Reviewerly/exHarmony huggingface dataset:
KAJIAN PEMASANGAN PEMBANGKIT LISTRIK TENAGA SURYA 100 kWp OFF GRID DI SOTA MERAUKE

The development of solar cell technology can convert sunlight into electrical energy becoming increasingly advanced and environmentally friendly, the Regional Government of Merauke built a Solar Power Plant in Sota District, Merauke Regency, Papua Province in the amount of 100 kWp off grid to electrify the District in the afternoon and assisted supply electricity in PLTD for 12 hours per day starting at 06.00 p.m. EIT until 06.00 a.m EIT with automatic load transfer equipment or ATS (Automatic Transfer Switch). Sota District is a tourist area located in the Zero point of Eastern Indonesia, directly adjacent to Papua New Guinea which requires electricity during the day. The PLTS of Sota Merauke has been operating since September 2016, with operating time during the day starting at 09.00 a.m. EIT to 04.00 p.m. EIT, but starting in November 2018 when 1 of the 4 inverters experienced an interruption, the operation of the PLTS was changed to operating at night when the peak load at 05.00 p.m. EIT until 10.00 p.m. EIT with separate operation through the Low Voltage Network. The results of the study found that the separate operation caused the power plant charging system to be suboptimal, so that the quality of the distribution of electrical energy was not good, and the potential saving cost of using the fuel PLTD of Sota Merauke was Rp.153.164.076 per year.
Augmented Reality Mobile Application Co-Design Experience: Delineating the Nuances from the Lens of a System Developer

With the rapid development of educational tools, co-design has been on the rise. Co-design is instrumental in successful product development as it merges two key perspectives, namely consumers&amp;rsquo; insights and professionals&amp;rsquo; knowledge. The roles of users, designers, and developers are now blurred as educators have begun to construct their own tools for teaching based on problems and ideas conceived in their classrooms, thereby assuming roles as designers and to some extent, as developers. A more common practice in recent days is educators&amp;rsquo; co-designing of tools with system developers to achieve their targeted goal. This study attempted to explore the process of co-designing an augmented reality (AR) mobile application and the role of a system developer in mediating the design process with non-designers, namely academic members of the Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. This study further aimed to delineate the nuances of the AR mobile application co-design experience from the perspective of the system developer. Semi-structured interviews, observations, and document analyses were conducted to examine the detailed process of co-designing and developing the AR application as well as to understand the developer&amp;rsquo;s interaction with the consumers. The main findings of this study suggest that the waterfall model of the Software Development Life Cycle was in use during the co-design process. This cycle generally involves five stages, namely (i) planning, (ii) analysis, (iii) design, (iv) implementation, and (v) maintenance, which are iterative in nature. The designer&amp;rsquo;s role in mediating the co-design process encompassed balancing their knowledge and experience with the needs of consumers that do not necessarily match the designer&amp;rsquo;s expectations. This was achieved through (i) precise communication, (ii) commitment to the delivery and quality of the AR mobile application, as well as to building cohesive working relationships, and (iii) motivation to work with co-designers during the development process. The findings shed light on the value of co-design and the complex role of designers in mediating the design process with non-designers, which when accounted for, can lead to more feasible project development.
Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows

We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available. We derive a lower bound to the conditioning variable log-probability using Schur complement properties in the spirit of Gaussian conditional sampling. Our derivation relies on partitioning flow's domain in such a way that the flow restrictions to subdomains remain bijective, which is crucial for the Schur complement application. Simulation from the variational conditional flow then amends to solving an equality constraint. Our contribution is three-fold: a) we provide detailed insights on the choice of variational distributions; b) we discuss how to partition the input space of the flow to preserve bijectivity property; c) we propose a set of methods to optimise the variational distribution. Our numerical results indicate that our sampling method can be successfully applied to invertible residual networks for inference and classification.
A Neoteric Image Encryption System Using Nonlinear Chaotic Strange Attractors

The science of securing the message by altering the information into an uninterruptable form so that it cannot be recognized is called cryptography. Currently, a wide range of algorithms have been employed for image encryption, but chaos blended image encryption techniques are preferred due to their excellent security and speed. This scheme primarily consists of two steps: chaos-based diffusion and scrambling of the pixel locations. This paper provides a concise introduction to chaotic image encryption and then an analysis of the properties of the chaotic attractors used. The inherent properties of the attractors which are relevant to cryptography are the salient reasons for their utilization in information security applications. Here, we introduce an innovative technique to encrypt a standard RGB image using the pseudorandom numbers generated by the 3D chaotic attractors which has an advanced response to a broad range of control parameters. The unpredictability of the attractors and its sensitivity to initial conditions has enhanced the security of the image immensely. This encryption scheme involves the chaotic bit streams and the image parameters being combined over a simple logical operation, thus reducing the possibility of retrieving the original data by intruders. From the results of the variety of statistical tests conducted, we come to an inference that encryption algorithm proposed is up to the mark and can be deployed for safe transmission of information.
Learning a Self-Expressive Network for Subspace Clustering

State-of-the-art subspace clustering methods are based on self-expressive model, which represents each data point as a linear combination of other data points. However, such methods are designed for a finite sample dataset and lack the ability to generalize to out-of-sample data. Moreover, since the number of self-expressive coefficients grows quadratically with the number of data points, their ability to handle large-scale datasets is often limited. In this paper, we propose a novel framework for subspace clustering, termed Self-Expressive Network (SENet), which employs a properly designed neural network to learn a self-expressive representation of the data. We show that our SENet can not only learn the self-expressive coefficients with desired properties on the training data, but also handle out-of-sample data. Besides, we show that SENet can also be leveraged to perform subspace clustering on large-scale datasets. Extensive experiments conducted on synthetic data and real world benchmark data validate the effectiveness of the proposed method. In particular, SENet yields highly competitive performance on MNIST, Fashion MNIST and Extended MNIST and state-of-the-art performance on CIFAR-10. The code is available at https://github.com/zhangsz1998/Self-Expressive-Network.
How Chaotic Are Recurrent Neural Networks

Recurrent neural networks (RNNs) are non-linear dynamic systems. Previous work believes that RNN may suffer from the phenomenon of chaos, where the system is sensitive to initial states and unpredictable in the long run. In this paper, however, we perform a systematic empirical analysis, showing that a vanilla or long short term memory (LSTM) RNN does not exhibit chaotic behavior along the training process in real applications such as text generation. Our findings suggest that future work in this direction should address the other side of non-linear dynamics for RNN.
Improvement of value orientations in the process of forming the professional competence of teachers

This study examines the issues of value orientations of a teacher and their development as an integral part of professional competence. We drew attention to the relationship between the value orientations and the value sphere within the process of teacher’s professional development, the conditionality of professional competence by the value system. The aim of the research is the theoretical and empirical study of value orientations as a component of the professional competence of teachers. Particular attention is paid to the role and significance of creativity in the teaching performance. Based on the analysis of current literary sources, we examined the phenomenon of professional creativity of a teacher, made an attempt to define this concept and considered its structure and structural components. The research methodology and its algorithm were selected due to the purpose of the study. Experimental work included a complex psychological and pedagogical experiment and diagnostics of the stated parameters before and after the implementation of the advanced training. The study was carried out on the basis of the Institute of Foreign Languages of the Federal State Autonomous Educational Institution of Higher Education “The Peoples’ Friendship University of Russia” (RUDN). The results of the study allow us to conclude that there is a significant relationship between the professional creativity of a teacher and the effectiveness of professional performance, which is confirmed by the results of statistical processing of the data obtained. In the Russian psychological and pedagogical literature on the problem of professional competence two basic concepts are discussed, competence and competency. Researchers argue that these concepts are multidimensional and systemic in nature. In Russia, the difference between these concepts is most thoroughly substantiated in the glossary of the Federal State Educational Standard. It is indicated that competencies consist of knowledge, skills and abilities, as well as of practical experience. The ability to apply them in professional activities is called competence.
A deterministic source of indistinguishable photons in a cluster state

Measurement-based quantum communication relies on the availability of highly entangled multi-photon cluster states. The inbuilt redundancy in the cluster allows communication between remote nodes using repeated local measurements, compensating for photon losses and probabilistic Bell-measurements. For feasible applications, the cluster generation should be fast, deterministic, and its photons - indistinguishable. We present a novel source based on a semiconductor quantum-dot device. The dot confines a heavy-hole, precessing in a finely tuned external weak magnetic field while periodically excited by a sequence of optical pulses. Consequently, the dot emits indistinguishable polarization-entangled photons, where the field strength optimizes the entanglement. We demonstrate Gigahertz rate deterministic generation of >90% indistinguishable photons in a cluster state with more than 10 photons characteristic entanglement-length.
Development Finance Strategies, Agricultural Growth and Poverty Reduction in Emerging Economies: Evidence from Nigeria

Development finance issues have appeared very topical in extant literatures and notable political cum socioeconomic discussions in recent time due basically to its desideratum and criticality to aggregate development of emerging economies.This article investigated development finance strategies and their effects on agriculture and poverty reduction in Nigeria.Specifically, the study examined the impact of agro-financing on agricultural development, and; the impact of industrial financing on poverty eradication in Nigeria.A computer based regression and correlation analysis aided by the IBM SPSS software version 23 was employed to test two formulated hypotheses of the study.Based on the findings from the empirical evaluation, the researcher made two major conclusions that; agro-finance does not have significant positive effect on agricultural development in Nigeria.Secondly, industrial finance does not have significant positive effect on poverty eradication in Nigeria.In line with these conclusions, the study recommends that government should place higher priority on directed credit control scheme such as ACGSF to enable such schemes yield the desired result of improving the agricultural sector and poverty reduction.Secondly, directed credit programmes to SMEs like small and medium enterprise equity investment scheme (SMEEIS) should be supervised adequately and made to work effectively.Lastly, proactive surveillance should be activated by government to checkmate unhealthy practices by lending institutions which could impinge lending integrity and dampen the trust of investors.
LibGuides UPRM: Ebook Collections @ the UPRM Library: Presentations

This ebook guide offers key information on ebook resources available at the library and how to find them. Presentations, videos and other media related to library ebooks
Intent Classification Using Pre-Trained Embeddings For Low Resource Languages

Building Spoken Language Understanding (SLU) systems that do not rely on language specific Automatic Speech Recognition (ASR) is an important yet less explored problem in language processing. In this paper, we present a comparative study aimed at employing a pre-trained acoustic model to perform SLU in low resource scenarios. Specifically, we use three different embeddings extracted using Allosaurus, a pre-trained universal phone decoder: (1) Phone (2) Panphone, and (3) Allo embeddings. These embeddings are then used in identifying the spoken intent. We perform experiments across three different languages: English, Sinhala, and Tamil each with different data sizes to simulate high, medium, and low resource scenarios. Our system improves on the state-of-the-art (SOTA) intent classification accuracy by approximately 2.11% for Sinhala and 7.00% for Tamil and achieves competitive results on English. Furthermore, we present a quantitative analysis of how the performance scales with the number of training examples used per intent.
NLP ‘RECIPES’ FOR TEXT CORPORA: APPROACHES TO COMPUTING THE PROBABILITY OF A SEQUENCE OF TOKENS

Investigation in the hybrid architectures for Natural Language Processing (NLP) requires overcoming complexity in various intellectual traditions pertaining to computer science, formal linguistics, logic, digital humanities, ethical issues and so on. NLP as a subfield of computer science and artificial intelligence is concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text (and speech) in order to create systems, such as: machine translation (converting from text in a source language to text in a target language), document summarization (converting from long texts into short texts), named entity recognition, predictive typing, et cetera. Undoubtedly, NLP phenomena have been implanted in our daily lives, for instance automatic Machine Translation (MT) is omnipresent in social media (or on the world wide web), virtual assistants (Siri, Cortana, Alexa, and so on) can recognize a natural voice or e-mail services use detection systems to filter out some spam messages. The purpose of this paper, however, is to outline the linguistic and NLP methods to textual processing. Therefore, the bag-of-n-grams concept will be discussed here as an approach to extract more details about the textual data in a string of a grouped words. The n-gram language model presented in this paper (that assigns probabilities to sequences of words in text corpora) is based on findings compiled in Sketch Engine, as well as samples of language data processed by means of NLTK library for Python. Why would one want to compute the probability of a word sequence? The answer is quite obvious – in various systems for performing tasks, the goal is to generate texts that are more fluent. Therefore, a particular component is required, which computes the probability of the output text. The idea is to collect information how frequently the n-grams occur in a large text corpus and use it to predict the next word. Counting the number of occurrences can also envisage certain drawbacks, for instance there are sometimes problems with sparsity or storage. Nonetheless, the language models and specific computing ‘recipes’ described in this paper can be used in many applications, such as machine translation, summarization, even dialogue systems, etc. Lastly, it has to be pointed out that this piece of writing is a part of an ongoing work tentatively termed as LADDER (Linguistic Analysis of Data in the Digital Era of Research) that touches upon the process of datacization[1] that might help to create an intelligent system of interdisciplinary information.
Safe Driving via Expert Guided Policy Optimization

When learning common skills like driving, beginners usually have domain experts standing by to ensure the safety of the learning process. We formulate such learning scheme under the Expert-in-the-loop Reinforcement Learning where a guardian is introduced to safeguard the exploration of the learning agent. While allowing the sufficient exploration in the uncertain environment, the guardian intervenes under dangerous situations and demonstrates the correct actions to avoid potential accidents. Thus ERL enables both exploration and expert's partial demonstration as two training sources. Following such a setting, we develop a novel Expert Guided Policy Optimization (EGPO) method which integrates the guardian in the loop of reinforcement learning. The guardian is composed of an expert policy to generate demonstration and a switch function to decide when to intervene. Particularly, a constrained optimization technique is used to tackle the trivial solution that the agent deliberately behaves dangerously to deceive the expert into taking over. Offline RL technique is further used to learn from the partial demonstration generated by the expert. Safe driving experiments show that our method achieves superior training and test-time safety, outperforms baselines with a substantial margin in sample efficiency, and preserves the generalizabiliy to unseen environments in test-time. Demo video and source code are available at: https://decisionforce.github.io/EGPO/
Research on Effective Strategies and Teaching Models to Improve High School Physics Learning Interest

Starting from the two subjects of "teacher" and "student", this paper puts forward effective strategies to improve learning interest from multiple angles and all directions.The measures cover teaching materials, classes, homework, rewards and punishments, cultivating habits, overcoming difficulties and so on.Then it summarizes and forms a certain teaching mode from three aspects: before class, in class and after class
Evaluation of Physical Education Teaching Based on Analytic Hierarchy Process

The evaluation of physical education (PE) teaching plays an important role in improving the teaching quality and students’ physical fitness. Taking five colleges in a Chinese province for example, this paper surveys the status quo of college PE teaching evaluation, and reveals several problems with the evaluation: the evaluation index system (EIS) is incomplete, and the subjects are not diverse. Based on the survey results and previous findings, questionnaire survey, the Delphi method, analytic hierarchy process (AHP), logic analysis, and mathematical statistics were combined to construct an EIS for college PE teaching evaluation, determine the weights of indices on each level, design a standard scale, and develop the evaluation framework and flow. The research results promote the development of college PE teaching, and provide a reference for reforming the teaching evaluation of other disciplines.
Does it matter who pays back Technical Debt? An empirical study of self-fixed TD

Context: Technical Debt (TD) can be paid back either by those that incurred it or by others. We call the former self-fixed TD, and it can be particularly effective, as developers are experts in their own code and are well-suited to fix the corresponding TD issues. Objective: The goal of our study is to investigate self-fixed technical debt, especially the extent in which TD is self-fixed, which types of TD are more likely to be self-fixed, whether the remediation time of self-fixed TD is shorter than non-self-fixed TD and how development behaviors are related to self-fixed TD. Method: We report on an empirical study that analyzes the self-fixed issues of five types of TD (i.e., Code, Defect, Design, Documentation and Test), captured via static analysis, in more than 44,000 commits obtained from 20 Python and 16 Java projects of the Apache Software Foundation. Results: The results show that about half of the fixed issues are self-fixed and that the likelihood of contained TD issues being self-fixed is negatively correlated with project size, the number of developers and total issues. Moreover, there is no significant difference of the survival time between self-fixed and non-self-fixed issues. Furthermore, developers are more keen to pay back their own TD when it is related to lower code level issues, e.g., Defect Debt and Code Debt. Finally, developers who are more dedicated to or knowledgeable about the project contribute to a higher chance of self-fixing TD. Conclusions: These results can benefit both researchers and practitioners by aiding the prioritization of TD remediation activities and refining strategies within development teams, and by informing the development of TD management tools.
Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters

Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Programming for Cyber Security encompasses extant and immediate research efforts in a vital problem domain, arguably occupying a position at the frontier where GP matters. Additionally, it prompts research questions around evolving complex behavior by expressing different abstractions with GP and opportunities to reconnect to the Machine Learning, Artificial Life, Agent-Based Modeling and Cyber Security communities. We present a framework called RIVALS which supports the study of network security arms races. Its goal is to elucidate the dynamics of cyber networks under attack by computationally modeling and simulating them.
Research on the Audio-Visual Language of Mobile Short Video Shooting

This article first introduces the current situation of mobile phone short video shooting, and then analyzes the composition differences of the vertical screen frames in the mobile phone short video shooting, the different techniques used in scenes, the light processing methods, the motion and scheduling during shooting, and the sound processing methods. Finally, it is hoped that these audio-visual language methods unique to mobile phone short videos can in turn promote the development of traditional film and television, thus proposing that mobile phone short videos will continue to move forward on the road of independent art in the future.