Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating a decision support system with smart grid infrastructures and ICT solutions towards energy cost reduction: An action plan to optimally schedule the operation of heating and electricity systems

Smart grid technology has recently attracted a lot of attention since it enables the effective use of widespread energy sources and supports that way actions towards energy optimization and cost reduction. The benefits of adopting and promoting such a technology becomes even more promising given the advances in the field of renewable energy and Information and Communications Technology (ICT), which allow managing the energy sources available according to the respective demand in a smart and environmental friendly way. The main scope of this paper is to present an action plan in order to schedule the operation of heating and electricity systems towards energy cost optimization by taking advantage of smart grid and ICT infrastructures. The action plan, which has been developed to operate within a Decision Support System, is currently tested in the University Campus of Savona (Italy) in order to indicate the usefulness of the solution in a real life application.
Changes in gene expression within the nucleus accumbens and striatum following sexual experience

Sexual experience, like repeated drug use, produces long‐term changes including sensitization in the nucleus accumbens and dorsal striatum. To better understand the molecular mechanisms underlying the neuroadaptations following sexual experience, we employed a DNA microarray approach to identify genes differentially expressed between sexually experienced and sexually naïve female hamsters within the nucleus accumbens and dorsal striatum. For 6 weeks, a stimulus male was placed in the home cage of one‐half of the hormonally primed, ovariectomized female hamsters. On the seventh week, the two experimental groups were subdivided, with one half paired with a stimulus male. In comparison with sexually naïve animals, sexually experienced hamsters receiving a stimulus male on week 7 exhibited an increase in a large number of genes. Conversely, sexually experienced female hamsters not receiving a stimulus male on week 7 exhibited a reduction in the expression of many genes. For directional changes and the categories of genes regulated by the experimental conditions, data were consistent across the nucleus accumbens and dorsal striatum. However, the specific genes exhibiting changes in expression were disparate. These experiments, among the first to profile genes regulated by female sexual behavior, will provide insight into the mechanisms by which both motivated behaviors and drugs of abuse induce long‐term changes in the mesolimbic and nigrostriatal dopamine pathways.
Knowledge Utilization in Projects – a Practice-based Perspective

Drawing upon Practice-based theorizing in general and Actor Network Theory and theories of Communities of Practices in particular the paper develops an analytical strategy for understanding “life” in projects. The analytical strategy is applied on empirical material from an 18-month ethnographic study of a construction project. The project is interpreted as constellation of networked practices, which always is in the making. Participation in this project is a learning process where existing practices are reproduced and developed. This understanding of “life” in the project, frames a concluding analysis and discussion of the utilization of knowledge in the project.
Word Sense Disambiguation in Contextual Dynamic Network Using Associative Concept Dictionary

Many of the Japanese ideographs (Chinese characters) have a few meanings. They should be disambiguated by using their contextual information. Some of the ideographs have a few different pronunciations depending on their meanings. For example, we have an ideograph which has two pronunciations, /hitai/ and /gaku/, the former means a forehead of the human body and the latter means an amount of money or a picture frame. Conventional methods for such disambiguation have been using statistical methods with co-occurrence of words in their context. In this research, Contextual Dynamic Network Model is developed using the Associative Concept Dictionary, which includes semantic relations among concepts/words and the relations are represented with qualitative distances. In this model, an interactive activation model is used in the contextual semantic network where the activation in the network is calculated using the distances. The proposed method can disambiguate word senses by constructing dynamically the contextual semantic network according to the input text including an ambiguous word. The interactive activation model based on the contextual semantic network is evaluated by using a Japanese newspaper, Mainichi shinbun. Its effectiveness is shown by comparison with a Naive Bayes method.
Lux0R: Detection of Malicious PDF-embedded JavaScript code through Discriminant Analysis of API References

JavaScript is a dynamic programming language adopted in a variety of applications, including web pages, PDF Readers, widget engines, network platforms, office suites. Given its widespread presence throughout different software platforms, JavaScript is a primary tool for the development of novel -rapidly evolving- malicious exploits. If the classical signature- and heuristic-based detection approaches are clearly inadequate to cope with this kind of threat, machine learning solutions proposed so far suffer from high false-alarm rates or require special instrumentation that make them not suitable for protecting end-user systems. In this paper we present Lux0R "Lux 0n discriminant References", a novel, lightweight approach to the detection of malicious JavaScript code. Our method is based on the characterization of JavaScript code through its API references, i.e., functions, constants, objects, methods, keywords as well as attributes natively recognized by a JavaScript Application Programming Interface (API). We exploit machine learning techniques to select a subset of API references that characterize malicious code, and then use them to detect JavaScript malware. The selection algorithm has been thought to be "secure by design" against evasion by mimicry attacks. In this investigation, we focus on a relevant application domain, i.e., the detection of malicious JavaScript code within PDF documents. We show that our technique is able to achieve excellent malware detection accuracy, even on samples exploiting never-before-seen vulnerabilities, i.e., for which there are no examples in training data. Finally, we experimentally assess the robustness of Lux0R against mimicry attacks based on feature addition.
Modelling Spirituality, Wellness and Quality of Life: A Study in Redang and Perhentian Islands

This paper reports findings of a study that was conducted to effect two major objectives. Firstly, to ascertain the criteria or factors that measure quality of life (QOL) from the perspectives of Perhentian and Redang island communities. Secondly, to test a hypothesised proposed model that illustrates the relationships between spirituality, wellness and QOL. Factor analysis was performed to determine the underlying factors of spirituality, wellness and QOL before proceeding to applying structural equation modelling (SEM), using AMOS 7 to the data set. The findings of the study suggest that the hypothesised model fits the sample data adequately. In addition, the findings of the study supported two hypotheses proposed in the study. Firstly, there is a direct relationship between spirituality and Wellness and, secondly, there is a direct relationship between wellness and QOL. However, the study failed to support the third hypothesis that asserts there is a direct relationship between spirituality and QOL. The implications of the study suggest that community's QOL can be improved by enhancing the community's different aspect of wellness which is turn is influenced by its spirituality level. In addition, factors termed as employment and family bond are crucial to improve QOL.
High-density interspecific genetic maps of kiwifruit and the identification of sex-specific markers

Kiwifruit (Actinidia chinensis Planchon) is an important specialty fruit crop that suffers from narrow genetic diversity stemming from recent global commercialization and limited cultivar improvement. Here, we present high-density RAD-seq-based genetic maps using an interspecific F1 cross between Actinidia rufa ‘MT570001’ and A. chinensis ‘Guihai No4’. The A. rufa (maternal) map consists of 2,426 single-nucleotide polymorphism (SNP) markers with a total length of 2,651 cM in 29 linkage groups (LGs) corresponding to the 29 chromosomes. The A. chinensis (paternal) map consists of 4,214 SNP markers over 3,142 cM in 29 LGs. Using these maps, we were able to anchor an additional 440 scaffolds from the kiwifruit draft genome assembly. Kiwifruit is functionally dioecious, which presents unique challenges for breeding and production. Three sex-specific simple sequence repeats (SSR) markers can be used to accurately sex type male and female kiwifruit in breeding programmes. The sex-determination region (SDR) in kiwifruit was narrowed to a 1-Mb subtelomeric region on chromosome 25. Localizing the SDR will expedite the discovery of genes controlling carpel abortion in males and pollen sterility in females.
The Whole Set of Symmetry Preserving Discrete Versions of a Heat Transfer Equation with a Source. 1

In this letter we present the set of invariant di erence equations and meshes which preserve the Lie group symmetries of the equation ut = (K(u)ux)x +Q(u): All special cases of K(u) and Q(u) that extend the symmetry group are considered. Numerical realization of some solutions of a linear heat equation is developed. The relations between invariant discrete models for heat equation and for Burgers equation are shown.
Ontology extraction for E-Learning — A fuzzy based approach

An increasing number of ontology extraction based on domain knowledge are adopted in E-learning. It is critical that domain ontologies cannot sufficiently handle imprecise and vague knowledge, but fuzzy ontology can effectively resolve data with uncertainty. This paper proposes novel fuzzy domain ontology acquisition algorithm for E-Learning application. The knowledge construction mechanism constructs fuzzy concept and relation based on concepts. We have then proposed our design for extracting concepts from heterogeneous text documents.
What can we learn from the surface chemical composition of the optical companions of Soft X-ray transients?

Several evolutionary sequences with low-mass secondaries ($M_{\rm d}=1.25$, 1.5 and 1.7 $M_\odot$) and black hole accretors ($M_{\rm bh}=5$ and 10 $M_\odot$) are calculated. The angular momentum losses due to magnetic braking and gravitational wave radiation are included. Using full nuclear networks (p-p and CNO cycles) we follow carefully the evolution of the surface composition of the secondary star. We find that the surface chemical composition of the secondary star may give additional information which helps to understand the formation of soft X-ray transients with black holes as accretors. We show that observations of isotope ratios 12 C/ 13 C, 14 N/ 15 N and 16 O/ 17 O with comparison to computed sequences allow estimates independent of spectroscopy of the mass of the secondary component. We find that our evolutionary calculations satisfactorily explain the observed $q=M_{\rm sg}/M_{\rm bh}-P_{\rm orb}$ distribution for Soft X-ray transients with orbital periods less than one day. Using our evolutionary calculations we estimate secondary masses and surface chemical abundances (C, N, O) for different systems. We distinguished three different phases in the SXT's evolution. The optical component shows (i) cosmic C, N, O abundances and 12 C/ 13 C isotopic ratio; (ii) cosmic C, N, O abundances but modified 12 C/ 13 C ratio; and (iii) depletion of C and enhanced of N abundances and strongly modified isotopic ratios of C, N, O.
A PREDICTIVE SPELLER FOR A BRAIN-COMPUTER INTERFACE BASED ON MOTOR IMAGERY

Communication is at the basis of human development and represents a fundamental aspect in the life of every individual. Persons suffering of motor disorders have limited possibilities to communicate and normally require assistive technologies to fulfill this primary need. A promising means to give back basic communication abilities to subjects affected by severe motor impairments are brain-computer interfaces (BCIs). A brain-computer interface is a system that bypasses any muscle or nerve mediation and translates signals acquired from the brain into commands for an external device. BCI systems are normally characterized by poor accuracies and low rates of information transfer, while communication is a complex process which requires a large set of language symbols to convey messages. The objective of this thesis is to develop a BCI spelling application for people with severe motor impairments, adopting Natural Language Processing (NLP) techniques to enhance the overall communication rate of the system. The BCI paradigm adopted is motor imagery. When the subject imagines to move a certain part of the body produces modifications to specific brain rhythms over the sensory-motor cortex which are detected in real-time from the electroencephalogram (EEG) and translated into commands for the spelling application. The approach chosen for maximizing the overall communication rate is two-fold: on one side there is an effort in gaining information transfer rate from the control signal, on the other side there is an effort in optimizing the way this information is employed for the purpose of verbal communication. Therefore this thesis focused both on the definition of novel features for classifying EEG signals and on the design of an original spelling application provided with language prediction capabilities. The results obtained vary significantly from subject to subject. With one subject we obtained high classification accuracies and the BCI spelling application reached an overall communication rate of about three characters per minute. With other two subjects we obtained lower accuracies and
A two‐step method for the introduction of single or multiple defined point mutations into the genome of Saccharomyces cerevisiae

The introduction of defined mutations into open reading frames (ORF) or non‐translated regions of the genome is important to study of the structure–function relationship of amino acid residues in proteins or that of sequence motifs at the genome level. We describe a simple two‐step method for the introduction of defined single or multiple point mutations into the genome of Saccharomyces cerevisiae. This method circumvents the need for plasmid‐based mutagenesis and thus ensures homogenous expression of the gene of interest within the cell population. It is based on the introduction of a selectable marker downstream of the gene of interest. This marker is then amplified with a gene‐specific primer that harbours the desired point mutation, creating a selectable marker‐tagged mutant version of the gene of interest. The mutant fragment is then integrated into the genome of a wild‐type strain through homologous recombination. Successive rounds of amplification of the mutant loci with primers that introduce additional point mutations upstream of existing mutations will generate multiple defined mutations within a single gene. As a proof of principle, we have employed this method to generate a temperature‐sensitive mutant version of the plasma membrane ATPase, pma1‐7, which bears two point mutations (Pro434Ala and Gly789Ser). Phenotypic analysis of a pma1‐7 haploid strain indicates that this allele has the same characteristics as the original pma1‐7 allele. It confers a temperature‐sensitive growth phenotype and the newly synthesized Pma1‐7 protein is unstable and rapidly degraded. Copyright © 2006 John Wiley & Sons, Ltd.
Anomaly Detection Technology Using BigGraph

Many difficulties are encountered along all three axes of Big Data (volume, variety, and velocity), which limit the applicability of established technology. BigGraph is a research project and platform for realising the vision of an intelligent society, where requirements along all three axes can be accommodated. In the fraud detection scenario, working with Fujitsu UK & Ireland, BigGraph researchers consider different types of information, link external data sources as streams, and consolidate existing knowledge in order to identify undiscovered and fraudulent anomalies in Big Data.
Building Blocks for Communication Over Noisy Quantum Networks

A capacity of a quantum channel characterizes the limits of reliable communication through a noisy quantum channel. This fundamental information-theoretic question is very well studied specially in the setting of many independent uses of the channel. An important scenario, both from practical and conceptual point of view, is when the channel can be used only once. This is known as the one-shot channel coding problem. We provide a tight characterization of the one-shot entanglement-assisted classical capacity of a quantum channel. We arrive at our result by introducing a simple decoding technique which we refer to as position-based decoding. We also consider two other important quantum network scenarios: quantum channel with a jammer and quantum broadcast channel. For these problems, we use the recently introduced convex split technique in addition to position-based decoding. Our approach exhibits that the simultaneous use of these two techniques provides a uniform and conceptually simple framework for designing communication protocols for quantum networks.
Supporting rule-based representations with corpus-derived lexical information.

The pervasive ambiguity of language allows sentences that differ in just one lexical item to have rather different inference patterns. This would be no problem if the different lexical items fell into clearly definable and easy to represent classes. But this is not the case. To draw the correct inferences we need to look how the referents of the lexical items in the sentence (or broader context) interact in the described situation. Given that the knowledge our systems have of the represented situation will typically be incomplete, the classifications we come up with can only be probabilistic. We illustrate this problem with an investigation of various inference patterns associated with predications of the form 'Verb from X to Y', especially 'go from X to Y'. We characterize the various readings and make an initial proposal about how to create the lexical classes that will allow us to draw the correct inferences in the different cases.
Generalized <formula formulatype="inline"><tex Notation="TeX">${ q}$</tex></formula>-Sampling Imaging

Based on the Fourier transform relation between diffusion magnetic resonance (MR) signals and the underlying diffusion displacement, a new relation is derived to estimate the spin distribution function (SDF) directly from diffusion MR signals. This relation leads to an imaging method called generalized q-sampling imaging (GQI), which can obtain the SDF from the shell sampling scheme used in q-ball imaging (QBI) or the grid sampling scheme used in diffusion spectrum imaging (DSI). The accuracy of GQI was evaluated by a simulation study and an in vivo experiment in comparison with QBI and DSI. The simulation results showed that the accuracy of GQI was comparable to that of QBI and DSI. The simulation study of GQI also showed that an anisotropy index, named quantitative anisotropy, was correlated with the volume fraction of the resolved fiber component. The in vivo images of GQI demonstrated that SDF patterns were similar to the ODFs reconstructed by QBI or DSI. The tractography generated from GQI was also similar to those generated from QBI and DSI. In conclusion, the proposed GQI method can be applied to grid or shell sampling schemes and can provide directional and quantitative information about the crossing fibers.
The Language of Emotion and Financial News

This paper deals with the use of various conceptually organized dictionaries used in the emerging subject of sentiment analysis which seeks to understand the emotions underlying the writings of and about individuals and organization. Four key sentiment dictionaries described are General Inquirer, Whissell Dictionary of Affect, WordNet Affect and SentiWordNet . The reliability of these dictionaries is estimated as a function of the overlap between their contents. The discussion is illustrated with an analysis of financial news texts relating to a brief and highly emotive takeover attempt of a national airline by its rival. This paper examines the terms of the “domain” of emotion in language and how this specialized language is utilized within another domain, that of finance.
Conformal ECOC Machines

Conformal prediction (CP) has received a signif-icant attention in the last decade due to its capability of pro-viding confidences for class predictions. However, the literature lacks of a computationally efficient implementation of CP for multi-class reduction techniques such as one-versus-all, ECOC etc. To address this issue we propose two implementations of CP for multi-class reduction: Conformal ECOC Machine (cECOC) and Conformal Poisson ECOC Machine (cpECOC). We show that both machines are computationally efficient and are capable of working with any reduction techniques based on binary code matrices. Moreover, we show that the second machine, cpECOC, probabilistically incorporates the error correction property of ECOC into CP framework using the Poisson binomial distribution. Conducted experiments on UCI datasets demonstrate that both machines output valid and efficient prediction sets.
The Queue Method: Handling Delay, Heuristics, Prior Data, and Evaluation in Bandits

Current algorithms for the standard multi-armed bandit problem have good empirical performance and optimal regret bounds. However, real-world problems often differ from the standard formulation in several ways. First, feedback may be delayed instead of arriving immediately. Second, the real world often contains structure which suggests heuristics, which we wish to incorporate while retaining the best-known theoretical guarantees. Third, we may wish to make use of an arbitrary prior dataset without negatively impacting performance. Fourth, we may wish to efficiently evaluate algorithms using a previously collected dataset. Surprisingly, these seemingly-disparate problems can be addressed using algorithms inspired by a recently-developed queueing technique. We present the Stochastic Delayed Bandits (SDB) algorithm as a solution to these four problems, which takes black-box bandit algorithms (including heuristic approaches) as input while achieving good theoretical guarantees. We present empirical results from both synthetic simulations and real-world data drawn from an educational game. Our results show that SDB outperforms state-of-the-art approaches to handling delay, heuristics, prior data, and evaluation.