR-based book use the Multimedia Development Methodology sourced from Luther, with six stages namely: Concept (concept), Design (design), Collecting material (material collecting), Making (assembly), Testing (testing), and Distribution (distribution). To create developments in the world of education and increase the attractiveness and effectiveness of learning, Augmented Reality technology can be utilized in the learning process both inside and outside of school. In order to achieve this, collaboration between digital creative actors or the IT profession and collaborators or teachers in creating AR media is needed in order to match the material and information to be conveyed.
The Moderating Effect of Managerial of Innovation on Leadership Styles and Job Satisfaction in Jordanian University Private

This study aimed to reveal the relationship between leadership styles of faculty deans in private universities with the job satisfaction of faculty members in Jordan.Two types of leadership styles were chosen, namely the autocratic leadership style and the free leadership style in the presence of a Moderating variable, which is the managerial of innovation of faculties deans in those universities.In those universities, the questionnaire was also distributed to the study sample in a random and systematic way, which consisted of (345) members.The study concluded that there is a negative relationship between the autocratic leadership style and the free leadership style and job satisfaction of the faculty members in the presence of a Moderating variable (managerial of innovation).
ACTIVITY RECOGNITION FOR AMBIENT SENSING DATA AND RULE BASED ANOMALY DETECTION

Abstract. After a brief look at the smart home, we conclude that to have a smart home, and it is necessary to have an intelligent management center. In this article, We have tried to make it possible for the smart home management center to be able to detect the presence of an abnormal state in the behavior of someone who lives in the house. In the proposed method, the daily algorithm examines the rate of changes of a person and provides a number which is henceforth called NNC (Number of normal changes) based on the person’s behavioral changes. We achieve the NNC number using a machine learning algorithm and performing a series of several simple statistical and mathematical calculations. NNC is a number that shows abnormal changes in residents’ behaviors in a smart home, i.e., this number is a small number for a regular person with constant planning and for a person who may not have any fixed principles and regular in personal life is a big number.To increase our accuracy in calculating NNC, we review all common machine learning algorithms and after tests we choose the decision tree because of its higher accuracy and speed and finally, NNC number is obtained by combining the Decision Tree algorithm with statistical and mathematical methods. In this method, we present a set of states and information obtained from the sensors along with the activities performed by the occupant of the house over a period of several days to the proposed algorithm. and the method ahead generates the main NNC number for those days for anyone living in a smart home. To generate this main NNC, we calculate each person’s daily NNC. That means we have daily NNCs for each person (based on his/her behaviors on that day) and the main NNC is the average of these daily NNC. We chose ARAS dataset (Human Activity Datasets in Multiple Homes with Multiple Residents) to implement our method and after tests and replications on the ARAS dataset, and to find anomalies in each person’s behavior in a day, we compare the main (average) NNC with that person’s daily NNC on that day. Finally, we can say, if the main NNC changes more than 30%, there is a possibility of an abnormality. and if the NNC changes more than 60% percent, we can say that an abnormal state or an uncommon event happened that day, and a declaration of an abnormal state will be issued to the resident of the house.
Schematic Refutations of Formula Schemata

Proof schemata are infinite sequences of proofs which are defined inductively. In this paper we present a general framework for schemata of terms, formulas and unifiers and define a resolution calculus for schemata of quantifier-free formulas. The new calculus generalizes and improves former approaches to schematic deduction. As an application of the method we present a schematic refutation formalizing a proof of a weak form of the pigeon hole principle.
The Relationship of Critical Thinking According to the Five-Factor Model Through Metacognition Mediation: A Path Analysis Model

The Relationship of Critical Thinking According to the Five-Factor Model Through Metacognition Mediation: A Path Analysis Model
Determinants of some special matrices over commutative finite chain rings

Abstract Circulant matrices over finite fields and over commutative finite chain rings have been of interest due to their nice algebraic structures and wide applications. In many cases, such matrices over rings have a closed connection with diagonal matrices over their extension rings. In this paper, the determinants of diagonal and circulant matrices over commutative finite chain rings R with residue field 𝔽 q are studied. The number of n × n diagonal matrices over R of determinant a is determined for all elements a in R and for all positive integers n . Subsequently, the enumeration of nonsingular n × n circulant matrices over R of determinant a is given for all units a in R and all positive integers n such that gcd( n , q ) = 1. In some cases, the number of singular n × n circulant matrices over R with a fixed determinant is determined through the link between the rings of circulant matrices and diagonal matrices. As applications, a brief discussion on the determinants of diagonal and circulant matrices over commutative finite principal ideal rings is given. Finally, some open problems and conjectures are posted
ITO: The Sponsored Token Technology

Blockchain technology can be made more efficient with an incentive mechanism using tokens.This article proposes an innovative method of initial token offerings (ITO), allowing issuers such as the government to sponsor and implement policy targeted at specific products, projects or technology.Sponsor's qualifications can gradually be relaxed and guided by a pre-determined process.With a combination of call auctioning and commanding price (CP) determination, the initial issue price is fixed by the sponsor and ultimately by the consensus of all stakeholders.This approach ensures that the initial token price is non-zero at launch and leaves room for revaluation in line with subsequent development of the project or technology.ITO can attract more enterprises, teams and individuals to participate in the innovation activities of critical projects or technological breakthroughs by reducing their economic costs and risks, thus accelerating project collaboration.It also combines a conducive regulatory environment and market forces to achieve flexibility and effective management of technological innovations.
The Impact of Applying Artificial Intelligence on the Quality of Decision-Making of Abu Dhabi Police General Headquarters

The study aims to examine the impact of applying artificial intelligence on the quality of making decisive security decisions in Abu Dhabi Police General Headquarters. The study uses hypothetical deductive approach to measure impact of applying artificial intelligence on the quality of decision-making. The study uses purposive sample of 100 respondents on staff of Abu Dhabi Police General Headquarters. The results showed that the importance of artificial intelligence was high. This indicates that managerial decision-making is directly or indirectly affected by artificial intelligence within the study sample. This makes the security sectors interested in the developments of artificial intelligence and its outputs and exploits them to save time in making decisions, and achieving quality and acceptance. The results further showed that the level of agreement on administrative decision-making has positive and significant relationship on System Sustainability and Development; Effectiveness of the program used as well as security system of Abu Dhabi Police General Headquarters. Artificial intelligence has become one of the main mechanisms that security sectors rely on in decision-making and represent the most important pillars of development that is indispensable under the changing strong competitions in the world of management, as the information and digital revolution cannot be overlooked and difficult to keep up with at the present time.
A Visual Saliency-Based Approach for Content-Based Image Retrieval

During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is used as a query to find the most similar images. One of the biggest challenges in CBIR system is to fill up the “semantic gap,” which is the gap between low-level visual features and the high-level semantic concepts of an image. In this paper, the authors have proposed a saliency-based CBIR system that utilizes the semantic information of image and users search intention. In the proposed model, firstly a significant region is identified with the help of method structured matrix decomposition (SMD) using high-level priors that highlight the prominent area of the image. After that, a two-dimensional principal component analysis (2DPCA) is used as a feature, which is compact and effectively used for fast recognition. Experiment results are validated on different image dataset having an extensive collection of semantic classifications.