Reviewerly/exHarmony huggingface dataset:
A Survey on Image Steganography Techniques Using Least Significant Bit

Steganography is the technique in which the information is hidden within the objects so that the viewer cannot track it down and only the reserved recipient will be able to see it. The data can be concealed in different mediums such as text, audio and video files. Hiding the information in image or picture files is called image steganography. This steganography method helps in protecting the data from malicious attacks. The image chosen for steganography is known as cover image and the acquired image as stego image. A digital image can be described using pixel values, and those values will be modified using least significant bit (LSB) technique. To increase the security, various LSB techniques had been proposed. We made a comparison on various image steganography techniques based on the parameters like robustness, imperceptibility, capacity and security. In this paper, based on the comparisons we have suggested few image steganography algorithms.
A Hybrid Duo-Deep Learning and Best Features Based Framework for燗ction燫ecognition

Human Action Recognition (HAR) is a current research topic in the field of computer vision that is based on an important application known as video surveillance. Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning, but they still face many challenges such as similarity in various actions and redundant features. We proposed a framework for accurate human action recognition (HAR) based on deep learning and an improved features optimization algorithm in this paper. From deep learning feature extraction to feature classification, the proposed framework includes several critical steps. Before training fine-tuned deep learning models – MobileNet-V2 and Darknet53 – the original video frames are normalized. For feature extraction, pre-trained deep models are used, which are fused using the canonical correlation approach. Following that, an improved particle swarm optimization (IPSO)-based algorithm is used to select the best features. Following that, the selected features were used to classify actions using various classifiers. The experimental process was performed on six publicly available datasets such as KTH, UT-Interaction, UCF Sports, Hollywood, IXMAS, and UCF YouTube, which attained an accuracy of 98.3%, 98.9%, 99.8%, 99.6%, 98.6%, and 100%, respectively. In comparison with existing techniques, it is observed that the proposed framework achieved improved accuracy.
Model to Cope With Requirements Engineering Issues for Software Development Outsourcing

The anticipated benefits of Software Development Outsourcing (SDO) are not achieved in case of several projects because of the issues that emanate from Requirements Engineering (RE) process. This research work presents a Requirements Engineering Practices (REP) model to cope with the customarily occurring issues of the RE process for SDO. To formulate the model, five workshops have been conducted and Root Cause Analysis has been performed by considering 43 commonly occurring SDO RE process issues, and 147 RE practices to tackle the issues. To discover the root causes for commonly transpiring issues, 5- Whys technique has been employed. The relevant RE practices that can be used to deal with the root causes, have been endorsed by applying Brainstorming technique. For the 43 frequently occurring issues, 89 root causes have been discovered. Afterwards, 124 relevant RE practices have been recommended to eradicate the root causes and hence to address the corresponding issues. Thus, REP model postulates the root causes for commonly occurring issues of the SDO RE process, maps the root causes to the relevant best RE practices to address the corresponding issues. The model has been evaluated by an expert panel and evaluation results have been analysed through Inter-Rater Reliability analysis and Analysis of Means. The REP model supports the RE process for SDO by i). evading the adoption of random and inappropriate RE practices for dealing with the common issues of the process, ii) helping to attain the expected benefits of SDO.
Color Template Matching Based on Fuzzy Density Clustering for Vision Sensor Based Shoe Detection in Human-Robot Coexisting Environment

Fast-Match Algorithm is a fast and randomized template matching algorithm based on affine transformations. However, it is not suitable for color images as they are converted into greyscale images, and hence the color information is lost. An improved algorithm named Color Fast Affine Template Matching (CFAsT-Match), was thus proposed which utilizes all the three color channels (R, G, B). Our previous works have demonstrated how CFAsT-Match can be effectively used in shoe detection problem, wherein a robot can successfully track a target person from consecutive frames captured by an onboard camera. This paper proposes a new technique which extends the template matching algorithm using the Fuzzy Density based Clustering Algorithm instead of the traditional DBSCAN Algorithm. Three variants of Fuzzy based DBSCAN algorithms have been implemented here and a novel similarity measure called Fuzzy-based-colour-sum-of-absolute-differences (FCSAD) has been proposed. Experimental results firmly demonstrate that Fuzzy DBSCAN based CFAsT-Match algorithms can comfortably outperform recently proposed crisp DBSCAN based CFAsT-Match algorithms for shoe detection in human-robot coexisting environments.
Extending SysML with Refinement and Decomposition Mechanisms to Generate Event-B Specifications

SysML, dedicated to system design, provides graphical models. One of the strengths of these graphical models is that they can be validated by domain experts. However, the semantics of SysML is given in natural language, which does not allow formal and rigorous reasoning necessary for critical systems for which safety and security are major concerns. Our project aims at modeling and verifying high-level architectures of critical complex systems, in particular railways systems, that must be validated by domain experts. For that, we propose to combine SysML and the Event-B formal method. To master the complexity of such systems, Event-B provides refinement and decomposition mechanisms that allow a step-by-step design and make proofs easier to discharge. This paper proposes to extend SysML with safety relevant Event-B mechanisms that enable an automatic translation from SysML diagrams to Event-B specifications. We focus on diagrams that facilitate high-level architecture design, namely package, block-definition, state-transition and sequence diagrams.
Less Is More: Matched Wavelet Pooling-Based Light-Weight CNNs With Application to Image Classification

We are concerned with the development of effective training strategies for light-weight deep networks, which facilitates harnessing the power of deep learning on hand-held devices. A vast literature has focused on enhancing the performance of deep convolution models (CNNs) by introducing changes in the model architecture and/or developing new training procedures. Recent literature has witnessed a growing focus on pooling layers, one example for this is wavelet pooling. Pooling is often adopted by CNNs to enlarge the receptive field, yet traditional pooling methods cause information loss which undermines further operations such as feature extraction. By capitalizing on spectral information, wavelet pooling (WP) has addressed this drawback in neighborhood pooling, and we have demonstrated its positive impact on light-weight CNN architectures such as MobileNets in previous research. In this paper, we report the following significant observation: Including all sub-bands of the wavelet decomposition does not necessarily yield higher accuracies than using certain sub- set of sub-bands, i.e., less is more. By identifying which sub-bands to be included in the pooling process for every image during training and testing, we propose a new training and inference procedures, namely, Matched Wavelet Pooling (MWP), for light-weight CNN architectures, and in particular, MobileNets. As an example, the image classification application is considered. On two widely adopted datasets, the proposed MWP algorithm consistently outperform previous pooling methods and attained an average of 20% accuracy increase compared to the recent literature.
Understanding the Impact of Chatbots on Purchase Intention

This paper aims to understand the use of chatbots as digital assistants in e-commerce sites. We investigate the impact of several chatbot attributes, specifically perceived usefulness, perceived ease of use, and trust on consumer attitude and satisfaction with the chatbot, and the effect of these two outcomes on purchase intention. A survey was carried out with 298 valid respondents to test the proposed model. The results indicate significant positive effects of both perceived usefulness and trust on both attitude towards chatbots and satisfaction. The findings did not reveal any significant impact of perceived ease of use on attitude and satisfaction, furthermore, these two outcomes did not significantly influence purchase intention calling for further research to help explain behavioral outcomes towards these relatively new conversational agents.
Adaptive Threshold-Based Database Preparation Method for Handwritten Image Classification

Problem-specific, well classified database is primary and most important requirement of all machine learning-based systems. Hand-written character classification and recognition system is also no exception to this. In terms of features the good database must have less intra-class variance and high inter-class variance. However, extracting unique features from digital handwritten character image (HCI) is one of the most challenging task. High variation in the writing style of the writers and similarity in the feature value between multiple classes of characters are the primary hurdles. Unfortunately this scenario leads to high false acceptance rate which interns results drastic decrease in accuracy of the classification and recognition system. As far as handwritten character classification and recognition system is concerned to overcome this hurdle we have developed a simple but effective adaptive threshold-based database preparation method. In the proposed method, Adaptive Threshold Value ( $$AT_v$$ ) is calculated based on the similarity score (SS) of an existing HCI images in the respective class. If the threshold value of a new sample is in acceptable range then it is added into the feature map of respective class. To verify the efficiency and accuracy of the proposed method, the series of experiments are conducted on two standard datasets (MNIST and VDM). For the experimentation high-level features extracted using Deep neural network (DNN) based architecture and the proposed adaptive threshold-based method is applied to place HCI to correct class. Experimental results state that, the proposed adaptive thresholding-based method produces promising results and reduced the false acceptance rate.
Independent Channel Residual Convolutional Network for Gunshot Detection

The main purpose of this work is to propose a robust approach for dangerous sound events detection (e.g.gunshots) to improve recent surveillance systems.Despite the fact that the detection and classification of different sound events has a long history in signal processing, the analysis of environmental sounds is still challenging.The most recent works aim to prefer the time-frequency 2-D representation of sound as input to feed convolutional neural networks.This paper includes an analysis of known architectures as well as a newly proposed Independent Channel Residual Convolutional Network architecture based on standard residual blocks.Our approach consists of processing three different types of features in the individual channels.The UrbanSound8k and the Free Firearm Sound Library audio datasets are used for training and testing data generation, achieving a 98 % F1 score.The model was also evaluated in the wild using manually annotated movie audio track, achieving a 44 % F1 score, which is not too high but still better than other state-of-the-art techniques.
Out-of-Distribution Detection Based on Feature Fusion in Neural Network Classifier Pre-Trained by PEDCC-Loss

Out-of-distribution (OOD) detection is related to the security and stability of deep learning models deployed in the real world.The existing OOD detection algorithms based on the neural network normally use a single scoring function to detect out-of-distribution examples, which start from the posterior probability and do not fully utilize the information of the pre-trained model.In this paper, based on our previous PEDCC-based work, feature fusion is explored in OOD detection to take maximum advantage of the pre-trained classifier features.Our improved method adopts a two-stage training approach, in which multiple OOD detection features of the first-stage neural network classifier are extracted as the input of the second-stage training.In addition, we propose the stop-near-saturation method, which can help the OOD detection algorithm find optimal network parameters without accessing OOD data.Extensive experiments on several public datasets and classification networks show that compared with other existing methods, this method has better OOD detection performance, and maintains the low computational complexity of the original PEDCC-based method.
Bidirectional Sampling-Based Motion Planning Without Two-Point Boundary Value Solution

Bidirectional path and motion planning approaches decrease planning time, on average, compared to their unidirectional counterparts. In single-query feasible motion planning, using bidirectional search to find a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">continuous</i> motion plan requires an edge connection between the forward search tree and the reverse search tree. Such a tree–tree connection requires solving a two-point boundary value problem (BVP). However, obtaining a closed-form two-point BVP solution can be difficult or impossible for many systems. While numerical methods can provide a reasonable solution in many cases, they are often computationally expensive, numerically unstable, or sensitive (to an initial guess) for the purposes of single-query sampling-based motion planning. To overcome this challenge, we present a novel bidirectional search strategy that does not require solving the two-point BVP. Instead of connecting the forward and reverse trees directly, the reverse tree’s cost information is used as a guiding heuristic for forward search. This enables the forward search to quickly grow down the reverse tree—converging to a fully feasible solution <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> a direct tree–tree connection and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> the solution to a two-point BVP. In this article, we propose two algorithms that use this strategy for single-query feasible motion planning for various dynamical systems, performing experiments in both simulation and hardware testbeds. We find that these algorithms perform better than or comparable to the existing state-of-the-art methods with respect to quickly finding an initial feasible solution.
CRAM: Code Recommendation With Programming Context Based on Self-Attention Mechanism

Code recommendation with programming context is to use the contextual code surrounding the missing code to automatically find that which of the code snippets would be useful to assist in the completion of the program. In this way, the developers need not take time to formulate explicit queries or write descriptions. Existing work only treats code as textual documents and use information retrieval techniques to retrieve relevant code snippets, which is difficult to capture the semantics of code adequately. The self-attention mechanism have achieved promising progress in various natural language processing tasks, especially for extracting deep semantic information from long sequences. Inspired from this, we propose a novel code recommendation with programming context based on self-attention mechanism (CRAM). The proposed approach first builds a small-scale candidate set from codebase. Then, it utilizes self-attention networks in the abstract syntax tree to capture the deep semantics of code, and finally recommend the relevant code to developers. We conduct several experiments to evaluate our approach in a large-scale codebase containing 741 148 code snippets. The experimental results show that CRAM can effectively recommend code and outperforms related work in recall, precision, and NDCG.
Lifelong Learning of Reactive Systems in Practice

This paper presents our lifelong learning framework for continuous quality control. The framework integrates automata learning, model checking, and monitoring into a six-phase continuous improvement cycle which is designed to capture entire system life-cycles. The technical backbone of our framework is ALEX, an open source, web-based learning tool for defining adequate test blocks, as well as for serving as test execution environment and as platform for learning Mealy machines. Keys to the industrial success of our framework are a) the guarantee that the level of quality can only increase when using our framework, b) the continuous improvement of originally customer-provided (regression) test suites, c) the maintenance of achieved quality levels even across system changes, and d) the visualization of system changes using automatically generated difference trees and difference automata. All this is illustrated using an adaptive cruise control system (ACC) that has been implemented in a one year students project.
LgNet: A Local-Global Network for Action Recognition and Beyond

This work addresses the task of action recognition in video sequences. In real world applications, this task is quite challenging due to the complex background of video content, the similarities between different types of actions, the dependence on a large amount of annotated data, and so on. Most of the existing methods fail to distinguish similar actions with the same static appearance and motion pattern. We attempt to address this issue from the perspective of a local-global view, considering videos as combinations of a set of action units (local semantic information) and their relations along temporal dimension (global relation information). To achieve this end, we propose a novel Local-global Networks (LgNet) to enhance recognition of similar action. Besides, we propose an end-to-end training method to decrease the reliance on annotated data. It combines self-supervised learning and supervised learning, which not only enables the model to learn video representations from a large number unannotated data but also avoids subsequent finetuning. The proposed training method can be flexibly equipped to a wide array of vision tasks. Experiments on several benchmark datasets show that our proposed model and training method achieve state-of-the-art performance.
Fine-tuning pre-trained models for Automatic Speech Recognition, experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)

This is a report on results obtained in the development of speech recognition tools intended to support linguistic documentation efforts.The test case is an extensive fieldwork corpus of Japhug, an endangered language of the Trans-Himalayan (Sino-Tibetan) family.The goal is to reduce the transcription workload of field linguists.The method used is a deep learning approach based on the language-specific tuning of a generic pre-trained representation model, XLS-R, using a Transformer architecture.We note difficulties in implementation, in terms of learning stability.But this approach brings significant improvements nonetheless.The quality of phonemic transcription is improved over earlier experiments; and most significantly, the new approach allows for reaching the stage of automatic word recognition.Subjective evaluation of the tool by the author of the training data confirms the usefulness of this approach.
VID2XML: Automatic Extraction of a Complete XML Data From Mobile Programming Screencasts

Developers often refer to video-hosting online platforms to find screencasts that provide a step-by-step guide to help them solve a programming task at hand or learn a new concept. More specifically, developers search for resources that help them design and implement effective mobile graphical user interfaces (GUI) using <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XML</monospace> . Although mobile programming screencasts contain a vast amount of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XML</monospace> data at developers’ disposal, they cannot be easily found and copied-pasted due to the image nature of videos. Given that the most common task developers perform online is copy-pasting, mobile programming screencasts must support that and be complemented with <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XML</monospace> data in a textual format. To overcome this challenge and aid developers, this paper presents <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vid2XML</monospace> , which is a three-phase approach that leverages both visual and textual information of video frames to locate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XML</monospace> region in video frames, locate the currently opened file, and extract <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XML</monospace> data for each file presented in video frames. We evaluated each phase of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vid2XML</monospace> in a comprehensive empirical evaluation on videos collected from YouTube. The results reveal that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vid2XML</monospace> is able to accurately (i) locate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XML</monospace> regions, outperforming four previous work, (ii) locate the bounding box of the selected file, and (iii) extract, fix, and merge <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XML</monospace> data for each file opened/created in a video.