Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Classification Algorithms in Data Mining

With the application of database deepening and the size of database expanding quickly, Data Mining has recently become the hotspot. Classification, the problem among them especially because of its extensive usage, has acquired more and more concerns presently. On account of this, the article carried on an overview according to the present condition of data mining's classification. Firstly, the article discussed in detail the classification methods that were researched widely, such as Decision Tree, Artificial Neural Network and Bayesian classification. Secondly, the article analyzed the new brought forward algorithms briefly. Lastly, according to the data mining's developmental conditions and the emphases of research, the article forecasted the trends of the next research of classification.
Noise Tolerance Analysis for Reliable Analog and Digital Computation in Living Cells

Biomolecular computing, encompassing computations performed by molecules, proteins and DNA, is a central area of focus in Synthetic Biology research and development, which attempt to apply engineering design principles in living cells. Two major computation paradigms have been implemented so far in living cells - analog paradigm that computes with a continuous set of numbers and digital paradigm that computes with two-discreet set of numbers. Here, we analyze the biophysical and technological limits of large-scale gene networks created based on analog and digital computation in living cells. More specifically, we calculate the precision of analog systems and the noise margin of digital systems in living cells. We conclude that both systems are challenging to operate with low protein levels. To overcome this challenge, we show that analog systems should operate with a Hill coefficient smaller than 1 and digital systems should be buffered. Furthermore, an analytical description of a biophysical model recently developed for positive feedback linearization circuits and used in analog synthetic biology, is presented. Finally, we suggest new directions for engineering biological circuits capable of computation.
Isolation, structure elucidation, and syntheses of isoneocryptotanshinone II and tanshinlactone a from Salvia miltiorrhiza

Two new components, isoneocryptotanshinone II (1) and tanshinlactone A (2), were isolated from an EtOH extract of Salvia miltiorrhiza. The structures of 1 and 2 were established by spectroscopic methods and total syntheses. Compound (2) exhibited moderate cytotoxic activities with a CD 50 range of 6.87-8.85 μg/mL against the HeLa (cervical epitheloid carcinoma), HepG2 (hepatocellular carcinoma) and OVCAR-3 (ovarian adenocarcinoma) cell lines.
Building Robust Distributed Infrastructure Networks

Many competing designs for Distributed Hash Tables exist exploring multiple models of addressing, routing and network maintenance. Designing a general theoretical model and implementation of a Distributed Hash Table allows exploration of the possible properties of Distributed Hash Tables. We will propose a generalized model of DHT behavior, centered on utilizing Delaunay triangulation in a given metric space to maintain the networks topology. We will show that utilizing this model we can produce network topologies that approximate existing DHT methods and provide a starting point for further exploration. We will use our generalized model of DHT construction to design and implement more efficient Distributed Hash Table protocols, and discuss the qualities of potential successors to existing DHT technologies. INDEX WORDS: P2P, Distributed Hash Table, Decentralized, Networking BUILDING ROBUST DISTRIBUTED INFRASTRUCTURE NETWORKS
A Robust, Format-Agnostic Scientific Data Transfer Framework

The olog approach of Spivak and Kent (PLoS ONE 7, 1 (2012) p e24274) is applied to the practical development of data transfer frameworks, yielding simple rules for construction and assessment of data transfer standards. The simplicity, extensibility and modularity of such descriptions allows discipline experts unfamiliar with complex ontological constructs or toolsets to synthesise multiple pre-existing standards, potentially including a variety of file formats, into a single overarching ontology. These ontologies nevertheless capture all scientifically-relevant prior knowledge, and when expressed in machine-readable form are sufficiently expressive to mediate translation between legacy and modern data formats. A format-independent programming interface informed by this ontology consists of six functions, of which only two handle data. Demonstration software implementing this interface is used to translate between two common diffraction image formats using such an ontology in place of an intermediate format.
DISCOURSE ANALYSIS OF THE GIFT OF THE MAGI (Smith's Discourse Modes)

Five Discourse Modes have been identified by Smith (2003), i.e. Narrative, Report, Descriptive, Information and Argument. Smith analyzes the properties that distinguish each mode, focusing on grammatical rather than lexical information. She has also examined presentational matters: topic and focus; variation in syntactic structure; and subjectivity, or point of view (2003, 1999, 1991). According to him, close linguistic analysis of the text through discourse modes helps understand the text. The short story “The Gift of the Magi” is analyzed applying the five modes of discourse with the objective if the analysis of the text could lead better understanding of the text. It is also analyzed if all the five modes are present in one type of text i.e. the Narrative here, and if their presence in the text enhances the understanding. The purpose of the analysis is to mark the stylistic effect of the Discourse Modes that may contribute to the better and proper understanding of the text, specifically from a pedagogical point of view.
ACE Inhibitory and Antioxidative Bioactive Peptides Derived from Hydrolyzed Soy Milk

Soy milk is a soybean processed product rich in protein as well as sources of bioactive peptides. Bioactive peptides defined as specific protein fragments that have a positive impact on body functions and conditions and may ultimately influence health. This study was conducted to explore the potential of hydrolyzed soy milk as a source of antioxidative and antihypertensive bioactive peptides through enzymatic hydrolysis. The initial treatment of soy milk protein was acidic precipitation with hydrochloric acid. Furthermore, protein precipitate was hydrolyzed using pepsin proteolytic enzyme with an enzyme: substrate ratio (1:5, 1:10 and 1:20). Protein hydrolysis was carried out for 0–48 hours at 37 °C in an acetate buffer pH 4.5. The soy milk protein hydrolysates were subjected to determination of % DH (Degree of Hydrolysis) and protein profile by SDS-PAGE (sodium dodecyl sulphate polyacrylamide gel electrophoresis). The antihypertensive assay was carried out by in vitro inhibition of the ACE enzyme (Angiotensin Converting Enzyme) and antioxidative activity assay using the DPPH method. The results showed that the optimum conditions for hydrolysis of soy milk were obtained at 40 hours with a % DH value of 53.24% in enzyme ratio 1:20 and the highest antihypertensive activity was obtained from 48 hours hydrolysis with % ACE inhibition value of 79.31%. The highest antioxidative activity of bioactive peptide was obtained at hydrolysis 48 hours with IC50 69.1034 µg / ml. After fractionated and LCMS characterized it was obtained 2 bioactive peptides with molecular weights of 8.954 and 2,696 kDa. These bioactive peptides from hydrolyzed of soymilk might be potential as an antihypertensive agent and reduce oxidative stress.
Proportional Fairness in Regular Topologies of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are being used in a wide variety of commercial civilian applications which favour regular deployment of sensor nodes (SNs) over a random deployment due to its simplicity of planning, ease of maintenance and precise node location information. In such regular deployments, it is necessary to ensure that every SN is allocated a fair share of the network’s resources in order to successfully obtain the sensed information from every portion of the area covered by the WSN. However, in the interest of maintaining the best possible network performance, the total throughput of the WSN needs to be maximized. Thus, we consider the problem of proportionally fair rate allocation to the SNs in regular WSNs to achieve maximum throughput while ensuring that every SN is allocated a fair portion of the available bandwidth. We follow a cross layer approach, considering both the transport layer session rates and the link layer transmission probabilities required to achieve the maximum total proportionally fair throughput for the square, hexagonal, and triangular topologies of WSN. Closed form expressions for each topology are derived, and they enable us to determine which topology has the best performance at different network sizes. Network simulations conducted in Qualnet and experiments conducted using TELOSB SNs validate our theoretical results and shed light on future directions of work.
Fault detection for linear stochastic system with intermittent observations via innovation sequence

This paper is concerned with the problem of fault detection for linear stochastic system with intermittent observations. It is proved that the innovation sequences of Kalman filter with intermittent observations is still a Gaussian white noises process. Then, the fault detection algorithm over a lossy network is given. If all the packets are received successfully, the proposed fault detection algorithm is equal to the existing method. Simulation results illustrate the effectiveness of the proposed approach.
L’acustica dell’orecchio esterno: un approccio a modelli strutturali misti per display uditivi virtuali - The external ear acoustics: a mixed structural modeling approach in virtual auditory displays

Riassunto Gli effetti acustici rilevanti per la percezione verticale del suono, quali le riflessioni sui contorni del padiglione auricolare e le risonanze all'interno delle cavita dell'orecchio, possono venir isolati e modellati da una combinazione di filtri sintetici e contributi misurati acusticamente. Questo approccio prende il nome di modellazione strutturale mista (MSM) e viene utilizzato, piu in generale, per descrivere la Head-Related Transfer Function (HRTF), che cattura gli effetti di testa, busto e orecchie dell'ascoltatore. Questo articolo impiega la MSM nella realizzazione di display uditivi capaci di adattarsi all'antropometria dell'ascoltatore per la resa della dimensione verticale del suono. ---------- Abstract The most important acoustic effects involved in vertical spatial sound perception, i.e. reflections on pinna contours and resonances inside the ear cavities, are isolated and modeled separately. The combination of such components that can be chosen to include either synthetic or measured components, is formalized in the Mixed Structural Modeling (MSM) approach which describes, more in general, the Head-Related Transfer Functions (HRTFs) capturing effects of head, torso, and external ear of the listener. This paper employs the MSM approach aiming at building personalized virtual auditory displays (VADs) able to adapt to listener anthropometry and to convey vertical localization cues.
Coastal Community Treatment Patterns in Riau Province

The purpose of this research are to know: (1) What is being done by the fisherman's family to utilize health facilities to get medication? (2) Is there any relation between the economic condition of the fishermen's family with the utilization of health facilities behavior? (3) Is there any relation between the type of a patient's disease with the utilization of health facilities behavior? The research location is chosen purposefully in sub district Pasir Limau Kapas in Rokan hilir regency. This location is chosen with consideration that this sub district is a coastal area and far from city service facilities access. The capital of this sub district is Panipahan village. The number of sampling is 15%. From the number of existing data then from each village is obtained 65 householders sample from Papahan village and 45 householders sample from Teluk Pulai village. The total number of samples is 110 householders. The result of the study finds that there are no people in utilizing health facilities who feel suffer from disease by only doing medical treatment or non medical treatment by changing from more than one treatment. Some proposed suggestion specially: the need of evaluation and revision of policy regulation regarded with: (1) Skill improvement of health care in the socialization effort of healthy living including introducing indication and contraindication of traditional medicine (2) Improvement of health infrastructure and facilities in order to be able to diagnose disease more thoroughly so that it can give medication appropriately.
An Algorithm for Title Classification on Scientific News

Nowadays, with the rapid development of Internet, news is growing explosively. In order to improve the practical value of scientific news, an algorithm of text categorization which is used to classify scientific news is designed. The number of scientific news is 16034, which is gotten from web pages. During the experiment, scientific news of life, medical scientific news, scientific news of earth, mathematical and physical scientific news, chemical scientific news and informational scientific news respectively achieved 72.04%, 64.05%, 71.59%, 67.88%, 66.84% and 61.35% accuracy rate. This algorithm gets good effect and improves the value of the scientific news collected from Web and the accuracy of scientific news detailed classification.
Hunting Malicious Attacks in Social Networks

This paper describes how the path fragments are rectifying during the transmission time and how the energy are handled in wireless sensor networks are explored. Wireless sensor networks are used to monitoring the application like chemical sensing, fire detection, weather condition. In this homogenous application some problems may occur during the data transmission time. There are various techniques that are available to rectifying path fragments problem and reduce the energy consumption during data transmission as well as continuous monitoring. The various algorithms mainly focused to solve the path fragments with lowest possible energy and end to end transmitting delay. The fault tolerances are classified based on the different kinds of fault which occur in WSN that are discussed in detail. In this paper present a survey on fault during the data transmitting time and how that are detect and recovery from that fault in WSNs in theoretical point of view research.
The influence of socioeconomic variability on the housing quality standard in sub-Saharan Africa

The study examined the relationships between residents’ socioeconomic characteristics and the housing quality standard in Abeokuta, Nigeria. Systematic sampling technique was used to select 198 household heads for questionnaire administration. The findings revealed that socioeconomic factors are correlated in the following proportions: educational level shows a moderate correlation with monthly income (r = .463, p < 0.01), length of stay (r = -.433, p < 0.01) and age of building (r = -.414, p < 0.05); monthly income displays a fairly strong correlation with length of stay (r = -.502, p < 0.01) and age of building (r = -.625, p < 0.01); household size presents a relatively strong correlation with length of stay (r = .766, p < 0.01), and a moderate correlation with age of building (r = .545, p < 0.01). The results of the multiple regression analysis produced an R = 0.387, meaning that socioeconomic variables explained the 38.7% variance in the housing quality standard (HQS) in the study area. Therefore, the study concluded that length of stay, educational level and age of building are strongly associated with the housing quality in Abeokuta.
A Hybrid Classification Algorithm Based on Subtractive Clustering and Incremental Learning

As real world data is increasing day by day, so there is a great need of improved classification method to handle it. Therefore, a hybrid classification algorithm based on subtractive clustering and incremental learning is proposed in this work. The algorithm is divided into two phases. In the first phase of the algorithm, subtractive clustering is used to select the cluster centers and disposing of redundant data samples. In the second phase new samples are introduced. The angle between the new sample and the old samples is used to find out the support vectors and accordingly the hyper plane is optimized . We have implemented our algorithm on standard Iris data sets. Experimental results have shown that our proposed algorithm has equal accuracy as that of the conventional support vector machine algorithm and is also faster in speed. The memory requirement of our proposed algorithm is also better than the conventional SVM algorithm.
Social annotation to enhance learning and assessment in higher education

Social annotation products make the thinking of learners transparent, visible, and easily accessible for sharing with others, self-reflection, and feedback. This is accomplished by enabling almost any number of users to have threaded discussions linked to selected sections of a page. Three professors share their experiences with the use of a social annotation product, HyLighter, to enhance teaching, learning, and assessment activities in three different areas of the college curriculum. One teaches screenplay writing. He describes how he used HyLighter to improve students' writing and critical review skills. A second teaches online graduate-level courses in pharmaceutical and forensic sciences. He describes the application of HyLighter to help students learn about chemical structures and related analytical principles. A third is a professor of educational psychology. She discusses her use of HyLighter to implement an alternative multiple-choice assessment approach in educational and developmental psychology courses. The chapter concludes with thoughts on the potential of social annotation technology to shift the focus of learning systems from content to be learned to what is going on inside the minds of learners.
Optimization of mobility parameters using fuzzy logic and reinforcement learning in self-organizing networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed.
Dynamic Task Assignment with Load Balancing in Cloud Platform

Load balancing is one of the major issue in cloud computing. Load balancing helps in achieving maximum resource utilization and user satisfaction. This mechanism transparently transfer load from heavily loaded process to under loaded process. In this paper we have proposed a hybrid technique for solving task assignment problem in cloud platform. PSO based heuristic has been developed to schedule random task in heterogeneous data centres. Here we have also used variants of Particle Swarm Optimization(PSO) which gives better result than PSO and other heuristics for load balancing in cloud computing environment.
A new dimeric naphtho-γ-pyrone from an endophytic fungus Aspergillus niger AKRN associated with the roots of Entandrophragma congoënse collected in Cameroon

Abstract A new dimeric naphtho-γ-pyrone, 2-hydroxydihydronigerone (1), along with five compounds, nigerone (2), pyrophen (3), kojic acid (4), 4-(hydroxymethyl)-5-hydroxy-2H-pyran-2-one (5), and p-hydroxyphenylacetic acid (6), was isolated from an endophytic fungus Aspergillus niger AKRN associated with the roots of Entandrophragma congoënse. The structure of the new compound has been elucidated using spectroscopic data including 1D and 2D NMR as well as the high-resolution mass spectrometry. Compounds 1–5 showed weak antimicrobial activity on five selected Gram-negative bacteria, namely Enterobacter aerogenes (CM64), Enterobacter cloacae (BM67), Klebsiella pneumonia (K2), and Escherichia coli (ATCC8739 and ATCC10536).
Morphosémantique pour l’appariement de termes dans le vocabulaire médical : approche multilingue

Cet article s’intéresse à la manière dont la morphosémantique peut contribuer à l’appariement multilingue de variantes terminologiques entre termes. L’approche décrite permet de relier automatiquement entre eux les noms et adjectifs composés savants d’un corpus spécialisé en médecine (synonymie, hyponymie, approximation). L’acquisition de relations lexicales est une question particulièrement cruciale lors de l’élaboration de bases de données et de systèmes de recherche d’information multilingues. La méthode est applicable à au moins cinq langues européennes dont elle exploite les caractéristiques morphologiques similaires des mots composés dans les langues de spécialité. Elle consiste en l’intéraction de trois dispositifs : (1) un analyseur morphosémantique monolingue, (2) une table multilingue qui définit des relations de base entre les racines gréco-latines des lexèmes savants, (3) quatre règles indépendantes de la langue qui infèrent, à partir de ces relations de base, les relations lexicales entre les lexèmes contenant ces racines. L’approche décrite est implémentée en français, où l’on dispose d’un analyseur morphologique capable de calculer la définition de mots construits inconnus à partir du sens de ses composants. Le corpus de travail est un lexique spécialisé médical d’environ 29000 lexèmes, que le calcul des relations de synonymie, hyponymie et approximation a permis de regrouper en plus de 3000 familles lexicales.