Reviewerly/exHarmony huggingface dataset:
Building English–Punjabi Parallel Corpus for Machine Translation

Parallel corpus is needed for many natural language processing tasks, like machine translation and multilingual document classification. The parallel corpus of English–Punjabi language pair is sparse in volume due to the semantic differences between two languages and Punjabi being a low resource language. In this paper, a parallel corpus for machine translation is being created and evaluated using the sentence alignment permutation metrics. Multiple translation corpora and human assessment together validate automatic evaluation metrics, which are important for the development of machine translation systems. The corpora considered are dialogues of the movie taken from the Wikipedia dumps. Further, the metrics are identified that define the corpora more accurately. The quality of the corpus is verified using the performance metrics based on distance metrics.
Human Body Fall Recognition System

Falling is one of the major risks for elderly people, kids and people with disabilities. The situation worsens when the victim suffers from serious injuries and is unable to get help on time. In this paper, we propose a method to detect a fall in real-time. The proposed detection method consists of three stages: Video analysis, Body Recognition and Trigger Alert. In this recognition system, human detection algorithms using OpenCV have been implemented. The application accuracy has been tested under different lighting settings and in different environment settings.
Feature selection via computational intelligence techniques

Feature selection (FS) has become an essential task in overcoming high dimensional and complex machine learning problems. FS is a process used for reducing the size of the dataset by separating or extracting unnecessary and unrelated properties from it. This process improves the performance of classification algorithms and reduces the evaluation time by enabling the use of small sized datasets with useful features during the classification process. FS aims to gain a minimal feature subset in a problem domain while retaining the accuracy of the original data. In this study, four computational intelligence techniques, namely, migrating birds optimization (MBO), simulated annealing (SA), differential evolution (DE) and particle swarm optimization (PSO) are implemented for the FS problem as search algorithms and compared on the 17 well-known datasets taken from UCI machine learning repository where the dimension of the tackled datasets vary from 4 to 500. This is the first time that MBO is applied for solving the FS problem. In order to judge the quality of the subsets generated by the search algorithms, two different subset evaluation methods are implemented in this study. These methods are probabilistic consistency-based FS (PCFS) and correlation-based FS (CFS). Performance comparison of the algorithms is done by using three well-known classifiers; k-nearest neighbor, naive bayes and decision tree (C4.5). As a benchmark, the accuracy values found by classifiers using the datasets with all features are used. Results of the experiments show that our MBO-based filter approach outperforms the other three approaches in terms of accuracy values. In the experiments, it is also observed that as a subset evaluator CFS outperforms PCFS and as a classifier C4.5 gets better results when compared to k-nearest neighbor and naive bayes.
Integrated Formal Tools for Software Architecture Smell Detection

The architecture smells are the poor design practices applied to the software architecture design. The smells in software architecture design can be cascaded to cause the issues in the system implementation and significantly affect the maintainability and reliability attribute of the software system. The prevention of architecture smells at the design phase can therefore improve the overall quality of the software system. This paper presents a framework that supports the detection of architecture smells based on the formalization of architecture design. Our modeling specification supports representing both structural and behavioral aspect of software architecture design; it allows the smells to be analyzed and detected with the provided tools. Our framework has been applied to seven architecture smells that violate different design principles. The evaluation has been conducted and the result shows that our detection approach gives accurate results and performs well on different size of models. With the proposed framework, other architecture smells can be defined and detected using the process and tools presented in this paper.
Information Security Assurance and the Role of Security Configuration Management: Substantive and Symbolic Perspectives

ABSTRACT This paper explores whether IT and audit professionals have different perceptions of the substantive and symbolic perspectives of information security assurance and the role of security configuration management (SCM) using a mixture of qualitative and quantitative approaches. Importance performance analysis (IPA) is utilized to identify differences in perceived importance and perceived controllability from both substantive and symbolic perspectives between these two professional groups. Our results suggest that SCM plays a vital role in maintaining consistency between the IT and audit professionals by enhancing their confidence in controlling and managing information security control sets. IPA also helps determine an information security program's strengths and weaknesses and supports remedial strategic actions more efficiently. Implications for both research and practice are discussed.
Opinion Extraction of Government Microblog Comments via BiLSTM-CRF Model

The comments of government social media contain a lot of netizens' opinions. To extract netizens' opinions quickly and accurately, this poster used BiLSTM-CRF model. To verify the effectiveness of the model, this poster selected a microblog of "China Police Online", crawled the comments and trained the model on the basis of manual annotation. Then the trained model was used to identify opinions of netizens from a large number of comments. The experiment showed that the model was effective and could accomplish the task of extracting opinions from comments of government microblog.
DigiMo - towards developing an emotional intelligent chatbot in Singapore

The paper is a work in progress report on the development of DigiMo, a chatbot with emotional intelligence. The chatbot development is based on a data collection and annotations of real dialogues between local Singaporeans expressing genuine emotions. The models were trained with cakechat, an open source sequence-to-sequence deep neural network. Perplexity measurements from automatic testing, as well as feedback from 6 expert evaluators confirmed the chatbot answers have high accuracy.
Efficient and Exact Multigraph Matching Search

A multigraph is modeled as a bag of graphs. Exact multigraph matching search aims to find all multigraphs that are the same as the query multigraphs from the data multigraph datasets. To the best of our knowledge, works regarding exact multigraph matching search have not been reported although they have a very wide range of application scenarios. In this article, we propose an efficient algorithm to solve the problem of exact multigraph matching search. We first propose a definition of exact multigraph matching and its Basic Method (BM), called BM, which has a considerable amount of graph isomorphism detection calculations and, thus, has very high computational complexity. Obviously, it is impractical to compare the query multigraph to each data multigraph in the multigraph datasets. To reduce the search space, multiple filtering conditions are proposed to obtain a candidate result set containing all the final results, including the cardinality filter, the vertex filter, the edge filter, the size filter, and the star filter. Then, each multigraph in the candidate result set is verified with the Improved BM (IBM) algorithm. Moreover, an offline and Multilayer Inverted Index (MII), named MII, is proposed to further accelerate the search process. Finally, we propose an Exact Multigraph Matching Search (EMMS) algorithm, based on the abovementioned technologies. We also analyze its time complexity. Extensive experiments on real datasets demonstrate the effectiveness and efficiency of the proposed algorithms.
Research on the Construction and Innovation of Lifelong Education System Under the Background of Big Data

The development of information technology provides technical support for the construction of lifelong education think tanks. It is an urgent need for lifelong education development to create a new type of life think tanks in combination with regional development. The advent of the era of big data has profoundly changed the social structure, social relations, social production and lifestyle, and people's way of thinking. The entire education ecosystem has been reshaped, information and knowledge are constantly changing, and continuous learning has become the basic needs of society members. In addition, China is in a period of social transformation, the quality of the population needs to be improved, the issue of equity in education needs to be solved, and the vitality of social innovation needs to be activated. The unique advantages of big data will help solve the above problems. Therefore, building a learning society for the whole people, lifelong and comprehensive learning is not only an effective way to promote China's sustainable development, but also an inevitable choice in the era of big data.
Three stage deep network for 3D human pose reconstruction by exploiting spatial and temporal data via its 2D pose

3D Human Pose Reconstruction (HPR) is a challenging task due to less availability of 3D ground truth data and projection ambiguity. To address these limitations, we propose a three-stage deep network having the workflow of 2D Human Pose Estimation (HPE) followed by 3D HPR; which utilizes the proposed Frame Specific Pose Estimation (FSPE), Multi-Stage Cascaded Feature Connection (MSCFC) and Feature Residual Connection (FRC) Sub-level Strategies. In the first stage, the FSPE concept with the MSCFC strategy has been used for 2D HPE. In the second stage, the basic deep learning concepts like convolution, batch normalization, ReLU, and dropout have been utilized with the FRC Strategy for spatial 3D reconstruction. In the last stage, LSTM deep architecture has been used for temporal refinement. The effectiveness of the technique has been demonstrated on MPII, Human3.6M, and HumanEva-I datasets. From the experiments, it has been observed that the proposed method gives competitive results to the recent state-of-the-art techniques.
Hardware-Based Parallelism Scheme for Image Steganography Speed up

Steganography is the art of concealing a document in another one. The main goal of steganography is hiding the existence of a message in a communication. Some steganography techniques are based on logical operations and calculations. The used algorithm for parallelism is an image steganography algorithm adapted from LSB substitution method in which secret image is embedded in a cover image. Since parallelism is the most used approach for speeding up the processes, in the proposed method, parallelization is used and is done in data level. In this paper different parallelism based implementations of a steganography method are presented. The parallelism schemes that are investigated in this research are (1) Parallelism using Graphical Processing Unit (GPU) and (2) parallelism using Network On a Chip (NOC). Experimental results show that for the chosen steganography algorithm, both NOC architecture and GPUs drastically decrease the execution time versus serial execution.
TransVis: Integrated Distant and Close Reading of Othello Translations

Studying variation among time-evolved translations is a valuable research area for cultural heritage. Understanding how and why translations vary reveals cultural, ideological, and even political influences on literature as well as author relations. In this article, we introduce a novel integrated visual application to support distant and close reading of a collection of Othello translations. We present a new interactive application that provides an alignment overview of all the translations and their correspondences in parallel with smooth zooming and panning capability to integrate distant and close reading within the same view. We provide a range of filtering and selection options to customize the alignment overview as well as focus on specific subsets. Selection and filtering are responsive to expert user preferences and update the analytical text metrics interactively. Also, we introduce a customized view for close reading which preserves the history of selections and the alignment overview state and enables backtracing and re-examining them. Finally, we present a new Term-Level Comparisons view (TLC) to compare and convey relative term weighting in the context of an alignment. Our visual design is guided by, used and evaluated by a domain expert specialist in German translations of Shakespeare.
Statistical analyses for meta-heuristic stochastic optimization algorithms

tutorial Share on Statistical analyses for meta-heuristic stochastic optimization algorithms: GECCO 2020 tutorial Authors: Tome Eftimov Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile , Peter Korošec Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 724–746https://doi.org/10.1145/3377929.3389881Published:08 July 2020Publication History 1citation129DownloadsMetricsTotal Citations1Total Downloads129Last 12 Months22Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Data Formats and Its Research Challenges in IoT: A Survey

In the current context, the increase in data generated by the heterogeneous IoT sensors results in several challenges to the IoT applications’ developers. One of the significant problems is handling nonstandardized zeta bytes of heterogeneous IoT data. Further, there is a lack of understanding of data generated by the sensors and incompatibility in representing the data. The objective of this survey paper is to introduce IoT sensors with their areas of applications, the different data formats, representing the data in an interchangeable format, and handling data as a stream. Further, this paper also presents challenges and research opening in IoT data.
A Multi-Objective Optimization Algorithm for Center-Based Clustering

Center-based clustering is a set of clustering problems that require finding a single element, a center, to represent an entire cluster. The algorithms that solve this type of problems are very efficient for clustering large and high-dimensional datasets. In this paper, we propose a similar heuristic used in Lloyd's algorithm to approximately solve (EMAX algorithm) a more robust variation of the k-means problem, namely the EMAX problem. Also, a new center-based clustering algorithm (SSO-C) is proposed, which is based on a swarm intelligence technique called Social Spider Optimization. This algorithm minimizes a multi-objective optimization function defined as a weighted combination of the objective functions of the k-means and EMAX problems. Also, an approximation algorithm for the discrete k-center problem is used as a local search strategy for initializing the population. Results of the experiments showed that SSO-C algorithm is suitable for finding maximum best values, however EMAX algorithm is better in finding median and mean values.
Quantifying DNN Model Robustness to the Real-World Threats

DNN models have suffered from adversarial example attacks, which lead to inconsistent prediction results. As opposed to the gradient-based attack, which assumes white-box access to the model by the attacker, we focus on more realistic input perturbations from the real-world and their actual impact on the model robustness without any presence of the attackers. In this work, we promote a standardized framework to quantify the robustness against real-world threats. It is composed of a set of safety properties associated with common violations, a group of metrics to measure the minimal perturbation that causes the offense, and various criteria that reflect different aspects of the model robustness. By revealing comparison results through this framework among 13 pre-trained ImageNet classifiers, three state-of-the-art object detectors, and three cloud-based content moderators, we deliver the status quo of the real-world model robustness. Beyond that, we provide robustness benchmarking datasets for the community.
Cockroach Swarm Optimization

In the chapter we focus on the basic working principles of the cockroach swarm optimization (CSO) algorithm cockroach swarm optimization. In order to provide a specific anchor for the later discussion, we describe the main algorithmic phases, and outline the original version in the pseudo-code form and provide its source-code in Matlab, and in C++ programming language. To demonstrate the algorithm’s operation step-by-step, we briefly present an application of CSO for numerical function optimization numerical function optimization. Finally, last section summarizes this chapter and contains concluding remarks.
An Efficient Multicore CPU Implementation for Convolution-Pooling Computation in CNNs

The main contribution of this paper is to present an efficient multicore CPU implementation of convolution-pooling computation in convolutional neural networks (CNNs). Since the convolution and pooling operations are performed several times in most CNNs, we propose a method to accelerate the operations. In our proposed multicore CPU implementation, we use convolution interchange to reduce the computational cost. Also, we implement convolution-pooling computation efficiently using DNNL that is an open source library for accelerating deep learning frameworks. The experimental results using Intel Corei9-7980XE CPU show that our proposed CPU implementation for the convolution-pooling is 1.42 to 2.82 times faster than the multiple convolution and then pooling by DNNL. Further, we incorporate the proposed implementation into TensorFlow to perform them as a TensorFloW operation. The incorporated implementation for the convolution-pooling is 1.18 to 2.42 times faster than straightforward implementation by primitives in TensorFlow.
Low dose 4D-CT super-resolution reconstruction via inter-plane motion estimation based on optical flow

In order to obtain the dynamic intuitive image of the patient's internal organs movement and minimize the potential risks of X-ray radiation at the same time, low dose four-dimensional computed tomography (4D-CT) has attracted a considerable interest in the high precision radiation therapy. But some susceptible artifacts, including device-dependent, image reconstruction times and patient's respiratory pattern, usually cause an inter-plane thickness that is much greater than intra-plane voxel resolutions. In this study, to estimate the respiratory motion and enhance the inter-plane resolution of multi-plane computed tomography (CT) images, a joint optimization framework was proposed using the combined local and global (CLG) variational optical flow and improved non-local iterative back projection (NLIBP). Note that, the premise of this work is that the anatomical information missing in one particular phase can be recovered from other phases in CT images. First, CLG variational optical flow model was constructed to estimate the respiratory motion (i.e., the optical flow fields) between different phases at the corresponding voxel positions, and then was solved by the fast alternating direction method of multipliers (ADMM). Secondly, the improved NLIBP algorithm characterized by non-local mean filter and image fusion strategy was employed to reconstruct high resolution (HR) inter-plane images based on the calculated motion fields. Finally, we explored different hyperparameter settings to achieve a good trade-off between the super resolution (SR) reconstruction performance and computational efficiency, and indicated the success of CLG variational optical flow method for estimating the displacement field between images. Experimental results on public lung 4D-CT datasets demonstrated that this proposed method is able to more effectively enhance texture structures while preserving edges, and outperforms current state-of-the-art methods both quantitatively and qualitatively.
Supporting the Exploration of Semantic Features in Academic Literature using Graph-based Visualizations

Literature search and recommendation systems have traditionally focused on improving recommendation accuracy through new algorithmic approaches. Less research has focused on the crucial task of visualizing the retrieved results to the user. Today, the most common visualization for literature search and recommendation systems remains the ranked list. However, this format exhibits several shortcomings, especially for academic literature. We present an alternative visual interface for exploring the results of an academic literature retrieval system using a force-directed graph layout. The interactive information visualization techniques we describe allow for a higher resolution search and discovery space tailored to the unique feature-based similarity present among academic literature. RecVis - the visual interface we propose - supports academics in exploring the scientific literature beyond textual similarity alone, since it enables the rapid identification of other forms of similarity, including the similarity of citations, figures, and mathematical expressions.
Cold Start Knowledge Tracing with Attentive Neural Turing Machine

Deep learning based knowledge tracing approaches achieve high accuracy in mastery prediction with pattern extraction on a large learning behavior data set. However, when there is little training data available, these approaches either fail to extract the key patterns or result in over fitting. Ideally, we aim to provide a similar learning experience to both the first group of learners, who interact with a new course or a new activity with little learning behavior data to provide personalized guidance, and the learners who interact with the course later. We propose a novel architecture, Attentive Neural Turing Machine (ANTM), to solve the cold start knowledge tracing problem. The proposed ANTM comprises an attentive controller module and differential reading and writing processes with extra memory bank. Accuracy (ACC) and Area Under Curve (AUC) measures are used for model performance comparison. Results show the proposed approach can learn fast and generalize well to unseen data. It achieves around 95% ACC trained with only 3 learners, while conventional deep learning based approaches achieve only 65% ACC with over prediction issues.