Reviewerly/exHarmony huggingface dataset:
Engaging young people in the co-design of a brand and online platform for a public health organization

This article explores how co-design was used to engage young people in the design of a new brand identity and online platform for the Auckland District Health Board’s Peer Sexuality Support Programme (PSSP). Unlike conventional branding practices that do not generally engage users as co-creators in the early discovery stages of the design process, in this research young people were brought into the design process as informants and co-designers. This drove both brand strategy and touchpoint design, positioning the brand to be better accepted by the young people involved in the programme. A series of discovery and evaluation co-design workshops, with a diverse range of PSSP youth leaders, offered game-like interactive activities and the opportunity for open discussions. Engaging young people in fun and meaningful ways helped them uncover and share insights that could only be possible by directly involving them in the design process. The outputs of this co-designed brand – FRANK – incorporated a full visual identity and a proposal for an online platform. The final brand design elicited strong positive responses from PSSP youth leaders, highlighting the importance of involving young people in the design of products and services that address issues affecting them.
An Improved Distributed Query for Large-Scale RDF Data

The rigid structure of the traditional relational database leads to data redundancy, which seriously affects the efficiency of the data query and cannot effectively manage massive data. To solve this problem, we use distributed storage and parallel computing technology to query RDF data. In order to achieve efficient storage and retrieval of large-scale RDF data, we combine the respective advantage of the storage model of the relational database and the distributed query. To overcome the disadvantages of storing and querying RDF data, we design and implement a breadth-first path search algorithm based on the keyword query on a distributed platform. We conduct the LUBM query statements respectively with the selected data sets. In experiments, we compare query response time in different conditions to evaluate the feasibility and correctness of our approaches. The results show that the proposed scheme can reduce the storage cost and improve query efficiency.
UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation

SemEval Task 4 Commonsense Validation and Explanation Challenge is to validate whether a system can differentiate natural language statements that make sense from those that do not make sense.Two subtasks, A and B, are focused in this work, i.e., detecting against-common-sense statements and selecting explanations of why they are false from the given options.Intuitively, commonsense validation requires additional knowledge beyond the given statements.Therefore, we propose a system utilising pre-trained sentence transformer models based on BERT, RoBERTa and DistillBERT architectures to embed the statements before classification.According to the results, these embeddings can improve the performance of the typical MLP and LSTM classifiers as downstream models of both subtasks compared to regular tokenised statements.These embedded statements are shown to comprise additional information from external resources which help validate common sense in natural language.
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View

The Transformer architecture is widely used in natural language processing. Despite its success, the design principle of the Transformer remains elusive. In this paper, we provide a novel perspective towards understanding the architecture: we show that the Transformer can be mathematically interpreted as a numerical Ordinary Differential Equation (ODE) solver for a convection-diffusion equation in a multi-particle dynamic system. In particular, how words in a sentence are abstracted into contexts by passing through the layers of the Transformer can be interpreted as approximating multiple particles' movement in the space using the Lie-Trotter splitting scheme and the Euler's method. Given this ODE's perspective, the rich literature of numerical analysis can be brought to guide us in designing effective structures beyond the Transformer. As an example, we propose to replace the Lie-Trotter splitting scheme by the Strang-Marchuk splitting scheme, a scheme that is more commonly used and with much lower local truncation errors. The Strang-Marchuk splitting scheme suggests that the self-attention and position-wise feed-forward network (FFN) sub-layers should not be treated equally. Instead, in each layer, two position-wise FFN sub-layers should be used, and the self-attention sub-layer is placed in between. This leads to a brand new architecture. Such an FFN-attention-FFN layer is Macaron-like, and thus we call the network with this new architecture the Macaron Net. Through extensive experiments, we show that the Macaron Net is superior to the Transformer on both supervised and unsupervised learning tasks. The reproducible codes and pretrained models can be found at this https URL
DaDoEval @ EVALITA 2020: Same-Genre and Cross-Genre Dating of Historical Documents

In this we paper present Tint 2.0, an open-source, fast and extendable Natural Language Processing suite for Italian based on Stanford CoreNLP.The new release includes some improvements of the existing NLP modules, and a set of new text processing components for finegrained linguistic analysis that were not available so far, including multi-word expression recognition, affix analysis, readability and classification of complex verb tenses.
GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering

This paper describes the system proposed by the Random team for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection.We focus our approach on the detection problem.Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or lost senses.To this end, we define a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods.We compare the proposed approach with a number of similarity-based thresholds.We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system.
Guidelines and Their Challenges in Implementing CSIRT in Ecuador

Increasing in cyber threats in Ecuador and worldwide, motivates to search cybersecurity mechanisms that support the cybersecurity incident response promptly, on time, so that operations of the organization are not affected. One primary strategy is to establish Cybersecurity Incident Response Teams (CSIRTs) to face cybersecurity attacks; organizations like ENISA and NIST have developed a set of steps necessary to establish a CSIRT. However, in Ecuador there are few CSIRT found in academic, financial, military, or government fields and many of them have a few years of building. The purpose of this study is to develop an analysis of the management and barriers that have been present in the implementation of CSIRTS in Ecuador and to identify possible actions to face these barriers and improve the cybersecurity incident response in the Country. Defining a guideline to avoid nonconformities when implementing a CSIRT and in this way, national security can be improved.
Stepping over Obstacles with Augmented Reality based on Visual Exproprioception

The purpose of this study is to analyze the different kinds of ex- proprioceptive visual cues on an Augmented Reality (AR) system during gait exercise, on top of understanding which cues provide the best visualizations in stepping over obstacles. The main problem for users is to understand the position of virtual objects relative to themselves. Since visual exproprioception provides information about the body position in relation to the environment, it has the possibility to yield positive effects with regards to position control and gait biomechanics in the AR system. This research was born as part of the collaboration with the staff of Takanohara Central Hospital in Japan. Twenty-seven individuals were invited to take part in the user study to test three visual interfaces. The task of the mentioned user study involves making the subjects cross and avoid virtual obstacles of different heights that come from different directions. The AR application was implemented in the experiment by using the Head-Mounted Display (HMD) Microsoft HoloLens. Data obtained from the experiment revealed that the interface projected in front of the user from a third-person point of view resulted to improvements in terms of posture, visual stimuli, and safety.
Team DiSaster at SemEval-2020 Task 11: Combining BERT and Hand-crafted Features for Identifying Propaganda Techniques in News

The identification of communication techniques in news articles such as propaganda is important, as such techniques can influence the opinions of large numbers of people.Most work so far focused on the identification at the news article level.Recently, a new dataset and shared task has been proposed for the identification of propaganda techniques at the finer-grained span level.This paper describes our system submission to the subtask of technique classification (TC) for the SemEval 2020 shared task on detection of propaganda techniques in news articles.We propose a method of combining neural BERT representations with hand-crafted features via stacked generalization.Our model has the added advantage that it combines the power of contextual representations from BERT with simple span-based and article-based global features.We present an ablation study which shows that even though BERT representations are very powerful also for this task, BERT still benefits from being combined with carefully designed task-specific features.
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models

Embedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem.A popular solution is to train a character-level neural network to reproduce the embeddings from a standard word embedding model.The trained network is then used to assign vectors to any input string, including OOV and rare words.We enhance this approach and introduce an algorithm that iteratively refines and improves both word-and character-level models.We demonstrate that our method outperforms the existing algorithms on 5 word similarity data sets, and that it can be successfully applied to job title normalization, an important problem in the e-recruitment domain that suffers from the OOV problem.
AI in Video Analysis, Production and Streaming Delivery

Abstract Video technologies evolve steadily with the evolution of machine learning and artificial intelligence which use cloud platform and video transcoding for better video production, delivery and live streaming. AI has profound effect on media and film industry, from content delivery to viewer’s experience. AI serves the richer and realistic experiences in personalization of user experience for video production and analysis process. AI changes the fact of manual tasks and facilitates deep content indexing. Quality assessment becomes easier when AI scrutinizes the content. Personal and interactive video provides new delightful viewing experiences. AI generates new level of interactions at scene by dichotomizing the videos and builds more practical access methods within the content.
Euclidean, Manhattan and Minkowski Distance Methods For Clustering Algorithms

The process of grouping a set of physical objects into classes of similar objects is called clustering. Clustering is a process of grouping the data into classes or cluster so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. This system studies how to compute dissimilarities between objects represented by interval scaled variables. This system is intended to implement the dissimilarity matrix for interval-scaled variables using Euclidean, Manhattan, and Minkowski distance methods. This stores a collection of proximities that are available for all pairs of n objects.
Towards the First Machine Translation System for Sumerian Transliterations

The Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history.We present the first attempt to translate Sumerian texts into English automatically.We publicly release high-quality corpora for standardized training and evaluation and report results on experiments with supervised, phrase-based, and transfer learning techniques for machine translation.Quantitative and qualitative evaluations indicate the usefulness of the translations.Our proposed methodology provides a broader audience of researchers with novel access to the data, accelerates the costly and time-consuming manual translation process, and helps them better explore the relationships between Sumerian cuneiform and Mesopotamian culture.
Continuous Development and Testing of Access and Usage Control

Context: Development and testing of access/usage control systems is a growing research area. With new trends in software development such as DevOps, the development of access/usage control also has to evolve. Objective: The main aim of this paper is to provide an overview of research proposals in the area of continuous development and testing of access and usage control systems. Method: The paper uses a Systematic Literature Review as a research method to define the research questions and answer them following a systematic approach. With the specified search string, 210 studies were retrieved. After applying the inclusion and exclusion criteria in two phases, a final set of 20 primary studies was selected for this review. Results: Results show that primary studies are mostly published in security venues followed by software engineering venues. Furthermore, most of the studies are based on the standard XACML access control language. In addition, a significant portion of the proposals for development and testing is automated with test assessment and generation the most targeted areas. Some general guidelines for leveraging continuous developing and testing of the usage and access control systems inside the DevOps process are also provided.
Imaging and analysis of near-infrared fluorescence from tiger chert and Native American tiger chert artifacts

Tiger chert was used extensively by Native American people of the Clovis lithic industry as the source material for stone tools. Although tiger chert occurs naturally only within the Bridger Formation of Southwest Wyoming, tiger chert artifacts have been found throughout the American west. This paper explores the previously unreported near-infrared fluorescence emitted from tiger chert upon visible excitation. The two objectives of this study were to demonstrate the utility of near-infrared fluorescence imaging (NIRFI) to tiger chert artifacts and to identify the chemical origin of the observed fluorescence. Tiger chert was analyzed by visible and NIRFI, SEM-EDX, ICP-OES, and fluorescence spectroscopy. NIRFI can be easily used to discriminate between tiger chert artifacts and the environment, since most materials such as soil and most rocks appear black in the resultant image. This suggests NIRFI should be utilized by archeologists at any site where tiger chert has been previously identified. Additionally, NIRFI was used to differentiate between tiger chert artifacts and an artificially weathered, recently-knapped tiger chert point, suggesting that NIRFI could be used to distinguish genuine tiger chert artifacts from counterfeits. Tiger chert fluorescence was confined to the manganese-and-iron-rich surface, suggesting the presence of desert varnish; however, red agate from the same site did not exhibit fluorescence, instead implicating tiger-chert-specific weathering. Thus, the chemical origin of fluorescence was not conclusively identified.
HearSmoking: Smoking Detection in Driving Environment via Acoustic Sensing on Smartphones

Driving safety has drawn much public attention in recent years due to the fast-growing number of cars. Smoking is one of the threats to driving safety but is often ignored by drivers. Existing works on smoking detection either work in contact manner or need additional devices. This motivates us to explore the practicability of using smartphones to detect smoking events in driving environment. In this paper, we propose a cigarette smoking detection system, named HearSmoking, which only uses acoustic sensors on smartphones to improve driving safety. After investigating typical smoking habits of drivers, including hand movement and chest fluctuation, we design an acoustic signal to be emitted by the speaker and received by the microphone. We calculate Relative Correlation Coefficient of received signals to obtain movement patterns of hands and chest. The processed data is sent into a trained Convolutional Neural Network for classification of hand movement. We also design a method to detect respiration at the same time. To improve system performance, we further analyse the periodicity of the composite smoking motion. Through extensive experiments in real driving environments, HearSmoking detects smoking events with an average total accuracy of 93.44 percent in real-time.
Hidden Markov Model Based Variable Structured Multiple Model Algorithm

In this study, a novel methodology is proposed for more accurate target tracking. The proposed method is basically built on the merging of interacting multiple model (IMM) structures using Hidden Markov Model (HMM). Thus, more models are used than the ordinary IMM algorithm, but more accurate state vectors are estimated by selecting the most likely ones. The proposed algorithm is compared with the variable structure IMM (VSIMM) algorithm, which is the most similar methodology in the literature, in MATLAB environment and the results are presented.
Hierarchical Network with Label Embedding for Contextual Emotion Recognition

Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recognition, a label embedding matrix is trained by joint learning, which contributes to the final prediction. Comparison experiments are conducted on Chinese emotional corpus RenCECps, and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task.
Deep Poisoning: Towards Robust Image Data Sharing against Visual Disclosure

Due to respectively limited training data, different entities addressing the same vision task based on certain sensitive images may not train a robust deep network. This paper introduces a new vision task where various entities share task-specific image data to enlarge each other's training data volume without visually disclosing sensitive contents (e.g. illegal images). Then, we present a new structure-based training regime to enable different entities learn task-specific and reconstruction-proof image representations for image data sharing. Specifically, each entity learns a private Deep Poisoning Module (DPM) and insert it to a pre-trained deep network, which is designed to perform the specific vision task. The DPM deliberately poisons convolutional image features to prevent image reconstructions, while ensuring that the altered image data is functionally equivalent to the non-poisoned data for the specific vision task. Given this equivalence, the poisoned features shared from one entity could be used by another entity for further model refinement. Experimental results on image classification prove the efficacy of the proposed method.
Solar UAV for the Inspection and Monitoring of Photovoltaic (PV) Systems in Solar Power Plants

This paper aims to design and fabricate a prototype of a solar-powered, fixed-wing, Unmanned Aerial Vehicle (UAV) with energy harvesting capabilities that can inspect and monitor panel arrays in solar power plants. In the energy sector, workers are susceptible to hazards such as working at large heights, high voltage contact, confined areas, and variable weather. Drone technology can be used to provide real-time, high-quality data that plant managers can use to conduct safe inspections, all at a low-cost. The primary goal for the UAV concept presented in this paper is use a lightweight, renewable energy source called Thermophotovoltaics, and attach the system to the wing of a drone to significantly extend its flight time without compromising the UAV’s weight, size, or maneuverability and provide power for long-duration missions without having to land. The UAV concept will incorporate three technologies: machine learning algorithms, artificial intelligence and path-planning, and recognition methods. These methods will be used to achieve high accuracy and precision information on the degradation or defect presence on individual solar panel modules. In addition, thermal and optical imaging may reveal compromises in the solar panel array via electrical errors or structural issues.
Managing several simultaneous lines of talk in Finnish multi-party mobile messaging

As smartphones have become an integral part of the everyday life of many, we have seen a proliferation of private messaging groups in mobile apps such as WhatsApp. One characteristic of group messaging is the constant presence of multiple simultaneous lines of talk – even more so than in the dyadic mode. In this article, we explore the linguistic and technologically afforded practices of managing simultaneous lines of talk – topics, sequences, larger activities - in mobile group messaging. Specifically, we analyze (i) how new lines of talk are initiated next to already existing ones, (ii) how two prior messages are responded to consecutively, and (iii) how non-adjacent messages are responded to. Our data consist of Finnish instant group messaging, and the analysis was conducted using the Conversation Analytic method. Our study complements previous studies on the management of multiple lines of talk in mobile messaging, and more generally, multiactivity in social interaction. In particular, it presents new insights into how practices such as ‘and’-prefacing, action-labelling and platform-provided reply-marking are utilized for organizing parallel activities.
A Closer Look at Few-Shot Crosslingual Transfer: Variance, Benchmarks and Baselines.

We present a focused study of few-shot crosslingual transfer, a recently proposed NLP scenario: a pretrained multilingual encoder is first finetuned on many annotations in a high resource language (typically English), and then finetuned on a few annotations (the ``few shots'') in a target language. Few-shot transfer brings large improvements over zero-shot transfer. However, we show that it inherently has large variance and it is necessary to report results on multiple sets of few shots for stable results and to guarantee fair comparison of different algorithms. To address this problem, we publish our few-shot sets. In a study of why few-shot learning outperforms zero-shot transfer, we show that large models heavily rely on lexical hints when finetuned on a few shots and then overfit quickly. We evaluate different methods that use few-shot annotations, but do not observe significant improvements over the baseline. This calls for better ways of utilizing the few-shot annotations.