Reviewerly/exHarmony huggingface dataset:
Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity

Originated from distributed learning, federated learning enables privacy-preserved collaboration on a new abstracted level by sharing the model parameters only. While the current research mainly focuses on optimizing learning algorithms and minimizing communication overhead left by distributed learning, there is still a considerable gap when it comes to the real implementation on mobile devices. In this article, we start with an empirical experiment to demonstrate computation heterogeneity is a more pronounced bottleneck than communication on the current generation of battery-powered mobile devices, and the existing methods are haunted by mobile stragglers. Further, non-identically distributed data across the mobile users makes the selection of participants critical to the accuracy and convergence. To tackle the computational and statistical heterogeneity, we utilize data as a tuning knob and propose two efficient polynomial-time algorithms to schedule different workloads on various mobile devices, when data is identically or non-identically distributed. For identically distributed data, we combine partitioning and linear bottleneck assignment to achieve near-optimal training time without accuracy loss. For non-identically distributed data, we convert it into an average cost minimization problem and propose a greedy algorithm to find a reasonable balance between computation time and accuracy. We also establish an offline profiler to quantify the runtime behavior of different devices, which serves as the input to the scheduling algorithms. We conduct extensive experiments on a mobile testbed with two datasets and up to 20 devices. Compared with the common benchmarks, the proposed algorithms achieve 2-100× speedup epoch-wise, 2–7 percent accuracy gain and boost the convergence rate by more than 100 percent on CIFAR10.
Efficient human motion prediction using temporal convolutional generative adversarial network

Human motion prediction from its historical poses is an essential task in computer vision; it is successfully applied for human-machine interaction and intelligent driving. Recently, significant progress has been made with variants of RNNs or LSTMs. Despite alleviating the vanishing gradient problem, the chain RNN often leads to deformities and convergence to the mean pose because of its low ability to capture long-term dependencies. To address these problems, in this paper, we propose a temporal convolutional generative adversarial network (TCGAN) to forecast high-fidelity future poses. The TCGAN uses hierarchical temporal convolution to model the long-term patterns of human motion effectively. In contrast to RNNs, the hierarchical convolution structure has recently proved to be a more efficient method for sequence-to-sequence learning in computational complexity, the number of model parameters, and parallelism. Besides, instead of traditional GANs, spectral normalization (SN) is embedded in the model to alleviate mode collapse. Compared with typical recurrent methods, the proposed model is feedforward and can produce the future poses in real-time. Extensive experiments on various human activity analysis benchmarks (i.e., H3.6M, CMU, and 3DPW MoCap) demonstrate that the model consistently outperforms the state-of-the-art methods in terms of accuracy and visualization for short-term and long-term predictions.
Reversible data hiding with automatic contrast enhancement for medical images

Recent developments in data hiding technology have made reversible data hiding (RDH) a popular research topic. This paper is focused on medical images and the up-to-date RDH methods, and proposes an automatic contrast enhancement algorithm called the RDHACEM algorithm considering two metrics: larger embedding capacity and better image visual quality. The proposed algorithm separates medical images into regions of interest (ROI) and regions of non-interest (NROI). It automatically stretches the ROI's grayscale histogram to simultaneously enlarge the embedding capacity of the ROI and enhance the image contrast. Experimental results show that the marked medical images generated by the proposed algorithm have better visual quality and larger embedding capacity in the ROI than those generated by the up-to-date RDH methods.
OnRL

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an "offline learning" paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this work, we close the gap by proposing OnRL, an online RL framework for real-time mobile video telephony. OnRL puts many individual RL agents directly into the video telephony system, which make video bitrate decisions in real-time and evolve their models over time. OnRL then aggregates these agents to form a high-level RL model that can help each individual to react to unseen network conditions. Moreover, OnRL incorporates novel mechanisms to handle the adverse impacts of inherent video traffic dynamics, and to eliminate risks of quality degradation caused by the RL model's exploration attempts. We implement OnRL on a mainstream operational video telephony system, Alibaba Taobao-live. In a month-long evaluation with 543 hours of video sessions from 151 real-world mobile users, OnRL outperforms the prior algorithms significantly, reducing video stalling rate by 14.22% while maintaining similar video quality.
Camera-to-Robot Pose Estimation from a Single Image

We present an approach for estimating the pose of an external camera with respect to a robot using a single RGB image of the robot. The image is processed by a deep neural network to detect 2D projections of keypoints (such as joints) associated with the robot. The network is trained entirely on simulated data using domain randomization to bridge the reality gap. Perspective-n-point (PnP) is then used to recover the camera extrinsics, assuming that the camera intrinsics and joint configuration of the robot manipulator are known. Unlike classic hand-eye calibration systems, our method does not require an off-line calibration step. Rather, it is capable of computing the camera extrinsics from a single frame, thus opening the possibility of on-line calibration. We show experimental results for three different robots and camera sensors, demonstrating that our approach is able to achieve accuracy with a single frame that is comparable to that of classic off-line hand-eye calibration using multiple frames. With additional frames from a static pose, accuracy improves even further. Code, datasets, and pretrained models for three widely-used robot manipulators are made available.
Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction

Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture, these approaches do not generalize well the problem at hand. This paper proposes a novel adversarial training framework to address this shortcoming, based on an adaptation of Generative Adversarial Networks (GANs) to the realm of sequential temporal data. The training works by putting one neural network against the other in a two-player game (hence the "adversarial" nature) which leads to predictions that are indistinguishable from the ground truth. We formally show that the worst-case accuracy of the proposed approach is at least equal to the accuracy achieved in non-adversarial settings. From the experimental evaluation it emerges that the approach systematically outperforms all baselines both in terms of accuracy and earliness of the prediction, despite using a simple network architecture and a naive feature encoding. Moreover, the approach is more robust, as its accuracy is not affected by fluctuations over the case length.
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator

Neural architecture search (NAS) has been very successful at outperforming human-designed convolutional neural networks (CNN) in accuracy, and when hardware information is present, latency as well. However, NAS-designed CNNs typically have a complicated topology, therefore, it may be difficult to design a custom hardware (HW) accelerator for such CNNs. We automate HW-CNN codesign using NAS by including parameters from both the CNN model and the HW accelerator, and we jointly search for the best model-accelerator pair that boosts accuracy and efficiency. We call this Codesign-NAS. In this paper we focus on defining the Codesign-NAS multiobjective optimization problem, demonstrating its effectiveness, and exploring different ways of navigating the codesign search space. For CIFAR-10 image classification, we enumerate close to 4 billion model-accelerator pairs, and find the Pareto frontier within that large search space. This allows us to evaluate three different reinforcement-learning-based search strategies. Finally, compared to ResNet on its most optimal HW accelerator from within our HW design space, we improve on CIFAR-100 classification accuracy by 1.3% while simultaneously increasing performance/area by 41% in just ~1000 GPU-hours of running Codesign-NAS.
Contrastive Adaptation Network for Single- and Multi-Source Domain Adaptation

Unsupervised domain adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To tackle this issue, this paper proposes contrastive adaptation network (CAN) that optimizes a new metric named Contrastive Domain Discrepancy explicitly modeling the intra-class domain discrepancy and the inter-class domain discrepancy. To optimize CAN, two technical issues need to be addressed: 1) the target labels are not available; and 2) the conventional mini-batch sampling is imbalanced. Thus we design an alternating update strategy to optimize both the target label estimations and the feature representations. Moreover, we develop class-aware sampling to enable more efficient and effective training. Our framework can be generally applied to the single-source and multi-source domain adaptation scenarios. In particular, to deal with multiple source domain data, we propose: 1) multi-source clustering ensemble which exploits the complementary knowledge of distinct source domains to make more accurate and robust target label estimations; and 2) boundary-sensitive alignment to make the decision boundary better fitted to the target. Experiments are conducted on three real-world benchmarks (i.e., Office-31 and VisDA-2017 for the single-source scenario, DomainNet for the multi-source scenario). All the results demonstrate that our CAN performs favorably against the state-of-the-art methods. Ablation studies also verify the effectiveness of each key component of our proposed system.
Efficient Hyperparameter Optimization for Convolution Neural Networks in Deep Learning: A Distributed Particle Swarm Optimization Approach

Convolution neural network (CNN) is a kind of powerful and efficient deep learning approach that has obtained great success in many real-world applications. However, due to its complex network structure, the intertwining of hyperparameters, and the time-consuming procedure for network training, finding an efficient network configuration for CNN is a challenging yet tough work. To efficiently solve the hyperparameters setting problem, this paper proposes a distributed particle swarm optimization (DPSO) approach, which can optimize the hyperparameters to find high-performing CNNs. Compared to tedious, historical-experience-based, and personal-preference-based manual designs, the proposed DPSO approach can evolve the hyperparameters automatically and globally to obtain promising CNNs, which provides a new idea and approach for finding the global optimal hyperparameter combination. Moreover, by cooperating with the distributed computing techniques, the DPSO approach can have a considerable speedup when compared with the traditional particle swarm optimization (PSO) algorithm. Extensive experiments on widely-used image classification benchmarks have verified that the proposed DPSO approach can effectively find the CNN model with promising performance, and at the same time, has greatly reduced the computational time when compared with traditional PSO.
Bitcoin: The biggest financial innovation of fourth industrial revolution and a portfolio's efficiency booster

Fourth industrial revolution has brought with it a consistently growing use of technology in all fields of life, including finance and investment. Amidst all this, Bitcoin also emerged on the surface of the financial markets in 2008, as one of the biggest innovations of our century. However, since then, it has largely been scrutinized for the benefits and risks that it puts forth in terms of the economic growth, financial system's stability and the welfare of overall society. In this study, we evaluate how the financial industry can use Bitcoin for improving the efficiency and wealth of the society, by focusing on portfolio optimization. Our results show that Bitcoin has an enormous tendency to improve an investor's risk-return profile. This efficiency is clearly visible in the upward shifts of the efficient frontiers, once Bitcoin is added in the universe of investable assets. Bitcoin's efficiency is valid for when the short selling was, and, was not restricted. It is important to note that our findings are based on the data that spans over a timeframe of a decade, and provides a medium to long term perspective on Bitcoin, which was not the case with the earlier studies.
ARFV: An Efficient Shared Data Auditing Scheme Supporting Revocation for Fog-Assisted Vehicular Ad-Hoc Networks

With the technology of data storage and shared services in the cloud, users can easily share data or revoke from their groups. To ensure the accuracy of the shared data, the key is used for group users to calculate the authenticators of the shared data and audit the data's integrity remotely. However, high-speed mobile terminals have posed challenges to key management in Vehicle Ad-hoc networks (VANETs). Besides, to maintain the integrity of the shared data, the computation and communication overheads must be increased in the revocation process when a vehicle user is removed from a group. For such, a new shared data auditing scheme is proposed in this paper, which supports user revocation by combining the existing certificateless signcryption method with a fog architecture. This scheme improves the efficiency of auditing and user revocation since it reduces many time-consuming operations, as well as features such as public auditing, mutual authentication, efficient and secure revocation are presented. Security analysis and experimental results demonstrate that the proposed scheme is both correct and safe, has more comprehensive functions and better efficiency than other existing ones.
HealthXAI: Collaborative and explainable AI for supporting early diagnosis of cognitive decline

Our aging society claims for innovative tools to early detect symptoms of cognitive decline. Several research efforts are being made to exploit sensorized smart-homes and artificial intelligence (AI) methods to detect a decline of the cognitive functions of the elderly in order to promptly alert practitioners. Even though those tools may provide accurate predictions, they currently provide limited support to clinicians in making a diagnosis. Indeed, most AI systems do not provide any explanation of the reason why a given prediction was computed. Other systems are based on a set of rules that are easy to interpret by a human. However, those rule-based systems can cope with a limited number of abnormal situations, and are not flexible enough to adapt to different users and contextual situations. In this paper, we tackle this challenging problem by proposing a flexible AI system to recognize early symptoms of cognitive decline in smart-homes, which is able to explain the reason of predictions at a fine-grained level. Our method relies on well known clinical indicators that consider subtle and overt behavioral anomalies, as well as spatial disorientation and wandering behaviors. In order to adapt to different individuals and situations, anomalies are recognized using a collaborative approach. We experimented our approach with a large set of real world subjects, including people with MCI and people with dementia. We also implemented a dashboard to allow clinicians to inspect anomalies together with the explanations of predictions. Results show that our system’s predictions are significantly correlated to the person’s actual diagnosis. Moreover, a preliminary user study with clinicians suggests that the explanation capabilities of our system are useful to improve the task performance and to increase trust. To the best of our knowledge, this is the first work that explores data-driven explainable AI for supporting the diagnosis of cognitive decline.
Product Decomposition of Periodic Functions in Quantum Signal Processing

We consider an algorithm to approximate complex-valued periodic functions $f(e^{i\theta})$ as a matrix element of a product of $SU(2)$-valued functions, which underlies so-called quantum signal processing. We prove that the algorithm runs in time $\mathcal O(N^3 \mathrm{polylog}(N/\epsilon))$ under the random-access memory model of computation where $N$ is the degree of the polynomial that approximates $f$ with accuracy $\epsilon$; previous efficiency claim assumed a strong arithmetic model of computation and lacked numerical stability analysis.
Developing teachers’ digital identity: towards the pedagogic design principles of digital environments to enhance students’ learning in the 21st century

Digitalisation provides valuable opportunities for learning; however, it imposes demands on teachers. Teachers are expected not only to be profound users of educational technologies but also to engage in the design of digital environments such as online courses, learning management systems, and mobile applications. This article argues that originated in cultural-historical traditions, Galperin’s pedagogical theory might offer an approach to outline the pedagogic design principles of digital environments to empower teachers to develop their digital identity, enhance students’ learning and their development as learners. Two empirical snapshots are presented to exemplify the use of Galperin’s theory to design assignments and modules in digital learning environments. By engaging in learning and design of digital environments based on the suggested design principles, teachers and students may reposition themselves as active agents in knowledge practices to nurture teacher digital identity and enhance students’ capacity in learning to learn.
Single-image deblurring with neural networks: A comparative survey

Neural networks (NNs) are becoming the tool of choice for sharpening blurred images. We discuss and categorize deblurring NNs. Then we evaluate seven NNs for non-blind deblurring (NBD), and seven NNs and four optimization techniques for blind deblurring (BD). To do this we use several current datasets containing pairs of sharp and blurred images, synthesized either by convolving sharp images with blur kernels or by averaging consecutive sharp images, so as to produce both uniform and non-uniform blurs. We also introduce a newly reorganized benchmark dataset in which blurred images have been classified using attributes that depend on the extent of the blur. We use this dataset to compare the effectiveness of single and multi-scale training in coping with large blurs. On NBD, NNs that use regularization with a denoising prior network outperform other denoising NNs; and NNs that use a deep image prior network outperform other deconvolution NNs. On BD, NNs outperform optimizations in signal-difference terms, but not in terms of perceptual fidelity. We found that multi-scale training helps NNs to deal with large blurs, and RNNs outperform CNNs. We also observed that GANs using a perceptual loss function produce artifacts; but also that some form of perceptual fidelity loss is required to get the best results from NNs. We contend that the domain bias of current datasets works against robustness and generality. And we discuss the potential of more sophisticated perceptual loss functions, attention techniques, and unsupervised learning.
Systematic literature reviews in software engineering—enhancement of the study selection process using Cohen’s Kappa statistic

Context: Systematic literature reviews (SLRs) rely on a rigorous and auditable methodology for minimizing biases and ensuring reliability. A common kind of bias arises when selecting studies using a set of inclusion/exclusion criteria. This bias can be decreased through dual revision, which makes the selection process more time-consuming and remains prone to generating bias depending on how each researcher interprets the inclusion/exclusion criteria. Objective: To reduce the bias and time spent in the study selection process, this paper presents a process for selecting studies based on the use of Cohen's Kappa statistic. We have defined an iterative process based on the use of this statistic during which the criteria are refined until obtain almost perfect agreement (k>0.8). At this point, the two researchers interpret the selection criteria in the same way, and thus, the bias is reduced. Starting from this agreement, dual review can be eliminated; consequently, the time spent is drastically shortened. Method: The feasibility of this iterative process for selecting studies is demonstrated through a tertiary study in the area of software engineering on works that were published from 2005 to 2018. Results: The time saved in the study selection process was 28% (for 152 studies) and if the number of studies is sufficiently large, the time saved tend asymptotically to 50%. Conclusions: Researchers and students may take advantage of this iterative process for selecting studies when conducting SLRs to reduce bias in the interpretation of inclusion and exclusion criteria. It is especially useful for research with few resources.