Miracle-dz/UniFAR-FTUs huggingface dataset:
Network analysis, architecture, and design

Traditionally, networking has had little or no basis in analysis or architectural development, with designers relying on technologies they are most familiar with or being influenced by vendors or consultants. However, the landscape of networking has changed so that network services have now become one of the most important factors to the success of many third generation networks. It has become an important feature of the designer? s job to define the problems that exist in his network, choose and analyze several optimization parameters during the analysis process, and then prioritize and evaluate these parameters in the architecture and design of the system.Network Analysis, Architecture, and Design, 3e, uses a systems methodology approach to teaching these concepts, which views the network (and the environment it impacts) as part of the larger system, looking at interactions and dependencies between the network and its users, applications, and devices. This approach matches the new business climate where customers drive the development of new services and the book discusses how networks can be architected and designed to provide many different types of services to customers. With a number of examples, analogies, instructor tips, and exercises, this book works through the processes of analysis, architecture, and design step by step, giving designers a solid resource for making good design decisions.With examples, guidelines, and general principles McCabe illuminates how a network begins as a concept, is built with addressing protocol, routing, and management, and harmonizes with the interconnected technology around it. Other topics covered in the book are learning to recognize problems in initial design, analyzing optimization parameters, and then prioritizing these parameters and incorporating them into the architecture and design of the system. This is an essential book for any professional that will be designing or working with a network on a routine basis. *Substantially updated design content includes ad hoc networks, GMPLS, IPv6, and mobile networking*Written by an expert in the field that has designed several large-scale networks for government agencies, universities, and corporations*Incorporates real-life ideas and experiences of many expert designers along with case studies and end-of-chapter exercises
A phylogenetic approach to chemical tagging. Reassembling open cluster stars

Context. The chemical tagging technique is a promising approach to reconstruct the history of the Galaxy by only using stellar chemical abundances. Different studies have undertaken this analysis and they raised several challenges. Aims. Using a sample of open clusters stars, we wish to address two issues: minimize chemical abundance differences which origin is linked to the evolutionary stage of the stars and not their original composition; evaluate a phylogenetic approach to group stars based on their chemical composition. Methods. We derived differential chemical abundances for 207 stars (belonging to 34 open clusters) using the Sun as reference star (classical approach) and a dwarf plus a giant star from the open cluster M67 as reference (new approach). These abundances were then used to perform two phylogenetic analyses, cladistics (Maximum Parsimony) and Neighbour-Joining, together with a partitioning unsupervised classification analysis with k-means. The resulting groupings were finally confronted to the true open cluster memberships of the stars. Results. We successfully reconstruct most of the original open clusters when carefully selecting a subset of the abundances derived differentially with respect to M67. We find a set of eight chemical elements that yields the best result, and discuss the possible reasons for them to be good tracers of the history of the Galaxy. Conclusions. Our study shows that unraveling the history of the Galaxy by only using stellar chemical abundances is greatly improved provided that i) we perform a differential spectroscopic analysis with respect to an open cluster instead of the Sun, ii) select the chemical elements that are good tracers of the history of the Galaxy, and iii) use tools that are adapted to detect evolutionary tracks such as phylogenetic approaches.
Measurement and Comparison of Efficiency of Saving and Loan Cooperatives in Jakarta, Indonesia, through the Stochastic Frontier Analysis Sudarmadji

Savings and loan cooperatives play an important role in the Indonesian economy as they also contribute to providing capital in the local market. This study examines the efficiency of savings and loan cooperatives in terms of number of members, total capital and operating costs and their effects on profits, total assets, disbursed loans and debt repayments. Two types of savings and loan cooperatives studied are savings and loan cooperatives owned by government employees and savings and loan cooperatives owned by the general public (community owned). The efficiency of these two types of cooperatives is studied separately and comparatively using the parametric Stochastic Frontier Analysis (SFA) method. and the research involved a total of 22 cooperatives from five areas in Jakarta. The results of the study used the SFA method and found that the average efficiency of community-owned cooperatives was greater than cooperatives owned by government employees.
The Charlotte (TM) intra-vehicular robot

NASA has identified telerobotics and telescience as essential technologies to reduce the crew extra-vehicular activity (EVA) and intra-vehicular activity (IVA) workloads. Under this project, we are developing and flight testing a novel IVA robot to relieve the crew of tedious and routine tasks. Through ground telerobotic control of this robot, we will enable ground researchers to routinely interact with experiments in space. Our approach is to develop an IVA robot system incrementally by employing a series of flight tests with increasing complexity. This approach has the advantages of providing an early IVA capability that can assist the crew, demonstrate capabilities that ground researchers can be confident of in planning for future experiments, and allow incremental refinement of system capabilities and insertion of new technology. In parallel with this approach to flight testing, we seek to establish ground test beds, in which the requirements of payload experimenters can be further investigated. In 1993 we reviewed manifested SpaceHab experiments and defined IVA robot requirements to assist in their operation. We also examined previous IVA robot designs and assessed them against flight requirements. We rejected previous design concepts on the basis of threat to crew safety, operability, and maintainability. Based on this insight, we developed an entirely new concept for IVA robotics, the CHARLOTTE robot system. Ground based testing of a prototype version of the system has already proven its ability to perform most common tasks demanded of the crew, including operation of switches, buttons, knobs, dials, and performing video surveys of experiments and switch panels.
Mutual query data sharing protocol for public key encryption through chosen-ciphertext attack in cloud environment

In this paper, we are proposing a mutual query data sharing protocol (MQDS) to overcome the encryption or decryption time limitations of exiting protocols like Boneh, rivest shamir adleman (RSA), Multi-bit transposed ring learning parity with noise (TRLPN), ring learning parity with noise (Ring-LPN) cryptosystem, key-Ordered decisional learning parity with noise (kO-DLPN), and KD_CS protocol’s. Titled scheme is to provide the security for the authenticated user data among the distributed physical users and devices. The proposed data sharing protocol is designed to resist the chosen-ciphertext attack (CCA) under the hardness solution for the query shared-strong diffie-hellman (SDH) problem. The evaluation of proposed work with the existing data sharing protocols in computational and communication overhead through their response time is evaluated.
International Journal of Electrical and Computer Engineering (IJECE)

Nur Adrinna Shafiqa Zakaria, Dalila Mat Said, Norzanah Rosmin, Nasarudin Ahmad, Mohamad Shazwan Shah Jamil, Sohrab Mirsaeidi School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia Centre of Electrical Energy Systems, Universiti Teknologi Malaysia, Johor Bahru, Malaysia Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru, Malaysia School of Electrical Engineering, Beijing Jiaotong University, Beijing, China
Optimizing Conditional Value-At-Risk of Black-Box Functions

This paper presents two Bayesian optimization (BO) algorithms with theoretical performance guarantee to maximize the conditional value-at-risk (CVaR) of a black-box function: CV-UCB and CV-TS which are based on the well-established principle of optimism in the face of uncertainty and Thompson sampling, respectively. To achieve this, we develop an upper conﬁdence bound of CVaR and prove the no-regret guarantee of CV-UCB by utilizing an interesting connection between CVaR and value-at-risk (VaR). For CV-TS, though it is straightforwardly performed with Thompson sampling, bounding its Bayesian regret is non-trivial because it requires a tail expectation bound for the distribution of CVaR of a black-box function, which has not been shown in the literature. The performances of both CV-UCB and CV-TS are empirically evaluated in optimizing CVaR of synthetic benchmark functions and simulated real-world optimization problems.
Physics Interpretable Shallow-Deep Neural Networks for Physical System Identification with Unobservability

The large amount of data collected in complex physical systems allows machine learning models to solve a variety of prediction problems. However, the directly applied learning approaches, especially deep neural networks (DNN), are difficult to balance between universal approximation to minimize error and the interpretability to reveal underlying physical law. Their performance drops even faster with system unobservability (of measurements) issues due to limited measurements. In this paper, we construct the novel physics interpretable shallow-deep neural networks to integrate exact physical interpretation and universal approximation to address the concerns in previous methods. We show that not only the shallow layer of the structural DNN extracts interpretable physical features but also the designed physical-input convex property of the DNN guarantees the true physical function recovery. While input convexity conditions are strict, the proposed model retains the representation capability to universally approximate for the unobservable system regions. We demonstrate its effectiveness by experiments on physical systems. In particular, we implement the proposed model on the forward kinematics and complex power flow reproduction tasks, with or without observability issues. We show that, besides the physical interpretability, our model provides consistently smaller or similar prediction error for system identification, compared to the state-of-art learning methods.
μ-Plasma assisted deposition of Titanium Dioxide thin films

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
The XXL Survey

We present the version of the point source catalogue of the XXL Survey that was used, in part, in the first series of XXL papers. In this paper we release, in our database in Milan and at CDS: (i) the X-ray source catalogue with 26 056 objects in two areas of 25 deg2 with a flux limit (at 3σ) of ~10−15 erg s−1 cm−2 in [0.5–2] keV, and ~ 3 × 10−15 erg s−1 cm−2 in [2–10] keV, yielding a 90% completeness limit of 5.8 × 10−15 and 3.8 × 10−14 respectively;(ii) the associated multiwavelength catalogues with candidate counterparts of the X-ray sources in the infrared, near-infrared, optical, and ultraviolet (plus spectroscopic redshift when available); and (iii) a catalogue of spectroscopic redshifts recently obtained in the southern XXL area. We also present the basic properties of the X-ray point sources and their counterparts. Other catalogues described in the second series of XXL papers will be released contextually, and will constitute the second XXLdata release.
An Overview of Raman Spectroscopy as Applied to Lignocellulosic Materials. Chapter 9

Compared with most materials, lignocellulosics have only been recently studied using Raman spectroscopy. Although Raman spectroscopy has been around for more than 60 years, compared with infrared (IR) spectroscopy its usage (even in other disciplines) has not been wide spread. This is because of a number of factors, including cost of Raman instrumentation, user unfamiliarity with the technique, and an impression that Raman provides information that is already available by IR. Actually, both Raman and IR spectroscopes complement each other.
Virtual data integration for a clinical decision support systems

Clinical decision support (CDS) supplies clinicians and their patients, and relevant staff with meaningful and timely information intelligently integrated or visualized to enhance health and the health sector. Data is the backbone of decision support systems, especially (clinical) ones. Data integration (either virtual or physical manner) is a powerful technique to manipulate a vast amount of heterogeneous data and prepare it as input for the decision-making process. The difficulties in manipulating data that have a physical data integration technique motivated the decision support developers to tend to data virtualization as a data integration technique. In this paper, a clinical decision support system was developed using the virtual data integration technique. The developed system was evaluated in terms of usability and its capability of providing clinical decision support. The evaluation findings indicate that the proposed system is highly usable and has a positive impact on supporting the clinical decision-making process.
Normal pressure hydrocephalus . Results of shunting correlated with diagnostic tests in 55 patients

Object: The purpose of this study was to evaluate the efficacy of ventricular shunting in normal pressure hydrocephalus (NPH). Methods: The authors retrospectively studied outcomes in NPH patients in whom shunts were implanted. At follow-up examinations up to one year, clinical symptoms were recorded according to a new NPH outcome rating scale, the modified Rankin score and the Mini-Mental State Examination. Comorbidities and complications during this period were also analyzed. Results: A total of 55 patients with NPH were included, with a male predominance (69% of men), an average age of 70 years. Comorbidities are multiple, hypertension is the most common factor in 55% of cases. 76% of patients are classified as having idiopathic NPH. The CSF tap test allows the evacuation of at least 40 ml of CSF and RCSF measurements in 55 patients. All these patients received a shunt withe programmable valve. An improvement in gait was observed in 88% of patients, 91% in balance and 84% in continence. The improvement in the field of neuropsychology is 34% after one year of shunt. 87% of patients were improved according to mRS. 27% of patients had complications and were treated conservatively or surgically. Conclusions: Ventricular shunt implantation is a safe procedure and can improve symptoms in 98% of patients with NPH in the medium term.
Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature

Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The disease can lead a person to death or a high level of body damage. To avoid significant damage, patients need sugar. The research aims at implementing an automatic system to detect hypoglycemia and perform automatic sugar injections to save a life. Receiving the benefits of the internet of things (IoT), the sensor’s data was transferred using the hypertext transfer protocol (HTTP) protocol. To ensure the safety of health-related data, blockchain technology was utilized. The glucose sensor and smartwatch data were processed via Fog and sent to the cloud. A Random Forest algorithm was proposed and utilized to decide hypoglycemic events. When the hypoglycemic event was detected, the system sent a notification to the mobile application and auto-injection device to push the condensed sugar into the victim’s body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed model's performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The system’s performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease.
Special Issue on Designing the future of technology with and for children

This special issue is a result of two workshops with the topic of Researchers' toolbox for the future . This workshop series is dedicated towards exploring, discussing, redefining, and formulating methods for imagining the future of technology, one that is empowering albeit provocative. In these workshops, we build on critical and/or future-oriented methods such as critical design, speculative design, design fictions, and others. Our goal is to compile a methodological toolbox for the future of human-computer interaction research; one that enables us to inquire, design, and critically examine our technological futures. The first workshop was held as part of the International Conference on Interaction Design and Children (IDC’20) in June 2020 and the second workshop was held as part of the Nordic Conference on Human-Computer Interaction (NordiCHI’20) in October 2020. Both workshops were organized online due to the current COVID-19 pandemic. This special issue contains some of the position papers for the workshops. In these workshops, researchers and practitioners discussed, critiqued, imagined, and redefined methods and approaches towards technology design for the future. The special focus in the workshops was designing future technology with children for children. The authors of this special issue want to find ways for children to imagine their own futures, not only adults imagining the futures for children. The papers in the special issue form an interesting combination to approach the topic. ‘Future Oriented Child-Centric Character Design and Interaction in Culturally Diverse Games’ by Giri discusses the need for cultural diversity and culturally meaningful interaction in game design. ‘Towards a Child-Led Approach for Children’s Activation’ by Miccolis brings to our attention children’s agency and bottom-up participation in the change-making in their community. Tuvi and Okkonen in ‘Children Centered Living Lab Approach Development in Education’ continue by discussing the potential of living lab approach – trying things out ‘in the wild’ – to bring valuable understanding for
 Permission and Header Based Android Application

: We propose a novel android malware detection system that uses a deep convolutional neural network Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. To solve these challenges, we propose system to identify malware both efficiently and effectively. The novel technique enabling effectiveness is the Semantic-based deep learning. We use Long Short Term Memory on the semantic structure of Android bytecode, avoiding missing the details of method-level bytecode semantics. To achieve efficiency, we apply Multilayer Perceptron on the xml files based on the finding that most malware can be efficiently identified using information only from xml files .
Internship and PhD proposal Regularization with generative neural networks for satellite imaging

Deep neural networks achieved a major breakthrough in computer vision in the past decade, enabling a dramatic increase in performance for supervised tasks such as image classification or segmentation. In the last few years, neural networks have also been successfully applied to image processing or reconstruction tasks such as denoising, deblurring, compression, pan-sharpening or super-resolution [3, 2]. The standard approach directly solves the image reconstruction task, by end-to-end training a deep convolutional network on a large amount of data. If a physical model is known (for instance, the point-spread function for the deblurring task), it can be used to simulate the pairs of clean and degraded images. In spite of their impressive performance, such end-to-end approaches can hardly be used in satellite imaging, for at least two reasons. First, such deep architectures are hardly interpretable and exhibit instabilitites, which limit the confidence in the reconstructed image. Second, their training require both a large amount of GPU time and the tuning of numerous hyper-parameters. This is particularly problematic in the context of satellite imaging, since the specificities of the physical model (noise level, PSF, spectral responses) require to retrain the network for each instrument. More recent works investigated an alternative way, which consists in using well-known model-based inversion, the neural networks being used only for regularization. The seminal work in the literature [1] capitalized on deep generative models, such as variational autoencoders (VAEs) or generative adversarial networks (GANs). It consists in a synthesis regularization, where the solution is searched in the latent space of the generative model. In a recent work [4], we showed that a direct inversion in the image space, termed analysis regularization, can lead to better results, especially in the case where the sought image does not exactly lie in the range of the generative model. This will be the case in satellite imaging, where the size, diversity and complex nature of images prevent to learn a generative model that perfectly captures the statistics of the images in the dataset.
Machine learning model to classify modulation techniques using robust convolution neural network

In wireless ccommunications receiver plays a main role to recognize modulation techniques which were used at the transmitter. While transferring information from transmitter to receiver, the receiver must retrieve original information. In order to achieve this goal we introduced a neural network architecture that recognizes the types of modulation techniques. The applications of deep learning can be categorized into classification and detection. The CNN architecture is used to perform feature extraction based on the layers to build a model which classifies the input data. A model that classifies the radio communication signals using deep learning method. The robust c (RCNN) is used to train the modulated signals; the transformations are used to help the neural network which estimate the signal to noise ratio of each signal ranges from -20dB to 18dB with loss and accuracy of 89.57% at SNR 0dB.