Reviewerly/exHarmony huggingface dataset:
Evaluation of 1D Selection Techniques for Mobile Visualizations

In spite of growing demand for mobile data visualization, few design guidelines exist to address its many challenges including small screens and low touch interaction precision. Both of these challenges can restrict the number of data points a user can reliably select and view in more detail, which is a core requirement for interactive data visualization. In this study, we present a comparison of the conventional tap technique for selection with three variations including visual feedback to understand which interaction technique allows for optimal selection accuracy. Based on the results of the user study, we provide actionable solutions to improve interaction design for mobile visualizations. We find that visual feedback, such as selection with a handle, improves selection accuracy three- to fourfold compared to tap selection. With a 75% accuracy, users could select a target item among 176 items total using the handle, but only from 60 items using tap. On the other hand, techniques with visual feedback took about twice as long per selection when compared to tap. We conclude designers should use selection techniques with visual feedback when the data density is high and improved selection precision is required for a visualization.
Insurance against Ransomware

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Arbitrary Style Transfer with Parallel Self-Attention

Neural style transfer aims to create artistic images by synthesizing patterns from a given style image. Recently, the Adaptive Instance Normalization (AdaIN) layer is proposed to achieve real-time arbitrary style transfer. However, we observed that if crucial features based on AdaIN can be further emphasized during transfer, both content and style information will be better reflected in stylized images. Furthermore, it is always essential to preserve more details and reduce unexpected artifacts in order to generate appealing results. In this paper, we introduce an improved arbitrary style transfer method based on the self-attention mechanism. A self-attention module is designed to learn what and where to emphasize in the input image. In addition, an extra Laplacian loss is applied to preserve structure details of the content while eliminating artifacts. Experimental results demonstrate that the proposed method outperforms AdaIN and can generate more appealing results.
Detecting Secret Messages in Images Using Neural Networks

Image-based applications are widely spread nowadays. Steganography is a type of data hiding methods that manage covering the presence of a confidential communication between two ends. That is obtained by concealing the secret medium into a cover medium to deliver a stego medium that ought to be unnoticeable to a third party. The contrary of steganography, image steganalysis, is about identifying the presence of stego images. Image steganography schemes are becoming more and more secure every day. Cyber criminals can utilize these schemes to conduct a secret and malicious communication. Therefore, image steganalysis is of great importance to interfere such communications. In this paper, a transform domain based steganalysis scheme is proposed that utilizes the architecture of AlexNet, which is an object classification DL scheme. Some modifications are performed on the existing AlexNet architecture to enhance the detection performance of the model. Experiments showed that FB-GAR steganography scheme was successfully detected with an accuracy of 74.72%. Also, the relationship between the capacity and the quality of stego images was studied in this paper for both FB-GAR and J-UNIWARD. Furthermore, the relationship between the correlation of the cover images and the capacity and quality of stego images was discussed.
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative Glasso and Projection

Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph signal compression and denoising. Previous graph learning algorithms either i) make assumptions on graph connectivity (e.g., graph sparsity), or ii) make edge weight assumptions such as positive edges only. In this paper, given an empirical covariance matrix ${\mathbf{\bar C}}$ computed from data as input, we consider an eigen-structural assumption on the graph Laplacian matrix L: the first K eigenvectors of L are pre-selected, e.g., based on domain-specific criteria, and the remaining eigenvectors are then learned from data. One example use case is image coding, where the first eigenvector is pre-chosen to be constant, regardless of available observed data. We first prove that the subspace $\mathcal{H}_{\mathbf{u}}^ + $ of symmetric positive semi-definite (PSD) matrices with the first K eigenvectors being {u <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> } in a defined Hilbert space is a convex cone. We then construct an operator to project a given positive definite (PD) matrix L to $\mathcal{H}_{\mathbf{u}}^ + $, inspired by the Gram-Schmidt procedure. Finally, we design an efficient hybrid graphical lasso / projection algorithm to compute the most suitable graph Laplacian matrix ${{\mathbf{L}}^ * } \in \mathcal{H}_{\mathbf{u}}^ + $ given ${\mathbf{\bar C}}$. Experimental results show that given the first K eigenvectors as a prior, our algorithm outperforms competing graph learning schemes using a variety of graph comparison metrics.
A Scalable Knowledge Graph Embedding Model for Next Point-of-Interest Recommendation in Tallinn City

With the rapid growth of location-based social networks (LBSNs), the task of next Point Of Interest (POI) recommendation has become a trending research topic as it provides key information for users to explore unknown places. However, most of the state-of-the-art next POI recommendation systems came short to consider the multiple heterogeneous factors of both POIs and users to recommend the next targeted location. Furthermore, the cold-start problem is one of the most thriving challenges in traditional recommender systems. In this paper, we introduce a new Scalable Knowledge Graph Embedding Model for the next POI recommendation problem called Skgem. The main originality of the latter is that it relies on a neural network-based embedding method (node2vec) that aims to automatically learn low-dimensional node representations to formulate and incorporate all heterogeneous factors into one contextual directed graph. Moreover, it provides various POIs recommendation groups for cold-start users, e.g., nearby, by time, by tag, etc. Experiments, carried out on a location-based social network (Flickr) dataset collected in the city of Tallinn (Estonia), demonstrate that our approach achieves better results and sharply outperforms the baseline methods. Source code is publicly available at: https://github.com/Ounoughi-Chahinez/SKGEM
Perceptual Quality Assessment for Recognizing True and Pseudo 4k Content

To meet the imperative demand for monitoring the quality of Ultra High-Definition (UHD) content in multimedia industries, we propose an efficient no-reference (NR) image quality assessment (IQA) metric to distinguish original and pseudo 4K contents and measure the quality of their quality in this paper. First, we establish a database including more than 3000 4K images composed of natural 4K images together with upscaled versions interpolated from 1080p and 720p images by fourteen algorithms. To improve computing efficiency, our model segments the input image and selects three representative patches by local variances. Then, we extract the histogram features and cut-off frequency features in the frequency domain as well as the natural scenes statistic (NSS) based features from the representative patches. Finally, we employ support vector regressor (SVR) to aggregate these extracted features as an overall quality metric to predict the quality score of the target image. Extensive experimental comparisons using seven common evaluation indicators demonstrate that the proposed model outperforms the competitive NR IQA methods and has a great ability to distinguish true and pseudo 4K images.
Optimum Feature Ordering for Dynamic Instance–Wise Joint Feature Selection and Classification

We introduce a supervised machine learning framework to perform joint feature selection and classification individually for each data instance during testing. In contrast to our prior work, we decide both the order and the number of features for each data instance. Specifically, our proposed solution dynamically selects the feature to review at each stage based on the already observed features and stops the selection process to make a prediction once it determines no classification improvement can be achieved. To gain insights, we analyze the properties of the proposed solution. Based on these properties, we propose a fast algorithm and demonstrate its effectiveness compared to the state–of–the–art using 4 publicly available datasets.
How Explicit Feature Traces Did Not Impact Developers’ Memory

Software features are intuitive entities used to abstract and manage the functionalities of a software system, for instance, in product-line engineering and agile software development. Nonetheless, developers rarely make features explicit in code, which is why they have to perform costly program comprehension and particularly feature location to (re-)gain knowledge about the code. In a previous paper, we conducted an experiment on how explicit feature traces impact developers' program comprehension by facilitating feature location. We found that annotating features in code improved program comprehension, while decomposing them into classes had a negative impact. Additionally, but not reported in that paper, we were concerned with understanding whether the different traces would impact developers' memory regarding the code and its features. To this end, we repeatedly asked our participants questions about the code on different levels of detail within time periods of two weeks. Since developers' memory decays over time, we expected that our participants would provide fewer correct answers over time, with differences depending on the feature traces in their code. Unfortunately, the actual results were inconclusive and up for interpretation, particularly due to challenges in designing an experiment on developers' memory. In this paper, we discuss our experimental design, the null results, and challenges for improving the methodology of future studies in this direction.
End-to-End Multilingual Automatic Speech Recognition for Less-Resourced Languages: The Case of Four Ethiopian Languages

The End-to-End (E2E) approach, which maps a sequence of input features into a sequence of graphemes or words, to Automatic Speech Recognition (ASR) is a hot research agenda. It is interesting for less-resourced languages since it avoids the use of pronunciation dictionary, which is one of the major components in the traditional ASR systems. However, like any deep neural network (DNN) approaches, E2E is data greedy. This makes the application of E2E to less-resourced languages questionable. However, using data from other languages in a multilingual (ML) setup is being applied to solve the problem of data scarcity. We have, therefore, conducted ML E2E ASR experiments for four less-resourced Ethiopian languages using different language and acoustic modelling units. The results of our experiments show that relative Word Error Rate (WER) reductions (over the monolingual E2E systems) of up to 29.83% can be achieved by just using data of two related languages in E2E ASR system training. Moreover, we have also noticed that the use of data from less related languages also leads to E2E ASR performance improvement over the use of monolingual data.
Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.
Knowledge Transfer for Efficient on-Device False Trigger Mitigation

In this paper, we address the task of determining whether a given utterance is directed towards a voice-enabled smart-assistant device or not. An undirected utterance is termed as a "false trigger" and false trigger mitigation (FTM) is essential for designing a privacy-centric non-intrusive smart assistant. The directedness of an utterance can be identified by running automatic speech recognition (ASR) and determining the user intent by analyzing the ASR transcript. Yet, in case of a false trigger, transcribing the audio using ASR itself is strongly undesirable. To alleviate this issue, we propose an LSTM-based FTM architecture which determines the user intent from acoustic features directly without explicitly generating ASR transcripts from the audio. The proposed models are small-footprint and can be run on-device with limited computational resources. During training, the model parameters are optimized using a knowledge transfer approach where a more accurate self-attention graph neural network model [1] serves as the teacher. Given the whole audio snippets, our approach mitigates 87% of false triggers at 99% true positive rate (TPR), and in a streaming audio scenario, the system listens to only 1.69s of the false trigger audio before rejecting it while achieving the same TPR.
Research on Video Automatic Feature Extraction Technology Based on Deep Neural Network

With the development of society, people have invested a lot of manpower and material resources in public safety and established various monitoring systems. Video content recognition is to get the theme of video through the analysis of video, and it is an abstract overview of video. Compared with video description and target detection, the results of video content recognition are more concise and abstract, so it can better meet the development needs of information networks with a huge number of videos. Video anomaly detection is to efficiently detect abnormal events from a large number of videos, thus ensuring public safety and preventing dangerous situations. Artificial intelligence system should have the ability to acquire its own knowledge and extract features from the original data. Aiming at the demand of real-time video big data processing ability of video monitoring system, this paper analyzes the automatic video feature extraction technology based on deep neural network, and studies the detection and location of abnormal targets in monitoring video.
Artificially Intelligent Technology for the Margins: A Multidisciplinary Design Agenda

There has been increasing interest in socially just use of Artificial Intelligence (AI) and Machine Learning (ML) in the development of technology that may be extended to marginalized people. However, the exploration of such technologies entails the development of an understanding of how they may increase and/or counter marginalization. The use of AI/ML algorithms can lead to several challenges, such as privacy and security concerns, biases, unfairness, and lack of cultural awareness, which especially affect marginalized people. This workshop will provide a forum to share experiences and challenges of developing AI/ML health and social wellbeing technologies with/for marginalized people and will work towards developing design methods to engage in the re-envisioning of AI/ML technologies for and with marginalized people. In doing so we will create cross-research area dialogues and collaborations. These discussions build a basis to (1) explore potential tools to support designing AI/ML systems with marginalized people, and (2) develop a design agenda for future research and AI/ML technology for and with marginalized people.
Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions

Decisions are an important type of artifacts in software development and maintenance, while decisions are not well-documented in projects due to limited human resources and budget. To this end, many studies focus on using automatic approaches to identify decisions from textual artifacts, e.g., mailing lists, issue tracking systems. In this paper, we present a replication study of our previous work (EASE2020), which conducted experiments to automatically identify decisions from the Hibernate developer mailing list. In addition, we utilized different datasets in the experiment with the aim of exploring the impact of the proprieties of dataset (i.e., the quality of positive samples, different negative samples in the dataset, and the size of the dataset) on classification results of decisions. The results show that (1) improving the quality of positive samples in the dataset can decently improve the classification results; (2) different negative samples in the dataset have an impact on the classification results; and (3) before the dataset size reaches 1200, increasing the size will improve the classification results.
Higher-order Recursion Schemes and Collapsible Pushdown Automata: Logical Properties

This article studies the logical properties of a very general class of infinite ranked trees, namely, those generated by higher-order recursion schemes. We consider, for both monadic second-order logic and modal <?TeX $\mu$?> -calculus, three main problems: model-checking, logical reflection (a.k.a. global model-checking, that asks for a finite description of the set of elements for which a formula holds), and selection (that asks, if exists, for some finite description of a set of elements for which an MSO formula with a second-order free variable holds). For each of these problems, we provide an effective solution. This is obtained, thanks to a known connection between higher-order recursion schemes and collapsible pushdown automata and on previous work regarding parity games played on transition graphs of collapsible pushdown automata.
Accurate 2D Facial Depth Models Derived from a 3D Synthetic Dataset

As Consumer Technologies (CT) seeks to engage and interact more closely with the end-user it becomes important to observe and analyze a user's interaction with CT devices and associated services. One of the most useful modes for monitoring a user is to analyze a real-time video stream of their face. Facial expressions, movements and biometrics all provide important information, but obtaining a calibrated input with 3D accuracy from a single camera requires accurate knowledge of the facial depth and distance of different features from the camera. In this paper, a method is proposed to generate synthetic high-accuracy human facial depth from synthetic 3D face models. The generated synthetic human facial dataset is then used in Convolutional Neural Networks (CNN's) for monocular depth facial estimation and the results of the experiments are presented.
H-GPR: A Hybrid Strategy for Large-Scale Gaussian Process Regression

With the massive volume of data emerging from both scientific and industrial domains, it has become a desideratum to improve the scalability of Gaussian process regression (GPR). There are two major approaches to assuage its $\mathcal{O}\left( {{n^3}} \right)$ training complexity: the aggregation based methods and the sparse approximation methods. This paper proposes a hybrid strategy called H-GPR to combine these two well-established approaches. We show that it is possible to improve the performance of aggregation based methods by removing some data points that severely violate its underlying assumption, and then this information loss can be recovered by a set of inducing points generated by the sparse approximation methods. A novel metric called conditional independent score is proposed, which measures to what extent the assumption made by the aggregation based methods is satisfied. A heuristic rule is developed to adjust the relative size of the local experts and the inducing subset, so that their distinction can be better reflected. Thorough experiments on synthetic and realistic datasets were performed, demonstrating that the proposed method can improve both the predictive means and variances.
MEAN: Multi - Element Attention Network for Scene Text Recognition

Scene text recognition is a challenging problem due to the wide variances in contents, styles, orientations, and image quality of text instances in natural scene images. To learn the intrinsic representation of scene texts, a novel multi-element attention (MEA) mechanism is proposed to exploit geometric structures from local to global levels in feature maps extracted from a scene text image. The MEA mechanism is a generalized form of self-attention technique. The elements in feature maps are taken as the nodes of an undirected graph, and three kinds of adjacency matrices are designed to aggregate information at local, neighborhood and global levels before calculating the attention weights. A multi-element attention network (MEAN) is implemented, which includes a CNN for feature extraction, an encoder with MEA mechanism and a decoder for predicting text codes. Orientational positional encoding is added to feature maps output by the CNN, and a feature vector sequence transformed from the feature maps is used as the input of the encoder. Experimental results show that MEAN has achieved state-of-the-art or competitive performance on seven public English scene text datasets (IIITSk, SVT, IC03, IC13, IC15, SVTP, and CUTE). Further experiments have been conducted on a selected subset of the RCTW Chinese scene text dataset, demonstrating that MEAN can handle horizontal, vertical, and irregular scene text samples.
Evaluation of Curved Raycasting-based Interactive Surfaces in Virtual Environments

As 3D user interfaces become more popular, quick and reliable aerial selection and manipulation are desired. We evaluated a virtual curved interactive surface with controllable curvature based on raycasting. The user operates the surface by pointing using a head-mounted display and grip-type controller. We investigated the operation speed and accuracy of curved interactive surfaces under various presentation conditions. To investigate the users' operation ability for different curved conditions, we experimented with multiple surface curvature radii, including completely flat conditions. The experimental results showed that varying the curvature of the display improved the pointing accuracy by 28% and the speed by 15% over the flat surface in the most effective cases. These findings can be applied to curved interactive surfaces with mid-air pointing for 2D-style applications such as GIS and photo browsers.