Miracle-dz/UniFAR-FTUs huggingface dataset:
Feedback Control Of Dynamic Systems

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Effects of challenge and skill on videogame flow

The purpose of this study is to reinvestigate the concept of flow engagement between expert and novice gamers, with flow being defined by Csikszentmihalyi (1997) as “the place of perfect harmony between expectations and accomplishments”. A previous study, conducted by Gascon (2015), concluded that there is a connection between player skill and game difficulty, meaning that players who are adequately challenged based on their skill level are more engaged in the game. The use of video games for this study is ideal, since the level of difficulty can be altered as needed. These results were obtained through a post-game survey based off of the Csikszentmihalyi flow theory model. While the Gascon study provided useful results, newer methods that more accurately measure flow exist and warrant a replication with these new methods. This new study will use Flow State Scale 2 and the Play Experience Scale as outlined in Pavlas (2012) that have been shown to more directly measure flow. This new study also aims to better utilize a distraction task that would have provided objective data of flow engagement that was attempted in the earlier study but was impaired by participants not reaching the distraction stimulus. This task, which provided a picture that would change, would provide evidence of flow if the participant was engaged in the game and does not detect the changing image. This data could have provided further reinforcement to the concept of flow while gaming, but was set up in a way that did not give subjects an opportunity to notice or not notice the task. The new study wants to incorporate this task by tailoring it to the average time previous subjects spent playing the game. This new study is currently in progress and no data has been collected, but the methods outlined will be used to do so. It is important to reinvestigate flow engagement with newer measurements in order to differentiate it between engagement, immersion or other concepts that are closely related to flow. Data collected from this study could assist in reinforcing the importance of flow in videogames, as well as other forms of entertainment.
STATEFUL DETECTION OF STEALTHY BEHAVIORS IN ANDROID APPS

STATEFUL DETECTION OF STEALTHY BEHAVIORS IN ANDROID APPS MOHSIN JUNAID, Ph.D. The University of Texas at Arlington, 2017 Supervising Professor: David C Kung, Jiang Ming The number of smartphones has increased greatly during the last few years. Among the popular mobile operating systems (such as iOS and Android) installed on these devices, Android captures most of the mobile market share. This also puts Android OS in a spotlight to attract malware attacks. A recent study shows that for the last two years, more than ∼99% of the mobile malware targeted Android OS [1]. Examples of such attacks are leakage of privacy-sensitive data available on the devices (such as phone number, contacts, photos, and SMS and call logs), recording audio and video files, silently making phone calls in the background, and encrypting device files. Many of them are profit-oriented (i.e., sending SMS to premium rate numbers can cause unexpected higher monthly bills for the users). Driven by the rich profit, the malware attacks are also becoming stealthier over time to maximize the long-term payoffs. A stealthy attack typically takes extra precautionary measures to stay undetected for a longer period of time. There are two types of stealthy attacks based on how stealth is achieved: (1) Type 1 attacks use hidden or uncommon program flows of Android apps to exhibit their malicious behaviors. (2) Type 2 attacks launch additional actions to hide their intended malicious behaviors. For example, the infamous Android.HeHe malware [2] carefully takes three actions to silently block incoming phone
Tensor Factorization for Heterogeneous and Spatio-temporal Data

We observe various kinds of interesting events occurring in our daily lives, and try to represent these events in a simple manner. A relational model is one of the de facto systems used to digitally store these events due to its flexible representation abilities compared to other data models that have strict predefined structures. We try to discover interpretable patterns from the relational data, which is a traditional way to understand the latent characteristic or trend in subjective phenomena. These patterns are employed to recover missing records that have not observed in relational data. We can obtain better predictions of future events by leveraging the latent trends than by directly using the observed data. However, in practical data analysis, we often encounter situations where the relational data are sparse and noisy, which means that the data contain fewer messy records than the total number of combinations of the attributes. Pattern extraction methods may provide undesired results due to the lack of information sources. In this dissertation, we attempt to solve the sparseness and noise problems by developing data analysis methods that adopt side information to discover interpretable and useful patterns. There are three essential challenges in this dissertation: (1) develop a method to merge heterogeneous information of associated relational data to extract patterns, (2) leverage spatio-temporal structures that can be represented as graphs or groups of data attributes to encourage latent patterns to be smooth, and (3) learn dependency for attributes in a data-driven manner and modify spatiotemporal structures to capture the patterns suitable for prediction problems. To tackle these challenges, we propose tensor factorization methods that extract an interpretable pattern by decomposing a tensor into latent factors. For the first challenge, we develop Non-negative Multiple Matrix Factorization (NMF), which decomposes a target matrix and auxiliary matrices simultaneously. As an exten-
Thread: Towards fine-grained precision reconfiguration in variable-precision neural network accelerator

In this work, we propose a neural network accelerator that supports ﬁ ner-grained precision tuning for both activations and weights. To the best of our knowledge, this is the ﬁ rst neural network accelerator supporting arbitrary bit widths within 8 bits for both neural weights and activations. Our accelerator combines the features of bit-parallel and bit-serial design methods so that we can accomplish high arithmetic-unit utilization and precision tuning ﬂ exibility simultaneously. According to the cycle accurate simulation and the synthesized result, the proposed accelerator achieves up to 1.77x energy-e ﬃ ciency improvement over the bit-serial design of Stripes for the evaluated workloads.
A Novel Segmentation Method for the Optic Nerve Head in the Retinal Images

Early detection of glaucoma is essential to minimizing the risk of visual loss.It has been shown that a good predictor of glaucoma is the morphological parameters of the optic nerve head.To realize the computer-aided measurement of these parameters,this paper presents a novel,automated method to segment the optic nerve head based on the improved Random walks segmentation method and morphological pre-processing in polar coordination.In contrast with the state-of-art algorithms,the overlap accuracy is 96.52%acquired in the comparing segmentation results for our clinic retinal images database with the golden standards and verifies the effectiveness of the proposed algorithm.
Sample Size Determination for Markovian Queueing Models

In this thesis, we focus on the sample size of two variants of the standard M/M/1 queueing model. The reason is that variants of the standard M/M/1 queueing model are extensively used in the real world. There are many fields in which queueing models can be utilized. In these applications, parameter plays an extremely important role. For example, the paper (Choudhury and Borthakur, 2008) studied inference for parameters of the M/M/1 queueing model. Therefore, in order to guarantee the precision of parameters estimated in these queueing models, the sample size determination is proposed in this thesis. Firstly, we show how a Bayesian approach could be applied to these models to obtain the minimal sample size required by the given precision. Then, we will illustrate in detail how to use R, a statistical software, to compute the sample size for these models.
An engineering study of the design, integration and control of antibody fragment production processes

This project investigates process options for the production of antibody fragments from Escherichia coli and quantifies the effects of processing decisions on the integration of a complete bioprocess sequence. Fab' antibody fragments were produced by E. coli fermentation at scales of up to 450L. Antibody expression was directed to the periplasmic space, however during initial fermentations over 50% of the Fab' leaked into the extracellular broth over the course of the induction period. Alterations to the fermentation were made to allow greater control of product location, and with the modified protocol 80-90% of the product was consistently retained within the periplasm. This fermentation strategy formed the basis for future downstream purification studies. A novel method for the recovery of periplasmic proteins has been characterised and modelled at scales from 65mL to 100L. 85% recovery of periplasmic Fab' was achieved following resuspension of cells in a Tris-EDTA extraction buffer at 60°C. Operation at high temperature also resulted in purification of the process stream by degradation of both contaminating E. coli proteins and incomplete or partially degraded Fab' fragments. Clarification of the process stream following periplasmic extraction was compared using a novel tubular bowl and an intermittent discharge disc-stack centrifuge. Operating at 95% biomass removal, 94% Fab' was recovered using the tubular bowl, compared to 73% with the disk-stack centrifuge. The improved recovery obtained with the tubular bowl was shown to be due directly to the greater level of liquid recovery. However lower throughputs were required for equivalent clarification when using this machine. The optical biosensor has been assessed as a technique for the monitoring of Fab' in real time. The biosensor gave comparable Fab' accumulation profiles to ELISA during fermentation. During chromatographic purification, the sensor provided an 4 accurate indication of Fab' breakthrough during column loading and correctly identified product containing fractions during column elution. The thesis concludes with a series of mass balance studies which compare the relative efficiencies of traditional purification processes and more novel process alternatives conducted at pilot scale. The results show that novel techniques such as whole broth extraction (performing the periplasmic extraction process on whole fermentation broth) and expanded bed adsorption offer potentially viable process alternatives, however the operational problems and reduced reliability compared to more conventional routes means further adaptation or optimisation is required before such techniques will be selected over conventional processing strategies.
A Data Forwarding Approach for Fire-Rescue Scenario with Multi-Type Mobile Nodes

The opportunistic mobile sensor network has been extensively applied in various public safety applications such as the fire rescue and earthquake rescue, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of humans staying in risk zones. However, due to some environmental events such as building structure damage, airflow push, and fire explosions, the sensor nodes sprinkled into the fire-rescue scenario may be kept moving. Thus, the contacts between nodes become momentary, and the data packets cannot be forwarded along stable communication paths. To this end, the opportunistic forwarding manner is adopted in the fire-rescue scenario to enable the data packets to be transferred to the rescue control center (RCC) through some discrete hops. The contributions of this paper are threefold. First, the nodes in the fire-rescue scenario are carefully investigated and classified into four types: small-range mobile nodes (SRNs), large-range mobile nodes (LRNs), firefighter nodes (FNs), and robot nodes (RNs). Second, we formulate the data forwarding problem, and the optimal proportions of SRNs, LRNs, and FNs in data holders are mathematically analyzed to obtain the maximum delivery ratio. Third, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, the optimal proportions of SRNs, LRNs, and FNs in data holders are maintained as far as possible through selecting different types of data holder candidates, and then the new data holders are determined from these data holder candidates and the adjacent RNs on basis of their expected delivery delay. Finally, the performance of DFAFR is analyzed through simulations of the fire-rescue scenario, and the results indicate that DFAFR can enhance the delivery ratio and shorten the delivery delay while the forwarding overhead is restricted.
Analysis of Interconnection Schemes for PV Systems Operating under Shadow Conditions

The output power of a PV system consisting of multiple strings and arrays of PV modules reduces drastically as shading increases on it. The shaded module in a string limits the output current of the entire string thereby reduces its output power. Different techniques are used for the optimal interconnection of modules in a PV system that not only helps in reducing the deteriorating effects of partial shading but also enhances the output power of the overall PV system. The performance and the cost analysis of different interconnection schemes including series-parallel (SP), honeycomb (HC), bridge-linked (BL), and total cross tied (TCT) with bypass diode are presented. Commonly existing shading patterns including diagonal (DI), short wide (SW), long wide (LW), short narrow (SN) and short wide (SW) are evaluated. Results show that TCT exhibits better output power under all shade patterns. For a typical 4×4 PV system operating under diagonal shading condition, 15.1% gain in output power can be achieved through TCT connection in comparison to conventional SP interconnection. Further, it has been found that the cost of extra conductors incurred due to TCT connection can be recovered through enhanced energy output within three quarters of the year.
Principle Line Extraction and Restoration Based on Wavelet Theory

As a vital branch in the study of biometrics-based technology,identification and verification by palm print has been a striking evidence for prior disease diagnosis and personal recognition,given its remarkable advantages like simplicity and stablility,etc.Especially,the extraction process of principle-line feature plays a key role.This paper presents a new approach to extract this novel characteristic.Unlike other traditional methods,its step is inherently simple and convenient using regular scanner.After the pre-process and alignment,we extract four spatial directional template and reach high convergence by adopting Symlet wavelets transformation method,and a series of morphological operations derived from ASF are utilized.Finally we use regression analysis and image fusion to eliminate divergence and disconnectedness in our result region,and successfully extract principle-line from numerous palm-lines.The experimental results with a large collection of different images showed its advantages compared with former work,and also illustrated its strong robustness,and provide effective and accurate statistics to clinical diagnosis,classification and encoding work at a later stage.
Making sense of models: How teachers use agent-based modeling to advance mechanistic reasoning

Computer modeling promotes mechanistic reasoning when learners build and analyze models of complex systems to explore causal mechanisms and use models to generate patterns. StarLogo Nova (SLN), an agent‐based modeling (ABM) environment, enables novice programmers to model a system's individual components and investigate its emergent, collective behavior. Through case analysis of teachers using SLN, we demonstrate how ABM advances thinking about mechanisms generating phenomenon. Teachers who used simulation combined with the decoding of SLN models utilized mechanistic reasoning to make sense of how and why complex phenomenon emerged. Practitioner Notes: What is already known about this topic Mechanisms, or causal explanatory processes, are critical in science. Mechanistic reasoning is concerned with why a phenomenon occurs.Traditionally in science classrooms, teachers incorporate pre‐packaged simulations that limit the ability to understand mechanisms within the simulations.Agent‐based modeling empowers learners to model at the micro‐level to understand how macro‐level phenomenon emerge in complex systems. What this paper adds StarLogo Nova, an agent‐based modeling environment, has features that support mechanistic reasoning.Using simulation combined with decoding of models in StarLogo Nova promotes mechanistic explanations of why phenomenon occurs.Teachers are able to scaffold student interactions in StarLogo Nova to support students' mechanistic reasoning. Implications for practice and/or policy When using computer simulations in instruction, incorporate decoding of models to engage students in uncovering why a phenomenon occurs.In professional development programs on scientific modeling, it is important for educators to learn how decoding contributes to students' understanding of mechanisms in models and experience modeling as a scientific practice. [ABSTRACT FROM AUTHOR] uracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Cloud Computing Task Scheduling based on Dynamically Adaptive Ant Colony Algorithm and QoS Perception

Cloud computing is designed to integrate resources and to improve resource utilization. In the cloud computing environment, due to the varied and widely distributed resources, dynamic and changeling user demand, how to close automatically and efficiently task scheduling for the user to the appropriate virtual resources become one of the cloud computing data center is facing enormous challenges. To this problem, an ant colony optimization scheduling algorithm is proposed, dynamic adaptive adjusting pheromone volatilization factor, the user's QoS requirements was developed for the resource selection. By Cloud Sim experiment, the results show that the algorithm can improve the global search ability and avoid falling into local optimum prematurely, can improve the algorithm convergence speed and can balance resources load.
Optimum Design of Energy Harvesting Relay for Two-Way Decode-and-Forward Relay Networks Under Max–Min and Max-Sum Criterions

We study the optimum design of an energy harvesting relay for two-way decode-and-forward (DF) relay networks. In the networks, the relay harvests energy as well as decodes information using the received signal from two sources with power splitting relaying (PSR) and time switching relaying (TSR) strategies. Since the two-way relay network has two opposite traffic flows, it can be considered as a special case of multi-user systems. Applying the max–min criterion for fairness and max-sum criterion for maximum resource utilization, we optimize the operations of the energy harvesting relay. Specifically, considering the transmission rate constraints of individual hops, we derive optimum power splitting coefficients and optimum time switching coefficients, respectively, for PSR-based and TSR-based networks under both criterions, and analytically calculate the resulting maximum transmission rates. Numerical results confirm that our analyses exactly match with exhaustive search simulations. The obtained optimum coefficients are given in closed-form, therefore, they can be easily adopted in simple two-way relay networks with limited computational power such as sensor/Internet of Things (IoT) networks and can help two-way DF relay networks.
Enhanced collision resolution for the IEEE 802.11 Distributed Coordination Function

The IEEE 802.11 standard relies on the Distributed Coordination Function (DCF) as the fundamental medium access control method. DCF uses the Binary Exponential Backoff (BEB) algorithm to regulate channel access. The backoff period determined by BEB depends on a contention window (CW) whose size is doubled if a station suffers a collision and reset to its minimum value after a successful transmission. BEB doubles the CW size upon collision to reduce the collision probability in retransmission. However, this CW increase reduces channel access time because stations will spend more time sensing the channel rather than accessing it. Although resetting the CW to its minimum value increases channel access, it negatively affects fairness because it favours successfully transmitting stations over stations suffering from collisions. Moreover, resetting CW leads to increasing the collision probability and therefore increases the number of collisions. Since increasing channel access time and reducing the probability of collisions are important factors to improve the DCF performance, and they conflict with each other, improving one will have an adverse effect on the other and consequently will harm the DCF performance. We propose an algorithm, Enhanced Collision Resolution Algorithm (ECRA), that solves collisions once they occur without instantly increasing the CW size. Our algorithm reduces the collision probability without affecting channel access time. We also propose an accurate analytical model that allows comparing the theoretical saturation and maximum throughputs of our algorithm with those of benchmark