Reviewerly/exHarmony huggingface dataset:
Introduction to the Special Section on Applying Machine Learning Systems for IoT Services in Industrial Informatics

Energy consumption has increased in recent decades at a rate ranging from 1.5% to 10% per year in the developed world. As a consequence, several efforts have been made to model energy consumption in order to achieve a better use of energy and to minimize environmental impact. Open problems in this area range from energy consumption forecasting to user profile mining, energy source planning, to transportation, among others. To address these problems, it is important to have suitable tools to model energy consumption data series, so that the analysts and CEOs can have knowledge about the underlying properties of the power demand in order to make high-level decisions. In this paper, we focus on the problem of energy consumption modelling, and provide a solution from the perspective of symbolic regression. More specifically, we develop hybrid genetic programming algorithms to find the algebraic expression that best models daily energy consumption in public buildings at the University of Granada as a testbed, and compare the benefits of Straight Line Programs with the classic tree representation used in symbolic regression. Regarding algorithm design, the outcomes of our experimentation suggest that Straight Line Programs outperform other representation models in the symbolic regression problems studied, and also that the hybridation with local search methods can improve the quality of the resulting algebraic expression. On the other hand, with regards to energy consumption modelling, our approach empirically demonstrates that symbolic regression can be a powerful tool to find underlying relationships between multivariate energy consumption data series.
Relationship Between Self-efficacy and Chinese Language Speaking Anxiety of International Students at Northeast Normal University

The existing research was conducted in order to explore the correlation between Self-efficacy and Chinese Language Speaking Anxiety among International students at NENU, Changchun, China.In that respect the intention of this survey is to probe whether international students' Chinese Language Speaking Anxiety reveal significant variation with respect to gender, Nationality, Time lived in China, Number of Chinese Courses learned and Frequency of communication with Chinese friends in Chinese language.The research group involved 131 international students i.e. (70 males =53% and 61 females= 47%) from NENU.The study employed the quantitative method conducted through surveys by adapted questionnaires developed by Horowitz's (1986) and Graumer Erickson, A.S.,Soukup, J.H.,Noonan, P.M., &McGurn, L.( 2016).The data were examined by means of independent T-test, one-way ANOVA and Pearson's correlation approaches.T-test reveals high degree of Chinese Language Speaking Anxiety among international students but found no any considerable distinction in terms of gender.While ANOVA results show a only significance difference between the numbers of Chinese courses the participants learnt.In last A Pearson's r data revealed a weak positive association between Self-efficacy and Chinese Language Speaking Anxiety among International students at NENU.
Scatterplot Summarization by Constructing Fast and Robust Principal Graphs from Skeletons

Principal curves are a long-standing and well-known method for summarizing large scatterplots. They are defined as self-consistent curves (or curve sets in the more general case) that locally pass through the middle of the scatterplot data. However, computing principal curves that capture well complex scatterplot topologies and are robust to noise is hard and/or slow for large scatterplots. We present a fast and robust approach for computing principal graphs (a generalization of principal curves for more complex topologies) inspired by the similarity to medial descriptors (curves locally centered in a shape). Compared to state-of-the-art methods for computing principal graphs, we outperform these in terms of computational scalability and robustness to noise and resolution. We also demonstrate the advantages of our method over other scatterplot summarization approaches.
Survey of Traditional Waterfall Model in SDLC

Abstract Software development life cycle or SDLC for short is a technique for planning, fabricating, and keeping up data and modern frameworks. Up until this point, there exist numerous SDLC models, one of which is the Waterfall show which involves five stages to be finished successively so as to build up a product arrangement. I consider the waterfall model before the other models because it has had a profound effect on software development and has additionally influenced many SDLC models prevalent today. Keywords: SDLC (System Development Life Cycle), Testing, waterfall model Cite this Article Mohit Saxena. Survey of Traditional Waterfall Model in SDLC. Current Trends in Information Technology. 2019; 9(1): 4–6p.
Context-sensitive smart devices - definition and a functional taxonomy

Smart devices are no longer fashionable artefacts but have found mainstream adoption. Smart devices help humans by being context-sensitive, run errands or execute tasks and support their daily operations. This paper defines these context-sensitive smart devices (CSSD). We review the context and context sensitivity exhibited by mobile phones and smart devices. We further discuss the functionality of these smart devices and provide a functional taxonomy classification. This functional taxonomy could act as a framework in the design and development of CSSD. We illustrate the application of the functional taxonomy in different contexts, bringing out the characteristics, challenges and applications in each context.
Polytopic Trees for Verification of Learning-Based Controllers

Reinforcement learning is increasingly used to synthesize controllers for a broad range of applications. However, formal guarantees on the behavior of learning-based controllers are elusive due to the blackbox nature of machine learning models such as neural networks. In this paper, we propose an algorithm for verifying learning-based controllers—in particular, deep neural networks with ReLU activations, and decision trees with linear decisions and leaf values—for deterministic, piecewise affine (PWA) dynamical systems. In this setting, our algorithm computes the safe (resp., unsafe) region of the state space—i.e., the region of the state space on which the learned controller is guaranteed to satisfy (resp., fail to satisfy) a given reach-avoid specification. Knowing the safe and unsafe regions is substantially more informative than the boolean characterization of safety (i.e., safe or unsafe) provided by standard verification algorithms—for example, this knowledge can be used to compose controllers that are safe on different portions of the state space. At a high level, our algorithm uses convex programming to iteratively compute new regions (in the form of polytopes) that are guaranteed to be entirely safe or entirely unsafe. Then, it connects these polytopic regions together in a tree-like fashion. We conclude with an illustrative example on controlling a hybrid model of a contact-based robotics problem.
Impact of the intra-day variability of the DNI on the energy yield of CSP plants

The operation and the electricity yield of CSP plants in two days with the same daily-accumulated DNI, even with the same Aperture Normal Irradiance (ANI) can be very different depending on other parameters such as the variability and distribution of the solar radiation. In this paper, we intend to quantify the impact of the intra-daily variability of the DNI on the production of CSP plants. To that end, we use the ND model [1] to generate several synthetic years with similar distribution and daily energy to a reference measured year but different levels of intra-daily variability. We use System Advisor Model (SAM, https://sam.nrel.gov/) to simulate the performance of two parabolic trough (PT) plants, with and without thermal storage and common configurations for the location of Seville, Spain. To test the influence of the DNI variability, we simulate the measured and the synthetic years and we compare the daily gross power produced. As result, a clear decrease in the gross production of the PT plant without TES system is observed when the intra-daily variability increases, with daily average differences of 47% respect to the case with lowest variability. This difference is reduced with the use of the TES system to 6 %, damping the dynamic effects of the DNI variability on the global response of the plant.
A Steganographic Model Using Wavelet and Arnold Cat Transform with Multi-Threshold Error-Correcting Coding

This paper is another attempt of a comprehensive solution in the field of steganography data transmission. We also consider software model prototype that fully implements the process of hidden messages transmission in digital photographs. All stages of hidden message processing on the whole way from the sender to the recipient are taken into account. The programming model uses a discrete wavelet transform and new hiding algorithms based on the “Arnold cat map” decomposition. The efficiency of using noise-resistant coding methods and multi-threshold decoding to ensure high probability of integrity and reliability of hidden messages when transmitting them through communication channels with a high level of noise is also shown.
when we dance the ocean, does it hear us?

Author(s): Hobart, Hiʻilei Julia | Abstract: Response to Yuki Kihara’s Siva in Motion (2012) and Kalisolaite ‘Uhila’s Ongo Mei Moana (2015)
Underwater Image Target Detection with Cascade Classifier and Image Preprocessing Method

Underwater image target detection is an important part of exploring the ocean. This paper adopts cascade classifier and image preprocessing method. Firstly, it selects candidate regions on a given picture, then extracts feature from them and finally uses the trained classifier to detect. It focuses on the self-defined training of the cascade classifier, and trains the cascade classifier by collecting a large number of underwater target images. Secondly, it uses some of image preprocessing to make the detection effect more accurate. Finally, the simulation results show that it can achieve the target detection of underwater image by using the method of self-defined cascade classifier and image preprocessing.
The Trust Machine? The Promise of Blockchain-Based Algorithmic Governance of Exchange

Distributed ledger technologies such as blockchains allow, under certain conditions, economic exchange without the need for trust. Thus, they can facilitate mutually beneficial transactions that trust mechanisms or institutions fail to support. Blockchains can significantly lower the costs of verification and facilitate contract enforcement in digital markets. When verification becomes easy and cheap, transaction intermediaries that provide contract fulfillment services may become redundant, enhancing efficiency and enabling decentralized marketplaces for digital goods at a large scale. At the same time, blockchains may create markets for goods and services that previously were produced either within firms or through volunteer labor. This symposium explores the promise of blockchain technology of enabling “trustless” transactions and carves out where and why trust will still be needed even in blockchain-based exchange. The four papers focus on the potential organizational implications of blockchain technologies, ranging from the boundaries of the firm to collective action with externalities and to entrepreneurial finance. The studies complement each other by providing theoretical, technical, and empirical perspectives. The papers converge around the argument that blockchain-type verification technologies are likely to enhance trust and facilitate transactions that previously may have failed, but they do not obviate the need for relational governance. Trust is altered, not made redundant, by distributed ledger technologies. Smart Contracts and Firm Boundaries Presenter: Hanna Halaburda; - Presenter: Yannis Kakos; NYU Stern Revisiting Ostrom in the Age of the Blockchain Presenter: Aija Elina Leiponen; Cornell U. Presenter: Marc-David Seidel; U. of British Columbia Presenter: Llewellyn D W Thomas; LaSalle U. Ramon Llull (How) Can Blockchain Technology Enhance Trust? Presenter: Peter Altmann; - Blockchains as Trust-Free systems? Exploring the Boundaries of Trust Presenter: Daniel Obermeier; - Presenter: Joachim Henkel; Technical U. of Munich
Architecture Proposal of Help Desk based on the framework ITIL 3.0

This article focuses on analyzing incident management through the use of the ITIL Information Technology Infrastructure Library V.3.0, as a framework to improve the efficiency of IT services. Through the aforementioned architecture, it will be possible to attend and provide solutions to hardware, software, networks and other computer components problems, thus improving the quality of service for those stakeholders.
Natural Language Processing (NLP)

In 2014, Microsoft launched a chatbot—an AI system that communicates with people—called Xiaoice. It was integrated into Tencent’s WeChat, the largest social messaging service in China. Xiaoice performed quite well, getting to 40 million users within a few years.
Literal and Enriched Meaning of Sentences with Weak Definites and Bare Singulars

This paper studies literal and enriched meanings of sentences with weak definites and bare singulars in complementary distribution. It discusses, among other things, whether or not they display reinforceability, defeasibility, at-issueness and projectivity, which are properties usually used in the literature to characterize different types of inferences. The main conclusions are that literal meanings are regular entailments and that the best way to analyze enriched meanings is as a combination of entailed and conversationally implicated content. This treatment can explain their mixed behavior and, furthermore, is compatible with Aguilar-Guevara and Zwarts’s (2011, 2013) analysis of weak definites and its possible extension to bare singulars.
The Relationship between Foreign Portfolio Investment, Foreign Direct Investment and Economic Performance of Nigerian Economy: (1980-2017): An Empirical Analysis

The study examined the impact of foreign portfolio investment and Foreign Direct Investment on the performance of the Nigerian Economy over a period of 1980-2017. The data used were purely secondary sourced from the central Bank of Nigeria statistical Bulletin and World Bank Development indicator. The ordinary least square (OLS) regression analysis was used. The findings revealed that the performance of the Nigerian Economy is directly related to inflow of foreign portfolio investment and foreign direct investment and it is also statistically significant at 5% level. This means that a good performance of the economy depends on the inflow of these variables, or that the variables serve as an engine of economic growth. The study therefore recommends that policy makers should work on improvement of economic incentives capable of mobilizing external resources to the country to engender macroeconomic stability. A stable economy will attract foreign investment and this result to increased inflow of foreign capital.
Leadership competencies of middle managers and employees' job performance: evidence from a Palestinian media channel

This paper aims to examine the relationship between leadership competencies and job performance at Al-Aqsa TV channel. A descriptive analytical design was adopted in this study. A total number of 288 questionnaires were distributed to Al-Aqsa TV channel employees. One hundred and eighty-one questionnaires were returned and were usable for statistical analysis. In this study, the leadership competency model was used to measure the leadership competencies at Al-Aqsa TV channel. The results showed that middle managers at Al-Aqsa TV possess leadership competencies at a medium level. The results also demonstrated a positive relationship between leadership competences and performance at Al-Aqsa TV channel. Job performance was mainly influenced by three competencies which are: building coalitions or communications, business acumen and leading change. The paper suggests that building coalitions/communications, business acumen, and leading change, leading people, results driven competencies must be considered while developing job performance development plans. Finally, this paper contributes to the media management empirical literature as the topic is not well developed in developing countries and especially in Palestine.
Syntactical analysis of essays

The main purpose of this research is to describe: (1) sentence type; (2) Unity sentence; and (3) sentence structure found in the class VI students elementary school in Buleleng Regency, Bali Province. The data source used to be the subject in this study is a class VI elementary school student in Buleleng Regency, Bali province. Meanwhile, the object is (1) sentence type; (2) unity sentence; and (3) sentence structure found in the class VI students elementary School in Buleleng Regency, Bali Province. The method used in collecting data is a document recording method. Collected data is analyzed with descriptive analysis. The results showed that: (1) The type of sentence found in the class VI elementary school students in Buleleng Regency, Bali Province can be divided according to (a) the contents, there are 219 sentences; (b) The number of the clauses, there are 74 single sentences, 145 compound sentences; (c) The predicate of the establishment, there are 216 verbal sentences and 3 nominal sentences; and (d) the nature of the actor's relationship, there are 201 active sentences and 18 passive sentences.
Deep Neural Network Quantizers Outperforming Continuous Speech Recognition Systems

In Automatic Speech Recognition (ASR), the acoustic model (AM) is modeled by a Deep Neural Network (DNN). The DNN learns a posterior probability in a supervised fashion utilizing input features and ground-truth labels. Current approaches combine a DNN with a Hidden Markov Model (HMM) in a hybrid approach, which achieved good results in the last years. Similar approaches using a discrete version, hence a Discrete Hidden Markov Model (DHMM), have been disregarded in recent past. Our approach revisits the idea of a discrete system, more precisely the so-called Deep Neural Network Quantizer (DNNQ), demonstrating how a DNNQ is created and trained. We introduce a novel approach to train a DNNQ in a supervised fashion with an arbitrary output layer size even though suitable target values are not available. The proposed method provides a mapping function exploiting fixed ground-truth labels. Consequently, we are able to apply a frame-based cross entropy (CE) training. Our experiments demonstrate that the DNNQ reduces the Word Error Rate (WER) by 17.6 % on monophones and by 2.2 % on triphones, respectively, compared to a continuous HMM-Gaussian Mixture Model (GMM) system.
Earthquakes Shake Up Groundwater Systems

Increased permeability temporarily boosts water flow.
Results and Findings

In this chapter, the analysis outcomes of all performed phases are presented. The term results is applied to the QUAN phase, and the term findings is used for both qualitative phases. In this sense, results refer to the processed raw data from the field, whereas findings come from new emerging patterns from a discussion of the results of a performed investigation or research.
A Motion Planning Scheme for Cooperative Loading Using Heterogeneous Robotic Agents

In this work, we present a decentralized motion planning and control architecture for the cooperative loading task using heterogeneous robotic agents operating in a cluttered workspace with static obstacles. Initially, we tackle the problem of calculating a set of feasible loading configurations via a Probabilistic Road Maps technique. Next, an optimal loading configuration is selected considering the connectivity of the space and the Euclidean distance between the robotic agents. A motion control scheme for each agent is designed and implemented in order to autonomously guide each robot to the desired loading configuration with guaranteed obstacle avoidance and convergence properties. The performance and the applicability of the proposed strategy is experimentally verified in a variety of loading scenarios using a redundant static manipulator and a mobile platform.
Towards Interoperable Open Statistical Data

An important part of Open Data is of statistical nature and describes economic and social indicators monitoring population size, inflation, trade, and employment. Combining and analysing Open Data from multiple datasets and sources enable the performance of advanced data analytics scenarios that could result in valuable services and data products. However, it is still difficult to discover and combine open statistical data that reside in different data portals. Although Linked Open Statistical Data (LOSD) provide standards and approaches to facilitate combining statistics on the Web, various interoperability challenges still exist. In this paper, we define interoperability conflicts that hamper combining and analysing LOSD from different portals. Towards this end, we start from a thorough literature review on databases and data warehouses interoperability conflicts. Based on this review, we define interoperability conflicts that may appear in LOSD. We defined two types of schema-level conflicts namely, naming conflicts and structural conflicts. Naming conflicts include homonyms and synonyms and result from the different URIs used in the data cubes. Structural conflicts result from different practices of modelling the structure of data cubes.
Hand Tracking Interface for Virtual Reality Interaction Based on MARG sensors

Recent years have seen an accelerated development of Virtual Reality (VR) and Augmented Reality (AR) systems. Through these systems, computers immerse users in a completely artificial environment or in one that is partially based on the user's physical surroundings, but partially created artificially. One primary objective of these systems is to cause the perception of actually “being there” (in the environment created by the computer). However, this perception of full immersion may be weakened by the need to use “unnatural” devices (e.g., game controller) to execute actions within the VR or AR environment. Accordingly, increased interest has emerged in developing mechanisms by which the user could interact with VR and AR environments using natural actions, such as the movements of his/her hands.
Research on Equipment Support Capability Evaluation based on Information Fusion

Abstract The evaluation of equipment support capability cannot be ignored in the work of equipment support, and the evaluation results directly affect the formulation and decision-making of the support scheme. In view of the multi-source uncertain information in the process of evaluation, this paper proposes an equipment support capability evaluation model based on information fusion, and uses the D-S evidence theory to model and evaluate the uncertainty. The results show that the proposed method is effective. It can be used in engineering practice.