Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Synthetic Data for Context-Aware Recommender Systems

Context-Aware Recommender Systems (CARS) have emerged as a different way of providing more precise and interesting recommendations through the use of data about the context in which consumers buy goods and/or services. CARS consider not only the ratings given to items by consumers (users), but also the context attributes related to these ratings. Several algorithms and methods have been proposed in the literature in order to deal with context-aware ratings. Although there are lots of proposals and approaches working for this kind of recommendation, adequate and public datasets containing user's context-aware ratings about items are limited, and usually, even these are not large enough to evaluate the proposed CARS very well. One solution for this issue is to crawl this kind of data from e-commerce websites. However, it could be very time-expensive and also complicated due to problems regarding legal rights and privacy. In addition, crawled data from e-commerce websites may not be enough for a complete evaluation, being unable to simulate all possible users' behaviors and characteristics. In this article, we propose a methodology to generate a synthetic dataset for context-aware recommender systems, enabling researchers and developers to create their own dataset according to the characteristics in which they want to evaluate their algorithms and methods. Our methodology enables researchers to define the user's behavior of giving ratings based on the Probability Distribution Function (PDF) associated to their profiles.
Rule induction methods with hierachical sampling

This paper proposes a method for hiearchical sampling for rule induction. The method generates training samples and test samples in a two-level hierarchical way, and compared the results between these two levels, which corresponding to second-order approximation of estimators in Edgeworth expansion. We applied this method to three medical datasets. The results show that this method gives better performance than conventional methods.
Internalization and presentation of myelin antigens by the brain endothelium guides antigen-specific T cell migration

Trafficking of myelin-reactive CD4+ T-cells across the brain endothelium, an essential step in the pathogenesis of multiple sclerosis (MS), is suggested to be an antigen-specific process, yet which cells provide this signal is unknown. Here we provide direct evidence that under inflammatory conditions, brain endothelial cells (BECs) stimulate the migration of myelin-reactive CD4+ T-cells by acting as non-professional antigen presenting cells through the processing and presentation of myelin-derived antigens in MHC-II. Inflamed BECs internalized myelin, which was routed to endo-lysosomal compartment for processing in a time-dependent manner. Moreover, myelin/MHC-II complexes on inflamed BECs stimulated the trans-endothelial migration of myelin-reactive Th1 and Th17 2D2 cells, while control antigen loaded BECs did not stimulate T-cell migration. Furthermore, blocking the interaction between myelin/MHC-II complexes and myelin-reactive T-cells prevented T-cell transmigration. These results demonstrate that endothelial cells derived from the brain are capable of enhancing antigen-specific T cell recruitment. DOI: http://dx.doi. org/10.7554/eLife.13149. 001
The elusive memristor: properties of basic electrical circuits

We present an introduction to and a tutorial on the properties of the recently discovered ideal circuit element, a memristor. By definition, a memristor M relates the charge q and the magnetic flux ϕ in a circuit and complements a resistor R, a capacitor C and an inductor L as an ingredient of ideal electrical circuits. The properties of these three elements and their circuits are a part of the standard curricula. The existence of the memristor as the fourth ideal circuit element was predicted in 1971 based on symmetry arguments, but was clearly experimentally demonstrated just last year. We present the properties of a single memristor, memristors in series and parallel, as well as ideal memristor–capacitor (MC), memristor–inductor (ML) and memristor–capacitor–inductor (MCL) circuits. We find that the memristor has hysteretic current–voltage characteristics. We show that the ideal MC (ML) circuit undergoes non-exponential charge (current) decay with two time scales and that by switching the polarity of the capacitor, an ideal MCL circuit can be tuned from overdamped to underdamped. We present simple models which show that these unusual properties are closely related to the memristor's internal dynamics. This tutorial complements the pedagogy of ideal circuit elements (R, C and L) and the properties of their circuits, and is aimed at undergraduate physics and electrical engineering students.
Multistrategy Learning for Information Extraction

Information extraction IE is the problem of lling out pre de ned structured sum maries from text documents We are in terested in performing IE in non traditional domains where much of the text is often ungrammatical such as electronic bulletin board posts and Web pages We suggest that the best approach is one that takes into ac count many di erent kinds of information and argue for the suitability of a multistrat egy approach We describe learners for IE drawn from three separate machine learning paradigms rote memorization term space text classi cation and relational rule induc tion By building regression models mapping from learner con dence to probability of cor rectness and combining probabilities appro priately it is possible to improve extraction accuracy over that achieved by any individ ual learner We describe three di erent mul tistrategy approaches Experiments on two IE domains a collection of electronic seminar announcements from a university computer science department and a set of newswire ar ticles describing corporate acquisitions from the Reuters collection demonstrate the e ec tiveness of all three approaches
Efficient Space Vector PWM Scheme for Multi-Level Inverters

The paper proposes a very efficient method for the FPGA based implementation of multi-level space vector pulse width modulation (SVPWM). The objective of the proposed scheme is to reduce the necessary processing requirements without any drawback. The Multi-level switching sequence with minimal switching transitions and optimal control of the divided dc-link voltages is generated automatically. Experimental results with an FPGA controlled 5-level inverter built from two IGBT based 30 kW 3-level inverters using harmonic cancelation are presented. Due to the straight forward VHDL respectively state machine programming the solution is extremely fast and allows a cost-efficient single chip implementation even for very high switching frequencies.
A New Computational Tool for Establishing Model Parameter Identifiability

We describe a novel method to establish a priori whether the parameters of a nonlinear dynamical system are identifiable--that is, whether they can be deduced from output data (experimental observations). This is an important question as usually identifiability is assumed, and parameters are sought without first establishing whether these can be inferred from a set of measurements. We highlight the connections between parameter identifiability and state observability. We show how observability criteria can be used to check for identifiability, and we use new, state of the art computational tools to implement our approach. Nonlinear dynamical systems are prevalent in systems biology, where they are often used to represent a biological system. Thus, examples from biology are used to illustrate our method.
Neuroserpin Mutation S52R Causes Neuroserpin Accumulation in Neurons and Is Associated with Progressive Myoclonus Epilepsy

Mutations in the Neuroserpin gene have been reported to cause familial presenile dementia. We describe a new family in which the S52R Neuroserpin mutation is associated with progressive myoclonus epilepsy in 2 siblings. The proband presented myoclonus and epilepsy at age 24, his brother and mother presented a similar disorder when they were 25. A clinical diagnosis of progressive myoclonus epilepsy was made on the proband and his brother. Skin and liver biopsies did not reveal the presence of cytological alterations in the proband. His neurological status worsened over the subsequent 19 yr during which he became demented and had uncontrollable seizures. He died at 43 yr of age from aspiration pneumonia. Neuropathologically, eosinophilic bodies, which were positive for periodic acid-Schiff and immunoreactive with antibodies against human neuroserpin, were present in the perikarya and cell processes of the neurons. They were found in large numbers in the cerebral cortex and substantia nigra and to a lesser extent, in most subcortical gray areas, spinal cord, and dorsal root ganglia. By electron microscopy, the intracytoplasmic bodies were contained within the membranes of the rough endoplasmic reticulum. Occasionally neuroserpin immunopositivity was seen throughout the cytoplasm, even without the presence of well-defined bodies. Our study characterizes for the first time the neuropathologic phenotype associated with hereditary progressive myoclonus epilepsy caused by the S52R Neuroserpin mutation.
VIDEO SEARCH ENGINE USING DUAL-MEDIA SEGMENTATION

Most work in the area of segmentation of broadcast material has concentrated solely on the video element of the media. This paper looks at an algorithm which, in addition, utilises the audio track as a means of identifying meaningful scene breaks. This work is set in the context of a web-based video search engine that is demonstrated using broadcast news, and the effectiveness of the algorithm as a story boundary detector is evaluated.
Petition Drives and Deliberation of Shareholders

We introduce protocols for a fully decentralized synchronization of data items among equal peers in an argumentation framework (for applications such as debates in petition signature drives, and deliberation among shareholders). Here, an argumentation is a structured exchange of justifications around issues (or motions). The motions are relevant to organizations, and the organizations are rules for constituents. A constituent has a certain voting weight (e.g., based on her shares in the organization). A peer is a group of agents representing a user. Each peer separately stores all the data items of interest to her. To achieve data coherence at convergence for connected sub-graphs in such a framework we introduce a method for generating unique identifiers for peers, organizations and their constituents, as well as for data items generated by them. This avoids conflicts between semantically distinct data. Social networks are very successful applications but few have proven or even formalized their properties. Our proposal is a framework and protocol for a new type of P2P Social Network, where peers can congregate into fully decentralized virtual constituencies mirroring externally existing organizations. The membership in such organizations is based on constituents witnessing about each other. A reference implementation in Java is made available and can be used to validate claimed basic functionality.
Exploring Community College Student and Alumni Experience and Value of Career and Technical Education

ABSTRACT This qualitative study explores how students and alumni of community college career and technical programs value their education. Participants for this study were cultivated from a variety of community colleges. The following prevalent themes emerged from the data, economic independence, access to a better lifestyle, and gaining self-confidence. It is concluded that career and technical education appears to be a means to an end for many of the participants of this study. Students and alumni appeared to value their community college career and technical education because it provided them with job skills, thereby, affording them social-mobility and economic stability in a relatively short time frame. Some participants were enrolled in a community college career and technical program as a stepping stone to earning a bachelor‘s degree.
Spatial patterns of watershed impervious cover relative to stream location

The urban stream syndrome may not be limited to streams in urbanized watersheds. We measured the spatial pattern of impervious cover in ∼82,800 small watersheds across the conterminous United States by comparing watershed-based and stream-based measures of imperviousness. The watershed-based measure was the commonly used watershed percentage impervious cover. The stream-based measure was the percentage of watershed stream length flowing through impervious cover. Spatial pattern of impervious cover was classified on a watershed basis as proximal to streams, distal to streams, and uniform by comparing the two measures of impervious cover. We used a classification threshold of ±5% to assign watersheds to the three classes (i.e., stream-based minus watershed-based ≥5% = proximal; watershed-based minus stream-based ≥5% = distal; else = uniform). We then applied the classification to two impervious cover thresholds, ≥5% and ≥15%. For ≥5% and ≥15% thresholds, impervious cover was distributed uniformly across ∼70% and ∼86% of the watersheds, respectively. For the remaining watersheds, the proximal spatial pattern was ∼12× and ∼4× greater than the distal spatial pattern for the ≥5% and ≥15% impervious cover thresholds, respectively. The proximal spatial pattern of impervious cover occurred predominantly in non-urbanized watersheds, resulting in a widespread occurrence of a relatively high percentage of streams flowing through relatively high impervious cover in watersheds where the total percentage impervious cover was relatively low. The spatial pattern of change in impervious cover between ca. 2001 and ca. 2006 did not avoid streams. Impervious cover increased in the vicinity streams in ∼55% of the watersheds with increases in impervious cover. During this period, the length of streams flowing through ≥5% and ≥15% impervious cover increased by ∼9800 km and ∼6900 km,
The Ultraviolet Galaxy Luminosity Function in the Local Universe from GALEX Data

We present the results of a determination of the galaxy luminosity function at ultraviolet wavelengths at redshifts of z = 0.0-0.1 from Galaxy Evolution Explorer (GALEX) data. We determined the luminosity function in the GALEX far-UV and near-UV bands from a sample of galaxies with UV magnitudes between 17 and 20 that are drawn from a total of 56.73 deg2 of GALEX fields overlapping the bJ-selected Two-Degree Field Galaxy Redshift Survey. The resulting luminosity functions are fainter than previous UV estimates and result in total UV luminosity densities of 1025.55±0.12 and 1025.72±0.12 ergs s-1 Hz-1 Mpc-3 at 1530 and 2310 Å, respectively. This corresponds to a local star formation rate density in agreement with previous estimates made with Hα-selected data for reasonable assumptions about the UV extinction.
Science Proficiency and Course Taking in High School: The Relationship of Science Course-Taking Patterns to Increases in Science Proficiency between 8th and 12th Grades.

This analysis uses data on science achievement and transcript reports of science course taking patterns of students from the National Education Longitudinal Study of 1988 (NELS:88) to estimate the relationship between science course taking and the change in science proficiency levels between 8th and 12th grades. It also explores the extent to which gains in science proficiency are related to student race-ethnicity, gender, and socioeconomic background after accounting for differences among these groups in science course taking. Findings indicate that 54% of students showed an increase in their science proficiency level while 35% stayed at the same level and 11% declined. Generally speaking, taking eight or more semesters of science was positively associated with an increase in science proficiency level and for students who started at the top science proficiency level in eighth grade, taking more advanced science courses was related to increases in science proficiency level. Asian and-white students were found to be more likely to increase in science proficiency level, and gender, race, and familial socioeconomic background continued to exhibit a relationship to chances of increasing in science proficiency even after adjusting for differences in science course-taking. Contains 45 references. (JRH) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ********************************************************************************
Regression Testing in Research And Practice

Regression testing is a costly but crucial problem in software development. Both the research community and the industry have paid much attention to this problem. However, are the issues they concerned the same? The paper try to do the survey of current research on regression testing and current practice in industry and also try to find out whether there are gaps between them. The observations show that although some issues are concerned both by the research community and the industry gay, there do exist gaps.
Real option evaluation in fuzzy environment ∗

Financial options are known from the financial world where they represent the right to buy or sell a financial value (mostly a stock) for a predetermined price (the exercise price), without having the obligation to do so. Real options in option thinking are based on the same principals as financial options. In real options, the options involve real assets as opposed to financial ones. To have a real option means to have the possibility for a certain period to either choose for or against something, without binding oneself up front. Real options are valued (as financial options), which is quite different with from discounted cashflow investment approaches. The real option rule is that one should invest today only if the net present value is high enough to compensate for giving up the value of the option to wait. Because the option to invest loses its value when the investment is irreversibly made, this loss is an opportunity cost of investing. However, the pure (probabilistic) real option rule characterizes the present value of expected cash flows and the expected costs by a single number, which is not realistic in many cases. In this paper we consider the real option rule in a more realistic setting, namely, when the present values of expected cash flows and expected costs are estimated by trapezoidal fuzzy numbers.
Exploiting Social Tags in Matrix Factorization Models for Cross-domain Collaborative Filtering

Cross-domain recommender systems aim to generate or enhance personalized recommendations in a target domain by exploiting knowledge (mainly user preferences) from other source domains. Due to the heterogeneity of item characteristics across domains, content-based recommendation methods are difficult to apply, and collaborative filtering has become the most popular approach to cross-domain recommendation. Nonetheless, recent work has shown that the accuracy of cross-domain collaborative filtering based on matrix factorization can be improved by means of content information; in particular, social tags shared between domains. In this paper, we review state of the art approaches in this direction, and present an alternative recommendation model based on a novel extension of the SVD++ algorithm. Our approach introduces a new set of latent variables, and enriches both user and item profiles with independent sets of tag factors, better capturing the effects of tags on ratings. Evaluating the proposed model in the movies and books domains, we show that it can generate more accurate recommendations than existing approaches, even in cold-start situations.
Study to selection of suppliers approach based on approved BP artificial neural network

Supplier selection is one of the most critical decisions in a supply chain. While it can contribute to the supply good suppliers, supply chain's overall incorrect selection can drive the whole chain into disarray. The back-propagation algorithm(BP) is a well-known method of training a multilayer feed-forward artificial neural networks(FFANNS). Although the algorithm is successful, it has some disadvantages. Because of adopting the gradient method by BP neural network, the problems including slowly learning convergent velocity and easily converging to local minimum can not be avoided. In addition, the selection of learning factor and inertial factor affects the convergence of BP neural network, which are usually determined by experiences. Therefore the effective application of BP neural network is limited. In this paper a new method in BP algorithm to avoid local minimum was proposed by means of adding gradually training data and hidden units. In addition, the paper also proposed a new model of controllable feed-forward neural network for supplier selection.
Adaptive MCMC Particle Filter for Nonlinear and Non-Gaussian State Estimation

The particle filter is well known as a state estimation method for nonlinear and non-Gaussian system. However, particle filter has the inherent drawbacks such as samples less of diversity and the computational complexity depends on the number of samples used for state estimation process. In this paper, the adaptive Markov chain Monte Carlo (MCMC) particle filter is proposed in order to overcome these drawbacks. In the new algorithm, the KLD-sampling and MCMC sampling are simultaneously used to improve the performance of particle filter. The computer simulations are performed to compare the adaptive MCMC particle filter algorithm, the MCMC particle filter and particle filter in performance. The simulation results demonstrated that the adaptive MCMC particle filter is very efficient and smaller time consumption compared to MCMC particle filter and particle filter. Therefore, the MCMC adaptive particle is more suitable to the nonlinear and nonGaussian state estimation.
Arabic term extraction using combined approach on Islamic document

While a wide range of methods has been conducted to English terminology extraction, relatively few studies have been applied to Arabic terms extractio n in Islamic corpus. In this paper, we present an e fficient approach for automatic extraction of Arabic Termino logy (SWTs, MWTs). The approach relies on two main filtering steps: the linguistic filter, where simple part of speech (POS) tagger is used to extra ct candidate MWTs matching given syntactic patterns, and the statistical filter where several statistical methods (PMI, Kappa, CHI-squire, T-test, Piatersky- Shapiro and Rank Aggregation) are used to rank candidate MWTs and we applied IF.IDF to rank the SWTs candidate. Our approach extracted the bi-gram candidates of MWTs Islamic term from corpus and evaluated the association measures (STWs and MWTs) by using the n-best evaluation method .