Reviewerly/exHarmony huggingface dataset:
Detection of Unobserved Common Cause in Discrete Data Based on the MDL Principle

Inference of causal structure in random variables from observed data only is a crucial science problem. This study categorizes the causal relationship between two discrete random variables into four categories, which include the case that variables have a common cause and that variables are independent in addition to two directions of the direct causality. Although several existing methods have been proposed for causal inference from a joint distribution of two discrete random variables that can select either direction of the direct causality, methods that can directly infer a causal relationship from the four categories mentioned are limited. We proposed the first method to infer a causal relationship without any assumption on the unobserved confounder in discrete data. Based on the minimum description length (MDL) principle, the proposed method calculates the code length of the observed data for each causal model and then selects a model which yields the minimum code length. We demonstrated that the proposed method is effective in detecting common causes and outperforms the existing methods in terms of the accuracy of inference using synthetic data. We further showed that the proposed method effectively detects an unobserved confounder in real-world data.
Legal Artificial Assistance Agent to Assist Refugees

Populations move across regions in search of better living possibilities, better life outcomes or going away from problems that affected their lives in the previous region they lived in. In the United States of America, this problem has been happening over decades. Intelligent Conversational Text-based Agents, also called Chatbots, and Artificial Intelligence are increasingly present in our lives and over recent years, their presence has increased considerably, due to the usability cases and the familiarity they are wining constantly. Using NLP algorithms for law in accessible platforms allows scaling of users to access a certain level of law expert who could assist users in need. This paper describes the motivation and circumstances of this problem as well as the description of the development of an Intelligent Conversational Agent system that was used by immigrants in the USA so they could get answers to questions and get suggestions about better legal options they could have access to. This system has helped thousands of people, especially in California
Improved Feature Point and Descriptor Extraction Network for SuperPoint

Image feature point and descriptor extraction is the basis of SLAM, SFM and 3D reconstruction tasks. In this paper, we study the SuperPoint network, which has good robustness in extracting feature points and descriptors, and introduces the idea of group convolution, replaces the normal convolution with group convolution, and introduces the Mish activation function to replace the ReLU activation function to solve the problem that some data fall into negative intervals. The experimental results show that the accuracy of single-strain estimation only decreases by 0.01 when the tolerance distance difference is 3, and the repetition rate of feature point detection increases by 0.3%, which has good robustness. In this paper, the network achieves lightness without excessive loss of accuracy.
Robust Learning with Noisy Label Detection and Counterfactual Correction

Data quality is of paramount importance in the training process of any machine learning model. Recently proposed methods for noisy learning focus on detecting noisy labeled data instances by using a fixed loss value threshold, and exclude detected noisy data instances in subsequent training steps. However, a predefined, fixed loss value threshold may not always be optimal, and excluding the detected noisy data instances can hurt the size of the training set. In this paper, we propose a new method, NDCC, that automatically selects a loss threshold to identify noisy labeled data instance, and uses counterfactual learning to repair them. To the best of our knowledge, NDCC is the first work to explore the feasibility of using counterfactual learning in the noisy learning domain. We demonstrate the performance of NDCC on Fashion–MNIST and CIFAR–10 datasets under a variety of label noise environments. Experimental results show the superiority of the proposed method compared to the state–of–the–art, especially in the presence of severe label noise.
Improving Robustness of Vision Transformers via Data-Augmented Virtual Adversarial Training

Inspired by the success of transformers in natural language processing, vision transformers have been proposed to address a wide range of computer vision tasks, such as image classification, object detection and image segmentation, and they have achieved very promising performance. However, the robustness of vision transformers has been relatively under-explored. Recent studies have revealed that pre-trained vision transformers are also vulnerable to white-box adversarial attacks on the downstream image classification task. The adversarial attacks (e.g., FGSM and PGD) designed for convolutional neural networks (CNNs) can also cause severe performance drop for vision transformers. In this paper, we evaluate the robustness of vision transformers fine-tuned with the off-the-shelf methods under adversarial attacks on CIFAR-10 and CIFAR-100. We further propose a data-augmented virtual adversarial training approach called MixVAT, which is able to enhance the robustness of pre-trained vision transformers against adversarial attacks on the downstream tasks with the unlabelled data. Extensive results on multiple datasets demonstrate the superiority of our approach over baselines on adversarial robustness, without compromising generalization ability of the model.
Explainable Recommendation Using Knowledge Graphs and Random Walks

A knowledge graph (KG) contains rich information about users and items. The relationship among users and items can help to generate intuitive explanations for recommended items. Many variations of KG-based recommendation algorithms use the shortest path from the user to the item in order to generate an explanation of the recommendation. However, the simple shortest path may not be useful in the case when the path is long, because the interpretation of the long path is difficult. Also, there may be no path between the user and the recommended item. In order to overcome these difficulties, we proposed an extension of the existing framework based on random walk with KG embedding. In the proposed framework, we use the most probable path in a random walk as an explanation. Thereby, our framework can even explain items that have no connection in the KG due to the latent connection resulting from random walk teleportation. Comparison experiment demonstrated that the framework can provide more suitable recommendations than the existing method. In addition, the experiment show the ability of the proposed method to generate explanation for all recommendations that have no path in the graph.
A Study On Security and Privacy Risks of Self-Disclosure On Social Networking Sites During COVID-19 Pandemic

Social networking sites (SNSs) contain a large amount of information that has been self-disclosed by users around the world because it provides a platform for millions of users to express their feelings, emotions, and even deepest thoughts. Some of these information are sensitive and private and can be used by hackers to launch social engineering attacks against the user or the company the user works for. Due to the physical restrictions imposed by the COVID-19 pandemic, more people turned to social media to stay connected with each other and they spent more time on social media and disclosed much more information than the pre-COVID pandemic. The objective of this research is to study the potential security risks and privacy concerns brought by the disclosed information on SNSs during the COVID-19 pandemic. We developed an automated tool to collect and analyze publicly accessible data from Twitter API using some personal keywords such as birthday, anniversary, mental health, suicide etc. to investigate the impact of the COVID-19 pandemic on the disclosed sensitive information.
Hybrid Cloud and HPC Approach to High-Performance Dataframes

Data pre-processing is a fundamental component in any data-driven application. With the increasing complexity of data processing operations and volume of data, Cylon, a distributed dataframe system, is developed to facilitate data processing both as a standalone application and as a library, especially for Python applications. While Cylon shows promising performance results, we experienced difficulties trying to integrate with frameworks incompatible with the traditional Message Passing Interface (MPI). While MPI implementations encompass scalable and efficient c ommunication routines, their process launching mechanisms work well with mainstream HPC systems but are incompatible with some environments that adopt their own resource management systems. In this work, we alleviated this issue by directly integrating the Unified Communication X (UCX) framework, which supports a variety of classic HPC and non-HPC process-bootstrapping mechanisms as our communication framework. While we experimented with our methodology on Cylon, the same technique can be used to bring MPI communication to other applications that do not employ MPI’s built-in process management approach.
SQ2SV: Sequential Queries to Sequential Videos retrieval

Current video retrieval models are one-to-one matching models, which limits them from learning from the sequential context of the videos. While most public datasets for this task are text-video pairs that are contextually independent, datasets such as YouCook2, Video Storytelling, and COIN consist of chronological text-video pair segments. This paper introduces a retrieval task Sequential Queries to Sequential Videos retrieval (SQ2SV) that retrieves multiple sets of sequential videos from sequential queries to utilize such contextual interdependence. To the best of our knowledge, this paper is the first a ttempt to introduce multiple sets of sequential videos retrieval. We not only introduce a new task but also build a task-specific model and its evaluation metric. Our model, UniSeq (UniVL-based sequential videos retrieval), is a sequential as well as a cross representation model. Our new metric ‘Video R@k’ evaluates the performance of a retrieval model in a unit of video, not in a unit of the video segment. Our best model outperforms the UniVL baseline in the original R@1 of YouCook2 by 0.40% and Video Storytelling by 1.09%. Furthermore, comparing the Video R@1 score, our model outperforms the baseline by 0.27% for YouCook2 and 0.94% for Video Storytelling.
Lossy Compression to Reduce Latency of Local Image Transfer for Autonomous Off-Road Perception Systems

Autonomous vehicles greatly rely on their perception system for navigation. Semantic segmentation provides a much better understanding of a vehicle’s surroundings than object detection. Unfortunately, complete image segmentation comes at a higher computational cost than object detection, which complicates developing a real-time perception system using semantic segmentation. Perception systems contain other bottlenecks too, and are not only limited by their deep learning model. An inherent amount of latency exists in data transfer, specifically through Ethernet. A vehicle’s camera feed must be transferred to an edge device for image processing as part of the autonomous driving decision-making process. This study investigates decreasing image transfer time by using various levels of JPEG compression as well as further understanding how compression affects the accuracy of semantic segmentation. Additionally, as most autonomous driving research focuses on urban environments, we look to explore autonomous unmanned ground vehicles (UGVs) in the off-road space by using the Rellis-3D dataset. We train and evaluate SwiftNet, a state-of-the-art semantic segmentation model, at different JPEG compression ratios and identify the accuracy. The transfer time of these different compression ratios is tested on three images. Results show a continual decrease in accuracy occurs as the compression ratios increase. When training SwiftNet on the train set with no compression, the highest compression ratio of 16.96 achieves a mean intersection over union (mIoU) score of 67.9% compared to the baseline achieving 78.9% mIoU. There is an increase in the accuracy of the higher compression ratios by training SwiftNet on the corresponding compression ratios; the highest compression ratio reaches 74.9% mIoU. Lastly, we notice a positive transfer speedup of these higher compression ratios when inducing JPEG compression in all transfer scenarios: (a) 1870 images (b) 10 images, (c) 1 image. Each scenario has a speedup of 1.18×, 1.14×, and 1.06×, respectively.
Low-rank Defenses Against Adversarial Attacks in Recommender Systems

Recommender systems are powerful tools which touch on numerous aspects of everyday life, from shopping to consuming content, and beyond. However, as other machine learning models, recommender system models are vulnerable to adversarial attacks and their performance could drop significantly with a slight modification of the input data. Most of the studies in the area of adversarial machine learning are focused on the image and vision domain. There are very few work that study adversarial attacks on recommender systems and even fewer work that study ways to make the recommender systems robust and reliable. In this study, we explore two state-of-the-art adversarial attack methods proposed by Tang et al. [1] and Christakopoulou et al. [2] and we report our proposed defenses and experimental evaluations against these attacks. In particular, we observe that low-rank reconstructions and/or transformation of the attacked data has a significant alleviating effect on the attack, and we present extensive experimental evidence to demonstrate the effectiveness of this approach. We also show that a simple classifier is able to learn to detect fake users from real users and can successfully discard them from the dataset. This observation elaborates the fact that the threat model does not generate fake users that mimic the same behavior of real users and can be easily distinguished from real users’ behavior. We also examine how transforming latent factors of the matrix factorization model into a low-dimensional space impacts its performance. Furthermore, we combine fake users from both attacks to examine how our proposed defense is able to defend against multiple attacks at the same time. Local low-rank reconstruction was able to reduce the hit ratio of target items from 23.54% to 15.69% while the overall performance of the recommender system was preserved.Adversarial machine learning, recommender systems, low-rank reconstruction
Text Spotting in Natural Scenes Based on Feature Pyramid Neural Network

The natural scene text recognition (STR) task has been a popular research field in computer vision due to its many applications. Earlier studies mainly relied on hand-crafted features, which often limited the recognition performance. In recent years, deep learning neural networks have made significant progress in STR tasks with the rise and development of deep learning. This study analyzes and evaluates model optimization strategies for text detection and recognition tasks. In the text detection model, the EAST model is used as the base to optimize the backbone of the front-end feature extraction and the feature fusion part of the middle part of the network. In the text recognition model, the SRN model is used as the base to optimize the backbone of front-end feature extraction. In the end-to-end architecture integration, MobileNetV3 is used as the base to train a text orientation classifier to achieve the goal of recognizing straight text. The experimental results show that after the improvement of this study, in the detection task, compared to the original EAST model [1], the precision can be increased by 6.9%, the recall rate can be increased by 2.3%, and the F-measure can be increased by 4.6%. In the identification task, compared to the original SRN model [2], the accuracy can be increased by 8.8%, and the normalized edit distance can be increased by 9.7%. Finally, this research also integrates the two tasks into an end-to-end system architecture, which solves the problem of straight writing in Chinese characters and makes the algorithm more practical. The mentioned models are available at https://github.com/Isaac93546/SRN_OCR.
The Method of Constructing Information Network Capability Evaluation Index System based on Graph Theory

The information network capacity index system is the basis for conducting its capacity evaluation. The traditional tree-like capability evaluation index system is difficult to adapt to the capability evaluation problem of such a complex system as information network. To solve this problem, we first sort out the information network capability evaluation indicator set, then use the directed graph theory to model the directional relationship between the information network capability evaluation indicators, and then build an indicator network. Finally, we calculate the reachability matrix according to the topological structure of the indicator network, and use the reachability matrix to carefully divide the levels of the evaluation indicators, and build an information network capability evaluation index system with a certain hierarchical structure.
SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge

The goal of causal representation learning is to map low-level observations to high-level causal concepts to learn interpretable and robust representations for various downstream tasks. Latent variable models such as the variational autoencoder (VAE) are frequently leveraged to learn disentangled representations. However, there are often complex non-linear causal relationships underlying the observed data that cannot be captured through disentangled representations or linear dependence assumptions. Further, an independent conditional prior assumption can make learning causal dependencies in the latent space more challenging. We propose a framework, coined SCM-VAE, which uses apriori causal knowledge, a structural causal prior, and a non-linear additive noise structural causal model (SCM) to learn independent causal mechanisms and identifiable causal representations. We conduct theoretical analysis and perform experiments on synthetic and real-world datasets to show the improved quality of learned causal representations and robustness under interventions.
Research Practice of the Scale Estimation Method Based on NESMA

Based on the common software scale estimation method, various functional point estimation method and NESMA, a scale estimation method suitable for space embedded software is proposed. By tailoring the estimation process, adding non-functional demand impact factor and fixed complexity weights, reduce the influence of individual subjective factors on estimation accuracy, and estimate the estimated workload of space embedded software as less than 15% different from the actual development workload of space embedded software in the space field.
Implementation of Biased Big Data to the Japanese Official Labor Statistics Using Supervised Learning under Covariate Shift

Recently, the National Statistical Institutes (NSIs) have started to use new data sources to produce official statistics. These new data sources often referred to as "Big Data" are not directly related to statistical production purposes. An advantage of using Big Data for official statistics is the speed of publication. Surveys designed to produce official statistics are time-consuming. On the other hand, we can obtain such new data sources with almost no time lag as the previous day ’s information is available the next day. However, the new data sources have a problem. They are likely selective with respect to the target population. This selection bias needs to be corrected when using these data sources to produce official statistics. In this study, we implemented Big Data to the Japanese official labor statistics, wage changes of hired career-changing employees. This economic indicator can potentially be indispensable for public policymakers and recruiters in private firms. If the indicator had been published quickly, they could have identified the degree of pressure on wage by the external labor market and could have used the information for their decision-making. The problem is the indicator is not published quickly. The time lag ranges from six to thirteen months. This means the indicator cannot be used to make decisions. To address this problem, we used transaction data of private employment agency and the idea of supervised learning under covariate shift to correct the selection bias. The proposed method can achieve early publication if a certain margin of error is allowed. The range of time lag reduces from a few days to five months, and policymakers and hiring managers could use the indicator for their decision-making.
KGIQ: Scalable Translation of User-Specified Examples into Knowledge-Graph Queries

Querying large-scale knowledge graphs (KGs) can be difficult for users in real-life scenarios, in which formal graph query languages potentially present usability barriers. Query-By-Example (QBE) approaches, which allow users to specify their query intent with examples, have become an emerging trend to address this issue. However, existing QBE approaches either require user-specified examples to provide values of all the KG attributes, or may return incorrect formalizations of user query intent due to the lack of user interaction. In this paper we propose an approach called Knowledge Graph Intuitive Querier (KGIQ) that addresses both challenges on large-scale KGs by using novel scalable algorithms. Unlike existing approaches, KGIQ ensures correct translation of user-specified examples into formal executable graph queries by interacting with users in a lightweight manner. Our experimental results suggest that KGIQ can correctly formalize graph queries on large-scale KGs with the help of uncomplicated user interactions and a feedback loop to improve user experience, while consistently outperforming the state of the art in terms of efficiency and outcome quality.
NOMABER: A Novel Framework for Multi-Type Abnormal Behaviour Recognition

Recently, using machine learning technology to realize abnormal behavior recognition in video surveillance to replace human monitoring has become a hot academic topic. In that case, constructing an efficient and unified framework for multi-type abnormal behavior recognition is a worthy topic in machine learning research. This research aims to design a lightweight recognition framework that can recognize various abnormal behaviors in real-time. We propose a Novel framewOrk for the Multi-type Abnormal BEhavior Recognition (NOMABER), which consists of three parts. Firstly, the improved image pre-processing module annotates the abnormal behaviors of image data sets. Secondly, the improved YOLOv5 module is used to identify the multi-type abnormal behaviors, and then the abnormal behaviors are classified by the output module. Finally, experiments on real data sets show that NOMABER is superior to the current methods of real-time performance, identification accuracy, and types of abnormal behaviors.