Reviewerly/exHarmony huggingface dataset:
On Hypergraph Supports.

Let $\mathcal{H}=(X,\mathcal{E})$ be a hypergraph. A support is a graph $Q$ on $X$ such that for each $E\in\mathcal{E}$, the subgraph of $Q$ on the elements in $E$ is connected. We consider hypergraphs defined on a host graph. Given a graph $G=(V,E)$, with $c:V\to\{\R,\B\}$ and a collection of connected subgraphs $\mathcal{H}$ of $G$, a primal support is a graph $Q$ on $\B(V)$ such that for each $H\in \mathcal{H}$, the subgraph $Q[\B(H)]$ on vertices $\B(H)=H\cap c^{-1}(\B)$ is connected. A \emph{dual support} is a graph $Q^*$ on $\mathcal{H}$ s.t. for each $v\in X$, the subgraph $Q^*[\mathcal{H}_v]$ is connected, where $\mathcal{H}_v=\{H\in\mathcal{H}: v\in H\}$. We present sufficient conditions on the host graph and hyperedges so that the resulting support comes from a restricted family. We primarily study two classes of graphs: $(1)$ If the host graph has genus $g$ and the hypergraphs satisfy a topological condition of being \emph{cross-free}, then there is a primal and a dual support of genus at most $g$. $(2)$ If the host graph has treewidth $t$ and the hyperedges satisfy a combinatorial condition of being \emph{non-piercing}, then there exist primal and dual supports of treewidth $O(2^t)$. We show that this exponential blow-up is sometimes necessary. As an intermediate case, we also study the case when the host graph is outerplanar. Finally, we show applications of our results to packing and covering, and coloring problems on geometric hypergraphs.
EVA: An End-to-End Exploratory Video Analytics System

In recent years, deep learning models have revolutionized computer vision, enabling diverse applications. However, these models are computationally expensive, and leveraging them for video analytics involves low-level imperative programming. To address these efficiency and usability challenges, the database community has developed video database management systems (VDBMSs). However, existing VDBMSs lack extensibility and composability and do not support holistic system optimizations, limiting their practical application. In response to these issues, we present our vision for EVA, a VDBMS that allows for extensible support of user-defined functions and employs a Cascades-style query optimizer. Additionally, we leverage Ray's distributed execution to enhance scalability and performance and explore hardware-specific optimizations to facilitate runtime optimizations. We discuss the architecture and design of EVA, our achievements thus far, and our research roadmap.
Using Pipeline Performance Prediction to Accelerate AutoML Systems

Automatic machine learning (AutoML) systems aim to automate the synthesis of machine learning (ML) pipelines. An important challenge these systems face is how to efficiently search a large space of candidate pipelines. Several strategies have been proposed to navigate and prune the search space, from the use of grammars to deep learning models. However, regardless of the strategy used, a major overhead lies in the evaluation step: for each synthesized pipeline p, these systems must both train and test p to guide the search and to identify the best pipelines. Given a time budget and computing resources, the evaluation cost limits how much of the search space can be explored. As a result, these systems may miss good pipelines. We propose ML4ML, an approach that aims to reduce the evaluation overhead for AutoML systems. ML4ML leverages the provenance of prior pipeline runs to predict performance without having to re-train and test the pipelines. We present results of an experimental evaluation which demonstrates that not only can ML4ML build a reliable predictive model with low mean absolute error, but the integration of this model with AutoML systems leads to substantial speedups, enabling the systems to explore a larger number of pipelines and primitive combinations and derive pipelines at a much lower cost.
A method for heavy metal estimation in mining regions based on SMA-PCC-RF and reflectance spectroscopy

Heavy metal contamination has long been a concern of intense research within the field of environmental protection. The exacerbation of heavy metal pollution due to mineral resource over-exploitation and the implementation of agricultural modernization has further emphasized the need for rapid monitoring. Although traditional geochemical survey methods have been deemed reliable, they lack the ability to conduct non-destructive and rapid surveys of large areas due to their high cost, low real-time capability, and cumbersome operations. We collected 120 soil samples in the field using Xin'an County, Henan Province as the research region, and obtained hyperspectral curves and contents of the samples using the spectrometer and chemical analysis. After preprocessing the spectral curves, we used the slime mold algorithm (SMA) to preselect feature wavebands, and then the mathematically transformed method was used to improve their correlation. Next, we calculated the correlation coefficients of these wavebands with six heavy metals, and the final modeled wavebands were obtained through precise feature selection based on the criterion that the correlation coefficient’s absolute value exceeded 0.298. The inversion model was also established by using adaptive boosting (AdaBoost), gradient boosted decision tree (GBDT), random forest (RF), and partial least squares (PLS). The results indicated that SMA-PCC can effectively downscale the high-dimensional hyperspectral data and obtain the feature wavebands with a high contribution to the modeling. We also observed that the mathematical transformation method improved the relevance between heavy metal elements and spectra. Among the four models, RF showed better overall inversion accuracy for all heavy metals (i.e., Zn: RAdaBoost2=0.85,RGBDT2=0.56,RRF2=0.89,RPLS2=0.57). This paper showcases that the algorithm presented can provide meaningful technical guidance for the large-scale investigation and assessment of heavy metal levels in soil.
Poster: BystandAR: Protecting Bystander Visual Data in Augmented Reality Systems

Augmented Reality (AR) devices are set apart from other mobile devices by the immersive experience they offer. While the powerful suite of sensors on modern AR devices is necessary for enabling such an immersive experience, they can create unease in bystanders (i.e., those surrounding the device during its use) due to potential bystander data leaks, which is called the bystander privacy problem. In this poster, we propose BystandAR, the first practical system that can effectively protect bystander visual (camera and depth) data in real-time with only on-device processing. BystandAR builds on a key insight that the device user's eye gaze and voice are highly effective indicators for subject/bystander detection in interpersonal interaction, and leverages novel AR capabilities such as eye gaze tracking, wearer-focused microphone, and spatial awareness to achieve a usable frame rate without offloading sensitive information. Through a 16-participant user study, we show that BystandAR correctly identifies and protects 98.14% of bystanders while allowing access to 96.27% of subjects. We accomplish this with average frame rates of 52.6 frames per second without the need to offload unprotected bystander data to another device.
ConvReLU++: Reference-based Lossless Acceleration of Conv-ReLU Operations on Mobile CPU

Many activation values of Convolutional Neural Networks (CNNs) are zeros due to ReLU (Rectified Linear Unit), one of the most common activation functions used in modern neural networks. Since ReLU outputs are zero for all negative inputs, existing CNN acceleration approaches estimate zero outputs to skip redundant computation, which has to sacrifice accuracy for efficiency and leads to dilemma trade-offs and cockamamie configuration. In this paper, we introduce a lossless acceleration method ConvReLU++ for CNN inference on mobile devices, which accurately detects and skips zero-outputs for speedup without failures. The key to early negative detection is adopting reference-based upper-bounds calculation. This ensures that as soon as the intermediate results become negative, the final results are guaranteed to be negative. Upon detection, the remaining computation can be skipped and the following ReLU output can be simply set to zero. We rigorously prove the losslessness property of ConvReLU++, analyze the theoretical FLOPs reduction, and show the compatibility of our method with vector-level parallelism on mobile platforms. We implement ConvReLU++ in popular mobile inference frameworks and evaluate it on common deep vision tasks. The results demonstrate that ConvReLU++ can achieve 2.90% to 8.91% latency reduction over the original inference framework on edge devices without sacrificing accuracy. Our code can be found at https://github.com/monster119120/conv_relu_plus_plus.
Demo: EMSAssist – An End-to-End Mobile Voice Assistant at the Edge for Emergency Medical Services

We present EMSAssist, the first end-to-end mobile voice assistant for emergency medical services (EMS). EMSAssist allows Emergency Medical Technicians (EMT) to verbally describe patients' signs and symptoms and uses EMTs' voice input to recommend top-5 EMS protocols. Through this demo, we allow the attendees to evaluate EMSAssist through a pair of Google Glass and a mobile phone. Both mobile devices can collect users' voices as input and output top-5 recommended protocols. A companion youtube video of using EMSAssist on the Google Glass is provided: https://www.youtube.com/watch?v=bj7aQJKf4aE
Demo: Exploiting Indices for Man-in-the-Middle Attacks on Collaborative Unpooling Autoencoders

In this demonstration, we introduce the vulnerability of indices in unpooling autoencoders. We show that this small factor can be maliciously exploited by performing man-in-the-middle attacks to eavesdrop on the victim's data, resulting in reconstruction and adversarial attacks. Such attacks especially make systems that integrate collaborative inference operations vulnerable. This demo presentation will empirically show the feasibility of index-based attacks by launching reconstruction and adversarial attacks on embedded/mobile computing platforms.
Demo: Near Real-time ChatGPT-AR

Augmented reality (AR) applications based on conventional approaches lack the adaptability to cater to different scene requirements and address users' personalized demands effectively. This demon presents ChatGPT-AR, a ChatGPT-powered near real-time voice-to-AR mobile application system, that can create different 3D-augmented spaces using voice commands. Further, ChatGPT-AR enables near real-time contextual editions in AR fashion.
A large-scale data security detection method based on continuous time graph embedding framework

Abstract Graph representation learning has made significant strides in various fields, including sociology and biology, in recent years. However, the majority of research has focused on static graphs, neglecting the temporality and continuity of edges in dynamic graphs. Furthermore, dynamic data are vulnerable to various security threats, such as data privacy breaches and confidentiality attacks. To tackle this issue, the present paper proposes a data security detection method based on a continuous-time graph embedding framework (CTDGE). The framework models temporal dependencies and embeds data using a graph representation learning method. A machine learning algorithm is then employed to classify and predict the embedded data to detect if it is secure or not. Experimental results show that this method performs well in data security detection, surpassing several dynamic graph embedding methods by 5% in terms of AUC metrics. Furthermore, the proposed framework outperforms other dynamic baseline methods in the node classification task of large-scale graphs containing 4321477 temporal information edges, resulting in a 10% improvement in the F1 score metric. The framework is also robust and scalable for application in various data security domains. This work is important for promoting the use of continuous-time graph embedding framework in the field of data security.
A General Mixture Model for Cognitive Diagnosis

Although the generalized deterministic inputs, noisy “and” gate model (G-DINA; de la Torre, 2011) is a general cognitive diagnosis model (CDM), it does not account for the heterogeneity that is rooted from the existing latent groups in the population of examinees. To address this, this study proposes the mixture G-DINA model, a CDM that incorporates the G-DINA model within the finite mixture modeling framework. An expectation–maximization algorithm is developed to estimate the mixture G-DINA model. To determine the viability of the proposed model, an extensive simulation study is conducted to examine the parameter recovery performance, model fit, and correct classification rates. Responses to a reading comprehension assessment were analyzed to further demonstrate the capability of the proposed model.
Locating Infectious Sources Using Bluetooth System of Smart Devices

Infectious diseases, such as COVID-19, may have a significant impact on human daily life and social activities. One effective method to prevent and control the spread of such diseases is to accurately locate the sources of infection and limit the possible exposure to the virus. This paper presents a method, system, big data storage and analysis to control the infection based on Bluetooth technology. GPS and Bluetooth positioning are combined to track the movement trajectory of each person with a smart device, locally store the location information and close contacts, as well as periodically update it on the cloud platform. Based on the related algorithms of big data, this method can provide personal and regional risk levels, providing an alarm function which can be triggered by being close enough to a high-risk area or if the infected person’s risk level is greater than the set threshold within the Bluetooth interconnection range. The system can provide a wealth of data on the location of infection sources and close contacts, offering valuable technical support for rapid and efficient epidemic prevention and control.
Cloud computing design of intelligent connected vehicles based on regional load

This paper attempts to use a software architecture based on observer mode, establish cloud computing data centers by region, register computing resources to the registry, and take the registry as the system coordinator to ensure that all vehicles can always find the best computing resources in the registry. The process is that all cloud computation registers themselves to the register with their connect info and geographical area. All the intelligent connected should get cloud service information from the register and the point of vehicle can estimate the best cloud server. It ensures that all the intelligent connected vehicles can transfer data to best cloud computing data center get best network and computing services. so as to achieve a reasonable allocation of computing resources and ensure the real-time and stability of data processing.
Design of China's intangible cultural heritage inheritance and protection system based on intelligent media technology

Intangible cultural heritage plays an irreplaceable role in the process of national cultural and economic development. The key factor to solve the problem of intangible cultural heritage protection is to solve the problem of "dissemination". Among them, under the background of contemporary culture, multimedia is not only a tool for people to live and learn, but also an important medium for the protection and dissemination of intangible cultural heritage. The application of intelligent media technology in the inheritance and dissemination of intangible cultural heritage will, on the one hand, break through people's cognitive structure of cultural dissemination, and make the inheritance subjects of intangible cultural heritage more diverse, the forms of dissemination more diverse and the aesthetic experience more profound. On the other hand, it will build a brand-new communication form, realize fission communication, and make the communication of intangible cultural heritage faster, more efficient and more accurate. Intelligent media technology can promote the more effective dissemination and higher quality utilization of intangible cultural heritage by establishing a larger cultural communication platform, stimulating wider cultural consumption, and transforming more cultural resources. This paper proposes that one of the innovation directions of intangible cultural heritage protection is multidisciplinary multimedia design application based on new media application, and finally forms multimedia interactive products with optimized user experience.
Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography

To date, most mammography-related AI models have been trained using either film or digital mammogram datasets with little overlap. We investigated whether or not combining film and digital mammography during training will help or hinder modern models designed for use on digital mammograms.To this end, a total of six binary classifiers were trained for comparison. The first three classifiers were trained using images only from Emory Breast Imaging Dataset (EMBED) using ResNet50, ResNet101, and ResNet152 architectures. The next three classifiers were trained using images from EMBED, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), and Digital Database for Screening Mammography (DDSM) datasets. All six models were tested only on digital mammograms from EMBED.The results showed that performance degradation to the customized ResNet models was statistically significant overall when EMBED dataset was augmented with CBIS-DDSM/DDSM. While the performance degradation was observed in all racial subgroups, some races are subject to more severe performance drop as compared to other races.The degradation may potentially be due to ( 1) a mismatch in features between film-based and digital mammograms ( 2) a mismatch in pathologic and radiological information. In conclusion, use of both film and digital mammography during training may hinder modern models designed for breast cancer screening. Caution is required when combining film-based and digital mammograms or when utilizing pathologic and radiological information simultaneously.
A new progressive open-set recognition method with adaptive probability threshold

In the traditional pattern classification method, it usually assumes that the object to be classified must lie in one of given (known) classes of the training data set. However, the training data set may not contain the class of some objects in practice, and this is considered as an Open-Set Recognition (OSR) problem. In this paper, we propose a new progressive open-set recognition method with adaptive probability threshold. Both the labeled training data and the test data (objects to be classified) are put into a common data set, and the k-Nearest Neighbors (k-NNs) of each object are sought in this common set. Then, we can determine the probability of object lying in the given classes. If the majority of k-NNs of the object are from labeled training data, this object quite likely belongs to one of the given classes, and the density of the object and its neighbors is taken into account here. However, when most of k-NNs are from the unlabeled test data set, the class of object is considered very uncertain because the class of test data is unknown, and this object cannot be classified in this step. Once the objects belonging to known classes with high probability are all found, we re-calculate the probability of the other uncertain objects belonging to known classes based on the labeled training data and the objects marked with the estimated probability. Such iteration will stop when the probabilities of all the objects belonging to known classes are not changed. Then, a modified Otsu’s method is employed to adaptively seek the probability threshold for the final classification. If the probability of object belonging to known classes is smaller than this threshold, it will be assigned to the ignorant (unknown) class that is not included in training data set. The other objects will be committed to a specific class. The effectiveness of the proposed method has been validated using some experiments.
Anytime bottom-up rule learning for large-scale knowledge graph completion

Abstract Knowledge graph completion is the task of predicting correct facts that can be expressed by the vocabulary of a given knowledge graph, which are not explicitly stated in that graph. Broadly, there are two main approaches for solving the knowledge graph completion problem. Sub-symbolic approaches embed the nodes and/or edges of a given graph into a low-dimensional vector space and use a scoring function to determine the plausibility of a given fact. Symbolic approaches learn a model that remains within the primary representation of the given knowledge graph. Rule-based approaches are well-known examples. One such approach is AnyBURL. It works by sampling random paths, which are generalized into Horn rules. Previously published results show that the prediction quality of AnyBURL is close to current state of the art with the additional benefit of offering an explanation for a predicted fact. In this paper, we propose several improvements and extensions of AnyBURL. In particular, we focus on AnyBURL’s capability to be successfully applied to large and very large datasets. Overall, we propose four separate extensions: (i) We add to each rule a set of pairwise inequality constraints which enforces that different variables cannot be grounded by the same entities, which results into more appropriate confidence estimations. (ii) We introduce reinforcement learning to guide path sampling in order to use available computational resources more efficiently. (iii) We propose an efficient sampling strategy to approximate the confidence of a rule instead of computing its exact value. (iv) We develop a new multithreaded AnyBURL, which incorporates all previously mentioned modifications. In an experimental study, we show that our approach outperforms both symbolic and sub-symbolic approaches in large-scale knowledge graph completion. It has a higher prediction quality and requires significantly less time and computational resources.
Continuous Integration and Continuous Deployment (CI/CD) for Web Applications on Cloud Infrastructures

Continuous Integration and Continuous Deployment (CI/CD) for Web Applications on Cloud Infrastructures
TbsNet: the importance of thin-branch structures in CNNs

The performance of a convolutional neural network (CNN) model is influenced by several factors, such as depth, width, network structure, size of the receptive field, and feature map scaling. The optimization of the best combination of these factors poses as the main difficulty in designing a viable architecture. This article presents an analysis of key factors influencing network performance, offers several strategies for constructing an efficient convolutional network, and introduces a novel architecture named TbsNet (thin-branch structure network). In order to minimize computation costs and feature redundancy, lightweight operators such as asymmetric convolution, pointwise convolution, depthwise convolution, and group convolution are implemented to further reduce the network's weight. Unlike previous studies, the TbsNet architecture design rejects the reparameterization method and adopts a plain, simplified structure which eliminates extraneous branches. We conduct extensive experiments, including network depth, width, etc. TbsNet performs well on benchmark platforms, Top 1 Accuracy on CIFAR-10 is 97.02%, on CIFAR-100 is 83.56%, and on ImageNet-1K is 86.17%. Tbs-UNet's DSC on the Synapse dataset is 78.39%, higher than TransUNet's 0.91%. TbsNet can be competent for some downstream tasks in computer vision, such as medical image segmentation, and thus is competitive with prior state-of-the-art deep networks such as ResNet, ResNeXt, RepVgg, ParNet, ConvNeXt, and MobileNet.