Reviewerly/exHarmony huggingface dataset:
Kernel-Based Image Filtering: Fast Algorithms and Applications

Image filtering is a fundamental preprocessing task in computer vision and image processing. While the dominant applications of kernel filtering are enhancement and denoising, it can also be used as a powerful regularizer for image reconstruction. In general, the brute-force implementations of kernel filtering is prohibitively expensive. They are often too slow for real-time applications. In the first half of the thesis, we propose fast algorithms for bilateral filtering (BLF) and nonlocal means (NLM). In particular, we demonstrate that by using the Fourier approximation of the underlying kernel, we can obtain state-of-the-art fast algorithms for BLF of grayscale images. We next extend the idea for fast filtering of color images, which involves the approximation of a three-dimensional kernel. We next propose a fast separable formulation for NLM of grayscale images. In the second half of the dissertation, we turn to some applications of kernel filtering. We introduce a scale-adaptive variant of BLF that is used for suppressing fine textures in images. We develop a fast implementation of a symmetrized variant of NLM that is used for regularization (i.e., as a prior) within the plug-and-play framework for image restoration. The core idea can be extended to other forms of kernel filtering.
Attack Models for Big Data Platform Hadoop

Hadoop is a very popular big data processing framework, however, due to its distributed and large-scale characteristics, its security problems have not been solved very well. Existing research does not systematically analyze attacks in big data platforms. This paper proposes four innovative hadoop attack models. Through adjusting heartbeat time, tampering intermediate data, blocking network, attackers prolong the execution time of jobs, and damage the correctness of job result. We implemented these attacks in hadoop and evaluate the effects of them through experiments. The experimental results show that our attacks are effective and harmful.
The Fluted Fragment with Transitivity

We study the satisfiability problem for the fluted fragment extended with transitive relations. We show that the logic enjoys the finite model property when only one transitive relation is available. On the other hand we show that the satisfiability problem is undecidable already for the two-variable fragment of the logic in the presence of three transitive relations.
Tobacco Industry Data Security Protection System

With the rapid development of information technology, large data technology has been fully applied in the tobacco industry, which improves the efficiency of analysis, processing, decision-making, marketing and distribution. However, the current data security is facing a serious threat, which is related to the safety of business secrets. In this paper, through the analysis of security situation and challenge of tobacco industry data, and the study of data processing mechanism used in tobacco industry, a data security protection system covering the data life cycle is put forward.
Preliminary Experiment of the Interactive Registration of a Trocar for Thoracoscopy with HoloLens Headset

During surgical procedures, it may be necessary to insert one or several trocars between the patient’s ribs. This is the case, for example, with mini-invasive cardiac surgery to replace one or several heart valves assisted by 3D thoracoscopy: a trocar must be inserted before the camera is introduced. Most systems that provide visual assistance are complex and expensive. They are based either on robotics or magnetic tracking, and allow to track a precise positioning of surgical instruments and to provide an augmented visualization on a screen inside the operating room. Nevertheless, few operating rooms for this type of intervention own this type of hardware and most surgeons place and insert a trocar without visual assistance. In this paper, we have proposed a first software prototype of trocar guidance which exploits the mixed reality framework through the use of the Microsoft HoloLens headset. The surgeon first defines the position and direction of insertion using the CT scan in order to minimize the risk of damaging the patient’s internal organs during the trocar insertion then an interactive registration may help him or her during the intervention. In this article, we describe the sequence of computing steps and the first obtained results about such kind of interactive registration.
Personal learning environment of the talented student - based on their own experiences

Aim. The purpose of the research is to depict how talented students create their personal learning environment. The author of that paper shows a difference that has occurred thanks to the comparison between the manner in which average students and talented students build their personal learning environment.&#x0D; Methods. The author has conducted quantitative research, by making a survey. The surveys were carried out among fifty talented students. This paper focuses on the presentation of their opinions. The respondents presented how they constructed their personal learning environment. Additionally, they expressed their viewpoints about their digital competences.&#x0D; Results. The analysis shows that talented students are familiar with the latest digital tools. It allows them to design their personal learning space effectively. They can collect and process information profitably. They are also keen to share their knowledge with other participants of an educational process via the Internet, using some complex and practical devices. Moreover, talented students prefer discussing educational problems with other students online. They use some appropriate tools, such as chat rooms and social networking services. According to the research, the author can conclude that talented students are able to build and improve their digital learning space very well. They seem to create their personal learning environment functionally and consciously.&#x0D; Conclusions. Talented learners use advanced searching strategies, methods of collecting and storing information, and also teamwork tools. It is particular that they are autonomous learners but they also like sharing knowledge with other learners. The level of information competences of the talented students is quite high.
Fast Chaotic Encryption for Hyperspectral Images

The information collected by hyperspectral images (HI) is essential in applications of remote sensing like object detection, geological process recognition, and identifying materials. However, HI information could be sensitive, and therefore, it should be protected. In this chapter, we show a parallel encryption algorithm specifically designed for HI. The algorithm uses multiple chaotic systems to produce a crossed multidimensional chaotic map for encrypting the image; the scheme takes advantage of the multidimensional nature of HI and is highly parallelizable, which leads to a time-efficient algorithm. We also show that the algorithm gets high-entropy ciphertext and is robust to ciphertext-only attacks.
Reactive Collision Avoidance using Essential Visibility Graphs

This paper deals with a distributed path planning and Collision Avoidance System (CAS) for multiple aircraft sharing the same airspace. The algorithm is compliant with aircraft limits on maximum turn rate and with environmental constraints resulting from no-fly zones and/or obstacles. The avoidance maneuver is calculated on the basis of the Essential Visibility Graph and Dubins curves. Numerical examples reproducing urban-like environments show the effectiveness of the proposed approach.
An Ensemble Co-Evolutionary based Algorithm for Classification Problems

In this paper, the authors propose a dual-population co-evolutionary approach using ensemble learning approach (E-SOCA) to simultaneously solve both feature subset selection and optimal classifier design. Different from previous studies where each population retains only one best individual (Elite) after co-evolution, in this study, an elite community will be stored and calculated together through an ensemble learning algorithm to produce the final classification result. Experimental results on standard UCI problems with a variety of input features ranging from small to large sizes shows that the proposed algorithm results in more accuracy and stability than traditional algorithms.
Framing Lifelong Learning as Autonomous Deployment: Tune Once Live Forever

Lifelong Learning in the context of Artificial Intelligence is a new paradigm that is still in its infancy. It refers to agents that are able to learn continuously, accumulating the knowledge learned in previous tasks and using it to help future learning. In this position paper we depart from the focus on learning new tasks and instead take a stance from the perspective of the life-cycle of intelligent software. We propose to focus lifelong learning research on autonomous intelligent systems that sustain their performance after deployment in production across time without the need of machine learning experts. This perspective is being applied to three European projects funded under the CHIST-ERA framework on several domains of application.
Automotive Rank Based ELM Using Iterative Decomposition

Nowadays, the Extreme learning machine (ELM) is playing a key role in machine intelligence and big data analytics due to its various advantages such as fast training rate, universal classification/regression and the capability of approximation.The standard ELM uses the Moore-Penrose generalized pseudo-inverse for solving the hidden layer activation matrix and also it identifies the output weights.Because of that, the standard ELM takes more time to train the features from the dataset.In ELM, scalability also considered as a one of the major concern while processing the large dataset.In order to overcome this concern, the Automotive Rank based ELM (AR-ELM) is proposed to obtain an effective tensor decomposition for diminishing the training time.Besides, the Bayesian approach is considered in this AR-ELM to remove the redundancy from the decomposed samples of the tensor.The major objective of this proposed AR-ELM is to process the large amount of dataset without depending on memory capacities.The recognition accuracy is improved by eliminating redundant information.The key idea of the AR-ELM is to reduce the training time while processing the huge dataset.The implementation and simulation of the AR-ELM is done in Spark Python 3.7.The performance of the AR-ELM is analysed in terms of accuracy, precision, recall and training time.The proposed methodology is compared with three existing methodologies such as basic ELM, ELM-TUCKER and ELM-PARAFAC.The recognition accuracy of the AR-ELM methodology with Hardlims activation function is 0.8879 for letter recognition dataset, it is high when compared to the basic ELM, ELM-TUCKER and ELM-PARAFAC that are 0.8102, 0.8375 and 0.834 respectively.
How does the visual aesthetics of positively-framed messages impact their motivational capacity?

Many people nowadays engage with motivational text messages embedded in a visual presentation. However, empirical studies of the impact of textual messages on users’ motivation to exercise have paid little attention to how their visual appeal impacts their motivational capacity. We conducted an online experiment to explore the impact of visual aesthetics on the motivational capacity of positively-framed textual messages. Participants were assigned to three conditions: positively-framed messages a) presented as text-only (no-aesthetics), b) presented with high visual appeal (beautiful), and c) presented with low visual appeal (ugly). Contrary to our expectations, the perceived visual appeal of a message show no impact on its motivational capacity in terms of how motivating it was perceived to be, nor on extrinsic identified behavior regulation. The implications of these results on the design of interactive systems, and physical activity trackers in particular, are discussed herein.
A Blockchain Infrastructure for Transportation in Low Income Country Cities, and Beyond

For our cities of tomorrow, it is essential that transport is organised in an efficient, resilient and equitable way; enabling economic growth, social cohesion and minimising environmental impacts, including Climate Change.In cities across the world, new flexible, sharing economy services are blurring the lines between private and public transportation.However, these new transport modes are creating a "digital divide" and lack the integration and co-ordination between other services.This is needed to create seamless and sustainable travel options for people, including those belonging to vulnerable groups.This exploratory paper examines the potential for Blockchain to play a pivotal role in addressing increasing congestion and pollution in growing cities of developing countries.It draws on preliminary research into the role of Automatic Fare Collection systems and related mobility market dynamics and trends in the cities of Cape Town, South Africa and Dehli, India.By creating viable new digital infrastructure for Low Income Country Cities (LICCs), who have less incumbent legacy systems, there is potential to establish a decentralised blockchain network across these territories.There would also be scope for this network to be scaled further into wealthier countries, through a secondary wave of adoption by Mobility-as-a-Service (MaaS).
Designing context-aware architectures for business-to-government information sharing

New developments in Information and Communication Technology (ICT), such as big data, the Internet of Things (IoT), and blockchain technology provide opportunities for businesses and government organisations to benefit from business-to-government (B2G) information sharing. For example, big data analytics might provide government organisations with knowledge on how to assess risks using the information they receive from businesses. However, B2G information sharing can entail risks as well. Sensitive data could fall in the wrong hands and by that the competitor of the business might obtain this data. In addition, B2G information sharing could be unlawful...
Overview of the Languages for Safe Smart Contract Programming

Blockchain technologies are gradually being found an application in many areas, especially in FinTech. As a result, a lot of blockchain platforms have emerged with the support of smart contracts that are intended to automate party interactions. However, it has been shown that they are prone to attacks and errors which lead to money loss. To date, there has been a wide range of approaches for making smart contracts safer that included analysis tools, reasoning models, and safer and more rigorous programming languages. In this paper, we provide an overview of smart contract programming languages design principles, related vulnerabilities, and future research areas. The provided overview is meant to outline the to date state of languages and to become a possible basis for future proceedings, and show approaches, used by the community, to reach safe and usable language for smart contracts. We have split all found vulnerabilities by source of their arising. Various languages’ characteristics such as abstraction level, paradigm, Turing completeness and main features are summarized in the table. Additional information about languages is provided, e.g. model of execution and tools for static analysis.
Measuring I2P Censorship at a Global Scale

The prevalence of Internet censorship has prompted the creation of several measurement platforms for monitoring filtering activities. An important challenge faced by these platforms revolves around the trade-off between depth of measurement and breadth of coverage. In this paper, we present an opportunistic censorship measurement infrastructure built on top of a network of distributed VPN servers run by volunteers, which we used to measure the extent to which the I2P anonymity network is blocked around the world. This infrastructure provides us with not only numerous and geographically diverse vantage points, but also the ability to conduct in-depth measurements across all levels of the network stack. Using this infrastructure, we measured at a global scale the availability of four different I2P services: the official homepage, its mirror site, reseed servers, and active relays in the network. Within a period of one month, we conducted a total of 54K measurements from 1.7K network locations in 164 countries. With different techniques for detecting domain name blocking, network packet injection, and block pages, we discovered I2P censorship in five countries: China, Iran, Oman, Qatar, and Kuwait. Finally, we conclude by discussing potential approaches to circumvent censorship on I2P.
Survey on Load Balancing in Cloud Computing

Cloud computing is one of the outstanding platforms that give storage of data in very essential cost and obtainable for all time over the internet . Load balancing aims at high user fulfilment and usage of resource ratio by guaranteeing a proficient and reasonable allocation of each computing resource. Load Balancing is essential for efficient operations in distributed environments many researchers have proposed several techniques to enhance load balancing and this paper too. We explore the various forms of algorithms projected by variety of researchers to resolve the matter of load equalization in cloud computing. This paper describes a survey on load balancing tactics in cloud environments.
Unsupervised Prediction of Negative Health Events Ahead of Time

The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health tracking. In recent years, unsupervised learning methods have drawn special attention of researchers to tackle the sparse annotation of health data and real-time detection of anomalies has been a central problem of interest. However, one problem that has not been well addressed before is the early prediction of forthcoming negative health events. Early signs of an event can introduce subtle and gradual changes in the health signal prior to its onset, detection of which can be invaluable in effective prevention. In this study, we first demonstrate our observations on the shortcoming of widely adopted anomaly detection methods in uncovering the changes prior to a negative health event. We then propose a framework which relies on online clustering of signal segment representations which are automatically learned by a specially designed LSTM auto-encoder. We benchmark our results on the publicly available MIT-PICS dataset and show the effectiveness of our approach by predicting Bradycardia events in infants 1.3 minutes ahead of time with 68% AUC score on average, with no label supervision. Results of our study can indicate the viability of our approach in the early detection of health events in other applications as well.
Using Semantic Fluency Models Improves Network Reconstruction Accuracy of Tacit Engineering Knowledge

Abstract Human- or expert-generated records that describe the behavior of engineered systems over a period of time can be useful for statistical learning techniques like pattern detection or output prediction. However, such data often assumes familiarity of a reader with the relationships between entities within the system — that is, knowledge of the system’s structure. This required, but unrecorded “tacit” knowledge makes it difficult to reliably learn patterns of system behavior using statistical modeling techniques on these written records. Part of this difficulty stems from a lack of good models for how engineers generate written records of a system, given their expertise, since they often create such records under time pressure using shorthand notation or internal jargon. In this paper, we model the process of maintenance work order creation as a modified semantic fluency task, to build a probabilistic generative model that can uncover underlying relationships between entities referenced within a complex system. Compared to more traditional similarity-metric-based methods for structure recovery, we directly model a possible cognitive process by which technicians may record work-orders. Mathematically, we represent this as a censored local random walk over a latent network structure representing tacit engineering knowledge. This allows us to recover implied engineering knowledge about system structure by processing written records. Additionally, we show that our model leads to improved generative capabilities for synthesizing plausible data.
Handling Challenges in Rule Based Machine Translation from Marathi to English

Machine translation is being carried out by the researchers from quite a long time.However, it is still a dream to materialize flawless Machine Translator and the small numbers of researchers has focussed at translating Marathi Text to English.Perfect Machine Translation Systems have not yet been fully built owing to the fact that languages differ syntactically as well as morphologically.Majority of the researchers have opted for Statistical Machine translation whereas in this paper we have addressed the challenges of Rule based Machine Translation.The paper describes the major divergences observed in language Marathi and English and many challenges encountered while attempting to build machine translation system form Marathi to English using rule based approach and rules to handle these challenges.As there are exceptions to the rules and limit to the feasibility of maintaining knowledgebase, the practical machine translation from Marathi to English is a complex task.
Comparative Study of Cryptography for Cloud Computing for Data Security

Background: Cloud computing is an emerging technique by which anyone can access the applications as utilities over the internet. Cloud computing is the technology which comprises of all the characteristics of the technologies like distributed computing, grid computing, and ubiquitous computing. Cloud computing allows everyone to create, to configure as well as to customize the business applications online. So the cloud computing techniques need security of information communicated between the sending and receiving entities. Objective: The secure data storage disadvantage of Cloud computing can be resolve up to some extent with the help of the implementation of the cryptographic algorithms during the storing and accessing of the data from the cloud servers. Method: In this paper we have compared four different recently implemented Cryptographic Algorithms which are Modified RSA (SRNN), Elliptic Curve Cryptography Algorithm, Client Side Encryption Technique and Hybrid Encryption Technique. Conclusion: Client Side Encryption Technique and Hybrid Encryption Technique are better than Modified RSA and Elliptic Curve Cryptography because Client Side Encryption Technique has an advantage that in this data is encrypted prior to upload the data on the cloud i.e. encryption at the client side which provides an additional layer of security to the data on the cloud. On the other hand, Hybrid Encryption Technique has an advantage that it uses the rapidity of the processing time of Symmetric Key Cryptography and robustness in key length of the Asymmetric Key Cryptography.