Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional Sampling from Invertible Generative Models with Applications to Inverse Problems

We consider uncertainty aware compressive sensing when the prior distribution is defined by an invertible generative model. In this problem, we receive a set of low dimensional measurements and we want to generate conditional samples of high dimensional objects conditioned on these measurements. We first show that the conditional sampling problem is hard in general, and thus we consider approximations to the problem. We develop a variational approach to conditional sampling that composes a new generative model with the given generative model. This allows us to utilize the sampling ability of the given generative model to quickly generate samples from the conditional distribution.
Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey

In many practical applications, it is often difficult and expensive to obtain enough large-scale labeled data to train deep neural networks to their full capability. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative. However, direct transfer often results in significant performance decay due to domain shift. Domain adaptation (DA) addresses this problem by minimizing the impact of domain shift between the source and target domains. Multi-source domain adaptation (MDA) is a powerful extension in which the labeled data may be collected from multiple sources with different distributions. Due to the success of DA methods and the prevalence of multi-source data, MDA has attracted increasing attention in both academia and industry. In this survey, we define various MDA strategies and summarize available datasets for evaluation. We also compare modern MDA methods in the deep learning era, including latent space transformation and intermediate domain generation. Finally, we discuss future research directions for MDA.
PHS: A Toolbox for Parallel Hyperparameter Search

We introduce an open source python framework named PHS - Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machine learning. Bayesian optimization is chosen as a sample efficient method to propose the next query set of parameters.
Understanding and Enhancing Mixed Sample Data Augmentation

Mixed Sample Data Augmentation (MSDA) has received increasing attention in recent years, with many successful variants such as MixUp and CutMix. Following insight on the efficacy of CutMix in particular, we propose FMix, an MSDA that uses binary masks obtained by applying a threshold to low frequency images sampled from Fourier space. FMix improves performance over MixUp and CutMix for a number of state-of-the-art models across a range of data sets and problem settings. We go on to analyse MixUp, CutMix, and FMix from an information theoretic perspective, characterising learned models in terms of how they progressively compress the input with depth. Ultimately, our analyses allow us to decouple two complementary properties of augmentations, and present a unified framework for reasoning about MSDA. Code for all experiments is available at https://github.com/ecs-vlc/FMix.
Black or White? How to Develop an AutoTuner for Memory-based Analytics

There is a lot of interest today in building autonomous (or, self-driving) data processing systems. An emerging school of thought is to leverage AI-driven "black box" algorithms for this purpose. In this paper, we present a contrarian view. We study the problem of autotuning the memory allocation for applications running on modern distributed data processing systems. We show that an empirically-driven "white-box" algorithm, called RelM, that we have developed provides a close-to-optimal tuning at a fraction of the overheads compared to state-of-the-art AI-driven "black box" algorithms, namely, Bayesian Optimization (BO) and Deep Distributed Policy Gradient (DDPG). The main reason for RelM's superior performance is that the memory management in modern memory-based data analytics systems is an interplay of algorithms at multiple levels: (i) at the resource-management level across various containers allocated by resource managers like Kubernetes and YARN, (ii) at the container level among the OS, pods, and processes such as the Java Virtual Machine (JVM), (iii) at the application level for caching, aggregation, data shuffles, and application data structures, and (iv) at the JVM level across various pools such as the Young and Old Generation. RelM understands these interactions and uses them in building an analytical solution to autotune the memory management knobs. In another contribution, called Guided-BO (GBO), we use RelM's analytical models to speed up BO. Through an evaluation based on Apache Spark, we showcase that the RelM's recommendations are significantly better than what commonly-used Spark deployments provide, and are close to the ones obtained by brute-force exploration; while GBO provides optimality guarantees for a higher, but still significantly lower cost overhead compared to the state-of-the-art AI-driven policies.
Segmentation-Based Method Combined with Dynamic Programming for Brain Midline Delineation

The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI). The automated midline delineation not only improves the assessment and clinical decision making for patients with stroke symptoms or head trauma but also reduces the time of diagnosis. Nevertheless, most of the previous methods model the midline by localizing the anatomical points, which are hard to detect or even missing in severe cases. In this paper, we formulate the brain midline delineation as a segmentation task and propose a three-stage framework. The proposed framework firstly aligns an input CT image into the standard space. Then, the aligned image is processed by a midline detection network (MD-Net) integrated with the CoordConv Layer and Cascade AtrousCconv Module to obtain the probability map. Finally, we formulate the optimal midline selection as a pathfinding problem to solve the problem of the discontinuity of midline delineation. Experimental results show that our proposed framework can achieve superior performance on one in-house dataset and one public dataset.
From Data Flows to Privacy Issues: A User-Centric Semantic Model for Representing and Discovering Privacy Issues

In today's highly connected cyber-physical world, people are constantly disclosing personal and sensitive data to different organizations and other people through the use of online and physical services. Such data disclosure activities can lead to unexpected privacy issues. However, there is a general lack of tools that help to improve users' awareness of such privacy issues and to make more informed decisions on their data disclosure activities in wider contexts. To fill this gap, this paper presents a novel user-centric, data-flow graph based semantic model, which can show how a given user's personal and sensitive data are disclosed to different entities and how different types of privacy issues can emerge from such data disclosure activities. The model enables both manual and automatic analysis of privacy issues, therefore laying the theoretical foundation of building data-driven and user-centric software tools for people to better manage their data disclosure activities in the cyber-physical world.
Understanding Ecosystem Data

There is a growing body of empirical studies on business ecosystems. Driven by different questions these studies typically employ a wide variety of data sources – ranging from open to proprietary, structured to unstructured – that contain a broad range of entities, relationships, activities, and issues of interest. Individually, these data sources offer the ability to investigate very targeted business ecosystem questions. However, when linked and combined these data sources can potentially open up many new lines of inquiry. The purpose of this study is to provide an overview of the scope and complexity of the business ecosystem data landscape, discuss what type(s) of information is captured in them, identify how data sources overlap and differ, discuss strengths and weaknesses, and suggest new types of analyses that could be generated when combined. In doing so this study aims to help researchers and practitioners with the data identification and selection process and stimulate novel data-driven ecosystem intelligence. The study concludes with theoretical and managerial implications.
Interventions in Primary Care to Promote Breastfeeding: A Systematic Review

Authors' conclusions The Baby Friendly Hospital Initiative is effective in promoting certain health outcomes in infants from Belarus. Whether those findings are applicable to United States primary care is unclear. Indirect evidence suggests that interventions with a component of lay support (e.g., peer support or peer counseling) are more effective than interventions with structured education or professional support in increasing both shortand long-term breastfeeding rate, compared to usual care. Prenatal combined with postnatal interventions are more effective than usual care in prolonging the duration of breastfeeding.
Collaboration Drivers andBreakdowns in Large Scale Software Customization

Software is not developed in a vacuum. Development teams and organizations must react to various incidents, such as changes in personnel, development practices, project objectives, and business environment, and all kinds of delays occurring during large-scale software development projects. These incidents challenge contemporary communication, collaboration and development practices. They also require an ability to adapt to the new situation. We argue that the problems in reacting to those incidents and the inability in implementing corrective actions are the main reasons for software project failures. We identify four drivers of collaboration. They are named as contract, individual person, group of collaborators, and development process, each being appropriate in different points of time. Yet their emphasis and appropriateness vary over time. This emphasizes the developers’ ability to transit between the drivers of collaboration.
Implementation of High-Speed Pseudo-Random-Number Generator with Chaotic and Random Neural Networks

Chaotic and random time series generated from improved chaotic and random neural network (CRNN) afford statistically appropriate pseudo-random number series for information security. Randomness of outputs of CRNN is empirically validated in detail, and control methods of an appropriate ratio of chaotic character and randomness in the time series for PRNG is reported. The rate of random number generation has reached 2.8530×10 12 b/s. In future, the generator may play an important role on implementing applications for protecting personal information on the Internet.
Close Encounters between AMC and MoviePass

Two threads regarding the recent feud between AMC Theaters and MoviePass have been quite popular in the news cycle: (i) the impending bankruptcy of MoviePass and (ii) the Twitter war between the two players. In this paper, we seek to evaluate if such developments are mere aberrations or if there is any economic rationale behind them. We develop a parsimonious microeconomic model for this purpose that sheds light on the competition/cooperation responses of a theater chain towards the entry of a non-essential intermediary. Our results have important implications for management and policy.
IT Risk Factor Disclosure and Stock Price Crashes

As firms are increasingly more dependent on Information Technology (IT) for their business strategies and value creation activities, risks associated with IT become one of the top concerns for corporate boards and managers. This study examines the impact of IT-related risk factor disclosure in Item 1A of the 10-K annual report on stock price crashes. We use Latent Dirichlet Allocation topic modeling to identify risk categories in risk disclosures between 2006 and 2017. IT risk emerged as one of the key risk categories. We find that IT risk disclosure is positively correlated with a firm’s future stock price crash risk. We further separate IT risk factor disclosures into two categories: IT value risk that relates to a firm’s use of and reliance on information technology for its operations to reach its goals and objectives, and cybersecurity risk that could lead to a loss or leak of data. We find that while the correlation between cyber security risk disclosure and a firm’s future crash risk is significant, IT value risk disclosures do not have a significant correlation.
Explaining Multidimensional Facebook Benefits: A Task-Technology Fit Approach

Facebook has emerged as the most popular Social Network Site (SNS). The literature has studied extensively the factors that explain Facebook usage. Despite this, not equal attention has been devoted to explaining the benefits of this SNS. The few studies have considered impacts as one-dimensional; however, the literature shows that benefits could be conceptualized as a multidimensional construct. Besides, little is known about using the Task-Technology Fit model (TTF) to assess Facebook. In addressing this gap, this study aims to develop and empirically test a model that explains Facebook benefits in a multiple-way using a tasktechnology fit approach. Data collected from 240 Facebook users, analyzed using partial least squares technique (PLS). The results support the model empirically. This research integrates benefits, use, and task-technology fit into a single model to provide a more comprehensive perspective. Also, a multidimensional view allows us to consider both utilitarian and hedonic benefits as dimensions of value that can spawn greater continued use.
Theoretical Underpinnings and Practical Challenges of Crowdsourcing as a Mechanism for Academic Study

Researchers in a variety of fields are increasingly adopting crowdsourcing as a reliable instrument for performing tasks that are either complex for humans and computer algorithms. As a result, new forms of collective intelligence have emerged from the study of massive crowd-machine interactions in scientific work settings as a field for which there is no known theory or model able to explain how it really works. Such type of crowd work uses an open participation model that keeps the scientific activity (including datasets, methods, guidelines, and analysis results) widely available and mostly independent from institutions, which distinguishes crowd science from other crowdassisted types of participation. In this paper, we build on the practical challenges of crowd-AI supported research and propose a conceptual framework for addressing the socio-technical aspects of crowd science from a CSCW viewpoint. Our study reinforces a manifested lack of systematic and empirical research of the symbiotic relation of AI with human computation and crowd computing in scientific endeavors.
Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip Recommendation

Trip planning/recommendation is an important task for a plethora of applications in urban settings (e.g., tourism, transportation, social outings), relying on services provided by Location-Based Social Networks (LBSN). To provide greater context-awareness in trajectory planning, LBSNs combine historical trajectories of users for generating various hand-crafted features—e.g., geo-tags of photos taken by tourists and textual characteristics derived from reviews. Those features are used to learn tourists’ preferences, which are then used to generate a travel plan recommendation. However, many such features are extracted based on prior knowledge or empirical analysis specific to particular datasets, rendering the corresponding solutions not to be generalizable to diverse data sources. Thus, one important question for managing mobility is how to learn an accurate tour planning model based solely on POI visits or user check-ins and without the efforts of hand-crafted feature engineering. Inspired by recent successes of deep learning in sequence learning, we develop a solution to the tour planning problem based on the semi-supervised learning paradigm. An important aspect of our solution is that it does not involve any feature engineering. Specifically, we propose the Trip Recommendation method via trajectory Encoder and Decoder—a novel end-to-end approach encoding historical trajectories into vectors, while capturing both the intrinsic characteristics of individual POIs and the transition patterns among POIs. We also incorporate historical attention mechanism in our sequence-to-sequence trip recommendation task to improve the effectiveness. Experiments conducted on multiple publicly available LBSN datasets demonstrate significantly superior performance of our method.
Privacy and Economics An introduction to the mini-track at HICSS’54 2021

The General Data Protection Regulation (GDPR) from ay 2018 was a formal marker – not only for Europe - that personal data needs protection. It illustrates a general agreement that in principle must organizations that collect and manage personal information also protect it from misuse. The protection principle is, however, contrasted with that the digital service industry for years has built up a business model around collecting, analyzing and selling information from private users. This has included both personal data and behavioral data to enhance and target marketing and secure profits Privacy and Economics are therefore two concepts that need to be considered simultaneously. This mini-track discusses the relations between privacy and economics and the challenges to the established business models.
Tourism clusters identification procedure and specifics

This paper intended to show the scope and complexity of the tourism clusters problem. Processes of clusters formation and functioning are of great interest for researchers in Russian Federation and abroad, especially considering governments' activity in this sphere. Key problems are as follows: Which economic activities have to be included in tourism cluster as supplementary? What are the problems of their turnover estimation? What are the criteria of tourism cluster formation? All of them have a long history, but currently are still far from being solved, among other things, we still need to calculate a share of complementary industries' sales and resources to be included in tourism activity and «wild» tourism turnover estimation. We propose to run a specialized survey to assess the economic effects of non-organized tourism to solve these problems. In terms of theory, it remains to be determined, what are criteria for local group of firms in tourism and complementary activities that make them arrange into a cluster considering the economic benefits of geographical concentration in certain location. Widely used procedure of cluster maturity estimation was applied to Russian Federation regions in comparison with world-class tourist clusters in Europe. We got an evidence of biases in cluster identification because of country and type of tourism specifics as well as limitations of estimating procedure. The paper shows that these problems are typical for not only Russian Federation but foreign countries too, including those with well-developed tourism industry. Clusters influence at companies efficiency is at least contradictory both from the side of demand and supply. Tourist product, resources and business processes specifics makes it much harder to extract cluster benefits and gives privileges first to large companies.
Visual Narratives and Collective Memory across Peer-Produced Accounts of Contested Sociopolitical Events

Studying cultural variation in recollections of sociopolitical events is crucial for achieving diverse understandings of such events. To date, most studies in this area have focused on analyzing variation in texts describing events. Here, we analyze variation in image usage across Wikipedia language editions to understand if, like text, visual narratives reflect distinct perspectives in articles about culturally-tethered events. We focus on articles about coup d’états as an example of highly contextual sociopolitical events likely to display such variation. The key challenge to examining variation in images is that there is no existing framework to use as a basis for comparison. To address this challenge, we use an iterative inductive coding process to arrive at a 46-item typology for categorizing the content of images relating to contested sociopolitical events, and a typology of network motifs that characterizes structural patterns of image use. We apply these typologies in a large-scale quantitative analysis that establishes clusters of image themes, two detailed qualitative case studies comparing Wikipedia articles on coup d’états in Soviet Russia and Egypt, and four quantitative analyses clustering image themes by language usage at the article level. These analyses document variation in imagery around particular events and variation in tendencies across cultures. We find substantial cultural variation in both content and network structure. This study presents a novel methodological framework for uncovering culturally divergent perspective of political crises through imagery on Wikipedia.
Integrative East–West Medicine Intervention for Chronic Daily Headache: A Case Report and Care Perspective

Chronic daily headache is a group of headache syndromes including most commonly chronic migraine and chronic tension-type headache, which often overlap, are complicated by medication overuse and are disabling, costly, and variable responsive to western pharmacotherapeutic interventions. There is growing research and awareness of integrative health approaches and therapies to address patients with chronic headache, yet limited examples of how to deliver this approach. This article reviews a commonly seen challenging case of a patient with overlapping chronic migraine and chronic tension-type headache complicated by medication overuse managed with an integrative east–west medicine intervention. This included person-centered biopsychosocial history taking, traditional Chinese medicine informed acupuncture, trigger point injections, and contributing factors modifications. A narrative review of the literature is presented to demonstrate an evidence-informed rationale for incorporating nonpharmacologic approaches to effectively help reduce the symptom burden of this patient population.