Reviewerly/exHarmony huggingface dataset:
Long-Term Forecasting Potential of Photo-Voltaic Electricity Generation and Demand Using R

For micro-grid cost-benefit analyses, both energy production and demand must be estimated on the long-term of one year. However, there remains a scarcity of studies predicting energy production and demand simultaneously and in the long-term. By means of programming in R and applying linear, non-linear, and support vector regression, we show the in depth analysis of the data of a micro-grid on solar power generation and building energy demand and its potential to be modeled simultaneously on the term of one year, in relation to electricity costs. We found solar power generation is linearly related to solar irradiance, but the effect of temperature on total output was less pronounced than anticipated. Building energy demand was found to be related to multiple parameters of both time and weather, and could be estimated through a quadratic function in relation to temperature. Models for both solar power generation and building energy demand could predict electricity costs within 8% of actual costs, which is not yet the ideal accuracy, but shows potential for future studies. These results provide important statistics for future studies where building energy consumption of any building type is correlated in detail to various time and weather parameters.
VR in Education: Ergonomic Features and Cybersickness

This paper describes theory (systemic structural model) of learning activity in the virtual reality, as well as demonstrates (on experimental data) in psychophysiological regulation of the cognitive activity in VR. Understanding of these changes can be used for learning improvement using VR environment regards the interrelationship between the structure and self-regulation of learning activity, avoiding potential cybersickness.
iSENSE2.0

Software engineers get questions of “how much testing is enough” on a regular basis. Existing approaches in software testing management employ experience-, risk-, or value-based analysis to prioritize and manage testing processes. However, very few is applicable to the emerging crowdtesting paradigm to cope with extremely limited information and control over unknown, online crowdworkers. In practice, deciding when to close a crowdtesting task is largely done by experience-based guesswork and frequently results in ineffective crowdtesting. More specifically, it is found that an average of 32% testing cost was wasteful spending in current crowdtesting practice. This article intends to address this challenge by introducing automated decision support for monitoring and determining appropriate time to close crowdtesting tasks. To that end, it first investigates the necessity and feasibility of close prediction of crowdtesting tasks based on an industrial dataset. Next, it proposes a close prediction approach named iSENSE2.0, which applies incremental sampling technique to process crowdtesting reports arriving in chronological order and organizes them into fixed-sized groups as dynamic inputs. Then, a duplicate tagger analyzes the duplicate status of received crowd reports, and a CRC-based (Capture-ReCapture) close estimator generates the close decision based on the dynamic bug arrival status. In addition, a coverage-based sanity checker is designed to reinforce the stability and performance of close prediction. Finally, the evaluation of iSENSE2.0 is conducted on 56,920 reports of 306 crowdtesting tasks from one of the largest crowdtesting platforms. The results show that a median of 100% bugs can be detected with 30% saved cost. The performance of iSENSE2.0 does not demonstrate significant difference with the state-of-the-art approach iSENSE , while the later one relies on the duplicate tag, which is generally considered as time-consuming and tedious to obtain.
HMPO: Human Motion Prediction in Occluded Environments for Safe Motion Planning

We present a novel approach to generate collisionfree trajectories for a robot operating in close proximity with a human obstacle in an occluded environment.The self-occlusions of the robot can significantly reduce the accuracy of human motion prediction, and we present a novel deep learningbased prediction algorithm.Our formulation uses CNNs and LSTMs and we augment human-action datasets with synthetically generated occlusion information for training.We also present an occlusion-aware planner that uses our motion prediction algorithm to compute collision-free trajectories.We highlight performance of the overall approach (HMPO) in complex scenarios and observe upto 68% performance improvement in motion prediction accuracy, and 38% improvement in terms of error distance between the ground-truth and the predicted human joint positions.
Sentiment Analysis: A Case Study among the selected Government Agencies in the Philippines

This paper describes a sentiment analysis method to analyze comments polarity and to facilitate selected government agencies such as DepEd, DPWH, DILG, DND, DSWD, and DOH to describe quantitatively the opinions and comments of active users and citizens on social networks such as YouTube.The method used was from the latest comments from the first 100 were selected from each video to form the final datasets used in the study.The comments were extracted from 1450 videos and contain a total number of 14,506 posts.The objectives of the study were (1) to analyze the citizen's attitude among the six government agencies in the category of positive and negative polarity.(2) to utilized the results in developing a decision support system.
Chatting with chatbots: Sign making in text-based human–computer interaction

This paper investigates the kind of sign making that goes on in text-based human–computer interaction, between human users and chatbots, from the point of view of integrational linguistics. A chatbot serves as a “conversational” user interface, allowing users to control computer programs in “natural language”. From the user’s perspective, the interaction is a case of semiologically integrated activity, but even if the textual traces of a chat may look like a written conversation between two humans the correspondence is not one-to-one. It is argued that chatbots cannot engage in communication processes, although they may display communicative behaviour. They presuppose a (second-order) language model, they can only communicate at the level of sentences, not utterances, and they implement communicational sequels by selecting from an inventory of executable skills. Instead of seeing them as interlocutors in silico, chatbots should be seen as powerful devices for humans to make signs with.
Implementation of K-Nearest Neighbor Algorithm for Customer Satisfaction

Customer satisfaction is one of the goals of the company in providing services to its customers both service and non-service companies. One of the camera rental service provider companies that are committed to customer satisfaction is Cikarang Camera Rental. This study aims to analyze customer satisfaction in Cikarang camera rental using the K Nearest Neighbor (KNN) algorithm. Customer satisfaction input attributes in this study include price, facilities, and dam loyalty services. The output results from the input attributes above are satisfied and not satisfied. This research is expected to help Cikarang Camera Rental to increase customer satisfaction and increase profit on Cikarang Camera Rental. The results of the research that can be achieved using the KNN algorithm are accuracy = 98%, classification recall = 86.67%, Classification precision = 100% and AUC = 0.750. The results of this study can be used as a reference to build an application that can facilitate companies in obtaining information about customer satisfaction.Keywords— Datamining, classification, KNN algorithm, customer satisfaction
Fast and accurate online sequential learning of respiratory motion with random convolution nodes for radiotherapy applications

Accurate prediction of tumor motion for motion adaptive radiotherapy has been a challenge as respiration-induced motion is non-stationary in nature and often subjected to irregularities. Despite having a plethora of works for predicting this motion, their tracking capabilities are usually prone to large prediction errors due to the time-varying irregularities and intra-trace variabilities. To overcome this, prediction models are re-trained at regular intervals. This solution however demands a trade-off between the re-training interval and prediction accuracy in estimating the future tumor location. This is because re-training with small interval increases the computational requirements whereas a larger interval hampers the prediction performance. To address these issues, a prediction model that relies on random convolution nodes (RCN) governed by local receptive fields (LRFs) is proposed for respiratory motion prediction. The innate nature of LRFs extracts the features that contribute to the local-patterns as well as the non-stationary patterns in recent samples and subsequently learn them using extreme learning machine (ELM) theories. To address the re-training issue, we propose an online sequential learning framework (OS-fRCN) that can update the model parameters at regular intervals. Suitability of the proposed OS-fRCN for respiratory motion prediction is evaluated on 304 respiratory motion traces. Performance analysis conducted at four prediction horizons (in-line with the commercially available radiotherapy systems) demonstrated that the proposed OS-fRCN method requires less computational complexity and yields robust, accurate prediction performance when compared with existing prediction methods.
Pending issues from digital inclusion in Ecuador: challenges for public policies, programs and projects developed and ICT-mediated teacher training

The policies of digital inclusion in Ecuador had a rebound since 2007, placing technology as a priority axis for government management. Despite this, there are still pending issues to be developed. This study is based on a qualitative approach that, through documentary research, bibliographic analysis and critical review of literature, can generate contributions that allow the identification of challenges and possible gaps to be faced by government entities in Ecuador when proposing actions that link education and technology. As main findings, it can be mentioned that the impacts and results from the application of the National Development Plans must transcend the figures, which allow to deepen the analysis in relation to this topic, together with initiatives and proposals that regarding digital inclusion cannot be developed in isolation and need to have comprehensive long-term management. Finally, it is essential to understand that it is not necessary to create only platforms, it is relevant to work from integral pedagogical designs that start by understanding teachers from their experiences, accompaniment and motivation for the design of a contextualized training structure on digital mediation issues.
Ultra-high-rate nonclassical light source with 50 GHz-repetition-rate mode-locked pump pulses and multiplexed single-photon detectors

Heralded single photons (HSPs) and entangled photon pairs (EPPs) via spontaneous parametric down-conversion are essential tools for the development of photonic quantum information technologies. In this paper, we report a novel ultra-high-rate nonclassical light source realized by developing 50 GHz-repetition-rate mode-locked pump pulses and multiplexed superconducting nanowire single-photon detectors. The presence of the single-photon state in the heralded photons with our setup was indicated by the second-order intensity correlation below 1/2 at the heralding rate over 20 Mcps. Even at the rate beyond 50 Mcps, the nonclassicality was still observed with the intensity correlation below unity. Moreover, our setup is also applicable to the polarization-EPP experiment, where we obtained the maximum coincidence rate of 1.6 Mcps with the fidelity of 0.881 ± (0.254 × 10 −3 ) to the maximally entangled state. Our versatile source could be a promising tool to explore various large-scale quantum-photonic experiments with low success probability and heavy attenuation.
Contact Tracing: A Clarion Call for National Training Standards

As of late May 2020, more than 1.5 million people had tested positive for coronavirus infection in the United States; however, only 3% of Americans had been tested. However, testing is only one of the key elements in the effort to control communicable diseases. There is a need to investigate others who may have been exposed to the virus; this can be accomplished through a foundational public health strategy – contact tracing. Most public health students and professionals have been introduced to the concept of contact tracing; however, competency in this area is undetermined. The purpose of this perspective is to call for national standards for contact tracing training programs that lead to a widely recognized certification process. Ethn Dis. 2020;30(3):437-440; doi:10.18865/ed.30.3.437
Color image encryption based on chaotic shit keying with lossless compression

In order to protect valuable data from undesirable readers or against illegal reproduction and modifications, there have been various data encryption techniques. Many methods are developed to perform image encryption. The use of chaotic map for image encryption is very effective, since it increase the security, due to its random behavior. The most attractive feature of deterministic chaotic systems is he extremely unexpected and random-look nature of chaotic signals that may lead to novel applications. A novel image encryption algorithm based on compression and hyper chaotic map techniques is proposed. Firstly the image is decomposed into three subbands R, G, and B then each band is compressed using lossless technique. The generated chaotic sequences from the 3D chaotic system are employed to code the compressed results by employing the idea of chaotic shift encoding (CSK) modulation to encode the three bands to generate the encrypted image. The experiments show that the proposed method give good results in term of security, feasibility, and robustness.
EEJE: Two-Step Input Transformation for Robust DNN Against Adversarial Examples

Adversarial examples are human-imperceptible perturbations to inputs to machine learning models. While attacking machine learning models, adversarial examples cause the model to make a false positive or a false negative. So far, two representative defense architectures have shown a significant effect: (1) model retraining architecture; and (2) input transformation architecture. However, previous defense methods belonging to these two architectures do not produce good outputs for every input, i.e., adversarial examples and legitimate inputs. Specifically, model retraining methods generate false negatives for unknown adversarial examples, and input transformation methods generate false positives for legitimate inputs. To produce good-enough outputs for every input, we propose and evaluate a new input transformation architecture based on two-step input transformation. To solve the limitations of the previous two defense methods, we intend to answer the following question: How to maintain the performance of Deep Neural Network (DNN) models for legitimate inputs while providing good robustness against various adversarial examples? From the evaluation results under various conditions, we show that the proposed two-step input transformation architecture provides good robustness to DNN models against state-of-the-art adversarial perturbations, while maintaining the high accuracy even for legitimate inputs.
MEMBANGUN BUDAYA MEMBACA PADA ANAK USIA DINI DI ERA DIGITAL

In this modern era, the interest in reading in children is very alarming. Nowadays children prefer to play gadgets or games rather than reading, even though reading is a window to the world. By reading we can add insights and knowledge that we have never known before. Children prefer to play games or social media rather than looking engrossed in spending time reading books.Keywords: reading, early childhood, digital age
Improving Second Language Speaking and Pronunciation through Smartphones

The ability to speak English fluently and confidently plays a vital role in enhancing employment opportunities of graduate students of India. Though English has been a second language for over decades in the country, teaching speaking skills with intelligible pronunciation to the first generation of learners remains a major challenge. The limited number of classes allotted for English speaking and pronunciation and lack of resources make it more challenging. Previous research suggests that Mobile Assisted Language Learning (MALL) offers anywhere and anytime learning opportunities for second language learners. However, there is little research on how mobile learning enhances speaking skills and pronunciation of tertiary level students from rural areas who lack exposure to English outside the classroom. The present paper aims at studying the impact of MALL in improving a) speaking skills b) segmental and suprasegmental features of pronunciation and c) the viability of bring your own device (BYOD) approach. Mixed methods approach has been adopted for collecting the data from twenty five (n=25) randomly selected students who underwent training with BYOD. The results showed a positive effect on the speaking skills and the overall intelligibility of the participants. The use of smartphones also resulted in improved learners’ engagement in the language learning. Teachers at tertiary level could benefit from the study, adopt digital technologies in L2 teaching and learning and further extend the scope of the study with their experiences.
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization

High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. However, the constrained computation and storage resources on these devices still pose significant challenges for real-time DNN inference executions. To address this problem, we propose a set of hardware-friendly structured model pruning and compiler optimization techniques to accelerate DNN executions on mobile devices. This demo shows that these optimizations can enable real-time mobile execution of multiple DNN applications, including style transfer, DNN coloring and super resolution.
Shannon-Cosine Wavelet Precise Integration Method for Locust Slice Image Mixed Denoising

A novel denoising method for removing mixed noise from locust slice images is proposed by means of Shannon-cosine wavelet and the nonlinear variational model for the image processing. This method includes two parts that are the sparse representation of the slice images and the novel numerical algorithm for solving the variation model on image denoising based on the sparse representation. In the first part, a parametric Shannon-cosine wavelet function is introduced to construct the multiscale wavelet transform matrix, which is applied to represent the slice images sparsely by adjusting the parameters adaptively based on the texture of the locust slice images. By multiplying the matrix with the signal, the multiscale wavelet transform coefficients of the signal can be obtained at one time, which can be used to identify the salt-and-pepper noises in the slice images. This ensures that the salt-and-pepper noise points are kept away from the sparse representation of the slice images. In the second part, a semianalytical method on solving the system of the nonlinear differential equations is constructed based on the sparse representation of the slice images, which is named the sparse wavelet precise integration method (SWPIM). Substituting the sparse representation of the slice images into the Perona–Malik model which is the famous edge-preserving smoothing model for removing the Gaussian noises of the biomedical images, a system of nonlinear differential equations is obtained, whose scale is far smaller than the one obtained by the difference method. The numerical experiments show that both the values of SSIM and PSNR of the denoised locust slice images are better than the classical methods besides the algorithm efficiency.
Concerning the Question of Determining the Role of Artificial Intellect in Music

In the present article the issue is raised about evaluation of the use of artificial intellect in the creation of musical compositions. By means of an interdisciplinary approach, the authors assert the thesis about the distinction between “creativity” and the “creative process”, thereby distinguishing the results of the composer’s personality from those created by means of artificial intellect (weak neuronal networks). The authors dwell in great detail on the process of creating musical compositions by using electronic computers and the latest neuronal networks. Special examples from music history show that modern computers possess unique sound generation technologies: for example, NSynth is able to synthesize a completely new sound, which simultaneously is partially a flute sound and partially resembles a sitar. Can we regard these latest digital processes in music as creativity? The authors of the article defend the position that in the present day they can only be described as a variant of the “creative process”, which leaves the right for authorship to the composer. The issues raised in the article are particularly relevant in the modern era which, considering the accelerated development of digital technologies, may well pass into the era of transhumanism, when the ongoing connection of the human and the artificial intellect would only exacerbate these problems to a greater degree. Keywords: music, musical composition, creativity, harmony, art, figure, artificial intellect, neuronal network, algorithm.
Geoinformation Support System for Real Estate Market

Investing in real estate is one of the major investment areas in the world. While buying real estate people often consider - apart from standard analysis like size, standard, age, equipment- location and market prices. There is no doubt that the access to a large set of data describing and presenting property environment at present, market participants (targeted users = buyers and sellers) use the services of intermediaries or real estate appraisers, which generate extra costs, or they conduct the research using their own computers or mobile devices.&#x0D; The main goal of idea is to create an automatically generated information system for real estate market members. The proposed solution can allow delivering filtered information to the user. It can be presented in conjunction with the location. The user can be able, while standing in or near the property. The proposed solution consists of: positioning based on the phone GNSS (Global Navigation Satellite Systems) receiver and radius specification. As a result, valuable information can be displayed in AR (Augmented Reality) technology or on a map where location and unit prices are visible.