Reviewerly/exHarmony huggingface dataset:
Artifact for "GenTree: Using Decision Trees to Learn Interactions for Configurable Software"

This document describes the artifact package accompanying the ICSE'21 paper "GenTree: Using Decision Trees to Learn Interactions for Configurable Software". The artifact includes GenTree source code, pre-built binaries, benchmark program specifications, and scripts to replicate the data presented in the paper. Furthermore, GenTree is applicable to new programs written in supported languages (C, C++, Python, Perl, Ocaml), or can be extended to support new languages easily. GenTree implementation is highly modular and optimized, hence, it can also be used as a framework for developing and testing new interaction inference algorithms. We hope the artifact will be useful for researchers who are interested in interaction learning, especially iterative and data-driven approaches.
Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.
Image Denoising Using Adaptive and Overlapped Average Filtering and Mixed-Pooling Attention Refinement Networks

Cameras are essential parts of portable devices, such as smartphones and tablets. Most people have a smartphone and can take pictures anywhere and anytime to record their lives. However, these pictures captured by cameras may suffer from noise contamination, causing issues for subsequent image analysis, such as image recognition, object tracking, and classification of an object in the image. This paper develops an effective combinational denoising framework based on the proposed Adaptive and Overlapped Average Filtering (AOAF) and Mixed-pooling Attention Refinement Networks (MARNs). First, we apply AOAF to the noisy input image to obtain a preliminarily denoised result, where noisy pixels are removed and recovered. Next, MARNs take the preliminary result as the input and output a refined image where details and edges are better reconstructed. The experimental results demonstrate that our method performs favorably against state-of-the-art denoising methods.
Developing Critical Readers in the Age of Literacy Acceleration

Research Article| April 01 2021 Developing Critical Readers in the Age of Literacy Acceleration Joanne Baird Giordano; Joanne Baird Giordano Joanne Baird Giordano previous collaborative work on two-year college readers and writers has been published in edited collections and in Teaching English in the Two-Year College, College Composition and Communication, Pedagogy, and College English. Their work has received the 2010 Mark Reynolds Teaching English in the Two-Year College Best Article Award and the 2017 Council of Writing Program Administrators' Outstanding Scholarship award. Giordano teaches at Salt Lake Community College; Hassel is professor of English at North Dakota State University. Search for other works by this author on: This Site Google Holly Hassel Holly Hassel Holly Hassel's previous collaborative work on two-year college readers and writers has been published in edited collections and in Teaching English in the Two-Year College, College Composition and Communication, Pedagogy, and College English. Their work has received the 2010 Mark Reynolds Teaching English in the Two-Year College Best Article Award and the 2017 Council of Writing Program Administrators' Outstanding Scholarship award. Giordano teaches at Salt Lake Community College; Hassel is professor of English at North Dakota State University. Search for other works by this author on: This Site Google Pedagogy (2021) 21 (2): 241–258. https://doi.org/10.1215/15314200-8811432 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Joanne Baird Giordano, Holly Hassel; Developing Critical Readers in the Age of Literacy Acceleration. Pedagogy 1 April 2021; 21 (2): 241–258. doi: https://doi.org/10.1215/15314200-8811432 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter Books & JournalsAll JournalsPedagogy Search Advanced Search critical reading, two-year colleges, information literacy, first-year writing Copyright © 2021 by Duke University Press2021 Issue Section: Articles You do not currently have access to this content.
A Contour Co-Tracking Method for Image Pairs

We proposed a contour co-tracking method for co-segmentation of image pairs based on active contour model. Our method comprehensively re-models objects and backgrounds signified by level set functions, and leverages Hellinger distance to measure the similarity between image regions encoded by probability distributions. The main contribution are as follows. 1) The new energy functional, combining a rewarding and a penalty term, relaxes the assumptions of co-segmentation methods. 2) Hellinger distance, fulfilling the triangle inequality, ensures a coherence measurement between probability distributions in metric space, and contributes to finding a unique solution to the energy functional. The proposed contour co-tracking method was carefully verified against five representative methods on four popular datasets, i.e., the images pair dataset (105 pairs), MSRC dataset (30 pairs), iCoseg dataset (66 pairs) and Coseg-rep dataset (25 pairs). The comparison experiments suggest that our method achieves the competitive and even better performance compared to the state-of-the-art co-segmentation methods.
The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval

Over the last few years, mobile robotics has experienced a great development thanks to the wide variety of problems that can be solved with this technology. An autonomous mobile robot must be able to operate in a priori unknown environments, planning its trajectory and navigating to the required target points. With this aim, it is crucial solving the mapping and localization problems with accuracy and acceptable computational cost. The use of omnidirectional vision systems has emerged as a robust choice thanks to the big quantity of information they can extract from the environment. The images must be processed to obtain relevant information that permits solving robustly the mapping and localization problems. The classical frameworks to address this problem are based on the extraction, description and tracking of local features or landmarks. However, more recently, a new family of methods has emerged as a robust alternative in mobile robotics. It consists of describing each image as a whole, what leads to conceptually simpler algorithms. While methods based on local features have been extensively studied and compared in the literature, those based on global appearance still merit a deep study to uncover their performance. In this work, a comparative evaluation of six global-appearance description techniques in localization tasks is carried out, both in terms of accuracy and computational cost. Some sets of images captured in a real environment are used with this aim, including some typical phenomena such as changes in lighting conditions, visual aliasing, partial occlusions and noise.
Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval

Deep supervised hashing for image retrieval has attracted researchers' attention due to its high efficiency and superior retrieval performance. Most existing deep supervised hashing works, which are based on pairwise/triplet labels, suffer from the expensive computational cost and insufficient utilization of the semantics information. Recently, deep classwise hashing introduced a classwise loss supervised by class labels information alternatively; however, we find it still has its drawback. In this paper, we propose an improved deep classwise hashing, which enables hashing learning and class centers learning simultaneously. Specifically, we design a two-step strategy on center similarity learning. It interacts with the classwise loss to attract the class center to concentrate on the intra-class samples while pushing other class centers as far as possible. The centers similarity learning contributes to generating more compact and discriminative hashing codes. We conduct experiments on three benchmark datasets. It shows that the proposed method effectively surpasses the original method and outperforms state-of-the-art baselines under various commonly-used evaluation metrics for image retrieval.
Preprocessing Techniques for End-To-End Trainable RNN-Based Conversational System

Spoken dialogue system interfaces are gaining increasing attention, with examples including Apple’s Siri, Amazon’s Alexa, and numerous other products. Yet most existing solutions remain heavily data-driven, and face limitations in integrating and handling data semantics. They mainly rely on statistical co-occurrences in the training dataset and lack a more profound knowledge integration model with semantically structured information such as knowledge graphs. This paper evaluates the impact of performing knowledge base integration (KBI) to regulate the dialogue output of a deep learning conversational system. More specifically, it evaluates whether integrating dependencies between the data, obtained from the semantic linking of an external knowledge base (KB), would help improve conversational quality. To do so, we compare three approaches of conversation preprocessing methods: i) no KBI: considering conversational data with no external knowledge integration, ii) All Predicates KBI: considering conversational data where all dialogue pairs are augmented with their linked predicates from the domain KB, and iii) Intersecting Predicates KBI: considering conversational data where dialogue pairs are augmented only with their intersecting predicates (to filter-out potentially useless or redundant knowledge). We vary the amount of history considered in the conversational data, ranging from 0% (considering the last dialogue pair only) to 100% (considering all dialogue pairs, from the beginning of the dialogue). To our knowledge, this is the first study to evaluate knowledge integration in the preprocessing phase of conversational systems. Results are promising and show that knowledge integration – with an amount of history ranging between 10% and 75%, generally improves conversational quality.
Temporal Rain Decomposition with Spatial Structure Guidance for Video Deraining

Recently, removing rain streaks from videos has drawn wide concerns in vision and multimedia communities. But existing works ignore the depicts of image inherent structure and rain location to cause details loss, and their adopted manners of exploiting temporal information are still insufficient. In this work, we propose a multi-frame deraining network with temporal rain decomposition and spatial structure guidance to more effectively accomplish video deraining. A learnable decomposition method is defined to learn the distribution of rain, where the location map acts on a single-frame deraining block. We construct a multi-frame fusion module with a detailed guidance map to integrate temporal and spatial information. Many evaluated experiments demonstrate that our algorithm performs favorably on video deraining tasks compared with other methods. The elaborate ablation study in terms of network architecture fully indicates the effectiveness of our network.
Visual Oriented Encoder: Integrating Multimodal and Multi-Scale Contexts for Video Captioning

Video captioning is a challenging task which aims at automatically generating a natural language description of a given video. Recent researches have shown that exploiting the intrinsic multi-modalities of videos significantly promotes captioning performance. However, how to integrate multi-modalities to generate effective semantic representations for video captioning is still an open issue. Some researchers proposed to learn multimodal features in parallel during the encoding stage. The downside of these methods lies in the neglect of the interaction among multi-modalities and their rich contextual information. In this study, inspired by the fact that visual contents are generally more important for comprehending videos, we propose a novel Visual Oriented Encoder (VOE) to integrate multimodal features in an interactive manner. Specifically, VOE is designed as a hierarchical structure, where bottom layers are utilized to extract multi-scale contexts from auxiliary modalities while the top layer is exploited to generate joint representations by considering both visual and contextual information. Following the encoder-decoder framework, we systematically develop a VOE-LSTM model and evaluate it on two mainstream benchmarks: MSVD and MSR-VTT. Experimental results show that the proposed VOE surpasses conventional encoders and our VOE-LSTM model achieves competitive results compared with state-of-the-art approaches.
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]

Abstract Very recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions. However, all the existing certified defense methods assume that the defenders have been informed of how the adversaries generate synonyms, which is not a realistic scenario. In this study, we propose a certifiably robust defense method by randomly masking a certain proportion of the words in an input text, in which the above unrealistic assumption is no longer necessary. The proposed method can defend against not only word substitution-based attacks, but also character-level perturbations. We can certify the classifications of over 50% of texts to be robust to any perturbation of five words on AGNEWS, and two words on SST2 dataset. The experimental results show that our randomized smoothing method significantly outperforms recently proposed defense methods across multiple datasets under different attack algorithms.
TSSE-DMM: Topic Modeling for Short Texts Based on Topic Subdivision and Semantic Enhancement

Short texts have been prevalent in Web sites and the emerging social media for several years, which makes it a critical task to identify intelligible topics from online data sources. However, the existing topic models over short texts cannot analyze the internal components of the learned topics, which is significant for improving the coherence and interpretability of topics. In this paper, we propose a novel topic model for short texts, named TSSE-DMM, for improving the coherence and interpretability of topics by the topic subdivision and alleviating the problem of text sparsity by the semantic enhancement strategy. Firstly, we subdivide each topic into 4 detailed aspects, namely the location aspect, the people & organization aspect, the core word aspect, and the background word aspect, to obtain the different and interpretable components of topics. Then, we combine the Generalized Polya Urn model and the joint word embedding to solve the problem of data sparsity. The extensive experimental results carried on three real-world text collections in two languages show that our model achieves better topic representations than the baseline methods. Moreover, our method has been adopted by the public service hotline platform of Jiangsu province in China.
Clusterface: Joint Clustering and Classification for Set-Based Face Recognition

Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios `on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.
Deep Adversarial Quantization Network for Cross-Modal Retrieval

In this paper, we propose a seamless multimodal binary learning method for cross-modal retrieval. First, we utilize adversarial learning to learn modality-independent representations of different modalities. Second, we formulate loss function through the Bayesian approach, which aims to jointly maximize correlations of modality-independent representations and learn the common quantizer codebooks for both modalities. Based on the common quantizer codebooks, our method performs efficient and effective cross-modal retrieval with fast distance table lookup. Extensive experiments on three cross-modal datasets demonstrate that our method outperforms state-of-the-art methods. The source code is available at https://github.com/zhouyu1996/DAQN.
Comparative Review on Sentiment analysis-based Recommendation system

Recommendation systems are ubiquitous these days and are used in nearly every domain; from learning which videos could be recommended to users on streaming websites, to products that can be sold on e-commerce platforms. These systems are driven by the copious amount of data that is scraped and collected from sources such as review platforms and social media websites. On this collected data, sentiment analysis can be performed to recommend products to users based on an overall analysis of sentiments conveyed using reviews, comments, or opinions. The information thus obtained is provided to already existing machine learning-based filtering techniques which include content-based, collaborative, and hybrid filtering. The aim of this paper is to provide a detailed review of various techniques used for sentiment-based recommendation systems and the inherent challenges in these techniques.
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images

This paper proposes the first object counting method for omnidirectional images. Because conventional object counting methods cannot handle the distortion of omnidirectional images, we propose to process them using stereographic projection, which enables conventional methods to obtain a good approximation of the density function. However, the images obtained by stereographic projection are still distorted. Hence, to manage this distortion, we propose two methods. One is a new data augmentation method designed for the stereographic projection of omnidirectional images. The other is a distortion-adaptive Gaussian kernel that generates a density map ground truth while taking into account the distortion of stereographic projection. Using the counting of grape bunches as a case study, we constructed an original grape-bunch image dataset consisting of omnidirectional images and conducted experiments to evaluate the proposed method. The results show that the proposed method performs better than a direct application of the conventional method, improving mean absolute error by 14.7% and mean squared error by 10.5%.
Domain Siamese CNNs for Sparse Multispectral Disparity Estimation

Multispectral disparity estimation is a difficult task for many reasons: it has all the same challenges as traditional visible-visible disparity estimation (occlusions, repetitive patterns, textureless surfaces), in addition of having very few common visual information between images (e.g. colour information vs. thermal information). In this paper, we propose a new CNN architecture able to do disparity estimation between images from different spectra, namely thermal and visible in our case. Our proposed model takes two patches as input and proceeds to do domain feature extraction for each of them. Features from both domains are then merged with two fusion operations, namely correlation and concatenation. These merged vectors are then forwarded to their respective classification heads, which are responsible for classifying the inputs as being same or not. Using two merging operations gives more robustness to our feature extraction process, which leads to more precise disparity estimation. Our method was tested using the publicly available LITIV 2014 and LITIV 2018 datasets, and showed best results when compared to other state-of-the-art methods.
Introduction to Intelligent User Interfaces

Recent advancements in artificial intelligence (AI) create new opportunities for implementing a wide range of intelligent user interfaces. Speech-based interfaces, chatbots, visual recognition of users and objects, recommender systems, and adaptive user interfaces are examples that have majored over the last 10 years due to new approaches in machine learning (ML). Modern ML-techniques outperform in many domains of previous approaches and enable new applications. Today, it is possible to run models efficiently on various devices, including PCs, smartphones, and embedded systems. Leveraging the potential of artificial intelligence and combining them with human-computer interaction approaches allows developing intelligent user interfaces supporting users better than ever before. This course introduces participants to terms and concepts relevant in AI and ML. Using examples and application scenarios, we practically show how intelligent user interfaces can be designed and implemented. In particular, we look at how to create optimized keyboards, use natural language processing for text and speech-based interaction, and how to implement a recommender system for movies. Thus, this course aims to introduce participants to a set of machine learning tools that will enable them to build their own intelligent user interfaces. This course will include video based lectures to introduce concepts and algorithms supported by practical and interactive exercises using python notebooks.