AI Communications - Volume 37, issue 4

Authors: Sutcliffe, Geoff | Desharnais, Martin

Article Type: Research Article

Abstract: The CADE ATP System Competition (CASC) is the annual evaluation of fully automatic, classical logic, Automated Theorem Proving (ATP) systems – the world championship for such systems. CASC-29 was the twenty-eighth competition in the CASC series. Twenty-four ATP systems competed in the various divisions. This paper presents an outline of the competition design and a commentated summary of the results.

Keywords: Automated Theorem Proving, competition

DOI: 10.3233/AIC-230325

Citation: AI Communications, vol. 37, no. 4, pp. 485-503, 2024

Authors: Gupta, Shikha | Deshmukh, Sonia | Kumar, Naveen

Article Type: Research Article

Abstract: Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life …event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models. Show more

Keywords: Manta ray foraging optimization, process model discovery, bio-inspired optimization, process mining, event log

DOI: 10.3233/AIC-220219

Citation: AI Communications, vol. 37, no. 4, pp. 505-524, 2024

Authors: Pahuja, Swimpy | Goel, Navdeep

Article Type: Review Article

Abstract: Critical applications ranging from sensitive military data to restricted area access demand selective user authentication. The prevalent methods of tokens, passwords, and other commonly used techniques proved deficient as they can be easily stolen, lost, or broken to gain illegitimate access, leading to data spillage. Since data safety against tricksters is a significant issue nowadays, biometrics is one of the unique human characteristic-based techniques that may give better solutions in this regard. The technique entails biometric authentication of users based on an individual’s inimitable physiological or behavioral characteristics to provide access to a specific application or data. This paper provides …a detailed description of authentication and its approaches, focusing on biometric-based authentication methods, the primary challenges they encounter, and how they have been addressed. The tabular view shows the benefits and downsides of various multimodal biometric systems, and open research challenges. To put it another way, this article lays out a roadmap for the emergence of multimodal biometric-based authentication, covering both the challenges and the solutions that have been proposed. Further, the urge to develop various multi-trait-based methods for secure authentication and data privacy is focused. Lastly, some multimodal biometric systems comprising fingerprint and iris modalities have been compared based on False Accept Rate (FAR), False Reject Rate (FRR), and accuracy to find the best secure model with easy accessibility. Show more

Keywords: Authentication, multimodal biometrics, identification, biometrics

DOI: 10.3233/AIC-220247

Citation: AI Communications, vol. 37, no. 4, pp. 525-547, 2024

Authors: , Monika | Singh, Pardeep | Chand, Satish

Article Type: Research Article

Abstract: Pedestrian intent prediction is an essential task for ensuring the safety of pedestrians and vehicles on the road. This task involves predicting whether a pedestrian intends to cross a road or not based on their behavior and surrounding environment. Previous studies have explored feature-based machine learning and vision-based deep learning models for this task but these methods have limitations in capturing the global spatio-temporal context and fusing different features of data effectively. To address these issues, we propose a novel hybrid framework HSTGCN for pedestrian intent prediction that combines spatio-temporal graph convolutional neural networks (STGCN) and long short-term memory …(LSTM) networks. The proposed framework utilizes the strengths of both models by fusing multiple features, including skeleton pose, trajectory, height, orientation, and ego-vehicle speed, to predict their intentions accurately. The framework’s performance have been evaluated on the JAAD benchmark dataset and the results show that it outperforms the state-of-the-art methods. The proposed framework has potential applications in developing intelligent transportation systems, autonomous vehicles, and pedestrian safety technologies. The utilization of multiple features can significantly improve the performance of the pedestrian intent prediction task. Show more

Keywords: STGCN, LSTM, skeleton, spatio temporal, pedestrian intent prediction

DOI: 10.3233/AIC-230053

Citation: AI Communications, vol. 37, no. 4, pp. 549-562, 2024

Authors: Noor, Sabah Binte | Siddiqui, Fazlul Hasan

Article Type: Research Article

Abstract: Plan deordering removes unnecessary ordering constraints between actions in a plan, facilitating plan execution flexibility and several other tasks, such as plan reuse, modification, and decomposition. Block deordering is a variant of plan deordering that encapsulates coherent actions into blocks to eliminate further ordering constraints from a partial-order plan (POP) and is useful in many applications (e.g., generating macro-actions and improving the overall plan quality). The existing block deordering strategy is formulated in propositional encodings. Finite-domain state variable encodings (e.g., SAS+ representation), in contrast with propositional encodings, can capture the internal structure and the behavior of state variables of a …planning instance through concise constructs such as causal graphs (CGs) and domain transition graphs (DTGs). This work redefines the semantics of block deordering terminologies and related plan deordering concepts in finite domain representation (FDR). Our proposed semantics also resolves some limitations of the existing block semantics and further enhance plan flexibility. In addition, this work exploits block deordering to eliminate redundant actions from a POP. A comparative analysis is also performed on block deordering with various deordering/reordering techniques using explanation-based order generalization (EOG) and MaxSAT. Our experiments on the benchmark problems from International Planning Competitions (IPC) show that our FDR formalism of block deordering significantly improves the plan execution flexibility while maintaining good coverage and execution time. Show more

Keywords: Partial-order planning, block deordering, plan deordering, plan annotation, finite-domain representation

DOI: 10.3233/AIC-230058

Citation: AI Communications, vol. 37, no. 4, pp. 563-583, 2024

Authors: Levshun, Diana | Levshun, Dmitry | Doynikova, Elena | Branitskiy, Alexander | Kotenko, Igor

Article Type: Research Article

Abstract: Nowadays, people spend a lot of time in the information space, communicating within various social platforms. Content of those platforms can influence people’s feelings and personalities, which is especially relevant for young people. In this research, we made an attempt to prove this hypothesis. For the experiment, we selected the VKontakte social network and analysed users profiles together with the results of the psychological tests passed by them. The goal of the experiment was to find correlations between the information provided within the social network communities and the users’ personalities. Moreover, in this paper, we made an attempt to enhance …the results of the classifier accuracy using the sentiment analysis. The experiments were conducted to test the sentiment analysis models, to analyse the proposed feature based on posts’ sentiment, and test the classifier for the detection of the potentially destructive impacts. The analysis of the correlation of the proposed feature with the communities that have potentially destructive impacts on anxiety is conducted. The analysis of the obtained results is provided. During the experiments, the authors found out that consideration of the posts’ sentiment allows increasing accuracy of the classifier for anxiety destructive impacts on 12.24 %. Additionally, we analysed the relationship between the user sentiments metric and destructiveness. We confirmed that the assessment of the user’s posts’ sentiment can be used to compile his psychological characteristics and determine possibility of destructiveness. Show more

Keywords: Social network, destructive impact, machine learning, protection from information, Ammon’s test

DOI: 10.3233/AIC-230154

Citation: AI Communications, vol. 37, no. 4, pp. 585-598, 2024

Authors: Lian, Zewei | Wang, Xiaogang | Lin, Junjie | Zhang, Liuhong | Tang, Mingming

Article Type: Research Article

Abstract: When the sensor dynamically collects point cloud data for object or map reconstruction, the registration effect is poor and reconstruction application is difficult with a too low overlap rate of the collected point cloud data. The reason is that the objects are covered, the sensor rotation angle is too large and the speed of movement is too fast. Because of these problems, this paper proposes a point cloud registration algorithm based on FPFH feature matching, combined with second-order spatial measures. Firstly, using the FPFH feature extraction algorithm, the features of each point are extracted, and then feature matching is performed …to generate the set of feature point pairs. Secondly, the second-order spatial measure is used to calculate the set of feature point pairs to obtain the second-order spatial measure matrix scores and sort them. Finally, the dichotomy method is used to find the appropriate second-order spatial measure scores for distinguishing the inner points (points in the overlap region) from the outer points (points that do not belong to the overlap region as well as the mismatched points and some disturbances). The contrast experiments between this algorithm and three common point cloud registration algorithms, FPFH-ICP, 4PCS-ICP, and NDT-ICP, on the Stanford dataset and 3DMatch dataset shows that the registration accuracy of the other algorithms decreases significantly with a low overlap rate. But this algorithm still has a high registration accuracy and is less affected by outliers than the other algorithms. Besides, this algorithm can still maintain a good registration effect on different data sets. Show more

Keywords: Low overlap, FPFH, second-order spatial measures, dichotomy, point cloud registration

DOI: 10.3233/AIC-230217

Citation: AI Communications, vol. 37, no. 4, pp. 599-617, 2024

Authors: Wang, Jingpin | Ge, Yuan | Zhao, Jie | Han, Chao

Article Type: Research Article

Abstract: Under the foggy environment, lane line images are obscured by haze, which leads to lower detection accuracy, higher false detection of lane lines. To address the above problems, a multi-layer feature fusion dehazing network based on CycleGAN architecture is proposed. Firstly, the foggy image is enhanced to remove the fog in the image, and then the lane line detection network is used for detection. For the dehazing network, a multi-layer feature fusion module is used in the generator to fuse the features of different coding layers of U-Net to enhance the network’s recovery of information such as details and edges, …and a frequency domain channel attention mechanism is added at the key nodes of the network to enhance the network’s attention to different fog concentrations. At the same time, to improve the discriminant effect of the discriminator, the discriminator is extended to a global and local discriminator. The experimental results show that the dehaze effect on Reside and other test data sets is better than the comparison method. The peak signal-to-noise ratio is improved by 2.26 dB compared to the highest GCA-Net algorithm. According to the lane detection of fog images, it is found that the proposed network improves the accuracy of lane detection on foggy days. Show more

Keywords: Image dehazing, lane line detection, multi-layer feature fusion, frequency channel attention networks, CycleGAN

DOI: 10.3233/AIC-230227

Citation: AI Communications, vol. 37, no. 4, pp. 619-635, 2024

Authors: Lin, Tian | Hua, Li | Linxuan, Li | Chuanao, Bai

Article Type: Research Article

Abstract: Traditional object detection algorithms operate within a closed set, where the training data may not cover all real-world objects. Therefore, the issue of open-world object detection has attracted significant attention. Open-world object detection faces two major challenges: “neglecting unknown objects” and “misclassifying unknown objects as known ones.” In our study, we address these challenges by utilizing the Region Proposal Network (RPN) outputs to identify potential unknown objects with high object scores that do not overlap with ground truth annotations. We introduce the reselection mechanism, which separates unknown objects from the background. Subsequently, we employ the simulated annealing algorithm to disentangle …features of unknown and known classes, guiding the detector’s learning process. Our method has improved on multiple evaluation metrics such as U-mAP, U-recall, and UDP, greatly alleviating the challenges faced by open world object detection. Show more

Keywords: Object detection, open-set recognition, unknown object detection, incremental learning, simulated annealing

DOI: 10.3233/AIC-230270

Citation: AI Communications, vol. 37, no. 4, pp. 637-653, 2024

Authors: Roth, Tom | Gao, Yansong | Abuadbba, Alsharif | Nepal, Surya | Liu, Wei

Article Type: Research Article

Abstract: Many adversarial attacks target natural language processing systems, most of which succeed through modifying the individual tokens of a document. Despite the apparent uniqueness of each of these attacks, fundamentally they are simply a distinct configuration of four components: a goal function, allowable transformations, a search method, and constraints. In this survey, we systematically present the different components used throughout the literature, using an attack-independent framework which allows for easy comparison and categorisation of components. Our work aims to serve as a comprehensive guide for newcomers to the field and to spark targeted research into refining the individual attack components.

Keywords: Text adversarial attacks, security, robustness, natural language processing

DOI: 10.3233/AIC-230279

Citation: AI Communications, vol. 37, no. 4, pp. 655-676, 2024