Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Effective Bug Reproduction for Mobile Applications

Bug reproduction is a critical task in software testing, as it helps developers to identify and fix bugs in the software. While some automated reproduction tools are designed to assist developers in reproducing bugs detected by automated testing tools, they are not entirely reliable. Thus, manual reproduction remains important. However, automated testing tools often generate testing results that are difficult for non-professional developers to understand, which complicates their efforts to reproduce bugs. In this paper, we propose RepAssistor, an approach that employs an interactive method to assist developers in reproducing bugs based on automated testing logs. RepAssistor is designed for reproducing bugs in mobile applications. It leverages deep learning (DL) and traditional computer vision (CV) techniques to analyze application screenshots, transforming automated testing logs into a graph representation. In this graph, edges represent test events while nodes represent application states. With this graph representation in place, RepAssistor is then able to monitor developers’ actions and continuously track which node they are at in this graph in real-time. Based on this understanding, RepAssistor dynamically calculates and updates the optimal path to guide developers to reproduce bugs. This guidance is conveyed to the developers through an interactive method, enabling effective communication and assistance throughout the bug reproduction process. Our experiments demonstrate that RepAssistor improves the performance of developers in bug reproduction tasks.
Lightweight Encryption and Authentication for Controller Area Network of Autonomous Vehicles

With the rapid development of autonomous vehicles (AVs), vehicle safety has attracted attention. Controller area network (CAN) bus without authentication and encryption mechanisms is a weakness of the in-vehicle network (IVN), and attackers always primarily target the CAN bus. Attacks on the AVs' CAN bus are more frequent because AVs have more external interfaces and sensors than ordinary vehicles. To address the abovementioned issues, this paper proposes a lightweight encryption and authentication scheme for the CAN bus of AVs using message authentication codes and Grain stream cipher. The scheme realizes efficient authentication between electronic control units and a counter re-synchronization protocol is used to ensure that authentication failure owing to counter inconsistency is prevented. Blom key management scheme is employed to rapidly distribute and update session keys, avoiding the complicated process of updating pairwise keys. In addition, considering the scalability of the IVN, a key distribution protocol for new nodes was provided. The security proof using Burrows-Abadi-Needham logic and security analysis prove that the proposed scheme can satisfy the security requirements of the IVN. In addition, performance analysis shows that the proposed scheme can meet real-time requirements and has little impact on the busload.
Using Space, Air, Marine, and Ground Assets for Disaster Response and Environmental Monitoring

Space, Air, In-situ, and Marine assets have been integrated into sensorwebs to enable detection, tracking, and response to a wide range of terrestrial phenomena. These techniques have been used to track flooding, cryosphere, volcanism, oceanographic events, and many other events of humanitarian, scientific, and environmental importance. We describe how sensorwebs have been deployed to monitor numerous phenomena to great impact and how artificial intelligence techniques such as machine learning, automated planning, and automated data analysis are being leveraged to enable these sensorwebs.
Fast Federated Low Rank Matrix Completion

For the low rank matrix completion problem, we develop a fast, communication-efficient and private algorithm, called Alternating Gradient Descent and Minimization (AltGDMin). The communication-efficiency and privacy claims assume a vertically federated setting in which each node observes some entries of a different subset of columns of the unknown LR matrix X*. By extensive simulation experiments, we demonstrate the superior communication efficiency of AltGDMin. We also numerically evaluate the sample complexity and show competitive performance compared to benchmark methods.
Host Intrusion Detection Method Based on Short Sequence of System Call

System call is the interface for application programs to interact with the kernel, and its security affects the security of the entire operating system. Therefore, constructing an intrusion detection system that can detect attacks during application program execution at the system call level has important application value. In this paper, we first analyze the semantic information of the system call and intrusion detection methods in the system call domain, then propose a detection method based on short sequence of the system call and deep learning techniques. In data preprocessing, we use sliding window technology to divide long system call sequences into equal-length short sequences. In order to fully utilize the semantic information between system calls, Word2vec word embedding technology is used to convert the system call into a vector representation. To reduce the false positive rate of the detection model, we train a Doc2vec model based on short sequences of the system call to construct a new dataset and train the detection model, thereby improving the detection precision of the normal type data. Finally, relevant experiments are conducted on the ADFA-LD dataset to verify the effectiveness of the proposed method in this paper.
Regularized RLS Adaptive Algorithm with Conjugate Gradient Method

The recursive least-squares (RLS) family of adaptive algorithms is attractive for the identification of long unknown systems due to their high convergence speeds. Despite the fact that the combination with the conjugate gradient (CG) iterative method can further improve the tracking capabilities of the RLS, the namely RLS-CG is still vulnerable to high-level interference signals. This paper proposes a regularization method for the RLS-CG, which aims to considerably reduce the adaptive filter’s update process during the manifestation of the high-interference scenarios. The theoretical model is validated using simulations.
Forest Recovery Detection Based on U-Net and LinkNet

Nowadays geographical information systems (GIS) software may be a huge problem for people, who want to learn the art of forestry. Not every such product offers tools for analysis with the help of artificial intelligence. The market of forestry software growth each year and researches in the field are out of human capabilities to grasp. However, despite all of that, there are not so much researches on topic of “reforestation” with the usage of machine learning techniques. The purpose of the work is to implement the system of reforestation and deforestation detection system as part of developing an accounting application for forestry. Totally two segmentation neural network architectures and three different encoders were used, in order to create a comparison characteristic of networks for the problem. Tests show high accuracy results (at least 94%) on each network in the project. Moreover, best neural network accuracy performance is 97.45%.
Truncated Sequential Procedure of Statistical Data Processing

The transition to Industry 4.0 involves the implementation of modern information technologies. This framework uses the doctrine of data-driven decision-making. The implementation of this doctrine includes the collecting, processing, and use of data for the formation and implementation of control influences in the relevant systems of the industry. During processing, two basic approaches are possible: based on a fixed sample size and with a random sample size within the framework of A. Wald’s sequential analysis. This paper is devoted to the synthesis and analysis of a truncated sequential statistical classification procedure with the arrangement of two decision thresholds according to Bellman’s approach.
Concept Bottleneck Model for Adolescent Idiopathic Scoliosis Patient Reported Outcomes Prediction

Post-surgical patient-reported outcomes (PROs) serve as a crucial subjective measure of surgical success for adolescent idiopathic scoliosis (AIS) patients. Leveraging pre-operative patient information to predict post-operative PROs is instrumental in improving pediatric patient care and providing invaluable insights for clinical decision-making. Recently, deep learning techniques have demonstrated encouraging results in developing predictive models for clinical decision support. However, the inherent black-box nature makes them non-interactive and challenging to troubleshoot during the training phase. To mitigate this issue, our study introduces an interactive concept bottleneck model to predict subjective rehabilitation outcomes for AIS patients. We assess three learning schemas - independent, sequential, and joint - to first comprehend the concepts, which are a set of post-operative radiographic data available during the training phase. Subsequently, these acquired concepts are employed to predict post-operative patient rehabilitation outcomes across five domains: pain, function, general satisfaction, self-image, and mental health. Our results demonstrated improvement compared to the existing baseline, with the joint learning schema yielding the highest F1 score in the function and pain domains, while sequential learning recorded the highest F1 score in the mental health and self-image domains. This proposed framework harbors the immense potential to aid pre-operative surgical planning and further enhance the transparency of AI models, thereby supporting real-world clinical decision-making.
WIP: Automatic DNN Deployment on Heterogeneous Platforms: the GAP9 Case Study

Emerging Artificial-Intelligence-enabled System-on-Chips (AI-SoCs) combine a flexible microcontroller with parallel Digital Signal Processors (DSP) and heterogeneous acceleration capabilities. In this Work-in-Progress paper, we focus on the GAP9 RISC-V SoC as a case study to show how the open-source DORY Deep Neural Network (DNN) tool flow can be extended for heterogeneous acceleration by fine grained interleaving of a dedicated Neural Engine and a cluster of RISC-V cores. Our results show that up to 91% of the peak accelerator throughput can be extracted in end-to-end execution of benchmarks based on MobileNet-V1 and V2.
Analysis of Temperature Fields in FGM Micro/Nano Solids by Moving Finite Element Method

This paper deals with the higher-grade theory of heat conduction in micro/nano solids with functionally graded material (FGM) properties. Making use the variation principles, the mathematical formulation of the boundary value problems is derived within higher-grade theory of heat conduction, i.e. the governing equation and the physically admissible boundary conditions are derived. Applying the developed formulation for FGM solid structures we can derive rather complex governing equation with variable coefficients and higher order derivatives of field variables. It is well known the high order derivatives of field variables can lead to decreasing of accuracy of the numerical solution. To eliminate this problem, the decomposition of the original governing partial differential equation (PDE) into the system of PDEs with lower order derivatives is proposed. For the numerical implementation the novel Moving Finite Element approximation method is utilized. Several numerical simulations are devoted to study the influence of micro-length scale parameter as well as the parameters of gradation of material parameters on the temperature field.
Modern Applications of High-Resolution Multispectral EuroPlanet Dataset

Remote sensing techniques, such as satellite imaging, have become vital for modern applications. The availability of acquiring high-resolution remote sensing images periodically has opened new possibilities for solving problems such as environmental analysis, climate change, and urban development. The study focuses on the performance evaluation of the EuroPlanet dataset which consists of 25,911 high-resolution multispectral satellite images and demonstrates the effectiveness of trained classifiers for land use and land cover classification. Furthermore, it was shown that the trained convolutional neural network can correctly classify the territory of Ukraine in multiple regions where manual observation is impossible due to difficult access.
A Bi-objective Distributed Optimization Algorithm for Multi-target Encirclement of Multi-robot System with Optimal Formation

This paper focuses on the multi-target encirclement of multi-robot system with different optimal formations. Two objective functions are considered: the distance between robot’s target and initial positions, and the distance between robot’s target position and the containment target’s position. Furthermore, an equality constraint is introduced to achieve a specific formation. Based on the above considerations, the multi-target encirclement problem is transformed into a bi-objective optimization problem, which is further converted into a single-objective optimization problem by employing weighted sum method. Subsequently, a distributed optimization algorithm with a fixed step size is designed based on projection operator. By adjusting the weights of objective functions, the containment effectiveness of multirobot system can be controlled. Additionally, the influence of objective functions’ weights on the containment performance of the multi-robot system and the effectiveness of the proposed algorithm are discussed by two simulation examples.
Spatio-Temporal Rule Constraint Guided Safe Reinforcement Learning for CPS

Cyber Physical System (CPS) integrates computational and physical processes, representing a next-generation intelligent system that combines environmental perception, computational communication, and control decision-making. During the design phase of the system, the formal method ensures that the system satisfies logical specifications and security requirements. In the operational phase of the system, deep reinforcement learning is widely employed for decision-making. However, when faced with uncertain scenarios and complex decision-making tasks, black-box-based deep reinforcement learning systems cannot guarantee safety, and spatio-temporal rule requirements cannot be captured by existing specifications. To overcome these challenges, we propose a spatio-temporal rule constraint guided safe reinforcement learning for CPS. Firstly, we extend the formal language used to describe the system specification to encompass spatio-temporal properties, enabling it to adequately capture the spatio-temporal rule requirements of CPS. Secondly, we introduce the spatio-temporal rule constrained hybrid automaton by extending the spatio-temporal properties onto the hybrid automaton to formalize the modeling of CPS and validate it through the model checking algorithm. Thirdly, on the basis of the validated model, a safe reinforcement learning method combined with formal model for spatio-temporal rule constraint guidance is proposed.
Embracing errors is more effective than avoiding them through constrained coding for DNA data storage

DNA is an attractive medium for digital data storage. When data is stored on DNA, errors occur, which makes error-correcting coding techniques critical for reliable DNA data storage. To reduce the number of errors, a common technique is to include constraints that avoid homopolymers (consecutive repeated nucleotides) and balance the GC content, as sequences with homopolymers and unbalanced GC contents are often associated with larger error rates. However, constrained coding comes at the cost of an increase in redundancy. An alternative is to control the errors by randomizing the sequences, embracing the extra errors, and paying for them with additional coding redundancy. In this paper, we determine the error regimes in which embracing errors is more efficient than constrained coding. We find that constrained coding is inefficient in most common error regimes for DNA data storage. Specifically, the error probabilities for homopolymers and unbalanced GC contents must be very large for constrained coding to achieve a higher code rate than unconstrained coding.
Assessment of Real-World Incident Detection Through a Component-Based Online Log Anomaly Detection Pipeline Framework

This study introduces an open-source, component-based pipeline framework for online log anomaly detection. It implements popular parsing, encoding, and anomaly detection methods as replaceable components, and compares their performance to industry-standard, rule-based methods using real-world incident log data. The goal of this study is to assess the suitability of using modern log anomaly detection methods for industry system monitoring.
Investigation of Machine Learning Regression Techniques to Predict Critical Heat Flux

A unifying model for Critical Heat Flux (CHF) prediction has been elusive for over 60 years. With the release of the data utilized in the making of the 2006 Groeneveld Lookup table (LUT), by far the largest public CHF database available to date, data-driven predictions on a large variable space can be performed. The popularization of machine learning techniques to solve regression problems allows for deeper and more advanced tools when analyzing the data. We compare three different machine learning algorithms to predict the occurrence of CHF in vertical, uniformly heated round tubes. For each selected algorithm (ν-Support vector regression, Gaussian process regression, and Neural network regression), an optimized hyperparameter set is fitted. The best performing algorithm is the Neural network, which achieves a standard deviation of the prediction/measured factor three times lower than the LUT, while the Gaussian process regression and the ν-Support vector regression both lead to two times lower standard deviation. All algorithms significantly outperform the LUT prediction performance. The neural network model and training methodology are designed to prevent overfitting, which is confirmed by data analysis of the predictions. Additionally, a feasibility study of transfer learning and uncertainty quantification is performed, to investigate potential future applications.
Is Automated Topic Model Evaluation Broken

Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. However, the field has coalesced around automated estimates of topic coherence, which rely on the frequency of word co-occurrences in a reference corpus. Contemporary neural topic models surpass classical ones according to these metrics. At the same time, topic model evaluation suffers from a validation gap : automated coherence, developed for classical models, has not been validated using human experimentation for neural models. In addition, a meta-analysis of topic modeling literature reveals a substantial standardization gap in automated topic modeling benchmarks. To address the validation gap, we compare automated coherence with the two most widely accepted human judgment tasks: topic rating and word intrusion. To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets. Automated evaluations declare a winning model when corresponding human evaluations do not, calling into question the validity of fully automatic evaluations independent of human judgments.
Efﬁcient Detailed Routing for FPGA Back-End Flow Using Reinforcement Learning

: Over the past few years, the computation capability of ﬁeld-programmable gate arrays (FPGAs) has increased tremendously. This has led to the increase in the complexity of the designs implemented on FPGAs and to the time taken by the FPGA back-end ﬂow. The FPGA back-end ﬂow comprises of many steps, and routing is one of the most critical steps among them. Routing normally constitutes more than 50% of the total time taken by the back-end ﬂow and an optimization at this step can lead to overall optimization of the back-end ﬂow. In this work, we propose enhancements to the routing step by incorporating a reinforcement learning (RL)-based framework. In the proposed RL-based framework, we use the (cid:101) -greedy approach and customized reward functions to speed up the routing step while maintaining similar or better quality of results (QoR) as compared to the conventional negotiation-based congestion-driven routing solution. For experimentation, we use two sets of widely deployed, large heterogeneous benchmarks. Our results show that, for the RL-based framework, the (cid:101) -greedy greedy approach combined with a modiﬁed reward function gives better results as compared to purely greedy or exploratory approaches. Moreover, the incorporation of the proposed reward function in the RL-based framework and its comparison with a conventional routing algorithm shows that the proposed enhancement requires less routing time while giving similar or better QoR. On average, a speedup of 35% is recorded for the proposed routing enhancement as compared to negotiation-based congestion-driven routing solutions. Finally, the speedup of the routing step leads to an overall reduction in the execution time of the back-end ﬂow of 25%.
and Haptic rendering of Ancient Woodcarvings in

: Virtual reality and 3D computer graphics are increasingly been employed on reconstruction of cultural heritage sites and archaeological museums in Europe, less effort has been spent on evaluating the use of different Information and Communication Technologies (ICT) as well as 3D computer graphics for reconstructing heritage sites in South Asia. This research presents a case study focused on the visualization and haptic rendering of ancient woodcarvings in Sri Lanka. Embekke is a very special shrine because it owns collection of the greatest woodcarvings in Sri Lanka. According to archaic documents, it was built during the 14th Century. The historical records suggest that the King Wickremabahu III, who reigned in Gampola from 1359 to 1374, built this shrine and dedicated to God Kataragama. This paper proposes a virtual reality framework for visualizing ancient woodcarvings in Embekke, that offers a wide set of features to support realistic rendering, advanced human computer interaction and stereoscopic display technology. In order to achieve a realistic visual simulation of woodcarvings, we used several texture mapping techniques, while haptic device has been utilized to simulate the sense of touch.
ATTRIBUTE CLASSIFIER WITH IMBALANCE COMPENSATION FOR ANOMALOUS SOUND DETECTION

This paper proposes an Attribute Classifier with Imbalance Compensation (ACIC) for DCASE 2023 Challenge Task 2. The goal is to perform anomalous sound detection by exploiting prior knowledge about machine attributes. First, we propose to use the weak prior knowledge provided by attribute for anomaly detection. Then, we design the Imbalance Compensation (IC) strategy to address the class imbalance problem of attributes. Finally, we propose a score fusion method based on ACIC to enhance the robustness of the model. Experimental results show that compensating for attribute class imbalance improves the exposure of anomalies
What is That Object Out There? Automated Satellite Modeling and Alternate Reality

We present a technique for automatically building a 3D model of a Resident Space Object (RSO) and determining its pose. This technique is applicable at a variety of ranges and the 3D point cloud representation increases in resolution with closing range. The pose estimation of the object provides useful intelligence. One example is for capture or docking. We will discuss the exploitation of our data as suitable for Virtual Reality (VR) and Alternate Reality (AR).