Provable Advantage in Quantum Phase Learning via Quantum Kernel Alphatron

The use of quantum computation to speed-up machine learning algorithms is among the most exciting prospective applications in the NISQ era. Here, we focus on the quantum phase learning problem, which is crucially important in understanding many-particle quantum systems. We prove that, under widely believed complexity theory assumptions, quantum phase learning problem cannot be efficiently solved by machine learning algorithms using classical resources and classical data. Whereas using quantum data, we prove the universality of quantum kernel Alphatron in efficiently predicting quantum phases, indicating clear quantum advantages in such learning problems. We numerically benchmark the algorithm for a variety of problems, including recognizing symmetry-protected topological phases and symmetry-broken phases. Our results highlight the capability of quantum machine learning in efficient prediction of quantum phases of many-particle systems.

“Liking” as an early and editable draft of long-run affective value

Psychological and neural distinctions between the technical concepts of “liking” and “wanting” pose important problems for motivated choice for goods. Why could we “want” something that we do not “like,” or “like” something but be unwilling to exert effort to acquire it? Here, we suggest a framework for answering these questions through the medium of reinforcement learning. We consider “liking” to provide immediate, but preliminary and ultimately cancellable, information about the true, long-run worth of a good. Such initial estimates, viewed through the lens of what is known as potential-based shaping, help solve the temporally complex learning problems faced by animals.

Problematika Pembelajaran Daring di Masa Pandemi Covid-19 Ditinjau dari Sudut Pandang Mahasiswa

This study aims to discover online learning problems amid the covid-19 pandemic. The method used is descriptive qualitative with a phenomenological approach. The research subjects were Billfath University Indonesian Language Education study program students. The research data is in survey results and interviews with Indonesian Language Education students. The results of surveys and interviews found that the biggest problems in online learning, namely: unstable internet networks and data packages used by students. These two problems ultimately impact the level of understanding of the material by students. In addition, online learning also impacts the ability of mobile phones to respond to data. The memory capacity of mobile phones certainly causes this has exceeded, thus hampering students in learning. Therefore, mutual agreement is needed between students and lecturers so that lecture activities can run smoothly and achieve the lecture's objectives.

A Journey From Neural Networks to Deep Networks

The chapter is about deep learning fundaments and its recent trends. The chapter mentions many advanced applications and deep learning models and networks to easily solve those applications in a very smart way. Discussion of some techniques for computer vision problem and how to solve with deep learning approach are included. After taking fundamental knowledge of the background theory, one can create or solve applications. The current state-of-the-art of deep learning for education, healthcare, agriculture, industrial, organizations, and research and development applications are very fast growing. The chapter is about types of learning in a deep learning approach, what kind of data set one can be required, and what kind of hardware facility is required for the particular complex problem. For unsupervised learning problems, Deep learning algorithms have been designed, but in the same way Deep learning is also solving the supervised learning problems for a wide variety of tasks.

An Insight for Cursive Context-Specific Printed Script Recognition

The greatest challenge of machine learning problems is to select suitable techniques and resources such as tools and datasets. Despite the existence of millions of speakers around the globe and the rich literary history of more than a thousand years, it is expensive to find the computational linguistic work related to Punjabi Shahmukhi script, a member of the Perso-Arabic context-specific script low-resource language family. This paper presents a deep insight into the related work with summary statistics, advocating the popularity and success of artificial neural networks and related techniques. The paper includes support from recent trends from the authentic sources based on the top-level researchers' feedback including the machine learning frameworks. A comprehensive comparison of the most popular deep learning techniques convolutional neural network and the recursive neural network has been presented for the cursive context-specific scripts of Perso-Arabic nature. The overview of the available benchmark datasets for machine learning problems, especially for the Perso-Arabic group, is added. This paper incorporates essential knowledge contents for the researchers in machine learning and natural language processing disciplines on the selection of algorithms, architectures, and resources.

PERILAKU PENCARIAN INFORMASI SISWA DALAM MODEL PEMBELAJARAN JARAK JAUH DI MASA PANDEMI COVID-19

The pandemic Covid-19 has impact most of human activities, one of them is education. The implementation of distance learning has influence for the parents, teachers, and students who are the main subjects in education, as though increased stress, less of competency development, or technical learning problems related to fulfill information needs. In case the problems abandoned, it will hinder the students-development, the development of sustainable education, and the progress of the nation. This research aims to determine the information behavior of students of SMAN 1 Cicalengka in overcome learning constraints during the pandemic Covid-19. This research uses a qualitative approach with descriptive methods. Data collection techniques is using triangulation data, through observation/surveys, interviews, and literature study. The population were all students of SMAN 1 Cicalengka with 5 informants. It uses the Ohotshi-Gottschalg-Duque information-behavior model as conceptual framework. The results showed that: First, students information needs were at the concious, visceral, and adapted levels; Second, information needs are mostly related with learning materials, motivation, health, hobbies, college even job vacancies; Third, students information behavior based on habits and intuitions, most of students do not understand the process of extracting because they do not recognize the domain of the information need. The implication that learning based on library is necessity at the level of educational unit that collaborates between teachers and school librarians.

Arabic Language Phoneme Pronunciation Difficulties Among Upper Basic Hausa-Speaking Students in Kano State, Nigeria

In the process of learning a foreign language, there are some indispensable learning problems, especially in the pronunciation aspect. Therefore, this study investigated Arabic Language phoneme pronunciation difficulties among Upper Basic Hausa-speaking Arabic Language students in Kano State, Nigeria. The total population for the study was all Upper Basic Hausa-speaking students of Arabic in Kano State, Nigeria. Two hundred (200) Hausa-speaking Arabic students were sampled from the 3 senatorial districts in the state involving 9 Local Government Areas using multistage sampling procedure. A Pronunciation Test adapted from Adebayo (2010) with a reliability coefficient of 0.75 was used for data collection. Only 180 Pronunciation test that were properly filled and returned were subjected to both descriptive and inferential statistical analysis. The percentage was used to answer the research questions while Chi-square was employed to test the hypotheses postulated at the 0.05 level of significance. The findings of this study revealed that a number of Arabic consonants constitute pronunciation difficulty for the students due to the presence of their corresponding consonant sounds in Hausa language, while all Arabic vowels did not constitute much pronunciation difficulty for students. The findings also revealed that there was no significant difference in the Arabic consonant and vowel sounds that constitute pronunciation difficulties for male and female Upper Basic Hausa-speaking students of Arabic from both public and private schools in Kano State. In the light of these findings, it was recommended among others that, teachers of Arabic language should pay more attention to the various Arabic consonant and vowel phonemes as well as the Arabic syllable patterns that constitute pronunciation difficulties for the Hausa-speaking students of Arabic. The attention of Hausa-speaking students of Arabic should also be drawn to the semantic implications of phonemic replacement, reduction or prolongation.

On the Performance of Generative Adversarial Network by Limiting Mode Collapse for Malware Detection Systems

Generative adversarial network (GAN) has been regarded as a promising solution to many machine learning problems, and it comprises of a generator and discriminator, determining patterns and anomalies in the input data. However, GANs have several common failure modes. Typically, a mode collapse occurs when a GAN fails to fit the set optimizations and leads to several instabilities in the generative model, diminishing the capability to generate new content regardless of the dataset. In this paper, we study conditional limiter solutions for mode collapse for the Intrusion Detection System (IDS) Control Flow GAN (ICF-GAN) model. Specifically, the ICF-GAN’s mode collapse instances are limited by a mini-batch method that significantly improves the model accuracy. Performance evaluation is conducted using numerical results obtained from experiments.

The Design of Web-Based Learning Using Google Sites for Teaching Heat and Temperature Topic

The limited variety of learning media will create various learning problems. One of the problems that arise is the lack of motivation to learn and low learning outcomes. These problems can be overcome by developing learning media integrated with technology such as web-based learning. This study aims to produce web-based learning using Google Sites on Heat and Temperature topics for high school students. The media development used the 4D model with the following stages: 1) Define, 2) Design, 3) Develop. The design and development stages of the media involve experts who assess two aspects, namely material and media design. The average result of media expert's validation is "very feasible". The average effect of material expert validation is "feasible." Based on the validation results by experts, this media is feasible to be used as a learning medium that helps students learn Heat and Temperature topics. The weakness in this research was that the learning design on the developed media was not very relevant to the learning objectives and basic competencies. Researchers hope to develop a learning media with learning designs that are more relevant to learning objectives and basic competencies in the future.

The Effectiveness of Online Learning Model Implementation

The study from home policy was implemented to reduce crowds as an effort to suppress the spread of Covid-19. All levels of education were asked to learn from home, guided and facilitated by educators and educational institutions. Ideally, the learning process should run smoothly even though learning activities are carried out online. However, the shift in learning modes from face-to-face to online has created many learning problems. This study aimed to assess the effectiveness of the implemention of online learning models in Indonesia. This was a descriptive study and a quantitative approach was used. Data were collected online using a questionnaire. Data were analyzed interactively through three components of analysis, namely data reduction, data presentation, and drawing conclusions or verification. Based on the results, it was found that the implementation of online learning was effective. 100% of the lecturers implemented online learning using LMS, of which 86.67% were skilled at using the LMS platform in carrying out online learning. The lecturers used various strategies to organize learning, including integrating cognitive materials in core activities to improve student preparation, involvement and independence in online learning activities. In addition, 50% of the lecturers have conducted student satisfaction surveys on online learning. Keywords: effectiveness, learning model, online learning