Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning-based Rate Adaptation for Adaptive 360-Degree Video Streaming

In this paper, we propose a deep reinforcement learning (DRL)-based rate adaptation algorithm for adaptive 360-degree video streaming, which is able to maximize the quality of experience of viewers by adapting the transmitted video quality to the time-varying network conditions. Specifically, to reduce the possible switching latency of the field of view (FoV), we design a new QoE metric by introducing a penalty term for the large buffer occupancy. A scalable FoV method is further proposed to alleviate the combinatorial explosion of the action space in the DRL formulation. Then, we model the rate adaptation logic as a Markov decision process and employ the DRL-based algorithm to dynamically learn the optimal video transmission rate. Simulation results show the superior performance of the proposed algorithm compared to the existing algorithms.
Ensemble Additive Margin Softmax for Speaker Verification

End-to-end speaker embedding systems have shown promising performance on speaker verification tasks. Traditional end-to-end systems typically adopt softmax loss as training criterion, which is not strong enough for training discriminative models. In this paper, we adapt the additive margin softmax (AM-Softmax) loss, which is originally proposed for face verification, to speaker embedding systems. Furthermore, we propose a novel ensemble loss, called ensemble additive margin softmax (EAM-Softmax) loss, for speaker embedding by integrating Hilbert-Schmidt independence criterion (HSIC) into the speaker embedding system with the AM-Softmax loss. Experiments on a large-scale dataset VoxCeleb show that AM-Softmax loss is better than traditional loss functions, and approaches using EAM-Softmax loss can outperform existing speaker verification methods to achieve state-of-the-art performance.
Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets

This paper addresses the problem of training a deep neural network for satellite image segmentation so that it can be deployed over images whose statistics differ from those used for training. For example, in postdisaster damage assessment, the tight time constraints make it impractical to train a network from scratch for each image to be segmented. We propose a convolutional encoder-decoder network able to learn visual representations of increasing semantic level as its depth increases, allowing it to generalize over a wider range of satellite images. Then, we propose two additional methods to improve the network performance over each specific image to be segmented. First, we observe that updating the batch normalization layers' statistics over the target image improves the network performance without human intervention. Second, we show that refining a trained network over a few samples of the image boosts the network performance with minimal human intervention. We evaluate our architecture over three data sets of satellite images, showing the state-of-the-art performance in binary segmentation of previously unseen images and competitive performance with respect to more complex techniques in a multiclass segmentation task.
Older People Inventing their Personal Internet of Things with the IoT Un-Kit Experience

We introduce the IoT Un-Kit Experience, a co-design approach that engages people in exploring, designing and generating personally meaningful IoT applications and that also serves as a means to explore IoT kit design through in-home workshops. Un-Kit represents a seemingly uncompleted set of sensors, actuators and media elements that have a decontextualized appearance - unfinished state, undefined purpose and unboxed form. The approach emphasises users contemplating and experiencing the IoT elements in their familiar space through detailed and layered conversation with researchers; rather than focusing on connecting up the kit itself, thus their ideas are not constrained by the kit or their competence with it. We illustrate the approach through in-home workshops with older adults, envisioned users of IoT who have had limited voice in its conception. The Un-kit approach supported participants to lead the process and to imagine new artfully integrated designs, with personally legible interactions and aesthetic qualities that fit their desire. We offer insights for a more situated and responsive approach to design of the IoT and its constituent kits.
Deep Manifold Structure Transfer for Action Recognition

While intrinsic data structure in subspace provides useful information for visual recognition, it has not yet been well studied in deep feature learning for action recognition. In this paper, we introduce a new spatio-temporal manifold network (STMN) that leverages data manifold structures to regularize deep action feature learning, aiming at simultaneously minimizing the intra-class variations of learned deep features and alleviating the over-fitting problem. To this end, the manifold prior is imposed from the top layer of a convolutional neural network (CNN), and is propagated across convolutional layers during forward-backward propagation. The observed correspondence of manifold structures in the data space and feature space validates that the manifold priori can be transferred across CNN layers. STMN theoretically recasts the problem of transferring the data structure prior into the deep learning architectures as a projection over the manifold via an embedding method, which can be easily solved by an Alternating Direction Method of Multipliers and Backward Propagation (ADMM-BP) algorithm. STMN is generic in the sense that it can be plugged into various backbone architectures to learn more discriminative representation for action recognition. Extensive experimental results show that our method achieves comparable or even better performance as compared with the state-of-the-art approaches on four benchmark datasets.
TSTSS: A two-stage training subset selection framework for cross version defect prediction

Cross Version Defect Prediction (CVDP) is a practical scenario by training the classification model on the historical data of the prior version and then predicting the defect labels of modules in the current version. Unfortunately, the differences of data distribution across versions may hinder the effectiveness of the trained CVDP model. Thus, it is not trivial to select a suitable training subset from the prior version to promote the CVDP performance. In this paper, we propose a novel method, called Two-Stage Training Subset Selection (TSTSS), to address this challenging issue. In the first stage, TSTSS utilizes a sparse modeling representative selection method to select an initial module subset from the prior version which can well reconstruct the data of the prior version. In the second stage, TSTSS leverages a dissimilarity-based sparse subset selection method to further refine the selected module subset, which enables the selected modules to well represent the modules of the current version. Finally, we use a novel weighted extreme learning machine classifier to construct the CVDP model. We evaluate the CVDP performance of TSTSS on 50 cross-version pairs using 6 indicators. The experiments show that TSTSS can efficiently improve the CVDP performance compared with 11 baseline methods.
An Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing

Virtualization technology, as a key technology in cloud computing, makes the virtual machine placement (VMP) play an important role in improving the energy efficiency of data centers. In this paper, an energy-aware algorithm named GATA is proposed for the VMP problem. It combines the genetic algorithm with the tabu search algorithm. The goal is to obtain an optimal VMP scheme to achieve energy efficiency while maximizing load balance among various resources. The algorithm is compared with two meta-heuristic algorithms and a newly proposed algorithm that is based on ant colony algorithm. The execution time of these algorithms is also discussed. The results show that the proposed algorithm is superior to those methods mentioned above.
Broadening Intellectual Diversity in Visualization Research Papers

Promoting a wider range of contribution types can facilitate healthy growth of the visualization community, while increasing the intellectual diversity of visualization research papers. In this paper, we discuss the importance of contribution types and summarize contribution types that can be meaningful in visualization research. We also propose several concrete next steps we can and should take to ensure a successful launch of the contribution types.
SAR image change detection based on deep denoising and CNN

The intrinsic noise of synthetic aperture radar (SAR) images has a big influence to the image processing performance, especially in change detection (CD). Image denoising is an important branch of image restoration which aims at enhancing the quality of images. The detection accuracy of CD depends greatly on the quality of red difference image (DI), therefore image denoising can be regarded as a vital step in SAR CD. However, few researches focused on this problem. In this study, an end-to-end deep denoising model is first designed to remove the noise of SAR images. With the help of abundant simulated SAR images, deep denoising model is trained effectively to estimate the noise component. Then clean image can be achieved by removing this noise component from the original SAR image. After denoising, the new image pair will generate a clean DI. At last, DI is classified into changed and unchanged areas by a three-layer Convolutional Neural Network (CNN). Three real SAR image pairs demonstrate the effectiveness of the proposed method.
Travelling Salesman Problem Optimization Using Genetic Algorithm

Optimization problem is which mainly focuses on finding feasible solution out of all possible solutions. Travelling salesman problem belongs to this one. As it is not possible to find its solution in definite polynomial time that is why it is considered as one of the NP-hard problem. This paper utilizes the optimization capability of genetic algorithm to find the feasible solution for TSP. The algorithm starts with the calculation of Euclidean distance between the towns to be visited by the salesman. Initial chromosome pool is generated using value encoding. Then best fit chromosomes are selected by applying roulette wheel selection which then goes through m-point crossover. Now we apply interchange mutation on the offsprings generated before. Now this whole process is repeated until the convergence of genetic algorithm.
Residual dense network for intensity-guided depth map enhancement

The depth maps captured by sensors always suffer from low resolution and random noise. Recently, by introducing the guidance from the color image, deep convolutional neural network (DCNN) shows significant improvements for depth map enhancement. However, most DCNN-based methods do not make full use of multi-scale guidance from the color image, thereby achieving sub-optimal performances. In this paper, we propose a novel DCNN to progressively reconstruct the high-resolution depth map guided by the intensity image. Specially, the multi-scale intensity features are extracted to provide guidance for the refinement of depth features as their resolutions are gradually enhanced. Furthermore, local residual learning and global residual learning are adopted in the output of each up-sampling sub-network and the whole network respectively. Such design can recover the high-frequency details from coarse to fine. In addition, according to the contiguous memory mechanism, the dense connections are designed to take the low-level features and the high-level features into account which further exploits the guidance from the intensity image. To balance the resource expense and the performance, the dimension reduction units are used to efficiently represent the features. The proposed network is compared with 17 state-of-the-art methods which shows improved performances.
DT-CNN: Dilated and Transposed Convolution Neural Network Accelerator for Real-Time Image Segmentation on Mobile Devices

A convolution neural network (CNN) accelerator is proposed for real-time image segmentation on mobile devices. The proposed CNN processor cuts down the redundant zero computations in dilated and transposed convolution for higher throughput. As a result, the overall computations of the image segmentation are reduced by 86.6% and the proposed CNN processor boosts up the throughput 6.7×. Moreover, the proposed processor utilizes RoI (Region of Interest) based image segmentation algorithm to reduce the overall computational requirement significantly. Although RoI based image segmentation degrades the image segmentation accuracy, the proposed dilation rate adjustment compensates for the accuracy degradation and maintains the accuracy of the full-size image segmentation. Finally, the proposed CNN processor is simulated in 65 nm CMOS technology, and it occupies 6.8 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The proposed processor consumes 196 mW and shows 211 frames-per-second (fps) at the image segmentation for human body parts.
Curvature Bag of Words Model for Shape Recognition

The object shape recognition of nonrigid transformations and local deformations is a difficult problem. In this paper, a shape recognition algorithm based on the curvature bag of words (CBoW) model is proposed to solve that problem. First, an approximate polygon of the object contour is obtained by using the discrete contour evolution algorithm. Next, based on the polygon vertices, the shape contour is decomposed into contour fragments. Then, the CBoW model is used to represent the contour fragments. Finally, a linear support vector machine is applied to classify the shape feature descriptors. Our main innovations are as follows: 1) A multi-scale curvature integral descriptor is proposed to extend the representativeness of the local descriptor; 2) The curvature descriptor is encoded to break through the limitation of the correspondence relationship of the sampling points for shape matching, and accordingly it forms the feature of middle-level semantic description; 3) The equal-curvature integral ranking pooling is employed to enhance the feature discrimination, and also improves the performance of the middle-level descriptor. The experimental results show that the recognition rate of the proposed algorithm in the MPEG-7 database can reach 98.21%. The highest recognition rates of the Swedish Leaf and the Tools databases are 97.23% and 97.14%, respectively. The proposed algorithm achieves a high recognition rate and has good robustness, which can be applied to the target shape recognition field for nonrigid transformations and local deformations.
Inaction as a Design Decision

This reflective essay documents an attempt to design self-tracking technologies for menopause. This process culminated in the decision to not design. The contribution of this essay is the knowledge produced through reflecting on inaction. From an investigation into current examples, it became clear that applying self-tracking to menopause was fundamentally inappropriate. These technologies were also found to risk resulting in more harm than good; both in essentializing and medicalizing a non-medical process, and in perpetuating notions of the bodily experience of the menopausal transition as a negative experience.
Deep Learning-Based Super-Resolution Reconstruction and Marker Detection for Drone Landing

There have been a significant number of recent studies on autonomous landing in unmanned aerial vehicles (UAVs).Early studies employed a global positioning system (GPS) receivers for this purpose.However, because GPS signals cannot be used in certain urban environments, prior studies used vision-based marker detection.To accurately detect a marker, a high-resolution camera on a drone must obtain a highquality image.This can not only be expensive but also increases the weight of the drone.In general, drones are only equipped with a frontal-viewing and fixed angle camera, and an additional downward-viewing camera becomes necessary for drone landing.Therefore, expensive and weighted high-resolution cameras are not feasible for use on drones.Nevertheless, most previous studies on vision-based drone landing use high-resolution images.To address such limitations, we propose a new method of drone landing using deep learning-based super-resolution reconstruction and marker detection on an image captured by a cost-effective and low-resolution visible light camera.The experimental results on two datasets demonstrate that our method exhibits higher performance than the existing methods in terms of super-resolution reconstruction and marker detection.
MOKBL+MOMs: An interpretable multi-objective evolutionary fuzzy system for learning high-dimensional regression data

This work presents a multi-objective evolutionary linguistic fuzzy system that addresses regression problems, especially those that are dimensional and scalable. A multi-objective knowledge base learning (MOKBL) is developed in the first stage of this model. MOKBL learns the most relevant and least redundant features by considering the desirability of the components of the fuzzy system. At the same time as feature selection, MOKBL slightly tunes the membership functions to provide greater initial adaptation of the fuzzy rule-based system components. In the second stage, multi-objective modifications (MOMs) are organized to modify the generated fuzzy system and to perform post-processing tasks. MOMs more finely tune the membership functions and prune additional rules. The newly proposed rule pruning method can eliminate weak rules from the rule base using the concepts of support and confidence. The membership functions tuning process is accomplished using the tasks of core displacement and width alteration of the symmetric functions. MOKBL+MOMs and its stages were validated using 28 real-world datasets and compared with two state-of-the-art regression solutions through non-parametric statistical tests. The experimental results confirmed the effectiveness of MOKBL+MOMs in terms of interpretability (complexity), accuracy, and time.
Rubber hands in space: the role of distance and relative position in the rubber hand illusion

The rubber hand illusion (RHI) is a perceptual phenomenon in which participants experience ownership over a fake model hand through synchronous visuotactile stimulation. Several studies have shown that the illusion occurs only when both hands are in close proximity to each other. In the present study, we systematically examined the role of relative position (lateral, distal) and distance (13–75 cm) of the model hand (with respect to participants’ real hand) on illusion experience across both lateral and distal positions. Furthermore, we also compared different facets of the subjective illusion experience; the experience of the model hand being part of one’s body (i.e., ownership) and the perceptual fusion of vision and touch (i.e., referral of touch). In two experiments we observed indications for a stronger illusion experiences in distal compared to lateral positions of identical distances, indicating that the illusory effects may vary as a function of the relative position of the hand. Our results also showed that manipulations of distance differently modulated both facets of the illusion. While ownership was restricted to near distances, referral of touch sensations remained stable at farther distances. These results are interpreted in relation to variations in sensory weighting across different planes.
Profit-Sensitive Spatial Scheduling of Multi-Application Tasks in Distributed Green Clouds

An increasing number of organizations choose distributed green data centers (DGDCs) and use their infrastructure resources to deploy and manage multiple applications that flexibly provide services to users around the world in a cost-effective way. The dramatic growth of tasks makes it highly challenging to maximize the total profit of a DGDC provider in a market, where the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs all vary with geographical sites. Different from existing studies, this paper designs a profit-sensitive spatial scheduling (PS3) approach to maximize the total profit of a DGDC provider by smartly scheduling all tasks of multiple applications to meet their response time constraints. PS3 can well utilize such spatial diversity of the above factors. In each time slot, the profit maximization for the DGDC provider is formulated as a constrained nonlinear program and solved by the proposed genetic-simulated-annealing-based particle swarm optimization. Real-life trace-driven simulation experiments demonstrate that PS3 realizes higher total profit and throughput than two typical task scheduling methods. Note to Practitioners-This paper investigates the profit maximization problem for a DGDC provider, while the average response time of all arriving tasks of each application is within their corresponding constraint. Existing task scheduling approaches fail to jointly consider the spatial variations in many factors, including the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs. Consequently, they cannot schedule all tasks of multiple applications within their response time constraints in a profit-sensitive way. In this paper, a profit-sensitive spatial scheduling (PS3) method that tackles the drawbacks of previous approaches is presented. It is achieved by adopting a proposed genetic-simulated-annealing-based particle swarm optimization algorithm that solves a constrained nonlinear program. Simulation experiments prove that compared with two typical scheduling approaches, it increases the total profit and throughput. It can be readily realized and incorporated into real-life industrial DGDCs. The future work should improve the proposed method by analyzing the indeterminacy in green energy and the uncertainty in tasks.
Quantifying and Alleviating the Language Prior Problem in Visual Question Answering

Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years. Although some progress has been achieved so far, several studies have pointed out that current VQA models are heavily affected by the language prior problem, which means they tend to answer questions based on the co-occurrence patterns of question keywords (e.g., how many) and answers (e.g., 2) instead of understanding images and questions. Existing methods attempt to solve this problem by either balancing the biased datasets or forcing models to better understand images. However, only marginal effects and even performance deterioration are observed for the first and second solution, respectively. In addition, another important issue is the lack of measurement to quantitatively measure the extent of the language prior effect, which severely hinders the advancement of related techniques.