Reviewerly/exHarmony huggingface dataset:
An Autonomous Service-Oriented Orchestration Framework for Software Defined Mobile Networks

5G network evolution results in new challenges for mobile network management and orchestration. The emerging SDN and NFV technologies are transforming the network infrastructure and network service components into compositions of programmable virtualized functions. The resulting flexibility allows the network to be conceived as services, which are aware of vertical application QoS/QoE requirements. While most recent works focus on flexible network control and improved resource utilization in emerging mobile network architectures, an end-to-end orchestration of both network infrastructure and vertical application is not adequately explored. In this work, we identify the challenges and propose a service oriented framework for application-aware end-to-end mobile network orchestration. We also present a prototypical realization of the proposed framework, which is an orchestration platform for virtualized mobile networks. To address the challenges of multi-stakeholder and multi-layer mobile network architecture, we employ event calculus for modeling the network layers, and abductive reasoning approach for autonomous policy translation and service provisioning in a 2-phase algorithm. The framework is validated with a 5G enhanced mobility management application as a service on our software defined mobile network testbed. Initial performance measurements are analyzed for practical insights into the proposed framework, as well as suggestions for its future development.
Anomaly Detection in Single Subject vs Group Using Manifold Learning

This paper compares several linear and non-linear multivariate models for the detection of abnormal patterns in neuroimaging data, when comparing a single subject to a normal control group. The proposed methods learn the manifold spanned by the normal controls using non-linear dimension reduction techniques. The image of a subject is projected on the control group manifold either via a standard projection or through an embedding/reconstruction scheme. A comparison of the reconstruction with the subject's original neuroimaging data allows for the detection of abnormal patterns by way of statistical tests on the residuals. The different abnormality detection methods are assessed on synthetic data and real (MRI) neuroimaging data. The importance of non-linear modeling of the manifold in the reduced-dimension subspace is highlighted, as well as robustness to large abnormalities.
Adversarial Placement Vector Learning

Automated jigsaw puzzle solving is a challenging problem with numerous scientific applications. We explore whether a Generative Adversarial Network (GAN) can output jigsaw piece placements. State-of-the-art GANs for image-to-image translation cannot solve the jigsaw problem in an exact fashion. Instead of learning image-to-image mappings, we propose a novel piece-to-location mapping problem and present a trainable generative model for producing output that can be interpreted as the placement of jigsaw pieces. This represents a first step in developing a complete learning-based generative model for piece-to-location mappings. We introduce four new evaluation measures for the quality of output locations and show that locations generated by our model perform favorably.
Speech Summarization for Tamil Language

In the education field, the amount of information in the form of audio and video recordings is available for every topic of interest and has gained a lot of research interest in summarization. Summarization is defined as a series of actions performed to express information in a concise form that can help users review the information available from a huge quantity of multimedia content in a shorter span of time. Summarization of speech files can be an effective mechanism to manage the large volume of information available in audio recordings. Summarization of a speech document usually addresses the following problems: generating a transcript from the input speech data, summarization, and rendering the output. The output can be in the form of either speech or text. In case of speech, prosodic information such as emotion of speakers that is conveyed only by speech can be presented and in case of text, hearing impaired people will benefit. This chapter discusses certain approaches that have been developed so far for extractive and abstractive speech summarization, and investigates speech summarization in the Indian language, a domain that has not been explored thus far. Also, this chapter discusses various speech recognition techniques, and their performance on recognizing Tamil speech data is analyzed. Various features used in the summarization of a spoken document are described, and related work on summarizing Tamil documents is presented.
Detecting Text in Manga Using Stroke Width Transform

The Japanese comic-book style known as manga is becoming a popular topic for researchers. This paper focuses on the problem of detecting text regions in manga pages. Because it is time-consuming and laborious to identify the text regions in images manually, an automatic approach is highly desirable. Here, we propose a new text-detection method for manga using a Stroke Width Transform (SWT) technique in conjunction with a Support Vector Machine (SVM). Conventional SWT-based text-detection techniques perform poorly with manga because both text and non-text objects have similar characteristics for strokes, lines, and shapes. To better suit manga, we propose modifying the rules for finding letter candidates, which improves the ability to capture text. An SVM is then used to classify image patches into letter and nonletter regions. We compared our proposed framework with a conventional framework and other text-detection methods including deep-learning techniques. In the results, our proposed method achieved the highest F-measure of 0.506.
Effects of sentence structure and word complexity on intelligibility in machine-to-human communications

With the rise of robotics and artificial intelligence, good communication between humans and machines becomes more important. However, users with language and hearing disadvantages may find synthetic speech systems to be difficult to understand. In this study, we explore the types of sentence structure and level of word complexity that affect intelligibility of speech in unfamiliar context. Using semantically unpredictable sentences, we found that sentence with more complex syntax such as relative pronouns and question words are harder to comprehend, while on the word level, it is the shorter and simpler words that contribute to misunderstandings. We found that although word frequency affects how well a word is recognised, the effect from the occurring frequency is much less than the effect of how phonetically distinctive the word is. There was also evidence of significant difference between native speakers and non-native speakers on how well they could understand the sentences. These results may help us in designing better dialogue system for machine to human interactions, especially in the healthcare arena, where often users have disadvantages in language and hearing abilities.
A Preliminary Strategy for Fall Prevention in the ASBGo Smart Walker

Fall-related injuries affect a large part of the population and related costs. Thus, there is a concern in studying a fall prevention strategy to minimize the consequences of falls. Walkers are assistive devices used to improve the balance, stability and reduce the load on the lower limb of the user. In this sense, there is a concern to improve the safety in smart walkers and, consequently, to prevent falls in these devices. However, in this field, the only approach is to stop the walker in risk situations. So, the aim of this paper is to define a preliminary strategy to prevent a fall event in the Adaptive System Behaviour Group (ASBGo) Smart Walker. For ASBGo Smart Walker, two modes of security are discussed in this paper. One approach is based on monitoring the center of mass and changing the trajectory when a near fall is detected. The other mechanism consists only in to stop the walker when a dangerous situation is detected. The first or the second mode are activated depending if the user drives the walker with the forearm on forearm support or not.
Łukasiewicz logics for cooperative games

Coalitional resource games (CRGs) provide a natural abstract framework with which to model scenarios in which groups of agents cooperate by pooling resources in order to carry out tasks or achieve individual goals. In this work, we introduce a richer and more general framework, called Łukasiewicz resource games (ŁRG), which is based on many-valued Łukasiewicz logics, whose formulae make it possible to specify the class of piecewise linear polynomial functions with integer and rational coefficients on [0,1]n. The use of Łukasiewicz logics provides a new approach to the representation of the scenario/situations modelled by CRGs. In ŁRGs, each agent is endowed with resources that can be allocated over a set of tasks, where the outcome of a task depends on the profile of resources that are allocated to it. We specify task outcomes using formulae of Łukasiewicz logic. In addition, agents have payoff functions over task outcomes, which are also specified by Łukasiewicz formulae. After motivating and introducing ŁRGs, we formalise notions of coalition structures and the core for ŁRGs and investigate the non-emptiness of the core both from a logical and computational perspective. We prove that ŁRGs are a proper generalisation of CRGs by showing how any CRG can be translated into a ŁRG that is strategically equivalent, in the sense that the former has a non-empty core if and only if so does the latter.
Detection of Frauds for Debit Card Transactions at Automated Teller Machine in Indonesia Using Neural Network

Fraud detection in an online banking transactions such as in Automated Teller Machine (ATM) is one of the important strategy implemented by banks to protect customer's account. Fraud detection requires a lot of investments, complex algorithms, training and testing. Fraud destroy the reputation of banks, loss of financials, and loss of the country's finances. This research is conducted in order to propose a model using neural network and data mining to detect fraud in debit card transaction. Neural network can be used as a benchmark to develop a logistic regression model in data mining. The evaluation of performance classifier (accuracy, sensitivity and specificity) in this research showed that the proposed model predicted class label tuple correct (76.3%). The result supports fraud analysis in debit card transaction in ATM. In conclusion the results show that the model has a good performance in detecting fraud in a debit card transaction.
Under Sampling Bagging Shapelet Transformation for Hydraulic System

In order to preserve the prediction of machine defects, fault conditions and normal conditions must be classified from time series data collected in real time from various sensors. For predictive maintenance of machine defects, fault conditions and normal conditions must be classified from time series data collected in real time from various sensors. Using the amount of time series data as it is classified is too large and inefficient. Therefore, a variety of studies has been conducted regarding effective classification. Above all, Shapelet transformation is known as a feature extraction method that enables effective classification from time series data based on information acquisition. In general, sampling and ensemble methods used to reduce noise and overfitting are expected to make shapelet transformations more stable. In this paper, we propose a framework to effectively classify time series data by adding Under Sampling Bagging technique to the shapelet transformation method.
Generalized space time autoregressive (gstar)-artificial neural network (ann) model with multilayer feedforward networks architecture

The GSTAR model is one linear space time model used to model time series data with inter-location linkages. One of the weaknesses of the GSTAR model is that the model has not been able to capture any nonlinear patterns that may arise. The ANN is one model of nonlinear artificial intelligence that has a flexible functional form and is a supervised engine learning that provides a good framework to represent a relationship in time series data. Due to the advantages of such ANN, it can be combined with GSTAR model. In this research conducted study of GSTAR-ANN model and its network architecture. The model is constructed in 3 layers namely input, hidden and output. The GSTAR model is used as input in the training process. Each input will receive an input signal and forward the signal to the hidden layer, then to the output layer. The GSTAR-ANN model has network architecture with one neuron unit at the output layer, p input neuron, q neuron in hidden layer (multilayer feedforward networks).
Blind Quality Evaluator for Screen Content Images via Analysis of Structure

Existing blind evaluators for screen content images (SCIs) are mainly learning-based and require a number of training images with co-registered human opinion scores. However, the size of existing databases is small, and it is labor-, time-consuming and expensive to largely generate human opinion scores. In this study, we propose a novel blind quality evaluator without training. Specifically, the proposed method first calculates the gradient similarity between a distorted image and its translated versions in four directions to estimate the structural distortion, the most obvious distortion in SCIs. Given that the edge region is easier to be distorted, the inter-scale gradient similarity is then calculated as the weighting map. Finally, the proposed method is derived by incorporating the gradient similarity map with the weighting map. Experimental results demonstrate its effectiveness and efficiency on a public available SCI database.
An Improved Interpolation based Reversible Data Hiding for Medical Images

Despite having a good embedding performance for natural images, performance of the Interpolation based Reversible Data Hiding (IRDH) schemes is yet to be verified for medical images. An IRDH scheme embeds only in the up-sampled pixels leaving the original pixels intact in an input image, which is strictly required in many medical and military image applications. This paper therefore presents an improved IRDH scheme and analysis of its embedding performance for medical images. Our proposed scheme utilizes local dispersion based adjustment to improve the quality of embedded images further. For higher embedding capacity, correlation between each interpolated pixel and its embedded versions is used. Experiments have been carried out for embedding data into a set of medical images, and our early results are evident of significant improvement in embedding performance.
A Cryptographic Algorithm Based On Aes Cipher Andnondeterministic Algorithm Approach For Key Generation

E-commerce and the financial services sector continue to rely hugely on the internet towards performing their activities. The need for a secured and protected environment to support the use of the internet has become critical in our time. A strong encryption system is required to protect data stored at a maximum level. The need for a strong and faster encryption algorithm to provide an enhanced network security has become relevant in recent time. This paper addresses the above issue by using a cryptographic method by applying the AES 256-bit algorithm (Rijndael) method of encryption and Non-Deterministic Algorithm (Monte-Carlo Simulation). An algorithm for encrypting and decrypting was built mainly for encrypting and decrypting documents coupled with non-deterministic approach for key generation. The system was designed, developed and the results obtained.
Thai-Isarn Dialect Parallel Corpus Construction for Machine Translation

Parallel corpus is an essential resource in Natural Language Processing (NLP) research, especially machine language translation. This paper presents the construction process of the Thai language and Isarn dialect bilingual parallel corpus, which includes word segmentation, translation and word alignment, part of speech (POS) tagging, and the parallel corpus design and construction. In the study, source sentences in Thai are segmented into a sequence of words by applying a Conditional Random Field (CRF) approach. We used the example and rule based Thai-Isarn machine translation system as a tool to generate the corresponding target sentence (Isarn dialect). The POS of each word is tagged using Hidden Markov Modeling (HMM). The source and target sentences with their POSs are validated by Isarn native speakers, who are expert in both Thai language and Isarn dialects. Lastly, the validated data were collected into the Thai Isarn parallel corpus.
A Flexible Dissimilarity Measure for Active and Passive 3D Structures and Its Application in the Fitness–Distance Analysis

Evolutionary design of 3D structures – either static structures, or equipped with some sort of a control system – is one of the hardest optimization tasks. One of the reasons are rugged fitness landscapes resulting from complex and non-obvious genetic representations of such structures and their genetic operators. This paper investigates global convexity of fitness landscapes in optimization tasks of maximizing velocity and height of both active and passive structures. For this purpose, a new dissimilarity measure for 3D active and passive structures represented as undirected graphs is introduced. The proposed measure is general and flexible – any vertex properties can be easily incorporated as dissimilarity components. The new measure was compared against the previously introduced measure in terms of triangle inequality satisfiability, changes in raw measure values and the computational cost. The comparison revealed improvements for triangle inequality and raw values at the expense of increased computational complexity. The investigation of global convexity of the fitness landscape, involving the fitness–distance correlation analysis, revealed negative correlation between the dissimilarity of the structures and their fitness for most of the investigated cases.
Joint Structured Graph Learning and Unsupervised Feature Selection

The central task in graph-based unsupervised feature selection (GUFS) depends on two folds, one is to accurately characterize the geometrical structure of the original feature space with a graph and the other is to make the selected features well preserve such intrinsic structure. Currently, most of the existing GUFS methods use a two-stage strategy which constructs graph first and then perform feature selection on this fixed graph. Since the performance of feature selection severely depends on the quality of graph, the selection results will be unsatisfactory if the given graph is of low-quality. To this end, we propose a joint graph learning and unsupervised feature selection (JGUFS) model in which the graph can be adjusted to adapt the feature selection process. The JGUFS objective function is optimized by an efficient iterative algorithm whose convergence and complexity are analyzed in detail. Experimental results on representative benchmark data sets demonstrate the improved performance of JGUFS in comparison with state-of-the-art methods and therefore we conclude that it is promising of allowing the feature selection process to change the data graph.
Modeling naïve bayes imputation classification for missing data

Naïve Bayes Imputation (NBI) is used to fill in missing values by replacing the attribute information according to the probability estimate. The NBI process divides the whole data into two sub-sets is the complete data and data containing missing data. Complete data is used for the imputation process at the lost value. The process is repeated for each missing attribute to generate complete data for classification. This research applies NBI for imputation and preprocessing as preparation of classification process. The trial of this study used NBI for imputation compared to using the mean and mode to predict the missing data. The data used for imputation is full train of complete data as a whole to predict the missing value so as to represent the entire data. The results of this study prove that imputation with NBI produces the right imputation with higher accuracy than other imputations. NBI with single imputation and multiple imputation results in better performance because of the right features. This study aims to calculate the effect of missing values on Naïve Bayes Imputation Algorithm is based on a probalistic model using mixed data. Empirically shows that the interaction between several methods of imputation and supervised classification results in differences in the performance of classification for the same imputation method.
Face alignment: improving the accuracy of fast models using domain‐specific unlabelled data and a teacher–student scheme

Face alignment is a crucial step in multiple face analysis and recognition tasks. The current state-of-the-art is comprised by very slow methods based on deep learning that require computationally heavy inference and very fast methods based on cascades of regressors that lack the ability to cope with complicated cases or extreme poses. The authors show how collecting a small subset of unlabelled domain-specific data can improve the accuracy of fast-inference models utilising data annotated by a slower one and a teacher–student architecture. In the proposed solution, they annotate a small subset of facial images belonging to two challenging domains using a slow but more accurate model, and this data is used to incrementally train a fast one. Their results show that by adding as little as a 5% of challenging data, they can reduce the error rate in a specific domain up to 30% without losing any generalisation abilities. This training scheme has applicability in numerous computer vision and engineering problems where computational power and model size are constrained by the application and platform or real-time operation is a requirement.
Non-Genuine Actors

Non-genuine actors, including social bots and sockpuppets, are automated or manipulated to post information and sentiment to various social media platforms. Though not all social bots and sockpuppets are nefarious, there is evidence that some of these non-genuine accounts—especially on Facebook and Twitter—are maliciously managed by hackers or state actors, to spread misinformation and spam. Trolls may or may not be automated, but are consciously or unconsciously divisive.
HBSD

Enterprise cloud bus (ECBS) is a multi-agent-based abstraction layer framework, responsible for publishing and discovery of services in an Inter-cloud environment. Our work focuses on the service discovery model (HBSD) using Hadoop that leads to the challenges of automatic web service discovery patterns. It has been observed that the RDBMS can handle only data sizes up to a few Terabytes but fails to scale beyond that, so Apache Hadoop can be used for parallel processing of massive datasets. This article provides a novel Hadoop based Service Discovery (HBSD) approach that can handle vast amount of datasets generated from heterogeneous cloud services. The novelty of the proposed architecture coordinates cloud participants, automate service registration pattern, reconfigure discover services and focus on aggregating heterogeneous services from Inter-cloud environments. Moreover, this particle states a novel and efficient algorithm (HBSDMCA) for finding the appropriate service as per user's requirements that can provide higher QoS to the user request for web services.