Reviewerly/exHarmony huggingface dataset:
Introduction to the Special Section on Security and Privacy of Medical Data for Smart Healthcare

introduction Share on Introduction to the Special Section on Security and Privacy of Medical Data for Smart Healthcare Editors: Amit Kumar Singh National Institute of Technology Patna, India National Institute of Technology Patna, IndiaSearch about this author , Jonathan Wu University of Windsor, Canada University of Windsor, CanadaSearch about this author , Ali Al-Haj Princess Sumaya University for Technology, Jordan Princess Sumaya University for Technology, JordanSearch about this author , Calton Pu Georgia Institute of Technology, USA Georgia Institute of Technology, USASearch about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 3August 2021 Article No.: 53pp 1–4https://doi.org/10.1145/3460870Online:09 June 2021Publication History 3citation88DownloadsMetricsTotal Citations3Total Downloads88Last 12 Months88Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Maximum Weight Trace Alignment Merging Problem

The Maximum Weight Trace (MWT) is an optimization problem for multiple sequence alignment that takes a set of sequences and weights on pairs of letters from different sequences and seeks a multiple sequence alignment that maximizes the sum of the weights for the pairs of letters that appear in the same column. MWT was introduced by Kececioglu in 1993, then proven to be NP-hard, and heuristics and exact solutions for MWT developed. Unfortunately none of the MWT methods are scalable to even moderate-sized datasets. Here we propose the MWT-AM problem (MWT for Alignment Merging), an extension of the MWT problem to be used in a divide-and-conquer setting, where we seek a merged alignment of a set of disjoint alignments that optimizes the MWT score. We present variations of GCM (the Graph Clustering Merger, originally developed for the MAGUS multiple sequence alignment method) that are specifically designed for MWT-AM. We show that the best of these variants, which we refer to as GCM-MWT, perform well for the MWT-AM criterion. We explore GCM-MWT in comparison to other methods for merging alignments, T-coffee and MAFFT–merge, and find that GCM-MWT produces more accurate merged alignments. GCM-MWT is available in open source form at https://github.com/vlasmirnov/MAGUS.
LightBlock: Reducing Bandwidth Required to Synchronize Blocks in Ethereum Network

Recently, with the vigorous development of Decentralized Finance (DeFi) on Ethereum blockchain, the economic value and user scale of Ethereum keep growing. However, the low transaction processing capability of Ethereum prevents its further development. One factor that limits the transaction processing capability is the bandwidth required to synchronize the blocks over the nodes in the Ethereum network. This work proposes a new block synchronization protocol, referred to as LightBlock, which is dedicated to reducing the bandwidth used by the Ethereum network to disseminate messages. The LightBlock protocol is of great significance to the improvement of TPS. Since Ethereum's block synchronization protocol is a kind of gossip protocol, the redundant message propagation is its inherent shortcoming. In the LightBlock protocol, we use transaction Hash to replace the transaction of the original block to form a LightBlock structure. The propagation of LightBlock structure requires a much smaller bandwidth resource than the propagation of the original block has. Moreover, in order to reduce the communication overhead of the new protocol, each node needs to predict the missing transactions of its neighbor nodes before propagating the LightBlock strcuture. Therefore, we set up three models to measure the similarity of the current Ethereum node transaction pool and other parameters. Through analysis, the size of LightBlock is reduced by an average of 83.55% compared to the size of the current block; after adding the function of predicting missing transactions, the probability of not increasing the number of additional communications increases from 0.2 to 0.398.
ControlFlag: a self-supervised idiosyncratic pattern detection system for software control structures

Software debugging has been shown to utilize upwards of half of developers’ time. Yet, machine programming (MP), the field concerned with the automation of software (and hardware) development, has recently made strides in both research and production-quality automated debugging systems. In this paper we present ControlFlag, a self-supervised MP system that aims to improve debugging by attempting to detect idiosyncratic pattern violations in software control structures. ControlFlag also suggests possible corrections in the event an anomalous pattern is detected. We present ControlFlag’s design and provide an experimental evaluation and analysis of its efficacy in identifying potential programming errors in production-quality software. As a first concrete evidence towards improving software quality, ControlFlag has already found an anomaly in CURL that has been acknowledged and fixed by its developers. We also discuss future extensions of ControlFlag.
Fourier Expression of the Quantum Radar Cross Section of a Dihedral Corner Reflector

In this paper, a Fourier expression of the quantum radar cross section (QRCS) of a dihedral corner reflector is proposed, which avoids the problems caused by atomic sampling on the target-object surface and is a powerful tool for analyzing the scattering characteristics of the reflector. We compare the result of the proposed expression with that of numerical calculations, and have preliminarily verified its correctness. On this basis, we discuss the influence of the wavelength of single photon pulses and the panel size of a dihedral corner reflector on QRCS in detail. Simulation results demonstrate that from a single factor of wavelength, a quantum radar using a small-wavelength quantum signal has better visibility to the reflector, and the quantum scattering intensity is proportional to the panel size. Compared with classical radar, quantum radar has an enhanced detection performance that is more stable within a wide angle range, which is insensitive to the wavelength of the quantum signal and panel size of the reflector and is helpful for the detection of stealth targets.
Representation Learning for Fine-Grained Change Detection

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.
Architecture Smells and Pareto Principle: A Preliminary Empirical Exploration

Architecture smells represent violations of best practices recommended for software architecture that adversely impact various quality attributes of a software system. Though architecture quality is considered very important by the software engineering community, architecture refactoring, given involved high risk and effort, is often avoided by software development teams. In this paper, we empirically explore the properties of architecture smells in the context of the Pareto principle. We investigate the degree of adherence of architecture smell occurrences to the Pareto principle and explore the influence of other related factors i.e., programming language and size of the repositories. To this end, we analyzed 750 Java and 361 C# repositories containing more than 50 million lines of code to detect seven kinds of architecture smells. We found that approximately 45% of the Java repositories follow the Pareto principle. Moreover, C# repositories show significantly higher adherence (66%) to the principle than the repositories written in Java. Our results indicate that the size of the repositories shows a low negative correlation with the Pareto categories. The results imply that software development teams can figure out a few vital components suffering from architecture smells by carrying out the Pareto analysis. It will allow them to optimize their efforts towards making their software architecture quality better.
MapReduce Task Scheduling in Heterogeneous Geo-Distributed Data Centers

Different data transmission times, processing times which are difficult to predict and node-dependent access times make MapReduce task scheduling rather complex. In this article, we consider the problem of scheduling MapReduce tasks to heterogeneous geo-distributed data centers to minimize the total tardiness. A new architecture is constructed to analyze data in the considered scenario. We model distinct data transmission levels, inter- and intra- data centers and heterogeneity of nodes mathematically. An algorithm framework is proposed to schedule MapReduce tasks to heterogeneous nodes in geographically distributed data centers. The proposed algorithm is suitable for both Hadoop MRv1 and MRv2. In terms of the number of idle containers detected in each heartbeat, the same number of tasks are selected from a sorted job sequence. For the map and reduce phases, two measurements are developed with data locality and completion time, respectively, based on which the classical Hungarian algorithm is adopted to optimally assign selected tasks to corresponding idle containers. Components and parameters of the proposal are statistically calibrated over a large set of random instances. A comparison of the proposed algorithm to existing methods for similar problems is carried out. Experimental results demonstrate the proposal is effective for the considered problem.
Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation

Interactive (a.k.a. conversational) recommendation systems provide the potential capability to personalize interactions with increasingly prevalent dialog-based AI assistants. In the conversational recommendation setting, a user often has long-term preferences inferred from previous interactions along with ephemeral session-based preferences that need to be efficiently elicited through minimal interaction. Historically, Bayesian preference elicitation methods have proved effective for (i) leveraging prior information to incrementally estimate uncertainty in user preferences as new information is observed, and for (ii) supporting active elicitation of preference feedback to quickly zero in on the best recommendations in a session. Previous work typically focused on eliciting preferences in the space of items or a small set of attributes; in the dialog-based setting, however, we are faced with the task of eliciting preferences in the space of natural language while using this feedback to determine a user’s preferences in item space. To address this task in the era of modern, latent embedding-based recommender systems, we propose a method for coembedding user-item preferences with keyphrase descriptions (i.e., not explicitly known attributes, but rather subjective judgments mined from user reviews or tags) along with a closed-form Bayesian methodology for incrementally estimating uncertainty in user preferences based on elicited keyphrase feedback. We then combine this framework with well-known preference elicitation techniques that can leverage Bayesian posteriors such as Upper Confidence Bounds, Thompson Sampling, and a variety of other methods. Our empirical evaluation on real-world datasets shows that the proposed query selection strategies effectively update user beliefs, leading to high-quality recommendations with a minimal number of keyphrase queries.
Multi-Metric Fusion Network for Image Quality Assessment

With the fast proliferation of multimedia applications, the reliable prediction of image/video quality is urgently needed. Many quality assessment metrics have been proposed in the past decades with various complexity and consistency with human ratings. The metrics are designed from different aspects, e.g., pixel level fidelity, structural similarity, information theory and data-driven. In this paper, we design a Multi-Metric Fusion Network (MMFN) for aggregating the quality scores predicted by diverse metrics to generate more accurate results. To be specific, we utilize the image features extracted from the pretrained network to adaptively rescale the predicted quality from different metrics, and leverage the fully-connected layers to regress a single scalar as the final score. Pairwise images can be further integrated into the training procedure by adding a Score2Prob layer. Experimental results on the validation and test sets demonstrate that our proposed MMFN achieves better prediction accuracy compared with other metrics.
Counting Pop-Stacked Permutations in Polynomial Time

Permutations that can be sorted greedily by one or more stacks having various constraints have been studied by a number of authors. A pop-stack is a greedy stack that must empty all entries whenever popped. Permutations in the image of the pop-stack operator are said to be pop-stacked. Asinowki, Banderier, Billey, Hackl, and Linusson recently investigated these permutations and calculated their number up to length 16. We give a polynomial-time algorithm to count pop-stacked permutations up to a fixed length and we use it to compute the first 1000 terms of the corresponding counting sequence. With the 1000 terms, we apply a pair of computational methods to prove some negative results concerning the nature of the generating function for pop-stacked permutations and to empirically predict the asymptotic behavior of the counting sequence using differential approximation.
Demonic Lattices and Semilattices in Relational Semigroups with Ordinary Composition

Relation algebra and its reducts provide us with a strong tool for reasoning about nondeterministic programs and their partial correctness. Demonic calculus, introduced to model the behaviour of a machine where the demon is in control of nondeterminism, has also provided us with an extension of that reasoning to total correctness. We formalise the framework for relational reasoning about total correctness in nondeterministic programs using semigroups with ordinary composition and demonic lattice operations. We show that the class of representable demonic join semigroups is not finitely axiomatisable and that the representation class of demonic meet semigroups does not have the finite representation property for its finite members. For lattice semigroups (with composition, demonic join and demonic meet) we show that the representation problem for finite algebras is undecidable, moreover the finite representation problem is also undecidable. It follows that the representation class is not finitely axiomatisable, furthermore the finite representation property fails.
Price Prediction of Bitcoin

This work aims to enhance the existing analysis made on bitcoin and predict the price of a Bitcoin by taking some parameters into consideration. After a huge research taking all the parameters which affect the price of the bitcoin value and identified daily changes in the bitcoin market. In this work all the data consists of different features over the past few year's daily records. This work is started by gaining all the information that all are needed to predict the bitcoin price. All the information was collected from the past few years and implemented the data into this work. In this work Support Vector Machine (SVM) algorithm is used as it gives much more accuracy better than previous algorithms. This study predicts sign of change in the price of bitcoin to the investors so that they can invest in this easily and also for the newcomers to this market or business.
User Expectations of Conversational Chatbots Based on Online Reviews

Open-domain chatbots that can engage in a conversation on any topic received significant attention in the last several years, which opened opportunities for studying user interaction with them. Drawing from reviews of chatbots posted on Google Play, we explore user experience and expectations of these agents in a mixed-method study. Results of statistical analysis reveal which social qualities of chatbots are the most significant for user satisfaction. Further, we employ natural language processing and qualitative methods to identify how users wish their chatbots to evolve in the future. While currently users mostly value the entertaining component of their experience, their expectations call for more human-like behavior of chatbots. The most prominent expectations include chatbots' abilities to treat and express emotions and be more attentive to the user. Based on these findings, we conclude with design implications, discussing the directions for developing social skills of open-domain chatbots.
Differentiable Diffusion for Dense Depth Estimation from Multi-view Images

We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view reprojection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians with analytic transmittance. Further, we develop an efficient optimization routine that can simultaneously optimize the 50k+ points required for complex scene reconstruction. We validate our routine using ground truth data and show high reconstruction quality. Then, we apply this to light field and wider baseline images via self supervision, and show improvements in both average and outlier error for depth maps diffused from inaccurate sparse points. Finally, we compare qualitative and quantitative results to image processing and deep learning methods.
Single-image de-raining using joint filter and multi-scale deep alternate-connection dense network

This paper aims at developing an effective approach to remove rain layer from an individual image. The proposed is a two-stage method named JF-MADN consisting of a Joint Filter (JF) extracting high frequency details from rainy image and a Multi-scale deep Alternate-connection Dense Network (MADN) separating rain streaks in a coarse-to-fine manner. In the first stage, rainy image is enhanced first through a high frequency emphasis filter. Next, a gradient operator is deduced based on trigonometric function and Laplacian operator to generate rain gradient prior. By combining it with rain dark channel prior, a rain-prior weighted statistic order filter could be constructed to separate high and low frequency components of rainy image. Then, the result image through a guided filter followed by would be subtracted by the original low frequency component to obtain residuals for final operation with high frequency component to generate high quality initial rain layer. In the second stage, by adopting multi-scale mechanism, this study develops two new alternate-connected fully convolutional DenseNets in different scale, WNet-129 and NNet-96, to implement de-raining. By combining element-wise addition in our alternate-connection structure with original concatenation in fully convolutional DenseNet, features could be constantly connected in our network. Experimental results demonstrate that the proposed framework outperforms existing single-image de-raining algorithms on testing rainy images. Code available at: http://www.imagetech-polynomials.com/derain.html.
ID-Unet: Iterative Soft and Hard Deformation for View Synthesis

View synthesis is usually done by an autoencoder, in which the encoder maps a source view image into a latent content code, and the decoder transforms it into a target view image according to the condition. However, the source contents are often not well kept in this setting, which leads to unnecessary changes during the view translation. Al-though adding skipped connections, like Unet, alleviates the problem, but it often causes the failure on the view conformity. This paper proposes a new architecture by performing the source-to-target deformation in an iterative way. Instead of simply incorporating the features from multiple layers of the encoder, we design soft and hard deformation modules, which warp the encoder features to the target view at different resolutions, and give results to the decoder to complement the details. Particularly, the current warping flow is not only used to align the feature of the same resolution, but also as an approximation to coarsely deform the high resolution feature. Then the residual flow is estimated and applied in the high resolution, so that the deformation is built up in the coarse-to-fine fashion. To better constrain the model, we synthesize a rough target view image based on the intermediate flows and their warped features. The extensive ablation studies and the final results on two different data sets show the effectiveness of the proposed model. https://github.com/MingyuY/Iterative-view-synthesis
RiftNeXt™

We propose a framework, RiftNeXtTM, to perform radio frequency (RF) scene context change detection and classification with Expert driven neural explainability. Our approach uses a deep learning based classifier to perform spectrum monitoring of Wi-Fi devices and usage patterns with an auxiliary classifier operating post-hoc to output human interpretable reasoning for classification declarations. The classification network operates on input spectrograms through a series of dilated causal convolution layers for feature extraction which are fed into classification layers. We have previously shown that dilated causal convolutions are well suited for RF applications, including RF fingerprinting, and extend their use here to new applications. The Explainability Module operates over an auxiliary dataset that is built based on domain expertise for learning how to reason over the classification network outputs. These two approaches, the deep learning classifier and Explainability Module are combined into a unique explainable deep learning approach that we apply to Wi-Fi spectrum monitoring. This paper provides results from this fused approach, leveraging the power of deep learning classification with user interpretable explainability.