Reviewerly/exHarmony huggingface dataset:
QUAD: Quadratic-Bound-based Kernel Density Visualization

Kernel density visualization, or KDV, is used to view and understand data points in various domains, including traffic or crime hotspot detection, ecological modeling, chemical geology, and physical modeling. Existing solutions, which are based on computing kernel density (KDE) functions, are computationally expensive. Our goal is to improve the performance of KDV, in order to support large datasets (e.g., one million points) and high screen resolutions (e.g., 1280 x 960 pixels). We examine two widely-used variants of KDV, namely approximate kernel density visualization (EKDV) and thresholded kernel density visualization (TKDV). For these two operations, we develop fast solution, called QUAD, by deriving quadratic bounds of KDE functions for different types of kernel functions, including Gaussian, triangular etc. We further adopt a progressive visualization framework for KDV, in order to stream partial visualization results to users continuously. Extensive experiment results show that our new KDV techniques can provide at least one-order-of-magnitude speedup over existing methods, without degrading visualization quality. We further show that QUAD can produce the reasonable visualization results in real-time (0.5 sec) by combining the progressive visualization framework in single machine setting without using GPU and parallel computation.
Automata Learning

We propose a generic categorical framework for learning unknown formal languages of various types (e.g. finite or infinite words, weighted and nominal languages). Our approach is parametric in a monad T that represents the given type of languages and their recognizing algebraic structures. Using the concept of an automata presentation of T-algebras, we demonstrate that the task of learning a T-recognizable language can be reduced to learning an abstract form of algebraic automaton whose transitions are modeled by a functor. For the important case of adjoint automata, we devise a learning algorithm generalizing Angluin's L*. The algorithm is phrased in terms of categorically described extension steps; we provide for a termination and complexity analysis based on a dedicated notion of finiteness. Our framework applies to structures like ω-regular languages that were not within the scope of existing categorical accounts of automata learning. In addition, it yields new learning algorithms for several types of languages for which no such algorithms were previously known at all, including sorted languages, nominal languages with name binding, and cost functions.
Explaining the semantics capturing capability of scene graph generation models

Deep neural network is a effective way for scene graph generation tasks. However, it also makes the scene graph generation models difficult to explain. For instance, the current standard metric cannot explain how capable neural network models are of capturing the semantics of relations. In this paper, we try to understand the semantics capturing capability of scene graph generation models based on three types of metrics: conformance recall, violation recall, and non-violation recall, which measure semantic properties of relations that are reflected by triples in scene graph generated by models. Evaluation of these metrics on three representative state-of-the-art scene graph generation models based on deep neural network in Visual Genome dataset shows that the proposed metrics can effectively explain the capability of models to capture different semantic properties and identify design problems in models. By extending the Visual Genome dataset with different sets of additional annotations, these metrics can also explaining whether the semantics capturing capability of deep neural network models can be improved by data enhancement.
FRAGMENT: A Web Application for Database Fragmentation, Allocation and Replication over a Cloud Environment

Fragmentation, allocation and replication are techniques widely used in relational databases to improve the performance of operations and reduce their cost in distributed environments. This article shows an analysis of different methods for database fragmentation, allocation and replication and a Web application called FRAGMENT that adopts the work technique that was selected in the analysis stage, because it presents a fragmentation and replication method, it is applied to a cloud environment, it is easy to implement, it focuses on improving the performance of the operations executed on the database, it shows everything necessary for its implementation and is based on a cost model. FRAGMENT analyzes the operations performed in any table of a database, proposes fragmentation schemes based on the most expensive attributes and allocates and replicates a scheme chosen by the user in a distributed environment in the cloud. This work shows a common problem in fragmentation methods, overlapping fragments, and provides an algorithm with an approach to address it. This algorithm results in the predicates that will define each fragment in a distributed environment. To validate the implemented technique, a second web application is presented, dedicated to simulate operations on sites and focused on producing a log file for the main application. Experiments with the TPC-E benchmark demonstrated lower response time of the queries executed against the distributed database generated by FRAGMENT compared with a centralized database.
What is the schema of your knowledge graph?

Large-scale knowledge graphs have become prevalent on the Web and have demonstrated their usefulness for several tasks. One challenge associated to knowledge graphs is the necessity to keep a knowledge graph schema (which is generally manually defined) that accurately reflects the knowledge graph content. In this paper, we present an approach that extracts an expressive taxonomy based on knowledge graph embeddings, linked data statistics and clustering. Our results show that the learned taxonomy is not only able to retain original classes but also identifies new classes, thus giving an up-to-date view of the knowledge graph.
Block-SMPC

With the rapid development of Internet of Things, the demand of data sharing is not only to ensure the data integrity, but also to protect user's individual privacy. Secure multi-party computation, as a technology paradigm based on cryptography technology, enables privacy protection in data sharing among multiple parties, while the implement of secure multi-party computation is limited due to the inefficient, complex computation protocol and frequent interaction. Fortunately, the emergence of blockchain technology provides excellent properties of decentralization, verifiability and high reliability for secure multi-party computation. In this paper, we propose a blockchain-based secure multi-party computation architecture for data sharing, where we design an aggregator consortium for data storage, verification and joint computation. Moreover, we introduce a straightforward secure multi-party computation scheme based on homomorphic encryption. In addition, we analyze and discuss this scheme in terms of data security, access flexibility, attack resistance and potential applications.
The Burden of Ending Online Account Sharing

Many people share online accounts, even in situations where high privacy and security are expected. Naturally, the sharing of these accounts does not endure forever. This paper reports the privacy and security challenges that people experience when they stop online account sharing. We conducted semi-structured interviews with 25 participants who stopped sharing at least one online account in the 12 months preceding the study. Our results suggest that users experience cognitive and psychosocial burdens when ending account sharing. We offer suggestions for how to improve the design of online accounts to support users better when they end account sharing.
Giving Voice to Silent Data: Designing with Personal Music Listening History

Music streaming services collect listener data to support personalization and discovery of their extensive catalogs. Yet this data is typically used in ways that are not immediately apparent to listeners. We conducted design workshops with ten Spotify listeners to imagine future voice assistant (VA) interactions leveraging logged music data. We provided participants with detailed personal music listening data, such as play-counts and temporal patterns, which grounded their design ideas in their current behaviors. In the interactions participants designed, VAs did not simply speak their data out loud; instead, participants envisioned how data could implicitly support introspection, behavior change, and exploration. We present reflections on how VAs could evolve from voice-activated remote controls to intelligent music coaches and how personal data can be leveraged as a design resource.
Incorporating uncertainties in student response modeling by loss function regularization

Most existing research works involving deep learning focus on performance improvement by developing new architectures or regularizers. However, in this paper we study the modeling of uncertainty in recurrent networks for the application of student response modeling, more specifically, knowledge tracing. Knowledge tracing is an application of time series machine learning. It consists of inferring the mastery level of a skill for a student as they navigate a question bank, thus adjusting curriculum for efficient learning. Deep Knowledge Tracing (DKT) takes the deep learning approach for knowledge tracing and has achieved better results than models like Bayesian Knowledge Tracing (BKT) and Performance Factor Analysis (PFA). However, the opaqueness of these deep knowledge tracing models also brings some criticisms. Providing an uncertainty score for each prediction helps mitigate this opaqueness. To investigate uncertainty modeling in DKT, we first examine a popular way of modeling data dependent uncertainties using Monte Carlo and show how it is insufficient to model variance in data. Second, we show how to incorporate sensible uncertainties by explicitly regularizing the cross entropy loss function. Third, we evaluate our method both in three different real datasets and in a more controlled way using synthetic data. Using synthetic data allows us to quantitatively understand the generated uncertainties. The results show that our method provides comparable results with standard deep knowledge tracing models as well as meaningful prediction uncertainties.
Structured Query Language Learning: Concept Map-Based Instruction Based on Cognitive Load Theory

Structured query language (SQL) is difficult to master because the execution process of SQL statements is invisible. When learning to construct an SQL query, learners must visualise the evolution process of the intermediate datasets of the SQL statement in working memory, which may burden learners' cognitive load and consequently jeopardise learning outcomes. This study describes the execution process of SQL statements by using concept maps to improve learners' understanding of SQL. An empirical experiment was conducted using two database courses, namely concept map-based and conventional instruction, to examine the relationship between concept maps and the understanding of SQL from a cognitive load theory perspective. The experimental results demonstrated the superiority of concept map-based instruction over conventional instruction because concept map-based instruction reduces extraneous load but increases germane load. Concept map construction facilitated learner engagement and promoted meaningful learning. Studying the instructors' concept maps helped learners follow the cognitive structures used by instructors to perform SQL queries, and enabled them to perceive the execution process of SQL queries relatively easily. These results potentially help educators understand the learning difficulties caused by the declarative nature of SQL and motivating researchers to resolve the inherent problem by considering learners' cognitive processes.
Ranking via partial ordering for answer selection

Answer selection with deep neural networks has been studied extensively owing to its strong capability of encoding semantic features. Most previous work has treated the candidates as independent individuals for ranking but ignored the relations between these. In this study, we propose a ranking method via partial ordering for answer selection, which is applicable to various tasks, such as question answering and reading comprehension. First, we propose a comparative network, namely Candidate vs. Candidate, which aims to discover the possible partial order relations between (candidate, candidate) pairs. Thereafter, a multi-task learning framework is constructed for the answer selection problem, in which the main task is representing the relevance between (question, answer) pairs, while the auxiliary sub-task is learning the partial order relations between (answer, answer) pairs. By jointly training the networks with abundant supervision information, a reasonable relevance function and a comparison function can be approximated for these tasks. The experimental results on four benchmarks indicate that ranking candidate answers via partial ordering can significantly improve the answer selection performance.
Parareligious-HCI: Designing for 'Alternative' Rationality in Rural Wellbeing in Bangladesh

A body of Human-Computer Interaction (HCI) research addresses a wide range of wellbeing issues including digital and online health-care, formal and informal healthcare and wellbeing infrastructure, and health informatics. We focus on the wellbeing practices among the underrepresented marginalized communities in rural Bangladesh. The goal of this work is to understand their wellbeing challenges and how those are connected to their religious beliefs and adaptations - that we define as 'Parareligion'. This paper presents our findings from a 3-year long ethnographic study with 150 participants in 15 villages. We extend this discussion by arguing for better inclusion of para-religious practices in Wellbeing-HCI discourse to design more culturally appropriate and sustainable technologies for such target communities.
Optimizing Machine Learning Workloads in Collaborative Environments

Effective collaboration among data scientists results in high-quality and efficient machine learning (ML) workloads. In a collaborative environment, such as Kaggle or Google Colabratory, users typically re-execute or modify published scripts to recreate or improve the result. This introduces many redundant data processing and model training operations. Reusing the data generated by the redundant operations leads to the more efficient execution of future workloads. However, existing collaborative environments lack a data management component for storing and reusing the result of previously executed operations. In this paper, we present a system to optimize the execution of ML workloads in collaborative environments by reusing previously performed operations and their results. We utilize a so-called Experiment Graph (EG) to store the artifacts, i.e., raw and intermediate data or ML models, as vertices and operations of ML workloads as edges. In theory, the size of EG can become unnecessarily large, while the storage budget might be limited. At the same time, for some artifacts, the overall storage and retrieval cost might outweigh the recomputation cost. To address this issue, we propose two algorithms for materializing artifacts based on their likelihood of future reuse. Given the materialized artifacts inside EG, we devise a linear-time reuse algorithm to find the optimal execution plan for incoming ML workloads. Our reuse algorithm only incurs a negligible overhead and scales for the high number of incoming ML workloads in collaborative environments. Our experiments show that we improve the run-time by one order of magnitude for repeated execution of the workloads and 50% for the execution of modified workloads in collaborative environments.
Analyzing Demographic Bias in Artificially Generated Facial Pictures

Artificial generation of facial images is increasingly popular, with machine learning achieving photo-realistic results. Yet, there is a concern that the generated images might not fairly represent all demographic groups. We use a state-of-the-art method to generate 10,000 facial images and find that the generated images are skewed towards young people, especially white women. We provide recommendations to reduce demographic bias in artificial image generation.
Refinement-Based Game Semantics for Certified Abstraction Layers

Formal methods have advanced to the point where the functional correctness of various large system components has been mechanically verified. However, the diversity of semantic models used across projects makes it difficult to connect these component to build larger certified systems. Given this, we seek to embed these models and proofs into a generalpurpose framework where they could interact. We believe that a synthesis of game semantics, the refinement calculus, and algebraic effects can provide such a framework.
Le Taureau: Deconstructing the Serverless Landscape &amp; A Look Forward

Akin to the natural evolution of programming in assembly language to high-level languages, serverless computing represents the next frontier in the evolution of cloud computing: bare metal -> virtual machines -> containers -> serverless. The genesis of serverless computing can be traced back to the fundamental need of enabling a programmer to singularly focus on writing application code in a high-level language and isolating all facets of system management (for example, but not limited to, instance selection, scaling, deployment, logging, monitoring, fault tolerance and so on). This is particularly critical in light of today's, increasingly tightening, time-to-market constraints. Currently, serverless computing is supported by leading public cloud vendors, such as AWS Lambda, Google Cloud Functions, Azure Cloud Functions and others. While this is an important step in the right direction, there are many challenges going forward. For instance, but not limited to, how to enable support for dynamic optimization, how to extend support for stateful computation, how to efficiently bin-pack applications, how to support hardware heterogeneity (this will be key especially in light of the emergence of hardware accelerators for deep learning workloads). Inspired by Picasso's Le Taureau, in the tutorial proposed herein, we shall deconstruct evolution of serverless --- the overarching intent being to facilitate better understanding of the serverless landscape. This, we hope, would help push the innovation frontier on both fronts, the paradigm itself and the applications built atop of it.
A Philosophical Treatise on the Connection of Scientific Reasoning with Fuzzy Logic

The present article studies the connection of scientific reasoning with fuzzy logic. Induction and deduction are the two main types of human reasoning. Although deduction is the basis of the scientific method, almost all the scientific progress (with pure mathematics being probably the unique exception) has its roots to inductive reasoning. Fuzzy logic gives to the disdainful by the classical/bivalent logic induction its proper place and importance as a fundamental component of the scientific reasoning. The error of induction is transferred to deductive reasoning through its premises. Consequently, although deduction is always a valid process, it is not an infallible method. Thus, there is a need of quantifying the degree of truth not only of the inductive, but also of the deductive arguments. In the former case, probability and statistics and of course fuzzy logic in cases of imprecision are the tools available for this purpose. In the latter case, the Bayesian probabilities play a dominant role. As many specialists argue nowadays, the whole science could be viewed as a Bayesian process. A timely example, concerning the validity of the viruses’ tests, is presented, illustrating the importance of the Bayesian processes for scientific reasoning.
RCA-SOC: A novel adversarial defense by refocusing on critical areas and strengthening object contours

The success of deep learning is greatly attributed to its representation capability especially in computer vision tasks. However, recent studies have shown that deep neural networks (DNNs) are often vulnerable to adversarial attacks. To determine the common ground of various attacks, we compare the difference between clean and adversarial examples via the model hidden feature visualization method, i.e. heatmap, as adversarial perturbations are usually imperceptible for human visual systems. It was observed that the adversarial examples generated by various attack methods are capable of fooling DNNs by scattering critical areas of the image and blurring object contours. Inspired by these findings, we created a direct but effective defense by Refocusing on Critical Areas and Strengthening Object Contours, briefly RCA-SOC. It is a pixel attention weight-based defense composed of a pixel channel attention and a pixel plane attention. Critical areas of the images can be reconstructed by the pixel channel attention, while object contour is strengthened by the pixel plane attention. The effect of RCA-SOC against different attacks were demonstrated on scalable models and datasets. Furthermore, current state-of-the-art defense methods were shown to improve when cascaded with RCA-SOC. To demonstrate its practical application, RCA-SOC also showed its effectiveness in a case study of not suitable for work (NSFW) recognition.