Miracle-dz/UniFAR-FTUs huggingface dataset:
Heritage Building Era Detection using CNN

The Indian subcontinent is a southern area of Asia continent which includes India, Bangladesh, Pakistan, Nepal, Bhutan, Maldives, and Sri Lanka. In the different periods, different rulers had ruled in these territories such as the Sultanate period (1206–1526) and Mughal period (1526–1540, 1555–1857). In addition, various ancient and heritage structure patterns show the different historical and religious characteristics of the old civilizations. This research presents a computational method for identifying the construction period or era of old heritage building by using feature detection. We have done this experiment for the application aspect, where the tour guide is not available this application will help the tourists for knowing the construction period or era by detecting the features of old spectacular architecture. In this study, we have focused on the constructional characteristics of old architectural sites by using Canny Edge Detector method. After using the Canny Edge Detector method, we have categorized different types of features among the buildings such as Dome, Minaret or Tower, Ratna, and Front. The different eras old structure contains different characteristics of the Dome, Minaret or Tower, Ratna, and Front. Finally, we have developed a neural network based model which can identify the building’s construction era of the Sultanate period and Mughal period.
P‐3.2: Measurement method of the Field of View for Near‐Eye Display device

We propose a novel measurement method of field of view (FOV) for near‐eye display types (Augmented Reality and Virtual Reality). This method can make use of the existing laboratory optical testing equipment, without additional components, and utilize the characteristics of light propagating along a straight line. In addition, we propose a method for calculating FOV, that is, the edge luminance is reduced to 50% of the central luminance.
Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak speciﬁc private information in the training data to an attacker, either through the models’ structure or their observable behavior. This article examines the factors that can allow a training set membership inference attacker or an attribute inference attacker to learn such information. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We ﬁnd that overﬁtting is sufﬁcient to allow an attacker to perform membership inference and, when the target attribute meets certain conditions about its inﬂuence, attribute inference attacks. We also explore the connection between membership inference and attribute inference, showing that there are deep connections between the two that lead to effective new attacks. We show that overﬁtting is not necessary for these attacks, demonstrating that other factors, such as robustness to norm-bounded input perturbations and malicious training algorithms, can also signiﬁcantly increase the privacy risk. Notably, as robustness is intended to be a defense against attacks on the integrity of model predictions, these results suggest it may be difﬁcult in some cases to simultaneously defend against privacy and integrity attacks.
Leveraging Mobile Technology in a School-Based Participatory Asthma Intervention: Findings From the Student Media-Based Asthma Research Team (SMART) Study

Background: Asthma places a heavy burden on Chicago's schoolchildren, particularly in low-income, minority communities. Recently, our group developed a 10-week afterschool program, the Student Asthma Research Team (START), which successfully engaged high school youth in a Photovoice investigation of factors impacting their asthma at school and in their community. Purpose: The present Student Media-Based Asthma Research Team (SMART) intervention adapted START to target middle school students with asthma during the school day. The 13-week SMART intervention leverages recent trends in mobile technology and the power of participatory media to improve student asthma self-management and outcomes, while enhancing asthma knowledge and support at the school and community levels through a student-led participatory media-based campaign. Methods: To assess the effectiveness of SMART, pre/postintervention data were collected from student participants, their caregivers, and their classmates. Results: Pre/post assessments identified significant increases in participant asthma knowledge (z = − 2.83; P < .05), self-efficacy (z = 2.00; P < .05), control (z = 2.01; P < .05), lung function (z = − 3.41; P < .01), and empowerment (z = 2.00; P < .05). Participants also produced 4 educational videos. Through dissemination of these videos, peer (z = − 2.72; P < .01) and parental asthma knowledge (z = − 2.64; P < .05) increased. Parental asthma caregiver-related quality of life (z = 2.83; P < .01) also improved. Translation to Health Education Practice: Future health education work should consider leveraging ongoing advances in mobile technology and adopting a participatory media-based approach.
Inter cell interference modeling and analysis in long term evolution (LTE)

Long Term Evolution (LTE) is promising standard for data rate and system capacity (coverage) in wirelesses communication history. Inter cell interference (ICI) is a dominate impairment of LTE systems. In interference, modeling and analysis have focused on the first tier only, but considering ICI arises from the second tier is very crucial in communication system standard and implementations too. In this research work interference modeling is proposed and analyzed for the first two subsequent tires under various channel environments. The work is analytically quantified with respect to standard parameters and system models using Matlab and other supportive tools. Thus, link level simulation results demonstrate, ICI beyond the first tier becomes active in urban environment and the smaller cell size increases ICI power in LTE.
Device-Circuit Co-Design Employing Phase Transition Materials for Low Power Electronics

Phase transition materials (PTM) have garnered immense interest in concurrent post-CMOS electronics, due to their unique properties such as - electrically driven abrupt resistance switching, hysteresis, and high selectivity. The phase transitions can be attributed to diverse material-specific phenomena, including- correlated electrons, filamentary ion diffusion, and dimerization. In this research, we explore the application space for these materials through extensive device-circuit co-design and propose new ideas harnessing their unique electrical properties. The abrupt transitions and high selectivity of PTMs enable steep (< 60 mV/decade) switching characteristics in Hyper-FET, a promising post-CMOS transistor. We explore device-circuit co-design methodology for Hyper-FET and identify the criterion for material down-selection. We evaluate the achievable voltage swing, energy-delay trade-off, and noise response for this novel device. In addition to the application in low power logic device, PTMs can actively facilitate non-volatile memory design. We propose a PTM augmented Spin Transfer Torque (STT) MRAM that utilizes selective phase transitions to boost the sense margin and stability of stored data, simultaneously. We show that such selective transitions can also be used to improve other MRAM designs with separate read/write paths, avoiding the possibility of read-write conflicts. Further, we analyze the application of PTMs as selectors in cross-point memories. We establish a general simulation framework for cross-point memory array with PTM based selector. We explore the biasing constraints, develop detailed design methodology, and deduce figures of merit for PTM selectors. We also develop a computationally efficient compact model to estimate the leakage through the sneak paths in a cross-point array. Subsequently, we present a new sense amplifier design utilizing PTM, which offers built-in tunable reference with low power and area demand. Finally, we show that the hysteretic characteristics of unipolar PTMs can be utilized to achieve highly efficient rectification. We validate the idea by demonstrating significant design improvements in a Cockcroft-Walton Multiplier, implemented with TS based rectifiers. We emphasize the need to explore other PTMs with high endurance, thermal stability, and faster switching to enable many more innovative applications in the future.
Alternative models for the modified form of ridge regularized linear model in discovering Markov boundary

It has been proved that the modified form of ridge regularized linear model (MRRLM) can discover a subset of Markov boundary of the target variable under some constrained conditions. However, MRRLM cannot be applied to the data sets with collinear variables due to covariance matrix is employed. To develop a suitable alternative model for MRRLM, we study the relationships of discovery performance of Markov boundary among MRRLM, ridge regression linear model (RRLM), and LASSO combining with permutation test through empirical method. In addition, we also proposed a new NVRRLM to discover Markov boundary of the target variable. The experimental results show that: (1) On the binary data sets, MRRLM has a basically similar performance with LASSO and RRLM; (2) On the continuous data sets, MRRLM has a basically similar discovery performance with LASSO but has higher discovery performance than RRLM; (3) The new NVRRLM can replace MRRLM on the data sets with collinear variables. The above experimental results demonstrate NVRRLM can effectively deal with variable collinearity problems.
Militarizing red teaming : agile and scalable process for cyber red teaming using adaptive planning and execution framework

Tuovinen, Jussi & Frilander, Kimmo Militarizing red teaming – Agile and scalable process for cyber red teaming using adaptive planning and execution framework Jyväskylä, University of Jyväskylä, 2019, 147 pp. Cyber Security Supervisor: Professor Martti Lehto The goal of red teaming is to create better plans, policies, procedures and products in any domain by challenging the current ones. This calls for assessment and critique of status quo. Red teaming is about mitigating future risks and communicating bad news. Red teaming research has focused in adversary emulation and penetration testing practices somewhat disregarding the remediations which are the key in building better security. Cyber threats are evolving and so should cyber red teaming research. Red teaming efforts should be conducted through a comprehensive planning and execution process which considers the complete information security lifecycle starting from planning of intelligence activities and ending to implementing remediations for security to the target organization. Red teaming should be a process that can be understood and adopted by organization and it should be also transparent and traceable. The research problem was to create a comprehensive agile red teaming framework by combining adaptive planning and execution framework in information security context. Design science research methodology was used to solve this challenge. Solid knowledge base and environment description about red teaming and information security was completed in accordance with information systems research framework. Adaptive planning and execution framework, intelligence, targeting and agile methodologies were introduced to support the creation of the framework. Challenges in red teaming were identified by a survey to five cyber security companies. Challenges were remediated by success factors identified from literature and survey. The framework was created, and it underwent two Delphi iterations with subject matter experts. Main result of the study is the comprehensive agile red teaming framework which incorporates the remediations drawn from subject matter experts, military and agile methods. The scope of this study was wide and therefore results can be considered general. The significance of the created framework lies in its novelty and possibilities to adapt it to any red teams’ purposes due to general outcome. Framework delivers a good basis for future work.
Protecting scientific workflows in clouds with an intrusion tolerant system

With the development of cloud computing technology, more and more scientific workflows are delivered to cloud platforms to complete. However, there are many threats in clouds due to the multi-tenant coexistence. In order to protect scientific workflows in clouds, the authors propose an intrusion tolerant scientific workflow system. In this system, the task executors containing multiple virtual machines are used for workflow sub-task execution to enhance reliability. Then lagged decision mechanism is presented to ensure uninterrupted workflow execution while checking the intermediate data, and assessing the confidence of these data. Inspired by moving target defence, they propose a dynamic task scheduling strategy based on resource circulation to periodically generate and recycle task executors, keeping the clean state of the workflow execution environment. Furthermore, temporary workflow intermediate data backup mechanism is presented, the stored intermediate data can be used for the re-execution of workflow sub-tasks with low confidence. Experiments are conducted in both the actual test environment based on OpenStack and the simulated test environment based on WorkflowSim toolkit. Experimental results demonstrate that the proposed system can effectively enhance intrusion tolerance of scientific workflows.
An alternative way of estimating a cumulative logistic model with complex survey data

Introduction: Fitting a Regression Model with Complex Survey Data The standard “design-based” framework for fitting a regression model to survey data was introduced by Fuller (1975) for linear regression and by Binder (1983) more generally. This framework treats the finite population as a realization of independent trials from a conceptual population. A maximum likelihood regression estimator could, in principle, be estimated from the finite-population values. The goal in the Fuller/Binder framework is to estimate the conceptual maximum-likelihood estimator, or its limit as the population grows arbitrarily large, from survey data. Skinner (1989) refers to this as the “pseudo-maximum-likelihood” approach.
Predicting the diffusion of brand's stories in social network

The emergence and growing of social media allows one con-sumer to communicate with thousands or millions other consumers. The consumer-generated stories about a brand or a product can be widely propagated and as a consequence can have a big impact on the market-place and indirectly aﬀect the success of the brand. Therefore, modeling the information diﬀusion in social media is crucial for business managers in order to both understand the information propagation and to better control it. Our research aims at predicting whether a tweet about a brand is going to be diﬀused and the level of the diﬀusion. We apply several machine learning classiﬁers using user-based, time-based and content-based features associated to tweets and developed several new features some content-based. We show that our method signiﬁcantly improves F-measure by about 4% compared to the state of art. We also show that the numbers of a user’s followers, number of communities that this user belongs to, and number of likes that a user has made on his time line are the most important features for the predictive model.
Provenance Analysis for Sensemaking

The articles in this special section examine the concept of "sensemaking", which refers to how we structure the unknown so as to be able to act in it. In the context of data analysis it involves understanding the data, generating hypotheses, selecting analysis methods, creating novel solutions, and critical thinking and learning wherever needed. Due to its explorative and creative nature, sensemaking is arguably the most challenging part of any data analysis.
Hybrid Learning Approach Based Aspect Category Detection for Sentiment Summarization with Co-Occurrence Data

User-generated reviews are precious decision-making resources. Identifying the feature categories mentioned in a specified review phrase (e.g. "food" and "service" in restaurant reviews) is a significant task for analyzing sentiment and mining opinion. Most prior researchers hold hand-crafted characteristics and a classification algorithm to achieve the assignment given a predefined aspect category set. The key step to achieve better efficiency is feature engineering that consumes a great deal of human effort and can be volatile when the product domain changes. A hybrid learning method is suggested in this project to automatically learn helpful characteristics for the identification of aspect categories. Specifically, on a big collection of reviews with noisy labels, a Hybrid Aspect Analysis Algorithm is first suggested to achieve ongoing word depictions. We subsequently suggest generating deeper and hybrid characteristics through the stacked neural networks on the word vectors. Finally, a logistic regression classifier is trained to predict the aspect category with hybrid characteristics. The tests are conducted on a SemEval-2014 benchmark dataset. In this paper we achieves the state of the art results with the F1 score of 90.10% on the dataset. Overall, our approach to representation learning outperforms traditional hand-crafted characteristics and embedding algorithms with current words.
<inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="modarressi-ieq1-2948345.gif"/></alternatives></inline-formula>NN: Power-Efficient Neural Network Acceleration Using Differential Weights

The enormous and ever-increasing complexity of state-of-the-art neural networks has impeded the deployment of deep learning on resource-limited embedded and mobile devices. To reduce the complexity of neural networks, this article presents $\Delta$ΔNN, a power-efficient architecture that leverages a combination of the approximate value locality of neuron weights and algorithmic structure of neural networks. $\Delta$ΔNN keeps each weight as its difference ($\Delta$Δ) to the nearest smaller weight: each weight reuses the calculations of the smaller weight, followed by a calculation on the $\Delta$Δ value to make up the difference. We also round up/down the $\Delta$Δ to the closest power of two numbers to further reduce complexity. The experimental results show that $\Delta$ΔNN boosts the average performance by 14%–37% and reduces the average power consumption by 17%–49% over some state-of-the-art neural network designs.
Local search metaheuristics for Capacitated Vehicle Routing Problem: a comparative study

This study is concerned with local search metaheuristics for solving Capacitated Vehicle Routing Problem (CVRP). In this problem the optimal design of routes for a fleet of vehicles with a limited capacity to serve a set of customers must be found. The problem is NP-hard, therefore heuristic algorithms which provide near-optimal polynomial-time solutions are still actual. This experimental analysis is a continue of previous research on construction heuristics for CVRP. It was investigated before that Clarke and Wright Savings (CWS) heuristic is the best among constructive algorithms except for a few instances with geometric type of clients’ distribution where Nearest Neighbor (NN) heuristic is better. The aim of this work is to make a comparison of best-known local search metaheuristics by criteria of error rate and running time with CWS or NN as initial algorithms because there were not found any such state-of-the-art comparative study. An experimental comparison is made using 8 datasets from well-known library because it is interesting to analyze “effectiveness” of algorithms depending on type of input data. Overall, five different groups of Pareto optimal algorithms are defined and described. Abstract . Задача маршрутизации – одна из широко известных задач комбинаторной оптимизации. Она состоит в
Validating the traditional use of medicinal plants in Maputaland to treat skin diseases

iv Publications and conference presentations.vi Table of contents.viii List of figures.xiii List of tables.xvii Abbreviations.xx Chapter one: General introduction 1.1 Overview of skin diseases.1 1.1.1 Bacterial skin infections.2 1.1.2 Fungal skin infections.4 1.1.3 Viral skin infections.5 1.1.4 Inflammatory skin disorders.6 1.1.5 Prevalence of skin diseases.8 1.2 Treatment of skin diseases.10 ix 1.2. 1 Conventional approaches and their limitations.10 1.2. 2 Medicinal plants commonly used to treat skin inflictions.11 1.2. 3 Ethnobotanical and ethnopharmacological approaches to the healing of skin diseases.13 1.2. 4 Plant combinations used to treat skin diseases.15 1.3 Phytochemical evaluation of topically administered plant preparations.15 1.4 Overview of the study area and the context of this study.16 1.5 Aims and objectives of the study.27 Chapter two: Antimicrobial screening of individual plants used in northern Maputaland to treat skin diseases 2.
An exploration of the current state-of-the-art in automatic music transcription - with proposed improvements using machine learning

The research field of automatic music transcription has vastly grown during the 21st century, where the goal is to transcribe a polyphonic music signal into annotated sheet music. Within this field, the subproblem of fundamental frequency estimation in a piece of music is a difficult problem, e.g., due to dissimilar structures in signals from different instruments playing the same note. This becomes further convoluted in a polyphonic signal consisting of several notes, where the harmonic overtones of the notes interact. To solve this and other issues, machine learning techniques have furthered the research in music transcription, which is the main focus of this thesis. This is undertaken by comparing the best performing fundamental frequency estimators from recent years, mainly from MIREX competitions from 2015-2017. These are recreated and evaluated on a customized test set consisting of MIDI files of various instruments. The evaluation consists both of typical music transcription measures such as precision, recall and accuracy, but also by deeper analysis in order to find the large-scale structural biases. The evaluation of the tests herein shows that the best performing models are THK1 and CT1 from MIREX 2017 which are based on CNN. This work has identified some structural errors in these methods pointing out potential for further improvements. In addition, a novel approach of applying complex-valued neural networks in music transcription is also examined, by modifying research in an existing deep complex neural network model. The proposed and improved model finishes on third place in the evaluation, indicating that complex neural networks may develop the research area of music transcription even further. (Less)