Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathwise optimality and state space collapse for the exponential rule

We study the problem of scheduling transmissions of multiple data users sharing the same wireless channel. The unique feature of this problem is the fact that the capacity (service rate) of the channel varies with time randomly and asynchronously for different users. Such a problem arises, for example, in the 3G CDMA high data rate (HDR) system, where multiple mobile users in a cell share the same downlink wireless channel by means of time-sharing. Time is divided into fixed size slots, and in each time slot, one user is scheduled for transmission. Each user constantly reports to the base station its "instantaneous" channel capacity, i.e. the rate at which data can be transmitted if this user is scheduled for transmission. In an HDR system (and in the generic variable channel model as well), a "good" scheduling algorithm should take advantage of channel variations by giving some form of priority to users with instantaneously better channels.
A modern approach to dysarthria classification

This work deals with the assessment of neurological diseases known as dysarthrias, using a novel approach based on objective and perceptual features extracted from pathological speech signals. A methodology for the classification of dysarthria is developed in which digital signal processing algorithms are used to appraise the severity of those features less reliably judged by the clinicians, while the others are taken directly from perceptual judgments or medical records. The assessment process evaluates the performance of two different classifiers and compares them with the traditional assessment system. The first approach is based on the lineal discriminant analysis and the second is a non-lineal technique based on self-organizing maps. The non-lineal classifier provided the highest percent of correct classification and the most accurate information on the relevance of the features in the classifier decision. It also provided a bi-dimensional representation of de data that allows a better understanding of the correspondence between the speech deviations and the location of the damage in the peripheral or central nervous system.
A Poly-phased, Time-interleaved Radio Frequency Digital-to-analog Converter (Poly-TI-RF-DAC)

Patel, Vipul J., Ph.D., Electrical Engineering Ph.D. Program, Department of Electrical Engineering, Wright State University, 2017. A Poly-phased, Time-Interleaved Radio Frequency Digital-to-Analog Converter (Poly-TI-RF-DAC). With increasing data rates and new communication standards owing to substantial advancements in digitizing and signal processing capabilities over the last century, the radio spectrum is increasingly becoming a finite and expensive resource. Furthermore, radar systems and 5G communication systems have revealed the need for gigahertz-frequency signal generation with flexible frequency planning and minimal spurious emissions. Advanced digital-to-analog converters (DACs), along with associated filtering, are critical enablers of these new systems and play a key role in meeting stringent system requirements. In conventional implementations, however, high quality filters are specific to one frequency band thereby limiting their operational agility. Therefore, a DAC architecture that removes undesired image replicas, mixer sidebands, and harmonic nonlinearities is an essential component for flexible frequency synthesis as it allows for reduced filtering requirements while enabling new software-defined transmitter techniques. This dissertation aims to provide a quantitative study and analysis of a poly-phased, time-interleaved Nyquist radio frequency digital-to-analog converter (Poly-TI-RF-DAC) for use in spectrum-agile and software-defined transmitters. Beginning with the fundamental understanding of high-speed DAC capabilities and limitations, the mathematical and behavioral analyses give insight into the signal, sideband, and image replica locations of the proposed architecture. Additionally, the use of a mixing DAC as the core structure is discussed along with how nonlinearities can be canceled with the appropriate choice of frequency planning.
Optimization of YARN Hierarchical Resource Scheduling Algorithm

We propose a hierarchical resource scheduling algorithm to solve the YARN resource problem, YARN is the resource scheduling management platform of Hadoop. Based on the resource scheduling DRF algorithm, the algorithm improves the fairness of the hierarchical resource scheduling by adjusting the calculation method of the usage rate of the parent node's primary resource. This paper establishes a hierarchical resource preemption model based on the characteristics of YARN task, and designs a framework to dynamically determine the preemptive task. The algorithm improves cluster resource’s utilization and rationality. The experimental results show our work can effectively shorten the overall run time and average YARN platform response time, improve the usage rate of the distributed cluster resources, and improve the Fair scheduling capability of YARN under the multiple queues and multiple resources.
A Technique to Improve Ridge Flows of Fingerprint Orientation Fields Estimation

An accurate estimated fingerprint orientation fields is a significant step for detection of singular points. Gradient-based methods are frequently used for estimating orientation fields but those methods are sensitive to noise. Fingerprints that perfect quality are seldom. They may be corrupted and degraded due to impression conditions or variations on skin. Enhancement of ridge flows improved the structure of orientation fields and hence increased the number of true singular points thereby conducting the overall performance of the classification process. In this paper, we provided discussion on the technique and implementation to improve local ridge flows of fingerprint orientation fields. That main technique have four steps; firstly, fingerprint segmentation; secondly, identification of noise areas and marking; thirdly, estimation of fingerprint orientation fields, and finally, enhancement of ridge flows using minimum variance of the cross centre block direction in squared gradients. A standard fingerprint database is used for testing of proposed technique to verify the tier of effectivity of algorithm. The experimental results suggest that our enhanced algorithm achieves visibly better ridge flows compare to other methods.
ANALYSIS OF THE SCIENCE LEARNING OBJECTS OF THE GREEK DIGITAL LEARNING OBJECT REPOSITORY FROM A LEARNING ACTIVITIES PERSPECTIVE

In recent years, learning objects have been playing a crucial role in the teaching process. However, research focused on the analysis of science learning objects is particularly limited. The present study aims to analyze the science learning objects of the Greek Digital Learning Object Repository that are intended for primary school from a learning activities perspective. A total of 178 learning objects were analyzed. The analysis of learning objects from the perspective of the learning activities (cognitive and metacognitive) they activate in the students was carried out in line with the analysis framework of Overman, Vermunt, Meijer, Bulte and Brekelmans (2013). The analysis of learning objects showed that they are dominated by low level cognitive learning activities, while the learning objects that activate high level cognitive learning activities are limited. Finally, no learning objects activating metacognitive learning activities in the students were detected.
Proposal of a Secure Modbus RTU Communication with Adi Shamir's Secret Sharing Method

Drinking fresh water, turning the lights on, travelling by tram, calling our family or getting a medical treatment are usual activities, but the underlying SCADA (Supervisory Control and Data Acquisition) systems like CIS (Critical Infrastructure Systems), ICS (Industrial Control Systems) or DCS (Distributed Control Systems) were always the target of many types of attacks, endangered the above mentioned simple activities. During the last decades because of the fast spread of the internet based services and the continuous technical development these systems become more vulnerable than ever. Full reconstruction and innovative changes in older SCADA systems has high cost, and it is not always rewarding. Communication protocols as Modbus (1979) serve as a main basis for SCADA systems, so security of Modbus has a major impact of the security of SCADA systems. Our paper raises and answers questions about the security of the Modbus RTU protocol. We focus on the serial Modbus protocol, because in that method we found many unsolved problems, like lack of authentication of the participants, lack of secure channel and so on. The aim of this paper to propose a secure communication alternative for Modbus RTU @ RS485 wire. The main advantage of the proposed method is the coexistence with traditional slaves and bus systems and only software update is necessary.
Microaggression, anxiety, trigger warnings, emotional reasoning, mental filtering, and intellectual homogeneity on campus: A study of what students think

ABSTRACT College campus life is changing based on pressures to create safe environments for students to learn. Much of this change has been outlined in an article by Lukianoff and Haidt entitled “The Coddling of the American Mind. ” The authors of the present article asked 188 students from two universities if microaggressions, trigger warnings, emotional reasoning, and mental filtering are prevalent on campus and whether colleges are promoting these concepts. The results show that the majority of students believe that universities must change the way these concepts are addressed to prevent intellectual homogeneity and to adequately prepare them for their business professions.
Modified Partitioning Algorithm for Privacy Preservation in Microdata Publishing with Full Functional Dependencies

Today's world has a growing concern on preserving privacy of sensitive information, in organizations where person-specific data are to be published for inferring useful information from the data for researchers. Such sensitive information should not be disclosed, at the same time the data published should carry some useful information to the researchers. According to Hui Wang et.al., finding a proper optimal partitioning scheme for creating anonymization groups is a NP-hard problem. It is also realized that databases serve as an important asset for several applications, especially in managing patient data and maintaining the privacy of the same turns out to be an important problem. In this paper we make an investigation on the threat from Full Functional Dependency (FFD) which is considered as a part of adversary knowledge. We propose an enhanced top-down greedy approach to deal with FFD Based privacy attack. The designed algorithm is robust when compared to existing since the frequency distribution of sensitive values act as a deciding factor for choosing split points in partitioning.
Bimodal Leaky Prediction for Error Resilient Source-Channel Coding and Its Adaptation to Video Coding - eScholarship

A novel video coding scheme based on leaky prediction is proposedand its superiority against conventional predictive video coding isdemonstrated in environments with moderate to heavy packet loss. Thestrength of the scheme stems from the fact that it explicitly takesinto account the two modes of operation (packet loss or no packetloss) at the decoder and optimizes the corresponding reconstructionfilters together with the prediction filter at the encodersimultaneously. The prediction coefficient tends to be significantlysmaller than the correlation coefficient between two correspondingframes.
Security Authentication for Student Cards’ Biometric Recognition Using Viola-Jones Algorithm

The unauthorized access to the university entrance could be gained by only flashing a student card. This unsecure situation shows the loophole of security authentication in a university. In order to overcome this, a biometric recognition could be the most suitable candidate as it varies uniquely from one person to another. A study on student cards’ biometric recognition using Viola-Jones algorithm is presented as it is proven as a powerful algorithm in terms of superb detection rates and speed. It is done by comparing the facial structures and features between the student card’s image and the card holder image, thus determining the similarity. The recognition performance is evaluated based on the percentage of similarity using 100 testing images of 50 students. The observation on results obtained the effectiveness of the Viola-Jones features in student cards’ biometric recognition rate.
A Dynamic Addressing Protocol on Code Messages for an Underwater Wireless Half Duplex Networks of Autonomous Sensors

This paper presents the new protocol for the organization of dynamic addressing nodes in the underwater wireless networks. In view of the extremely limited frequency band and significant delays inherent in the underwater acoustic data transmission channel, as well as the nonlinearity of the propagation paths of the acoustic signal in water, an approach is proposed for constructing a dynamic addressing protocol based on avoiding collisions at the receiving point. The protocol takes into account the peculiarities of the physical layer of the data transmission and is designed for servicing the network of autonomous non-synchronized nodes.
Efficient Regular Expression Compression Algorithm for Deep Packet Inspection: Efficient Regular Expression Compression Algorithm for Deep Packet Inspection

A memory efficient regular expression compression algorithm is devised for deep packet inspection. First, a parameter DR (distending rate) is defined to quantify the explosive quality of regular expressions. Then based on DR, a regular expression cutting algorithm is proposed to downsize the storage needs of individual regular expression, by detecting and confining the parts which cause DFA (deterministic finite automaton) states’ exponential growth. Then according to the relation of different regular expressions, a selective grouping algorithm is introduced for regular expression sets, which could cut down the number of finite automata, and reduce the runtime memory consumption.
Dota 2 Bot Competition

Multiplayer online battle arena (MOBA) games are a recent huge success both in the video game industry and the international eSports scene. These games encourage team coordination and cooperation, short and long-term planning, within a real-time combined action and strategy gameplay. Artificial intelligence (AI) and computational intelligence (CI) in games research competitions offer a wide variety of challenges regarding the study and application of AI techniques to different game genres. These events are widely accepted by the AI/CI community as a sort of AI benchmarking that strongly influences many other research areas in the field. This paper presents and describes in detail the Dota 2 (Defense of the Ancients 2) Bot competition and the Dota 2 AI framework that supports it. This challenge aims to join both the MOBAs and AI/CI game competitions, inviting participants to submit AI controllers for the successful MOBA Dota 2 to play in 1v1 matches, which aims for fostering research on AI techniques for real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original F2P game.
An Emerging Learning Mode: The Effects of Instrumental Beliefs, Extrinsic Influences and Personal Traits on Undergraduate Students Adoption of Mobile Learning

Mobile learning is an emerging and important learning mode in higher education. Understanding students’ adoption of mobile learning is critical to the success of mobile learning implement. Drawing on the innovation diffusion theory and prior research related to extrinsic influences and personal traits, a mobile learning adoption model was developed and empirically examined against data collected from 206 college students in China. The results demonstrate that educational compatibility, perceived university support, perceived peer students readiness, and self-efficacy for change positive influence college students’ intention to adopt mobile learning. However, the influences of perceived relative advantage and complexity of using mobile learning on behavior intention were not significant.
Knowledge Management in Software Development Based on the Leavitt s Model Traditional versus Agile Methods

— Knowledge management has been a challenging issue in software projects. Due to the knowledge driven nature of the software products, paying enough attention to this concept is necessary. Recently, Agile methods are widely used in software companies. A big challenge in these methods in associated with knowledge management, is lack of formal communication and documentation. Meanwhile, several knowledge management models have been proposed in which Leavitt’s model is famous one. This study tries to adopt Leavitt’s model to Agile methods and map this model to structure of the Agile methods.
Summary of NIST latent fingerprint testing workshop

The central focus of this paper is on the Latent Testing Workshop, held on the National Institute of Standards and Technology (NIST) Campus April 5 to 6, 2006 and the lessons that were learned from it. The primary goal of the workshop was to gather information for the creation of a “Latent Challenge,” whose purpose is to stimulate Latent Automated Fingerprint Identification System (AFIS) vendors to submit their AFIS prototype systems for evaluation. To lay the foundations for such testing it is necessary that: 1) suitable test sets be identified and prepared; 2) the Application Programming Interface (API) is defined; and 3) the effective methods of performance scoring be defined. To provide background and context, past and present latent fingerprint activity at NIST is discussed. While the primary focus is on latent fingerprints, the paper also surveys relevant general biometrics activity.
A Hardware and Soft ware Integrated Approach for Adaptive Thr ead Management in Multicore Multithreaded Microprocessors

OF THE DISSERTATION A HARDWARE AND SOFTWARE INTEGRATED APPROACH FOR ADAPTIVE THREAD MANAGEMENT IN MULTICORE MULTITHREADED MICROPROCESSORS by Lichen Weng Florida International University, 2012 Miami, Florida Professor Chen Liu, Major Professor The Multicore Multithreaded Microprocessor maximizes parallelism on a chip for the optimal system performance, such that its popularity is growing rapidly in high-performance computing. It increases the complexity in resource distribution on a chip by leading it to two directions: isolation and unification. On one hand, multiple cores are implemented to deliver the computation and memory accessing resources to more than one thread at the same time. Nevertheless, it limits the threads’ access to resources in different cores, even if extensively demanded. On the other hand, simultaneous multithreaded architectures unify the domestic execution resources together for concurrently running threads. In such an environment, threads are greatly affected by the inter-thread interference. Moreover, the impacts of the complicated distribution are enlarged by variation in workload behaviors. As a result, the microprocessor requires an adaptive management scheme to schedule threads throughout different cores and coordinate them within cores. In this study, an adaptive thread management scheme was proposed, integrating both hardware and software approaches. The instruction fetch policy at the hardware level took the responsibility by prioritizing domestic threads, while the Operating System scheduler at the software level was used to pair threads dynami-
Frame-Based Compressed Sensing Of Speech Signal

In this paper, compressed sensing (CS) of speech signal using Frame-based adaptive technique has been proposed. we propose 3 sampling strategies in a frame-based adaptive CS framework. The first stage is frame based adaptive CS framework. In this stage, each speech sequence is divided into non-overlapping frames and all frames in a speech sequence are processed independently. Second stage is Partial Sampling and Frame Analysis. This stage can estimate the amount of intensity changes in the two frames. Last stage is adaptive sampling was used to reconstruction the signal using orthogonal matching pursuit (OMP). In the experimental results shows that the significant improvement of speech reconstruction quality by using frame based adaptive technique compare to the non-adaptive technique.
Utilizing Public Displays to Enable Location-Centric Information Sharing

Location-centric information sharing means that information can be shared at a specific geographic location by any person, regardless of the person's actual geographic location. Conceptually, we position location-centric information sharing as a contribution to P3 (People to People to Geographic Place) research which is about understanding the (new) meaning of place in social networks. Technically, we describe a proof of concept that links recent developments in mobile technology (smartphones and tablet computers, such as iPads) and large public displays and that demonstrates how off-the-shelf technology can be used to enable the remote sourcing of highly location-sensitive information. We are currently looking into installing the AskAway prototype for long-term embedded evaluation at a number of locations including universities, start-up companies and coffee shops.
4G, LTE-Advanced Pro and The Road to 5G, Third Edition

The upcoming 5G specifications from 3GPP, to be available in 2018, will include LTE-Advanced Pro as well as a new 5G radio-access technology. This practical and very successful book, written by engineers working closely with 3GPP, gives insight into the newest technologies and standards adopted by 3GPP, with detailed explanations of the specific solutions chosen and their implementation in LTE, LTE-Advanced, and LTE-Advanced Pro, as well as providing a detailed description of the path to 5G and the associated underlying technologies. This edition has been thoroughly revised and updated to reflect the large extensions to LTE as introduced in 3GPP Releases 12 and 13 and the role of LTE in the upcoming 5G era. New to this edition includes updated content on: 4G and 5G Radio Access Spectrum for 4G and 5G Machine-Type Communication Device-to-Device Communication License-assisted Access Full-dimension MIMOSmall-cell enhancements, eIMTA, FDD+TDD aggregation, dual connectivity Requirements on and general structure of 5G wireless access, addressing the existing and new usage scenarios for 5GTechnical solutions for the new 5G radio-access technology The authors of this book all work at Ericsson Research and have been deeply involved in 3G and 4G development and standardization. They are leading experts in the field and are today actively contributing to the standardization of 4G and 5G within 3GPP. The leading book on 3GPP specifications for LTE, LTE-Advanced, and LTE-Advanced Pro covering up to and including Release 13, written by Ericsson engineers who are heavily involved in the development of 3GPP specificationsTen new chapters and coverage of all major features introduced with Release 12 and 13Two completely new chapters on 5G wireless access including a detailed description of the key technology components under development by 3GPP