Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling Parsing and Unparsing

Any expert researcher in the field of parsing knows exactly what parsing is, and does not need any definition for it. With some expendable mental effort, we can all read one another’s papers on “parsing” that do not explicitly state whether the focus is on the process of syntactic analysis of tokenised sequences, or on the generalised scannerless parsing, or on extracting linked structured information from flat textual data, or on converting concrete syntax trees to abstract ones — all these activities, and many others, at some point have been referred to as “parsing”, with details emerging from the context. Yet, some of these processes are inherently different than others, and that distinction is not apparent in any way to outsiders and to students studying compiler construction or software language engineering, in particular. Recently we have proposed a unified model of parsing in a broad sense [6] — a lattice-like diagram with nodes/cells corresponding to different kinds of software artefacts such as parse trees or token sequences and edges representing existing mappings between them which are (or can be) referred to as “parsing” and “unparsing”. It builds on top of our previous work on understanding (un)parsing [1,4, etc] and refers to many well-known (un)parsing activities. The model can be “renarrated” in order to explain a particular parsing technology [5], which we already successfully use in teaching [2]. The twelve kinds of artefacts commonly found in software language engineering can be seen on Figure 1: ♦ Str — a string, a file, a byte stream. ♦ Tok — a finite sequence of tokens which, when concatenated, yield Str. Includes spaces, line breaks, comments, etc — collectively, layout. ♦ TTk — a finite sequence of typed tokens, possibly with layout removed, some classified as numbers, strings, etc. ♦ Lex — a lexical source model (ILA, TEBA, srcML, etc) that adds grouping to typing; a possibly incomplete tree connecting most tokens within one structure. ♦ For — a forest of parse trees, a parse graph or an ambiguous parse tree. ♦ Ptr — an unambiguous parse tree, the leaves can be concatenated to form Str. ♦ Cst — a parse tree with concrete syntax information. Structurally similar to Ptr, but abstracted from layout and other minor details. Comments could still be a part of the Cst model, depending on the use case. ♦ Ast — a tree which contains only abstract syntax information. ♦ Pic — a picture, which can be an ad hoc model, a natural model [3] or a rendering of a formal model. Str (string) Tok (tokens) TTk (typed tokens) Lex (lexical model) For (parse forest) Ptr (parse tree) Cst (concrete syntax tree) Ast (abstract syntax tree) Pic (rasterised picture) Dra (vector drawing) Gra (graph model) Dia (diagram) to ke n is e co n ca t st ri p fo rm at parse unparse parse unparse st ri p fo rm at im p lo d e
Diversity and Reach of Penn State Extension Programs and Effect of Brokerage and Network Position on Extension Program Outcomes through Social Network AnalysiS (SNA)

This study was designed to understand the diversity and reach of Cooperative Extension programs in Pennsylvania delivered by Penn State Extension and the influence of network variables (brokerage and centrality) on program outcomes (program business performance and demand for the program) using Social Network Analysis (SNA). The study was conducted at Penn State Extension (PSE), the outreach wing of the College of Agricultural Sciences at the Pennsylvania State University. The population for this study consisted of all the programs offered by Penn State Extension and the program stakeholders. The sampling method used for this study was a ‘census’ of all programs and their stakeholders. The study utilized the SNA methodology and ex-post facto research design. The independent variables used in the study were the network variables, which included five types of brokers (liaison, gatekeeper, representative, itinerant, and coordinator), and degree centrality of Extension programs. There were two dependent variables, change in program business performance and change in demand for the programs. The independent variables were analyzed using UCINET 6 and network maps were drawn using NetDraw’s spring embedding algorithm. Data were analyzed using the Statistical Package for Social Sciences (SPSS 21). Binary logistic regression was used to test the hypotheses. The study had four hypotheses regarding influence of network variables (degree centrality, gatekeeper brokerage, consultant brokerage, and liaison brokerage) on Extension program outcomes (program business performance and demand for the
The Investigation of Elite Athletes’ Psychological Resilience

The performances of the athletes can change from competition to competition under the pressure of heavy exercise and success anxiety during the season. The help obtained from sports psychology in order to minimize these fluctuations in the performance of the athlete is an important component in increasing the psychological resilience of the athlete. The aim of this study is to examine the psychological resilience of elite athletes in terms of various variables. This research is a descriptive survey model study in terms of defining psychological resilience levels of elite athletes. In this study, the Psychological Resilience Scale developed by Friborg et al. (2005) and adapted to Turkey by Basım and Çetin (2011), and the personal data form developed by the researcher for collecting personal information were used to determine the psychological resilience of elite athletes. The data of the research was collected from 147 national athletes in different branches, including 79 males and 68 females. SPSS 21.00 statistical package program was used in data analysis. Descriptive statistics, independent groups t-test, one-way variance analysis, correlation and regression analyses were used in data analysis. As a result of the study, it was found that the psychological resilience of elite athletes differed according to the athlete’s gender, sports branch, their tendency to use doping, whether the athletes make self-talk or not, but did not differ according to the athlete’s age, sports experience, and smoking and alcohol use.
Attachment Style of Volunteer Counselors in Telephone Emergency Services Predicts Counseling Process

Telephone emergency services (TES) provide emotional support and aim to prevent suicide. The current study examines a potential change of volunteer counselors’ attachment characteristics during TES training and investigates the predictive influence of counselor attachment for their competence and working alliance with callers. We recruited 261 volunteers enrolled in training for paraprofessional counseling in the German Telephone Emergency Service (TelefonSeelsorge). Participants were assessed three times during their training (mean training duration 13.3 months) and responded to questionnaires on adult attachment (Experience in Close Relationships-Revised) and their counseling competence (adapted Development of Psychotherapists Common Core Questionnaire). In addition, they indicated the quality of the working alliance (adapted Working Alliance Inventory – Short, Revised) with their client callers upon training completion. Results showed that attachment anxiety, but not attachment avoidance, significantly decreased during training. Lower attachment avoidance predicted better working alliances with callers as well as better general skillfulness. Implications for the training of volunteer telephone counselors are discussed.
Skala Wymiarów Rozwoju Tożsamości DIDS: Wersja zrewidowana

The present article describes the process of revision of the Polish adaptation of the popular identity measure – The Dimensions of Identity Development Scale. The article presents theoretical assumptions of the scale, reasons for undertaking the process of revision of the Polish adaptation of the DIDS, and the results of three studies in which the revised questionnaire was used. The obtained results suggest that in its current version the Polish adaptation of the DIDS is a reliable and factorially valid measure. The associations of the five identity processes measured by the DIDS (exploration in breadth, commitment making, exploration in depth, identification with commitment, ruminative exploration) with rumination, reflection, worry, shame, guilt, indecisiveness, and educational identity dimensions support the validity of the Polish adaptation of the measure.
Scaling real-time event detection to massive streams

In today’s world the internet and social media are omnipresent and information is accessible to everyone. This shifted the advantage from those who have access to information to those who do so first. Identifying new events as they emerge is of substantial value to financial institutions who consider realtime information in their decision making processes, as well as for journalists that report about breaking news and governmental agencies that collect information and respond to emergencies. First Story Detection is the task of identifying those documents in a stream of documents that talk about new events first. This seemingly simple task is non-trivial as the computational effort increases with every processed document. Standard approaches to solve First Story Detection determine a document’s novelty by comparing it to previously seen documents. This results in the highest reported accuracy but even the currently fastest system only scales to 10% of the Twitter stream. In this thesis, we propose a new algorithm family, called memory-based methods, able to scale to the full Twitter stream on a single core. Our memory-based method computes a document’s novelty up to two orders of magnitude faster than state-of-the-art systems without sacrificing accuracy. This thesis additional provides original work on the impact of processing unbounded data streams on detection accuracy. Our experiments reveal for the first time that the novelty scores of state-of-the-art comparison based and memory-based methods decay over time. We show how to counteract the discovered novelty decay and increase detection accuracy. Additionally, we show that memory-based methods are applicable beyond First Story Detection by building the first real time rumour detection system on social media streams.
Maternal and paternal infant-directed speech to girls and boys: An exploratory study

ABSTRACT We compared systematically the structure and the content of maternal and paternal speech to infant boys and girls while we also investigated the effect of infant’s age. Six girls and five boys were observed in the interaction with their mothers and their fathers (dyads came from the same families) at home from the second to the sixth month after birth at 15- day intervals. Detailed analysis of time revealed that fathers favour systematically the temporal structure of their speech to both girls and boys, while mothers favour boys in infant emotion/attention thematic sequences and girls in infant attention speech. Variations are counterbalanced in the way mothers and fathers respond sensitively to known marked changes in infant- parent communication over the first 6 months of life. These results are discussing in relation to the theory of innate intersubjectivity and the complementary company and care both parents give to developing infant girls and boys.
Practice that Transforms Intergenerational Programs: A Model of Theory- and Evidence Informed Principles

ABSTRACT Intergenerational practitioners have identified documentation of impact as the greatest challenge. Intergenerational program outcomes are best understood when interpreted through a theory-research-practice framework. We present theory and evidence informing an intergenerational model developed and tested over several years in the current paper. Theories emphasizing relationships, both interpersonal and with the environment, figure centrally in the model. Empirical data from multiple sources further support the practices. Steps needed to further test the model are described. A validated, parsimonious practice model can support successful implementation and evaluation of intergenerational programs, meeting an identified need for training and evaluation resources.
Structural Road Accident Models

DRAG (from un modele de la Demande Routiere, des Accidents et leur Gravite) is a complex computer model that simulates accident propensities under detailed conditions. The DRAG approach constitutes the largest road accident modelling effort ever undertaken. Gaudry is the creator and developer of DRAG and this work explains its nature, purpose and value. Such a model, which explains accidents for a whole region, province or country, has advantages in answering many questions asked about accidents (such as the role of the economic cycle, weather, prices, insurance etc.) that other models fail to take fully into account. DRAG research is underpinned by a fundamental theoretical innovation which sees a 3-level approach to analysing the determinants of the number of road victims in a given accident: Exposure (the distance travelled), Accident Frequency Risk and Accident Severity Risk (i.e. the number of victims of each type of accident). In applying flexible mathematical forms, the DRAG model also allows significant conceptual and empirical claims to be made about the importance of detecting responses that reverse themselves (to alcohol, and to other factors). Under Gaudry's guidance, a family of DRAG-inspired models has grown up, based on their development for different countries and this book explores each national model in turn. As more countries seek to adopt the DRAG approach, this book serves as a timely, unique and valuable guide to the model. Format: Hardback Pagination: 370 Price: £108.99 $195.99 €154.99 Publication Date: 17th Nov 2000 ISBN: 9780080430614
ACHIEVEMENT AND GENDER EFFECTS ON 5TH GRADER'S ACQUISITION OF SCIENCE PROCESS SKILLS IN A SOCIOECONOMICALLY DISADVANTAGED NEIGHBORHOOD

Science process skills (SPSs) play a significant role in science education. This research aimed to determine the acquisition level of 5th-grade students in SPSs with classroom activities. The research was a case study, and the data were collected by focus group interviews, document analysis, and observations. The sample of the research was six students from the fifth grade selected based on maximum variation sampling. Science process skills focused worksheets, focus group interview schedule, and observation forms were used as data collection tools. As a result, it was found out that the students were at the average or above the average level in observing, predicting, measuring, comparing and classifying skills; and they were at below the average level in inferring, organizing data, identifying and using experimental materials, processing data and formulating models, controlling variables, experimenting, interpreting and inferring. Also, it was determined that students with high academic achievement have more acquisition in SPSs. Besides, it was found out that the development of SPSs is gender neutral, it depends on the number of activities that are made in the classroom and what is the role of the students in these activities. Keywords: gender in science process skills, lower secondary school, science achievement, science activities, science process skills.
Board Faultlines and Firm Innovativeness: The bridging role of women directors

In this study, we analyze whether having women shareholder as well as employee representatives on a co-determined supervisory board is significantly related to firm innovativeness as measured by R&D intensity. Based on faultline theory, we hypothesize that it is not the mere presence of women on boards, but rather the presence of women in both functional groups, shareholder and employee representatives, that positively affects firm innovativeness as measured by R&D intensity. The underlying rationale is that the presence of women in both functional groups might cross-cut and thus bridge the representative faultline thereby enhancing boardroom interactivity and innovativeness. Our empirical analysis is based on a sample of 105 listed companies in the German indices DAX30, MDAX50, S-DAX and TecDAX30 from 2000–2015. We find that it is neither the share of women on the supervisory board as a whole nor the share of women in each of the two functional groups that affects firm innovativeness, but rather their presence in both groups, shareholder and employee representatives.
Studying the Impact of Partition on Data Reduction for Very Large Spatio-temporal Datasets

—Nowadays, huge amounts of data are being collected with spatial and temporal components from sources such as metrological, satellite imagery etc. Efficient visualisation as well as discovery of useful knowledge from these datasets is therefore very challenging and becoming a massive economic need. Data Mining has emerged as the technology to discover hidden knowledge in very large amounts of data. Furthermore, data mining techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. As a consequence, instead of dealing with a large size of raw data, we can use these representatives to visualise or to analyse without losing important information. Recently, we proposed a new approach based on different clustering techniques for data reduction to help analyse large spatio-temporal data. This approach is based on the partition of huge datasets due to the memory constraint. In this paper, we evaluate the impact of various numbers of partitions on our data reduction approach.
Comparison of ANFIS and ANN modeling for predicting the behavior of a catalytic methane reformer

Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) were applied in modeling of methane mixed reforming in a packed bed catalytic reactor. These methods were developed by use of data collected from a methane reforming pilot plant using CO2 and steam and in process conditions near to MIDREX reforming plant in sponge Iron production. Different reaction temperatures from 700 to 1100 C with different values of carbon dioxide, steam, hydrogen, methane and carbon monoxide, were randomly selected and used to generate around 5000 data set of input- output data. Both networks achieve quite satisfying scientific results with acceptable deviations. However, it is hard to say which one is better as they have close output values but ANN marginally outperformed ANFIS in predicting the reaction outputs by varying the inputs. The prediction performances of these models are compared. The accuracies of the two models were evaluated in terms of square correlation coefficient (R2) and mean square error (MSE).
Low-resolution ship detection from high-altitude aerial images

Ship detection from optical images taken by high-altitude aircrafts such as unmanned long-endurance airships and unmanned aerial vehicles has broad applications in marine fishery management, ship monitoring and vessel salvage. However, the major challenge is the limited capability of information processing on unmanned high-altitude platforms. Furthermore, in order to guarantee the wide detection range, unmanned aircrafts generally cruise at high altitudes, resulting in imagery with low-resolution targets and strong clutters suffered by heavy clouds. In this paper, we propose a low-resolution ship detection method to extract ships from these high-altitude optical images. Inspired by a recent research on visual saliency detection indicating that small salient signals could be well detected by a gradient enhancement operation combined with Gaussian smoothing, we propose the facet kernel filtering to rapidly suppress cluttered backgrounds and delineate candidate target regions from the sea surface. Then, the principal component analysis (PCA) is used to compute the orientation of the target axis, followed by a simplified histogram of oriented gradient (HOG) descriptor to characterize the ship shape property. Finally, support vector machine (SVM) is applied to discriminate real targets and false alarms. Experimental results show that the proposed method actually has high efficiency in low-resolution ship detection.
A Simple Multibody 2d-model for Early Postural Checks in Workplace Design

Posture prediction is one of the most important aspects of virtual modeling tools used for the workplace design: once the work point to reach is defined, the posture prediction module allows simulating, through inverse kinematics, the posture the operator is likely to assume. The paper presents a simple multibody 2D-model created for early postural checks in the design phase. The tool is a spreadsheet created in Microsoft Excel environment, with the support of Visual Basic. The principal output of the model in terms of angles of trunk bending and upper arm elevation, set in compliance with the technical standards, are compared to the results of an established software tool for ergonomic analyses, the 3D Static Strength Prediction Program (3DSSPP). Finally, possible differences in terms of moments on the L5/S1 and the shoulder joints introduced by the simplified kinematics of the 2D manikin are discussed.
A review of quadratic discriminant analysis for high‐dimensional data

Quadratic discriminant analysis (QDA) is a classical and flexible classification approach, which allows differences between groups not only due to mean vectors but also covariance matrices. Modern high‐dimensional data bring us opportunities and also challenges. In the framework of classical QDA, the inverse of each sample covariance matrix is essential, but high‐dimensionality causes singularity in sample covariance matrices. To overcome this technical difficulty, several high‐dimensional QDA approaches with desirable theoretical properties emerge in recent years. We are to discuss the challenges, some existing works, and possibly several future directions with regard to high‐dimensional QDA.
Expert committee classifier for hand motions recognition from EMG signals

espanolEste trabajo presenta el diseno e implementacion de una nueva tecnica para el reconocimiento de cuatro movimientos de la mano (flexion (FL), extension (EX), apertura (OP) y cierre (CL)) para respuesta en tiempo real a partir de senales electromiograficas EMG generadas desde dos musculos del antebrazo: palmaris longus y extensor digitorum. El trabajo se desarrollo en dos principales etapas: el hardware de bajo costo para la adquisicion y adecuacion analogica de las senales EMG; el sistema de procesamiento para la identificacion y clasificacion de los movimientos capturados para respuesta en tiempo real. El sistema fue integrado mediante una aplicacion hardware-software usando MATLAB y se usaron tecnicas de procesamiento para el analisis discriminante. Tres metodos fueron evaluados para el reconocimiento de patrones obteniendo tasas de reconocimiento del 98% con el nuevo metodo propuesto. EnglishThis paper presents the design and implementation of a novel technique for the recognition of four hand motions for real time response (flexion (FL), extension (EX), opening (OP) and closure (CL)) from electromyographic (EMG) signals generated from two forearm muscles: palmaris longus and extensor digitorum. The development of the work had two main stages: the low cost hardware for acquisition and conditioning of the EMG analog signals and the processing system for the identification and classification of the movement performed for real time response; the entire system was integrated in a hardware-software application using MATLAB and processing techniques for the discriminant analysis were performed. Three methods were evaluated for pattern recognition getting 98% recognition rates with the method proposed which had the best performance.