Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Layer Random Walk Method for Local Dynamic Community Detection in Brain Functional Network

Detecting the time-varying community structure of brain functional network is very important to reveal dynamic properties of the human brain. Although several community detection methods have been proposed, they are limited in real application due to their poor performance in large dynamic network and difficulty in parameter setting without prior knowledge. To address these problems, this paper proposes a novel dynamic community detection method for the brain network based on random walk, named as ML-RW. This method uses local community discovery instead of global community detection to improve its ability to deal with large dynamic networks. Specifically, ML-RW first selects a query brain region and sends out multiple random walkers starting from this region to explore local community structures of all networks in dynamic network simultaneously. It updates the visiting probability vector of each walker by aggregating the transition probabilities from itself and two temporally adjacent networks. Since the influence strength from one network to another is adaptively tuned according to the relevance of visiting histories of two networks, ML-RW could guarantee the temporal smoothness of the detected modular structure without introducing the hyper-parameter and thus avoids the problem of parameter setting in existing methods. Experiments on two public real neuroimaging datasets demonstrate that our proposed method has more potential to capture subtle community variations in the brain region, stronger ability to discover biomarkers for brain diseases, and higher test-retest reliability than the conventional method.
A Phased Optimization Method for Robot Dragging Teaching without Torque Sensors

Aiming at the requirements of the robot dragging teaching in practical application, the dragging process is divided and the corresponding dragging control strategy is adopted, so as to improve the flexibility of the robot dragging. On the basis of the known robot dynamics model, the disturbance observer based on generalized momentum is used to estimate the external force of the robot, and the admittance control algorithm is adopt to realize the dragging following the external force. In order to further improve the performance of the dragging teaching, the whole dragging process is divided into static state, traction state and braking state, and the corresponding control strategy is proposed to optimize the starting stage and braking stage in the dragging process. Experimental results show that: increasing torque compensation can effectively reduce the starting time of robot joints, and the starting time can be reduced by 40.22% ~ 51.31% compared without torque compensation; the introduction of variable stiffness parameters in the braking stage can effectively reduce the braking time of robot joints, and the braking time can be reduced by 77.65% ~ 83.97% compared with no braking scheme.
Android Malware Identification and Polymorphic Evolution Via Graph Representation Learning

Developing techniques to identify malware is critical. The polymorphic nature of malware makes it difficult to detect, especially if the detection is done with Hash-based based techniques. Image-based binary representations have been shown to be more robust to popular polymorphic obfuscation techniques. In contrast to image-based techniques, in this paper, we employed a graph-based technique that extracts control flow graphs from Android APK binary. To process the resulting graph, we use a procedure combining a new graph representation learning method, called Inferential SIR-GN for Graph representation, that preserves graph structural similarities, with XGboost, which is a standard machine learning model. Then, we apply this procedure to MALNET, which is a publicly available cybersecurity database that provides image and graph-based Android APK binary representations for a total 1,262,024 million Android APK binary with 47 types and 696 families. Experimental results show that this graph-based procedure is even more accurate than the image-based approach. Moreover, this paper provides a procedure that, by leveraging Inferential SIR-GN is able to create malware polymorphic evolution representations to use during the train of the XGboost that strengthens the malware classification tasks when the train and test datasets are split temporally according to the binary creation date. This means that our procedure can predict malware polymorphic evolution.
A Human Mobility Data Driven Hybrid GNN+RNN Based Model For Epidemic Prediction

Epidemic simulation traditionally serves as one of the important methods to forecast how an epidemic may spread among a population. However, there are two key limitations that restrict the scope of such methods. The first limitation is that the existing tools rely on different sets of static parameters (e.g., infection probability, recovering probability) for simulating an epidemic spread that may fail to capture the dynamic nature of population interactions that acts as a dominant factor in an epidemic spread scenario such as COVID-19 pandemic. To handle this challenge, we propose a machine learning based model that combines a Graph Convolutional Neural Network (GCN) and a Recurrent Neural Network (RNN). It integrates the ability of the GCN to capture spatial dependency in human interaction and the ability of the RNN to incorporate temporal effects of the virus spread. The second limitation is that these methods do not address the computation overhead problem when dealing with time-dynamic graphs. Training a GCN on a very large graph suffers from the communication overhead from different graph partitions and the computation overheads stemming from partitioning dynamic graphs. This limitation impacts the scalability of the existing systems. To solve this challenge, we partition the graph in a computationally less expensive manner by partitioning the graph using the min-cut principle. We conducted comprehensive large scale real-world human mobility data driven experiments. Our experimental result shows that the proposed machine learning based forecasting model achieves overall 84% classification accuracy with greater than 72% precision and 62% recall. Also, the proposed graph partitioning approach reduces computation time and commutation overhead by a significant margin.
Hidden Buyer Identification in Darknet Markets via Dirichlet Hawkes Process

Darknet markets are underground markets for various illicit transactions, including selling or brokering drugs, weapons, and stolen credit cards. To combat these illicit activities in cyberspace, it is critical to understand the activity behaviors of participants in the darknet markets. Currently, many studies focus on studying the activities of vendors. However, there is no much work on analyzing buyers. The key challenge is that the buyers are anonymized in darknet markets. To ensure the anonymity of transactions, we only observe the first a nd last digits of a buyer’s ID, such as "a**b", on most of the darknet markets. To tackle this challenge, we propose a hidden buyer identification model, called UNMIX, which can group transactions from one hidden buyer into one cluster given a transaction sequence from an anonymized ID. UNMIX is able to model the temporal dynamics information as well as the product, comment, and vendor information associated with each transaction. Then, the transactions with similar patterns in terms of time and content are grouped as a subsequence from one hidden buyer. Experiments on the data collected from three real-world darknet markets and one DBLP publication dataset demonstrate the effectiveness of our approach measured by various clustering metrics. Case studies on real transaction sequences explicitly show that our approach can group transactions with similar patterns into the same clusters.
Predicting lncRNA-protein interactions based on graph autoencoders and collaborative training

Long non-coding RNAs(lncRNAs) play an important role in various biological processes. lncRNAs usually perform their molecular functions by interacting with proteins. Therefore, it is essential to predict potential lncRNA-protein associations for disease prevention and disease treatment. Label-propagation-based methods are widely used for predicting associations among biological entities. However, in these approaches, similarity computation and label propagation are separate procedures, which decrease the effectiveness of label propagation. Moreover, the prediction accuracy of existing models is also not ideal. In this paper, we proposed an end-to-end deep learning lncRNA-protein Interaction predictor through Graph Autoencoders and Collaborative training (LPIGAC). Different from previous studies, our model implemented two graph autoencoders on lncRNA graph and protein graph respectively, and trained these two graph autoencoders collaboratively. Graph autoencoders on lncRNA graph and protein graph are competent to reconstruct score matrix through initial association matrix, which is equivalent to propagate labels on graphs. This end-to-end framework can strengthen the robustness and precision of the label propagation procedure. Cross validations indicate that LPIGAC outperforms current lncRNA-protein associations prediction methods. Case studies demonstrate that LPIGAC is competent to detect potential lncRNA-protein associations. Source code of our paper is available at https://github.com/zhanglabNKULPIGAC.
Human-Centered Clustering for Time Series Data

Clustering is a fundamental step of several data science pipelines leading to compute groups of objects with high similarity. In this paper, we focus on the problem of clustering time series and we show how we can make this process human-centered. In particular, by relying on FeatTS , a feature-based semi-supervised framework, we show how parameters can be tuned by the users in an intuitive fashion. The considered parameters are: (1) the learning threshold leading to choose how much labeled data are needed as input; (2) the cutting threshold leading to prune time series whose distance is below this threshold; (3) the number of clusters desired as output leading to create different clusters than the number of classes reflected by the labeled data. In particular, while the first two parameters allow to tune the amount of the supervision and tweak the number of significant features, the third parameter leads to showcase the robustness of the clustering process with respect to the number of clusters provided as input. Finally, the significance of the entire multi-step clustering pipeline is empirically demonstrated through a handful of ablation tests.
A discussion of damage mitigation of natural disasters: How to increase citizens' preparedness

It is widely recognised that community-wide support and learning is important to ensure that people are adequately prepared for natural disasters. To enhance preparedness of citizens, a critical theme is how to utilise the benefit of information Communication Technology (ICT) in the scheme of ICT-based learning programmes to raise citizens' perceptions of disaster risk and how to enable citizens to be ready for the future disasters. And in so doing, collaboration with the public sector, local disaster management NPOs and other stakeholders would be important topic. However, for such training programmes to be meaningful is important that they are based on citizens' sense of urgency about all aspects of disasters and a clear understanding by policy makers of what elements should be prioritised in the programme. This study first recognises the existence of a gap between citizens' perceptions of the real and the virtual scape of natural disasters, and then, based on data from a survey of citizens' attitudes in X city in Japan, quantitatively captures the factors and elements of disasters that influence citizens' perceptions of city resilient, and suggests implications for policy makers and relevant stakeholders. The results will be presented to policy makers and stakeholders. This is expected to provide models and scales of analysis for ICT implementation in disaster prevention and mitigation, i.e. coping with natural disasters brought about by climate change.
A Survey on the Fine-grained Complexity of Constraint Satisfaction Problems Based on Partial Polymorphisms

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. A Survey on the Fine-grained Complexity of Constraint Satisfaction Problems Based on Partial Polymorphisms Miguel Couceiro, Lucien Haddad, Victor Lagerkvist
An efficient and flexible inference system for serving heterogeneous ensembles of deep neural networks

Ensembles of Deep Neural Networks (DNNs) has achieved qualitative predictions but they are computing and memory intensive. Therefore, the demand is growing to make them answer a heavy workload of requests with available computational resources. Unlike recent initiatives on inference servers and inference frameworks, which focus on the prediction of single DNNs, we propose a new software layer to serve with flexibility and efficiency ensembles of DNNs.Our inference system is designed with several technical innovations. First, we propose a novel procedure to found a good allocation matrix between devices (CPUs or GPUs) and DNN instances. It runs successively a worst-fit to allocate DNNs into the memory devices and a greedy algorithm to optimize allocation settings and speed up the ensemble. Second, we design the inference system based on multiple processes to run asynchronously: batching, prediction, and the combination rule with an efficient internal communication scheme to avoid overhead.Experiments show the flexibility and efficiency under extreme scenarios: It successes to serve an ensemble of 12 heavy DNNs into 4 GPUs and at the opposite, one single DNN multi-threaded into 16 GPUs. It also outperforms the simple baseline consisting of optimizing the batch size of DNNs by a speedup up to 2.7X on the image classification task.
Point cloud indexing using Big Data technologies

We present a system for scalable indexing of very large point cloud data in the cloud using common Big Data technologies. We adjusted the procedure for building the common modifiable-nested-octree data-structure to the Map-Reduce paradigm using Morton-Codes for fast point grouping and sampling. Our system requires only two shuffle operations independently of the size of the point cloud, thus enabling linear scalability with the number of cluster nodes. Combining the modifiable-nested-octree with a hash-based grid also allows our system to efficiently deal with data updates. Based on a series of experiments with freely available point cloud datasets of up to 82 billion points we show that our system outperforms the state-of-the-art by up to a factor of 3. As our system is cloud-native, even the largest point cloud datasets can be indexed efficiently. Storing the resulting data-structure in a distributed database enables fast range queries supporting level of detail for web-based rendering.
Analysis of Gene Expression Cancer Data Set: Classification of TCGA Pan-cancer HiSeq Data

In our research, supervised machine learning algorithms were applied to analyze and compare their capability of cancer classification. Our research used eight machine learning algorithms: Decision Tree, Gradient Boosting, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Machine learning models were generated by training the algorithms on the TCGA Pancancer HiSeq data set. This data set is an RNA sequencing (RNA-seq) data set consisting of five separate cancer types such as breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), and Prostate adenocarcinoma (PRAD). The data set was preprocessed with feature selection, oversampling, and normalization techniques. The preprocessed methods were implemented by selecting only the best features and thus removing the inconsequential features, balancing the sample size of each cancer type, and rescaling the values of numeric attributes. Our goal was to determine which algorithm generates a classification model that shows the best performance when categorizing cancer types by employing the following evaluation measures: accuracy, precision, recall, area under curve (AUC) score, F-1 score, and processing time.
Analysis of Queuing Delay in LTE System: Model and Validation

We address the problem of controlling the end-to-end per-packet delay for real-time video communication. We provide a simple analytical model for predicting delay for the Constant Bit Rate (CBR) traffic in the uplink Long Term Evolution (LTE). We initially understand the LTE uplink scheduling mechanism using Network Simulator-3 (NS-3) and then utilize it to present a Packet Data Convergence Protocol (PDCP) queue length evolution model for the real LTE network. We identified a way to obtain a delay in queuing model using the average length of the queue and mean arrival rate. We provide a solution approach to obtain queuing delay and empirical validation against the simulation and experimental results.
Color Based Object Categorization Using Histograms of Oriented Hue and Saturation

In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.
Open Data Lake to Support Machine Learning on Arctic Big Data

The era of big data is evolving with the introduction of the data lake concept. While a data warehouse provides a well-structured model to manage big data, a data lake accepts data of any types and formats with or without schema and provides access to the data for diverse communities of users. A data lake provides flexible, agile, and scalable solution to manage the ever-increasing volume of big data we are witnessing in the world today, including many siloed data collected over the years by researchers through Arctic expeditions. In this paper, we present our conceptual model of a data lake for integrating the diverse huge amount of data collected by researchers during Arctic expedition. We also design a baseline metadata using a data-driven approach to manage the disparately huge structured, semi-structured, and unstructured data collected from the Arctic region. The resulting open data lake not only effectively manages big Arctic data but also supports machine learning on these big data.
Seasonal Changes of Glacier Lakes in Tibetan Plateau Revealed by Multipolarization SAR Data

In the context of global warming, monitoring glacial lakes is of great significance for revealing climate change and mitigating glacier lake outburst floods (GLOFs). Multitemporal mapping of glacier lakes through optical satellite remote-sensing imageries can only provide us comparative analysis of glacier lake changes during long time intervals. In recent years, the rapid development of synthetic aperture radar (SAR) has made it possible to observe seasonal changes of glacial lakes. This letter proposed a polarization enhancement-based maximum interclass variance (PE-MIV) method to accurately and automatically mapping the seasonal cycles of glacial lakes based on multipolarization SAR data. Taking the Gongcuo and Langcuo glacial lakes in Laigu village, Tibetan Plateau, as examples, based on 113 Sentinel-1 SAR imageries covering 2017 to 2020 with a 12-day revisiting time, the seasonal boundary changes of both lakes were extracted automatically with a relative accuracy of 93.59%. The seasonal variations of glacial lake area are closely related to temperature and precipitation. This letter demonstrated that the satellite SAR (short revisiting time, multipolarization, etc.) is an effective tool to monitor areal changes of glacier lakes, which can be widely applied in detecting glacier lakes, GLOFs, and their response to climate change.
Identifying and Characterizing Opioid Addiction States Using Social Media Posts

Opioid addiction constitutes a significant contemporary health crisis that is multifarious in its complexity. Modeling the epidemiology of any addiction is challenging in its own right. For opioid addiction, the challenge is exacerbated due to the difficulties in collecting real-time data and the circumscribed nature of information opioid users may disclose owing to stigma associated with prescription misuse. Given this context, identifying the progression of individuals through the stages of (opioid) addiction is one of the more acute problems in epidemiological modeling whose solution is crucial for designing specific interventions at both personal and population levels. We describe a computational approach for determining and characterizing addiction stages of opioid users from their social media posts. The proposed approach combines recurrent neural network learning with information-theoretic analysis of word-associations and context-based word embedding to determine addiction stage-specific language usage. Users who have a high likelihood for relapsing back to drug-use are identified and characterized using propensity score matching and logistic regression. Experimental evaluations indicate that the proposed approach can distinguish between various addiction stages and identify users prone to relapse with high accuracy as evidenced by F1 scores of 0.88 and 0.79 respectively.
Network-based link prediction of scientific concepts – a Science4Cast competition entry

We report on a model built to predict links in a complex network of scientific concepts, in the context of the Science4Cast 2021 competition. We show that the network heavily favours linking nodes of high degree, indicating that new scientific connections are primarily made between popular concepts, which constitutes the main feature of our model. Besides this notion of popularity, we use a measure of similarity between nodes quantified by a normalized count of their common neighbours to improve the model. Finally, we show that the model can be further improved by considering a time-weighted adjacency matrix with both older and newer links having higher impact in the predictions, representing rooted concepts and state of the art research, respectively.