Reviewerly/exHarmony huggingface dataset:
Multiple-view D<sup>2</sup>NNs array: realizing robust 3D object recognition

As an optical-based classifier of the physical neural network, the independent diffractive deep neural network ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">D</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">N</mml:mi> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> </mml:math> ) can be utilized to learn the single-view spatial featured mapping between the input lightfields and the truth labels by preprocessing a large number of training samples. However, it is still not enough to approach or even reach a satisfactory classification accuracy on three-dimensional (3D) targets owing to already losing lots of effective lightfield information on other view fields. This Letter presents a multiple-view <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">D</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">N</mml:mi> <mml:mi mathvariant="normal">N</mml:mi> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> </mml:math> array (MDA) scheme that provides a significant inference improvement compared with individual <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">D</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">N</mml:mi> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> </mml:math> or Res- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">D</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">N</mml:mi> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> </mml:math> by constructing a different complementary mechanism and then merging all base learners of distinct views on an electronic computer. Furthermore, a robust multiple-view <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">D</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">N</mml:mi> <mml:mi mathvariant="normal">N</mml:mi> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> </mml:math> array (r-MDA) framework is demonstrated to resist the redundant spatial features of invalid lightfields due to severe optical disturbances.
Indian Sign Language Recognition Using Machine Learning Techniques

Abstract Automatic conversion of sign language to text or speech is indeed helpful for interaction between deaf or mute people with people who even do not have knowledge of sign language. This is the demand of current times to develop an automatic system to convert ISL signs to normal text and vice versa. This will be beneficial for both communities to express their fillings to one another in accessing publicly available facilities like ticketing, banking services, traffic signals, etc. A new feature extraction and selection technique using structural features and some of the best available classifiers are proposed to recognize ISL signs for better communication for computer‐human interface. This paper narrates a system for automatic recognition of ISL immobile numeric signs, in which a standard digital camera was only used to acquire the signs, no wearable devices are required to capture electrical signals. The system is intended to convert isolated digit signs into text, that is, each entered sign image should contain precisely one numeric sign. To recognize ISL sign images in real time environment, a sign database containing ISL digits is created which contains 5000 images, 500 images for each numeral sign (0‐9). Among two classifiers, the k‐Nearest Neighbor outperforms the Naive Bayes classifier in stipulations of classification accuracy.
Bootstrapped Unsupervised Sentence Representation Learning

As high-quality labeled data is scarce, unsupervised sentence representation learning has attracted much attention.In this paper, we propose a new framework with a two-branch Siamese Network which maximizes the similarity between two augmented views of each sentence.Specifically, given one augmented view of the input sentence, the online network branch is trained by predicting the representation yielded by the target network of the same sentence under another augmented view.Meanwhile, the target network branch is bootstrapped with a moving average of the online network.The proposed method significantly outperforms other state-of-the-art unsupervised methods on semantic textual similarity (STS) and classification tasks.It can be adopted as a post-training procedure to boost the performance of the supervised methods.We further extend our method for learning multilingual sentence representations and demonstrate its effectiveness on cross-lingual STS tasks.
Learning Light-Weight Translation Models from Deep Transformer

Recently, deep models have shown tremendous improvements in neural machine translation (NMT). However, systems of this kind are computationally expensive and memory intensive. In this paper, we take a natural step towards learning strong but light-weight NMT systems. We proposed a novel group-permutation based knowledge distillation approach to compressing the deep Transformer model into a shallow model. The experimental results on several benchmarks validate the effectiveness of our method. Our compressed model is 8 times shallower than the deep model, with almost no loss in BLEU. To further enhance the teacher model, we present a Skipping Sub-Layer method to randomly omit sub-layers to introduce perturbation into training, which achieves a BLEU score of 30.63 on English-German newstest2014. The code is publicly available at https://github.com/libeineu/GPKD.
Prototyping a Smart Contract Based Public Procurement to Fight Corruption

Corruption in public procurement is a worldwide appearance that causes immense financial and reputational damages. Especially in developing countries, corruption is a widespread issue due to secrecy and lack of transparency. An important instrument for transparency and accountability assurance is the record which is managed and controlled by recordkeeping systems. Blockchain technology and more precisely blockchain-based smart contracts are emerging technological tools that can be used as recordkeeping systems and a tool to mitigate some of the fraud involving public procurement records. Immutability, transparency, distribution and automation are some of the features of smart contracts already implemented in several applications to avoid malicious human interference. In this paper, we discuss some of the frauds in public procurement, and we propose smart contracts to automatize different stages of the public procurement procedure attempting to fix their biggest current weaknesses. The processes we have focused on include the bidding process, supplier habilitation and delivery verification. In the three subprocesses, common irregularities include human fallibility, improper information disclosure and hidden agreements which concern not only governments but also civil society. To show the feasibility and usability of our proposal, we have implemented a prototype that demonstrates the process using sample data.
Factors that influence data sharing through data sharing platforms: A qualitative study on the views and experiences of cohort holders and platform developers

Background Infrastructures are being developed to enhance and facilitate the sharing of cohort data internationally. However, empirical studies show that many barriers impede sharing data broadly. Purpose Therefore, our aim is to describe the barriers and concerns for the sharing of cohort data, and the implications for data sharing platforms. Methods Seventeen participants involved in developing data sharing platforms or tied to cohorts that are to be submitted to platforms were recruited for semi-structured interviews to share views and experiences regarding data sharing. Results Credit and recognition, the potential misuse of data, loss of control, lack of resources, socio-cultural factors and ethical and legal barriers are elements that influence decisions on data sharing. Core values underlying these reasons are equality, reciprocity, trust, transparency, gratification and beneficence. Conclusions Data generators might use data sharing platforms primarily for collaborative modes of working and network building. Data generators might be unwilling to contribute and share for non-collaborative work, or if no financial resources are provided for sharing data.
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark

Commonsense AI has long been seen as a near impossible goal---until recently. Now, research interest has sharply increased with an influx of new benchmarks and models. We propose two new ways to evaluate commonsense models, emphasizing their generality on new tasks and building on diverse, recently introduced benchmarks. First, we propose a new multitask benchmark, Rainbow, to promote research on commonsense models that generalize well over multiple tasks and datasets. Second, we propose a novel evaluation, the cost equivalent curve, that sheds new insight on how the choice of source datasets, pretrained language models, and transfer learning methods impacts performance and data efficiency. We perform extensive experiments---over 200 experiments encompassing 4800 models---and report multiple valuable and sometimes surprising findings, e.g., that transfer almost always leads to better or equivalent performance if following a particular recipe, that QA-based commonsense datasets transfer well with each other, while commonsense knowledge graphs do not, and that perhaps counter-intuitively, larger models benefit more from transfer than smaller ones. Last but not least, we introduce a new universal commonsense reasoning model, UNICORN, that establishes new state-of-the-art performance across 8 popular commonsense benchmarks, aNLI (87.3%), CosmosQA (91.8%), HellaSWAG (93.9%), PIQA (90.1%), SocialIQa (83.2%), WinoGrande (86.6%), CycIC (94.0%) and CommonsenseQA (79.3%).
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction

We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG).Especially in this presumed sentential RE setting, the context of a single sentence is often sparse.This paper introduces the KGPool method to address this sparsity, dynamically expanding the context with additional facts from the KG.It learns the representation of these facts (entity alias, entity descriptions, etc.) using neural methods, supplementing the sentential context.Unlike existing methods that statically use all expanded facts, KGPool conditions this expansion on the sentence.We study the efficacy of KGPool by evaluating it with different neural models and KGs (Wikidata and NYT Freebase).Our experimental evaluation on standard datasets shows that by feeding the KGPool representation into a Graph Neural Network, the overall method is significantly more accurate than state-of-the-art methods.
Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos

Despite the recent progress, 3D multi-person pose estimation from monocular videos is still challenging due to the commonly encountered problem of missing information caused by occlusion, partially out-of-frame target persons, and inaccurate person detection. To tackle this problem, we propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses that does not require camera parameters. In particular, we introduce a human-joint GCN, which unlike the existing GCN, is based on a directed graph that employs the 2D pose estimator's confidence scores to improve the pose estimation results. We also introduce a human-bone GCN, which models the bone connections and provides more information beyond human joints. The two GCNs work together to estimate the spatial frame-wise 3D poses and can make use of both visible joint and bone information in the target frame to estimate the occluded or missing human-part information. To further refine the 3D pose estimation, we use our temporal convolutional networks (TCNs) to enforce the temporal and human-dynamics constraints. We use a joint-TCN to estimate person-centric 3D poses across frames, and propose a velocity-TCN to estimate the speed of 3D joints to ensure the consistency of the 3D pose estimation in consecutive frames. Finally, to estimate the 3D human poses for multiple persons, we propose a root-TCN that estimates camera-centric 3D poses without requiring camera parameters. Quantitative and qualitative evaluations demonstrate the effectiveness of the proposed method.
Language Model Evaluation Beyond Perplexity

We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language.To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the humangenerated text on which they were trained.We provide a framework-paired with significance tests-for evaluating the fit of language models to these trends.We find that neural language models appear to learn only a subset of the tendencies considered, but align much more closely with empirical trends than proposed theoretical distributions (when present).Further, the fit to different distributions is highly-dependent on both model architecture and generation strategy.As concrete examples, text generated under the nucleus sampling scheme adheres more closely to the typetoken relationship of natural language than text produced using standard ancestral sampling; text from LSTMs reflects the natural language distributions over length, stopwords, and symbols surprisingly well.
Mimicking the Brain’s Cognition of Sarcasm From Multidisciplines for Twitter Sarcasm Detection

Sarcasm is a sophisticated construct to express contempt or ridicule. It is well-studied in multiple disciplines (e.g., neuroanatomy and neuropsychology) but is still in its infancy in computational science (e.g., Twitter sarcasm detection). In contrast to previous methods that are usually geared toward a single discipline, we focus on the multidisciplinary cross-innovation, i.e., improving embryonic sarcasm detection in computational science by leveraging the advanced knowledge of sarcasm cognition in neuroanatomy and neuropsychology. In this work, we are oriented toward sarcasm detection in social media and correspondingly propose a multimodal, multi-interactive, and multihierarchical neural network ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M_{3}N_{2} $ </tex-math></inline-formula> ). We select Twitter, image, text in image, and image caption as the input of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M_{3}N_{2} $ </tex-math></inline-formula> since the brain’s perception of sarcasm requires multiple modalities. To reasonably address the multimodalities, we introduce singlewise, pairwise, triplewise, and tetradwise modality interactions incorporating gate mechanism and guide attention (GA) to simulate the interactions and collaborations of involved regions in the brain while perceiving multiple modes. Specifically, we exploit a multihop process for each modality interaction to extract modal information multiple times using GA for obtaining multiperspective information. Also, we adopt a two-hierarchical structure leveraging self-attention accompanied by attention pooling to integrate multimodal semantic information from different levels mimicking the brain’s first- and second-order comprehensions of sarcasm. Experimental results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M_{3}N_{2} $ </tex-math></inline-formula> achieves competitive performance in sarcasm detection and displays powerful generalization ability in multimodal sentiment analysis and emotion recognition.
Cryptocurrencies and Fraudulent Transactions: Risks, Practices, and Legislation for Their Prevention in Europe and Spain

Cryptocurrencies have been developing very rapidly in recent years, and their use is becoming more and more widespread in different areas. The use of digital currencies for legal uses is advancing along with technological development, but, at the same time, criminal activities are also emerging to take advantage of this boom. The aim of this paper has been, first, to analyze the various ways in which individuals and criminal organizations have taken advantage of the phenomenon of cryptocurrencies to carry out fraudulent activities such as laundering money of illicit origin and, second, to provide an overview of the legal tools that have been developed in this regard in Europe and, more specifically, in Spain to combat these activities. Undoubtedly, cryptocurrencies bring great benefits to the economy, but it is also necessary to know the risks and abuses that have been developed to prevent them.
A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation

Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model.
Towards Generating and Evaluating Iconographic Image Captions of Artworks

To automatically generate accurate and meaningful textual descriptions of images is an ongoing research challenge. Recently, a lot of progress has been made by adopting multimodal deep learning approaches for integrating vision and language. However, the task of developing image captioning models is most commonly addressed using datasets of natural images, while not many contributions have been made in the domain of artwork images. One of the main reasons for that is the lack of large-scale art datasets of adequate image-text pairs. Another reason is the fact that generating accurate descriptions of artwork images is particularly challenging because descriptions of artworks are more complex and can include multiple levels of interpretation. It is therefore also especially difficult to effectively evaluate generated captions of artwork images. The aim of this work is to address some of those challenges by utilizing a large-scale dataset of artwork images annotated with concepts from the Iconclass classification system. Using this dataset, a captioning model is developed by fine-tuning a transformer-based vision-language pretrained model. Due to the complex relations between image and text pairs in the domain of artwork images, the generated captions are evaluated using several quantitative and qualitative approaches. The performance is assessed using standard image captioning metrics and a recently introduced reference-free metric. The quality of the generated captions and the model's capacity to generalize to new data is explored by employing the model to another art dataset to compare the relation between commonly generated captions and the genre of artworks. The overall results suggest that the model can generate meaningful captions that indicate a stronger relevance to the art historical context, particularly in comparison to captions obtained from models trained only on natural image datasets.
Larger-Scale Transformers for Multilingual Masked Language Modeling

Recent work has demonstrated the effectiveness of cross-lingual language model pretraining for cross-lingual understanding. In this study, we present the results of two larger multilingual masked language models, with 3.5B and 10.7B parameters. Our two new models dubbed and outperform XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on average while handling 99 more languages. This suggests larger capacity models for language understanding may obtain strong performance on high-resource languages while greatly improving low-resource languages. We make our code and models publicly available.
University Graduate Students and Digital Competence: Are Future Secondary School Teachers Digitally Competent?

Information and communication technology is very important both for teacher training and for delivering quality education. Therefore, university graduates must have a level of qualification in digital competencies suitable for their entry into the world of work. The research analyzes various aspects related to the use of technological resources by graduate students who were currently studying for a university master’s degree in compulsory secondary education, and the relationship between their use based on gender and/or age. A descriptive cross-sectional survey is carried out. We use an ad hoc questionnaire that shows acceptable reliability. The results show that graduate students make traditional use of ICT—both for searching for information and using digital resources for teaching. Moreover, differences were found based on gender and age regarding participants’ perception of their digital competence.