Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching \Prediction: Machine Learning and Statistics"

The course Prediction: Machine Learning and Statistics is taught currently at MIT to mathematically oriented non-experts. The course focuses generally on predictive modeling from data, and contains topics within data mining, machine learning, and statistics, often going back and forth between machine learning and statistical views of various algorithms and concepts. The course is structured in three parts: an overview of most of the \top 10" algorithms in data mining based on the ICDM survey (Wu et al., 2008), statistical learning theory and kernels, and Bayesian analysis. We present insights from this course.
Algorithm for Image Quality Assessment Based on Color Features

As the most direct and efficient features to describe images,color features currently become one of the most significant parameters in image quality assessment.Based on the theory of structural similarity,a model of full-reference objective quality assessment system for image on the basis of color features is proposed,which takes the affect of chrominance on the human visual perception into consideration.After the theoretical analysis,a new similarity model using color moments of partitions is established in this paper.Experimental results show that the model has high consistency with the subjective results.
Combining Institutional Frameworks and Agent-based Simulation for the Design of Enforcement Policies

Computer science advocates institutional frameworks as an effective tool for mod- elling policies and reasoning about their interplay. In practice the rules or policies, of which the institutional framework consists, are often specified using a formal language, which allows for the full verification and validation of the framework (e.g. the consistency of policies) and the interplay between the policies and actors (e.g. violations). However, when modelling large- scale realistic systems, with numerous decision-making entities, scalability and complexity issues arise making it possible only to verify certain portions of the problem or reducing the scale. In the social sciences, agent-based modelling is a popular tool for analysing how entities interact within a system and react to the system properties. Agent-based modelling allows the modelling of complex decision-making entities and experimentation with large numbers of different parameter sets for these entities in order to explore their effects on overall system performance. In this paper we describe how to achieve the best of both worlds, namely verification of a formal specification combined with the testing of large-scale systems with numerous different actor configurations. Hence, we offer an approach that allows for reasoning about policies, policy making and their effects, on a more comprehensive level than has been possible to date. We present a prototype combining an institutional framework, whose specifications can be translated into a computational model under the answer set semantics, and an agent-based simulation. Using a simplified contract enforcement example, we demonstrate the functional- ities of this prototype and show how it can help to assess an appropriate fine level in case of contract violations.
Engaging Non-Citizens in an Age of Uncertainty

Abstract Problem, research strategy, and findings: Arnstein’s ladder has informed how planners redistribute power among constituents and increase citizen participation. Since the late 1960s, the non-citizen population has increased in the United States. This demographic shift has affected planning and community engagement because many immigrants experience disparate access to public goods and services more so than native-born residents. Non-citizens are also particularly vulnerable to shifting political landscapes due to citizenship status. I use 29 interviews with immigrant-serving nonprofits to identify unique challenges in serving non-citizen clients after the 2016 election. Immigrant nonprofit experiences are informative because they may be the first and only organization to provide non-citizens with services and resources in times of uncertainty. The interviews inform how planners can improve non-citizen engagement practices and redistribute political power. In particular, interviewees highlight how non-citizens experience barriers to public services and spaces due to fear of deportation and abrupt changes in their citizenship status. As a result, non-citizens are selective in how and where they engage. Thus, they connect to informal and formal spaces that may exist beyond their neighborhoods. These experiences also increase non-citizen dependency on nonprofits. Takeaway for practice: Non-citizens engage with planners differently than do native-born residents because of legal status, disparate access to spaces and resources, and varying relationships with government agencies. The results of this study offer implications for planners, such as spending more time to build trust with non-citizens through frequent outreach and offering small incentives, using alternative spaces for engagement and pushing for policies that decouple public institutions from immigration services, and partnering with nonprofits to support non-citizens and immigrant-friendly policies. As planners improve immigrant incorporation through these considerations, they can improve how they balance power, constituent representation, and meeting residents’ needs.
GSM and Wi-Fi Module Based Advanced Smart Irrigation Monitoring System using IoT

This paper proposes an Automated Irrigation System in Farming using Internet of Things (IoT) which aids farmers in acquiring information on mobile phone regarding temperature and soil moisture for efficient environment monitoring. This will promote the gross yield and quality of the production. An irrigation system built on automation technology saves the farmer’s expenses and electricity, and yield time. The technology of IoT has made the life of ordinary people easier by introducing revolutionary changes in every field, where the changes include the automation of tools. IoT defines the system of physical thing rooted with software, sensors as well as additional technologies. The design methodology of Automated Irrigation System in Farming using IoT-based devices is gradually enhancing the agriculture production, enhancing it, making it economical environmental-friendly, and decreasing waste production to conserve the yield. It is designed with Arduino Technology integrated with different sensors and poses a GSM module for SMS alerts to a specified mobile number and a Wi-Fi module producing a live data feed acquired online from Thingsspeak.com. The prototype developed has been tested on live agriculture fields, which gives the highest precision of 98% in data feeds.
Comparison Of Methods For Evaluating Mechanical Stress In The Rotor Of High-Speed Machines

High-speed rotating electrical machines are interesting solutions to achieve high-power density, especially in transportation industry. Such machines are considered as high-speed ones when the mechanical aspects like stress and vibrations cannot anymore be neglected during their sizing. Machine topologies having a solid rotor (homopolar, reluctance and asynchronous) are suited to high speed operation. Permanent magnet machines can also operate at high-speed if they are properly sized. This paper proposes analytical evaluation of the stresses in the rotor caused by the centrifugal effects in order to determine the speed limit, ensuring a mechanical sizing of the rotating parts of both solid rotor and permanent magnet machines which will operate in their elastic regime. Comparisons of the developed models with simulations using finite element method as well as retro-engineering on industrial applications serve to validate and analyze the analytical findings. □
Automatic Image Tagging

Automatic Image Tagging seeks to assign relevant words (e.g. “jungle”, “boat”, “trees”) to images that describe the actual content found in the images without intermediate manual labelling. Current approaches are largely based on categorization, and treat the tags independently, so an annotation (jungle,trees) is just as plausible as (jungle,snow). The goal of this dissertation was to develop a probabilistic model (the Continuous Relevance Model) to take into account the dependencies between keywords so as to provide more precise annotations. The main findings suggest that, under certain conditions, taking into account keyword correlation, coupled with an efficient method (beam search) to search over sets of tags is an effective method to increase annotation accuracy.
Tax Incentive Heterogeneity Between Shareholders, Voting Rights Power, and Capital Structure

Using a sample of public European firms and a multitude of shifts in cross-country taxation data, we examine the influence of individual shareholders’ tax incentives on capital structure. We find that the largest shareholder’s tax incentive for debt positively influences leverage. We also find that the second-largest shareholder’s tax incentive for debt is incrementally relevant for leverage. However, tax incentive heterogeneity between shareholders reduces the positive influence of the largest shareholder’s tax incentive on leverage. Finally, we document that the relevance of the largest shareholder’s tax incentive for capital structure decisions is increasing in the level of voting rights power.
Development of Arterial Spin Labeling Techniques forQuantitative Perfusion Measurements at 3 Tesla

Microvascular perfusion is an important parameter of high clinical value as it provides important information on tissue viability and function. In contrast to dynamic contrastenhanced magnetic resonance imaging (DCE-MRI), arterial spin labeling (ASL) is a method to assess quantitative perfusion values non-invasively by MRI. The main goal of this work was the development of a 3D ASL technique that allows the quantitative perfusion measurement within a whole volume. Further, the diagnostic signifcance of ASL was tested by applying it in dedicated renal disease models. ASL yielded significantly (P < 0.01) different values in healthy kidneys ((500+-91) ml/100g/min) compared to kidneys with acute kidney injury ((287+-83) ml/100/min) as well as for acutely rejected transplanted kidneys compared to chronically rejected grafts. A comparison to DCE-MRI showed no signifcant differences. While literature reports on 3D ASL sequences show a prevalence for spin-echo based data acquisitions, the presented sequence employs a 3D balanced steady-state free precession (bSSFP) readout that has the ability to overcome some of the drawbacks of spin-echo based 3D sequences. The developed 3D ASL technique includes several features, each of which has been optimized separately. Special emphasis was put on design, simulation, and implementation of a slice-selective adiabatic inversion pulse. In an initial measurement, the whole-brain perfusion of a healthy volunteer was assessed with an isotropic resolution of 3mm. Mean perfusion values were f_GM = (51+-17) ml/100g/min and f_WM = (24+-8) ml/100/min for gray and white matter, respectively, which are in very good agreement with findings from the "gold-standard" method of 15O-positron emission tomography. The diagnostic value of ASL in combination with the possibility to assess absolute perfusion values in a whole volume makes it a promising technique for future clinical diagnosis.
An algorithm for assembly job shop scheduling problem

The problem of assembly job shop scheduling (AJSS) is studied with the objective of minimizing the weighted sum of earliness and tardiness penalties. An insertion search algorithm is proposed to solve the problem. The operations of job with tardiness or earliness are shifted left or right to optimize the sequence. Simulation results validate the effectiveness of the proposed model and algorithm. Introduction An assembly job shop (AJS) refers to a shop which involves both processing and assembly operations. It is assumed that each job has a tree structure of component and subassemblies that assembly together to build up the end job [1], as shown in Fig.1. These subassemblies in turn have sub-sub-assemblies and so on. These are called as multi level assembly jobs. Higher-level subassembly cannot be assembled until all preceding lower-levels components and/or subassemblies are completed. In an AJS, subassemblies/assemblies undergo operations in a serial fashion as per the precedence constrains and wait for the arrival of its mating components at the assembly station, for the assembly operation to start [2]. As the number of levels increases the complexity of scheduling also increases. This makes the AJS problems quite challenging.
Vision Recognition in Micro-Robot Soccer System Based on Region Projection

This paper deals with the vision recognition system for recognizing vehicle robots and football based on object projection in micro-robot soccer system. The vision recognition is processed with two stages. In the first stage, a grid search scanning from top to bottom and left to right is processing to find an object point inside an unrecognizing object(robots or football), then a precision analyzing and recognizing procedure based on object region projection is processing for determining the robots and football's position and also the robots' orientation in the second stage. The recognition algorithm is robust in treating noisy image captured by CCD, even in the situation while multiple vehicle robots are closely adjacent to each other. The algorithm is also very efficient, it takes only 1.2 ms in PII350 PC for recognizing and identifying six vehicle robots in a captured frame, which is composed of 800 pixels×600 pixels.
Quantitative Analysis of Context Field in Natural Language Processing

Context is the necessary resource not only for corpus based linguistic but also for solving the problem in computational linguistics. But what is the size of context window? Because there is no method to define the effective field of context window by quantitative analysis, this paper put forward one computational method to do it with quantitative analysis. First, context position is weighted by information gain; second, the position weight function is constructed according to weight of context positions, and then the function is integraled to arrive to the information ratio 85% to define the size of windows. The result is [-8,+9] in Chinese and [-16,+13] in English for context window. The result explained quantitatively the value and function of context in natural language processing.
A French Weblog Corpus for New Insights on Blog Post Tagging

The rapid evolution and informational growth of blogs requires enhanced functionality for searching, navigating and linking content. This paper presents the French Blog Annotation Corpus \textsc{FLOG}, intended to provide a research testbed for the study of annotation practices, and specifically tagging and categorizing blog posts. The corpus covers a ten year time span of blog posts on cooking, law, video games and technology. Statistical analysis of the corpus suggests that tag annotation of posts is more frequent than category attribution, but on the other hand categories provide a richer semantic structure for post classification and search. The review of the state of the art on automatic tag suggestion shows that tag suggestion tools are not of widespread use yet between bloggers, which might be a consequence of methods that do not take into account the past tagging history of the blog, the context of the post within the blog and the tagging pattern of each blog author.
Analyzing the Benefits of Graphics Processing Units for Computation-Intensive Applications on Hadoop

Due to the ever expanding amount of data that is being generated in the “Big Data era” there is an ever increasing challenge of processing this data. This work aims to tackle the challenge of improving the performance of processing unstructured data being generated by combining two different technologies of General Purpose Graphics Processor Units (GPGPUs) and Apache Hadoop. Many researchers have focused on improving either the GPGPU or Apache Hadoop; very little amount of evaluation data is available in combination of the two technologies, which we aim to study in our work. JCuda, JCublas and JCuFFT were used in conjunction with the CUDA library to incorporate GPGPU computation within the Apache Hadoop framework. We utilized the Nvidia Tesla M2050 and up to 16 compute nodes to evaluate the integration of the GPGPU and Apache Hadoop framework with three different use cases. Two synthetic benchmarks, matrix multiplication, fast Fourier transformations, and a real world application, image processing using the Gaussian blur filter. We were able to achieve up to, 6.91×, 2.48× and 1.49× of overall performance improvements for matrix multiplication, FFT and Gaussian blur respectively. We expect that our work will be useful to the reader to gauge the amount of performance that they would be able to achieve under certain workloads for their own work with the combination of GPGPU and Apache Hadoop.
Prediction of Alopecia Areata using Machine Learning Techniques

Alopecia areata (AA) is a chronic, autoimmune condition that attacks anagen hair follicles, causing sudden hair loss, resulting in circular bald patches. The disorder may be developed in both adults as well as children and the prevalence of the same is approximated to be 1 in every 1000 people, putting around 2% of the broad population at a risk of developing this disease at some point in their lives. Existing techniques to assess the disorder are heavily supported by naked-eye examination and thus have demonstrated nominal accuracy. In recent years, Machine Learning has paved the way for enhanced diagnosis of diseases in various fields of healthcare, including Dermatology. This study postulates a significant role of computer-aided diagnosis of AA to equip medical practitioners with a more accurate form of prediction and classification. Our proposed framework is in relevance to the categorization of healthy hair and hair with symptoms that indicate AA. For the dataset, a 1000 images of healthy hair were collected via web scraping and the Figaro1k dataset. Over 500 AA images were web scraped along with some of the same from the Dermnet dataset. To improve the dataset further, the images were preprocessed by performing image enhancement, segmentation and data augmentation. This study aims to elevate the quality of diagnosis carried out for accurate prediction of Alopecia areata in the field of Dermatology by conducting a comparative study for classification by implementing SVM, KNN, Random Forest Classifier, Gaussian Naive Bayes and CNN algorithms which gave 85%, 78%, 88%, 80% and 92% accuracy respectively.
Cloud Computing: VM placement & Load Balancing

In recent years, cloud computing has become a popular paradigm for hosting and delivering services over the internet. The key technology that makes cloud computing possible is server virtualization, which enables dynamic sharing of physical resources. Through virtualization, a cloud service provider can ensure QoS delivered to the user while achieving higher server utilization and energy efficiency. Virtualization introduces the problem of virtual machine placement and also increases the overheads in load balancing. This paper discusses the various the various algorithms dealing with VM placement and load balancing in cloud environment.
Higher Technological Education Graduates' Entrepreneurship: Results and Conclusions of a Nation-Wide Research Study

Higher education graduates constitute one of the most important parts of the young workforce, and their transition to the labour market is among the main concerns of governments, policy makers and higher education institutes. It has also been argued by numerous scientists that there is a noticeable recognition of the relationship between entrepreneurship and economic growth, and moreover increasing self-employment rates tend to bring subsequent decreases of unemployment. This paper concentrates on the investigation of technological higher education graduates’ entrepreneurship through the analysis of the results of a nation-wide research study carried out in Greece. The conducted survey was addressed through a structured questionnaire to a large random, yet stratified sample of 708 self-employed graduates of five consecutive years. Descriptive statistics was used for investigating the main variables sketching graduates’ entrepreneurship, such as their demography and education, their professional status, the characteristics of their enterprises, the satisfaction from self-employment; and multivariate analysis was applied for detecting the variables affecting significantly the turn to entrepreneurship. The main findings are highlighted and future relevant work is suggested.
Getting the point: Which kinds of gestures by pedagogical agents improve multimedia learning?

Previous studies have shown that students learn better from an online lesson when a gesturing pedagogical agent is added (Mayer & DaPra, 2012; Wang, Li, Mayer, & Liu, 2018). The goal of this study is to pinpoint which aspect of a gesturing pedagogical agent causes an improvement in learning from an online lesson. College students learned about neural transmission in an online multimedia lesson that included a pedagogical agent who displayed specific pointing gestures (i.e., pointing to the specific component in the diagram being mentioned in the narration), general pointing gestures (i.e., pointing in the general direction of the diagram), nonpointing gestures (moving hands as beats, moving an arm up or down, or crossing two hands), or no gestures. An analysis of students’ eye movements during learning showed that students in the specific-pointing group paid more attention to task-related elements than did students in the other groups (as indicated by fixation time and fixation count on the target area of interest). Students in the specific-pointing group also performed better than the other groups on retention and transfer tests administered immediately after the lesson and after a 1-week delay. The results show that an active ingredient in effective pedagogical agents is the use of specific pointing gestures. This work helps clarify the embodiment principle and image principle by isolating specific pointing (or deictic gestures) as a key feature that makes gesturing effective in multimedia lessons. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
Do Demographic Characteristics Moderate the Acceptance and Use of Moodle Learning System among Business Students

Continuous development of new educational digital learning such as the Moodle Learning System (MLS) for students, has become an integral part of the evolution of modern educational systems. Despite considerable effort by academic institutions in motivating students to use new digital learning for blended and virtual learning, there is practical evidence of mixed feelings among students whether to accept or not to accept the use of this new technology. This study aims to provide an integrated view of user acceptance of digital learning based on validated constructs from well tested and proven theoretical models. This study used random sampling and hierarchical regression analysis through structural equation modelling in AMOS to determine the relationships among conceptualised constructs. Research results indicated that social influence has a significant influence on behavioural intention and behavioural intention has a significant influence on system usage. Gender, age and experience are vital moderators in the relationships between performance expectancy and behavioural intention, social influence and behavioural intention, facilitating conditions and system usage among business students in tertiary institutions.