Miracle-dz/UniFAR-FTUs huggingface dataset:
The Data Mining Based on the Co-Author Network

Co-authoring a manuscript usually connotes a strong influential connection between researchers. Based on data extraction, build the co-author network which maps 511 nodes connections, the nodes represent authors and the links represent the connections with one another coauthors. Within the network, we use software UCINET to analyze its properties. Furthermore, we apply the grey relational analysis method into measuring the author influence and impacts, after extracting five indexes which are the degree, closeness, betweenness, eigenvector and the number of joint publications. By combining network topology and the algorithm ,it will be much better to tap and identify information between authors. Furthermore, it can provide the development of science and technology management and technology with some important guidance.
Leaf Features Based Plant Classification Using Artificial Neural Network

This paper presents the classification of plant leaf images with biometric features. Traditionally, the trained taxonomic perform this process by following various tasks. The taxonomic usually classify the plants based on flowering and associative phenomenon. It was found that this process was time consuming and difficult. The biometric features of plants leaf like venation make this classification easy. Leaf biometric feature are analyzed using computer based method like morphological feature analysis and artificial neural network based classifier. ANN model take input as the leaf venation morphological feature and classify them into four different species. The result of this classification based on leaf venation is achieved 96.53% accuracy in the training of the model for classification of leaves provide the 91% accuracy in testing to classify the leaf images.
Nozzilla: A Novel Peer to Peer Architecture for Video Streaming

Many peer-to-peer video streaming systems use application-level multicast where the peers are responsible of forwarding the video packets between them, usually following the path of one or more tree overlays. Since the performance of such systems depends on the uplink capacity available at each peer (measured as bandwidth, delay and jitter), previous work focuses on sharing the load responsibility between all peers by dividing the video trafﬁc into several stripes and requiring a peer to be an interior node in one multicast tree. However, although these techniques consider the uplink capacity limit, they do not adapt easily for situations where the resources are either very abundant or very scarce. This thesis proposes Nozzilla, a new approach based on structured peer-to-peer that dynamically takes into account the available capacity resources. Increased efﬁciency and reliability is achieved in several ways. First, the peers in the overlay are grouped according to their current resources, improving the search for a suitable parent inside the multicast tree. Second, the system uses path diversity by splitting the video trafﬁc in several stripes and distributes each stripe along a different multicast tree. Third, the protocol can be used in a quality-of-service enabled network, which can assign different classes of service for each video stripe. This makes possible the usage of techniques such as multi-description coding and scalable video coding and is more robust to failures since the loss of a single stripe does not cause the loss of video reception. Finally, the algorithm improves the performance of the multicast tree by increasing the load distribution between peers and reducing the resource demand on the root.
An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

Concerning the prediction problems’accuracy of the state-of-charge(SOC) of the battery，this paper proposes a prediction method based on an improved genetic algorithm-radial basis function neural network for power battery charged state. The prediction method, based on intensity of information interaction and neural activity, adjusts the size of the neural network online and solves the problem that radial basis function neural network structure adjustment influences the accuracy of charged state prediction. The simulation results show that，compared with the method of radial basis function neural network based on genetic algorithm , the accuracy of charged state prediction is more stable and more precise.
24-bit Low-Power Low-Cost Digital Audio Sigma-Delta DAC

This paper describes a low-power low-cost 24-bit Σ-digital-to-analog converter (DAC) for portable digital-audio applications.The interpolation filter uses a no-multiplier scheme to implement the arithmetic units and reading-writing common storage scheme for the delay-line to significantly reduce the die area.A 15-level quantizer,third-order,single-stage Σ-modulator is employed to reduce the passband quantization noise,relax the out-of-band filtering requirements,and enhance immunity to clock jitter.A data weighted averaging algorithm is used to mitigate the nonlinearity caused by capacitor mismatch.A direct charge transfer switched-capacitor low-pass filter (DCT-SC LPF) is used to reconstruct the analog signal to reduce the kT/C noise and capacitor mismatch effect with a small increase of the power dissipation.The chip was fabricated in the SMIC 0.13 μm 1P5M CMOS process.The cell area of the digital part is 0.056 mm 2 and the total area of the analog part is 0.34 mm 2.The supply voltage is 1.2 V for the digital circuit and 3.3 V for the analog circuit.The power consumption of the analog part is 3.5 mW.The audio DAC achieves a 100 dB dynamic range and an 84 dB peak signal-to-noise-plus-distortion ratio over a 20 kHz passband.The results show that these performances are good enough for high quality portable audio applications.
Dagging Meta Classifiers with Support Vector Machine

Brain-Computer Interface (BCI) processes the brain signals into computer understandable signals which in turn are converted into control signals. Thus, BCI acts as a communication system between the brain and an external device. The brain signals are captured in the form of Electroencephalogram (EEG) and BCI system transforms it into control signals by extracting features, pattern recognition and classification. In this paper, the features from the EEG are extracted using Gaussian smoothened Fast Hartley Transform (GS-FHT) which computes the energies of different motor imagery. Dagging Meta classifiers are used with Support Vector Machine for classification of the extracted features. The proposed methodology was evaluated using publicly available BCI Competition dataset IVA. The evaluations of preprocessed signals show that the extracted features from EEG were interpretable and the classification accuracy obtained were satisfactory.
Computational models of problems with writing of English as a second language learners

Learning a new language is a challenging endeavor. As a student attempts to master the grammar usage and mechanics of the new language, they make many mistakes. Detailed feedback and corrections from language tutors are invaluable to student learning, but it is time consuming to provide such feedback. In this thesis, I investigate the feasibility of building computer programs to help to reduce the efforts of English as a Second Language (ESL) tutors. Specifically, I consider three problems: (1) whether a program can identify areas that may need the tutor’s attention, such as places where the learners have used redundant words; (2) whether a program can auto-complete a tutor’s corrections by inferring the location and reason for the correction; (3) for detecting misusages of prepositions, a common ESL error type, whether a program can automatically construct a set of potential corrections by finding words that are more likely to be confused with each other (known as a confusion set). The viability of these programs depends on whether aspects of the English language and common ESL mistakes can be described by computational models. For each task, building computational models faces unique challenges: (1) In highlighting redundant areas, it is difficult to precisely define “redundancy” in a computer’s language. (2) In auto-completing tutors’ annotations, it is difficult for computers to correctly interpret how many writing problems were addressed during revision. (3) In confusion set construction, it is difficult to infer which words are more likely confused with the given word. To address these challenges, this thesis presents different model alternatives for each task. Empirical experiments demonstrate the degrees of success to which computational models can help with detecting and correcting ESL writing problems
Robust Phase Synchronization for DVB-RCS systems

In this paper, we present robust phase recovery algorithm and its implementation for DVB-RCS (Digital Video Broadcasting - Return Channel System via Satellite) system. We investigate the performances and complexities of a single phase estimator using Turbo decoder. In addition, we identify the practical drawback when we employ a multiple PSP (Per Survivor Process) phase estimator for DVB-RCS turbo code transmission system. To validate it, we analyze how the double binary recursive turbo code structure applied to DVB-RCS system affects the performance of the phase recovery systems. Basically, the LMS (Least Mean Square) and ML (Maximum Likelihood) phase estimation schemes are used for the iterative phase estimations. We also propose scaled LMS algorithms for the FPGA implementation of the double binary turbo coded system with the single phase estimator. Keywords—turbo code, phase estimation, least mean square, 3GPP, double binary turbo code, DVB- RCS.
Computer-mediated communication in English for specific purposes: A case study with computer science students at Universiti Teknologi Malaysia

This paper will describe an ESP approach to the design and implementation of computer-mediated communication (CMC) tasks for computer science students at Universiti Teknologi Malaysia, and discuss the effectiveness of the chat feature of Windows NetMeeting as a tool for developing specified language skills. CMC tasks were set within a programme of sustained-content language instruction (SCLI), a variation on the content-based instruction approach. Various studies have confirmed the potential of SCLI as a means of familiarising language learners with academic genres and the language skills expected of them in the content classroom. To date, however, there has been little or no research into the use of CMC within SCLI. We found that students who followed a programme of SCLI using CMC ESP tasks made significant improvements in their oral communication skills, and also achieved higher scores than their peers in a computer science project undertaken in the semester following the treatment.
Field Evaluation of Blue Force Tracking with a 2D/3D Command and Control System

Decision makers in complex environments have a difficult task and researchers all over the world try to develop different decision aids to support them. This paper presents a study where a research platform with a combined 2D- and 3D-tool was used as a decision aid during two Military Operation in Urban Terrain exercises. Blue Force Tracking was used on some vehicles and soldiers to monitor their movements. The purpose of the study was to investigate if 3D visualization and distributed information in a C2 system, including Blue Force Tracking (BFT), gives the commander better SA. The results show that the commanders found the decision aid and the BFT very useful. The system enabled good SA and a shared awareness of what was going on in the battlefield. 2D was used 80% of the time and 3D was used for specific tasks where detailed information was needed to improve SA.
An analog VLSI neural network architecture with on-chip learning

A user-configurable analog VLSI feedforward neural network architecture that adds only 10% to chip area relative to a fixed topology is described. Central to the architecture is a novel synapse circuit that consumes 4500 /spl mu/m/sup 2/ in a 2-/spl mu/m technology. Hybrid dynamic and non-volatile weight storage allows fast learning as well as reliable long-term storage. Measured synapse current-voltage curves from a test chip are presented. The synapse includes a weight increment circuit that adds offset of only 1 part in 13 bits allowing analog-domain on-chip learning. Weight update circuits that implement a semiparallel weight perturbation learning algorithm are presented. <<ETX>>
Multimedia learning in an interactive self-explaining environment: What works in the design of agent

Students learned about electric motors by asking questions and receiving answers from an on-screen pedagogical agent named Dr. Phyz who stood next to an on-screen drawing of an electric motor. Students performed better on a problem-solving transfer test when Dr. Phyz's explanations were presented as narration rather than on-screen text (Experiment 1), when students were able to ask questions and receive answers interactively rather than receive the same information as a noninteractive multimedia message (Experiments 2a and 2b), and when students were given a prequestion to guide their self-explanations during learning (Experiment 3). Deleting Dr. Phyz's image from the screen had no significant effect on problem-solving transfer performance (Experiment 4). The results are consistent with a cognitive theory of multimedia learning and yield principles for the design of interactive multimedia learning environments. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
Track Your Stress: Konzeption und Realisierung einer mobilen (Android) Anwendung zur Messung des Stresslevels

Ein immer wichtiger werdendes Thema in unserer heutigen Gesellschaft ist Stress. Von vielen Personen unbewusst oder bewusst unterschatzt, schleichen sich stetig verschiedene Stressfaktoren und Stressausloser in unser Leben. Sei dies im alltaglichen Leben, Zuhause, bei der Arbeit, beim Sport oder sogar im Familienurlaub. Die meisten Menschen kommen nicht darum herum, hin und wieder gestresst zu sein und stressbedingte Krankheiten werden in unserer Gesellschaft leider immer haufiger. Wie negativ sich andauernder Stress oder zu viel Stress auf die Gesundheit eines jeden Menschen auswirken kann, wird immer ofter studiert und ist auch teilweise schon erwiesen worden. Langfristig sollte es das Ziel sein, moglichst alle Stressursachen zu verringern oder gar zu eliminieren, um der Gesellschaft ein stressfreies und somit auch gesunderes Leben zu ermoglichen, denn schon in der Antike gab es die Weisheit „Mens sana in corpore sano“, was grob ubersetzt „ein gesunder Geist in einem gesunden Korper“ bedeutet1. In Zukunft sollen daher auch Unternehmen und Arbeitgeber dazu verpflichtet sein, den Stresslevel eines Arbeitnehmers zu erfassen und auf Grundlage dieser Erhebungen den Stresslevel einer jeden Person moglichst gering zu halten. Das Projekt Track Your Stress soll ein Anfang sein, um das Stresslevel einer jeden Person messen und bestimmen zu konnen, sowie die teilnehmenden Personen sensibler auf dieses oftmals unterschatzte Thema reagieren zu lassen. Hierfur zeigt die Arbeit, wie auf mobilen (Android) Geraten das jeweils eigene Stresslevel, unter unterschiedlichen Bedingungen und zu unterschiedlichen Zeitpunkten, wahrgenommen wird und wie dies visualisiert werden kann. Dies wird im Detail vorgestellt, sowie aus technischer Sicht betrachtet. Weiter wird aufgezeigt, wie ein solches Projekt architektonisch zu realisieren ist. Zusatzlich wird in dieser Arbeit auf spezielle Implementierungsthemen eingegangen. Am Ende entstand eine voll funktionsfahige mobile Anwendung zur Unterstutzung der Messung des Stresslevels einer jeden Person bzw. eines jeden Nutzers der mobilen Anwendung. Des Weiteren kann das Projekt in dieser Form auch auf andere Zwecke ubertragen und genutzt werden, wie z.B. zur Ermessung und Unterstutzung bei chronischen Erkrankungen, bezuglich deren Symptome und daraus entstehenden Einschrankungen etc.
Array Diagnosis using Compressed Sensing in Near Field

This paper will present a technique for array diagnosis using a small number of measured data acquired by a near-field system by making use of the concepts of compressed sensing technique in image processing. Here, the high cost of large array diagnosis in near-field facilities is mainly caused by the time required for the data acquisition. So there is a need to decrease the measurement time and at the same time the reconstruction of an array must be satisfactory. The proposed technique uses less number of measurement points compared to other proposed techniques like back-propagation method and standard matrix inversion method. Keywords: Arrays, Compressed sensing, near field
Introduction to Digital Signal Processing

From the Publisher: Utilizing classroom tested projects and exercises, this supplemental text and disk exposes advanced undergraduate and graduate students to digital signal processing (DSP) in a computer environment. These exercises and projects offer practical applications to real-world problems with easy modification to provide students with variety and fresh testing from year to year. The software is easy to use with an on-line help function which explains the usage of all DSP functions.
Elman Backpropagation Single Hidden Layer Models for Estimating Shelf Life of Kalakand

Elman artificial neural network (ANN) models were developed using backpropagation networks with a single hidden layer and sigmoid activation functions for predicting the shelf life of kalakand stored at 6 o C. Input variables were moisture, titratable acidity, free fatty acids, tyrosine and peroxide value of the product, while the overall acceptability score was taken as the output variable. It was found that ANN with 5a20a1 topology gave the best fitting with the experimental data, which made it possible to predict the shelf life of kalakand.
Literacy Development in the Early Years: Helping Children Read and Write

1. Foundations of Early Literacy Development: Surveying the Past to the Present. 2. Assessing Early Literacy Development to Design Instruction. 3. Literacy and Diversity: Meeting Individual Needs of English Language Learners, Children with Disabilities and Other Diverse Concerns. 4. Language and Vocabulary Development. 5. Strategies to Figure out Words: Phonological Awareness, Phonemic Awareness, and Phonics. 6. Developing Comprehension of Text and Concepts About Books. 7. Writing, Spelling, and Literacy Development. 8. Motivating Reading and Writing Using Children's Literature, Literacy Centers, Technology, and Play. 9. Organizing and Managing the Literacy Program. 10. Family Literacy Partnerships: Home and School Working Together.
Applying Pharmacokinetic and Pharmacodynamic Models in the Operating Room: Validation of Response Surface Models

Pharmacokinetics are used to model drug concentrations in the body. These predictions can be combined with pharmacodynamic response surface models that predict the effect of multiple drugs acting on the body. This study combined several pharmacokinetic and pharmacodynamic models to predict “adequate anesthesia. ” These predictions were compared to observations in patients. While these specific model combinations are not accurate predictors of anesthesia for the recovery of responsiveness and tracheal intubation, a few combinations are reasonable predictors of the loss of responsiveness and also for the analgesia necessary for the first skin incision. The Schnider propofol model and a fentanyl scaling factor of 1.2 are empirically the most accurate PK models in combination with the pharmacodynamic models used.
Semantic Models for the Security of Sequential and Concurrent Programs

The present thesis investigates conndentiality, i.e., the problem of determining whether a given program (possibly acquired from an untrusted source) has secure information ow. In a setting of extensional security, whether or not a diierence in a program's behaviour is detectable by a low-level observer as the sensitive data is varied, determines whether or not the program is insecure. Such a view induces an equivalence relation on program behaviours in the low-level observer's (or attacker's) view. We present a formalisation of the attacker's view: this is done via the mathematical machinery of partial equivalence relations in the case of sequential programs (covering nondeterminism and probabilistic covert channels) and bisimulations in the case of concurrent programs (arguing for scheduler-independent security and covering synchronisation, timing and probabilistic covert channels). In both cases, we arrive at compositional security speci-cations; this facilitates straightforward soundness proofs for compositional security analyses, such as type-based analyses. By such a formalisation of secure information ow, we contribute to a rigorous understanding of potential threats and a higher conndence in security certiication based on formal deenitions. We further concentrate on integrating the formalisation of the security of computation at the level of speciic programming languages (sequential and concurrent as considered in this thesis) into the security of complex systems at a more abstract level. With this objective in mind, we propose a sound and complete translation of a timing-sensitive security speciication for simple multi-threaded programs into a more general security framework. i This thesis is a collection of four articles devoted to semantic models for program security. The articles correspond to Chapters 2{5 and are published (in order) in the the following sources: 1.
Synthesis and evaluation of fault-tolerant quantum computer architectures

Fault-tolerance is the cornerstone of practical, large-scale quantum computing, pushed into its prominent position with heroic theoretical efforts. The fault-tolerance threshold, which is the component failure probability below which arbitrarily reliable quantum computation becomes possible, is one standard quality measure of fault-tolerant designs based on recursive simulation. However, there is a gulf between theoretical achievements and the physical reality and complexity of envisioned quantum computing systems. This thesis takes a step toward bridging that gap. We develop a new experimental method for estimating fault-tolerance thresholds that applies to realistic models of quantum computer architectures, and demonstrate this technique numerically. We clarify a central problem for experimental approaches to fault-tolerance evaluation namely, distinguishing between potentially optimistic pseudo-thresholds and actual thresholds that determine scalability. Next, we create a system architecture model for the trapped-ion quantum computer, discuss potential layouts, and numerically estimate the fault-tolerance threshold for this system when it is constrained to a local layout. Finally, we place the problem of evaluation and synthesis of fault-tolerant quantum computers into a broader framework by considering a software architecture for quantum computer design. Thesis Supervisor: Isaac L. Chuang Title: Associate Professor of Electrical Engineering and Computer Science