Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning in Energy‐Efficient Neuromorphic Computing

This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture CoDesign also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks.
Preserving Privacy and Fairness for an Innovative E-Commerce Model: Penny M-lottery

Lottery games are prevalent worldwide because they provide participants with the opportunity to win a big fortune. With the development of the Internet, the practicability of online lotteries has advanced, because players can purchase lottery tickets online rather than at physical stores. However, the price of participating in traditional e-lottery is high so that not most players can join the game. Therefore, a novel type of lottery called penny lottery has been introduced. The price of penny lottery participation is lower than that of a traditional lottery, but it still offers people the opportunity to be a millionaire; thus, leading to fascination for participants with budget deficits. In recent years, the technology of mobile devices has developed rapidly. Applications are increasingly available in mobile environments. Consequently, we aim to develop an efficient mobile penny lottery mechanism called penny M-lottery. This mechanism could realize the fairness and offer privacy to participants. In addition, the process accuracy is verified through BAN logic and the security is confirmed by AVISPA. Experimental results revealed that the mechanism can be implemented in mobile environments because of the low computational costs and communication overheads.
THE INFLUENCE OF FERRITE ON RETAINED AUSTENITE CHARACTERISTICS IN THERMOMECHANICALLY-PROCESSED LOW-SILICON CONTENT TRIP-ASSISTED STEELS

The outstanding balance of strength-ductility exhibited in newly-developed low silicon content TRIP-assisted steels can be achieved through precise controlling of microstructures which are composed of allotrimorphic ferrite, bainitic ferrite, retained austenite, and martensite. Ferrite as the first transformation product of austenite decomposition during cooling stage of any TMP processing has a profound effect on microstructural characteristics of subsequent transformation products and, hence, on resulted mechanical properties. In this study, a typical multi-stage TMP program was simulated in laboratory by using an automated hot-compression machine and the effects of ferrite grain size and its fraction on microstructural characteristics of subsequent austenite transformation products were investigated. Microstructure of samples were characterized by optical and electron microscopy, XRD, and Mössbauer spectroscopy. The results indicated that by increasing the amount of ferrite, retained austenite volume fraction, after a slight plateau, decreases. Ferrite grain refinement has also found to not only increase retained austenite fraction but also changes particle morphology to the desired type of interlath film-like.
SDN-Managed Provisioning of Named Computing Services in Edge Infrastructures

Pushed by the challenging demands of fifth generation (5G) use cases and the recent advancements in virtualization technologies, edge network devices are rapidly evolving from simple forwarders to softwarized infrastructures augmented with computing and storage capabilities. Such a trend blurs the distinction between IT and telco domains and paves the way for the integrated management of network and computing resources. In this paper, we focus on the interplay of the Software Defined Networking (SDN) and Named Data Networking (NDN) paradigms as key drivers for the orchestration of computing services in softwarized edge infrastructures. We propose a new framework that brings out the best of the SDN centralized intelligence to take “smart” decisions and inject rules for service allocation (e.g., retrieve input data, execute a function over them), and the best of the adaptive NDN forwarding plane and its native in-network caching to request and deliver services by name. Within the framework, we devise a service allocation strategy that aims at selecting as an executor, among the potential candidates, the one which is able to guarantee the shortest service provisioning delay for each service request, while accounting for the network topology, the links status, and the available computing resources in the edge nodes. Performance evaluations testify to the superiority of our proposal against benchmarking solutions from the literature under different operation settings.
You Shall Not Pass! Measuring, Predicting, and Detecting Malware Behavior

Researchers have been fighting malicious behavior on the Internet for several decades. The arms race is far from being close to an end, but this PhD work is intended to be another step towards the goal of making the Internet a safer place. My PhD has focused on measuring, predicting, and detecting malicious behavior on the Internet; we focused our efforts towards three different paths: establishing causality relations into malicious actions, predicting the actions taken by an attacker, and detecting malicious software. This work tried to understand the causes of malicious behavior in different scenarios (sandboxing, web browsing), by applying a novel statistical framework and statistical tests to determine what triggers malware. We also used deep learning algorithms to predict what actions an attacker would perform, with the goal of anticipating and countering the attacker’s moves. Moreover, we worked on malware detection for Android, by modeling sequences of API with Markov Chains and applying machine learning algorithms to classify benign and malicious apps. The methodology, design, and results of our research are relevant state of the art in the field; we will go through the different contributions that we worked on during my PhD to explain the design choices, the statistical methods and the takeaways characterizing them. We will show how these systems have an impact on current tools development and future research trends.
Quantifying the potential for improved management of weather risk using sub‐seasonal forecasting: The case of UK telecommunications infrastructure

Reliable and affordable telecommunications are an integral part of service‐based economies, but the nature of the associated physical infrastructure leads to considerable exposure to weather. With unique access to observational records of the UK fixed‐line telecommunications infrastructure, an end‐to‐end demonstration of how extended range forecasts can be used to improve the management of weather risk is presented, assessing forecast value on both short‐term “operational” (weeks) and long‐term “planning” timeframes (months/years). A robust long‐term weather‐related fault‐rate climatology is first constructed at weekly resolution, based on the ERA‐Interim reanalysis. A clear dependence of winter fault rates on large‐scale atmospheric circulation indices is demonstrated. The European Centre for Medium‐Range Weather Forecasts (ECMWF) sub‐seasonal forecast system is subsequently shown to produce skilful forecast of winter weekly fault rates at lead times of three to four weeks ahead (i.e. days 14–20 and 21–28). Forecast skill at a given lead time is, however, a necessary rather than a sufficient condition for improved risk management. It is shown that practical decision‐making leads to dependencies across multiple forecasts times that cannot be modelled using traditional “cost‐loss matrix” methods as errors in previous forecasts influence the value of subsequent forecasts. A parsimonious model representing operational decision‐making for fault repair scheduling is therefore constructed to show that fault‐rate forecast skill does improve both short‐ and long‐term management outcomes (in this case meeting performance targets more often in the short term, or reducing the resources required to achieve these targets in the long term). Consequently, it is argued that new methods are needed for forecast skill assessment in complex decision environments.
(Z)-1-(2,4-Difluorophenyl)-3-phenyl-2-(1H-1,2,4-triazol-1-yl)prop-2-en-1-one

In the title molecule, C17H11F2N3O, the triazole ring makes dihedral angles of 83.00 (5) and 16.63 (5)°, respectively, with the phenyl and benzene rings. Weak intermolecular C—H⋯F and C—H⋯N interactions contribute to the crystal packing.
Hyperspectral band quality analysis based on dictionary representation

Hyperspectral image can acquire hundreds of bands with wavelengths ranging from visible spectrum to infrared, and the rich discriminant information has led to the widespread applications in the military and civil fields. However, due to the influence of imaging devices, some bands are polluted by noise, which bring great inconvenience to subsequent processing. Therefore, in order to quickly and accurately find low quality and noisy bands in hyperspectral image, we propose a hyperspectral image quality analysis method based on band dictionary representation. Firstly, a representative band dictionary is constructed to accurately represent the dominant information in hyperspectral image. Then, the band subset dictionary is used to reconstruct all the remaining bands in the hyperspectral image and obtain the reconstruction coefficients of each band. Finally, representation error is calculated to analyze the quality of each band. By comparing the representation errors of the bands, the quality of each band can be estimated. Experimental results on three real-world hyperspectral images demonstrate the proposed method can effectively and quickly select low quality bands and noisy bands without any priors.
Fuzzy Expert System for the Detection of Glaucoma and Cataract

: Glaucoma and Cataract are eye diseases which shares common symptoms like, high pressure on eye, damage of optic nerves, lens size and sometimes loss of eyesight. Blindness occurs when left untreated that affects peripheral vision. Early diagnosis of glaucoma and cataract requires regular checkups which is too expensive and time consuming. This work proposes fuzzy based decision constructed to overcome glaucoma and cataract at initial stage. Fuzzy rule-based system helps the medical practitioners to give accurate results by considering patients symptoms. The entire test performed on fuzzy system was done in presence of ophthalmologist, who further found these systems as precise and useful by comparing accuracy, sensitivity and specificity which results 97%, 98% and 96% respectively. This method is efficient and having low computational value.
Using Accelerometer Signals to Classify Prehensile Hand Movements

USING ACCELEROMETER SIGNALS TO CLASSIFY PREHENSILE HAND MOVEMENTS Nirav R. Sheth George Mason University, 2019 Thesis Director: Dr. Zoran Durić Prehensile movement is crucial for activities of daily living (ADLs) such as grooming and self-care. In humans, the hand is the primary device utilized for prehensile movements. Recognizing the relationship between a hand’s prehensile patterns and accelerometer data can be instrumental in developing assisted and enhanced functional hand devices. An accelerometer measures the linear acceleration acting on the part of the body where the sensor is placed. This thesis demonstrates the usefulness of several features, based on accelerometer data, towards recognizing prehensile movement of the hand while performing 47 movements, grips and neutral rest. Particular emphasis is given to measuring the usefulness of the features to identify movement transitions. A random forest classifier is used to recognize motion onset and offset as well as various phases of movement. The results showed that the accelerometer-based features were effective in recognizing motion onset and offset and moments of transitions. However, they were not as effective in recognizing various phases of prehensile movement. Chapter 1: Introduction The ability to grasp, known as prehensile movement, is important for an individual to perform basic activities of daily living (ADLs). ADLs are critical for survival and for independent living. ADLs include feeding, mobility, performing personal hygiene, administering self-care, etc. Besides walking, most ADLs are performed through upper extremity (UE) movements. In addition to ADLs, UE movements are critical for work activities and self-defense. The hand is the terminal UE device and thus utilized most for prehensile movements. Machine recognition of the hand’s prehensile patterns can be instrumental in developing assisted and enhanced living devices. Such devices include robotic hands, gesture-based touch-less interfaces, assisted typing software, and more. A hand can move with multiple degrees of freedom and form different grip types due to various joints in the skeletal structure. Therefore, recognizing prehensile hand movements is challenging and requires precision.
Radio resource management framework for energy‐efficient communications in the Internet of Things

The Internet of Things (IoT) is the vision of a global network that connects various physical world objects to the IT infrastructure through a wireless medium. Despite the availability of a number of mature radio access technologies such as GSM, Long‐Term Evolution, Wi‐Fi, and due to the current progress made in developing 5G technology, more and more IoT operators are opting to use low‐power wide‐area (LPWA) technologies due to their low cost and easy deployment. However, recent studies show that the radio resource allocation used in these technologies is not scalable. This limitation often results in packet collisions, retransmission and unnecessary waste of scarce energy resources. In this paper, we propose a radio resource management framework, based on software‐defined network (SDN), to overcome the inefficient radio resource allocation of LPWA technologies. This is possible through the centralized nature of SDN, which allows collecting network monitoring information to analyze and calculate the optimal channel assignment configuration across the IoT network. We perform software‐defined radio‐based spectrum monitoring within the real IoT network platform in 868 MHz bands in which the latest IoT technologies, ie, LoRa and SigFox, operate. We demonstrate, through extensive simulations, that the proposed approach provides a better radio resource allocation for LPWA, reduces the number of packet collisions, and significantly improves the energy efficiency of the IoT communications.
An efficient approach for cement strength prediction

In this paper, a simple and computationally efficient approach is proposed to predict the cement strength. It is based on the mathematical concept of covariance matrix and polynomial coefficients. The polynomial coefficients are used to represent the features of cement strength data set. The efficiency and feasibility of the proposed approach is demonstrated on the cement strength data set collected from the cement industry for 2 days, 7 days and 28 days samples. Based on the number of dynamic input variables of the cement strength, the different prediction models such as SOM, linear and nonlinear regression and artificial neural network are designed and the various experimentations are carried to evaluate the proposed approach. Experimental results have shown the effectiveness of the proposed approach in the form of RMSE, R-square coefficients and computational time. It is observed that the proposed polynomial coefficient-artificial neural network approach performs better and predict the cement strength efficiently as compared to other existing methods.
recommendations from an expert commiftee13

An expert committee was convened to deter- mine specific criteria for overweight to be integrated into routine preventive screening of adolescents. Body mass index (BMI) should be used routinely to screen for overweight adolescents. Youths with BMIs � 95th percentile for age and sex, or > 30 (in kg/rn2) should be considered overweight and referred for in- depth medical follow-up to determine underlying diagnoses. Ad- olescents with BMIs � 85th percentile but < 95th percentile or � 30, should be considered at risk of overweight, and should be referred to a second-level screen. The second-level screen in- cludes family history, blood pressure, total cholesterol, large prior increment in BMI, and concern about weight. If youths are positive for any of the items on the second-level screen they should be referred for further medical assessment. Am J Clin Nutr 1994;59:307-16.
Designing Application-Specific Heterogeneous Architectures from Performance Models

In this paper, we propose an approach for designing application-specific heterogeneous systems based on performance models through combining accelerator and processor core models. An application-specific program is profiled by the dynamic execution trace and is used to construct a data flow model of the accelerator. Modeling of the processor is partitioned into an instruction set architecture (ISA) execution and a micro-architecture specific timing model. These models are implemented on FPGAs to take advantage of their parallelism and speed up the simulation when architecture complexity increases. This approach aims to ease the design of multi-core multi-accelerator architecture, consequently contributes to explore the design space by automating the design steps. A case study is conducted to confirm that presented design flow can model the accelerator starting from an algorithm, validate its integration in a simulation framework, allowing precise performance to be estimated. We also assess the performance of our RISC-V single-core and RISC-V-based heterogeneous architecture models.
McLaurin series as a new technique to improve encryption process

Now days many institutions and organizations needs to protect their information and secret data from theft and many other attempts to destroy and fabricate these data and information by unauthorized persons who tries to manipulate by the privacy of these institutions and organizations. So, it is necessary to find a secure way to protect such a data and information, therefore in this paper a new method proposed to develop the encryption techniques through using the McLaurin series as a new cryptosystem, and through using different cryptanalysis techniques and tools, the proposed method was inevitable against the different attacks, it is also a one way function technique.
EVALUATION OF A PORTABLE KETOMETER IN SMALL RUMINANTS

Within the productive cycle of sheep and goat farming, the periparturient period is critical both for animal health and performance [20]. Prolific, high producing ewes and goats, especially underand over-conditioned animals, are at higher risk for negative energy balance periparturiently and therefore, more susceptible to pregnancy toxemia during late pregnancy and to ketosis during lactation [30, 3].
Real time condition monitoring of hydraulic brake system using naive bayes and bayes net algorithms

The vehicles usage is increasing day by day due to the recent, technological development in the automotive field. In a competitive global market in order to survive, the reliability needs to be ensured, through a proper monitoring system. Brake system is one such control component, in which much focus is very much essential. An efficient brake system should provide reliable and effective performance in order to ensure the safety. If it is not properly monitored, it may lead to a serious catastrophic effect such as accidents, brake down, etc. Hence, the brake system needs to be monitored continuously. In this study, an experimental investigation was carried out for monitoring the brake system using vibration signals. An experimental setup which resembles the brake system was fabricated. The vibration signals were acquired under various brake condition such as good and faulty. From the acquired vibration signals, the features were extracted using statistical and histogram feature extraction techniques and feature selection was carried out. The selected features were then classifieds using a Naive Bayes and Bayes Net a classifier. The classification accuracy of all the algorithms were compared for finding the best feature classifier model for monitoring the brake condition.
Integrating Online Georeferenced Epidemiological Analysis and Visualization into a Telemedicine Infrastructure – First Results

With the objective to perform a first evaluation of the impact of the integration of a graphic spatial epidemiology tool that allows quasi-realtime georeferenced data visualization into a telemedicine infrastructure, this work presents GISTelemed, an online module specialized on indexing structured and semi-structured data, as well as querying the indexed content using structured and free-text search.We evaluated GISTelemed accordingly to the guidelines published by the US Centers for Disease Control and Prevention and input provided by a questionnaire customized according to AdEQUATE (questionnAire for Evaluation of QUAlity in TElemedicine systems). 39 healthcare professionals from 13 municipalities participated in the evaluation. We analyzed data from questionnaires using descriptive statistics, being Lernability and Comfort the characteristics that received the best evaluation. Quantitative evaluation based upon leprosis cases detected through tele-dermatology showed a sensitivity and PPV of respectively 77.2% and 95.3%. 22.8% of the cases detected were un-notified cases.Results from our case study show a good evaluation regarding the perceived software quality”. We conclude that the integration of spatial epidemiology tools to the STT/SC system, besides enabling visualization of data in maps, allowed users to analyze the evolution of morbidities and their co-occurrences.
Nash Bargaining-Based Economic Analysis of Opportunistic Cognitive Cellular Networks

We analyse a cognitive cellular network comprising of a primary and a secondary network operator (PNO and SNO). The SNO implements distributed detection to opportunistically access the licensed band. In return, the PNO requests the SNO to pay a remuneration. The PNO judiciously balances between the revenue gained from its subscribers and the payment earned from the SNO. On the other hand, the SNO must earn a positive utility to maintain its infrastructure. Consequently, a two-person non-cooperative bargaining game is formulated where the operators agree upon the value of the following factors: base station activity probabilites and the payment from the SNO. The PNO is aware of detection and false-alarm probabilities of spectrum sensing by the SNO and hence can infer how the interference created by missed detection degrades its capacity. We establish that our formulated game has a unique Nash solution. For linear demands, we derive an approximate closed-form solution. Numerical analysis reveals several insightful results. For example, we exhibit that, if the user number of the SNO lies below a certain threshold, the cognitive network appears to be economically unsustainable. We also show the functional dependence of the bargaining outcome on various system parameters.