Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding ‘Hobby’ Farmers: A ‘Parish Study’ Methodology for Qualitative Research

This paper presents a methodology for undertaking research with a ‘difficult to reach population’ – hobby farmers. In an investigation designed to assess agrarian transition processes in a peri-urban locale, data was sought on every agricultural holding in a Scottish parish (municipality). This ‘parish study’ methodology combined participant mapping and qualitative interviewing with photo elicitation. Participant mapping was found to be useful for identifying farmers who are not normally included in rural social research, leading to a high response rate and a respondent pool for photo elicitation. The method also enabled the analysis of agrarian identity and land use. However, the parish study method is not suited to studies of dispersed groups and is more resource intensive than standard qualitative interview-based studies. A number of ethical and EU General Data Protection Regulation (GDPR) issues also arise around identifying participants, mapping, photos and incentivising participation in the research. The utility of the parish study method is demonstrated through two key findings of the research: the problematic definition of ‘hobby farmers’ in the study site and the trajectories towards de facto land abandonment in a peri-urban locale. deep, dense, accounts A thick description… presents detail, context, emotion, and the webs of social relationships that join persons to one another. Thick description involves emotionality and self-feelings. It inserts history into experience. It establishes the significance of an experience, or the sequence of events, for the person or persons in question.
Systematic Generation of Risk Evaluation Systems basedon Temporal Motivational Theory

This paper provides a schematic, systematic and structured approach todeveloping Bayesian belief networks to assess risks in contexts dened by activities.The method ameliorates elicitation, specication and validation of expert knowledgeby reusing a schematic structures based on reasoning of risks based on the temporal motivationaltheory. The method is based on earlier work that took a rst signicant steptowards reducing the complexity of development of Bayesian belief networks by clusteringand classifying variables in Bayesian belief networks as well as associating the processwith human deciions making. It may be possible to reduce the role of a facilitiatoror even remove the facilitator altogether by using this method. The method is partiallyvalidated and further work is required on this topic.
Fully sampled matrix transducer for real time 3D ultrasonic imaging

A 2D array for acquiring 3D images is described. Interconnection methods are presented that connect elements of the array to integrated circuits (ICs) within the transducer. These ICs simultaneously process signals from ALL elements forming a fully sampled array. Comparisons are made with mechanical and sparse array solutions. Several signal processing options for use within the ICs are presented.
Preserving feature layout information for object recognition

In this paper, we have proposed a method to preserve layout information of feature maps, which is vanished in fully connected layer, for object classification tasks. In bag‐of‐features framework, codebook encodes an image to produce a group of response maps by convolution operation. And the maps hold location information of features in regular grids. To obtain mid‐level representation, it is common to concatenate all the features into a long feature vector. Based on the representation, a linear classifier or full‐connected layer is implemented to predict labels. However, because of the disappearance of regular girds, the spatial information of features vanish before being fed to the higher layer, even though it is preserved in the feature extraction process. In this paper, this problem is addressed by applying spatial description feature (SDF) to preserve more useful information with modified spatial pyramids. In addition, to enhance the performance of SDFs, we have designed a forward–backward strategy to select receptive fields. In the experiment, it is shown that the knowledge of feature spatial layout can promote classification and the forward–backward learning scheme can generate a compact and high‐performance pipeline. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
A Multi-Dimensional Issue Clustering from the Perspective Consumers' Interests and R&D

The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers` interests, and (ii) related R&D terms.
Pharmacological Research on Osteoarthritis Treated by Tougu Xiaotong Capsule

Objective To study the pharmacological mechanism of Tougu Xiaotong capsule(TGXTC). Methods The parameters of network features,including degree distribution,betweenness centrality,closeness centrality,characteristic path length and network heterogeneity were used in the experiment,and TGXTC ligand-target interaction network and drug ligand-target interaction network were compared.Results Network features showed that both networks belong to scale-free networks and have some key points.The values of heterogeneity and characteristic path length in both networks were close.Conclusion The nonlinear regulation pattern of "one to many,many to one" exists in TGXTC ligand-target interaction network and drug ligand-target interaction network,which reveal the pharmacological behavior of TGXTC.
The Application of RS Erasure Codes in the Cloud Storage

Cloud storage as a storage mode which designed to provide users with cheap,efficient,secure,scalable,customizable storage services,have become the trend of storage services.But because of its cloud storage node is not controllable,how to protect data stored in the cloud security issues facing challenges.RS erasure codes by the introduction of cloud storage,by fragmentation polymerization mechanism for cloud storage data security protection provides a new solution.
Modifying social dimensions of human faces with ModifAE

Author(s): Atalla, Chad E | Advisor(s): Cottrell, Gary | Abstract: At first glance, humans extract social judgments from faces, including how trustworthy, attractive, and aggressive they look. These impressions have profound social, economic, and political consequences, as they subconsciously influence decisions like voting and criminal sentencing. Therefore, understanding human perception of these judgments is important for the social sciences. In this work, we present a modifying autoencoder (ModifAE, pronounced ``modify'') that can model and alter these facial impressions. We assemble a face impression dataset large enough for training a generative model by applying a state-of-the-art (SOTA) impression predictor to faces from CelebA. Then, we apply ModifAE to learn generalizable modifications of these continuous-valued traits in faces (e.g., make a face look slightly more intelligent or much less aggressive). ModifAE can modify face images to create controlled social science experimental datasets, and it can reveal dataset biases by creating direct visualizations of what makes a face salient in social dimensions. The ModifAE architecture is also smaller and faster than SOTA image-to-image translation models, while outperforming SOTA in quantitative evaluations.
“Not like that, not for that, not by them”: social media affordances of critique

ABSTRACT This paper has two objectives: to explicate the sociotechnical conditions that facilitate critique on social media platforms (specifically: Tumblr); and to operationalize a “working theory” from Foucault’s conceptualization of critique. We analyze resistant practices observed (in ethnographic study) in a Not Safe For Work (NSFW) community on Tumblr, arguing that the potential for critique arises there at the intersection of platform architecture and use cultures. Specifically, we show how critique emerges from shared practices of ethics, most visibly enacted through what we call voluntary vulnerability and paying it forward. This potential for critique is arguably at risk with Tumblr’s recent NSFW ban.
The ambiguous status of laryngeals in nasal vowel-consonant harmony

Walker (2000) generalizes a nasalized segment constraint hierarchy based on typological frequency (i.e., natural markedness), and artificial grammar learning is assumed to be able to test natural markedness. In order to verify if the results of artificial grammar learning match natural markedness (i.e., the more unmarked the pattern is, the easier it is to learn), this study examines nasal harmony by looking at one kind of natural markedness, a nasalized segment constraint hierarchy, and proposes experiments that could be conducted to test natural markedness.
Mitigating Visually Induced Motion Sickness

Virtual reality has grown rapidly over the past decade, yet visually induced motion sickness (VIMS), continues to affect the usability of this technology. Aside from medicine, physical hand-eye-coordination tasks have been found to be effective in mitigating symptoms of VIMS, however the need for equipment outside of virtual reality limits the usefulness of these mitigation techniques. In this study, 21 participants were sickened via a virtual obstacle course and used one of two mitigation techniques. The first, natural decay, is simply waiting outside the virtual environment (VE) for symptoms to subside; the other was a virtual peg-in-hole task, performed in the VE with a gamepad. A paired samples t-test confirmed that the virtual obstacle course induced VIMS. Both mitigation techniques significantly lessened the symptoms of VIMS, but there were no significant differences in the effectiveness of mitigation between the two techniques. A virtual mitigation method allowing continued immersion in a VE would pave the way for long-term immersion virtual reality studies, involving topics such as vigilance or training.
Optimised auto-scaling for cloud-based web service

Elasticity and cost-effectiveness are two key features for ensuring that cloud-based web services appeal to more businesses. However, true elasticity and cost-effectiveness in the pay-per-use cloud business model has not yet been fully achieved. The explosion of cloud-based web services brings new challenges to enable the automatic scaling up and down of service provision when the workload is time-varying. This research studies the problems associated with these challenges. It proposes a novel scheme to achieve optimised auto-scaling for cloudbased web services from three levels of cloud structure: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). At the various levels, auto-scaling for cloud-based web services has different problems and requires different solutions. At the SaaS level, this study investigates how to design and develop scalable web services, especially for time-consuming applications. To achieve the greatest efficiency, the optimisation of service provision problem is studied by providing the minimum functionality and fastest scalability performance concerning the speed-up curve and QoS (Quality of Service) of the SLA (Service-Level Agreement). At the PaaS level, this work studies how to support dynamic re-configuration when workloads change and the effective deployment of various kinds of web services to the cloud. To achieve optimised auto-scaling of this deployment, a platform is designed to deploy all web services automatically with the minimal number of cloud resources by satisfying the QoS of SLAs. At the IaaS level for two infrastructure resources of virtual machine (VM) and virtual network (VN), this research focuses on studying two types of cloud-based web service: computation-intensive and bandwidth-intensive. To address the optimised auto-scaling problem for computation-intensive cloud-based web service, data-driven VM auto-scaling approaches are proposed to handle the workload in both stable and dynamic environments. To address the optimised autoscaling problem for bandwidth-intensive cloud-based web service, this study proposes a novel approach to predict the volume of requests and dynamically adjust the software defined network (SDN)-based network configuration in the cloud to auto-scale the service with minimal cost. This research proposes comprehensive and profound perspectives to solve the auto-scaling optimisation problems for cloud-based web services. The proposed approaches not only enable cloud-based web services to minimise resource consumption while auto-scaling service provision to achieve satisfying performance, but also save energy consumption for the global realisation of green computing. The performance of the proposed approaches has been evaluated on a public platform (e.g. Amazon EC2) with the real dataset workload of web services. The experiment results demonstrate that the proposed approaches are practicable and achieve superior performance to other benchmark methods.
AN EXPLORATORY STUDY OF TWITTER SPORTS CONSUMPTION MOTIVES AND RELATIONSHIP WITH FUTURE INTENTIONS: A PERSPECTIVE FROM ATHLETES’ FOLLOWERS

The purpose of the study is to examine the relationship between Twitter consumption motives with future behavioural intentions from athletes’ followers’ perspective. Data were gathered from respondents using a structured selfadministered questionnaire in a university located in southern Gauteng, South Africa. Exploratory factor analysis was used to identify the dimensions of Twitter consumption motive using principal component analysis. A five-factor structure emerged and these factors were labeled skills, information, economic, pass time and accessibility. Moderate, positive correlation coefficients were observed between the economic, information and pass time dimension with future intentions. The regression analysis showed significant predictive relationships with the economic and information variable with future behavioural intentions. This study provides a focused examination of Twitter consumption motives, which can offer valuable insights into athletes’ managers seeking to nurture interactions with the outside world, especially in the social media context. Therefore, understanding the motives that drive users’ interest in Twitter engagement will also provide sport marketers with information that can be used to develop targeted promotional campaigns in order to increase followership
Noun Phrase Structure

Panare, also known as E'napa Woromaipu, is a seriously endangered Cariban language spoken by about 3,500 people in Central Venezuela. A Typological Grammar of Panare by Thomas E. Payne and Doris L. Payne, is a full length linguistic grammar, written from a modern functional/typological perspective.
Seismic modelling of early B-type pulsators observed by BRITE – I. θ Ophiuchi

We analyse time-series observations from the BRITE-Constellation of the well known $\beta$ Cephei type star $\theta$ Ophiuchi. Seven previously known frequencies were confirmed and nineteen new frequency peaks were detected. In particular, high-order g modes, typical for the SPB (Slowly Pulsating B-type star) pulsators, are uncovered. These low-frequency modes are also obtained from the 7-year SMEI light curve. If g modes are associated with the primary component of $\theta$ Oph, then our discovery allows, as in the case of other hybrid pulsators, to infer more comprehensive information on the internal structure. To this aim we perform in-depth seismic studies involving simultaneous fitting of mode frequencies, reproducing mode instability and adjusting the relative amplitude of the bolometric flux variations. To explain the mode instability in the observed frequency range a significant increase of the mean opacity in the vicinity of the $Z$-bump is needed. Moreover, constraints on mass, overshooting from the convective core and rotation are derived. If the low-frequency modes come from the speckle B5 companion then taking into account the effects of rotation is enough to explain the pulsational mode instability.
Entrepreneurial Mindset and Multicultural Communication Skills: a Reflection on the ECMT+ Intensive Programme

Streamlining the process of entrepreneurship higher education in the field of shaping an entrepreneurial mindset, communication skills, and the professional competences necessary for working in a multicultural environment is one of the significant challenges of the Eras-mus+ Strategic Partnership Project titled Entrepreneurship and Communication in Multicultural Teams (ECMT+). The purpose of the conducted research is to explore and work out practical methods and tools for entrepreneurship education to support the learning and teaching processes in higher education. The aim of this paper is to present a model of entrepreneurship teaching formats – methods, tools, contents – to enhance the development of entrepreneurial attitudes and multicultural communication skills. Both descriptive and explanatory techniques were used in the presented study.
Improved Algorithm for Sequential Pattern Mining Based on PrefixSpan

Aiming at the PrefixSpan algorithm produce huge amount of project databases in mining sequence patterns, this paper proposes an Improved PrefixSpan algorithm for Mining Sequential Patterns(IPMSP) alaorithm.By avoid produce duplicated project databases with the same prefix pattern through checking the prefix with regard to prefix of the sequence database and abnegating the non-frequent items and project databases which sequential number is lower than minimum support in the recursive mining process, the performance of PrefixSpan is well improved.Experiment results shows that the time and space performance of IPMSP algorithm are better than that of PrefixSpan.
Zellig Sabbettai Harris

Harris was born on October 23, 1909, in what was to become the Soviet Union, but he was brought to Philadelphia when he was four. His entire academic career lay at the University of Pennsylvania, as student and professor, until he formally retired in 1979. For years, however, he commuted to Israel, where his wife Bruria Kaufman, a theoretical physicist and former assistant of Einstein, was a professor at the Weizmann Institute. I wish that I could refer to other memorials which would say something of the man himself. I met him once, and found him courteous and charming. Instant tributes in the Linguist list, by Bruce Nevin and Michael Kac, include some relevant anecdotes. <1> But it would be impertinent for an obituarist so little connected with him to attempt to say more.
Coupled Hidden Markov Model for Process Mining of Invisible Prime Tasks

Process mining provides process improvement in a variety of application domains. A primary focus of process mining is transferring information from event logs into process model. One of the issues of process mining is dealing with invisible prime tasks. An invisible prime task is an additional task in the process model to assist in showing real processes. However, a few of algorithm solves the issue. This research proposes an algorithm for dealing with invisible prime tasks. The proposed algorithm contains rules and equations utilizing probability of state transition of Coupled Hidden Markov and double time-stamped in event logs. The rules and equations are used for determining invisible prime tasks and parallel control-flows patterns. In addition to dealing with invisible prime tasks, the experiment results also show that the proposed algorithm obtains right parallel control-flow patterns from non-complete event logs. This proposed algorithm also decreases usage of the invisible prime task in A# algorithm without reducing the quality of discovered process models. It has proven with the fitness of process models obtained by the proposed algorithm are relatively high as those obtained by A# algorithm.
Gratitude Enhanced Mindfulness (GEM): A pilot study of an internet-delivered programme for self-management of pain and disability in people with arthritis

ABSTRACT A study was conducted to test an online mindfulness and gratitude intervention for self-management of arthritis. It was hypothesized that the intervention would decrease pain anxiety, intensity and interference, fear of movement, and increase pain self-efficacy for a group of self-referred people with arthritis. The intervention consisted of four once-weekly programmes which included animated guides, audio guided mindfulness exercises and a gratitude programme. One hundred and fifty-one people enrolled in the study. Data from 81 people who completed the trial and questionnaires was analysed. Pain anxiety, pain interference, pain intensity, fear of movement and pain self-efficacy were all improved by the intervention as hypothesized. This pilot study lends support to both gratitude and mindfulness being appropriate interventions for physical health conditions and demonstrates how they can be used in combination. Additionally, this shows the potential of online delivery for positive health interventions.
A Test Set Optimization Method Based on Ant Algorithm

The increase of digital circuit integrity and complexity has made fault diagnosis of circuits more and more difficult.The scale of test set has become very large because of redundancy,which costs lots of time and memory unnecessarily.It is important to acquire optimal test set for test application.Test set optimization,which can eliminate the redundancy,is one of key problems in fault diagnosis of digital circuits.Ant colony optimization,a new kind of random optimization algorithm,has become a better alternative to genetic algorithm in some areas.That algorithm has such advantages as less parameters and simple operations,so it is easier to be adopted.We propose a method based on ant colony optimization that solves test set optimization better than classic algorithm or genetic algorithm.The better performance of the proposed method is demonstrated by experimental results.