Reviewerly/exHarmony huggingface dataset:
CLARIFYING THE EFFECTS OF DIGITALIZATION ON (HIGHER) EDUCATION

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 8114-8121ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1991Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Analysis of Clear Sky Index Defined by Various Ways Using Solar Resource Map Based on Chollian Satellite Imagery

Clear sky indices were estimated by various ways based on in-situ observation and satellite-derived solar irradiance. In principle, clear sky index defined by clear sky solar irradiance indicates the impacts of cloud on the incoming solar irradiance. However, clear sky index widely used in energy sciences is formulated by extraterrestrial irradiance, which implies the extinction of solar irradiance due to mainly aerosol, water vapor and clouds drops. This study examined the relative difference of clear sky indices and then major characteristics of clear sky irradiance when sky is clear are investigated. Clear sky is defined when clear sky index based on clear sky irradiance is higher than 0.9. In contrast, clear sky index defined by extraterrestrial irradiance is distributed between 0.4 and 0.8. When aerosol optical depth and air mass coefficient are relative larger, solar irradiance is lower due to enhanced extinction, which leads to the lower value of clear sky index defined by extraterrestrial irradiance.
Machine Learning Based Taxonomy and Analysis of English Learners' Translation Errors

This study extracts the comments from a large scale of Chinese EFL learners' translation corpus to study the taxonomy of translation errors. Two unsupervised machine learning approaches are used to obtain the computational evidences of translation error taxonomy. After manually revision, ten types of English to Chinese (E2C) and eight types Chinese to English (C2E) translation errors are finally confirmed. There probably exists three categories of top-level errors according to the hierarchical clustering results. In addition, three supervised learning methods are applied to automatically recognize the types of errors, among which the highest performance reaches F1 = 0.85 on E2C and F1 = 0.90 on C2E translation. Further comparison to the intuitive or theoretical studies on translation taxonomy shows some phenomenon accompanied by language skill improvement of Chinese learners. Analysis on translation problems based on machine learning provides the objective insight and understanding on the students' translations.
Innovative monitoring instruments as support tools for natural risks management

Within the geotechnical field, monitoring systems are particularly important for what concern the description of natural phenomena and data collection for risks prevention purposes. For this reason, the need for instruments able to guarantee a continuous and prolonged control of the area has led to the development of new monitoring systems featuring higher reliability, durability and accuracy withrespect to traditional approaches. With these tools, it is possible to monitor various parameters with automatic procedures and high frequency sampling, allowing to perform statistical analyses and to provide a more accurate description of the ongoing phenomenon.Another relevant aspect is the ability to realize automated systems able to remotely elaborate data recorded by on-site instrumentation.This feature has led to the development of specific software and webbased interactive platforms integrated with various technologies.These features made it possible to develop Early Warning Systems (EWS) with a near real-time approach, allowing for a more effective and reliable management of emergencies.
LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning.

We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a low-cost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. State-of-the-art is to train a conditional variational auto-encoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing. We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along near-optimal paths that are otherwise difficult-to-sample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.
Exploration and Research on Fault Maintenance System of New Generation Weather Radar

In order to improve the standardization, specialization, intelligence and timeliness of the new generation weather radar fault repair, the technical threshold of radar fault repair is greatly reduced, so that the general operators can carry out radar fault repair work. In order to achieve this goal, this paper designs a new generation weather radar fault repair system, which aims to solve two problems: one is to solve the causes and locations of the new generation weather radar faults which can be quickly and accurately detected and diagnosed[1]; the other is to solve the problem that the grass-roots radar operational staff are inexperienced in maintenance and will not be repaired, and to provide visual through the maintenance system. Maintenance methods and steps with expert intelligence level [2], so that general radar operators can operate radar fault repair according to video steps, and have the technical level of maintenance experts, to achieve breakthroughs in technical difficulties of radar fault repair, to achieve both disease detection and treatment effect, to improve the efficiency of the use of new generation weather radar and to achieve modern technical equipment support. Chemistry plays an important role and significance.
Dimensionality Reduction Technique on SIFT Feature Vector for Content Based Image Retrival

Scale-Invariant Feature Transform (SIFT) descriptors plays very important role for depicting and matching the digital images between various views. There are various practical issues like keypoint localization and image retrieval that uses the SIFT descriptors for matching the image content among different views. The problem associated with these descriptors of an image is that calculation and matching of SIFT features descriptors are very cumbersome and slow process.For removing this problem the proposed method presents a technique that reduces the complexity, size and the time for matching of SIFT descriptors used in robot localization and indoor image retrieval.The proposed method reduces the number of SIFT descriptors and the complexity of every SIFT descriptor for determining an image. Our outcomes demonstrate that there is a negligible loss of exactness in feature retrieval while accomplishing a critical decrease in picture descriptor estimate and coordinating time. Proposed technique diminishes the descriptor size (number of descriptors) and furthermore speed up the searching of an image from extensive dataset. Decreasing the descriptor estimate also reduces the storage space required for storing the image descriptor.
Continuation Semantics for Multi-Quantifier Sentences: Operation-Based Approaches

Classical scope-assignment strategies for multi-quantifier sentences involve quantifier phrase (QP)-movement. More recent continuation-based approaches provide a compelling alternative, for they interpret QP's in situ - without resorting to Logical Forms or any structures beyond the overt syntax. The continuation-based strategies can be divided into two groups: those that locate the source of scope-ambiguity in the rules of semantic composition and those that attribute it to the lexical entries for the quantifier words. In this paper, we focus on the former operation-based approaches and the nature of the semantic operations involved. More specifically, we discuss three such possible operation-based strategies for multi-quantifier sentences, together with their relative merits and costs.
Toward Cross-theory Discourse Relation Annotation

In this exploratory study, we attempt to automatically induce PDTB-style relations from RST trees. We work with a German corpus of news commentary articles, annotated for RST trees and explicit PDTB-style relations and we focus on inducing the implicit relations in an automated way. Preliminary results look promising as a high-precision (but low-recall) way of finding implicit relations where there is no shallow structure annotated at all, but mapping proves more difficult in cases where EDUs and relation arguments overlap, yet do not seem to signal the same relation.
The Role of Utterance Boundaries and Word Frequencies for Part-of-speech Learning in

In this study, we address the problem of part-of-speech (or syntactic category) learning during language acquisition through distributional analysis of utterances. A model based on Redington et al.’s (1998) distributional learner is used to investigate the informativeness of distributional information in Brazilian Portuguese (BP). The data provided to the learner comes from two publicly available corpora of child directed speech. We present preliminary results from two experiments. The first one investigates the effects of different assumptions about utterance boundaries when presenting the input data to the learner. The second experiment compares the learner’s performance when counting contextual words’ frequencies versus just acknowledging their co-occurrence with a given target word. In general, our results indicate that explicit boundaries are more informative, frequencies are important, and that distributional information is useful to the child as a source of categorial information. These results are in accordance with Redington et al.’s findings for English.
Annotating Shallow Discourse Relations in Twitter Conversations

We introduce our pilot study applying PDTB-style annotation to Twitter conversations. Lexically grounded coherence annotation for Twitter threads will enable detailed investigations of the discourse structure of conversations on social media. Here, we present our corpus of 185 threads and annotation, including an inter-annotator agreement study. We discuss our observations as to how Twitter discourses differ from written news text wrt. discourse connectives and relations. We confirm our hypothesis that discourse relations in written social media conversations are expressed differently than in (news) text. We find that in Twitter, connective arguments frequently are not full syntactic clauses, and that a few general connectives expressing EXPANSION and CONTINGENCY make up the majority of the explicit relations in our data.
Revisiting a single-stage method for face detection

Although accurate, two-stage face detectors usually require more inference time than single-stage detectors do. This paper proposes a simple yet effective single-stage model for real-time face detection with a prominently high accuracy. We build our single-stage model on the top of the ResNet-101 backbone and analyze some problems with the baseline single-stage detector in order to design several strategies for reducing the false positive rate. The design leverages the context information from the deeper layers in order to increase recall rate while maintaining a low false positive rate. In addition, we reduce the detection time by an improved inference procedure for decoding outputs faster. The inference time of a VGA (640×480) image was only approximately 26 ms with a Titan X GPU. The effectiveness of our proposed method was evaluated on several face detection benchmarks (Wider Face, AFW, Pascal Face, and FDDB). The experiments show that our method achieved competitive results on these popular datasets with a faster runtime than the current best two-stage practices.
HeroMirror interactive

Appropriately chosen user interfaces are essential parts of immersive augmented reality experiences. Regular user interfaces cannot be efficiently used for interactive, real-time augmented reality applications. In this study, a gesture controlled educational gaming experience is described where gesture recognition relies on deep learning methods. Our implementation is able to replace a depth-camera based gesture recognition system using conventional camera while ensuring the same level of recognition accuracy.
Using artificial intelligence in the fight against spam

More than 40 years ago, on 3 May 1978, a computer vendor in the US sent the first spam email in history. It was sent by marketing manager Gary Thuerk to a list of 320 people who were active at that time on Arpanet, a predecessor of today's Internet, and was to invite them to the launch of a new computer in Los Angeles and San Mateo.
A semi-parametric statistical test to compare complex networks

Abstract The modelling of real-world data as complex networks is ubiquitous in several scientific fields, for example, in molecular biology, we study gene regulatory networks and protein–protein interaction (PPI)_networks; in neuroscience, we study functional brain networks; and in social science, we analyse social networks. In contrast to theoretical graphs, real-world networks are better modelled as realizations of a random process. Therefore, analyses using methods based on deterministic graphs may be inappropriate. For example, verifying the isomorphism between two graphs is of limited use to decide whether two (or more) real-world networks are generated from the same random process. To overcome this problem, in this article, we introduce a semi-parametric approach similar to the analysis of variance to test the equality of generative models of two or more complex networks. We measure the performance of the proposed statistic using Monte Carlo simulations and illustrate its usefulness by comparing PPI networks of six enteric pathogens.
Bayesian flexible mixture model with rating conversion on multi-criteria recommender system

Purpose Multi-criteria recommender systems (MC-RSs) allow users to express their preference in multiple aspects. Bayesian flexible mixture model (BFMM) is a model-based RS which extends FMM from single-criterion to MC. However, results of BFMM have a preference on different rating pattern problem. In single-criterion, FMM with decoupled normalization and W’s transposed function try to solve this problem. However, these techniques are applied to each criterion separately. Then, the relationship among criteria will be lost. This paper aims to solve different rating pattern problems and loss of the relationship between criteria. Design/methodology/approach The proposed method is combining between BFMM and rating conversion. First, mean and variance normalization is applied to make MC ratings of an active user and a neighbor lying on the same plane. After that, a pattern of each user is extracted using principal component analysis (PCA). Next, the pattern is used to convert neighbors’ MC ratings to the active user aspect. After that, converted MC ratings of neighbors are aggregated to be overall ratings using multiple linear regression (MLR). Finally, overall rating of the active user toward the target item is predicted using weighted average on the derived neighbors’ overall ratings where the similarity from BFMM acts as a weight. Findings The experimental results show that the proposed method where all criteria ratings are converted simultaneously can improve the performance of recommendation. Originality/value The proposed method predicts overall rating of the active user by converting MC ratings of each neighbor to the active user aspect at the same time, which can reduce the loss of the relationship between criteria.
A Cooperative Obstacle-Avoidance Approach for Two-Manipulator Based on A* Algorithm

During the movement process of two 6-dof manipulators, there may be collisions between manipulator and space obstacles or itself. To solve this problem, this paper use A* algorithm to finish the cooperative obstacle-avoidance path planning. Firstly, the model of obstacles is established. A* algorithm is used to plan a feasible path in the static obstacles environment for the main manipulator. Then, the virtual obstacles is set on the feasible path of the main manipulator. A* algorithm is applied to plan a feasible path for the second manipulator. In order to prevent the interference between the manipulators, ensure the smoothness of the manipulators in motion, and the time-energy optimum. By establishing the optimization model of inverse solutions, we realize the two-manipulator smooth motion without interference. Finally, the effectiveness of the algorithm is verified by simulation experiments.
Audio Watermarking with Encryption

Up to this point, the basic audio watermarking techniques in the various domains with its results and properties are discussed. In these all techniques, watermark image is directly inserted into sample values or transform coefficients of host audio signal. In this chapter, the audio watermarking techniques with encryption are discussed with its various properties and results. The two types of encryption such as Arnold scrambling and compressive sensing (CS) based are used in these techniques. This is an advanced audio watermarking technique where the host audio signal or watermark image is in its encrypted domain. The various types of approaches for audio watermarking are explained by researchers in the literature [1–5].
RightJob: Application of Text Data Mining to Curriculum Selection and Development

We applied text data mining techniques from machine learning to position (job) descriptions posted on a university job search site, Bureau of Labor Statistics (BLS) standard U.S. job descriptions, course descriptions, and curricula descriptions. Our work compared Term Frequency-Inverse Document Frequency (TD-IDF) to Latent Semantic Indexing (LSI) and found that TD-IDF was preferred in this application. We used TD-IDF to measure the extent of coherence among the collections of our documents. We then leveraged those measurements to developed novel approaches to assist students and curricula designers in answering these questions: (1) for students, given an interest in specific jobs, which degrees and courses are most relevant; (2) for students, given courses that have been taken, which jobs are most likely to result in initial interviews; (3) for curricula designers, how aligned are degree programs with specific groups of jobs (for example, with STEM jobs); (4) for TM curricula designers, to what extent do current and proposed degrees address different job opportunities. Other similar applications are possible by composing our Python and JMP code. Our work could be extended by providing open source implementation of the algorithms.
Body temperature and ECG monitoring system for cardiac problem patient

This project is the development of body temperature and ECG monitoring by using web server database. The project focuses on subject who has Sinus Tachycardia problem who is having an excessive heart rate above 100 beats per minute. The data is continuous and can be saved. Besides that, body temperature is one of the four main vital signs that must be monitored to ensure safe and effective care. This project is developed by using AD8232 Single Lead Heart Rate Monitor and LM60. The sensors are connected to Beaglebone Black. The acquired ECG signal from Beaglebone Black is recorded continuously in web server together with the body temperature of the patient. The recorded ECG graph and body temperature can be viewed and accessed by users at any place with internet using secured WSN. Thus, allowing easy supervision anywhere and anytime.
Use of Spatial Information via Markov and Conditional Random Fields in Histopathological Images

This study aims to increase the segmentation accuracy by using spatial information in biomedical histopathological images. The first step in the study is to provide pre-segmentation of H & E stained images using supervised learning methods, which are k-nearest neighbors algorithm, support vector machine and random forest. In order to build necessary classifier models, several training sets are created from intracellular and extra-cellular image patches extracted from histopathological images. As a two-class classification approach, supervised learning based segmentation are applied to test images in the evaluations. Spatial information should be used to improve the segmentation accuracy of output image obtained in the classification step. In the second step of the study, Markov and conditional random fields methods are utilized to exploit spatial information in histopathological images as a post processing approach. Comparative results prove that the use of spatial information via Markov and conditional random fields can be used to improve the segmentation accuracy of histopathological images.
Intelligent user interface design of application programs

Abstract The modern software is used in all areas of human activity. Application programs allows users to carry out the professional activity at the new, better level. The most important component of any application is the interface on which not only specialist productivity depends, but also quality of the performed work. As the contingent of modern users is very different, it is reasonable to develop the software and its interface part taking into account features of target audience. Use of elements of artificial intelligence at design of application programs will allow to consider features of training of users as much as possible. Creation of adaptive interfaces is caused by high growth rates of functionality of modern program systems, need of simplification of their operation and also to reduction of requirements to the professional level of users. In article the technique of development of the adaptive interface on the basis of individual characteristics of users and also their emotional status is described. Users were divided into five groups: the beginner, normal, confident, qualified and the administrator. Key parameters of individual users characteristics and also their possible emotional states are revealed. The automated system which can create groups based on the analysis of characteristics of users and their emotional state is developed, each group is provided by the adapted prototype of the interface of the application program.
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias

We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions. We also generalize adjustment criteria and formulas from the acyclic setting to the general one (i.e. ioSCMs). Such criteria then allow to estimate (conditional) causal effects from observational data that was (partially) gathered under selection bias and cycles. This generalizes the backdoor criterion, the selection-backdoor criterion and extensions of these to arbitrary ioSCMs. Together, our results thus enable causal reasoning in the presence of cycles, latent confounders and selection bias. Finally, we extend the ID algorithm for the identification of causal effects to ioSCMs.