OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Trajectory Planning and Control for USV with Vessel Dynamics and Differential Flatness
Venue: arXiv cs.RO 2022
Year: 2022

Unmanned surface vessels (USVs) are widely used in ocean exploration and environmental protection fields. To ensure that USV can successfully perform its mission, trajectory planning and motion tracking are the two most critical technologies. In this paper, we propose a novel trajectory generation and tracking method for USV based on optimization theory. Specifically, the USV dynamic model is described with differential flatness, so that the trajectory can be generated by dynamic RRT* in a linear invariant system expression form under the objective of optimal boundary value. To reduce the sample number and improve efficiency, we adjust the trajectory through local optimization. The dynamic constraints are considered in the optimization process so that the generated trajectory conforms to the kinematic characteristics of the under-actuated hull, and makes it easier to be tracked. Finally, motion tracking is added with model predictive control under a sequential quadratic programming problem. Experimental results show the planned trajectory is more in line with the kinematic characteristics of USV, and the tracking accuracy remains a higher level.
Banknote Recognition for Visually Impaired People (Case of Ethiopian note)
Venue: arXiv cs.HC 2022
Year: 2022

Currency is used almost everywhere to facilitate business. In most developing countries, especially the ones in Africa, tangible notes are predominantly used in everyday financial transactions. One of these countries, Ethiopia, is believed to have one of the world highest rates of blindness (1.6%) and low vision (3.7%). There are around 4 million visually impaired people; With 1.7 million people being in complete vision loss. Those people face a number of challenges when they are in a bus station, in shopping centers, or anywhere which requires the physical exchange of money. In this paper, we try to provide a solution to this issue using AI/ML applications. We developed an Android and IOS compatible mobile application with a model that achieved 98.9% classification accuracy on our dataset. The application has a voice integrated feature that tells the type of the scanned currency in Amharic, the working language of Ethiopia. The application is developed to be easily accessible by its users. It is build to reduce the burden of visually impaired people in Ethiopia.
Measuring the Interpretability of Unsupervised Representations via Quantized Reverse Probing
Venue: arXiv cs.CV 2022
Year: 2022

Self-supervised visual representation learning has recently attracted significant research interest. While a common way to evaluate self-supervised representations is through transfer to various downstream tasks, we instead investigate the problem of measuring their interpretability, i.e. understanding the semantics encoded in raw representations. We formulate the latter as estimating the mutual information between the representation and a space of manually labelled concepts. To quantify this we introduce a decoding bottleneck: information must be captured by simple predictors, mapping concepts to clusters in representation space. This approach, which we call reverse linear probing, provides a single number sensitive to the semanticity of the representation. This measure is also able to detect when the representation contains combinations of concepts (e.g., "red apple") instead of just individual attributes ("red" and "apple" independently). Finally, we propose to use supervised classifiers to automatically label large datasets in order to enrich the space of concepts used for probing. We use our method to evaluate a large number of self-supervised representations, ranking them by interpretability, highlight the differences that emerge compared to the standard evaluation with linear probes and discuss several qualitative insights. Code at: {\scriptsize{\url{https://github.com/iro-cp/ssl-qrp}}}.
Measurement of the Usage of Web Clips in Underground Economy
Venue: arXiv cs.CR 2022
Year: 2022

In this paper, we study the ecosystem of the abused Web Clips in underground economy. Through this study, we find the Web Clips is wildly used by perpetrators to penetrate iOS devices to gain profit. This work starts with 1,800 user complaint documents about cyber crimes over Web Clips. We firstly look into the ecosystem of abused Web Clips and point out the main participants and workflow. In addition, what is the Web Clips used for is demystified. Then the main participants, including creators, distributors, and operators are deeply studied based on our dataset. We try to reveal the prominent features of the illicit Web Clips and give some mitigation measures. Analysis reveals that 1) SSL certificate is overwhelmingly preferred for signing Web Clips instances compared with certificate issued by Apple. The wildly used SSL certificates can be aggregated into a limited group. 2) The content of the abused Web Clips falls into a few categories, `Gambling', `Fraud', and `Pornography' are among the top categories. 3) Instant messenger (IM) and live streaming platform are the most popular medium to trick victims into deploying the Web Clips. 4) The Web Clips are operated by a small amount of perpetrators, and the perpetrators tend to evade detection by taking technical approach, such as registering domain names through oversea domain name service provider, preferring easy-to-acquire new gTLD (global Top Level Domain), and deploying anti-crawler tricks. Our study gives hints on investigation of cyber crime over Web Clips, we hope that this work can help stakeholders to stay ahead of the threat.
ESSYS* Sharing #UC: An Emotion-driven Audiovisual Installation
Venue: arXiv cs.MM 2022
Year: 2022

We present ESSYS* Sharing #UC, an audiovisual installation artwork that reflects upon the emotional context related to the university and the city of Coimbra, based on the data shared about them on Twitter. The installation was presented in an urban art gallery of Círculo de Artes Plásticas de Coimbra during the summer and autumn of 2021. In the installation space, one may see a collection of typographic posters displaying the tweets and listening to an ever-changing ambient sound. The present audiovisuals are created by an autonomous computational creative approach, which employs a neural classifier to recognize the emotional context of a tweet and uses this resulting data as feedstock for the audiovisual generation. The installation's space is designed to promote an approach and blend between the online and physical perceptions of the same location. We applied multiple experiments with the proposed approach to evaluate the capability and performance. Also, we conduct interview-based evaluation sessions to understand how the installation elements, especially poster designs, are experienced by people regarding diversity, expressiveness and possible employment in other commercial and social scenarios.
Data Leakage in Notebooks: Static Detection and Better Processes
Venue: arXiv cs.SE 2022
Year: 2022

Data science pipelines to train and evaluate models with machine learning may contain bugs just like any other code. Leakage between training and test data can lead to overestimating the model's accuracy during offline evaluations, possibly leading to deployment of low-quality models in production. Such leakage can happen easily by mistake or by following poor practices, but may be tedious and challenging to detect manually. We develop a static analysis approach to detect common forms of data leakage in data science code. Our evaluation shows that our analysis accurately detects data leakage and that such leakage is pervasive among over 100,000 analyzed public notebooks. We discuss how our static analysis approach can help both practitioners and educators, and how leakage prevention can be designed into the development process.
A hybrid Bayesian network for medical device risk assessment and management
Venue: arXiv cs.LG 2022
Year: 2022

ISO 14971 is the primary standard used for medical device risk management. While it specifies the requirements for medical device risk management, it does not specify a particular method for performing risk management. Hence, medical device manufacturers are free to develop or use any appropriate methods for managing the risk of medical devices. The most commonly used methods, such as Fault Tree Analysis (FTA), are unable to provide a reasonable basis for computing risk estimates when there are limited or no historical data available or where there is second-order uncertainty about the data. In this paper, we present a novel method for medical device risk management using hybrid Bayesian networks (BNs) that resolves the limitations of classical methods such as FTA and incorporates relevant factors affecting the risk of medical devices. The proposed BN method is generic but can be instantiated on a system-by-system basis, and we apply it to a Defibrillator device to demonstrate the process involved for medical device risk management during production and post-production. The example is validated against real-world data.
Investigating Reasons for Disagreement in Natural Language Inference
Venue: arXiv cs.CL 2022
Year: 2022

We investigate how disagreement in natural language inference (NLI) annotation arises. We developed a taxonomy of disagreement sources with 10 categories spanning 3 high-level classes. We found that some disagreements are due to uncertainty in the sentence meaning, others to annotator biases and task artifacts, leading to different interpretations of the label distribution. We explore two modeling approaches for detecting items with potential disagreement: a 4-way classification with a "Complicated" label in addition to the three standard NLI labels, and a multilabel classification approach. We found that the multilabel classification is more expressive and gives better recall of the possible interpretations in the data.
A Survey of Neural Trees
Venue: arXiv cs.LG 2022
Year: 2022

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term as neural trees (NTs). This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability. We first propose a thorough taxonomy of NTs that expresses the gradual integration and co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their interpretability and performance, and suggest possible solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions towards this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/zju-vipa/awesome-neural-trees
Responsibility: An Example-based Explainable AI approach via Training Process Inspection
Venue: arXiv cs.LG 2022
Year: 2022

Explainable Artificial Intelligence (XAI) methods are intended to help human users better understand the decision making of an AI agent. However, many modern XAI approaches are unintuitive to end users, particularly those without prior AI or ML knowledge. In this paper, we present a novel XAI approach we call Responsibility that identifies the most responsible training example for a particular decision. This example can then be shown as an explanation: "this is what I (the AI) learned that led me to do that". We present experimental results across a number of domains along with the results of an Amazon Mechanical Turk user study, comparing responsibility and existing XAI methods on an image classification task. Our results demonstrate that responsibility can help improve accuracy for both human end users and secondary ML models.
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design
Venue: arXiv cs.LG 2022
Year: 2022

Aircraft industry is constantly striving for more efficient design optimization methods in terms of human efforts, computation time, and resource consumption. Hybrid surrogate optimization maintains high results quality while providing rapid design assessments when both the surrogate model and the switch mechanism for eventually transitioning to the HF model are calibrated properly. Feedforward neural networks (FNNs) can capture highly nonlinear input-output mappings, yielding efficient surrogates for aircraft performance factors. However, FNNs often fail to generalize over the out-of-distribution (OOD) samples, which hinders their adoption in critical aircraft design optimization. Through SmOOD, our smoothness-based out-of-distribution detection approach, we propose to codesign a model-dependent OOD indicator with the optimized FNN surrogate, to produce a trustworthy surrogate model with selective but credible predictions. Unlike conventional uncertainty-grounded methods, SmOOD exploits inherent smoothness properties of the HF simulations to effectively expose OODs through revealing their suspicious sensitivities, thereby avoiding over-confident uncertainty estimates on OOD samples. By using SmOOD, only high-risk OOD inputs are forwarded to the HF model for re-evaluation, leading to more accurate results at a low overhead cost. Three aircraft performance models are investigated. Results show that FNN-based surrogates outperform their Gaussian Process counterparts in terms of predictive performance. Moreover, SmOOD does cover averagely 85% of actual OODs on all the study cases. When SmOOD plus FNN surrogates are deployed in hybrid surrogate optimization settings, they result in a decrease error rate of 34.65% and a computational speed up rate of 58.36 times, respectively.
Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots
Venue: arXiv cs.CY 2022
Year: 2022

Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not behave in undesired manners, providing offensive or toxic responses to users. This is not a trivial task as state-of-the-art chatbot models are trained on large, public datasets openly collected from the Internet. This paper presents a first-of-its-kind, large-scale measurement of toxicity in chatbots. We show that publicly available chatbots are prone to providing toxic responses when fed toxic queries. Even more worryingly, some non-toxic queries can trigger toxic responses too. We then set out to design and experiment with an attack, ToxicBuddy, which relies on fine-tuning GPT-2 to generate non-toxic queries that make chatbots respond in a toxic manner. Our extensive experimental evaluation demonstrates that our attack is effective against public chatbot models and outperforms manually-crafted malicious queries proposed by previous work. We also evaluate three defense mechanisms against ToxicBuddy, showing that they either reduce the attack performance at the cost of affecting the chatbot's utility or are only effective at mitigating a portion of the attack. This highlights the need for more research from the computer security and online safety communities to ensure that chatbot models do not hurt their users. Overall, we are confident that ToxicBuddy can be used as an auditing tool and that our work will pave the way toward designing more effective defenses for chatbot safety.
Analyzing and Calibrating Risk Assessment by Software Developers
Venue: arXiv cs.SE 2022
Year: 2022

In software project management, risk management is a critical factor. Project managers use existing lists of risk or perform brainstorming to identify the risks. However, it is not easy to perceive all the risks objectively. As a result, some risks are perceived based on subjective impression, which leads to risk biases. So, our goals are (i) We clarify the risk perception of developers to enhance the reliability of the brainstorming, and (ii) we calibrate the risk assessment based on a mathematical model to make more accurate risk list. In the analysis, we collected data concerning the risk perception of 69 professional software developers via a questionnaire. The average number of years of experience among these professionals was 18.3. Using the dataset, we applied factor analysis to clarify the factors that affect the evaluation of risk impact. The questionnaire was based on the risk perception theory established by Slovic, in which "dread" and "unknown" are the major factor of risk perception. The analysis result shows that (i) risk experience (i.e., whether a developer actually faced the risk or not) sometimes affects risk assessment (evaluation of risk impact), (ii) risk perception is considered to be based on unknown and dread factors, and (iii) risk assessment can be calibrated by a mathematical model (the average absolute error was 0.20).
Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application
Venue: arXiv cs.LG 2022
Year: 2022

Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
MalDetConv: Automated Behaviour-based Malware Detection Framework Based on Natural Language Processing and Deep Learning Techniques
Venue: arXiv cs.CR 2022
Year: 2022

The popularity of Windows attracts the attention of hackers/cyber-attackers, making Windows devices the primary target of malware attacks in recent years. Several sophisticated malware variants and anti-detection methods have been significantly enhanced and as a result, traditional malware detection techniques have become less effective. This work presents MalBehavD-V1, a new behavioural dataset of Windows Application Programming Interface (API) calls extracted from benign and malware executable files using the dynamic analysis approach. In addition, we present MalDetConV, a new automated behaviour-based framework for detecting both existing and zero-day malware attacks. MalDetConv uses a text processing-based encoder to transform features of API calls into a suitable format supported by deep learning models. It then uses a hybrid of convolutional neural network (CNN) and bidirectional gated recurrent unit (CNN-BiGRU) automatic feature extractor to select high-level features of the API Calls which are then fed to a fully connected neural network module for malware classification. MalDetConv also uses an explainable component that reveals features that contributed to the final classification outcome, helping the decision-making process for security analysts. The performance of the proposed framework is evaluated using our MalBehavD-V1 dataset and other benchmark datasets. The detection results demonstrate the effectiveness of MalDetConv over the state-of-the-art techniques with detection accuracy of 96.10%, 95.73%, 98.18%, and 99.93% achieved while detecting unseen malware from MalBehavD-V1, Allan and John, Brazilian, and Ki-D datasets, respectively. The experimental results show that MalDetConv is highly accurate in detecting both known and zero-day malware attacks on Windows devices.
Automated Validation of Insurance Applications against Calculation Specifications
Venue: arXiv cs.SE 2022
Year: 2022

Insurance companies rely on their Legacy Insurance System (LIS) to govern day-to-day operations. These LIS operate as per the companys business rules that are formally specified in Calculation Specification (CS) sheets. To meet ever-changing business demands, insurance companies are increasingly transforming their outdated LIS to modern Policy Administration Systems (PAS). Quality Assurance (QA) of such PAS involves manual validation of calculations implementation against the corresponding CS sheets from the LIS. This manual QA approach is effort-intensive and error-prone, which may fail to detect inconsistencies in PAS implementations and ultimately result in monetary loss. To address this challenge, we propose a novel low-code no-code technique to automatically validate PAS implementation against CS sheets. Our technique has been evaluated on a digital transformation project of a large insurance company on 12 real-world calculations through 254 policies. The evaluation resulted in effort savings of approximately 92 percent against the conventional manual validation approach.
Suspicious and Anomaly Detection
Venue: arXiv cs.CV 2022
Year: 2022

In this project we propose a CNN architecture to detect anomaly and suspicious activities; the activities chosen for the project are running, jumping and kicking in public places and carrying gun, bat and knife in public places. With the trained model we compare it with the pre-existing models like Yolo, vgg16, vgg19. The trained Model is then implemented for real time detection and also used the. tflite format of the trained .h5 model to build an android classification.
Sign Language Detection
Venue: arXiv cs.CV 2022
Year: 2022

With the advancements in Computer vision techniques the need to classify images based on its features have become a huge task and necessity. In this project we proposed 2 models i.e. feature extraction and classification using ORB and SVM and the second is using CNN architecture. The end result of the project is to understand the concept behind feature extraction and image classification. The trained CNN model will also be used to convert it to tflite format for Android Development.
DIY-IPS: Towards an Off-the-Shelf Accurate Indoor Positioning System
Venue: arXiv cs.NI 2022
Year: 2022

We present DIY-IPS - Do It Yourself - Indoor Positioning System, an open-source real-time indoor positioning mobile application. DIY-IPS detects users' indoor position by employing dual-band RSSI fingerprinting of available WiFi access points. The app can be used, without additional infrastructural costs, to detect users' indoor positions in real time. We published our app as an open source to save other researchers time recreating it. The app enables researchers/users to (1) collect indoor positioning datasets with a ground truth label, (2) customize the app for higher accuracy or other research purposes (3) test the accuracy of modified methods by live testing with ground truth. We ran preliminary experiments to demonstrate the effectiveness of the app.