Reviewerly/exHarmony huggingface dataset:
Research on skin quality detection system based on facial key point detection

Aiming at the problem that the sampling parts need to be manually marked in the process of facial skin quality detection, a skin quality detection system based on the detection of key points of the face is proposed to automatically locate the sampling parts. In order to realize real-time and fast detection of face key points on portable devices, the system adopts an improved lightweight MobileNetV2 network. Adjusted the model structure to make the model parameters smaller to improve the detection rate. At the same time, in order to improve the detection accuracy of the network, an efficient channel attention (ECA) module is added on the basis of the inverted residual block, which solves the problem of information loss after deep convolution, and can accurately detect face rotation, occlusion, etc. Feature points. Automatic localization is achieved by projecting key points to the face, so as to detect the skin quality of the sampled part. The trained model is tested on the WFLW dataset, and the average error rate of face key points is 1.54%. The experimental results show that the improved network adopted by the system further improves the detection accuracy, which effectively realizes the automatic positioning of the sampling parts in the facial skin quality detection system.
EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer

Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. Moreover, we propose a new numerically stable loss function that easily controls the balance between the sensitivity and specificity of cancer detection. The proposed approach is evaluated using a breast ultrasound dataset with 1,511 images. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively. We validate the model using a virtual mobile device, and the average inference time is 0.35 seconds per image.
Category-Graded Algebraic Theories and Effect Handlers

We provide an effect system CatEff based on a category-graded extension of algebraic theories that correspond to category-graded monads. CatEff has category-graded operations and handlers. Effects in CatEff are graded by morphisms of the grading category. Grading morphisms represent fine structures of effects such as dependencies or sorts of states. Handlers in CatEff are regarded as an implementation of category-graded effects. We define the notion of category-graded algebraic theory to give semantics of CatEff and prove soundness and adequacy. We also give an example using category-graded effects to express protocols for sending receiving typed data.
Maximal Initial Learning Rates in Deep ReLU Networks

Training a neural network requires choosing a suitable learning rate, which involves a trade-off between speed and effectiveness of convergence. While there has been considerable theoretical and empirical analysis of how large the learning rate can be, most prior work focuses only on late-stage training. In this work, we introduce the maximal initial learning rate $\eta^{\ast}$ - the largest learning rate at which a randomly initialized neural network can successfully begin training and achieve (at least) a given threshold accuracy. Using a simple approach to estimate $\eta^{\ast}$, we observe that in constant-width fully-connected ReLU networks, $\eta^{\ast}$ behaves differently from the maximum learning rate later in training. Specifically, we find that $\eta^{\ast}$ is well predicted as a power of depth $\times$ width, provided that (i) the width of the network is sufficiently large compared to the depth, and (ii) the input layer is trained at a relatively small learning rate. We further analyze the relationship between $\eta^{\ast}$ and the sharpness $\lambda_{1}$ of the network at initialization, indicating they are closely though not inversely related. We formally prove bounds for $\lambda_{1}$ in terms of depth $\times$ width that align with our empirical results.
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models

Generative models have recently undergone significant advancement due to the diffusion models. The success of these models can be often attributed to their use of guidance techniques, such as classifier or classifier-free guidance, which provide effective mechanisms to trade-off between fidelity and diversity. However, these methods are not capable of guiding a generated image to be aware of its geometric configuration, e.g., depth, which hinders their application to areas that require a certain level of depth awareness. To address this limitation, we propose a novel guidance method for diffusion models that uses estimated depth information derived from the rich intermediate representations of diffusion models. We first present label-efficient depth estimation framework using internal representations of diffusion models. Subsequently, we propose the incorporation of two guidance techniques based on pseudo-labeling and depth-domain diffusion prior during the sampling phase to self-condition the generated image using the estimated depth map. Experiments and comprehensive ablation studies demonstrate the effectiveness of our method in guiding the diffusion models towards the generation of geometrically plausible images.
Differentially Private Decentralized Optimization with Relay Communication

To address the privacy leakage problem in decentralized composite convex optimization, we proposes a novel differentially private decentralized primal--dual algorithm named DP-RECAL with operator splitting method and relay communication mechanism. We study the relationship between communication and privacy leakage, thus defining a new measure: local communication involvement (LCI). To the best of our knowledge, compared with existing differentially private algorithms, DP-RECAL is the first to take advantage of the relay communication mechanism to experience less LCI so as to reduce the overall privacy budget. In addition, we prove that DP-RECAL is convergent with uncoordinated network-independent stepsizes and establish the linear convergence rate of DP-RECAL under metric subregularity. Furthermore, taking the least squares problem as an example, DP-RECAL presents better privacy performance and communication complexity than listed differentially private decentralized algorithms. Numerical experiments on real-world datasets verify our analysis results and demonstrate that DP-RECAL can defend deep leakage from gradients (DLG) attacks.
UAV-Assisted Image Acquisition: 3D UAV Trajectory Design and Camera Control

In this paper, we consider a new unmanned aerial vehicle (UAV)-assisted oblique image acquisition system where a UAV is dispatched to take images of multiple ground targets (GTs). To study the three-dimensional (3D) UAV trajectory design for image acquisition, we first propose a novel UAV-assisted oblique photography model, which characterizes the image resolution with respect to the UAV's 3D image-taking location. Then, we formulate a 3D UAV trajectory optimization problem to minimize the UAV's traveling distance subject to the image resolution constraints. The formulated problem is shown to be equivalent to a modified 3D traveling salesman problem with neighbourhoods, which is NP-hard in general. To tackle this difficult problem, we propose an iterative algorithm to obtain a high-quality suboptimal solution efficiently, by alternately optimizing the UAV's 3D image-taking waypoints and its visiting order for the GTs. Numerical results show that the proposed algorithm significantly reduces the UAV's traveling distance as compared to various benchmark schemes, while meeting the image resolution requirement.
Authenticable Additive Homomorphic Scheme and its Application for MEC-based IoT

The integration of Internet of Things (IoT) and cloud computing are always seen as promising technologies to enhance streamlined data collection, share and exchange. Although the advances in edge computing, particularly mobile edge computing (MEC), could enhance the performance of data collection and computation via computing offloading, security and privacy impediments have made new challenges to data integrity and confidentiality, in particular when multiple edges or nodes at different locations collect IoT data. Homomorphic encryption therefore has shown promising advantages for cloud computing, offering arithmetic operations to be carried out on the encrypted data without revealing the secret key. While fully homomorphic encryption introduced by Gentry, in 2009, allows both additive and multiplicative operations, it has shown significant implementation drawbacks due to the parameters generation and memory consumption. In this work, we focus on partially homomorphic encryption, which can be efficiently computed. However, it is challenging to add authentication feature for the verification and aggregation capability. We propose a novel secure and privacy preserving authenticable homomorphic encryption (AHEC) scheme. We demonstrate an application of our AHEC scheme for MEC-based IoT systems and provide security analysis to prove that our scheme is secure against chosen plaintext attack (IND-CPA) and unforgeability (UNF) under DDH-ZN2 and Lift-DH-ZN2 assumptions. Experimental results show that our proposed scheme is efficient for practical applications.
Analysis of Web Monitoring Servers and Tools for Cloud Computing Services

This research aims to find best cloud service monitoring tools and techniques according to trending parameters. The services of cloud computing are most popular services now a days. Many organizations are migrating to cloud technology in order to reduce the management’s complications of resources. The effective monitoring methodologies should be implemented, to certify the Quality of Experience (QoE) and Quality of Services (QoS) of cloud computing services for end users. For this purpose, cloud services should be monitor and control using different tools and techniques. There are so many server-based or web-based monitoring tools are available. In this paper, analysis of different tools is based on their pricing, working efficiency, key features, platforms and environments in which they could be used. This paper also analyzes the cloud computing structure and investigates that which type of monitoring services are required at which level. This research paper shows that no monitoring tool is mature enough to fulfil all requirements and desires of clients. This research also interprets that which tool is recommended for which kind of platform and environment.
Knowledge Ecosystems: An Intelligent Agent for Knowledge Discovery

One of the major objectives of any knowledge ecosystem is the effective management of its main resource - knowledge. As a general rule, this is to be achieved through developing an environment for interaction between system participants, simplifying the decision-making process and stimulating innovation. The fundamental elements of the knowledge ecosystem are software agents. Located in the ecosystem environment, agents receive data about internal events, interpret them and execute commands that affect the environment. The paper proposes one of the possible implementations of an intelligent agent for knowledge discovery (KD-agent). The input for such an agent is a priori dictionary of features and a training set. As the outcome of the KD-agent activity previously unknown patterns are revealed and can be interpreted within the subject domain. The effectiveness of the proposed approach is demonstrated on the example of model data analysis.
Implementation of Vufind Application As Web Scale Discovery Services To Improve Easiness Access of Open E‐Resources: Case Study At University of Jember Library

The increase number and vast of information resources in library, both internal or external resources, encourage University of Jember library to use an aplication base on Web Scale Discovery Services (WSDS) for managing open access e‐Resources. This study provide an overview of implementation open source Vufind discovery tools in order to give new e‐resources search Services at University of Jember library, so user able to access various open access databases in one indexer portal. This research uses descriptive qualitative method, with a case study approach. Data was collected by observation, interview, and document tracing methods. In this study, interviews were conducted informally, so that researchers and informants were in a natural situation. Meanwhile, data testing was carried out using the triangulation method. As results, the technical problems in implementing Vufind are in harvesting and editing metadata, because there is a lot of differences of metadata form myriad databases. Other research result show that Implementation Vufind Web Scale Discovery Services at University of Jember Library is very useful in facilitating access to various databases open access e‐resources
Discriminator-Cooperated Feature Map Distillation for GAN Compression

Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an awesome ''performance maker'', knowledge distillation is demonstrated to be particularly efficacious in exploring low-priced GANs. In this paper, we investigate the irreplaceability of teacher discriminator and present an inventive discriminator-cooperated distillation, abbreviated as DCD, towards refining better feature maps from the generator. In contrast to conventional pixel-to-pixel match methods in feature map distillation, our DCD utilizes teacher discriminator as a transformation to drive intermediate results of the student generator to be perceptually close to corresponding outputs of the teacher generator. Furthermore, in order to mitigate mode collapse in GAN compression, we construct a collaborative adversarial training paradigm where the teacher discriminator is from scratch established to co-train with student generator in company with our DCD. Our DCD shows superior results compared with existing GAN compression methods. For instance, after reducing over 40x MACs and 80x parameters of CycleGAN, we well decrease FID metric from 61.53 to 48.24 while the current SoTA method merely has 51.92. This work's source code has been made accessible at https://github.com/poopit/DCD-official.
The 2HD+a model: collider, dark matter and gravitational wave signals

We perform a comprehensive study of a model in which the Higgs sector is extended to contain two Higgs doublet fields, with the four types of possibilities to couple to standard fermions, as well as an additional light pseudoscalar Higgs boson which mixes with the one of the two doublets. This 2HD+a model includes also a stable isosinglet massive fermion that has the correct thermal relic abundance to account for the dark matter in the Universe. We summarize the theoretical constraints to which the model is subject and then perform a detailed study of the phenomenological constraints. In particular, we discuss the bounds from the LHC in the search for light and heavy scalar resonances and invisible states and those from high precision measurements in the Higgs, electroweak and flavor sectors, addressing the possibility of explaining the deviation from the standard expectation of the anomalous magnetic moment of the muon and the $W$-boson mass recently observed at Fermilab. We also summarize the astrophysical constraints from direct and indirect detection dark matter experiments. We finally conduct a thorough analysis of the cosmic phase transitions and the gravitational wave spectrum that are implied by the model and identify the parameter space in which the electroweak vacuum is reached after single and multiple phase transitions. We then discuss the prospects for observing the signal of such gravitational waves in near future experiments such as LISA, BBO or DECIGO.
Image To Tree with Recursive Prompting

Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps of a recursive process. We design and train a two-stage model which leverages the UNet and Transformer architectures and introduces an image-based prompting technique. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.
Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain

In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.
Empirical Differential Privacy

We show how to achieve differential privacy with no or reduced added noise, based on the empirical noise in the data itself. Unlike previous works on noiseless privacy, the empirical viewpoint avoids making any explicit assumptions about the random process generating the data.
HierVL: Learning Hierarchical Video-Language Embeddings

Video-language embeddings are a promising avenue for injecting semantics into visual representations, but existing methods capture only short-term associations between seconds-long video clips and their accompanying text. We propose HierVL, a novel hierarchical video-language embedding that simultaneously accounts for both long-term and short-term associations. As training data, we take videos accompanied by timestamped text descriptions of human actions, together with a high-level text summary of the activity throughout the long video (as are available in Ego4D). We introduce a hierarchical contrastive training objective that encourages text-visual alignment at both the clip level and video level. While the clip-level constraints use the step-by-step descriptions to capture what is happening in that instant, the video-level constraints use the summary text to capture why it is happening, i.e., the broader context for the activity and the intent of the actor. Our hierarchical scheme yields a clip representation that outperforms its single-level counterpart as well as a long-term video representation that achieves SotA results on tasks requiring long-term video modeling. HierVL successfully transfers to multiple challenging downstream tasks (in EPIC-KITCHENS-100, Charades-Ego, HowTo100M) in both zero-shot and fine-tuned settings.
Accelerating Voting by Quantum Computation

Studying the computational complexity and designing fast algorithms for determining winners under voting rules are classical and fundamental questions in computational social choice. In this paper, we accelerate voting by leveraging quantum computation: we propose a quantum-accelerated voting algorithm that can be applied to any anonymous voting rule. We show that our algorithm can be quadratically faster than any classical algorithm (based on sampling with replacement) under a wide range of common voting rules, including positional scoring rules, Copeland, and single transferable voting (STV). Precisely, our quantum-accelerated voting algorithm outputs the correct winner with high probability in $\Theta\left(\frac{n}{\text{MOV}}\right)$ time, where $n$ is the number of votes and $\text{MOV}$ is {\em margin of victory}, the smallest number of voters to change the winner. In contrast, any classical voting algorithm based on sampling with replacement requires $\Omega\left(\frac{n^2}{\text{MOV}^2}\right)$ time under a large class of voting rules. Our theoretical results are supported by experiments under plurality, Borda, Copeland, and STV.
A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models

In this paper, a privacy preserving image classification method is proposed under the use of ConvMixer models. To protect the visual information of test images, a test image is divided into blocks, and then every block is encrypted by using a random orthogonal matrix. Moreover, a ConvMixer model trained with plain images is transformed by the random orthogonal matrix used for encrypting test images, on the basis of the embedding structure of ConvMixer. The proposed method allows us not only to use the same classification accuracy as that of ConvMixer models without considering privacy protection but to also enhance robustness against various attacks compared to conventional privacy-preserving learning.