Reviewerly/exHarmony huggingface dataset:
EAS-GCN: Enhanced Attribute-Aware and Structure-Constrained Graph Convolutional Network

Recently, graph neural networks (GNNs) have achieved significant success in many graph-based tasks. However, most GNNs are inherently restricted by over-smoothing, which limits performance improvement. In this paper, we propose an Enhanced Attribute-aware and Structure-constrained Graph Convolutional Network (EAS-GCN). Specifically, EAS-GCN first uses degree prediction to incorporate graph local structure information into autoencoder-specific representation. A delivery mechanism is then designed to pass the autoencoder-specific representation to the corresponding GCN layer. Autoencoder mainly assists GCN in learning enhanced attribute information, and node degree prediction assists GCN in learning local structure information. Furthermore, we theoretically analyze autoencoder could help alleviate the over-smoothing in GCN. Experimental results show that EAS-GCN enjoys high accuracy on the node classification task and can better alleviate over-smoothing.
GADAL: An Active Learning Framework for Graph Anomaly Detection

Graph Neural Networks (GNNs) have been widely used in graph-based anomaly detection tasks, and these methods require a sufficient amount of labeled data to achieve satisfactory performance. However, the high cost for data annotation leads to some well-designed algorithms in low practicality in real-world tasks. Active learning has been used to find a trade-off between labeling cost and model performance, while few prior works take it into anomaly detection. Therefore, we propose GADAL, a novel Active Learning framework for Graph Anomaly Detection, which employs a multi-aspects query strategy to achieve high performance within a limited budget. First, we design an abnormal-aware query strategy based on the scalable sliding window to enrich abnormal patterns and alleviate the class imbalance problem. Second, we design an inconsistency-aware query strategy based on the effective degree to capture the most specificity nodes in information aggregation. Then we provide a hybrid solution for the above query strategies. Empirical studies demonstrate that our query strategy significantly outperforms other strategies, and GADAL achieves a comparable performance to the state-of-art anomaly detection methods within less than 3% of the budget.
Integrated Bigdata Analysis Model for Industrial Anomaly Detection via Temporal Convolutional Network and Attention Mechanism

Bigdata analysis has been the key to the abnormal detection of industrial systems using the Industrial Internet of Things (IIoT). How to effectively detect anomalies using industrial spatial-temporal sensor data is a challenging issue. Deep learning-based anomaly detection methods have been widely used for abnormal detection and fault identification with limited success. Temporal Convolutional Network (TCN) has the advantages of parallel structure, larger receptive field and stable gradient. In this work, we propose a new industrial anomaly detection model based on TCN, called IAD-TCN. In order to highlight the features related to anomalies and improve the detection ability of the model, we also introduce attention mechanism into the model. The experimental results over real industrial datasets show that the IAD-TCN model outperforms the traditional TCN model, the long short-term memory network (LSTM) model, and the bidirectional long short-term memory network model (BiLSTM).
Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.
SgIndex: An Index Structure Supporting Multiple Graph Queries

With the rise of social networks, traffic navigation and other fields, graph applications become increasing extensive. To improve query efficiency, indexes are built to manage large-scale graph data. However, these indexes cost large memory space and can only support one single graph operation. We propose a two-layered index structure SgIndex, in which the first layer stores subgraph information, and the second layer stores adjacency information to support multiple path operations and subgraph matching queries. We propose a subgraph matching algorithm based on path join, which completes subgraph matching by searching SgIndex twice. The experimental results show that SgIndex achieves better performance on path queries and subgraph matching than existing index structures, and reduces memory overhead.
An Improved Yin-Yang-Pair Optimization Algorithm Based on Elite Strategy and Adaptive Mutation Method for Big Data Analytics

In order to analyze and explore the potential value of big data more effectively, intelligent optimization algorithms are applied to this field increasingly. However, data often has the characteristics of high dimension for big data analytics. As the dimension of data increases, the performance of the optimization algorithm degrades dramatically. The Yin-Yang-Pair Optimization (YYPO) is a lightweight single-objective optimization algorithm, which has stronger competitive performance compared with other algorithms and has significantly lower computational time complexity. Nevertheless, it also suffers from the drawbacks of easily falling into local optimum and elitism deficiency, resulting in unsatisfactory performance on high-dimensional problems. To further improve the performance of YYPO in solving high-dimensional problems for big data analytics, this paper proposes an improved Yin-Yang-Pair Optimization based on elite strategy and adaptive mutation method, namely CM-YYPO. First, the crossover operator using an elite strategy is introduced to record the individual optimal generated by point P1 as elite. After the splitting stage, the elite to cross-disturb point P1 is utilized to improve the global search performance of YYPO. Subsequently, the mutation operator with an improved adaptive mutation method is used to mutate point P1 to improve the local search performance of YYPO. The proposed CM-YYPO is evaluated by 28 test functions used in the Single-Objective Real Parameter Algorithm competition of the Congress on Evolutionary Computation 2013. The performance of CM-YYPO is compared with YYPO, YYPO-SA1, YYPO-SA2, A-YYPO, and four other single-objective optimization algorithms with superior performance, which are Salp Swarm Algorithm, Sine Cosine Algorithm, Grey Wolf Optimizer and Whale Optimization Algorithm. The experimental results show that, the proposed CM-YYPO can achieve more stable optimization capability and higher computational accuracy on high-dimensional problems, which prospects a promising idea to solve high-dimensional problems in the field of big data analytics.
Robust Clustered Federated Learning with Bootstrap Median-of-Means

Federated learning (FL) is a new machine learning paradigm to collaboratively learn an intelligent model across many clients without uploading local data to the server. Non-IID data across clients is a major challenge for the FL system because its inherited distributed machine learning framework is designed for the scenario of IID data across clients. Clustered FL is a type of FL method to solve non-IID challenges using a client clustering method in the FL context. However, existing clustered FL methods suffer the challenge of processing client-wise outliers which could be produced by minority clients with abnormal behaviour patterns or be derived from malicious clients. This paper is to propose a novel Federated learning framework with Robust Clustering (FedRoC) to tackle client-wise outliers in the FL system. Specifically, we will develop a robust federated aggregation operator using a bootstrap median-of-means mechanism that can produce a higher breakdown point to tolerate a larger proportion of outliers. We formulate the proposed FL framework into a bi-level optimization problem, and then a stochastic expectation-maximization method is adopted to solve the optimization problem in an alternative updating manner by considering EM steps and distributed computing simultaneously. The experiments on three benchmark datasets have demonstrated the effectiveness of the proposed method that outperforms other baseline methods in terms of evaluation criteria.
A Proposal of Blockchain and NFC-Based Electronic Voting System

One of the most significant ways for a community to make a decision and the most democratic event in today’s countries is voting. Therefore, conducting fair and trusted elections is a basic prerequires. To that end, a wide range of electronic voting systems has been proposed with great potential to minimize the cost of the process, ensure the participation of larger numbers and improve the conventional voting mechanisms. Security, reliability, secrecy and other challenges keep e-voting systems that have been implemented from being used on a large scale. Introducing new and trustworthy technologies such as blockchain and NFC to the voting process can make it faster, more efficient and less vulnerable to security breaches. In this paper, we introduce a new hybrid NFC and blockchain-based electronic voting system as a solution to address current e-voting concerns and issues. It ensures public and transparent voting process while maintaining voter anonymity.
Text-Driven Stylization of Video Objects

We tackle the task of stylizing video objects in an intuitive and semantic manner following a user-specified text prompt. This is a challenging task as the resulting video must satisfy multiple properties: (1) it has to be temporally consistent and avoid jittering or similar artifacts, (2) the resulting stylization must preserve both the global semantics of the object and its fine-grained details, and (3) it must adhere to the user-specified text prompt. To this end, our method stylizes an object in a video according to two target texts. The first target text prompt describes the global semantics and the second target text prompt describes the local semantics. To modify the style of an object, we harness the representational power of CLIP to get a similarity score between (1) the local target text and a set of local stylized views, and (2) a global target text and a set of stylized global views. We use a pretrained atlas decomposition network to propagate the edits in a temporally consistent manner. We demonstrate that our method can generate consistent style changes over time for a variety of objects and videos, that adhere to the specification of the target texts. We also show how varying the specificity of the target texts and augmenting the texts with a set of prefixes results in stylizations with different levels of detail. Full results are given in the supplementary and in full resolution in the project webpage: https://sloeschcke.github.io/Text-Driven-Stylization-of-Video-Objects/ .
Methods Improving Solar Power System Efficiency Based on Geographical Coordinates and Sun Position Calculators

Many extensive research efforts has been conducted on solar energy over the past decades. Granted, scientists believe that the sun can actually provide more than enough energy to satisfy the world's energy needs. However solar power is limited in terms of producing power unfailingly in all conditions. This paper presents new methods of increasing solar system power efficiency without using sensors, but rather using sun position calculators and algorithms based on the astronomical equation. A solar tracking system was implemented and compared using three different control methods; a tracking code that uses Algerian postal codes, a GPS based tracker and a graphical user interface. The system was designed and simulated using Arduino microcontroller with MATLAB and Proteus environments.
Cautious Distributed Belief

This paper introduces and studies a notion of cautious distributed belief. Different from the standard distributed belief, the cautious distributed belief of a group is inconsistent only when all group members are individually inconsistent. The paper presents basic results about cautious distributed belief, investigates whether it preserves properties of individual belief, and compares it with standard distributed belief. Although both notions are equivalent in the class of reflexive models, this is not the case in general. While we argue that an understanding of the concept of cautious distributed belief from first principles is important, cautious distributed belief can be expressed using standard distrbuted belief and we show that the propositional language extended only with cautious distributed belief is in fact strictly less expressive than the propositional language extended only with standard distributed belief. We, finally, identify a small extension of the language making the former as expressive as the latter.
Some results and properties of non-incidence topological spaces in simple graphs

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Asmhan Flieh Hassan, Ammar Mousa Jafar; Some results and properties of non-incidence topological spaces in simple graphs. AIP Conf. Proc. 13 February 2023; 2414 (1): 040096. https://doi.org/10.1063/5.0114876 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Mining Significant Utility Discriminative Patterns in Quantitative Databases

Drawing a discriminative pattern in quantitative datasets is often represented to return a high utility pattern (HUP). The traditional methods output patterns with a utility above a pre-given threshold. Nevertheless, the current user-centered algorithm requires outputting the results in a timely manner to strengthen the interaction between the mining system and users. Pattern sampling can return results with a probability guarantee in a short time, and it could be a candidate technology to mine such discriminative patterns. In this paper, a novel approach named HUPSampler is proposed to sample one potential HUP, which is extracted with probability significance according to its utility in the database. HUPSampler introduces an interval constraint on the length of HUP and randomly extracts an integer k according to the utility proportion firstly; then, the HUPs could be obtained efficiently from a random tree by using a pattern growth way, and finally, it returns a HUP of length k randomly. The experimental study shows that HUPSampler is efficient in regard to memory usage, runtime, and utility distribution. In addition, case studies show that HUPSampler can be significantly used in analyzing the COVID-19 epidemic by identifying critical locations.
The Development of Contextual Learning-Based Big Books on the Science Literacy Ability of Grade V Elementary School Students

The purpose of this study is to produce an effective and valid contextual learning-based big book used to improve the science literacy skills of grade V elementary school students. This type of research uses Research and Development (RnD) using the ADDIE model. The subjects in this study were grade V students of SDN 2 Gamping, SDN 2 Suruh, and SDN 2 Karangsoko. The data collection technique in this study is a test. The research instruments used include observations, interviews, prototype assessments, expert validation, and science literation ability tests. Contextual learning-based big books are declared valid for use through expert test validation. The test results of teaching materials, materials, languages, and practitioners obtained an average of 92.93%, which is very valid. The results of the application of big books in small-scale and field-scale tests have an increase in the average science literacy ability of students before and after the use of big books. The results of the hypothesis test obtained a significance of 0.000 (&lt; 0.05) that there was an increase in the average science literacy ability of students before and after the use of big books. so that contextual learning-based big books meet the categories of valid, practical, interesting, and can improve the science literacy skills of elementary school student.
Research on pattern recognition of different music types in the context of AI with the help of multimedia information processing

Music is a form of art in which the sounds are timed and organized. Music is a kind of entertainment that mixes sounds in a way that people like, find fascinating, or to which they desire to dance. Most music is created via one or more people's vocal or instrumental efforts. By the dictionary, music is defined as having at least one of the following three elements: rhythm, melody, and harmony. Music is utilized in therapy because of its apparent benefits on behavior. Various physiological circumstances have different metabolite expression patterns that can be studied using pattern recognition in multimedia information processing. Music therapy includes various activities, including singing, playing instruments, dancing, and listening to music. Music-making with artificial intelligence (AI) uses neural networks, which are massive collections of computer bits that aim to stimulate brain activity. The neural network (NN) may be bombarded with music to see if it picks up on patterns the way the human brain does when repeatedly exposed to new stimuli. It will get the hang of them eventually. Experts believed that AI would be unable to generate music unless it first mimics a human-created data collection. By providing a conceptual paradigm for multimedia information processing. The end effect will be entirely different depending on how many hours of music are placed into it. For AI to learn from patterns or features in data on its own, it needs big data (BD), fast, repeated processing, and complex algorithms. The use of technology makes the process of creating analytical models much faster. A new AI-BD tool is an opportunity, not a danger for people currently working as artists. People are beginning to ask what constitutes acceptable work as AI grows more prominent in the music and art industries to gain efficiency of 97.8%. Future music will be heavily impacted by listeners' bodies and emotions all the time. For example, wearable technology may detect a person's mood and play the music that matches it. It is the next step in personalization. The AI-BD methodology improves the efficiency, accuracy, etc., compared to other existing models by gaining 97.8%, performance analysis 97.2%, reliability ratio 95.6%, and survivability analysis 98.2%.
Towards Query-Efficient Black-Box Attacks: A Universal Dual Transferability-Based Framework

Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting with it many times and producing global perturbations. However, all pixels are not equally crucial to the target model; thus, indiscriminately treating all pixels will increase query overhead inevitably. In addition, existing black-box attacks take clean samples as start points, which also limits query efficiency. In this article, we propose a novel black-box attack framework, constructed on a strategy of dual transferability (DT), to perturb the discriminative areas of clean examples within limited queries. The first kind of transferability is the transferability of model interpretations. Based on this property, we identify the discriminative areas of clean samples for generating local perturbations. The second is the transferability of adversarial examples, which helps us to produce local pre-perturbations for further improving query efficiency. We achieve the two kinds of transferability through an independent auxiliary model and do not incur extra query overhead. After identifying discriminative areas and generating pre-perturbations, we use the pre-perturbed samples as better start points and further perturb them locally in a black-box manner to search the corresponding adversarial examples. The DT strategy is general; thus, the proposed framework can be applied to different types of black-box attacks. We conduct extensive experiments to show that, under various system settings, our framework can significantly improve the query efficiency of existing black-box attacks and attack success rates.
PSEV-BF Methodology for Object Recognition of Birds in Uncontrolled Environments

Computer vision methodologies using machine learning techniques usually consist of the following phases: pre-processing, segmentation, feature extraction, selection of relevant variables, classification, and evaluation. In this work, a methodology for object recognition is proposed. The methodology is called PSEV-BF (pre-segmentation and enhanced variables for bird features). PSEV-BF includes two new phases compared to the traditional computer vision methodologies, namely: pre-segmentation and enhancement of variables. Pre-segmentation is performed using the third version of YOLO (you only look once), a convolutional neural network (CNN) architecture designed for object detection. Additionally, a simulated annealing (SA) algorithm is proposed for the selection and enhancement of relevant variables. To test PSEV-BF, the repository commons object in Context (COCO) was used with images exhibiting uncontrolled environments. Finally, the APIoU metric (average precision intersection over union) is used as an evaluation benchmark to compare our methodology with standard configurations. The results show that PSEV-BF has the highest performance in all tests.
Lossless Reconstruction of Convolutional Neural Network for Channel-Based Network Pruning

Network pruning reduces the number of parameters and computational costs of convolutional neural networks while maintaining high performance. Although existing pruning methods have achieved excellent results, they do not consider reconstruction after pruning in order to apply the network to actual devices. This study proposes a reconstruction process for channel-based network pruning. For lossless reconstruction, we focus on three components of the network: the residual block, skip connection, and convolution layer. Union operation and index alignment are applied to the residual block and skip connection, respectively. Furthermore, we reconstruct a compressed convolution layer by considering batch normalization. We apply our method to existing channel-based pruning methods for downstream tasks such as image classification, object detection, and semantic segmentation. Experimental results show that compressing a large model has a 1.93% higher accuracy in image classification, 2.2 higher mean Intersection over Union (mIoU) in semantic segmentation, and 0.054 higher mean Average Precision (mAP) in object detection than well-designed small models. Moreover, we demonstrate that our method can reduce the actual latency by 8.15× and 5.29× on Raspberry Pi and Jetson Nano, respectively.