Reviewerly/exHarmony huggingface dataset:
Managing the Cloud Continuum: Lessons Learnt from a Real Fog-to-Cloud Deployment

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.
Lightweight Attended Multi-Scale Residual Network for Single Image Super-Resolution

Recently, deep convolutional neural networks (CNN) have been widely applied in the single image super-resolution (SISR) task and achieved significant progress in reconstruction performance.However, most of the existing CNN-based SR models are impractical to real-world applicants due to numerous parameters and heavy computation.To tackle this issue, we propose a lightweight attended multi-scale residual network (LAMRN) in this work.Specially, we present an attended multi-scale residual block (AMSRB) to extract multi-scale features, where we embed the efficient channel attention block (ECA) to enhance the discrimination of features.Besides, we introduce a double-attention fusion (DAF) block to fuse the low-level and high-level features efficiently.We use spatial attention and channel attention to obtain guidance from the low-level and high-level features, which is used to guide the feature fusion.Extensive experimental results demonstrate that our LAMRN achieves competitive performance against the state-ofthe-art methods with similar parameters and computational operations.
Diagnosing Vision-and-Language Navigation: What Really Matters

Vision-and-language navigation (VLN) is a multimodal task where an agent follows natural language instructions and navigates in visual environments.Multiple setups have been proposed, and researchers apply new model architectures or training techniques to boost navigation performance.However, there still exist non-negligible gaps between machines' performance and human benchmarks.Moreover, the agents' inner mechanisms for navigation decisions remain unclear.To the best of our knowledge, how the agents perceive the multimodal input is under-studied and needs investigation.In this work, we conduct a series of diagnostic experiments to unveil agents' focus during navigation.Results show that indoor navigation agents refer to both object and direction tokens when making decisions.In contrast, outdoor navigation agents heavily rely on direction tokens and poorly understand the object tokens.Transformer-based agents acquire a better cross-modal understanding of objects and display strong numerical reasoning ability than non-Transformer-based agents.When it comes to vision-and-language alignments, many models claim that they can align object tokens with specific visual targets.We find unbalanced attention on the vision and text input and doubt the reliability of such cross-modal alignments. 1
A Critical Study on Data Leakage in Recommender System Offline Evaluation

Recommender models are hard to evaluate, particularly under offline setting. In this article, we provide a comprehensive and critical analysis of the data leakage issue in recommender system offline evaluation. Data leakage is caused by not observing global timeline in evaluating recommenders e.g., train/test data split does not follow global timeline. As a result, a model learns from the user-item interactions that are not expected to be available at the prediction time. We first show the temporal dynamics of user-item interactions along global timeline, then explain why data leakage exists for collaborative filtering models. Through carefully designed experiments, we show that all models indeed recommend future items that are not available at the time point of a test instance, as the result of data leakage. The experiments are conducted with four widely used baseline models—BPR, NeuMF, SASRec, and LightGCN, on four popular offline datasets—MovieLens-25M, Yelp, Amazon-music, and Amazon-electronic, adopting leave-last-one-out data split. 1 We further show that data leakage does impact models’ recommendation accuracy. Their relative performance orders thus become unpredictable with different amount of leaked future data in training. To evaluate recommendation systems in a realistic manner in offline setting, we propose a timeline scheme, which calls for a revisit of the recommendation model design.
Spatial irradiance estimation in a thermosolar power plant by a mobile robot sensor network

An algorithm for mapping Direct Normal Irradiance (DNI) in thermosolar power plants using a Mobile Robotic Sensor Network (RSN) is presented. The algorithm selects measurement spots and allocates the RSN accordingly to carry out the dynamic estimation of DNI. A generic thermosolar power plant with a fleet of vehicles is used as a simulated case study to assess the performance of the algorithm. The results show that the proposed method allows us to obtain a spatial estimation of the DNI that could improve the performance of the plant, and that outperforms estimations based on a single pyrheliometer.
Shadow Generation for Composite Image in Real-World Scenes

Image composition targets at inserting a foreground object into a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible with background while ignoring the shadow effect of foreground on the background. In this work, we focus on generating plausible shadow for the foreground object in the composite image. First, we contribute a real-world shadow generation dataset DESOBA by generating synthetic composite images based on paired real images and deshadowed images. Then, we propose a novel shadow generation network SGRNet, which consists of a shadow mask prediction stage and a shadow filling stage. In the shadow mask prediction stage, foreground and background information are thoroughly interacted to generate foreground shadow mask. In the shadow filling stage, shadow parameters are predicted to fill the shadow area. Extensive experiments on our DESOBA dataset and real composite images demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Object-Shadow-Generation- Dataset-DESOBA.
NLQuAD: A Non-Factoid Long Question Answering Data Set

We introduce NLQuAD, the first data set with baseline methods for non-factoid long question answering, a task requiring documentlevel language understanding.In contrast to existing span detection question answering data sets, NLQuAD has non-factoid questions that are not answerable by a short span of text and demanding multiple-sentence descriptive answers and opinions.We show the limitation of the F1 score for evaluation of long answers and introduce Intersection over Union (IoU), which measures position-sensitive overlap between the predicted and the target answer spans.To establish baseline performances, we compare BERT, RoBERTa, and Longformer models.Experimental results and human evaluations show that Longformer outperforms the other architectures, but results are still far behind a human upper bound, leaving substantial room for improvements.NLQuAD's samples exceed the input limitation of most pretrained Transformer-based models, encouraging future research on long sequence language models. 1
Developing Virtual Communication Skills in Online Learning Based on Modified PBL During the Covid-19 Pandemic

This study aims to determine the virtual communication skills of students in the Vertebrate Zoology course, the subject of the Reptile Class with the example of the Varanus salvator species. The research was conducted by survey method using an adaptation observation sheet from the 2012 Greenstein instrument. Data collection was carried out on modified online Problem Based Learning (PBL) using Zoom meeting and WhatsApp Group (WAG). While presentation and organizing stages were carried out by class discussion on Zoom, the problems that have been formulated were discussed by each group using a WAG. The data obtained were analyzed quantitatively, using the percentage formula (Akbar, 2013) in the book Learning Device Instruments and showed the results that the percentage of oral communication skills was 86.73%, of the skills to understand the intent/purpose of communication was 85.65%, of the skills to use communication strategies was 86.57%, and of presentation skills was 85.19%, with each criterion falling in the category ‘Exceeded Expectations’. Meanwhile, viewing the percentage results and aiming the target of 84.57%, the two receptive communication skills listening and reading were included in the ‘Satisfactory’ category. The general percentage of communication skills of 85.16% as average was included in the category ‘Exceeding Expectations’. This finding suggests that communication which included oral communication skills, receptive communication skills, understanding the purpose of communication, using communication strategies, communicating clearly for a purpose, and presentation skills can be achieved virtually.
Adapting Interactional Observation Embedding for Counterfactual Learning to Rank

Counterfactual Learning to Rank (CLTR) becomes an attractive research topic due to its capability of training ranker with click logs. However, CLTR inherently suffers from a large amount of bias caused by confounders, variables that affect both the observation (examination) behavior and click behavior. Recent efforts to correct bias mostly focus on position bias, which assumes that each observation in a ranking list is isolated and only depends on the position. Though effective, users often engage with documents in an interactive manner. Ignoring the interactions between observations/clicks would incur a large interactional observation bias no matter how much data is collected. In this work, we leverage the embedding method to develop an Interactional Observation-Based Model (IOBM) to estimate the observation probability. We argue that while there exist complex observed and unobserved confounders for observation/click interactions, it is sufficient to use the embedding as a proxy confounder to uncover the relevant information for the prediction of the observation propensity. Moreover, the embedding could offer an alternative to the fully specified generative model for observation and decouples the complex interaction structure of observations/clicks. In our IOBM, we first learn the individual observation embedding to capture position and click information. Then, we learn the interactional observation embedding to uncover their local interaction structure. To filter out irrelevant information and reduce contextual bias, we utilize query context information and propose the intra-observation attention and the inter-observation attention, respectively. We conduct extensive experiments on two LTR benchmark datasets, demonstrating that the proposed IOBM consistently achieves better performance over the baseline models in various click situations and verifying its effectiveness of eliminating interactional observation bias.
Does Typological Blinding Impede Cross-Lingual Sharing?

Bridging the performance gap between highand low-resource languages has been the focus of much previous work.Typological features from databases such as the World Atlas of Language Structures (WALS) are a prime candidate for this, as such data exists even for very low-resource languages.However, previous work has only found minor benefits from using typological information.Our hypothesis is that a model trained in a cross-lingual setting will pick up on typological cues from the input data, thus overshadowing the utility of explicitly using such features.We verify this hypothesis by blinding a model to typological information, and investigate how cross-lingual sharing and performance is impacted.Our model is based on a cross-lingual architecture in which the latent weights governing the sharing between languages is learnt during training.We show that (i) preventing this model from exploiting typology severely reduces performance, while a control experiment reaffirms that (ii) encouraging sharing according to typology somewhat improves performance.
Modeling Context in Answer Sentence Selection Systems on a Latency Budget

Answer Sentence Selection (AS2) is an efficient approach for the design of open-domain Question Answering (QA) systems.In order to achieve low latency, traditional AS2 models score question-answer pairs individually, ignoring any information from the document each potential answer was extracted from.In contrast, more computationally expensive models designed for machine reading comprehension tasks typically receive one or more passages as input, which often results in better accuracy.In this work, we present an approach to efficiently incorporate contextual information in AS2 models.For each answer candidate, we first use unsupervised similarity techniques to extract relevant sentences from its source document, which we then feed into an efficient transformer architecture fine-tuned for AS2.Our best approach, which leverages a multi-way attention architecture to efficiently encode context, improves 6% to 11% over noncontextual state of the art in AS2 with minimal impact on system latency.All experiments in this work were conducted in English.* Work was conducted while the author was an intern at Amazon Alexa. The math of pi explained, as simply as possibleHow many digits of pi we really need?Pi, you may also remember from grade school, is not an ordinary number.It's irrational, meaning it has an endless number of decimals that never repeat.Though even cutting off pi at 15 digits allows for extremely precise measurements.If you were to draw a circle with a diameter of 25 billion miles, using 15 digits of pi, you'd only arrive at a measurement of the circumference that's off by 1.5 inches, NASA's Marc Rayman explained in a post on NASA's JPL website.And that's good enough.Of course, that hasn't stopped people from looking for more and more digits of pi.Currently, there are more than 22.4 trillion known digits, which show no hint of ending or repeating.Further reading: pi and pie.
Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.
A Data Preservation Method Based on Blockchain and Multidimensional Hash for Digital Forensics

Since digital forensics becomes more and more popular, more and more attention has been paid to the originality and validity of data, and data preservation technology emerges as the times require. However, the current data preservation models and technologies are only the combination of cryptography technology, and there is a risk of being attacked and cracked. And in the process of data preservation, human participation is also needed, which may lead to data tampering. To solve problems given, this paper presents a data preservation model based on blockchain and multidimensional hash. With the decentralization and smart contract characteristics of blockchain, data can be automatically preserved without human participation to form a branch chain of custody in the unit of case, and blockchain has good antiattack performance, which is the so-called 51% attack. Meanwhile, in order to solve the problem of data confusion and hard to query caused by the excessive number of cases, hash, cryptography, and timestamps are used to form a serialized main chain of custody. Because of the confliction problem of hash and judicial trial needs to absolutely guarantee the authenticity and validity of data, multidimensional hash is used to replace regular hash. In this way, the data preservation becomes an automatic, nonhuman-interventional process. Experiments have been carried out to show the security and effectiveness of the proposed model.
Ultimate limits of thermal pattern recognition

Quantum channel discrimination presents a fundamental task in quantum information theory, with critical applications in quantum reading, illumination, data readout, and more. The extension to multiple quantum channel discrimination has seen a recent focus to characterize potential quantum advantage associated with quantum-enhanced discriminatory protocols. In this paper, we study thermal imaging as an environment localization task, in which thermal images are modeled as ensembles of Gaussian phase insensitive channels with identical transmissivity, and pixels possess properties according to background (cold) or target (warm) thermal channels. Via the teleportation stretching of adaptive quantum protocols, we derive ultimate limits on the precision of pattern classification of abstract, binary thermal image spaces, and show that quantum-enhanced strategies may be used to provide significant quantum advantage over known optimal classical strategies. The environmental conditions and necessary resources for which advantage may be obtained are studied and discussed. We then numerically investigate the use of quantum-enhanced statistical classifiers, where quantum sensors are used in conjunction with machine-learning image classification methods. Proving definitive advantage in the low-loss regime, this work motivates the use of quantum-enhanced sources for short-range thermal imaging and detection techniques for future quantum technologies.
CovRelex: A COVID-19 Retrieval System with Relation Extraction

This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers.This work aims at building a system supporting users efficiently in acquiring knowledge across a huge number of COVID-19 scientific papers published rapidly.Our system can be accessed via https://www.jaist.ac.
Writer Identification Using Microblogging Texts for Social Media Forensics

Establishing authorship of online texts is fundamental to combat cybercrimes. Unfortunately, text length is limited on some platforms, making the challenge harder. We aim at identifying the authorship of Twitter messages limited to 140 characters. We evaluate popular stylometric features, widely used in literary analysis, and specific Twitter features like URLs, hashtags, replies or quotes. We use two databases with 93 and 3957 authors, respectively. We test varying sized author sets and varying amounts of training/test texts per author. Performance is further improved by feature combination via automatic selection. With a large number of training Tweets (>500), a good accuracy (Rank-5>80%) is achievable with only a few dozens of test Tweets, even with several thousands of authors. With smaller sample sizes (10-20 training Tweets), the search space can be diminished by 9-15% while keeping a high chance that the correct author is retrieved among the candidates. In such cases, automatic attribution can provide significant time savings to experts in suspect search. For completeness, we report verification results. With few training/test Tweets, the EER is above 20-25%, which is reduced to < 15% if hundreds of training Tweets are available. We also quantify the computational complexity and time permanence of the employed features.
Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.
Mobile Learning as the Key to Higher Education Innovation: A Systematic Mapping

The study of educational innovations has attracted increasing attention from academics and researchers around the world. Educational innovation proposes the implementation of new approaches or practices that are beneficial and make an impact on individuals or academic communities. The current educational model of many higher education institutions (HEIs) was not designed for this generation of “digital natives”. For this reason, HEIs face the challenge of building teaching strategies that generate meaningful educational experiences. This research seeks to address this issue through a systematic mapping that includes empirical research papers from 2015 to 2020 that study innovations in educational practices using mobile devices. A qualitative and quantitative approach was applied using a four-stage research methodology to evidence innovation in higher education. After employing the selected methodology and applying all the exclusion criteria, 27 papers related to the research topic were identified. Mapping was also performed between the corpus of papers and five dimensions on educational innovation (the purpose of learning, the context of learning, the role of the teacher, the role of the learner, and the evidence of the outcome). The findings reveal that the role of the teacher is the dimension that is least analyzed in innovation initiatives, whereas the most analyzed dimension is the purpose of learning. The goal of this work was to explore and identify educational innovations and unveil uncovered fields of research to generate opportunities for new lines of research in educational innovation.