Reviewerly/exHarmony huggingface dataset:
Structure Extension of Tree-Augmented Naive Bayes

Due to the simplicity and competitive classification performance of the naive Bayes (NB), researchers have proposed many approaches to improve NB by weakening its attribute independence assumption. Through the theoretical analysis of Kullback–Leibler divergence, the difference between NB and its variations lies in different orders of conditional mutual information represented by these augmenting edges in the tree-shaped network structure. In this paper, we propose to relax the independence assumption by further generalizing tree-augmented naive Bayes (TAN) from 1-dependence Bayesian network classifiers (BNC) to arbitrary k-dependence. Sub-models of TAN that are built to respectively represent specific conditional dependence relationships may “best match” the conditional probability distribution over the training data. Extensive experimental results reveal that the proposed algorithm achieves bias-variance trade-off and substantially better generalization performance than state-of-the-art classifiers such as logistic regression.
Implementing a concept network model

The same concept can mean different things or be instantiated in different forms, depending on context, suggesting a degree of flexibility within the conceptual system. We propose that a feature-based network model can be used to capture and predict this flexibility. We modeled individual concepts (e.g., banana, bottle) as graph-theoretical networks, in which properties (e.g., yellow, sweet) were represented as nodes and their associations as edges. In this framework, networks capture within-concept statistics that reflect how properties relate to one another across instances of a concept. We extracted formal measures of these networks that capture different aspects of network structure, and explored whether a concept’s network structure relates to its flexibility of use. To do so, we compared network measures to a text-based measure of semantic diversity, as well as to empirical data from a figurative-language task and an alternative-uses task. We found that network-based measures were predictive of the text-based and empirical measures of flexible concept use, highlighting the ability of this approach to formally capture relevant characteristics of conceptual structure. Conceptual flexibility is a fundamental attribute of the cognitive and semantic systems, and in this proof of concept we reveal that variations in concept representation and use can be formally understood in terms of the informational content and topology of concept networks.
Artificial Neural Networks as Emerging Tools for Earthquake Detection

As seismic networks continue to spread and monitoring sensors become more efficient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismicrisk. These have fueled the research for the automation of interpretation tasks such as event detection, event identification, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recent advances are related to the application of Artificial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and theestimation of on set times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.
Motion-induced error reduction for binary defocusing profilometry via additional temporal sampling

The recently proposed binary defocusing technique has brought speed breakthroughs for three-dimensional (3D) shape measurement with a digital fringe projection system. Despite this, motion-induced phase error is still inevitable due to the multi-shot nature of the phase-shifting algorithm. To alleviate this problem, this paper proposes a motion-induced error reduction method by taking advantage of additional temporal sampling. Particularly, each illuminated fringe pattern will be captured twice in one projection cycle, resulting in two sets of phase shifted fringe images being obtained. Due to the mechanism of binary defocusing projection, the motion-induced phase error could be effectively separated from the fixed phase shift value by evaluating the difference between the two phase maps. Based on this, an iterative compensation strategy is further applied to compensate the phase error until high-quality phase maps are generated. Meanwhile, different synchronization schemes are also proposed and tested to evaluate the error compensation effects. Both simulation and experiments demonstrated that the proposed methods can substantially reduce motion-introduced measurement errors. Since defocused 1-bit binary patterns are utilized to bypass rigid camera-projector synchronization, this method has potential for high-speed applications.
Detecting and Characterizing Lateral Phishing at Scale

Author(s): Ho, G; Cidon, A; Gavish, L; Schweighauser, M; Paxson, V; Savage, S; Voelker, GM; Wagner, D | Abstract: We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefit-ting from both the implicit trust and the information in the hijacked user's account. We develop a classifier that finds hundreds of real-world lateral phishing emails, while generating under four false positives per every one-million employee-sent emails. Drawing on the attacks we detect, as well as a corpus of user-reported incidents, we quantify the scale of lateral phishing, identify several thematic content and recipient targeting strategies that attackers follow, illuminate two types of sophisticated behaviors that attackers exhibit, and estimate the success rate of these attacks. Collectively, these results expand our mental models of the 'enterprise attacker' and shed light on the current state of enterprise phishing attacks.
A Fault Prediction Of Equipment Based On CNN-LSTM Network

Unit equipment is the key to industrial production, and predicting unit failures is the focus of improving equipment productivity. In order to improve the accuracy and reliability of predicting, and consider the use of multiple related influencing factors for prediction. This paper presents a fault prediction method based on CNN-LSTM. Firstly, the data of multiple variables that affects the predicted value over a period of time are formed into a large matrix. Then, the Long short-term memory network is trained by the feature information extracted from the convolutional neural network. This can predict device data at future time points and build models that use large amounts of data for predicting. Finally, the rationality and effectiveness of the proposed method are verified by the comparison between the evaluation example and the LSTM network.
Text Typing in VR Using Smartphones Touchscreen and HMD

In this work, we were interested in using smartphone touchscreen keyboard for text typing in virtual environments (VEs) with head-mounted display. We carried out an experiment comparing the smartphone to the ordinary devices: gamepad and HTC Vive Controllers. We represented the touchscreen keyboard in the VE with a virtual interface and the fingertips with tracked green circles. A confirm-on-release paradigm was employed for text typing. Results showed that the smartphone did not fully outperformed the other devices. However, unlike the other devices, smartphones users tended to correct progressively their error while typing thanks to their familiarity with the device.
VPModel: High-Fidelity Product Simulation in a Virtual-Physical Environment

In the development of a new product, the design team must describe the expected effects of the final products to potential users and stakeholders. However, existing prototyping tools can only present a product imperfectly, due to limitations at different levels. Specifically, the physical product model, which may be the product of 3D printing, could lack a visual interface; the presentation of the product through modeling software such as Rhinoceros 3D does not provide good realistic tactile perception; or the interface platforms, such as Axure RP, used to display the interactive effects differ from those to be used in the actual operation. Thus, we present the VPModel, a high-fidelity prototyping tool, able to integrate multiple prototyping methods simultaneously. It combines a touchable 3D-printed product model (3DPM) and a corresponding visualized virtual model, and the interactive interfaces are rendered synchronously in a mixed-reality device. Through the tangible, visual, and interactive demonstration, designers and normal users can each obtain a similar experience to the experience of the finished product. Furthermore, the VPModel also enhances design practices by enabling comparisons between modular models. However, the implementation of this system is a challenging task, which subsumes several fundamental problems as sub-tasks: object detection, real-time matching, hand-gesture detection and action recognition. To achieve the expected goals of the VPModel, this system uses physical hardware (a Microsoft MR HoloLens headset, a Leap Motion Controller, and a 3D printer) and existing machine learning algorithms. To evaluate our VPModel, we report the user experience of 16 participants, evaluated using a closed-ended questionnaire survey, a quantitative analysis of task performance, and a qualitative analysis of open-ended interviews. The results show a significant improvement in realism and enjoyment using the VPModel over the two traditional camera prototype approaches. In summary, the VPModel can be used to support design strategy and to convey design concepts fully and efficiently, which indicates a potential use for the VPModel in shortening product development cycles and reducing communication costs.
A Radar Signal Recognition System Based on Non-Negative Matrix Factorization Network and Improved Artificial Bee Colony Algorithm

The development of cognitive radio and electronic warfare brings new challenges to radar electronic reconnaissance, the recognition of radar signal plays an extreme important role in radar electronic reconnaissance. In order to realize the reliable recognition of radar signal at the condition of low signal-to-noise ratio (SNR), we propose a new radar signal recognition system based on non-negative matrix factorization network (NMFN) and ensemble learning, which can recognize radar signals including BPSK, LFM, NLFM, COSTAS, FRANK, P1, P2, P3 and P4. First, we explore feature extractor based on convolutional neural network (CNN), which applies transfer learning to solve the problem of small sample size. Second, we propose non-negative matrix factorization network to extract features, which can reduce the redundant information. Third, we develop feature fusion algorithm based on stacked autoencoder (SAE), which can acquire essential expression of features and reduce dimension of features. Finally, we propose improved artificial bee colony algorithm (IABC) as the strategy of ensemble learning, which can improve the recognition rate. The simulation results show that the recognition rates reach 94.23% at −4 dB, 99.82% at 6 dB.
ILIME: Local and Global Interpretable Model-Agnostic Explainer of Black-Box Decision

Despite outperforming humans in different supervised learning tasks, complex machine learning models are criticised for their opacity which make them hard to trust especially when used in critical domains (e.g., healthcare, self-driving car). Understanding the reasons behind the decision of a machine learning model provides insights into the model and transforms the model from a non-interpretable model (black-box) to an interpretable one that can be understood by humans. In addition, such insights are important for identifying any bias or unfairness in the decision made by the model and ensure that the model works as expected. In this paper, we present ILIME, a novel technique that explains the prediction of any supervised learning-based prediction model by relying on an interpretation mechanism that is based on the most influencing instances for the prediction of the instance to be explained. We demonstrate the effectiveness of our approach by explaining different models on different datasets. Our experiments show that ILIME outperforms a state-of-the-art baseline technique, LIME, in terms of the quality of the explanation and the accuracy in mimicking the behaviour of the black-box model. In addition, we present a global attribution technique that aggregates the local explanations generated from ILIME into few global explanations that can mimic the behaviour of the black-box model globally in a simple way.
On Tractable Computation of Expected Predictions

Computing expected predictions of discriminative models is a fundamental task in machine learning that appears in many interesting applications such as fairness, handling missing values, and data analysis. Unfortunately, computing expectations of a discriminative model with respect to a probability distribution defined by an arbitrary generative model has been proven to be hard in general. In fact, the task is intractable even for simple models such as logistic regression and a naive Bayes distribution. In this paper, we identify a pair of generative and discriminative models that enables tractable computation of expectations, as well as moments of any order, of the latter with respect to the former in case of regression. Specifically, we consider expressive probabilistic circuits with certain structural constraints that support tractable probabilistic inference. Moreover, we exploit the tractable computation of high-order moments to derive an algorithm to approximate the expectations for classification scenarios in which exact computations are intractable. Our framework to compute expected predictions allows for handling of missing data during prediction time in a principled and accurate way and enables reasoning about the behavior of discriminative models. We empirically show our algorithm to consistently outperform standard imputation techniques on a variety of datasets. Finally, we illustrate how our framework can be used for exploratory data analysis.
Retrofitting Contextualized Word Embeddings with Paraphrases

Contextualized word embedding models, such as ELMo, generate meaningful representations of words and their context.These models have been shown to have a great impact on downstream applications.However, in many cases, the contextualized embedding of a word changes drastically when the context is paraphrased.As a result, the downstream model is not robust to paraphrasing and other linguistic variations.To enhance the stability of contextualized word embedding models, we propose an approach to retrofitting contextualized embedding models with paraphrase contexts.Our method learns an orthogonal transformation on the input space, which seeks to minimize the variance of word representations on paraphrased contexts.Experiments show that the retrofitted model significantly outperforms the original ELMo on various sentence classification and language inference tasks.
Successor Uncertainties: exploration and uncertainty in temporal difference learning

Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning. Randomised value functions (RVF) can be viewed as a promising approach to scaling PSRL. However, we show that most contemporary algorithms combining RVF with neural network function approximation do not possess the properties which make PSRL effective, and provably fail in sparse reward problems. Moreover, we find that propagation of uncertainty, a property of PSRL previously thought important for exploration, does not preclude this failure. We use these insights to design Successor Uncertainties (SU), a cheap and easy to implement RVF algorithm that retains key properties of PSRL. SU is highly effective on hard tabular exploration benchmarks. Furthermore, on the Atari 2600 domain, it surpasses human performance on 38 of 49 games tested (achieving a median human normalised score of 2.09), and outperforms its closest RVF competitor, Bootstrapped DQN, on 36 of those.
Multi-Task Learning with High-Order Statistics for x-Vector Based Text-Independent Speaker Verification

The x-vector based deep neural network (DNN) embedding systems have demonstrated effectiveness for text-independent speaker verification. This paper presents a multi-task learning architecture for training the speaker embedding DNN with the primary task of classifying the target speakers, and the auxiliary task of reconstructing the first- and higher-order statistics of the original input utterance. The proposed training strategy aggregates both the supervised and unsupervised learning into one framework to make the speaker embeddings more discriminative and robust. Experiments are carried out using the NIST SRE16 evaluation dataset and the VOiCES dataset. The results demonstrate that our proposed method outperforms the original x-vector approach with very low additional complexity added.
Prosodic Phrase Alignment for Machine Dubbing

Dubbing is a type of audiovisual translation where dialogues are translated and enacted so that they give the impression that the media is in the target language.It requires a careful alignment of dubbed recordings with the lip movements of performers in order to achieve visual coherence.In this paper, we deal with the specific problem of prosodic phrase synchronization within the framework of machine dubbing.Our methodology exploits the attention mechanism output in neural machine translation to find plausible phrasing for the translated dialogue lines and then uses them to condition their synthesis.Our initial work in this field records comparable speech rate ratio to professional dubbing translation, and improvement in terms of lip-syncing of long dialogue lines.
A New Users Rating-Trend Based Collaborative Denoising Auto-Encoder for Top-N Recommender Systems

To promote online businesses and sales, e-commerce industry focuses to fulfill users' demands by giving them top set of recommendations which are ranked through different ranking measures.Deep learning based auto-encoder models have further improved the performance of recommender systems. A state-of-the-art collaborative denoising auto-encoder (CDAE) models user-item interactions as a corrupted version of users rating inputs. However, this architecture still lacks users' ratings-trend information which is an important parameter to recommend top-N items to users. In this paper, building upon CDAE characteristics, we propose a novel users rating-trend based collaborative denoising auto-encoder (UT-CDAE) which determines user-item correlations by evaluating rating-trend(High or Low) of a user towards a set of items. This inclusion of a user's rating-trend provides additional regularization flexibility which helps to predict improved top-N recommendations. The correctness of the suggested method is verified through different ranking evaluation metrics i.e., (mean reciprocal rank, mean average precision and normalized discounted gain), for various input corruption values, learning rates and regularization parameters.Experiments on standard ML-100K and ML-1M datasets show that suggested model has improved performance overstate-of-the-art denoising auto-encodermodels.
Towards Enhanced Security for Certificateless Public-Key Authenticated Encryption with Keyword Search

Certificateless Public-key Authenticated Encryption with Keyword Search (CLPAEKS) is derived from the Public-key Authenticated Encryption with Keyword Search (PAEKS) and simultaneously combines the features of the Public Key Cryptography (CLPKC). In a CLPAEKS scheme, the ciphertext is designed to meet the need for both confidentiality and authentication, i.e., on one hand, the ciphertext is the encryption of the keyword; on the other hand, adversaries are incapable of generating a valid ciphertext without the owner’s private key. He et al. formalized security models for CLPAEKS and proposed a CLPAEKS scheme. However, we find their models are incomplete to capture the security requirements for CLPAEKS and re-formalize the security requirements for CLPAEKS in terms of trapdoor privacy and ciphertext indistinguishability. Besides, we point out that their scheme is vulnerable to the Keyword Guessing Attack (KGA) by a malicious receiver, which is not considered in their security model. Then we modify He et al.’s scheme and prove that the new scheme meets the new security requirements.
A Large-Scale Empirical Study on Control Flow Identification of Smart Contracts

Background: Millions of smart contracts have been deployed to Ethereum for providing various applications. Recent studies discovered many severe security and performance issues in smart contracts by applying static program analysis techniques to them. Given a smart contract, the majority of these analysis techniques need to first construct its control flow graph, which connects basic blocks through control flow transfers (CFTs), before conducting further analysis. Aims: The objective of this work is to understand the capabilities of static program analysis techniques to identify CFTs, and to investigate how static program analysis techniques can be improved if the CFTs are complemented. Method: We perform a comprehensive empirical study on six widely-used tools for smart contract analysis by using all deployed smart contracts to understand their capabilities to recognize CFTs. We capture all execution traces of all smart contracts to evaluate the number of CFTs covered by traces that are not found by those tools. We enhance a state-of-the-art tool, OYENTE for discovering vulnerabilities in smart contracts with the CFTs covered by traces to investigate how the tool is improved. Results: These studied tools fail to identify all CFTs due to several reasons, e.g., incomplete code patterns. Execution traces effectively complement these tool in recognizing CFTs. By including the CFTs covered by traces, the false negative rate of OYENTE can be reduced by up to 30%. Conclusions: Our study underlines the ineffectiveness of static analysis techniques due to the incapabilities of CFT identification.