Reviewerly/exHarmony huggingface dataset:
Estimation of Hand Posture with Straight Line Detection for a Hand Pose Rally System

There is an event called "stamp rally" that collects checkpoint stamps. The event needs some rally tools. If a participant loses his/her rally tools, it will be difficult for him/her to continue the rally. We are developing the hand pose rally system, which is one of the "gesture interface". Our system attempts to identify an individual by the posture of the participant's hand captured by the USB camera. By bending and stretching five fingers, 32 types of hand postures are achieved. This system estimates which of the 32 types of hand postures whom he/she has presented. We have been constructing a posture estimation method. However, the accuracy was not so good depending on the posture of the hand. In this paper, we focus only on the posture estimation part of the hand pose rally system, and consider a method to improve the estimation accuracy.
Analysis on the Problems and Countermeasures of Online Education and Teaching in Colleges and Universities in the Post-epidemic Period

The outbreak of the new crown epidemic has caused the stagnation of offline teaching, and the popularity of online teaching has doubled. With the effective control of the epidemic, major colleges and universities have begun to open classes, offline education and teaching are in a state of restart, and the popularity of online education has decreased. Effectively improving the shortcomings of online education and teaching, allowing it to continue to give full play to its advantages in the post-epidemic period, and realizing the organic combination of online and offline teaching has become a topic for every college educator to think about. This article focuses on issues related to online education and teaching in colleges and universities in the post-epidemic period.
Distributed Extended Object Tracking Information Filter Over Sensor Networks

This work aims to design a distributed extended object tracking (EOT) system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpendicular axis-symmetric objects to have individual uncertainty. To incorporate the MEM into the information filter (IF) style, we use the moment-matching technique to derive two pair linear models with only additive noise. The separation is merely in a fashion, and the cross-correlation between states is preserved as parameters in each other's model. As a result, the closed-form expressions are transferred into an alternating iteration of two linear IFs. With the two models, a centralized IF is proposed wherein the measurements are converted into a summation of innovation parts. Later, under a sensor network with the communication nodes and sensor nodes, we present two distributed IFs through the consensus on information and consensus on measurement schemes, respectively. Moreover, we prove the estimation errors of the proposed filter are exponentially bounded in the mean square. The benefits are testified by numerical experiments in comparison to state-of-the-art filters in literature.
ANALISIS DETERMINAN PERTUMBUHAN EKONOMI DENGAN PENDEKATAN ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM (ANFIS)

This research is an application to realize a system that capable of providing data and information between levels of economic growth and Indonesian’s Export between 2010-2020. Data from the system that created dug deeper to find out the prediction of drug distribution in the future. The system to be built is the system that is able to predict the level of export needs that will happen in time (month/year) that you want based on the data of time (month/year) using ANFIS system. The ANFIS system will search the best function to predict the export needs in year 2010. Furthermore, the output is used as the data in 2010. The data output as prediction will be matched with actual data, whether the resulting function of ANFIS system has a small error. If so, then the function obtained is optimal. Keyword: Time Series Prediction, neural network, ANFIS.
Revealing the Relation Between Students’ Reading Notes and Scores Examination with NLP Features

Predicting students’ exam scores has been a popular topic both in educational psychology and data mining areas for many years. Currently, many researchers devote efforts to predict exam score precisely with student behavior data and exercise content data. In this paper, we present the Topic-Based Latent Variable Model (TB-LVM) to predict the midterm and final scores with students’ textbook reading notes. We compare the Topic-Based Latent Variable Model with the Two-Step LDA model. For TB-LVM, the standard deviations of the maximum likelihood estimation and method of moments for the midterm exam are 7.79 and 7.63, respectively. The two standard deviations for the final exam are 8.68 and 7.72, respectively. The results are much better than the results of the Two-Step LDA model, which is 14.38 for the midterm exam and 16.55 for the final exam. Finally, we also compare with the knowledge graph embedding method to predict exam scores.
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima

Escaping from saddle points and finding local minimum is a central problem in nonconvex optimization. Perturbed gradient methods are perhaps the simplest approach for this problem. However, to find $(\epsilon, \sqrt{\epsilon})$-approximate local minima, the existing best stochastic gradient complexity for this type of algorithms is $\tilde O(\epsilon^{-3.5})$, which is not optimal. In this paper, we propose LENA (Last stEp shriNkAge), a faster perturbed stochastic gradient framework for finding local minima. We show that LENA with stochastic gradient estimators such as SARAH/SPIDER and STORM can find $(\epsilon, \epsilon_{H})$-approximate local minima within $\tilde O(\epsilon^{-3} + \epsilon_{H}^{-6})$ stochastic gradient evaluations (or $\tilde O(\epsilon^{-3})$ when $\epsilon_H = \sqrt{\epsilon}$). The core idea of our framework is a step-size shrinkage scheme to control the average movement of the iterates, which leads to faster convergence to the local minima.
PROBLEM SOLVING BERBANTUAN APLIKASI PINTAR SMART CHEMISTRY MELALUI SMARTPHONE UNTUK MENINGKATKAN KETERAMPILAN BERPIKIR TINGKAT TINGGI DAN HASIL BELAJAR

Abstract. This study aims to determine whether there are any differences in the students’ achievement of higher order thinking skills and the student learning outcomes through the application of problem solving learning assisted by smart chemistry applications on stichiometry. The population was 105 students of X MIPA SMAN 11 Banjarmasin. The sampling technique was conducted by purposive sampling with 2 experimental classes and 1 control class. Each class consisted of 35 students. The method used was quasi-experimental with a nonequivalent control group design. The data collection was gathered using test and non-test techniques. The data analysis technique used was descriptive and inferential analysis techniques using one way ANAVA test. The results showed that higher order thinking skills, learning outcomes, knowledge, skills and attitudes of the experimental class were better than the control class.Keywords: problem solving, smart chemistry, higher order thinking skills, learning outcomes, stoichiometry.Abstrak. Penelitian ini bertujuan untuk mengetahui ada tidaknya perbedaan pencapaian keterampilan berpikir tingkat tinggi dan hasil belajar peserta didik, melalui penerapan pembelajaran problem solving berbantuan aplikasi smart chemistry pada topik stikiometri. Populasi penelitian yaitu sebanyak 105 peserta didik X MIPA SMAN 11 Banjarmasin. Pengambilan sampel dilakukan melalui teknik purposive sampling dengan 2 kelas eksperimen dan 1 kelas kontrol. Masing-masing kelas terdiri dari 35 peserta didik. Metode yang digunakan adalah eksperimen semu dengan desain nonequivalent control group. Pengumpulan data menggunakan teknik tes dan nontes. Teknik analisis data yang digunakan yaitu teknik analisis deskriptif dan inferensial menggunakan uji ANAVA 1 jalur. Hasil penelitian menunjukkan bahwa keterampilan berpikir tingkat tinggi, hasil belajar pengetahuan, keterampilan dan sikap dari kelas eksperimen lebih baik dari kelas kontrol.Kata kunci: problem solving, smart chemistry, keterampilan berpikir tingkat tinggi, hasil belajar, stoikiometri.
A Sequence to Sequence Model for Extracting Multiple Product Name Entities from Dialog

E-commerce voice ordering systems need to recognize multiple product name entities from ordering utterances. Existing voice ordering systems such as Amazon Alexa can capture only a single product name entity. This restrains users from ordering multiple items with one utterance. In recent years, pre-trained language models, e.g., BERT and GPT-2, have shown promising results on NLP benchmarks like Super-GLUE. However, they can't perfectly generalize to this Multiple Product Name Entity Recognition (MPNER) task due to the ambiguity in voice ordering utterances. To fill this research gap, we propose Entity Transformer (ET) neural network architectures which recognize up to 10 items in an utterance. In our evaluation, the best ET model (conveRT + ngram + ET) has a performance improvement of 12% on our test set compared to the non-neural model, and outperforms BERT with ET as well. This helps customers finalize their shopping cart via voice dialog, which improves shopping efficiency and experience.
Precise runtime analysis for plateau functions

To gain a better theoretical understanding of how evolutionary algorithms (EAs) cope with plateaus of constant fitness, we propose the n-dimensional Plateauk function as benchmark and analyze how different variants of the (1 + 1) EA optimize it. The Plateauk function has a plateau of second-best fitness in a ball of radius k around the optimum. As EA, we regard the (1 + 1) EA using an arbitrary unbiased mutation operator. Denoting by α the random number of bits flipped in an application of this operator and assuming that Pr[α = 1] has at least some small sub-constant value, we show that for all constant k ≥ 2, the runtime T follows a distribution close to the geometric one with success probability equal to the probability to flip between 1 and k bits divided by the size of the plateau. Consequently, the expected runtime is the inverse of this number, and thus only depends on the probability to flip between 1 and k bits, but not on other characteristics of the mutation operator. Our result also implies that the optimal mutation rate for standard bit mutation here is approximately k/(en). Our main analysis tool is a combined analysis of the Markov chains on the search point space and on the Hamming level space, an approach that promises to be useful also for other plateau problems.
A 3D Object Detection Framework for Intelligent Driving using YOLOv4

As the continuous evolution of the vehicles' industry, the self-driving technology has become an indispensable part in today's intelligent vehicles. While 3D object detection is the most core technique in the self-driving technology. A little improvement in the accuracy and efficiency of a 3D object detector will be of the utmost importance to intelligent driving and human life. So this paper focused on the improvement of detecting accuracy based on the yolov4, which is also an effective object detection network. This work transformed yolov4 from a 2D detector into a 3D detector, and also improved the original yolov4 neural network with the CSPDarknet53 backbone network. The proposed framework has a more effective and accurate performance in the 3D object detecting for intelligent driving.
Implementation of Automatic Plant Sprinkler Control System in RT 07 RW 08 Pabuaran Mekar Subdistrict, Cibinong, Bogor

The COVID-19 pandemic is forcing humans to adapt and be more creative. The Women Farmers Forum (KWT) group in Pabuaran Mekar receives assistance for the success of the food security program in the form of P2L (Sustainable Food Courts). The purpose of the program is to ensure that the community receives quality food from its own environment to maintain and improve the nutrition of the surrounding community. The problems faced in KWT are watering plants that still rely on humans, which is carried out in the morning and afternoon. The manufacture of an automatic plant sprinkler control system reduces the role of humans because it is equipped with a timer program. Users need to know about how to use the system to make it more durable and it is given from a presentation that is aimed as training. The survey at the end of the presentation provided feedback that the activities carried out were very good with a score of 65% of respondents in terms of more than the presentation material, and 79% of respondents rated it very much in accordance with their benefits and expectations. More than 87.5% of the respondents scored an excellent overall quality of the activities.
LibGuides: Library Services for Summer 2021: Home

This guide will provide information about our services during this social distancing time and to the end of Summer 2021 semester
Mitigasi bencana banjir berbasis sekolah

The partnerof this Community Service (PKM) was SMA 5 Parepare. The problems were: (1) lack of knowledge regarding the causes of floods and disaster mitigation, and (2) lack of socialization on flood mitigation.Activities carried out in the form of socialization with the methods used were: lectures, question and answer, and video playback causes flooding. The results achieved were: (1) knowledge of partners increases through socializaion about the importance of environmental sustainability, flooding and various causes, as well as school-based flood disaster mitigation efforts, and (2) Public awareness (students) increases in supporting flood mitigation efforts.
A Visual Pattern of Two Decades of Literature on Mobile Learning: A Bibliometric Analysis

Mobile learning, or m-Learning, has grown in popularity significantly over the last few decades, as evidence of educators and students worldwide using the device as a teaching and learning tool continues to accumulate. The pattern of mobile-learning research from 2001 to 2020 is determined by bibliometric analysis. The study retrieved 3,874 documents for further analysis, based on the keywords associated with mobile learning in the article’s title. The maps depicted the connections between the researchers, countries, all keywords, titles, and abstracts. The title and abstract of this study are used to visualise the co-occurring terms of various phases or concepts associated with mobile learning that were extracted from the Scopus database. The findings indicate strong and direct connections between the concepts in e-learning, implying a significant and direct research connection. China was the leading country in mobile-learning research, and the leading journal was Computers and Education. The top author’s keywords in terms of co-occurrence were "mobile learning", "e-learning", "students", "learning systems", and "m-learning". To conduct a two-decade analysis, this study excludes any publications from the years 1984 and 2021. These critical analyses of prior work are valuable and indispensable resources for mobile-learning scholars and practitioners. It is believed that online-learning applications have increased students’ engagement; and it has eliminated the accessibility gap. Consequently, mobile learning is expected to maintain its popularity over the next few decades.
CIRC for Improving Reading Comprehension Skill in 4th Grade Elementary School Students

This study aims to determine whether the learning design using the CIRC type in cooperative learning model can improve reading comprehension skills in fourth grade students of elementary schools. This study focused on the steps of the CIRC type of this model emphasizes student’s teamwork in finding the main ideas of some texts. The learning process is centered on student and their group activities. Meanwhile, teacher provides some guidance for each group. This study used a quasi-experimental method and used a nonequivalent control group design. This research was conducted in 4th grade elementary students in one of school in Lampung Tengah Regency (22 participants). The result shows that the design of CIRC type in cooperative learning models can improve the reading ability of 4th grade elementary students’ understanding
OpenMG: An Open-Source Middleware Interface for Gesture Recognition

The interface between the user and a healthcare simulation is a critical component, often driving engagement, immersion, and usage. However, the majority of interface designs leverage the mouse and keyboard, despite the availability of newer hardware platforms. With a goal of enabling a more natural user interface, the OpenMG effort designed an open-source gesture database for healthcare simulations. The system utilizes a hardware-agnostic approach to capture gestures onto a universal hand model, supplying a readily available database mapping gestures to intended actions. This manuscript reports on the development of the OpenMG software package, initial functionality testing, and conclusions including future research avenues for improving human-computer interaction in healthcare simulation.
A sketch of Akum (Southern Jukunoid)

This article presents data on the little researched Southern Jukunoid language Akum which is spoken in five villages of the Cameroon-Nigerian border area. Akum shows the typical Benue-Congo syllable structure (CV, CVC) as well as typical sounds of the Benue-Congo consonant inventory (double and secondary articulation). As is known from other Southern Jukunoid languages, only the consonants r, b, g, and nasals are permitted in word-final position and – because they are unreleased – the distinction voiced/voiceless is neutralized. The number and qualities of phonemically distinct vowels remains debatable. Concerning the nominal morphology, the Akum nominal prefix system is reduced in several aspects compared to its Southern Jukunoid relatives: it only has a set of 4 different nominal prefixes which are vocalic in form and it shows only marginal agreement on adjectives. The quinary numeral system and SVO basic word order are similar to its Southern Jukunoid relatives Bezen, Yukuben and Kuteb.
Towards Comparative Physical Interpretation of Spatial Variability Aware Neural Networks: A Summary of Results

Given Spatial Variability Aware Neural Networks (SVANNs), the goal is to investigate mathematical (or computational) models for comparative physical interpretation towards their transparency (e.g., simulatibility, decomposability and algorithmic transparency). This problem is important due to important use-cases such as reusability, debugging, and explainability to a jury in a court of law. Challenges include a large number of model parameters, vacuous bounds on generalization performance of neural networks, risk of overfitting, sensitivity to noise, etc., which all detract from the ability to interpret the models. Related work on either model-specific or model-agnostic post-hoc interpretation is limited due to a lack of consideration of physical constraints (e.g., mass balance) and properties (e.g., second law of geography). This work investigates physical interpretation of SVANNs using novel comparative approaches based on geographically heterogeneous features. The proposed approach on feature-based physical interpretation is evaluated using a case-study on wetland mapping. The proposed physical interpretation improves the transparency of SVANN models and the analytical results highlight the trade-off between model transparency and model performance (e.g., F1-score). We also describe an interpretation based on geographically heterogeneous processes modeled as partial differential equations (PDEs).
Perspectives on Auditing and Regulatory Compliance in Blockchain Transactions

The recent advent of blockchain technology is anticipated to revolutionize the operational processes of several industries including banking, finance, real estate, retail and benefit governmental as well as corporate information management structures. The underlying principles of information immutability, traceability, and verifiability built-in blockchain transactions may lead to greater adoption of distributed crypto-ledger applications in auditing automation, compliance monitoring, and guaranteeing high assurance. This chapter discusses the contemporary applications of blockchain technology in information auditing, exploring aspects such as data recording, accuracy, verification, transparency, and overall value of a decentralized blockchain crypto-ledger for auditors. Opportunities for timeliness, completeness, and reconciliation in appraising regulatory compliance of organizations employing blockchain-based contractual frameworks are also investigated. The chapter reviews the existing and anticipated challenges blockchain applications pose to traditional regulatory compliance models and the inherent risks for businesses and stakeholders. We highlight the impact of operational concerns such as decentralized transactions, network complexity, transaction reversals, credential management, software quality, and human resources. Finally, the chapter provides perspective on assurance complexities involved in transforming from proprietary to blockchain-based framework while adhering to IT control obligations dictated by three major auditing standards Sarbanes Oxley Act (SOX), Control Objectives for Information Technologies (COBIT), and International Standardization Organization (ISO) /International Electrotechnical Commission (IEC) 27001.
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data

Moving beyond testing on in-distribution data works on Out-of-Distribution (OOD) detection have recently increased in popularity. A recent attempt to categorize OOD data introduces the concept of near and far OOD detection. Specifically, prior works define characteristics of OOD data in terms of detection difficulty. We propose to characterize the spectrum of OOD data using two types of distribution shifts: covariate shift and concept shift, where covariate shift corresponds to change in style, e.g., noise, and concept shift indicates a change in semantics. This characterization reveals that sensitivity to each type of shift is important to the detection and confidence calibration of OOD data. Consequently, we investigate score functions that capture sensitivity to each type of dataset shift and methods that improve them. To this end, we theoretically derive two score functions for OOD detection, the covariate shift score and concept shift score, based on the decomposition of KL-divergence for both scores, and propose a geometrically-inspired method (Geometric ODIN) to improve OOD detection under both shifts with only in-distribution data. Additionally, the proposed method naturally leads to an expressive post-hoc calibration function which yields state-of-the-art calibration performance on both in-distribution and out-of-distribution data. We are the first to propose a method that works well across both OOD detection and calibration and under different types of shifts. View project page at https://sites.google.com/view/geometric-decomposition.