Reviewerly/exHarmony huggingface dataset:
Navigation of Autonomous Mobile Robots in Diverse Terrain

Optimal path planning is essential for successful operation of mobile robots. Many applications require a robot to operate outdoors, frequently in diverse terrain conditions. These include earth and space exploration, surveillance, and search and rescue. Many existing path planning algorithms utilize either genetic algorithms (GAs) or fuzzy logic. However, these algorithms fail to consider the effect of terrain conditions when determining the optimal path. Additionally, they are unable to deliver real-time adjustments required for operation in a dynamic environment and focus solely on path planning without regard to trajectory planning required to facilitate movement. This paper proposes a navigation algorithm that utilizes a genetic algorithm to compute a near-optimal path through varying terrain and the associated trajectory planning required to follow that path. Fuzzy linguistic variables are used to represent the terrain conditions to address the uncertainty and imprecision inherent in terrain data. The navigation algorithm presented is robust, allowing rapid changes in the path to adapt to changes in dynamic environments.
Cloud based End to End Service Security

In today's internet era, the increased rate of using cloud services has been one of the most burning issue. Even though the various server available with high encryptesd cloud services as it is almost used by popular platform such as Yahoo, Gmail, Microsoft Outlook Express. However, Due to distributed and centralised data repository of information in the cloud services are more lean for attackers. And they are more vulnerabke to attack if cloud based system is not perfectly configured. User perosonal data and shared information is not secured on centralised locations which results in lack of security mecanism.. If an attack targeting a server is successful, information for all users on that server may be compromised. The flow of this research paper is to be introduced with a cloud based communication paradigm which also referred to enhanced cloudsim architecture at different layers with the scope of providing end to end security architecture. We have performed simulation and include results in support of end to end security with various protocol analysis for communication in between cloud based web services. Here we analyse security in the client and cloud server as FTP, HTTP for Remote server uploading and downloading time response and size specifications.
Refining Indeterministic Choice: Imprecise Probabilities and Strategic Thinking

Often, uncertainty is present in processes that are part of our routines. Having tools to understand the consequences of unpredictability is convenient. We introduce a general framework to deal with uncertainty in the realm of distribution sets that are descriptions of imprecise probabilities. We propose several non-biased refinement strategies to obtain sensible forecasts about results of uncertain processes. Initially, uncertainty on a system is modeled as the non-deterministic choice of its possible behaviors. Our refinement hypothesis translates non-determinism into imprecise probabilistic choices. Imprecise probabilities allow us to propose a notion of uncertainty refinement in terms of set inclusions. Later on, unpredictability is tackled through a strategic approach using uncertainty profiles and angel/daemon games ([Formula: see text]-games). Here, imprecise probabilities form the set of mixed strategies and Nash equilibria corresponds to natural uncertainty refinements. We use this approach to study the performance of Web applications — in terms of response times — under stress conditions.
ExSDM: Novel Content-based Image Retrieval based on Sparse Distributed Memory Model

In recent times, image retrieval has garnered an increasing amount of interest due to the introduction of image datasets of significant size. Many methods have been suggested to retrieve images swiftly and accurately. However, the majority of these techniques are centered on the representation of the image. It is felt that alongside the representation of the image, smart storage is required that can rise to the demands of the task. A possible solution is to model human visual memory, retrieving images by imitating the brain's detection processes. This paper proposes a memory model that can be employed as smart memory for efficiently retrieving images based on image hashes. The memory model accepts hash code inputs derived from DWT and DCT transformations. The model is evaluated in terms of the memory capacity and the accuracy of the image retrieval. The results demonstrate that this model has a greater capacity and is significantly quicker than other types of memory models.
OACV

Modern avionics system, especially those developed for modern aircraft, require extremely high portability, interoperability, standardization and accuracy. But during the model integration of avionics component models based on the multi-team development method, component models developed by different teams, especially component data models, may exists some problems during interoperation, including message semantic ambiguities, mismatched metrics, missing information, as well as inaccurate and non-standard model definitions. Therefore, this paper analyzes the key attributes such as portability, interoperability, standardization, and accuracy of data models, including abstract data models, logical data models, and platform data models in the data architecture of avionics-based model-driven development. Based on the definition of standardized software component, an Object Constraint Language (OCL)-based avionics component model verification method (OACV) is proposed. OCL is used to perform constraint validation on the data model to ensure semantic and syntactic consistency and standardization of interoperable data. In order to verify the correctness of the constraint verification of data models using OCL, this paper further proposes a formal description of verification of these data models based on OCL. Based on this, this article designs and implements OACV System. Finally, the actual project requirements of avionics were extracted, and a verification case was developed. The verification result shows that OACV is feasible and effective.
Ретроспективи становлення конференції «Technology Entertainment Design» (TED) як бренда

The article discusses the historical aspects and factors that have contributed to the emergence of TED, as the conference has not only lost its position for thirty years, but continues to gain worldwide popularity. Technology Entertainment Design is a world-renowned conference whose mission is to spread thoughts, ideas that can motivate us to make personal and societal changes, that is, to inspire people from around the globe to make the world better. The emergence of TED as an international brand was facilitated by the fact that the areas of activity began to expand significantly due to the launch of new successful programs and initiatives: the TED Conference; TEDGlobal Conference; the annual TED Prize; open project ‘Translations’; open project ‘TV’; TED Fellows; TEDx conferences. TEDGlobal is a conference dedicated to human ingenuity through the study of ideas, innovations and creativity from around the world. The TEDx format invites individuals or groups of people to organize local, self-organized events around the world. The costs of this program include ongoing training and seminars for participants, as well as reaching out to new communities, as the main goal is to support this global international community. The TED Fellows project helps innovators from around the world become part of the TED community and with it help to achieve even greater success. The TED Youth and Education Initiative is TED-Ed, which aims to organize the exchange of interesting ideas and knowledge between teachers and students from around the world. The sources analyzed have highlighted a number of factors that contributed to the emergence of TED as a brand, namely: the increase in the number of interesting projects and initiatives launched by TED, the uniqueness of the conference format; peculiarities of the selection of speakers, the relevance of the topics of speeches, the requirements for speaking, the reach of various target audiences, the attraction of innovative technologies.
Context-Aware Information for Smart Retailers

Smart stores use ubiquitous digital signages to show information, images and videos, on their items, e.g., selling prices and advertisements. However, most signages tend to always or periodically show the same information to anyone. This paper presents a framework for easily developing novel digital signage whose content are aware of contextual changes in the real world. To spatially bind items and information, the framework automatically deploys programs for displaying contents at digital signage close to the items. It was constructed as a general-purpose middleware system for selecting and deploying programs with information according to context.
Experimental Validation of a Robust H∞ Control Method on Miniaturized Optical Image Stabilizer Prototypes

Abstract This paper validates a robust H∞ controller design method, experimentally, on miniaturized prototypes of the magnetically actuated lens-tilting optical image stabilizers (OISs) with product variabilities. Five small-scale OIS prototypes with product variations are constructed by three-dimensional (3D) printing. For the prototypes, the model parameters are identified based on experimental frequency response data of the prototypes. Using the identified model, a robust H∞ controller is designed to guarantee the robust stability of the closed-loop system and to optimize the closed-loop performance. The experimental results reveal larger and more complex uncertainties in miniaturized OISs with mass-produced parts compared to large-scale prototypes. Despite the increased amount of uncertainties, it is demonstrated that the robust H∞ controller still outperforms the conventional controllers in terms of robust closed-loop stability, performance, and controller order for practical implementation on a mobile phone device.
Multi-Resolution Texture-Based 3D Level Set Segmentation

This article presents a novel three-dimensional level set method for the segmentation of textured volumes. The algorithm combines sparse and multi-resolution schemes to speed up computations and utilise the multi-scale nature of extracted texture features. The method's performance is also enhanced by graphics processing unit (GPU) acceleration. The segmentation process starts with an initial surface at the coarsest resolution of the input volume and moves to progressively higher scales. The surface evolution is driven by a generalised data term that can consider multiple feature types and is not tied to specific descriptors. The proposed implementation of this approach uses features based on grey level co-occurrence matrices and discrete wavelet transform. Quantitative results from experiments performed on synthetic volumes showed a significant improvement in segmentation quality over traditional methods. Qualitative validation using real-world medical datasets, and comparison with other similar GPU-based algorithms, were also performed. In all cases, the proposed implementation provided good segmentation accuracy while maintaining competitive performance.
Vector-Based Feature Representations for Speech Signals: From Supervector to Latent Vector

There are two basic types of feature representations for speech signals. The first type refers to probabilistic models, such as the Gaussian mixture model (GMM). The second type refers to vector-based feature representations, such as the Gaussian supervector (GSV). Since vector-based feature representations are easier to use and process, they are more popular than probabilistic model-based feature representations. In this paper, we begin by explaining the rationale behind two widely used vector-based feature representations, viz. GSV and the i-vector, and then make extensions. GSV is a supervector (SV) based on maximum a posteriori (MAP) adaptation. Its computation is simple and fast, but its dimensionality is high and fixed. While the i-vector is a latent vector (LV) based on factor analysis (FA). Although the computation can be time-consuming because of additional model parameters, its dimensionality is changeable. To generalize GSV, we propose the MAP SV, which is also based on MAP adaptation but can have an even higher dimensionality and thus carry more information. To boost the computational efficiency of the i-vector, we adopt the concept of the mixture of factor analyzers (MFA) and propose the MFA LV, which exhibits a similar flexibility in dimensionality but is faster in computation. The experimental results for speaker identification and verification tasks demonstrate that, MAP SV can be more robust than GSV, and MFALV is comparable to or even better than the i-vector in effectiveness and meanwhile maintains a higher computational efficiency. With a powerful backend, GSV and MAP SV are comparable to the i-vector and MFALV, but the latter two are more flexible in dimensionality.
A Practice-Based Approach to Security Management: Materials, Meaning and Competence for Trainers of Healthcare Cybersecurity

Managing how new digital technologies are integrated into different contexts has become a key component needed for effective international security management. This chapter focuses on rethinking our approach to the integration of digital technologies within (cyber)security work. Most analyses of security take for granted a problematic split between technologies involved in securing specific contexts and the humans involved with or operating such devices. By shifting to a practice theory approach, we offer a more holistic view of security by examining not only the implementation of technologies or human factors but also how this affects the meaning these practices hold. We highlight how this comes into being in the healthcare domain by drawing on interviews with trainers who are actively involved in providing this type of change management. Trainers relate to the daily practices of healthcare staff they train by taking into account the materials they work with, the skills they need and which meaning these practices may hold for them building on this. As such, trainers can be conceived of as mediators of practices and a practice theory approach offers a clearer sense of how to implement security change management.
Design of Application for Students Attendance Checking in Islamic Boarding School

Abstract This research aims to create applications that can facilitate in conducting absences this is done to see the liveliness on the santri (a student who schools at Islamic boarding school) in the Koran as well as facilitate reporting on the offer in the form of software that can be open in Smartphone. The methods used in developing this application was observation. The results of this research are a system used to take attendance absences in each session a lesson in a payroll activity. This application is made so that the santri do not do cheating during absences so that information would be more valid.
Notable Site Recognition using Deep Learning on Mobile and Crowd-sourced Imagery

Being able to automatically recognize notable sites in the physical world using artificial intelligence embedded in mobile devices can pave the way to new forms of urban exploration and open novel channels of interactivity between residents, travellers and cities. Although the development of outdoor recognition systems has been a topic of interest for a while, most works have been limited in geographic coverage due to lack of high quality image data that can be used for training site recognition engines. As a result, prior systems usually lack generality and operate on a limited scope of pre-elected sites. In this work we design a mobile system that can automatically recognise sites of interest and project relevant information to a user that navigates the city. We build a collection of notable sites using Wikipedia and then exploit online services such as Google Images and Flickr to collect large collections of crowd-sourced imagery describing those sites. These images are then used to train minimal deep learning architectures that can be effectively deployed to dedicated applications on mobile devices. By conducting an evaluation and performing a series of online and real-world experiments, we recognise a number of key challenges in deploying site recognition system and highlight the importance of incorporating mobile contextual information to facilitate the visual recognition task. The similarity in the feature maps of objects that undergo identification, the presence of noise in crowd-sourced imagery and arbitrary user induced inputs are among the factors the impede correct classification for deep learning models. We show how curating the training data through the application of a class-specific image de-noising method and the incorporation of information such as user location, orientation and attention patterns can allow for significant improvement in classification accuracy and the election of an end-to-end system that can effectively be used to recognise sites in the wild.
Agribusiness Information System of Dragon Fruit Planation

Abstract The purpose of this study is to examine e-commerce information systems in agriculture. This system is useful for distributing information about seed purchases online in the local area and can increase investment funds to overcome the problem of buying in trade. The research method used consisted of data collection methods (observation, interviews and literature studies) and FGD (Focus Group Discussion). The research results show the establishment of an e-commerce system that can help horticultural seed shops in several regions or communities to introduce and sell horticultural seeds online with a transaction system or COD. Besides, product, customer, and sales data can be used in this system. The use of information systems (SI) by Small and Medium Enterprises (SMEs) in developed countries increases the number of SI applications and still low compared to large companies. Meanwhile, in developing countries, the use of SI by SMEs is still relatively low. The use of SI by SMEs is an interesting thing to know. Big companies have to stay competitive because they have adequate resources in a better position.
FATE:Fingerprints Automatically Targeting and Extracting for image source identification

The convenience of obtaining perfect photos with mobile devices not only promotes the quality of modern life, but also facilitates privacy hacker for crimes like taking images secretly. Such dilemma causes widely concerns and calls for forensic community to reverse-engineer the evil images, which can be seen as a source identification problem. In recent years, convolutional neural network (CNN) has jumped to the crown jewel due to its high performance in image recognition. In this light, we devise a CNN-based system named FATE (Fingerprints Automatically Targeting and Extracting) which can identify the image source out of various models of mobile devices. To verify our FATE system, we first set up a dataset consisted of images from six different model mobile devices, then we train FATE on our dataset and test it with real data. Besides, we introduce another two stat-of-the-art CNN models for comparison. The results show that our FATE beats down the rivals and achieves an accuracy of 98.5% in recognition.
Comprehensive Fuzzy Evaluation for Simulated Training of Aerial Mechanist

Abstract Performance evaluation is an important part of simulated training, and also a challenging project in the development of simulators for aerial mechanist training. In this paper, the evaluation method is explained in detail, and then the evaluation model is put forward, and finally the application of comprehensive fuzzy evaluation is discussed in this paper.
Improved adaptive type-2 fuzzy filter with exclusively two fuzzy membership function for filtering salt and pepper noise

Image denoising is one of the preliminary steps in image processing methods in which the presence of noise can deteriorate image quality. This paper presents an improved two-stage fuzzy filter for filtering salt and pepper noise from the images to enhance the image quality. In the first stage, the pixels in the image are categorized as good or noisy based on adaptive thresholding using type-2 fuzzy logic with exclusively two different membership functions in the filter window. In the second stage, the noisy pixels are denoised using modified ordinary fuzzy logic in the respective filter window. The proposed filter is validated on standard images with various noise levels. The proposed filter removes the noise and preserves beneficial image characteristics, i.e., edges and corners at higher noise levels. The performance of the proposed filter is compared with the various state-of-the-art methods in terms of peak signal-to-noise ratio and computation time. The average PSNR values for the noise percentage 20%, 50% and 80% are the 37%, 31% and 27%. To show the effectiveness of statistical filter tests, i.e., the Friedman test and Bonferroni−Dunn (BD) test are also carried out, which ascertain that the proposed filter outperforms in comparison to various filtering approaches.
Developing a theory based on the causes of technical debt injection into software projects in Colombia

Abstract Engineering, like sciences such as physics, seeks to ensure that the conclusions reached can be verified through experiments, and that subsequent theories allow predictions to be made in future observations. This procedure is applied in this article, whose objective is the identification of the causes that favor the injection or presence of technical debt, and its application in software projects in Colombia, to build a theory that allows explaining this behavior. In turn, these causes could be also extrapolated to physical research projects. Results showed that “focus on producing more at the expense of quality” is the most cited cause in general. Conversely, “planning and management” is the most cited category of causes of technical debt. The concept of technical debt is well understood by software practitioners and that most efforts need to be invested by researchers on offering strategies and tools to support technical debt management.
Designing Self-healing Software Using Role-Based Multi Agent System Approach

Abstract Multi-Agent System (MAS) is one of the popular approaches on modeling behaviour and states in complex system, and has been widely implemented in various field, both on modelling computer-based system or social system condition. There are many approaches and ways on modeling MAS, such as Gaia, OORam, Tropos, and MaSE. In general, the approaches usually starting from defining agents then followed by defining the attributes and roles of each agent. The problem could arise when some agents can share the same roles, because the roles should be defined on each agent. This problem can be resolved by using Role-based MAS or RoMAS approach, that emphasized on defining the role then assigning agent into the defined roles. Using this approach, we can share the role among agents and the agents could build or designed separated from the roles. On this research, we designed the conceptual model for self-healing software that will be modeled using RoMAS. The model show organization of roles that has 3 main roles which are logger, controller and executor. We also identify the breakdown for each roles and it has 3 generic roles such as getter, comparator and setter. The generic roles have responsibilities to collect data, control the system and execute the recovery plan if the system fail. By defining the role, we can propose the architecture of self-healing software that can be independent from agents that would be assigned for executing the role.