Reviewerly/exHarmony huggingface dataset:
An Aspect-Centralized Graph Convolutional Network for Aspect-Based Sentiment Classification

Recent works on aspect-based sentiment classification have manifested the great effectiveness of modeling syntactic dependency with graph neural networks (GNN). However, these works ignore the fact of sentiment information decreasing over dependency paths due to the complex syntactic structure. To tackle the above limitation, we explore a novel solution of constructing an Aspect-Centralized Graph (ACG) for each aspect. Specifically, we directly link all words in a sentence to the aspect word and create a more effective way for the interaction between aspects and opinion words. Based on it, we also incorporate syntactic information into the new graph. To achieve this, we substitute edges of ACG with weighed values, which are calculated from the syntactic relative distance between the aspect and context words on the original dependency graph. Then we propose an Aspect Centralized Graph Convolutional Network (ACGCN) to extract aspect-specific features and effectively interact them with context representations. Extensive experiments on five benchmark datasets show that our model achieves better performance over most baseline models and extensively boosts the performance with BERT.
Joint Phase Unwrapping and Speckle Filtering by Using Convolutional Neural Networks

In this paper the effectiveness of a CNN based interferometric phase unwrapping algorithm combined with phase noise filtering is analysed. In particular, the considered processing chain relies on a pre-processing step with the nonlocal filter InSAR-BM3D followed by a deep CNN solution for restoring the absolute phase. The analyses is conducted on simulated data with different coherence values and aims at comparing the performance of the unwrapping with and without the pre-processing step. This paper is the first step towards a unique deep learning solution for jointly unwrapping and restoring the absolute phase.
Robotic Information Gathering using Semantic Language Instructions

This paper presents a framework that uses language instructions to define the constraints and objectives for robots gathering information about their environment. Designing autonomous robotic sampling missions requires deep knowledge of both autonomy systems and scientific domain expertise. Language commands provide an intuitive interface for operators to give complex instructions to robots. The key insight we leverage is using topological constraints to define routing directions from the language instruction such as ‘route to the left of the island.’ This work introduces three main contributions: a framework to map language instructions to constraints and rewards for robot planners, a topology constrained information gathering algorithm, and an automatic semantic feature detection algorithm for upwelling fronts. Our work improves on existing methods by not requiring training data with language instruction to planner constraint pairs, allowing new robotic domains such as marine robotics to use our method. This paper provides results demonstrating our framework producing correct constraints for 84.6% of instructions, from a systematically generated corpus of over 1.1 million instructions We also demonstrate the framework producing robot plans from language instructions for real-world scientific sampling missions with the Slocum underwater glider.
Nearly Optimal Quantum Algorithm for Generating the Ground State of a Free Quantum Field Theory

We devise a quasilinear quantum algorithm for generating an approximation for the ground state of a quantum field theory (QFT). Our quantum algorithm delivers a super-quadratic speedup over the state-of-the-art quantum algorithm for ground-state generation, overcomes the ground-state-generation bottleneck of the prior approach and is optimal up to a polylogarithmic factor. Specifically, we establish two quantum algorithms -- Fourier-based and wavelet-based -- to generate the ground state of a free massive scalar bosonic QFT with gate complexity quasilinear in the number of discretized-QFT modes. The Fourier-based algorithm is limited to translationally invariant QFTs. Numerical simulations show that the wavelet-based algorithm successfully yields the ground state for a QFT with broken translational invariance. Furthermore, the cost of preparing particle excitations in the wavelet approach is independent of the energy scale. Our algorithms require a routine for generating one-dimensional Gaussian (1DG) states. We replace the standard method for 1DG-state generation, which requires the quantum computer to perform lots of costly arithmetic, with a novel method based on inequality testing that significantly reduces the need for arithmetic. Our method for 1DG-state generation is generic and could be extended to preparing states whose amplitudes can be computed on the fly by a quantum computer.
Reinforced fuzzy clustering-based rule model constructed with the aid of exponentially weighted ℓ2 regularization strategy and augmented random vector functional link network

Fuzzy rule-based models are widely employed to tackle regression problems due to their simplicity and comprehensibility. Numeric functions (e.g., linear ones) are generally utilized to represent the conclusion part of a rule, but this simple module cannot well describe the behavior of data located within local area defined by the premise part. In this study, a novel reinforced fuzzy clustering-based rule model (RFCRM) is presented to address this issue. First, information granulation (Fuzzy C-Means) approach is used to reveal the structure in the data and divide the input space into local regions, as well as form the premise parts. Second, the augmented Random Vector Functional Link Networks (RVFLNs) are employed as the conclusion parts to strengthen the description and representation of the behavior of data positioned within the local regions. Weighted Least Squares Error estimation with regularization is exploited to estimate the coefficients of the connection weights of RVFLN. Also, an exponential weight method benefited from the weight function theory encountered in harmonic analysis is introduced to improve the penalty strategy of regularization in coefficient estimation. More specifically, an exponential weighted ℓ2 regularization (EWℓ2) strategy equipped with the exponential functions as the penalty terms is proposed to realize the targeted penalty on coefficients. The merit of EWℓ2 over ordinary ℓ2 is that different types of polynomial terms can be identified and penalized separately in coefficient estimation. This strategy not only prevents the decline of generalization ability, but also effectively enhances the prediction potential of the model. By combining weighted LSE based on EWℓ2 with FCM partition and the augmented RVFLN leads to better prediction accuracy as well as lower complexity (viz., number of rules). Finally, detailed experimental studies are conducted to demonstrate the effectiveness of the proposed RFCRM.
CAMNet: A controllable acoustic model for efficient, expressive, high-quality text-to-speech

Spoken language is becoming one of the key components of human–machine interaction, both to send information to the machine – e.g. voice control – and to receive from it – e.g. virtual assistants. In this scenario, text-to-speech (TTS) models have become an essential artificial intelligence capacity. Even though this interaction can be based on neutral style speech, generating speech with different styles, pitches and speaking rates may improve user experience. With this in view, this paper presents CAMNet, a controllable acoustic model for efficient, expressive, high-quality TTS. CAMNet is based on deep convolutional TTS (DCTTS), a state-of-art acoustic model which is efficient and produces neutral speech. DCTTS was first adapted to generate Bark cepstrum acoustic features in order to integrate well with the LPCNet (linear prediction coefficient) neural vocoder and to remove the reduction factor which demanded the presence of an upsampling network before the vocoder – i.e. the CAMNet output can be directly fed into LPCNet. Next, style transfer functionality was added by means of a novel characterisation of the prosodic information from the Bark cepstrum acoustic features and a new approach to inject this information into the convolutional layers. Finally, controllability is provided via a variational auto-encoder module which creates a smoothed disentangled latent space which allows interpolation and extrapolation of reference styles as well as independent and simultaneous control of two generative factors: pitch and speaking rate. Moreover, this controllability is implemented using a simple offset-based approach. To sum up, CAMNet is an efficient acoustic model which provides a simple but consistent controllability on coarse-grained expression, pitch and speaking rate while still providing high-quality synthesised speech.
Feature Imitating Networks

We introduce a novel approach to neural learning: the Feature-Imitating-Network (FIN). A FIN is a neural network with weights that are initialized to reliably approximate one or more closed-form statistical features, such as Shannon’s entropy. In this paper, we demonstrate that FINs (and FIN ensembles) provide best-in-class performance for a variety of downstream signal processing and inference tasks, while using less data and requiring less fine-tuning compared to other networks of similar (or even greater) representational power. We conclude that FINs can help bridge the gap between domain experts and machine learning practitioners by enabling re-searchers to harness insights from feature-engineering to enhance the performance of contemporary representation learning approaches.
Self-Attention Fusion Module for Single Remote Sensing Image Super-Resolution

Single image super-resolution (SISR) is an important procedure to improve many remote sensing applications. Global features play an important role in pixel generation of SISR. In this paper, we proposed a self-attention fusion module named as SAF module which combines spatial attention and channel attention in parallel to handle this problem. Our self-attention fusion module can be flexibly added to many popular deep-learning-based SISR models to further improve their representation ability and learn global features. Experiments on UC Merced dataset indicate that SAF module can improve the performance of classic SISR models and achieve state-of-the-art super-resolution results.
Empirically Evaluating the Semantic Qualities of Language Vocabularies

Developing and representing conceptualizations is a critical element of conceptual modeling language design. Designers choose a set of suitable concepts for describing a domain and make them the core of the language using suggestive terms that convey their meaning to language users. Additional documentation and training material, such as examples and guides, aims at ensuring that the chosen terms indeed evoke the concepts designers intended. However, there is no guarantee that language designers and users will eventually understand the correspondence between terms and concepts in the same way. This paper proposes a framework for empirically evaluating the vocabulary appropriateness of modeling languages and characterizing its absence in terms of established language design issues. The framework is based on the definition of a set of abstract empirical constructs that can be operationalized into different concrete measures, depending on study requirements and experimental design choices. We offer examples of such measures and demonstrate how they inform language design through a hypothetical language design scenario using a mix of realistic and simulated data.
Anomaly detection of data and topology patterns in WSNs

Wireless sensor networks are often distributed which makes detection of cyber-attacks or misconfiguration hard. Topology and data patterns change may result from attacks leading to the compromise of data and service availability or indicate operational problems. Graphs are often used to model topology and data paths to describe and compare state of a system. For anomaly detection, the definition of normal patterns, deviation from normal, and criteria when to declare anomaly are required. In this contribution the process of acquisition of normal patterns (ground truth), and criteria when to declare anomaly based on graph comparison are proposed. The anomaly detection is suitable for deployment at the edge of a network. Finally, the inability to define all security threats is addressed by a custom tree-based classifier which only requires normal patterns for training. A simulated wireless sensor network was used to acquire data and apply the method. Our experiments show that data and topology change can be detected at the edge of a network.
Hypersphere for Branching Node for the Family of Isolation Forest Algorithms

We propose Finite Boundary (FB) version of Isolation Forest (IF), Split Selection Criterion iForest (SciForest) and Extended Isolation Forest (EIF) algorithms using hypersphere as branching boundary for enhanced consistency in anomaly score. EIF substitutes axis parallel hyperplanes with slanted hyperplanes as in SciForest for a remedy of the problem of inconsistent anomaly score. EIF offers an improvement of computation speed over SciForest algorithm by removing the search for the optimum hyperplane for branching. We identify inconsistency in anomaly score by EIF for a synthetic 2-D spiral dataset and inconsistency in anomaly score for single blob of 2-D synthetic gaussian dataset by SciForest to empirically show that the slanted hyperplanes alone is insufficient. First, we explain the abnormal decrease of anomaly score for anomalous data points due to the unexpected increase in the number of branching for anomalous data points by the infinite extensions of hyperplanes. Second, we propose to use hyper-sphere as a suitable option for generalized branching decision boundary. Next, we empirically show that the anomaly scores suffer not from the artifacts of axis parallelism of the hyper-planes of IF, by comparing anomaly scores with finite boundary hyper-sphere as branching decision boundary against the slanted hyperplanes and highlight the redundant extension of the infinite hyperplanes as the dominant cause of the inconsistency in anomaly score. Third, we apply FB version of IF (FBIF), EIF (FBEIF) and SciForest (FBSciForest) to several standard 2-D synthetic datasets to assess robustness and computation speed in comparison to EIF, SciForest and IF.
Bit Security as Computational Cost for Winning Games with High Probability

We introduce a novel framework for quantifying the bit security of security games. Our notion is defined with an operational meaning that a \(\lambda \)-bit secure game requires a total computational cost of \(2^\lambda \) for winning the game with high probability, e.g., 0.99. We define the bit security both for search-type and decision-type games. Since we identify that these two types of games should be structurally different, we treat them differently but define the bit security using the unified framework to guarantee the same operational interpretation. The key novelty of our notion of bit security is to employ two types of adversaries: inner adversary and outer adversary. While the inner adversary plays a "usual" security game, the outer adversary invokes the inner adversary many times to amplify the winning probability for the security game. We find from our framework that the bit security for decision games can be characterized by the information measure called the Rényi divergence of order 1/2 of the inner adversary. The conventional "advantage," defined as the probability of winning the game, characterizes our bit security for search-type games. We present several security reductions in our framework for justifying our notion of bit security. Many of our results quantitatively match the results for the bit security notion proposed by Micciancio and Walter in 2018. In this sense, our bit security strengthens the previous notion of bit security by adding an operational meaning. A difference from their work is that, in our framework, the Goldreich-Levin theorem gives an optimal reduction only for "balanced" adversaries who output binary values in a balanced manner.
Synthesizing Spoken Descriptions of Images

Image captioning technology has great potential in many scenarios. However, current text-based image captioning methods cannot be applied to approximately half of the world's languages due to these languages lack of a written form. To solve this problem, recently the image-to-speech task was proposed, which generates spoken descriptions of images bypassing any text via an intermediate representation consisting of phonemes (image-to-phoneme). Here, we present a comprehensive study on the image-to-speech task in which, 1) several representative image-to-text generation methods are implemented for the image-to-phoneme task, 2) objective metrics are sought to evaluate the image-to-phoneme task, and 3) an end-to-end image-to-speech model that is able to synthesize spoken descriptions of images bypassing both text and phonemes is proposed. Extensive experiments are conducted on the public benchmark database Flickr8k. Results of our experiments demonstrate that 1) State-of-the-art image-to-text models can perform well on the image-to-phoneme task, and 2) several evaluation metrics, including BLEU3, BLEU4, BLEU5, and ROUGE-L can be used to evaluate image-to-phoneme performance. Finally, 3) end-to-end image-to-speech bypassing text and phonemes is feasible.
Use of Speaker Metadata for Improving Automatic Pronunciation Assessment

Pronunciation assessment remains a subjective task which depends on a pronunciation reference hold as canonical. Whether a second language (L2) speaker is able to replicate said reference is decided by an assessor who perceives the identity of the sounds produced. It is known that the assessor has a bias caused by the perception of the speaker, hence the definition of a standard for L2 pronunciation is crucial in a formal assessment. In Computer Assisted Pronunciation Assessment (CAPA), the definition of a pronunciation standard for L2 is not trivial due to limited L2 data annotated for mispronunciations. Inspired on the assessor's bias, this work explores an alternative to a conventional Automatic Speech Recognition approach for CAPA by using speaker metadata along with acoustic observations for mispronunciation detection. A combination of Bidirectional Long-Short Memory with self-attention was used to detect pronunciation errors in short speech segments. It was found that the use of categorical metadata can have a positive effect in the classification of mispronounced segments depending on the sparsity and balance of the classes. It was also found that different assessors can be influenced differently by information about the speaker's linguistic background. The effect of the metadata was tested on data from Dutch children learners of English as L2 in schools across the Netherlands. The limited speaker diversity of the corpus made the task a challenge worth keep exploring.
Differential-Linear Cryptanalysis of the Lightweight Cryptographic Algorithm KNOT

KNOT is one of the 32 candidates in the second round of NIST’s lightweight cryptography standardization process. The KNOT family consists of bit-slice lightweight Authenticated Encryption with Associated Data (AEAD) and hashing algorithms. In this paper, we evaluate the security for the initialization phase of two members of the KNOT-AEAD family by differential-linear cryptanalysis.
How to Find Ternary LWE Keys Using Locality Sensitive Hashing

Let \(As = b + e \bmod q\) be an LWE-instance with ternary keys \(s,e \in \{0, \pm 1\}^n\). Let s be taken from a search space of size \(\mathcal {S}\). A standard Meet-in-the-Middle attack recovers s in time \(\mathcal {S}^{0.5}\). Using the representation technique, a recent improvement of May shows that this can be lowered to approximately \(\mathcal {S}^{0.25}\) by guessing a sub-linear number of \(\varTheta (\frac{n}{\log n})\) coordinates from e. While guessing such an amount of e can asymptotically be neglected, for concrete instantiations of e.g. NTRU, BLISS or GLP the additional cost of guessing leads to complexities around \(\mathcal {S}^{0.3}\).
Uniform concentration bounds for frequencies of rare events

New Vapnik–Chervonenkis type concentration inequalities are derived for the empirical distribution of an independent random sample. Focus is on the maximal deviation over classes of Borel sets within a low probability region. The constants are explicit, enabling numerical comparisons.
Trans* Vulnerability and Digital Research Ethics

Trans* communities across the United States are under assault. Researchers seeking to work with trans* people and other multiply marginalized and underrepresented communities must attend to ethical research practices within the communities in which they participate. Digital research ethics is particularly murky with issues of embodiment, vulnerability, and unclear IRB guidance. Comparing two transparency activist organizations—Wikileaks and DDoSecrets—we introduce “qubit ethics,” a trans*material, trans-corporeal ethics of care as praxis within vulnerable online communities. We then demonstrate how this unique approach to research design allows for the complex entanglements that is trans* life, particularly digital life. Finally, we present clear take-aways for qubit-ethics informed social justice research.
A SHAP Value-Based Approach to Stock Price Evaluation of Manufacturing Companies

In this study, we propose the usage of Shapley values as explanatory variables’ values to represent operational competence measures and try to relate such operational competencies to stock price evaluation. For this analysis, we used the data of Japanese manufacturing companies of three industries, automobiles, electronics and precision machinery equipment. Based on the analysis results, we advocate this type of approach can be expected to provide more reliable information because such operational measures are considered to signal the status of management quality competence.
Dual-Targeted Textfooler Attack on Text Classification Systems

Deep neural networks provide good performance on classification tasks such as those for image, audio, and text classification. However, such neural networks are vulnerable to adversarial examples. An adversarial example is a sample created by adding a small adversarial noise to an original data sample in such a way that it will be correctly classified by a human but misclassified by a deep neural network. Studies on adversarial examples have focused mainly on the image field, but research is expanding into the text field as well. Adversarial examples in the text field that are designed with two targets in mind can be useful in certain situations. In a military scenario, for example, if enemy models A and B use a text recognition model, it may be desirable to cause enemy model A tanks to go to the right and enemy model B self-propelled guns to go to the left by using strategically designed adversarial messages. Such a dual-targeted adversarial example could accomplish this by causing different misclassifications in different models, in contrast to single-target adversarial examples produced by existing methods. In this paper, I propose a method for creating a dual-targeted textual adversarial example for attacking a text classification system. Unlike the existing adversarial methods, which are designed for images, the proposed method creates dual-targeted adversarial examples that will be misclassified as a different class by each of two models while maintaining the meaning and grammar of the original sentence, by substituting words of importance. Experiments were conducted using the SNLI dataset and the TensorFlow library. The results demonstrate that the proposed method can generate a dual-targeted adversarial example with an average attack success rate of 82.2% on the two models.