Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost Guarantee for Individual Fairness on Spectral Clustering

The graph mining algorithm has been widely used in various fields in recent years, among which the spectral clustering algorithm is based on spectral graph theory, which has the ability to cluster on an arbitrarily shaped sample space and converge to the global optimal solution compared with the traditional clustering algorithm. As algorithmic fairness has become a research hot-spot recently, more and more fairness constraints have been introduced into spectral clustering. Most studies focus on group fairness, with only a small number providing individual-level fairness constraints. Existing fair spectral clustering algorithms focus only on whether the clustering results are fair or the decreased rate of fairness loss. To this end, we propose an Individual Fair Spectral Clustering with Cost constraints (IFSCC), which ensures the clustering effect while also improving a certain degree of individual fairness. The experimental results show that IFSCC has the lowest COST value while having a comparable clustering effect compared to other individual fair spectral clustering algorithms. To the best of our knowledge, this paper is the first study to make a trade-off between the clustering effect and fairness performance.
A Canonical Queuing System with Heterogeneous Servers and Finite Storage: Optimality and Application

It is well known that the blocking probability of an M/M/1/1 system can be reduced by evenly splitting the capacity of its single server into two servers, which leads to an M/M/2/2 system with homogeneous servers. In this paper, we extend the analysis to an M/M/2/m $(m \geq 2)$ system with heterogeneous servers. For this system, we analytically prove that the blocking probability can be further reduced. As a matter of fact, there exists an optimal service ratio at which the blocking probability reaches its minimum. Moreover, we prove the optimality of average system size. The analysis is accompanied by graphical profiles. Due to its compact configuration of parallel servers and finite storage, this system has direct applications to a variety of emerging communication paradigms.
Vision Transformers for Biomedical Applications *

Advanced artificial intelligence (AI) methods, such as attention mechanisms using Transformer architecture, typically used for large language models, have proven their potential in biomedical applications. ViT is proven to be successful in various biomedical applications, such as cancer detection, remote photoplethysmography (rPPG), COVID-19 detection, and lung disease detection. As such, this paper presents a systematic review of the research gaps and future directions of Vision Transformers (ViT)s in biomedical applications to investigate their efficiency in integrating transformers with computer vision applications in the biomedical domain. The paper also explores the lightweight Mobile Vision Transformer (MoViT) models and their applications in the biomedical field. The reported outcomes of applying ViTs in detecting diseases, estimated heart rates and surgical applications are promising and have the potential to be transformed into treatment monitoring and medical diagnosis. It is also worth highlighting that ViT has demonstrated promising results in COVID-19 detection. In future work, it is recommended to improve the performance on ViT through adapting pre-trained models, the addition of more convolution layers, and hyperparameter optimisation. MoViT is a promising lightweight, advanced AI model that is useful in real-time computer vision applications in the biomedical field. ViTs are promising for improving medical research.
O-Minus Decomposition for Multiview Tensor Subspace Clustering

With the powerful ability to exploit the latent structure of self-representation information, multiple off-the-shelf low rank tensor constraints have been employed in multiview tensor subspace clustering (MTSC) for achieving significant performance. However, current approaches mainly suffer from a series of problems, such as the deficient exploration of self-representation due to the unbalanced unfolding matrices, the inability to simultaneously capture both intraview and interview information, and so forth. All these will lead to MTSC with insufficient access to global information, which is contrary to the target of multiview clustering. To alleviate these problems, we propose a new tensor decomposition called O-minus decomposition (OMD) for multiview clustering. Specifically, based on the tensor ring format, we present the O-minus structure, which consists of a circle with an efficient bridge linking two weakly correlated factors. In this way, the information from intraview and interview can be better obtained simultaneously. Moreover, the enhanced capacity to capture global low-rank information will be achieved. The alternating direction method of multipliers is used to solve the proposed optimization model for OMD-MVC. Numerical experiments on six benchmark datasets demonstrate the superiority of our proposed method in terms of F-score, precision, recall, normalized mutual information, adjusted rand index, and accuracy.
ISAA: Boost Repair Process by Constructing the Degree Constrained Optimal Repair Tree for Erasure-coded Systems

To ensure data reliability, large-scale distributed systems usually adopt erasure codes to restore failed nodes. However, existing erasure-coded repair strategies will cause heavy network traffics, which will increase the repair time. In order to boost the repair process, we consider optimizing the repair path which can be abstracted to a repair tree. Moreover, we add a degree constraint to each node to avoid local congestion. In this paper, we study the degree constrained optimal repair tree, which is an NP-hard problem. Current methods cannot find the optimal solution in a short time in complex non-uniform bandwidth networks. To obtain the optimal repair tree, an improved simulated annealing algorithm (ISAA) based on the Prufer code representation is proposed in this paper. In addition, we simulate the repair process of erasure codes in a non-uniform bandwidth network and experiments show that the repair time reduction can reach up to 66.4% and 88.6% with ISAA over Repair Pipelining and Partial-Parallel-Repair.
Generalized Co-Prime MIMO Radar for DOA Estimation With Enhanced Degrees of Freedom

In this paper, we propose a generalized co-prime multiple-input multiple-output (MIMO) configuration for direction of arrival estimation. Compared with the conventional co-prime MIMO radar that requires prototype co-prime arrays for transmitter and receiver, the proposed configuration enlarges the inter-element spacing of transmitter with an integer expansion factor. By vectorizing the sample covariance matrix, the generalized sum and difference coarray (GSDC) concept is defined for exploiting the advantages of proposed configuration. The analytical expressions for the expansion factor, the maximum consecutive lags, and the number of unique lags are derived carefully. It is verified that GSDC can obtain more degrees of freedom (DOFs) with the increase of expansion factor. Specifically, with <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(M+N)$ </tex-math></inline-formula> sensors, GSDC can provide <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(M^{2}N^{2})$ </tex-math></inline-formula> DOFs, whereas the conventional one only has <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(MN)$ </tex-math></inline-formula> DOFs. Simulation results demonstrate the usefulness of proposed configuration utilizing both spatial smoothing and sparse representation algorithms.
Transparent File Deduplication with Reduced Update Cost on Encryption Enabled Mobile Devices

Data deduplication has been long studied to achieve data reduction. However, deploying deduplication on encryption enabled mobile systems might consume much memory footprint and computation time for hash calculations. Moreover, frequent file updates on deduplicated files could badly degrade the deduplication efficacy due to the increased file-system metadata penalty. Considering the characteristics of mobile devices, an efficient data deduplication method is proposed in this paper. First, it separates the hash calculation into foreground and background stages. The background stage calculates the hash values of potentially duplicate files while the foreground stage quickly hashes the file which is being written using a lightweight hash algorithm. Second, a dual-level node structure is proposed to improve the file update efficacy for deduplicated files, saving more storage space against the file re-splitting. Besides, we implement a superlink call to make deduplication process compatible with file-based encryption. These methods are combined to realize a transparent file deduplication (TFDedup) approach, which eliminates redundant data and reduces the associated cost of file update. Experimental results show that TFDedup succeeds to lower the space consumption when serving file updates by 55.6% and accelerate the deduplication process by 50.3%.
Efficient Generation of Approximate Region-based Geo-maps from Big Geotagged Data

smart city applications scenarios, such as traffic monitoring, require regularly generating region-based geographical maps (geo-maps) such as choropleth, to uncover statistical patterns in the data, therefore helping municipalities to achieve better urban planning. However, with tremendous avalanches of big data arriving in fast streams, it is becoming cumbersome and inefficient to achieve the visualization task in a timely manner. Having said that, spatial approximate query processing presents itself as an indispensable and reliable solution in cases of data overloading. In this paper, we focus on presenting a novel system for generating efficiently region-based geo-maps from fast arriving big georeferenced data streams. We specifically present ApproxGeoViz. It is a system for generating approximate region-based maps from fast arriving data relying on a novel stratified-like spatial sampling method. We have built a standard-compliant prototype and tested its performance on real smart city data. Our results show that ApproxGeoViz is efficient in terms of time-based and accuracy-based QoS constraints such as running time and approximate map accuracy.
Robust Listwise Learning-to-Rank Approach for Database Query Optimizer

Finding the optimal query plan within a large planning space plays a crucial role in determining the performance of an SQL statement. Since traditional cost model-based approaches rely on manual parameter adjustments and DBA experience, the learned query optimizer has been proposed as an alternative to automatically determine the optimal query plan. However, recent methods face challenges in capturing all candidate query plan relationships and adapting to dynamic environments. In this paper, we design an improved learned query optimizer, Li-Lero, that could plug into the existing DBMS with non-intrusive modification. Inspirations from learning to rank, we employ a listwise approach to train a ranker to obtain the optimal with minimal execution time. Rather than computing the real execution time, such a ranker only measures the relative relationship of candidate plans. Furthermore, a robustness enhancement method is proposed to improve the dynamic adaptability of the model by utilizing the masking technique. We implement Li-Lero and demonstrate its outstanding performance using PostgreSQL. Comparative evaluations are conducted using two data sets with different data distributions, and results show that our proposed method achieves high prediction accuracy and is robust to dynamic environments.
Exploring Wi-Fi Privacy Disclosure: A Novel Approach to User Identity Prediction Based on Traffic Multi-level Information

Nowadays, people are accustomed to network communication via Wireless Fidelity (Wi-Fi) when using mobile phones and IoT devices in fixed places (such as homes and dormitories). This means that a large amount of Wi-Fi traffic data will be generated every day. This paper focuses on the leakage of user identity privacy in traffic, namely: can user identity attributes be inferred from real home Wi-Fi traffic data? If malicious attackers can obtain user identities so "conveniently", it means that the success rate of malicious activities such as phishing and fraud can be easily increased. Therefore, to explore this question, we propose a new method for predicting user attributes based on user network behavior similarity. Then we collected real world data for verification and compared it with other methods. Finally, this paper also analyzes the current response methods for Wi-Fi privacy disclosure and points out the problems and future research directions.
Accelerating Point Clouds Classification in Dynamic Graph CNN with GPU Tensor Core

Point clouds play a crucial role in various fields such as robotics, 3D modeling, and autonomous driving. DGCNN, as a representative work in this domain, has exhibited superior performance compared to classic models like PointNet++. Such models often display significant computational demands and encounter notable performance bottlenecks, so how to accelerate their calculations has become a noteworthy issue. Nevertheless, due to the inherent disparities between point cloud tasks and conventional convolution tasks, existing algorithms and architectural paradigms designed for convolution frequently prove to be ill-suited for point clouds, such as Tensor Core (TC). There have been studies focused on reducing DGCNN model complexity to improve computational efficiency, but research specifically tailored to TC acceleration remains relatively limited. Therefore, we present an encapsulation module that bridges the computation of model in Torch level with the components of the CUDA backend, introducing TC into DGCNN. It dynamically adjusts parameters and computation flow based on input data and transforms batch computations into streaming computations. Additionally, we propose a new computational organization approach, which involves the restructuring of two critical computation steps within DGCNN: the Get Graph Feature operation and the Conv operation. It significantly enhances the computational speed of these two operations while reducing memory redundancy of tensor data involved in the intermediate processes. In the experiment, our modifications achieve an average acceleration of 1.80X for the Get Graph Feature operation and 1.46X for the Conv operation, all while not compromising result accuracy.
Spectrum estimation and harmonic analysis

In the choice of an estimator for the spectrum of a stationary time series from a finite sample of the process, the problems of bias control and consistency, or "smoothing," are dominant. In this paper we present a new method based on a "local" eigenexpansion to estimate the spectrum in terms of the solution of an integral equation. Computationally this method is equivalent to using the weishted average of a series of direct-spectrum estimates based on orthogonal data windows (discrete prolate spheroidal sequences) to treat both the bias and smoothing problems. Some of the attractive features of this estimate are: there are no arbitrary windows; it is a small sample theory; it is consistent; it provides an analysis-of-variance test for line components; and it has high resolution. We also show relations of this estimate to maximum-likelihood estimates, show that the estimation capacity of the estimate is high, and show applications to coherence and polyspectrum estimates.
A Reactive Power Controller for a Two-Stage Transformerless PV Grid System by Employing Virtual Inertia Control

The rising integration of photovoltaic (PV) energy with the grid has several impacts on electrical power systems which demands a properly designed control scheme to maintain the power balance along with the voltage stability at the grid integration point commonly known as the Point of Common Coupling (PCC). As the PV systems are non-inertial and produce only active power, hence without regulating the reactive power the $PV$ microgrids often cause reactive power disturbances as well as transmission line impedance errors to the grids. Thus, while penetrating with grids, maintaining active and reactive powers simultaneously with the proper controlling of voltage and frequency of the PV systems is a challenging task. In this work, a virtual inertia control scheme is proposed for a two-stage transformerless PV grid system to maintain both active and reactive power of the system while changing system voltage under variable irradiance conditions. The control scheme is virtual and based on the dynamic equations involved with the synchronous generator. The overall system is simulated under MATLAB/SIMULINK and results are presented showing the control parameter performances including the DC bus voltage, grid voltage, frequency, active power, and reactive power. The results show that with the variation of system voltage, the control scheme is capable of maintaining the active and reactive power by varying the frequency along with the grid and inverter voltage and current.
TFS-index-based Multi-keyword Ranked Search Scheme Over Cloud Encrypted Data

Traditional searchable encryption schemes for clouds are generally based on TF-IDF vector space model, but they ignore the high-dimensional sparse characteristic of encrypted vectors. It will lead to substantial computational cost of inner product, and thus slows the search speed. In this paper, we propose a two-layer fast search index-based multi-keyword ranked search scheme (TFSRS) to address this problem. In the proposed TFSRS scheme, a keyword clustering-based equal-length dictionary partition (KCEDP) strategy is adopted to compress the document and search vectors, which benefits the process of inner product. Based on the strategy, a novel KCEDP-based vector space model (KCEDP-VSM) is proposed, and based on which, a two-layer fast search index (TFS-index) is presented. By using the TFS-index, secure inner product and symmetric encryption, the efficient multi-keyword ranked search scheme over encrypted cloud data is proposed. Experimental results show the better performance of the proposed schemes in search efficiency.
Realistic Sketch Face Generation via Sketch-Guided Incomplete Restoration

Generating realistic sketches of human faces is an important research direction in the fields of computer vision and computer graphics. However, generating high-quality sketch faces remains challenging, especially when dealing with incomplete input sketch images. In this paper, we propose a framework based on the U-Net generative network, which progressively optimizes network performance through a three-stage training strategy. We introduce the Sketch Guidance and Completion Network (SGCN), which is capable of restoring missing features, completing blurry edges, and handling occluded regions in sketch images. By employing multiple discriminators and adversarial training strategies, we enhance the realism and preserve fine details in the generated results. Through experiments on publicly available sketch face datasets, we demonstrate significant improvements in reconstructing incomplete sketch faces using our approach. The generated images exhibit enhanced completeness, realism, and retain fine details and important facial features. Quantitative evaluations validate the effectiveness and superiority of our method.
Efficient rate-distortion optimized media streaming for tree-reducible packet dependencies

In packetized media streaming systems, packet dependencies are often modeled as a directed acyclic graph called the dependency graph. We consider the situation where the dependency graph is reducible to a tree. This occurs, for instance, in MPEG1 video streams that are packetized at the frame level. Other video coding standards such as H.264 also allow tree-reducible dependencies. We propose in this context efficient dynamic programming algorithms for finding rate-distortion optimal transmission policies. The proposed algorithms are much faster than previous exact algorithms developed for arbitrary dependency graphs.
Hybrid Parallel Inference for Large Model on Heterogeneous Clusters for High Throughput

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. Data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× −3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy.
Multilabel Chromosome Classification Based on Denver and Chromosome Information

Karyotyping is essential for identifying genetic ab-normalities, assisting with prenatal screening, gaining insight into the genetics of cancer, researching evolution, and advancing research. It offers useful knowledge on chromosomal anomalies and makes contributions to many fields of biology, genetics, and medicine. A key component of karyotyping is chromosome classification, which entails classifying and arranging chromo-somes according to their size, shape, and banding patterns. While Denver categorization of chromosomes offers a defined and widely acknowledged system for naming and classifying human chromosomes into 7 groups, chromosome classification categorizes the human chromosomes from the metaphase images to 23 or 24 classes. In order to improve the effectiveness of the karyotyping system, a multilabel chromosomal classification approach is developed in this study. It makes use of the combined information from the 24 class chromosome classification infor-mation and the Denver classification information. The suggested method uses two Convolution Neural Networks, one for 24 class classification and one for Denvor classification. The proposed model is experimented with the public ChromosomeNet dataset as well as a private dataset generated at Regional Cancer Center, Thiruvananthapuram, Kerala and obtained the testing accuracy of 97 % and 70 % accuracies respectively. For the comparative study, CNN features of the 24-class classifier are used to train the traditional classifiers Decision Tree, Random Forest and SVM and proved that the proposed multilabel classifier outperforms the other models.