Miracle-dz/UniFAR-FTUs huggingface dataset:
High-resolution material structuring using ultrafast laser non-diffractive beams

ABSTRACT Scales in the 100 nm range represent a generic cornerstone for laser material processing, enabling novel size-dependent functions on surfaces and in the bulk and thus a new range of technological applications. On these scales, the processed material acquires optical, transport or contact properties that do not only rely on local effects on singular topographic features but involve increasingly collective behaviors. Rapid access to sub-100 nm features with intense coherent light represents nevertheless a challenge in laser structuring in view of the optical diffraction limit. Ultrafast non-diffractive beams with controllable time envelopes can overcome this limit and achieve super-resolved processing, a prerequisite for the next generation of flexible and precise material processing tools. They show a remarkable capacity of structuring transparent materials with high degree of accuracy and exceptional aspect ratio. This capacity relies on triggering fast hydrodynamic and material fracture effects with characteristic spatial scales in the nm range. Reviewing the present achievements and technical potential, we discuss from a dynamic viewpoint the physical mechanisms enabling structural features beyond diffraction limit achieved using ultrafast Bessel beams and indicate applications of high technical relevance. Abbreviations: PCM: phase contrast microscopy; SEM: scanning electron microscopy; NBOHC: non-bridging oxygen hole center
Sales Forecasting: Machine Learning Solution to B2B Sales Opportunity Win-Propensity Computation

This research is focused on finding an optimal machine learning solution for computation of a sales win-propensity score for B2B software sales. Currently sales propensity scoring at opportunity level is a manual, time-consuming and subjective task carried out by a salesperson. Customised model stack involving Random Forests, GLM, boosting, trees, and neural networks is a proposed solution in this research. Unlike reviewed related works, research focused on developing an approach using open-source tools and commonly met sales variables, hence proposed solution can be lifted and re-purposed cross-industry. Integration of provided solution into CRM will allow for accuracy improvement in sales forecasting, result in better planning, more effective resource management, saving time and costs.
Early Prediction of Patient Mortality Based on Routine Laboratory Tests and Predictive Models in Critically Ill Patients

We propose a method for quantitative analysis of predictive power of laboratory tests and early detection of mortality risk by usage of predictive models and feature selection techniques. Our method allows automatic feature selection, model selection, and evalu- ation of predictive models. Experimental evaluation was conducted on patients with renal failure admitted to ICUs (medical intensive care, surgical intensive care, cardiac, and cardiac surgery recovery units) at Boston’s Beth Israel Deaconess Medical Center. Data are extracted from Multi parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database. We built and evaluated different single (e.g. Logistic regression) and ensemble (e.g. Random Forest) learning methods. Results revealed high predictive accuracy (area under the precision-recall curve (AUPRC) values >86%) from day four, with acceptable results on the second (>81%) and third day (>85%). Random forests seem to provide the best predictive accuracy. Feature selection techniques Gini and ReliefF scored best in most cases. Lactate, white blood cells, sodium, anion gap, chloride, bicar - bonate, creatinine, urea nitrogen, potassium, glucose, INR, hemoglobin, phosphate, total bilirubin, and base excess were most predictive for hospital mortality. Ensemble learn- ing methods are able to predict hospital mortality with high accuracy, based on laboratory tests and provide ranking in predictive priority.
Characterization of the behaviour of welded steel joints through modal components

The analysis and design of joints constitutes one of the most important aspects when designing steel structures. It is a common practice to model the joint behaviour by zero‐length rotational springs placed between the beam and corresponding column, or by mechanical models composed of springs and rigid bars. These models have serious limitations when considering the interactions between the shear, bending and axial forces and deformations of the panel zone.
Distributed and Adaptive Efficient Energy Aware Routing Procedure for MANETs

In mobile ad hoc networks {MANET}, power or energy consumption is a complex representation to define extensive communication between different nodes. To define energy routing scenario with respect to efficient energy maintenance to decrease the over burden of the protocol hierarchy wireless communication. To achieve these goals, a Synchronization-based Efficient Energy (SEE) calculation is supported along with the position opportunistic routing protocol previously to reduce overhead burden of server to maintain efficient energy. It consists distributed and random nature in which node consists local communication by facilitate packet transmitting in between different intermediate nodes based on backward communication. Because of random routing scenario of ad hoc networks, maintenance of capacity of each node for relay configuration at each node is a complex task. Due to that further extension of SEE, we introduce Collaborative & Efficient Energy Aware Distributed (CEEAD) approach, which consists routing representation in energy awareness scenario for wireless networks and it also supports cluster head representation of each node based on its random changes of power consumption in network communication. Our proposed approach consists cluster connectivity at each node because random routing process for wireless ad hoc networks. Our experimental results show effective increasing of network life time with scalable data communication in wireless networks.
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains—astronomy, genetics, and material science—via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.
Concept discovery and argument bundles in the web of experiences

Millions of people interact and share interesting information every day in the Social Web. From daily conversations to comments about products in e-commerce sites, the content generated by people in these sites is huge and diverse. Among the wide diversity of user-contributed content on the web, there is a particular kind that has the potential of being put to good use by intelligent systems: human experiences. People very often use other people's experiences before making decisions, and when these kind of human experiences are expressed and recorded on the web, they can be shared with by large number of people. Nevertheless sometimes this content is not easily accessible, so a person trying to book a hotel may read a few reviews over a few hotels - but cannot possibly read them all. There is a clear need for an in-depth analysis of this kind of information, based on textual expressions of human particular experiences. Our approach, in the framework of the Web of Experiences, aims at acquiring practical knowledge from individual experiences with entities in the real world expressed in textual form. Moreover, this knowledge has to be represented in a way that facilitates the reuse of the experiential knowledge by other individuals with different preferences. Our approach has three stages: First, we extract the most salient set of aspects used by the individuals to describe their experiences with the entities in a domain. Second, using the set of extracted aspects, we group them in concepts to create a concept vocabulary that models the set of issues addressed in the reviews. Third, using the vocabulary of concepts, we create a bundle of arguments for each entity. An argument bundle characterizes the pros and cons of an entity, aggregating practical knowledge from judgments written by individuals with different biases and preferences. Moreover, we show how argument bundles allow us to define the notions of user query and the satisfaction degree of a bundle by a user query, proving that argument bundles are not only capable of representing practical knowledge but they are also useful to perform inference given a set of user preferences specified in a query. We evaluate the argument bundles of our approach with the Amazon score ratings and the camera characterizations of Dpreview. We show that pro and con arguments are very close to those listed in Dpreview. Evaluating entity rankings, we show that Dpreview and our approach give congruent rankings, while Amazon's is not congruent neither with Dpreview's or ours.
Experimental Validation of INS Monitor against GNSS Spoofing

Global Navigation Satellite System (GNSS) spoofing attacks are a critical threat to navigation integrity. We previously developed an analytical framework to evaluate the performance of Inertial Navigation System (INS) monitors against spoofing attacks, which is necessary for eventual clarification and certification of the INS monitor. In this paper, we develop and execute an experimental setup to verify the resistance of the monitor against worstcase GNSS spoofing attacks. The test setup includes generation of the worst-case spoofed code and carrier phase GPS signals (using a GNSS simulator) that feed the receiver through a cable without needing to broadcast via transmitter. Using these signals, the miss-detection rates of the Kalman filter innovations-based monitor, has been observed through a tightly-coupled integration of a tactical grade inertial and a standalone GNSS receiver on a static platform. The experimental evaluation technique in this paper is fundamental because it is the first hardware test to quantify spoofing resistance of a widely used IMU with worst-case spoofing signals. We realistically demonstrate that, even if the spoofer tracks the position of the receiver and filters the high-frequency tracking errors, the monitor is still capable of detecting that with low missed detection probability.
Comparative Study of Optimized Wireless Sensor Network Routing Protocols

Nowadays, Wireless sensor network applications are very important in most industrial fields. Most strategies are used to save energy of WSNs to prolong their life time. This problem attracted attention of many researches and many methods were proposed to optimize the energy consumption of WSNs. This paper presents a comparative study between two meta-heuristics algorithms are used to optimize this problem. One of them is the improved harmony search algorithm, and the other is the particle swarm optimization algorithm. Showing the strategies which are used to build its routing protocol and overviews on optimization algorithm which depend on build its solutions. Energy model used to calculate fitness of each solution. Improved algorithms are used in routing to solve the proposed problem. And propose termination criteria, simulation and results for each algorithm. At last this of them preferred to implement.
A New Approach for Customer Churn Prediction in Telecom Industry

Since its inception, the field of Data Mining and Knowledge Discovery from Databases has been driven by the need to solve many practical problems. With the rapid development of telecommunication industry, the service providers are inclined more towards expansion of the subscriber base. To meet the need of surviving in the competitive environment, the retention of existing customers has become a huge challenge. In the survey done in the Telecom industry, it is stated that the cost of acquiring a new customer is far more that retaining the existing one. Therefore, by collecting knowledge from the telecom industries can help in predicting the association of the customers as whether or not they will leave the company. The required action needs to be undertaken by the telecom industries in order to initiate the acquisition of their associated customers for making their market value stagnant. This paper describe a framework that was proposed to conduct for the churn prediction model using Naïve Bayes algorithm for classification task and then apply Elephant Herding Optimization algorithm for solving optimization task. Elephant Herding Optimization is a metaheuristic algorithm. The proposed methodology thereby generates optimal customers who will leave the service provider which is
Sistem Monitoring Suhu, Kelembaban, Dan Pengenendali Penyiraman Tanaman Hidroponik Menggunakan Blynk Android

Hidroponik merupakan cara bercocok tanam yang tidak menggunakan tanah sebagai media tanam, tetapi hanya menggunakan air yang mengandung nutrisi yang diperlukan tanaman. Salah satu faktor keberhasilan penanaman dengan metode ini dipengaruhi oleh bagaimana cara pemilik melakukan perawatan untuk tanamannya. Perawatan dilakukan utamanya untuk memastikan sirkulasi atau penyiraman air nutrisi tersebut diberikan sesuai waktunya dalam jumlah yang cukup. Ada saatnya pemilik tanaman hidroponik tidak berada di dekat area penanaman tersebut sehingga tidak dapat secara langsung melakukan perawatan terhadap tanamannya. Agar pemilik tanaman hidroponik tetap dapat merawat dan memantau kondisi lingkungan tanamannya meskipun jauh dari lokasi penanaman, proyek dalam skripsi ini memberikan alternatif solusi untuk mengatasi permasalahan tersebut. Alat yang dibuat dalam proyek ini menggabungkan kemampuan arduino mega sebagai sistem akuisisi data yang dilengkapi ethernet shield untuk pengiriman data melalui jaringan internet, sensor DHT11 untuk membaca suhu dan kelembaban, aplikasi khusus android blynk sebagai alat bantu pemantuan, dan RTC untuk pewaktuan secara real time. Arduino Mega juga dihubungkan dengan relay untuk mengatur penyalaan pompa penyiram atau sirkulator air. Berdasarkan pengujian yang dilakukan didapatkan bahwa setiap modul dapat bekerja dengan baik sesuai fungsnya. Dengan kualitas jaringan sesuai pengujian (delay rata-rata ke server blynk 1242ms, diperoleh proses pengiriman perintah dan ekseskusi penyiraman air dan pengiriman data lingkungan memerlukan waktu sekitar 1-2 menit.
Detection of Possible Match-fixing in Tennis Games

: This study seeks ways that can immediately detect match-fixing in a tennis match. We first explore the number of rallies observed in tennis matches of the ATP and WTA leagues to determine whether they follow the Benford’s law. We also artificially manipulate practice games to investigate whether the number of ralleys observed in manipulated matches also follows the Benford’s law. Experimental results demonstrate that the numbers collected from fixed games and the expected frequencies predicted by Benford’s Law are different. Based on the lessons learned, we develop a machine-learning-based model for detecting whether a given match is fixed or not. Our model shows a high accuracy in detecting fixed tennis matches, which has a great utility for fair tennis play.
Forget Memory: Creating Better Lives for People with Dementia

Memory loss can be one of the most terrifying aspects of a diagnosis of dementia. Yet the fear and dread of losing our memory make the experience of the disease worse than it needs to be, according to cultural critic and playwright Anne Davis Basting. She says, Forget memory. Basting emphasizes the importance of activities that focus on the present to improve the lives of persons with Alzheimer's disease and other dementias. Based on ten years of practice and research in the field, Basting's study includes specific examples of innovative programs that stimulate growth, humor, and emotional connection; translates into accessible language a wide range of provocative academic works on memory; and addresses how advances in medical research and clinical practice are already pushing radical changes in care for persons with dementia. Bold, optimistic, and innovative, Basting's cultural critique of dementia care offers a vision for how we can change the way we think about and care for people with memory loss.
Design of an on-chip Hilbert fractal inductor using an improved feed forward neural network for Si RFICs

This paper presents an efficient modeling of Hilbert fractal inductors by improved feed forward neural network trained hybrid particle swarm optimization and gravitational search algorithm (FNNPSOGSA). The proposed model computes the effective inductance value (L) and quality factor (Q) of Hilbert fractal inductors with metal trace width, effective fractal length, frequency, and oxide thickness as input parameters. In contrast to the traditional feed forward neural network, the proposed FNNPSOGSA has been designed with fewer hidden neurons with much-enhanced learning and generalization capabilities. As a consequence, the proposed model achieves better speed and is as accurate as electromagnetic simulations. From the simulation results, it is proved that the proposed model is a good alternative for complex fractal inductor design.
Otantiklik Ölçeğinin Türkçe’ye Uyarlanması: Geçerlik ve Güvenirlik Çalışması

Bu calismada Otantiklik Olcegi (Authenticity Scale) Turkce formunun psikometrik ozellikleri incelenmistir. Arastirma iki asamada gerceklestirilmistir. Birinci asamada olcegin Turkce formu lisans ve ogretmenlik sertifika programinda egitim goren 165 (97 kadin, 68 erkek) universite ogrencisine uygulanmis, toplanan veriler uzerinden dogrulayici faktor analizi yapilmistir. Arastirmanin ikinci asamasinda ise 240 (161 kadin, 79 erkek) universite ogrencisine ulasilmis ve Otantiklik Olceginin uyum ve yordama gecerligi incelenmistir. Dogrulayici faktor analizinde uyum indeksi degerleri (/ sd = 1.49, RMSEA = . 05, CFI = . 95, IFI = . 95, GFI = . 92 olarak bulunmustur. Olcegin Cronbach Alpha ic tutarlilik katsayilari sirasiyla Yabancilasma icin . 79, Dis Etkileri Kabullenme icin . 67 ve Otantik Yasam icin . 62 olarak bulunmustur. Olcegin uyum gecerligi icin yapilan analizde ise her bir alt boyut oznel iyi olus ve ozerklik ihtiyaci doyumu ile anlamli duzeyde iliskili bulunmustur. Ayrica ozerklik doyumu kontrol edilerek yapilan asamali regresyon analizinde, otantikligin oznel iyi olusa ait varyansa onemli duzeyde katki sagladigi gorulmustur. Sonuc olarak, bulgular Otantiklik Olceginin Turkce formunun gecerlik ve guvenirligini destekler niteliktedir.
Level of Reflective Thinking of Undergraduate and Higher Education Students at An-Najah National University

The purpose of this study was to determine the level of reflective thinking of bachelor and higher education students at An-najah National University, furthermore, determine the differences in reflective thinking according to type of college, gender and academic level variables. To achieve these purposes Eysenck & Welson Reflectiveness scale had been applied to (641) students. The results revealed a good level of reflective thinking of students at An-Najah National University, where the mean of response was (23.21) degree out of (30), with percentage (77.66%). In addition, the results revealed that there were significant differences in reflective thinking due to type of college and academic level variables, while there were no significant differences in reflective thinking due to gender. Based on the study findings, the researcher recommended to encourage academic staff in participating in thinking training programs. هدفت هذه الدراسة تحديد مستوى التفكير ألتأملي لدى طلبة البكالوريوس والدراسات العليا في جامعة النجاح الوطنية، إضافة إلى تحديد الفروق في مستوى التفكير ألتأملي تبعا لمتغيرات، نوع الكلية، والجنس، والمستوى الدراسي. ولتحقيق ذلك أجريت الدراسة على عينة قوامها (641) طالبا وطالبة، وذلك بواقع (550) من طلبة البكالوريوس و(91) من طلبة الماجستير، ولغاية قياس التفكير ألتأملي طبق مقياس ايزنك وولسون والذي اشتمل على (30) فقرة. أظهرت نتائج الدراسة أن مستوى التفكير ألتأملي لدى طلبة البكالوريوس والدراسات العليا في جامعة النجاح الوطنية كان جيدا حيث وصل المتوسط الحسابي للإجابة على مقياس ايزنك وولسون إلى (23.21) درجة من أصل (30) درجة، وبنسبة مئوية (77.66%). كما أظهرت النتائج انه توجد فروق ذات دلالة إحصائية عند مستوى الدلالة (? =0.05) في مستوى التفكير ألتأملي لدى طلبة البكالوريوس والدراسات العليا في جامعة النجاح الوطنية بين طلبة الكليات العلمية والإنسانية ولصالح طلبة الكليات الإنسانية، وبين طلبة البكالوريوس والماجستير ولصالح طلبة الماجستير، بينما لم تكن الفروق دالة إحصائيا تبعا للجنس.
Ranking the social media platform user pages using Big Data

Проаналiзовано платформи соцiальних середовищ Iнтернету залежно вiд їхнього кон- тенту. Здiйснено класифiкацiю, яка дала змогу виокремити групи за певними озна- ками. Для ранжування сторiнок користувачiв вiртуальних спiльнот запропоновано використовувати модифiкований алгоритм PageRank. Побудовано пiдхiд, який осно- вується на використаннi лексичного аналiзу, алгоритму ранжування та упорядкуван- ня даних з використанням парадигми MapReduce. Реалiзовано програмне забезпечен- ня для ранжування сторiнок користувачiв. Проаналiзовано результати оброблених даних та формування PageRank користувачiв платформи.
Intelligent Fault Diagnosis of Rotating Machinery Using ICD and Generalized Composite Multi-Scale Fuzzy Entropy

A new intelligent fault diagnosis algorithm of rotating machinery based on intrinsic characteristic-scale decomposition (ICD), generalized composite multi-scale fuzzy entropy (GCMFE), Laplacian score (LS), and particle swarm optimization-based support vector machine (PSO-SVM) is proposed in this paper. First, ICD is applied to decompose a vibration signal into a sum of product components. Second, GCMFE is proposed to evaluate the complexity of the decomposed vibration signals. GCMFE can overcome the drawbacks of the MFE method, and the superiority of GCMFE is validated using a simulated signal. Third, the LS method is utilized to select the extracted fault features. In the end, the selected features are input into the PSO-SVM to classify different health conditions. The simulated and experimental results validate the superiority of the proposed method in fault feature extraction compared with three other methods: ICD-MFE, ICD-CMFE, and GCMFE.