Miracle-dz/UniFAR-FTUs huggingface dataset:
Penelope differently: feminist re-visions of myth

This thesis examines feminist rewritings of the Penelope myth and the intersections between poetry, myth, and feminist theory. The theoretical framework develops from Rosi Braidotti’s theory of memory and subjectivity which has its roots in the work of Michel Foucault. In Braidotti’s understanding, subjectivity is constructed through narratives of the past including myth. In order to support new, minority, and dissident subjectivities, a re-remembering of mythical narratives needs to happen. This process is linked to Judith Butler’s recent work on narrating the self and to Adrienne Rich’s idea of “Re-vision”. What Butler’s theory adds to Braidotti’s is the notion of dispossession: that as subjects we do not own our identities. We are, instead, dependent on others for recognition. This co-dependence based notion of subjectivity has ethical implications for how we interact with one another and what kind of narratives we iterate and reiterate. The writers discussed in this thesis, namely, Francisca Aguirre, Katerina Anghelaki-Rooke, Gail Holst-Warhaft, and Margaret Atwood, not only rewrite Penelope, but perform Re-visions of the myth. They look back at it with a critical eye and remake it. This thesis further contends that Re-vision provides contemporary feminist writers with a reading and writing strategy that allows them to engage with myth in a way that parallels feminist theory’s efforts to construct new forms of subjectivity. Chapter 1 frames feminist appropriations of myth in a contemporary context and discusses Adrienne Rich’s theory of Re- vision. The next four chapters focus on specific writers who carry out a sustained dialogue with Penelope; they each take an element of the myth and tease it out towards a modern relevance. In looking at how Penelope is revised, this thesis demonstrates that women writers are engaged in a process of remaking canonical, mythic texts in such a way that speaks to contemporary issues of ethical subjectivity and self-making.
3/2-Institutions: an institution theory for conceptual blending

We develop an extension of institution theory that accommodates implicitly the partiality of the signature morphisms and its syntactic and semantic effects. This is driven primarily by applications to conceptual blending, but other application domains are possible (such as software evolution). The particularity of this extension is a reliance on ordered-enriched categorical structures.
Beyond the Iambic-Trochaic Law: the joint influence of duration and intensity on the perception of rhythmic speech*

The Iambic-Trochaic Law (ITL) asserts that listeners associate greater acoustic intensity with group beginnings and greater duration with group endings. Some researchers have assumed a natural connection between these perceptual tendencies and universal principles underlying linguistic categories of rhythm. The experimental literature on ITL effects is limited in three ways. Few studies of listeners' perceptions of alternating sound sequences have used speech-like stimuli, cross-linguistic testing has been inadequate and existing studies have manipulated intensity and duration singly, whereas these features vary together in natural speech. This paper reports the results of three experiments conducted with native Zapotec speakers and one with native English speakers. We tested listeners' grouping biases using streams of alternating syllables in which intensity and duration were varied separately, and sequences in which they were covaried. The findings suggest that care should be taken in assuming a natural connection between the ITL and universal principles of prosodic organisation.
Line feature extraction and matching in palmprint

Paimprint is a new biometric method to recognize a person. The most important feature of paimprint is the lines. In this paper, a set of line detector is devised for paimprint. There are two parameters in these detectors, one controls the smoothness and connection of the lines, the other controls the width of lines which can be detected. The lines in different directions are detected by corresponding direction detectors and then fused into one edge image. In training stage, the lines of the training samples are represented and stored with chain code. In the verification stage, the lines are matched using Hausdorif distance. Experimental results show the efficiency of this method.
Toward the integration of uncertainty and probabilities in spatial multi-criteria risk analysis

Quantitative risk assessment supports decision-making processes in an increasing variety of contexts. Within the domain of environmental decision-making, the spatial distribution of impacts, vulnerabilities and consequences associated to different risk-mitigation alternatives calls for a different framework, i.e. spatial multi-criteria risk analysis. In this paper, we propose the combined use of a hierarchical Bayesian modelling approach and Geographic Information Systems to integrate uncertainty and model probabilities into an overall risk map. The research aims at testing the operability of the integration between Bayesian modelling and spatial analysis in the context of spatial risk processes for resource allocation. To this end, we applied the model on a case study dealing with tanker oil spill risk in the Mediterranean Sea. The innovative contribution of the study stems from both the context of application point of view and the use of Bayesian modelling to calculate not only probabilities but an overall spatial risk measure. lost crops, etc., which typically occur in such decision problems (e.g. Ferretti and Montibeller, 2017). There are several reasons that can explain the above weaknesses. First, the lack of publicly accessible spatial data about vulnerability and probability of occurrence spatial distributions; second, the need to involve risk analysts to facilitate probability elicitation using appropriate protocols (e.g. structured expert judgment elicitation protocols, Dias et al. (2018)); third, the need to allocate enough time to the necessary preliminary phase of expert training in probabilities elicitation (e.g. Keeney and Winterfeldt, 1991). However, preliminary ideas and suggestions on how to formalize a framework for spatial risk analysis and how to spatially elicit preference information have recently been proposed (e.g. Keller and Simon, 2017, Ferretti and Montibeller, 2017). In this paper, we propose the combined use of a hierarchical Bayesian modelling approach (e.g. Kalinina et al., 2016, Spada et al., 2014) and GIS to update prior probability maps and obtain an overall risk map. The research aims at testing the feasibility and operability of the integration between hierarchical Bayesian modelling and spatial analysis in the context of spatial risk processes for resource allocation. To this end, we applied the model on a case study dealing with tanker oil spill risk in the Mediterranean Sea. The main reasons for the selection of this context of application can be summarized as follows: (i) international
Power-Efficient Approaches to

Noise and radiation-induced soft errors (transient faults) in computer systems have increased significantly over the last few years and are expected to increase even more as we move toward smaller transistor sizes and lower supply voltages. Fault detection and recovery can be achieved through redundancy. The emergence of chip multiprocessors (CMPs) makes it possible to execute redundant threads on a chip and provide relatively low-cost reliability. State-of-the-art implementations execute two copies of the same program as two threads (redundant multithreading), either on the same or on separate processor cores in a CMP, and periodically check results. Although this solution has favorable performance and reliability properties, every redundant instruction flows through a high-frequency complex out-of-order pipeline, thereby incurring a high power consumption penalty. This paper proposes mechanisms that attempt to provide reliability at a modest power and complexity cost. When executing a redundant thread, the trailing thread benefits from the information produced by the leading thread. We take advantage of this property and comprehensively study different strategies to reduce the power overhead of the trailing core in a CMP. These strategies include dynamic frequency scaling, in-order execution, and parallelization of the trailing thread. Index Terms—Reliability, power, transient faults, soft errors, redundant multithreading (RMT), heterogeneous chip multiprocessors, dynamic frequency scaling.
Optimization Strategy Considering Energy Storage Systems to Minimize Energy Production Cost of Power Systems

Using renewable energy by integrating Energy Storage Systems (ESS) can serve to decrease energy production cost of Distributed Generators (DGs) as long as their operation is optimally managed. In this context, this work proposes the optimization of the energy production cost considering a two-level charging strategy for ESSs. For a 24 hours operation cycle, the optimization tries to reduce the total cost by minimizing the quantity of energy to be procured from the Distributed Generators (DGs) at each time period. Additionally, the objective is to determine the quantity of energy surplus generated by Renewable Energy (RE) to be stored in the storage system and determining the using of ESS by applying two levels for proper charging of the battery. The proposed formulation allows simultaneous optimizing DGs and REs, which interact directly when a strong increase in demand is present. In this work, the problem is modeled as a Mixed Integer Linear Programming (MILP) optimization problem. This approach is implemented under the GAMS 24.7. 1 environment and is validated by using OSICPLEX. The given model can easily combine different sources of energy (renewables with battery and generators); by organizing the resources with high performance and flexibility. The optimization cost is implemented in a real case and the obtained results confirm the effectiveness of the proposed methodology.
Чрезкожные пункционно-дренирующие вмешательства с ультразвуковым контролем в лечении непаразитарных кист печени

Американская администрация и японское правительство официально не рассматривают КНР в качестве главного противника, однако практически все международные эксперты полагают, что могучее китайское государство последовательно вырастает в серьезного конкурента за влияние в Восточноазиатском регионе. Среди специалистов считается, что для США и Японии исключительно важным фактором является необходимость иметь ис0 ключительно ясную концепцию взаимоотношений с Китаем на ближайшие 15–20 лет. Ре0 гионализация в Восточной Азии привела ее участников к поискам противовесов и попыт0 кам создания некоего равновесия с помощью гармонизации международных отношений. Китайская сложившаяся формула восточноазиатской интеграции содержит весьма зна0 чительное количество вполне убедительных аргументов в пользу важности заметного усиления региональной составляющей. Та же Япония, например, не хочет сдаваться на милость Китая и пытается противопоставить общественное мнение практически всех стран Восточной Азии, ощущающих серьезную тревогу в отношении непредсказуемых последствий усиливающегося китайского доминирования в Восточноазиатском регионе. Восточноазиатские государства внимательно следят за динамикой роста военной мощи КНР, особенно в ее новом количественном и качественном выражении. По оценкам международных экспертов, Китай в силу своего заметно растущего экономи0 ческого, демографического, военного и политического потенциала становится одним из самых влиятельных игроков на азиатской сцене, а в перспективе способен реально доми0 нировать во всех знаковых азиатских делах. Ключевые слова: КНР; Китай; Япония; регионализация; региональное соперничество; ре0 гиональное лидерство; восточноазиатская интеграция; Восточноазиатское сообщество
Youth Outcome Questionnaire (Y-OQ).

Cost Paper-and-pencil version: $75 one-time license fee for individual therapist, group rates available for group practices for unlimited duplication of form; $25 manual plus $25 shipping/handling fee. Computer admin and scored: $200/year licensing fee per clinician for up to 3 versions of OQ measures including YOQ, OQ, SOQ, BRPS, $150 one time installation fee, and $25 shipping/handling fee. Please contact website or publisher for most up-to-date cost information.
A Method of Discriminating Microblog Topic Position based on the Text Classification with Correlation of Subject

How to discriminate accurately the microblog topic position is one of the highlights in the short essay mining. This paper proposes a method based on the text classification with correlation of subject, which can discriminate users for the topic who is to support or oppose. The correlation of subject often leads to the text that have greatly different features. The method first obtain the topic keywords by extraction technology and mutual information, then classify the text to conversation corpus with the correlation of subject, at last adopt different method to analyze the comprehensive microblog topic position. The experimental results show that the method of correlated adopting machine learning and the uncorrelated adopting dictionary can greatly improve the discrimination accuracy. On this basis, we construct a model, can be used for the relevant government departments to monitor the Internet public opinion and business evaluate the products market, etc.
Special Education Administration at a Crossroads: Availability, Licensure, and Preparation of Special Education Administrators.

3 Introduction 4 Supply and Demand 5 Preparation 7 Preparation Program Availability 7 Preparation Program Content 8 Standards-Driven Reforms and Special Education Leadership Preparation 10 Certification and Licensure 12 Alternate Paths to Certification and Licensure 12 State Professional Development Provisions for Special Education Administrators 13 Supporting and Developing the Special Education Work Force 14 Vision and Communication 15 Resources and Paper Work 16 Professional Development 16 Principals 17 Special Education Administration at the Crossroads 18 References 19
Embedded Feature Selection Method for a Network-Level Behavioural Analysis Detection Model

Feature selection in network-level behavioural analysis studies is used to represent the network datasets of a monitored space. However, recent studies have shown that current behavioural analysis methods at the network-level have several issues. The reduction of millions of instances, disregarded parameters, removed similarities of most of the traffic flows to reduce information noise, insufficient number of optimised features and ignore instances which are not an entity are amongst the other issue that have been identified as the main issues contributing to the inability to predict zero-day attacks. Therefore, this paper aims to select the optimal features that will improve the prediction and behavioural analysis. The training dataset will be trained to use the embedded feature selection method which incorporates both the filter and wrapper method. Correlation coefficient, r and weighted score, wj will be used. The accepted or selected features will be optimised uses Beta distribution functions, β, to find its maximum likelihood, Ɩmax. The final selected features will be trained by the Bayesian Network classifier and tested through several testing datasets. Finally, this method was compared to several other feature selection methods. Final results show the proposed selection method’s performance against other datasets consistently outperform other methods.
MSA: Jointly Detecting Drug Name and Adverse Drug Reaction Mentioning Tweets with Multi-Head Self-Attention

Twitter is a popular social media platform for information sharing and dissemination. Many Twitter users post tweets to share their experiences about drugs and adverse drug reactions. Automatic detection of tweets mentioning drug names and adverse drug reactions at a large scale has important applications such as pharmacovigilance. However, detecting drug name and adverse drug reaction mentioning tweets is very challenging, because tweets are usually very noisy and informal, and there are massive misspellings and user-created abbreviations for these mentions. In addition, these mentions are usually context dependent. In this paper, we propose a neural approach with hierarchical tweet representation and multi-head self-attention mechanism to jointly detect tweets mentioning drug names and adverse drug reactions. In order to alleviate the influence of massive misspellings and user-created abbreviations in tweets, we propose to use a hierarchical tweet representation model to first learn word representations from characters and then learn tweet representations from words. In addition, we propose to use multi-head self-attention mechanism to capture the interactions between words to fully model the contexts of tweets. Besides, we use additive attention mechanism to select the informative words to learn more informative tweet representations. Experimental results validate the effectiveness of our approach.
Large-Scale Graph Biconnectivity in MapReduce

The MapReduce framework, originally proposed by Google, and its open source implementation, Hadoop, are nowadays considered the standard frameworks, both in industry and academia, to deal with petabyte scale datasets. In this paper we describe a two-rounds MapReduce approach to biconnectivity in undirected graphs, that is the computation of the set of articulation points, the set of bridges and the set of biconnected components of a graph G. We recall that an articulation point (resp. a bridge) is a vertex (resp. an edge) whose removal increases the number of connected components. A biconnected component is a maximal biconnected subgraph, i.e., it does not include neither articulation points nor bridges. In order to minimize the communication cost, the algorithm is based on a summary of the input data set, that is a compact data structure from which queries on biconnectivity properties can be answered. This summary, called navigational sketch, was originally designed in the data streams framework and was implicitly proved to be incrementally maintainable.Here we define it in a different framework in order to prove that it is mergeable . Mergeability is the key property of summaries in distributed or parallel computation: in particular, it provides a way to split the computation of the summary across separate subsets of the original data set, and thus to exploit the parallelism of the MapReduce framework. We finally discuss a scenario in which it is assumed that the machines have limited memory, showing tradeoffs between the memory available and the number of rounds of the algorithm. We conclude the paper with an experimental analysis that, on the basis of different executions of an Hadoop implementation of the algorithm against large-scale real world graphs, confirms the effectiveness of our approach.
A Lightweight Traffic Anomaly Detection Model in SDN Based on Decision Tree

This paper establishes a SDN-based network traffic anomaly detection model based on decision tree. Firstly, five statistical indexes are presented to describe the behavior of network traffic, then normal and abnormal traffic data to train the machine learning model to detect traffic anomaly. Four machine learning algorithms, decision tree, k-nearest neighbor, support vector machine and naive Bayes, are used to predict the detection rate, false alarm rate and other indicators. Experiments shows that decision tree algorithm is suitable for detecting traffic anomaly in SDN network. Keywords—SDN; network security; anomaly detection
An Interactive Tool for the Elicitation of Subjective Probabilities in Probabilistic Seismic-Hazard Analysis

Abstract In probabilistic seismic‐hazard analysis, epistemic uncertainties are commonly treated within a logic‐tree framework in which the branch weights express the degree of belief of an expert in a set of models. For the calculation of the distribution of hazard curves, these branch weights represent subjective probabilities. A major challenge for experts is to provide logically consistent weight estimates (in the sense of Kolmogorovs axioms), to be aware of the multitude of heuristics, and to minimize the biases which affect human judgment under uncertainty. We introduce a platform‐independent, interactive program enabling us to quantify, elicit, and transfer expert knowledge into a set of subjective probabilities by applying experimental design theory, following the approach of Curtis and Wood (2004). Instead of determining the set of probabilities for all models in a single step, the computer‐driven elicitation process is performed as a sequence of evaluations of relative weights for small subsets of models. From these, the probabilities for the whole model set are determined as a solution of an optimization problem. The result of this process is a set of logically consistent probabilities together with a measure of confidence determined from the amount of conflicting information which is provided by the expert during the relative weighting process. We experiment with different scenarios simulating likely expert behaviors in the context of knowledge elicitation and show the impact this has on the results. The overall aim is to provide a smart elicitation technique, and our findings serve as a guide for practical applications. Online Material: Interactive software for the elicitation of expert knowledge.
Steroids for Severe Alcoholic Hepatitis: More Risk Than Reward?

Alcoholic hepatitis (AH) is a condition marked by inflammatory liver injury in patients with chronic excessive alcohol consumption. Severe alcoholic hepatitis (SAH), defined by a Maddrey’s Discriminant Function greater than 32, can be associated with greater than 70% mortality rate within 6 months in patients who demonstrate persistent liver impairment even after medical therapy. Historically, the primary treatment option for SAH has been corticosteroids, although reported efficacy has been variable.
Mapping Urban Green Spaces Based on an Object-Oriented Approach

The advent of technology and its implications on especially remote sensing image processing using High Resolution Satellite Images (HRSI) to map land cover provide researchers to monitor land changes, make landscape analyses, and manage land transformation. One of land dynamics that should be mapped for the sustainability of urban area is green spaces. Urban green spaces, such as parks, playgrounds, and residential greenery may promote both mental and physical health. Besides, they contribute to ecosystem services such as reducing heat island effect and carbon storage, aiding water regulation etc. Therefore, mapping urban green infrastructure from a high-resolution satellite image provides an important tool to conduct studies, researches, and projects for sustainable development of urban areas. As the material of this research, one of the orthophotos of Aydin urban area exemplifies the park, the green cover in the agricultural area, the playground, and the residential garden, was used. For classifying land cover from the orthophoto with Object-Based Image Analysis (OBIA), eCognition Developer 9.0 software was utilized. To combine spectral and shape features, multiresolution segmentation was implemented. Additionally, features as brightness and ratio green were used for the extraction of urban green areas. In this research, urban green areas were successfully extracted from the orthophoto and accuracy assessment was performed on the classified image. OBIA of high resolution imagery enables to extract detailed information of various targets on urban areas. The result of accuracy assessment of the classification achieved 84.68% overall accuracy. To increase the accuracy via manual interventions, manual classification tool of eCognition Developer 9.0 may be used if needed.