Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic quantification of radiographic knee osteoarthritis severity and associated diagnostic features using deep convolutional neural networks

“Automatic Quantification of Radiographic Knee Osteoarthritis Severity and Associated Diagnostic Features using Deep Convolutional Neural Networks” A. Joseph Antony Due to the increasing prevalence of knee Osteoarthritis (OA), a debilitating kneejoint degradation, and total joint arthoplasty as a serious consequence, there is a need for effective clinical and scientific tools to assess knee OA in its early stages. This thesis investigates the use of machine learning algorithms and deep learning architectures, in particular convolutional neural networks (CNN), to quantify the severity and clinical radiographic features of knee OA. The goal is to offer novel and effective solutions to automatically assess the severity of knee OA achieving on par with human accuracy. Instead of conventional hand-crafted features, it is proposed in this thesis that automatically learning features in a supervised manner can be more effective for fine-grained knee OA image classification. The main contributions of this thesis are as follows. First, the use of off-the-shelf CNNs are investigated for classifying knee OA images through transfer learning by fine-tuning the CNNs. Second, CNNs are trained from scratch to quantify the knee OA severity optimising a weighted ratio of two loss functions: categorical cross entropy and mean-squared error. Third, CNNs are jointly trained to quantify the clinical features of knee OA: joint space narrowing (JSN) and osteophytes along with the KL grades. This improves the overall quantification of knee OA severity producing simultaneous predictions of KL grades, JSN and osteophytes. Two public datasets are used to evaluate the approaches, the OAI and the MOST, with extremely promising results that outperform existing approaches. In summary, this thesis primarily contributes to the field of automated methods for localisation and quantification of radiographic knee OA.
Study of Process Migration Techniques for Load Balancing in Distributed Operating System

In current scenario distributed operating system is most interesting research topic and the load balancing in a distributed operating system is a really challenging technique. Load in a distributed operating system can be balanced by process migration technique. This paper is focus on different algorithms for implementing process migration mainly in homogeneous distributed system as well as it also focus on a heterogeneous system.
Customer involvement in scaled agile framework implementations

Trienekens, J., Himawan H.B. , & van Moll J., (2017). Customer Involvement in Scaled Agile Framework Implementations. In: P. Lorenz, J.Trienekens (editors), Proceedings of The Second International Conference on Advances in Computation, Communications and Services, ACCSE2017, Volume 2 (page 3-6), ISBN: 978-1-61208-570-8
Literature Review in Mobile Technologies and Learning

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Literature Review in Mobile Technologies and Learning Laura Naismith, Mike Sharples, Giasemi Vavoula, Peter Lonsdale
Z-MAC an Analytical Model for Wireless Sensor Networks

This paper presents an analytical model for estimating throughput and energy consumption in Z-MAC protocol for Wireless Sensor Networks. The analytical design includes transmission power control and transmission of frames through one hop, two hop and multi hop. Proposed model reduces collision under low contention level as well as high contention level with the use of explicit contention notification. The proposed protocol has been simulated using MATLAB. The simulations reveal better results for throughput and energy consumption of the proposed model as compared to Z-MAC protocol.
The role of terminological knowledge bases in specialized translation: the use of umbrella concepts

A complex knowledge acquisition process, which is a necessary prelude to specialized translation or technical writing, requires optimal terminological resources and an accurate representation of specialized knowledge. Terms are the linguistic units that designate our conceptualization of objects, processes, states and attributes in a specialized domain. Therefore, they play a key role in understanding, representing, transmitting, and acquiring specialized knowledge. However, most translation-oriented terminological resources fail to reflect the complexity and dynamicity of conceptualization (Faber & San Martín, 2010: 118), since the vast majority of specialized dictionaries, thesauri, and glossaries are term-based rather than concept-based. Without a doubt, translators need to be proficient in the use of specialized language in order to translate specialized texts. Moreover, they also need to know how to access different terminological resources and how to acquire a certain amount of their conceptual content. Although it does not necessarily signify that they have as much knowledge in a particular field as a domain expert, they must reach a minimum threshold so as to guarantee high-quality work. For this reason, translators can acquire specialized knowledge in a cost-effective way if they have access to useful knowledge resources and if they have good documentation and search skills. For example, according to López Rodríguez et al. (2013: 50), ontology-based terminological databases can solve different problems related to translation, information retrieval, and knowledge management. This is possible because they link terms that designate the same concept in different languages, showing both conceptual and linguistic information whilst at the same time fomenting data interoperability. Accordingly, lexicographic and terminographic tools should endeavor to reflect the dynamic way in which we conceptualize the world around us. Tercedor et al. (2012: The following chapter is available in:
A MACHINE LEARNING BASED LANGUAGE MODEL TO IDENTIFY COMPROMISED USERS

Identifying compromised accounts on online social networks that are used for phishing attacks or sending spam messages is still one of the most challenging problems of cyber security. In this research, the author explore an artificial neural network based language model to differentiate the writing styles of different users on short text messages. In doing so, the aim is to be able to identify compromised user accounts. The results obtained indicate that one can learn the language model on one dataset and can generalize it to different datasets with high accuracy and low false alarm rates without any modifications to the language model.
Parallel vision algorithm design and implementation : 1988 end of year report

Abstract : The Apply programming language has been extended to allow variable- sized image computations, and also to allow border mirroring, in which pixels accessed outside the borders are produced by copying pixels from the interior of the image. Implementation and design decisions are discussed. Apply and the Warp programming language W2 were used to implement the second DARPA image understanding benchmark. The results of this implementation are reported. Experience with this benchmark suggests a method for performing global image computations in a machine independent manner, using the divide and conquer model. Implications of this model for algorithms in the image understanding benchmark are discussed. It is shown that this model is capable of computing any algorithm in which data is accessed in a fixed order, regardless of the data values, and in which the final computation is reversible: that is, it produces the same results if the data values are reversed in order.
The Role of Sociolinguistics in Second Language Acquisition

Poone Abbasi Mesrabadi Dr. Mohammad Hashamdar Islamic Azad University, Karaj Branch ABSTRACT Learning a new language also involves learning a broad system of norms for social relations. This study broadly showed how EFL learners’ speech act is conveyed from their native cultures when they are communicating in English and demonstrated that there are some possibilities of cross-cultural misunderstanding when interlocutors are engaged in the speech act of complimenting with native speakers of English. Interactional sociolinguistics can present perception into the linguistic and cultural heterogeneity traits of today’s communicative contexts, and document its effect on people’s lives. In spite of where we live, heterogeneousness is all around us and influences much of what we do in our daily life. Politeness, linguistic gests normally present respect and care for others, is assumed to be delicate to the social dispensation of power. Variation permeates the speech of foreign language learners. The late 1960s and early 1970s proved the development of linguistics, both the quantitative study of linguistic variation pioneered by Labov (1966, 1969) and the systematic investigation of second language acquisition (SLA), exemplified by studies such as Cazden, Cancino, Rosansky, and Schumann (1975) and Hakuta (1976).
Defect-based delay testing of resistive vias-contacts a critical evaluation

This defect-based study analyzes statistical signal delay properties and delay fault test pattern constraints in the CMOS deep submicron environment. Delay fault testing has uncertainty, or noise, in its attempt to detect defects that slow a signal. CMOS resistive vias and contacts were used as a delay defect target. Data were taken from a scan-based test chip (Veqtor) on the Philips 0.25 /spl mu/m technology. Methods to improve delay fault defect detection are given.
Dynamic spatial and network sampling

This paper considers some designs for sampling and interventions in dynamic networks and spatial temporal settings. The sample spreads through the population largely by tracing network links, although random sampling or spatial designs may be used in addition. To investigate the effectiveness of different designs for finding units on which to make observations and introduce interventions is investigated through simulations. For this purpose a dynamic spatial network model is developed based on simple stochastic processes. The sampling processes considered have both acquisition processes and attrition process by which units are added and removed from the sample. The effect of an intervention introduced with a given sampling design is assessed by the change in the resulting equilibrium distribution or, in more detail, by the distribution of sample paths resulting from the intervention strategy.
An Efficient & Secure Content Contribution and Retrieval content in Online Social Networks using Level-level Security Optimization & Content Visualization Algorithm

Online Social Networks (OSNs) is currently popular interactive media to establish the communication, share and disseminate a considerable amount of human life data. Daily and continuous communications imply the exchange of several types of content, including free text, image, audio, and video data. Security is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). However, there are no content-based preferences supported, and therefore it is not possible to prevent undesired messages. Providing the service is not only a matter of using previously defined web content mining and security techniques. To overcome the issues, Level-level Security Optimization & Content Visualization Algorithm is proposed to avoid the privacy issues during content sharing and data visualization. It adopts level by level privacy based on user requirement in the social network. It evaluates the privacy compatibility in the online social network environment to avoid security complexities. The mechanism divided into three parts namely like online social network platform creation, social network privacy, social network within organizational privacy and network controlling and authentication. Based on the experimental evaluation, a proposed method improves the privacy retrieval accuracy (PRA) 9.13% and reduces content retrieval time (CRT) 7 milliseconds and information loss (IL) 5.33%.
SISA—Automatic Indexing System for Scientific Articles: Experiments with Location Heuristics Rules Versus TF-IDF Rules

: Indexing is contextualized and a brief description is provided of some of the most used automatic indexing systems. We describe SISA, a system which uses location heuristics rules, statistical rules like term frequency (TF) or TF-IDF to obtain automatic or semi-automatic indexing, depending on the user’s preference. The aim of this research is to ascertain which rules (location heuristics rules or TF-IDF rules) provide the best indexing terms. SISA is used to obtain the automatic indexing of 200 scientific articles on fruit growing written in Portuguese. It uses, on the one hand, location heuristics rules founded on the value of certain parts of the articles for indexing such as titles, abstracts, keywords, headings, first paragraph, conclusions and references and, on the other, TF-IDF rules. The indexing is then evaluated to ascertain retrieval performance through recall, precision and f-measure. Automatic indexing of the articles with location heuristics rules provided the best results with the evaluation measures.
The vital role of business processes for a business model: the case of a startup company

The present study highlights the importance of alignment between a business model (BM) and business processes. The authors employ a case study method and analyze a young company focused on R&D in high technology. In order to explicate the observations, the researchers invoke the newly developed ‘VIP framework’ (Solaimani and Bouwman, 2012). The research reveals that the business processes (BP) carried out in the company must fit the stated business model. The case study demonstrates how some of the processes are not optimal and efficient, and that the two main requirements for achieving a higher level coherence and consistency between BM and BP are an enhancement in human resource management and information systems. This research fills a research gap in understanding the connection between BM and BP and contributes to the illumination of their significance. Besides, a newly developed BM/BP alignment framework is empirically applied for the first time.
A CMOS lock-in amplifier for low-power biomedical applications

An analogue Lock-In Amplifier (LIA) is employed in an optical receiver front end to retrieve signals from an extremely noisy environment. The conventional LIA consists of a current source, Transimpedance Amplifier (TIA), set of active Gm-C filter and Phase Locked Loop (PLL). In this work, a high gain low power TIA making use of Operational Transconductance Amplifier (OTA) is proposed. This structure has a 54% improvement in gain and consumes 8% lesser power compared to replica biasing TIA. The gain boosting circuit in the NMOS current steering charge pump reduces the power consumption by 38% compared to conventional structure. In the modified Phase Frequency Detector (PFD), True Single Phase Clock (TSPC) logic is utilised which consumes 50% lesser power compared to conventional design. So, the low power LIA designed with proposed structures can be used for low power biomedical applications. Simulations are done using Cadence Spectre, GPDK 180 nm technology.
Adaptive Industrial IoT Security Threats Intelligent Perception "Robots"

This paper concept to study the security of the automatic discovery method of the threat under the industrial space. By building an adaptive cyberspace security threat to intelligent perception of "robots", the use of its "complex eye" sensor adaptive perception, continuous perception of complex and changeable network space behavior and state changes; Using its tentacles data, automatically connect to a variety of threats from different sources of information; use of its powerful central control intelligence analysis capabilities, self-learning every day, like the alpha dog of self-evolution, accumulating abnormal behavior and threats in cyberspace knowledge base; Using its unique language man-machine cooperation and collaboration security experts, update cyberspace threats and abnormal behavior characteristic of knowledge; finally able to automatically identify threats, and timely warning.
Optical manipulation and advanced analysis ofcells using an innovative optofluidic platform

This doctoral research project aims to analyse complex processes of living cells using Digital Holographic Microscopy (DHM) as a three-dimensional (3D) imaging tool. DHM is a real-time, high-throughput, label-free and quantitative phase imaging technique which permits advanced cell analysis in microfluidic environment. In particular, an innovative optofluidic platform is implemented, composed of a DHM modulus and aided by holographic optical tweezers (HOT) for optical manipulation and a fluorescence modulus. This platform has been used for blood disease screening, cell manipulation studies and tracking of migrating cells. In this thesis, three main topics have been investigated. The first topic focuses on diagnostics, which plays several critical roles in healthcare. Here a novel and cost-effective approach for detecting real blood disorders such as iron-deficiency anaemia and thalassemia at lab-on-chip scale is shown. In addition, cell dynamics studies were performed by DHM. In particular, a study regarding the temporal evolution of cell morphology and volume during blue light exposure is reported. The second topic aims to investigate cell mechanics. To this end, the capabilities of HOT were used to enable the generation and the independent high-precision control of an arbitrary number of 3D optical traps. The combination of HOT and DHM provides the possibility to manipulate cells, detect nano-mechanical cell response in the pN range, and reveal cytoskeleton formation. To confirm the formation of the cytoskeleton structures after the stimulation, a fluorescence imaging system was used as control. Finally, the third topic focuses on cell manipulation using an innovative electrode-free dielectrophoretic approach (DEP) for investigating smart but simple strategies for orientation and immobilization of biological samples such as bacteria and fibroblast. In particular, the light-induced DEP is achieved using ferroelectric iron- doped lithium niobate crystal as substrate. In this way, a dynamic platform that can dynamically regulate the cell response has been developed. In this case, DHM is going to be used as a time-lapse imaging tool for the characterization of dynamic cell processes. In conclusion, the results show that DHM is a highly relevant method that allows novel insights into dynamic cell biology, with applications in cancer research and toxicity testing. In addition, this study could pave the way for detecting and quantifying circulating tumor cells and for providing multidimensional information on tumour metastasis. In this framework, the optofluidic platform is a promising tool for both identification and characterization of “foreign” cancer cells in the blood stream in order to achieve an early diagnosis.
Exploring AI Language Assistants with Primary EFL Students.

The main objective of this study was to identify ways to incorporate voice-driven Artificial Intelligence (AI) effectively in classroom language learning. This nine month teacher-led design research study employed technology probes (Amazon’s Alexa, Apple’s Siri, Google voice search) and co-design methods with a class of primary age English as a Foreign Language (EFL) students to explore and develop ideas for classroom activities using AI language assistants. Speaking to AI assistants was considered highly engaging by all students. Students were observed to speak more English when using AI assistants in group work, and to spontaneously reformulate, self-correct, and joyfully and playfully persist with speaking English in their attempts to get AI assistants to do what they wanted them to do.
Playing, debugging, learning: A proposal between game and instructional designs via extended prototyping

This article investigates the phenomenon of open and participative development (e.g. beta testing, Kickstarter projects)—i.e. extended prototyping—in digital entertainment as a potential source of insights for instructional interventions. Despite the increasing popularity of this practice and the potential implications for educators and instructional designers, little efforts have been done in enlightening the topic. This study aims to address this lack by staging a bridge between Instructional Design and Game Design with an empirical inquiry. N:130 subjects (beta testers and Steam Early Access and Kickstarter users) were recruited with a quantitative questionnaire about their contribution to open development instances. Behavioral patterns, effective policies and managements, and subjective profiles and opinions were gathered and tied to instructional design models and concepts. Results point to successful techniques in designing and applying such a process, while mistakes and unproductive tactics are highlighted as well. To summarize, instruction can take advantage of an increased participation of targeted audiences/learners to its development phases. Benefits span transparency, engagement, and commitment. However, poor communication and incoherence between testing and final product may weaken the overall outcome.
Blind segmentation of time-series: A two-level approach

Change-point detection is an indispensable tool for awide variety of applications which has been extensively studied in the literature over the years. However, the development of wireless devices and miniature sensors that allows continuous recording of data poses new challenges that cannot be adequately addressed by the vast majority of existing methods. In this work, we aim to balance statistical accuracy with computational efficiency, by developing a hierarchical two-level algorithmthat can significantly reduce the computational burden in the expense of a negligible loss of detection accuracy. Our choice is motivated by the idea that if a simple test was used to quickly select some potential change-points in the first level, then the second level which consists of a computationally more expensive algorithm, would be applied only to a subset of data, leading to a significant run-time improvement. In addition, in order to alleviate the difficulties arising in high-dimensional data, we use a data selection technique which gives more importance to data that are more useful for detecting changes than to others. Using these ideas, we compute a detection measure which is given as the weighted sum of individual dissimilarity measures and we present techniques that can speed up some standard change-point detection methods. Experimental results on both artificial and real-world data demonstrate the effectiveness of developed approaches and provide a useful insight about the suitability of some of the state-of-the-art methods for detecting changes in many different scenarios.