Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence, Cloud Librarianship, and Infopreneurship Initiatives for Inclusiveness

Artificial intelligence (AI) is a broad and complex area of study, which can be difficult for non-specialists to understand. Yet, its ultimate promise is to create computer systems that manifest human intelligence. This chapter coins “Machinzation” for the application of literary machine (computer) to human operations. This clearly has major implications for library and information science profession. In principle and practice, AI has penetrated virtually all walks of human life. Many authors have previously provided in-depth overviews of AI technologies. Service is the vocal point of librarianship and particularly in the era where information is the fifth and most important factor of production. Cloud computing stems from the principle of AI while when applied into the operations and routines in libraries and information center gives a brand new concept “CloudLibrarianship.” The new concept is dealt with in this work. Emergence of this concept opens up the entrepreneur opportunities in the information sector of the economy-inforpreneurship. This chapter therefore examines certain key aspects of AI that determine its potential utility as a tool for enhancing and supporting library operations.
Annotation Framework for Hate Speech Identification in Tweets: Case Study of Tweets During Kenyan Elections

Considering the colossal amount of user-generated content on social media, it has become increasingly difficult to monitor hateful content being published on public online spaces, especially during the electioneering periods, particularly in Kenya. In this regard, it is crucial to automate the identification of hate speech in order to manage the volume, variety, veracity and velocity of this content. In this research, we postulate a supervised machine learning approach whereby annotation of the training data set is critical in determining the performance of the trained classifier. Therefore, we develop an annotation framework based on Sternberg's (2003) hate theory and test its performance in classifying about 5k tweets using 3 human annotators per tweet. Preliminary results indicate an intercoder reliability score of 0.5027 based on Krippendorff's alpha.
RETRACTED CHAPTER: Differential Evolution Clustering and Data Mining for Determining Learning Routes in Moodle

Data mining techniques are being widely used in the field of education from the arise of e-learning platforms like Moodle, WebCT, Claroline, and others, and the virtual learning system they entail. Information systems store all activities in files or databases which, correctly processed, may offer relevant data to the teacher. This paper reports the use of data mining techniques and Differential Evolution Clustering for discovering learning routes frequently applied in the Moodle Platform. Data were obtained form 4.115 university students monitored in an online course using Moodle 3.1. Firstly, students were grouped according to the data from a final qualifications report in a course. Secondly, the data of the Moodle logs about each cluster/group of students was used separately with the aim of obtaining more specific and precise models of the students behavior in the processes.
Chi squared feature selection over Apache Spark

We live in the age of big data and distributed computing. The current large scale computation frameworks are based on a scaling-out approach for distributing tasks over a cluster of commodity machines. Apache Spark is one of these frameworks that has excelled in many computational tasks. Implementation of statistical learning algorithms over Spark is a challenging task. A bad implementation may lead to a significant decrease in performance and a waste of cluster time and money. Poor performance is mostly due to a lack of understanding of the data in hand and Spark's underlying mechanisms more than it is due to a deficit in the framework itself. In this paper, we consider the use case of X2 feature selection which is very popular in supervised learning pipelines. Our implementation follows the algorithm of the Scikit-learn Python machine learning library which is different than the algorithm used by the Spark machine learning library. The Spark ML library implementation of X2 feature selection accepts only categorical features. Our alternative implementation is more suitable for numerical features. We experiment in particular with features of high sparsity such as n-gram counts. We study the best partitioning scheme of the data and the optimal number of partitions. Our experiments are run over the Databricks platform.
Improving image classifiers for small datasets by learning rate adaptations

Our paper introduces an efficient combination of established techniques to improve classifier performance, in terms of accuracy and training time. We achieve two-fold to ten-fold speedup in nearing state of the art accuracy, over different model architectures, by dynamically tuning the learning rate. We find it especially beneficial in the case of small datasets, where reliability of machine reasoning is lower. We validate our approach by comparing our method versus vanilla training on CIFAR-10. We also demonstrate its practical viability by implementing on an unbalanced corpus of diagnostic images.
Semantic Textual Similarity and Factorization Machine Model for Retrieval of Question-Answering

Question and Answering (QA) in many collaborative social networks such as Yahoo!-answers, Stack Overflow have attracted copious users to post and transfer knowledge between users. This paper proposes an Adaptive global T-max Long Short-Term Memory-Convolutional Neural Network (ALSTM-CNN) method to retrieve semantically matching questions from historical questions and forecast the best answers by saving their effort and time. Moreover, a novel Field-aware Factorization Machine (FFM) classifier is adapted to rank the high-quality answers from large sparse data. This method has certain advantages include: (a) effectively learns the similarity based on simple pertained models with various multiple dimensions, (b) does not uses handcrafted features. This algorithm shows robust performance for various tasks (i.e., measuring textual similarity and paraphrase identification), when it employs on datasets such as Semantic Textual Similarity (STS) benchmark, Sentence Involving Compositional Knowledge (SICK), Microsoft Research Paraphrase Corpus (MRPC) and Wikipedia Question Answer dataset. The performance of our proposed method is compared with different classifiers and the result shows a better accuracy measure than other state-of-the-art methods.
Concept Recognition with Convolutional Neural Networks to Optimize Keyphrase Extraction

For knowledge management purposes, it would be useful to automatically classify and tag documents based on their content. Keyphrase extraction is one way of achieving this automatically by using statistical or semantic methods. Whereas corpus-index-based keyphrase extraction can extract relevant concepts for documents, the inverse document index grows exponentially with the number of words that candidate concepts can have. Document-based heuristics can solve this issue, but often result in keyphrases that are not concepts. To increase concept precision, or the percentage of extracted keyphrases that represent actual concepts, we contribute a method to filter keyphrases based on a pre–trained convolutional neural network (CNN). We tested CNNs containing vertical and horizontal filters to decide whether an n-gram (i.e, a consecutive sequence of N words) is a concept or not, from a training set with labeled examples. The classification training signal is derived from the Wikipedia corpus, assuming that an n-gram certainly represents a concept if a corresponding Wikipedia page title exists. The CNN input feature is the vector representation of each word, derived from a word embedding model; the output is the probability of an n-gram to represent a concept. Multiple configurations for vertical and horizontal filters are analyzed and optimised through a hyper-parameterization process. The results demonstrated concept precision for extracted keywords of between 60 and 80% on average. Consequently, by applying a CNN-based concept recognition filter, the concept precision of keyphrase extraction was significantly improved. For an optimal parameter configuration with an average of five extracted keyphrases per document, the concept precision could be increased from 0.65 to 0.8, meaning that on average, at least four out of five keyphrases extracted by our algorithm were actual concepts verified by Wikipedia titles.
Tracing sharing in an imperative pure calculus

We introduce a type and effect system, for an imperative object calculus, which infers sharing possibly introduced by the evaluation of an expression, represented as an equivalence relation among its free variables. This direct representation of sharing effects at the syntactic level allows us to express in a natural way, and to generalize, widely-used notions in literature, notably uniqueness and borrowing . Moreover, the calculus is pure in the sense that reduction is defined on language terms only, since they directly encode store. The advantage of this non-standard execution model with respect to a behaviorally equivalent standard model using a global auxiliary structure is that reachability relations among references are partly encoded by scoping. • Syntactic treatment of imperative object oriented calculus. • Blocks define a hierarchical memory. • Type and effect system tracing the sharing effect due to expression execution. • Operational semantics does not require external memory but rewrites expressions. • Subject reduction enforces properties of uniqueness and safe manipulation of object graphs.
Higher-order asymptotic refinements in the multivariate Dirichlet regression model

The likelihood ratio test statistic provides the basis for testing inference on the regression parameters in the class of multivariate Dirichlet regression models, which is very useful in modeling multivariate positive observations summing to one. We focus on the small-sample case, where the reference chi-squared distribution gives a poor approximation to the true null distribution of the likelihood ratio statistic. Our simulation results suggest that the likelihood ratio test tends to be extremely liberal when the sample size is small. We derive a general Bartlett correction factor in matrix notation for the likelihood ratio test statistic, which reduces the size distortion of the test, and also consider a bootstrap-based Bartlett correction. We also employ the Skovgaard’s adjustment to the likelihood ratio statistic. We numerically compare the proposed tests with the usual likelihood ratio test. Our simulation results suggest that the proposed corrected tests can be interesting alternatives to usual likelihood ratio test since they lead to very accurate inference even for very small samples. We also present an empirical application for illustrative purposes.
USING QR CODES IN DENTAL PRACTICE

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 434-439ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0147Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Decentralized Beacons: Attesting the Ground Truth of Blockchain State for Constrained IoT Devices

Internet of Thing devices (IoT devices) are often constrained in terms of computing, memory, storage, power, and network resources. This makes them ill-suited to operate as first-class citizens on a blockchain, such as Ethereum, preventing the IoT devices from attaining the security guarantees that are available to better resourced nodes that are able to operate as full, validating nodes on the blockchain. IoT devices may use so-called light protocols to interact with the blockchain with minimal resource requirements, but these protocols provide only probabilistic security guarantees. In this position paper, we propose a new mechanism where an operator of IoT devices is able to send a “ground truth state” to the devices via a new mechanism, which we call “decentralized beacons”, enabling them to gain full security guarantees of the blockchain state.
Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language $$p\mathcal{BC}{+}$$

We extend probabilistic action language $$p\mathcal{BC}{+}$$ with the notion of utility in decision theory. The semantics of the extended $$p\mathcal{BC}{+}$$ can be defined as a shorthand notation for a decision-theoretic extension of the probabilistic answer set programming language $$\mathrm{LP}^\mathrm{{MLN}}$$ . Alternatively, the semantics of $$p\mathcal{BC}{+}$$ can also be defined in terms of Markov Decision Process (MDP), which in turn allows for representing MDP in a succinct and elaboration tolerant way as well as leveraging an MDP solver to compute a $$p\mathcal{BC}{+}$$ action description. The idea led to the design of the system pbcplus2mdp, which can find an optimal policy of a $$p\mathcal{BC}{+}$$ action description using an MDP solver.
Graph Pattern Index for Neo4j Graph Databases

Nowadays graphs have become very popular in domains like social media analytics, healthcare, natural sciences, BI, networking, etc. Graph databases (GDB) allow simple and rapid retrieval of complex graph structures that are difficult to model in traditional information systems based on a relational DBMS. GDB are designed to exploit relationships in data, which means they can uncover patterns difficult to detect using traditional methods. We introduce a new method for indexing graph patterns within a GDB modelled as a labelled property graph. The index is based on so called graph pattern trees of variations and stored in the same database where the database graph. The method is implemented for Neo4j GDB engine and analysed on three graph datasets. It enables to create, use and update indexes that are used to speed-up the process of matching graph patterns. The paper provides details of the implementation, experiments, and a comparison between queries with and without using indexes.
Automatic Construction of a Polish Legal Dictionary with Mappings to Extra-Legal Terms Established via Word Embeddings

The primary objective of this research is finding correspondence between legal and extra-legal terms in Polish by employing unsupervised methods, such as statistics and word embeddings. We investigate the possibility to construct a legal dictionary automatically by employing statistical methods for identifying the legal terms (including multi-word entities) and then finding correspondence between these terms and extralegal terminology used by laymen, by employing word embeddings inducing algorithms. We compare two popular libraries word2vec and GloVe in a synthetic experiment showing the superiority of word2vec CBOW negative sampling variant in the described problem.
Supervised learning methods application to sentiment analysis

The field of artificial intelligence (AI) is constantly growing and finding new ways to solve real world problems. One of the AI knowledge and research fields is natural language processing (NLP) which attempts to categorise and process human language data in an effort to utilise machines to understand humans.
Taxi Service Pricing Based on Online Machine Learning

In present paper we propose an approach to the dynamic taxi service pricing based on data mining from the large database of cases accumulated in the company. We believe that historical data on decisions made by experienced managers in the past contain valuable information on the most subtle parameters that influence decision making, but cannot be taken into account in the direct optimization formulations proposed in the literature. The analysis of existing clustering methods is carried out and the Enhanced Self-Organizing Incremental Neural Network (ESOINN) is chosen which is the most suitable for online learning with a previously unknown number of clusters. A comparative analysis of the accuracy of the proposed method has been carried out. It is discovered that ESOINN shows the best accuracy of prediction in a realistic situation with acceptable waiting time. We also presented a web application implementing the proposed approach.
A novel method based on entity relationship for online transaction fraud detection

Fraud detection is the focus of research in Internet financial domain. The methods of both expert rules and machine learning detect fraud by seeking commonness among groups and individual differences between normal and abnormal transactions. However, they don't consider the relationship among attributes of transaction entities that may affect the precision of fraud detection. Different from the grid data, each node in relational network has different numbers of neighbors nodes, and the arrangement among them is disordered. Thus, this paper adds the relationship of transaction entities to machine learning model, which can effectively connect the graph domain and attributes space. First, the relational network between the transaction entity and the attribute entity is extracted from the transaction record. It is essentially a heterogeneous non-connected sparse bipartite graph with characteristics on nodes. However, the heterogeneous information network can not be uniformly characterized, and we should find and utilize the hidden relationship among the transaction entities we focused. Thus, Node Shrinkage Homogenization Algorithm is proposed for homogenization. Based on this homogeneous network, we put forward the method of graph-based Neighborhood Information Aggregation Gradient Boosting Decision Tree (NIAGBDT), so that the transaction features are integrated from its neighbor through the relational network. The experiments show that compared with current state of the art approaches, our algorithm has a significant improvement more than ten percent.
Hybrid Cryptosystem Combination Algorithm Of Hill Cipher 3x3 and Elgamal To Secure Instant Messaging For Android

Abstract Instant messaging is commonly used in communication, which made a high level of security as an essential part. To secure the huge amount of messages, one of the techniques used is hybrid cryptosystem. A Symmetric Hill Cipher 3x3 and ElGamal Asymmetric Algorithm combination will be used to perform the hybrid encryption scheme. Symmetric Hill Cipher with a square 3x3 key matrix for the encryption process and ElGamal Asymmetric for encrypt the key of Hill Cipher. The strength of Asymmetric ElGamal is the difficulty of calculating discrete logs in a large prime modulus. In this research, the running time is directly proportional to the length of text during the encryption and decryption process.
Object occlusion guided stereo image retargeting

The resizing of stereo images while preserving salient content and geometric consistency between the image pair is an essential problem to be addressed for 3D visualization. Existing stereo image retargeting techniques are observed to be incurring salient object deformation. In this paper, we formulate a seam carving method to perform stereo image retargeting using graph-cuts having node size as the number of pixels in one of the stereo images. We define the object map with depth ordering of each object from the camera. The seams are allowed to pass along the object-object occlusion boundary at depth discontinuity in order to prevent salient object deformation. We propose adaptive occlusion boundary weights as a function of an object area to be occluded to preserve small objects. The seam passing through object-object occlusion boundary in one image may not follow the exact boundary in the other image. We propose a refinement of the seam to follow object-object occlusion boundary in both the stereo images. By comparing the proposed method with other state-of-the-art stereo image retargeting methods, we show that the proposed method is able to preserve the salient object(s) along with geometric consistency between the stereo image pair.
Wireless motion controlled dual six axis robotic arms with rover

A robot is a machine, which is designed to carry out a complex series of actions either automatically or controlled manually by using remote device. The objective of this project is to design a wireless motion controlled dual six axis robotic arms with rover using motion technology. This robot can be used in national defense system, for border surveillance, repair works in critical areas, mine detection and diffusing. In this work concluded, the robot is designed and assembled in such a way that all the operations can be carried out by using only hand motions along with push buttons.
On problems about judicious bipartitions of graphs

Bollobás and Scott [5] conjectured that every graph G has a balanced bipartite spanning subgraph H such that for each v∈V(G), dH(v)≥(dG(v)−1)/2. In this paper, we show that every graphic sequence has a realization for which this Bollobás-Scott conjecture holds, confirming a conjecture of Hartke and Seacrest [8]. On the other hand, we give an infinite family of counterexamples to this Bollobás-Scott conjecture, which indicates that ⌊(dG(v)−1)/2⌋ (rather than (dG(v)−1)/2) is probably the correct lower bound. We also study bipartitions V1,V2 of graphs with a fixed number of edges. We provide a (best possible) upper bound on e(V1)λ+e(V2)λ for any real λ≥1 (the case λ=2 is a question of Scott [12]) and answer a question of Scott (Problem 3.9 in [12]) on max⁡{e(V1),e(V2)}.
Short-term Path Prediction for Virtual Open Spaces

In predictive redirected walking applications, reliable path prediction is essential for an effective redirection. So far, most predictive redirected walking algorithms introduced many restrictions to the virtual environment in order to simplify this path prediction. Path prediction is time-consuming and is also prone to errors due to potentially impulsive and even irrational walking behaviour of humans in virtual environments. Therefore, many applications confine users in narrow virtual corridors or mazes in order to minimise larger deviations from intended and predictable walking patterns. In this paper, we present a novel approach for short-term path prediction which can be applied to virtual open space predictive redirected walking. We introduce a drop-shaped trajectory prediction which is described using a Lemniscate of Bernoulli. The drop's contour is discretised and we show how this is used to determine potential user trajectories in the virtual environment.
Representation Learning for Minority and Subtle Activities in a Smart Home Environment

Daily human activity recognition using sensor data can be a fundamental task for many real-world applications, such as home monitoring and assisted living. One of the challenges in human activity recognition is to distinguish activities that have infrequent occurrence and less distinctive patterns. We propose a dissimilarity representation-based hierarchical classifier to perform two-phase learning. In the first phase, the classifier learns general features to recognise majority classes, and the second phase is to collect minority and subtle classes to identify fine difference between them. We compare our approach with a collection of state-of-the-art classification techniques on a real-world third-party dataset that is collected in a two-user home setting. Our results demonstrate that our hierarchical classifier approach outperforms the existing techniques in distinguishing users in performing the same type of activities. The key novelty of our approach is the exploration of dissimilarity representations and hierarchical classifiers, which allows us to highlight the difference between activities with subtle difference, and thus allows the identification of well-discriminating features.