Miracle-dz/UniFAR-FTUs huggingface dataset:
On the performance of adaptive HARQ with no channel state information feedback

This paper presents an adaptive power control scheme for hybrid automatic repeat request (HARQ) systems with no channel state information feedback. The power control process is developed by noting that the throughput of particular HARQ systems may exhibit a staircase behavior. In such scenarios, the throughput remains fixed for a wide range of signal-to-noise ratios (SNRs). Consequently, the transmit power can be reduced significantly while the throughput remains almost unchanged. The obtained results reveal that invoking power optimization algorithms can achieve a significant power saving of about 80% for particular scenarios. The system considered in this work is a truncated HARQ with turbo product codes (TPC). Chase combining is also used to combine the retransmitted packets with the original transmission. HARQ acknowledgment/negative acknowledgment (ACK/NACK) feedback is used to estimate the packet error rate from which the system throughput is computed.
An industrial Fault Diagnosis System based on Bayesian Networks

This paper presents a DC motor fault diagnosis system based on Bayesian networks. This was done by the design of a new electromechanical test bed allowing the collection of functioning data from a real world industrial Direct current (DC) Motor. The data collection will help in the construction of Bayesian networks models. These data are collected from sensors measuring different types of variables that are directly related to the industrial system. Without doing any mathematical modeling that describes the physical properties of the studied DC motor, the proposed tool provides with the help of Bayesian networks parameters and structure learning algorithms, the base to construct a fault diagnosis tool that can be extended to a fault prognosis tool.
DAHLIA 2.0: A Visual Analyzer of Database Usage in Dynamic and Heterogeneous Systems

Understanding the links between application programs and their database is useful in various contexts such as migrating information systems towards a new database platform, evolving the database schema, or assessing the overall system quality. However, data-intensive applications nowadays tend to access their underlying database in an increasingly dynamic way. The queries that they send to the database server are usually built at runtime, through String concatenation, or Object-Relational-Mapping (ORM) frameworks. This level of dynamicity significantly complicates the task of adapting programs to an evolving database schema. In this paper, we present DAHLIA 2.0, an interactive visualization tool that allows developers to analyze the database usage in order to support data-intensive software evolution and more precisely, program-database co-evolution.
Extending Intuitionistic Logic with Subtraction

This paper is an exercise in formal and philosophical logic. I will show how intuitionistic propositional logic can be extended with a new two-place connective, not expressible in the traditional language of intuitionistic logic (the language of conjunction, disjunction, negation and implication). The new system will be shown to be a conservative extension of intuitionistic logic. After examining the formal properties of this extension, the task will be to consider whether this is an `acceptable' extension of intuitionistic logic. It will turn out that on some intuitionistic considerations the extension is acceptable, and on others it is not. In this paper I presume that the reader has some idea both of the formal properties of intuitionistic logic, and some motivating philosophical principles which inform the development of intuitionistic logic. Readers wanting such an introduction can do no better than look at some of the excellent, extensive literature on intuitionism [3, 5, 8, 12]. Other work has been done on extending intuitionistic propositional logic with new connectives. Gabbay [6] considers extending the logic with propositional connectives. Our new connective is not one he considers. De Jongh too considers extending intuitionistic logic by adding arbitrary conjunction and disjunction. In his interesting paper [2] he shows that once you make such an addition to the basic logic, you end up with a proper class of propositional connectives expressible. In what follows we will consider adding just one new binary connective to the language of intuitintionistic propositional logic.
DENİZLİ İLİNDE BULUNAN RESMİ VE ÖZEL ANAOKULU BAHÇELERİNİN KARŞILAŞTIRMALI OLARAK İNCELENMESİ

Bu arastirmada Denizli ilinde bulunan resmi ve ozel anaokullarinin bahcelerinin fiziksel ozellikleri,kullanimi ve gelistirilmesine yonelik uygulamalar ve karsilasilan problemler incelenmistir.Arastirmaya toplam 26 anaokulu dahil edilmistir. Veri toplama araci olarak Chancellor tarafindangelistirilen toplam 43 sorudan olusan olcme araci, okul yoneticileri tarafindan yanitlanmistir.Arastirmada elde edilen bulgulara gore, her iki turdeki anaokulunun okul bahcelerinin dogalunsurlari yeterli olcude icermedigi belirlenmistir. Okul bahcesine iliskin yazili kurallari bulunanozel anaokullarinin daha cok oldugu; ancak bu kurallarin daha cok mudur tarafindan belirlendigigorulmustur. Ayrica okul bahcesinin gelistirilmesi surecinde resmi anaokullarinin toplumla dahacok isbirligi yaptigi, buna karsilik her iki anaokulu turunde de cocuklarin karar verme sureclerineyeterli duzeyde dahil edilmedigi gozlenmistir. Resmi ve ozel anaokullarinda okul bahcelerininbirer ogrenme ortami olarak algilandigi ancak bilissel gelisim alaninin her iki okul turunde de okulbahcesinde yurutulen etkinliklerde amaclanmadigi saptanmistir. Arastirma sonunda elde edilenbulgular ilgili alanyazin isiginda tartisilmis ve birtakim onerilerde bulunulmustur.
Study of a novel multi-band antenna for body-centric wireless networks

Body-centric wireless networks are used for connectivity between on-body and on-off body communications for various applications for rescue, diagnostics and medical usage. Multiple features of modern portable and wearable devices necessitate antenna operation at a number of frequencies. A compact, low profile and multi-band antenna is presented for body-centric wireless networks in this study. The conventional microstrip rectangular patch antenna has been converted into a multi-band antenna by using layers of mercury and liquid crystal polymer (LCP). The antenna performance in free space and in body-mounted configurations are evaluated and compared using computer simulations. The proposed antenna supports six frequencies for operation at ISM/Wi-Fi/C band. A minimal shift in the operating frequencies while operating in on-body configuration makes this the proposed antenna very resilient to frequency de-tuning caused by the human body presence. The antenna also offers high peak gain values (>7.68 dBi) in the two configurations at all of the operating frequencies.
Hybrid flower pollination algorithm with rough sets for feature selection

Flower pollination algorithm (FPA) optimization is a new evolutionary computation technique that inspired from the pollination process of flowers. In this paper, a model for multi-objective feature selection based on flower pollination algorithm (FPA) optimization hybrid with rough set is proposed. The proposed model exploits the capabilities of filter-based feature selection and wrapper-based feature selection. Filter-based approach can be described as data oriented methods that not directly related to classification performance. Wrapper-based approach is more related to classification performance but it does not face redundancy and dependency among the selected feature set. Therefore, we proposed a multi-objective fitness function that uses FPA to the find optimal feature subset. The multi-objective fitness function enhances classification performance and guarantees minimum redundancy among selected features. At begin of the optimization process, fitness function uses mutual information among feature as a goal for optimization. While at some later time and using the same population, the fitness function is switched to be more classifier dependent and hence exploits rough-set classifier as a guide to classification performance. The proposed model was tested on eight datasets form UCI data repository and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA).
Gateway : A System to Automatically Decompose Complex Environments

This article presents a methodology, and an associated system called Gateway, for automatically breaking complex Markov environments into a series of smaller regions. In turn, these regions form a kind of hierarchy that is useful in improving learning efficiency. The methodology has its roots in the cognitive mapping literature and requires adding little or no prior knowledge to the Gateway system.
Adaptive Distribution of Vocabulary Frequencies: A Novel Estimation Suitable for Social Media Corpus

This paper aims to propose a mathematical model that evaluates the distribution of the vocabulary frequency terms in proportion to a probabilistic ideal. Once we are able to evaluate it, the main objective of this work is to use it in order to examine text demising. We propose this new metric based on the classic Zipf's law statistic method. The experimental set to test the classic Zipf's law and our developed model is based on some books of the classic literature and some tweets sets of Twitter. Thus, our main result is that the model proposed in this work is more sensitive to the presence of text noises than Zipf's law and is asymptotically quicker, suitable to corpus of social media networks.
Grammar-Based Lexicon Extension for Aligning German Radiology Text and Images

For efficient diagnosis processes, the multitude of heterogeneous medical data requires seamless integration. In order to automatically align radiology reports and images based on the pathological anatomical entities they describe, a preceding sentence classification is necessary. However, the lexical resource used has to contain semantic information about the pathological classification of each entity. We introduce an approach to extend medical lexical resources with pathology classification information and, at the same time, with new classified vocabulary. Our algorithm is based on a semi-supervised learning algorithm and incorporates a semantic contextfree grammar combined with a RadLexbased lexicon.
The Properties of X-Ray and Optical Light Curves of X-Ray Novae

We have collected the available data from the literature and from public data archives covering the past two decades for the long-term X-ray and optical light curves of X-ray nova (XN) outbursts. XN outbursts are due to episodic accretion events, primarily in low-mass X-ray binaries normally characterized by low mass transfer rates. Dynamical studies indicate that most XNs contain a black hole. The soft X-ray emission during outburst traces the accretion rate through the inner edge of the accretion disk, while the optical light curve traces the physical conditions at the outer disk—thus, collectively, they contain information on the time-dependent behavior of accretion processes through the disks. In this paper, we carry out for the first time a systematic, statistical study of XN light curves that are classified into five morphological types. Basic light-curve parameters, such as the outburst peak flux, amplitude, luminosity, rise and decay timescales, the observed and expected outburst durations, and the total energy radiated, are tabulated and discussed. We find that the rise timescales have a flat distribution while the decay timescales have a much narrower and near-Gaussian distribution, centered around 30 days and dominated by the strongest outbursts. The peak luminosity is also distributed like a Gaussian, centered around 0.2 in Eddington units, while the total energy released has a much broader distribution around 1044 ergs. We find no intrinsic difference between black hole and neutron star systems in their distribution of peak amplitudes. We identify and discuss additional light-curve features, such as precursors, plateaus, and secondary maxima. The plateaus exhibited in the light curves of black hole sources are found to have, on average, longer durations, and they are followed by longer decays. The identified secondary maxima seem to occur mostly in black hole systems. For the frequency of outbursts, we find that the average XN outburst rate is about 2.6 yr-1 for events greater than 0.3 crab and that the mean recurrence time between outbursts from a single source is 6 yr. The spatial and log N-log S distribution of the XN sources, with limited statistics, agrees with a source population in the Galactic disk, as observed from a point at a distance of 8.5 kpc from the Galactic center. Finally, we point out that the observed XN light-curve properties can be explained in general by a disk thermal instability model, although some important problems still remain.
Image Retrieval for Highly Similar Objects

In content-based image retrieval, precision is usually regarded as the top metric used for performance measurement. With image databases reaching hundreds of millions of records, it is apparent that many retrieval strategies will not scale. Data representation and organization has to be better understood. This paper focuses on: (a) feature selection and optimal representation of features and (b) multidimensional tree indexing structure. The paper proposes the use of a forward and conditional backward searching feature selection algorithm. The data is then put through a minimum description length based optimal non-uniform bit allocation algorithm to reduce the size of the stored data, while preserving the structure of the data. The results of our experiments show that the proposed feature selection process with a minimum description length based non-uniform bit allocation method gives a system that improves retrieval time and precision.
Why is there money? Endogenous derivation of `money' as the most liquid asset: a class of examples

Summary. The monetary character of trade, use of a common medium of exchange, is shown to be an outcome of an economic general equilibrium. Monetary structure can be derived from price theory in a modified Arrow-Debreu model. Two constructs are added: transaction costs and market segmentation in trading posts (with a separate budget constraint at each transaction). Commodity money arises endogenously as the most liquid (lowest transaction cost) asset. Government-issued fiat money has a positive equilibrium value from its acceptability for tax payments. Scale economies in transaction cost account for uniqueness of the (fiat or commodity) money in equilibrium.
Enhanced Constraint Dependency Grammar Parsers

Constraint Dependency Grammar CDG is a constraint based grammatical formalism that has a weak generative capacity beyond context free gram mars and supports a very exible parsing algorithm for working with feature grammars however the running time of the parser is O n Hence we have investi gated how to improve the running time of the parser by applying feature constraints di erentially and by using aggregate unary constraints which can be applied in O n time Additional speedup was achieved by in tegrating the ltering algorithm more tightly with the parser and by supporting the use of a varying number of roles for word classes
Extraction of tongue feature related to TCM physique based on image processing

System of traditional Chinese medicine has always been emphasized by people, have heavy depending on the individual difference factors in disease, the role of development, this kind of subtle change, tongue can fast and accurate present right now. Using tongue diagnosis to identification of traditional Chinese medicine (TCM) physique, which in clinical can be applied to the health, health physical examination and individual health insurance health, for the medical “cure not ill” provide convenient diagnostic method. This paper describes a quantitative study on the tongue characters related to Chinese medicine physique by the means of the image processing.
Genetic Variability in Key Genes in Prostaglandin E2 Pathway (COX-2, HPGD, ABCC4 and SLCO2A1) and Their Involvement in Colorectal Cancer Development

The pro-carcinogenic effects of prostaglandin E2 (PGE2) in colonic mucosa are not only regulated by the rates between Cyclooxygenase-2 (COX-2) biosynthesis and 15-Hydroxyprostaglandin Dehydrogenase (15-PGDH)-dependent degradation but also the steady-state levels of PGE2 in extracellular microenvironment, maintained by key specific prostaglandin transporters, the Multidrug Resistance Protein (MRP4) (efflux carrier) and Prostaglandin Transporter (PGT) (influx carrier). To understand the contribution of genetic variability in genes coding for COX-2/15-PGDH/MRP4/PGT proteins in CRC development, we conducted a hospital-based case-control study involving 246 CRC patients and 480 cancer-free controls. A total of 51 tagSNPs were characterized using the Sequenom platform through multiplexed amplification followed by mass-spectrometric product separation or allelic discrimination using real-time PCR. Seven tagSNPs were implicated in CRC development: the rs689466 in COX-2 gene, the rs1346271 and rs1426945 in 15-PGDH, the rs6439448 and rs7616492 in PGT and rs1751051 and rs1751031 in MRP4 coding genes. Upon a stratified analysis a measurable gene-environment interaction was noticed between rs689466 and smoking habits, with individuals ever-smokers carriers of rs689466 GG homozygous genotype having a nearly 6-fold increased susceptibility for CRC onset (95%CI: 1.49–22.42, P = 0.011). Furthermore, the multifactor dimensionality reduction (MDR) analysis identified an overall four-factor best gene-gene interactive model, including the rs1426945, rs6439448, rs1751051 and rs1751031 polymorphisms. This model had the highest cross-validation consistency (10/10, P<0.0001) and an accuracy of 0.6957 and was further associated with a 5-fold increased risk for CRC development (95%CI: 3.89–7.02, P<0.0001). In conclusion, specific low penetrance genes in the pro-carcinogenic PGE2 pathway appear to modulate the genetic susceptibility for CRC development. A clearer understanding on CRC etiology through the identification of biomarkers of colorectal carcinogenesis might allow a better definition of risk models that are more likely to benefit from targeted preventive strategies to reduce CRC burden.
Active Learning via Perfect Selective Classification

We discover a strong relation between two known learning models: stream-based active learning and perfect selective classification (an extreme case of 'classification with a reject option'). For these models, restricted to the realizable case, we show a reduction of active learning to selective classification that preserves fast rates. Applying this reduction to recent results for selective classification, we derive exponential target-independent label complexity speedup for actively learning general (non-homogeneous) linear classifiers when the data distribution is an arbitrary high dimensional mixture of Gaussians. Finally, we study the relation between the proposed technique and existing label complexity measures, including teaching dimension and disagreement coefficient.
APPLICATION OF A GIS AS A MODELING TOOL FOR REMOTE SENSING IMAGE ANALYSIS OF AGRICULTURAL FIELDS

In the context of the analysis of remotely sensed data the question arises of how to analyse large volumes of data. In the specific case of agricultural fields in flat areas these fields can often be modelled in terms of geometric primitives such as triangles and rectangles. In this case the options are classical i.e. bottom-up, starting at the pixel level and resulting in a segmented, labelled image or topdown, starting with a model for image partitioning and resulting in a minimum cost estimation of shape hypotheses with corresponding parameters. Standard bottom-up classification methods usually concern the pixel as a main element and try to label the pixel individually. But various errors are involved in the image analysis with these methods. Mixed pixels, simplicity of the basic assumptions in the classification algorithms, sensor effects, atmospheric effects, and radiometric overlap of land cover objects lead to the wrong detection in image analysis. In this paper we propose a Model-Based Image Analysis (MBIA) approach to analyze the remotely sensed data. In this manner using the available knowledge about the remote sensing system we generate some hypothesis maps and then test them using the radiometric measurements (images). In order to test the method we used the boundaries of the agricultural fields stored in a GIS to model the objects in the scene. The results of the method have been compared with the result of a traditional Maximum-Likelihood classification and a standard Object-Based Classification using the boundaries. Using this approach we could reach to the 94% overall accuracy.
Rule-based reasoning about qualitative spatiotemporal relations

This paper is about a novel rule-based approach for reasoning about qualitative spatiotemporal relations among technology-rich autonomous objects, to which we refer to as artifacts. The objective of our work is to provide means for defining spatiotemporal constraints - i.e. logical combinations of spatial relations to artifacts at certain time intervals - at a high level of abstraction, and to recognize relative situations therewith. Such constraints are defined with rules that infer high-level relationships for newly recognized situations, which in turn can be used in other constraints. At any time, the history of known relationships can be queried in order to trigger predefined actions. We decided for qualitative abstractions of both spatial and temporal relationships, as they reflect the semantics of natural language terms and thus facilitate dealing with relationships at the application programming level. The core concepts of this reasoning approach are presented, and the implementation of a middleware for spatiotemporal reasoning as well as evaluation results are discussed.
Coupling dialogue and prosody computation in spoken dialogue generation

We introduce a concept-to-speech (CTS) system that generates prosodic structure compositionally, in a spoken dialogue agent architecture. Representations from the semantic interpretation, task modeling, and dialogue strategy selection components drive the computation of accentuation, pitch accent type selection, and choice of melodic contour, respectively. These principled couplings of dialogue and prosody computation extend both the theory and practice of concept-to-speech generation.
Mining and Visualizing the Evolution of Subgroups in Social Networks

A social network consists of people who interact in some way such as members of online communities sharing information via the WWW. To learn more about how to facilitate community building e.g. in organizations, it is important to analyze the interaction behavior of their members over time. So far, many tools have been provided that allow for the analysis of static networks and some for the temporal analysis of networks - however only on the vertex and edge level. In this paper we propose two approaches to analyze the evolution of two different types of online communities on the level of subgroups. The first method consists of statistical analyses and visualizations that allow for an interactive analysis of subgroup evolutions in communities that exhibit a rather membership structure. The second method is designed for the detection of communities in an environment with highly fluctuating members. For both methods, we discuss results of experiments with real data from an online student community