Reviewerly/exHarmony huggingface dataset:
Asymmetric Correlation Quantization Hashing for Cross-Modal Retrieval

In recent years, cross-modal hashing (CMH) has attracted considerable attention due to its ability to learn across different modalities and its high efficiency for similarity retrieval applications. This procedure is computationally inexpensive when dealing with large-scale multi-modalities datasets. However, they do not form the ideal representative model to fully exploit multi-modal data&#x2019;s underlying properties despite their successful performance. We identify that: (i) most CMH models in their current forms transform the real data points into discrete compact binary codes, which can limit their ability to prevent the loss of important information and thereby produce suboptimal results. (ii) the discrete-binary constraint model is hard to implement, and relaxing the binary constraints is a common property in most existing methods, which often leads to significant quantization errors. (iii) handling the CMH in a symmetry domain leads to a complex and inefficient optimization problem. This paper addresses the above challenges and proposes a novel Asymmetric Correlation Quantization Hashing (ACQH) method. ACQH learns a projection matrix for each heterogeneous modality to map the data point into a low-dimensional semantic space and constructs a compositional quantization to generate hash codes, using the pairwise semantic similarity preservation and the pointwise label regression. As a specific instantiation of our model, we use discrete iterative optimization to obtain the unified hash codes across different modalities. Extensive experiments show that ACQH outperforms state-of-the-art methods on several diverse datasets.
PETGEN

What should a malicious user write next to fool a detection model? Identifying malicious users is critical to ensure the safety and integrity of internet platforms. Several deep learning based detection models have been created. However, malicious users can evade deep detection models by manipulating their behavior, rendering these models of little use. The vulnerability of such deep detection models against adversarial attacks is unknown. Here we create a novel adversarial attack model against deep user sequence embedding-based classification models, which use the sequence of user posts to generate user embeddings and detect malicious users. In the attack, the adversary generates a new post to fool the classifier. We propose a novel end-to-end Personalized Text Generation Attack model, called PETGEN, that simultaneously reduces the efficacy of the detection model and generates posts that have several key desirable properties. Specifically, PETGEN generates posts that are personalized to the user's writing style, have knowledge about a given target context, are aware of the user's historical posts on the target context, and encapsulate the user's recent topical interests. We conduct extensive experiments on two real-world datasets (Yelp and Wikipedia, both with ground-truth of malicious users) to show that PETGEN significantly reduces the performance of popular deep user sequence embedding-based classification models. PETGEN outperforms five attack baselines in terms of text quality and attack efficacy in both white-box and black-box classifier settings. Overall, this work paves the path towards the next generation of adversary-aware sequence classification models.
A brightness-adaptive kernel prediction network for inverse tone mapping

The inverse tone mapping (iTM) technique that produces a high dynamic range (HDR) image from one single standard dynamic range (SDR) image has received much attention in industry and academia recently. However, existing methods to recover HDR images mainly focus on overexposed regions but ignore underexposed regions. The underexposed regions in an image are susceptible to noise and artifacts, which will reduce the quality of the image and the user’s visual experience. Therefore, in this paper, we propose a brightness-adaptive iTM model based on deep learning to focus on the content restoration of both the overexposed and underexposed regions in an SDR image simultaneously. In this model, instead of directly predicting the HDR output, we adopt an encoder-decoder network to predict spatially adaptive kernels, which further convolute the input SDR image to produce the HDR result. With the spatially adaptive kernels, the input regions with different exposures can be adaptively mapped by making full use of neighborhood information. Importantly, brightness-adaptive skip connections in the encoder-decoder network, as well as a region loss, are designed to force the proposed model to attach importance to overexposed and underexposed regions. Besides, a global branch is employed in our encoder to exploit both the global and local brightness. Extensive qualitative and quantitative experiments demonstrate that the proposed approach outperforms recent methods on multiple metrics.
Classification of Spam Emails using Deep learning

The Internet has become an integral part of modern life. One of the most critical aspects of the Internet is collaboration. Email is a communication tool that can be used for both personal and professional purposes. Spam messages are not intended to be received by addressee of emails, and therefore are often regarded as unwanted bulk emails. Every day, a wide range of people use email to connect globally. Currently, large numbers of Spam emails are logic genes. Being in large quantities already causes real frustration for both internet users and providers. For instance, it degrades user analysis data, encourages network virus migration, expands stack on arrangement movement, absorbs mail server storage, wastes time and network bandwidth, and depletes the vitality of real emails among the Spam. It is therefore necessary to prevent the spread of Spam. Given the fact that there are several data mining techniques beneficial in preserving security, they can also be of use in classifying Spam email. As for the present work, the Min-hash technique is combined with the Deep Neural Network (DNN) algorithm to classify emails into Spam and Ham. The results indicate that a remarkably high accuracy rate (98%) is obtained by using this combination, which means that it is an effective method to be adopted and further developed in the field of Spam detection and classification.
Structural Compression of Convolutional Neural Networks with Applications in Interpretability

Deep convolutional neural networks (CNNs) have been successful in many tasks in machine vision, however, millions of weights in the form of thousands of convolutional filters in CNNs make them difficult for human interpretation or understanding in science. In this article, we introduce a greedy structural compression scheme to obtain smaller and more interpretable CNNs, while achieving close to original accuracy. The compression is based on pruning filters with the least contribution to the classification accuracy or the lowest Classification Accuracy Reduction (CAR) importance index. We demonstrate the interpretability of CAR-compressed CNNs by showing that our algorithm prunes filters with visually redundant functionalities such as color filters. These compressed networks are easier to interpret because they retain the filter diversity of uncompressed networks with an order of magnitude fewer filters. Finally, a variant of CAR is introduced to quantify the importance of each image category to each CNN filter. Specifically, the most and the least important class labels are shown to be meaningful interpretations of each filter.
Machine-Learning-Based Parameter Estimation of Gaussian Quantum States

We propose a machine learning framework for parameter estimation of single mode Gaussian quantum states.Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data.For phase-space displacement and squeezing parameter estimation, this is achieved by introducing Expectation-Maximization (EM) based algorithms, while for phase parameter estimation an empirical Bayes method is applied.The estimated prior distribution parameters along with the observed data are used for finding the optimal Bayesian estimate of the unknown displacement, squeezing and phase parameters.Our simulation results show that the proposed algorithms have estimation performance that is very close to that of 'Genie Aided' Bayesian estimators, that assume perfect knowledge of the prior parameters.In practical scenarios, when numerical values of the prior distribution parameters are not known beforehand, our proposed methods can be used to find optimal Bayesian estimates from the observed measurement data.
Toward Explainable Deep Anomaly Detection

Anomaly explanation, also known as anomaly localization, is as important as, if not more than, anomaly detection in many real-world applications. However, it is challenging to build explainable detection models due to the lack of anomaly-supervisory information and the unbounded nature of anomaly; most existing studies exclusively focus on the detection task only, including the recently emerging deep learning-based anomaly detection that leverages neural networks to learn expressive low-dimensional representations or anomaly scores for the detection task. Deep learning models, including deep anomaly detection models, are often constructed as black boxes, which have been criticized for the lack of explainability of their prediction results. To tackle this explainability issue, there have been numerous techniques introduced over the years, many of which can be utilized or adapted to offer highly explainable detection results.
Comparative study of random search hyper-parameter tuning for software effort estimation

Empirical studies on software effort estimation have employed hyper-parameter tuning algorithms to improve model accuracy and stability. While these tuners can improve model performance, some might be overly complex or costly for the low dimensionality datasets used in SEE. In such cases a method like random search can potentially provide similar benefits as some of the existing tuners, with the advantage of using low amounts of resources and being simple to implement. In this study we evaluate the impact on model accuracy and stability of 12 state-of-the-art hyper-parameter tuning algorithms against random search, on 9 datasets of the PROMISE repository and 4 sub-datasets from the ISBSG R18 dataset. This study covers 2 traditional exhaustive tuners (grid and random searches), 6 bio-inspired algorithms, 2 heuristic tuners, and 3 model-based algorithms. The tuners are used to configure support vector regression, classification and regression trees, and ridge regression models. We aim to determine the techniques and datasets for which certain tuners were 1) more effective than default hyper-parameters, 2) more effective than random search, 3) which models(s) can be considered "the best" for which datasets. The results of this study show that hyper-parameter tuning was effective (increased accuracy and stability) in 862 (51%) of the 1,690 studied scenarios. The 12 state-of-the-art tuners were more effective than random search in 95 (6%) of the 1,560 studied (non-random search) scenarios. Although not effective in every dataset, the combination of flash tuning, logarithm transformation and support vector regression obtained top ranking in accuracy on the highest amount (8 out of 13) of datasets. Hyperband tuned ridge regression with logarithm transformation obtained top ranking in accuracy on the highest amount (10 out of 13) of datasets. We endorse the use of random search as a baseline for comparison for future studies that consider hyper-parameter tuning.
Synthetic to Realistic Imbalanced Domain Adaption for Urban Scene Perception

Deep neural networks technique has achieved impressive performance on semantic segmentation, while its training process requires a large amount of pixel-wise labeled data. Domain adaptation, as a promising solution, can break the restriction by training the model on synthetic data, and generalizing it in real-world data. However, there is still a lack of attention paid to the imbalance problems on semantic segmentation adaptation, including the imbalance problem between 1) source and target data and 2) different classes. To solve these problems, a progressive hierarchical feature alignment method is proposed in this article. To alleviate the data imbalance problem, the network is progressively trained by the data from multisource domains, so as to obtain domain-invariant features. To address the class imbalance problem, the features are aligned hierarchically across domains. According to the experimental results, our method shows the competitive adapted segmentation performance on three benchmark datasets.
Using machine learning to generate test oracles: a systematic literature review

Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field.
New Framework for Sequences With Perfect Autocorrelation and Optimal Crosscorrelation

In this paper, we give a new framework for constructing perfect sequences, called generalized Milewski sequences, over various alphabets including Polyphase (PSK) as well as Amplitude-and-Polyphase (APSK) in general, and for constructing optimal sets of such perfect sequences by using combinatorial designs, called circular Florentine arrays. Specifically, we prove that, given any positive integer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m\geq 1$ </tex-math></inline-formula> , (i) there exists a perfect sequence of period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mN^{2}$ </tex-math></inline-formula> for any positive integer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> if there exists a perfect sequence (polyphase or not) of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> ; (ii) an optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -set of perfect sequences of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mN^{2}$ </tex-math></inline-formula> can be constructed if there exist both a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k \times N$ </tex-math></inline-formula> circular Florentine array and an optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -set of perfect sequences all of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> . This enables us to find some optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -set of perfect sequences where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k &gt; p_{\text {min}}-1$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{\text {min}}$ </tex-math></inline-formula> is the smallest prime factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mN^{2}$ </tex-math></inline-formula> .
BDDR: An Effective Defense Against Textual Backdoor Attacks

Deep neural networks (DNNs) have been recently shown to be vulnerable to backdoor attacks. The infected model performs well on benign testing samples, however, the attacker can trigger the infected model to misbehave by the backdoor. In the field of natural language processing (NLP), some backdoor attack methods have been proposed, and achieved high attack success rates on a variety of popular models. However, researches on the defense of textual backdoor attacks are lacking and the defense effects are bad at present. In this paper, we propose an effective textual backdoor defense model, namely BDDR, which contains two steps: (1) detecting suspicious words in the sample and (2) reconstructing the original text by deletion or replacement. In the replacement part, we use the pre-trained masking language model taking BERT as an example to generate replacement words. We conduct exhaustive experiments to evaluate our proposed defense model by defending against various backdoor attacks on two infected models trained using two benchmark datasets. Overall, BDDR reduces the attack success rate of word-level backdoor attacks by more than 90%, and reduces the attack success rate of sentence-level backdoor attacks by more than 60%. The experimental results show that our proposed method can always significantly reduce the attack success rate compared with the baseline method.
VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification

The use of data analysis techniques in electronic health records (EHRs) offers great promise in improving predictive risk modeling. Although useful, these analysis techniques often suffer from a lack of interpretability and transparency, especially when the data is high-dimensional. The emergence of a type of computational system known as visual analytics has the potential to address these issues by integrating data analysis techniques with interactive visualizations. This paper introduces a visual analytics system called VERONICA that utilizes the natural classification of features in EHRs to identify the group of features with the strongest predictive power. VERONICA incorporates a representative set of supervised machine learning techniques—namely, classification and regression tree, C5.0, random forest, support vector machines, and naive Bayes to support users in developing predictive models using EHRs. It then makes the analytics results accessible through an interactive visual interface. By integrating different sampling strategies, analytics algorithms, visualization techniques, and human-data interaction, VERONICA assists users in comparing prediction models in a systematic way. To demonstrate the usefulness and utility of our proposed system, we use the clinical dataset stored at ICES to identify the best representative feature groups in detecting patients who are at high risk of developing acute kidney injury.
Multimodel anomaly detection on spatio-temporal logistic datastream with open anomaly detection architecture

Logistic companies rely on heavily Vehicle Tracking Systems (VTS) to provide instant and valuable data about the vehicle’s condition, the cargoes’ statuses, and the trips. The data that is collected from the vehicles and drivers are generally not fully utilized to its full potential other than day-to-day business activities because of the difficulties created by the big data aspects of the logistic data in storing, processing, analyzing, and reporting which require expertise and infrastructure. Real-time analysis of the data coming from VTS and mobile devices provides enormous value in terms of businesses. In this research, by using real-world data supplied by an international logistic company, we have established a multimodel streaming analytic framework that detects trajectory anomalies and identifies drivers in parallel and near-real-time. The framework can be enhanced with any number of trajectory or driver identification models, all of which can be run in parallel and contribute to the overall anomaly detection process by the “consensus” of the included models. In this study, we provide a sample implementation of the framework with one trajectory and one behavior anomaly detection model suitable for logistic spatio-temporal datastream. Provided models do not require any business-specific data and only depend on the basic VTS and location data. An isolation-based model is used for trajectory anomaly detection which does not need the source and the destination while identifying sub-trajectories. The outputs of both models are combined on a dashboard which shows the vehicles’ abnormal sub-trajectories along with the notification of whether or not the vehicles are being driven by the assigned driver. • Trajectory Based Anomaly Detection takes into account the vehicle’s route. • Anomaly detection based on driver behavior addresses driving characteristics. • Driver and trajectory based anomaly analysis was carried out together. • This work based on stream processing so the anomalies are detected in near-real-time.
Web Service Network Embedding Based on Link Prediction and Convolutional Learning

Extensive efforts have been applied to develop efficient feature extraction algorithms, which aim to achieve optimal results in many fundamental tasks such as Web-based software service clustering, recommendation and composition. However, one common issue for existing methods is that mined features are problem dependent, causing poor generalization ability across different applications. Recent studies show that we can represent networked data (e.g., citation networks and social networks) as low-dimensional vectors with rich structure and content information preserved, which can then greatly facilitate many downstream tasks such as classification and clustering. In this article, we focus on the problem of Web service network embedding, which aims to learn low-dimensional vectors to represent services by encoding both Mashup-API composition structure and service functional content. We first propose a novel probabilistic topic model to predict potential links between Mashups and APIs in the service network. Then, we develop a Service Graph Convolutional Network (Service-GCN) to learn vector representations of services, where each service (e.g., Mashup or API) forms its representation through message passing between neighborhood services over the network. We evaluate the network embedding quality on two real-world datasets for downstream classification and clustering tasks. Experimental results show that the average performance of our method improves 20.7 percent (Micro-F1) in service classification and 19.0 percent (Accuracy) in Mashup clustering compared to the state-of-the-art, which verified the effectiveness of the proposed approach for learning vector representations of Web services.
Automated detection of unstructured context-dependent sensitive information using deep learning

With the rapid growth of cloud computing and remote workforces, organizations are increasingly aware of the risk of data leaks and data exfiltration . Handling such risk has become more challenging because organizations today usually deal with big data. In particular, organizations must deal with massive amounts of unstructured data. As a result, modern data leak prevention (DLP) solutions must support automated methods to detect and identify confidential and sensitive information in both structured and unstructured data. In this paper, we demonstrate the benefits of using deep learning to identify unstructured context-dependent sensitive information, in contrast to traditional machine learning methods and rule-based methods.