Reviewerly/exHarmony huggingface dataset:
The study on data migration from relational database to graph database

Abstract Under the background of big data, using relational databases to manage massive data may have some problems just like storage capacity and query efficiency. So, there is a new kind of databases called NoSQL to store data. However, the data models of NoSQL databases are different from relation databases. In order to finish migrating historical data from relational databases to NoSQL databases, in terms of graph database in the NoSQL databases, this paper takes ER diagram as the original model, graph model as target, and makes some transformational rules by using the relationships of entities. And this paper proposes an algorithm which can finish data migration by traversing ER diagram and using the transformational rules. This method can reduce the impact of model differences between relational databases and graph databases, ensure the integrity constraint of data, and automatically complete data migration. The experimental results show the validity and correctness of the data migration.
Anomaly Detection in Metro Passenger Flow Based on Random Matrix Theory

Anomaly detection in metro passenger flow is significant for the operation of metro system. Metro smart card data can provide more accurate data sources for detect anomalous metro passenger flow. This paper proposes a data-driven method based on random matrix theory (RMT) to detect anomaly in metro passenger flow. The method mainly includes three parts: matrix construction in metro passenger flow, the transform of raw matrix and the detection with RMT (M-P Law and Ring Law). Two cases are designed and conducted to validate the performance of the method based on RMT. The results indicate that the method can achieve an acceptable detection performance.
Collaborative work as a learning strategy to teach mathematics incorporating robotics using led godt education system and fischertechnik in seventh graders at the school Isidro Caballero Delgado in Floridablanca Santander Colombia

Abstract This article describes the impact of collaborative work to teach Mathematics in Seventh graders (70 students) at the school Isidro Caballero Delgado, Ocaña, Colombia. Led godt education system and fischertechnik allowed a great interaction and participation and provided the tools to incorporate robotics in the classroom. Moreover, these tools are used as a learning strategy to achieve understanding and analysis in the solving problems teaching math. Students became more critical and responsible facing daily solutions and a better understanding of basic problems of geometry, math and statistics. Students also learnt about and improved interpersonal abilities and improved social interaction. It also allowed making learning effective creating a better environment to improve the acquisition of mathematical concepts.
Consonant Phoneme Based Extreme Learning Machine (ELM) Recognition Model for Foreign Accent Identification

Foreign accent automatic identification has a key role in many speech systems, such as speech recognition, speaker identification, voice conversion, and immigration screenings, etc. English speakers exhibit dialectal differences or non-native accents on specific features of their speech, and these features can be used to identify the dialect or native language of the speaker. In this paper, we proposed the consonant phoneme based Extreme Learning Machine (ELM) recognition model for accent identification based on the different pronunciation of English consonant phonemes by Arab native speakers. Mel-Frequency Cepstrum Coefficients (MFCCs) and the normalized energy parameter along with their first and second derivatives are used as acoustic features and trained with ELMs, SVMs and DBN classifiers. ELM classifier showed fast learning, and better performance, based on KFold validation with an accuracy of 88% and standard deviation (σ=0.0167), 76% by SVM and 64% with DBN classifier respectively. Our proposed ELM and SVM model showed an 11%, 16% increase in accuracy respectively over the previous work model by using the same classifier on multiple words based acoustic model to identify regional accents.
A fuzzy clustering based color-coded diagram for effective illustration of blood perfusion parameters in contrast-enhanced ultrasound videos

Early identification and diagnosis of tumors are of great significance to improve the survival rate of patients. Amongst other techniques, contrast-enhanced ultrasound is an important means to help doctors diagnose tumors. Due to the advantages of high efficiency, accuracy and objectivity, more and more computer-aided methods are used in medical diagnosis. Here we propose, a color-coded diagram based on quantitative blood perfusion parameters for contrast-enhanced ultrasound video. The method realizes the static description of the dynamic blood perfusion process in contrast-enhanced ultrasound videos and reveal the blood perfusion characteristics of all regions of the tissue providing assistance to the doctors in their clinical diagnosis. For effective illustration of the blood perfusion through tissues, we propose (a) an improved block matching algorithm to eliminate the image distortions caused by breathing; (b) compute the time-grayscale intensity curve for each pixel to obtain four different quantitative blood perfusion parameters; and finally (c) employ the fuzzy C-means clustering algorithm to cluster the blood perfusion parameters, where each parameter is associated with a particular color. Thus based on the correspondence between the pixel and the blood perfusion parameters, all the pixels are color-coded to obtain the color-coded diagram. To the best of our knowledge, the proposed technique is one-of-its-kind to color code the contrast-enhanced ultrasound videos using blood perfusion parameters in order to understand the hemodynamic characteristics of the benign and malignant lesion. In our experiments, various contrast-enhanced ultrasound videos corresponding to several real-world cases were color-coded and the results of the experiments illustrated that the proposed color-coded diagrams are consistent with the diagnosis presented by the physicians. The experimental results suggested that the proposed method can comprehensively describe the blood perfusion characteristics of tissues during the angiography process thereby effectively assisting the doctors in diagnosis.
A Collaborative and Sustainable Edge-Cloud Architecture for Object Tracking with Convolutional Siamese Networks

Convolutional Neural Networks (CNNs) are becoming popular in Internet-of-Things (IoT) based object tracking areas, e.g., autonomous driving, commercial surveillance, and intelligent traffic management. However, due to limited processing power of embedded devices and network bandwidth, how to simultaneously guarantee fast object tracking with high accuracy and low energy consumption is still a major challenge, which makes IoT-based vision applications unreliable and unsustainable. To address this problem, this article proposes a collaborative edge-cloud architecture that resorts to cloud for object tracking performance enhancement. By properly offloading computations to cloud and periodically checking tracking status of edge devices through convolutional Siamese networks, our novel edge-cloud architecture enables interactive collaborations between edge devices and cloud servers in order to quickly and accurately rectify tracking errors. Comprehensive experimental results on well-known video object tracking benchmarks show that our architecture can not only significantly improve the performance of object tracking, but also can save the energy consumption of edge devices.
Spot Price Prediction Based Dynamic Resource Scheduling for Web Applications

Resource scheduling algorithms are crucial for cloud web applications to minimize resource rental costs of virtual machines elastically rented from public clouds while guaranteeing performances. Most existing algorithms only focus on virtual machines with fixed prices without considering spot virtual machines which have dynamic and cheaper prices. A few existing algorithms have considered the renting of spot resources to decrease rental costs. However, trends of dynamic prices are not considered which are beneficial to decrease rental costs further. Meanwhile, although spot virtual machines are cheaper, fluctuated prices are likely to produce out-of-bid events which make spot virtual machines unreliable. In this paper, a spot price prediction and minimum renting interval based resource scheduling method is proposed. Spot prices are predicted to identify cheap and stable spot types to improve the reliability. Frequent spot type switching is avoided in some extent by setting a minimum renting interval. The proposals are evaluated on CloudSim and experimental results show a better performance than existing algorithms.
Sampling Signals On Meet/Join Lattices

We present a novel sampling theorem, and prototypical applications, for Fourier-sparse lattice signals, i.e., data indexed by a finite semilattice. A semilattice is a partially ordered set endowed with a meet (or join) operation that returns the greatest lower bound (smallest upper bound) of two elements. Semilattices can be viewed as a special class of directed graphs with a strictly triangular adjacency matrix, which thus cannot be diagonalized. Our work does not build on prior graph signal processing (GSP) frameworks but on the recently introduced discrete-lattice signal processing (DLSP), which uses the meet as shift operator to derive convolution and Fourier transform. DLSP is fundamentally different from GSP in that it requires several generating shifts that capture the partial-order- rather than the adjacency-structure, and a diagonalizing Fourier transform is always guaranteed by algebraic lattice theory. We apply and demonstrate the utility of our novel sampling scheme in three real-world settings from computational biology, document representation, and auction design.
Implementation of Voice User Interfaces to Enhance Users’ Activities on Moodle

Voice user interfaces (VUIs) are effective and intuitive to improve user interaction with various types of information-technology-based systems. VUIs are increasingly becoming suitable for use in various practical applications, e.g., voice-operated smartphones or smart speakers such as Amazon Echo or Google Home. If VUIs, which enable hands-free and intuitive use, become available to a learning management system (LMS) such as Moodle, learning activities on the LMS can be made easier and help motivate the users. In addition, if it is possible to search LMS help documentation by speech via a VUI and listen to the search results, the work efficiency of LMS content creators may be improved. In this research, we have developed a voice app for learners to attempt quizzes on Moodle sites, and a voice app for users to search MoodleDocs (Moodle online documents).
Region-based analysis

This chapter is concerned with how we can characterise regions in an image, often by their texture. We shall first consider some of the grouping techniques that have been developed to form regions within an image. These essentially change the image resolution from high to very low. We shall then consider what is meant by texture and then how we can use Fourier transform techniques, statistics and region measures to describe it. We shall then look at how the measurements provided by these techniques, the description of the texture, can be collected together to recognise it.
SIAS-miner: mining subjectively interesting attributed subgraphs

Abstract Data clustering, local pattern mining, and community detection in graphs are three mature areas of data mining and machine learning. In recent years, attributed subgraph mining has emerged as a new powerful data mining task in the intersection of these areas. Given a graph and a set of attributes for each vertex, attributed subgraph mining aims to find cohesive subgraphs for which (some of) the attribute values have exceptional values. The principled integration of graph and attribute data poses two challenges: (1) the definition of a pattern syntax (the abstract form of patterns) that is intuitive and lends itself to efficient search, and (2) the formalization of the interestingness of such patterns. We propose an integrated solution to both of these challenges. The proposed pattern syntax improves upon prior work in being both highly flexible and intuitive. Plus, we define an effective and principled algorithm to enumerate patterns of this syntax. The proposed approach for quantifying interestingness of these patterns is rooted in information theory, and is able to account for background knowledge on the data. While prior work quantified the interestingness for the cohesion of the subgraph and for the exceptionality of its attributes separately, then combining these in a parameterized trade-off, we instead handle this trade-off implicitly in a principled, parameter-free manner. Empirical results confirm we can efficiently find highly interesting subgraphs.
Securing personal items in public space

While we put great effort in protecting digital devices and data, there is a lack of research on usable techniques to secure personal items that we carry in public space. To better understand situations where ubiquitous technologies could help secure personal items, we conducted an online survey (N=101) in which we collected real-world stories from users reporting on personal items, either at risk of, or actually being lost, damaged or stolen. We found that the majority of cases occurred in (semi-)public spaces during afternoon and evening times, when users left their items. From these results, we derived a model of incidents involving personal items in public space as well as a set of properties to describe situations where personal items may be at risk. We discuss reoccurring properties of the scenarios, potential multimedia-based protection mechanisms for securing personal items in public space as well as future research suggestions.
What we can learn from how trivalent conditionals avoid triviality

A trivalent theory of indicative conditionals automatically enforces Stalnaker's thesis – the equation between probabilities of conditionals and conditional probabilities. This result holds because the trivalent semantics requires, for principled reasons, a modification of the ratio definition of conditional probability in order to accommodate the possibility of undefinedness. I explain how this modification is motivated and how it allows the trivalent semantics to avoid a number of well-known triviality results, in the process clarifying why these results hold for many bivalent theories. In short, the slew of triviality results published in the last 40-odd years need not be viewed as an argument against Stalnaker's thesis: it can be construed instead as an argument for abandoning the bivalent requirement that indicative conditionals somehow be assigned a truth-value in worlds in which their antecedents are false.
Graph-based Indexing Method for Searching in RDF Data

RDF is a generic graph-based data model of Semantic Web, and SPARQL is a query language for accessing the RDF data. With the increasing size of RDF data, answering complex SPARQL queries is expensive because multiple self-joins are needed to process. In this work, we consider an indexing and searching approach based on the graph structure of RDF data to reduce the number of join operations. It can speed up the queries' performance and support chain and star shaped SPARQL query. Chain and star shaped subgraphs are extracted from the RDF data graph by considering the structure of edges around each vertex. The subgraphs obtained are stored as the index, named as CS-index. To execute a query, the query is firstly decomposed into query subgraphs based on the common join variable of its all triple patterns. And the query results are retrieved by searching the query subgraphs in CS-index, not in the whole data graph. The proposed index structure and searching approach tend to speed up the query response time by reducing the number of joins. We conduct a performance study on LUBM data set and see that our method outperforms the contest by a few orders of magnitude.
Mitigating Challenges in the Elicitation and Analysis of Transparency Requirements

Software systems are getting more and more complex, with an increasing integration of machine-learning based decisions. The ubiquitous presence of these systems makes users more dependent on them and their correctness in many aspects of daily life. Thus, there is a rising need to make software systems and their decisions more comprehensible. This seems to call for more transparency in software-supported decisions. Therefore, transparency requirements have to be understood, elicited and translated to lower level requirements. However, there is a lack of understanding about the requirements engineering process for transparency and how the different roles, e.g., UX designers, data scientists, and other stakeholders, have to interact in this process. In order to fill this gap, the requirements engineering process for transparency requirements needs to be thoroughly investigated. For this purpose, I intend to conduct empirical studies with practitioners and other stakeholders to gain a deeper understanding of the process and its associated problems. Based on the results, additional research will be conducted to investigate and propose solutions with the purpose of supporting requirements engineering when transparency is required.
Lautum Regularization for Semi-Supervised Transfer Learning

Transfer learning is a very important tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a small number of training examples in the latter. Yet, discrepancies between the underlying distributions of the source and target data are commonplace and are known to have a substantial impact on algorithm performance. In this work we suggest a novel information theoretic approach for the analysis of the performance of deep neural networks in the context of transfer learning. We focus on the task of semi-supervised transfer learning, in which unlabeled samples from the target dataset are available during the network training on the source dataset. Our theory suggests that one may improve the transferability of a deep neural network by imposing a Lautum information based regularization that relates the network weights to the target data. We demonstrate the effectiveness of the proposed approach in various transfer learning experiments.
Learned emergence in selfish collective motion

To understand the collective motion of many individuals, we often rely on agent-based models with rules that may be computationally complex and involved. For biologically inspired systems in particular, this raises questions about whether the imposed rules are necessarily an accurate reflection of what is being followed. The basic premise of updating one's state according to some underlying motivation is well suited to the realm of reservoir computing; however, entire swarms of individuals are yet to be tasked with learning movement in this framework. This work focuses on the specific case of many selfish individuals simultaneously optimizing their domains in a manner conducive to reducing their personal risk of predation. Using an echo state network and data generated from the agent-based model, we show that, with an appropriate representation of input and output states, this selfish movement can be learned. This suggests that a more sophisticated neural network, such as a brain, could also learn this behavior and provides an avenue to further the search for realistic movement rules in systems of autonomous individuals.
Knowledge recommender system for complex product development using ontology and vector space model

Knowledge recommendation has become a trend to improve the efficiency of acquiring knowledge during product development. As such, coping with the challenge of constantly discovering new knowledge is critical for knowledge reuse. This article proposes a knowledge recommendation framework based on design tasks, to facilitate the capturing and sharing of knowledge in manufacturing firms. An ontology-based knowledge representation model is designed and developed to organize the knowledge resources and a vector space model-based knowledge recommendation model is built to hasten the knowledge reuse process. A case study demonstrates the effectiveness of the proposed method.
Destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming

For theoretical analyses there are two specifics distinguishing GP from many other areas of evolutionary computation: the variable size representations, in particular yielding a possible bloat (i.e. the growth of individuals with redundant parts); and also the role and the realization of crossover, which is particularly central in GP due to the tree-based representation. Whereas some theoretical work on GP has studied the effects of bloat, crossover had surprisingly little share in this work. We analyze a simple crossover operator in combination with randomized local search, where a preference for small solutions minimizes bloat (lexicographic parsimony pressure); we denote the resulting algorithm Concatenation Crossover GP. We consider three variants of the well-studied Majority test function, adding large plateaus in different ways to the fitness landscape and thus giving a test bed for analyzing the interplay of variation operators and bloat control mechanisms in a setting with local optima. We show that the Concatenation Crossover GP can efficiently optimize these test functions, while local search cannot be efficient for all three variants independent of employing bloat control.
An Evolutionary Computing Approach to Solve Object Identification Problem for Fall Detection in Computer Vision-Based Video Surveillance Applications

This chapter proposes to present the design and development of an evolutionary computation (EC) based system which solves two interesting real-world problems. In the first phase of the system, the object detection problem of image processing is considered. An EC framework is designed to solve it, by formulating it as an optimization problem, considering the minimal required features to detect an object in a given image. The differential evolution (DE) algorithm is used in the framework. The objectives considered are to maximize the accuracy of detection and minimize the number of features required. Belga LOGOS dataset is considered, and 10 different feature descriptors are individually applied for object detection. Later applied the combination of features using the proposed EC framework and compared the results. Individual descriptors gave an average accuracy of 72.35% whereas the proposed method gave an average accuracy of 81.15% which shows the efficiency of the proposed framework. In the second phase, a real-world problem of fall detection of elderly people is considered for surveillance applications. A vision-based solution is proposed instead of using any external devices such as sensors and accelerometers to detect the fall. The advantage of this method is that it doesn’t need the subject to carry any devices and is cost-effective as it is based on only the surveillance videos. To detect the fall, the person needs to detected and tracked during their activities. Initially, people are tracked using core computer vision technique later applied the EC framework developed in Phase I to detect the people and the results are compared. Core computer vision techniques gave an accuracy of 97.16% whereas the proposed optimization framework gave an accuracy of 98.65%. This proved the efficiency of the proposed approach.