Reviewerly/exHarmony huggingface dataset:
Graph Learning for Fake Review Detection

Fake reviews have become prevalent on various social networks such as e-commerce and social media platforms. As fake reviews cause a heavily negative influence on the public, timely detection and response are of great significance. To this end, effective fake review detection has become an emerging research area that attracts increasing attention from various disciplines like network science, computational social science, and data science. An important line of research in fake review detection is to utilize graph learning methods, which incorporate both the attribute features of reviews and their relationships into the detection process. To further compare these graph learning methods in this paper, we conduct a detailed survey on fake review detection. The survey presents a comprehensive taxonomy and covers advancements in three high-level categories, including fake review detection, fake reviewer detection, and fake review analysis. Different kinds of fake reviews and their corresponding examples are also summarized. Furthermore, we discuss the graph learning methods, including supervised and unsupervised learning approaches for fake review detection. Specifically, we outline the unsupervised learning approach that includes generation-based and contrast-based methods, respectively. In view of the existing problems in the current methods and data, we further discuss some challenges and open issues in this field, including the imperfect data, explainability, model efficiency, and lightweight models.
Nonfinite-modality data augmentation for brain image registration

Brain image registration is fundamental for brain medical image analysis. However, the lack of paired images with diverse modalities and corresponding ground truth deformations for training hinder its development. We propose a novel nonfinite-modality data augmentation for brain image registration to combat this. Specifically, some available whole-brain segmentation masks, including complete fine brain anatomical structures, are collected from the actual brain dataset, OASIS-3. One whole-brain segmentation mask can generate many nonfinite-modality brain images by randomly merging some fine anatomical structures and subsequently sampling the intensities for each fine anatomical structure using random Gaussian distribution. Furthermore, to get more realistic deformations as the ground truth, an improved 3D Variational Auto-encoder (VAE) is proposed by introducing the intensity-level reconstruction loss and the structure-level reconstruction loss. Based on the generated images and trained improved 3D VAE, a new Synthetic Nonfinite-Modality Brain Image Dataset (SNMBID) is created. Experiments show that pre-training on SNMBID can improve the accuracy of registration. Notably, SNMBID can be a landmark for evaluating other brain registration methods, and the model trained on the SNMBID can be a baseline for the brain image registration task. Our code is available at https://github.com/MangoWAY/SMIBID_BrainRegistration.
AI Technologies for Machine Supervision and Help in a Rehabilitation Scenario

We consider, evaluate, and develop methods for home rehabilitation scenarios. We show the required modules for this scenario. Due to the large number of modules, the framework falls into the category of Composite AI. Our work is based on collected videos with high-quality execution and samples of typical errors. They are augmented by sample dialogues about the exercise to be executed and the assumed errors. We study and discuss body pose estimation technology, dialogue systems of different kinds and the emerging constraints of verbal communication. We demonstrate that the optimization of the camera and the body pose allows high-precision recording and requires the following components: (1) optimization needs a 3D representation of the environment, (2) a navigation dialogue to guide the patient to the optimal pose, (3) semantic and instance maps are necessary for verbal instructions about the navigation. We put forth different communication methods, from video-based presentation to chit-chat-like dialogues through rule-based methods. We discuss the methods for different aspects of the challenges that can improve the performance of the individual components. Due to the emerging solutions, we claim that the range of applications will drastically grow in the very near future.
Beetle Antennae Search: Using Biomimetic Foraging Behaviour of Beetles to Fool a Well-Trained Neuro-Intelligent System

Deep Convolutional Neural Networks (CNNs) represent the state-of-the-art artificially intelligent computing models for image classification. The advanced cognition and pattern recognition abilities possessed by humans are ascribed to the intricate and complex neurological connection in human brains. CNNs are inspired by the neurological structure of the human brain and show performance at par with humans in image recognition and classification tasks. On the lower extreme of the neurological complexity spectrum lie small organisms such as insects and worms, with simple brain structures and limited cognition abilities, pattern recognition, and intelligent decision-making abilities. However, billions of years of evolution guided by natural selection have imparted basic survival instincts, which appear as an “intelligent behavior”. In this paper, we put forward the evidence that a simple algorithm inspired by the behavior of a beetle (an insect) can fool CNNs in image classification tasks by just perturbing a single pixel. The proposed algorithm accomplishes this in a computationally efficient manner as compared to the other adversarial attacking algorithms proposed in the literature. The novel feature of the proposed algorithm as compared to other metaheuristics approaches for fooling a neural network, is that it mimics the behavior of a single beetle and requires fewer search particles. On the contrary, other metaheuristic algorithms rely on the social or swarming behavior of the organisms, requiring a large population of search particles. We evaluated the performance of the proposed algorithm on LeNet-5 and ResNet architecture using the CIFAR-10 dataset. The results show a high success rate for the proposed algorithms. The proposed strategy raises a concern about the robustness and security aspects of artificially intelligent learning systems.
Role of Human-AI Interaction in Selective Prediction

Recent work has shown the potential benefit of selective prediction systems that can learn to defer to a human when the predictions of the AI are unreliable, particularly to improve the reliability of AI systems in high-stakes applications like healthcare or conservation. However, most prior work assumes that human behavior remains unchanged when they solve a prediction task as part of a human-AI team as opposed to by themselves. We show that this is not the case by performing experiments to quantify human-AI interaction in the context of selective prediction. In particular, we study the impact of communicating different types of information to humans about the AI system's decision to defer. Using real-world conservation data and a selective prediction system that improves expected accuracy over that of the human or AI system working individually, we show that this messaging has a significant impact on the accuracy of human judgements. Our results study two components of the messaging strategy: 1) Whether humans are informed about the prediction of the AI system and 2) Whether they are informed about the decision of the selective prediction system to defer. By manipulating these messaging components, we show that it is possible to significantly boost human performance by informing the human of the decision to defer, but not revealing the prediction of the AI. We therefore show that it is vital to consider how the decision to defer is communicated to a human when designing selective prediction systems, and that the composite accuracy of a human-AI team must be carefully evaluated using a human-in-the-loop framework.
Anatomizing Bias in Facial Analysis

Existing facial analysis systems have been shown to yield biased results against certain demographic subgroups. Due to its impact on society, it has become imperative to ensure that these systems do not discriminate based on gender, identity, or skin tone of individuals. This has led to research in the identification and mitigation of bias in AI systems. In this paper, we encapsulate bias detection/estimation and mitigation algorithms for facial analysis. Our main contributions include a systematic review of algorithms proposed for understanding bias, along with a taxonomy and extensive overview of existing bias mitigation algorithms. We also discuss open challenges in the field of biased facial analysis.
PUMA: Performance Unchanged Model Augmentation for Training Data Removal

Preserving the performance of a trained model while removing unique characteristics of marked training data points is challenging. Recent research usually suggests retraining a model from scratch with remaining training data or refining the model by reverting the model optimization on the marked data points. Unfortunately, aside from their computational inefficiency, those approaches inevitably hurt the resulting model's generalization ability since they remove not only unique characteristics but also discard shared (and possibly contributive) information. To address the performance degradation problem, this paper presents a novel approach called Performance Unchanged Model Augmentation (PUMA). The proposed PUMA framework explicitly models the influence of each training data point on the model's generalization ability with respect to various performance criteria. It then complements the negative impact of removing marked data by reweighting the remaining data optimally. To demonstrate the effectiveness of the PUMA framework, we compared it with multiple state-of-the-art data removal techniques in the experiments, where we show the PUMA can effectively and efficiently remove the unique characteristics of marked training data without retraining the model that can 1) fool a membership attack, and 2) resist performance degradation. In addition, as PUMA estimates the data importance during its operation, we show it could serve to debug mislabelled data points more efficiently than existing approaches.
Calibration of line-structured light vision sensors based on simultaneous polarization imaging

Abstract Stray light from line-structured light vision sensors easily interferes with the center extraction accuracy of a structured light strip. In this paper, a high-accuracy calibration technique using polarized beam splitting simultaneous imaging is proposed. First, two polarization component images with different polarization directions are obtained through a single measurement. The method overcomes problems related to stray light and errors caused by the shaking of the instruments. Therefore, false polarization information can be avoided in this way. Second, an image fusion algorithm is used to improve the imaging quality of the light bar. Experiments are performed to show that the light strips no longer have local overexposure, and the calibration accuracy of the system is improved.
Co-clustering Interactions via Attentive Hypergraph Neural Network

With the rapid growth of interaction data, many clustering methods have been proposed to discover interaction patterns as prior knowledge beneficial to downstream tasks. Considering that an interaction can be seen as an action occurring among multiple objects, most existing methods model the objects and their pair-wise relations as nodes and links in graphs. However, they only model and leverage part of the information in real entire interactions, i.e., either decompose the entire interaction into several pair-wise sub-interactions for simplification, or only focus on clustering some specific types of objects, which limits the performance and explainability of clustering. To tackle this issue, we propose to Co-cluster the Interactions via Attentive Hypergraph neural network (CIAH). Particularly, with more comprehensive modeling of interactions by hypergraph, we propose an attentive hypergraph neural network to encode the entire interactions, where an attention mechanism is utilized to select important attributes for explanations. Then, we introduce a salient method to guide the attention to be more consistent with real importance of attributes, namely saliency-based consistency. Moreover, we propose a novel co-clustering method to perform a joint clustering for the representations of interactions and the corresponding distributions of attribute selection, namely cluster-based consistency. Extensive experiments demonstrate that our CIAH significantly outperforms state-of-the-art clustering methods on both public datasets and real industrial datasets.
Construction of Power Fault Knowledge Graph Based on Deep Learning

A knowledge graph can structure heterogeneous knowledge in the field of power faults, construct the correlation between different pieces of knowledge, and solve the diversification, complexity, and island of fault data. There are many kinds of entities in power fault defect text, the relationship between entities is complex, and the data are often mixed with noise. It is necessary to research how to effectively mine the target data and separate the salient knowledge from the noise. Moreover, the traditional entity and relationship extraction methods used in the construction of a power fault knowledge graph cannot fully understand the text semantics, and the response accuracy is low. The Log system usually contains all kinds of information related to faults and a log analysis helps us collect fault information and perform association analysis. Therefore, a Bidirectional Sliced GRU with Gated Attention mechanism (BiSGRU-GA) model is proposed to detect the anomalous logs in the power system, this enriches the fault knowledge base and provides a good data resource for the construction of the knowledge graph. A new Bidirectional GRU with Gated Attention mechanism and Conditional Random Fields and a BERT input layer (BBiGRU-GA-CRF) model is proposed by introducing a BERT layer and Attention Mechanism into the Bidirectional GRU (BiGRU) model to more fully understand the context information of fault sentences and improve the accuracy of entity recognition of fault sentences. Aiming to solve the problems of large calculation cost and propagation error which occur in the traditional relationship extraction model, an improved Bidirectional Gated Recurrent Unit neural network with fewer parameters and the Gated Attention Mechanism (BiGRU-GA) model is proposed. This new model introduces an improved Gated Attention Mechanism to achieve better effects in relationship extraction. Compared with Bidirectional Long Short-Term Memory with Attention Mechanism (BiLSTM-Attention), the accuracy, recall, and F-measure of the model were improved by 1.79%, 13.83%, and 0.30% respectively, and the time cost is reduced by about 16%. The experimental results show that the BiGRU-GA model can capture local features, reduce the training time cost, and improve the model recognition effect.
oVRcome – Self-guided virtual reality for specific phobias: A randomised controlled trial

Objective: Mobile health applications for mental health are widely accessible but most have had limited research evaluation. Virtual reality exposure therapy is an emerging treatment for specific phobias. Most virtual reality studies have investigated high-end virtual reality devices, typically only available in research and limited clinical settings for a single phobia. This study evaluated the effectiveness of oVRcome, a mobile health application combining self-guided virtual reality exposure and cognitive behaviour therapy, for five specific phobias. Methods: This is a 2-arm 6-week randomised controlled trial, with a waitlist control group and follow-up at week 12. Participants were required to live in New Zealand; be aged 18–64 years; have a fear of flying, heights, spiders, dogs and needles; score above 4 on the Brief Standard Self-rating scale for phobic patients; and have access to a smartphone and Internet. oVRcome consists of six modules of psychoeducation, relaxation, mindfulness, cognitive techniques, exposure through virtual reality and relapse prevention over 6 weeks. The primary outcome was the change from baseline to week 6 on the Severity Measures for Specific Phobia – Adults. All analyses were performed on intention-to-treat set. Results: A total of 126 participants were randomised, and 109 completed the follow-up at week 6, for a retention rate of 86.5%. The mean change in Severity Measures for Specific Phobia – Adults score from baseline to week 6 was greater in the active group compared with the waitlist group (active group −20.53 [standard deviation = 8.24]; waitlist group: − 12.31 [standard deviation = 10.66]; p &lt; 0.001). The effect size for this difference was 0.86. Conclusion: Self-guided use of the oVRcome app was effective at reducing severity of specific phobia symptoms in a sample of people with a self-reported fear of flying, heights, spiders, dogs or needles. Trial registry clinicaltrials.gov NCT04909177.
Accuracy Enhancement of Prediction Method using SMOTE for Early Prediction Student's Graduation in XYZ University

According to the Minister of Education and Culture of the Republic of Indonesia's regulations from 2014, one of the essential elements in implementing higher education is the student's study duration.Higher education institutions will use early graduation prediction as a guide when developing policy.According to XYZ University data, the student study period is Grade Point Average (GPA), Gender, and Age are all aspects to consider.Using a dataset of 8491 data, the Prediction of Early Graduation of Students based on XYZ University data was examined by this study, particularly in the information systems and informatics study program.The aim is to find significant features and compare three prediction models: Artificial Neural Networks (ANN), K-Nearest Neighbor (K-NN) method, and Support Vector Machines (SVM).The Challenge in the development of a prediction model is imbalanced data.The Synthetic Minority Oversampling Technique (SMOTE) handles the class imbalance problem.Next, the machine learning models are trained and then compared.Prediction results increase.The best test accuracy value is on ANN with a data Imbalance of 62.5% to 70.5% after using SMOTE, compared to the accuracy test on the K-NN method with SMOTE 69.3%, while the SVM method increased to 69.8%.The most significant increase in recall value to 71.3% occurred in the ANN.
Counterfactual Building and Evaluation via eXplainable Support Vector Data Description

Increasingly in recent times, the mere prediction of a machine learning algorithm is considered insufficient to gain complete control over the event being predicted. A machine learning algorithm should be considered reliable in the way it allows to extract more knowledge and information than just having a prediction at hand. In this perspective, the counterfactual theory plays a central role. By definition, a counterfactual is the smallest variation of the input such that it changes the predicted behaviour. The paper addresses counterfactuals through Support Vector Data Description (SVDD), empowered by explainability and metric for assessing the counterfactual quality. After showing the specific case in which an analytical solution may be found (under Euclidean distance and linear kernel), an optimisation problem is posed for any type of distances and kernels. The vehicle platooning application is the use case considered to demonstrate how the outlined methodology may offer support to safety-critical applications as well as how explanation may shed new light into the control of the system at hand.
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism

Entity alignment (EA) aims to find the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source KGs.For a long time, most researchers have regarded EA as a pure graph representation learning task and focused on improving graph encoders while paying little attention to the decoding process.In this paper, we propose an effective and efficient EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI).Specifically, we derive two sets of isomorphism equations: (1) Adjacency tensor isomorphism equations and (2) Gramian tensor isomorphism equations.By combining these equations, DATTI could effectively utilize the adjacency and inner correlation isomorphisms of KGs to enhance the decoding process of EA.Extensive experiments on public datasets indicate that our decoding algorithm can deliver significant performance improvements even on the most advanced EA methods, while the extra required time is less than 3 seconds.
Word Order Does Matter and Shuffled Language Models Know It

Recent studies have shown that language models pretrained and/or fine-tuned on randomly permuted sentences exhibit competitive performance on GLUE, putting into question the importance of word order information.Somewhat counter-intuitively, some of these studies also report that position embeddings appear to be crucial for models' good performance with shuffled text.We probe these language models for word order information and investigate what position embeddings learned from shuffled text encode, showing that these models retain information pertaining to the original, naturalistic word order.We show this is in part due to a subtlety in how shuffling is implemented in previous work -before rather than after subword segmentation.Surprisingly, we find even Language models trained on text shuffled after subword segmentation retain some semblance of information about word order because of the statistical dependencies between sentence length and unigram probabilities.Finally, we show that beyond GLUE, a variety of language understanding tasks do require word order information, often to an extent that cannot be learned through fine-tuning.* Equal contribution.Order was decided by a coin toss.
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents

Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task.Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehension task from single document, Multi-Doc2Dial task faces challenges of both seeking information from multiple documents and generating conversation response simultaneously.This paper summarizes our entries to agent response generation subtask in Multi-Doc2Dial dataset.We propose a three-stage solution, Grounding-guided goal-oriented dialogues generation(G4), which predicts groundings from retrieved passages to guide the generation of the final response.Our experiments show that G4 achieves SacreBLEU score of 31.24 and F1 score of 44.6 which is 60.7% higher than the baseline model.