Miracle-dz/UniFAR-FTUs huggingface dataset:
Politeness in professional contexts: foreign-language teacher training

Abstract This paper examines the professional context of teachers of English as a Foreign Language (EFL), whose first language is not English but who are required to help learners adhere to target-language (TL) politeness norms and practices. Many of these teachers have had little or no contact with TL countries/cultures and have limited professional training in this area. This paper highlights the specific context of 39 Mexican EFL teachers who reflected on their understandings and “teaching” of politeness. I argue that by employing existing resources and knowledge and with further training, bilingual teachers can be helped to take “possession” of politeness rather than having to unquestioningly teach appropriate, socially-accepted, socially-expected usage.
Parsing the 'Me' in #MeToo

India's #MeToo movement began in late-2018, and was largely a platform for some privileged women sharing their accounts of sexual harassment. Beyond issues of access to digital technology, our paper investigates why various sections of India's female and LGBTQ+ population chose not to engage with the #MeToo movement. Focusing on experiences with sexual harassment, we conducted 44 qualitative interviews with middle-class working women, feminist and queer activists, academics, and other stakeholders working against gender-based violence, to understand their perspectives on #MeToo. Our paper explores why some survivors bypass the legal infrastructure to speak out against sexual harassment using #MeToo, while others choose not to participate despite having access to social media platforms. Using the lens of infrastructure, we outline the imbrication of social media movements with existing social norms and legal infrastructures. Further, we highlight how infrastructural politics are connected to patriarchy, colonialism, caste, class, and gender struggles.
The reactivity of phosphorus - sulfur- nitrogen ring systems. Synthesis and NMR study

In dit proefschrift worden reacties behandeld van anorganische ringsystemen van het type (NPC1 2)m(NS0C1)n (m + n = 3; m =/ 0) met enkele nucleofiele reagentia; bij deze reacties worden een of meer chloorliganden gesubstitueerd. Zie: Samenvatting
Dance self-learning application and its dance pose evaluations

In this study, we propose a variety of dance pose evaluation methods required for a smartphone-based application that helps dance self-learning. Previously, various dance-pose-evaluation methods and dance-learning applications have been proposed for the same purpose, but some problems were identified in the actual application process, such as the absence of joints due to occlusion, the overfitting of the torso, and the evaluation of rotational poses. To mitigate these problems, this study proposes three methods to a) solve the view occlusion problem that precludes evaluation by creating a state variable referred to as visibility, b) solve the torso overfitting problem by creating a keypoint referred to as the core, and c) estimate the rotation of the body based on a vector obtained between the core and torso joints. Furthermore, we conducted experiments using a dance self-learning application running on a real smartphone and various real-world datasets. Based on the experimental results, we demonstrate that the newly proposed method can provide effective feedback to students who try to learn to dance alone, by performing robust dance pose evaluations in different scenarios.
CoRoNNa: a deep sequential framework to predict epidemic spread

We propose CoRoNNa, a deep framework for epidemic prediction to analyse the spread of COVID-19 and, potentially, of other unknown viruses, based on a flexible integration of sequential and convolutional components. Importantly, our framework is general and can be specialised according to different analysis objectives. In this paper, the specific purpose is to optimise CoRoNNa for analysing the impact of different mobility containment policies on the epidemic. To this end, we integrate cumulative viral diffusion statistics and local demographic and mobility information of several countries. Our analysis confirms that mobility data have a strong, but delayed, effect on the viral spread. In this context, CoRoNNa has superior performances when compared with other frameworks that incorporate multivariate lagged predictors, and with simple LSTM models. On the contrary, no method is able to predict daily cases based only on lagged viral diffusion statistics.
BertLoc: duplicate location record detection in a large-scale location dataset

Due to a significant increase in the number of location services as well as services which rely on location information such as real-time maps, there is an enormous need to provide accurate location information to end users. In order to acquire the location records, generally, users or other systems initiate the location query to the location search engine, and the location search engine provides the best matching results. However, there are often inconsistency, noise, and ambiguity in the location datasets. In particular, there are many cases where the same location is recorded as different names from varying data sources, which can not only confuse users, but also introduce inaccurate results. Therefore, detecting the duplicate location information in a large database as well as accurately merging them into a single location record are critical. In this work, we propose BertLoc, a novel deep learning-based architecture to detect the duplicate location represented in different ways (e.g., Cafe vs. Coffee House) and effectively merge them into a single and consistent location record. BertLoc is based on Multilingual Bert Model followed by BiLSTM and CNN to effectively compare and determine whether given location strings are the same location or not. We evaluate BertLoc trained with more than half a million location data used in real service in South Korea and compare the results with other popular baseline methods. Our experimental results show that BertLoc outperforms other popular baseline methods with 0.952 F1-score, and shows great promise in detecting duplicate records in a large-scale location dataset.
Wikipedia Beyond the English Language Edition

Do models of collaboration among contributors of Wikipedia generalize beyond the larger, western editions of the encyclopedia? In this study, we expanded upon the known collaborative mechanisms on the English Wikipedia and demonstrated that the collaboration model is best captured through the interplay of these mechanisms. We annotated talk page conversations for types of power plays or vies for control over edits that are made to articles, to understand how policy and power play mechanisms in editors' discussions account for behavior in English (EN), Farsi (FA), and Chinese (ZH) language editions of Wikipedia. Our findings show that the same power plays used in EN exist in both FA and ZH but the frequency of their usage differs across the editions. These variations suggest that editors in different language communities value contrasting types of policies to compete for power while discussing and editing articles. Our study contributes to a deeper understanding of how collaboration models developed from a western perspective translate to non-western languages.
Aarith: an arbitrary precision number library

Developing customized arithmetic hardware units is a very difficult and error-prone process, especially when using non-standard bit-widths for number representation. In the early stages of development, the actual details of the physical implementation might not be of interest. Synthesis from hardware description languages like VHDL or Verilog takes a large amount of (computation) time, effectively preventing an agile experimentation cycle. As a remedy we present aarith, an easy to use, header-only C++ large number library for hardware simulation. Aarith supports unsigned and signed two's complement integers of arbitrary bit-widths as well as IEEE 754 floating point numbers with arbitrary bit-widths for the exponent and the mantissa. Aarith allows to easily manipulate the individual bits of the numbers, making it an ideal framework for developing and functionally evaluating customized arithmetic (hardware) units. The design goal is to provide a framework that is simple to learn and use. The provided number types are intended to blend in with the native number types. The framework is publicly available as open-source software.
Automation and prioritization of replica balancing in HDFS

The Hadoop Distributed File System (HDFS) is a reliable storage engine designed to run over commodity hardware. To provide reliability and read performance, HDFS has a storage model based on data replication and works best when the file blocks are evenly spread across the cluster. HDFS Balancer is an Apache Hadoop daemon created for replica balancing on the file system. However, the tool is not optimized to meet potential usage demands of reliability and availability during data redistribution, besides requiring to be manually configured and triggered. In this work, we present a solution for replica balancing that takes advantage of the combined use of a proactive and a reactive approach. The former is addressed through the active monitoring of the computational environment by an agent-server structure. The latter is based on the customization of the default operation policy of the HDFS Balancer. As shown by the evaluation results, the solution automates the use of the HDFS Balancer and allows it to execute according to the reliability of the racks and the availability of the data stored in the cluster.
When Forcing Collaboration is the Most Sensible Choice

Individuals share increasing amounts of personal multimedia data, exposing themselves (uploaders) as well as others (data subjects). Non-consensual sharing of multimedia data that depicts others raises so-called multiparty privacy conflicts (MPCs), which can have severe consequences. To limit the incidence of MPCs, a family of Precautionary mechanisms have recently been developed that force uploaders to collaborate with the other data subjects to prevent MPCs. However, there is still very little work on understanding how users perceive the precautionary mechanisms together with which ones they prefer and why. In addition, precautionary mechanisms have some limitations, e.g., they require linking content to the co-owners' identity. Therefore, we also explore alternatives to precautionary mechanisms and propose a new class of solutions-Dissuasive mechanisms-that aim at deterring the uploaders from sharing without consent. We then present a user-centric comparison of precautionary and dissuasive mechanisms, through a large-scale survey (N = 1792; a representative sample of adult Internet users). Our results showed that respondents prefer precautionary to dissuasive mechanisms. These enforce collaboration, provide more control to the data subjects, but also they reduce uploaders' uncertainty around what is considered appropriate for sharing. We learned that threatening legal consequences is the most desirable dissuasive mechanism, and that respondents prefer the mechanisms that threaten users with immediate consequences (compared with delayed consequences). Dissuasive mechanisms are in fact well received by frequent sharers and older users, while precautionary mechanisms are preferred by women and younger users. We discuss the implications for design, including considerations about side leakages, consent collection, and censorship.
Reducing the subscription latency in reliable causal publish-subscribe systems

Publish-subscribe systems are a powerful abstraction to build distributed applications. This paper addresses the problem of reducing subscription latency in reliable publish-subscribe systems. In most systems that offer reliability guarantees, a subscriber needs to wait until its subscription has been propagated throughout the entire system, and known by all relevant publishers, before starting to receive events. Interestingly, this may happen even when a previously deployed subscription covers a new one. In this paper, we study the properties that need to be satisfied to reduce the subscription latency and propose a new publish-subscribe system that leverages causal order multicast to offer low subscription latency when these conditions are met.
Decentralised Learning in Federated Deployment Environments

Decentralised learning is attracting more and more interest because it embodies the principles of data minimisation and focused data collection, while favouring the transparency of purpose specification (i.e., the objective for which a model is built). Cloud-centric-only processing and deep learning are no longer strict necessities to train high-fidelity models; edge devices can actively participate in the decentralised learning process by exchanging meta-level information in place of raw data, thus paving the way for better privacy guarantees. In addition, these new possibilities can relieve the network backbone from unnecessary data transfer and allow it to meet strict low-latency requirements by leveraging on-device model inference. This survey provides a detailed and up-to-date overview of the most recent contributions available in the state-of-the-art decentralised learning literature. In particular, it originally provides the reader audience with a clear presentation of the peculiarities of federated settings, with a novel taxonomy of decentralised learning approaches, and with a detailed description of the most relevant and specific system-level contributions of the surveyed solutions for privacy, communication efficiency, non-IIDness, device heterogeneity, and poisoning defense.
Delivery Work and the Experience of Social Isolation

The isolating nature of platform-based work, particularly gig work involving deliveries, has created unintended consequences over how workers engage with peers, friends, family, and society in general. We performed a qualitative study involving interviews with 21 delivery workers in Bangalore, India to understand how workers experienced and responded to social isolation perpetuated by the nature and daily function of their work. We found that the stigma and individual nature of app-based delivery work restricts access to inter-relational and instrumental support. As a response, workers organized peer networks for both companionship and emergency assistance. We analyze how the cultural context of India heightens these experiences, and offer ideas for mitigating the risks of isolation as a result of gig work.
Fuzzy time series for predicting phenological stages of apple trees

In southern Brazil, apple production is influenced by several factors that affect productivity, where climate conditions are among the most challenging factors. The thermal conditions along spring and summer influence the development of apple trees. The phenology of apple trees vary across harvests, mainly due to the air temperature. The number of days between phenological events increases as the minimum air temperature decreases. Predictive models to estimate phenological stages in apple trees as a function of the climate are essential to anticipate problems, helping agricultural technicians and engineers. Studies on the relationship between meteorological factors and phenological stages of other cultures, including fruit trees, are available in the literature. However, no previous work employs Fuzzy Time Series prediction mechanisms in the context of apple trees. In this work, we propose a methodology to evaluate different Fuzzy Time Series variants and select the best performing one to predict the duration of phenological stages of apple trees as a function of temperature. In particular, we are considering univariate and multivariate methods. We conducted experiments using real data from a Brazilian agricultural institution, whose results evidence the effectiveness on the prediction of phenological stages of apple trees.
DiverJS: path exploration heuristic for difference analysis of event-driven code

The existing methods of difference analysis cannot cope with event-driven code well, since they explore only the input space for program input like integers and strings, but do not explore the event space for all possible event sequences. This paper proposes a novel heuristic, as well as a tool called DiverJS, for performing difference analysis that copes well with both the input space and event space. To efficiently explore huge space, DiverJS prunes redundant event sequences based on Dynamic Partial Order Reduction (DPOR). DiverJS also stochastically switches two exploration heuristics: (1) one aims to increase code coverage, using shared variable information, derived from Write-Read (WR) set and dynamic taint analysis, (2) the other aims to guide the execution to the location of code changes, using the distance between the branch to be negated and the change. We conducted a preliminary experiment to evaluate the detection accuracy of program behavioral differences, and the efficiency of exploration by the number of paths. The result shows DiverJS outperformed the existing methods; DiverJS detected the differences with higher detection accuracy in fewer paths, which suggests our DiverJS's difference analysis is effective and efficient.
Automatic Vulnerability Detection in Embedded Devices and Firmware

In the era of the internet of things (IoT), software-enabled inter-connected devices are of paramount importance. The embedded systems are very frequently used in both security and privacy-sensitive applications. However, the underlying software (a.k.a. firmware) very often suffers from a wide range of security vulnerabilities, mainly due to their outdated systems or reusing existing vulnerable libraries; which is evident by the surprising rise in the number of attacks against embedded systems. Therefore, to protect those embedded systems, detecting the presence of vulnerabilities in the large pool of embedded devices and their firmware plays a vital role. To this end, there exist several approaches to identify and trigger potential vulnerabilities within deployed embedded systems firmware. In this survey, we provide a comprehensive review of the state-of-the-art proposals, which detect vulnerabilities in embedded systems and firmware images by employing various analysis techniques, including static analysis, dynamic analysis, symbolic execution, and hybrid approaches. Furthermore, we perform both quantitative and qualitative comparisons among the surveyed approaches. Moreover, we devise taxonomies based on the applications of those approaches, the features used in the literature, and the type of the analysis. Finally, we identify the unresolved challenges and discuss possible future directions in this field of research.
Finding Place in a Design Space

Many organizations have adopted design processes that integrate community voices to discover the real problems that communities face. Online discussion forums offer a familiar and flexible technology that can help facilitate discussion around problems and potential solutions. However, we lack understanding about what information community members share, how that information is structured, and how social interactions affect design processes at scale. This paper presents a mixed-methods analysis of Canvas, a learning management system, which enables users to contribute to the design of the platform by sharing and deliberating on problems and solutions in a discussion forum. We collected and analyzed 1412 ideas and 18,335 associated comments shared on the Canvas discussion forum. We found that the distributed nature of design information, the presence of duplicate ideas, and contributors' gaming behaviors made it difficult for the community to make sense of the design discussion. These gaming behaviors also constitute a new concern for participatory design research. Finally, we reflect on how Canvas community members contribute information to a shared design space and how future systems could more effectively coordinate community design efforts.
Yet another BLE technology based tracking system

There are numerous systems for analyzing human mobility for outdoor environments at the scale of large regions or entire cities. Most of them use mobile phones to capture data information. However, these systems are agnostic to a specific business field, do not consider indoor movement, do not take into account the participants' employee or client category, and usually do not assign semantics to the visited locations. In this work, we first developed a low-cost tracking system to detect, collect, process, and store users' movement and presence data in indoor environments. Such an approach takes into consideration the users' category and location semantic. We collected these data in two university buildings used by faculty, staff, and students. We suggest that our work could monitor social distancing in indoor environments due to its human presence detection capability, whenever required, such as for the COVID-19 pandemic.
ISSumSet: a tweet summarization dataset hidden in a TREC track

A key issue for Twitter users relates to the summarization of the continuous and overwhelming stream of information. Many approaches for tweet summarization were proposed in the literature. It is however difficult to compare them given the lack of standard and accessible test collection. This absence can be due to the efforts needed to construct such a (large) dataset. In this paper, we propose to capitalize on the dataset proposed for the TREC Incident Streams track, which was not intended to evaluate automatic summarization. We show why and how this dataset is usable for this purpose, focusing on extractive summarization. Indeed, when producing additional annotations on a subset of the TREC Incident Streams (IS) dataset with particular initial assessors' annotations, it appears to respect the criteria identified in the literature for automatic summarization. For this, we studied the original TREC IS dataset and then proposed a subset summarizing each event, based on the initial assessors' annotations. This subset is evaluated according to the criteria previously mentioned. Several widely used state-of-the-art models for automatic text summarization, some specific to tweets and some adapted to tweet summarization, were finally tested on the proposed dataset. For easy reproducibility, the code used to build the dataset, our additional annotations, and the experiments made on the dataset are provided on our Github.
Object Detection Using Deep Learning Methods in Traffic Scenarios

The recent boom of autonomous driving nowadays has made object detection in traffic scenes a hot topic of research. Designed to classify and locate instances in the image, this is a basic but challenging task in the computer vision field. With its powerful feature extraction abilities, which are vital for object detection, deep learning has expanded its application areas to this field during the past several years and thus achieved breakthroughs. However, even with such powerful approaches, traffic scenarios have their own specific challenges, such as real-time detection, changeable weather, and complex lighting conditions. This survey is dedicated to summarizing research and papers on applying deep learning to the transportation environment in recent years. More than 100 research papers are covered, and different aspects such as key generic object detection frameworks, categorized object detection applications in traffic scenario, evaluation metrics, and classified datasets are included. Some open research fields are also provided. We believe that it is the first survey focusing on deep learning-based object detection in traffic scenario.