Reviewerly/exHarmony huggingface dataset:
A Tight Lower Bound For Non-Coherent Index Erasure.

The index erasure problem is a quantum state generation problem that asks a quantum computer to prepare a uniform superposition over the image of an injective function given by an oracle. We prove a tight Ω(√n) lower bound on the quantum query complexity of the non-coherent case of the problem, where, in addition to preparing the required superposition, the algorithm is allowed to leave the ancillary memory in an arbitrary function-dependent state. This resolves an open question of Ambainis, Magnin, Roetteler, and Roland (CCC 2011), who gave a tight bound for the coherent case, the case where the ancillary memory must return to its initial state.To prove our main result, we first extend the so-called automorphism principle (Hoyer et al. STOC 2007) to the general adversary method for state conversion problems (Lee et al. STOC 2011), which allows one to exploit the symmetries of these problems to lower bound their quantum query complexity. Using this method, we establish a strong connection between the quantum query complexity of non-coherent symmetric state generation problems and the well-known Krein parameters of association schemes. Krein parameters are usually hard to determine, nevertheless, we give a novel way of computing certain Krein parameters of a commutative association scheme defined over partial permutations. We believe the study of this association scheme may also be of independent interest.
A Thermal Camera-based Activity Recognition Using Discriminant Skeleton Features and RNN

Recognizing human activities from sensor data is one of the key areas of image processing, computer vision, and pattern recognition researches today. The target of human activity recognition (HAR) is usually to detect and analyze distinguished activities from the data acquired via different sensors (e.g. thermal cameras). This work proposes a HAR approach from videos recorded via a thermal camera. Skeletons of human bodies are extracted from thermal frames using an opensource deep convolutional neural network (CNN)-based approach named OpenPose. It is generally applied on videos of typical color cameras. However, this work adopts OpenPose on thermal images to extract useful features so that the HAR system can be deployed in environments with low lights as well. Once skeletons of human silhouettes are obtained from the thermal images, robust spatiotemporal features are extracted followed by discriminant analysis. Finally, the discriminant features are fed into a deep recurrent neural network (RNN) for activity training and recognition. The proposed HAR method can be applied to monitor the users such as elderly in both bright and dark environments to prolong their independent life, unlike other typical color cameras which are generally applied in bright environments.
DCMN: Double Core Memory Network for Patient Outcome Prediction with Multimodal Data

More and more healthcare data are becoming readily available nowadays. These data can help the healthcare professionals and patient themselves to better understand the patient status and potentially lead to improved care quality. However, the analysis of these data are challenging because they are large-scale and heterogeneous, high-dimensional and sparse, temporal but irregularly sampled. In this paper, we propose a method called Double Core Memory Networks (DCMN) to integrate information from different modalities of the longitudinal patient data and learn a joint patient representation effective for downstream analytical tasks such as risk prediction. DCMN is designed not only to disentangle the temporal and non-linear intra-modal dependencies for the data within each modality but also to capture the long-term inter-modal interactions. DCMN models are the end-to-end memory networks with two external memory cores where each modality of data is compressed and stored. Each memory core has an information-flow controller named query to interact with an external memory module. In addition, we incorporate a gating mechanism into basic DCMN model to perform dynamic regulation of memory interaction. DCMN models have multiple computational layers (hops) allowing data of different modalities interacting with each other recurrently along with a mechanism of alternating access of external memory for each memory core hop-by-hop. We evaluate DCMN models on two outcome prediction tasks, including a mortality prediction on the public Medical Information Mart for Intensive Care III (MIMIC-III) database and a cost prediction on the Hospital Quality Monitoring System (HQMS) dataset. Experimental results demonstrate that our DCMN models are more competitive over the baseline methods in the multimodal prediction setting.
Measuring user relevance in online debates through an argumentative model

Online debating forums are important social media for people to voice their opinions and engage in debates with each other. Measuring user relevance on these forums can be useful to identify different user profiles or behaviors in online debates, for example, users that tend to participate at the beginning of a debate and whose comments trigger participation, or users that post relevant comments but are not replied too much. To help users to distinguish such different user profiles, we propose graded measures based on users’ influence, the controversy that they generate throughout the debates, their contribution to the polarization of the debates, and their social acceptance, that we extract by analyzing the debates in which the users participate. Our approach is based on an argumentation-based analysis that represents a debate as a valued argumentation framework, in which comments of a debate are arguments, the attack relation between arguments models disagreement between comments, and values for arguments represent the overall support of users for comments. Finally, we test our measures with a sample of users from Reddit debates, identifying four main groups of users, from users with almost no impact on the debate to very active ones with decisive comments for the outcome of the debate.
Computing multiple guiding paths for sampling-based motion planning

Path planning of 3D solid objects leads to search in a six-dimensional configuration space, which can be solved by sampling-based motion planning. The well-known issue of sampling-based planners is the narrow passage problem, which is caused by the presence of small regions of the configuration space that are difficult to cover by random samples. Guided-based planners cope with this issue by increasing the probability of sampling along an estimated solution (a guiding path). In the case of six-dimensional configuration space, the guiding path needs to be computed in the configuration space rather than in the workspace. Fast computation of guiding paths can be achieved by solving a similar, yet simpler problem, e.g., by reducing the size of the robot. This results in an approximate solution (path) that is assumed to be located near the solution of the original problem. The guided sampling along this approximate solution may, however, fail if the approximate solution is too far from the desired solution. In this paper, we cope with this problem by sampling the configuration space along multiple approximate solutions. The approximate solutions are computed using a proposed iterative process: after a path (solution) is found, it forms a region where the subsequent search is inhibited, which boosts the search of new solutions. The performance of the proposed approach is verified in scenarios with multiple narrow passages and compared with the state-of-the-art planners.
Outliers Detection in a Quality Control Procedure for Measurements of Solar Radiation

The correct design of Photovoltaic Systems and Solar Heating Systems should be based on solar irradiance measurements (global, sky-diffuse and direct-beam) acquired on land surface. This paper presents a quality control procedure for global (IG), sky-diffuse (ID) and direct-beam (IB) solar irradiance measurements on horizontal surface, considering the application of a impossible values identification method with predefined thresholds and a statistical techniques for outliers detection (identifying measures suspected to be correct). To evaluate and quantify the occurrence of physically impossible and suspected correct measures, it was also proposed to apply this procedure in six years of measurements (2005 to 2010) of the Solar Radiometry Laboratory of the School of Agricultural Sciences of the São Paulo State University (UNESP) of Botucatu (SP) - Brazil. The procedure was based on the International Commission on Illumination (CIE) fundamental quality control of solar measurements and statistical ranges according to the different sky cover classification (KT). The results indicated that the dataset used for the demonstration contained approximately 1.00%, 1.49% and 1.88% of global, sky-diffuse and direct-beam irradiance values on horizontal surfaces, respectively, flagged as invalid by the procedure. This amount represents measures physically impossible or suspected to be correct (outlier). The techniques for outliers detection, proposed in this paper, in combination with the CIE quality control criteria, should be applied in solar measurements to provide greater security for future investigations pointing the valid and invalid measures.
Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires.
Preservation and Reuse of Historical Seismic Data in Mexico: SISMOMex and the Online “National Seismogram Library”

Abstract The SISMOMex project represents more than 110 yr of seismological information from Mexico. Its objective is the preservation, search, recovery, systematization, reuse, and dissemination of data and information from the seismograms generated by the Servicio Sismológico Nacional (SSN, National Seismological Service of Mexico) and published material about earthquakes and seismology in Mexico. SISMOMex is the combination of resources in any format (print, electronic, multimedia, and so on) and their corresponding registry in a database and an institutional repository. The database and the repository have an interface for an online search, allowing access to open-access materials, through a link to the electronic resources generated or acquired. The project seeks to preserve for the future all the products generated by the SSN since the beginning of its operations, and organize the “National Seismogram Library” (Sismoteca Nacional en Línea), which is the physical place that stores the seismograms generated by the SSN. Similarly, it allows, under citation, the reuse of the data by interested researchers and students. These seismograms contain unique and unreproducible information. With the project presented here, support is provided mainly to the scientific community, by directing the search of historical and present data and information on the seismic activity of Mexico to a single place.
On Scene Flow Computation of Gas Structures with Optical Gas Imaging Cameras

Gas leak inspection and gas leak quantification are nowadays of high relevance within the oil and gas industry as well as in many other industrial sectors. This has been driven by safety-related issues, economic losses and the considerable climate impact caused by such unwanted gas releases. Due to the latter, the efforts for developing new and more reliable measurement techniques for detecting and quantifying greenhouse gases such as methane have increased in the recent years. In this work, a stereo camera system based on optical gas imaging cameras is used for computing dense 3D velocity information, i.e. scene flow, of escaping gas structures. Here, the optical flow, the disparity and the disparity change in likely gas image regions are computed utilizing classical variational methods. The accuracy of the applied methods and their applicability under real conditions in a biogas plant are characterized and tested. The results show that the recovered 3D gas velocity field per camera frame approaches the average 3D velocity field of the measured gas structure. The accuracy of the used method is affected, among others, when the imaged gas structures exhibit a low contrast.
White-box compression: Learning and exploiting compact table representations

We formulate a conceptual model for white-box compression, which represents the logical columns in tabular data as an openly deﬁned function over some actually stored physical columns. Each block of data should thus go accompanied by a header that describes this functional mapping. Because these compression functions are openly deﬁned, database systems can exploit them using query optimization and during execution, enabling e.g. better ﬁlter predicate pushdown. In addition, we show that white-box compression is able to identify a broad variety of new opportunities for compression, leading to much better compression factors. These opportunities are identiﬁed using an automatic learning process that learns the functions from the data. We provide a recursive pattern-driven algorithm for such learning. Finally, we demonstrate the effectiveness of white-box compression on a new benchmark we contribute hereby: the Public BI benchmark provides a rich set of real-world datasets. We believe our basic prototype for white-box compression opens the way for future research into transparent compressed data representations on the one hand and database system architectures that can eﬃciently exploit these on the other, and should be seen as another step into the direction of data management systems that are self-learning and optimize themselves for the data they are deployed on.
Machine Learning for Bitcoin Pricing — A Structured Literature Review

Bitcoin, as the most popular cryptocurrency, has received increasing attention from both investors and researchers over recent years. One emerging branch of the research on bitcoin focuses on empirical bitcoin pricing. Machine learning methods are well suited for predictive problems, and researchers frequently apply these methods to predict bitcoin prices and returns. In this study, we analyze the existing body of literature on empirical bitcoin pricing via machine learning and structure it according to four different concepts. We show that research on this topic is highly diverse and that the results of several studies can only be compared to a limited extent. We further derive guidelines for future publications in the field to ensure a sufficient level of transparency and reproducibility.
Risk Evaluation Using Nominal Group Technique for Cloud Computing Risk Assessment in Healthcare

Emerging of cloud computing with flexibility, improve accessing data, and cost-saving makes this technology accessible and growing fast. As a result of the emergence of cloud computing bring interest to industries to used cloud computing. Although cloud computing brings so many benefits to customers, the previous study reveals that cloud computing penetration in the Healthcare area is still low. With effective cloud risk assessment methodology will gain the confidence to cloud users in this technology. Study in cloud risk assessment methodology still infant and the complexity in identifying security risk still debating. This paper explores the risk assessment process by highlighting the method in the risk evaluation process. Risk evaluation is an essential phase in the risk assessment process. It compares the result from the risk analysis process and determines whether to accept or tolerate the risk criteria to decide on the risk analysis. In this study, the Nominal Group Technique (NGT) is introduced to compare risk analysis results in the earlier phase. Since risk evaluation based on organizational objectives, external and internal context and stakeholders' views, NGT is promising for effective results. This study not only contributing to the prioritizing list of risks and threats in a systematical manner but indirectly NGT process makes stakeholders aware of the current cloud security risk situation in the organization. Equal opportunity expressing views in this focus group discussion is hope can generate a brilliant solution in risk assessment results.
Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency

Abstract Objective While electronic health record (EHR) systems store copious amounts of patient data, aggregating those data across patients can be challenging. Visual analytic tools that integrate with EHR systems allow clinicians to gain better insight and understanding into clinical care and management. We report on our experience building Tableau-based visualizations and integrating them into our EHR system. Materials and Methods Visual analytic tools were created as part of 12 clinician-initiated quality improvement projects. We built the visual analytic tools in Tableau and linked it within our EPIC environment. We identified 5 visual themes that spanned the various projects. To illustrate these themes, we choose 1 exemplary project which aimed to improve obstetric operating room efficiency. Results Across our 12 projects, we identified 5 visual themes that are integral to project success: scheduling &amp; optimization (in 11/12 projects); provider assessment (10/12); executive assessment (8/12); patient outcomes (7/12); and control and goal charts (2/12). Discussion Many visualizations share common themes. Identification of these themes has allowed our internal team to be more efficient and directed in developing visualizations for future projects. Conclusion Organizing visual analytics into themes can allow informatics teams to more efficiently provide visual products to clinical collaborators.
Deep Learning Language Modeling Workloads: Where Time Goes on Graphics Processors

Language Modeling is at the core of many natural language processing tasks. We analyze two such recent models: a Gated Convolutional Network (GCN) with five layers on the Wikitext-2 dataset and a Transformer network with 24 layers on the Google Billion Word dataset. We find that when executed on modern graphics processors, 30% - 40% of the execution time is due to the final adaptive softmax layer. Analytical modeling of the computation and memory demands of the GCN shows that this behavior will persist even if the hidden state is increased - which could be needed to improve accuracy or to support a wider vocabulary. We present variations of the adaptive softmax layer that reduce execution time for the layer by 40% and that scale better with the hidden state.
Turnaround: Exploring Care Relations in Dementia Through Design

People with dementia often experience a lack of social engagement after moving from the home environment into long-term care. Professional caregivers aim to build social relations with residents in dementia care homes to empower and socially include them during everyday care. However, the natural imbalance between the caregiver and the cared-for positions the person with dementia in a passive role, making it difficult for them to initiate and build relations with caregivers. In this paper, we present 'Turnaround' as a design exploration into care relations and the potential role of design to support these relations. Turnaround is a musical interface to facilitate collaborative acts of music-making. Our preliminary results reveal how the role of resident and caregiver shifted towards equal partnership during the exploration and interaction with Turnaround. Furthermore, we argue that technologies should foster partnerships in care activities by facilitating shared forms of expression and reinforcement of agency.
Real-time tracking and mining of users’ actions over social media

With the advent of Web 2.0 technologies and social media, companies are actively looking for ways to know and understand what users think and say about their products and services. Indeed, it has become the practice that users go online using social media like Facebook to raise concerns, make comments, and share recommendations. All these actions can be tracked in real-time and then mined using advanced techniques like data analytics and sentiment analysis. This paper discusses such tracking and mining through a system called Social Miner that allows companies to make decisions about what, when, and how to respond to users? actions over social media. Questions that Social Miner allows to answer include what actions were frequently executed and why certain actions were executed more than others.
Self-Supervised Discovering of Causal Features: Towards Interpretable Reinforcement Learning

Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent's decision-making process is generally not transparent. The lack of interpretability hinders the applicability of RL in safety-critical scenarios. In this paper, we propose a self-supervised interpretable framework, which employs a self-supervised interpretable network (SSINet) to discover and locate fine-grained causal features that constitute most evidence for the agent's decisions. We verify and evaluate our method on several Atari 2600 games as well as Duckietown. The results show that our method renders causal explanations and empirical evidences about how the agent makes decisions and why the agent performs well or badly. Moreover, our method is a flexible explanatory module that can be applied to most vision-based RL agents. Overall, our method provides valuable insight into interpretable vision-based RL.