Reviewerly/exHarmony huggingface dataset:
Spectral Responses of Heavy Metal Contaminated Soils in the Vicinity of a Hydrothermal Ore Deposit: A Case Study of Boksu Mine, South Korea

This paper investigated the spectral characteristics of heavy metal contaminated soils of a hydrothermal ore deposit developed in carbonate host rock associated with heavy metal concentration and mineral composition. The results showed that spectral response of heavy metal contaminated soils was statistically correlated with zinc, cadmium, and lead concentrations. Empirical equations for predicting zinc, cadmium, and lead concentrations were derived. Spectral characteristics of the soils were expressed by smectite, chlorite, tremolite, and talc which resulted from hydrothermal alteration and weathering products of the parent rocks. The stepwise multiple linear regression (SMLR) model of zinc, cadmium, and lead was statistically satisfactory with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> greater than 0.7. The SMLR results indicated that the spectral response to cadmium and zinc concentration was sensitive to reflectance at 1850 nm and first derivative at ~950 and 2154 nm corresponding to the smectite absorption features. On the other hand, lead concentration is closely related to first derivatives at 1453, 2316, and 2337 nm, which are absorption features of chlorite, tremolite, and talc. These results revealed that the spectral bands sensitive to the heavy metal concentration varied with the geochemical absorption mechanism between specific minerals and heavy metal elements. Therefore, the geological setting of the soils is one of the major controlling factors associated with spectral response to heavy metal contamination. Given the fact that a hydrothermal ore deposit is one of the most widely distributed types, the laboratory result of this paper may be applied to the real-world cases with similar geological environments.
A Stochastic SPICE Model for Superconducting Nanowire Single Photon Detectors and Other Nanowire Devices

Superconducting nanowire devices, such as the superconducting nanowire single photon detector (SNSPD) or nanocryotron, have a time-dependent stochasticity that depends on the current flowing through them. When modeling complex circuits made of several such devices (for instance, an array of SNSPDs), the ability to include this randomness can be important for predicting unwanted effects and interactions within the circuit. We present a modification of the model described by Berggren et al. that allows for the inclusion of this stochasticity into the nanowire device model. We then verify the model against experiment using a tungsten silicide SNSPD, and show that the modified model replicates the stochasticity of the physical device.
Single image rain removal using image decomposition and a dense network

Removing rain from a single image is a challenging task due to the absence of temporal information. Considering that a rainy image can be decomposed into the low-frequency (LF) and high-frequency (HF) components, where the coarse scale information is retained in the LF component and the rain streaks and texture correspond to the HF component, we propose a single image rain removal algorithm using image decomposition and a dense network. We design two task-driven sub-networks to estimate the LF and non-rain HF components of a rainy image. The high-frequency estimation sub-network employs a densely connected network structure, while the low-frequency sub-network uses a simple convolutional neural network (CNN). We add total variation (TV) regularization and LF-channel fidelity terms to the loss function to optimize the two subnetworks jointly. The method then obtains de-rained output by combining the estimated LF and non-rain HF components. Extensive experiments on synthetic and real-world rainy images demonstrate that our method removes rain streaks while preserving non-rain details, and achieves superior de-raining performance both perceptually and quantitatively.
Multimodal Sentiment Analysis: A Multitask Learning Approach

Multimodal sentiment analysis has recently received an increasing interest. However, most methods have considered that text and image modalities are always available at test time. This assumption is often violated in real environments (e.g. social media) since users do not always publish a text with an image. In this paper we propose a method based on a multitask framework to combine multimodal information when it is available, while being able to handle the cases where a modality is missing. Our model contains one classifier for analyzing the text, another for analyzing the image, and another performing the prediction by fusing both modalities. In addition to offer a solution to the problem of a missing modality, our experiments show that this multitask framework improves generalization by acting as a regularization mechanism. We also demonstrate that the model can handle a missing modality at training time, thus being able to be trained with image-only and text-only examples.
Effectful Normal Form Bisimulation

Normal form bisimulation, also known as open bisimulation, is a coinductive technique for higher-order program equivalence in which programs are compared by looking at their essentially infinitary tree-like normal forms, i.e. at their Böhm or Lévy-Longo trees. The technique has been shown to be useful not only when proving metatheorems about $$\lambda $$ -calculi and their semantics, but also when looking at concrete examples of terms. In this paper, we show that there is a way to generalise normal form bisimulation to calculi with algebraic effects, à la Plotkin and Power. We show that some mild conditions on monads and relators, which have already been shown to guarantee effectful applicative bisimilarity to be a congruence relation, are enough to prove that the obtained notion of bisimilarity, which we call effectful normal form bisimilarity, is a congruence relation, and thus sound for contextual equivalence. Additionally, contrary to applicative bisimilarity, normal form bisimilarity allows for enhancements of the bisimulation proof method, hence proving a powerful reasoning principle for effectful programming languages.
Learning Automata Based Incremental Learning Method for Deep Neural Networks

Deep learning methods have got fantastic performance on lots of large-scale datasets for machine learning tasks, such as visual recognition and neural language processing. Most of the progress on deep learning in recent years lied on supervised learning, for which the whole dataset with respect to a specific task should be well-prepared before training. However, in the real-world scenario, the labeled data associated with the assigned classes are always gathered incrementally over time, since it is cumbersome work to collect and annotate the training data manually. This suggests the manner of sequentially training on a series of datasets with gradually added training samples belonging to new classes, which is called incremental learning. In this paper, we proposed an effective incremental training method based on learning automata for deep neural networks. The main thought is to train a deep model with dynamic connections which can be either “activated” or “deactivated” on different datasets of the incremental training stages. Our proposed method can relieve the destruction of old features while learning new features for the newly added training samples, which can lead to better training performance on the incremental learning stage. The experiments on MNIST and CIFAR-100 demonstrated that our method can be implemented for deep neural models in a long sequence of incremental training stages and can achieve superior performance than training from scratch and the fine-tuning method.
Analyzing and Identifying Data Breaches in Underground Forums

Recently, underground forums play a crucial role in trading and exchanging leaked personal information.Meanwhile, the forums have been gradually used as data breaches' information sources.Therefore, it shows an upward trend in announcing the results of data theft by posting in the forums.Identifying these threads can make the compromised third-party respond quickly to the data breach incident.For this purpose, we presented a system to identify the threads which are related to data breaches automatically.The system can monitor and discover data breaches in underground forums in real-time.In addition, the study further revealed the wording characteristics of the threads by applying the feature extraction method based on LDA topic model.In this paper, the data set was collected from the surface web and the dark web.Besides, to improve the performance of the system, we compared various supervised classification algorithms in this application scenario and selected the best method for the classifier.Through the system, we identified more than 92% of data breach threads on the experimental data set.
Hybrid Private Record Linkage

Private record linkage protocols allow multiple parties to exchange matching records, which refer to the same entities or have similar values, while keeping the non-matching ones secret. Conventional protocols are based on computationally expensive cryptographic primitives and therefore do not scale. To address these scalability issues, hybrid protocols have been proposed that combine differential privacy techniques with secure multiparty computation techniques. However, a drawback of such protocols is that they disclose to the parties both the matching records and the differentially private synopses of the datasets involved in the linkage. Consequently, differential privacy is no longer always satisfied. To address this issue, we propose a novel framework that separates the private synopses from the matching records. The two parties do not access the synopses directly, but still use them to efficiently link records. We theoretically prove the security of our framework under the state-of-the-art privacy notion of differential privacy for record linkage (DPRL). In addition, we develop a simple but effective strategy for releasing private synopses. Extensive experimental results show that our framework is superior to the existing methods in terms of efficiency.
Fluent Translations from Disfluent Speech in End-to-End Speech Translation

Elizabeth Salesky, Matthias Sperber, Alexander Waibel. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A Case Study on Neural Headline Generation for Editing Support

Kazuma Murao, Ken Kobayashi, Hayato Kobayashi, Taichi Yatsuka, Takeshi Masuyama, Tatsuru Higurashi, Yoshimune Tabuchi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Method of Data Control in the Residue Classes

Methods of data control in the residue classes are considered in the article.The main advantage of non-positional notation in the residue classes lays in the possibility of an organization of the process of quick implementation of modular arithmetic operations of addition, subtraction and multiplication.The base of the method is the procedure of generating and using the positional indication of non-positional code.That allows increasing efficiency of the procedure of data control granted by the residue classes.
The AM4I Architecture and Framework for Multimodal Interaction and Its Application to Smart Environments

Technologies, such as smart sensors, actuators, and other kinds of devices, are often installed in our environments (e.g., our Homes) and available to integrate our daily lives. Despite their installation being motivated by the pursuit of automation and increased efficiency, making these environments usable, acceptable and enjoyable in a sustainable, energy efficient way is not only a matter of automation. Tackling these goals is a complex task demanding the combination of different perspectives including building and urban Architecture, Ubiquitous Computing and Human-Computer Interaction (HCI) to provide occupants with the means to shape these environments to their needs. Interaction is of paramount relevance in the creation of adequate relations of users with their environments, but it cannot be seen independently from the ubiquitous sensing and computing or the environment's architecture. In this regard, there are several challenges to HCI, particularly in how to integrate this multidisciplinary effort. Although there are several solutions to address some of these challenges, the complexity and dynamic nature of the smart environments and the diversity of technologies involved still present many challenges, particularly for its development. In general, the development is complex, and it is hard to create a dynamic environment providing versatile and adaptive forms of interaction. To participate in the multidisciplinary effort, the development of interaction must be supported by tools capable of facilitating co-design by multidisciplinary teams. In this article, we address the development of interaction for complex smart environments and propose the AM4I architecture and framework, a novel modular approach to design and develop adaptive multiplatform multilingual multi-device multimodal interactive systems. The potential of the framework is demonstrated by proof-of-concept applications in two different smart environment contexts, non-residential buildings and smart homes.
An Approach to Extraction Midsagittal Plane of Skull From Brain CT Images for Oral and Maxillofacial Surgery

To prepare for the oral and maxillofacial surgery for the facial symmetry of patients, midsagittal plane of skull in brain computed tomography (CT) images is calculated with points manually chosen from skull by doctor. But the extracted midsagittal plane of the skull is different by different doctor. Even the extracted midsagittal plane of the same patient is also different by the same doctor in different times. The manually extracting operation usually takes a long time to increase the doctor’s workload. Aimed at this problem, a semi-automatic extracting method for midsagittal plane of skull is proposed in this paper. First, the brain tissue is extracted by region growing method and the oriented bounding box (OBB) of the brain tissue is built. Second, the middle symmetry plane of the OBB of brain tissue is extracted as the initial midsagittal plane, which is updated by the mathematical translation and rotation method. Finally, the symmetrical characteristic of the brain tissue based on the updated symmetry plane is calculated by the mutual information method. This procedure is executed iteratively until the symmetrical characteristic of the brain tissue based on the new symmetry plane is no more different from the previous result. The final extracted symmetry plane is the midsagittal plane of skull in brain CT images of the patient. The midsagittal plane which is extracted manually by doctor is used to compare and evaluate the accuracy of this semi-automatic extracting symmetry plane method. The experimental results from both qualitative and quantitative analyses showed that the method can reach or approach the accuracy of manual extracted, but the stable level of this method is significantly higher than the manual method and this method can shorten the operate time to reduce the doctor’s workload.
Text steganography on RNN-Generated lyrics

We present a Recurrent Neural Network (RNN) Encoder-Decoder model to generate Chinese pop music lyrics to hide secret information. In particular, on a given initial line of a lyric, we use the LSTM model to generate the next Chinese character or word to form a new line. In so doing, we generate the entire lyric from what has been generated so far. Using common lyric formats and rhymes we extracted, we generate lyrics embedded with secret information to meet the visual and pronunciation requirements. We carry out experiments and theoretical analysis, and show that lyrics generated by our method offer higher embedding capacities for steganography, which also look more natural than the existing steganography methods based on text generations.
Assessment of metal and metalloid contamination in soils trough compositional data: the old Mortórios uranium mine area, central Portugal

Soils from the old Mortórios uranium mine area were studied to look for contamination, as they are close to two villages, up to 3 km away, and used for agriculture. They are mainly contaminated in U and As and constitute an ecological threat. This study attempts to outline the degree to which soils have been affected by the old mining activities through the computation of significant hot clusters, Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the analyzed elements represent the soil's entirety. However, in geochemical studies these elements are just a fraction of the total soil composition. Thus, considering compositional data is pivotal. The spatial characterization, considering raw and compositional data together, allowed a broad discussion about not only the concentrations' spatial distribution, but also a better understanding on the possibility of trends of "relative enrichment" and, furthermore an insight in U and As fate. The highest proportions (compositional data) on U (up to 33%), As (up to 35%) and Th (up to 13%) are reached in the south-southeast segment. However, the highest concentrations (raw data) occur in north and northwest of the studied area, pointing out to a "relative enrichment" toward the south-southeast zone. The Mondego Sul area is mainly contaminated in U and As, but also in Co, Cu, Pb and Sb. The Mortórios area is less contaminated than the Mondego Sul area.
Detection and Repair of Architectural Inconsistencies in Java

Java is one of the most widely used programming languages. However, the absence of explicit support for architectural constructs, such as software components, in the programming language itself has prevented software developers from achieving the many benefits that come with architecture-based development. To address this issue, Java 9 has introduced the Java Platform Module System (JPMS), resulting in the first instance of encapsulation of modules with rich software architectural interfaces added to a mainstream programming language. The primary goal of JPMS is to construct and maintain large applications efficiently-as well as improve the encapsulation, security, and maintainability of Java applications in general and the JDK itself. A challenge, however, is that module declarations do not necessarily reflect actual usage of modules in an application, allowing developers to mistakenly specify inconsistent dependencies among the modules. In this paper, we formally define 8 inconsistent modular dependencies that may arise in Java-9 applications. We also present DARCY, an approach that leverages these definitions and static program analyses to automatically (1) detect the specified inconsistent dependencies within Java applications and (2) repair those identified inconsistencies. The results of our experiments, conducted over 38 open-source Java-9 applications, indicate that architectural inconsistencies are widespread and demonstrate the benefits of DARCY in automated detection and repair of these inconsistencies.
Adaptive Neuro-Filtering Based Visual Servo Control of a Robotic Manipulator

This paper focuses on the solutions to flexibly regulate robotic by vision. A new visual servoing technique based on the Kalman filtering (KF) combined neural network (NN) is developed, which need not have any calibration parameters of robotic system. The statistic knowledge of the system noise and observation noise are first given by Gaussian white noise sequences, the nonlinear mapping between robotic vision and motor spaces are then on-line identified using standard Kalman recursive equations. In real robotic workshops, the perfect statistic knowledge of the noise is not easy to be derived, thus an adaptive neuro-filtering approach based on KF is also studied for mapping on-line estimation in this paper. The Kalman recursive equations are improved by a feedforward NN, in which the neural estimator dynamic adjusts its weights to minimize estimation error of robotic vision-motor mapping, without the knowledge of noise variances. Finally, the proposed visual servoing based on adaptive neuro-filtering has been successfully implemented in robotic pose regulation, and the experimental results demonstrate its validity and practicality for a six-degree-of-freedom (DOF) robotic system which the hand-eye without calibrated.
Self-Improving Generative Artificial Neural Network for Pseudorehearsal Incremental Class Learning

Deep learning models are part of the family of artificial neural networks and, as such, they suffer catastrophic interference when learning sequentially. In addition, the greater number of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, we propose the Self-Improving Generative Artificial Neural Network (SIGANN), an end-to-end deep neural network system which can ease the catastrophic forgetting problem when learning new classes. In this method, we introduce a novel detection model that automatically detects samples of new classes, and an adversarial autoencoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder, and a novelty-detection module implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN can retain previous knowledge while incorporating gradual forgetfulness of each learning sequence at a rate of about 7% per training step. Moreover, SIGANN can detect new classes that are hidden in the data with a median accuracy of 43 % and, therefore, proceed with incremental class learning.
Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation

Nowadays, the recommendation systems are applied in the fields of e-commerce, video websites, social networking sites, which bring great convenience to people's daily lives. The types of information are diversified and abundant in recommendation systems; therefore the proportion of unstructured multimodal data such as text, image, and video is increasing. However, due to the representation gap between different modalities, it is intractable to effectively use unstructured multimodal data to improve the efficiency of recommendation systems. In this paper, we propose an end-to-end multimodal interest-related item similarity model (multimodal IRIS) to provide recommendations based on the multimodal data source. Specifically, the multimodal IRIS model consists of three modules, i.e., multimodal feature learning module, the interest-related network (IRN) module, and item similarity recommendation module. The multimodal feature learning module adds knowledge sharing unit among different modalities. Then, IRN learns the interest relevance between target item and different historical items respectively. Finally, the multimodal feature learning, IRN, and item similarity recommendation modules are unified into an integrated system to achieve performance enhancements and to accommodate the addition or absence of different modal data. Extensive experiments on real-world datasets show that, by dealing with the multimodal data which people may pay more attention to when selecting items, the proposed multimodal IRIS significantly improves accuracy and interpretability on top-N recommendation task over the state-of-the-art methods.