Reviewerly/exHarmony huggingface dataset:
Wireless Quantized Federated Learning: A Joint Computation and Communication Design

Recently, federated learning (FL) has sparked widespread attention as a promising decentralized machine learning approach which provides privacy and low delay. However, communication bottleneck still constitutes an issue, that needs to be resolved for an efficient deployment of FL over wireless networks. In this paper, we aim to minimize the total convergence time of FL, by quantizing the local model parameters prior to uplink transmission. More specifically, the convergence analysis of the FL algorithm with stochastic quantization is firstly presented, which reveals the impact of the quantization error on the convergence rate. Following that, we jointly optimize the computing and communication resources as well as the number of quantization bits, in order to guarantee minimized convergence time, subject to energy and quantization error requirements. The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed. Moreover, the proposed method is shown to result in faster convergence compared with baseline schemes. Finally, useful insights for the selection of the quantization error tolerance are provided.
Optimal Machine Learning Based Automated Malaria Parasite Detection and Classification Model Using Blood Smear Images

Malaria is a deadly disease which can be spread by the Plasmodium parasites.The existence of malaria can be identified by professional microscopists who examine the microscopic blood smear images.But it remains a challenge owing to the unavailability of experts, poor resolution images, and insufficient diagnostic quality.Therefore, image processing and machine learning (ML) models can be employed to detection of malaria parasites using blood smear images.With this motivation, this study introduces an optimal machine learning based automated malaria parasite detection and classification (OML-AMPDC) model using blood smear images.The proposed OML-AMPDC technique primarily undergoes preprocessing in two stages namely adaptive filtering (AF) based noise removal and contrast enhancement using CLAHE technique.Besides, the feature extraction process was implemented using Local Derivative Radial Patterns (LDRP).In addition, random forest (RF) classifier is applied to allot proper class labels to the blood smear images.Finally, particle swarm optimization (PSO) algorithm was utilized for optimally choose two parameters of the RF model, named maximum number of levels in every decision tree (max_depth) and number of trees in the forest (n_estimators).The design of PSO algorithm helps for enhancing the classification performance of the RF method.A wide-ranging experimental analysis is performed using benchmark dataset and the results reported the betterment of the OML-AMPDC technique over the recent approaches.
Your lottery ticket is damaged: Towards all-alive pruning for extremely sparse networks

Network pruning has been widely adopted for reducing computational costs and memory consumption in low-resource devices. Recent studies show that the lottery ticket hypothesis achieves high accuracy under high compression ratios (i.e., 80–90% of the parameters in original networks are removed). Nevertheless, finding well-trainable networks with sparse parameters (i.e., < 10% of the parameters remaining) is still a challenging task, commonly believed by the lack of model capacity. This paper revisits the training process of existing pruning methods and observe that dead connections, which do not contribute to model capacity, occur in existing pruning methods. To this end, we propose a novel pruning method, namely all-alive pruning (AAP), which produces the pruned networks with only trainable weights with no dead connections. Notably, AAP is broadly applicable to various pruning methods and model architectures. We demonstrate that AAP equipped with the existing pruning methods (e.g., iterative pruning, one-shot pruning, and dynamic pruning) consistently improves the accuracy of the original methods at high compression ratios on the various image- and language-based tasks.
Relationship between Social Networking Addiction and Academic Performance in Students of University

There is no doubt whatsoever that social networks affects our lives both positively and negatively.Social media addiction is one of the negative impacts of social networking and affects students on a global scale.The research seeks to find out whether there is a statistically significant relationship between the patterns of social addiction and the academic performance by gender of students at the university.The study used the descriptive approach, through a random sample of 383 students from Al Ain University, United Arab Emirates.The results of the study showed a strong negative relationship between social media addiction and academic performance.68.93% (264 participants), were within the moderate level of addiction, and the Mann-Whitney U test for nominal qualitative variables revealed that social networking addiction was higher.In male participants compared to social networking addiction in female participants.The results of the research are important at the local level due to the social and economic well-being of the Emirati society, and consequently the spread of the Internet in a wide field and the ease of children's possession of information technology tools.The researchers recommend conducting more studies at the local and global levels to spread awareness of the effects of this phenomenon.We suggested to university leaders, lecturers, and parents to adopt effective educational and training measures in guiding children to exploit social networks in effective education and to advise them about the dangers of Internet addiction and unhelpful networks.
Fundamental properties of beamsplitters in classical and quantum optics

A lossless beamsplitter has certain (complex-valued) probability amplitudes for sending an incoming photon into one of two possible directions. We use elementary laws of classical and quantum optics to obtain general relations among the magnitudes and phases of these probability amplitudes. Proceeding to examine a pair of (nearly) single-mode wavepackets in the number-states n1 and n2 that simultaneously arrive at the splitter's input ports, we find the distribution of photon-number states at the output ports using an argument inspired by Feynman's scattering analysis of indistinguishable Bose particles. The result thus obtained coincides with that of the standard quantum-optical treatment of beamsplitters via annihilation and creation operators â and â†. A simple application of the Feynman method provides a form of justification for the Bose enhancement implicit in the well-known formulas ân=nn−1 and â†n=n+1n+1.
Nonequispaced Fast Fourier Transform Boost for the Sinkhorn Algorithm

This contribution features an accelerated computation of Sinkhorn's algorithm, which approximates the Wasserstein transportation distance, by employing nonequispaced fast Fourier transforms (NFFT).The proposed algorithm allows approximations of the Wasserstein distance by involving not more than O(n log n) operations for probability measures supported by n points.Furthermore, the proposed method avoids expensive allocations of the characterizing matrices.With this numerical acceleration, the transportation distance is accessible to probability measures out of reach so far.Numerical experiments using synthetic and real data affirm the computational advantage and superiority.
GUDN: A novel guide network with label reinforcement strategy for extreme multi-label text classification

Extreme multi-label text classification (XMTC) is an emerging and essential task in natural language processing. Its objective is to retrieve the most relevant labels for a text from a large set of labels while balancing time and accuracy. Although large-scale pre-trained models have brought new perspectives to this task, more attention should be given to valuable fine-tuned methods and the significant semantic gap between texts and labels. In this paper, we propose a novel guide network (GUDN) with a label reinforcement strategy based on label semantics to help fine-tune pre-trained models for classification. Experimental results demonstrate that GUDN outperforms state-of-the-art methods on Eurlex-4k and achieves competitive results on other popular datasets. In addition, we find that meaningless tokens can harm the Transformer-based model’s classification accuracy in another experiment. We conclude that GUDN is effective in the presence of solid semantics. Our source code is available at https://t.hk.uy/aFSH.
Refining a deep learning-based formant tracker using linear prediction methods

In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward–backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest formants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local spectral peaks shown by the model-driven LP-based methods. The refinement procedure can be plugged into the DeepFormants tracker with no need for any new data learning. Two refined DeepFormants trackers were compared with the original DeepFormants and with five known traditional trackers using the popular vocal tract resonance (VTR) corpus. The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis. In addition, by tracking formants using VTR speech that was corrupted by additive noise, the study showed that the refined DeepFormants trackers were more resilient to noise than the reference trackers. In general, these results suggest that LP-based model-driven approaches, which have traditionally been used in formant estimation, can be combined with a modern data-driven tracker easily with no further training to improve the tracker’s performance.
SLISEMAP: Combining Supervised Dimensionality Reduction with Local Explanations

Abstract We introduce a Python library, called slisemap , that contains a supervised dimensionality reduction method that can be used for global explanation of black box regression or classification models. slisemap takes a data matrix and predictions from a black box model as input, and outputs a (typically) two-dimensional embedding, such that the black box model can be approximated, to a good fidelity, by the same interpretable white box model for points with similar embeddings. The library includes basic visualisation tools and extensive documentation, making it easy to get started and obtain useful insights. The slisemap library is published on GitHub and PyPI under an open source license.
FFBDNet: Feature Fusion and Bipartite Decision Networks for Recommending Medication Combination

Recommending medication combinations for patients is an essential part of artificial intelligence in the healthcare field. Existing approaches improve the effect of recommendations by considering how to make full use of patients’ electronic health records or by introducing additional external knowledge, but there is still room for improving the fusion of heterogeneous and diverse knowledge and the effect between accuracy and drug-drug interaction (DDI) rate. To fill this gap, we propose the Feature Fusion and Bipartite Decision Networks (FFBDNet) to leverage external knowledge and improve accuracy and DDI rate. FFBDNet is equipped with a patient feature encoder which extract useful information from current and historical visits of patient to supplement the patient’s health status, a medication feature encoder which can easily fuse the heterogeneous and diverse external knowledge of medications as feature, and a bipartite decision module to give medication recommendation results. FFBDNet also has a greedy loss function to improve accuracy and DDI rate. We demonstrate the effectiveness of FFBDNet by comparing with several state-of-the-art methods on a benchmark dataset. FFBDNet outperformed all baselines in all effective measures, reduced relatively the DDI rate by 97.65% from existing EHR data, and also is shown to improve 1.02% on Jaccard similarity.
Elastica Models for Color Image Regularization

The choice of a proper regularization measure plays an important role in the field of image processing. One classical approach treats color images as two- dimensional surfaces embedded in a five-dimensional spatial-chromatic space. In this case, a natural regularization term arises as the image surface area. Choosing the chromatic coordinates as dominating over the spatial ones, we can think of the image spatial coordinates could as a parameterization of the image surface manifold in a three-dimensional color space. Minimizing the area of the image manifold leads to the Beltrami flow or mean curvature flow of the image surface in the three-dimensional color space, while minimizing the elastica of the image surface yields an additional interesting regularization. Recently, we proposed a color elastica model, which minimizes both the surface area and the elastica of the image manifold. In this paper, we propose to modify the color elastica and introduce two new models for color image regularization. The revised measures are motivated by the relations between the color elastica model, Euler’s elastica model, and the total variation model for gray level images. Compared to our previous color elastica model, the new models are direct extensions of Euler’s elastica model to color images. The proposed models are nonlinear and challenging to minimize. To overcome this difficulty, two operator-splitting methods are suggested. Specifically, nonlinearities are decoupled by the introduction of new vector- and matrix-valued variables. Then, the minimization problems are converted to initial value problems which are time-discretized by operator splitting. Each subproblem, after splitting, either has a closed-form solution or can be solved efficiently. The effectiveness and advantages of the proposed models are demonstrated by comprehensive experiments. The benefits of incorporating the elastica of the image surface as regularization terms compared to common alternatives are empirically validated.
A Design Framework for Ingestible Play

Ingestible sensors have become smaller and more powerful and allow us to envisage new human-computer interactions and bodily play experiences inside our bodies. Users can swallow ingestible sensors, which facilitate interior body sensing functions that provide data on which play experiences can be built. We call bodily play that uses ingestible sensors as play technologies “ingestible play”, and we have adopted a research-through-design (RtD) approach to investigate three prototypes. For each prototype, we conducted a field study to understand the player experiences. Based upon these results and practical design experiences, we have developed a design framework for ingestible play. We hope this work can guide the future design of ingestible play; inspire the design of play technologies inside the human body to expand the current bodily play design space; and ultimately extend our understanding of how to design for the human body by considering the bodily experience of one’s interior body.
New Probabilistic, Dynamic Multi-Method Ensembles for Optimization Based on the CRO-SL

In this paper, new probabilistic and dynamic (adaptive) strategies for creating multi-method ensembles based on the coral reef optimization with substrate layers (CRO-SL) algorithm are proposed. CRO-SL is an evolutionary-based ensemble approach that is able to combine different search procedures for a single population. In this work, two different probabilistic strategies to improve the algorithm are analyzed. First, the probabilistic CRO-SL (PCRO-SL) is presented, which substitutes the substrates in the CRO-SL population with tags associated with each individual. Each tag represents a different operator which will modify the individual in the reproduction phase. In each generation of the algorithm, the tags are randomly assigned to the individuals with similar probabilities, obtaining this way an ensemble that sees more intense changes with the application of different operators to a given individual than CRO-SL. Second, the dynamic probabilistic CRO-SL (DPCRO-SL) is presented, in which the probability of tag assignment is modified during the evolution of the algorithm, depending on the quality of the solutions generated in each substrate. Thus, the best substrates in the search process will be assigned higher probabilities than those which showed worse performance during the search. The performances of the proposed probabilistic and dynamic ensembles were tested for different optimization problems, including benchmark functions and a real application of wind-turbine-layout optimization, comparing the results obtained with those of existing algorithms in the literature.
Path-Integrated X-Ray Images for Multi-Surface Digital Image Correlation (PI-DIC)

X-ray imaging offers unique possibilities for Digital Image Correlation (DIC), opening the door for full-field deformation measurements of a test article in complex environments where optical DIC suffers severe biases or is impossible. While X-ray DIC has been performed in the past with standard DIC codes designed for optical images, the path-integrated nature of X-ray images places constraints on the experimental setup, predominantly that only a single surface of interest moves/deforms. These requirements are difficult to realize for many practical situations and limit the amount of information that can be garnered in a single test. Other X-ray based diagnostics such as Digital Volume Correlation (DVC) and Projection DVC (P-DVC) overcome these obstacles, but DVC is limited to quasi-static tests, and both DVC and P-DVC necessitate high-resolution computed tomography (CT) scan(s) and often require a potentially invasive pattern throughout the volume of the specimen. This work presents a novel approach to measure time-resolved displacements and strains on multiple surfaces from a single series of 2D, path-integrated (PI) X-ray images, called PI-DIC. The principle of optical flow or conservation of intensity—the foundation of DIC—was reframed for path-integrated images, for an exemplar setup comprised of two plates moving and deforming independently. Synthetic images were generated for rigid translations, rigid rotations, and uniform stretches, where each plate underwent a unique motion/deformation. Experimental specimens were fabricated (either an aluminum plate with tantalum features or a plastic plate with steel features) and the two specimens were independently translated. PI-DIC was successfully demonstrated with the synthetic images and validated with the experimental images. Prescribed displacements were recovered for each plate from the single set of path-integrated, deformed images. Errors were approximately 0.02 px for the synthetic images with 1.5% image noise, and 0.05 px for the experimental images. These results provide the foundation for PI-DIC to measure motion and deformation of multiple, independent surfaces with subpixel accuracy from a single series of path-integrated X-ray images.
Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage

Data deduplication is of vital importance for mobile cloud computing to cope with the explosive growth of outsourced mobile data. In order to ensure the privacy of sensitive mobile data against an untrusted cloud, Message-Locked Encryption (MLE) has been proposed to enable deduplication over ciphertext. However, MLE prohibits data access control since it uses deterministic content-derived encryption keys. Recently, a lightweight rekeying-aware encrypted deduplication system (REED) has been proposed to achieve dynamic access control for secure data deduplication. However, REED is vulnerable to key-retaining attack and stub-retaining attack, which leads to insecure access revocation, and thus cannot support secure dynamic access control. In response, we present AC-Dedup, an encrypted deduplication storage system that supports <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secure dynamic access control</i> for mobile cloud storage. At the core of AC-Dedup are two novel encryption techniques named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mixed message locked encryption</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random stub re-encryption</i> to resist the two types of attacks, respectively. To the best of our knowledge, AC-Dedup is the first practical system that achieves secure data deduplication and secure dynamic access control simultaneously. We conduct security analysis and experimental evaluation on mobile device and cloud platform with real-world IoT datasets. The results show that AC-Dedup enables secure and efficient dynamic access control while preserving deduplication effectiveness.
Maximizing the benefits from sharing cyber threat intelligence by government agencies and departments

Abstract The primary objective of the current study is to analytically examine the economic benefits an organization can obtain by receiving and processing cyber threat intelligence (CTI) shared by the US government. Our results show that the benefits from receiving CTI are closely associated with the difference between the threat level indicated by the CTI and the receiving organization’s prior belief of the threat level. In addition, for the same difference between the threat levels indicated by the CTI and the organization’s prior belief, our analyses show that the magnitude of adjustments to an organization’s cybersecurity investments is inversely related to the organization’s prior belief of the threat level. Thus, larger benefits can be obtained when the receiving organization’s prior belief of a threat level is lower. Taken together, our results suggest that the common belief that it is optimal for a federal government agency or department to focus on sharing CTI related to vulnerabilities with the highest threat level is misguided. More generally, the benefits from CTI sharing can be improved if producers of CTI could develop a clearer understanding of the prior beliefs that organizations have concerning their threat level and focus on sharing CTI that is significantly different from those prior beliefs.