Reviewerly/exHarmony huggingface dataset:
Dsr: An Accurate Single Image Super Resolution Approach For Various Degradations

Convolution neural networks have achieved unprecedented success for image super resolution (SR). However, such methods typically assume a predetermined degradation that deviates from real-world cases, resulting in poor performance frequently. To improve upon this, researchers have proposed various methods to handle super resolution under multiple degradations. Despite, such methods fail to capture an accurate image prior, which is a crucial part of reconstructing image details. In this work, we propose a novel framework called Decoupled Super Resolver (DSR) with both promising performance and applicability. DSR employs a LR Finer to project a degraded image back to its clean version and a Combinational Super Resolver to retrieve a more comprehensive and accurate prior. The latter module further enables DSR to output high-resolution images by combining both image-specific knowledge and external statistics. Extensive experiments under various degradation settings demonstrate the effectiveness of DSR by setting new state-of-the-arts on multiple benchmarks.
Continuous time hidden Markov model for longitudinal data

Hidden Markov models (HMMs) describe the relationship between two stochastic processes, namely, an observed outcome process and an unobservable finite-state transition process. Given their ability to model dynamic heterogeneity, HMMs are extensively used to analyze heterogeneous longitudinal data. A majority of early developments in HMMs assume that observation times are discrete and regular. This assumption is often unrealistic in substantive research settings where subjects are intermittently seen and the observation times are continuous or not predetermined. However, available works in this direction restricted only to certain special cases with a homogeneous generating matrix for the Markov process. Moreover, early developments have mainly assumed that the number of hidden states of an HMM is fixed and predetermined based on the knowledge of the subjects or a certain criterion. In this article, we consider a general continuous-time HMM with a covariate specific generating matrix and an unknown number of hidden states. The proposed model is highly flexible, thereby enabling it to accommodate different types of longitudinal data that are regularly, irregularly, or continuously collected. We develop a maximum likelihood approach along with an efficient computer algorithm for parameter estimation. We propose a new penalized procedure to select the number of hidden states. The asymptotic properties of the estimators of the parameters and number of hidden states are established. Various satisfactory features, including the finite sample performance of the proposed methodology, are demonstrated through simulation studies. The application of the proposed model to a dataset of bladder tumors is presented.
Improving Neural Chinese Word Segmentation with Lexicon-enhanced Adaptive Attention

Chinese word segmentation (CWS) is an important research topic in information retrieval (IR) and natural language processing (NLP). Significant progresses have been made by deep neural networks with context features. However, these deep models may fail to deal with rare or ambiguous words, thus limit the overall CWS performance. In this paper, we propose a lexicon-enhanced adaptive attention network (LAAN), which takes full advantage of external lexicons to deal with the rare or ambiguous words. Specifically, we devise an adaptive attention mechanism to learn the lexicon-aware representation. In addition, we propose a fusion gate to effectively integrate the additional word information with context information to improve the performance of CWS. LAAN is evaluated on four benchmark datasets, and the experimental results demonstrate that LAAN has robust superiority over the compared methods.
Any-Width Networks

Despite remarkable improvements in speed and accuracy, convolutional neural networks (CNNs) still typically operate as monolithic entities at inference time. This poses a challenge for resource-constrained practical applications, where both computational budgets and performance needs can vary with the situation. To address these constraints, we propose the Any-Width Network (AWN), an adjustable-width CNN architecture and associated training routine that allow for fine-grained control over speed and accuracy during inference. Our key innovation is the use of lower-triangular weight matrices which explicitly address width-varying batch statistics while being naturally suited for multi-width operations. We also show that this design facilitates an efficient training routine based on random width sampling. We empirically demonstrate that our proposed AWNs compare favorably to existing methods while providing maximally granular control during inference.
Analyzing cerebral infarction using support vector machine with artificial bee colony and particle swarm optimization feature selection

Abstract Early diagnosis of cerebral infarction is essential since many patients cannot be cured where the diagnosis is made at an advanced stage. In case an infarct occurs, the tissue in the brain die and stop the circulation of blood, which carries oxygen and nutrients to the body. Therefore, this study uses a machine learning Support Vector Machine (SVM) for early detection of the disorder. To produce the best classification accuracy and fast computing time, feature selection is performed on cerebral infarction data, including Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). After classification, infarction data with the best features are classified using SVM. The classification results of ABC-SVM and PSO-SVM methods are compared with the accuracy of 90.36% for ABC-SVM and 86.74% for PSO-SVM. Therefore, the best approach used in classification is the SVM method with ABC feature selection.
A Fault Detection and Isolation Design for a Dual Pitot Tube Air Data System

We design a fault detection and isolation algorithm using the integrated navigation system approach for detecting and potentially isolating a single Pitot tube failure in the safety-critical air data system used in Unmanned Aerial System (UAS). The design requires two Pitot tubes, one Inertial Measurement Unit (IMU), and a GNSS receiver. The algorithm utilizes two identical synthetic air data systems (SADS) as the bases for estimation. Each SADS works independently with a Pitot tube while sharing the IMU and the Global Navigation Satellite Systems (GNSS) receiver. The detection capability is achieved using residual thresholding techniques, and the isolation functionality is obtained via simple decision logic. This air data fault detection algorithm is designed to meet the integrity and continuity requirements. Both innovation-based and residual-based test statistics using various sliding window sizes are compared to see their effectiveness. A mathematical derivation is presented to show the relationship between the residual-based test and the observability matrix. A new Minimal Detectable Error (MDE) of the airspeed V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> is developed, and protection level calculations for the angle-of-attack a and sideslip ß are derived. A simulation is presented to showcase the air data fault detection and isolation algorithm. Specifically, the impact of the external disturbance, the motion of the dynamics and the airspeed fault on a and ß state errors, and the detection capability are discussed. Furthermore, the protection levels of a and ß are illustrated.
Query-level Early Exit for Additive Learning-to-Rank Ensembles

Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers to investigate algorithms to make faster the traversal of the additive ensemble or to early terminate the evaluation of documents that are unlikely to be ranked among the top-k. In this paper, we investigate the novel problem of query-level early exiting, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble. Besides the obvious advantage on query latency and throughput, we address the possible positive impact on ranking effectiveness. To this end, we study the actual contribution of incremental portions of the tree ensemble to the ranking of the top-k documents scored for a given query. Our main finding is that queries exhibit different behaviors as scores are accumulated during the traversal of the ensemble and that query-level early stopping can remarkably improve ranking quality. We present a reproducible and comprehensive experimental evaluation, conducted on two public datasets, showing that query-level early exiting achieves an overall gain of up to 7.5% in terms of [email protected] with a speedup of the scoring process of up to 2.2x.
Dual Relations Network for Collaborative Filtering

Collaborative filtering (CF) is one of the most effective and popular recommendation methods. Deep learning has achieved great success in various tasks and extensive work has applied deep learning into collaborative filtering. However, existing deep learning based CF methods only capture either user-item relations (users' affection for target items) or item-item relations (association between target items and users' historical items), which may lead to sub-optimal recommendation accuracy. In this paper, we propose deep dual relations network, a neural network based approach that can model user-item relations and item-item relations simultaneously. Furthermore, considering the fact that a historical item a user prefers will have a more positive impact on his decision on a new item, user-item relations should play an important role in item-item relations modeling, which is not investigated by previous work. To solve this problem, we propose a special architecture, named affection-based attention network to capture the impact of user-item relations on item-item relations modeling. Comprehensive experimental results on four real-world datasets demonstrate the effectiveness of our proposed model.
Deep Learning for massive MIMO: Challenges and Future prospects

The wireless networks today are complex, massive and have dynamic capacity demands. Increase in demand resulted into trouble in managing and monitoring the network components. Thus, intelligent data-driven designs and approaches are required so that the 5th generation (5G) of mobile systems can be reformed for enabling self-organizing capabilities. Thus, in the last decade, mathematical models are designed and adapted among modems. This paper presents a comprehensive outline of the emerging research on deep learning-based models for massive MIMO systems. In most of the work, Deep learning models are used for redesigning the conventional communication system. It may involve channel encoding, decoding, detection, recognition, antenna selection, modulation, etc. It will be interesting to comprehend that replacement of the communication system with a profoundly new architecture such as deep learning based autoencoder, convolutional neural network, etc. These Deep learning-based models show promising performance enhancements with a few limitations and can be efficiently used with massive MIMO.
On the Distribution of Minima in Intrinsic-Metric Rotation Averaging

Rotation Averaging is a non-convex optimization problem that determines orientations of a collection of cameras from their images of a 3D scene. The problem has been studied using a variety of distances and robustifiers. The intrinsic (or geodesic) distance on SO(3) is geometrically meaningful; but while some extrinsic distance-based solvers admit (conditional) guarantees of correctness, no comparable results have been found under the intrinsic metric. In this paper, we study the spatial distribution of local minima. First, we do a novel empirical study to demonstrate sharp transitions in qualitative behavior: as problems become noisier, they transition from a single (easy-to-find) dominant minimum to a cost surface filled with minima. In the second part of this paper we derive a theoretical bound for when this transition occurs. This is an extension of the results of [24], which used local convexity as a proxy to study the difficulty of problem. By recognizing the underly- ing quotient manifold geometry of the problem we achieve an n-fold improvement over prior work. Incidentally, our analysis also extends the prior l2 work to general lp costs. Our results suggest using algebraic connectivity as an indicator of problem difficulty.
Cloud SaaS Security Issues and Challenges

The World and the business is growing with data. The requirements about data and data management are also evolving every year. Most of the cybersecurity solutions and tools already have Cloud-based alternative or are only available as a Service. This trend can contain several benefits and disadvantages, but with the necessary preparations, a welldesigned and well-managed system can highlight the positive factors. This paper examines the current Software as a Service situation focusing on the Cybersecurity.
Unsupervised extractive multi-document text summarization using a genetic algorithm

The task of Extractive Multi-Document Text Summarization (EMDTS) aims at building a short summary with essential information from a collection of documents. In this paper, we propose an EMDTS method using a Genetic Algorithm (GA). The fitness function considering two unsupervised text features: sentence position and coverage. We propose the binary coding representation, selection, crossover, and mutation operators. We test the proposed method on the DUC01 and DUC02 data set, four different tasks (summary lengths 200 and 400 words), for each of the collections of documents (in total, 876 documents) are tested. Besides, we analyze the most frequently used methodologies to summarization. Moreover, different heuristics such as topline, baseline, baseline-random, and lead baseline are calculated. In the results, the proposed method achieves to improve the state-of-art results.
Handling and measuring inconsistency in non-monotonic logics

We address the issue of quantitatively assessing the severity of inconsistencies in non-monotonic frameworks. While measuring inconsistency in classical logics has been investigated for some time now, taking the non-monotonicity into account poses new challenges. In order to tackle them, we focus on the structure of minimal strongly K-inconsistent subsets of a knowledge base K—a sound generalization of minimal inconsistent subsets to arbitrary, possibly non-monotonic, frameworks which induces a generalization of Reiter's famous hitting set duality between minimal inconsistent and maximal consistent subsets of a knowledge base. We propose measures based on this notion and investigate their behavior in a non-monotonic setting by revisiting existing rationality postulates, analyzing the compliance of the proposed measures with these postulates, and by investigating their computational complexity. Motivated by the observation that a knowledge base of a non-monotonic logic can also be repaired by adding formulas – whereas Reiter's duality is only concerned about removing –, we also investigate situations where we are given potential additional assumptions to repair a knowledge base. For this, we characterize the minimal modifications to a knowledge base in terms of a hitting set duality
Enhancing Generic Segmentation With Learned Region Representations

Deep learning approaches to generic (non-semantic) segmentation have so far been indirect and relied on edge detection. This is in contrast to semantic segmentation, where DNNs are applied directly. We propose an alternative approach called Deep Generic Segmentation (DGS) and try to follow the path used for semantic segmentation. Our main contribution is a new method for learning a pixel-wise representation that reflects segment relatedness. This representation is combined with a CRF to yield the segmentation algorithm. We show that we are able to learn meaningful representations that improve segmentation quality and that the representations themselves achieve state-of-the-art segment similarity scores. The segmentation results are competitive and promising.
Two-branch fusion network with attention map for crowd counting

Predicting the number and distribution of people in a crowded image is challenging work, due to the scale variations and complex background. In this work, we propose a two-branch fusion model with attention map to deal with the above two problems. Compared with previous works, our method designs a network focusing on fusing density maps which generated from different branches, instead of fusing features which captured from different scales. Two paralleled branches are followed with first ten layers of VGG16 model to extract different scales features from input image. And a dynamic weighting strategy is used to fuse the density maps generated from the different branches to get the final high quality density map. In addition, our model adopts attention mechanism to modify the density map of each branch. Our method has been evaluated on different datasets (ShanghaiTech, WorldExpo’10, UCF-QRNF and UCF_CC_50). The results present that our model has lower error than many other methods on these four datasets. The source code is available at https://github.com/jiezishu737/two_branches_code.
A METHODOLOGY TO IDENTIFY AND ADDRESS IMPROVEMENT POTENTIALS IN COMMUNICATION PROCESSES OF DISTRIBUTED PRODUCT DEVELOPMENT – AN INITIAL APPROACH

Abstract The advantages of distributed development teams help companies to address megatrends like globalization and individualization. However, development teams are facing challenges according to increasing requirements on communication processes. This approach provides a methodology to identify and address improvement potentials in communication processes of distributed product development by including the dimensions technology, organization and human involved in the development process. The validation of the methodology's process steps was carried out together with a machine tool manufacturer.
Is the Uplink Enough? Estimating Video Stalls from Encrypted Network Traffic

Today’s traffic projections speak of almost 58% video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50% encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.
System Error Prediction for Business Support Systems in Telecommunications Networks

Reliability and stability have been treated as the major requirements for the Business Support System (BSS) in telecommunications networks. It is crucial and essential for service providers to maintain good operating state of the BSS. In this article, we aim at system error prediction for a BSS, i.e., we predict occurrences of the abnormal state or behavior of the BSS. Because the occurrences of system errors are rare events in the BSS (i.e., the dataset of system status is highly imbalanced), it is highly challenging to use machine learning or deep learning algorithms to predict system error for the BSS. To address this challenge, we propose a machine learning-based framework for the system error prediction and a Frequency-based Feature Creation (FFC) algorithm to create new features to improve prediction. By adding the time-series information created by the existing features, the proposed FFC can amplify the effects of important features. Our experimental results show that the FFC significantly improves the prediction performance for the Random Forest algorithm.
A General Re-Ranking Method Based On Metric Learning For Person Re-Identification

When Person Re-identification is considered as a retrieval task, re-ranking becomes a critical part of improving the re-identification accuracy. Most of the existing re-ranking methods focus on k -nearest neighbors, which requires a lot of queries and memory. In this paper, we propose a Feature Relation Map based Similarity Evaluation (FRM-SE) model to tackle this problem. The Feature Relation Map is utilized to automatically mine the latent relation between the k -neighbors through convolution operation. The re-ranking distance is learned through the FRM-SE model with metric learning. Further, we optimize the existing re-ranking method to utilize the advantage of the FRM-SE model for maintaining a balance between accuracy and complexity. The proposed approach is validated on two benchmark datasets, Market1501 and CUHK03. Results show that our re-ranking method is superior to the state-of-the-art re-ranking methods. Furthermore, in the transfer learning setting, the model trained on either Market1501 or CUHK03 can achieve a comparable accuracy improvement on the DuekMTMC dataset, which validates the generalization of our SE model.
Can Facial Pose and Expression Be Separated With Weak Perspective Camera?

Separating facial pose and expression within images requires a camera model for 3D-to-2D mapping. The weak perspective (WP) camera has been the most popular choice; it is the default, if not the only option, in state-of-the-art facial analysis methods and software. WP camera is justified by the supposition that its errors are negligible when the subjects are relatively far from the camera, yet this claim has never been tested despite nearly 20 years of research. This paper critically examines the suitability of WP camera for separating facial pose and expression. First, we theoretically show that WP causes pose-expression ambiguity, as it leads to estimation of spurious expressions. Next, we experimentally quantify the magnitude of spurious expressions. Finally, we test whether spurious expressions have detrimental effects on a common facial analysis application, namely Action Unit (AU) detection. Contrary to conventional wisdom, we find that severe pose-expression ambiguity exists even when subjects are not close to the camera, leading to large false positive rates in AU detection. We also demonstrate that the magnitude and characteristics of spurious expressions depend on the point distribution model used to model the expressions. Our results suggest that common assumptions about WP need to be revisited in facial expression modeling, and that facial analysis software should encourage and facilitate the use of the true camera model whenever possible.