Reviewerly/exHarmony huggingface dataset:
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs

Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge graphs. More specifically, latent distance approaches model the relationships among entities via a distance between latent representations. Translating embedding models (e.g., TransE) are among the most popular latent distance approaches which use one distance function to learn multiple relation patterns. However, they are mostly inefficient in capturing symmetric relations since the representation vector norm for all the symmetric relations becomes equal to zero. They also lose information when learning relations with reflexive patterns since they become symmetric and transitive. We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework to collaboratively combine variant latent distance-based terms. Our solution is based on two principles: 1) we use a limit-based loss instead of a margin ranking loss and, 2) by learning independent embedding vectors for each of the terms we can collectively train and predict using contradicting distance terms. We further demonstrate that MDE allows modeling relations with (anti)symmetry, inversion, and composition patterns. We propose MDE as a neural network model that allows us to map non-linear relations between the embedding vectors and the expected output of the score function. Our empirical results show that MDE performs competitively to state-of-the-art embedding models on several benchmark datasets.
Using Natural Language Relations between Answer Choices for Machine Comprehension

When evaluating an answer choice for Reading Comprehension task, other answer choices available for the question and the answers of related questions about the same paragraph often provide valuable information. In this paper, we propose a method to leverage the natural language relations between the answer choices, such as entailment and contradiction, to improve the performance of machine comprehension. We use a stand-alone question answering (QA) system to perform QA task and a Natural Language Inference (NLI) system to identify the relations between the choice pairs. Then we perform inference using an Integer Linear Programming (ILP)-based relational framework to re-evaluate the decisions made by the standalone QA system in light of the relations identified by the NLI system. We also propose a multitask learning model that learns both the tasks jointly.
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, researchers have been investigating approaches to boost accuracy through either deeper or wider network structures, which brings with them the exponential increment of the computational and storage cost, delaying the responding time. In this paper, we propose a general training framework named self distillation, which notably enhances the performance (accuracy) of convolutional neural networks through shrinking the size of the network rather than aggrandizing it. Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself. The networks are firstly divided into several sections. Then the knowledge in the deeper portion of the networks is squeezed into the shallow ones. Experiments further prove the generalization of the proposed self distillation framework: enhancement of accuracy at average level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as maximum. In addition, it can also provide flexibility of depth-wise scalable inference on resource-limited edge devices.Our codes will be released on github soon.
The CSI effect on cold case investigations

Much has been made about the potential impact of fictional crime scene shows in the media on jury verdicts. However, there has been little discussion about how those programs, and the myriad of other true crime shows and documentaries may otherwise influence the actual investigative process. The intent of this paper is to discuss ways that such shows may impact the investigative process. The focus will be on the investigation of old, unsolved cases, commonly referred to as cold cases, because of the increasing focus on forensics in all aspects of the investigation and the number of television shows that feature such cases. This paper is not intended to be an academic study of the issue, as it relies much on anecdotal evidence and the experience of the author and others. The purpose is to hopefully generate further discussion and research with the goal of improving the investigative process.
Quantum correlations from dynamically modulated optical nonlinear interactions

We investigate optical nonlinear interactions in a dynamic environment by studying the generation of photons in spontaneous parametric down conversion inside a nonlinear cavity where the optical path length is periodically modulated in time. We show that the temporal dynamics of the cavity modify the nonlinear interaction and the generated continuous-variable time-frequency entangled biphoton state evolves into a tunable discrete higher-dimensional state in the nonadiabatic modulation regime where the modulation timescales are much faster than the photon lifetime. In this regime, the system mimics effects of a quantum random walk in a photonic lattice with many associated effects, including localized and delocalized wave functions of the generated photons. We also propose generation of time-frequency hyperentangled states in the adiabatic limit. Our analysis shows that the proposed system is promising for applications in quantum simulation and information processing in the time-frequency domain.
Connecting Heterogeneous Electronic Health Record Systems Using Tangle

Currently, Electronic Medical Records (EHRs) are centralized and arduous to move around. Getting crucial information in front of a doctor shouldn’t be as difficult as it is. We want to solve a core problem of how to distribute transparent, updated medical data to a network of caregivers securely. Due to the current centralized nature of data ownership, there would be no guarantee to deliver untampered up-to-date information unless distributed ledgers or shared data structure is used. The number of patients is continually increasing, and it requires a system that would be able to keep track of a decentralized record of each patient, but at the same time, it should be able to scale to the proportions necessary for handling the increasing network throughput. Our approach from this study is to provide a new enhanced secure and trustable network platform between care providers and patients by sharing data using Tangle, where data is structured using a Directed Acyclic Graph (DAG) to store its EHRs ledger, with the primary motivation of being scalable.
Advantages of document deep thematic indexing for the information center acquisitions service

The paper presents the main technological stages of deep the matic indexing and its benefitsfor the information center acquisitions service. The most common problems of journal thematic attribution as a whole in global citation indexes are considered. It is argued that deep document indexing leads to an unlimited number of document subject headings thus creating serials multiple thematic profiles that represent an increasing number of interdisciplinary studies. The specific examples demonstrate the dynamic links formation between the publication and a certain set of thematic headings, which makes it possible to identify productive publications with different basic profiles for specific subject areas. A comparative study of 2016–2017 chemistry document arrays for VINITI RAS and Scopus journals selections was conducted. The Scopus journals analysis was carried out using the Scimagotools. In the Scopus database, the most representative thematic category is determined as "Chemistry, miscellaneous", which includes both polythematic and highly specialized publications. The maximum flow of Russian editionsarticles is noted in the field of physical chemistry with a significant number of productive journals with a basic physical profile. This model of deep thematic indexing allows the information center acquisitionsservice to monitor the content of various subject areas and ensure the completeness of the resulting documentsetsfor the information products.
Development of a New International Behavioral-Cognitive Internet Security Questionnaire: Preliminary Results from Croatian and German samples

This study is based on previous results obtained with the validated Users' Information Security Awareness Questionnaire (UISAQ) and its main critiques concerning length and international usage. The aim was to develop a new international short version of the UISAQ, Behavioral-cognitive internet security questionnaire (BCISQ, Velki & Šolić, 2018). Authors gathered information on risky behavior and security awareness among 250 Croatian students and 225 German adults using the same instrument translated to German. The BCISQ consisted of 2 subscales with a total of 10 items. Model fit for both groups (Croatian and German) was tested using the Software program R. For both groups, the analyses did not confirm a two-factor structure. Due to large covariances between the measurement errors of the last two questions on the cognitive scale it is recommended to split this scale into two separate subscales, risk and importance. The resulting three-factor model shows a good fit for the Croatian (CFI=0.97, TLI=0.96, RMSEA=0.04, SRMR=0.04, χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =46.61) and the German sample (CFI=0.93, TLI=0.90, RMSEA=0.05, SRMR=0.05, χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =51.44). Future studies should try to further develop the third subscale (risk) of the BCISQ as well as to test the BCISQ in other cultures and languages.
DARNet: Deep Active Ray Network for Building Segmentation

In this paper, we propose a Deep Active Ray Network (DARNet) for automatic building segmentation. Taking an image as input, it first exploits a deep convolutional neural network (CNN) as the backbone to predict energy maps, which are further utilized to construct an energy function. A polygon-based contour is then evolved via minimizing the energy function, of which the minimum defines the final segmentation. Instead of parameterizing the contour using Euclidean coordinates, we adopt polar coordinates, i.e., rays, which not only prevents self-intersection but also simplifies the design of the energy function. Moreover, we propose a loss function that directly encourages the contours to match building boundaries. Our DARNet is trained end-to-end by back-propagating through the energy minimization and the backbone CNN, which makes the CNN adapt to the dynamics of the contour evolution. Experiments on three building instance segmentation datasets demonstrate our DARNet achieves either state-of-the-art or comparable performances to other competitors.
Disclosing Citation Meanings for Augmented Research Retrieval and Exploration

In recent years, new digital technologies are being used to support the navigation and the analysis of scientific publications, justified by the increasing number of articles published every year. For this reason, experts make use of on-line systems to browse thousands of articles in search of relevant information. In this paper, we present a new method that automatically assigns meanings to references on the basis of the citation text through a Natural Language Processing pipeline and a slightly-supervised clustering process. The resulting network of semantically-linked articles allows an informed exploration of the research panorama through semantic paths. The proposed approach has been validated using the ACL Anthology Dataset containing several thousands of papers related to the Computational Linguistics field. A manual evaluation on the extracted citation meanings carried to very high levels of accuracy. Finally, a freely-available web-based application has been developed and published on-line.
Effective Sentence Scoring Method using Bidirectional Language Model for Speech Recognition

In automatic speech recognition, many studies have shown performance improvements using language models (LMs). Recent studies have tried to use bidirectional LMs (biLMs) instead of conventional unidirectional LMs (uniLMs) for rescoring the $N$-best list decoded from the acoustic model. In spite of their theoretical benefits, the biLMs have not given notable improvements compared to the uniLMs in their experiments. This is because their biLMs do not consider the interaction between the two directions. In this paper, we propose a novel sentence scoring method considering the interaction between the past and the future words on the biLM. Our experimental results on the LibriSpeech corpus show that the biLM with the proposed sentence scoring outperforms the uniLM for the $N$-best list rescoring, consistently and significantly in all experimental conditions. The analysis of WERs by word position demonstrates that the biLM is more robust than the uniLM especially when a recognized sentence is short or a misrecognized word is at the beginning of the sentence.
The management of first party fraud in e-tailing: a qualitative study

Purpose First-party fraud in which retail consumers commit fraud against retailers is a growing problem. However, to date studies on retail crime have focused almost entirely on fraudulent consumer behaviours in physical stores. With the growth of e-commerce, the losses to retailers from this fraud are growing so there is strong need to research this problem from multiple perspectives. The paper aims to discuss this issue. Design/methodology/approach The authors conducted three case studies and a total of 24 semi-structured interviews with retail managers, and evaluated their existing prevention-related documentation. Fraud management lifecycle theory was used to organise and discuss the findings. Findings The authors found that many retailers are treating this problem as just a cost of doing business online and have no detailed plans for dealing with this problem or any reporting to law enforcement agencies. However, they have begun working with delivery companies for delivery accuracy. Use of convenience stores as collection points is also showing early improvements. Research limitations/implications The small number of cases and interviews used is a limitation of this study. However, the authors believe that the findings are useful for advancing knowledge in this emerging research area. Practical implications This study provides insight into existing management practices in this domain, and makes recommendations on how to improve the management of first-party fraud. The study also makes a case for increased managerial interest and involvement in reducing first-party fraud. The study also helps bridge a glaring gap in existing literature and provides useful leads for further research. Originality/value To the authors’ knowledge, this is the first study to evaluate the existing practices employed to manage first-party fraud in e-retail.
Learning spectrograms with convolutional spectral kernels

We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolution between two imaginary radial basis functions. We present a principled framework to interpret CSK, as well as other deep probabilistic models, using approximated Fourier transform, yielding a concise representation of input-frequency spectrogram. Observing through the lens of the spectrogram, we provide insight on the interpretability of deep models. We then infer the functional hyperparameters using scalable variational and MCMC methods. On small- and medium-sized spatiotemporal datasets, we demonstrate improved generalization of GP models when equipped with CSK, and their capability to extract non-stationary periodic patterns.
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees

Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. However, this strong performance comes at the cost of interpretability (i.e. users are unable to understand the relationships a trained random forest has learned and why it is making its predictions). In particular, it is challenging to understand how the contribution of a particular feature, or group of features, varies as their value changes. To address this, we introduce Disentangled Attribution Curves (DAC), a method to provide interpretations of tree ensemble methods in the form of (multivariate) feature importance curves. For a given variable, or group of variables, DAC plots the importance of a variable(s) as their value changes. We validate DAC on real data by showing that the curves can be used to increase the accuracy of logistic regression while maintaining interpretability, by including DAC as an additional feature. In simulation studies, DAC is shown to out-perform competing methods in the recovery of conditional expectations. Finally, through a case-study on the bike-sharing dataset, we demonstrate the use of DAC to uncover novel insights into a dataset.
BERTSel: Answer Selection with Pre-trained Models

Recently, pre-trained models have been the dominant paradigm in natural language processing. They achieved remarkable state-of-the-art performance across a wide range of related tasks, such as textual entailment, natural language inference, question answering, etc. BERT, proposed by Devlin et.al., has achieved a better marked result in GLUE leaderboard with a deep transformer architecture. Despite its soaring popularity, however, BERT has not yet been applied to answer selection. This task is different from others with a few nuances: first, modeling the relevance and correctness of candidates matters compared to semantic relatedness and syntactic structure; second, the length of an answer may be different from other candidates and questions. In this paper. we are the first to explore the performance of fine-tuning BERT for answer selection. We achieved STOA results across five popular datasets, demonstrating the success of pre-trained models in this task.
Differential phase encoded measurement-device-independent quantum key distribution

We present a measurement-device-independent quantum key distribution (MDI-QKD) using single photons in a linear superposition of three orthogonal time-bin states, for generating the key. The orthogonal states correspond to three distinct paths in the delay line interferometers used by two (trusted) sources. The key information is decoded based on the measurement outcomes obtained by an untrusted third party Charles, who uses a beamsplitter to measure the phase difference between pulses traveling through different paths of the two delay lines. The proposed scheme combines the best of both differential-phase-shift (DPS) QKD and MDI-QKD. It is more robust against phase fluctuations, and also ensures protection against detector side-channel attacks. We prove unconditional security by demonstrating an equivalent protocol involving shared entanglement between the two trusted parties. We show that the secure key rate for our protocol compares well to existing protocols in the asymptotic regime. For the decoy-state variant of our protocol, we evaluate the secure key rate by using a phase-post-selection technique. Finally, we estimate the bit error rate and the phase error rate, in the finite key regime.
Statistical Shape Models

This chapter deals with the topic of statistical shape models (SSMs). It is shown how these can be viewed as an extension of the concept of inferential statistics to the domain of shapes rather than univariate or multivariate data. The relationship between SSMs and the field of dimensionality reduction is described. The steps involved in the formation of SSMs are outlined: parameterizing the shape, aligning the centroids, computing the covariance matrix, and computing its eigenvectors and eigenvalues. It is also shown how new shapes can be produced from an SSM. A review of the biomedical applications of SSMs is provided. Finally, the chapter ends with a guided exercise on the formation of an SSM of the left ventricle of the heart using MATLAB.
Parsimonious Deep Learning: A Differential Inclusion Approach with Global Convergence.

Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error. However, compressive networks are desired in many real world applications and direct training of small networks may be trapped in local optima. In this paper, instead of pruning or distilling an over-parameterized model to compressive ones, we propose a parsimonious learning approach based on differential inclusions of inverse scale spaces, that generates a family of models from simple to complex ones with a better efficiency and interpretability than stochastic gradient descent in exploring the model space. It enjoys a simple discretization, the Split Linearized Bregman Iterations, with provable global convergence that from any initializations, algorithmic iterations converge to a critical point of empirical risks. One may exploit the proposed method to boost the complexity of neural networks progressively. Numerical experiments with MNIST, Cifar-10/100, and ImageNet are conducted to show the method is promising in training large scale models with a favorite interpretability.
Abbreviation Explorer - an interactive system for pre-evaluation of Unsupervised Abbreviation Disambiguation

We present Abbreviation Explorer, a system that supports interactive exploration of abbreviations that are challenging for Unsupervised Abbreviation Disambiguation (UAD). Abbreviation Explorer helps to identify long-forms that are easily confused, and to pinpoint likely causes such as limitations of normalization, language switching, or inconsistent typing. It can also support determining which long-forms would benefit from additional input text for unsupervised abbreviation disambiguation. The system provides options for creating corrective rules that merge redundant long-forms with identical meaning. The identified rules can be easily applied to the already existing vector spaces used by UAD to improve disambiguation performance, while also avoiding the cost of retraining.
Bi-Intra Prediction for Versatile Video Coding

This paper presents a novel Bi-Intra Prediction (BIP) algorithm to improve intra coding performance for the next generation of video coding. In the proposed algorithm, a new predictor is generated by combining two existing intra prediction modes as an additional mode, being able to provide more accurate prediction. To remove the number of unnecessary combinations, restrictions on the search candidates and block sizes are carried out based on the statistical analyses. In addition, an efficient mode coding method of syntax elements in BIP is introduced to improve the coding performance. Moreover, a rough mode decision scheme is adopted to avoid high computation complexity in encoder side. Experimental results show that compared with the Versatile Video Coding reference software VTM-1.0, the proposed algorithm achieves 0.71%, 0.46% and 0.43% BD-Rate gains on average for Y, U and V components under all intra configuration, respectively, and 0.32%, 0.39%, 0.49% BD-Rate gains under random access configuration.