Reviewerly/exHarmony huggingface dataset:
Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning.

We envision that in the near future, humanoid robots would share home space and assist us in our daily and routine activities through object manipulations. One of the fundamental technologies that need to be developed for robots is to enable them to detect objects and recognize them for effective manipulations and take real-time decisions involving those objects. In this paper, we describe a novel architecture that enables multiple low-compute NAO robots to perform real-time detection, recognition and localization of objects in its camera view and take programmable actions based on the detected objects. The proposed algorithm for object detection and localization is an empirical modification of YOLOv3, based on indoor experiments in multiple scenarios, with a smaller weight size and lesser computational requirements. Quantization of the weights and re-adjusting filter sizes and layer arrangements for convolutions improved the inference time for low-resolution images from the robot s camera feed. YOLOv3 was chosen after a comparative study of bounding box algorithms was performed with an objective to choose one that strikes the perfect balance among information retention, low inference time and high accuracy for real-time object detection and localization. The architecture also comprises of an effective end-to-end pipeline to feed the real-time frames from the camera feed to the neural net and use its results for guiding the robot with customizable actions corresponding to the detected class labels.
Evaluating Naïve Bayes Automated Classification for GBAORD

The Indonesian Government Budget Appropriations or Outlays for Research and Government (GBAORD) has been analyzed manually every year to measure the government expenditures in research and development. The analysis process involved several experts in making the budget classification. This method, commonly known as manual classification, has its downsides, which are time consumption and inconsistent result. Therefore, a study about implementing the machine learning method in GBAORD budget classification to avoid inconsistency is proposed in the previous research. For further analysis, this paper evaluates the performance of the Naïve Bayes algorithm for the GBAORD budget classification. This paper aims to measure the robustness of the Naïve Bayes to classify GBAORD data taken from 2017 until 2019. This paper uses three models of Naive Bayes with different preprocessing methods and features. This paper concludes that using the Naïve Bayes algorithm in Indonesian GBAORD budget classification is suitable since the robustness of the algorithm is proved to be high with 96.788+-0.185% average accuracy.
Manufacturing Method and Evaluation Analysis for the Practice of Standard Products

In the Polytechnic University of Japan, the practice for the standard production system is carried out. It incorporates three methods: the learning-practice connecting method, the project-based learning method and the working group learning method. The practice for the standard production system employs actual processes for product development in a company and we produced a data logger system including wireless communication. The design and production practice is carried out in 144 hours. We address two questionnaires to the students at the middle and the end of the practice. Based on the questionnaires, we evaluated the student answers for the practice.
Pakistan's Blasphemy Laws and the Role of Forensic Psychiatrists

Humans have been persecuted for the crime of blasphemy since biblical times. Today, about one quarter of the world's countries and territories, most of them Muslim-majority nations, still have anti-blasphemy laws. Pakistan is among the countries where blasphemy is punishable by death. In many instances, the accused are killed by mobs before legal proceedings even begin. Often, it is people with mental illness and members of religious minorities who end up being accused of blasphemy. A psychiatric evaluation can be beneficial in informing the court about symptoms of mental illness in the context of a blasphemy accusation. Psychiatrists can also provide crucial information to the courts and the general public about trauma, persecution, and safety in asylum cases where the asylum seeker is fleeing persecution after being accused of blasphemy in Pakistan or another country that criminalizes blasphemy.
Simon, S - Biographical entry - Index to the AMA archive

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Preface to the Special Issue on Foundations of Coordination Languages and Self-Adaptive Systems (Foclasa 2018)

A system of polynomial ordinary differential equations (odes) is specified via a vector of multivariate polynomials, or vector field, F. A safety assertion ψ⟶[F]ϕ means that the trajectory of the system will lie in a subset ϕ (the postcondition) of the state-space, whenever the initial state belongs to a subset ψ (the precondition). We consider the case when ϕ and ψ are algebraic varieties, that is, zero sets of polynomials. In particular, polynomials specifying the postcondition can be seen as a system's conservation laws implied by ψ. Checking the validity of algebraic safety assertions is a fundamental problem in, for instance, hybrid systems. We consider a generalized version of this problem, and offer an algorithm that, given a user specified polynomial set P and an algebraic precondition ψ, finds the largest subset of polynomials in P implied by ψ (relativized strongest postcondition). Under certain assumptions on ϕ, this algorithm can also be used to find the largest algebraic invariant included in ϕ and the weakest algebraic precondition for ϕ. Applications to continuous semialgebraic systems are also considered. The effectiveness of the proposed algorithm is demonstrated on several case studies from the literature.
The Orange and the Rose - Subject - Index to the AMA archive

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Analysis on News Articles Classification using Machine Learning

News articles order is an administered learning approach in which news articles are relegated classification names dependent on probability showed by a preparation set of marked articles. A framework for programmed classification of news articles into a standard arrangement of classifications has been executed. The proposed work will utilize Term Frequency-Inverse Document Frequency (TF-IDF) term weighting plan for improvement of grouping systems to get more advanced outcomes and utilize two administered learning draws near, i.e., Support Vector Machine (SVM) and K-Nearest neighbor (kNN) and think about the exhibitions of the two classifiers. Every news report is preprocessed and changed into a term-archive lattice. Besides, various methodologies, for example, stemming, stop word expulsion, include decrease have been executed for both execution and precision upgrades.
Bismo Jelantik Joyodiharjo PENDEKATAN BIOMIMICRY PADA DESAIN SARANA SEARCH AND RESCUE DENGAN TEKNOLOGI ADDITIVE MANUFACTURING

As a young species of 200.000 years, human is part of earth ecosystem. For 3.8 billion years, other livingbeings have evolved, adapting and selected by nature, until we came and disrupt the balance. Populationgrowth and climate change impact all of earth’s ecosystem. Urbanization and deforestation resulted in floodingin parts of Indonesia. Flooding is worse in urban areas as more risks involved. The Search and Rescueoperation can benefit from remote controlled unmanned vehicles to help save people. Conventional propulsionuses propeller, however in urban flooding condition, there are lots of debris in the water and the propeller canget stuck. Biomimicry uses nature’s genius to solve problem with emphasis on sustainability. This approachseeks nature’s advice in designing an alternative solution to the propeller problem in certain condition. Mobuliformswimmers, such as Manta Rays, present a model of efficient and debris-proof propulsion that can be used inthe search and rescue unmanned vehicles. This paper will highlight the Biomimicry process in designing aSearch and Rescue vehicle, called RCM3D-SAR that uses inspiration from Manta Ray’s pectoral fins. Thedesign is based on additive manufacturing technology and form follows function approach.
Curdie, Daniel - Biographical entry - Index to the AMA archive

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Designing Pedagogic Conversational Agents through Data Analysis

Pedagogical Conversational Agents are systems or programs that represent a resource and a means of learning for students, making the teaching and learning process more enjoyable. The aim is to improve the teaching-learning process. Currently, there are many agents being implemented in multiple knowledge domains. In our previous work, a methodology for designing agents was published, the result of which was Agent Dr. Roland, the first conversational agent for Early Childhood Education. In this paper, we propose the use of Data Analytics techniques to improve the design of the agent. Two new techniques are applied: KDDIAE, application of (Knowledge Discovery in Databases) to the Data of the Interaction between Agents and Students – Estudiantes in Spanish, and BIDAE (use of Data Analytics to obtain information of agents and students). The use of KDDIAE and BIDAE proves the existence of a fruitful relationship between learning analytics and learning design. Some samples of rules related to learning analytics and design are the following: (Learning Analytics) Children who initially do not know how to solve the exercise, after receiving help, are able to understand and solve it à (Learning Design) An agent for small children should be able to provide help. In addition, help should be entertaining and tailored to their characteristics because it is a resource that children actually use; or (Learning Analytics) Younger children use more voice interaction à (Learning Design) An agent interface for young children must incorporate voice commands. A complete list of rules related to learning analytics and design is provided for any researcher interested in PCA design. 72 children were able to use the new Dr. Roland after applying the learning analytics-design rules. They reported a 100 % satisfaction as they all enjoyed interacting with the agent.
PENERAPAN ALGORITMA MD5 SEBAGAI PENGAMAN AKUN PADA APLIKASI WEB EMUSRENBANG KOTA BINJAI

Encryption is a technique for hiding passwords into writing that changes from their original form. Encryption aims to increase the security of passwords on an account so that it is not misused by people who are not responsible or who have an interest in seeing data on the application or the web.Emusrenbang application in Binjai City is a website that aims to accommodate community proposals in Binjai so that every community in Binjai city can submit proposals without having to come to the Kelurahan office. Each community has an account to be able to submit proposals based on Local Government Work Plans (OPD). To increase security for each user account is equipped by adding a password. To prevent passwords from being read easily, passwords are encrypted using the MD5 algorithm implemented in PHP. MD5 is an algorithm. It is expected that the emusrenbang web application can cut costs in implementing the emusrenbang from villages, sub-districts, the Regional Representative Council and the Regional Parliament Council (PokD), and can increase public trust with government transparency in carrying out musrenbang in the city of Binjai.Keywords: Cryptography, MD5 Algorithm
Nested Barycentric Coordinate System as an Explicit Feature Map

We propose a new embedding method which is particularly well-suited for settings where the sample size greatly exceeds the ambient dimension. Our technique consists of partitioning the space into simplices and then embedding the data points into features corresponding to the simplices' barycentric coordinates. We then train a linear classifier in the rich feature space obtained from the simplices. The decision boundary may be highly non-linear, though it is linear within each simplex (and hence piecewise-linear overall). Further, our method can approximate any convex body. We give generalization bounds based on empirical margin and a novel hybrid sample compression technique. An extensive empirical evaluation shows that our method consistently outperforms a range of popular kernel embedding methods.
Phase measurement by quadriwave lateral shearing interferometry using spatial light modulator

Quadriwave lateral shearing interferometry (QLSI) is well established phase imaging technique. Information about phase distribution within this method is encoded into complex pattern of intensity distribution. So called difference fronts are then extracted via fast Fourier transform (FFT) technique and optimally merged with various integration algorithms, depending on character of measured phase distribution. QLSI experiment is typically build with the use of dedicated shearing device, based on combination of special amplitude grating with phase chessboard. In our experiment, we perform the diffractive shear through phase spatial light modulator (SLM), which also serves as known in advance phase test. In this context, we present a comparison of measurement results with the use of different illumination sources.
Instructional Media and English: Using Macromedia Flash to Teach EFL Writing

The objectives of this study were to find out whether or not (1) Macromedia Flash enhanced students’ writing competence, and (2) there was significant difference between the students taught by using Macromedia Flash and those who were not. This study employed quasi experimental design, pretest and posttest nonequivalent groups. There were 39 students of one of junior high schools in Jambi participating in this study. They were grouped in two different classes. The experimental group consisted of 20 students while the other 19 students were in control group. The data were collected by using writing test. Students’ paragraphs writing scores in pretest and posttest were analyzed through the use of paired sample and independent sample t-test statistical analysis. The findings showed that first, based on the result of paired sample t-test, there was significant improvement of the students’ writing competence after the intervention. Second, with regard to the result of independent sample t test, there was significant difference of students’ writing competence in experimental group and control group.
PENINGKATAN HASIL BELAJAR IPA MELALUI MODEL PEMBELAJARAN COOPERTIVE TIPE TEAM PAIR SOLO

This research was conducted at Indra Makmu 2 Public Middle School for 3 months in the odd semester of the 2017/2018 school year which aims to improve student learning outcomes. The research subjects were all class VII students, amounting to 30 people, with 11 men and 19 women. In collecting data, researchers used instruments or data collection techniques in the form of granules of test questions totaling 10 items and observation sheets. After the data is collected the researcher conducts data processing and analysis by comparing the results of tests and observations from the initial conditions, the first and second cycles. The results showed an increase in completeness of students in the first cycle to 63% (19 students) from the initial condition which was only 17% (5 students), and at the end of cycle II completeness increased again to 87% (24 students).&#x0D;
Study on Skill Data Analysis and Training Method of Highly Skilled Installer in Information Network Cabling

This paper presents an effective training method using skill data to acquire highly skills in a short time. Therefore, a skill analysis has been performed to clarify highly skill of a model worker of “information network cabling” who set a world record in the Optical Fiber Speed Challenge 2019. We attempted to formalize the characteristics of highly skill by a motion capture software and mathematically modeling the body parts affecting work time by multiple regression analysis. At the same time, we present a scientific training methods using those skill data, such as showing that the improvement of work movement by this mathematical model leads to the efficiency of work time.
Learning Numerical Observers using Unsupervised Domain Adaptation

Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers for task-based image quality assessment. However, labeling large amounts of experimental data to train deep neural networks is tedious, expensive, and prone to subjective errors. Computer-simulated image data can potentially be employed to circumvent these issues; however, it is often difficult to computationally model complicated anatomical structures, noise sources, and the response of real world imaging systems. Hence, simulated image data will generally possess physical and statistical differences from the experimental image data they seek to emulate. Within the context of machine learning, these differences between the sets of two images is referred to as domain shift. In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones. In the proposed method, a DL-NO will initially be trained on computer-simulated image data and subsequently adapted for use with experimental image data, without the need for any labeled experimental images. As a proof of concept, a binary signal detection task is considered. The success of this strategy as a function of the degree of domain shift present between the simulated and experimental image data is investigated.
Changes in Description of “Silla Village Register” in the “Korean History” Textbook Since the Second Educational Period

This paper reviews changes in descriptions related to “Silla Village Registerr(新羅村落文書)” in high school Korean history textbooks from the second curriculum to the 2009 revision. During this period, the content of the “Silla Village Register” was not out of place. Since the revision of the 2009 school curriculum, changes in descriptions and descriptions of the “Silla Village Registerr” have begun to appear as the research achievements of the historians have begun to be reflected in Korean history textbooks. The seven textbooks in the curriculum revised in 2009 include “Silla Village Registerr” The relevant data are contained, and the date of preparation of the documents is recorded in 695 years in two textbooks. This is not only the result of sincere implementation of the writing standards of the curriculum, but also the result of making good use of the research achievements of the Historical Society.
Perceived threats in Plastic money use and dealing with simple utilize action and security endeavours in India

The advantage of plastic cash is its openness and adequacy with gentility than money in wallet in addition to prepared idea with the serious issue of fraudsters who may abuse the privileges of the authentic partners. At present, the processing assets, correspondence innovation, accessibility of the cards in focused market are raising India to be set at the developing edge of prominence of plastic cash. Individuals after demonetization are strongly utilizing the innovation; also it's a stage towards the digitalization of India. With expanding populace of the PDA clients, individuals are progressively mindful about the alerts expected to seek after while utilizing P-cards. Anyway as contrast with the created countries, in a place where there is towns India, the issue turn out to be progressively risky as there are petite infrastructural office and proficiency, high level of less talented populace, old traditions and non-reach of the administration offices, ruining the p-cash appropriateness and security and computerized India crusade. In this way, other than the plastic cash prizes and commitments to the digitalization, present paper contributed towards a scope of security issues notwithstanding system to make cash ensured and dependable.
Australian Academy of Science - Organisation - Index to the AMA archive

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Neuro-evolutionary Frameworks for Generalized Learning Agents

The recent successes of deep learning and deep reinforcement learning have firmly established their statuses as state-of-the-art artificial learning techniques. However, longstanding drawbacks of these approaches, such as their poor sample efficiencies and limited generalization capabilities point to a need for re-thinking the way such systems are designed and deployed. In this paper, we emphasize how the use of these learning systems, in conjunction with a specific variation of evolutionary algorithms could lead to the emergence of unique characteristics such as the automated acquisition of a variety of desirable behaviors and useful sets of behavior priors. This could pave the way for learning to occur in a generalized and continual manner, with minimal interactions with the environment. We discuss the anticipated improvements from such neuro-evolutionary frameworks, along with the associated challenges, as well as its potential for application to a number of research areas.
The Effect of Using Group Work Technique on Students’ Reading Comprehension at the First Year of Senior High School 4 Mandau

This research was aimed to find out The Effect of Using Group Work Technique on Students’ Reading Comprehension. The design of this research was quasi experimental. The population was the total of tenth grade students that consist of 10 classess. Meanwhile the sample was proportion of the population that consist of 2 classes. X6 was experimental class and X9 was control class. The number of sample was selected by using cluster sampling. To analyze the data the writer gave pretest and posttest then analyzed the data by using independent sample t-test by using SPSS 16 version and effect size formula. After analyzing the data, where the 2-tailed value was smaller than 0.05 (0.000 &lt; 0.05). The result of effect size was 0.3 categorized as large effect. Thus, null hypothesis (Ho) is rejected, and alternative hypothesis (Ha) is accepted. So, it can be concluded that there was a significant effect of using Group Work Technique on Students’ Reading Comprehension