Reviewerly/exHarmony huggingface dataset:
Unsupervised Cross-Media Retrieval Using Domain Adaptation With Scene Graph

Existing cross-media retrieval methods are usually conducted under the supervised setting, which need lots of annotated training data. Generally, it is extremely labor-consuming to annotate cross-media data. So unsupervised cross-media retrieval is highly demanded, which is very challenging because it has to handle heterogeneous distributions across different media types without any annotated information. To address the above challenge, this paper proposes Domain Adaptation with Scene Graph (DASG) approach, which transfers knowledge from the source domain to improve cross-media retrieval in the target domain. Our DASG approach takes Visual Genome as the source domain, which contains image knowledge in the form of scene graph. The main contributions of this paper are as follows: First, we propose to address unsupervised cross-media retrieval by domain adaptation. Instead of using the labor-consuming annotated information of cross-media data in the training stage, our DASG approach learns cross-media correlation knowledge from Visual Genome, and then transfers the knowledge to cross-media retrieval through media alignment and distribution alignment. Second, our DASG approach utilizes fine-grained information via scene graph representation to enhance generalization capability across domains. The generated scene graph representation builds (subject$\rightarrow $ relationship$\rightarrow $ object) triplets by exploiting objects and relationships within image and text, which makes the cross-media correlation more precise and promotes unsupervised cross-media retrieval. Third, we exploit the related tasks including object and relationship detection for learning more discriminative features across domains. Leveraging the semantic information of objects and relationships improves cross-media correlation learning for retrieval. Experiments on two widely-used cross-media retrieval datasets, namely Flickr-30K and MS-COCO, show the effectiveness of our DASG approach.
Joint data center cooling and workload management: A thermal-aware approach

Information technology (IT) equipment and cooling infrastructure are key contributors to the total energy expenditure in a data center. There is typically significant power wastage due to inefficient cooling control and thermal-oblivious management of workload. Recent thermal-aware data center management techniques have not taken a unified approach in controlling IT and cooling systems. In this paper, we find that considering thermal effects of server workloads, in conjunction with control parameters of the cooling unit saves more power than optimizing each of them separately. We leverage a low complexity holistic data center model that considers thermal interactions between IT and cooling unit entities. This thermal model provides control decisions with fine-grained control variables. We propose joint cooling and workload management (JCWM), which has the potential to save a considerable amount of power by exploring synergies between the workload scheduler and operational parameters of the cooling unit. In addition, we provide a significant caveat for the power efficiency of server consolidation methods when taking into account associated thermal effects.
Camera calibration using synthetic random speckle pattern and digital image correlation

Accurate camera calibration is of fundamental importance to various vision-based 3D metrological techniques. Despite camera calibration methods using regular planar calibration targets (e.g., checkerboard or circular pattern) have been widely adopted, their accuracy is less-than-desirable due to the limited number and the low registration accuracy of control points. This work presents an alternative calibration method that uses a calibration target based on a synthetic random speckle pattern. Specifically, a set of regularly distributed control points is first specified on the synthetic speckle pattern displayed on a monitor. These control points are then precisely matched to their counterparts on the captured calibration images using the state-of-the-art digital image correlation (DIC) algorithm. Compared with the existing camera calibration methods, the proposed method possesses the advantages of much more effective control points, higher control point match accuracy, which lead to more accurate and precise estimation of camera parameters. To evaluate the performance of the proposed calibration method, simulated calibration tests using images with varied noise levels and real calibration tests were performed on the planar calibration targets using speckle, circular and checkerboard patterns. The influences of various DIC calculation parameters (i.e. the number of control points and the subset size) on calibration results are also studied using noiseless simulated images. The reprojection errors on synthetic noiseless images and real images are computed as 0.004 and 0.038 pixels, respectively, confirming the high calibration accuracy delivered by the proposed method.
DeepRemaster

The remastering of vintage film comprises of a diversity of sub-tasks including super-resolution, noise removal, and contrast enhancement which aim to restore the deteriorated film medium to its original state. Additionally, due to the technical limitations of the time, most vintage film is either recorded in black and white, or has low quality colors, for which colorization becomes necessary. In this work, we propose a single framework to tackle the entire remastering task semi-interactively. Our work is based on temporal convolutional neural networks with attention mechanisms trained on videos with data-driven deterioration simulation. Our proposed source-reference attention allows the model to handle an arbitrary number of reference color images to colorize long videos without the need for segmentation while maintaining temporal consistency. Quantitative analysis shows that our framework outperforms existing approaches, and that, in contrast to existing approaches, the performance of our framework increases with longer videos and more reference color images.
THE EFFECT OF JOB SATISFACTION AND ORGANIZATIONAL COMMITMENT ON NURSES’ PERFORMANCE

The purpose: The purpose of this study was to find the impact of organizational commitment and Job satisfaction on nurses’ job performance.&#x0D; Methodology: The study conducted at the hospital of Ibn al-Athir and employed a quantitative method by structured questionnaires to collect the data. 200 questionnaires were distributed randomly and only 108 valid surveys likewise; the valid questionnaires have been analyzed by SPSS to exam the impact of organizational commitment and job satisfaction on job performance.&#x0D; Findings: the results indicated there is a positive and significant impact of Job satisfaction on nurses' performance. However, the three components of organizational commitment, Affective Commitment, Continuance Commitment, and Normative Commitment have a positive and significant impact on nurse's performance. The findings of this research indicate that employees ' job satisfaction and organizational commitment components play a critical role in nurses' performance.&#x0D; Implications: of this study to enrich the body of literature in the context of Iraq, which is suffering from a lack of studies related to nurses' performance.&#x0D; Novelty: the study provided useful and valuable recommendations to hospitals to increase nurse performance in the context of Iraq.
Compressible FHE with Applications to PIR

Homomorphic encryption (HE) is often viewed as impractical, both in communication and computation. Here we provide an additively homomorphic encryption scheme based on (ring) LWE with nearly optimal rate ($$1-\epsilon $$ for any $$\epsilon >0$$). Moreover, we describe how to compress many Gentry-Sahai-Waters (GSW) ciphertexts (e.g., ciphertexts that may have come from a homomorphic evaluation) into (fewer) high-rate ciphertexts. Using our high-rate HE scheme, we are able for the first time to describe a single-server private information retrieval (PIR) scheme with sufficiently low computational overhead so as to be practical for large databases. Single-server PIR inherently requires the server to perform at least one bit operation per database bit, and we describe a rate-(4/9) scheme with computation which is not so much worse than this inherent lower bound. In fact it is probably less than whole-database AES encryption – specifically about 2.3 mod-q multiplication per database byte, where q is about 50 to 60 bits. Asymptotically, the computational overhead of our PIR scheme is $$\tilde{O}(\log \log \mathsf {\lambda }+ \log \log \log N)$$, where $$\mathsf {\lambda }$$ is the security parameter and N is the number of database files, which are assumed to be sufficiently large.
Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments

Recently unsupervised learning of depth from videos has made remarkable progress and the results are comparable to fully supervised methods in outdoor scenes like KITTI. However, there still exist great challenges when directly applying this technology in indoor environments, e.g., large areas of non-texture regions like white wall, more complex ego-motion of handheld camera, transparent glasses and shiny objects. To overcome these problems, we propose a new optical-flow based training paradigm which reduces the difficulty of unsupervised learning by providing a clearer training target and handles the non-texture regions. Our experimental evaluation demonstrates that the result of our method is comparable to fully supervised methods on the NYU Depth V2 benchmark. To the best of our knowledge, this is the first quantitative result of purely unsupervised learning method reported on indoor datasets.
Significance of Subband Features for Synthetic Speech Detection

In text-to-speech or voice conversion based synthetic speech detection, it is a common practice that spectral information over the entire frequency band is used for feature representation. We propose a new method, referred to as subband transform, that characterizes the signals by subband. It is found that subband transform captures the artifacts in synthetic speech more effectively than full band transform. We propose equal subband transform, octave subband transform, and mel subband transform for three novel features, namely, constant-Q equal subband transform (CQ-EST), constant-Q octave subband transform (CQ-OST) and discrete Fourier mel subband transform (DF-MST). We evaluate the three features on the ASVspoof 2015, noisy ASVspoof 2015 and ASVspoof 2019 logical access corpora. The experiments show that the proposed CQ-EST feature achieves an average equal error rate of 0.056% on ASVspoof 2015 evaluation set. The study observes that the features based on subband transform outperform those based on full band transform under both clean and noisy conditions. In addition, the tandem detection cost function of CQ-OST can reach 0.188 on ASVspoof 2019 logical access evaluation set.
Not All Parts Are Created Equal: 3D Pose Estimation by Modeling Bi-Directional Dependencies of Body Parts

Not all the human body parts have the same degree of freedom (DOF) due to the physiological structure. For example, the limbs may move more flexibly and freely than the torso does. Most of the existing 3D pose estimation methods, despite the very promising results achieved, treat the body joints equally and consequently often lead to larger reconstruction errors on the limbs. In this paper, we propose a progressive approach that explicitly accounts for the distinct DOFs among the body parts. We model parts with higher DOFs like the elbows, as dependent components of the corresponding parts with lower DOFs like the torso, of which the 3D locations can be more reliably estimated. Meanwhile, the high-DOF parts may, in turn, impose a constraint on where the low-DOF ones lie. As a result, parts with different DOFs supervise one another, yielding physically constrained and plausible pose-estimation results. To further facilitate the prediction of the high-DOF parts, we introduce a pose-attribution estimation, where the relative location of a limb joint with respect to the torso, which has the least DOF of a human body, is explicitly estimated and further fed to the joint-estimation module. The proposed approach achieves very promising results, outperforming the state of the art on several benchmarks.
Two-qubit quantum probes for the temperature of an Ohmic environment

We address a particular instance where open quantum systems may be used as quantum probes for an emergent property of a complex system, as the temperature of a thermal bath. The inherent fragility of the quantum probes against decoherence is the key feature making the overall scheme very sensitive. The specific setting examined here is that of quantum thermometry, which aims to exploit decoherence as a resource to estimate the temperature of a sample. We focus on temperature estimation for a bosonic bath at equilibrium in the Ohmic regime (ranging from sub-Ohmic to super-Ohmic), by using pairs of qubits in different initial states and interacting with different environments, consisting either of a single thermal bath or of two independent ones at the same temperature. Our scheme involves pure dephasing of the probes, thus avoiding energy exchange with the sample and the consequent perturbation of temperature itself. We discuss the role of correlations among the probes and the presence of a local versus a global bath. We show that entanglement improves thermometry at short times if the two qubits are embedded in a common bath, whereas if the interaction time is not constrained, then coherence rather than entanglement is the key resource in quantum thermometry.
Fast Depth Prediction and Obstacle Avoidance on a Monocular Drone Using Probabilistic Convolutional Neural Network

Recent studies employ advanced deep convolutional neural networks (CNNs) for monocular depth perception, which can hardly run efficiently on small drones that rely on low/middle-grade GPU(e.g. TX2 and 1050Ti) for computation. In addition, the methods which can effectively and efficiently produce probabilistic depth prediction with a measure of model confidence have not been well studied. The lack of such a method could yield erroneous, sometimes fatal, decisions in drone applications (e.g. selecting a waypoint in a region with a large depth yet a low estimation confidence). This paper presents a real-time onboard approach for monocular depth prediction and obstacle avoidance with a lightweight probabilistic CNN (pCNN), which will be ideal for use in a lightweight energy-efficient drone. For each video frame, our pCNN can efficiently predict its depth map and the corresponding confidence. The accuracy of our lightweight pCNN is greatly boosted by integrating sparse depth estimation from a visual odometry into the network for guiding dense depth and confidence inference. The estimated depth map is transformed into Ego Dynamic Space (EDS) by embedding both dynamic motion constraints of a drone and the confidence values into the spatial depth map. Traversable waypoints are automatically computed in EDS based on which appropriate control inputs for the drone are produced. Extensive experimental results on public datasets demonstrate that our depth prediction method runs at 12Hz and 45Hz on TX2 and 1050Ti GPU respectively, which is 1.8X~5.6X faster than the state-of-the-art methods and achieves better depth estimation accuracy. We also conducted experiments of obstacle avoidance in both simulated and real environments to demonstrate the superiority of our method to the baseline methods.
An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation

This paper studies graph-based recommendation, where an interaction graph is built from historical responses and is leveraged to alleviate data sparsity and cold start problems. We reveal an early summarization problem in previous graph-based models, and propose Neighborhood Interaction (NI) model to capture each neighbor pair (between user-side and item-side) distinctively. NI model is more expressive and captures more complicated structural patterns behind user-item interactions. To enrich the neighborhood information, we also introduce Graph Neural Networks (GNNs) and Knowledge Graphs (KGs) to NI, resulting an end-to-end model, namely Knowledge-enhanced Neighborhood Interaction (KNI). Our experiments on 4 real world datasets show that, compared with state-of-the-art feature-based, meta path-based, and KG-based recommendation models, KNI achieves superior performance in click-through rate prediction (1.1%-8.4% absolute AUC improvements) and outperforms by a wide margin in top-N recommendation.
An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale

Today, the overall goal of energy transition planning is to seek an optimal strategy for increasing the share of renewable sources in existing power networks, such that the growing power demand is satisfied at manageable short/long term investment. In this paper we address the problem of PV penetration in electricity networks, by considering both (1) the spatial issue of site selection and size, and (2) the temporal aspect of hourly load and demand satisfaction, in addition with the investment and maintenance costs to guarantee a viable and reliable solution. We propose to address this spatio-temporal optimization problem through an integrated GIS and robust optimization model, that allows handling of the ubiquitous dependencies between resource and demand time variability and the selection of optimal sites of renewable power generation. Our approach contributes to the integration of the multi-dimensional and combinatorial aspects of this problem, gathering geographical layers (regional or national scale) and temporal packing (hourly time stamp) constraints, and cost functions. This model computes the optimal geographical location and size of PV facilities allowing energy planning targets to be met at minimal cost in a reliable manner. In this paper, we illustrate our approach by studying the penetration of large-scale solar PV in the French Guiana’s power system. Among the results, we show for instance that: (1) our approach performs geographical aggregation with real contextual data, i.e. balances the intermittency of RE sources by spreading out the corresponding installations (location + size) across the territory; (2) the total installed PV capacity can be doubled by removing the 35% penetration limit on intermittent power without exceeding hourly demand; (3) the safest investment scenario is below 30 MW of new PV facilities (≈45 M€ and 2 plants), though it is theoretically possible to install up to 45 MW (>120 M€ and 11 plants).
A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing

Nowadays Network function virtualization (NFV) has drawn immense attention from many cloud providers because of its benefits. NFV enables networks to virtualize node functions such as firewalls, load balancers, and WAN accelerators, conventionally running on dedicated hardware, and instead implements them as virtual software components on standard servers, switches, and storages. In order to provide NFV resources and meet Service Level Agreement (SLA) conditions, minimize energy consumption and utilize physical resources efficiently, resource allocation in the cloud is an essential task. Since network traffic is changing rapidly, an optimized resource allocation strategy should consider resource auto-scaling property for NFV services. In order to scale cloud resources, we should forecast the NFV workload. Existing forecasting methods are providing poor results for highly volatile and fluctuating time series such as cloud workloads. Therefore, we propose a novel hybrid wavelet time series decomposer and GMDH-ELM ensemble method named Wavelet-GMDH-ELM (WGE) for NFV workload forecasting which predicts and ensembles workload in different time-frequency scales. We evaluate the WGE model with three real cloud workload traces to verify its prediction accuracy and compare it with state of the art methods. The results show the proposed method provides better average prediction accuracy. Especially it improves Mean Absolute Percentage Error (MAPE) at least 8% compared to the rival forecasting methods such as support vector regression (SVR) and Long short term memory (LSTM).
A Probabilistic Formulation of Unsupervised Text Style Transfer

We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques. Our probabilistic approach models non-parallel data from two domains as a partially observed parallel corpus. By hypothesizing a parallel latent sequence that generates each observed sequence, our model learns to transform sequences from one domain to another in a completely unsupervised fashion. In contrast with traditional generative sequence models (e.g. the HMM), our model makes few assumptions about the data it generates: it uses a recurrent language model as a prior and an encoder-decoder as a transduction distribution. While computation of marginal data likelihood is intractable in this model class, we show that amortized variational inference admits a practical surrogate. Further, by drawing connections between our variational objective and other recent unsupervised style transfer and machine translation techniques, we show how our probabilistic view can unify some known non-generative objectives such as backtranslation and adversarial loss. Finally, we demonstrate the effectiveness of our method on a wide range of unsupervised style transfer tasks, including sentiment transfer, formality transfer, word decipherment, author imitation, and related language translation. Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach generalizes. Further, we conduct experiments on a standard unsupervised machine translation task and find that our unified approach matches the current state-of-the-art.
Challenges in passenger use of mixed reality headsets in cars and other transportation

Abstract This paper examines key challenges in supporting passenger use of augmented and virtual reality headsets in transit. These headsets will allow passengers to break free from the restraints of physical displays placed in constrained environments such as cars, trains and planes. Moreover, they have the potential to allow passengers to make better use of their time by making travel more productive and enjoyable, supporting both privacy and immersion. However, there are significant barriers to headset usage by passengers in transit contexts. These barriers range from impediments that would entirely prevent safe usage and function (e.g. motion sickness) to those that might impair their adoption (e.g. social acceptability). We identify the key challenges that need to be overcome and discuss the necessary resolutions and research required to facilitate adoption and realize the potential advantages of using mixed reality headsets in transit.