Reviewerly/exHarmony huggingface dataset:
Building a Security Framework for Smart Cities: A Case Study from UAE

Smart cities have evolved in the last years, leading the cities to implement initiatives related to technical aspects to improve quality of life. City-rankings have become a central tool for assessing the attractiveness of urban regions. The development of smart cities, however, is not without risk. Cities and citizens are putting more and more responsibilities in urban systems. Hence, all key stakeholders should provide an effective safety and security response to any situation affecting their citizens or organizations. Special attention should be paid with respect to development of services aimed at reducing cybercrime. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. The paper provides an overview of security aspects of a cyber security in smart cities. It starts with exploration of various definitions, threats, and risks in cyber security as well as threats from environments both internal and external and how these threats are currently mitigated with tools, processes and technologies as smart cities utilizes IoT with big data. Presently, most of the data is transmitted and collected online. Hackers usually try to exploit the vulnerabilities using various tools in order to know more about the customers and then misuse the customer's information. Enterprises, on the other hand, continue to collect more information in order to improve services, infrastructure, and security. They collect tons of data from customers in order to complete their requirements for the different services they provide. The main concern with the collected data is that it can be vulnerable to misuse by hackers. Building a security framework based on concepts and repositories of big data and leveraging on the intelligence of predictive analytics can help build a security system that can counter these threats and help to guard from these risk and threats to a large extent. Various concepts, applications, and technologies interact to cover every aspect of the digital citizen's life. Understanding this privacy-challenging environment is the basic requirement for the development of effective protection mechanisms. Thus, the paper aims to address sentiments on cyber security technologies and cybercrime awareness in order to come up with recommendations for innovative solutions. A survey has been conducted and the findings have been analyzed of a case study to come up with recommendations for building a conceptualizing security framework for smart cities. The survey is conducted in the United Arab Emirates (UAE), one of the most advanced countries in the MENA region, who is applying smart city concepts.
Deductive Synthesis of Min-Max-Sort Using Multisets in Theorema

We demonstrate the deductive synthesis of the Min-Max-Sort algorithm using multisets in the frame of the Theorema system. Starting from the logical specification of the sorting function (input and output conditions), we show how to construct a synthesis conjecture, from whose proof the algorithm can be constructed. For the proof we choose those inference methods and induction principles such that the synthesized algorithm consists of selecting at each step the minimum and the maximum of the list, and moving them at the ends of the list. We also show how to add, on a logical basis, a specific flag in order to stop the recursion as soon as the list is already sorted. During the main proof new conjectures are produced for the synthesis of auxiliary algorithms, and this process repeats in a cascading fashion until all necessary algorithms are produced. Our proof techniques, which are in natural style, include a novel approach using multisets and the use of cover sets for realizing Noetherian induction. The later has the advantage that the concrete induction hypotheses are created dynamically during the proof of the corresponding induction conclusion, thus no concrete induction principle or algorithm scheme is needed in advance. The synthesis mechanism is implemented in the frame of the Theorema system which allows the construction of mathematical theories, proving in natural style, and computing with the synthesized algorithms.
Plane Space Representation in Context of Mode-Based Symmetry Plane Detection

This paper describes various representations of the space of planes. The main focus is on the plane space representation in the symmetry plane detection in $$E^3$$ where many candidate planes for many pairs of points of the given object are created and then the most often candidate is found as a mode in the candidate space, so-called Mode-based approach. The result depends on the representation used in the mode-seeking process. The most important aspect is how well distances in the space correspond to similarities of the actual planes with respect to the input object. So, we describe various usable distance functions and compare them both theoretically and practically. The results suggest that, when using the Mode-based approach, representing planes by reflection transformations is the best way but other simpler representations are applicable as well. On the other hand, representations using 3D dual spaces are not very appropriate. Furthermore, we introduce a novel way of representing the reflection transformations using dual quaternions.
Method for Image Copyright Protection Based on Construction of Highly Robust Watermarks

In this paper, a new approach to multimedia data watermarking is proposed. The key idea of the method is to embed a specially constructed protective information instead of a simple bitmap or a bit sequence. A two-dimensional complex spectrum is constructed on the basis of the binary sequence, which serves as a primary watermark used when verification. Via discrete Fourier transform a highly robust watermark in the form of a noise-like raster image is calculated and embedded into the carrier data. The aim of the experimental research is to analyze the applicability of the method in the task of copyright protection for raster images. It is shown that the proposed noise-like watermarks can be imperceptibly embedded into the spatial domain of the protected image using the strategy of additive watermarking. Furthermore, the study on robustness has shown that resulting method demonstrates clear advantages over existing analogues.
Abstractive Text Summarization on Google Search Results

The Internet has become the forefront of our lives, technological advancements help people in an efficient and better way. The capitalists, educators, analysts all share a lot of content on the web so that it becomes available to users and helps them to connect with people. However, there is an issue in presenting the data to the users so that they can get the best information with the least effort. The search engine only helps to filter out irrelevant documents, leaving difficult tasks such as reading and summarizing to ourselves.Our paper proposes a model that will set an interface for web scraping using Python, The SSN techniques will prepare an abstractive summary from the data appearing in the search result links. This will allow people to access the best information with minimal efforts. The time-consuming and tedious tasks such as retrieving and summarizing are left to the computers. For all the impatient users, who don’t have time to read the text carefully, such a system can be very useful to them.
Analysis of iOS SQLite Schema Evolution for Updating Forensic Data Extraction Tools

Files in the backup of iOS devices can be a potential source of evidentiary data. Particularly, the iOS backup (obtained through a logical acquisition technique) is widely used by many forensic tools to sift through the data. A significant challenge faced by several forensic tool developers is the changes in the data organization of the iOS backup. This is due to the fact that the iOS operating system is frequently updated by Apple Inc. Many iOS application developers release periodical updates to iOS mobile applications. Both these reasons can cause significant changes in the way user data gets stored in the iOS backup files. Moreover, approximately once every couple years, there could be a major iOS release which can cause the reorganization of files and folders in the iOS backup. Directories in the iOS backup contain SQLite databases, plist files, XML files, text files, and media files. Android/iOS devices generally use SQLite databases since it is a lightweight database. Our focus in this paper is to analyze the SQLite schema evolution specific to iOS and assist forensic tool developers in keeping their tools compatible with the latest iOS version. Our recommendations for updating the forensic data extraction tools is based on the observation of schema changes found in successive iOS versions.
Journalistic transparency using CRFs to identify the reporter of newspaper articles in Spanish

Journalistic transparency rises as a key issue against the lack of credibility to which journalists are exposed, as well as the media manipulators and fake news providers. With the use of Natural Language Processing (NLP) and Machine Learning (ML), it is possible to automate the extraction of information from newspaper articles to know what the sources of information are to verify their veracity. Along with this article, we present the application of Conditional Random Fields (CRFs) for a specific type of Entity Recognition (ER) task, namely, to identify what we have called the “reporter” in newspaper articles, i.e., who or what is the provider of the information. Thus, we have created a labelled corpus for the Spanish language and trained and analysed several CRFs models with a set of specific features. The obtained results suppose a solid baseline for our goal.
Recognition of Hand written and Printed Text of Cursive Writing Utilizing Optical Character Recognition

Optical character recognition is one of the capabilities of computers to perceive text from external sources, for example, e-structures, e-archives, etc. OCR is a technique to recognize handwritten or printed text by a PC. It converts handwritten, printed, or photo of any documents into digitized format. OCR is mainly applied in the research fields of pattern recognition, machine learning, and computer vision. Here in this paper, we use OCR and k-nearest neighbor algorithm and classifier for text recognition. The project is equipped to recognize both handwritten and printed for cursive writing of the following styles; San-Serif, Tahoma, Comic Sans, and Calibri. The technique will recognize both the upper case and lower case text of the above mentioned style. The steps involved in the proposed methodology are pre-processing, segmentation, feature extraction, and recognition. The project recognizes the style of the text with an accuracy of 95.60%.
Inside Importance Factors of Graph-Based Keyword Extraction on Chinese Short Text

Keywords are considered to be important words in the text and can provide a concise representation of the text. With the surge of unlabeled short text on the Internet, automatic keyword extraction task has proven useful in other information processing applications. Graph-based approaches are prevalent unsupervised models for this task. However, most of these methods emphasize the importance of the relation between words without considering other importance factors. Furthermore, when measuring the importance of a word in a text, the damping factor is set to 0.85 following PageRank. To the best of our knowledge, there is no existing work investigating the impact of the damping factor on the keyword extraction task. In addition, there are few publicly available labeled Chinese short text datasets for this task. In this article, we investigate the importance parts of words in a given document and propose an improved graph-based method for keyword extraction from short documents. Moreover, we analyze the impact of importance factors on performance. We also provide annotated long and short Chinese datasets for this task. The model is performed on Chinese and English datasets, and results show that our model obtains improvements in performance over the previous unsupervised models on short documents. Comparative experiments show that the damping factor is related to the text length, which is neglected in traditional methods.
Story Completion with Explicit Modeling of Commonsense Knowledge

Growing up with bedtime tales, even children could easily tell how a story should develop; but selecting a coherent and reasonable ending for a story is still not easy for machines. To successfully choose an ending requires not only detailed analysis of the context, but also applying commonsense reasoning and basic knowledge. Previous work [8] has shown that language models trained on very large corpora could capture common sense in an implicit and hard-to-interpret way. We explore another direction and present a novel method that explicitly incorporates commonsense knowledge from a structured dataset [11], and demonstrate the potential for improving story completion.
The Development Prospect of English Translation Software Based on Artificial Intelligence Technology

Abstract At present, information technology has developed by leaps and bounds. In particular, some advanced information technologies such as artificial intelligence technology and computer technology have been widely used in all aspects of social production and life. How to effectively use artificial intelligence technology to solve problems in translation is the ultimate purpose of this paper. This paper first introduces the development of computer translation. Secondly, the paper mainly introduces the basic principle and system composition of the case based machine translation. On the basis of the above research, a simple english-chinese machine translation system is explored to realize the help of computer-aided translation to domestic teaching. The basic process is to extract the key words from the English sentences or sentence fragments input by the user or the system, and search the sentences containing the key words by the key words, so as to obtain the possible translation of examples. Finally, the realization, experimental results and evaluation of the translation system are introduced. The development prospect of English translation software is analyzed from the experimental results.
Software visualization of natural semantics of imperative languages - a teaching tool

In this work, we discuss the role and need of a teaching tool for the visualization of natural semantics method of imperative programming languages. The design, development and use of a compiler that can interpret simple programming language Jane statements to a tree that represents the semantic method of natural semantics is presented. A formal definition of Jane programming language and production rules in natural semantics is showed, as well. The developed teaching tool can provide particular visual steps in the process of finding the meaning of well-structured input program.
Design and Development of an Augmented Reality Mobile Application for Medical Training

The ageing population has led to increasing demand for additional medical staff and training resources. However, the cost of medical training equipment is costly. Considering that augmented reality (AR) technology has been shown to enhance training experience and trainer-trainee interactions in various sectors, the aim of this project is to develop a low-cost AR mobile application framework, which is expandable for use in various medical training modules. On the displays of the trainers' and trainees' mobile devices, the application overlays AR effects that simulate physiological symptoms onto a training manikin. Under the control of the trainer, the effects can be changed for evaluating the trainee's timely responses. Initial trials focused on anesthetic injection-induced rash effects. The application was tested on three mobile platforms under different environmental settings, and was found to function satisfactorily.
Super-resolution reconstruction of real infrared images acquired with unmanned aerial vehicle

Super-resolution (SR) technology provides a far promising computational imaging approach in obtaining a high-resolution (HR) image (or image sequences) from observed multiple low-resolution (LR) images by incorporating complementary information. In this paper, a three-stage SR method is proposed to generate a HR image from infrared (IR) LR Images acquired with Unmanned Aerial Vehicle (UAV). The proposed method integrates a high-level image capturing process and a low-level SR process. In this integrated process, we incorporate UAV path optimization, sub-pixel image registration, and sparseness constraint into a computational imaging framework of a region of interest (ROI). To refine ROI complementary feathers, we design an optimal flight control scheme to acquire adequate image sequences from multi-angles. In particular, a phase correlation approach achieving reliable sub-pixel image feature matching is adapted, on the basis of which an effective sparseness regularization model is built to enhance the fine structures of the IR image. Unlike most traditional multiple-frame SR algorithms that mainly focus on signal processing and achieve good performances when using standard test datasets, the performed experiments with real-life IR sequences indicate the three-stage SR method can also deal with practical LR IR image sequences collected by UAVs. The experimental results demonstrate that the proposed method is capable of generating HR images with good performance in terms of edge preservation and detail enhancement.
Learning Intuitive Physics by Explaining Surprise

The IntPhys Challenge aims to evaluate how well algorithms capture "common sense" about the physical world by measuring the ability to detect violations of intuitive physics in dynamic multi-object visual scenes. One approach to this problem is to define or learn a detailed model of the observations and dynamics and to then detect violations of that model. While viable, this approach poses challenges in acquiring an accurate enough model that can handle detailed non-linear object interactions, such as visual occlusion and collisions. In this work, we consider an alternative approach, the Surprise and Explain (SnE) framework, which aims for simplicity while remaining highly flexible. The key idea is to exploit the assumption that, for the vast majority of time, objects follow simple dynamic models, e.g. linear dynamics. Further, when the simple dynamics are occasionally violated ("surprises") due to non-linear interactions, e.g. collisions and occlusion, it is assumed that there is a small set of detectable explanations for the surprise. Violations of intuitive physics then correspond to surprises for which an explanation cannot be inferred. This paper develops an instantiation of the SnE framework and demonstrates its potential in the IntPhys Challenge by placing 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> at the time of this paper's submission. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution

In the past few years supervised and adversarial learning have been widely adopted in various complex computer vision tasks. It seems natural to wonder whether another branch of artificial intelligence, commonly known as Reinforcement Learning (RL) can benefit such complex vision tasks. In this study, we explore the plausible usage of RL in super resolution of remote sensing imagery. Guided by recent advances in super resolution, we propose a theoretical framework that leverages the benefits of supervised and reinforcement learning. We argue that a straightforward implementation of RL is not adequate to address ill-posed super resolution as the action variables are not fully known. To tackle this issue, we propose to parameterize action variables by matrices, and train our policy network using Monte-Carlo sampling. We study the implications of parametric action space in a model-free environment from theoretical and empirical perspective. Furthermore, we analyze the quantitative and qualitative results on both remote sensing and non-remote sensing datasets. Based on our experiments, we report considerable improvement over state-of-the-art methods by encapsulating supervised models in a reinforcement learning framework.
A Blockchain based Solution to Know Your Customer (KYC) Dilemma

Know Your Customer (KYC) is done as a mandatory entry step into any financial institution. However, even today the amount of manual intervention involved in the process is staggering. Often the data is centrally stored, and the computer programs acting are also centrally governed thus are not tamperproof making them susceptible to vulnerabilities and attacks. Different organizations do not have a unified application where the KYC information can be seamlessly shared between them without any risk of repudiation from any of the participating organizations. Our application, that is based on blockchain technology, aims to provide this platform as a service to financial institutions as an electronic-KYC solution, in the process of making the life of the end consumer easier.
Application of particle swarm optimization BP neural network algorithm in image compression

Abstract In order to overcome the problems of weak global search ability, slow convergence speed and easy to fall into local minima in the process of image compression of BP neural network model, an image compression model based on particle swarm optimization algorithm and improved BP algorithm is proposed. In this model, a group of optimal approximate solutions of weights and thresholds of BP network are obtained through global search of particle swarm optimization according to objective function, and then the approximate solution is taken as the initial value of BP model, and the improved BP algorithm is used to conduct quadratic optimization training on these weights and thresholds to obtain the final image compression model. The experimental results show that with the same error accuracy, the quality of model compressed image reconstruction based on particle swarm BP neural network algorithm is significantly higher than that of BP and improved BP algorithm models.
Importance of rooting in an Android data acquisition

Since Android became the leading handheld device operating system worldwide, the probability of an Android device being subject to usage in a criminal case is increasing, therefore, Android data acquisition is a necessity in investigations. However, with the increasing security level and complexity of each new model and operating system released, forensic acquisition is getting more and more difficult. Special tools and techniques are needed in criminal investigation in order to acquire data from an android device. This paper explores various tools and techniques available in order to perform logical, physical acquisition and conduct a data acquisition followed by an analysis of the extracted data.