Miracle-dz/UniFAR-FTUs huggingface dataset:
Near-Boundary Data Selection for Fast Suppor Vector Machines

Support Vector Machines(SVMs) have become more popular than other algorithms for pattern classification. The learning phase of a SVM involves exploring the subset of informative training examples (i.e. support vectors) that makes up a decision boundary. Those support vectors tend to lie close to the learned boundary. In view of nearest neighbor property, the neighbors of a support vector become more heterogeneous than those of a non-support vector. In this paper, we propose a data selection method that is based on the geometrical analysis of the relationship between nearest neighbors and boundary examples. With real-world problems, we evaluate the proposed data selection method in terms of generalization performance, data reduction rate, training time and the number of support vectors. The results show that the proposed method achieves a drastic reduction of both training data size and training time without significant impairment to generalization performance compared to the standard SVM.
Isolation of 18 microsatellite loci in the desert mistletoe Phoradendron californicum (Santalaceae) via 454 pyrosequencing1

Premise of the study: Microsatellite primers were developed for the parasitic mistletoe Phoradendron californicum to investigate to what extent population genetic structure depends on host tree distribution within a highly fragmented landscape. Methods and Results: Fourteen unlinked polymorphic and four monomorphic nuclear microsatellite markers were developed using a genomic shotgun pyrosequencing method. A total of 187 alleles plus four monomorphic loci alleles were found in 98 individuals sampled in three populations from the Sonoran Desert in the Baja California peninsula (Mexico). Loci averaged 13.3 alleles per locus (range 4–28), and observed and expected heterozygosities within populations varied from 0.167–0.879 and 0.364–0.932, respectively. Conclusions: Levels of polymorphism of the reported markers are adequate for studies of diversity and fragmentation in natural populations of this parasitic plant. Cross-species amplifications in P. juniperinum and P. diguetianum only showed four markers that could be useful in P. diguetianum.
Comparison of six serological assays for human immunodeficiency virus antibody detection in developing countries

Three commercially available assays for the detection of human immunodeficiency virus (HIV) antibodies-Vironostika enzyme immunoassay (EIA), Wellcozyme competitive EIA, and JLC Allaman indirect immunofluorescence assay--were tested on 300 serum samples from African subjects with and without HIV-related conditions. Two experimental assays both rapid and simple to perform (Biotech dip stick and Cambridge Bioscience latex agglutination) were also evaluated on the same serum samples. The results were compared with those of a commercial Western blot (WB) (immunoblot) assay from Biotech, used as the reference technique. All assays were tested in the laboratory of the AIDS Project in Kigali, Rwanda. Calculated specificity ranged from 90.8% (dip stick) to 98.6% (Vironostika EIA, Wellcozyme competitive EIA, and Cambridge Bioscience latex agglutination). Sensitivity ranged from 95.2% (Cambridge Bioscience latex agglutination) to 98.0% (Vironstika EIA) and JLC indirect immunofluorescence assay). However, the sensitivity of the latex agglutination test improved to 98.6% after the prozone effect was controlled for by serial twofold dilution of latex agglutination-negative, WB-positive samples. In situations with a high prevalence of HIV infection, any one of these tests can be regarded as an alternative to the more expensive, time-consuming, and difficult WB assay.
Effect of Ozone Treatment on Deoxynivalenol and Wheat Quality

Deoxynivalenol (DON) is a secondary metabolite produced by Fusarium fungi, which is found in a wide range of agricultural products, especially in wheat, barley, oat and corn. In this study, the distribution of DON in the wheat kernel and the effect of exposure time to ozone on DON detoxification were investigated. A high concentration of toxin was found in the outer part of the kernel, and DON was injected from the outside to the inside. The degradation rates of DON were 26.40%, 39.16%, and 53.48% after the samples were exposed to 75 mg/L ozone for 30, 60, and 90 min, respectively. The effect of ozonation on wheat flour quality and nutrition was also evaluated. No significant differences (P > 0.05) were found in protein content, fatty acid value, amino acid content, starch content, carbonyl and carboxyl content, and swelling power of ozone-treated samples. Moreover, the ozone-treated samples exhibited higher tenacity and whiteness, as well as lower extensibility and yellowness. This finding indicated that ozone treatment can simultaneously reduce DON levels and improve flour quality.
Towards Verifying Parallel Algorithms and Programs using Coloured Petri Nets

Coloured Petri nets have proved to be a useful formalism for modeling distributed algorithms, i.e., algorithms where nodes communicate via message passing. Here we describe an approach for modeling parallel algorithms and programs, i.e., algorithms and programs where processes communicate via shared memory. The model is verified for correctness, here to prove absence of mutual exclusion violations and to find deadand live-locks. The approach can be used in a model-driven development approach, where code is generated from a model, in a modelextraction approach, where a model is extracted from running code, or using a combination of the two, supporting extracting a model from an abstract description and generation of correct implementation code. We illustrate our idea by applying the technique to a parallel implementation of explicit state-space exploration.
Complexity for Self-Stabilizing Sensor Networks

Mobile sensor networks have been studied using population protocols in [2]. In this model, a collection of agents (called petrels in this paper) are described as finite state machines moving unpredictably around. They can have two kinds of interactions, only with a fixed antenna, or with each other petrels. The goal of the precedent jobs have been to compute the set of computable functions by the petrels on a set of inputs (with or without failures). In [9] we build self-stabilizing algorithms that allow to count the number of petrels. In this paper we define the random movement of the petrels and compute the complexity of these algorithms, which depends on the topology. key words: randomized algorithms, distributed algorithms, self-stabilizing system, sensor networks, counting. Eligible for Brief Announcement. Eligible for Best Student Paper Award.
Improving the robustness in feature detection by local contrast enhancement

This paper presents a new feature detector, with improved local contrast performance. The proposed method is based on an improved non-linear version of the classic Difference of Gaussians, which exhibits increased sensitivity to low contrast. Additionally, it does not employ computationally expensive or memory demanding routines. In order to evaluate the degree of illumination invariance that the proposed, as well as, other existing detectors exhibit, a new benchmark image database has been created. It features a greater variety of imaging conditions, compared to existing databases, containing real scenes under various degrees and combinations of uniform and non-uniform illumination. Experimental results show that the proposed detector extracts greater number of features, with high level of repeatability, compared to other existing ones. These results are evident for both uniform and non-uniform illumination, evincing a favorable usage of the proposed feature detector by robotic platforms working in outdoor working environments.
Pose pooling kernels for sub-category recognition

The ability to normalize pose based on super-category landmarks can significantly improve models of individual categories when training data are limited. Previous methods have considered the use of volumetric or morphable models for faces and for certain classes of articulated objects. We consider methods which impose fewer representational assumptions on categories of interest, and exploit contemporary detection schemes which consider the ensemble of responses of detectors trained for specific pose-keypoint configurations. We develop representations for poselet-based pose normalization using both explicit warping and implicit pooling as mechanisms. Our method defines a pose normalized similarity or kernel function that is suitable for nearest-neighbor or kernel-based learning methods.
Analysis and Determination of Inner Lip texture Descriptors for Visual Speech Representation

The problem of visual speech representation for bimodal based speech recognition includes particular challenges in themodeling of the inner lip texture reflecting different pronunciations,such as the appearance of teeth and tongue. This paper proposesand analyzesseveral possible statistical inner lip texture descriptors to determine an effective and discriminantfeature. Simply usinggrayscale without full specification of the underlying colour model tends to loss some significant discriminative information. Therefore thorough exploration on the color space components selection in computing the local inner lip texture is thus a primary goal of the present research. The L channel of Lab color space is finally determined as the basis for the development of the inner lip texture model. Through feature level fusion, the final classification of visual speech is performedbased on the proposed inner lip texture descriptor and standard geometric features. Together with audio speech,this paper furthers the development ofthe CHMM based bimodal Chinese character pronunciationrecognition system. The experimental results show that the local inner texture descriptors, such as the color moment with geometric feature,outperform the holistic inner texture descriptors, such as the statistical histogram, in representing visual speechwith theclose discriminability but low dimensionality.
Journal of Internet Banking and Commerce Cross-and Up-selling Techniques in E-commerce Activities

His researches and didactics focuses on informatization strategy of organization, systems analysis, knowledge and information management, ecommerce, organizational performance and electronic business strategies. He has published books and articles in many national and foreign journals and publishing houses. He has gained extensive experience as system analyst and as application specialist in formulating and realization of informatization strategy in organizations Msc Paweł Weichbroth is an Assistant at The Department of Management at the Gdansk University of Technology. His researches and didactics focus on expert systems, data mining and information systems. He has published eight articles in national journals and publishing houses so far. He has been Microsoft Certified Trainer for three years and holds Microsoft Certified Engineer (Security and Messaging) certificates since 2006. In spare time, he plays tennis and volleyball and reads history books. Abstract In the article the Cross-and Up-selling marketing techniques were presented in the spectrum of e-commerce. Their aim is to raise both the value of a single sales transaction and confidence as well as to lower the risk of taking the customers by the competitors. In the article iterative client service model was presented, indicating particular roles of the Cross-and Up-selling techniques in the processes of automation and integration marketing activities. -2-In the context of customer relationship management those are of great importance as they give reasons for conducting effective advertisement, promotion and loyal campaigns. The work is finished by summary in which authors pay attention to economic advantages resulting from using presented techniques.
A constraint logic programming approach to 3D structure determination of large protein complexes

The paper describes a novel framework, constructed using constraint logic programming and parallelism, to determine the association between parts of the primary sequence of a protein and α-helices extracted from 3-dimensional low-resolution descriptions of large protein complexes. The association is determined by extracting constraints from the 3D information, regarding length, relative position, and connectivity of helices, and solving these constraints with the guidance of a secondary structure prediction algorithm. Parallelism is employed to enhance performance on large proteins. The framework provides a fast, inexpensive alternative to determine the exact tertiary structure of unknown proteins.
A survey of current work in biomedical text mining

The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5-10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.
From a "Cold" to a "Warm" Start in Recommender Systems

Human is surrounded by a tremendous and scary amount of information on the web. That highlights the continuous need of recommendation systems in the different domains. Unfortunately cold start problem is still an important issue in these systems on new users and new items. The problem becomes more critical in systems that contain resources that lives too shortly like offers on products which stays only for few days (short life resources-SLiR). In this work we highlight how iSoNTRE (the intelligent Social Network Transformer into Recommendation Engine) solves this problem by using users' information in online social networks to overcome the cold start problem on new users, as well as iSoNTRE uses conceptual similarity, this overcomes the problem on new items, and on short life resources also. The work has been evaluated on Twitter on real users and results show that iSoNTRE succeeded in recommending offers to users with 14% of open rate on recommended offers, which is high compared to general open rate in social media, especially when we have nothing about users or offers before.
Algorithms for Alarm Placement in Systems with Fault

In this paper, we consider systems that can be modeled as directed acyclic graphs such that nodes represent components and directed edges represent fault propagation between components. Some components can be equipped with alarms that ring when they detect faulty behavior. We study algorithms that attempt to minimize the number of alarms to be placed so that a fault at any single component can be detected and uniquely diagnosed. We rst show that the minimization problem is NP-hard, even when restricted to three level graphs in which all nodes have outde-gree two or less. We present optimal algorithms for three special classes of graphs, and a polynomial-time approximation algorithm for the general case. Using a reduction from the minimum dominating set problem , we show that the ratio of the number of alarms placed by the approximation algorithm to the optimum required is the best that can be achieved within a constant factor.
Influence of terpenoids and acarbose on glycosyltransferases produced by strain Leuconostoc mesenteroides URE 13

A study of the influence of different plant terpenoids and amino sugar derivate acarbose on the activity of glycosyltransferase complex and purified dextransucrase from Leuconostoc mesenteroides URE 13 strain was carried out. All the tested terpenoids showed an inhibitory effect on glycosyltransferases from strain URE 13 at concentration 0.34 mmol. Out of all studied diterpenoids splendidin showed the strongest inhibitory effect decreasing the activity of both glycosyltransferase complex and dextransucrase with 70% and 90%, respectively. The triterpenoid ursolic showed the second strongest inhibitory effect as the enzyme complex and dextransucrase from strain URE 13 retain 27% and 13% of their initial enzyme activity. Despite the higher degree of inhibition of purified dextransucrase, compared to the enzyme complex, a complete inhibition of the enzyme was not observed at the highest used terpenoid concentration (3.42 mmol). When acarbose was used as an inhibitor, a complete inhibition of dextransucrase was observed at concentration of 6.9 mmol, while the enzyme complex retained 8% of its enzyme activity. Ki values of 0.28 mmol for splendidin, 0.37 mmol for ursolic acid and 0.29 mmol for acarbose were determined from the kinetic studies of purified dextransucrase.
Multiuser Broadcast Erasure Channel With Feedback—Capacity and Algorithms

We consider the N-user broadcast erasure channel with N unicast sessions (one for each user) where receiver feedback is regularly sent to the transmitter in the form of ACK/NACK messages. We first provide a generic outer bound to the capacity of this system; we then propose a virtual-queue-based inter-session mixing coding algorithm, determine its rate region, and show that it achieves capacity under certain conditions on channel statistics, assuming that instantaneous feedback is known to all users. Removing this assumption results in a rate region that asymptotically differs from the outer bound by 1 bit as L → ∞, where L is the number of bits per packet (packet length). For the case of arbitrary channel statistics, we present a modification of the previous algorithm whose rate region is identical to the outer bound for N = 3, when instant feedback is known to all users, and differs from the bound by 1 bit as L → ∞, when the three users know only their own ACK. The proposed algorithms do not require any prior knowledge of channel statistics.
High-cost, high-capacity backbone for global brain communication

Network studies of human brain structural connectivity have identified a specific set of brain regions that are both highly connected and highly central. Recent analyses have shown that these putative hub regions are mutually and densely interconnected, forming a “rich club” within the human brain. Here we show that the set of pathways linking rich club regions forms a central high-cost, high-capacity backbone for global brain communication. Diffusion tensor imaging (DTI) data of two sets of 40 healthy subjects were used to map structural brain networks. The contributions to network cost and communication capacity of global cortico-cortical connections were assessed through measures of their topology and spatial embedding. Rich club connections were found to be more costly than predicted by their density alone and accounted for 40% of the total communication cost. Furthermore, 69% of all minimally short paths between node pairs were found to travel through the rich club and a large proportion of these communication paths consisted of ordered sequences of edges (“path motifs”) that first fed into, then traversed, and finally exited the rich club, while passing through nodes of increasing and then decreasing degree. The prevalence of short paths that follow such ordered degree sequences suggests that neural communication might take advantage of strategies for dynamic routing of information between brain regions, with an important role for a highly central rich club. Taken together, our results show that rich club connections make an important contribution to interregional signal traffic, forming a central high-cost, high-capacity backbone for global brain communication.
Saner: Composing Static and Dynamic Analysis to Validate Sanitization in Web Applications

Web applications are ubiquitous, perform mission- critical tasks, and handle sensitive user data. Unfortunately, web applications are often implemented by developers with limited security skills, and, as a result, they contain vulnerabilities. Most of these vulnerabilities stem from the lack of input validation. That is, web applications use malicious input as part of a sensitive operation, without having properly checked or sanitized the input values prior to their use. Past research on vulnerability analysis has mostly focused on identifying cases in which a web application directly uses external input in critical operations. However, little research has been performed to analyze the correctness of the sanitization process. Thus, whenever a web application applies some sanitization routine to potentially malicious input, the vulnerability analysis assumes that the result is innocuous. Unfortunately, this might not be the case, as the sanitization process itself could be incorrect or incomplete. In this paper, we present a novel approach to the analysis of the sanitization process. More precisely, we combine static and dynamic analysis techniques to identify faulty sanitization procedures that can be bypassed by an attacker. We implemented our approach in a tool, called Saner, and we applied it to a number of real-world applications. Our results demonstrate that we were able to identify several novel vulnerabilities that stem from erroneous sanitization procedures.
Numerical analysis of the Cahn-Hilliard equation and approximation for the Hele-Shaw problem, Part II: Error analysis and convergence of the interface

In this second part of the series, we focus on approximating the Hele-Shaw problem via the Cahn-Hilliard equation ut + ∆(e∆u − ef(u)) = 0 as e ց 0. The primary goal of this paper is to establish the convergence of the solution of the fully discrete mixed finite element scheme proposed in [21] to the solution of the Hele-Shaw (Mullins-Sekerka) problem, provided that the HeleShaw (Mullins-Sekerka) problem has a global (in time) classical solution. This is accomplished by establishing some improved a priori solution and error estimates, in particular, an L(L)-error estimate, and making full use of the convergence result of [2]. Like in [20, 21], the cruxes of the analysis are to establish stability estimates for the discrete solutions, use a spectrum estimate result of Alikakos and Fusco [3] and Chen [12], and establish a discrete counterpart of it for a linearized Cahn-Hilliard operator to handle the nonlinear term.