Reviewerly/exHarmony huggingface dataset:
Grocery Recommendation using Improvised Apriori Algorithm

In the present world from small companies to giant company's product recommendation system plays a very major role, also people are very much interested in online shopping these days, so Recommendations are utilized to make the customer's job easier and faster. The majority of these recommendations are made based on their previous transaction history and association rules generated from it. Apriori algorithm is the most widely used method for generating association rules based on frequent product sets. But it has a drawback, while generating association rules it uses the whole transactions list and frequent product sets, ignoring the seasonal transactions for generating rules. Hence seasonal transactions are not considered for mining rules, if a person is buying a cake for Christmas, it's very likely that recommender system recommends birthday balloons, birthday caps as recommendations. But actually, it should recommend Christmas related products for recommendation. To solve this problem, we proposed a model using chi square test with improvised apriori algorithm.
Reverse ETL for Improved Scalability, Observability, and Performance of Modern Operational Analytics - A Comparative Review

We are on the verge of the Fourth Industrial revolution, in which data analytics and industry insights will play a critical role in data-driven, quicker, and better decision-making. A data warehouse (DW) is a store of strategic data accumulated over time using ETL techniques from various sources. Traditional DW operations primarily consist of obtaining data from many sources, converting the data into a suitable format, and then putting the data into a DW schema for further analysis. Reverse ETL fits into the current data stack and is distinct from regular ETL as firms pursue creative data strategies. This paper will discuss the significance and process of reverse ETL, why it is taking center stage in cloud environments to eliminate data silos created by SQL in data engineering, and why modern analytics prefers reverse ETL for scalability, performance, and better visual representations.
Device Fingerprinting in Wireless Networks using Deep Learning

Device fingerprinting involves identifying devices based on attributes provided by their configuration and usage. In this work a Deep Neural Network (DNN) architecture is designed for device fingerprinting. DNN is fed Inter Arrival Time (IAT) and Transmission Time (TT) of preprocessed wireless network traffic. The DNN consists of multiple Convolution Neural Networks (CNN), Rectified Linear Units (ReLU), and maximum pooling layers. As a final step, two fully connected layers, a softmax layer and a classification layer, are applied to classify devices. To evaluate the proposed model, two benchmark datasets, SIGCOMM-2004 and SIGCOMM-2008, were used. Using only 200 frames, it can accurately fingerprint 74 devices in SIGCOMM-2004 and 48 devices in SIGCOMM-2008 with accuracy of 97.04% and 97.70% respectively. The experimental results indicate that the proposed method is more efficient, since it requires fewer frames and produces a higher level of accuracy.
Real-time Face Tracking Based on a Configurable Ensemble of Detectors

The deployment of face tracking capabilities at the network edge requires (near) real-time performance under strict computational and energy constraints. Existing approaches often use object detectors with low complexity for tracking to satisfy limited resource constraints. An obvious problem with limiting complexity is that it has a direct impact on performance (e.g., ability to detect faces), especially at lower resolutions. In this study, we present a novel face tracking system for edge computing devices, which combines a tracking-by-detection algorithm with an ensemble of detectors. This system utilizes an online decision-making strategy based on extracting scene information from a density map to inform the active configuration of object detectors and image resolution. Using this system, we enhance real-time processing capability and reduce energy consumption by adaptively making trade-offs in resolution and active detectors to maintain tracking performance while minimizing resource costs. The proposed face tracking system is coupled with a multi-frame bounding box matching algorithm to provide multi-facial tracking functionality. We demonstrate the effectiveness of our system through experiments using the Multiple Object Tracking (MOT) Head Tracking 21 dataset.
Imaging Through Turbulence with GANs

We present a self-supervised and self-calibrating multi-shot approach to imaging through atmospheric turbulence, called TurbuGAN. Our approach requires no paired training data, adapts itself to the distribution of the turbulence, flexibly leverages prior knowledge of images from different domains, outperforms existing approaches, and can generalize from tens to tens of thousands of measurements. We achieve such functionality through an adversarial sensing framework adapted from CryoGAN [1], with a discriminator network to match the distributions of captured and simulated measurements. We extend CryoGAN by (1) generalizing the forward measurement model to incorporate physically accurate and computationally efficient models for light propagation through anisoplanatic turbulence, (2) enabling adaptation to slightly misspecified forward models, and (3) flexibly leveraging prior knowledge of images from different domains using pretrained generative networks. We validate the effectiveness of TurbuGAN with simulated experiments using realistic models for atmospheric turbulence-induced distortion. An extended version of this manuscript is available [2].
VLSI Hardware Architecture of Neural A* Path Planner

Path planning is an elemental and critical task in modern autonomous systems. Recently, Neural <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{A}^{\ast}$</tex> , a data-driven neural planner reformulated from the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{A}^{\ast}$</tex> search algorithm, has been proposed with high performance. However, because the path search procedure of the Neural <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{A}^{\ast}$</tex> path planner is mainly composed of matrix operations, such computation-intensive nature poses severe challenges for deployment on the time-constrained power-constrained embedded platforms. In this paper, we propose an efficient hardware architecture for the Neural <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{A}^{\ast}$</tex> path planner with specifically path search scheme. The hardware accelerator is implemented and synthesized using CMOS 28nm technology. Evaluation result shows the proposed hardware design achieves order-of-magnitude increase over CPU and GPU implementations with respect to area efficiency and energy efficiency.
Domain-Invariant Feature Alignment Using Variational Inference for Partial Domain Adaptation

The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an optimal source domain with identical label space is a challenging task. Partial domain adaptation alleviates this problem of procuring a labeled dataset with identical label space assumptions and addresses a more practical scenario where the source label set subsumes the target label set. This, however, presents a few additional obstacles during adaptation. Samples with categories private to the source domain thwart relevant knowledge transfer and degrade model performance. In this work, we try to address these issues by coupling variational information and adversarial learning with a pseudo-labeling technique to enforce class distribution alignment and minimize the transfer of superfluous information from the source samples. The experimental findings in numerous cross-domain classification tasks demonstrate that the proposed technique delivers superior and comparable accuracy to existing methods.
Adaptive Regression Trees for Nonlinear Adaptive Filtering

Decision trees provide interperable parametric nonlinear models with effective algorithms to train the models on a fixed set of training data. In this paper we devise a method of making these models adaptive, so they adjust to data with changing statistics. This is accomplished by partitioning the trees in the model into sets called groves, then aging out groves, similar to the aging of errors in a recursive least-squares models. As old groves age out, new groves are introduced which compensate both for the changes in statistics of the data and the aging of the data. The adaptive algorithm is tested on a prediction problem on a simulated nonlinear signal.
Data Sculpting: Interpretable Algorithm for End-to-End Cohort Selection

Many scientific and medical analysis involves fitting a parametric model over a heterogeneous data set. The model is often chosen to be low capacity (e.g. logistic regression) in order to make statistical inference about the association between each feature and the outcome (e.g. odds ratio). However the simple model often cannot capture the heterogeneity in the data. For example, a subset of the data might follow a clean logistic relation, but other data points could follow different relations so that the fitting a logistic regression over the entire set may not find any association. In this paper, we propose a novel algorithm, Data Sculpting, for simultaneously learning to select a subset of the data while fitting the desired parametric model on the selected cohort. Data Sculpting retains the statistical inference convenience of the original model, while leveraging end-to-end differentiable optimization (via the Concrete selector) to learn interpretable rules for selecting the cohort. Extensive experiments demonstrate that Data Sculpting is efficient, robust and substantially improves over the standard approaches.
Adaptive NN-based OFDM Receivers: Computational Complexity vs. Achievable Performance

Ahstract― We revisit the design and retraining capabilities of neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers that combine channel estimation, equalization and soft-demapping for time-varying and frequencyselective wireless channels. Attracted by the inherent advantages of small NNs in terms of computational complexity during inference and (re-)training, we first analyze the performance of different neural receiver architectures, including versions with reduced complexity. We observe, that such neural receivers with reduced complexity show an expected but graceful degradation in terms of their achievable bit error rate (BER) performance. Further, we focus on the adaptive retraining of site-specific NN-based receivers and show that performance losses due to a reduced number of parameters can be compensated by a continuous retraining of the receiver on-the-fly and that generalization can be achieved through adaptivity. Finally, we propose an improved retraining process via data augmentation and demonstrate a better performance and faster adaptation to current channel conditions.
Soft Linear Algebra Over Noisy GF(q) Matrices

The problem of solving <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A\mathbf{x}=\mathbf{b}$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> is tall and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> and b are known only probabilistically may be solved over real or complex matrix using least squares or total least squares methods. But such problems also arise over a finite fields <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GF(q)$</tex> for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q\geq 2$</tex> , where these methods are not applicable. This paper describes linear algebra-based “soft” approaches to such problems. It extends previous work where the computations were done only over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GF(2)$</tex> . The significant computational method is a soft inner product theorem, stated and proved here over an arbitrary finite field, which can be applied to a variety of linear algebra computations, such as computing products, determinants, matrix inverses, etc. Other soft numerical methods (division, LU, Gauss-Jordan) are also discussed.
Handcrafted and Neural Network Based Features for Outlier Modulation Detection

Outlier detection is an important topic in radio frequency signal detection and classification. In this paper, two methods for outlier signal detection in the presence of strongly and moderately confusing modulations are proposed. The first method feeds selected handcrafted features to the distance discrimination classifiers for outlier detection. By removing the redundant features, this method can reach above 95% sensitivity and specificity at signal-to-noise ratio (SNR) equals 0dB and nearly 100% at high SNRs for mild confusers. In the second method, handcrafted features and neural network based features are used as inputs to several state-of-the-art machine learning classifiers such as one-class support vector machine, isolation forest, and one-class neural networks. From the results, the first method can achieve 83.90% accuracy; isolation forest and one-class neural networks with handcrafted features can reach 79.58% and 76.76% accuracy over all outlier signals at SNR=5dB which are noticeably higher than existing works in this field.
Towards Bayesian Learning of the Architecture, Graph and Parameters for Graph Neural Networks

Real life data often arises from relational structures that are best modeled by graphs. Bayesian learning on graphs has emerged as a framework which allows us to model prior beliefs about network data in a mathematically principled way. The approach provides uncertainty estimates and can perform very well on a small sample size when provided with an informative prior. Much of the work on Bayesian graph neural networks (GNNs) has focused on inferring the structure of the underlying graph and the model weights. Although research effort has been directed towards network architecture search for GNNs, existing strategies are not Bayesian and return a point estimate of the optimal architecture. In this work, we propose a method for principled Bayesian modelling for GNNs that allows for inference of a posterior over the architecture (number of layers, number of active neurons, aggregators, pooling), the graph, and the model parameters. We evaluate our proposed method on three mainstream datasets.
Deep Learning Based Modulation for Quantized SIMO Communications

High resolution analog to digital converters (ADCs) are major sources of power consumption in conventional fully digital receivers. To reduce power consumption, it is suggested to use a small number of low resolution ADCs. However, for a given number of low resolution ADCs, the best modulation scheme and corresponding quantization strategy that maximizes the spectral efficiency are still unknown. In this paper, a point-to-point singleinput multiple-output system with a hybrid receiver using low resolution ADCs is considered. A novel deep learning based modulation scheme and the associated quantization strategy are developed to maximize the spectral efficiency. It is observed that the derived modulation scheme can achieve optimal high SNR rates and perform near Shannon capacity at low and intermediate SNRs. Our results provide new insights into the optimal utilization of low resolution ADCs and optimal power-performance trade-off in terms of the spectral efficiency.
Joint Bandwidth Allocation, Computation Control, and Device Scheduling for Federated Learning with Energy Harvesting Devices

Federated learning (FL) is a promising technique for distilling artificial intelligence from massive data distributed in Internet-of-Things (IoT) networks while keeping their privacy. However, the efficient deployment of FL faces several challenges due to e.g., limited radio resources, computation capability, and battery capacity of IoT devices. To address these challenges, in this work, the energy harvesting technique is first enabled on IoT devices for supporting their long-term training. Then, the convergence rate of the FL algorithm is derived, which indicates that for reducing the learning latency, the data utility, i.e., the number of training samples, should be maximized in each training iteration. To this end, a data utility maximization problem for each iteration is formulated, under the constraints of limited time, bandwidth, computation frequency, and energy supply. The problem is mixed-integer and non-convex, and hence NP-hard. A joint bandwidth allocation, computation control, and device selection scheme is proposed. In the scheme, an energy-efficient training data contribution indicator is first derived for each device, and then a sequential device scheduling scheme is designed.
Neural Network Based Tracking of Maneuvering Unmanned Aerial Vehicles

The motion model of an unmanned aerial vehicle (UAV) is a time-varying function that is often unknown to the tracking entity, which makes accurate tracking of highly maneuvering UAVs a challenging problem. Most state-of-the-art tracking techniques employ a fixed set of parametric models that approximate the possible maneuvers along the target trajectory to a reasonable accuracy. However, such predetermined motion models might not be adequate for frequent and aggressive maneuvers performed by small UAVs. To this end, we build a data driven adaptive filtering algorithm that improves the tracking accuracy by using a recurrent neural network (RNN)-based motion model that is trained on realistic simulated data generated from a medium fidelity simulink model of a fixed-wing UAV. We then train another feed-forward neural network in conjunction with the pretrained RNN-based motion model, to adaptively combine the incoming current measurement with the predicted state based on the output of a change detection algorithm that detects any increase/decrease in the uncertainty in the predicted states. Our analysis and results show that the proposed tracking algorithm outperforms the state-of-the-art interactive multiple model (IMM) algorithm for highly maneuvering trajectories.
Secret Computing over Multiple Access Channels

We propose a novel secure computational system for wireless networks using the superposition property of the multiple access channel (MAC) for wireless over-the-air computing (OTAC). The proposed scheme leverages the propagation environment as a computation medium and further embeds homomorphic encryption (HE) elements to cryptographically secure the communication and computation of data. The resulting wireless system constitutes under its nomographic decomposition a universal wireless computing environment for noisy floating-point or complex inputs with security by design.
Learning Stable Graph Neural Networks via Spectral Regularization

Stability of graph neural networks (GNNs) characterizes how GNNs react to graph perturbations and provides guarantees for architecture performance in noisy scenarios. This paper develops a self-regularized graph neural network (SR-GNN) solution that improves the architecture stability by regularizing the filter frequency responses in the graph spectral domain. The SR-GNN considers not only the graph signal as input but also the eigenvectors of the underlying graph, where the signal is processed to generate task-relevant features and the eigenvectors to characterize the frequency response at each layer. We train the SR-GNN by minimizing the cost function and regularizing the frequency response close to one. The former improves the architecture performance, while the latter tightens the perturbation stability and alleviates the information loss through multi-layer propagation. We further show the SR-GNN preserves the permutation equivariance, which allows to explore the internal symmetries of graph signals and to exhibit transference on similar graph structures. Numerical results with source localization and movie recommendation corroborate our findings and show the SR-GNN yields a comparable performance with the vanilla GNN on the unperturbed graph but improves substantially the stability.
On Building Efficient and Robust Neural Network Designs

Neural network models have demonstrated outstanding performance in a variety of applications, from image classification to natural language processing. However, deploying the models to hardware raises efficiency and reliability issues. From the efficiency perspective, the storage, computation, and communication cost of neural network processing is considerably large because the neural network models have a large number of parameters and operations. From the standpoint of robustness, the perturbation in hardware is unavoidable and thus the performance of neural networks can be degraded. As a result, this paper investigates effective learning and optimization approaches as well as advanced hardware designs in order to build efficient and robust neural network designs.
PBFT Consensus Algorithm Based on Quality of Service Improvement

A consortium chain is a type of blockchain that is only open to some groups. The Practical Byzantine Fault Tolerance (PBFT) algorithm is commonly used in the consortium chain. However, there are problems with the simple selection of primary nodes and complicated communication processes. Given the above problems, this paper proposes to improve PBFT algorithm combined with node quality of service. The node quality of service model is established through multiple indicators of nodes, and nodes are divided into consistency nodes and light nodes according to the priority of service quality to ensure that the consensus process consists of nodes with higher priority are completed. Through the experimental results of this paper, it can be see that the improved scheme can significantly lessen the number of communications, improve the system throughput, and has good fault tolerance and security.
Information Extraction and Visualization for Cyber Financial Fraud Case

With the increasing popularity of the Internet and intelligent communication devices, communication means and information access have become more diversified, which has also led to the frequent occurrence of cyber fraud. In order to reduce the occurrence of cyber fraud and conduct in-depth research on the characteristics of cyber fraud, this paper collects the criminal judgment documents of the first instance of cyber fraud cases, analyzes the extractable information and extracts the information, visualize and analyzes the extracted information, and obtains the characteristics of cyber fraud cases via visual analysis. The extracted information is visually processed, and the characteristics of cyber fraud cases are analyzed as follows: in cyber fraud cases, the defendants are mostly male, most of them are between 20 and 30 years old, most of them are junior high school educated, and most of them are unemployed; The locations of the cases are mainly distributed in the southern coastal provinces and central China. The trend of team crime is obvious, and the amount involved is large, generally more than ten thousand Yuan; the main types of fraud cases are profit inducement.