Reviewerly/exHarmony huggingface dataset:
Analytical justification of vanishing point problem in the case of stairways recognition

Stair region detection and recognition from a stair candidate image is a challenging work in the computer vision research area. In the last few decades, researchers use many recognition systems to recognize and verify the stair region from other analogous objects. However, all the verification systems such as vanishing point (VP) do not achieve the desired result for various reasons. In this regard, a method is proposed in this paper to investigate the vanishing point’s problem arising in the case of stair region verification based on the three basic criteria, i.e. focal angle of the camera, height of the camera from the ground, and distance of the camera from the stair image. For that, primarily, the stair region is extracted by utilizing the geometrical features of a stair. The detected stair candidate region is verified through the y coordinate value of the vertical VP, i.e.y < 0. However, the y coordinate value of VP does not verify the stair region from all the scenarios. This paper investigates and justifies this problem utilizing the experimental analysis and introduces a mathematical model to estimate the location of the VP of the stair region. Finally, support vector machine (SVM) classifier is utilized instead of VP to recognize the stair candidate region and the performance of SVM is compared with respect to the VP. For that, rotational invariant uniform local binary pattern (LBP) is used for feature extraction. Stair images captured under different orientation and illumination conditions have been used to test the proposed method to evaluate the resultant accuracy.
Geochemical Evidences of Paleoenvironmental Changes in Late Quaternary Lacustrine Sediments of the Konya Closed Basin (Konya, Turkey)

Important geochemical evidence reflecting Quaternary climatic changes was obtained by evaluating geochemical data from 51 samples and datings from 3 samples by ESR method from sediments of a 7-m-deep trench in Great Konya lake area at the southwest of Adakale village (Konya, Turkey). The geochemical data indicate that the study area was influenced, four times, by a climate having strong precipitation, which caused strong erosion and high detrital input between 25,276 ± 1732-year BP and approximately 50,000-year BP. These periods correspond to climatic changes during the last glacial periods, the warm climate of D-O-2, D-O-5, D-O-6 and D-O-14 events and the cold climate of H2, H3, H4 or H5 and H6 events.
Convergenze semantiche tra musei, archivi e biblioteche. Ontologie per le relazioni interpersonali

This paper presents the state of the art of the cataloguing description of personal relationships between creators of archival records and people that are related to the life cycle of cultural objects, as highlighted by museums, archives and libraries records (i.e. GLAM, translated in italian MAB). Since people are fundamental access points to the cultural heritage, the representation of relationships between people and corporate bodies in Linked Open Data is of increasing interest. Nonetheless, a shared and comprehensive vocabulary of personal relationships is not available yet. Leveraging on Semantic Web technologies, and ontologies in particular, the aim is to identify existing ontologies that allow to represent relationships in their context (i.e. time, space and events); to map and classify different types of personal relationships; and to provide a formal representation of relationships not available in existing ontologies. As a result, we present an ontology, with a controlled vocabulary, for the representation of the scenario of personal relationships in the cultural heritage domain.
M-Pesa adoption and usage

Globally, there exits the largest ever youth population, otherwise known as the "youth bulge." Youth populations are especially sizable in developing countries like Kenya, where despite economic growth the challenge of creating job opportunities for this growing population persists. Concurrently, Kenya is experiencing tremendous growth in ICT adoption, most commonly the mobile phone. As mobile phones were quickly diffusing through Kenya, another unique innovation, M-Pesa, was created. M-Pesa is Kenya's leading mobile payment provider, and it has transformed financial services in the country. Using Diffusion of Innovations as a theoretical framework, this study explores M-Pesa adoption and use by Kenyan entrepreneurs. This study utilized semi-structured interviews with 28 young entrepreneurs in order to understand how the perceived attributes of M-Pesa influence adoption and continued usage.
Improving fuzzy c-means clustering via quantum-enhanced weighted superposition attraction algorithm

Fuzzy clustering has become an important research field in pattern recognition and data analysis. As supporting unsupervised mode of learning, fuzzy clustering brings about unique opportunities to reveal structural relationships in data. Fuzzy c-means clustering is one of the widely preferred clustering algorithms in the literature. However, fuzzy c-means clustering algorithm has a major drawback that it can get trapped at some local optima. In order to overcome this shortcoming, this study employs a new generation metaheuristic algorithm. Weighted Superposition Attraction Algorithm (WSA) is a novel swarm intelligence-based method that draws inspiration from the superposition principle of physics in combination with the attracted movement of agents. Due to its high converging capability and practicality, WSA algorithm has been employed in order to enhance performance of fuzzy-c means clustering. Comprehensive experimental study has been conducted on publicly available datasets obtained from UCI machine learning repository. The results point out significant improvements over the traditional fuzzy c-means algorithm.
COMBAHO: A deep learning system for integrating brain injury patients in society

In the last years, the care of dependent people, either by disease, accident, disability, or age, is one of the current priority research topics in developed countries. Moreover, such care is intended to be at patients home, in order to minimize the cost of therapies. Patients rehabilitation will be fulfilled when their integration in society is achieved, either in the family or in a work environment. To address this challenge, we propose the development and evaluation of an assistant for people with acquired brain injury or dependents. This assistant is twofold: in the patient’s home is based on the design and use of an intelligent environment with abilities to monitor and active learning, combined with an autonomous social robot for interactive assistance and stimulation. On the other hand, it is complemented with an outdoor assistant, to help patients under disorientation or complex situations. This involves the integration of several existing technologies and provides solutions to a variety of technological challenges. Deep leaning-based techniques are proposed as core technology to solve these problems.
Political-Electoral Marketing and Influencing Factors in Student Representatives’ Elections Under a Fuzzy Logic Approach

This study aimed to determine the causal factors, under uncertainty, related to the decisions taken by students in the University of Guayaquil as voters; at the time of electing a candidate as a student representative; similarly, how effective are electoral marketing strategies applied by parties and student groups for vote-catching. We proposed a methodology based on Fuzzy Logic techniques and cognitive maps to create causality models relying on expert criteria. Being these models useful for decision making, as well as analysis of hypothetical scenarios based on the underlying concept structures that have the most significant causal weight related to the effectiveness of electoral marketing strategies.
An Efficient Model and Algorithm for Privacy-Preserving Trajectory Data Publishing

Since Abul et al. first proposed the k-anonymity based privacy protection for trajectory data, the researchers have proposed a variety of trajectory privacy-preserving methods, these methods mainly adopt the static anonymity algorithm, which directly anonymize processing and data publishing after initialization. They do not take into account the real application scenarios of moving trajectory data. The objective of this paper is to realize the dynamic data publishing of high dimensional vehicle trajectory data privacy protection under (k, δ) security constraints. First of all, we propose the partition storage and calculation for trajectory data. According to the spatial and temporal characteristics of vehicle trajectory data, we choose the sample point $$ (x_{2} ,y_{2} ,t) $$ at the time $$ t_{i} $$ as partition fields, partition storage of the trajectory data according to the time sequence and the location of the running vehicle is $$ Region\left( {m,n} \right)\_\left( {x_{i} ,y_{i} ,t_{i} } \right) $$ . The computation of data scanning in trajectory data clustering and privacy processing is reduced greatly through this method. Secondly, the dynamic clustering method is used to cluster the regional data. According to the characteristics of the vehicle trajectory data, $$ \left( {x_{i} ,y_{i} ,t_{m - n} } \right) $$ as the release data identifier, trajectory attributes of the vehicle as the sensitive attributes, we use Data Partitioning and Cartesian Product (DPCP) method to cluster trajectory data under the (k, δ) security constraints. Thirdly, the anonymization function $$ f_{DPCP} $$ is used to preserve the privacy of clustering trajectory data. In each sampling time slice, $$ f_{DPCP} $$ function is used to generalize the location data in the grouping. Through the continuous algorithm optimization and the experimental verification of real trajectory data, this model and algorithm can effectively protect privacy under the security constraint of (k, δ). By means of data simulation and data availability evaluation, the data processed by the anonymization method has a certain usability under the threshold of δ. At the same time, the experimental results are compared with the classical NWA algorithm, and DLBG, the method in this paper have been proved to be advanced in time cost and data availability evaluation.
Discussion on the Effective English Teaching Methods in Universities and Colleges

As an international language, English is getting more and more attention. Middle school English is relatively simple, however, university and college English involves the English communication level in the later period of students, therefore, the requirements for English learning are higher, but the effect of university and college English teaching is not obvious. This paper puts forward some suggestions for several teaching methods in university and college English classroom.
Exploring the Performance of Tagging for the Classical and the Modern Standard Arabic

The part of speech (PoS) tagging is a core component in many natural language processing (NLP) applications. In fact, the PoS taggers contribute as a preprocessing step in various NLP tasks, such as syntactic parsing, information extraction, machine translation, and speech synthesis. In this paper, we examine the performance of a modern standard Arabic (MSA) based tagger for the classical (i.e., traditional or historical) Arabic. In this work, we employed the Stanford Arabic model tagger to evaluate the imperative verbs in the Holy Quran. In fact, the Stanford tagger contains 29 tags; however, this work experimentally evaluates just one that is the VB ≡ imperative verb. The testing set contains 741 imperative verbs, which appear in 1,848 positions in the Holy Quran. Despite the previously reported accuracy of the Arabic model of the Stanford tagger, which is 96.26% for all tags and 80.14% for unknown words, the experimental results show that this accuracy is only 7.28% for the imperative verbs. This result promotes the need for further research to expose why the tagging is severely inaccurate for classical Arabic. The performance decline might be an indication of the necessity to distinguish between training data for both classical and MSA Arabic for NLP tasks.
Objects Discovery Based on Co-Occurrence Word Model With Anchor-Box Polishing

The state-of-the-art objects discovery approaches can be categorized into two: deep learning methods based on a convolutional neural network with region proposals, and conventional machine learning methods based on topic models, low-rank matrix factorization, or image processing. The deep learning methods for objects discovery are based on sacrificing computational complexity to achieve precision, and the training time can be long without GPU platforms, whereas the conventional methods usually lack detection accuracy. To effectively address the problems of the training complexity and the detection speed, we present a new objects discovery approach by proposing a two-stage (training and verification) method. In the training stage, a topic model with words co-occurrence prior is proposed on the basis of Latent Dirichlet Allocation (LDA) model, in which the co-occurrence information among the features is sufficiently utilized. In the verification stage, we propose an anchor-box polishing algorithm that fine-tunes the detection results corresponding to the pre-trained topic model from some conventional algorithms with fast detection time. The experiments on various datasets demonstrate that the proposed approach can improve the detection performance in terms of efficiency and computing costs. It is also robust to objects different in colors, lightings, scales, and so on. Interestingly, the proposed method can be combined with many fast but inaccurate detection algorithms, in which it enhances the model's flexibility.
Complexity: Let's Not Make This Complicated

Examines the concept of "keeping it simple" with respect to software engineering. It’s funny how keeping it simple in software development can often mean revising and refactoring an existing system until it is elegant enough to afford adaptation and change. Simplicity and elegance are the goals of many developers when they’re designing software. Developers often view complexity as the opposite of simplicity, but I argue that complexity is not the right word. I think complicated software is really what people are worried about. In other fields, such as physics or even education,3 complexity refers to how agents, individuals, and entities interact with one another via a small set of rules or processes to produce intricate and interesting behaviors, much like birds or fish flocking and swarming together in complex patterns without collisions. Analogously, developers want their code to compose a solution both clearly and elegantly, allowing for dynamism and adaptability.
Deep Learning for Document Representation

While machines can discover semantic relationships in natural written language, they depend on human intervention for the provision of the necessary parameters. Precise and satisfactory document representation is the key to supporting computer models in accessing the underlying meaning in written language. Automated text classification, where the objective is to assign a set of categories to documents, is a classic problem. The range of studies in text classification is varied, ranging from studying a sophisticated approach for document representation to developing the best possible classifiers. A common representation approach in text classification is bag-of-words, where documents are represented by a vector of the words that appear in each document. Although bag-of-words is very simple to generate, the main challenge in such a presentation is that the resulting vector is very large and sparse. This sparsity and the need to ensure semantic understanding of text documents are the major challenges in text categorization. Deep learning-based approaches provide a fixed length vector in a continuous space to represent words and documents. This chapter reviews the available document representation methods that include five deep learning-based approaches: Word2Vec, Doc2Vec, GloVe, LSTM, and CNN.
Why do I believe that Quantum Key Distribution (QKD) is finally about to reach telecom markets and grow out of its present exotic standing?

QKD has had for decades the stigma of practical irrelevance. A new wave of interest in Quantum Technology coupled with emerging practical application scenarios is paving the way for a radical change in its future.
Feature Selection in Big Data using Filter Based Techniques

Today organizations are capturing and processing a very large amount of data that is informative which is further continuing to increase in an exponential manner. The analysis of data of this size to make a decision in decision making system often goes inaccurate and it computationally overburdens the system. The Hadoop is a working model which is based on a framework known as the Map-Reduce which efficiently process big data and reduce computation complexity. The methods of feature selection primarily aim at the elimination of irrelevant, redundant and noisy features that could otherwise bring down the performance of classification. But most of the traditional methods lack the level of scalability that is needed to cope with the various instances and bring about some successful results within a limited time. The work brings about feature selection that is based on the Minimal Redundancy Maximal Relevance (MRMR) along with the Information Gain (IG) which makes use of the paradigm known as the MapReduce for obtaining the feature subsets from all big datasets. The methods of feature selection were evaluated by making use of the classifiers such as the Naïve Bayes and the K-Nearest Neighbour (KNN). The results of the research show that this proposed method has achieved a good demonstration compared to the other methods.
The Use of Two Media of Instruction in Biology: A Quasi-Experimental Study

This paper determined the effectiveness of the two media of instruction, English and Filipino, in selected topics in Biology using quasi-experimental research.Two sections of Grade 8 students were the respondents of this study.The researchers found that the difference in scores of the two groups was statistically significant.Students who were subjected to English as a medium of instruction recorded a significantly higher posttest score than those students who were taught using Filipino.Thus, English as a medium of instruction is more effective in teaching selected topics in Biology.
The reform challenge: Australian patent law and the emergence of 3D printing

The social benefits and costs of patents in new areas of technology are not always easy to elucidate. Law and policy reform bodies have been reluctant to recommend express exclusions from patenting, leaving this task to the courts. In recent times, the Supreme Court of the United States and the High Court of Australia have been interventionist in setting the threshold requirements for patentable subject matter. As yet, however, these issues have not been considered specifically in the context of 3D printing. This chapter examines Australian case law on gene sequences, software and business methods and methods of medical treatment, concluding that there remains considerable uncertainty on the thresholds of patentability. There is no certainty that software or business methods will continue to be patentable, and in the bioprinting area there is some doubt as to whether methods of treatment that utilise 3D printing will be patentable. The chapter then turns to an examination of the effectiveness of patent protection. This analysis shows that actions for primary infringement have limited scope, as do exceptions to infringement, although secondary infringement actions may have broader application.
Development of Digital Competences of Teachers of Social Sciences at Secondary Vocational Schools

The paper points out to digital competence as an essential part of the competence model of a teacher in education. It points out to the fact that existing competence models need to be further explored, decompiled, and illustrated by the digital competences extensions. This is related to the importance of applying digital technologies to teaching and management.
Identification of Smartphone Applications by Encrypted Traffic Analysis

The requirements of smartphone users have shifted from the quality of service (i.e., throughput) to the quality of experience. Also, the amount of encrypted traffic has increased to protect personal information. Therefore, to provide a quality mobile network experience for smartphone users, network operators need to identify applications from the encrypted traffic and control their traffic. In this paper, we propose a method of identifying applications running on a specific smartphone by analyzing only the time series patterns in IP traffic without inspecting the encrypted traffic. The proposed method estimates application flow with a two-level probabilistic state transition model and identifies applications on the basis of the statistics per estimated flow. Through experiments identifying applications running on a smartphone, we evaluated the estimation accuracy of proposed method.
How democratized production challenges society’s ability to regulate

This chapter seeks to explain why some emerging technologies pose more problems than others. Just because a technology disrupts and democratizes a practice does not make the change good. Technologies such as 3D printing and Crispr/Cas9 may help improve society and also increase the risk of catastrophic outcomes. These technologies may force changes in tort or contract law and in regulatory systems such as the Food and Drug Act or Environmental Protection Act. But emphasis on the law alone misses the larger point. Cost and ease alter risk management. Many can find out how to build a nuclear bomb, but the costs to build one are high. In contrast, new technologies promise to wed powerful, widespread knowledge with low production cost so that someone can engage in sophisticated creation in the proverbial garage. Such technology forces us to rethink the tools and nature of governance and regulation in the 21st century.
On Structure Priors for Learning Bayesian Networks

To learn a Bayesian network structure from data, one popular approach is to maximize a decomposable likelihood-based score. While various scores have been proposed, they usually assume a uniform prior, or ``penalty,'' over the possible directed acyclic graphs (DAGs); relatively little attention has been paid to alternative priors. We investigate empirically several structure priors in combination with different scores, using benchmark data sets and data sets generated from benchmark networks. Our results suggest that, in practice, priors that strongly favor sparsity perform significantly better than the uniform prior or even the informed variant that is conditioned on the correct number of parents for each node. For an analytic comparison of different priors, we generalize a known recurrence equation for the number of DAGs to accommodate modular weightings of DAGs, a result that is also of independent interest.
Extracting Primary Objects and Spatial Relations from Sentences

In verbal human-robot interaction natural language utterances have to be grounded in visual scenes by the robot. Visual language grounding is a challenging task that includes identifying a primary ...