Reviewerly/exHarmony huggingface dataset:
Multimodal news analytics using measures of cross-modal entity and context consistency

Abstract The World Wide Web has become a popular source to gather information and news. Multimodal information, e.g., supplement text with photographs, is typically used to convey the news more effectively or to attract attention. The photographs can be decorative, depict additional details, but might also contain misleading information. The quantification of the cross-modal consistency of entity representations can assist human assessors’ evaluation of the overall multimodal message. In some cases such measures might give hints to detect fake news, which is an increasingly important topic in today’s society. In this paper, we present a multimodal approach to quantify the entity coherence between image and text in real-world news. Named entity linking is applied to extract persons, locations, and events from news texts. Several measures are suggested to calculate the cross-modal similarity of the entities in text and photograph by exploiting state-of-the-art computer vision approaches. In contrast to previous work, our system automatically acquires example data from the Web and is applicable to real-world news. Moreover, an approach that quantifies contextual image-text relations is introduced. The feasibility is demonstrated on two datasets that cover different languages, topics, and domains.
Deep learning in photonics: introduction

The connection between Maxwell’s equations and neural networks opens unprecedented opportunities at the interface between photonics and deep learning. This feature issue highlights recent research progress at the interdisciplinary field of photonics and deep learning and provides an opportunity for different communities to exchange their ideas from different perspectives.
A technical review of wireless security for the internet of things: Software defined radio perspective

The increase of cyberattacks by using the Internet of Things devices has exposed multiple vulnerabilities not only on devices but also on IoT infrastructures. Now, small devices can affect different networks and their services. This paper presents a review of the vulnerabilities on wireless technologies that gives connectivity to IoT, and the experiences using Software Defined Radio SDR for implementing wireless attacks to IoT technologies are assessed. A systematic literature review was conducted. The types of vulnerabilities and attacks that can affect the wireless technologies that stand the IoT ecosystem and SDR platforms were compared, and recent methods to overcome these attacks were identified. On the IoT reference model, the perception layer was identified as the most vulnerable. Most attacks at this level occur due to hardware limitations, physical device exposure, and heterogeneity of technologies. Future cybersecurity systems based on SDR technologies have notable advantages due to their flexibility and adaptability to new communication technologies and their potential to develop advanced tools. However, the IoT’s cybersecurity challenges are so complex that merging SDR hardware with cognitive and intelligent techniques is highly advisable. Some of these techniques could include deep learning to adapt mitigation systems to rapid technological changes.
Mapping structural complexity using geophysics: A new geostatistical approach applied to greenstone belts of the southern Superior Province, Canada

With the increasing size and complexity of geoscientific databases, statistical tools and machine learning algorithms are needed to highlight subtle patterns within dense data clouds and reduce human-related bias in geological mapping. This study presents a new tool that maps structural complexity using circular variance and spherical dispersion in two Archean greenstone belts near Dryden and Timmins in the Superior Province, Ontario. Structural complexity quantitatively highlights areas where structures are folded, juxtaposed, and/or parallel. Bedding measurements and autodetected aeromagnetic lineaments are used as inputs to test if structural complexity can refine or add confidence to existing geological interpretations. Linear, low variance trends frequently delineate the location and extent of deformation zones, which likely reflect parallel transposed fabrics in high strain corridors. Areas of high variance spatially correlate with fold traces. Sharp boundaries between high and low variance anomalies are interpreted as faults and/or shear zones that juxtapose structural blocks. The near continuous and representative nature of autodetected magnetic lineaments was effective in capturing structural complexity at a regional scale; however, spherical dispersion captured the highest resolution of structural complexity at local scales (<1:30000). Key considerations when performing structural complexity analysis are scale, data density, and optimization of input parameters (e.g., neighborhood radius, lineament length, minimum neighborhood populations). Gold deposits are found <~1700 m from low (< μ - 1σ) and < ~1500 m from high circular variance anomalies (> μ + 1σ). Additionally, the gold grade of large deposits (> 1 Moz) increases with proximity to high variance anomalies. When paired with expert knowledge, these techniques will increase repeatability in future exploration endeavors, making exploration a more rigorous process with increased confidence.
Mobile Apps to Fight the COVID-19 Crisis

The COVID-19 pandemic led to a multi-faceted global crisis, which triggered the diverse and quickly emerging use of old and new digital tools. We have developed a multi-channel approach for the monitoring and analysis of a subset of such tools, the COVID-19 related mobile applications (apps). Our approach builds on the information available in the two most prominent app stores (i.e., Google Play for Android-powered devices and Apple’s App Store for iOS-powered devices), as well as on relevant tweets and digital media outlets. The dataset presented here is one of the outcomes of this approach, uses the content of the app stores and enriches it, providing aggregated information about 837 mobile apps published across the world to fight the COVID-19 crisis. This information includes: (a) information available in the mobile app stores between 20 April 2020 and 2 August 2020; (b) complementary information obtained from manual analysis performed until mid-September 2020; and (c) status information about app availability on 28 February 2021, when we last collected data from the mobile app stores. We highlight our findings with a series of descriptives, which depict both the activities in the app stores and the qualitative information that was revealed by the manual analysis.
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels, each built with a specific criterion (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting, the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. This is not considered by traditional offline evaluation protocols. Hence, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. We report experiments on publicly available datasets on the movie domain and notice that under a carousel setting the ranking of the algorithms change. In particular, when a SLIM carousel is available, matrix factorization models tend to be preferred, while item-based models are penalized. We also propose to extend ranking metrics to the two-dimensional carousel layout in order to account for a known position bias, i.e., users will not explore the lists sequentially, but rather concentrate on the top-left corner of the screen.
A multivariate intersection over union of SiamRPN network for visual tracking

Abstract SiamPRN algorithm performs well in visual tracking, but it is easy to drift under occlusion and fast motion scenes because it uses $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -smooth loss function to measure the regression location of bounding box. In this paper, we propose a multivariate intersection over union (MIOU) loss in SiamRPN tracking framework. Firstly, MIOU loss includes three geometric factors in regression: the overlap area ratio, the center distance ratio, and the aspect ratio, which can better reflect the coincidence degree of target box and prediction box. Secondly, we improve the definition of aspect ratio loss to avoid gradient explosion, improve the optimization performance of prediction box. Finally, based on SiamPRN tracker, we compared the tracking performance of $$\ell _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -smooth loss, IOU loss, GIOU loss, DIOU loss, and MIOU loss. Experimental results show that the MIOU loss has better target location regression than other loss functions on the OTB2015 and VOT2016 benchmark, especially for the challenges of occlusion, illumination change and fast motion.
Ada-Sise: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks

Explainable AI (XAI) is an active research area to interpret a neural network’s decision by ensuring transparency and trust in the task-specified learned models. Recently, perturbation-based model analysis has shown better interpretation, but backpropagation techniques are still prevailing because of their computational efficiency. In this work, we combine both approaches as a hybrid visual explanation algorithm and propose an efficient interpretation method for convolutional neural networks. Our method adaptively selects the most critical features that mainly contribute towards a prediction to probe the model by finding the activated features. Experimental results show that the proposed method can reduce the execution time up to 30% while enhancing competitive interpretability without compromising the quality of explanation generated.
Computational 2D and 3D Medical Image Data Compression Models

In this world of big data, the development and exploitation of medical technology is vastly increasing and especially in big biomedical imaging modalities available across medicine. At the same instant, acquisition, processing, storing and transmission of such huge medical data requires efficient and robust data compression models. Over the last 2 decades, numerous compression mechanisms, techniques and algorithms were proposed by many researchers. This work provides a detailed status of these existing computational compression methods for medical imaging data. Appropriate classification, performance metrics, practical issues and challenges in enhancing the two dimensional (2D) and three dimensional (3D) medical image compression arena are reviewed in detail.
SoK: Efficient Privacy-preserving Clustering

Abstract Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care. In many of these applications, sensitive information is clustered that should not be leaked. Moreover, nowadays it is often required to combine data from multiple sources to increase the quality of the analysis as well as to outsource complex computation to powerful cloud servers. This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering. We implement and benchmark today’s four most efficient fully private clustering protocols by Cheon et al. (SAC’19), Meng et al. (ArXiv’19), Mohassel et al. (PETS’20), and Bozdemir et al. (ASIACCS’21) with respect to communication, computation, and clustering quality. We compare them, assess their limitations for a practical use in real-world applications, and conclude with open challenges.
Structural risk minimization for quantum linear classifiers

Quantum machine learning (QML) models based on parameterized quantum circuits are often highlighted as candidates for quantum computing&amp;apos;s near-term “killer application&amp;apos;&amp;apos;. However, the understanding of the empirical and generalization performance of these models is still in its infancy. In this paper we study how to balance between training accuracy and generalization performance (also called structural risk minimization) for two prominent QML models introduced by Havlíček et al. \cite{havlivcek:qsvm}, and Schuld and Killoran \cite{schuld:qsvm}. Firstly, using relationships to well understood classical models, we prove that two model parameters – i.e., the dimension of the sum of the images and the Frobenius norm of the observables used by the model – closely control the models&amp;apos; complexity and therefore its generalization performance. Secondly, using ideas inspired by process tomography, we prove that these model parameters also closely control the models&amp;apos; ability to capture correlations in sets of training examples. In summary, our results give rise to new options for structural risk minimization for QML models.
Is Continued Improvement After Automated Virtual Reality Exposure Therapy for Spider Phobia Explained by Subsequent in-vivo Exposure? A First Test of the Lowered Threshold Hypothesis

Consumer Virtual Reality (VR) technology offers a powerful, immersive medium for scalable dissemination of mental health interventions. Decades of research has shown VR exposure therapy to be efficacious in the treatment of anxiety disorders and that the fear reduction generalizes to real-world stimuli. Many studies also report continued improvement over time, after discontinuing VR use. The lowered threshold hypothesis states that this continued improvement is moderated by lowering the threshold to conduct subsequent in-vivo exposure. The current study is the first to formally test this hypothesis, using data from a recent trial on automated VR exposure therapy for spider phobia, in which participants ( n = 49) were followed for 1 year, completing assessments 1 week, 3 and 12 months post-treatment. The assessment included validated self-report of phobia symptoms, a standardized behavioral approach test featuring a real spider, and a questionnaire for self-reporting frequency of in-vivo exposures since last assessment. Number of in-vivo exposures was found to be independently associated with greater symptom decrease in longitudinal outcome models. In sequential structural equation models, immediate post-treatment symptom reduction was associated with subsequent in-vivo exposures, which in turn was associated with continued symptom reduction. However, this applied only to self-reported phobia symptoms (not behavioral avoidance) and no associations were found past 3 months. Our findings offer preliminary, partial support for the lowered threshold hypothesis, suggesting that VR exposure interventions may benefit from including explicit in-virtuo to in-vivo transitioning components.
A Voice Based Assistant Using Google Dialogflow and Machine Learning

Virtual Personal Assistant (VPA) is one of the most successful results of Artificial Intelligence, which has given a new way for the human to have its work done from a machine. This paper gives a brief survey on the methodologies and concepts used in making of an Virtual Personal Assistant (VPA) and thereby going on to use it in different software applications. Speech Recognition Systems, also known as Automatic Speech Recognition (ASR), plays An important role in virtual assistants in order to help user have a conversation with the system. In this project, we are trying to make a Virtual Personal Assistant ERAA which will include the important features that could help in assisting ones’ needs. Keeping in mind the user experience, we will make it as appealing as possible, just like other VPAs. Various Natural Language Understanding Platforms like IBM Watson and Google Dialogflow were studied for the same. In our project, we have used Google Dialogflow as the NLU Platform for the implementation of the software application. The User-Interface for the application is designed with the help of Flutter Software Platform. All the models used for this VPA will be designed in a way to work as efficient as possible. Some of the common features which are available in most of the VPAs will be added. We will be implementing ERAA via a smartphone application, and for future scope, our aim will be to implement it on the desktop environment. The following Paper ensure to provide the methodologies used for development of the application. It provides the obtained outcomes of the features developed within the application. It shows how the available natural language understanding platforms can reduce the burden of the user, and therefore going on to develop a robust software application.
Securing internet applications from routing attacks

Application-layer and network-layer defenses are critical for fortifying routing attacks.
Talking About My Generation

Since the invention of JavaScript 25 years ago, website functionality has been continuously shifting from the server-side to the client-side. Web browsers have evolved into an application platform, and HTML5 emerged as a first-class environment for building rich cross-platform applications. This additional functionality on the client-side comes with the added risk of new security issues with increasingly severe consequences. In this work, we investigate the prevalence of DOM-based Cross-Site Scripting (DOM-based XSS) in the top 100,000 most popular websites using a novel targeted exploit generation technique based on dynamic data-flow tracking. In total, this work finds 15,710 potentially insecure dataflows where information from the URL is injected into the HTML of the Web page. Using large-scale exploit generation and validation services, 7199 of these flows lead to JavaScript execution, across 711 different domains. This represents a successful exploit rate of 45.82%, improving on previous methods by factors of 1.8 and 1.9 respectively.
RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search

To support complex search tasks, where the initial information requirements are complex or may change during the search, a search engine must adapt the information delivery as the user’s information requirements evolve. To support this dynamic ranking paradigm effectively, search result ranking must incorporate both the user feedback received, and the information displayed so far. To address this problem, we introduce a novel reinforcement learning-based approach, RLIRank. We first build an adapted reinforcement learning framework to integrate the key components of the dynamic search. Then, we implement a new Learning to Rank (LTR) model for each iteration of the dynamic search, using a recurrent Long Short Term Memory neural network (LSTM), which estimates the gain for each next result, learning from each previously ranked document. To incorporate the user’s feedback, we develop a word-embedding variation of the classic Rocchio Algorithm, to help guide the ranking towards the high-value documents. Those innovations enable RLIRank to outperform the previously reported methods from the TREC Dynamic Domain Tracks 2017 and exceed all the methods in 2016 TREC Dynamic Domain after multiple search iterations, advancing the state of the art for dynamic search.
What Do You Mean by Trust? Establishing Shared Meaning in Interdisciplinary Design for Assistive Technology

Abstract The topic of trust has attracted increasing interest within HRI research, and is particularly relevant in the context of social robots and their assistance of older people at home. To make this abstract concept of trust more tangible for developers of robotic technologies and to connect it with older people’s living spaces and their daily practices, we propose a light-weight method drawing on elicitation cards to be used at early stages of participatory design. The cards were designed to serve as a guide for qualitative interviews at ideation phases. This was accomplished by using the cards connected to the living spaces of the participants, their daily practices, and ‘provocative’ questions to structure conversations. We developed the method with 10 inexperienced interviewers who conducted 10 qualitative interviews on the topic of trust without cards, and who tested the cards with 10 older adults. Our findings indicate that the method served as a powerful facilitator of conversations around the topic of trust and enabled interviewers to engage with everyday practices of older adults; it also facilitated a more active role for older adults during the conversations. As indicators of findings that can come from the cards, salient trust-related themes that emerged from the analysis of card usage were the desire for control, companionship, privacy, understandability, and location-specific requirements with regards to trust.
Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels

Neural language models are known to have a high capacity for memorization of training samples.This may have serious privacy implications when training models on user content such as email correspondence.Differential privacy (DP), a popular choice to train models with privacy guarantees, comes with significant costs in terms of utility degradation and disparate impact on subgroups of users.In this work, we introduce two privacypreserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a novel triplet-loss term.We compare our methods with DP through extensive evaluation.We show the advantages of our regularizers with favorable utility-privacy trade-off, faster training with the ability to tap into existing optimization approaches, and ensuring uniform treatment of under-represented subgroups.* Work done as part of an MSR internship. 1 A naïve example is an attacker querying "My account number is" and hoping to receive a user's account number.
Evaluation performance recall and F2 score of credit card fraud detection unbalanced dataset using SMOTE oversampling technique

Abstract Unbalanced data becomes an interesting research and continues to be studied because of its uniqueness. Unbalanced data requires special treatment prior to making the data balance. In this paper, our study to investigate the performance of unbalanced dataset using diverse oversampling proportion. We use SMOTE to gerentae new syntethic data, then we classify using random forest algorithm. In our experiment we generate new sampling with start 20%, 40%, 60%, 80%, and 100% of majority class, so that the data balancing until 50%: 50%. Each new generated data, we train the data using classification technique. Then, evaluate each algorithm performance. We show that the highest F2 score i.e: 85.34 and 84.93. The new data generated is 60% of majority class, result F2 score 85.34, then the new data generated from 100% of majority class result F2 score 84.93.