Reviewerly/exHarmony huggingface dataset:
Identifying Groups of Fake Reviewers Using a Semisupervised Approach

Online product reviews have become increasingly important in digital consumer markets where they play a crucial role in making purchasing decisions by most consumers. Unfortunately, spammers often take advantage of online reviews by writing fake reviews to promote/demote certain products. Most of the previous studies have focused on detecting fake reviews and individual fake reviewer-ids. However, to target a particular product, fake reviewers work collaboratively in groups and/or create multiple fake ids to write reviews and control the sentiments of the product. This article addresses the problem of finding such fake reviewer groups. More specifically, we propose a top-down framework for candidate fake reviewer groups’ detection based on the DeepWalk approach on reviewers’ graph data and a (modified) semisupervised clustering method, which can incorporate partial background knowledge. We validate our proposed framework on a real review dataset from the Google Play Store, which has partial ground-truth information about 2207 fraud reviewer-ids out of all 38 123 reviewer-ids in the dataset. Our experimental results demonstrate that the proposed approach is able to identify the candidate spammer groups with reasonable accuracy. The proposed approach can also be extended to detect groups of opinion spammers in social media (e.g. fake comments or fake postings) with temporal affinity, semantic characteristics, and sentiment analysis.
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

Machine Learning as a Service (MLaaS) is a popular and convenient way to access a trained machine learning (ML) model trough an API. However, if the user’s input is sensitive, sending it to the server is not an option. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. As a solution, we propose MLCapsule, a guarded offline deployment of MLaaS. MLCapsule executes the machine learning model locally on the user’s client and therefore the data never leaves the client. Meanwhile, we show that MLCapsule is able to offer the service provider the same level of control and security of its model as the commonly used server-side execution. Beyond protecting against direct model access, we demonstrate that MLCapsule allows for implementing defenses against advanced attacks on machine learning models such as model stealing, reverse engineering and membership inference.
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs

Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representations in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach. To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions. Furthermore, we introduce a reparameterisable sampling algorithm for the hyperbolic normal distribution to enable the gradient-based learning of HVGNN. Extensive experiments show that HVGNN outperforms state-of-the-art baselines on real-world datasets.
Frenetic at the SBST 2021 Tool Competition

Frenetic is a genetic approach that leverages a curvature-based road representation. Given an autonomous driving agent, the goal of Frenetic is to generate roads where the agent fails to stay within its lane. In other words, Frenetic tries to minimize the “out of bound distance”, which is the distance between the car and either edge of the lane if the car is within the lane, and proceeds to negative values once the car drives off. This work resembles classic aspects of genetic algorithms such as mutations and crossover, but introduces some nuances aiming at improving diversity of the generated roads.
Data synthesis via differentially private markov random fields

This paper studies the synthesis of high-dimensional datasets with differential privacy (DP). The state-of-the-art solution addresses this problem by first generating a set M of noisy low-dimensional marginals of the input data D , and then use them to approximate the data distribution in D for synthetic data generation. However, it imposes several constraints on M that considerably limits the choices of marginals. This makes it difficult to capture all important correlations among attributes, which in turn degrades the quality of the resulting synthetic data. To address the above deficiency, we propose PrivMRF, a method that (i) also utilizes a set M of low-dimensional marginals for synthesizing high-dimensional data with DP, but (ii) provides a high degree of flexibility in the choices of marginals. The key idea of PrivMRF is to select an appropriate M to construct a Markov random field (MRF) that models the correlations among the attributes in the input data, and then use the MRF for data synthesis. Experimental results on four benchmark datasets show that PrivMRF consistently outperforms the state of the art in terms of the accuracy of counting queries and classification tasks conducted on the synthetic data generated.
The Future of Care Work: Towards a Radical Politics of Care in CSCW Research and Practice

Computer-Supported Cooperative Work (CSCW) and Human- Computer Interaction (HCI) have long studied how technology can support material and relational aspects of care work, typically in clinical healthcare settings. More recently, we see increasing recognition of care work such as informal healthcare provision, child and elderly care, organizing and advocacy, domestic work, and service work. However, the COVID-19 pandemic has underscored long-present tensions between the deep necessity and simultaneous devaluation of our care infrastructures. This highlights the need to attend to the broader social, political, and economic systems that shape care work and the emerging technologies being used in care work. This leads us to ask several critical questions: What counts as care work and why? How is care work (de)valued, (un)supported, or coerced under capitalism and to what end? What narratives drive the push for technology in care work and whom does it benefit? How does care work resist or build resilience against and within oppressive systems? And how can we as researchers advocate for and with care and caregivers? In this one-day workshop, we will bring together researchers from academia, industry, and community-based organizations to reflect on these questions and extend conversations on the future of technology for care work.
Real-Time Depth Video-Based Rendering for 6-DoF HMD Navigation and Light Field Displays

This paper presents a novel approach to provide immersive free navigation with 6 Degrees of Freedom in real-time for natural and virtual scenery, for both static and dynamic content. Stemming from the state-of-the-art in Depth Image-Based Rendering and the OpenGL pipeline, this new View Synthesis method achieves free navigation at up to 90 FPS and can take any number of input views with their corresponding depth maps as priors. Video content can be played thanks to GPU decompression, supporting free navigation with full parallax in real-time. To render a novel viewpoint, each selected input view is warped using the camera pose and associated depth map, using an implicit 3D representation. The warped views are then blended all together to generate the chosen virtual view. Various view blending approaches specifically designed to avoid visual artifacts are compared. Using as few as four input views appears to be an optimal trade-off between computation time and quality, allowing to synthesize high-quality stereoscopic views in real-time, offering a genuine immersive virtual reality experience. Additionally, the proposed approach provides high-quality rendering of a 3D scenery on holographic light field displays. Our results are comparable - objectively and subjectively - to the state of the art view synthesis tools NeRF and LLFF, while maintaining an overall lower complexity and real-time rendering.
Query Processing in Blockchain Systems: Current State and Future Challenges

When, in 2008, Satoshi Nakamoto envisioned the first distributed database management system that relied on cryptographically secured chain of blocks to store data in an immutable and tamper-resistant manner, his primary use case was the introduction of a digital currency. Owing to this use case, the blockchain system was geared towards efficient storage of data, whereas the processing of complex queries, such as provenance analyses of data history, is out of focus. The increasing use of Internet of Things technologies and the resulting digitization in many domains, however, have led to a plethora of novel use cases for a secure digital ledger. For instance, in the healthcare sector, blockchain systems are used for the secure storage and sharing of electronic health records, while the food industry applies such systems to enable a reliable food-chain traceability, e.g., to prove compliance with cold chains. In these application domains, however, querying the current state is not sufficient—comprehensive history queries are required instead. Due to these altered usage modes involving more complex query types, it is questionable whether today’s blockchain systems are prepared for this type of usage and whether such queries can be processed efficiently by them. In our paper, we therefore investigate novel use cases for blockchain systems and elicit their requirements towards a data store in terms of query capabilities. We reflect the state of the art in terms of query support in blockchain systems and assess whether it is capable of meeting the requirements of such more sophisticated use cases. As a result, we identify future research challenges with regard to query processing in blockchain systems.
CG-SSD: Corner guided single stage 3D object detection from LiDAR point cloud

Detecting accurate 3D bounding boxes of the object from point clouds is a major task in autonomous driving perception. At present, the anchor-based or anchor-free models that use LiDAR point clouds for 3D object detection use the center assigner strategy to infer the 3D bounding boxes. However, in the real-world scene, due to the occlusions and the effective detection range of the LiDAR system, only part of the object surface can be covered by the collected point clouds, and there are no measured 3D points corresponding to the physical object center. Obtaining the object by aggregating the incomplete surface point clouds will bring a loss of accuracy in direction and dimension estimation. To address this problem, we propose a corner-guided anchor-free single-stage 3D object detection model (CG-SSD). Firstly, the point clouds within a single frame are assigned to regular 3D grids. 3D sparse convolution backbone network composed of residual layers and sub-manifold sparse convolutional layers are used to construct bird’s eye view (BEV) features for further deeper feature mining by a lite U-shaped network; Secondly, a novel corner-guided auxiliary module (CGAM) with adaptive corner classification algorithm is proposed to incorporate corner supervision signals into the neural network. CGAM is explicitly designed and trained to estimate locations of partially visible and invisible corners to obtain a more accurate object feature representation, especially for small or partial occluded objects; Finally, the deep features from both the backbone networks and CGAM module are concatenated and fed into the head module to predict the classification and 3D bounding boxes of the objects in the scene. The experiments demonstrate that CG-SSD achieves the state-of-art performance on the ONCE benchmark for supervised 3D object detection using single frame point cloud data, with 62.77% mAP. Additionally, the experiments on ONCE and Waymo Open Dataset show that CGAM can be extended to most anchor-based models which use the BEV feature to detect objects, as a plug-in and bring +1.17%∼+14.23% AP improvement. The code is available at https://github.com/mrqrs/CG-SSD.
Heterogeneous multi-sensor tracking for an autonomous surface vehicle in a littoral environment

Sensor fusion plays a key part in autonomous surface vehicles, however, the high cost of sensors makes the barrier of entry in this research field quite high. In this work, we present a complete system for sensor fusion on the milliAmpere autonomous ferry research platform as well as an open sensor fusion dataset for maritime tracking across two environments. Individual sensors and their detection pipelines are evaluated across various detection metrics. We also evaluate the tracking performance of the sensors both individually and in fusion using a multi-sensor extension of the JIPDA multi-target tracker. We find that the different environments have distinct challenges precluding the use of only a single sensor. Utilizing multiple sensors, either individually or in fusion, can mitigate these issues increasing the safety margins of the situational awareness system.
IHSSAO: An Improved Hybrid Salp Swarm Algorithm and Aquila Optimizer for UAV Path Planning in Complex Terrain

In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the leader mechanism of SSA, tent chaotic map, and pinhole imaging opposition-based learning strategy. Firstly, the tent chaotic map is utilized to substitute the randomly generated initial population in the original algorithm to increase the diversity of the initial individuals. Secondly, we integrate the leader mechanism of SSA into the position update formulation of the basic AO, which enables the search individuals to fully utilize the optimal solution information and enhances the global search capability of AO. Thirdly, we introduce the pinhole imaging opposition-based learning in the proposed IHSSAO to enhance the capability to escape from the local optimization. To verify the effectiveness of the proposed IHSSAO algorithm, we tested it against SSA, AO, and five other advanced meta-heuristic algorithms on 23 classical benchmark functions and 17 IEEE CEC2017 test functions. The experimental results indicate that the proposed IHSSAO is superior to the other seven algorithms in most cases. Eventually, we applied the IHSSAO, SSA, and AO to solve the UAV path planning problem. The experimental results verify that the IHSSAO is superior to the basic SSA and AO for solving the UAV path planning problem in complex terrain.
An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Nowadays, more and more people are sharing and expressing their feelings through social media platforms such as Twitter, Facebook and YouTube. Sentiment analysis is a process that explores, identifies and categorizes content. People that belong to multilingual communities tend to communicate through multiple regional languages. This type of text is represented using different languages and is known as code-mixed data. The proposed system utilizes a code-mixed data set of Tamil–English languages from FIRE 2021. To handle the class imbalance problem, re-sampling is performed and the impact is analyzed. Pre-processing of input text data can play a vital role in code-mixed data classification by removing unnecessary content. This research work aims to explore the impact of pre-processing on Tamil code-mixed data by employing various pre-processing steps such as emojis removal; repeated characters removal; and punctuations, symbols and number removal. The pre-processed text is applied to traditional machine learning, deep learning, transfer learning and hybrid deep learning models, and the accuracy of all these models before and after pre-processing is compared. Traditional machine learning models depend on various weighting schemes for the feature selection process. The main objective of this research work is to build hybrid deep learning models combining Convolutional Neural Network (CNN) with Long–Short Term Memory (LSTM) and Convolutional Neural Network (CNN) with Bi-Long–Short Term Memory (LSTM) in order to capture the local and global features implicitly from the code-mixed data for conducting sentiment analysis, and then classify the Tamil code-mixed data into positive, negative, mixed_feelings and unknown_state. The performance of hybrid deep learning models were evaluated by comparing them with state-of-art methods that include various traditional machine learning techniques such as random forest, multinomial Naive Bayes, logistic regression and linear Support Vector Classification (SVC); deep learning techniques such as LSTM, BiLSTM, BiGRU (Bidirectional Gated Recurrent Unit) and CNN; and a transfer learning method, IndicBERT. This research work also summarizes the precision, recall, F1-score, accuracy, macro-average, weighted-average and confusion matrix for all mentioned models. The result indicates that among all the different models employed, the hybrid deep learning model, especially the CNN+BiLSTM model performs better, with an accuracy of 0.66 with preprocessed Tamil code-mixed data.
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information

Transformer-based language models usually treat texts as linear sequences.However, most texts also have an inherent hierarchical structure, i. e., parts of a text can be identified using their position in this hierarchy.In addition, section titles usually indicate the common topic of their respective sentences.We propose a novel approach to formulate, extract, encode and inject hierarchical structure information explicitly into an extractive summarization model based on a pre-trained, encoderonly Transformer language model (HiStruct+ model), which improves SOTA ROUGEs for extractive summarization on PubMed and arXiv substantially.Using various experimental settings on three datasets (i.e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively, which differs from our model only in that the hierarchical structure information is not injected.It is also observed that the more conspicuous hierarchical structure the dataset has, the larger improvements our method gains.The ablation study demonstrates that the hierarchical position information is the main contributor to our model's SOTA performance.
Electronic Health Records Sharing Model Based on Blockchain With Checkable State PBFT Consensus Algorithm

With the popularity of IoT devices and cloud technology in the medical industry. Sharing EHRs (Electronic Health Records) among medical institutions improves the accuracy of medical diagnosis and promotes the development of public medical. However, it is difficult to share EHRs among hospitals, and patients typically don&#x2019;t know about the usage of their health records. In this paper, we propose a patient-controlled EHRs sharing scheme based on cloud computing collaborating blockchain technology. The medical abstract and the access strategy are stored in the blockchain to avoid being tampered with. To achieve the fine-grained access control, we propose the attribute-based encryption scheme and multi-keyword encryption scheme to encrypt EHRs. Moreover, we proposed a node-state-checkable Practical Byzantine Fault Tolerance consensus algorithm (sc-PBFT) to prevent the Byzantine nodes from sneaking into the consortium blockchain. First, we check the state of the elected master node to avoid the master node having any malicious records. Then, using pre-prepared, prepare, and commit processes to complete the consensus request submitted by the client. At last, the proposed consensus algorithm evaluates the state of the master node according to the completion of the three-stage process to reduce the impact of the malicious node on the whole consortium blockchain. By doing this, the malicious node will be marked and isolated into the isolation area. The experimental results show that the proposed sc-PBFT algorithm has better handling capability and lower consensus latency. Compared with the PBFT algorithm in the case of Byzantine nodes, sc-PBFT not only improves the robustness of the consortium blockchain network but also improves the handling capability.
XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Machine intelligence models are robust in classifying the datasets for data analytics and for predicting the insights that would assist in making clinical decisions. The models would assist in the disease prognosis and preliminary disease investigation, which is crucial for effective treatment. There is a massive demand for the interpretability and explainability of decision models in the present day. The models’ trustworthiness can be attained through deploying the ensemble classification models in the eXplainable Artificial Intelligence (XAI) framework. In the current study, the role of ensemble classifiers over the XAI framework for predicting heart disease from the cardiovascular datasets is carried out. There are 303 instances and 14 attributes in the cardiovascular dataset taken for the proposed work. The attribute characteristics in the dataset are categorical, integer, and real type and the associated task related to the dataset is classification. The classification techniques, such as the support vector machine (SVM), AdaBoost, K-nearest neighbor (KNN), bagging, logistic regression (LR), and naive Bayes, are considered for classification purposes. The experimental outcome of each of those algorithms is compared to each other and with the conventional way of implementing the classification models. The efficiency of the XAI-based classification models is reasonably fair, compared to the other state-of-the-art models, which are assessed using the various evaluation metrics, such as area under curve (AUC), receiver operating characteristic (ROC), sensitivity, specificity, and the F1-score. The performances of the XAI-driven SVM, LR, and naive Bayes are robust, with an accuracy of 89%, which is assumed to be reasonably fair, compared to the existing models.
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution

Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods. However, these approaches suffer from essential shortsightedness created by only utilizing the standard self-attention-based reasoning. In this paper, we introduce an effective hybrid SR network to aggregate enriched features, including local features from CNNs and long-range multi-scale dependencies captured by transformers. Specifically, our network comprises transformer and convolutional branches, which synergetically complement each representation during the restoration procedure. Furthermore, we propose a cross-scale token attention module, allowing the transformer branch to exploit the informative relationships among tokens across different scales efficiently. Our proposed method achieves state-of-the-art SR results on numerous benchmark datasets.