Reviewerly/exHarmony huggingface dataset:
The Possibilities of Applying Blockchain Technology in Shipping

Blockchain technology is a decentralized database which stores the registry of property and transactions using a peer-to-peer network, secured by cryptography, and over time, its history is locked into blocks of data which are cryptographically connected and secured. The blockchain technology was primarily designed for application in cryptocurrency transactions and related digital agreements. Due to its immutable and infallible record of all transactions within the network, the potential of this technology for broad application has been recognized. This paper analyzes the possible advantages of applying blockchain technology in logistics and maritime transport.
Self-Organizing Nebulous Growths for Robust and Incremental Data Visualization

Non-parametric dimensionality reduction techniques, such as t-SNE and UMAP, are proficient in providing visualizations for datasets of fixed sizes. However, they cannot incrementally map and insert new data points into an already provided data visualization. We present Self-Organizing Nebulous Growths (SONG), a parametric nonlinear dimensionality reduction technique that supports incremental data visualization, i.e., incremental addition of new data while preserving the structure of the existing visualization. In addition, SONG is capable of handling new data increments, no matter whether they are similar or heterogeneous to the already observed data distribution. We test SONG on a variety of real and simulated datasets. The results show that SONG is superior to Parametric t-SNE, t-SNE and UMAP in incremental data visualization. Specifically, for heterogeneous increments, SONG improves over Parametric t-SNE by 14.98 % on the Fashion MNIST dataset and 49.73% on the MNIST dataset regarding the cluster quality measured by the Adjusted Mutual Information scores. On similar or homogeneous increments, the improvements are 8.36% and 42.26% respectively. Furthermore, even when the above datasets are presented all at once, SONG performs better or comparable to UMAP, and superior to t-SNE. We also demonstrate that the algorithmic foundations of SONG render it more tolerant to noise compared to UMAP and t-SNE, thus providing greater utility for data with high variance, high mixing of clusters, or noise.
Global Solar Radiation Models in Minas Gerais, Southeastern Brazil

Accurate and complete global solar radiation ( H s ) data at a specific region are crucial for regional climate assessment, crop growth modeling, and all operations that use solar energy. However, in the Minas Gerais state, Southeastern Brazil (SEB), the number of weather stations that measure global solar radiation is scarce, and when it is available, it presents gaps in the time series. An attractive alternative to solve the data gap problem is to estimate global solar radiation using empirical models. In this study, thirteen models based on maximum and minimum air temperatures, precipitation, sunshine duration, and extraterrestrial solar radiation were compared in the daily estimation of H s. Data from 10 weather stations, from 1999 to 2017, located in Minas Gerais were used. Also, cluster analysis was used to group the localities (weather stations) with similar patterns of model performance, climatic classification (Köppen–Geiger and Thornthwaite), and seasonal data variability, considering minimum and maximum air temperatures, precipitation, sunshine duration, and global solar radiation. Although it is apparently simple, studies on this subject are scarce and the few existing ones in Minas Gerais have flaws, which justifies this study. The models were evaluated by root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (BIAS), Willmott’s index of agreement ( d ), and performance index ( c -index). Models based on sunshine duration, such as those proposed by Ertekin and Yaldiz and by Newland, showed the best performance (average c -index = 0.71). Models based on temperature and precipitation showed the worst results (average c -index = 0.41). Cluster analysis showed that there is a similar pattern between the performance of the models, climatic classification, and seasonal variability of data among the localities of Minas Gerais. In general, models that presented extremely poor performance were formed with weather stations located in the dry zone, but with different climate classification, and models that presented very good (and good) performance were composed by weather stations located in the humid zone (dry subhumid) with the same climate classification and similar seasonal variability. Furthermore, the models based on temperature have a tendency to overestimate radiation values below 10 MJ·m −2 day −1 and to underestimate values higher than 25 MJ·m −2 day −1 . This point is a limitation of the model for estimating global solar radiation below and above these levels, showing the influence of atmospheric systems and atmospheric attenuation mechanisms of global solar radiation.
Copy That! Editing Sequences by Copying Spans

Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code. In this paper, we argue that common seq2seq models (with a facility to copy single tokens) are not a natural fit for such tasks, as they have to explicitly copy each unchanged token. We present an extension of seq2seq models capable of copying entire spans of the input to the output in one step, greatly reducing the number of decisions required during inference. This extension means that there are now many ways of generating the same output, which we handle by deriving a new objective for training and a variation of beam search for inference that explicitly handles this problem. In our experiments on a range of editing tasks of natural language and source code, we show that our new model consistently outperforms simpler baselines.
Transition-based DRS Parsing Using Stack-LSTMs

We present our submission to the IWCS 2019 shared task on semantic parsing, a transitionbased parser that uses explicit word-meaning pairings, but no explicit representation of syntax.Parsing decisions are made based on vector representations of parser states, encoded via stack-LSTMs (Ballesteros et al., 2017), as well as some heuristic rules.Our system reaches 70.88% f-score in the competition.
AATEAM: Achieving the Ad Hoc Teamwork by Employing the Attention Mechanism

In the ad hoc teamwork setting, a team of agents needs to perform a task without prior coordination. The most advanced approach learns policies based on previous experiences and reuses one of the policies to interact with new teammates. However, the selected policy in many cases is sub-optimal. Switching between policies to adapt to new teammates' behaviour takes time, which threatens the successful performance of a task. In this paper, we propose AATEAM – a method that uses the attention-based neural networks to cope with new teammates' behaviour in real-time. We train one attention network per teammate type. The attention networks learn both to extract the temporal correlations from the sequence of states (i.e. contexts) and the mapping from contexts to actions. Each attention network also learns to predict a future state given the current context and its output action. The prediction accuracies help to determine which actions the ad hoc agent should take. We perform extensive experiments to show the effectiveness of our method.
Leveraging Multi-View Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation

We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images. To do so, we construct a multi-view dataset by collecting numerous customer product photos online, which exhibit large illumination variations that make them suitable for training of reflectance separation and can facilitate object-level decomposition. The main contribution of our approach is a proposed image representation based on local color distributions that allows training to be insensitive to the local misalignments of multi-view images. In addition, we present a new guidance cue for unsupervised training that exploits synergy between highlight separation and intrinsic image decomposition. Over a broad range of objects, our technique is shown to yield state-of-the-art results for both of these tasks.
Asymmetric Optical Image Encryption With Silhouette Removal Using Interference and Equal Modulus Decomposition

We propose an asymmetric optical image encryption scheme with silhouette removal by using interference and equal modulus decomposition (EMD).Plaintext is first separated into two complex value masks with the same modulus using EMD in the Fresnel transform domain.The two masks are encoded into four phase-only masks (POMs), two of which are treated as ciphertexts and other two as plaintext-dependent private keys by using the inverse Fresnel transform with different diffraction distances and interference-based encryption.Any information about the plaintext, including its silhouette, cannot be retrieved using one, two, or even three of the four POMs.Our scheme also avoids the constraint of the same modulus in EMD and eliminates the vulnerability against the iterative amplitude-phase attack and advanced iterative amplitude-phase attack.Numerical simulations were used to verify the validity and security of our proposed method.
Learning Feature Interactions with Lorentzian Factorization Machine

Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions. Recent advances in this area are empowered by deep learning methods which could learn sophisticated feature interactions and achieve the state-of-the-art result in an end-to-end manner. These approaches require large number of training parameters integrated with the low-level representations, and thus are memory and computational inefficient. In this paper, we propose a new model named “LorentzFM” that can learn feature interactions embedded in a hyperbolic space in which the violation of triangle inequality for Lorentz distances is available. To this end, the learned representation is benefited by the peculiar geometric properties of hyperbolic triangles, and result in a significant reduction in the number of parameters (20% to 80%) because all the top deep learning layers are not required. With such a lightweight architecture, LorentzFM achieves comparable and even materially better results than the deep learning methods such as DeepFM, xDeepFM and Deep &amp; Cross in both recommendation and CTR prediction tasks.
Randomized deduplication with ownership management and data sharing in cloud storage

In commercial cloud services, the client-side deduplication is widely used to save the system resource of servers. However, this kind of deduplication technique is vulnerable to the collusive authentication attack, brute-force attack and duplicate-faking attack. Most existing schemes cannot resolve those problems efficiently. Besides, how to realize the ownership management in client-side deduplication to ensure the forward and backward secrecy of outsourced data is also a hot issue. In this paper, we propose a randomized client-side deduplication scheme, which uses a randomized deduplication protocol to prevent the collusive authentication attack and offline brute-force attack launched by the outside adversaries, and stores each data according to two file tags to resist duplicate-faking attack. In addition, we realize a more available ownership management and data sharing with the aid of dynamic Key-Encrypting Key tree. Security and performance analysis show that the proposed scheme can achieve the desired security requirements while saving the system resource efficiently.
DWM: A Decomposable Winograd Method for Convolution Acceleration

Winograd's minimal filtering algorithm has been widely used in Convolutional Neural Networks (CNNs) to reduce the number of multiplications for faster processing. However, it is only effective on convolutions with kernel size as 3x3 and stride as 1, because it suffers from significantly increased FLOPs and numerical accuracy problem for kernel size larger than 3x3 and fails on convolution with stride larger than 1. In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions. DWM decomposes kernels with large size or large stride to several small kernels with stride as 1 for further applying Winograd method, so that DWM can reduce the number of multiplications while keeping the numerical accuracy. It enables the fast exploring of larger kernel size and larger stride value in CNNs for high performance and accuracy and even the potential for new CNNs. Comparing against the original Winograd, the proposed DWM is able to support all kinds of convolutions with a speedup of ∼2, without affecting the numerical accuracy.
AdaFilter: Adaptive Filter Fine-Tuning for Deep Transfer Learning

There is an increasing number of pre-trained deep neural network models. However, it is still unclear how to effectively use these models for a new task. Transfer learning, which aims to transfer knowledge from source tasks to a target task, is an effective solution to this problem. Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task. Despite its popularity, in this paper we show that fine-tuning suffers from several drawbacks. We propose an adaptive fine-tuning approach, called AdaFilter, which selects only a part of the convolutional filters in the pre-trained model to optimize on a per-example basis. We use a recurrent gated network to selectively fine-tune convolutional filters based on the activations of the previous layer. We experiment with 7 public image classification datasets and the results show that AdaFilter can reduce the average classification error of the standard fine-tuning by 2.54%.
Data Analysis: Types, Process, Methods, Techniques and Tools

The process of evaluating data using analytical and logical reasoning to examine each component of the data provided. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources is gathered, reviewed, and then analyzed to form some sort of finding or conclusion. There are a variety of specific data analysis method, some of which include data mining, text analytics, business intelligence, and data visualizations. Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis. This research article based on data analysis, it’s types, process, methods, techniques & tools.
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System

Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack comprehensive investigation. The existing attacks in image domain could not be directly applicable due to the distinct properties of ECGs in visualization and dynamic properties. Thus, this paper takes a step to thoroughly explore adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the properties of ECGs to design effective attacks schemes under two attacks models respectively. Our results demonstrate the blind spots of DNN-powered diagnosis systems under adversarial attacks, which calls attention to adequate countermeasures.
Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification

Cross-domain sentiment classification aims to leverage useful knowledge from a source domain to mitigate the supervision sparsity in a target domain. A series of approaches depend on the pivot features that behave similarly for polarity prediction in both domains. However, the engineering of such pivot features remains cumbersome and prevents us from learning the disentangled and transferable representations from rich semantic and syntactic information. Towards learning the pivots and representations simultaneously, we propose a new Transferable Pivot Transformer (TPT). Our model consists of two networks: a Pivot Selector that learns to detect transferable n-gram pivots from contexts, and a Transferable Transformer that learns to generate domain-invariant representations by modeling the correlation between pivot and non-pivot words. The Pivot Selector and Transferable Transformer are jointly optimized through end-to-end back-propagation. We experiment with real tasks of cross-domain sentiment classification over 20 domain pairs where our model outperforms prior arts.
MultiLing 2019: Financial Narrative Summarisation

The Financial Narrative Summarisation task at MultiLing 2019 aims to demonstrate the value and challenges of applying automatic text summarisation to financial text written in English, usually referred to as financial narrative disclosures.The task dataset has been extracted from UK annual reports published in PDF file format.The participants were asked to provide structured summaries, based on real-world, publicly available financial annual reports of UK firms by extracting information from different key sections.Participants were asked to generate summaries that reflects the analysis and assessment of the financial trend of the business over the past year, as provided by annual reports.The evaluation of the summaries was performed using Au-toSummENG and Rouge automatic metrics.This paper focuses mainly on the data creation process.
Active Learning with Query Generation for Cost-Effective Text Classification

Labeling a text document is usually time consuming because it requires the annotator to read the whole document and check its relevance with each possible class label. It thus becomes rather expensive to train an effective model for text classification when it involves a large dataset of long documents. In this paper, we propose an active learning approach for text classification with lower annotation cost. Instead of scanning all the examples in the unlabeled data pool to select the best one for query, the proposed method automatically generates the most informative examples based on the classification model, and thus can be applied to tasks with large scale or even infinite unlabeled data. Furthermore, we propose to approximate the generated example with a few summary words by sparse reconstruction, which allows the annotators to easily assign the class label by reading a few words rather than the long document. Experiments on different datasets demonstrate that the proposed approach can effectively improve the classification performance while significantly reduce the annotation cost.
A Smart Collaborative Authentication Framework for Multi-Dimensional Fine-Grained Control

The emergence of the 5G network has brought broad prospects for the massive terminal access and ubiquitous Internet of Things (IoTs).Potential attacking opportunities triggered by this progress are severely impacting the security fortress of current networks, especially in the edge access part.However, due to the unitary protection and inferior isolation, available security schemes are incapable of effectively eliminating these hidden perils.Motivated by these facts, we propose a Multi-dimensional Fine-grained Control (MFC) framework to strengthen safety and reliability in Radio Access Networks (RANs).First, we comprehensively survey and summarize the existing security schemes to grasp respective effects and limitations.Second, the MFC framework is established to describe the model structure and implementation processes.An identifier mapping mechanism is designed to achieve network isolation.We perform the security analysis of MFC by theoretically comparing diversified policies.Third, an integrated set of the authentication prototype system is created with wireless environment parameters settings.Specific verification scenarios are illustrated.Finally, we test the performances of the MFC framework.Validation results demonstrate that the proposed scheme can accomplish reliable security control at the access side.Comparing to multiple schemes, the performances, in terms of time and concurrency, are optimized.Therefore, the MFC framework is feasible for applications in 5G or IoT.
Improving Neural Relation Extraction with Positive and Unlabeled Learning

We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning. This approach first applies reinforcement learning to decide whether a sentence is positive to a given relation, and then positive and unlabeled bags are constructed. In contrast to most previous studies, which mainly use selected positive instances only, we make full use of unlabeled instances and propose two new representations for positive and unlabeled bags. These two representations are then combined in an appropriate way to make bag-level prediction. Experimental results on a widely used real-world dataset demonstrate that this new approach indeed achieves significant and consistent improvements as compared to several competitive baselines.
Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention's sentence-specific context. This is inadequate for highly overlapping and/or noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro-f1 and micro-f1 respectively.