Reviewerly/exHarmony huggingface dataset:
Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data

In response to the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge 2019), we developed machine learning algorithms to predict the fluid intelligence (FI) score using T1-weighed magnetic resonance imaging (MRI) data. 122 volumetric scores of regions of interest from 3739 samples provided in the training set were used to train the models and 415 samples were assigned as validation samples. We performed feature reduction using principal factors factor analysis on the training set volume. 36 factors explaining 100% of the total variance were extracted; the top 18 explained 80% of the variance. We estimated three types of models based on: (1) all regional brain volumes, (2) the 18 top factors or (3) the 36 complete factors. We used Scikit-Learn's grid search method to search the hyperparameter spaces of eight different machine learning algorithms. The best model, a Nu support vector regression model (NuSVR) using 36 factor scores as features, yielded the highest validation score (R2 = 0.048) and a relatively low training score (0.22), the latter of which was important for reducing the degree of over-fitting. The mean squared errors (MSEs) for the training and validation samples were 68.2 and 68.6, respectively; the correlation coefficients were 0.54 and 0.21 (p < 0.0001 for both). The final MSE for the test set was 95.63. Learning curve analysis suggests that the current training sample size is still too small; increasing sample size should improve predictive accuracy. Overall, our results suggest that, given a large enough sample, machine learning methods with structural MRI data may be able to accurately estimate fluid intelligence.
Deep Learning for Text Data on Mobile Devices

With the rise of Artificial Intelligence (AI), it is becoming a significant phenomenon in our lives. As with many other powerful tools, AI brings many advantages but many risks as well. Predictions and automation can significantly help in our everyday lives. However, sending our data to servers for processing can severely hurt our privacy. In this paper, we describe experiments designed to find out whether we can enjoy the benefits of AI in the privacy of our mobile devices. We focus on text data since such data are easy to store in large quantities for mining by third parties. We measure the performance of deep learning methods in terms of accuracy (when compared to fully-fledged server models) and speed (number of text documents processed in a second). We conclude our paper with findings that with few relatively small modifications, mobile devices can process hundreds to thousands of documents while leveraging deep learning models.
Handwritten Text Segmentation Using Pixel Based Approach

This paper presents a simple approach for the character segmentation of the handwritten words by bounding box approach and pixel based approach. The handwritten character segmentation is a tedious process because of their unconstrained writing styles. The handwritten words are classified based on their writing method. The non-touching words are segmented by the bounding box approach and the touching words are segmented by the pixel-wise approach. The novel part of this paper is to avoid over segmentation by thresholding automatically. The characters which are separated are then subjected to recognition using KNN, the non-parametric classifier. The features extracted for the classifying the model are Histogram of Oriented Gradients (HOG), Log-Gabor filters, concatenation of both the features and some geometric features. The segmentation accuracy is also responsible for the performance of the recognition. A benchmark database of IAM handwritten words is used for the handwritten segmentation and recognition. A random subset of words are taken into account and carried out for the proposed work. This paper achieves the segmentation rate of 94.45% and the recognition rate for 50-50% training and testing ratio is 85.89%.
Greedy Structure Learning of Hierarchical Compositional Models

In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter.Existing approaches to this problem are limited by making strong a-priori assumptions about the object's geometric structure and require segmented training data for learning.In this paper, we propose a novel framework for learning hierarchical compositional models (HCMs) which do not suffer from the mentioned limitations.We present a generalized formulation of HCMs and describe a greedy structure learning framework that consists of two phases: Bottom-up part learning and top-down model composition.Our framework integrates the foreground-background segmentation problem into the structure learning task via a background model.As a result, we can jointly optimize for the number of layers in the hierarchy, the number of parts per layer and a foregroundbackground segmentation based on class labels only.We show that the learned HCMs are semantically meaningful and achieve competitive results when compared to other generative object models at object classification on a standard transfer learning dataset.
Unsupervised Temporospatial Neural Architecture for Sensorimotor Map Learning

The ability to learn the sensorimotor maps of unknown environments without supervision is a vital capability of any autonomous agent, be it biological or artificial. An accurate sensorimotor map should be able to encode the agent's world and equip it with the capability to anticipate or predict the results of its actions. However, to design a robust autonomous learning technique for an unknown, dynamic, partially observable, or noisy environment remains a daunting task. This article proposes a temporospatial merge grow when required (TMGWR) network for continuous self-organization of an agent's sensorimotor awareness in noisy environments. TMGWR is an adaptive neural algorithm that learns the sensorimotor map of an agent's world using a time series self-organizing strategy and the grow when required (GWR) algorithm. The algorithm is compared with growing neural gas (GNG), GWR, and time GNG in terms of their disambiguation performance, sensorial representation accuracy, and sensorimotor-link error, a new metric that is developed in this article to evaluate how well a sensorimotor map represents causality in the agent's world. The outcomes of the experiments show that TMGWR is more efficient and suitable for sensorimotor map learning in noisy environments than the competing algorithms.
Density estimation of multivariate samples using Wasserstein distance

Density estimation is a central topic in statistics and a fundamental task of machine learning. In this paper, we present an algorithm for approximating multivariate empirical densities with a piecewise constant distribution defined on a hyperrectangular-shaped partition of the domain. The piecewise constant distribution is constructed through a hierarchical bisection scheme, such that locally, the sample cannot be statistically distinguished from a uniform distribution. The Wasserstein distance has been used to measure the uniformity of the sample data points lying in each partition element. Since the resulting density estimator requires significantly less memory to be stored, it can be used in a situation where the information contained in a multivariate sample needs to be preserved, transferred or analysed.
Scribbles for Metric Learning in Histological Image Segmentation

Segmentation is a fundamental process in biomedical image analysis that enables various types of analysis. Segmenting organs in histological microscopy images is problematic because the boundaries between regions are ambiguous, the images have various appearances, and the amount of training data is limited. To address these difficulties, supervised learning methods (e.g., convolutional neural networking (CNN)) are insufficient to predict regions accurately because they usually require a large amount of training data to learn the various appearances. In this paper, we propose a semi-automatic segmentation method that effectively uses scribble annotations for metric learning. Deep discriminative metric learning re-trains the representation of the feature space so that the distances between the samples with the same class labels are reduced, while those between ones with different class labels are enlarged. It makes pixel classification easy. Evaluation of the proposed method in a heart region segmentation task demonstrated that it performed better than three other methods.
Fast, Small, and Simple Document Listing on Repetitive Text Collections

Document listing on string collections is the task of finding all documents where a pattern appears. It is regarded as the most fundamental document retrieval problem, and is useful in various applications. Many of the fastest-growing string collections are composed of very similar documents, such as versioned code and document collections, genome repositories, etc. Plain pattern-matching indexes designed for repetitive text collections achieve orders-of-magnitude reductions in space. Instead, there are not many analogous indexes for document retrieval. In this paper we present a simple document listing index for repetitive string collections of total length n that lists the $$ ndoc $$ distinct documents where a pattern of length m appears in time $${{\,\mathrm{\mathcal {O}}\,}}(m+ ndoc \cdot \lg n)$$ . We exploit the repetitiveness of the document array (i.e., the suffix array coarsened to document identifiers) to grammar-compress it while precomputing the answers to nonterminals, and store them in grammar-compressed form as well. Our experimental results show that our index sharply outperforms existing alternatives in the space/time tradeoff map.
Language-independent development environment support for dynamic runtimes

There are many factors for the success of a new programming language implementation. Existing language implementation frameworks such as Truffle or RPython have focused on run-time performance and security, or on providing a comprehensive set of libraries.
Evaluating ProDirect manipulation in hour of code

We examine whether augmenting traditional coding environments with ProDirect manipulation improves several learning measures. ProDirect manipulation is a novel user interaction model that provides a bidirectional link between code and outputs. Instead of reasoning abstractly about the output a program might produce, users instead directly manipulate outputs (e.g., using a keyboard and mouse). Program text is then updated to reflect the change.
Efficient late binding of dynamic function compositions

Adaptive software becomes more and more important as computing is increasingly context-dependent. Runtime adaptability can be achieved by dynamically selecting and applying context-specific code. Role-oriented programming has been proposed as a paradigm to enable runtime adaptive software by design. Roles change the objects’ behavior at runtime and thus allow adapting the software to a given context. However, this increased variability and expressiveness has a direct impact on performance and memory consumption. We found a high overhead in the steady-state performance of executing compositions of adaptations. This paper presents a new approach to use run-time information to construct a dispatch plan that can be executed efficiently by the JVM. The concept of late binding is extended to dynamic function compositions. We evaluated the implementation with a benchmark for role-oriented programming languages leveraging context-dependent role semantics achieving a mean speedup of 2.79× over the regular implementation.
A New Perspective in Designing an Optimized Fault Tolerant Reversible Multiplier

This paper presents a new approach to design a cost effective fault tolerant reversible multiplier circuit. Unlike existing multiplier circuits that have separate circuits for partial product generation and multi-operand addition, the proposed multiplier, designed using a combination of six distinct blocks, incorporates these two circuits together. It also provides an algorithm that depicts the way of using the blocks in designing a n×n fault tolerant reversible multiplier. The proposed multiplier is simulated to verify the correctness of the design and algorithm. The comparative study shows that the proposed multiplier is more optimized than any existing fault tolerant reversible multiplier in terms of quantum cost, garbage outputs and number of gates used. The proposed multiplier can be used to design a reversible fault tolerant arithmetic logic unit.
A Study on the Effect of Blockchain on Personal Information Protection

In this paper, Blockchain is mentioned as the next-generation core IT technology.As an immature technology, there are not many practical use cases, but it is expected to be widely applied in various industries such as cryptocurrency, finance, public, etc. to increase efficiency and enable new services that did not exist in the past.Nevertheless, the generalization of blockchain technology is still difficult.In particular, from the viewpoint of personal information protection, GDPR of Europe, etc., is becoming stronger.Considering that the core of the blockchain is the change of information sharing and processing method, it is very important how the blockchain can affect, especially from the viewpoint of privacy, and how the Privacy Act can be applied to the blockchain.However, the discussion on this part also seems to be insufficient.Therefore, in this paper, blockchain By analyzing the implications and implications of technologies and services using them from the perspective of the Privacy Act, we will discuss how the blockchain will be used to prevent leakage of privacy.
Your Money or Your Life—Modeling and Analyzing the Security of Electronic Payment in the UC Framework

EMV, also known as Chip and PIN, is the world-wide standard for card-based electronic payment. Its security wavers: over the past years, researchers have demonstrated various practical attacks, ranging from using stolen cards by disabling PIN verification to cloning cards by pre-computing transaction data. Most of these attacks rely on violating certain unjustified and not explicitly stated core assumptions upon which EMV is built, namely that the input device (e.g. the ATM) is trusted and all communication channels are non-interceptable. In addition, EMV lacks a comprehensive formal description of its security. In this work we give a formal model for the security of electronic payment protocols in the Universal Composability (UC) framework. A particular challenge for electronic payment is that one participant of a transaction is a human who cannot perform cryptographic operations. Our goal is twofold. First, we want to enable a transition from the iterative engineering of such protocols to using cryptographic security models to argue about a protocol's security. Second, we establish a more realistic adversarial model for payment protocols in the presence of insecure devices and channels. We prove a set of necessary requirements for secure electronic payment with regards to our model. We then discuss the security of current payment protocols based on these results and find that most are insecure or require unrealistically strong assumptions. Finally, we give a simple payment protocol inspired by chipTAN and photoTAN and prove its security. Our model captures the security properties of electronic payment protocols with human interaction. We show how to use this to reason about necessary requirements for secure electronic payment and how to develop a protocol based on the resulting guidelines. We hope that this will facilitate the development of new protocols with well-understood security properties.
Ontological and Fuzzy Set Similarity between Perception-Based Words

Fuzzy short text semantic similarity measures allow the inclusion of human perception based words to be within the similarity measurement which results in better correlation on the meaning of the short text with human understanding. Existing measures such as FUSE and FAST rely on the creation of fuzzy ontological structures from the modelling of perception words using type-1 or type-2 fuzzy sets. Due to the complex methodology of creating these ontologies, fuzzy word representation cannot be guaranteed due to language evolution. This paper presents a comparative study of simpler fuzzy set similarity measures. The results surprisingly indicate that a very simple fuzzy set similarity measure created from the center of gravity (COG) distance between type-2 fuzzy sets has a very high correlation with the FUSE semantic similarity measure.
Radar target classification using improved Dempster–Shafer theory

This study considers the problem of coarse classification of targets using multifunction radar. Several methods are available for classification such as decision trees, Dempster–Shafer, Bayes, neural networks, etc. A different approach to assign the mass functions based on fuzzy logic in the Dempster–Shafer framework is proposed in this study. The method is evaluated for classification of different kinds of targets like aircraft, ballistic missiles, satellites, chaff and actual clouds, and unknown targets. With the proposed method, improvement in classification accuracy is observed, compared to existing mass functions. The technique is found to be computationally efficient and suitable for real-time systems.
Improving collective interpretation by extended potentiality assimilation for multi-layered neural networks

The present paper aims to extend the potential learning method to overcome the problem of collective interpretation, which aims to interpret multi-layered neural networks by compressing them into the simplest ones. In the process of compression, positive, negative, and complicated weights have had unfavourable effects for interpretation. To deal with the problems of collective interpretation, the potential learning is extended only to use positive weights. In addition, to obtain more appropriate weights for interpretation, the number of candidate weights for higher potentialities is first increased as much as possible. Then, from among many candidates, more appropriate weights are selected as more important ones. This extended potentiality learning is expected to produce more stable and more simple representations for easy interpretation. The extended method was applied to three datasets, namely, an artificial dataset, a real eye-tracking dataset, and a student evaluation dataset. In all cases, it was observed that the selectivity of connection weights could be increased. Correspondingly, the majority of connection weights became positive, and the collective weights were quite similar to the regression coefficients of the logistic regression analysis. Finally, for the third dataset (student evaluations), the extended method could extract more explicit input-output relations, compared with the logistic regression analysis, while improving generalisation performance.
Empirical Comparison of Cross-Validation and Test Data on Internet Traffic Classification Methods

Abstract In this paper, we compare two validation methods that are used to estimate the performance of classification algorithms in a non-problem-specific knowledge scenario. One way to measure the performance of a classification algorithm is to determine its prediction error rate. However, this value cannot be calculated but estimated. In this work, we apply and compare two common methods used for estimation namely: test data and cross-validation. Precisely, we analyze and compare the statistical properties of the K-fold cross-validation and test data estimators of the prediction error rates of six classifiers namely; Naïve Bayes, KNN, Random Forest, SVM, J48, and OneR. From the study, the statistical property of repeated cross-validation tends to stabilize the prediction error estimation which in turn reduces the variance of the prediction error estimator when compared with test data. The NIMS dataset collected over a network was employed in the experimental study.
Development of Reliable Wireless Communication System for Secure Blockchain-based Energy Trading

Blockchain technology is being developed rapidly in recent years. It has been applied to stock trading, supply chain auditing, crowd-funding, energy trading, and Internet-of-Things. Specifically, in Internet-of-Things applications, how the data is transmitted before reaching a blockchain platform reliably is a challenging issue. In this paper, we focus on an energy trading scenario with Internet-of-Things blockchain applications for energy trading. In this scenario, energy generated from renewable sources can be traded in a peer-to-peer manner among micro-grids. This requires a high level of data communication reliability. Blockchain is adopted as an effective decentralized transaction management solution. In particular, we propose a proof-of-concept for reliable data transmission from smart meters to the blockchain based on Sigfox technology and the Ethereum. To enhance reliability, we configure the base station antenna as a block-based mode and propose a physical data layer for wireless communication. We perform extensive experiments to evaluate the performance of the proposed wireless communication system for the blockchain.
What Is Anomaly Detection?

In this chapter, you will learn about anomalies in general, the categories of anomalies, and anomaly detection. You will also learn why anomaly detection is important and how anomalies can be detected and the use case for such a mechanism.
User interface prototype generation from agile requirements specifications written in concordia

User interface prototypes (UIP) are widely used to get feedback before building a software feature. They can prevent misunderstandings between the software development team and other stakeholders (e.g., users, investors) that lead to rework or a resulting software that does not meet their needs. UIP can also be a valuable resource in Agile software development, in which feedback is key. In this paper, we present an approach to generate UIP automatically from Agile requirements specifications written in Concordia and its corresponding prototype tool. The tool is able to generate UIP for web-based applications. We evaluated the approach and the tool with questionnaires, and the results revealed that: (i) the generated UIP are very similar to those drawn by respondents; (ii) the generated source code has good enough quality to be reused by developers; and (iii) they save design and development time.
Previous Studies and Differences Between Lean Management and World Class Manufacturing

The main objective of this study is to determine the relationship between Lean Management (LM) and World Class Manufacturing (WCM) practices of aeronautical company in Italy. The methodology should help managers to select those principles and tools of Lean Manufacturing and WCM that are expected to be effective in their company. The research aims to integrate the two models and to achieve a global manufacturing performance in order to maximize the company performance. To this end, a methodological approach, based on Fuzzy Cognitive Maps (FCMs) and the Analytic Hierarchy Process (AHP), is proposed to quantitatively evaluate relationships between LM and WCM. The proposed model allows to identify key factors of Lean and WCM practices and to measure the quantitative relationship between these strategies. The final output is a dynamic analysis designed to identify how relations change with each individual factor.