Miracle-dz/UniFAR-FTUs huggingface dataset:
Lossy DCT-based compression of remote sensing images with providing a desired visual quality

Modern remote sensing (RS) systems produce a huge amount of data that should be passed to potential users from sensors or saved. Then, compression is an operation that is extremely useful where lossy compression has found many applications. A requirement to it is not to loose useful information contained in RS data and to provide a rather high compression ratio (CR). This has to be done in automatic manner and quickly enough. One possible approach to ensure minimal or appropriate loss of useful information is to provide a desired visual quality of compressed images where introduced distortions are invisible. In this paper, we show how this can be done for coders based on discrete cosine transform (DCT) that employ either uniform or non-uniform quantization of DCT coefficients. For multichannel images that contain sub-band images with different dynamic range, it is also proposed to carry out preliminary normalization. Additionally, compression performance can be improved if sub-band images are compressed in groups. Then, either introduced distortions are smaller for a given CR or a larger CR is provided for a given level of compressed data quality. Examples for real-life data are presented.
Cooperative wireless communications in the presence of limited feedback

Dans cette these, les techniques de cooperation ont ete etudiees pour un canal multi-acces multi-relais compose d'au moins deux sources qui communiquent avec une seule destination a l'aide d'au moins deux nœuds de relayage en mode semi-duplex. Le multiplexage par repartition dans le temps est suppose. Tout d'abord, l’algorithme d’adaptation de lien est execute par l'ordonnanceur centralise. Durant la premiere phase de transmission, les sources transmettent chacune a leur tour leur message respectif pendant des intervalles de temps consecutifs. Dans chaque intervalle de temps dans la deuxieme phase, la destination planifie un nœud pour transmettre les redondances, mettant en œuvre un protocole cooperatif d'Hybrid Automatic Repeat reQuest (HARQ), ou les canaux de controle limites bidirectionnels sont disponibles depuis les sources et les relais vers la destination. Dans la premiere partie de la these, les strategies de selection des nœuds centralise sont proposees pour la deuxieme phase de transmission. Les decisions d’ordonnancement sont prises en fonction de la connaissance des ensembles de sources correctement decodees par chaque noeud et ayant comme objectif de maximiser l’efficacite spectrale moyenne. L'analyse de la probabilite de coupure de l'information ainsi que les simulations Monte-Carlo (MC) sont effectues afin de valider ces strategies. Dans la seconde partie, un algorithme d’adaptation de lien lent est propose afin de maximiser l’efficacite spectrale moyenne sous contrainte de verification d'une qualite de service individuelle cible pour une famille donnee de schemas de modulation et de codage, reposant sur l'information sur la distribution des canaux signalee. Les debits des sources discrets sont determines en utilisant l’approche "Genie-Aided" suivie d’un algorithme iteratif de correction de debit. Les simulations MC montrent que l’algorithme d’adaptation de lien propose offre des performances proches de celles de la recherche exhaustive. Dans la troisieme partie, les performances de protocole HARQ a redondance incrementale (IR) avec codage mono et multi-utilisateur, ainsi que l'HARQ de type Chase Combining avec codage mono-utilisateur sont comparees. Les simulations MC montrent que l'IR-HARQ avec codage mono-utilisateur offre le meilleur compromis entre performance et complexite pour le scenario de petit nombre de sources. Un schema de codage pratique est propose et valide a l'aide de simulations MC.
Ontology driven information retrieval

Ontology-driven information retrieval deals with the use of entities specified in domain ontologies to enhance search and browse. The entities or concepts of lightweight ontological resources are traditionally used to index resources in specialised domains. Indexing with concepts is often achieved manually and reusing them to enhance search remains a challenge. Other challenges range from the difficulty in merging multiple ontologies for use in retrieval to the problem of integrating concept-based search into existing search systems. We mainly encounter these challenges in enterprise search environments, which have not kept pace with Web search engines and mostly rely on full-text search systems. Full-text search systems are keyword-based and suffer from well-known vocabulary mismatch problems. Ontologies model domain knowledge and have the potential for use in understanding the unstructured content of documents. In this thesis, we investigate the challenges of using domain ontologies for enhancing search in enterprise systems. Firstly, we investigate methods for annotating documents by identifying the best concepts that represent their contents. We explore ways to overcome the challenges of insufficient textual features in lightweight ontologies and introduce an unsupervised method for annotating documents based on generating concept descriptors from external resources. Specifically, we augment concepts with descriptive textual content by exploiting the taxonomic structure of an ontology to ensure that we generate useful descriptors. Secondly, the need often arises for cross-ontology reasoning when using multiple ontologies in ontology-driven search. Once again, we attempt to overcome the absence of rich features in lightweight ontologies by exploring the use of background knowledge for the alignment process. We propose novel ontology alignment techniques which integrate string metrics, semantic features, and term weights for discovering diverse correspondence types in supervised and unsupervised ontology alignment. Thirdly, we investigate different representational schemes for queries and documents and explore semantic ranking models using conceptual representations. Accordingly, we propose a semantic ranking model that incorporates the knowledge of concept relatedness and a predictive model to apply semantic ranking only when it is deemed beneficial for retrieval. Finally, we conduct comprehensive evaluations of the proposed methods and discuss our findings.
A Q-Sort Study: service user and trainee psychologists’ perspectives of recovery in a community-based project.

The concept of recovery in mental health has changed over time and reflects our increased understanding of the factors that are important to service users and mental health professionals in defining recovery. This thesis investigates service user and trainee psychologists’ perspectives of recovery in a community-based project. The first part of this thesis reviews the literature on the development of our understanding of recovery in mental health. The search strategy that was developed yielded 9 studies that met the inclusion criteria and a further 3 studies were added following a hand-search of relevant literature. Four themes were identified that charted the development of our understanding of recovery: i) Recovery over the past decade; ii) Changing Perceptions of Recovery; iii) The rise of service user involvement; and iv) New ways of providing services. The review identified that there was a move from a medical model towards a recovery based model. The second part of this thesis is a Q-methodological study that investigates service user and trainee psychologists’ perceptions of recovery in a community based project. The sample of 23 participants comprised 12 service users and 11 trainee clinical psychologists. Participants sorted 50 statements related to the process of recovery from mental health problems. A Centroid Factor Analysis revealed a 3 Factor Solution. Factor 1 was solely endorsed by trainee clinical psychologists and proposed that understanding and social support is the key to recovery. Factor 2 was solely endorsed by service-users and proposed that attending Growthpoint and medication was the key to recovery. Factor 3 was endorsed by both service-users and trainee psychologists and proposed that attending Growthpoint and personal growth was the key to recovery. A visual representation of the conceptual space is presented through the use of multidimensional scaling. An in depth interpretation of the factors is presented and the implications of the research are discussed. The third part of this thesis is a reflective paper whereby the author uses a narrative approach to compare her experience of recovery from diabetes to the experience of recovery from mental health problems. The author uses Q-Sort methodology to compare factor viewpoints that emerged from the Q-Sort analysis and combines this with the model of Reflective Practice developed by Atkins & Murphy (1994).
A Platform of Resynthesizing a Clock Architecture Into Power-and-Area Effective Clock Trees

To trigger events for application-specific data transfer among registers in a multimillion-gate system-on-chip (SoC), various kinds of clock signals, selectively driven by different frequency-dependent sources and/or dividers (DIVs), are usually centralized in one or more clock generation modules, where clock gating cells (CGCs), multiplexers (MUXes) and DIVs are used to create the clocks required by different functional operations in an SoC. These modules will introduce uncommon and longer timing paths for clock propagations and further make the clock tree synthesis (CTS) process become more challenging due to the on-chip-variation (OCV) effects. In addition, high volume of switching activities in the increased number of clock logic cells will consume more power. In this article, a novel design platform, merging and replacing of multiple multiplexers and dividers (MRMMD), is developed to intelligently identify those suspicious clock architectures and resynthesize them into a power-and-area effective and less complicated clock structure. Using our resynthesis platform, not only the number of clock-related timing paths and their corresponding logic levels can be reduced, but also the corresponding analysis and implementations of clock skew minimizations during CTS become much easier. The experimental results implemented in TSMC 55- and 28-nm process nodes on optimizing some industrial clock architectures showed that significant reductions of area, power, latency, skew and clock path, logic level, OCV impact, total wire length, and implementation runtime are achieved using our MRMMD platform.
Associação da Classificação da OMS e da AHR com fatores clínico-patológicos do câncer de boca

It has been shown that histopathological classifications assist in the analysis of the degree of malignancy in oral squamous cell carcinoma (OSCC). This work reviewed the profile of 44 patients diagnosed in INCA in 1999 and applied WHO classification and Histopathological Risk Assessment (HRA), associating them with clinicopathological features. Most OSCC were classified as moderately differentiated (WHO) and intermediate-risk (HRA). No statistically significant correlation was found between clinicopathological features and WHO and HRA analysis. Studies comprising a larger sample are necessary to validate results found here.
Relationship between Bone Mass and Habitual Physical Activity and Calcium Intake in 8–11-Year-Old Boys and Girls

This study aimed to determine the relationship between bone mineral content, habitual physical activity, and calcium intake in children. Fifty-seven children, aged 8–11 years, wore pedometers for seven days to assess activity. Calcium intake was estimated by a 4-day food diary. Bone mineral content (BMC) and areal density (BMD) were measured at the total proximal femur and femoral neck using dual energy X-ray absorptiometry. Regression analysis was used to assess contributions of physical activity and calcium intake to BMC, residualized for bone area and body mass. Physical activity explained 11.6% of the variance in residualized BMC at the proximal femur and 14.3% at the femoral neck (p < 0.05). Calcium intake added to the variance explained at the proximal femur only (9.8%, p < 0.05). This study provides evidence for an association between BMC and habitual physical activity.
The impact of skin and soft tissue infection on college athletic programs and prevention policies

THE IMPACT OF SKIN AND SOFT TISSUE INFECTIONS ON COLLEGE ATHLETIC PROGRAMS AND PREVENTION POLICIES Alice Kimble April 8, 2013 The majority of skin and soft tissue infections (SSTIs) are caused by Methicillinresistant Staphylococcus aureus (MRSA), and antibiotic resistant strains pose a greater challenge for effective prevention and treatment. Although recommendations outlining procedures for prevention have been provided by the Centers for Disease Control and Prevention (CDC) and the National Athletic Trainers Association (NATA), the actual adherence by the athletic training staff is unclear (Centers for Disease Control and Prevention, 2010; National Athletic trainers' Association, 2010). MRSA can result in devastating illness and staggering healthcare costs, and deaths of athletes have been reported (Ellis, 2007). The current study used the Health Belief Model to examine the impact of SST Is on college athletic programs prevention policies. A survey was developed and sent to department heads of colleges and universities that were NATA members. Data were collected on the prevalence of SSTIs, their impact on policy, and variation in standards of treatment. Responses from 78 trainers indicated that although respondents were aware of guidelines for prevention and treatment and most had policies in place, they varied as to
Measuring the Global Recursive DNS Infrastructure: A View From the Edge

The Domain Name System (DNS) is one of the most critical Internet subsystems. While the majority of ISPs deploy and operate their own DNS infrastructure, many end users resort to third-party DNS providers with hopes of enhancing their privacy, security, and web performance. However, bad user choices and the uneven geographical deployment of DNS providers could render insecure and inefficient DNS configurations for millions of users. In this paper, we propose a novel and flexible measurement method to (1) study the infrastructure of recursive DNS resolvers, including both ISP’s and third-party DNS providers’ deployment strategies; and (2) study end-user DNS choices, both in a timely manner and at a global scale. For that, we leverage the outreach capacity of online advertising networks to distribute lightweight JavaScript-based DNS measurement scripts. To showcase the potential of our technique, we launch two separate ad campaigns that triggered more than 3M DNS lookups, which allow us to identify and study more than 76k recursive DNS resolvers giving support to more than 25k eyeball ASes in 178 countries. The analysis of the data offers new insights into the DNS infrastructure, such as user preferences towards third-party DNS providers (namely, Google, OpenDNS, Level3, and Cloudflare recursive DNS resolvers account for ~13% of the total DNS requests triggered by our campaigns), and into deployment decisions of many ISPs providing both mobile and fixed access networks to separate the DNS infrastructure serving each type of access technology.
Multiphase Active Energy Recycling Technique for Overshoot Voltage Reduction in Internet-of-Things Applications

Large supply voltage ripple will cause the Internet-of-Things (IoT) operation to fail. Conventional bulky clamp circuit simply eliminates overshoot redundant energy but with reduced efficiency. In this article, the multiphase active energy recycling (MP-AER) technique uses an active recovery mechanism with multiple 33-nH bond wires to recycle energy from the overshoot voltage for increasing efficiency. The experimental results show that when the load current in the IoT application suddenly drops from 1.7 to 0.3 A, the ultrafast energy recovery of 47.1 mW/ $\mu \text{s}$ suppresses the output overshoot voltage from 507 to 95 mV, an increase of 81.3%. The energy recovered by the MP-AER technique achieves a peak efficiency of 93% when the input voltage is 3 V and the loading current is 300 mA.
Analytical and Comparative Study of Face Detection and Face Recognition Techniques for Visually Impaired and Blind People

: It is from the beginning of the mankind history that humans suffered innumerable disabilities, however from all these disabilities the most long-lasting and common is blindness. As per the statistics given by World Health Organization around 39 million people are blind whereas 285 million people are visually impaired and around 246 million where detected to have low vision. With this given statistics by W.H. O around 90 percent of world’s population who are visually impaired are in-habitants of developing countries. The new innovations in the domain of Science and Technology we have discovered that technology is used to ease mankind’s life [1]. Thus, the main objective of this research paper would be to showcase measure which would recognize the people by detecting the face which is apparently most important function of our eyes. This is considered to be as a boon for mankind who cannot see as well as visionless and especially if a blind’s important part like retina and veins which are dysfunctional. This research will not only initiate the measures to put an end to difficulties experienced by the visually impaired but we shall also study the differences between the algorithm which would act as a catalyst in the process.
Design and implementation of a software system for the composition of a database and automated trading system on different cryptocurrency trading markets

Cryptocurrency is a means of digital exchange that, by its design and operation, complies with the functions of traditional money allowing the exchange of goods and services by using cryptography to verify and store transactions in a public ledger. The purpose of this project is to design and implement a cryptocurrency trading platform able to connect to multiple exchange APIs in order to gather market data information as well as place orders automatically on different market pairs. The platform is designed to allow the integration of new cryptocurrency exchange APIs. Furthermore, the software also allows the configuration of different trading strategies based on real time market data information. The present project will constitute the basis of future trading research analysis on the cryptocurrency market. In addition, an analysis, description and comparison of the most important cryptocurrencies and exchanges is presented. More specifically, the working principles and the key technological differences between most relevant cryptocurrenies based on market capitalization are discussed. A general overview of characteristics, digital coins offered, current performance and fee analysis of different cryptocurrency exchanges is conducted. Finally, an evaluation of trading strategies based on real data gathered by the designed platform is performed.
Inferencia de la respuesta afectiva de los espectadores de un video

espanolEste proyecto propone el analisis de la posible dependencia entre el contenido audiovisual de una produccion multimedia y el impacto causado por esta en sus espectadores. Para ello, nos apoyamos en diferentes areas de conocimiento tales como comunicacion audiovisual, vision por computador, sistemas multimodales, sensores biometricos, analisis de redes sociales, analisis de opinion o computacion afectiva, entre otras, con el objetivo de disenar nuevos modelos computacionales que permitan predecir las reacciones de los espectadores de un video de forma transversal a los medios y momentos en que estas se producen. Trabajamos principalmente con dos tipos de respuesta: la respuesta cognitiva y emocional inmediata de los espectadores durante el visionado, que medimos utilizando tecnicas de neurociencia y sensores biometricos, y la reaccion expresada en redes sociales, cuyo impacto es cuantificado mediante el analisis automatico de diferentes metadatos recabados para dichos videos, tales como popularidad, patrones de comparticion, valoraciones y comentarios realizados en las redes. EnglishIn this project we propose the automatic analysis of the relation between the audiovisual characteristics of a multimedia production and the impact caused in its audience. With this aim, potential synergies are explored between different areas of knowledge including, among others: audiovisual communication, computer vision, multimodal systems, biometric sensors, social network analysis, opinion mining, and affective computing. Our efforts are oriented towards combining these technologies to introduce novel computational models that could predict the reactions of spectators to multimedia elements across different media and moments. On the one hand, we study the cognitive and emotional response of the spectators while they are watching the media instances, using neuroscience techniques and biometric sensors. On the other hand, we also study the reaction shown by the audience on social networks by relying on the automatic collection and analysis of different metadata related to the media elements, such as popularity, sharing patterns, ratings and commentaries. |
Filter Bank based Multicarrier Systems for Wireless Communications

This work covers different aspects of filter bank based multicarrier systems. First, we present the basic theory, fundamental operations, efficient structures for the realization of the system and a complexity analysis. Then, schemes for channel estimation are derived based on appropriate subcarrier models. Also methods to compensate and mitigate the effects of the propagation channel are presented. An extension from single-input-single-output to multiple-input-multiple-output is also discussed in this work.
Sparse deep predictive coding captures contour integration capabilities of the early visual system

Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to process context-dependent information in the early visual cortex. While numerous models have accounted for feedback effects at either neural or representational level, none of them were able to bind those two levels of analysis. Is it possible to describe feedback effects at both levels using the same model? We answer this question by combining Predictive Coding (PC) and Sparse Coding (SC) into a hierarchical and convolutional framework applied to realistic problems. In the Sparse Deep Predictive Coding (SDPC) model, the SC component models the internal recurrent processing within each layer, and the PC component describes the interactions between layers using feedforward and feedback connections. Here, we train a 2-layered SDPC on two different databases of images, and we interpret it as a model of the early visual system (V1 & V2). We first demonstrate that once the training has converged, SDPC exhibits oriented and localized receptive fields in V1 and more complex features in V2. Second, we analyze the effects of feedback on the neural organization beyond the classical receptive field of V1 neurons using interaction maps. These maps are similar to association fields and reflect the Gestalt principle of good continuation. We demonstrate that feedback signals reorganize interaction maps and modulate neural activity to promote contour integration. Third, we demonstrate at the representational level that the SDPC feedback connections are able to overcome noise in input images. Therefore, the SDPC captures the association field principle at the neural level which results in a better reconstruction of blurred images at the representational level.