Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Web Application using Streamlit and Machine Learning

Credit Card Fraud is one of the major threads in the financial industry. Due to the covid-19 pandemic and the advance in technologies, the number of users is increasing, with the increased use of credit cards. Due to more use of credit cards, Fraud cases also increase day by day. The research community striving hard to explore myriad credit card fraud detection techniques, but changes in technology and the varying nature of credit card fraud make it difficult to develop an effective technique for the detection of credit card fraud. This research work used a real-world credit card dataset. To detect the fraud transaction within this dataset three machine learning algorithms are used (i.e. Random Forest, Logistic regression, and AdaBoost) and compared the machine learning algorithms based on their Accuracy and Mathews Correlation Coefficient (MCC) Score. In these three algorithms, the Random Forest Algorithm achieved the best Accuracy and MCC score. The Streamlit framework is used to create the machine learning web application.
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection

Generic event boundary detection (GEBD) is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries. The main challenge of this task is perceiving various temporal variations of diverse event boundaries. To this end, this paper presents an effective and end-to-end learnable framework (DDM-Net). To tackle the diversity and complicated semantics of event boundaries, we make three notable improvements. First, we construct a feature bank to store multi-level features of space and time, prepared for difference calculation at multiple scales. Second, to alleviate inadequate temporal modeling of pre-vious methods, we present dense difference maps (DDM) to comprehensively characterize the motion pattern. Finally, we exploit progressive attention on multi-level DDM to jointly aggregate appearance and motion clues. As a result, DDM-Net respectively achieves a significant boost of 14% and 8% on Kinetics-GEBD and TAPOS benchmark, and outperforms the top-1 winner solution of LOVEU Challenge@CVPR 2021 without bells and whistles. The state-of-the-art result demonstrates the effectiveness of richer motion representation and more sophisticated aggregation, in handling the diversity of GEBD. The code is made available at https://github.com/MCG-NJU/DDM.
Multiscale Wavelet Graph AutoEncoder for Multivariate Time-Series Anomaly Detection

In industrial facilities or IT systems, there are lots of multivariate time series generated from various metrics. Anomaly detection in multivariate time series is of great importance in applications such as fault diagnosis and root cause discovery. Recently, some unsupervised methods have made great progress in this task, especially the reconstruction architecture of autoencoders (AEs), learning normal distribution, and producing a significant error for anomalies. Although AEs can reconstruct subtle abnormal patterns well with the powerful generalization ability, it also leads to a high false negative. Moreover, these AE-based models ignore the dependence among variables at different time scales. In this article, we propose a novel anomaly detection framework named Multiscale wavElet Graph AE (MEGA). The main idea is to integrate discrete wavelet transform (DWT) into AE to decompose multivariate time series into multifrequency components and then reconstruct them, thereby highlighting various anomalies in specific frequency bands. Meanwhile, for anomalies caused by the changes of intervariable dependence, we introduce a dynamic graph module to capture such dependence on the decomposed multiscale frequency components. Experiments have been carried out on three public multivariate time-series anomaly detection datasets. The results demonstrate that the MEGA outperforms the state-of-the-art baselines.
Cross-domain Ensemble Distillation for Domain Generalization

Domain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domain ensemble distillation (XDED), that learns domain-invariant features while encouraging the model to converge to flat minima, which recently turned out to be a sufficient condition for domain generalization. To this end, our method generates an ensemble of the output logits from training data with the same label but from different domains and then penalizes each output for the mismatch with the ensemble. Also, we present a de-stylization technique that standardizes features to encourage the model to produce style-consistent predictions even in an arbitrary target domain. Our method greatly improves generalization capability in public benchmarks for cross-domain image classification, cross-dataset person re-ID, and cross-dataset semantic segmentation. Moreover, we show that models learned by our method are robust against adversarial attacks and unseen corruptions.
Cyber Security Maturity Assessment Framework for Technology Startups: A Systematic Literature Review

Cybersecurity has gained increasing importance among firms of different sizes and industries due to the significant rise of cyber-attacks over time. Technology startups are particularly vulnerable to cyber-attacks due to the lack of cyber security measures. This is because of limited human capital and financial resources to quantify cyber risks and allocate appropriate investments to cyber security. Technology startups are suppliers and vendors to large organisations such as MNCs, government and financial institutions. They could possibly have a network connection back to the large organisations and might even store confidential information of these large organisations such as financial records, personal data and other proprietary information. As such, with the lack of appropriate cyber security measures, technology startups may be an attack vector for malicious hackers to gain entry to the large organisations. Focusing on technology startups, this study conducted a systematic literature review on cyber security maturity assessment frameworks. This study addressed five research questions on the existing cyber security maturity assessment frameworks in various industries, the target for implementation, cyber security maturity level, shared control domains of these frameworks, and the quantification of the return of cyber security investments. Referring to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist, a detailed analysis was performed on 24 published research articles (out of 650) from reputable journals and conference proceedings from January 2011 to June 2022. The results revealed the lack of an end-to-end cyber security maturity assessment framework for technology startups. Despite the similarities in the cyber security maturity level for certain frameworks, the results revealed no singular framework that can evaluate the cyber security maturity level of technology startups. The results further revealed the lack of studies on the quantification of the return of cyber security investments in an end-to-end cyber security maturity assessment framework for technology startups. This put the startup in a vulnerable position since management is not able to obtain relevant data on the startup’s cyber maturity posture and without such information, they are not able to appropriately justify their security investments to mitigate the evolving cyber risks.
The Possibility of Inducing the Proteus Effect for Social VR Users

The Proteus effect in Virtual Reality (VR) happens when the user's behavior or attitude are affected by their avatar's appearance. The virtual body appearance is somehow affecting the emotional, behavioral and psychological state of its user. In recent years, manipulation of an avatar has become a more common situation because of social VR platforms. It is considered that the Proteus effect can be induced even for social VR users. However, there are possibilities to disrupt the Proteus effect by becoming accustomed to embodying an avatar or attachment to their avatar. In this paper, we investigated whether the Proteus effect can be induced even for social VR users. We experimented on how an artist-like avatar affects the score of creativity on brainstorming in comparison with a common avatar. Fourteen VRChat users participated in our experiment. We evaluate the number of ideas and the quality of ideas during brain-storming. As result, there are no significant differences and interactions between conditions regarding any measures. In addition, there are no equivalencies except the number of selected unique ideas. It implies that an artist-like avatar does not significantly affect the user's creativity in this experiment.. However, six of the fourteen participants reported that the artist-like avatar's appearance affected their thinking during task execution. Our results suggest that further research is needed to fully understand the elicitations, implications and limitations of the Proteus effect in VR.
Context-Enhanced Stereo Transformer

Stereo depth estimation is of great interest for computer vision research. However, existing methods struggles to generalize and predict reliably in hazardous regions, such as large uniform regions. To overcome these limitations, we propose Context Enhanced Path (CEP). CEP improves the generalization and robustness against common failure cases in existing solutions by capturing the long-range global information. We construct our stereo depth estimation model, Context Enhanced Stereo Transformer (CSTR), by plugging CEP into the state-of-the-art stereo depth estimation method Stereo Transformer. CSTR is examined on distinct public datasets, such as Scene Flow, Middlebury-2014, KITTI-2015, and MPI-Sintel. We find CSTR outperforms prior approaches by a large margin. For example, in the zero-shot synthetic-to-real setting, CSTR outperforms the best competing approaches on Middlebury-2014 dataset by 11 $$\%$$ . Our extensive experiments demonstrate that the long-range information is critical for stereo matching task and CEP successfully captures such information( $$^1$$ Code available at: github.com/guoweiyu/Context-Enhanced-Stereo-Transformer ).
VALHALLA: Visual Hallucination for Machine Translation

Designing better machine translation systems by considering auxiliary inputs such as images has attracted much attention in recent years. While existing methods show promising performance over the conventional text-only translation systems, they typically require paired text and image as input during inference, which limits their applicability to real-world scenarios. In this paper, we introduce a visual hallucination framework, called VALHALLA, which requires only source sentences at inference time and instead uses hallucinated visual representations for multi-modal machine translation. In particular, given a source sentence an autoregressive hallucination transformer is used to predict a discrete visual representation from the input text, and the combined text and hallucinated representations are utilized to obtain the target translation. We train the hallucination transformer jointly with the translation transformer using standard backpropagation with crossentropy losses while being guided by an additional loss that encourages consistency between predictions using either groundtruth or hallucinated visual representations. Extensive experiments on three standard translation datasets with a diverse set of language pairs demonstrate the effectiveness of our approach over both text-only baselines and state-of-the-art methods. Project page: http://www.svcl.ucsd.jects/valhalla.edu/pro.
3-D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion

3-D scene flow characterizes how the points at the current time flow to the next time in the 3-D Euclidean space, which possesses the capacity to infer autonomously the nonrigid motion of all objects in the scene. The previous methods for estimating scene flow from images have limitations, which split the holistic nature of 3-D scene flow by estimating optical flow and disparity separately. Learning 3-D scene flow from point clouds also faces the difficulties of the gap between synthesized and real data and the sparsity of LiDAR point clouds. In this article, the generated dense depth map is utilized to obtain explicit 3-D coordinates, which achieves direct learning of 3-D scene flow from 2-D images. The stability of the predicted scene flow is improved by introducing the dense nature of 2-D pixels into the 3-D space. Outliers in the generated 3-D point cloud are removed by statistical methods to weaken the impact of noisy points on the 3-D scene flow estimation task. Disparity consistency loss is proposed to achieve more effective unsupervised learning of 3-D scene flow. The proposed method of self-supervised learning of 3-D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds. The comparisons of multiple scene flow metrics are shown to demonstrate the effectiveness and superiority of introducing pseudo-LiDAR point cloud to scene flow estimation.
Preserving Privacy using Distributed Ledger Technology in Intelligent Transportation System

Intelligent Transportation System (ITS) is an enhanced application which mainly aims towards the curation of smart and interconnectivity services to ease the existing transportation systems. It is a field with a plethora of future possibilities so as to provide efficient and advanced techniques by solving and tackling the existing challenges and problems. Especially in cities and countries which aim towards developing smart cities, ITS plays an essential role in advancing the transportation sector with regards to the city. Using a number of AI and ML techniques, a variety of approaches, algorithms and techniques can be put to effective utilization to boost the ITS ecosystem. However, with every boon compliment few banes and plausible bottlenecks. One of the major concerns while using V2V networks is the privacy and security issue. The data being transferred amidst vehicles mainly caters to the status updates on the vehicles adjacent to it, the speed, and driver's information in case of any accidents or wherever required. In such cases, the privacy of the drivers details and personal information are at stake and often poses a major security challenge. Furthermore, there are chances of data breaches and similar attacks by malicious users which can lead to huge data loss and tampering of data. A possible solution to this problem is to approach it through the perspective of Blockchain. Blockchain is a concept of sharing information/ data in a distributed and decentralized manner. The numerous blocks present in a blockchain which are responsible for storing information do so by encrypting and securing the data and hence can be integrated with a V2V system for overcoming the privacy and security challenges.
Multi-Agent Reinforcement Learning-Based Coordinated Dynamic Task Allocation for Heterogenous UAVs

The coordinated dynamic task allocation (CDTA) problem for heterogeneous unmanned aerial vehicles (UAVs) in the presence of environment uncertainty is studied in this paper. Dynamic task allocation mainly solves the problem of resource reallocation after new tasks appear, so that the multi-UAV systems can quickly respond to further information and objectives. In this paper, the CDTA strategy for heterogenous UAVs is proposed through proposer-responser mechanism and prioritized experience replay, in which the multi-agent reinforcement learning (MARL)-based coordinated network is constructed to propose request, and the Q-network is developed to approximate expected return to determine the responser whether to participate in the dynamic task. The CDTA algorithm considers the uncertainty of dynamic task and has a high scalability in different UAV groups, which can reduce the burden of online calculation and increase the speed of online operation effectively. The experiment proves that the priority experience replay speeds up the convergence of the algorithm, and the scalability of the algorithm is verified within 10-180 UAVs. Comparison simulations with the game theory-based and reinforcement learning-based methods are provided to show the effectiveness of the proposed algorithm.
A Fast Knowledge Distillation Framework for Visual Recognition

While Knowledge Distillation (KD) has been recognized as a useful tool in many visual tasks, such as supervised classification and self-supervised representation learning, the main drawback of a vanilla KD framework is its mechanism that consumes the majority of the computational overhead on forwarding through the giant teacher networks, making the entire learning procedure inefficient and costly. The recently proposed solution ReLabel suggests creating a label map for the entire image. During training, it receives the cropped region-level label by RoI aligning on a pre-generated entire label map, which allows for efficient supervision generation without having to pass through the teachers repeatedly. However, as the pre-trained teacher employed in ReLabel is from the conventional multi-crop scheme, there are various mismatches between the global label-map and region-level labels in this technique, resulting in performance deterioration compared to the vanilla KD. In this study, we present a Fast Knowledge Distillation (FKD) framework that replicates the distillation training phase and generates soft labels using the multi-crop KD approach, meanwhile training faster than ReLabel since no post-processes such as RoI align and softmax operations are used. When conducting multi-crop in the same image for data loading, our FKD is even more efficient than the traditional image classification framework. On ImageNet-1K, we obtain 80.1% Top-1 accuracy on ResNet-50, outperforming ReLabel by 1.2% while being faster in training and more flexible to use. On the distillation-based self-supervised learning task, we also show that FKD has an efficiency advantage.
Geometric Anchor Correspondence Mining with Uncertainty Modeling for Universal Domain Adaptation

Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label space. However, domain shift and category shift make UniDA extremely challenging, which mainly lies in how to recognize both shared “known” samples and private “unknown” samples. Previous works rarely explore the intrinsic geometrical relationship between the two domains, and they manually set a threshold for the overconfident closed-world classifier to reject “unknown” samples. Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues. Specifically, we first develop a random walk-based anchor mining strategy together with a high-order attention mechanism to build correspondence across domains. Then a global joint local domain alignment paradigm is designed, i.e., geometric adversarial learning for global distribution calibration and subgraph-level contrastive learning for local region aggregation. Toward accurate target private samples detection, GATE introduces a universal incremental classifier by modeling the energy uncertainty. We further efficiently generate novel categories by manifold mixup, and minimize the open-set entropy to learn the “unknown” threshold adaptively. Extensive experiments on three benchmarks demonstrate that GATE significantly out-performs previous state-of-the-art UniDA methods.
A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

A hybrid multi-modal ensemble learning model is proposed for short-term solar irradiance forecasting based on historical observations and sky images in this paper. In the proposed model, historical observations of solar irradiance are utilized to extract temporal characteristics, and ground-based sky images are used as an exogenous input to offer cloud cover information. A powerful ensemble learning model, Extreme Gradient Boosting (XGBoost), is employed to capture the function relationships between input features and future observations. Since mean squared error loss is sensitive to the extreme large or small historical irradiance, a novel loss function is proposed to improve robustness of XGBoost. In order to find out the best controlling parameters, Rao-1 algorithm is employed for its easy operation. To validate performance of the proposed method, a solar irradiance dataset containing three-year historical observations and ground-based sky images collected from Folsom is employed. Meanwhile, five commonly applied methods, LASSO, Ridge regression, support vector regression, boosted regression trees, the generic XGBoost, are considered as benchmarking methods. The forecasting horizons from 5 to 30 min are considered for all compared methods while two metrics, mean absolute error and root mean squared error, are computed. Experimental results prove that the proposed hybrid model has better forecasting performance compared with benchmarking methods over all forecasting horizons.
Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

The local path planning, as one of the navigation stages, plays a significant role in the energy consumption of Self-Guided Vehicles (SGV).Since SGV must operate for several hours on a single battery charge to transport loads, its energy consumption is a critical issue.Therefore, this article puts forward an approach for boosting the energy efficiency of the local path planning stage using load position.Unlike other similar works which solely use robots' kinematic and kinetic constraints to develop energy-efficient local path planners, this article considers the effect of load position on SGV's dynamic.In this regard, first, the kinetic model of the differential drive SGV is developed to consider the change of SGV's Center of Mass (CoM) affected by load properties.Second, machine learning methods are used to create two learning models for online estimation of the position of CoM (PoCoM) and prediction of required energy of sample trajectories.Hence, the generated SGV's kinetic model is used to train the learning models.Finally, estimated parameters are employed to add a new constraint to extend the cost function of the local path planner.The outcomes of the study show that the proposed planner generates smoother and shorter paths to pass obstacles and corridors than a general one.Thus, SGV's energy consumption decreases by considering the load effect.