Reviewerly/exHarmony huggingface dataset:
On the mapping between Hopfield networks and Restricted Boltzmann Machines

Hopfield networks (HNs) and Restricted Boltzmann Machines (RBMs) are two important models at the interface of statistical physics, machine learning, and neuroscience. Recently, there has been interest in the relationship between HNs and RBMs, due to their similarity under the statistical mechanics formalism. An exact mapping between HNs and RBMs has been previously noted for the special case of orthogonal (uncorrelated) encoded patterns. We present here an exact mapping in the case of correlated pattern HNs, which are more broadly applicable to existing datasets. Specifically, we show that any HN with $N$ binary variables and $p<N$ arbitrary binary patterns can be transformed into an RBM with $N$ binary visible variables and $p$ gaussian hidden variables. We outline the conditions under which the reverse mapping exists, and conduct experiments on the MNIST dataset which suggest the mapping provides a useful initialization to the RBM weights. We discuss extensions, the potential importance of this correspondence for the training of RBMs, and for understanding the performance of deep architectures which utilize RBMs.
Online Strategies Enhancing Mathematics Teacher Knowledge for the Digital Age: Discourse and Critical Reflection

This study designed online graduate courses to enrich inservice mathematics teachers’ Technological Pedagogical Content Knowledge (TPACK). The effort identified key experiences to engage teachers in discourse and critical reflections for relearning, rethinking, and redefining teaching and learning as they know and learned it, transforming their TPACK with respect to teaching with digital technologies. The experiences modeled inquiry tasks merging content, technology and pedagogy as described in TPACK, connecting teachers with experiences as students learning about and with technologies. Critical reflections on the experiences as learners and as teachers combined with the online community of learners’ discourse, transforming their teacher knowledge. The collection of strategies involving discourse and critical reflection did enhance the participants’ TPACK, providing recommendations for designing online inservice teacher education courses.
On Some Computational and Applications of Finite Fields

Finite field is a wide topic in mathematics. Consequently, none can talk about the whole contents of finite fields. That is why this research focuses on small content of finite fields such as polynomials computational, ring of integers modulo p where p is prime or a power of prime. Most of the times, books which talk about finite fields are rarely to be found, therefore one can know how arithmetic computational on small finite fields works and be able to extend to the higher order. This means how integer and polynomial arithmetic operations are done for Z p such as addition, subtraction, division and multiplication in Z p followed by reduction of p (modulo p). Since addition is the same as subtraction and division is treated as the inverse of the multiplication, thus in this paper, only addition and multiplication arithmetic operations are applied for the considered small finite fields (Z 2 − Z 17 ). With polynomials, one can learn from this paper how arithmetic computational through polynomials over finite fields are performed since these polynomials have coefficients drawn from finite fields. The paper includes also construction of polynomials over finite fields as an extension of finite fields with polynomials. This lead to arithmetic computational tables for the finite fields F q [x]/f(x), where f(x) is irreducible over F q . From the past decades, many researchers complained about the applications of some topics in pure mathematics and therefore the finite fields play more important role in coding theory, which involves error-coding detection and error-correction as well as cyclic codes. As a result, this research paper shows these applications among others.
PENGEMBANGAN PROTOTYPE SISTEM UJIAN BERBASIS COMPUTER BASED TEST (CBT) PADA PROGRAM STUDI TEKNIK INFORMATIKA POLITEKNIK NEGERI AMBON

One of the uses of information technology in education is by building a Computer Based Test (CBT) application. The implementation of CBT is very much needed in exam activities in the lecture environment, because with the implementation of this exam, it makes it very easy in the correction phase and can minimize cheating, including question leakage, cheating on exams and even changes in exam scores. Development of a prototype examination system in the Computer-based Ambon State Polytechnic Informatics Engineering study program or also known as the Computer Based Test (CBT) means the test implementation system uses a computer as the test medium. The implementation of the CBT exam is considered very helpful for education providers. The method used in building this application is a prototype. By using this application it makes it easier for lecturers to perform the retest process efficiently
Application of Quantum Learning Models to Increase Student Motivation and Learning Outcomes

The learning atmosphere greatly influences the teaching and learning process in the classroom. therefore, the figure of teacher is needed to transform conventional learning models into more enjoyable learning models. One effective and fun learning model is Quantum Learning. The purpose of this study was to determine the increase in student motivation and learning outcomes with the Quantum Learning model. The location of this research is at SMAN 2 Gunungputri. This study uses a quasi-experimental design. The study sample consisted of two classes with a sample of 60 people. The control class applies the conventional learning model while the experimental class applies the Quantum Learning model. The research instruments were learning outcomes and motivational questionnaires. The results of the study found that student learning outcomes using the Quantum Learning Model increased by 0.49. Modification of the learning technique through various learning methods is needed to explore the potential of students.
Passive tracking of a target based on Supervisory adaptive EKF and CKF

This study proposed a neural network structure for passive tracking of a target by an observer. Since the passive tracking measurement equation is nonlinear, the extended Kalman filter (EKF) and cubature Kalman filter (CKF) methods were implemented. Due to the nonlinear nature of the passive tracking measurement equation, the conventional extended and cubature Kalman filters are not good candidates where bearing-only target tracking is a standard and traditional passive tracking method. The effectiveness of Kalman filters dramatically depended on measurement noise covariance (R). Since R is challenging to be determined and changed with environmental variations, an on-line adaptive filter is proposed. The adaptive structure is founded on the double-layer perceptron neural network, where its weights were updated by the steepest descent method to tune covariance matrix R. In the numerical simulations, it is assumed that tracking of a target was carried out in an underwater environment by sonar measurement. In this paper, in addition to the proposed method, the neural network extended Kalman filter (NNEKF), neural network cubature Kalman filter (NNCKF), Sage Husa adaptive cubature Kalman filter (SHCKF) are used to track the target. The simulation results show the effectiveness of the proposed method.
LibGuides: Music: Guitar for New and Experienced Players: Popular Music

Instructional books, CDs, and DVDs for Guitar in many styles, as well as songbooks and sheet music.
Sea Change: The Rising Tide of Pro Bono Legal Services for the Creative Community

Despite the ubiquity of the internet economy and the increasing importance of the creative sector, pro bono legal services have not generally been available to those needing assistance in intellectual property, technology and related fields of the law. Client services available from traditional legal services entities and law firm pro bono hours typically offer general civil and sometimes criminal representation but not more specialized legal help. Little to no relevant intellectual property or related pro bono expertise has been widely available from private law firms, state, local, or national bar associations or legal services entities. Pro bono legal assistance at law school clinics has historically mirrored these generalized offerings. Until recently, very few law school clinics were engaged in any kind of practice serving the creative clients in their communities. In addition, public interest and consumer policy advocacy and impact litigation in IP and technology law by nonprofit groups and academic centers has only recently emerged.The creation of the earliest pro bono intellectual property law school clinics was revolutionary and the clinics were somewhat controversial in clinical legal education when they first arrived on the scene. However, the vision for these new clinics was simple and modeled on the goals of existing clinical programs. Law students would represent clients in the creative community who had real IP and related legal problems for no fee. Students interested in intellectual property, technology and related areas would finally enjoy an experiential opportunity long available to their classmates in other fields of law. These students would also gain an understanding and appreciation for pro bono service and the public interest dimensions inherent in the intellectual property regime. Most importantly, the new clinics would be a resource for specialized pro bono legal advice to the many communities of creators and those hoping to enter the new internet economy but unable to access assistance in the traditional legal marketplace. In other articles I have explored the growth and maturing of this new and vibrant community of law school intellectual property law and technology clinics. In this Article, I suggest that the rapid growth and success of the work taken on by the new IP clinic community and the community’s innovative collaboration with the U.S. Patent and Trademark Office is inspiring a rising tide in the availability of pro bono expert legal assistance for the creative community throughout the wider legal marketplace.
Recent Trends in Named Entity Recognition (NER)

The availability of large amounts of computer-readable textual data and hardware that can process the data has shifted the focus of knowledge projects towards deep learning architec- ture. Natural Language Processing, particularly the task of Named Entity Recognition is no exception. The bulk of the learning methods that have produced state-of-the-art results have changed the deep learning model, the training method used, the training data itself or the encoding of the output of the NER system. In this paper, we review significant learning methods that have been employed for NER in the recent past and how they came about from the linear learning methods of the past. We also cover the progress of related tasks that are upstream or downstream to NER eg. sequence tagging, entity linking etc. wherever the processes in question have also improved NER results.
Multilateral Responses to Cybercrimes in the SADC Region: The Case of Zimbabwe and South Africa

This study sought to understand the multilateral responses to cyber crimes in the SADC region with specific reference to Zimbabwe and South Africa. The research examined the concept of cybercrimes, its causes, motivations, and implications. The research further examined mechanisms and legislative frameworks available to curb cybercrimes. The qualitative research methodologies were used for the study. Data was purposively collected from information technology experts, academia, the security sector, lawyers, law enforcement agencies, journalists, and diplomats. The key findings of the research revealed that the understanding of cybercrimes is not consistent as the term has no specific referent in law. The study deduced that although cyber espionage, extortion, fame and entertainment are some of the motivations behind cybercrimes in the SADC region are attributed to high unemployment rates, especially among educated ICT graduates and poverty in general. The study also established that SADC countries lack a comprehensive legal framework to combat cyber crimes.
Rethinking network for classroom video captioning

Many people believe that the understanding of classroom activities can benefit the parents and education experts to analyze the teaching situation. However, employing workers to supervise the events in the classroom costs lots of human resources. The deployment of surveillance video systems is considered to be a good solution to this problem. Converting videos captured by cameras into descriptions can further reduce data transmission and storage costs. In this paper, we propose a new task named Classroom Video Captioning (CVC), which aims at describing the events in classroom videos with natural language. We collect classroom videos and annotate them with sentences. To tackle the task, we employ an effective architecture called rethinking network to encode the visual features and generate the descriptions. The extensive experiments on our dataset demonstrate that our method can describe the events in classroom videos satisfactorily.
Proving prediction prudence

We study how to perform tests on samples of pairs of observations and predictions in order to assess whether or not the predictions are prudent. Prudence requires that that the mean of the difference of the observation-prediction pairs can be shown to be significantly negative. For safe conclusions, we suggest testing both unweighted (or equally weighted) and weighted means and explicitly taking into account the randomness of individual pairs. The test methods presented are mainly specified as bootstrap and normal approximation algorithms. The tests are general but can be applied in particular in the area of credit risk, both for regulatory and accounting purposes.
Reporting Automation Through a Web Based Daily Inpatient Census System

In the industrial era 4.0, the development of information technology is very fast in all fields, especially in the health sector. The hospital information system is designed to support data service activities and is accurate, integrated and safe. The data is used as a hospital report, one of which is a report on the daily census recapitulation of inpatients. This type of research is a descriptive study with data collection methods through interviews and observations using a cross sectional approach. The subjects of this study were ward nurses and medical record officers in the reporting section and the object of this study was the recording and processing of daily inpatient census data. The instruments in this study were observation guidelines and interview guidelines. Computerized Web-based Daily Census of Inpatient was obtained from the input process of patient data, diagnosis data, ward data, patient data entry, patient data moved, patient data transferred, patient data out. Then the data is processed to produce reports in the form of patient reports, diagnostic reports, ward reports, incoming patient reports, moving patient reports, transferred patient reports, outpatient reports, daily census recapitulation of inpatients, and calculation of indicators using PHP programming language with SQL database. The daily census process for inpatients at Hidayah Boyolali Hospital is still done manually, so that in its implementation there are still deficiencies in data filling that can affect the preparation of reports. By improving the system used, from manual to computerized systems, data processing will be easier.
Design of Blockchain-Based Precision Health-Care Using Soft Systems Methodology

Purpose: The purpose of this paper is to describe the application of soft systems methodology (SSM) to address the problematic situation of low opt-in rates for Precision Health-Care (PHC).Design/methodology/approach: The design logic is that when trust is enhanced and compliance is better assured, participants such as patients and their doctors would be more likely to share their medical data and diagnosis for the purpose of precision modeling.Findings: The authors present the findings of an empirical study that confronts the design challenge of increasing participant opt-in to a PHC repository of Electronic Medical Records and genetic sequencing. Guided by SSM, the authors formulate design rules for the establishment of a trust-less platform for PHC which incorporates key principles of transparency, traceability and immutability.Research limitations/implications: The SSM approach has been criticized for its lack of “rigour” and “replicability”. This is a fallacy in understanding its purpose – theory exploration rather than theory confirmation. Moreover, it is unlikely that quantitative modeling yields any clearer an understanding of complex, socio-technical systems.Practical implications: The application of Blockchain, a platform for distributed ledgers, and associated technologies present a feasible approach for resolving the problematic situation of low opt-in rates.Social implications: A consequence of low participation is the weak recall and precision of descriptive, predictive and prescriptive analytic models. Factors such as cyber-crime, data violation and the potential for misuse of genetic and medical records have led to a lack of trust from key stakeholders – accessors, participants, miners and regulators – to varying degrees.Originality/value: The application of Blockchain as a trust-enabling platform in the domain of an emerging eco-system such as precision health is novel and pioneering.
Geocognitive Technologies Improve Basin- and Petroleum-System Analyses

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197610, “Application of Geocognitive Technologies to Basin- and Petroleum-System Analyses,” by Paolo Ruffo, Marco Piantanida, SPE, and Floriana Bergero, Eni, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11-14 November. The paper has not been peer reviewed. Eni and IBM developed a cognitive engine exploiting a deep-learning approach to scan documents searching for basin geology concepts, extracting information about petroleum system elements (e.g., formation name; geological age; and lithology of source rocks, reservoirs, and seals), and enabling basin geologists to perform automated queries to collect all information related to a basin of interest. The cognitive engine succeeded in identifying the correct formations, lithologies, and geological ages of the petroleum systems with an accuracy in the range of 75 to 90%. Introduction While commercial databases often provide summary information about basins that can be extracted easily with queries or even interactive tools, the explorationist needs to integrate such information with more up-to-date and in-depth descriptions of structural and sedimentary events occurring in the basin, descriptions that can be found only in unstructured documents. Key information about basins can be scattered across paragraphs, tables, and image captions of hundreds of technical articles, or can be embedded within pictures. Even when exploiting a traditional search engine with the name of the desired basin, the results can be unsatisfactory: first, not all the results might be relevant; second, many different variants of the basin name are often used within publications. In the optimistic hypothesis that the subset of relevant documents is found by the search engine, all key concepts related to a basin need to be understood by the geologist by careful examination of the paper text and images. Moreover, even if the published information (structured and unstructured) on a basin is found, there are different opinions expressed by different authors, in addition to the uncertainty of the data itself (such as the age of a formation or the details of the geological evolution of the basin), so that multiple conceptual models of the basin can be drawn from the uncertain and scarce information available. Some of these conceptual models are more probable while others are less probable, but sometimes the latter happen to be economically more valuable. Therefore, recovery of all relevant information about a basin is crucial, but also important is the preservation of differing opinions about the data - what might be termed minority reports.
Exploring ensembles and uncertainty minimization in denoising networks

The development of neural networks has greatly improved the performance in various computer vision tasks. In the filed of image denoising, convolutional neural network based methods such as DnCNN break through the limits of classical methods, achieving better quantitative results. However, the epistemic uncertainty existing in neural networks limits further improvements in their performance over denoising tasks. Therefore, we develop and study different solutions to minimize uncertainty and further improve the removal of noise. From the perspective of ensemble learning, we implement manipulations to noisy images from the point of view of spatial and frequency domains and then denoise them using pre-trained denoising networks. We propose a fusion model consisting of two attention modules, which focus on assigning the proper weights to pixels and channels. The experimental results show that our model achieves better performance on top of the baseline of regular pre-trained denoising networks.
Regulation of Algorithmic Tools in the United States

Policymakers in the United States have just begun to address regulation of artificial intelligence technologies in recent years, gaining momentum through calls for additional research funding, piece-meal guidance, proposals, and legislation at all levels of government. This Article provides an overview of high-level federal initiatives for general artificial intelligence (AI) applications set forth by the U.S. president and responding agencies, early indications from the incoming Biden Administration, targeted federal initiatives for sector-specific AI applications, pending federal legislative proposals, and state and local initiatives. The regulation of the algorithmic ecosystem will continue to evolve as the United States continues to search for the right balance between ensuring public safety and transparency and promoting innovation and competitiveness on the global stage.
Rancang Bangun Sistem Informasi Peminjaman Buku Perpustakaan SMP Ibnu Aqil Berbasis WEB

Ibnu Aqil is an educational institution characterized by Islam with the understanding of ahlussunah wal jama'ah, founded by the Haji Agus Salim Bogor Foundation in 1992, amidst the rapid development of information technology, libraries at Ibnu Aqil Middle School in book data collection and book lending are still running manually, for example In borrowing data collection, it is still recorded in books so that it has shortcomings, namely making it difficult for librarians to search for borrower data, checking book availability cannot be done, recording borrowing and returning books often writing errors. Based on the description above, the writer tries to analyze and study the feasibility of the system running at Ibnu Aqil Middle School which the author wrote on a scientific proposal entitled Design and Build Information System for Book Borrowing in the Library of WEB-Based Ibnuaqil Middle School. in the library that can manage book lending and calculate fines automatically so that you can find out the weaknesses that exist in the library to improve the quality of better service to students. The development of this system uses the waterfall method and uses Data Flow Diagrams (DFD) for structured modeling, the programming languages ​​used are PHP, javascript, HTML, CSS, PhpMyAdmin as the database. This information system can make it easier to carry out the transaction process for borrowing books in Ibnu Aqil's library.