Reviewerly/exHarmony huggingface dataset:
Universal upper bounds for Gaussian information capacity

The most natural way to describe an information-carrying system containing a specific noise is an additive white Gaussian-noise (AWGN) channel. In bosonic quantum systems (especially the Gaussian case), although the classical information capacity for a phase-insensitive and thermal-noise channel is additive based on a proof of the minimum output entropy conjecture, several open questions remain. By generalizing the Gaussian noise model from thermal noise to general Gaussian noise, we rigorously revisit and calculate these strong upper bounds on the information capacity for single-mode with general Gaussian-noise channels. In this study, we use the quantum entropy power inequality (QEPI) approach. This framework gives a new formula for finding upper bounds on the information capacity of bosonic Gaussian channels.
Morphing Strategy Design for UAV based on Prioritized Sweeping Reinforcement Learning

This paper proposes an improved deep deterministic policy gradient (DDPG) algorithm in the morphing policy designing for a kind of morphing unmanned aerial vehicles (UAVs) Considering that random selection in reinforcement learning structure is not always an efficient iterative update method, prioritized sweeping approach is introduced into the DDPG-based deep reinforcement learning framework, and the original DDPG algorithm is optimized to avoid random selection of state action pairs (SAPs). Consequently, the efficiency reduction problem in the traditional reinforcement learning structure is weakened. The proposed improved DDPG algorithm has better learning performance and can make reasonable decisions about environmental changes. A simulation experiment is carried out on the designed algorithm. By building a reinforcement learning model of the Markov decision process, the simulation results verify the effectiveness and superiority of the designed algorithm.
Modeling “Equitable and Sustainable Well-being” (BES) Using Bayesian Networks: A Case Study of the Italian Regions

Measurement of well-being has been a highly debated topic since the end of the last century. While some specific aspects are still open issues, a multidimensional approach as well as the construction of shared and well-rooted systems of indicators are now accepted as the main route to measure this complex phenomenon. A meaningful effort, in this direction, is that of the Italian “Equitable and Sustainable Well-being” (BES) system of indicators, developed by the Italian National Institute of Statistics (ISTAT) and the National Council for Economics and Labour (CNEL). The BES framework comprises a number of atomic indicators measured yearly at regional level and reflecting the different domains of well-being (e.g. Health, Education, Work & Life Balance, Environment,...). In this work we aim at dealing with the multidimensionality of the BES system of indicators and try to answer three main research questions: (I) What is the structure of the relationships among the BES atomic indicators; (II) What is the structure of the relationships among the BES domains; (III) To what extent the structure of the relationships reflects the current BES theoretical framework. We address these questions by implementing Bayesian Networks (BNs), a widely accepted class of multivariate statistical models, particularly suitable for handling reasoning with uncertainty. Implementation of a BN results in a set of nodes and a set of conditional independence statements that provide an effective tool to explore associations in a system of variables. In this work, we also suggest two strategies for encoding prior knowledge in the BN estimating algorithm so that the BES theoretical framework can be represented into the network.
JITO: a tool for just-in-time defect identification and localization

In software development and maintenance, defect localization is necessary for software quality assurance. Current defect localization techniques mainly rely on defect symptoms (e.g., bug reports or program spectrum) when the defect has been exposed. One challenge task is: can we locate buggy program prior to the appearance of the defect symptom. Such kind of localization is conducted at an early stage (e.g., when buggy program elements are being checked-in) which can be an early step of continuous quality control.
A Total Variation Denoising Method Based on Median Filter and Phase Consistency

The total variation method is widely used in image noise suppression. However, this method is easy to cause the loss of image details, and it is also sensitive to parameters such as iteration time. In this work, the total variation method has been modified using a diffusion rate adjuster based on the phase congruency and a fusion filter of median filter and phase consistency boundary, which is called the MPC-TV method. Experimental results indicate that MPC-TV method is effective in noise suppression, especially for the removing of speckle noise, and it can also improve the robustness of iteration time of TV method on noise with different variance.
Using Variational Multi-view Learning for Classification of Grocery Items

An essential task for computer vision-based assistive technologies is to help visually impaired people to recognize objects in constrained environments, for instance, recognizing food items in grocery stores. In this paper, we introduce a novel dataset with natural images of groceries-fruits, vegetables, and packaged products-where all images have been taken inside grocery stores to resemble a shopping scenario. Additionally, we download iconic images and text descriptions for each item that can be utilized for better representation learning of groceries. We select a multi-view generative model, which can combine the different item information into lower-dimensional representations. The experiments show that utilizing the additional information yields higher accuracies on classifying grocery items than only using the natural images. We observe that iconic images help to construct representations separated by visual differences of the items, while text descriptions enable the model to distinguish between visually similar items by different ingredients.
Online Alternate Generator Against Adversarial Attacks

The field of computer vision has witnessed phenomenal progress in recent years partially due to the development of deep convolutional neural networks. However, deep learning models are notoriously sensitive to adversarial examples which are synthesized by adding quasi-perceptible noises on real images. Some existing defense methods require to re-train attacked target networks and augment the train set via known adversarial attacks, which is inefficient and might be unpromising with unknown attack types. To overcome the above issues, we propose a portable defense method, online alternate generator, which does not need to access or modify the parameters of the target networks. The proposed method works by online synthesizing another image from scratch for an input image, instead of removing or destroying adversarial noises. To avoid pretrained parameters exploited by attackers, we alternately update the generator and the synthesized image at the inference stage. Experimental results demonstrate that the proposed defensive scheme and method outperforms a series of state-of-the-art defending models against gray-box adversarial attacks.
Computing a longest common almost-increasing subsequence of two sequences

Given a positive constant c, a sequence S=〈s1,s2,…,sk〉 of k numbers is said to be almost increasing if and only if si>max1≤j<i⁡sj−c for all 1<i≤k. A longest common almost-increasing subsequence (LCaIS) between two input sequences is a longest common subsequence that is also an almost increasing sequence. We found out that the existing algorithm proposed by Moosa et al. [1] to find an LCaIS of two sequences without repeated elements gives an incorrect result for some instances. In this work, we present a dynamic programming algorithm that can correctly compute an LCaIS between any two sequences with repeated elements in O(nml) time and O(nm) space, where n and m are the lengths of two input sequences and l is the length of the output LCaIS.
REMOVED: Artificial intelligence for diabetes case management: The intersection of physical and mental health

Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate in a large state-wide population whether we can use artificial intelligence (AI) techniques to identify clusters of patient trajectories within the broader diabetes population in order to create cost-effective, narrowly-focused case management intervention strategies to reduce development of complications. This approach combined data from: 1) claims, 2) case management notes, and 3) social determinants of health from ~300,000 real patients between 2014 and 2016. We categorized complications as five types: Cardiovascular, Neuropathy, Opthalmic, Renal, and Other. Modeling was performed combining a variety of machine learning algorithms, including supervised classification, unsupervised clustering, natural language processing of unstructured care notes, and feature engineering. The results showed that we can predict development of diabetes complications roughly 83.5% of the time using claims data or social determinants of health data. They also showed we can reveal meaningful clusters in the patient population related to complications and mental health that can be used to cost-effective screening program, reducing the number of patients to be screened down by 85%. This study outlines creation of an AI framework to develop protocols to better address mental health comorbidities that lead to complications development in the diabetes population. Future work is described that outlines potential lines of research and the need for better addressing the 'people side' of the equation.
Human-Centric Emotion Estimation Based on Correlation Maximization Considering Changes With Time in Visual Attention and Brain Activity

A human-centric emotion estimation method based on correlation maximization with consideration of changes with time in visual attention and brain activity when viewing images is proposed in this paper. Owing to the recent developments of many kinds of biological sensors, many researchers have focused on multimodal emotion estimation using both eye gaze data and brain activity data for improving the quality of emotion estimation. In this paper, a novel method that focuses on the following two points is introduced. First, in order to reduce the burden on users, we obtain brain activity data from users only in the training phase by using a projection matrix calculated by canonical correlation analysis (CCA) between gaze-based visual features and brain activity-based features. Next, for considering the changes with time in both visual attention and brain activity, we obtain novel features based on CCA-based projection in each time unit. In order to include these two points, the proposed method analyzes a fourth-order gaze and image tensor for which modes are pixel location, color channel and the changes with time in visual attention. Moreover, in each time unit, the proposed method performs CCA between gaze-based visual features and brain activity-based features to realize human-centric emotion estimation with a high level of accuracy. Experimental results show that accurate human emotion estimation is achieved by using our new human-centric image representation.
Transferable Calibration with Lower Bias and Variance in Domain Adaptation

Domain Adaptation (DA) enables transferring a learning machine from a labeled source domain to an unlabeled target one. While remarkable advances have been made, most of the existing DA methods focus on improving the target accuracy at inference. How to estimate the predictive uncertainty of DA models is vital for decision-making in safety-critical scenarios but remains the boundary to explore. In this paper, we delve into the open problem of Calibration in DA, which is extremely challenging due to the coexistence of domain shift and the lack of target labels. We first reveal the dilemma that DA models learn higher accuracy at the expense of well-calibrated probabilities. Driven by this finding, we propose Transferable Calibration (TransCal) to achieve more accurate calibration with lower bias and variance in a unified hyperparameter-free optimization framework. As a general post-hoc calibration method, TransCal can be easily applied to recalibrate existing DA methods. Its efficacy has been justified both theoretically and empirically.
A Turn to Smart Contracts and Future Applications towards Construction Innovation: A Hybrid-Metric Review

Research into smart contracts (SC) over the past decade has increased substantially, with colossal efforts stemming from the computer science and engineering domains. As SC remains a relatively new research paradigm for many areas, efforts from the construction management (CM) field has not been noted yet. This study examines the SC research trend right from the publication on the subject matter until 2018. It focuses on the longitudinal examination of SC research in terms of subject area concentration, and countries, contributions towards SC research and the key sources of SC research. The study also attempts to investigate the efforts made by construction management researchers towards the exploration and application of SC in construction management processes. A comprehensive bibliometric search of SC documents generated a search result of 1,002 publications. A total of 182 publications were examined since only peer-reviewed journal papers were considered for this review study. 2018 recorded the highest number of publications (over 400% increase of the previous year, 2017). China, the U.S., and the U.K. have been the leading countries contributing immensely towards SC research. Despite the tremendous efforts over the past decade of SC research, the results show a significant lack of interdisciplinary collaborations among researchers. Moreover, a huge gap of SC research in construction/project management-related domains was identified, revealing the need for critical explorations on SC research and the applicability of SC during the planning, procuring, executing, and managing construction project-related processes. This study is, therefore, intended to incite a deeper engagement in SC research from construction management (CM) researchers, identifying the benefits and challenges of the application of SC systems in CM processes. Thus, contributing to a deepened understanding of SC in CM.
Deep Learning based Intelligent Recognition Method in Heterogeneous Communication Networks

Friendly signal coexistence problem over unlicensed bands has been received strongly attention in design next-generation wireless communication systems. Typically, it is very challenge to recognize wireless fidelity (WiFi) signal and long-term evolution (LTE) signal over the unlicensed bands (LTE-U) in heterogeneous communication networks. The main reason is that LTE-U may occupy the spectrum resources of WiFi. Hence it is necessary to solve this problem and then to lay the foundation for the friendly coexistence of LTE-U and WiFi technology. In this paper, we proposed a deep learning based intelligent recognition method for identifying LTE-U and WiFi signals in heterogeneous communication networks. First, we collect LTE-U and WiFi signal samples and introduce random phase offset and two data forms to them. Second, we use deep learning algorithms to train these samples to get the best preprocessing method and neural network algorithm parameters. Finally, experiments are conducted to show that our proposed method can efficiently recognize LTE and WiFi signals with excellent recognition accuracy and robustness.
Robust Prediction When Features are Missing

Predictors are learned using past training data which may contain features that are unavailable at the time of prediction. We develop an approach that is robust against outlying missing features, based on the optimality properties of an oracle predictor which observes them. The robustness properties of the approach are demonstrated on both real and synthetic data.
Identifying key developers using artifact traceability graphs

Developers are the most important resource to build and maintain software projects. Due to various reasons, some developers take more responsibility, and this type of developers are more valuable and indispensable for the project. Without them, the success of the project would be at risk. We use the term key developers for these essential and valuable developers, and identifying them is a crucial task for managerial decisions such as risk assessment for potential developer resignations. We study key developers under three categories: jacks, mavens and connectors. A typical jack (of all trades) has a broad knowledge of the project, they are familiar with different parts of the source code, whereas mavens represent the developers who are the sole experts in specific parts of the projects. Connectors are the developers who involve different groups of developers or teams. They are like bridges between teams.
Enabling Efficient Privacy-Assured Outlier Detection Over Encrypted Incremental Data Sets

Outlier detection is widely used in practice to track the anomaly on incremental data sets, such as network traffic and system logs. However, these data sets often involve sensitive information, and sharing the data to third parties for anomaly detection raises privacy concerns. In this article, we present a privacy-preserving outlier detection (PPOD) protocol for incremental data sets. The protocol decomposes the outlier detection algorithm into several phases and recognizes the necessary cryptographic operations in each phase. It realizes several cryptographic modules via efficient and interchangeable protocols to support the above cryptographic operations and composes them in the overall protocol to enable outlier detection over encrypted data sets. To support efficient updates, it integrates the sliding window model to periodically evict the expired data in order to maintain a constant update time. We build a prototype of PPOD and systematically evaluates the cryptographic modules and the overall protocols under various parameter settings. Our results show that PPOD can handle encrypted incremental data sets with a moderate computation and communication cost.
GATAE: Graph Attention-based Anomaly Detection on Attributed Networks

Anomaly detection on attributed network has broad applications in many practical scenarios. Most of existing methods figure out the anomaly detection task by using graph convolution networks to embed the attributed networks. However, these methods will inevitably suffer over-smoothing problems. To approach this problem, in this paper, we propose a graph attention-based autoencoder model. Firstly, we encode the attributed network with a graph attention network. The attention mechanism not only alleviate the over-smoothing problem, but also help encoder learn nodes' representation better. Secondly, we use two decoders to reconstruct the original network and obtain reconstruction errors subsequently. Thus, we are able to detect anomalies by measuring the reconstruction errors. Experiments on real-word datasets show that our proposed model has better performance than other baseline methods in the area under a receiver operating characteristic curve (AUC).
Multi-View Instance Matching with Learned Geometric Soft-Constraints

We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity of neighboring objects, and variability in scale. We propose to turn object instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a Siamese convolutional neural network that learns to match two views of the same object given many candidate image cut-outs. In addition to image features, we propose utilizing location information about the camera and the object to support image evidence via soft geometric constraints. Our method is compared to existing patch matching methods to prove its edge over state-of-the-art. This takes us one step closer to the ultimate goal of city-wide object mapping from street-level imagery to benefit city administration.
The application of the methodology for secure cyber–physical systems design to improve the semi-natural model of the railway infrastructure

The paper suggests a new methodology for secure cyber–physical systems design. The proposed methodology consists of two main cycles. The main goal of the first cycle is in design of the system model, while the second one is about development of the system prototype. The key idea of the methodology is in providing of the most rational solutions that are improving the security of cyber–physical systems. Such solutions are called alternatives and built according to functional requirements and non-functional limitations to the system. Each cycle of the methodology consists of the verification process and seven stages that are associated with the used cyber–physical system model. The objective of the verification process is in checking of constructed models and prototypes in terms of their correctness and compatibility. The model represents cyber–physical systems as sets of building blocks with network between them, takes elements internal structure into account and allows direct and reverse transformations. The novelty of the suggested methodology is in the combination of design, development and verification techniques within a single approach. To provide an example of the design methodology application, in this paper it is used to improve the semi-natural model of the railway infrastructure.
A randomized controlled trial on the effects of embedded computer language switching

Polyglot programming, the use of multiple programming languages during the development process, is common practice in modern software development. This study investigates this practice through a randomized controlled trial conducted under the context of database programming. Participants in the study were given coding tasks written in Java and one of three SQL-like embedded languages. One was plain SQL in strings, one was in Java only, and the third was a hybrid embedded language that was closer to the host language. We recorded 109 valid data points. Results showed significant differences in how developers of different experience levels code using polyglot techniques. Notably, less experienced programmers wrote correct programs faster in the hybrid condition (frequent, but less severe, switches), while more experienced developers that already knew both languages performed better in traditional SQL (less frequent but more complete switches). The results indicate that the productivity impact of polyglot programming is complex and experience level dependent.