Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust subspace estimation via low-rank and sparse decomposition and applications in computer vision

Recent advances in robust subspace estimation have made dimensionality reduction and noise and outlier suppression an area of interest for research, along with continuous improvements in computer vision applications. Due to the nature of image and video signals that need a high dimensional representation, often storage, processing, transmission, and analysis of such signals is a difficult task. It is therefore desirable to obtain a low-dimensional representation for such signals, and at the same time correct for corruptions, errors, and outliers, so that the signals could be readily used for later processing. Major recent advances in low-rank modelling in this context were initiated by the work of Candès et al. [17] where the authors provided a solution for the long-standing problem of decomposing a matrix into low-rank and sparse components in a Robust Principal Component Analysis (RPCA) framework. However, for computer vision applications RPCA is often too complex, and/or may not yield desirable results. The low-rank component obtained by the RPCA has usually an unnecessarily high rank, while in certain tasks lower dimensional representations are required. The RPCA has the ability to robustly estimate noise and outliers and separate them from the low-rank component, by a sparse part. But, it has no mechanism of providing an insight into the structure of the sparse solution, nor a way to further decompose the sparse part into a random noise and a structured sparse component that would be advantageous in many computer vision tasks. As videos signals are usually captured by a camera that is moving, obtaining a low-rank component by RPCA becomes impossible. In this thesis, novel Approximated RPCA algorithms are presented, targeting different shortcomings of the RPCA. The Approximated RPCA was analysed to identify the most time consuming RPCA solutions, and replace them with simpler yet tractable alternative solutions. The proposed method is able to obtain the exact desired rank for the low-rank component while estimating a global transformation to describe camera-induced motion. Furthermore, it is able to
Analysis of Soft Robotics Based on the Concept of Category of Mobility

Soft robotics is an emerging field of research where the robot body is composed of flexible and soft materials. It allows the body to bend, twist, and deform to move or to adapt its shape to the environment for grasping, all of which are difficult for traditional hard robots with rigid bodies. However, the theoretical basis and design principles for soft robotics are not well-founded despite their recognized importance. For example, the control of soft robots is outsourced to morphological attributes and natural processes; thus, the coupled relations between a robot and its environment are particularly crucial. In this paper, we propose a mathematical foundation for soft robotics based on category theory, a branch of abstract mathematics where any notions can be described by objects and arrows. It allows for a rigorous description of the inherent characteristics of soft robots and their relation to the environment as well as the differences compared to conventional hard robots. We present a notion called the category of mobility to well describe the subject matter. The theory has been applied to a model system and analysis to highlight the adaptation behavior observed in universal grippers, which are a typical example of soft robotics. The aim of the present study is not to offer concrete engineering solutions to existing robotics but to provide clear mathematical description of soft robots by category theory and to imply its potential abilities by a simple soft gripper demonstration. This paper paves the way to developing a theoretical background and design principles for soft robotics.
Dynamical Analysis of Two-Microorganism and Single Nutrient Stochastic Chemostat Model with Monod-Haldane Response Function

In this paper, we formulate and investigate a two-microorganism and single nutrient chemostat model with Monod-Haldane response function and random perturbation. First, for the corresponding deterministic system, we introduce the conditions of the stability of the equilibrium points. Then, using Lyapunov function and Itô’s formula, we investigate the existence and uniqueness of the global positive solution of the stochastic chemostat model. Furthermore, we explore and obtain the criterions of the extinction and the permanence for the stochastic model. Finally, numerical simulations are carried out to illustrate our main results.
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations.
Recherche d'Information Sociale en Langue Arabe : Cas de Facebook

Cet article propose une approche de recherche d'information (RI) en langue arabe sur Facebook, qui exploite toutes les traces des utilisateurs (ex. polarite, partage, j'aime, haha) laissees sur des publications Facebook pour estimer leur importance sociale. Notre objectif est de montrer comment ces signaux peuvent jouer un role vital dans l'amelioration de la recherche en langue arabe sur Facebook. Premierement, des polarites (positive ou negative) portee par les signaux textuels (ex. commentaires) et non textuels (ex. les reactions j'adore et triste) ont ete identifiees pour chaque publication Facebook. Par consequent, la polarite de chaque com-mentaire exprime sur une publication donnee, est estimee sur la base d'un modele neuronal de sentiment en langue arabe. Deuxiemement, des signaux en fonction de leur complementarite ont ete regroupes en utilisant des algorithmes de selection. Troisiemement, des algorithmes de learning to rank ont ete appliques pour re-ordonner les resultats de recherche de Facebook en fonction des groupes de signaux selectionnes. Enfin, des experimentations sont realisees sur 13500 publications Facebook, collectees a partir de 45 requetes en langue arabe. Les experi-mentations revelent des resultats prometteurs pour la RI en langue arabe sur Facebook.
Reconfigurable Battery Systems

In a reconfigurable battery pack, the connections among cells can be changed during operation to form different configurations. This can lead a battery, a passive two-terminal device, to a smart battery that can reconfigure itself according to the requirement to enhance operational performance. Several hardware architectures with different levels of complexities have been proposed. Some researchers have used existing hardware and demonstrated improved performance on the basis of novel optimization and scheduling algorithms. The possibility of software techniques to benefit the energy storage systems is exciting, and it is the perfect time for such methods as the need for high-performance and long-lasting batteries is on the rise. This novel field requires new understanding, principles, and evaluation metrics of proposed schemes. In this article, we systematically discuss and critically review the state of the art. This is the first effort to compare the existing hardware topologies in terms of flexibility and functionality. We provide a comprehensive review that encompasses all existing research works, starting from the details of the individual battery including modeling and properties as well as fixed-topology traditional battery packs. To stimulate further research in this area, we highlight key challenges and open problems in this domain.
Facebook User Reactions Towards Marijuana Content

—We have developed content and sentiment analysis of marijuana-related posts on Facebook based upon users’ emotional reactions, including LOVE , HAHA , WOW , SAD , and ANGRY as well as the number of LIKE s and comments posts received. In particular, we extracted posts and resultant reactions from the High Times Magazine page ( https://www.facebook.com/HIGHTIMESMag ) - a longstanding monthly publication known for advocating for the legalization of cannabis. A sentiment analysis tool was built using Google Cloud Prediction API to estimate the sentiment of each post from the text contained therein. The sentiment scores calculated by our tool achieved an accuracy of greater than 90% compared with sentiments heuristically generated based upon user reactions to the posts. Our research also provides insight into Facebook users’ activities towards marijuana that may potentially beneﬁt public health surveillance applications.
The Impact of Relational Characteristics on Consumer Responses to Word of Mouth on Social Networking Sites

ABSTRACT Previous research has consistently found an effect of the valence (positive vs. negative) of electronic word of mouth in general and of word of mouth on a social networking site (sWOM) specifically on consumer responses. The current study investigates how interpersonal and person-to-site relational characteristics (homophily, tie strength, and source credibility) moderate this effect on consumer responses to sWOM (behavioral and positive word-of-mouth intention). The results show that interpersonal homophily and source credibility both significantly reinforce the effect of sWOM valence on behavioral intention and positive word-of-mouth intention. Only considering person-to-site relational characteristics as antecedents, (person-to-site) homophily and source credibility reinforce the effect of sWOM valence on behavioral intention and on positive word-of-mouth intention. However, including both the interpersonal and the person-to-site relational characteristics as antecedents results in all person-to-site relational characteristics becoming nonsignificant as moderators. This study advances the sWOM literature by concurrently examining how both interpersonal and person-to site relational characteristics moderate the effect of message valence on sWOM responses. The findings imply that marketers should try to stimulate sWOM from credible sources that are homophilous to the target audience, as these relationships reinforce the positive impact of sWOM valence on behavioral intentions.
Semantics and Controllability of Time-Aware Business Processes

We present an operational semantics for time-aware business processes, that is, processes modeling the execution of business activities, whose durations are subject to linear constraints over the integers. We assume that some of the durations are controllable, that is, they can be determined by the organization that executes the process, while others are uncontrollable, that is, they are determined by the external world. Then, we consider controllability properties, which guarantee the completion of the execution of the process, satisfying the given duration constraints, independently of the values of the uncontrollable durations. Controllability properties are encoded by quantiﬁed reachability formulas, where the reachability predicate is recursively deﬁned by means of constrained Horn clauses (CHCs). These clauses are automatically derived from the operational semantics of the process. Finally, we present two algorithms for solving the so called weak and strong controllability problems. Our algorithms reduce these problems to the veriﬁcation of a set of quantiﬁed integer constraints, which are simpler than the original quantiﬁed reachability formulas, and can eﬀectively be handled by state-of-the-art CHC solvers.
Investigating the design of Smart Objects in the domain of forgetfulness

When we forget things, we feel anxious which can impact our day negatively. Some individuals believe they are forgetful, so emphatically, it disrupts their day. There has been little discussion about perceived forgetfulness in design and HCI, combined with few studied smart objects to aid with memory. However, embedded systems, radio frequency identification (RFID) and HCI research provides inspiration towards creating a solution. Challenges of creating a day-to-day smart object that can enhance a user’s lifestyle are explored and recommended design guidelines for creating a smart object in a specific domain are the focus of this thesis. Using an experience-centred approach, ‘Message Bag’ and ‘Tag Along’ are two purpose built object-based memory aids that have emerged as a result of investigating the design processes for smart objects. The work examines smart objects in the context of forgetting what items to pack in a bag. A solution presented is a device consisting of an RFID system involving (a) pre-tagging essential items; (b) scanning those tagged items and; (c) viewing a corresponding light illuminate, to communicate to the user. Although the conceptual model is simple, success depends on a combination of technical design, usability and aesthetics. These scanning interactions result in a person feeling more confident as suggested through autoethnography reporting, real-world, third person engagements single user walkouts, conference demos, professional critiques, and residential weekends with potential users (focus group) studies conducted. My work involved extensive autobiographical research and design-led enquiries. Testing was undertaken with investigative prototypes, followed by field testing high-fidelity prototypes. This involved an in-the-wild comparative study involving six users over several months. Results show that people feel more confident and respondents claim no longer needing to continually check items are packed, thus ‘gaining time’, and feeling less forgetful. Although the application of RFID is not new to ubiquitous computing, this implementation, styling and system immediacy is novel. This thesis presents the development of ten prototypes as well as design guidelines. The research provides a solid base for further exploration, and includes discovery of the importance of a user’s style universe and extreme ease-of-use. I conclude with the presentation of early positive results including; (i) the unique form factor becomes a reminder itself and; (ii) usability coupled with the intuitive nature of the system is shown to be essential. We found that when you are creating a smart object, usability and an intuitive nature is even more important than in a standard system. When dealing within the domain of forgetfulness, this is paramount. Submitted for the degree of Doctor of Philosophy Queen Mary University of London 2017
Nutritional Ketoacidosis During Incremental Exercise in Healthy Athletes

Purpose: Ketosis, achieved through ingestion of ketone esters, may influence endurance exercise capacity by altering substrate metabolism. However, the effects of ketone consumption on acid-base status and subsequent metabolic and respiratory compensations are poorly described. Methods: Twelve athletically trained individuals completed an incremental bicycle ergometer exercise test to exhaustion following the consumption of either a ketone ester [(R)-3-hydroxybutyrate-(R)-1,3-butanediol] or a taste-matched control drink (bitter flavoured water) in a blinded, cross-over study. Respiratory gases and arterialised blood gas samples were taken at rest and at regular intervals during exercise. Results: Ketone ester consumption increased blood D-β-hydroxybutyrate concentration from 0.2 to 3.7 mM/L (p < 0.01), causing significant falls versus control in blood pH to 7.37 and bicarbonate to 18.5 mM/L before exercise. To compensate for ketoacidosis, minute ventilation was modestly increased (p < 0.05) with non-linearity in the ventilatory response to exercise (ventilatory threshold) occurring at a 22 W lower workload (p < 0.05). Blood pH and bicarbonate concentrations were the same at maximal exercise intensities. There was no difference in exercise performance having consumed the ketone ester or control drink. Conclusion: Athletes compensated for the greater acid load caused by ketone ester ingestion by elevating minute ventilation and earlier hyperventilation during incremental exercise.
Psychological Demands of International Rugby Sevens and Well-Being Needs of Elite South African Players

Rugby sevens was included in the 2016 Olympic Games, with South Africa’s Blitzboks winning bronze. They also won the 2016/2017 and 2017/2018 World Rugby Sevens Series. Whilst peak performance is paramount at the elite level there is a growing responsibility to address player well-being and off-the-field player needs. This study explored the psychological demands of international rugby sevens and the well-being needs of elite players. Twenty professional players (age range: 21–33 years) participated in semi-structured interviews. Qualitative content analysis yielded five categories of psychological demands: (1) tournament structure, (2) resilience, (3) cope with physicality, (4) perform when fatigued, and (5) perform under pressure. The prevailing team culture included: (1) team cohesion, (2) clear purpose, (3) work ethic, (4) team values, (5) happy environment, (6) relationships with coaching staff, and (7) faith. Various psychological skills [(1) goal-directed behavior, (2) compartmentalization, (3) deal with anxiety, (4) motivation, (5) imagery, and (6) self-confidence] and mental strategies [(1) coping, (2) “back-to-zero,” (3) creativity, and (4) cognitive triggers] utilized by the players are discussed. Their general well-being and individual needs were: (1) physical needs, (2) financial concerns/needs, (3) preparation for life after rugby career, (4) support structures, and (5) mental (ill) health. This information could be useful to develop an integrated sport psychological and well-being program aimed at improving performance and facilitating psychological well-being both during and after retirement from elite sport.
Formation of Si/SiO2 Luminescent Quantum Dots From Mesoporous Silicon by Sodium Tetraborate/Citric Acid Oxidation Treatment

We propose a rapid, one-pot method to generate photoluminescent (PL) mesoporous silicon nanoparticles (PSiNPs). Typically, mesoporous silicon (meso-PSi) films, obtained by electrochemical etching of monocrystalline silicon substrates, do not display strong PL because the silicon nanocrystals (nc-Si) in the skeleton are generally too large to display quantum confinement effects. Here we describe an improved approach to form photoluminescent PSiNPs from meso-PSi by partial oxidation in aqueous sodium borate (borax) solutions. The borax solution acts to simultaneously oxidize the nc-Si surface and to partially dissolve the oxide product. This results in reduction of the size of the nc-Si core into the quantum confinement regime, and formation of an insulating silicon dioxide (SiO2) shell. The shell serves to passivate the surface of the silicon nanocrystals more effectively localizing excitons and increasing PL intensity. We show that the oxidation/dissolution process can be terminated by addition of excess citric acid, which changes the pH of the solution from alkaline to acidic. The process is monitored in situ by measurement of the steady-state PL spectrum from the PSiNPs. The measured PL intensity increases by 1.5- to 2-fold upon addition of citric acid, which we attribute to passivation of non-radiative recombination centers in the oxide shell. The measured PL quantum yield of the final product is up to 20%, the PL activation procedure takes <20 min, and the resulting material remains stable in aqueous dispersion for at least 1 day. The proposed phenomenological model explaining the process takes into account both pH changes in the solution and the potential increase in solubility of silicic acid due to interaction with sodium cations.
Relationship between empathy and motivation in undergraduate medical students

Background: Undergraduate medical education is important for encouraging empathy which is a critical component of patient-physician communication. Studies show a decline in empathy once medical students enter their clinical years. Since empathy is also a “motivated phenomenon”, the current study aims to explore the relationship between empathy and students’ motivation types. Methods: This cross-sectional study used a total sampling approach to recruit medical students in years 1-5. The Jefferson Scale of Physician Empathy (JSPE) was used to measure empathy in medical students and the Academic Motivation Scale (AMS) was utilised to assess student motivation. Following descriptive analyses, the differences in empathy scores based on motivation type was assessed using Kruskal-Wallis test and post-hoc Mann-Whitney test. Furthermore, the Spearman’s rank correlation analysis was completed to assess the relationship between students’ empathy and motivation type. The analyses were completed for each of year 1-5. Results: A total of 827 completed questionnaires (71.3% response rate) were analysed, showing strong internal consistency. Most students displayed high intrinsic and high controlled motivation. Motivation type was found to be consistently associated with empathy. Conclusions: The present study highlights the association of motivation with empathy in undergraduate medical students with an increasingly low empathy score the more the motivation profile is towards being Low Intrinsic and Low Controlled.