Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent resampling methods for video compression

Video compression is the core process that allows efficient storage and transmission of digital video. The main concept is to exploit redundancies in the video signal, with the goal of achieving the best possible trade-off between bitrate and quality. This very active research field has seen great improvements in the last 30 years, from the simple methods of the early 1990s, to more complex but highly optimized systems of modern compression formats. At the same time, improvements in consumer devices and the availability of faster Internet connections has allowed for the growing popularity of digital video. Nonetheless, with an ever-increasing demand for better quality and more immersive experiences from consumers, pressure for improved compression algorithms persists. Since current systems are already highly optimized, the challenge arises for innovative techniques to provide further enhancements. This thesis explores novel intelligent resampling-based methods for future video compression systems, from texture synthesis to spatial resolution adaptation. Texture synthesis has been proposed previously for the coding of difficult scenes, specially those featuring dynamic textures. However, the classification of dynamic textures is usually too broad and not well understood. For this reason, an extensive analysis of encoding statistics is presented based on a new homogenous texture dataset. In addition, limitations of prior dynamic texture synthesis approaches are presented and a new method is proposed. Moreover, we explore another type of resampling approach, known as spatial resolution adaptation. Our proposed framework dynamically adapts the spatial resolution of the input video based on a prediction that uses low-level visual features and later reconstructs the original resolution video at the decoder. Several methods are studied to achieve improved quality of the reconstructed video, including the use of Convolutional Neural Networks (CNNs) trained on a large dataset of videos using both pixel-based loss functions and a perceptually-inspired framework. Experimental results show that the proposed spatial resolution adaptation system achieves significant gains over HEVC using objective quality metrics, visual comparisons and subjective tests. In addition, an early version of the proposed approach was submitted to the Video Compression Technology Grand Challenge at ICIP 2017, and won first prize. For these reasons, we believe that this work provides a valuable contribution and offers significant potential for enhancing video coding systems.
Modeling, Analysis, and Optimization of Random Wireless Networks: Stochastic Geometry Approach

Recently, stochastic geometry has been shown to be a very powerful tool to model, analyze, and design networks with random topologies such as wireless ad hoc and sensor networks as well as multi-tier cellular networks. In stochastic geometry analysis, point processes are used to model the positions and the channel access behaviors of the nodes. The thesis develops analytical frameworks to characterize the performance of large-scale wireless networks with random topologies. In particular, I use stochastic geometry tools to model, analyze, and design ad hoc networks, star-connected sensor networks, and infrastructure-based two-tier cellular networks. I have optimized the tradeoff between outage probability and spatial frequency reuse efficiency in carriersensing-multiple-access based ad hoc networks. I have developed a novel spectrumefficient design paradigm for star-connected wireless sensor networks. For downlink transmission in cellular networks with cognitive femto access points (FAPs), I have quantified the performance gain imposed by cognition and developed a paradigm to optimize the spectrum sensing threshold for cognitive FAPs. Finally, I have developed a novel modeling paradigm for uplink transmission in cellular networks and obtained simple expressions for network performance metrics including the outage probability and average rate. Furthermore, I have revealed a transition point in the behavior of uplink transmission in cellular networks that depends on the relative values of the network parameters.
Sequence Labeling With Deep Gated Dual Path CNN

Sequence labeling, such as part-of-speech (POS) tagging, named entity recognition (NER), text chunking, is a classic task in natural language processing. Most existing neural networks models for sequence labeling are based on recurrent neural networks. Recently, convolutional neural networks have been proposed to replace the recurrent components for sequence labeling. However, they are usually shallow compared to deep convolutional networks that achieve start-of-the-art performance in other fields. Due to the vanishing gradient problem, these models usually can not work well when simply increasing the number of layers. In this paper, we propose using deep CNN architecture in sequence labeling, which can capture a large context through stacked convolutions. To reduce the vanishing gradient problem, the proposed method incorporates gated linear units, residual connections, and dense connections. Experimental results on three sequence labeling tasks show that the proposed model can achieve competitive performance to the RNN-based state-of-the-art method while maintaining $2.41\times$ faster speed, even with up to 10 convolutional layers.
A Flexible Multicarrier Scheme Based on the Discrete Orthogonal Stockwell Transform

Aiming to reduce the computational complexity of a resource (bit and power) allocation technique, this article introduces an orthogonal time-frequency division multiplexing (OTFDM) scheme. This OTFDM scheme makes use of the discrete orthogonal Stockwell transform (DOST) and exploits temporal and spectral information of power line channels for achieving very low data rate degradation and less computational complexity during the resource allocation process. Also, we present closed-form expression for the tile normalized signal-to-noise ratio (nSNR) for the OTFDM scheme and the existing relationship with its counterpart for the orthogonal frequency division multiplexing (OFDM) scheme. Besides, we demonstrate that OFDM and single carrier-cyclic prefix schemes are particular cases of the OTFDM scheme, showing the flexibility of OTFDM for handling distinct scenarios. Numerical analyses show that OTFDM outperforms OFDM in terms of computational complexity reduction and data rate loss when a resource allocation technique applies. Both schemes attain a similar symbol error ratio under the impulsive noise presence. Finally, OTFDM offers a lower peak-to-average power ratio than OFDM.
Point of Sale (POS) Network with Embedded Fingerprint Biometric Authentication

The steady growth in electronic transactions has promoted the Automated Teller Machine (ATM) thereby making it the main transaction channel for carrying out financial transactions. Conventional method of identification based on possession of ID cards or exclusive knowledge like a social security number or a password are not all together reliable. However, this has also increased the amount of fraudulent activities carried out on Automated Teller Machines (ATMs) thereby calling for efficient security mechanisms and increasing the demand for fast and accurate user identification and authentication in ATMs. In this paper, an embedded fingerprint biometric authentication scheme for Point of Sale (POS) network as additional security option to the ATM card is proposed. A fingerprint biometric technique was fused with personal identification numbers (PIN's) for authentication to upgrade the security level. The proposed idea will solve the problems that may face the customers such as theft, counterfeiting, oblivion or loss the card. Therefore, the customer will be identified once putting his finger on the reader (based on finger scanning) and the system recognizes the customer without requiring keys or cards of support. We have distributed a questioner to 586 responders, the obtained results show the importance of fingerprint biometric authentication for POS network as additional security option to the ATM card.
Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning

OF DISSERTATION Relation Prediction over Biomedical Knowledge Bases for Drug Repositioning Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task using existing biomedical knowledge bases. Our approaches can be broadly labeled as link prediction or knowledge base completion in computer science literature. Specifically, first we investigate the predictive power of graph paths connecting entities in the publicly available biomedical knowledge base, SemMedDB (the entities and relations constitute a large knowledge graph as a whole). To that end, we build logistic regression models utilizing semantic graph pattern features extracted from the SemMedDB to predict treatment and causative relations in Unified Medical Language System (UMLS) Metathesaurus. Second, we study matrix and tensor factorization algorithms for predicting drug repositioning pairs in repoDB, a general purpose gold standard database of approved and failed drug–disease indications. The idea here is to predict repoDB pairs by approximating the given input matrix/tensor structure where the value of a cell represents the existence of a relation coming from SemMedDB and UMLS knowledge bases. The essential goal is to predict the test pairs that have a blank cell in the input matrix/tensor based on the shared biomedical context among existing non-blank cells. Our final approach involves graph convolutional neural networks where entities and relation types are embedded in a vector space involving neighborhood information. Basically, we minimize an objective function to guide our model to concept/relation embeddings such that distance scores for positive relation pairs are lower than those for the negative ones. Overall, our results demonstrate that recent link prediction methods applied to automatically curated, and hence imprecise, knowledge bases can nevertheless result in high accuracy drug candidate prediction with appropriate configuration of both the methods and datasets used.
SCHEDULING FOR MASSIVE MIMO USING CHANNEL AIGING UNDER QOS CONSTRAINTS

Massive multiple-input multiple-output (MIMO) networks support QoS (Quality of Service) by adding a new sublayer Service Data Adaption Protocol on the top of Packet Data Convergence Protocol layer to map between QoS flows and data radio bearers. In downlink for Guaranteed Bit Rate (GBR) flows, the gNB guarantees the Guaranteed Flow Bit Rate (GFBR) that defines the minimum bit rate the QoS flow can provide. So, one of the most important requirements is the minimum rate. The channel aiging helps to improve the sum-rate of Massive MIMO systems by serving more users to increase the spatial multiplexing gain without incurring additional pilot overhead. In this paper, a novel scheduler, termed QoS-Aware scheduling, is designed and proposed for Massive MIMO to use the channel aiging to increase the sum-rate but guarantee the minimum bit rate per user to support QoS. We investigate how many users are enough to serve to maximize the sum-rate while keeping the data rate per user meeting a given threshold. Through the numerical analysis we confirmed that QoS-Aware scheduling can guarantee a minimum rate per user and get a higher useful through-put (goodput) than conventional channel aiging schedulers.
Effects of Control Device and Task Complexity on Performance and Task Shedding During a Robotic Arm Task

EFFECTS OF CONTROL DEVICE AND TASK COMPLEXITY ON PERFORMANCE AND TASK SHEDDING DURING A ROBOTIC ARM TASK Shelby K. Long Old Dominion University, 2019 Director: Dr. James P. Bliss The use of robotic arms across domains is increasing, but the relationship between control features and performance is not fully understood. The goal of this research was to investigate the difference in task performance when using two different control devices at high and low task complexities when participants can shed tasks to automation. In this experiment, 40 undergraduates (24 females) used two control devices, a Leap Motion controller and an Xbox controller, to teleoperate a robotic arm in a high or low complexity peg placement task. Simultaneously, participants were tasked with scanning images for tanks. During the experiment, participants had the option to task shed the peg task to imperfect automation. Analyses indicated a significant main effect of control device on task completion rate and time to first grasp the peg, with completion rate higher and time lower when using the Leap. However, participants made significantly more errors with the Leap Motion controller than with the Xbox controller. Participants in both conditions task shed similarly with both control devices and task shed at similar times. The 2 x 2 mixed ANOVAs somewhat supported the proposed hypotheses. The results of this study indicate that control device impacts performance on a robotic arm task. The Leap Motion controller supports increased task completion rate and quicker peg grasps in high and low task complexity when compared with the Xbox controller. This supports the extension of Control Order Theory into three-dimensional space and suggests that the Leap Motion controller can be implemented in some domains. However, the criticality and frequency of errors should be carefully considered.
Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior

Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human–subject study with 30 participants in a virtual reality environment. The study manipulated two factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk type (signalized and unsignalized crossing). Results indicate that pedestrians' trust in AVs was influenced by AV driving behavior as well as the presence of a signal light. In addition, the impact of the AV's driving behavior on trust in the AV depended on the presence of a signal light. There were also strong correlations between trust in AVs and certain observable trusting behaviors such as pedestrian gaze at certain areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also present implications for design and future research.
Secure Real-Time Traffic Data Aggregation With Batch Verification for Vehicular Cloud in VANETs

The vehicular cloud provides many significant advantages to Vehicular ad-hoc Networks (VANETs), such as unlimited storage space, powerful computing capability and timely traffic services. Traffic data aggregation in the vehicular cloud, which can aggregate traffic data from vehicles for further processing and sharing, is very important. Incorrect traffic data feedback may affect traffic safety; therefore, the security of traffic data aggregation should be ensured. In this paper, by using the property of data recovery in the message recovery signature (MRS), we propose a secure real-time traffic data aggregation scheme for vehicular cloud in VANETs. In the proposed scheme, the validity of vehicles’ signatures are verified, and then the original traffic data is recovered from signatures. Moreover, the proposed scheme supports batch verification for multiple vehicles’ signatures. Due to advantages of the MRS, security features such as data confidentiality, privacy preservation and reply attack resistance are preserved. In addition, the comparison and simulation results indicate that the proposed scheme is superior in comparison to previous schemes with respect to the communication and computational cost.
Automatic Lyrics Alignment and Transcription in Polyphonic Music: Does Background Music Help?

Automatic lyrics alignment and transcription in polyphonic music are challenging tasks because the singing vocals are corrupted by the background music. In this work, we propose to learn music genre-specific characteristics to train polyphonic acoustic models. We first compare several automatic speech recognition pipelines for the application of lyrics transcription. We then present the lyrics alignment and transcription performance of music-informed acoustic models for the best-performing pipeline, and systematically study the impact of music genre and language model on the performance. With such genre-based approach, we explicitly model the music without removing it during acoustic modeling. The proposed approach outperforms all competing systems in the lyrics alignment and transcription tasks on well-known polyphonic test datasets.
An Efficient Collaboration and Incentive Mechanism for Internet of Vehicles (IoV) With Secured Information Exchange Based on Blockchains

With the rapid development of Internet of Things (IoT), mobile crowdsensing (MCS), i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, MCS lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet of Vehicles (IoV)—a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice, some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multivehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we first propose a new model for the scenario of two vehicles collaboration, considering the situation of the emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Second, for an emergent task, a novel time-window-based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.
Sample Complexity Bounds for the Linear Quadratic Regulator

Author(s): Tu, Stephen Lyle | Advisor(s): Recht, Benjamin | Abstract: Reinforcement learning (RL) has demonstrated impressive performance in various domains such as video games, Go, robotic locomotion, and manipulation tasks. As we turn towards RL to power autonomous systems in the physical world, a natural question to ask is, how do we ensure that the behavior observed in the laboratory reflects the behavior that occurs when systems are deployed in the real world? How much data do we need to collect in order to learn how to control a system with a high degree of confidence? This thesis takes a step towards answering these questions by establishing the Linear Quadratic Regulator (LQR) as a baseline for comparison of RL algorithms. LQR is a fundamental problem in optimal control theory for which the exact solution is efficiently computable with perfect knowledge of the underlying dynamics. This makes LQR well suited as a baseline for studying the sample complexity of RL algorithms which learn how to control from observing repeated interactions with the system.The first part of this thesis focuses on model-based algorithms which estimate a model of the underlying system, and then build a controller based on the estimated dynamics. We show that the classic certainty equivalence controller, which discards confidence intervals surrounding the estimated dynamics, is efficient in regimes of low uncertainty. For regimes of moderate uncertainty, we propose a new model-based algorithm based on robust optimization, and show that it is also sample efficient.The second part studies model-free algorithms which learn intermediate representations instead, or directly search for the parameters of the optimal controller. We first look at the classical least-squares policy iteration algorithm, and establish an upper bound on its sample complexity. We then use tools from asymptotic statistics to characterize the asymptotic behavior of both the certainty equivalence controller and the popular policy gradient method on a particular family of LQR instances, which allows us to directly compare the bounds. This comparison reveals that the model-free policy gradient method has polynomial in state/input dimension and horizon length worse sample complexity than the model-based certainty equivalence controller. Our experiments corroborate this finding and show that model-based algorithms are more sample efficient than model-free algorithms for LQR.
Comparative Study of Pulmonary Function Tests in Sedentary Individuals and Dynamic Exercising People

The field of exercise physiology is at the dawn of a new era. Exercise can play a role in shaping one’s life style.Jogging, and walking are dynamic exercises, play a dominant role in individual’s total health. The pulmonary physicians admit the dynamic exercise is beneficial to prevent respiratory diseases . The dynamic exercise’s positive impact includes pulmonary physioloy. The pulmonary function tests evaluate the respiratory system. They are ventilatory function tests. The oxygen consumption and carbon dioxide production may go up to 25 times depending on severity of the exercise and the alveolar and arterial PO2 and PCO2 are maintained normal during the strenuous exercise, by increase in pulmonary ventilation and changes at the tissues level. Materials and Methods: study was conducted on subjects ranging from 24-26 years, 44-46 years and 64-66 years male in good health and their hemoglobin is more than 10gms/ 100ml. Different parameters (FVC, FEV1, FEV1/FVC %, PEF 50%, RR) of the study are recorded on the computerized Spirometer. Results and Conclusion: The results showed significantly increased (p<0.001) mean FVC, mean FEV1, mean FEV% and mean PEF in male atheletes practicing regular dynamic exercise and more so in young persons. This suggests that regular dynamic exercise has improved lung function.
Mothers Knowledge, Attitude and Practices about child immunization: A study in district Faisalabad, Pakistan

Objectives: To explore the knowledge, attitude and practices of the mothers about children immunization and to access the vaccination status of the children. Methodology: Data was collected on pre-designed questionnaire and analysed by descriptive statistics. Results: It was found that 26.5% respondents had not knowledge about routine vaccination schedule. Only 37% respondents were well aware about the name of common diseases of the children. 76% children were fully immunized and 17% children were partially immunized and BCG coverage was 94% in the study area. Conclusion: Mothers had poor knowledge but positive attitude towards the importance of children vaccination and vaccination schedule. Mostly, mothers practice child vaccination to prevent diseases but did not know about the communicable diseases and age appropriate immunization.