Reviewerly/exHarmony huggingface dataset:
A Text Localization Method Based on Weak Supervision

Recently, numerous deep learning based scene text detection methods have achieved promising performances in different text detecting tasks. Most of these methods are trained in a supervised way, which requires a large amount of annotated data. In this paper, we explore a weakly supervised method to locate text regions in scene images. We propose a fully convolutional network (FCN) architecture to implement binary classification. The training data we used do not need any text location annotation, we only need to divide the training data into two categories according to whether it contains text or not. We can obtain the text localization map (TLM) directly from the last convolutional layer. By setting a fixed threshold, the TLM is converted to a mask map. Then the connected component analysis and the text proposals method based on Maximally Stable Extremal Regions (MSERs) are used to get the text region bounding boxes. We conduct comprehensive experiments on standard text datasets. The results show that our text localization method achieves comparable recall performance with other methods and has more stable property.
Efficient Computer Forensic Analysis Using Machine Learning Approaches

In this digital era, the number of Cybercrimes is increasing that has resulted in increased number of pending cybercrimes cases such as artifacts as a malware, hacking and cyber fraud or e-harassment. In order to deal with these cases, digital forensics must include the concrete law enforcement in the court of law. In digital forensics, it is challenging task to detect reliable evidence because of worldwide use and advancements in digital communication technologies.Common approaches such as file signature analysis and the data carving can be done using the forensics tools, however, digital evidence examiners are keen to find the relevant data which helps in finding the truth behind the case. To reduce the examination time in the data examination or analysis process, this paper explores the role of unsupervised pattern recognition to identify the notable artefact. The Self-Organising Map (SOM) is used to automatically cluster notable artefacts. In this work, four cases are presented to demonstrate the use of SOM in examining the digital data saved in a CSV format. Multiple SOMs are created including Extension Mismatch SOM that represents the intentional changes done on the default extension of the file in order to hide it from the forensic examiner. Other types of SOM are created for the EXIF Metadata (i.e. MAC attributes). USB Device Attached (Device Make, Device Model, Device ID, Date/Time, Source File, Tags).
An Empirical Study to Detect the Collision Rate in Similarity Hashing Algorithm Using MD5

Similarity Hashing (SimHash) is a widely used locality-sensitive hashing algorithm employed in the detection of similarity, in large-scale data processing, including plagiarism detection and near-duplicate web document detection. Collision resistance is a crucial property of cryptographic hash algorithms that are used to verify the message integrity in internet security applications. A hash function is said to be collision-resistant if it is hard to find two different inputs that hash to the same output. In this paper, we present an empirical study to facilitate the detection of collision rate when SimHash is employed to check the integrity of the message. The analysis was performed using bit sequences with length varying from 2 to 32 and Message Digest 5 (MD5) as the internal hash function. Furthermore, to enable faster collision detection with more significant speedup and efficient space utilization, we parallelized the process using a distributed data-parallel approach with synchronous computation and optimum load balancing. Collision detection is desirable, owing to its applicability in digital signature systems, proof-of-work systems, and distributed content systems. Our empirical study revealed a collision rate of 0% to 0.048% in SimHash (with MD5) with the variation in the length of the bit sequence.
A Lexico-Syntactic-Semantic Approach to Recognizing Textual Entailment

Given two textual fragments, called a text and a hypothesis, respectively, recognizing textual entailment (RTE) is a task of automatically deciding whether the meaning of the second fragment (hypothesis) logically follows from the meaning of the first fragment (text). The chapter presents a method for RTE based on lexical similarity, dependency relations, and semantic similarity. In this method, called LSS-RTE, each of the two fragments is converted to a dependency graph, and the two obtained graph structures are compared using dependency triple matching rules, which have been compiled after a thorough and detailed analysis of various RTE development datasets. Experimental results show 60.5%, 64.4%, 62.8%, and 61.5% accuracy on the well-known RTE1, RTE2, RTE3, and RTE4 datasets, respectively, for the two-way classification task and 54.3% accuracy for three-way classification task on the RTE4 dataset.
Development of MMT Analyzer for ATSC 3.0

With the increasing volume of video streaming on the Internet, whether through on-demand or real-time services, the need arose for the development of transport mechanisms and protocols with greater flexibility and control of the quality of the transmitted service. To meet this new issue and ensure the evolution of broadcast television services, MPEG Media Transport (MMT), was developed to transport the hybrid delivery of multimedia streams in broadcast and broadband networks. This paper proposes the implementation of a specialized MMT Protocol (MMTP) analyzer for Advanced Television System Committee (ATSC) 3.0 developed in C language and integrated as a plug-in for the Open-Source Wireshark software.
Development of English Learning System Using Gamification Approach

English has been an international language for communication amongst global citizens. A good English ability is highly required for Indonesians to be able to communicate with other people around the world. However, learners in Indonesia frequently encounter a myriad of challenges in learning English, including their reluctance to study new English vocabulary, worry of making mistakes, and feeling bored with the conventional teaching methods. English learning system using gamification approach serves as one of the solutions to improve learners' motivation in learning English. The use of gamification concept integrates several elements embedded in game applications, such as point collection, achievement, ranking, which are wrapped as English learning materials. Website-based learning system can be accessed without any place and time constraint, so that learners can study English autonomously and flexibly. This type of English learning system is developed using a software development model `Waterfall' with some certain procedures, including needs analysis, designing, implementation, and system test. This learning system has been examined using validation test using Black box. Based on a sample of the conducted test, it shows that the functional system has been running as designed and the result of the tests showed a 100% validity level. Based on the questionnaire, gamification elements could increase student motivation.
Multi-Task Deep Learning Design and Training Tool for Unified Visual Driving Scene Understanding

Visual driving scene perception systems have been gained popularity among the autonomous driving research community following the advent of deep learning technology. Moreover, the multi-task deep learning model has been an important tool with respect to unifying the tasks performed in a driving scene perception system, such as scene classification, object detection, segmentation, depth estimation. In this paper, we introduce our developed multi-task deep-learning model design and training tool, for unified road scene perception model. Additionally, we also propose a sequential auxiliary multi-task training method that can train a multi-task model, using different datasets for each tasks. Finally, we present a unified road segmentation and depth estimation model, based on multi-task deep learning, to verify the efficiency and feasibility of our developed tool. Experimental results for KITTI datasets show that our tool-based unified road segmentation and depth estimation model can successfully segment the driving road and estimate its depth.
Intelligent document‐filling system on mobile devices by document classification and electronization

Abstract Both of an automatic classification method for original documents based on image feature and a layout analysis method based on rule hypothesis tree are proposed. Then an intelligent document‐filling system by electronizing the original documents, which can be applied to cellphones and pads is designed. When users are filling documents online, information can be automatically input to the financial information system merely by taking photos of the original documents. By this means can not only save time but also ensure the accuracy between the data online and that on the original documents. Experiments show that the accuracy of document classification is 88.38%, the accuracy of document‐filling is 87.22%, and it takes 5.042 seconds dealing with per document. The system can be applied to financial, government, libraries, electric power, enterprises and many other industries, which has high economic and application value.
Inferential statistics in quantitative data analysis

This chapter discusses the feasibility of investigating cause–effect relations, the traditional basis of inferential statistics. We also specifically advocate the generalized (mixed effects) regression framework, including linear mixed effects regression models (mixed models), multinomial logistic regression and generalized additive (mixed) models (GAM[M]s), in which the time series data, treated as single-cluster data, become the target of inferential statistics. All of these represent important statistical developments of the last 45 years and provide an extremely useful set of tools for analyzing data within the field of applied linguistics. Our goal is to focus on what these models are, how they work and why/when applied linguists should use them. We close with a critical discussion of limitations of inferential statistics and desiderata for future research, referring to Chapter 8, “Advancing quantitative research methods”.
Adaptive Teacher-and-Student Model for Heterogeneous Domain Adaptation

In heterogeneous domain adaptation (HDA), since the feature spaces of the source and target domains are different, knowledge transfer from the source to the target domain is really challenging. How to align the different feature spaces and then adaptively transfer the related knowledge is critical for HDA. In this paper, we develop an adaptive teacher-and-student model for heterogeneous domain adaptation (AtsHDA). In AtsHDA, the source domain as a teacher and the target domain as a student are aligned or co-adapted to each other first, so that their correlation can be maximized. Then the target domain adaptively learns from the source domain. Specifically, there is a balance between the learning by the target domain itself and the instruction from the source domain. That is, when the guidance from the source domain is helpful for learning, the learning of target classifier emphasizes the instruction of source knowledge, and considers its own knowledge more, otherwise. Further, an ensemble method is designed to decide such a balance. Finally, empirical results show that AtsHDA can achieve competitive results compared with the state-of-arts.
Parallel implementation for ECCP based on Montgomery ladder algorithm

Abstract An Elliptic Curve Crypto-Processor (ECCP) is a favourite public-key cryptosystem. It is used for embedded systems due to its small key size and its high security arithmetic unit. It is applied in constrained devices which often run on batteries and have limited processing, storage capabilities and low power. A finite field polynomial multiplier takes the most implementation effort of an ECCP because it is the most consuming operation for time and area. So, it is preferable to optimize this operation especially for light devices where the small area is needed. This research introduces a hardware design for parallel ECCP binary implementation that is based on Montgomery ladder algorithm. This implementation is targeted for GF(2 163 ) and GF(2 409 ) where the executed time are 2.9 μs and 29 μs respectively. The implementation is performed on Xilinx ISE Virtex6.
A strategy based in ICT for inclusion of a student with multiple disability into regular education

The purpose of this work is to share innovative and creative activities based on the use of new technologies, which translate into new, more efficient educational practices for educational rehabilitation; meaning the inclusion of students with disabilities in regular academic programs.
An Iterative Approach to Super-Resolution using Multiple Low-Resolution Images

Keeping in view the increasing requirements of super resolution in a wide range of applications, this paper adds to the existing literature of alternative algorithmic approach to develop a high resolution image from multiple low resolution images. The paper, after a review of the existing methods in super resolution, presents an iterative approach using multiple low-resolution images building on top of established tools like New Edge Directed Interpolation, through a novel approach. The approach tries to discover higher frequencies from a multiplicity of data samples available, and preserves these edges across iterations. The proposed method has been tested on images of a range of complexities, including on satellite images, and the results are promising.
Privacy-enhancing Cloud Computing Solution for Big Data

Variety of users cyber data are daily collected by enterprises and governments. These data are processed in order to improve users lifestyle, e.g. improving healthcare and optimizing traffic. Cloud computing is often the only possible strategy which allows processing these big amount of information due to the associated costs. However, data owners remain reluctant to outsource their data to a cloud which may read, use or even sell these data leading on user privacy leakage. These threats have been already observed by EU organization and reflected in many regulations and strategies.This paper present data splitting techniques as one of the most promising technology for privacy-preserving computation in a cloud. At first, we propose a new protocol for secure scalar product resistant against honest-but-curious colluding cloud. Then, we compared our new proposal with two most efficient state-of-the-art schemes. These schemes are not secure in a colluding security model. While using higher security level of our proposal, we achieved comparable performance. The protocols were implemented in real environment based on Amazon Web Services and, then, an FPGA processor cards implementation is considered in order to speed up the computations.
Comparison of SCRUM and KANBAN in the Learning Management System implementation process

Paper represents comparative analysis of SCRUM and Kanban on the example of building a Learning Management System. According to calculations Kanban gave better results when time is not limiting factor and the project would end two months earlier. On the other hand, when it is necessary to carry out detailed monitoring and focus on time Scrum has yielded slightly better results. These results provide guidance that can help decide the method for a particular project.
Data Clustering by Nature-inspired Algorithms and Chaotic Maps

A data clustering procedure based on the usage of nature inspired (NI) algorithms combined with chaotic sequences is proposed. Four NI algorithms: Particle Swarm Optimization (PSO), Multi Swarm Optimization (MSO), Cuckoo Search Algorithm (CSA) and Black Hole Algorithm (BHA) where combined with nine chaotic maps. The procedure was validated using a medical database and the results were compared to those obtained by the k-means algorithm and also other clustering methods proposed in literature. The chaotic versions of PSO and MSO offer near optimal results faster, but the chaotic version of CSA offers more precise solutions after increasing the number of iterations.
FCDM: A Methodology Based on Sensor Pattern Noise Fingerprinting for Fast Confidence Detection to Adversarial Attacks

Deep neural networks (DNNs) have shown phenomenal success in many real-world applications. However, a concerning weakness of DNNs is their vulnerability to adversarial attacks. Although there exist some methods to detect adversarial attacks, they often suffer from high computational cost and constraints on certain types of attacks, and ignore external features that could aid during attack detection. In this article, we propose fast confidence detection method (FCDM), an innovative method for fast confidence detection of adversarial attacks based on measuring the integrity of sensor pattern noise fingerprinting embedded in input examples. We note that the existing adversarial detectors are often designed as a binary classifier to differentiate clean or adversarial examples. However, the detection of adversarial examples can be much more complicated than such a scenario. Our key insight is that the confidence level of detecting an input sample as an adversarial example is a more useful info for the system to properly take an action to resist potential attacks. The experimental results show that FCDM is capable to give a confidence distribution model of the most popular adversarial attacks. And, using the confidence distribution model, FCDM can quickly determine the confidence level of the input sample. Based on different properties of the confidence distribution models associated with these adversarial attacks, FCDM can provide early attack warning including even the possible attack types of the adversarial attack examples. FCDM also has the following advantages: 1) it is effective for both a white-box attack and black-box attack; 2) it do not depend on the class of adversarial attacks and can be used as both known attack defense and unknown attack defense; and 3) it does not need to know the details of the DNN model and does not affect the functionality of the DNN. Since fast confidence detection method (FCDM) is a computationally heavy task, we propose an FPGA-based accelerator based on a series of optimization techniques, such as the quantization, data reuse and operation replacement, etc. We implement our method on an FPGA platform and achieve a system clock frequency of 279 MHz with a power consumption of the only 0.7626 W. Moreover, in the real system performance test, we obtain a high efficiency of 29.740 IPS/W and a low latency of just 44.1 ms with very marginal accuracy loss.
An AI driven Genomic Profiling System and Secure Data Sharing using DLT for cancer patients

In the pharmacogenomic and theranostic approach of treating melanoma, a continuous monitoring of the disease and the mutations associated with the disease is essential. Such a monitoring system has been designed and developed based upon the concept `One-shot learning', a machine learning technique adapted to work with a relatively small number of training images. The samples have been exhaustively studied through genomics, epigenomics, metagenomics and environmental genomics, finding the genetic signature behind proneness of these attributes. The mutations CDK4, CDKN2A, BRAF and KIT have been included in the analysis. The prediction accuracy of the machine is found to substantially high suggesting the device for the theranostic and pharmacogenomic strategies of controlling melanoma. A Distributed Ledger Technology (DLT) based system has been proposed for real time data sharing, training and analysis enabling hospitals and research labs to communicate with each other and conduct a cost-effective diagnostic workflow.
Non-bipolar Evaluation and Visualization of Online Text Reviews

Research in the field of Sentiment analysis is blowing day by day exponentially in the recent past and currently it is the much-acknowledged discipline. This is due to the outpouring users of internet and thus generated effusion of data in the form of reviews, comments, blogs, communications, etc. These are treasure trove of information needed to comprehend the fact-based opinions of diverse consumers. Elucidating those contents is propitious to various stakeholders as right sentiments can be gathered through it. But trading with such ginormous unstructured inputs embodies diverse challenges. These challenges trigger the raise in desideratum and thus summon the need to instigate pioneering logics to meet the same and to optimize the existing approaches. Sentiment analysis and opinion mining terms are used equivalently. This existed from ancient days. But the modes employed to perform this were different like in the form of surveys, elections, etc. It was carried out either by individuals or a group or an organization. Individuals used to consult friends or family before changing themselves from strangers to customers of any product or audience of any events. Groups or Organizations used to conduct surveys. But gradually most of the decisions were to be data-driven and thus appropriate decisions were to be made in short-run to bridge the gap between businesses and consumers and to gain the competitive advantage in market. So now this process was to be automated for mutual benefits by exploiting the escalation in technology. Various approaches are emerging every day to strengthen and ease the process. In the same direction, this paper addresses how sentiment analysis of online reviews can serve as suggestions to strangers and help them better in gaining additional insights to make well-judged decisions or choice by visualizing the analysis in the graph form.
Predictive use cases of CNN based multi label classification for programming languages

Multi-label classification refers to classifying data into two or more, usually independent, set of output labels. This approach is suitable for deep learning applications in multi-faceted subjects like software development, where it is desirable to yield multiple outcomes. This paper proposes a CNN based deep learning model on public datasets of programming language platforms like GitHub and Stack Overflow to infer intelligence to aid decision making process regarding the choice of programming languages for a given software development requirement. For this research, we've developed a training model with pre-trained vector embedding layer and multi-channel one dimensional CNN layers, followed by Multi Layer Perceptron layer to provide multi label outputs. We have managed to achieve 92%, 98% accuracy and 22%, 4% loss with our two experimental setups for Github and Stack Overflow respectively. The model performed well when tested on software development requirements. Stack Overflow dataset was observed to be noticeably better performing than the Github dataset for actual software development use cases. The implications of these models were also found to be good for trend prediction and source code use cases.