Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decision System for Predicting Diabetes using Neural Networks

Diabetic retinopathy (DR) is an eye fixed ill complete by the impairment of polygenic disorder and that we purchased to acknowledge it before of calendar for sensible treatment. On these lines, 2 social occasions were perceived, specifically non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR). During this paper, to dissect diabetic retinopathy, 3 models like Probabilistic Neural framework (PNN), Bayesian Classification and Support vector machine (SVM) square measure pictured and their displays square measure thought-about. The live of the unwellness unfold within the membrane are often recognized by analytic the elements of the membrane. The elements like veins, hemorrhages of NPDR image and exudates of PDR image square measure off from the unrefined photos victimization the icon prepare techniques, fed to the classifier for gathering a complete of 350 structure photos were used, out of that100 were used for designing and 250 pictures were used for testing. Exploratory results show that PNN has an accuracy of 89.6 % Bayes Classifier incorporates a exactness of 94.4% and SVM has an exactitude of 97.6%. What is more our system is equally continue running on 130 pictures open from "DIARETDB0: Evaluation Database and Procedure for Diabetic Retinopathy" and also the results show that PNN incorporates a exactness of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has a precision of 95.38%.
Recent Advancements in Intrusion Detection Systems for the Internet of Things

Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.
Ethical Considerations for Learning Game, Simulation, and Virtual World Design and Development

The goal of this chapter is to identify ethical concerns that instructional designers should be aware of when designing and developing learning games, simulations, and virtual worlds. Partly taken from ethical considerations that researchers are required to follow as part of standard institutional review board processes for the protection of human subjects, we suggest specific ethical principles which designers should consider prior to and during the design of these complex learning systems as well as during the evaluation of the products. We provide examples from existing and past learning games, simulations, and multi-user virtual environments that have either followed these principles or left questions to be addressed and propose a series of ethical considerations in future designs.
Evaluating Machine Learning Techniques for Smart Home Device Classification

Smart devices in the Internet of Things (IoT) have transformed the management of personal and industrial spaces. Leveraging inexpensive computing, smart devices enable remote sensing and automated control over a diverse range of processes. Even as IoT devices provide numerous benefits, it is vital that their emerging security implications are studied. IoT device design typically focuses on cost efficiency and time to market, leading to limited built-in encryption, questionable supply chains, and poor data security. In a 2017 report, the United States Government Accountability Office recommended that the Department of Defense investigate the risks IoT devices pose to operations security, information leakage, and endangerment of senior leaders [1]. Recent research has shown that it is possible to model a subject’s pattern-of-life through data leakage from Bluetooth Low Energy (BLE) and Wi-Fi smart home devices [2]. A key step in establishing pattern-of-life is the identification of the device types within the smart home. Device type is defined as the functional purpose of the IoT device, e.g., camera, lock, and plug. This research hypothesizes that machine learning algorithms can be used to accurately perform classification of smart home devices. To test this hypothesis, a Smart Home Environment (SHE) is built using a variety of commercially-available BLE and Wi-Fi devices. SHE produces actual smart device traffic that is used to create a dataset for machine learning classification. Six device types are included in SHE: door sensors, locks, and temperature sensors using BLE, and smart ii bulbs, cameras, and smart plugs using Wi-Fi. In addition, a device classification pipeline (DCP) is designed to collect and preprocess the wireless traffic, extract features, and produce tuned models for testing. K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forests (RF) classifiers are built and tuned for experimental testing. During this experiment, the classifiers are tested on their ability to distinguish device types in a multiclass classification scheme. Classifier performance is evaluated using the Matthews correlation coefficient (MCC), mean recall, and mean precision metrics. Using all available features, the classifier with the best overall performance is the KNN classifier. The KNN classifier was able to identify BLE device types with an MCC of 0.55, a mean precision of 54%, and a mean recall of 64%, and Wi-Fi device types with an MCC of 0.71, a mean precision of 81%, and a mean recall of 81%. Experimental results provide support towards the hypothesis that machine learning can classify IoT device types to a high level of performance, but more work is necessary to build a more robust classifier.
Evaluasi Implementasi Sistem Informasi Administrasi di Lembaga Komputer dan Pelatihan (LKP) XYZ Surakarta

Computer and Training Institutions (CGC) XYZ Surakarta own academic information systems. This journal discusses the evaluation study reception administration information system in LKP using the Unified Theory Of Acceptance and Use of Technology (UTAUT) to information systems administration. The purpose of this study was to determine the variables that affect the acceptance UTAUT administration information system for the existing users in the LKP and provides recommendations for improvement in the future system. The data collection techniques used were interviews, observation, and literature. This research was conducted using quantitative methods by means of propagation through questionnaires given to students. Variables used in this UTAUT include performance expectancy, effort expectancy, social influence and facilitating conditions. The study population was learner courses.
A network approach to understanding obesogenic environments for children in Pennsylvania

Abstract Network methods have been applied to obesity to map connections between obesity-related genes, model biological feedback mechanisms and potential interventions, and to understand the spread of obesity through social networks. However, network methods have not been applied to understanding the obesogenic environment. Here, we created a network of 32 features of communities hypothesized to be related to obesity. Data from an existing study of determinants of obesity among 1,288 communities in Pennsylvania were used. Spearman correlation coefficients were used to describe the bivariate association between each pair of features. These correlations were used to create a network in which the nodes are community features and weighted edges are the strength of the correlations among those nodes. Modules of clustered features were identified using the walktrap method. This network was plotted, and then examined separately for communities stratified by quartiles of child obesity prevalence. We also examined the relationship between measures of network centrality and child obesity prevalence. The overall structure of the network suggests that environmental features geographically co-occur, and features of the environment that were more highly correlated with body mass index were more central to the network. Three clusters were identified: a crime-related cluster, a food-environment and land use-related cluster, and a physical activity-related cluster. The structure of connections between features of the environment differed between communities with the highest and lowest burden of childhood obesity, and a higher degree of average correlation was observed in the heaviest communities. Network methods may help to explicate the concept of the obesogenic environment, and ultimately to illuminate features of the environment that may serve as levers of community-level intervention.
Personal Publications Lists Serve as a Reliable Calibration Parameter to Compare Coverage in Academic Citation Databases with Scientific Social Media

A Review of: Hilbert, F. , Barth, J., Gremm, J., Gros, D. , Haiter, J., Henkel, M. , Reinhardt, W. , & Stock, W.G. (2015). Coverage of academic citation databases compared with coverage of scientific social media: personal publication lists as calibration parameters. Online Information Review 39(2): 255-264. http://dx.doi. org/10.1108/OIR-07-2014-0159 Abstract Objective – The purpose of this study was to explore coverage rates of information science publications in academic citation databases and scientific social media using a new method of personal publication lists as a calibration parameter. The research questions were: How many publications are covered in different databases, which has the best coverage, and what institutions are represented and how does the language of the publication play a role? Design – Bibliometric analysis. Setting – Academic citation databases (Web of Science, Scopus, Google Scholar) and scientific social media (Mendeley, CiteULike, Bibsonomy). Subjects – 1,017 library and information science publications produced by 76 information scientists at 5 German-speaking universities in Germany and Austria. Methods – Only documents which were published between 1 January 2003 and 31 December 2012 were included. In that time the 76 information scientists had produced 1,017 documents. The information scientists confirmed that their publication lists were complete and these served as the calibration parameter for the study. The citations from the publication lists were searched in three academic databases: Google Scholar, Web of Science (WoS), and Scopus; as well as three social media citation sites: Mendeley, CiteULike, and BibSonomy and the results were compared. The publications were searched for by author name and words from the title. Main results – None of the databases investigated had 100% coverage. In the academic databases, Google Scholar had the highest amount of coverage with an average of 63%, Scopus an average of 31%, and lowest was WoS with an average of 15%. On social media sites, Bibsonomy had the highest coverage with an average of 24%, Mendeley had an average coverage of 19%, and the lowest coverage was CiteULike with an average of 8%. Conclusion – The use of personal publication lists are reliable calibration parameters to compare coverage of information scientists in academic citation databases with scientific social media. Academic citation databases had a higher coverage of publications, in particular, Google Scholar, compared to scientific social media sites. The authors recommend that information scientists personally publish work on social media citation databases to increase exposure. Formulating a publication strategy may be useful to identify journals with the most exposure in academic citation databases. Individuals should be encouraged to keep personal publication lists and these can be used as calibration parameters as a measure of coverage in the future.
Robust design of a vibro-impact electromechanical system

In this paper, the robust design with an uncertain model of a vibro-impact electro-mechanical system is done. The electro-mechanical system is composed of a cart, whose motion is excited by a DC motor, and an embarked hammer into this cart. The hammer is connected to the cart by a nonlinear spring component and by a linear damper, so that a relative motion exists between them. A linear flexible barrier, placed outside of the cart, constrains the hammer movements. Due to the relative movement between the hammer and the barrier, impacts can occur between these two elements. The developed model of the system takes into account the influence of the DC motor in the dynamic behavior of the system. Some system parameters are uncertain, such as the stiffness and the damping coefficients of the flexible barrier. The objective of the paper is to perform an optimization of this electro-mechanical system with respect to design parameters (spring component, and gap of the barrier) in order to maximize the impact power under the constraint that the electric power consumed by the DC motor is lower than a maximum value. This optimization is formulated in the framework of robust design due to the presence of uncertainties in the model.
Diagnosis of sensor faults in a combustion engine control system with the artificial neural network

The work presents the investigations carried out on a spark-ignition internal combustion engine with gasoline direct injection. The tests were carried out under conditions of simulated damage to the air temperature sensor, engine coolant temperature sensor, fuel pressure sensor, air pressure sensor, intake manifold leakage, and air flow disturbances. The on-board diagnostic system did not detect any damage because the sensor indications were within acceptable limits. The engine control system in each case changed its settings according to the adaptive algorithm. Signal values in cycles from all available sensors in the engine control system and data available in the on-board diagnostic system of the car were recorded. A large amount of measurement data was obtained. They were used to create a statistical function that classifies sensor faults using an artificial neural network. A set of training data has been prepared accordingly. During learning the neural network, a hit rate of over 99% was achieved.
Context and Implications Document for: A systematic review of interleaving as a concept learning strategy

Interleaving is a technique which involves modifying the order of practice tasks or examples such that items of different types appear together, rather than in ‘blocks’ of similar items. For example, interleaved examples of artworks might feature the work of several different artists mixed together, in contrast to a ‘block’ of several paintings by the same artist. The latter example formed the basis of a research study by Kornell and Bjork (2008) which suggested that not only is interleaving highly beneficial for learning, learners tend to lack insight into its benefits and are therefore unlikely to adopt the technique spontaneously. Subsequent studies extended this effect to a number of topic areas, including the learning of concepts in chemistry, biology, statistics and psychology, and there is also a body of work on the interleaved practice of maths problems. Thanks to this research, interleaving has begun to be cited as an ‘evidence-based’ approach to learning, and recommended to educators and to students who are pursuing independent study. However, there remain a number of issues and unanswered questions about the technique. How strong is the interleaving benefit, and how widely does it apply in terms of both learners and materials? What kind of issues might educators wish to keep in mind, for example in terms of the type of items that they interleave, the number of examples given, or the duration of the process? How similar should the examples be, and would—as argued by Carvalho and Goldstone (2015)—the effect cease to hold if the studied items are too different from one another? Are interleaving benefits primarily a matter of improved memory for previously studied items, or does the
Time Series Forecasting with Statistical Neural Network using Continuous Wavelet Decomposition

We propose a forecasting model based on the combined efficiency of the artificial neural network and wavelet transform in modeling time series data. The data used were decomposed into continuous wavelet signals on a scale of 10. Each of the decomposed series was subjected to correlation test with the original data. We compare the new model’s performance with the conventional time series regression model (TSRM) and the wavelet neural network (WNN) forecasting model. The WNN model performed better than the TSRM. The analysis also showed that except in extremely rare cases, all the wavelet series performed optimally compared to the original data.
Genome data analysis, protein function and structure prediction by machine learning techniques

The raw information of a typical human genome has been generated at 2001 by Human Genome Project. However, since there are a huge amount of data, it is still a big challenge for people to understand them, and extract useful structure and function information, such as the function of genes, the structure of proteins encoded by gene, and the function of proteins. Understanding these information is crucial for us to improve longevity and quality of life, and has a lot of applications, such as genomic medicine, drug design, and etc. In the meantime, machine learning techniques are growing rapidly and are good at processing large datasets, but many of them are limited for the impact on larger real world problems. In this thesis, three major contributions are described. First of all, we generate gene-gene interaction network from human genome conformation data by Hi-C technique, and the relationship of gene function and gene-gene interaction has been discovered. Second, we introduce a novel framework SMISS, which uses new source of information from gene-gene interaction network and uses a new way to integrate difference sources of information for protein function prediction. Finally, we introduce a tool called DeepQA which use machine learning technique to evaluate how well is the predicted protein structure, and a method MULTICOM for protein structure prediction. All of these protein structure and function prediction methods are available as software and web servers which are freely available to the scientific communities. xvii Chapter
Performance Analysis of DTN Routing Protocols: Single-Copy and Multi-Copy in ICMN Scenario

Delay tolerant networks are qualified by asymmetric flow and intermittent connectivity where direct path is not foundbetween nodes. In this research, we analyse two routing schemes' performance, single and multi-copy simultaneously in an intermittently connected mobile network. Among various routing protocols, first contact from single copy whilespray-and-wait, prophet, and epidemic from multi-copy are chosen to analyse their performance. Aside from various performance metrics, routing protocols are analysed in terms of overhead, delivery, average buffer time and latency for varying number of mobile nodes, time-to-live and message generation rates respectively. Opportunistic network environment simulator coded by java programming language is taken as the tool of simulation for the investigation of these routing protocols. According to the investigated result of the specified routing techniques, we can conclude that the performance of spray-and-wait is good while bad for epidemic withall the metrics used here. On the other hand, first contact does not showgood performance among all the routing protocols with delivery probability and average buffer time.
Endometriumun rejenerasyon kapasitesi: Endometrial kök hücreler

Endometrium her ay gerceklesen cogalma ve dejenerasyon dongusu nedeniyle postpubertal ve premenopozal donemler arasinda yuksek bir rejenerasyon kapasitesine sahiptir. Bu yapi icerisinde gorev alan kok hucrelerin varligina dair calismalar son 10 yil icerisinde giderek artmistir. Elde edilen sonuclar endometriumda kok hucrelerin varligini desteklemekte, ancak bu hucreleri tespit etmek icin spesifik hucre yuzey belirtecleri hakkinda daha fazla calismaya ihtiyac duyulmaktadir. Bu surec icerisinde, PDGFR-b (platelet derived growth factor receptor-beta) ve SUSD2 (sushi domain containing 2) gibi daha cok endometrial stromal kok hucrelere yonelik olmak uzere bazi spesifik hucre yuzey belirtecleri de saptanmistir. Bu hucrelerin daha spesifik bir sekilde tanimlanmalari, nedenleri tam aciklanamamis bircok endometrial patolojinin anlasilmasi icin de yol gosterici olabilir. Bu veriler bize yapilacak calismalar isiginda endometrial kaynakli kok hucrelerin rejeneratif tipta kullanim alani bulacagina isaret etmektedir.
Feedforward Equalizers for MDM–WDM in Multimode Fiber Interconnects

Abstract In this paper, we present new tap configurations of a feedforward equalizer to mitigate mode coupling in a 60-Gbps 18-channel mode-wavelength division multiplexing system in a 2.5-km-long multimode fiber. The performance of the equalization is measured through analyses on eye diagrams, power coupling coefficients and bit-error rates.
Influence of detectors efficiency normalization on small animal PET image quality

LabPET GE 4 (a small animal positron emission tomograph) image acquisition is done by 1536 independents channels. Differences in the rate counting of each channel must be corrected so as not compromise image quality. Equipment user manual recommends that normalization of the detectors efficiency be made as often as possible and always whenever there are hardware or software parameters changes - included, but not limited to channels parameters adjustments, electronic cards replacements, channels activation or inactivation or software update. This work evaluates the normalization effects on the image quality parameters. PET image acquisition were performed using recommended parameters by the NEMA NU 4-2008 standards. Image was reconstructed in different ways using different normalization files. The tests performed indicated that the image quality parameters do not vary significantly with different normalization data. Therefore, a daily routine of normalizations is not justified, suggesting a periodical frequency of one month or more for this procedure.