Reviewerly/exHarmony huggingface dataset:
Image Deblurring Methodology using Wiener Filter &amp; Genetic Algorithm

The Blind Image Deconvolution/ Deblurring (BID) issue was acknowledged in the mid1960 yet despite everything it stays a difficult errand for the picture handling research network to locate an effective, solid and in particular a differently relevant deblurring plan.This exploration is centered on the improvement of rebuilding plans for genuine obscured pictures.The essential target is to structure a BID plan that is powerful in term of Point Spread Function (PSF) estimation, proficient as far as rebuilding speed, and viable as far as reclamation quality.An ideal plan will require a deblurring measure to go about as a criticism of value with respect to the deblurred picture and lead the estimation of the obscuring PSF.The obscured picture and the assessed PSF would then be able to be passed on to any traditional reclamation channel for deblurring.The deblurring measures exhibited in this exploration incorporate visually impaired non-Gaussianity measures just as visually impaired Image Quality Measures (IQMs).These measures are visually impaired as in they can check the nature of a picture straightforwardly from it iii without the need to reference a brilliant picture.The non-Gaussianity measures incorporate spatial and phantom kurtosis measures; while the picture quality analyzers incorporate the Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE) record and Re-obscuring based Peak Signal to Noise Ratio (RPSNR) measure.Likewise, parametric and self-assertively molded (non-parametric or conventional) PSFs were treated for the visually impaired deconvolution of pictures.Full-reference and non-reference IQMs have been used to check the nature of deblurred pictures for the BID plans.The essentialness of the examination work lies in the BID plan's capacity to deal with parametric and selfassertively molded PSFs utilizing a solitary calculation, for single-shot obscured pictures, with improved streamlining through GA related to different input IQMs.
Speech Synthesis: Past and Present

The article describes the development of the speech synthesis methods from the beginnings to the present. The main approaches that have played an important role in the development of the speech synthesis, as well as modern advanced methods are considered. The extensive bibliography on this issue is also given.
Distance Aware VM Allocation Process to Minimize Energy Consumption in Cloud Computing

Background: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. Aim and Objective: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. Methods: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Results and Conclusion: Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.
Corrections to: Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

The original version of the book was inadvertently published with certain errors. These have now been corrected.
Device Driver and System Call Isolation in Embedded Devices

The number of low-end embedded devices in today's Internet of Things and Cyber-Physical Systems is increasing along with their security concerns. Memory isolation mechanisms are often absent, programming flaws lead to malfunctioning applications, which in turn can crush the whole system. A common design approach in these devices is to have applications, operating system components, and device driver libraries reside in a single non-isolated address space, which represents one vast attack surface. Furthermore, with increasing network connectivity and frequent dynamic updates, new or modified applications and services are uploaded, opening space for even more attacks. Isolating the execution of applications in these systems is still a challenge. In this work we provide a holistic hardware/software co-designed approach for memoryisolation, which prevents corruption of the state of the operating system and applications from a buggy software, including device drivers, interrupt service routines, and misused system calls. We implemented low-cost architectural extensions in a RISC-V-based microcontroller which work together with kernel-based protection concepts. Our evaluation shows that applications as well as the kernel can enjoy the benefits of the proposed memory isolation with minimal impact on performance and an insignificant increase in the area of the MCU.
PassGrid: Towards Graph-Supplemented Textual Shoulder Surfing Resistant Authentication

With the rapid development of intelligent mobile devices and network applications, user authentication plays an important role to help protect people’s privacy and sensitive information. A large number of authentication textual and graphical schemes have been proposed in the literature, but the majority of them are vulnerable to shoulder surfing attacks, or have to sacrifice usability. Motivated by this challenge, we propose a graph-supplemented textual shoulder surfing resistant authentication system, called PassGrid. With a series of one-time login indicators and cyclic movable blocks with textual elements, PassGrid prevents attackers from guessing the passwords even with the help of a camera. To reduce users’ workload, they only have to memorize one set of the password. Our user study shows that PassGrid can achieve good performance regarding security and usability, i.e., average login time consumption of 22s with a small password length.
Scene Graph Contextualization in Visual Commonsense Reasoning

Leveraging structured visual representations such as scene graphs is beneficial to high-level computer vision tasks such as captioning or visual question answering. The recent Visual Commonsense Reasoning challenge focuses on the cognition aspects of question answering. The paper also introduces a Reasoning to Cognition (R2C) architecture tailored for this problem. We propose a modification to the R2C network that takes into account the knowledge encoded in the image by extracting the scene graph, embedding the facts and attending to them.
YOLO-FD: YOLO for Face Detection

Face detection is a fundamental step for any face analysis approach. However, it remains as an unsolved problem in computer vision, specially, when it comes to the variability and distractions of in-the-wild environments. Moreover, a face detector must be accurate and fast to be used in surveillance/biometrics scenarios. In order to overcome these limitations, this paper proposes a customized version of the state-of-the-art object detector, YOLOv3, for face detection. The modifications aim at building a real-time, accurate model capable of detecting faces as small as 16 pixels in 34 FPS. Furthermore, this model was evaluated on three of the most difficult benchmarks for face detection, Wider Faces, UCCS and UFDD, showing a good score balance across them. Also, the comparison with the state-of-the-art shown that it was possible to achieve the second best FPS and the fifth best score on Wider Faces. Finally, the model will be available in https://github.com/luanps/yolofd .
GORO 2.0: Evolving an Ontology for Goal-Oriented Requirements Engineering

Goal-Oriented Requirements Engineering (GORE) gained prominence by covering some of the limitations of traditional Requirements Engineering (RE). As a result, many GORE modeling languages have been proposed since this field emerged. Aiming at providing formal semantics to the concepts of GORE, the Goal-Oriented Requirements Ontology (GORO) was proposed as a common vocabulary for this domain. However, the first version of GORO lacks important concepts and its applicability was not demonstrated in practice. In this paper, we present GORO 2.0, an evolution of the first version of GORO that overcomes several limitations of its first version, presenting new concepts such as obstacles, conflicts and contributions.
Cybersecurity Incident Response : A Socio-Technical Approach

This thesis examines the cybersecurity incident response problem using a socio-technical approach. The motivation of this work is the need to bridge the knowledge and practise gap that exists becau ...
Context aware adaptive mobile learning framework for bottom of pyramid people (BOP)

When the learners are truly interested in learning, they learn faster, participate actively, and they pull knowledge.Unlike school education, adult learning mostly occurs with the initiation of the adult learner, and they then pull the relevant knowledge.Vocational education is the master key that opens the door to the economic and social development of a country and it provides an opportunity for Bottom of Pyramid people (BOP) to acquire a sustainable livelihood.Due to many difficulties and commitments of their lives, BOP people are not engaging in a continuous learning process.Since there are many adaptive mobile learning systems available, there is no proper mechanism to achieve the sustainable livelihood of BOP people through vocational education which is tightly coupled with their lifestyle and motivates them to get in learning activities.The context aware adaptive mobile learning framework is an attempt to push vocational knowledge for Bottom of Pyramid (BOP) people, who are not ready to pull knowledge.In this paper, we present mobile learning content design, concept design, system architecture, Adaptivity components, the system implementation and evaluation of the system This system guides BOP people in their vocations through adaptive content delivery mechanisms using mobile technology together with vocational and motivational factors to educate the user in a transparent and non-resistive manner.Social science based models integrated into systems to carry out adaptive delivery of content based on the end user learning behavior, vocation, social and psychological factors.
Tools for supporting language learning for Sakha

This paper presents an overview of the available linguistic resources for the Sakha language, and presents new tools for supporting language learning for Sakha. The essential resources include a morphological analyzer, digital dictionaries, and corpora of Sakha texts. Based on these resources, we implement a language-learning environment for Sakha in the Revita CALL platform. We extended an earlier, preliminary version of the morphological analyzer/transducer, built on the Apertium finite-state platform. The analyzer currently has an adequate level of coverage, between 86% and 89% on two Sakha corpora. Revita is a freely available online language learning platform for learners beyond the beginner level. We describe the tools for Sakha currently integrated into the Revita platform. To the best of our knowledge, at present, this is the first large-scale project undertaken to support intermediate-advanced learners of a minority Siberian language.
The saturation of population fitness as a stopping criterion in genetic algorithm

Genetic Algorithm is an algorithm imitating the natural evolution process in solving optimization problems. All feasible (candidate) solutions would be encoded into chromosomes and undergo the execution of genetic operators in evolution. The evolution itself is a process searching for optimum solution. The searching would stop when a stopping criterion is met. Then, the fittest chromosome of last generation is declared as the optimum solution. However, this optimum solution might be a local optimum or a global optimum solution. Hence, an appropriate stopping criterion is important such that the search is not ended before a global optimum solution is found. In this paper, saturation of population fitness is proposed as a stopping criterion for ending the search. The proposed stopping criteria was compared with conventional stopping criterion, fittest chromosomes repetition, under various parameters setting. The results show that the performance of proposed stopping criterion is superior as compared to the conventional stopping criterion.
Autoencoder-based patch learning for real-world image denoising

Internal patch prior (e.g. self-similarity) has achieved a great success in image denoising. However, it is a challenging task to utilize clean external natural patches for denoising. Natural image patch comes from very complex distributions which are hard to learn without supervision. In this paper, we use an autoencoder to discover and utilize these underlying distributions to learn a compact representation that is more robust to realistic noises. By exploiting learned external prior and internal self-similarity jointly, we develop an efficient patch sparse coding scheme for real-world image denoising. Numerical experiments demonstrate that the proposed method outperforms many state-of-the-art denoising methods, especially on removing realistic noise.
AUGMENTED REALITY IN OPEN AND DISTANCE LEARNING

Open and Distance Learning constantly seeks to enrich learning experiences and processes by trying out innovative technologies and brings forward the best practices.Among these innovative technologies, Augmented Reality, draws attention due to the interactivity it enables within learning environments.Augmented Reality allows for the integration of theory and practice, and offers the potential to deliver learning through open and distance practices on disciplines such as medicine and engineering, which were once deemed impossible through distance learning mechanisms.This article aims to provide brief information on Augmented Reality and discusses its potential in the field of education.
Implications of Resurgence in Artificial Intelligence for Research Collaborations in Software Engineering

Challenges of implementing successful research collaborations between industry and academia in software engineering are varied and many. Differing timelines, metrics, expectations, and perceptions of these two communities are some common obstacles, which need be analyzed and discussed, to discover synergies and strengthen collaborations between researchers and practitioners. In this report, we present insights from the 6th International Workshop on Software Engineering Research and Industrial Practice held at the International Conference on Software Engineering 2019. Specifically, one particular topic dominated the discussion - the resurgence of artificial intelligence and machine learning algorithms in software engineering research and industry practice, and its implications for the collaboration between these two communities. We present takeaways from keynote talks on this subject, insights from paper presentations, and findings from the discussion session.
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models

We present a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI), which links them to concept unique identifiers (CUIs) in a knowledge base, for the PharmaCoNER shared task on pharmaceutical drugs and chemical entities.We proposed a neural NER model that captures the surrounding semantic information of a given sequence by capturing the forwardand backward-context of bidirectional LSTM (Bi-LSTM) output of a target span using contextual span representation-based exhaustive approach.The NER model enumerates all possible spans as potential entity mentions and classify them into entity types or no entity with deep neural networks.For representing span, we compare several different neural network architectures and their ensembling for the NER model.We then perform dictionary matching for CI and, if there is no matching, we further compute similarity scores between a mention and CUIs using entity embeddings to assign the CUI with the highest score to the mention.We evaluate our approach on the two sub-tasks in the shared task.Among the five submitted runs, the best run for each sub-task achieved the F-score of 86.76% on Sub-task 1 (NER) and the F-score of 79.97% (strict) on Sub-task 2 (CI).
Prediction method of cyanobacterial blooms spatial-temporal sequence based on deep belief network and fuzzy expert system

The process of cyanobacteria bloom in rivers and lakes is a highly non-stationary and non-linear process. The existing cyanobacterial bloom prediction method mainly uses time series model and single intelligent model, but time series model and single intelligent model cannot effectively explain the cyanobacterial bloom generation process, and the prediction accuracy is not high. In view of the above deficiencies, this paper proposes to use the cyanobacteria bloom spatiotemporal sequence data for modeling. Considering the characteristics of large-scale nonlinear trend term and small-scale residual term in the cyanobacteria bloom spatial-temporal sequence, the deep belief networks is used to model and explain the large-scale nonlinear trend term of the cyanobacteria bloom spatiotemporal sequence. Then use the time autocorrelation model and the multivariate spatiotemporal autocorrelation model to model and interpret the small-scale residual term; finally, after superimposing the large-scale nonlinear trend term and the small-scale residual term, the adaptive neuro-fuzzy system model is used to predict the chlorophyll a value of the water. Therefore, a fuzzy spatial and temporal sequence prediction method based on fuzzy expert system is proposed. The model verification results show that compared with the existing time series model and single intelligent model, the method can more fully explain the non-stationary and nonlinear dynamic changes of the cyanobacterial bloom spatial-temporal sequence. It provides a new method for accurately predicting cyanobacteria bloom in rivers and lakes.
A Level Set Based Method for Land Masking in Ship Detection Using SAR Images

This paper presents an efficient approach to obtain land masking using synthetic aperture radar (SAR) images, which is based on level set method and fully convolutional network (FCN) classification. First, the level set method is applied to the cropped SAR input image for initial contours. Second, FCN model has been trained to classify the input image by two labels of water and land which will find the possible region existing real coastlines, named region of interest (RIO). Third, to select the desired contour from extracted boundaries in the first step, according to the proportions to be covered in RIO of step two. Then, final land masking can be obtained after morphological processing and color filling. The method proposed in this paper is fast and accurate enough for ship detection in high-resolution SAR images. It is also robust to speckle noise and geographical changes. Experimental results on GF-3 SAR images show good performance of this method.
Real-time Linked Dataspaces: A Data Platform for Intelligent Systems Within Internet of Things-Based Smart Environments

Around 18,000 BCE, Paleolithic tribespeople marked notches into sticks, or bones, to keep track of trading activity or supplies. The tribespeople would compare the notches on their prehistoric data storage devices (their tally sticks) to make basic calculations that would allow them to make predictions such as how long their food supplies would last. From these early examples, we can trace a gradual evolution of the ability of humans to store, analyse, and share information.
Digital Forensic Analysis on iDevice : Jailbreak iOS 12.1.1 as a Case Study

Jailbreak has an issue in data alteration, as it modifies file(s) in the device to allow user to extract more data than without jailbreaking. This issue raises controversy of the use of jailbreaking in digital forensic investigation, as data integrity is a prominent requirement in a court proceeding. This study aims to analyze the process of jailbreak, what is actually done by the jailbreak code in a device, and what data is actually modified by the jailbreak code. By using the latest version of iOS system, this study uses the voucher_swap exploit as a representation of semi-tethered jailbreaking method to investigate the effects of jailbreak on data integrity on a idevice. The investigation is conducted based on to what extent data can be extracted from the jailbreak device, hash value comparison of the data, and source code analysis to scrutinize the effect of jailbreak to the system and user data inside the device. Results of this study suggest that jailbreak is acceptable to prepare idevice in digital forensic investigations to acquire more data, as it maintains the integrity of user data. These results may help forensic communities in their decision about the acceptability of jailbreaking in idevide forensic investigations.
Fundamentals of Real-time Linked Dataspaces

Dataspaces can provide an approach to enable data management in smart environments that can help to overcome technical, conceptual, and social/organisational barriers to information sharing. However, there has been limited work on the use of dataspaces within smart environments and the necessary support services for real-time events and data streams. This chapter introduces the Real-time Linked Dataspace (RLD) as a data platform for intelligent systems within smart environments. The RLD combines the pay-as-you-go paradigm of dataspaces with linked data, knowledge graphs, and (near) real-time processing capabilities. The RLD has been specifically designed to support the sharing and processing of data between intelligent systems within smart environments. We propose a set of specialised dataspace support services to enable the requirements of loose administrative proximity and semantic integration for event and stream systems. These requirements form the foundation of the techniques and models used to process events and streams within the RLD.