Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronous Rendezvous for Networks of Marine Robots in Large Scale Ocean Monitoring

We develop a synchronous rendezvous strategy for a network of minimally actuated mobile sensors or active drifters to monitor a set of Lagrangian Coherent Structure (LCS) bounded regions, each exhibiting gyre-like flows. This paper examines the conditions under which a pair of neighboring agents achieves synchronous rendezvous relying solely on the inherent flow dynamics within each LCS bounded region. The objective is to enable drifters in adjacent LCS bounded regions to rendezvous in a periodic fashion to exchange and fuse sensor data. We propose an agent-level control strategy to regulate the drifter speed in each monitoring region as well as to maximize the time the drifters are connected and able to communicate at every rendezvous. The strategy utilizes minimal actuation to ensure synchronization between neighboring pairs of drifters to ensure periodic rendezvous. The intermittent synchronization policy enables a locally connected network of minimally actuated mobile sensors to converge to a common orbit frequency. Robustness analysis against possible disturbance in practice and simulations are provided to illustrate the results.
Analysis of Different Approaches to Distributed Multiuser MIMO in the 802.11ac

The 802.11ac is a significant landmark in wireless communications, as it pushes towards new rate limits by utilizing downlink multiuser multiple-input multiple-output (MIMO) beamforming to transmit data to various locations simultaneously. However, successful beamforming relies on intelligent user selection which requires, in turn, extensive overhead of channel calibration between the AP and each of the candidate users. The large overhead involved in the user selection procedure overwhelms the multiuser gain and hinders the utilization of multiuser MIMO. The phenomenon is even more acute when APs handle large groups of mobile users, which frequently associate and disconnect, making the process of acquiring channel state from all users and selecting the appropriate group even harder. Thus, the subtle relation between the achievable rate of a scheduling algorithm and the overhead it requires is significant for the 802.11ac performance analysis. In this paper, we provide a rigorous analysis of distributed algorithms that schedule a group of users for the downlink. In particular, we accommodate common scheduling methods for the 802.11ac protocol and analyze both their achievable rate and their calibration process overhead. Both analysis and extensive simulations depict the superiority of simple threshold-based methods in terms of the throughput.
Computer Security and Privacy in the Interactions Between Victim Service Providers and Human Trafficking Survivors

A victim service provider, or VSP, is a crucial partner in a human trafficking survivor's recovery. VSPs provide or connect survivors to resources such as medical care, legal services, employment opportunities, etc. In this work, we study VSP-survivor interactions from a computer security and privacy perspective. Through 17 semi-structured interviews with staff members at VSPs and survivors of trafficking, we surface the role technology plays in VSP-survivor interactions as well as related computer security and privacy concerns and mitigations. Our results highlight various tensions that VSPs must balance, including building trust with their clients (often by giving them as much autonomy as possible) while attempting to guide their use of technology to mitigate risks around revictimization. We conclude with concrete recommendations for computer security and privacy technologists who wish to partner with VSPs to support and empower trafficking survivors.
Peer Assisted Learning Strategy for Improving Students’ Physiologic Literacy

Research about the implementation of the Peer Assisted Learning (PAL) strategy in Plant Physiology lecture has carried out, in which it aims to improve students’ physiologic literacy. The PAL strategy began with a briefing by the lecturers to the students tutor about pretest questions, followed by the interaction between student tutors with their peers to discuss response problems, terminated by answering responsiveness questions individually. This study used a quasi-experimental method, one - group pre-test post-test design. This design includes a group of students observed in the pre-test phase (tests carried out before PAL treatment) which is then followed by treatment with PAL and ends with post-test. The other students group (control) was given the pre-test and post-test only. The results showed that the PAL strategy can increase student’s physiologic literacy significantly. One of the weaknesses of students’ physiologic literacy is that they have not been able to read the graph. The faculties are encouraged to begin introducing and teaching material using a variety of strategies with scientific literacy aspects, for example teaching research-based material. All students respond positively to the PAL strategy.
Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

Previous studies have shown how individual differences in creativity relate to differences in the structure of semantic memory. However, the latter is only one aspect of the whole mental lexicon, a repository of conceptual knowledge that is considered to simultaneously include multiple types of conceptual similarities. In the current study, we apply a multiplex network approach to compute a representation of the mental lexicon combining semantics and phonology and examine how it relates to individual differences in creativity. This multiplex combination of 150,000 phonological and semantic associations identifies a core of words in the mental lexicon known as viable cluster, a kernel containing simpler to parse, more general, concrete words acquired early during language learning. We focus on low (N = 47) and high (N = 47) creative individuals’ performance in generating animal names during a semantic fluency task. We model this performance as the outcome of a mental navigation on the multiplex lexical network, going within, outside, and in-between the viable cluster. We find that low and high creative individuals differ substantially in their access to the viable cluster during the semantic fluency task. Higher creative individuals tend to access the viable cluster less frequently, with a lower uncertainty/entropy, reaching out to more peripheral words and covering longer multiplex network distances between concepts in comparison to lower creative individuals. We use these differences for constructing a machine learning classifier of creativity levels, which leads to an accuracy of 65 . 0 ± 0 . 9 % and an area under the curve of 68 . 0 ± 0 . 8 % , which are both higher than the random expectation of 50%. These results highlight the potential relevance of combining psycholinguistic measures with multiplex network models of the mental lexicon for modelling mental navigation and, consequently, classifying people automatically according to their creativity levels.
Multiple Comparisons With the Best and Difference Test Statistic for Supplier Selection for Nonlinear Profiles

It is vital to choose the right supplier to reduce cost and provide high-quality products. However, a gap remains because the supplier’s process is mainly measured using qualitative and intangible criteria. Further, with technological advances, the measurement of quality characteristics is transforming through smart data sensors. With a specific time or space measurements can be done at high frequency. The functional relationships between the measures or profiles can be established. The profiles indicate the pattern in the data. The literature focused on the case when quality characteristics are described by linear profile and consider symmetric tolerance. However, in a real-world application, nonlinear profiles and asymmetric tolerance is frequently found. This study proposed multiple comparisons with the best and the difference test statistic methods to select the best suppliers when the quality characteristics are described by nonlinear profile with asymmetric tolerances. A Monte Carlo simulation study is conducted, computer programs are written in the R programming language. The result indicated in terms of rejecting inferior suppliers, the multiple comparisons with the best method perform better than the difference test statistics. With the proposed methods, managers can make decisions using a single, easy-to-understand index. Also, these methods can handle any number of suppliers. For the convenience of a decision-maker, critical values, and profile size requirements are provided. An illustrative example is included to give a better insight into the proposed methods.
Image Denoiser Using Convolutional Neural Network with Deconvolution and Modified Residual Network

Due to improvements in hardware and software performance, deep learning algorithms have been used in many areas and have shown good results. In this paper, we propose a noise reduction framework based on a convolutional neural network (CNN) with deconvolution and a modified residual network (ResNet) to remove image noise. Simulation results show that the proposed algorithm is superior to the conventional noise eliminator in subjective and objective performance analyses. key words: image, denoise, deep learning, CNN
A dimension-based information security culture model and its relationship with employees’ security behavior: A case study in Malaysian higher educational institutions

ABSTRACT Despite strong recommendations by scholars to establish Information Security Culture (ISC), the lack of ISC guidelines persists, particularly in aspects that could effectively improve employees’ security behavior in an organization. This study proposes an ISC model based on seven new formulated dimensions to examine its influence on employees’ Information Security Policy (ISP) compliance behavior. The dimensions represent specific aspects of ISC and were formulated based on widely accepted concepts of Organizational Culture and ISC. The model was tested at 19 out of 21 public universities in Malaysia and validated using Partial Least Square Structural Equation Modelling (PLS-SEM). Findings revealed all seven dimensions are significant in contributing to the underlying concept of ISC, with Information Security Knowledge being the most important dimension. This ISC concept was also found to be significant in influencing ISP compliance behavior. This study contributes to ISC literature in terms of conceptualization and operationalization of an ISC concept based on the new comprehensive dimensions in relation to ISP compliance behavior. The model could be employed by practitioners in assessing, improving and cultivating a positive ISC that would effectively influence employees’ security behavior in higher educational institutions.
Statistical quality-of-service aware adaptive transmission for free space optical communication systems

Despite the growing attention for free space optical (FSO) networks, state-of-the-art literature lacks appropriate adaptive transmission (AT) schemes that can exploit the stochastic propagation channel of the FSO networks and guarantee delay quality-of-service (QoS) in the link-layer. Specifically, weatherinduced channel impairments not only affect the error-rate or outage probability but also increase the end-to-end queueing-delay of the FSO network. This dissertation aims to improve QoS-aware throughput of the terrestrial FSO communication systems by designing innovative AT schemes. The key goal of the developed AT schemes is to maximize the supportable arrival rate while ensuring the QoS in terms of certain delay-bound violation probability constraints. We first analyze the effective capacity (EC) of terrestrial FSO communication systems. We derive accurate closed-form expressions of the achievable EC by considering several FSO channel impairments. Through asymptotic analysis, we reveal insights on the EC increment. Next, we present delay-QoS aware discrete-rate AT schemes for FSO communication systems with parallel optical beams and average/peak transmit power constraints. Our results suggest that to improve the delay-throughput trade-off, only suitable optical channels need to be activated. Simulation results demonstrate that joint adaptation of transmission parameters of the active optical beams improve throughput of the FSO communication systems in the strict statistical-QoS constraints. Subsequently, we develop AT schemes for FSO backhaul/fronthaul networks. We first study AT schemes for buffer-aided parallel decode-and-forward relaying assisted hybrid radio-frequency (RF)/FSO backhaul networks. Two different hybrid RF/FSO system configurations are considered, and AT schemes for both configurations are developed. These schemes maximize the arrival rate subject to the total queue-occupancy constraint. Next, we develop delay-QoS aware joint power allocation and relaying link selection for amplify-and-forward relay assisted uplink FSO fronthaul networks. Finally, we investigate joint FSO fronthaul and millimeter-wave access link optimization for the downlink cloud small cell network subject to end-to-end queue-length-bound violation probability constraints. Extensive simulations are performed to evaluate the performance of the proposed AT schemes by considering several FSO channel impairments and statistical-QoS requirements. Simulation results demonstrate that our proposed AT schemes substantially improve the statistical-QoS aware arrival rate in the FSO backhaul/fronthaul networks.
Decision tree pruning via multi-objective evolutionary computation

 Abstract —To date, decision trees are among the most used classification models. They owe their popularity to their efficiency during both the learning and the classification phases and, above all, to the high interpretability of the learned classifiers. This latter aspect is of primary importance in those domains in which understanding and validating the decision process is as important as the accuracy degree of the prediction. Pruning is a common technique used to reduce the size of decision trees, thus improving their interpretability and possibly reducing the risk of overfitting. In the present work, we investigate on the integration between evolutionary algorithms and decision tree pruning, presenting a decision tree post-pruning strategy based on the well-known multi-objective evolutionary algorithm NSGA-II. Our approach is compared with the default pruning strategies of the decision tree learners C4.5 (J48 - on which the proposed method is based) and C5.0. We empirically show that evolutionary algorithms can be profitably applied to the classical problem of decision tree pruning, as the proposed strategy is capable of generating a more variegate set of solutions than both J48 and C5.0; moreover, the trees produced by our method tend to be smaller than the best candidates produced by the classical tree learners, while preserving most of their accuracy and sometimes improving it.
Applications of the Blockchain using cryptography

We have witnessed the rise of cryptocurrencies in the past eight years. Bitcoin and Ethereum are the world’s most successful cryptocurrencies with market capitalisations of $37bn and $21bn respectively in June 2017. The innovation behind these cryptocurrencies is the blockchain which is an immutable and censorship resistant public ledger. Bitcoin introduced the blockchain to trade a single asset (i.e. bitcoins), whereas Ethereum adopted the blockchain to store and execute expressive smart contracts. In this thesis, we consider cryptographic protocols that bootstrap trust from the blockchain. This includes secure end-to-end communication between two pseudonymous users, payment protocols, payment networks and decentralised internet voting. The first three applications rely on Bitcoin, whereas the final e-voting application is realised using Ethereum. First, it is important to highlight that Bitcoin was designed to protect the anonymity (or pseudonymity) for financial transactions. Nakamoto proposed that financial privacy is achievable by storing each party’s pseudonym (and not their real-world identity) in a transaction. We highlight that this approach for privacy has led to real-world authentication issues as merchants are failing to re-authenticate customers in post-transaction correspondence. To alleviate these issues, we propose an end-to-end secure communication protocol for Bitcoin users that does not require any trusted third party or public-key infrastructure. Instead, our protocol leverages the Blockchain as an additional layer of authentication. Furthermore, this insight led to the discovery of two attacks in BIP70: Payment Protocol which is a community-accepted standard used by more than 100,000 merchants. Our attacks were acknowledged by the leading payment processors including Coinbase, BitPay and Bitt. As well, we have proposed a revised Payment Protocol that prevents both attacks. Second, Bitcoin as deployed today does not scale. Scalability research has focused on two directions: 1) redesigning the Blockchain protocol, and 2) facilitating ‘off-chain transactions’ and only consulting the Blockchain if an adjudicator is required. We focus on the latter and provide an overview of Bitcoin payment networks. These consist of two components: payment channels to facilitate off-chain transactions between two parties, and the capability to fairly exchange bitcoins across multiple channels. We compare Duplex Micropayment Channels and Lightning Channels, before discussing Hashed Time Locked Contracts which
Gradient Descent for Non-convex Problems in Modern Machine Learning

Machine learning has become an important tool set for artificial intelligence and data science across many fields. A modern machine learning method can be often reduced to a mathematical optimization problem. Among algorithms to solve the optimization problem, gradient descent and its variants like stochastic gradient descent and momentum methods are the most popular ones. The optimization problem induced from classical machine learning methods is often a convex and smooth one, for which gradient descent is guaranteed to solve it efficiently. On the other hand,modern machine learning methods, like deep neural networks, often require solving a non-smooth and non-convex problem. Theoretically, non-convex mathematicaloptimization problems cannot be solved efficiently. However, in practice, gradient descent and its variants can find a global optimum efficiently. These competing factsshow that often there are special structures in the optimization problems that can make gradient descent succeed in practice. This thesis presents technical contributions to fill the gap between theory andpractice on the gradient descent algorithm. The outline of the thesis is as follows. In the first part, we consider applying gradient descent to minimize the empiricalrisk of a neural network. We will show if a multi-layer neural network with smooth activation function is sufficiently wide, then randomly initialized gradient descent can efficiently find a global minimum of the empirical risk. We will also show the same result for the two-layer neural network with Rectified Linear Unit (ReLU) activation function. It is quite surprising that although the objective function of neural networks is non-convex, gradient descent can still find their global minimum. Lastly, we will study structural property of the trajectory induced by the gradient descent algorithm. In the second part, we assume the label is generated from a two-layer teacher convolutional neural network and we consider using gradient descent to recover the teacher convolutional neural network. We will show that if the input distribution is Gaussian, then gradient descent can recovered a one-hidden-layer convolutional neural network in which both the convolutional weights and the output wights are unknown parameters to be recovered. We will also show that the Gaussian input assumption can be relaxed to a general structural assumption if we only need to recover a single convolutional filter. In the third part, we study conditions under which gradient descent fails. Wewill show gradient descent can take exponential time to optimize a smooth function with the strict saddle point property for which the noise-injected gradient can optimize in polynomial time. While our focus is theoretical, whenever possible, we also present experiments that illustrate our theoretical findings.
CALL beliefs in context: a study of US high school foreign language learners

Abstract Recent calls from applied linguistics and from CALL have emphasized the importance of situating the understanding and use of digital tools for language learning within layered contexts. An important component of these layered contexts is societal discourses of technology, which are multiple and far from neutral. In response to these calls, this study examines how students at one San Francisco Bay Area high school understand technology in foreign language learning and how these beliefs relate to regional discourses of technology. The study assumed an ecological theoretical frame and deployed a multi-pronged research design: a survey study (n = 283), a case study (n = 3), and a discourse analysis of a regional newspaper corpus (n = 372). Findings indicate that students held largely utilitarian beliefs about CALL that both aligned with and diverged from regional discourses. Instances of alignment suggest the potential influence of larger societal discourses on student beliefs about CALL while instances of divergence underscore the need to address the larger role of technology in society when considering CALL beliefs and practices.
3.3-mA 2.8-GHz bufferless LC oscillator directly driving a 10-mm on-chip clock distribution line

A bufferless LC oscillator can potentially offer an attractive solution for low-power, high-speed clock distribution due to the absence of repeaters on the clock distribution line and utilization of LC resonance. However, conventional bufferless LC oscillators suffer from a fundamental tradeoff between the frequency and power consumption due to their highfrequency sensitivity. In this paper, we have introduced a low-frequency sensitivity bufferless LC oscillator that is directly connected to a 10-mm on-chip clock distribution line in the TSMC 0.18-μm 1-Poly 6-Metal CMOS technology. The core area of the LC oscillator is only 270 × 280 μm2. The measurement results show that a 2.8-GHz oscillation frequency, 3.3-mA current consumption, and −112.8 dBc/Hz phase noise at 1MHz offset can be achieved.