Reviewerly/exHarmony huggingface dataset:
Adaptive Sampling and Quick Anomaly Detection in Large Networks

The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well as the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. Note to Practitioners—Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies.
A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis

Recognizing the type of connected devices to a network helps to enforce security policies. In smart grids, identifying massive number of grid metering terminals based on network traffic analysis is almost blank and existing research has not proposed a targeted end-to-end model to solve the flow classification problem. Therefore, we proposed a hierarchical terminal recognition approach that applies the details of grid data. We have formed a two-level model structure by segmenting the grid data, which uses the statistical characteristics of network traffic and the specific behavior characteristics of grid metering terminals. Moreover, through the selection and reconstruction of features, we combine three algorithms to achieve accurate identification of terminal types that transmit network traffic. We conduct extensive experiments on a real dataset containing three types of grid metering terminals, and the results show that our research has improved performance compared to common recognition models. The combination of an autoencoder, K-Means and GradientBoost algorithm achieved the best recognition rate with F1 value of 98.3%.
SAR MARITIME OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK

Insufficient data of SAR target recognition task leads to low accuracy and poor generalization of model and the SAR imaging mechanism leads to the insignificant difference between ship targets, which make recognition difficult. To overcome the above problems, we propose a SAR maritime method using siamese networks for model pre-training. Siamese network produce sample pairs to ease training sample insufficiency, and output difference of sample pairs to help model learning heterogeneous difference. Then, transfer the pre-training parameters of feature exaction layer to an end-to-end model. Finally, the end-to-end convolutional neural network is obtained by fine-tuning the parameters with supervised information. Experimental results show that the SAR maritime target recognition method based on siamese network training can effectively improve the recognition accuracy under the training condition of a small number of samples.
Quantum Annealing With Integer Slack Variables for Grid Partitioning

Quantum annealing (QA) can be used to efficiently solve quadratic unconstrained binary optimization (QUBO) problems. Grid partitioning (GP), which is a classic NP-hard integer programming problem, can potentially be solved much faster using QA. However, inequality constraints in the GP optimization model are difficult to handle. In this study, a novel solution framework based on QA is proposed for GP problems. The integer slack (IS) and binary expansion methods are applied to transform GP problems into QUBO problems. Instead of introducing continuous variables in traditional slack methods, the proposed IS method can avoid complex iteration processes when using QA. The case study demonstrates that the IS method obtains accurate feasible solutions with less calculation time.
What’s Driving Conflicts Around Differential Privacy for the U.S. Census

The U.S. Census Bureau’s use of differential privacy has been fiercely debated among interested parties. Accuracy loss has been at the forefront, but conflicting confidentiality notions help explain why common ground is lacking. We propose three ways of understanding confidentiality conflicts and offer suggestions for researchers and organizations adopting formal privacy.
Towards interpretable machine learning for clinical decision support

A major challenge in delivering reliable and trustworthy computational intelligence for practical applications in clinical medicine is interpretability. This aspect of machine learning is a major distinguishing factor compared with traditional statistical models for the stratification of patients, which typically use rules or a risk score identified by logistic regression. We show how functions of one and two variables can be extracted from pre-trained machine learning models using anchored Analysis of Variance (ANOVA) decompositions. This enables complex interaction terms to be filtered out by aggressive regularisation using the Least Absolute Shrinkage and Selection Operator (LASSO) resulting in a sparse model with comparable or even better performance than the original pre-trained black-box. Besides being theoretically well-founded, the decomposition of a black-box multivariate probabilistic binary classifier into a General Additive Model (GAM) comprising a linear combination of non-linear functions of one or two variables provides full interpretability. In effect this extends logistic regression into non-linear modelling without the need for manual intervention by way of variable transformations, using the pre-trained model as a seed. The application of the proposed methodology to existing machine learning models is demonstrated using the Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forests (RF) and Gradient Boosting Machines (GBM), to model a data frame from a well-known benchmark dataset available from Physionet, the Medical Information Mart for Intensive Care (MIMIC-III). Both the classification performance and plausibility of clinical interpretation compare favourably with other state-of-the-art sparse models namely Sparse Additive Models (SAM) and the Explainable Boosting Machine (EBM).
Deep Semantic Hashing with Structure-Semantic Disagreement Correction via Hyperbolic Metric Learning

Semantic hashing is a crucial component of content based search and retrieval systems. To achieve an effective semantic hashing for images, it is essential to map them to hash space in a way that preserves the semantic information. Most state-of-the-art deep semantic hashing approaches do not fully take into account the structural information and the inherent hierarchy in the dataset. Also, the distribution of hash codes is primarily driven by semantic information that comes from the supervision labels. We propose a semantic hashing framework which utilizes the hyperbolic metric learning to learn the structural and hierarchical information. This information is leveraged in the form of proxy labels for training the hashing network with the proposed novel Structure-Semantic Disagreement (SSD) loss. SSD enforces the model to learn to hash with semantic as well as structural information, leading to more robust and uniformly distributed hash codes. Tests on multiple public domain datasets establish the effectiveness of the proposed approach. Moreover, the developed SSD loss can also be applied to other classification models to improve the representation by enforcing the model to use the structure information more effectively.
Straggler-Resilient Secure Aggregation for Federated Learning

We present CodedSecAgg, a straggler-resilient secure aggregation scheme for federated learning. CodedSecAgg intro-duces redundancy on the devices' data across the network, which is leveraged during the iterative learning phase at the central server to update the global model based on the responses of a subset of the devices. Compared to other schemes in the literature, which deal with device dropouts by ignoring the contribution of dropped devices, the proposed scheme does not suffer from the client-drift problem. We apply CodedSecAgg to a classification problem on the MNIST dataset. For a scenario with 120 devices, we show that CodedSecAgg outperforms state-of-the-art LightSecAgg in terms of latency by a factor of 6.6 to 15.8, depending on the number of colluding agents, for an accuracy of 95%.
Fault Resilience of DNN Accelerators for Compressed Sensor Inputs

The sensor subsystem is a crucial component in a Deep Neural Network (DNN) inference framework. However, the high amount of sensor data being generated manifests as an energy bottleneck in resource-constrained edge devices. Towards this end, we propose SeNNse, a novel sensor compression methodology that optimizes the energy requirement of sensor subsystems, which involves a two-step approach of subsampling and subsequent supersampling of the sensor images via inter-polation. However, such compressed sensor inputs may result in substantial performance degradation in the presence of bit-flip faults manifested in DNN accelerators (as shown in this paper). Such faults occur frequently in semiconductor device memory due to variegated reasons, ranging from impingement of high-energy particles to structural deformities. We evaluate our approach on Multilayer Perceptrons (MLP), trained on MNIST, EMNIST, and CIFAR-10 datasets. Our proposed SeNNse frame-work furnishes maximum energy savings of 62.1%, with a negligible reduction in classification accuracy. However, our results also indicate larger performance degradation, of up to 21.56%, due to bit-flip faults for such compressed inputs, which is mainly attributed to the concise set of input activations being fed to the neural networks during inference.
SATMeas - Object Detection and Measurement: Canny Edge Detection Algorithm

In today’s modern world, detecting objects in real-time and measuring their dimensions has become a challenge in many areas of industry. Many big warehouses, courier companies, airport containers at airports etc. cannot always get precise/accurate measurement using human hands. In our research we have developed an application SATMeas to detect the object and give the measurements of the object in real-time. We have utilized the canny edge detection algorithm; we made some morphological changes to improve the algorithm to obtain the desired changes. These morphological procedures we did are combination of nonlinear procedures performed generally on the arrangement of pixels without changing their numeral values, erosion and dilation are the keys for morphological operations. We use the OpenCV function cv2.findContours to detect contours by identifying the shapes of the objects inside the edge outline. This is done after edge detection and closing any gaps between edges. We determine pixels per metric variable by relying on a reference object. The Euclidean distance between sets of center points was then determined to get the calculations. Putting it all together, we developed an application SATMeas to detect object in real time and measure it as well. In this way, the outlines of the distinctive objects within the frame were kept. The proposed method works exceptionally quick and efficiently, it gives 98–99% success in determining the measurement of the objects. Hope other scientists can add more features to it and industrialists can commercialize it for better and modern industrial use.
Decision Support System for Student Selection Recipients of COVID-19 Impacts using MOORA

SMPN 174 Jakarta is a junior high school in East Jakarta that has 719 students and 22 classrooms, including being one of the schools with government programs in the form of assistance such as Smart Jakarta Card, Smart Indonesia Program, and School Operational Assistance. To meet educational needs and distribute to students who are entitled to assistance. Currently, the selection process for student beneficiaries at SMPN 174 Jakarta is still done manually, so there is a risk of making decisions that are not on target and require a longer time. Therefore, a decision support system was made to select students who receive assistance. The development of the decision support system used in this research is the Multi-Objective Optimization Method on the Basic of Ratio (MOORA). The tests include black-box testing and comparison testing of calculation results with manual calculations. The Decision Support System for Selection of Student Recipients for the Impact of COVID-19 has been successfully created, and the results of selecting recipients of this assistance are obtained based on the calculation of the highest optimization value. The verifier has approved them with a percentage of 66.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> strongly agreeing and 33.3% agreeing.
Adaptive Active Contour Model for Brain Tumor Segmentation

For accurately diagnosing the severity of brain tumors in MRI images, Glioma segmentation is a significant step. The Glioma segmentation is due to noise and weak edges of organs in medical images. The geodesic active contour model (GACM) is a standard method for the segmentation of complex organ structures based on edge maps. The GACM performs poorly due to this noise and weak edges. So, the authors propose a method that uses adaptive kernels instead of a constant kernel for creating strong edge maps for GACM. The kernels used in phase congruency are Log Gabor kernels, which resemble similar anisotropic properties like Gabor kernels. They have replaced these with adaptive kernels. This adaptive kernel-based phase congruency provides a robust edge map to be used in GACM. Experimentation shows that when compared with state-of-the-art edge detection techniques, adaptive kernels enhance the weak as well as strong edges and improve the overall performance. Moreover, the proposed methodology substantially requires fewer parameters compared to existing ACM methods.
Predicting Salaries with Random-Forest Regression

For companies it is essential to know the market price of the salaries of their current and prospective employees. Predicting such salaries is challenging, as many factors need to be considered, and large real datasets for learning are scarce. For this reason, research on salary predictions is comparably rare and limited. In this study, we investigate whether and how an advanced machine-learning approach, namely ensembles of random-forest regression, can achieve high-quality salary predictions. We use a large real dataset of more than three million employees and more than 300 professions. Our approach learns –for each profession– a random-forest regression model to predict salaries. In our evaluation, we show that this approach performs better than related work on salary prediction by machine-learning approaches with a mean absolute percentage error (MAPE) of 17.1%. We identify reducing the number of possible values of categorical variables, training separate models as well as outlier handling as the key factors for the results achieved.
Application of Federated Learning Approaches for Time-Series Classification in eHealth Domain

Contemporary Machine Learning approaches (e.g., Deep Learning) need huge volumes of data to build accurate and robust statistical models. Nowadays, such data are often collected by distinct and geographically distributed entities and successively transmitted to and stored by centralized nodes that implement the learning process. However, this approach exposes data to security and privacy issues during the transmitting process. In order to demonstrate the benefits which Federate learning can bring concerning these issues, this paper proposes an application of the Federated Learning approach to training a time series (TS)-based model for the early identification of the level of risk associated with patients with hypertension in a federated healthcare environment. The results demonstrate the goodness of the solution in comparison with traditional centralized approaches. The solution achieves good results; indeed, the accuracy is over 0.90%. The goodness of the model is also confirmed by the values of recall and precision, equal to 82% and 96%, respectively
Range-Free Localization Using Extreme Learning Machine and Ring-Shaped Salp Swarm Algorithm in Anisotropic Networks

Node localization is one of the basic requirements in various Internet of Things applications. Among a wide range of localization schemes, the range-free localization algorithm is promising as a cost-effective technique. However, the localization accuracy of this technique is susceptible to various anisotropy factors, such as the existence of holes, nonuniform node distribution, and dynamic radio propagation pattern. To this end, an accurate range-free localization model using extreme learning machine (ELM) and ring-shaped salp swarm algorithm (SSA) is proposed for anisotropic wireless sensor networks. First, the integer hop count between two adjacent nodes is quantized as a real number according to the Jaccard coefficient of their shared neighbor nodes. Second, exploiting the strong generalization and fast learning speed of ELM, a distance mapping model based on the modified real hop count is developed for solving anisotropic signal attenuation. Third, the coordinate calculation of normal nodes is formulated as a minimum problem by taking into account the weighted squared error of estimated distance, and the bounding box method is utilized to initialize the possible location boundary area of normal nodes. Finally, the SSA based on the ring-shaped topology is designed to compute the coordinates of normal nodes. Extensive simulations on several network topologies are conducted with the effect of multiple anisotropic factors. Experimental results show that the proposed algorithm is superior to other developed ones not only in localization accuracy but also in robustness against network anisotropy.
Recognitions of collision avoidance for unmanned surface vessels with visible light camera using broad learning system

Unmanned surface vessels(USVS) are equipped with various types of sensors responsible for the function of the surface vessel to sense the environment. Vision sensors have the advantages of direct recognition effects, low costs and wide recognition contents compared with obstacle avoidance sensors. Traditional recognition algorithms based on vision sensors often use deep learningapproaches, such as YOLO,Fast-RCNN,and SSD etc. Although these algorithms have high accuracy, they also have disadvantages such as complex network structure, high-performance computing equipment, and time-consuming training. In this paper, we propose a fast way to optimize the broad learning system(BLS) structure using the integration algorithm, and use the BLS as an individual learner,The k-fold cross validation approach is taken to avoid model overfitting. And Improv integration learning so as to vote integrate the recognition results of individual learners and output the final results. The experimental reaults that our method achieved a high accuracy on the dataset. compared with both of the single BLS and the traditional recognition algorithm.
Image Translation Based Nuclei Segmentation for Immunohistochemistry Images

Numerous deep learning based methods have been developed for nuclei segmentation for H &E images and have achieved close to human performance. However, direct application of such methods to another modality of images, such as Immunohistochemistry (IHC) images, may not achieve satisfactory performance. Thus, we developed a Generative Adversarial Network (GAN) based approach to translate an IHC image to an H &E image while preserving nuclei location and morphology and then apply pre-trained nuclei segmentation models to the virtual H &E image. We demonstrated that the proposed methods work better than several baseline methods including direct application of state of the art nuclei segmentation methods such as Cellpose and HoVer-Net, trained on H &E and a generative method, DeepLIIF, using two public IHC image datasets.
Aspect Detection in Sportswear Apparel Reviews for Opinion Mining

Manufacturers and brand owners apply sentiment analysis techniques on customer reviews to identify customer opinions on their products and services. Sentiment analysis at the document level or sentence level does not provide a complete view of the customer opinion because customers may express their opinion on different aspects of the product or service within a single review. This issue has inspired aspect-level opinion mining. Two core tasks are involved with aspect-level opinion mining: aspect detection and aspect-based sentiment analysis. This research is aimed at the first task - aspect detection. The focused domain is sportswear apparel, which has been largely overlooked in the field of opinion mining. Accordingly, this paper presents a new dataset produced with manual annotations by domain experts, according to a newly defined aspect taxonomy. This research compares the performance of a set of pre-trained language models for the considered task, and achieves state-of the-art performance for sportswear apparel reviews using a novel ensemble method.