Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Scheduling for Autonomous Robotics

This project report describes a hybrid genetic algorithm that works as a schedule generator for a complex robotic harvesting task. The task is set to a dynamic environment with a robotic opponent, making responsiveness of the planning algorithm particularly important. To solve this task, many previous scheduling algorithms were studied. Genetic algorithms have successfully been used in many dynamic scheduling tasks, due to their ability to incrementally adapt and optimize solutions when changes are made to the environment. Many of the previous approaches also used a separate heuristic to quicly adapt solutions to the new environment, making the algorithm more responsive. In addition, the study of previous work revealed the importance of population diversity when making a responsive genetic algorithm. Implementation was based on a genetic algorithm made as the author's fifth year specialization project for solving a static version of the same task. This algorithm was hybridized with a powerful local search technique that proved essential in generating good solutions for the complex harvesting task. When extending the algorithm to also work in a dynamically changing environment, several adaptations and extensions needed to be made, to make it more responsive. The extensions and adaptations include a fast-response heuristic for immediate adaptation to environmental changes, a decrease in genotype size to speed up local searches and a contingency planning module intending to solve problems before they arise. Experiments proved that the implemented dynamic planner successfully adapted its plans to a changing environment, clearly showing improvements compared to running a static plan. Further experiments also proved that the dynamic planner was able to deal with erroneous time estimates in its simulator module in a good way. Experiments on contingency planning gave no clear results, but indicated that using computational resources for planning ahead may be a good choice, if the contingency to plan for is carefully selected. As no unequivocal results were obtained, further studies of combining genetic algorithms and contingency planning may be an interesting task for future efforts.
Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era

The revolution of cooperative connected and automated vehicles is about to begin and a key milestone is the introduction of short range wireless communications between cars. Given the tremendous expected market growth, two different technologies have been standardized by international companies and consortia, namely IEEE 802.11p, out for nearly a decade, and short range cellular-vehicle-to-anything (C-V2X), of recent definition. In both cases, evolutions are under discussion. The former is only decentralized and based on a sensing before transmitting access, while the latter is based on orthogonal resources that can be also managed by an infrastructure. Although studies have been conducted to highlight advantages and drawbacks of both, doubts still remain. In this work, with a reference to the literature and the aid of large scale simulations in realistic urban and highway scenarios, we provide an insight in such a comparison, also trying to isolate the contribution of the physical and medium access control layers.
Step-by-Step Guide to Oral History

Your stories and the stories of the people around you are unique, valuable treasures for your family and your community. You and your family members can preserve unwritten family history using oral history techniques. Likewise you and your community can discover and preserve unwritten history large and small. Oral history is so flexible that people of all ages can adapt the techniques of asking and listening to create and learn about history and historical narratives.
Quality of health information on the Web: user perceptions of relevance and reliability

Relevance is the central concept in information retrieval yet studies of the quality of health Web sites have focused exclusively on the reliability of on-line health information. Results from the Web survey component of a multi-method study of Canadian women who seek health information on the Web reveal the importance of relevance as an attribute of quality assessment. Gender differences in health conditions and the geographical context of health information and delivery of health services are identified by study participants as dimensions of the contextual or situational relevance of health information. Survey respondents rated 13 types of health information sources in terms of their perceived relevance and reliability. Health care practitioners, books and pamphlets/fact sheets received high ratings on both attributes of source quality. Web sites received favourable ratings on relevance but intermediate on reliability.
The data-driven pilot and the risk of personal sensitivity to a negative outcome

ABSTRACT The past decade has seen a gradual mistrust of the human-centric decision-making approaches with both researchers and practitioners advocating an increased use of data-driven decision making to lower decision-making risk. This is seen as vital in the aviation field with one of the greatest risks to passenger safety being errors in pilot decision making. Although the introduction of information systems has attempted to lower the risk of fatal crashes, human decision-making is still required. This article seeks to understand if a pilot will oscillate between human-centric decision-making approaches and information system based decision-making approaches. A case study approach was iteratively built to investigate this phenomenon. From this case study, the emergent theme of an individual’s personal sensitivity to a negative outcome is presented and discussed. The implications of these themes for information systems and the associated risks in the aviation field are then presented.
Presenting a prototype for pull based load balancing of web servers

The traffic and the number of users on the Internet are increasing, and service providers must respond to this demand if they are not to loose both customers and revenue. In order to provide a satisfactory level of QoS, the providers must increase server performance by aggregating multiple physical host into a web farm, which by working together, sharing the total load of the requests, will act as one unified server. Various algorithms and technology exists for performing this load sharing, and this thesis will introduce a new approach in this field. In the traditional load balancing (LB) strategies, the requests are pushed to the individual servers, which are passively forced to accept the requests. The new approach presented in this paper, differs from the traditional techniques, in that the servers themselves are more involved in the decision making associated with load balancing. Requests are stored in a central queue, and the individual requests is processed by the servers at the server’s own convenience, leaving the server with full control over it’s own resources. The paperwill present and compare this algorithmwith the traditional load balancing algorithms, and investigate possible benefits of this strategy.
Predictability effects in language acquisition

Human language has two fundamental requirements: it must allow competent speakers to exchange messages efficiently, and it must be readily learned by children. Recent work has examined effects of language predictability on language production, with many researchers arguing that so-called “predictability effects” function towards the efficiency requirement. Specifically, recent work has found that talkers tend to reduce linguistic forms that are more probable more heavily. This dissertation proposes the “Predictability Bootstrapping Hypothesis” that predictability effects also make language more learnable. There is a great deal of evidence that the adult grammars have substantial statistical components. Since predictability effects result in heavier reduction for more probable words and hidden structure, they provide infants with direct cues to the statistical components of the grammars they are trying to learn. The corpus studies and computational modeling experiments in this dissertation show that predictability effects could be a substantial source of information to languagelearning infants, focusing on the potential utility of phonetic reduction in terms of word duration for syntax acquisition. First, corpora of spontaneous adult-directed and childdirected speech (ADS and CDS, respectively) are compared to verify that predictability effects actually exist in CDS. While revealing some differences, mixed effects regressions on those corpora indicate that predictability effects in CDS are largely similar (in kind and magnitude) to predictability effects in ADS. This result indicates that predictability effects are available to infants, however useful they may be. Second, this dissertation builds probabilistic, unsupervised, and lexicalized models for learning about syntax from words and durational cues. One series of models is based on Hidden Markov Models and learns shallow constituency structure, while the other series is based on the Dependency Model with Valence and learns dependency structure. These models are then used to measure how useful durational cues are for syntax acquisition, and to what extent their utility in this task can be attributed to effects of syntactic predictability on word duration. As part of this investigation, these models are also used to explore the venerable “Prosodic Bootstrapping Hypothesis” that prosodic structure, which is cued in part by word duration, may be useful for syntax acquisition. The empirical evaluations of these models provide evidence that effects of syntactic predictability on word duration are easier to discover and exploit than effects of prosodic structure, and that even gold-standard annotations of prosodic structure provide at most a relatively small improvement in parsing performance over
Random Matrix Theory Analysis of Cross-Correlation in the Nigerian Stock Exchange

In this paper we use Random Matrix Theory (RMT) to analyse the eigenstructure of the empirical correlations of 82 stocks which are consistently traded in the Nigerian Stock Exchange (NSE) over a 4-year study period 3 August 2009 to 26 August 2013. We apply the Marcenko-Pastur distribution of eigenvalues of a purely random matrix to investigate the presence of investment-pertinent information contained in the empirical correlation matrix of the selected stocks. We use hypothesised standard normal distribution of eigenvector components from RMT to assess deviations of the empirical eigenvectors to this distribution for different eigenvalues. We also use the Inverse Participation Ratio to measure the deviation of eigenvectors of the empirical correlation matrix from RMT results. These preliminary results on the dynamics of asset price correlations in the NSE are important for improving risk-return trade-offs associated with Markowitz’s portfolio optimization in the stock exchange, which we achieve by cleaning up the correlation matrix. We propose to measure extent of closeness or otherwise in selected sectors of the NSE and the Johannesburg Stock Exchange (JSE) in our future work. Key terms: Random matrix theory; Correlation matrix; Inverse participation ratio; Eigenvalue and eigenvector; Portfolio optimization.
Parameter Selection of an LTV-MPC Controller for Vehicle Path Tracking Considering CPU Computational Load

There exist two intrinsic shortcomings on model predictive control (MPC) strategy, namely the extensive online calculation burden and the complex tuning process, which prevent MPC from being applied to a wider extent. To tackle these two drawbacks, different methods were proposed with majority of them treating these two issues independently. However, parameter tuning in fact has double-sided effects on both controller performance as well as real-time computational burden. Due to the lack of theoretical tools for globally analyzing the complex conflicts among MPC parameter tuning, controller performance optimization as well as computational burden easement, a look-up table based online parameter selection method is proposed in this paper to help a vehicle track its reference path under both the stability and computational capacity constraints. Matlab-CarSim conjoint simulation shows the effectiveness of the proposed strategy.
Architecture Concept of a Wireless Body Area Sensor Network for Health Monitoring of Elderly People

Emerging Wireless Body Area Sensor Networks are a promising technology which enables health monitoring of elderly people and enhances their independent living. In this paper, we propose the system architecture and protocol stack of a Wireless Body Area Sensor Network for health monitoring of elderly people. The architecture can be deployed in both indoor and outdoor environments and enables transmission of both routine and emergency vital signs.
A 0.016 mm2 0.26- $\mu$ W/MHz 60–240-MHz Digital PLL With Delay-Modulating Clock Buffer in 65 nm CMOS

Ultra-low-power systems, such as wearables and Internet-of-Things (IoT), require power- and volume-efficient micro-controller units (MCUs) capable of operating across a wide range of frequencies under extreme power constraints. This paper presents the techniques to implement clock generators that cater to the needs of such MCUs and other similar ultra-low-power applications. RC relaxation oscillators (RCOs) are shown to achieve excellent frequency stability when generating clocks in kilohertz to megahertz range but are not very power efficient (5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>/MHz). Their power efficiency further degrades at higher frequencies because of additional power needed to compensate for the impact of temperature-dependent comparator’s delay on frequency stability. On the other hand, ring oscillators (ROs) can generate higher frequency and lower noise clocks more power efficiently but exhibit very poor voltage and temperature sensitivity. In view of these complementary tradeoffs offered by RC oscillator and RO, this paper seeks to combine their advantages by using a phase-locked loop (PLL) to multiply the frequency of an RC oscillator using an RO. A new type-II digital PLL (DPLL) that uses a delay-modulating clock buffer to implement the proportional control and a low-area digital-to-analog converter to implement the digitally controlled oscillator is presented. Fabricated in the 65 nm CMOS process, the prototype PLL generates 50–300 MHz output frequencies from a reference clock in the range of 0.5–5 MHz. The DPLL occupies an active area of 125 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}\times125\mu \text{m}$ </tex-math></inline-formula> and achieves ±0.33% period jitter while consuming 63.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> at an output frequency of 240 MHz. This translates to an excellent power efficiency of 0.26 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>/MHz at 0.8-V supply voltage.
An investigation of carers expectations of recovery and progress in their relatives with an acquired brain injury

This thesis is comprised of a research component (Volume I) and five clinical practice reports (Volume II). Volume 1 consists of the research component in the form of two papers. The first paper in Volume I is a review of the literature, which examines the evidence for family involvement in acquired brain injury rehabilitation services and has been prepared for submission to Disability and Rehabilitation Journal. The second paper is an empirical study to investigate carers’ expectations of recovery and their engagement in the rehabilitation process with individuals with Acquired Brain Injury. This paper has been prepared for submission to Neuropsychological Rehabilitation (see Appendix 1 for submission guidelines). Volume II is comprised of five clinical practice reports that present work undertaken in the areas of adult mental health, older adults, child and Neurorehabilitation. The first report presents a cognitive and psychodynamic formulation of a man experiencing intrusive thoughts in an older adults mental health service. The second report describes a service evaluation to evaluate the outcome of the implementation of a recovery approach within an Older Adults Mental Health service. The third report presents a case study of cognitive-behaviour therapy with a 10 year- old girl with a fear of vomiting and anxiety. The fourth report is a single case experimental design to evaluate the use of compensatory strategies in a 35-year old man with unilateral spatial neglect as a result of acquired brain injury. An abstract for the fifth report presents a case study of a 26 year- old man with schizophrenia who experienced difficulties associated with persistent persecutory delusions. Cognitive-behavioural assessment, formulation and intervention with this client is presented.
Sensor side-channel attacks on user privacy: analysis and mitigation.

Mobile smartphones are equipped with an increasingly large number of precise and sophisticated sensors. These sensors vastly enhance the user’s GUI experience, but they also raise the risk of directly or indirectly leaking their private information. Mobile operating systems (e.g., Android and iOS) mitigate such leakages by implementing applevel sandboxing and resource permissions. These protections may suffice for traditional privacy attacks using traditional hardware, however, they fail when attacks exploit sidechannels that bypass the protections. One example of such side-channels is the motion sensors (Accelerometer, Gyroscope and Magnetometer) embedded in most modern smartphones. In this dissertation, we demonstrate two attacks that exploit the motion sensors on smartphones to infer accurate private information about the users such as their typed passwords and significant locations. To protect users from the above attacks and other location / sensor side-channel attacks, we propose the design and implementation of a mitigation framework called MATRIX for the Android ecosystem. In the first part, we investigated the feasibility of keystroke inference when user taps on a soft keyboard of a smartphone are captured by the Gyroscope and stereoscopic Microphones sensors co-resident on the smartphone. Our experiments demonstrate that by building machine learning models specific to the keyboard, using a combination of multiple sensors and adequate filtering, it is possible to infer keystrokes with an accuracy of 90-94% on the standard Android QWERTY and Numeric keyboards. In the second part, we investigated the feasibility of inferring a vehicular user’s locations and traveled routes with high accuracy using information captured by the Accelerometer, Gyroscope and Magnetometer sensors co-resident on the smartphone. We modeled location inference as a maximum likelihood route identification problem on a graph generated from OpenStreetMap. Our simulations from 11 cities worldwide demonstrate that it is possible to output a ranked list of 10 routes containing the traveled route with
Dynamically updating relevance judgements in probabilistic information systems via users' feedback

A decision maker's performance relies on the availability of relevant information. In many environments, the relation between the decision maker's informational needs and the information base is complex and uncertain. A fundamental concept of information systems, such as decision support and document retrieval, is the probability that the retrieved information is useful to the decision maker's query. This paper presents a sequential, Bayesian, probabilistic indexing model that explicitly combines expert opinion with data about the system's performance. The expert opinion is encoded into probability statements. These statements are modified by the users' feedback about the relevance of the retrieved information to their queries. The predictive probability that a datum in the information base is applicable to the current query is a logistic function of the expert opinion and the feedback. This feedback enters the computation through a measure of association between the current query-datum pair with previous, relevant query-datum pairs. When this measure is based on the proportional matching of multiple attributes, the predictive probabilities have a recursive formula that makes the model computationally feasible for large information bases.
First Order Methods for High Resolution Image Denoising

In this paper we are interested in comparing theperformance of some of the most relevant first order non-smoothoptimization methods applied to the Rudin, Osher and Fatemi(ROF) Image Denoising Model and a Primal-Dual Chambolle-Pock Image Denoising Model. Because of the properties of theresulting numerical schemes it is possible to handle these computationspixelwise, allowing implementations based on parallelparadigms which are helpful in the context of high resolutionimaging.
Superpixel Labeling Priors and MRF for Aerial Video Segmentation

Video segmentation is a task of partitioning pixels that exhibit homogeneous appearance and motion into coherent spatial–temporal groups, which is still challenging for aerial applications. In this paper, a principled combination of superpixel labeling priors and Markov random field (S-MRF) is proposed for aerial video segmentation. The proposed approach has several contributions: 1) we develop a metadata-based global projection model with coordinate transformation to estimate motion information between frames; 2) the superpixel labeling priors from previous frames are incorporated into the segmentation of the current frame, leading to a highly efficient probabilistic label propagation algorithm; and 3) we perform an MRF optimization on the initial segments with propagated labeling priors to improve the temporal coherency. In addition, a new video dataset is collected and will be made publicly available to evaluate the performance of aerial video segmentation algorithms. The experimental results show that the proposed approach outperforms the state-of-the-art video segmentation methods.
DyVSoR: dynamic malware detection based on extracting patterns from value sets of registers

To control the exponential growth of malware files, security analysts pursue dynamic approaches that automatically identify and analyze malicious software samples. Obfuscation and polymorphism employed by malwares make it difficult for signature-based systems to detect sophisticated malware files. The dynamic analysis or run-time behavior provides a better technique to identify the threat. In this paper, a dynamic approach is proposed in order to extract features from binaries. The run-time behavior of the binary files were found and recorded using a homemade tool that provides a controlled environment. The approach based on DyVSoR assumes that the run-time behavior of each binary can be represented by the values of registers. A method to compute the similarity between two binaries based on the value sets of the registers is presented. Hence, the values are traced before and after invoked API calls in each binary and mapped to some vectors. To detect an unknown file, it is enough to compare it with dataset binaries by computing the distance between registers, content of this file and all binaries. This method could detect malicious samples with 96.1% accuracy and 4% false positive rate. The list of execution traces and the dataset are reachable at: http://home.shirazu. ac.ir/˷ sami/malware