Reviewerly/exHarmony huggingface dataset:
Participant Selection for Hierarchical Federated Learning in Edge Clouds

Federated learning (FL) has been emerging as a new distributed machine learning paradigm recently. Although FL can protect the data privacy of participants by keeping their training data on local devices, there are recent works raising new privacy concerns especially when workers or the parameter server of FL are untrustworthy or malicious. One effective way to solve the problem is using hierarchical federated learning (HFL) where a few middle-layer aggregators (or called group leaders) are used to aggregate local model updates from workers and send group model updates to the parameter server. In this paper, we consider the participant selection problem of HFL in an edge cloud with multiple FL models, where each model needs to select one parameter server, a few group leaders and a certain amount of workers from edge servers to jointly perform HFL. We first formulate this problem as a non-linear integer programming, aiming to minimize the total learning cost of all models while satisfying the constrained edge resources. We then design a three-stage algorithm by decoupling the original problem into three sub-problems and solving them iteratively. Simulations with real-world datasets and FL models confirm that our proposed algorithm can efficiently reduce the average total learning cost in edge cloud compared with existing methods.
The epistemological foundations of data science: a critical review

Abstract The modern abundance and prominence of data have led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally.
Event-driven daily activity recognition with enhanced emergent modeling

With the population aging, elderly health monitoring is triggering more studies on daily activity recognition as the fundamental of ambient assisted living. It is remarkable that activity recognition remains difficulties including how to adequately extract feature structure and settle the issue of activity confusion. To address these challenges, we propose a novel activity modeling method under the emergent paradigm with marker-based stigmergy and the directed-weighted network with additional context-aware information. In the modeling process, stigmergy is first introduced to aggregate the context information at the low level for generating activity pheromone trails, and then the constructed stigmergic trails are represented in form of directed-weighted network with distinguishability of individual pheromone source corresponding to location. The potential advantage is that the robust trails with distinguishable individual initial positions are feasible to supplement user’s daily habits and thus both inter-class and intra-class distances can be kept at acceptable levels. Experiments on Aruba demonstrates that the proposed emergent modeling method can effectively deal with the problems of feature extraction and activity ambiguity and achieve good classification performance.
Wavefront optical spacing of freeform surfaces and its measurement using CGH interferometry

• The concept of wavefront optical spacing is proposed and the effect of wavefront optical spacing on surface figure is theoretically derived. • A new cat-eye CGH method is proposed to achieve wavefront optical spacing measurements. • The effect of wavefront optical spacing on the surface figure and optical system adjustment is analyzed. • Cat-eye CGH method achieves the wavefront optical spacing test with several micron precision. For optical surfaces, the distance between the wavefront at ideal design position and that at real surface testing position along the wavefront propagation direction is an important parameter. It determines the on-axis curvature deviation and best-fit sphere radius of the surface under testing, and thus, affects the optical distance, effective focal length, and tolerance design of the optical system. We define the distance as wavefront optical spacing (WOS or spacing d ). The analytical form of the WOS can be utilised in critical tolerance balancing, and its test results can aid in optical system redesign. Aiming at a computer-generated hologram (CGH) interferometric test, in this study, we propose the WOS concept and deduce the coupling relation between the surface error and spacing d , particularly in freeform surface conditions. A cat-eye CGH interferometric method was presented to test the spacing error within a precision of several microns. The simulation and error budget demonstrate that this method can measure the WOS of freeform surfaces. The experimental results indicate that the metrology accuracy is 10.2 ± 4.3 μm ( P = 95%) (absolute accuracy), 4.5 μm (repeatability), and 2.1 ppm (relative accuracy).
Similarity learning with deep CRF for person re-identification

The core of person re-identification (Re-ID) lies in robustly estimating similarities for each probe-gallery image pair. A common practice in existing works is to calculate the similarity of each image pair independently, ignoring relations between different probe-gallery pairs. In this paper, we present a deep learning conditional random field (Deep-CRF) graph to model group-wise similarities within a batch of images, and regard the Re-ID task as a CRF node labeling problem. Unlike the existing deep CRF based approach where the CRF inference is only involved in the training stage, our method intends to fully exploit the potential of CRF model, exhibiting inference consistency in both training and testing. Specifically, we design unary potentials for computing each probe-gallery similarity separately. To efficiently encode relationships between different probe-gallery pairs, pairwise potentials are built on an arbitrary node pair whose learning is achieved by a joint matching strategy using bidirectional LSTM. We pose the CRF inference as a RNN learning process, where unary and pairwise potentials are jointly optimized in an end-to-end manner. Extensive experiments on three large-scale person Re-ID datasets demonstrate the effectiveness of the proposed method. Our Deep-CRF achieves the best results compared with the previous graph-based deep learning approaches and substantially exceeds the existing deep CRF framework by 8% in Rank1 accuracy on CUHK03 dataset. It also behaves competitive among the current state-of-the-art methods.
A sinusoidal social learning swarm optimizer for large-scale optimization

Large-scale optimization problems are much more difficult compared to traditional optimization problems because they have a larger search space and more numerous local optimum. This paper presents a sinusoidal social learning swarm optimizer (SinSLSO) to effectively tackle large-scale optimization problems. In SinSLSO, sinusoidal function is employed to dynamically adjust the learning probability of particles in the population to balance exploration and exploitation capabilities. Meanwhile, the trapezoidal population size reduction strategy is utilized to make a trade-off between the diversity and convergence speed of SinSLSO. In addition, a new learning strategy is designed to prevent SinSLSO from trapping into a local optimum. Experiments are carried out on two widely used sets of large-scale benchmark functions (i.e., CEC2010 and CEC2013) and the SinSLSO is compared with eleven state-of-the-art algorithms. What is more, the proposed SinSLSO is applied to feature selection problem. The comparison results illustrate the competitive performance of SinSLSO in terms of the quality on most of test problems.
A multi-granularity convolutional neural network model with temporal information and attention mechanism for efficient diabetes medical cost prediction

As the cost of diabetes treatment continues to grow, it is critical to accurately predict the medical costs of diabetes. Most medical cost studies based on convolutional neural networks (CNNs) ignore the importance of multi-granularity information of medical concepts and time interval characteristics of patients' multiple visit sequences, which reflect the frequency of patient visits and the severity of the disease. Therefore, this paper proposes a new end-to-end deep neural network structure, MST-CNN, for medical cost prediction. The MST-CNN model improves the representation quality of medical concepts by constructing a multi-granularity embedding model of medical concepts and incorporates a time interval vector to accurately measure the frequency of patient visits and form an accurate representation of medical events. Moreover, the MST-CNN model integrates a channel attention mechanism to adaptively adjust the channel weights to focus on significant medical features. The MST-CNN model systematically addresses the problem of deep learning models for temporal data representation. A case study and three comparative experiments are conducted using data collected from Pingjiang County. Through experiments, the methods used in the proposed model are analyzed, and the super contribution of the model performance is demonstrated.
A study on small magnitude seismic phase identification using 1D deep residual neural network

Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data has been achieved. This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods. In this study, we develop 1D deep Residual Neural Network (ResNet), for tackling the problem of seismic signal detection and phase identification. This method is trained and tested on the dataset recorded by the Southern California Seismic Network. Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases. Compared to previously proposed deep learning methods, the introduced framework achieves around 4% improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center. The model generalizability is also tested further on the STanford EArthquake Dataset. In addition, the experimental result on the same subset of the STanford EArthquake Dataset, when masked by different noise levels, demonstrates the model’s robustness in identifying the seismic phases of small magnitude.
Deep Reinforcement Learning for Adaptive Learning Systems

The adaptive learning problem concerns how to create an individualized learning plan (also referred to as a learning policy) that chooses the most appropriate learning materials based on a learner’s latent traits. In this article, we study an important yet less-addressed adaptive learning problem—one that assumes continuous latent traits. Specifically, we formulate the adaptive learning problem as a Markov decision process. We assume latent traits to be continuous with an unknown transition model and apply a model-free deep reinforcement learning algorithm—the deep Q-learning algorithm—that can effectively find the optimal learning policy from data on learners’ learning process without knowing the actual transition model of the learners’ continuous latent traits. To efficiently utilize available data, we also develop a transition model estimator that emulates the learner’s learning process using neural networks. The transition model estimator can be used in the deep Q-learning algorithm so that it can more efficiently discover the optimal learning policy for a learner. Numerical simulation studies verify that the proposed algorithm is very efficient in finding a good learning policy. Especially with the aid of a transition model estimator, it can find the optimal learning policy after training using a small number of learners.
Exploiting spatio‐temporal knowledge for video action recognition

Abstract Action recognition has been a popular area of computer vision research in recent years. The goal of this task is to recognise human actions in video frames. Most existing methods often depend on the visual features and their relationships inside the videos. The extracted features only represent the visual information of the current video itself and cannot represent the general knowledge of particular actions beyond the video. Thus, there are some deviations in these features, and the recognition performance still requires improvement. In this sudy, we present a novel spatio‐temporal knowledge module (STKM) to endow the current methods with commonsense knowledge. To this end, we first collect hybrid external knowledge from universal fields, which contains both visual and semantic information. Then graph convolution networks (GCN) are used to represent and aggregate this knowledge. The GCNs involve (i) a spatial graph to capture spatial relations and (ii) a temporal graph to capture serial occurrence relations among actions. By integrating knowledge and visual features, we can get better recognition results. Experiments on AVA, UCF101‐24 and JHMDB datasets show the robustness and generalisation ability of STKM. The results report a new state‐of‐the‐art 32.0 mAP on AVA v2.1. On UCF101‐24 and JHMDB datasets, our method also improves by 1.5 AP and 2.6 AP, respectively, over the baseline method.
Multidimensional Red Fox meta-heuristic for complex optimization

Modern applications of computer science need efficient algorithms to solve complex optimization tasks. This necessity is especially well visible in multidimensional problems, where with growing number of optimization variables applied algorithms often loose precision or computing becomes very long. In this paper we present an improved Multidimensional Red Fox Optimization algorithm (MRFO). The initial RFO idea was ameliorated with new approach to local search, with even faster motion of the population toward regions of optimum. Secondly, in the reproduction phase additional mathematical operations addressing the problem of individuals crossing assumed optimization domain were formulated. Compared to RFO, the computational complexity of MRFO grows significantly slower with increasing dimensionality of an optimization task. Numerical results on the well-known COCO BBOB benchmark show that proposed modifications have merit and lead to higher efficacy of MRFO compared to the baseline model. The results are also competitive to DE-best - an efficient Differential Evolution implementation. • A new metaheuristic Multidimensional Red Fox Optimization algorithm (MRFO). • Improved local search and reproduction compared to baseline Red Fox (RFO) formulation. • Specifically devoted to solving high-dimensional problems. • Outperforming RFO in both convergence speed and solution quality. • Competitive results to Differential Evolution algorithm.
Feta

Zero-Knowledge protocols have increasingly become both popular and practical in recent years due to their applicability in many areas such as blockchain systems. Unfortunately, public verifiability and small proof sizes of zero-knowledge protocols currently come at the price of strong assumptions, large prover time, or both, when considering statements with millions of gates. In this regime, the most prover-efficient protocols are in the designated verifier setting, where proofs are only valid to a single party that must keep a secret state.
FeIDo

Two-factor authentication (2FA) mitigates the security risks of passwords as sole authentication factor. FIDO2---the de facto standard for interoperable web authentication---leverages strong, hardware-backed second factors. However, practical challenges hinder wider FIDO2 user adoption for 2FA tokens, such as the extra costs (20-30 per token) or the risk of inaccessible accounts upon token loss/theft.
Workload consolidation in alibaba clusters

Web companies typically run latency-critical long-running services and resource-intensive, throughput-hungry batch jobs in a shared cluster for improved utilization and reduced cost. Despite many recent studies on workload consolidation, the production practice remains largely unknown. This paper describes our efforts to efficiently consolidate the two types of workloads in Alibaba clusters to support the company's e-commerce businesses.
Low-Latency Hardware Private Circuits

Over the last years, the rise of the IoT, and the connection of mobile - and hence physically accessible - devices, immensely enhanced the demand for fast and secure hardware implementations of cryptographic algorithms which offer thorough protection against SCA attacks. Among a variety of proposed countermeasures against SCA, masking has transpired to be a promising candidate, attracting significant attention in both, academia and industry. Here, abstract adversary models have been derived, aiming to accurately model real-world attack scenarios, while being sufficiently simple to enable formally proving the SCA resilience of masked implementations on an algorithmic level. In the context of hardware implementations, the robust probing model has become highly relevant for proving SCA resilience due to its capability to model physical defaults like glitches and data transitions. As constructing a correct and secure masked variant of large and complex circuits is a challenging task, a new line of research has recently emerged, aiming to design small, masked subcircuits - realizing for instance a simple AND gate - which still guarantee security when composed to a larger circuit. Although several designs realizing such composable subcircuits - commonly referred to as gadgets - have been proposed, negligible research was conducted in order to find trade-offs between different overhead metrics, like randomness requirement, latency, and area consumption.
Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization

Visual localization is employed for indoor navigation and embedded in various applications, such as augmented reality and mixed reality. Image retrieval and geometrical measurement are the primary steps in visual localization, and the key to improving localization efficiency is to reduce the time consumption of the image retrieval. Therefore, a hierarchical clustering-based image-retrieval method is proposed to hierarchically organize an off-line image database, resulting in control of the time consumption of image retrieval within a reasonable range. The image database is hierarchically organized by two stages: scene-level clustering and sub-scene-level clustering. In scene-level clustering, an improved cumulative sum algorithm is proposed to detect change points and then group images by global features. On the basis of scene-level clustering, a feature tracking-based method is introduced to further group images into sub-scene-level clusters. An image retrieval algorithm with a backtracking mechanism is designed and applied for visual localization. In addition, a weighted KNN-based visual localization method is presented, and the estimated query position is solved by the Armijo–Goldstein algorithm. Experimental results indicate that the running time of image retrieval does not linearly increase with the size of image databases, which is beneficial to improving localization efficiency.
VEViD: Vision Enhancement via Virtual diffraction and coherent Detection

Abstract The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls. In modern times, this idea has been extended, for example, to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves. Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks. Specifically, diffraction followed by coherent detection becomes an image enhancement tool. Vision Enhancement via Virtual diffraction and coherent Detection (VEViD) reimagines a digital image as a spatially varying metaphoric “lightfield” and then subjects the field to the physical processes akin to diffraction and coherent detection. The term “Virtual” captures the deviation from the physical world. The light field is pixelated and the propagation imparts a phase with dependence on frequency which is different from the monotonically-increasing behavior of physical diffraction. Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image. The phase of the output, not the intensity, represents the output image. VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm. The algorithm is extremely fast, interpretable, and reduces to a compact and intuitively-appealing mathematical expression. We demonstrate image enhancement of 4k video at over 200 frames per second and show the utility of this physical algorithm in improving the accuracy of object detection in low-light conditions by neural networks. The application of VEViD to color enhancement is also demonstrated.
The impact of intelligent personal assistants on learners’ autonomous learning of second language listening and speaking

Research has revealed the positive impact of intelligent personal assistants (IPAs) on L2 learners’ oral development and learning attitude. These studies, however, focused mostly on the in-class use of IPAs, with existing research on the out-of-class use being exploratory. To fill the gap of lacking empirical investigations on IPA-based autonomous second language learning (ASLL), this study recruited 34 college EFL learners to use Google Assistant (GA) in their respective homes for six weeks and examined their listening and speaking development afterwards. Each week, participants received an email about the new commands for them to explore, and half of them were additionally given sustained teacher guidance (i.e. weekly evaluation forms) on the usefulness of each command to better enhance their participation. Statistical analysis on the participants’ learning gains demonstrated that participants receiving weekly evaluation forms made significant improvement in both listening and speaking skills, and they even outperformed those without weekly evaluation forms in their oral development. Nevertheless, there was no significant difference in the two groups’ listening gains. These findings demonstrate that, with sustained teacher guidance, IPAs have great potential in L2 oral development in ASLL context, but more long-term studies on IPAs’ impact for L2 listening should be conducted.