Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving Differentially Private Location Privacy in Edge-Assistant Connected Vehicles

Connected vehicles can provide safer and more satisfying services for drivers by using information sensing and sharing. However, current network architecture cannot support massive and real-time data transmissions due to the poor-quality wireless links. To provide real-time data processing and improve drivers’ security, edge computing is regarded as a promising method to offer more efficient services by placing computing and storage resources at the network edge. In this paper, we will introduce the concept of edge-assistant connected vehicles and propose some promising applications to reduce the network traffic and provide real-time services with the help of massive edge nodes. Unlike in the traditional cloud-based connected vehicles, edge nodes are introduced to enable vehicles to obtain real-time and distributed processing services. Furthermore, considering the location privacy issue in the new architecture, we propose a novel differentially privacy-preserving location-based service usage framework deployed on the edge node, designed to provide an adjustable privacy protection solution to balance the utility and privacy. Finally, we conduct extensive experiments to verify the proposed framework.
From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification

Emotion cause identification aims at identifying the potential causes that lead to a certain emotion expression in text. Several techniques including rule based methods and traditional machine learning methods have been proposed to address this problem based on manually designed rules and features. More recently, some deep learning methods have also been applied to this task, with the attempt to automatically capture the causal relationship of emotion and its causes embodied in the text. In this work, we find that in addition to the content of the text, there are another two kinds of information, namely relative position and global labels, that are also very important for emotion cause identification. To integrate such information, we propose a model based on the neural network architecture to encode the three elements (i.e., text content, relative position and global label), in an unified and end-to-end fashion. We introduce a relative position augmented embedding learning algorithm, and transform the task from an independent prediction problem to a reordered prediction problem, where the dynamic global label information is incorporated. Experimental results on a benchmark emotion cause dataset show that our model achieves new state-ofthe-art performance and performs significantly better than a number of competitive baselines. Further analysis shows the effectiveness of the relative position augmented embedding learning algorithm and the reordered prediction mechanism with dynamic global labels.
Combining Automated Measurement-Based Cost Modeling With Static Worst-Case Execution-Time and Energy-Consumption Analyses

Predicting the temporal behavior of embedded real-time systems is a crucial but challenging task, as it is with the energetic behavior of energy-constrained systems, such as IoT devices. To carry out static analyses in order to determine the worst-case execution time or the worst-case energy consumption of tasks, cost models are inevitable. However, these models are rarely available on a fine-grained level for commercial-off-the-shelf hardware platforms. In this letter, we present NEO, an end-to-end toolchain that automatically generates cost models, which are then integrated into an existing static-analysis tool. NEO exploits automatically generated benchmark programs, which are measured on the target platform and investigated in a virtual machine. Based on the gathered data, we formulate mathematical optimization problems that eventually yield both worst-case execution-time and energy-consumption cost models. In our evaluations with an embedded hardware platform (e.g., ARM Cortex-M0+), we show that the open-source toolchain is able to precisely bound programs’ resources while achieving acceptable accuracy.
Role of Infrastructure Adequacy in the Adoption of Digital Book Production and Distribution Hardware

Digital book production machines are adopted in many academic and educational environments to enhance book production and distribution beyond what could be accomplished through the printed book. In developing economies like Nigeria where book production and distribution is badly affected by poor infrastructure, adoption of these machines is likely to make a positive difference, but it appears that publishers are not eager to adopt them for reasons yet to be clearly understood. Explanations in available literature revolve around infrastructure deficit in the areas of power supply, telecommunication bandwidth, postal facilities, Internet service providers (ISP) facilities, digital content developers, venture capital and e-payment instruments. This study, therefore, investigated the extent of adoption of print on demand (POD) distribution machines and compact disc (CD/DVD) replicating machines in Nigeria using a survey design involving 109 publishers selected purposively. Findings indicate that adoption level is low (POD, 9.2%; CD/DVD, 12%) and interest very high (POD, 61.5%; CD/DVD, 61.5%). Only the perceived adequacy of digital contents development facilities and financial facilities played statistically significant roles in the adoption level of print on demand machines and CD/DVD replicating machines. Thus, it could be inferred that infrastructure adequacy plays a significant role in digital hardware adoption but not as extensively as the literature purports.
Chlamydia pneumoniae İnfeksiyonunun Astimdaki Rolü

OZET Chlamydia pneumoniae (C. pneumoniae) alt ve ust solunum yolu infeksiyonlarina neden olabilen, hucre ici parazitizm gos- teren bir infeksiyon ajanidir. Son zamanlarda, solunum yolu infeksiyonlarinda bir etken olmasinin yanisira astim ve arteri- yoskleroz gibi kronik hastaliklar ile iliskili olabilecegi uzerinde durulmaktadir. Yapilan calismalarda, C. pneumoniae ile olu- san akut solunum yolu infeksiyonlarinin wheezing gelisimini tetikledigi, daha once asemptomatik olan kisilerde astimi bas- latabildigi ve astimda ataga neden olabildigi ileri surulmektedir. Bu calismada, C. pneumoniae ve astim arasindaki iliskiyi belirlemeye yonelik calismalar ozetlenmis, gelisecek yeni tedavi yaklasimlari uzerinde durulmustur. ANAHTAR KELIMELER: Chlamydia pneumoniae, bronsiyal astim SUMMARY THE ROLE OF CHLAMYDIA PNEUMONIAE INFECTION IN ASTHMA Chlamydia pneumoniae is a common and important intracellular bacterium implicated in upper and lower respiratory tract infections in humans. C. pneumoniae infection has also been associated with chronic diseases such as atherosclero- sis and asthma. Recent reports contain suggestive data supporting a role for C. pneumoniae infection in acute wheezing and in the exacerbation, initiation and promotion of asthma in both children and adults. In this article, current eviden- ce linking C. pneumoniae infection to asthma and potential therapeutic implications of the C. pneumoniae asthma asso- ciation are discussed.
Towards a deeper understanding of current conversational frameworks through the design and development of a cognitive agent

In this exciting era of cognitive computing, conversational agents have a promising utility and are the subject of this thesis. Conversational agents aim to offer an alternative to traditional methods for humans to engage with technology. This can mean to reduce human effort to complete a task using reasoning capabilities and by exploiting context, or allow voice interaction when traditional methods are not available or inconvenient. This thesis explores technologies that power conversational applications such as virtual assistants, chatbots and conversational agents to gain a deeper understanding of the frameworks used to build them. This thesis introduces Foodie, a conversational kitchen assistant built using IBM Watson technology. The aim of Foodie is to assist families in improving their eating habits through recipe recommendations taking into account personal context, such as allergies and dietary goals, while helping reduce food waste and managing grocery budgets. This thesis discusses Foodie’s architecture, and derives a design methodology for building conversational agents. This thesis explores context-aware systems and their representation in conversational applications. Through Foodie, we characterize the contextual data and define its methods of interaction with the application. Foodie reasons using IBM Watson’s conversational services to recognize users’ intents and understand events related to the users and their context. This thesis discusses our experiences in building conversational agents with Watson, including features that may improve the development experience for creating rich conversations.
Medicolegal Aspects of Cannabis and Its Metabolites Detection Window in Oral Fluid And Urine

Cannabis is one of the most widely used illicit drugs. Oral fluid substance use testing is increasing and has many advantages over urine testing especially for recent substance use diagnosis. The aim of this study was to establish the possibility of detection of recent cannabis smoking through oral fluid and urine testing and its medicolegal aspects. Forty subjects were asked to provide oral fluid and urine samples after a reported period of abstinence. They provided 167 oral fluid samples during abstinence over 5 min – 30 h and 206 urine samples during extended observed abstinence over 1 - 90 days. Cannabis was detected using One Step cannabinoid urine test. The first negative oral fluid sample was that obtained 22 – 30 h after abstinence while the first negative urine sample was detected after seven days of abstinence. Cannabis was detected in urine samples for up to 70 days. These data suggest that oral fluid can be used to discriminate recent from former cannabis use indicating that the user is under the effect of cannabis.
Neuroregenerative-Rehabilitative Therapy for Spinal Cord Injury

Spinal cord injury is one of the leading causes of disability worldwide. Current mainstay treatment strategies consist of surgical and medical management in acute and subacute stage. Rehabilitative management in the chronic stage. None of the existing strategies can repair the damage to the spinal cord and recover neurological functioning. Stem cells have promising results in pre-clinical and clinical studies. Various pre-clinical studies have evidenced neuro-regenerative capabilities of stem cells and shown neural recovery. Clinical studies have also shown improvements in neurological functions and quality of life. This chapter discusses about different types of cells available, routes of administration available to transplant these cells, dosages of cell and optimum time after injury at which cells should be transplanted based on world-wide literature. We have also discussed results following our protocol of intrathecal transplantation of autologous bone marrow mononuclear cells. Although, not a cure, stem cell therapy further improves quality of life, functional independence and reduces secondary complications when combined with existing treatment strategies; neuroregenerative rehabilitative therapy.
Antidiabéticos orais (metformina e gliburida) no tratamento do diabetes mellitus gestacional (DMG): revisão sistemática com metanálise

Diabetes mellitus gestacional (DMG) e um problema medico comum apos a gravidez, principalmente no segundo e terceiro trimestres. A hiperglicemia esta associada a resultados adversos na gravidez em mulheres com diabetes mellitus gestacional. A principal abordagem para o seu controle e a terapia dietetica associada ou nao a atividades fisicas, com a adicao de insulina quando estas intervencoes nao sao suficientes. Apesar de representar uma terapia efetiva para controlar a glicemia materna, a administracao de insulina e cara e inconveniente, necessitando de habilidades para seu manuseio. Alem do custo elevado da terapia com insulina, verificase a alta complexidade em relacao ao seu uso e a rejeicao das pacientes ao tratamento. Dessa forma, o uso de antidiabeticos oraiss orais tem sido recomendado como uma opcao mais barata, simples e de facil aceitacao para o tratamento do DMG. No entanto, a seguranca, os riscos e as possiveis opcoes de tratamento nao sao completamente conhecidos, acarretando duvidas para a indicacao dessa terapeutica. No total, 1819 mulheres gravidas com diagnostico de DMG participaram dos 9 estudos incluidos. Destas, 621 foram tratadas com metformina, 454 com gliburida e 744 com insulina. O numero total de participantes por estudo variou de 23 a 733. A idade gestacional media de entrada em cada grupo de intervencao nos estudos variou entre 25,44 e 30,8 semanas. O IMC variou entre 22,82 e 39,70kg/m². Estudos do tipo ensaio clinico controlado randomizado que comparam regimes farmacologicos para tratamento de pacientes com diabetes mellitus gestacional (DMG) incluindo: antidiabetico oral (gliburida e metformina) e insulina em qualquer dose. Nao foram encontradas evidencias de aumento de desfechos adversos maternos e neonatais com o uso de gliburida e metformina comparados com insulina.
A taxonomy of user guidance devices for e-lexicography

Abstract The aim of this article is to give an overview of the nature of current lexicographic user guidance devices and to suggest a possible classification or taxonomy to serve as a guideline for future compilations of such tools to assist users in communicative and cognitive situations, especially in text production, text reception and computer assisted vocabulary learning. The higher levels of the taxonomy are broadly based on the Function Theory of Lexicography. The lower level of tools that support the communicative function describes the nature of the interaction of the user with the tool – interactive, automated linking/checking and automated translation. The lower level of the tools that describe the cognitive function can be further expanded, based on the nature of the cognition e.g. into self-study of corpus-based analyses and computer-aided learning. We expect this work to provide a broader basis for the discussion of this dispersed set of tools, and for a more principled design and development of new ones.
Metastatic breast cancer: Patterns of metastasis and novel biomarkers

Breast cancer is a heterogeneous disease and is the leading cause of cancerrelated deaths among women, with metastatic disease the principle cause of mortality. In recent years great advances have been made in stratifying breast cancer into a variety of subtypes based on morphological appearance, molecular characteristics and genomic signatures, casting light on the diverse intra-tumour and inter-tumour molecular portrait of the disease. In the era of personalized cancer management, it is imperative to further our understanding of the molecular make-up and clinical behaviour of breast cancer disease so as to tailor treatment and surveillance for recurrence appropriately. In this work we demonstrate the disparate metastatic patterns and outcomes following metastasis of the two major histological subtypes of breast cancer. Furthermore, in a retrospective analysis of metastatic breast cancer patients we identified the initial molecular subtype of primary breast cancer may be different to the subtype of the metastatic disease. This can have significant implications for patient survival and treatment strategies.
Title of dissertation : AGENT MODELING IN STOCHASTIC REPEATED GAMES

Title of dissertation: AGENT MODELING IN STOCHASTIC REPEATED GAMES Kan Leung Cheng, Doctor of Philosophy, 2014 Dissertation directed by: Professor Dana Nau Department of Computer Science There are many situations in which two or more agents (e.g., human or computer decision makers) interact with each other repeatedly in settings that can be modeled as repeated games. In such situations, there is evidence that agents sometimes deviate greatly from what conventional game theory would predict. There are several reasons why this might happen, one of which is the focus of this dissertation: sometimes an agent’s preferences may involve not only its own payoff (as specified in the payoff matrix), but also the payoffs of the other agent(s). In such situations, it is important to be able to understand what an agent’s preferences really are, and how those preferences may affect the agent’s behavior. This dissertation studies how the notion of Social Value Orientation (SVO), a construct in social psychology to model and measure a person’s social preference, can be used to improve our understanding of the behavior of computer agents. Most of the work involves the life game, a repeated game in which the stage game is chosen stochastically at each iteration. The work includes the following results: • Using a combination of the SVO theory and evolutionary game theory, we studied how an agent’s SVO affects its behavior in Iterated Prisoner’s Dilemma (IPD). Our analysis provides a way to predict outcomes of agents playing IPD given their SVO values. • In the life game, we developed a way to build a model of agent’s SVO based on observations of its behavior. Experimental results demonstrate that the modeling technique works well. • We performed experiments showing that the measured social preferences of computer agents have significant correlation with that of their human designers. The experimental results also show that knowing the SVO of an agent’s human designer can be used to improve the performance of other agents that interact with the given agent. • A limitation of the SVO model is that it only looks at agents’ preferences in one-shot games. This is inadequate for repeated games, in which an agent’s actions may depend on both its SVO and whatever predictions it makes of the other agent’s behavior. We have developed an extension of the SVO construct called the behavioral signature, a model of how an agent’s behavior over time will be affected by both its own SVO and the other agent’s SVO. The experimental results show that the behavioral signature is an effective way to generalize SVO to situations where agents interact repeatedly. AGENT MODELING IN STOCHASTIC REPEATED GAMES
ONLINE ASSESSMENT THROUGH MOODLE PLATFORM

The differences between secondary education and Higher Education (HE) are numerous, both in terms of teaching methodologies and as well as evaluation/validation procedures. Until a few years ago, the HE assessment practices were reduced, with some exceptions from practical/laboratory curricular units, to written exams, carried out on the same day and at the same time by all, or, at most, two frequencies (tests) over the course of the semester or year. With growing concerns in terms of learning outcomes and students’ success, globalisation and the massification of education, several alternative models have been tested, both in terms of teaching/learning methodologies (flipped, project-based, “blended”, among others) and assessment practices (portfolio, continuous, segmented, formative, and others). The fast development of electronic devices has been promoting the development and sharing of several digital educational tools and their use seem to be a good choice to promote actual students’/professors’ interaction and corresponding socialization, even in some assessment tasks. In this particular case, online activities can be transformed into powerful self-assessment resources for students, stimulating reflection and promoting independent learning. Moodle, as an open Learning Management System (LMS), has all the requisites and potential features to be a fine supporting tool to several activities, specifically to assessing ones. In this paper, we will briefly describe Moodle potentialities, with some application examples based on literature review and present the work developed on several Mathematic curricular units from different schools of the Polytechnic of Porto, where Moodle is used as an online assessment tool, to promote a continuous/segmented model with specific and different activities designed for each course.
Replicating DeepMind StarCraft II reinforcement learning benchmark with actor-critic methods

Reinforcement Learning (RL) is a subfield of Artificial Intelligence (AI) that deals with agents navigating in an environment with the goal of maximizing total reward. Games are good environments to test RL algorithms as they have simple rules and clear reward signals. Theoretical part of this thesis explores some of the popular classical and modern RL approaches, which include the use of Artificial Neural Network (ANN) as a function approximator inside AI agent. In practical part of the thesis we implement Advantage Actor-Critic RL algorithm and replicate ANN based agent described in [Vinyals et al., 2017]. We reproduce the state-of-the-art results in a modern video game StarCraft II, a game that is considered the next milestone in AI after the fall of chess and Go.
Long-term continuous terlipressin infusion in cirrhotic patients with hepatorenal syndrome or refractory ascites awaiting liver transplantation is associated with an increase in plasma sodium

We read with interest the study by Eriksen et al. regarding the impact of terlipressin on plasma sodium (PNa). An analogue of vasopressin, terlipressin is used to treat the effects of systemic vasodilatation and renal dysfunction in patients with portal hypertension. It acts on vascular smooth muscle V1 receptors to induce splanchnic vasoconstriction and on renal collecting duct V2 receptors to mobilise aquaporin channels. Eriksen et al. noted that these effects promote hyponatraemia and hypothesised that this may be more profound in non-cirrhotic patients, who do not demonstrate urinary sodium retention or high aquaporin expression at baseline. The authors retrospectively compared PNa fluctuations in non-cirrhotics and cirrhotics with portal hypertension receiving bolus terlipressin for variceal bleeding. They found that non-cirrhotic patients had a greater reduction in mean PNa during terlipressin therapy (8.3 vs. 1.8mmol/l, p1⁄4 0.048) and a larger increase in mean PNa post its cessation (12.6 vs. 2.3mmol/l, p1⁄4 0.03). In both groups, an increased cumulative dose and duration of terlipressin was associated with more marked hyponatraemia (p1⁄4 0.009 vs. p1⁄4 0.02). They concluded that terlipressin posed a potentially dangerous risk of hyponatraemia in non-cirrhotic patients with variceal bleeding. We agree that clinicians using terlipressin in this setting should proceed with caution, but offer safety data regarding its prolonged use in cirrhotic patients with other portal hypertensive complications. Based on safety and efficacy data from a Cochrane review and randomised control trial, 4 our centre has developed a protocol for delivering continuous terlipressin infusion (CTI) in the outpatient setting to cirrhotics with hepatorenal syndrome (HRS) (as defined by the International Club of Ascites) or refractory ascites who are awaiting liver transplantation. We have therefore prospectively observed the effect of CTI on PNa in this novel cohort. Data was collected on all patients who received at least two weeks of CTI prior to 31 March 2018. Patients commenced bolus terlipressin as inpatients (0.85mg Q6h) and received volume expansion with albumin (40 g/day for three days). Suitable patients were transitioned to CTI. Informed consent was obtained and patients were transitioned to outpatient CTI, delivered via a peripherally inserted central