Reviewerly/exHarmony huggingface dataset:
Software Support for Discourse-Based Textual Information Analysis: A Systematic Literature Review and Software Guidelines in Practice

The intrinsic characteristics of humanities research require technological support and software assistance that also necessarily goes through the analysis of textual narratives. When these narratives become increasingly complex, pragmatics analysis (i.e., at discourse or argumentation levels) assisted by software is a great ally in the digital humanities. In recent years, solutions have been developed from the information visualization domain to support discourse analysis or argumentation analysis of textual sources via software, with applications in political speeches, debates, online forums, but also in written narratives, literature or historical sources. This paper presents a wide and interdisciplinary systematic literature review (SLR), both in software-related areas and humanities areas, on the information visualization and the software solutions adopted to support pragmatics textual analysis. As a result of this review, this paper detects weaknesses in existing works on the field, especially related to solutions’ availability, pragmatic framework dependence and lack of information sharing and reuse software mechanisms. The paper also provides some software guidelines for improving the detected weaknesses, exemplifying some guidelines in practice through their implementation in a new web tool, Viscourse. Viscourse is conceived as a complementary tool to assist textual analysis and to facilitate the reuse of informational pieces from discourse and argumentation text analysis tasks.
Synthetic Class Specific Bangla Handwritten Character Generation Using Conditional Generative Adversarial Networks

Bangla handwritten character recognition is known to be one of the most classical problem in the field of machine learning. In order to solve a machine learning problem one must thing is dataset. The more varied data a model sees the better it learns. Generative adversarial networks (GANs) are a group of neural networks that are used in unsupervised machine learning. It helps to resolve many difficult operations such as image generation from description, transforming low resolution image into high resolution, retrieving image contents given a small pattern etc. GAN's have many other promising applications in machine learning. There are many variations available for GAN. One of the variation of GAN is Conditional Generative Adversarial Networks(cGAN). This kind of GAN is used for generating a specific type of image. In this work we have used cGAN for generating Class Based Character Generation. This work can help researchers to generate handwritten characters to enhance the perfomance of deep learning models. We have trained this model to generate 50 Basic Bangla Characters, 10 Bangla Numerals and 24 Compound characters.
Memory-Tight Reductions for Practical Key Encapsulation Mechanisms

The efficiency of a black-box reduction is an important goal of modern cryptography. Traditionally, the time complexity and the success probability were considered as the main aspects of efficiency measurements. In CRYPTO 2017, Auerbach et al. introduced the notion of memory-tightness in cryptographic reductions and showed a memory-tight reduction of the existential unforgeability of the RSA-FDH signature scheme. Unfortunately, their techniques do not extend directly to the reductions involving intricate RO-programming. The problem seems to be inherent as all the other existing results on memory-tightness are lower bounds and impossibility results. In fact, Auerbach et al. conjectured that a memory-tight reduction for security of Hashed-ElGamal KEM is impossible.
User acceptance of voice biometrics in managing the physical access to a secure area of an international airport

Voice biometric systems are going to be increasingly used in real-life scenarios, in substitution of or in combination with other ways to verify a person's claimed identity. This paper is a field study exploring the acceptance of a voice biometric system by the end-users. We address a scenario where voice authentication is used by authorised personnel to physically access a secure area within an Italian international airport. The innovative features of the biometric technology take account of extreme environmental noise conditions and several spoofing countermeasures. The Unified Theory of Acceptance and Use of Technology is adopted as a reference framework to assess the impact on end-users operating under strong security constraints. Personnel reactions are captured by questionnaires, whereas expected benefits are identified against the running reliability (system performance automatically detected). For these aims, the observation design is addressed by issues of Acceptance and Perception of Security. The results show how the system works in a real-life scenario, even under simulated attacks, and to what extent it is accepted by both the Business User and the authorised personnel participating in the trial.
A medical image segmentation method based on hybrid active contour model with global and local features

Summary In this article, we proposed an improved region‐based active contour model based on curve evolution theory and variational level set method. Our method can be used to segment image with intensity inhomogeneity, such as medical computed tomography images. Our model contains a local intensity fitting term that makes the evolution curve stop at boundaries of the object and a global expansion term that makes the evolution curve have the chance to get to every location in the image. Therefore, our model has a good performance to solve the problem of flexible initialization, which exposed in region‐scalable fitting energy model. For the curvature term that occurs during the calculation, we calculated it with a more efficiency and accuracy method. Compared with other models, our model shows good segmentation result and less computation expense. Finally, we will present some experimental results, especially the result of contrast experiment.
A New Framework for Detecting Insider Attacks in Cloud-Based E-Health Care System

The effect of insider attack on the e-Healthcare system can lead to false examination of patient's health records which have led to unaccountability of data usage and high financial cost as a result of data breaches in the e-healthcare without a highly efficient detection approach. A number of health centers have been faced with legal and reputational consequences as a result. This therefore requires the proposition of an efficient technique that can make this problem addressed most especially eHealth systems on the cloud environment as operations are currently operating with cloud services. Until such approaches are proposed, health records could be attacked and peradventure lead to poor treatment of patients due to misinformation and hence causing the death of individuals. This need serves as a key motivation for this research. In this, we proposed a new framework for detecting insider attacks in Cloud-based Healthcare system using watermarking extraction and logging detection technique. The framework was implemented using Microsoft Azure, Opennebula cloud management software emulation, PhP web scripting language, MySQL database server. The approach gave an output of the number of activities performed by users with the permission update of legal and illegal intrusion into the system using an audit trail. The approach showed high level of precision, recall and accuracy which makes it performance excellent to implement from the evaluation conducted at the end of the research.
Comparison of Linear Genome Representations for Software Synthesis

In many genetic programming systems, the program variation and execution processes operate on different program representations. The representations on which variation operates are referred to as genomes. Unconstrained linear genome representations can provide a variety of advantages, including reduced complexity of program generation, variation, simplification and serialization operations. The Plush genome representation, which uses epigenetic markers on linear genomes to express nonlinear structures, has supported the production of state-of-the-art results in program synthesis with the PushGP genetic programming system. Here we present a new, simpler, non-epigenetic alternative to Plush, called Plushy, that appears to maintain all of the advantages of Plush while providing additional benefits. These results illustrate the virtues of unconstrained linear genome representations more generally, and may be transferable to genetic programming systems that target different languages for evolved programs.
A long short-term memory deep learning framework for explainable recommendation

Due to the growing quantity of information available on the Web, recommender systems have become crucial component for the success of online shopping stores. However, most of the existing recommender systems were only designed to improve the recommendation results and ignore the explainable recommendation aspect. Therefore, in this paper we propose a long short-term memory deep learning framework for explainable recommendation, that is able to generate an efficient explanation for any rating made by users for a recommended item. Such a framework would help users to choose a product with confident after reading the automatically generated explanation by our framework. The generated explanation is a concise sentence that shows the reason behind a recommendation, i.e., why a user should select that product. Extensive experiments on a real-world dataset from Amazon are conducted with the goal to evaluate the effectiveness of the proposed method in terms of loss and accuracy metrics. The experimental results demonstrate the effectiveness of our method according to the diversity in generating explainable recommendation.
On Mismatched Detection and Safe, Trustworthy Machine Learning

Instilling trust in high-stakes applications of machine learning is becoming essential. Trust may be decomposed into four dimensions: basic accuracy, reliability, human interaction, and aligned purpose. The first two of these also constitute the properties of safe machine learning systems. The second dimension, reliability, is mainly concerned with being robust to epistemic uncertainty and model mismatch. It arises in the machine learning paradigms of distribution shift, data poisoning attacks, and algorithmic fairness. All of these problems can be abstractly modeled using the theory of mismatched hypothesis testing from statistical signal processing. By doing so, we can take advantage of performance characterizations in that literature to better understand the various machine learning issues.
A Robust Batik Image Classification using Multi Texton Co-Occurrence Descriptor and Support Vector Machine

Batik is a unique pattern that symbolises characteristics and is a cultural heritage that is recognised by UNESCO. Batik is a research topic in image processing for image retrieval, object detection, pattern recognition, and classification. More and more new batik patterns and combinations between patterns are becoming increasingly difficult to recognise. Several studies have been proposed, such as KNN, Support Vector Machine, Convolutional Neural Network, SIFT, etc. to classify batik patterns. However, until now, it has not provided a reliable model proven from the accuracy that is still low. This study proposes the method of extracting Batik Image features using Multi Texton Co-Occurrence Descriptor (MTCD) with the Support Vector Machine (SVM) classifier validated with Logistic Regression (LR) to classify batik with high accuracy. The dataset used in testing uses Batik 300 and Batik 41k. The experimental results show that MTCD and SVM are a combination of very reliable techniques in classifying batik images. The accuracy obtained using SVM and LR is 1.0 and 1.0. Thus MTCD, SVM, and LR can be used to classify batik images effectively and reliably.
Unpaired Person Image Generation With Semantic Parsing Transformation

In this paper, we tackle the problem of pose-guided person image generation with unpaired data, which is a challenging problem due to non-rigid spatial deformation. Instead of learning a fixed mapping directly between human bodies as previous methods, we propose a new pathway to decompose a single fixed mapping into two subtasks, namely, semantic parsing transformation and appearance generation. First, to simplify the learning for non-rigid deformation, a semantic generative network is developed to transform semantic parsing maps between different poses. Second, guided by semantic parsing maps, we render the foreground and background image, respectively. A foreground generative network learns to synthesize semantic-aware textures, and another background generative network learns to predict missing background regions caused by pose changes. Third, we enable pseudo-label training with unpaired data, and demonstrate that end-to-end training of the overall network further refines the semantic map prediction and final results accordingly. Moreover, our method is generalizable to other person image generation tasks defined on semantic maps, e.g., clothing texture transfer, controlled image manipulation, and virtual try-on. Experimental results on DeepFashion and Market-1501 datasets demonstrate the superiority of our method, especially in keeping better body shapes and clothing attributes, as well as rendering structure-coherent backgrounds.
A Study of Solving Traveling Salesman Problem with Genetic Algorithm

Travelling salesman problem is one of the most important problems in the optimization area. To solve the problem of traveling salesman, the GA (genetic algorithm) can be seen as an appropriate method. In the genetic algorithm, there are many parameters needing to be set in advance. To figure out the effects of parameters in GA, we conducted experiments with different parameters and compared the performance. In addition, we try to improve the algorithm in aspects of crossing and mutating. The improved GA obtained a better performance, showing the proposed improvement is efficient.
Learning Interpretable Hidden State Structures for Handwritten Numeral Recognition

A novel paradigm of learning interpretable hidden state structures from the Multi-Layer Perceptron (MLP) neural network is proposed, in this work, that considers hidden state activations as the feature vector. The classification task at hand is handwritten numeral recognition. The k-means Elbow method is used to analyze the interpretability of the clusters formed from each hidden layer. Only the hidden layer that forms well-defined and linearly separable clusters, corresponding to the different numeral classes, is considered for the feature extraction. An interpretable structure thus emerges from the hidden representations that are learnt using a suitable classifier. Direct learning of hidden states using the linear k-Nearest Neighbor (kNN) classifier and the Support Vector Machine (SVM) with linear kernel yields high accuracies. Two different MLP configurations are tested for the purpose. Patch-based hidden structure learning with feature fusion is found to further improve the results. Experiments on the benchmark MNIST handwritten numeral image dataset prove the efficacy of our interpretability-based approach as compared to existing works.
Automated Identification of Security Requirements

Early characterization of security requirements supports system designers to integrate security aspects into early architectural design. However, distinguishing security related requirements from other functional and non-functional requirements can be tedious and error prone. To address this issue, machine learning techniques have proven to be successful in the identification of security requirements. In this paper, we have conducted an empirical study to evaluate the performance of 22 supervised machine learning classification algorithms and two deep learning approaches, in classifying security requirements, using the publicly availble SecReq dataset. More specifically, we focused on the robustness of these techniques with respect to the overhead of the pre-processing step. Results show that Long short-term memory (LSTM) network achieved the best accuracy (84%) among non-supervised algorithms, while Boosted Ensemble achieved the highest accuracy (80%), among supervised algorithms.
Improve Regression Network on Depth Hand Pose Estimation With Auxiliary Variable

The regression based deep neural networks have achieved state-of-the-arts performance on depth 3D hand pose estimation task. This paper focuses on improving the regression mapping between features and pose joints. Inspired by the distribution modeling ability of Variational Autoencoders, we introduce an auxiliary variable into the regression network. During training, the auxiliary variable is modeled by an inference distribution that learns the underlying structural kinematics of human hand. Different with other regression methods on hand poses, our network estimates the pose joints from input depth features and the learned auxiliary variable as well. We show that by introducing the auxiliary variable, the regression is benefited from 1) regularization modeled by inference distribution; and 2) prior information carried by the auxiliary model. The effectiveness of the proposed regression method is evaluated with extensively self-comparative experiments and in comparison with other regression methods on hand pose datasets. The proposed network is easy to train in an end-to-end manner and can work with various feature extraction methods. We apply the proposed regression method to an existing hand pose estimation system, and improves the estimation accuracy by 18.35% and 16.65% on public hand pose datasets.
Non‐uniform image blind deblurring by two‐stage fully convolution network

Deep neural networks have recently demonstrated high performance for deblurring. However, few methods are designed for both non-uniform image blur estimation and removal with highly efficient. In this study, the authors proposed a fully convolutional network that outputs estimated blur and restored image in one feed-forward pass for the non-uniformly blurred image of any input-size. The proposed network contains two subnets. The parameter estimation subnet P-net predicts pixel-wise parameters of multiple blur types with high accuracy. The output of P-net is used as a condition, which guides the blur removal subnet G-net to restore a high quality latent sharp image. P-net and G-net are ultimately integrated into a single framework called PG-net, which guarantees the consistency of parameter estimation and blur removal, thereby improves algorithm efficiency. Experiment results show that the authors blur parameter estimation method as well as their deblurring method outperforms the comparison methods both quantitatively and qualitatively.
SETLBI: An Integrated Platform for Semantic Business Intelligence

With the growing popularity of Semantic Web technologies, more and more organizations natively manage data using Semantic Web standards, in particular RDF. This development gives rise to new requirements for Business Intelligence tools to enable analyses in the style of On-Line Analytical Processing (OLAP) over RDF data. In this demonstration, we therefore present the SETLBI (Semantic Extract-Transform-Load and Business Intelligence) integration platform that brings together the Semantic Web and Business Intelligence technologies. SETLBI covers all phases of integration: target definition, source to target mappings generation, semantic and non-semantic source extraction, data transformation, and target population and update. It facilitates Data Warehouse designers to build a semantic Data Warehouse, either from scratch or by defining a multi-dimensional view over existing RDF data sources, and further enables OLAP-style analyses.
Neural Zero-Shot Fine-Grained Entity Typing

Fine-grained entity typing is a task to assign types to entity mentions dependent on mentions’ context. Due to the heavy work of human annotation, high quality training data is always not enough, zero-shot fine-grained entity typing becomes important. Previous zero-shot works rely on hand-crafted features and suffers from noisy distant supervision induced training data. In this paper, we propose a Neural Zero-Shot Fine-Grained Entity Typing (NZFET) model. NZFET is an end-to-end neural model free from hand-crafted features. The entity type attention in NZFET makes model focus on information relevent to the entity type. In our experiments, NZFET obtains better results on popular datasets than previous works. And we show experimentally that our method is robust against noisy training data.
Markov chain Monte Carlo methods: Theory and practice

In many situations, especially in Bayesian statistical analysis, it is required to draw samples from intractable probability distributions. A common way to obtain approximate samples from such distributions is to make use of Markov chain Monte Carlo (MCMC) algorithms. Two questions arise when using MCMC algorithms. The first of these is how long the underlying Markov chain must run before it can be used to draw approximate samples from the desired distribution. The second question is that of how often states of the chain can be used as approximate samples from the desired distribution so that the samples are roughly independent. This chapter provides insight into how to answer both of these questions in the course of describing how MCMC algorithms are used in practice. In this chapter, common types of MCMC algorithms are described, and Bayesian estimation using the output of the chain is also discussed.
Fast and Accurate Traffic Measurement With Hierarchical Filtering

Sketches have been widely used to record traffic statistics using sub-linear space data structure. Most sketches focus on the traffic estimation of elephant flows (i.e., heavy hitters) due to their importance to many network optimization tasks, e.g., traffic engineering and load balancing. In fact, the information of aggregate mice flows (e.g., all the mice flows with the same source IP) is also crucial to many security-associated tasks, e.g., DDoS detection and network scan detection. However, the previous solutions, e.g., measuring each individual flow or using multiple sketches for independent measurement tasks, will result in worse estimation error or higher computational overhead. To conquer the above disadvantages, we propose an accurate traffic measurement framework with multiple filters, called Sketchtree, to efficiently measure both elephant flows and aggregate mice flows. These filters in Sketchtree are organized in a hierarchical manner, and help to alleviate the hash collision and improve the measurement accuracy, as the number of flows through hierarchical filters in turn will be decreased gradually. We also design some mechanisms to improve the resource utilization efficiency. To validate our proposal, we have implemented Sketchtree and conducted experimental evaluation using real campus traffic traces. The experimental results show that Sketchtree can increase the processing speed by 100 percent, and reduce the measurement error by over 30 percent compared with state-of-the-art sketches.
Does QUIC Suit Well With Modern Adaptive Bitrate Streaming Techniques?

This letter provides a thorough analysis of various recent adaptive bitrate streaming (ABR) techniques over Google's QUIC protocol. More specifically, we focus on the recent developments of the ABR techniques using control theoretic and reinforcement learning based approaches, and compare their performances over HTTP/TCP and HTTP/QUIC in terms of a wide range of video quality of experience (QoE) parameters. We observe that these ABR techniques are more compatible with HTTP/TCP compared to HTTP/QUIC, and the features of QUIC demands for new ABR technique which can balance the three fundamental QoE parameters - bitrate, smoothness and re-buffering during a video playback.