Reviewerly/exHarmony huggingface dataset:
CSCNN: Algorithm-hardware Co-design for CNN Accelerators using Centrosymmetric Filters

Convolutional neural networks (CNNs) are at the core of many state-of-the-art deep learning models in computer vision, speech, and text processing. Training and deploying such CNN-based architectures usually require a significant amount of computational resources. Sparsity has emerged as an effective compression approach for reducing the amount of data and computation for CNNs. However, sparsity often results in computational irregularity, which prevents accelerators from fully taking advantage of its benefits for performance and energy improvement. In this paper, we propose CSCNN, an algorithm/hardware co-design framework for CNN compression and acceleration that mitigates the effects of computational irregularity and provides better performance and energy efficiency. On the algorithmic side, CSCNN uses centrosymmetric matrices as convolutional filters. In doing so, it reduces the number of required weights by nearly 50% and enables structured computational reuse without compromising regularity and accuracy. Additionally, complementary pruning techniques are leveraged to further reduce computation by a factor of $2.8-7.2\times $ with a marginal accuracy loss. On the hardware side, we propose a CSCNN accelerator that effectively exploits the structured computational reuse enabled by centrosymmetric filters, and further eliminates zero computations for increased performance and energy efficiency. Compared against a dense accelerator, SCNN and SparTen, the proposed accelerator performs $3.7\times $, $1.6\times $ and $1.3\times $ better, and improves the EDP (Energy Delay Product) by $8.9\times $, $2.8\times $ and $2.0\times $, respectively.
Solidifier

The exploitation of smart-contract vulnerabilities can lead to catastrophic losses. Formal verification can be a useful tool in identifying these vulnerabilities before deployment. We present an encoding of Solidity and the Ethereum blockchain using Boogie, an intermediate verification language. Based on this formalisation, we create Solidifier: a bounded model checker for Solidity. Distinctive features of our encoding are precisely capturing Solidity's unorthodox memory model, a notion of lazy blockchain exploration, and memory-precise verification harnesses. Unlike much of the work in this area, our modus operandi is not matching contracts against specific known behavioural patterns that might lead to vulnerabilities. Rather, we provide a tool to find errors/bad states - be they vulnerabilities or not - that might be reached through behaviours that might not follow such a pattern.
Cyber Third-Party Risk Management: A Comparison of Non-Intrusive Risk Scoring Reports

Cybersecurity is a concern for organizations in this era. However, strengthening the security of an organization’s internal network may not be sufficient since modern organizations depend on third parties, and these dependencies may open new attack paths to cybercriminals. Cyber Third-Party Risk Management (C-TPRM) is a relatively new concept in the business world. All vendors or partners possess a potential security vulnerability and threat. Even if an organization has the best cybersecurity practice, its data, customers, and reputation may be at risk because of a third party. Organizations seek effective and efficient methods to assess their partners’ cybersecurity risks. In addition to intrusive methods to assess an organization’s cybersecurity risks, such as penetration testing, non-intrusive methods are emerging to conduct C-TPRM more easily by synthesizing the publicly available information without requiring any involvement of the subject organization. In this study, the existing methods for C-TPRM built by different companies are presented and compared to discover the commonly used indicators and criteria for the assessments. Additionally, the results of different methods assessing the cybersecurity risks of a specific organization were compared to examine reliability and consistency. The results showed that even if there is a similarity among the results, the provided security scores do not entirely converge.
Automated Essay Scoring via Example-Based Learning

Automated essay scoring (AES) is the task of assigning grades to essays. It can be applied for quality assessment as well as pricing on User Generated Content. Previous works mainly consider using the prompt information for scoring. However, some prompts are highly abstract, making it hard to score the essay only based on the relevance between the essay and the prompt. To solve the problem, we design an auxiliary task, where a dynamic semantic matching block is introduced to capture the hidden features with example-based learning. Besides, we provide a hierarchical model that can extract semantic features at both sentence-level and document-level. The weighted combination of the scores is obtained from the features above to get holistic scoring. Experimental results show that our model achieves higher Quadratic Weighted Kappa (QWK) scores on five of the eight prompts compared with previous methods on the ASAP dataset, which demonstrate the effectiveness of our model.
Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records

Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model (HGM) on electronic health record (EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.
Detection of Makeup Presentation Attacks based on Deep Face Representations

Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spoofing (MIFS) database. It is shown that makeup presentation attacks might seriously impact the security of the face recognition system. Further, we propose an attack detection scheme which distinguishes makeup presentation attacks from genuine authentication attempts by analysing differences in deep face representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection system employs a machine learning-based classifier, which is trained with synthetically generated makeup presentation attacks utilizing a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database reveal a detection equal error rate of 0.7% for the task of separating genuine authentication attempts from makeup presentation attacks.
Adaptive L2 Regularization in Person Re-Identification

We introduce an adaptive L2 regularization mechanism in the setting of person re-identification. In the literature, it is common practice to utilize hand-picked regularization factors which remain constant throughout the training procedure. Unlike existing approaches, the regularization factors in our proposed method are updated adaptively through backpropagation. This is achieved by incorporating trainable scalar variables as the regularization factors, which are further fed into a scaled hard sigmoid function. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets validate the effectiveness of our framework. Most notably, we obtain state-of-the-art performance on MSMT17, which is the largest dataset for person re-identification. Source code is publicly available at https://github.com/nixingyang/AdaptiveL2Regularization.
Characterizing fuzzy simulations for fuzzy labeled transition systems in fuzzy propositional dynamic logic

We formulate and prove logical characterizations of fuzzy simulations for fuzzy labeled transition systems under the Gödel semantics. Apart from the usual setting, we consider also the setting that allows graded modal operators, which are used to talk about the number of successors with a certain property of the current state. Our characterizations are formulated w.r.t. the existential fragment of fuzzy propositional dynamic logic and its extension with graded modal operators. The results concern fuzzy preservation of existential modal formulas under fuzzy simulations as well as the Hennessy-Milner property of fuzzy simulations.
Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data

High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments.
A Comparison of Naive Bayes Methods, Logistic Regression and KNN for Predicting Healing of Covid-19 Patients in Indonesia

Since it was declared a global pandemic by the World Health Organization (WHO), the number of cases of Covid-19 patients who died has continued to increase.One of the countries with the highest death rate in the world is Indonesia.On Saturday, April 4, 2020, Indonesia reached the highest death rate for Covid-19 patients, around 9.11%.This number must be suppressed so that there are no more victims.For this reason, it is necessary to know actually the factors that can reduce the risk of death and predict the chance of curing Covid-19 patients.In data mining, there are several methods that can be used to predict a patient's recovery rate by considering several variables.The variables used in this study were age and gender.Naive Bayes Method, logistic regression, and K-Nearest Neighbor (KNN) are the methods to be chosen in this study to analyze their most accurate performance.The result shows that KNN has the highest accuracy, which is 0.750 compared to logistic regression which has a value of 0.703 as well as Naive Bayes which has the same value.Meanwhile, the level of precision of the three models shows that KNN also has the highest value, namely 0.750 than logistic regression and Naive Bayes which have the same value, namely 0.700.The recall value of the three also shows that the kNN remains the highest with 0.750 compared to the two comparison models which have the same value, namely 0.708.
Building and benchmarking an Arabic Speech Commands dataset for small-footprint keyword spotting

The introduction of the Google Speech Commands dataset accelerated research and resulted in a variety of new deep learning approaches that address keyword spotting tasks. The main contribution of this work is the building of an Arabic Speech Commands dataset, a counterpart to Google’s dataset. Our dataset consists of 12000 instances, collected from 30 contributors, and grouped into 40 keywords. We also report different experiments to benchmark this dataset using classical machine learning and deep learning approaches, the best of which is a Convolutional Neural Network with Mel-Frequency Cepstral Coefficients that achieved an accuracy of ∼98%. Additionally, we point out some key ideas to be considered in such tasks.
Online Time-Varying Topology Identification Via Prediction-Correction Algorithms

Signal processing and machine learning algorithms for data sup-ported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging task, as the problem is often ill-posed, and becomes even harder when the graph structure is time-varying. In this paper, we address the problem of dynamic topology identification by building on recent results from time-varying optimization, devising a general-purpose online algorithm operating in non-stationary environments. Because of its iteration-constrained nature, the proposed approach exhibits an intrinsic temporal-regularization of the graph topology without explicitly enforcing it. As a case-study, we specialize our method to the Gaussian graphical model (GGM) problem and corroborate its performance.
Expressivity of parameterized and data-driven representations in quality diversity search

We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of variation are unknown or too complex to encode manually, generative models can provide a learned latent space to approximate these factors. When used as a search space, however, the range and diversity of possible outputs are limited to the expressivity and generative capabilities of the learned model. We compare the output diversity of a quality diversity evolutionary search performed in two different search spaces: 1) a predefined parameterized space and 2) the latent space of a variational autoencoder model. We find that the search on an explicit parametric encoding creates more diverse artifact sets than searching the latent space. A learned model is better at interpolating between known data points than at extrapolating or expanding towards unseen examples. We recommend using a generative model's latent space primarily to measure similarity between artifacts rather than for search and generation. Whenever a parametric encoding is obtainable, it should be preferred over a learned representation as it produces a higher diversity of solutions.
Data-Driven Synthesis of Provably Sound Side Channel Analyses

We propose a data-driven method for synthesizing static analyses to detect side-channel information leaks in cryptographic software. Compared to the conventional way of manually crafting such static analyzers, which can be tedious, error prone and suboptimal, our learning-based technique is not only automated but also provably sound. Our analyzer consists of a set of type-inference rules learned from the training data, i.e., example code snippets annotated with the ground truth. Internally, we use syntax-guided synthesis (SyGuS) to generate new recursive features and decision tree learning (DTL) to generate analysis rules based on these features. We guarantee soundness by proving each learned analysis rule via a technique called query containment checking. We have implemented our technique in the LLVM compiler and used it to detect power side channels in C programs that implement cryptographic protocols. Our results show that, in addition to being automated and provably sound during synthesis, our analyzer can achieve the same empirical accuracy as two state-of-the-art, manually-crafted analyzers while being 300X and 900X faster, respectively.
BlahBlahBot: Facilitating Conversation between Strangers using a Chatbot with ML-infused Personalized Topic Suggestion

It is a prevalent behavior of having a chat with strangers in online settings where people can easily gather. Yet, people often find it difficult to initiate and maintain conversation due to the lack of information about strangers. Hence, we aimed to facilitate conversation between the strangers with the use of machine learning (ML) algorithms and present BlahBlahBot, an ML-infused chatbot that moderates conversation between strangers with personalized topics. Based on social media posts, BlahBlahBot supports the conversation by suggesting topics that are likely to be of mutual interest between users. A user study with three groups (control, random topic chatbot, and BlahBlahBot; N=18) found the feasibility of BlahBlahBot in increasing both conversation quality and closeness to the partner, along with the factors that led to such increases from the user interview. Overall, our preliminary results imply that an ML-infused conversational agent can be effective for augmenting a dyadic conversation.
Tracing Contacts With Mobile Phones to Curb the Pandemic:Topics and Stances in People’s Online Comments About the Official German Contact-Tracing App

The Covid-19 pandemic has led to a health crisis with 90 million infections and two million deaths by the end of January 2021. To prevent an overload of medical capacities, quickly identifying potentially infected persons is vital to stop the spread of the virus. Mobile apps for tracing people's contacts seem effective, but raise public concerns, e. g., about privacy. Hence, they are contested in public discourse. We report a large-scale NLP-supported analysis of people's comments about the German contact-tracing app on news websites, social media and app stores. We identified prevalent topics, stances, and how commenting developed over time. We found privacy to be among the most debated topics discussed from various perspectives. Commenting peaked at one point in time, when public discourse centered on the potential tracing protocols and their privacy protection. We encourage further research on the link between the public discussions and actual adoption rates of the app.
Deep Learning Based Face Mask Detection and Crowd Counting

Without any uncertainty, the COVID-19 pandemic has set the world to a stop. The infection is spreading rapidly and could be a peril to the human race. Seeing the prerequisite of the hour, one needs to continuously make certain safeguards of that one being wearing a mask in any way at all times. To make a secure environment that leads to open safety, we propose a proficient learning and computer vision based approach concentrated on the real-time robotized observation of individuals to find unmasked faces in open areas. Subsequently, the recommended solution favors the society by sparing time and helps in bringing down the spread of coronavirus. It can be implemented successfully when lockdown is lifted completely bringing about people in open get-togethers, shopping centers, etc.. Automated inspection will reduce manpower to oversee the public and can also be used in any place.
Social VR as the New Normal?

Due to the COVID-19 pandemic, online meetings have become the new normal amongst business professionals. The usage of video conferencing services such as Zoom has skyrocketed, and the usage of Social Virtual Reality (VR) services have also been taken under consideration to be the new normal as it enables users to have a spatial online presence; nonetheless, the usage of Social VR has been considerably lower compared to video conferencing services. The purpose of this study is to investigate the user interactions of business professionals regarding the web-based Social VR platform, Mozilla Hubs, and suggest alterations regarding the user experience in order to understand why the usage of Social VR is low amongst business professionals and to promote the usage of the platform that could resolve the issue having a lack of spatial presence in the online arena.
Remote XR Studies: Exploring Three Key Challenges of Remote XR Experimentation

HCI and social science experimentation that explores or uses extended reality (XR) has been particularly impacted by the recent Covid-19 pandemic. This is due to typical deployment of XR experiments inside laboratories, and a paucity of research into how to effectively conduct remote XR experimentation. This first CHI Remote XR workshop aims to explore the current state of the art around three main themes of remote XR experimentation: (i) participant recruitment and screening; (ii) data collection, including limitations and affordances of existing research and XR tools; and (iii) software frameworks and requirements for the effective design of encapsulated remote XR user studies. This workshop brings together researchers and practitioners in XR to explore these recently emerged themes and to imagine how effective future remote XR research might be conducted.
Designing AI to Work WITH or FOR People?

Artificial Intelligence (AI) can refer to the machine learning algorithms and the automation applications built on top of these algorithms. Human-computer interaction (HCI) researchers have studied these AI applications and suggested various Human-Centered AI (HCAI) principles for an explainable, safe, reliable, and trustworthy interaction experience. While some designers believe that computers should be supertools and active appliances, others believe that these latest AI systems can be collaborators. With today's AI algorithm breakthroughs, in this panel we ask whether the supertool or the collaboration metaphors best support work and play? How can we design AI systems to work best with people or for people? What does it take to get there? This panel will bring together panelists with diverse backgrounds to engage the audience through the discussion of their shared or diverging visions on the future of human-AI interaction design.
Siamese Capsule Network for End-to-End Speaker Recognition in the Wild

We propose an end-to-end deep model for speaker verification in the wild. Our model uses thin-ResNet for extracting speaker embeddings from utterances and a Siamese capsule network and dynamic routing as the back-end to calculate a similarity score between the embeddings. We conduct a series of experiments and comparisons on our model versus baseline solutions, showing that our model outperforms the benchmarks using substantially less amount of training data. We also perform additional experiments to study the impact of different speaker embeddings on the Siamese capsule network. We show that the best performance is achieved by using embeddings obtained directly from the feature aggregation module of the front-end and passing them to higher capsules using dynamic routing.