Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Factors Affecting Consumers’ Behavioural Intention Towards Online Shopping: An Exploratory Study

As e-commerce has grown, the shopping landscape has changed from ofﬂine to online, sparking a lot of interest in research about online shopping in recent years. The variety of factors affects purchasing decisions. Here, the term & channel of shopping refers to online shopping; the main aim of this paper is to identify the inﬂuencing variables that affect young consumers & online purchasing choices in the context of India. In this study, we experimentally examine variables like website attributes, customer attributes, product attributes that impact the behavioural intention towards online shopping which impact the customer’s loyalty. For the purpose of understanding the impact on intention, this study, this is based on the TAM. Target respondents are 100 to perform empirical investigation. Exploratory Factor Analysis has been applied to check the relationship among the multiple constructs. The ﬁndings support the notion that are website attributes, customer attributes, product attributes factors that impact the behavioural intention towards online shopping which impact the customer’s loyalty. The outcomes of this study may enable the creation of more effective new-age online marketing platforms by the marketers.
Multilingual Stance Detection in Social Media: Four Political Debates on Twitter

Stance Detection is the task of automatically determining whether the author of a text is in favor, against, or neutral towards a given target. In this paper we investigate the portability of tools performing this task across diﬀerent languages, by analyzing the results achieved by a stance detection system (i.e. MultiTACOS) trained and tested in a multilingual setting. First of all, a set of resources on topics related to politics for English, French, Italian, Spanish and Catalan is provided which includes: novel corpora collected for the purpose of this study, and benchmark corpora exploited in stance detection tasks and evaluation exercises known in literature. We focus in particular on the novel corpora by describing their development and by comparing them with the benchmarks. Second, MultiTACOS is applied with diﬀerent sets of features especially designed for stance detection, with a speciﬁc focus to exploring and combining both features based on the textual content of the tweet (e.g., style and aﬀective load) and features based on contextual information that do not emerge directly from the text. Finally, for better highlighting the contribute of the features that most positively aﬀect system performance in the multilingual setting, a features analysis is provided, together with a qualitative analysis of the misclassiﬁed tweets for each of the observed languages, devoted to reﬂect on the open challenges.
Edinburgh Research Explorer The construction of the efficient office

The office has been a central site of organisational planning, accountability and control since the 19 th century. Yet it has been the subject of relatively little accounting research. Through the dual theoretical lenses of Foucaultian and Labour Process theories, this study employs historical photo-elicitation methodology to investigate the implementation of management control and accountability in the scientifically managed office which emerged in the US during the late 19 th and early 20 th centuries. Our analysis reveals the manner in which accounting records created new modes of disciplinary control and surveillance within the office and how accounting tasks were deskilled in a gradually feminised and mechanised office environment. We also witness the role of accounting in the physical structuring of office space through the assembly line arrangement of office furniture to facilitate paper flows and the installation of record keeping systems of surveillance. In addition, our visually derived historical account of these transformations in office administration allows us to reflect on some contemporary issues. The production line design and efficiency so promoted by scientific management served as a forerunner to the today’s open plan office, as well as influencing contemporary office management philosophies such as Activity-Based Working (ABW). Furthermore, we seek to inform current debates on the role of accounting in contemporary neo-liberal society. In the history of the scientific office, we gain an early glimpse of the subsequent role that accounting comes to play within a neo-liberal agenda as a powerful technology of micro-measurement and micro management.
Title : Relationships Between the External and Internal Training Load in Professional Soccer : What Can We Learn From Machine Learning ?

Purpose: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Methods: Training data were collected from 38 professional soccer players over two seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using two machine learning techniques, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (LASSO), and one naive baseline method. The predictions were based on a large set of external load indicators. Using each technique, one group model involving all players and one individual model for each player was constructed. These models’ performance on predicting the reported RPE values for future training sessions was compared to the naive baseline’s performance. Results: Both the ANN and LASSO models outperformed the baseline. Additionally, the LASSO model made more accurate predictions for the RPE than the ANN model. Furthermore, decelerations were identified as important external load indicators. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Conclusions: Machine learning techniques may have added value in predicting the RPE for future sessions to optimize training design and evaluation. Additionally, these techniques may be used in conjunction with expert knowledge to select key external load indicators for load monitoring.
Seed Manual for Forest Trees

The choices and relative values of different seed sources the choice of seed origin by species systems of seed and plant identification and certification seed supply and aspects of the law flower, fruit and seed development and morphology identification and assessment of cone and seed crops cone and seed collection and handling before processing the processing of cones and seeds seed storage seed testing seed dormancy, seed treatment and seed sowing.
A Surface Displacement Analysis for Volcan Pacaya from October 2001 through March 2013 by Means of 3-D Modeling of Precise Position GPS Data

Table 9.2. Best RMS misfit calculations for all tested Kode 100 models for each of three time periods surrounding the May 2010 eruption at Volcán Pacaya. 63 Table 9.3. Best RMS misfit calculations for all tested Kode 200 and 500 models for each of three time periods surrounding the May 2010 eruption at Volcán
Adaptive thresholding for sparse image reconstruction

The performance of the class of sparse reconstruction algorithms which is based on the iterative thresholding is highly dependent on a selection of the appropriate threshold value, controlling a trade-off between the algorithm execution time and the solution accuracy. This is why most of the state-of-the-art reconstruction algorithms employ some method of decreasing the threshold value as the solution converges toward the optimal one. To address this problem we propose a data-driven adaptive threshold selection method based on the fast intersection of confidence intervals (FICI) method, with which we have augmented the two-step iterative shrinkage thresholding (TwIST) algorithm. The performance of the proposed algorithm, denoted as the FICI-TwIST algorithm, has been evaluated on a problem of image reconstruction with the missing pixels, exploiting image sparsity in the discrete cosine transformation domain. The obtained results have shown competitive performance in comparison with a number of state-of-the-art sparse reconstruction algorithms, even outperforming them in some scenarios.
AniProtDB: A Collection of Consistently Generated Metazoan Proteomes for Comparative Genomics Studies.

To address the void in the availability of high-quality proteomic data traversing the animal tree, we have implemented a pipeline for generating de novo assemblies based on publicly available data from the NCBI Sequence Read Archive, yielding a comprehensive collection of proteomes from 100 species spanning 21 animal phyla. We have also created the Animal Proteome Database (AniProtDB), a resource providing open access to this collection of high-quality metazoan proteomes, along with information on predicted proteins and protein domains for each taxonomic classification and the ability to perform sequence similarity searches against all proteomes generated using this pipeline. This solution vastly increases the utility of these data by removing the barrier to access for research groups who do not have the expertise or resources to generate these data themselves and enables the use of data from non-traditional research organisms that have the potential to address key questions in biomedicine.
Brain Computer Interface using Genetic Algorithm with modified Genome and Phenotype Structures

The human machine interface research in the light of modern fast computers and advanced sensors is taking new heights. The classification and processing of neural activity in the brain accessed by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), EEG Electroencephalogram (EEG) etc., are peeling off new paradigms for pattern recognition in human brain-machine interaction applications. In the present paper, an effective novel scheme based upon a synergetic approach employing the Genetic Algorithm (GA), Support Vector Machine and Wavelet packet transform for motor imagery classification and optimal Channel selection is proposed. GA with SVM acting as the objective function is employed for simultaneous selection of features and channels optimally. The binary population of GA is uniquely represented in three-dimensional structure and a new cross-over operator for GA are introduced. The new modified cross-over operator is proposed for the modified three-dimensional population. The ‘data set I’ of ‘BCI Competition IV’ is taken for evaluation of the efficacy of the proposed scheme. For subject ‘a’ accuracy is 88.9 6.9 with 10 channels, for subject ‘b’ accuracy is 79.20±5.36with 11 channels, for subject ‘f’ accuracy is 90.50±3.56 with 13 channels, and for subject ‘g’ accuracy is 92.23±3.21with 12 channels. The proposed scheme outperforms in terms of classification accuracy for subjects ‘a, b, f, g’ and in terms of number of channels for subject ‘a’ and that for subject ‘b’ is same as reported earlier in literature. Therefore, proposed scheme contributes
Mining Semantic Relations in Data References to Understand the Roles of Research Data in Academic Literature

Research data serves important roles in scientific discovery and academic innovation. To appropriately assign credit for data work and to measure the value of research data, it is essential to articulate how data are actually used in research. We leveraged a combination of computational methods and human analysis to characterize different types of data use by mining semantic relations from the phrases where data are referenced in academic literature. In particular, we investigated references to data in the bibliography of a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). After retrieving and extracting semantic relations as subject-relation-object triples, we used rule-based methods to classify them. We then annotated samples from 11 frequent classes of data reference triples and found that they vary primarily along two dimensions of data use: proximity and function. Proximity describes the distance between the author and the data they reference (e.g., direct or indirect engagement). Function describes the role that data plays in each reference (e.g., describing interaction or providing context). These semantic relationships between authors and data reveal the ways data are used in scientific publications. Evidence of the variety of ways data are used can help stakeholders in research data curation and stewardship - including data providers, data curators, and data users - recognize the myriad ways that their investments in data sharing are realized.
New materials versus new design: study on the physico-chemical and electrical properties of thin SiOxNy layers for their use in RF-MEMS switches

: In this work we present results from an experimental study on the physico-chemical and electrical properties of thin dielectric layers (SiO x N y ) deposited using a plasma assisted process. An application of those dielectric layers in RF-MEMS switches is aimed at. We look for a better understanding of dielectric charging phenomenon as it is known to be the main cause for the failure and low reliability in the actuation of RF-MEMS switches. In order to improve the performance of dielectric layers used in RF-MEMS we have tested a new concept: instead of elaborating new materials we assembled a dielectric multi-layer structure that gains from design rather than from composition. To achieve this we have started with a characterization of the mono-layers deposited singly. The multi-layer structure was found to be a promising candidate for suitable modulation of the conductive properties of the deposited dielectric layers for their use in RF-MEMS switches.
: Probability and Statistics Existence and regularity of law density of a pair (diffusion, first component running maximum)

: Let X be a continuous $d$-dimensional diffusion process and M the running supremum of the first component. We show that, for any t>0, the law of the (d+1) random vector (M_t,X_t) admits a density with respect to the Lebesgue measure using Malliavin's calculus. In case $d=1$ we prove the regularity of this density.
Usability of Programming Languages

This is a practical course, in which most of the learning will be achieved by the design and execution of original research experiments. The purpose of these lecture notes is to give a broad introduction to research in the field, both classic research (as collected in the Hoc, Green Samurcay and Gilmore book Psychology of Programming – henceforth called ‘PoP’), and contemporary research currently being carried out in the major world centres.
Leveraging Connected and Automated Vehicles for Participatory Traffic Control

This report lays the theoretical groundwork for participatory traffic control that integrates traditional infrastructure like traffic signals with connected and automated vehicles (CAVs) acting as mobile actuators. The study is divided into two main parts. First, we introduce a robust traffic state estimation method that leverages real-time data from CAVs to yield precise insights into the transportation system. Second, we present an analytical framework to guide the strategic control of CAVs, aiming to indirectly influence the behavior of human-driven vehicles and optimize traffic flow across various time periods and transportation facilities. Our research serves as a foundational step towards the practical deployment of participatory traffic control systems, contributing to the creation of smarter, more efficient transportation networks for the future.
Access to conceptual understanding – Summer courses for linear algebra and analysis after the first semester

The transition from school to university mathematics is known to present a major challenge to many students, resulting in poor performances and high dropout rates during the first semesters. In this paper we present the preliminary results on summer courses in Linear Algebra and Analysis after the first semester, which were designed based on the Abstraction in Context framework (Dreyfus & Kidron, 2014) and on the self-determination theory (Deci & Ryan, 2000). In particular, we investigate the potential of this course design to contribute to the motivation of the students and to their ability to engage in concept construction. These qualitative preliminary results will be used in further research to quantitatively assess the effect of these courses as well
Nonlinear Dynamics of MEMS Arches Assuming Out-of-Plane Actuation Arrangement

In this work, the nonlinear dynamics of a microbeam shallow arch actuated through an out-of-plane electrostatic force arrangement is investigated. A reduced order model is developed to analyze the static, free vibration, and nonlinear dynamic response of the microstructure under different direct current and alternating current load conditions. A numerical investigation is conducted by comparing the response of the arch near primary and secondary resonances using a nonparallel plates actuation scheme where the arch itself forms a moving electrode. The results show that the nonparallel excitation can be efficient for primary and secondary resonances excitation. Moreover, unlike the classical parallel plates method, where the structure is vulnerable to the dynamic pull-in instability, this nonparallel excitation arrangement can provide large amplitude motion while protecting the structure from the so-called static and dynamic pull-in instabilities. In addition to primary resonance, secondary resonances are demonstrated at twice and one-half the primary resonance frequency. The ability to actuate primary and/or secondary resonances without reaching the dynamic pull-in instability can serve various applications where large strokes increase their performance, such as for resonator-based sensitive mass sensors.
Adaptive Anti-disturbance Switching Control for Switched T-S Fuzzy Systems

In this paper, an adaptive anti-disturbance switching (AADS) control strategy is proposed for switched Takagi-Sugeno fuzzy systems (ST-SFSs) subject to multi-source disturbances. The disturbances consist of two parts: the available unmodeled disturbance and the disturbance modeled by dynamic neural network. Firstly, a novel adaptive disturbance observer is designed to approximate the dynamic neural network modeled disturbance. Secondly, the attenuation performance from the output to the available disturbance is analyzed by the L 2 gain index. Thirdly, a controller based on the adaptive disturbance observer is constructed for the ST-SFS under the average dwell time switching signal limitation. Further, under the designed adaptive disturbance observer
IJCAI-ECAI Workshop “Interactions between Analogical Reasoning and Machine Learning” (IARML 2022)

Prompt learning, a recent thread in few-shot learning for pre-trained language models (PLMs), has been explored for completing word analogies in the extractive way. In this paper, we reformulate the analogy task as masked analogy completion task with the use of prompting to derive a generative model for analogies beyond words. We introduce a simple prompt-based fine-tuning paradigm for language modeling on answered prompts of analogies in the sequence-to-sequence framework. To convert discrete terms of analogies into linear sequences, we present a symbolic prompt template. The sequence-tosequence model is fine-tuned to fill in the missing span of masked prompts deduced from different masking schemes on phrase analogies extracted from a small corpus. We analyze the out-of-distribution performance on sentence analogies which are unseen cases. Our experiments demonstrate that promptbased fine-tuning with the objective of language modeling enables models to achieve significantly better performance on in-distribution cases than PLMs. Masked prompt learning with one-term masking exhibits the best out-of-distribution generalization on sentence analogies, with a difference of only 3 characters from references.
Information Sciences Institute

S itting in one of the conference rooms or offices on the west side of the Information Sciences Institute’s headquarters in Marina del Rey, California, it’s hard to ignore the view of the ocean and the sailboats in the marina. Somehow, ISI researchers manage to come to the office every day and get something done. They are too busy designing and building the next generation of information systems to be distracted by the sun and palm trees outside. Part of the University of Southern California’s Viterbi School of Engineering, the Information Sciences Institute employs 350 engineers, scientists, graduate students and staff. ISI researchers are guided by the institute’s founding vision: to bridge the gap between academia and industry by conducting research that transforms ideas from theory into practice. HISTORY ISI was founded in 1972 to help the Defense Advanced Research Projects Agency (DARPA) study emerging technologies related to computer communications. At that time, DARPA was interested in technologies that could be used for the ARPANET, the experimental communications network that was the predecessor of the Internet. Starting with early work on the ARPANET, ISI participated in the development of many of the core technologies that make up the Internet. ISI researcher Jon Postel co-authored many of the key Internet standards, including IPv4, SMTP, and FTP, and was the long-time editor of the “request for comments,” or RFC, series that defined many of the core principles and protocols of the Internet. In 1983, another ISI researcher, Paul Mockapetris, invented the domain name system (DNS), the service that translates domain names into IP addresses. Today, ISI still hosts one of the Internet’s 13 DNS root servers. ISI also played a key role in the administration of the early Internet. In addition to his role as the RFC editor, Postel also served as the administrator of the Internet Assigned Numbers Authority (IANA), the entity that manages IP addresses and top-level domain names such as . com and . net. ICANN, the organization that runs IANA and many of the Internet’s other governing boards today, is still headquartered in the same building as ISI. ISI played a key role in the administration of the early Internet, and it still hosts one of the Internet’s 13 DNS root servers. ” Information Sciences Institute Marina del Rey, California
Addressing Cold Start With Dataset Transfer In E-Commerce Learning To Rank

Learning To Rank (LTR) models are a crucial component for the relevance ranking in e-commerce applications. When properly trained, the LTR models yield state-of-the-art ranking performance. A popular approach to training the LTR models involves minimising a loss over historical customer interactions. Historical customer interactions are preferred over manually annotated data since the latter is costly to acquire and quickly becomes stale. Historical customer interactions are not always available when a new e-commerce platform is launched, or an existing platform is extended to new products. In this paper, we present the use of inverse propensity weighting as a strategy to create datasets where no historical customer interaction is available. We show results of an online A/B test which demonstrate the efficacy of our proposed strategy. Due to a simple practical application of the proposed strategy, we believe this can help LTR practitioners.