Reviewerly/exHarmony huggingface dataset:
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

In order to better understand the reason behind model behaviors (i.e., making predictions), most recent work has exploited generative models to provide complementary explanations.However, existing approaches in natural language processing (NLP) mainly focus on "WHY A" rather than contrastive "WHY A NOT B", which is shown to be able to better distinguish confusing candidates and improve model performance in other research fields.In this paper, we focus on generating Contrastive Explanations with counterfactual examples in NLI and propose a novel Knowledge-Aware generation framework (KACE).Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples.After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations.Experimental results show that contrastive explanations are beneficial to clarify the difference between predicted answer and other answer options.Moreover, we train an BERT-large based NLI model enhanced with contrastive explanations and achieve an accuracy of 91.9% on SNLI, gaining an improvement of 5.7% against ETPA ("Explain-Then-Predict-Attention") and 0.6% against NILE ("WHY A").
Transformer-Style Relational Reasoning with Dynamic Memory Updating for Temporal Network Modeling

Network modeling aims to learn the latent representations of nodes such that the representations preserve both network structures and node attribute information. This problem is fundamental due to its prevalence in numerous domains. However, existing approaches either target the static networks or struggle to capture the complicated temporal dependency, while most real-world networks evolve over time and the success of network modeling hinges on the understanding of how entities are temporally connected. In this paper, we present TRRN, a transformer-style relational reasoning network with dynamic memory updating, to deal with the above challenges. TRRN employs multi-head self-attention to reason over a set of memories, which provides a multitude of shortcut paths for information to flow from past observations to the current latent representations. By utilizing the policy networks augmented with differentiable binary routers, TRRN estimates the possibility of each memory being activated and dynamically updates the memories at the time steps when they are most relevant. We evaluate TRRN with the tasks of node classification and link prediction on four real temporal network datasets. Experimental results demonstrate the consistent performance gains for TRRN over the leading competitors.
Research on Intelligent Construction Algorithm of Subject Knowledge Thesaurus Based on Literature Resources

Abstract The implementation of National Science and Technology Innovation Strategy demands exponential growing in knowledge services on literature information institutions. It is the most important knowledge organization tool for Information Retrieval, which can be widely used for semantic citation, organization and retrieval of literature resources. This study aims to develop an innovative algorithm for constructing subject thesaurus based on massive literature resource data and mining academic neologisms, also the semantic relationship between academic neologisms and subject system. We firstly collect a dataset of literature corpus, corresponding work for data pre-processing carried out. Then using the FastText model to complete academic neologisms mining, we construct an automatic categorization model of academic neologisms based on the Bert and TextCNN algorithm. The algorithm proposed in this study is validated by 8.1 million multi-source and heterogeneous literature data in the field of marine disciplines. The result shows that the algorithm can effectively replace 90% of the manual annotation volume, mine a large number of high-quality marine neologisms and successfully build the marine science knowledge base with a pass rate of 82.6% reviewed by expert, which present high accuracy and certain engineering application prospects.
Learning not to take the bait: a longitudinal examination of digital training methods and overlearning on phishing susceptibility

As phishing becomes increasingly sophisticated and costly, interventions that improve and prolong resistance to attacks are needed. Previous research supported digital training as a method to reduce phishing susceptibility. However, the effects of training degrade with time. Therefore, we investigate overlearning as an approach that may increase skill retention through repetition and developing automaticity. We performed a longitudinal experiment crossing overlearning with anti-phishing digital training (rule-based, mindfulness, and control). Participants were tested using email identification tests (immediately following and 10 weeks after training) and mock phishing messages delivered to their inboxes (1 week and 8 weeks following training). Results showed that compared to rule-based training, mindfulness training resulted in significantly greater retention in terms of better email discrimination and less susceptibility to phishing attacks but similar levels of caution towards phishing after 2 months. Overlearning resulted in significantly less susceptibility to phishing attacks and more caution towards phishing compared to no overlearning but did not impact the digital training approaches. Even so, mindfulness was more beneficial compared to overlearning. Altogether, the results demonstrate the stability of the benefits of mindfulness training over time in terms of mitigating phishing susceptibility without influencing the chances of missing legitimate emails.
RankDetNet: Delving into Ranking Constraints for Object Detection

Modern object detection approaches cast detecting objects as optimizing two subtasks of classification and localization simultaneously. Existing methods often learn the classification task by optimizing each proposal separately and neglect the relationship among different proposals. Such detection paradigm also encounters the mismatch between classification and localization due to the inherent discrepancy of their optimization targets. In this work, we propose a ranking-based optimization algorithm for harmoniously learning to rank and localize proposals in lieu of the classification task. To this end, we comprehensively investigate three types of ranking constraints, i.e., global ranking, class-specific ranking and IoU-guided ranking losses. The global ranking loss encourages foreground samples to rank higher than background. The class-specific ranking loss ensures that positive samples rank higher than negative ones for each specific class. The IoU-guided ranking loss aims to align each pair of confidence scores with the associated pair of IoU overlap between two positive samples of a specific class. Our ranking constraints can sufficiently explore the relationships between samples from three different perspectives. They are easy-to-implement, compatible with mainstream detection frameworks and computation-free for inference. Experiments demonstrate that our RankDetNet consistently surpasses prior anchor-based and anchor-free baselines, e.g., improving RetinaNet baseline by 2.5% AP on the COCO test-dev set without bells and whistles. We also apply the proposed ranking constraints for 3D object detection and achieve improved performance, which further validates the superiority and generality of our method.
Applying the principles of zero-trust architecture to protect sensitive and critical data

In today's digital landscape, data is a company's most valuable asset, and energy companies, particularly electric, oil and gas, remain at risk of hacking attempts due to their major social and economic importance. Using key zero-trust principles can help to ensure that IT systems are protected, mitigating the risk to company operations and sensitive and critical data. Data is a company's most valuable asset, and energy companies, particularly electric, oil and gas, remain at risk of hacking attempts due to their major social and economic importance. Zero-trust principles can help to ensure that IT systems are protected, mitigating the risk to company operations and sensitive and critical data. However, as David Greenwood of ISN Solutions explains, this model often requires careful planning to ensure productivity and that access to data needed for daily work is maintained.
Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.
Asymmetric multiparameter encryption of hyperspectral images based on hybrid chaotic mapping and an equal modulus decomposition tree

We present an asymmetric encryption scheme for hyperspectral images using hybrid chaotic maps (HCMs) and an equal modulus decomposition tree (EMDT) structure in a discrete multiple-parameter fractional Fourier transform (dmpFrFT) domain. The original hyperspectral image was scrambled by an HCM and then encrypted into asymmetric ciphertext using the EMDT. In the EMDT, each pair of the band images of the scrambled hyperspectral image were regarded as leaf nodes, while the encryption modules using chaotic random phase mask, dmpFrFT, and improved equal modulus decomposition were regarded as branch nodes, and the encryption process was implemented along the paths from the leaf nodes to the topmost branch node. The EMDT structure could provide multiparameter encryption, real-valued output, and different pairs of band images with different secret keys and encryption/decryption paths. Compared with the previous optical encryption approaches for hyperspectral images, our asymmetric cryptosystem had larger key space, less data amount of storage and transmission, and stronger resistance to statistical attacks. Various numerical simulations verified the performance of our proposed asymmetric cryptosystem.
A Dual-Store Structure for Knowledge Graphs

To effectively manage increasing knowledge graphs in various domains, a hot research topic, knowledge graph storage management, has emerged. Existing methods are classified to relational stores and native graph stores. Relational stores are able to store large-scale knowledge graphs and convenient in updating knowledge, but the query performance weakens obviously when the selectivity of a knowledge graph query is large. Native graph stores are efficient in processing complex knowledge graph queries due to its index-free adjacent property, but they are inapplicable to manage a large-scale knowledge graph due to limited storage budgets or inflexible updating process. Motivated by this, we propose a dual-store structure which leverages a graph store to accelerate the complex query process in the relational store. However, it is challenging to determine what data to transfer from relational store to graph store at what time. To address this problem, we formulate it as a Markov Decision Process and derive a physical design tuner DOTIL based on reinforcement learning. With DOTIL, the dual-store structure is adaptive to dynamic changing workloads. Experimental results on real knowledge graphs demonstrate that our proposed dual-store structure improves query performance up to average 43.72% compared with the most commonly used relational stores.
Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Summary Software development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy‐based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global–local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well‐known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.
Fast local representation learning via adaptive anchor graph for image retrieval

Linear Discriminant Analysis (LDA) is one of most important methods in dimensionality reduction domain, which is limited with Gaussian assumption. Because of the complexity of real data, data often presents non-Gaussian distribution that points in same class can be divided into several sub-clusters and center point is not enough to describe the distribution of data. In order to solve non-Gaussian data, LDA-based methods consider local structure information through measuring each pairwise distance of full connection graph. However, the strategy of establishing fully-connected graph is at expense of high computational complexity and limits it practical and industrial applications. We propose a Fast Local Representation Learning (FLRL) method which leverages anchor points to establish anchor-based graph and uses similarity matrix to depict the relationships of each pairwise connections. Notably, to avoid the affect of noises and redundant features in original space, anchor points and similarity matrix are updated alternately in subspace that local structure of data will be more precise to learn. Extensive pattern classification and image retrieval experiments on several synthetic datasets, well-known datasets and deep features datasets demonstrate the advantages of our method over the state-of-the-art methods. Our source code available on: https://github.com/superzcy/FLRL.
Building a Pediatric Medical Corpus: Word Segmentation and Named Entity Annotation

Word segmentation and named entity annotation are essential foundations for medical text information extraction. This paper focuses on clinical pediatric diseases and takes the existing medical named entities and entity-relationship labeling systems as references. Under the guidance of the Chinese word segmentation and named entity labeling, the specifications for pediatric medical texts have been constructed in this paper. This paper also applies a self-developed distributed annotation platform to pre-annotate and manually proofread the named entities for many times. The corpus consists of 38,805 medical entries which can be divided into nine categories. Among the medical entries, there are 504 entries of common pediatric diseases, 7,085 entries of body parts, 12,907 entries of clinical manifestations, and 4,354 entries of medical procedures. This paper constructs the largest corpus with pediatric medical word segmentation and named entity annotation, which provides a data basis for related research.
Evaluating Document Coherence Modeling

Abstract While pretrained language models (LMs) have driven impressive gains over morpho-syntactic and semantic tasks, their ability to model discourse and pragmatic phenomena is less clear. As a step towards a better understanding of their discourse modeling capabilities, we propose a sentence intrusion detection task. We examine the performance of a broad range of pretrained LMs on this detection task for English. Lacking a dataset for the task, we introduce INSteD, a novel intruder sentence detection dataset, containing 170,000+ documents constructed from English Wikipedia and CNN news articles. Our experiments show that pretrained LMs perform impressively in in-domain evaluation, but experience a substantial drop in the cross-domain setting, indicating limited generalization capacity. Further results over a novel linguistic probe dataset show that there is substantial room for improvement, especially in the cross- domain setting.
Light Field Rendering for non-Lambertian Objects

In this paper we propose a solution for view synthesis of scenes presenting highly non-Lambertian objects.While Image-Based Rendering methods can easily render diffuse materials given only their depth, non-Lambertian objects present non-linear displacements of their features, characterized by curved lines in epipolar plane images.Hence, we propose to replace the depth maps used for rendering new viewpoints by a more complex "non-Lambertian map" describing the light field's behavior.In a 4D light field, diffuse features are linearly displaced following their disparity, but non-Lambertian feature can follow any trajectory and need to be approximated by non-Lambertian maps.We compute those maps from nine input images using Bezier or polynomial interpolation.After the map computation, a classical Image-Based Rendering method is applied to warp the input images to novel viewpoints.
Cyclic Defense GAN Against Speech Adversarial Attacks

This paper proposes a new defense approach for counteracting state-of-the-art white and black-box adversarial attack algorithms. Our approach fits into the implicit reactive defense algorithm category since it does not directly manipulate the potentially malicious input signals. Instead, it reconstructs a similar signal with a synthesized spectrogram using a cyclic generative adversarial network. This cyclic framework helps to yield a stable generative model. Finally, we feed the reconstructed signal into the speech-to-text model for transcription. The conducted experiments on targeted and non-targeted adversarial attacks developed for attacking DeepSpeech, Kaldi, and Lingvo models demonstrate the proposed defense's effectiveness in adverse scenarios.
Infinite but Rare: Valuation and Pricing in Marketplaces for Blockchain-Based Virtual Items

Blockchain technologies have enabled the creation of decentralized applications which let users own and transact scarce digital assets. Although still in its infancy, the industry has attracted interest from organizations such as Formula One, the NBA, and several football (soccer) clubs to create marketplaces for trading branded digital collectables. We introduce a novel dataset and study how buyers value and sellers price blockchain-based digital collectables. We find that buyers value digital collectables much like we would expect them to value physical collectables despite the near-zero marginal cost of producing digital items. Sellers appear uncertain in how to value items and have a tendency to price too high and earn a lower revenue as a result, highlighting an inefficiency resulting from a lack of price histories and a high cost of identifying comparable items in this market. We propose a machine learning approach to value items at scale and develop a proof-of-concept decision support tool to help sellers value their digital items, addressing the pressing need for information transparency in this new market.
Microseismic monitoring to assess rock mass damaging through a novel damping ratio-based approach

Monitoring rock mass damaging through the analysis of seismic records is an affirmed tool to manage rock fall risk in view of mitigation strategies. This study proposes a novel approach, named DAmping RaTio Anomalies Monitoring (DaRtAM), to detect the rock mass damaging by measuring variation over time of the damping ratio related to in-situ recorded vibrational events. Although damping-based damage detection techniques are well known for structural monitoring, only few applications exist for rock mass damage investigation not including in-situ long term rock mass monitoring. Here, a three-month-long seismic dataset recorded on a rock wall (Acuto Field Laboratory) by means of six one-component accelerometers was analysed with a STA/LTA algorithm that allowed to extract 19013 vibrational events. The damping ratio of the signals was derived as a function of frequency, averaged in a daily time window and analysed over time to output short-term anomalies and long-term trend. Although the limited time period analysed did not allow to observe significant trend variation of the damping ratio, 131 short-term anomalies were found in the collected seismic records. As it results from this study, the proposed approach is suitable for detecting rock mass damaging and, as a future perspective, will be applied to yearly-lasting vibrational datasets. This approach may be more effective with data acquired in environments characterised by recurrent forcings, such as coastal cliffs forced by sea and wind storms, rock walls stressed by thermal cycles, quarry walls involved in human activities or rocky trenches close to roads and railways.
ALiBERT

We consider Automated List Inspection (ALI), a content-based text recommendation system that assists auditors in matching relevant text passages from notes in financial statements to specific law regulations. ALI follows a ranking paradigm in which a fixed number of requirements per textual passage are shown to the user. Despite achieving impressive ranking performance, the user experience can still be improved by showing a dynamic number of recommendations. Besides, existing models rely on a feature-based language model that needs to be pre-trained on a large corpus of domain-specific datasets. Moreover, they cannot be trained in an end-to-end fashion by jointly optimizing with language model parameters. In this work, we alleviate these concerns by considering a multi-label classification approach that predicts dynamic requirement sequences. We base our model on pre-trained BERT that allows us to fine-tune the whole model in an end-to-end fashion, thereby avoiding the need for training a language representation model. We conclude by presenting a detailed evaluation of the proposed model on two German financial datasets.