Reviewerly/exHarmony huggingface dataset:
Efficient copyright protection for three CT images based on quaternion polar harmonic Fourier moments

The current zero-watermarking scheme mainly offers the copyright protection to one image. Although the scheme can be used for many images repeatedly, the repeat operation requires a lot of time and large storage space. Using the quaternion polar harmonic Fourier moments (QPHFM), the paper considers three CT images as a whole to construct the key image and proposes a robust zero-watermarking algorithm offering the copyright protection to three CT images simultaneously, improving the efficiency of watermarking system and saving the storage space. The algorithm first considers the three CT images as three imaginary parts of an array of pure quaternion and calculates their QPHFM and then constructs feature image using the amplitude of QPHFM, and finally makes the chaotic scrambling to the feature image and then the XOR operation to the feature image and watermark image to generate key image. Experiment results prove the proposed scheme can resist common image processing attacks and geometric attacks effectively and thus can be well applied to the copyright protection of three images.
Multi-Scale Detector for Accurate Vehicle Detection in Traffic Surveillance Data

The recent research by deep learning has shown many breakthroughs with high performance that were not achieved with traditional machine learning algorithms. Particularly in the field of object detection, commercial products with high accuracy in the real environment are applied through the deep learning methods. However, the object detection method using the convolutional neural network (CNN) has a disadvantage that a large number of feature maps should be generated in order to be robust against scale change and occlusion of the object. Also, simply raising the number of feature maps does not improve performance. In this paper, we propose to integrate additional prediction layers into conventional Yolo-v3 using spatial pyramid pooling to complement the detection accuracy of the vehicle for large scale changes or being occluded by other objects. Our proposed detector achieves 85.29% mAP, which outperformed than those of the DPM, ACF, R-CNN, CompACT, NANO, EB, GP-FRCNN, SA-FRCNN, Faster-R CNN2, HAVD, and SSD-VDIG on the UA-DETRAC benchmark data-set consisting of challenging real-world-traffic videos.
Robust Architecture for Programmable Universal Unitaries

The decomposition of large unitary matrices into smaller ones is important, because it provides ways to realization of classical and quantum information processing schemes. Today, most of the methods use planar meshes of tunable two-channel blocks, however, the schemes turn out to be sensitive to fabrication errors. We study a novel decomposition method based on multi-channel blocks. We have shown that the scheme is universal even when the block`s transfer matrices are chosen at random, making it virtually insensitive to errors. Moreover, the placement of the variable elements can be arbitrary, so that the scheme is not bound to specific topologies. Our method can be beneficial for large-scale implementations of unitary transformations by techniques, which are not of wide proliferation today or yet to be developed.
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation

In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmentation. Enforcing label consistency across the matches makes the final segmentation algorithm robust to seasonal changes. We describe how these 2D-2D matches can be generated with little human interaction by geometrically matching points from 3D models built from images. Two cross-season correspondence datasets are created providing 2D-2D matches across seasonal changes as well as from day to night. The datasets are made publicly available to facilitate further research. We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions.
Introducing Care 4.0: An Integrated Care Paradigm Built on Industry 4.0 Capabilities

Western developed health and care policy is shifting from a patriarchal medical model to a co-managed and integrated approach. Meanwhile, the fourth industrial revolution (Industry 4.0) is transforming manufacturing in line with the digital consumer revolution. Digital health and care initiatives are beginning to use some of the same capabilities to optimize healthcare provision. However, this is usually limited to self-management as part of an organization-centric delivery model. True co-management and integration with other organizations and people is difficult because it requires formal care organizations to share control and extend trust. Through a co-design lens, this paper discusses a more person-centered application of Industry 4.0 capabilities for care. It introduces 'Care 4.0', a new paradigm that could change the way people develop digital health and care services, focusing on trusted, integrated networks of organizations, people and technologies. These networks and tools would help people co-manage and use their own assets, in the context of their own care circle and community. It would enable personalized services that are more responsive to care needs and aspirations, offering preventative approaches that ultimately create a more flexible and sustainable set of integrated health and social care services that support meaningful engagement and interactions.
Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Summary Cloud computing introduced a new paradigm in IT industry by providing on‐demand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state‐of‐the‐art multiobjective VM placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.
A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: Performance evaluation

Cloud environments have been defined in order to provide the effective services and have different advantages but there are encounters some challenges such as load balancing. This paper presents a hybrid of firefly and Improved Particle Swarm Optimization (IPSO) algorithms in order to reach the better average load for making and improving the important metrics such as effective resource utilization and the response time of tasks respectively. This research has been provided some indicators for evaluating the performance of proposed hybrid method too. The results presented the better performance other than similar methods as well as flexible behavior in average load minimization through multi objectives optimization.
A Short Review on Different Clustering Techniques and Their Applications

In modern world, we have to deal with huge volumes of data which include image, video, text and web documents, DNA, microarray gene data, etc. Organizing such data into rational groups is a critical first step to draw inferences. Data clustering analysis has emerged as an effective technique to accurately accomplish the task of categorizing data into sensible groups. Clustering has a rich association with researches in various scientific domains. One of the most popular clustering algorithms, k-means algorithm was proposed as early as 1957. Since then, many clustering algorithms have been developed and used, to group data in various commercial and non-commercial sectors alike. In this paper, we have given concise description of the existing types of clustering approaches followed by a survey of the fields where clustering analytics has been effectively employed in pattern recognition and knowledge discovery.
Directional statistics-based deep metric learning for image classification and retrieval

L2-normalization is an effective method to enhance the discriminant power of deep representation learning. However, without exploiting the geometric properties of the feature space, the generally used gradient based optimization methods are failed to track the global information during training. In this paper, we propose a novel deep metric learning model based on the directional distribution. By defining the loss function based on the von Mises–Fisher distribution, we propose an effective alternative learning algorithm by periodically updating the class centers. The proposed metric learning not only captures the global information about the embedding space but also yields an approximate representation of the class distribution during training. Considering classification and retrieval tasks, our experiments on benchmark datasets demonstrate an improvement from the proposed algorithm. Particularly, with a small number of convolutional layers, a significant accuracy upsurge can be observed compared to widely used gradient-based methods.
Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach

Due to the rate adaptation in hypertext transfer protocol adaptive streaming, the video quality delivered to the client keeps varying with time depending on the end-to-end network conditions. Moreover, the varying network conditions could also lead to the video client running out of the playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). Hence, it is important to quantify the perceptual QoE of the streaming video users and to monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Toward this end, we present long short-term memory (LSTM)-QoE, a recurrent neural network-based QoE prediction model using an LSTM network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time-varying QoE. Based on an evaluation over several publicly available continuous QoE datasets, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides an excellent performance across these datasets. Furthermore, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for the QoE prediction.
Deep Private-Feature Extraction

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a user's device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy trade-off. We then implement and evaluate the performance of DPFEon smartphones to understand its complexity, resource demands, and efficiency trade-offs. Our results on benchmark image datasets demonstrate that under moderate resource utilization, DPFE can achieve high accuracy for primary tasks while preserving the privacy of sensitive information.
Gradient Coding From Cyclic MDS Codes and Expander Graphs

Gradient coding is a technique for straggler mitigation in distributed learning. In this paper we design novel gradient codes using tools from classical coding theory, namely, cyclic MDS codes, which compare favorably with existing solutions, both in the applicable range of parameters and in the complexity of the involved algorithms. Second, we introduce an approximate variant of the gradient coding problem, in which we settle for approximate gradient computation instead of the exact one. This approach enables graceful degradation, i.e., the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> error of the approximate gradient is a decreasing function of the number of stragglers. Our main result is that normalized adjacency matrices of expander graphs yield excellent approximate gradient codes, which enable significantly less computation compared to exact gradient coding, and guarantee faster convergence than trivial solutions under standard assumptions. We experimentally test our approach on Amazon EC2, and show that the generalization error of approximate gradient coding is very close to the full gradient while requiring significantly less computation from the workers.
How Do Engineers Perceive Difficulties in Engineering of Machine-Learning Systems? - Questionnaire Survey

There is increasing interest in machine learning (ML) techniques and their applications in recent years. Although there has been intensive support by frameworks and libraries for the implementation of ML-based systems, investigation into engineering disciplines and methods is still at the early phase. The most pressing issue in this field is identifying the essential challenges for the software engineering research community as engineering of ML-based systems requires novel approaches due to the essentially different nature of ML-based systems. In this paper, we analyze the results of a questionnaire administered to 278 people who have worked on ML-based systems in practice, clarify the essential difficulties and their causes as perceived by practitioners, and suggest potential research directions.
Dynamic Pseudo Label Decoding for Continuous Sign Language Recognition

Continuous sign language recognition is a weakly supervised problem to translate video sequence to sign gloss sequence, where temporal boundary of each sign gloss is not annotated. The CNN-RNN-CTC framework shows effectiveness in this task by estimating pseudo label for each clip and retraining the feature extractor alternately. The quality of pseudo labels greatly impacts the final performance. In contrast of existing methods which select labels of maximum posterior probability, we propose a dynamic pseudo label decoding method to find a reasonable alignment path via dynamic-programming. Our approach filters out apparently wrong labels and generates pseudo labels which conform to natural word order of sign language. To further boost the performance after iterative optimization, we introduce a temporal ensemble module equipped with BGRU and 1D-CNN to integrate features from different time scales. Experiments on two continuous sign language benchmarks with large vocabulary show the effectiveness of our proposed method.
Weighted aspect-based opinion mining using deep learning for recommender system

The main goal of Aspect-Based Opinion Mining is to extract product's aspects and the associated user opinions from the user text review. Although this serves as vital source information for enhancing rating prediction performance, few studies have attempted to fully utilize it for better accuracy of recommendation systems. Most of these studies typically assign equal weights to all aspects in the opinion mining process, however, in practices; users tend to give different priority on different aspects of the product when reaching overall ratings. In addition, most of the existing methods typically rely on handcrafted, rule-based or double propagation methods in the opinion mining process which are known to be time-consuming and often inclined to errors. This could affect the reliability and performance of the recommender systems (RS). Therefore, in this paper, we propose a weighted Aspect-based Opinion mining using Deep learning method for Recommender system (AODR) that can extract product's aspects and the underlying weighted user opinions from the review text using a deep learning method and then fuse them into extended collaborative filtering (CF) technique for improving the RS. The proposed method is basically comprised of two components: (1) Aspect-based opinion mining module which aims to extract the product aspects from the review text to generate aspect rating matrix. (2) Recommendation generation component that uses tensor factorization (TF) technique to compute weighted aspect ratings and finally infer the overall rating prediction. We evaluate the proposed model in terms of both aspect extraction and recommendation performance. Experiment results on different datasets show that our AODR model achieves better results compared to the baselines.
Online Planning for Target Object Search in Clutter under Partial Observability

The problem of finding and grasping a target object in a cluttered, uncertain environment, target object search, is a common and important problem in robotics. One key challenge is the uncertainty of locating and recognizing each object in a cluttered environment due to noisy perception and occlusions. Furthermore, the uncertainty in localization makes manipulation difficult and uncertain. To cope with these challenges, we formulate the target object search task as a partially observable Markov decision process (POMDP), enabling the robot to reason about perceptual and manipulation uncertainty while searching. To further address the manipulation difficulty, we propose Parameterized Action Partially Observable Monte-Carlo Planning (PA-POMCP), an algorithm that evaluates manipulation actions by taking into account the effect of the robot's current belief on the success of the action execution. In addition, a novel run-time initial belief generator and a state value estimator are introduced in this paper to facilitate the PA-POMCP algorithm. Our experiments show that our methods solve the target object search task in settings where simpler methods either take more object movements or fail.
IoU-Balanced loss functions for single-stage object detection

Single-stage object detectors have been widely applied in computer vision applications due to their high efficiency. However, the loss functions adopted by single-stage detectors hurt the localization accuracy seriously. Firstly, the cross-entropy loss for classification is independent of the localization task and drives all the positive examples to learn as high classification scores as possible regardless of localization accuracy. Thus, there exist many detections with high classification scores but low IoU or detections with low classification scores but high IoU. Secondly, for the smooth L1 loss, the gradient is dominated by the outliers with poor localization accuracy. In this work, IoU-balanced loss functions consisting of IoU-balanced classification loss and IoU-balanced localization loss are proposed to solve these problems. IoU-balanced classification loss pays more attention to positive examples with high IoU and enhances the correlation between classification and localization tasks. IoU-balanced localization loss decreases the gradient of examples with low IoU and increases the gradient of examples with high IoU, which improves the localization accuracy of models. Extensive experiments on MS COCO, PASCAL VOC, Cityscapes and WIDERFace demonstrate that IoU-balanced losses can substantially improve the popular single-stage detectors, especially the localization accuracy. On COCO test-dev, the proposed methods can substantially improve AP by 1.0%∼1.7% and AP75 by 1.0%∼2.4%. On PASCAL VOC, Cityscape and WIDERFace, it can also substantially improve AP by 1.0%∼1.5% and AP80, AP90 by ∼3.9%. The source code will be made publicly available.
Worth a thousand interpersonal words: Emoji as affective signals for relationship-oriented digital communication

Computer-mediated communication (CMC) is pervasive in our lives, influencing social interaction including human courtship. To connect with potential partners via CMC, modern relationship-seekers must master faster and shorter methods of communicating self-disclosure and affect. Although CMC can lack crucial sensory information in this context, emojis may provide useful aid. Across two studies, we assessed attitudes toward and frequency of emoji use, and whether signaling affect via emoji use relates to more romantic and sexual opportunities. Our findings suggest that emoji use with potential partners is associated with maintaining connection beyond the first date, and more romantic and sexual interactions over the previous year. This research provides evidence that emojis convey important affective information to potential partners, and are potentially associated with more successful intimate connection. Implications for multiple theoretical models and methodologies are discussed.
Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework

We develop a general-purpose agent-based simulation and modeling approach to analyze how user–recommender interactions affect recommender systems in the long run. Our explorations show that, over time, user–recommender interactions consistently lead to the longitudinal performance paradox of recommender systems. In particular, users’ reliance on recommendations, while helping users discover relevant items, actually hurts the future diversity of items that are recommended and consumed as well as slows down the system’s learning pace (i.e., the rate of predictive accuracy improvement). We also demonstrate unique benefits of certain hybrid consumption strategies—that is, that take advantage of both popularity- and personalization-based recommendations—in facilitating improvements in consumption relevance over time. Because users’ consumption strategies can significantly influence the longitudinal performance of recommender systems, it is important for designers to analyze the histories of a system’s recommendations and users’ choices to infer and understand users’ consumption strategies. This would enable the system to anticipate users’ consumption behavior and strategically adjust the system’s parameters according to its long-term performance objectives.