Reviewerly/exHarmony huggingface dataset:
Edge-Assisted Hierarchical Federated Learning with Non-IID Data

Federated Learning (FL) is capable of leveraging massively distributedprivate data, e.g., on mobile phones and IoT devices, to collaboratively traina shared machine learning model with the help of a cloud server. However, itsiterative training process results in intolerable communication latency, andcauses huge burdens on the backbone network. Thus, reducing the communicationoverhead is critical to implement FL in practice. Meanwhile, the modelperformance degradation due to the unique non-IID data distribution atdifferent devices is another big issue for FL. In this paper, by introducingthe mobile edge computing platform as an intermediary structure, we propose ahierarchical FL architecture to reduce the communication rounds between usersand the cloud. In particular, a Hierarchical Federated Averaging (HierFAVG)algorithm is proposed, which allows multiple local aggregations at each edgeserver before one global aggregation at the cloud. We establish the convergenceof HierFAVG for both convex and non-convex objective functions with non-IIDuser data. It is demonstrated that HierFAVG can reach a desired modelperformance with less communication, and outperform the traditional FederatedAveraging algorithm.
A Reduced-Order Flow Model for Fluid–Structure Interaction Simulation of Vocal Fold Vibration

We present a novel reduced-order glottal airflow model that can be coupled with the three-dimensional (3D) solid mechanics model of the vocal fold tissue to simulate the fluid-structure interaction (FSI) during voice production. This type of hybrid FSI models have potential applications in the estimation of the tissue properties that are unknown due to patient variations and/or neuromuscular activities. In this work, the flow is simplified to a one-dimensional (1D) momentum equation-based model incorporating the entrance effect and energy loss in the glottis. The performance of the flow model is assessed using a simplified yet 3D vocal fold configuration. We use the immersed-boundary method-based 3D FSI simulation as a benchmark to evaluate the momentum-based model as well as the Bernoulli-based 1D flow models. The results show that the new model has significantly better performance than the Bernoulli models in terms of prediction about the vocal fold vibration frequency, amplitude, and phase delay. Furthermore, the comparison results are consistent for different medial thicknesses of the vocal fold, subglottal pressures, and tissue material behaviors, indicating that the new model has better robustness than previous reduced-order models.
Differential Privacy for the Vast Majority

Differential privacy has become one of the widely used mechanisms for protecting sensitive information in databases and information systems. Although differential privacy provides a clear measure of privacy guarantee, it implicitly assumes that each individual corresponds to a single record in the result of a database query. This assumption may not hold in many database query applications. When an individual has multiple records, strict implementation of differential privacy may cause significant information loss. In this study, we extend the differential privacy principle to situations where multiple records in a database are associated with the same individual. We propose a new privacy principle that integrates differential privacy with the Pareto principle in analyzing privacy risk and data utility. When applied to the situations with multiple records per person, the proposed approach can significantly reduce the information loss in the released query results with a relatively small relaxation in the differential privacy guarantee. The effectiveness of the proposed approach is evaluated using three real-world databases.
Review on Facial Expression Recognition System Using Machine Learning Techniques

Facial expressions are the most convenient way of expressing one’s thoughts. The aim facial expression recognition (FER) algorithm is to extort the discriminative and distinguishable characteristic of a face. Multiple methods have been devised to identify face and facial expression. Facial expressions not only depict the feelings of any individual but it is also used to judge his/her intellectual views. This paper not only includes the introduction of the face detection and facial expression recognition but also provide an exploration on the recent previous researches to extract the useful and capable method for facial expression recognition. The identification of various facial expressions are done through geometric features, appearance features and hybrid features. This paper presents a literature summary of the various strategies used for facial expression reputation. The comparative study is also carried out using various preprocessing, feature extraction and classification techniques used for facial expression recognition.
An overview of optical flow-based approaches for motion segmentation

The goal of motion segmentation is to segregate a visual scene into independently moving objects. It is an indispensable pre-processing step for various tasks in computer vision and has evolved as an active and flourishing research area in the last few decades. In the sequences captured using a monocular camera, motion segmentation is typically performed by analyzing apparent motion of pixels in the image plane, i.e. the optical flow. Optical flow is generally contemplated as an appropriate representation of image motion. Numerous techniques for reliable flow estimation and subsequent advancements in their framework have been proposed in the last couple of decades and are outlined briefly in this work. The paper attempts to give a summary of diverse optical flow-based approaches used for robust segmentation of static or dynamic scenes containing rigidly moving objects and discusses in brief the shortcomings associated with them.
Sublinear quantum algorithms for training linear and kernel-based classifiers

We investigate quantum algorithms for classification, a fundamental problem in machine learning, with provable guarantees. Given $n$ $d$-dimensional data points, the state-of-the-art (and optimal) classical algorithm for training classifiers with constant margin runs in $\tilde{O}(n+d)$ time. We design sublinear quantum algorithms for the same task running in $\tilde{O}(\sqrt{n} +\sqrt{d})$ time, a quadratic improvement in both $n$ and $d$. Moreover, our algorithms use the standard quantization of the classical input and generate the same classical output, suggesting minimal overheads when used as subroutines for end-to-end applications. We also demonstrate a tight lower bound (up to poly-log factors) and discuss the possibility of implementation on near-term quantum machines. As a side result, we also give sublinear quantum algorithms for approximating the equilibria of $n$-dimensional matrix zero-sum games with optimal complexity $\tilde{\Theta}(\sqrt{n})$.
Haze and Thin Cloud Removal Using Elliptical Boundary Prior for Remote Sensing Image

Remote sensing images play important roles in various earth surface observation applications. However, the hazy state of surface atmosphere can visually decrease the contrast and availability of remote sensing images. In this paper, we propose a haze and thin cloud removal method for single visible remote sensing images, which aims to robustly estimate haze thickness, atmospheric light, and transmission value from a remote sensing image with dense haze or thin cloud, and finally recovers a haze-free image. An elliptical boundary prior (EBP) is proposed to transform the haze thickness in each local patch from the pixels cluster in the spectral space, which is surrounded by an ellipse. With the aim of preventing highlight objects influences, an atmospheric light estimation approach is presented. The correlation of transmission and haze thickness is reconstructed to develop the scattering model for remote sensing images. The experimental results demonstrate that the proposed method can not only significantly improve the contrast and restore textures of various kinds of hazy remote sensing images but also well preserve the spectral information of visible bands.
ScarletNAS: Bridging the Gap Between Scalability and Fairness in Neural Architecture Search.

To discover powerful yet compact models is an important goal of neural architecture search. Previous two-stage one-shot approaches are limited by search space with a fixed depth. It seems handy to include an additional skip connection in the search space to make depths variable. However, it creates a large range of perturbation during supernet training and it has difficulty giving a confident ranking for subnetworks. In this paper, we discover that skip connections bring about significant feature inconsistency compared with other operations, which potentially degrades the supernet performance. Based on this observation, we tackle the problem by imposing an equivariant learnable stabilizer to homogenize such disparities. Experiments show that our proposed stabilizer helps to improve the supernet's convergence as well as ranking performance. With an evolutionary search backend that incorporates the stabilized supernet as an evaluator, we derive a family of state-of-the-art architectures, the SCARLET series of several depths, especially SCARLET-A obtains 76.9% top-1 accuracy on ImageNet. The models and evaluation code are released online this https URL.
Random deep neural networks are biased towards simple functions

We prove that the binary classifiers of bit strings generated by random wide deep neural networks with ReLU activation function are biased towards simple functions. The simplicity is captured by the following two properties. For any given input bit string, the average Hamming distance of the closest input bit string with a different classification is at least sqrt(n / (2π log n)), where n is the length of the string. Moreover, if the bits of the initial string are flipped randomly, the average number of flips required to change the classification grows linearly with n. These results are confirmed by numerical experiments on deep neural networks with two hidden layers, and settle the conjecture stating that random deep neural networks are biased towards simple functions. This conjecture was proposed and numerically explored in [Valle Perez et al., ICLR 2019] to explain the unreasonably good generalization properties of deep learning algorithms. The probability distribution of the functions generated by random deep neural networks is a good choice for the prior probability distribution in the PAC-Bayesian generalization bounds. Our results constitute a fundamental step forward in the characterization of this distribution, therefore contributing to the understanding of the generalization properties of deep learning algorithms.
A Flexible Generative Framework for Graph-based Semi-supervised Learning

We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-supervised learning tasks. However, conventional graph-based regularization methods and recent graph neural networks do not fully leverage the interrelations between the features, the graph, and the labels. In this work, we propose a flexible generative framework for graph-based semi-supervised learning, which approaches the joint distribution of the node features, labels, and the graph structure. Borrowing insights from random graph models in network science literature, this joint distribution can be instantiated using various distribution families. For the inference of missing labels, we exploit recent advances of scalable variational inference techniques to approximate the Bayesian posterior. We conduct thorough experiments on benchmark datasets for graph-based semi-supervised learning. Results show that the proposed methods outperform state-of-the-art models under most settings.
Living With Intelligent Sensors

This qualitative study is part of a larger randomized prospective intervention study that examined the clinical and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. It explored the perceptions of older adults and family members on the sensor system's usefulness, impact on daily routine, privacy, and sharing of health information. This study was conducted in 13 assisted-living facilities in Missouri, and 55 older adults were interviewed. Data were collected over five points in time with a total of 188 interviews. From these five participant interview iterations, the following themes emerged: (1) understanding and purpose, (2) daily life and benefits, (3) impact on privacy, and (4) sharing of information. Three themes emerged from one round of family interviews: (1) benefits of bed sensors, (2) family involvement/staff interaction, and (3) privacy protection versus sensor benefits. The sensor suite was regarded as helpful in maintaining independence, health, and physical functioning. Responses suggest that the willingness to adopt the sensor suite was motivated by both a decline in functional status and a desire to remain independent. Participants were willing to share their health data with providers and select family members. Recommendations for future practice are provided.
Bayesian Optimization with Discrete Variables

Bayesian Optimization (BO) is an efficient method to optimize an expensive black-box function with continuous variables. However, in many cases, the function has only discrete variables as inputs, which cannot be optimized by traditional BO methods. A typical approach to optimize such functions assumes the objective function is on a continuous domain, then applies a normal BO method with a rounding of suggested continuous points to nearest discrete points at the end. This may cause BO to get stuck and repeat pre-existing observations. To overcome this problem, we propose a method (named Discrete-BO) that manipulates the exploration of an acquisition function and the length scale of a covariance function, which are two key components of a BO method, to prevent sampling a pre-existing observation. Our experiments on both synthetic and real-world applications show that the proposed method outperforms state-of-the-art baselines in terms of convergence rate. More importantly, we also show some theoretical analyses to prove the correctness of our method.
Augmented Reality for Supervising Multirobot System in Agricultural Field Operation

Agriculture is shifting from farmers manually operating machines to monitoring autonomous machines. Thus, the task of a farmer is fleet management and taking care of safe operation. Situational awareness during the operation is important. Augmented reality (AR) is a powerful tool for visualizing information in real-time. To demonstrate the use of AR in agricultural fleet management, in this paper we present a novel AR system to help the farmer supervise the operation of two autonomous agricultural machines. The paper discusses the requirements for AR application, and we present the architecture of the system and the results of a demonstration carried out in a test field.
Linking knowledge management to competitive strategies of knowledge-based SMEs

Purpose This paper aims to study the effect of knowledge management in formulating competitive strategies for knowledge-based small- and medium-sized enterprises (SMEs) in Malaysia. Design/methodology/approach A quantitative approach of a survey was carried out on 135 owners and managers of knowledge-based SMEs in Malaysia. Structural equation modeling technique was used to investigate the relationship between knowledge management and competitive strategies. SmartPLS software is used to analyze the quantitative data. Only SMEs which are involved in R&amp;D and innovation were selected to get the right respondents who meet the objective of the study. Findings The findings show mixed results. Most dimensions of knowledge management have significant relationships to differentiation strategy except for knowledge creation and knowledge acquisition, with only knowledge acquisition showing a significant relationship to cost leadership. Findings reveal that knowledge management has a positive effect on competitive strategies with more inclination toward differentiation strategy, compared to cost leadership strategy which does synchronize with their commitment in research and development and innovation. Research limitations/implications This study is only focused on knowledge-based SMEs in central Malaysia. Second, the use of a survey approach minimized the flow of information. Practical implications SMEs do have knowledge management practices but may not be exploiting it well. Mapping knowledge management practices would help SMEs identify their strengths and weaknesses to explore better business opportunities. This proves that SMEs are leveraging their resources through knowledge application, dissemination, storage and protection to be different than their competitors. However, their apparent lack of knowledge in knowledge acquisition and knowledge creation should be addressed accordingly, as it is important for their future continuous sustainability. Originality/value This paper contributes to the literature of knowledge management relating to competitive strategies in SMEs. The study offers insights on how competitive strategies were formulated through knowledge management. The mixed results reveal a new different outlook of knowledge management relating to competitive strategies.
A Construction Method of (2, 3) Visual Cryptography Scheme

Visual cryptography scheme (VCS) shares a binary secret image into multiple shadows, stacking qualified set of shadows will decode the secret image without computer resources. In this paper, we propose a (2, 3)-VCS, which can share one or two secret images into 3 shadows, stacking any two shadows will reveal the secret image. The shadow size is 3/2 times of the secret image. The contrasts of the revealed image are 1/3 and 1/2 when we share one and two secret images, respectively. We can also reveal the secret image by XOR decoding operation, and the visual quality of the revealed image can be further improved. By XOR decoding operation, the contrasts of the revealed image will be 2/3 and 1 when we share one or two secret images, respectively. In the case of sharing two secret images, the revealed two secret images have no distortion. Theoretic analysis and experimental results demonstrate the feasibility and advantages of the proposed (2, 3)-VCS.
Three-Dimension Transmissible Attention Network for Person Re-Identification

In this work, we propose a Three-Dimensional Transmissible Attention Network (3DTANet) for Person Re-Identification, which can transmit the attention information from layer to layer and attend to the person image from a three-dimensional perspective. Main contributions of the 3DTANet are: (i) A novel Transmissible Attention (TA) mechanism is introduced, which can transfer attention information between convolution layers. Different from traditional attention mechanism, not only can it convey accumulated attention information layer by layer but also guide the network to retain holistic attention information. (ii) We propose a Three-Dimension Attention (3DA) mechanism, which is capable of extracting a three-dimensional attention map. While previous researches on image attention mechanism extracts channel or spatial attention information separately, 3DA mechanism pays attention to channel and spatial information simultaneously, thereby making them play better complementary role in attention extraction. (iii) A new loss function named L2-norm Multi-labels Loss (L2ML) is applied to acquire higher recognition accuracy calculated by multi labels of same ID and corresponding feature representation. Quite different from the common loss functions, L2-norm Multi-labels Loss is specifically good at optimizing feature distance. In brief, 3DTANet gains two-fold benefit toward higher accuracy. For one thing, the attention information is informative and can be transmitted, feature being more representative. For another, our model is computationally lightweight and can be easily applied to real scenarios. We extensively conduct experiments on four Person Re-Identification benchmark datasets. Our model achieves rank-1 accuracy of 87.50% on CUHK03, 96.23% on Market-1501, 92.50% on DukeMTMC-reID and 76.60% on MSMT17-V2 respectively. The results confirm that the 3DTANet can extract more representative features and attain a higher recognition accuracy, outperforming the state-of-the-art methods.
Emoji Skin Tone Modifiers

Emoji are widely used in computer-mediated communication to express concepts and emotions. Skin tone modifiers were added in 2015 with the hope of better representing user diversity, and, indeed, recent work has shown that these modifiers are especially popular among darker-skinned users, who are a minority on Twitter. Previous work also showed that the vast majority of tone-modified emoji have a tone similar to the user’s own skin tone, suggesting that self-representation is a major factor in tone use. In this article, we first show that the basic finding (users mainly choose a tone that is similar to their own skin tone) generalizes to different sub-populations of users, including users from majority-Black regions. We then extend the analysis of tone use to quantify and examine cases where users modulate their tones—that is, for a particular emoji, they choose either to use a different tone than their usual one or no tone at all (after having previously used one). We show that even though these uses constitute only a small proportion of emoji usage, many instances are readily classifiable as ways of representing other people. The evidence we present is therefore crucial in working toward a broader understanding of the connection between emoji and identity expression online. We also offer explanations for why the darkest emoji skin tones are not used, by examining aspects of their design, which make them less suited to self-representational usage. This highlights the need for careful consideration of both design and human diversity when creating emoji. Moreover, despite early fears in the media, we find little evidence of negative usage, even when tones are used in a non-self-representational manner. In sum, our findings lend even more support to the highly positive role that emoji and skin tone modifiers play in identity and expression in computer-mediated communication.