Reviewerly/exHarmony huggingface dataset:
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

We advocate a new resource allocation framework, which we call resource rationing, for wireless federated learning (FL). Unlike existing resource allocation methods for FL, resource rationing focuses on balancing resources across learning rounds so that their collective impact on FL performance is explicitly captured. This new framework can be integrated seamlessly with existing resource allocation schemes to optimize the convergence of FL. In particular, a novel “lat-er-is-better” principle is at the front and center of resource rationing and is validated empirically in several instances of wireless FL. We also point out technical challenges and research opportunities that are worth pursuing. Resource rationing highlights the benefits of treating the emerging FL as a new class of service that has its own characteristics, and designing communication algorithms for this particular service.
A comparative study of navigation interfaces in virtual reality environments: A mixed-method approach

Recently, motion-based navigation interfaces have been widely utilized in virtual reality (VR) environments. However, improper navigation interfaces can negatively impact the VR experience, and because different interfaces have different characteristics, the navigation experience may vary. Although comparative studies have been conducted with various interfaces, information obtained by focusing on qualitative evaluation was limited. Thus, this study explores the effects from three navigation interfaces (walking-in-place (WIP), joystick, and teleportation) on user performance, sense of presence, workload, usability, and motion sickness through a mixed-method design. Twenty-one participants were asked to perform a navigation task using selected navigation interfaces. The results indicated different advantages and disadvantages in the navigation interfaces for each evaluation metric. In particular, it was found that more research on user safety is required for the WIP interface. The findings of this study are expected to contribute to the development of guidelines for applying navigation interfaces to specific VR environments.
Concentration-distance from centroids (C-DC) multifractal modeling: A novel approach to characterizing geochemical patterns based on sample distance from mineralization

Various fractal models have been implemented to separate populations and characterize spatial distributions in geochemical data derived from regional mapping programs. This study compares the conventional number-size with a proposed concentration-distance from centroids (C-DC) fractal model to detect geochemically anomalous populations. These models have been applied to centered log-ratio transformed data of VMS-style mineralization related element concentrations in till from a low-density national survey conducted across Sweden. The C-DC model has been applied to the distance between till samples and centroids of geological sub-provinces containing a number of VMS-style deposits, and is derived from the concentration-distance (C-D) approach originally developed using a radial-density (R-D) model. The efficiencies of the models in detecting a multivariate geochemical response to known mineralization are compared using a variant of the overall accuracy matrix. The C-DC model provides accuracy of classification similar to the N-S model. Therefore, Monte Carlo simulation was applied to quantify uncertainties in setting of population thresholds using the two fractal models. It demonstrated greater efficiency of the C-DC model.
Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions

When people observe events, they are able to abstract key information and build concise summaries of what is happening. These summaries include contextual and semantic information describing the important high-level details (what, where, who and how) of the observed event and exclude background information that is deemed unimportant to the observer. With this in mind, the descriptions people generate for videos of different dynamic events can greatly improve our understanding of the key information of interest in each video. These descriptions can be captured in captions that provide expanded attributes for video labeling (e.g. actions/objects/scenes/sentiment/etc.) while allowing us to gain new insight into what people find important or necessary to summarize specific events. Existing caption datasets for video understanding are either small in scale or restricted to a specific domain. To address this, we present the Spoken Moments (S-MiT) dataset of 500k spoken captions each attributed to a unique short video depicting a broad range of different events. We collect our descriptions using audio recordings to ensure that they remain as natural and concise as possible while allowing us to scale the size of a large classification dataset. In order to utilize our proposed dataset, we present a novel Adaptive Mean Margin (AMM) approach to contrastive learning and evaluate our models on video/caption retrieval on multiple datasets. We show that our AMM approach consistently improves our results and that models trained on our Spoken Moments dataset generalize better than those trained on other video-caption datasets.http://moments.csail.mit.edu/spoken.html
SMS Spam Detection using Machine Learning and Deep Learning Techniques

The number of people using mobile devices increasing day by day. SMS (short message service) is a text message service available in smartphones as well as basic phones. So, the traffic of SMS increased drastically. The spam messages also increased. The spammers try to send spam messages for their financial or business benefits like market growth, lottery ticket information, credit card information, etc. So, spam classification has special attention. In this paper, we applied various machine learning and deep learning techniques for SMS spam detection. we used a dataset from UCI and build a spam detection model. Our experimental results have shown that our LSTM model outperforms previous models in spam detection with an accuracy of 98.5%. We used python for all implementations.
Certificateless privacy preserving public auditing for dynamic shared data with group user revocation in cloud storage

With the increasing popularity of data sharing among users of a group in clouds, shared data auditing has become an important issue in the cloud auditing field. To address this issue, many shared data auditing schemes have been proposed in the literature based on either public key infrastructure (PKI) or identity-based cryptography (IBC). However, these schemes suffer from issues of complex certificate management or key escrow problem. To address these problems, recently, a certificateless shared auditing scheme was put forward. However, it cannot support data dynamics and does not protect data privacy against a verifier, i.e., the verifier can derive data content when verifying the data integrity, which affects the scheme's security. This paper proposes certificateless privacy preserving public auditing scheme for dynamic shared data with group user revocation in cloud storage (CLPPPA). CLPPPA protects the privacy of data from the verifier by leveraging a random masking technique. Further, CLPPPA also supports shared data dynamics and group user revocation. We formally prove the security of CLPPPA under computational Diffie-Hellman (CDH) and discrete logarithm (DL) assumptions in the Random Oracle Model (ROM). The performance of CLPPPA is evaluated by theoretical analysis, experimental results, and compared with the state-of-the-art ones. The results demonstrate that CLPPPA achieves desirable efficiency.
CS-BPSO: Hybrid feature selection based on chi-square and binary PSO algorithm for Arabic email authorship analysis

Email authorship analysis is a challenging task involving the detection of an author’s style to help determine their identity. Emails represent a widespread application of big data, and email authorship analysis is widely performed in the forensic linguistics field. However, the high-dimensional feature space encountered in authorship analysis affects the classification performance. Moreover, the Arabic language is highly inflected and involves certain unique characteristics, which pose critical challenges in identifying the context. Therefore, the selection of prominent features is a critical step in realizing authorship analysis. Swarm intelligence (SI) algorithms are widely adopted to address such feature selection problems. In this study, an efficient hybrid feature selection algorithm based on binary particle swarm optimization (BPSO) and chi-square BPSO (CS-BPSO) was developed to enhance the performance of Arabic email authorship analysis. Static and dynamic features were considered. Experiments were conducted on Arabic email messages collected from a sample population to test the algorithm performance using three popular classifiers: support vector machine (SVM), K-nearest neighbour (KNN), and naïve Bayes (NB) classifiers. Different metrics, specifically, the accuracy, precision, recall, and f1-score, were considered as performance measures. The results showed that the CS-BPSO method achieves impressive results using dynamic features. The findings were quite satisfactory in terms of solving multiple types of difficulties, e.g., imbalanced dataset, small dataset, and short text.
A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera

Solar irradiance fluctuates mainly due to clouds. A sky camera offers images with high temporal and spatial resolutions for a specific solar photovoltaic plant. The cloud cover from sky images is suitable for forecasting local fluctuations of solar irradiance and thereby solar power. Because no study applied deep learning for forecasting cloud cover using sky images, this study attempted to apply the long short-term memory algorithm in deep learning. Cloud cover data were collected by image processing of sky images and used for developing the deep learning model to forecast cloud cover 10 min ahead. The forecasted cloud cover data were plugged into solar radiation models as input in order to predict global horizontal irradiance. The forecasted results were grouped into three categories based on sky conditions: clear sky, partly cloudy, and overcast sky. By comparison with solar irradiance measurement at a ground station, the proposed model was evaluated. The proposed model outperformed the persistence model under high variability of solar irradiance such as partly cloudy days with relative root mean square differences for 10-min-ahead forecasting are 25.10% and 39.95%, respectively. Eventually, this study demonstrated that deep learning can forecast the cloud cover from sky images and thereby can be useful for forecasting solar irradiance under high variability.
Multiple-qubit controlled unitary quantum gate for Rydberg atoms using shortcut to adiabaticity and optimized geometric quantum operations

Multiple-qubit quantum logic gates are an important element in the implementation of quantum computers. The direct construction of multiple-qubit quantum logic gates in an efficient way has important values compared to the construction of multiple-qubit gates using a series of two-qubit and single-qubit gates. We propose a scheme to construct a multiple-qubit ${\mathrm{C}}_{k}\mathrm{U}$ gate ($k$ denotes the number of control qubits and U means the arbitrary universal operation performed on the target qubit) in a neutral atom platform through the Rydberg blockade effect by successively exciting them to Rydberg states. This scheme takes advantage of the shortcut to adiabaticity of inverse engineering, geometric quantum operations, as well as optimized control theory. The geometric quantum computation considered in this manuscript guarantees the robustness to operational errors. Meanwhile, inverse engineering-based shortcut to adiabaticity provides a further advantage in terms of the speed of the system evolution compared to adiabatic processes. An additional feature of our multiple-qubit quantum logic gate is that arbitrary operation on the target atom can be realized by adjusting the amplitude and phase of the laser fields. Numerical simulation of the master equation based on the full Hamiltonian demonstrates the high fidelity of the proposed scheme and its robustness to operational errors and spontaneous emission.
Lévy State-Space Models for Tracking and Intent Prediction of Highly Maneuverable Objects

In this article, we present a Bayesian framework for maneuvering object tracking and intent prediction using novel α-stable Lévy state-space models, expressed in continuous time as Lévy processes. In contrast to conventional (fully) Gaussian formulations, the proposed models are driven by heavy-tailed α-stable noise and are, thus, much more able to capture extreme values/behaviors. This can better characterize sharp changes in the state, which may be induced by sudden and frequent maneuvers such as swift turns or abrupt accelerations. In particular, they are represented in a conditionally Gaussian series form, which ensures the tractability of the applied inference algorithms. A corresponding estimation strategy with the Rao-Blackwellized particle filter is then proposed, and an efficient intent inference procedure is introduced. Here, the underlying intent, driving the target's long-term behavior (e.g., reaching its final destination), is modeled as a latent variable. Real vessel data from maritime surveillance and human computer interactions (e.g., cursor data from motor-impaired interface users) are utilized to demonstrate the effectiveness of the proposed approach. It is shown to deliver noticeable improvements in the tracking and intent prediction performance (whenever relevant) compared with a more conventional Gaussian dynamic model.
Extreme Multi-label Learning for Semantic Matching in Product Search

We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more. Because of large catalog spaces and real-time latency constraints, semantic matching algorithms not only desire high recall but also need to have low latency. Conventional lexical matching approaches (e.g., Okapi-BM25) exploit inverted indices to achieve fast inference time, but fail to capture behavioral signals between queries and products. In contrast, embedding-based models learn semantic representations from customer behavior data, but the performance is often limited by shallow neural encoders due to latency constraints. Semantic product search can be viewed as an eXtreme Multi-label Classification (XMC) problem, where customer queries are input instances and products are output labels. In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products. We consider hierarchical linear models with n-gram features for fast real-time inference. Quantitatively, our method maintains a low latency of 1.25 milliseconds per query and achieves a 65% improvement of [email protected] (60.9% v.s. 36.8%) over a competing embedding-based DSSM model. Our model is robust to weight pruning with varying thresholds, which can flexibly meet different system requirements for online deployments. Qualitatively, our method can retrieve products that are complementary to existing product search system and add diversity to the match set.
Scaling Up Graph Neural Networks Via Graph Coarsening

Scalability of graph neural networks remains one of the major challenges in graph machine learning. Since the representation of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes from previous layers, the receptive fields grow exponentially, which makes standard stochastic optimization techniques ineffective. Various approaches have been proposed to alleviate this issue, e.g., sampling-based methods and techniques based on pre-computation of graph filters.
Domain generalization on medical imaging classification using episodic training with task augmentation

Medical imaging datasets usually exhibit domain shift due to the variations of scanner vendors, imaging protocols, etc. This raises the concern about the generalization capacity of machine learning models. Domain generalization (DG), which aims to learn a model from multiple source domains such that it can be directly generalized to unseen test domains, seems particularly promising to medical imaging community. To address DG, recent model-agnostic meta-learning (MAML) has been introduced, which transfers the knowledge from previous training tasks to facilitate the learning of novel testing tasks. However, in clinical practice, there are usually only a few annotated source domains available, which decreases the capacity of training task generation and thus increases the risk of overfitting to training tasks in the paradigm. In this paper, we propose a novel DG scheme of episodic training with task augmentation on medical imaging classification. Based on meta-learning, we develop the paradigm of episodic training to construct the knowledge transfer from episodic training-task simulation to the real testing task of DG. Motivated by the limited number of source domains in real-world medical deployment, we consider the unique task-level overfitting and we propose task augmentation to enhance the variety during training task generation to alleviate it. With the established learning framework, we further exploit a novel meta-objective to regularize the deep embedding of training domains. To validate the effectiveness of the proposed method, we perform experiments on histopathological images and abdominal CT images.
Reversible Data Hiding in Encrypted Image via Secret Sharing Based on <i>GF</i>(<i>p</i>) and <i>GF</i>(2⁸)

Secret sharing is a useful method which divides a secret message into several shares for security. During the recovery procedure, only when sufficient shares are obtained, the secret message can be recovered. This paper proposes two novel reversible data hiding schemes in encrypted image via secret sharing over Galois fields <inline-formula> <tex-math notation="LaTeX">${{GF}}{({p})}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${{GF}}{(}{{2}^{{8}}}{)}$ </tex-math></inline-formula>. The content owner first applies a specific encryption method through block and pixel permutation and Shamir&#x2019;s secret sharing. Then, the theoretical demonstration is introduced to explain that the generated shares are suitable for data embedding over <inline-formula> <tex-math notation="LaTeX">${{GF}}{({p})}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${{GF}}{(}{{2}^{{8}}}{)}$ </tex-math></inline-formula>. Finally, two embedding algorithms over <inline-formula> <tex-math notation="LaTeX">${{GF}}{({p})}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${{GF}}{(}{{2}^{{8}}}{)}$ </tex-math></inline-formula> are presented, and on the receiver side, with different keys, additional data can be extracted correctly and original image can be recovered losslessly. Experimental results show that our schemes can achieve better rate-distortion performance than some state-of-the-art schemes.
Anomaly detection with variational quantum generative adversarial networks

Generative adversarial networks (GANs) are a machine learning framework comprising a generative model for sampling from a target distribution and a discriminative model for evaluating the proximity of a sample to the target distribution. GANs exhibit strong performance in imaging or anomaly detection. However, they suffer from training instabilities, and sampling efficiency may be limited by the classical sampling procedure. We introduce variational quantum-classical Wasserstein GANs to address these issues and embed this model in a classical machine learning framework for anomaly detection. Classical Wasserstein GANs improve training stability by using a cost function better suited for gradient descent. Our model replaces the generator of Wasserstein GANs with a hybrid quantum-classical neural net and leaves the classical discriminative model unchanged. This way, high-dimensional classical data only enters the classical model and need not be prepared in a quantum circuit. We demonstrate the effectiveness of this method on a credit card fraud dataset. For this dataset our method shows performance on par with classical methods in terms of the $F_1$ score. We analyze the influence of the circuit ansatz, layer width and depth, neural net architecture parameter initialization strategy, and sampling noise on convergence and performance.
D<sup>2</sup>IM-Net: Learning Detail Disentangled Implicit Fields from Single Images

We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IM-Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term.
HS-KDNet: A Lightweight Network Based on Hierarchical-Split Block and Knowledge Distillation for Fault Diagnosis With Extremely Imbalanced Data

Because of the cost, it is unrealistic to sample the failure state for a long time, which makes the data collected from the scenario of engineering usually extremely imbalanced. However, imbalanced training data pose a negative effect on the fault diagnosis algorithms based on the data driven. When the data are extremely imbalanced, this problem becomes more challenging. Furthermore, to reduce the deployment cost, in industrial practice, it is often required that the parameters and computation of the deployed diagnosis model should be within a certain range, which puts forward the requirement of lightweight for diagnosis model. Therefore, in this article, a novel lightweight framework for fault diagnosis with extremely imbalanced data, called HS-KDNet, is proposed. Soft labels generated by knowledge distillation can represent the similarity between categories, i.e., through to learn the soft labels, the information about all categories are considered in each update of the parameters, not only the information about the current samples. Consequently, unlike traditional data re-balancing strategies based on generating pseudo samples, we utilized knowledge distillation to suppress the adverse effects of imbalanced data for the first time. On two classical bearing datasets, the effectiveness and superiority of the proposed HS-KDNet were demonstrated, and the experimental results shown that, except for HS-KDNet, knowledge distillation can significantly inhibit the adverse effects of imbalanced data on other simple models.