An online bi-objective scheduling algorithm for service provisioning in cloud computing

Abstract: To reduce costs and risks, service providers can purchase Infrastructure-as-a-Service (IaaS) instances from public clouds to deploy service applications and deliver job-execution services to users. However, it is challenging to make optimal instance purchasing and job scheduling decisions due to diverse instance billing methods and random service demands. In order to minimize time average service costs while ensuring quality of service (QoS), we consider that service providers purchase combinations of reserved and on-demand instances to meet the workload of their service system. Service providers need to dynamically determine the amount of on-demand and reserved instances to purchase for job executions, and then schedule jobs to run at the appropriate time. In this paper, we first propose a method to dynamically purchase reserved instances based on the historical usage data of on-demand and reserved instances. Then, we use a variable-length cycle scheduling mode for the job processing system and propose an online bi-objective scheduling algorithm based on Lyapunov optimization to schedule jobs. Even without the knowledge about the future, our online algorithm can dynamically purchase on-demand instances in an optimal way under the current number of reserved instances, and schedule jobs in varying time periods according to the arrival of jobs and optimization objectives. Based on both synthetic and real-world data, extensive experimental results show that our algorithm can achieve bi-objective optimization of minimizing time average cost while ensuring QoS.


DSRNet: Depth Super-Resolution Network guided by blurry depth and clear intensity edges

Abstract: Although high resolution (HR) depth images are required in many applications such as virtual reality and autonomous navigation, their resolution and quality generated by consumer depth cameras fall short of the requirements. Existing depth upsampling methods focus on extracting multiscale features of HR color image to guide low resolution (LR) depth upsampling, thus causing blurry and inaccurate edges in depth. In this paper, we propose a depth super-resolution (SR) network guided by blurry depth and clear intensity edges, called DSRNet. DSRNet differentiates effective edges from a number of HR edges with the guidance of blurry depth and clear intensity edges. First, we perform global residual estimation based on an encoder–decoder architecture to extract edge structure from HR color image for depth SR. Then, we distinguish effective edges from HR edges in the decoder side with the guidance of LR depth upsampling. To maintain edges for depth SR, we use intensity edge guidance that extracts clear intensity edges from HR image. Finally, we use residual loss to generate accurate high frequency (HF) residual and reconstruct HR depth maps. Experimental results show that DSRNet successfully reconstructs depth edges in SR results as well as outperforms the state-of-the-art methods in terms of visual quality and quantitative measurements.1


A Novel Fall Detection Framework With Age Estimation Based on Cloud-Fog Computing Architecture

Abstract: Falls are one of the main causes of injuries and even fatalities among the elderly. However, few existing research on fall detection focuses on the supervision of the elderly. Furthermore, the face image of the target usually exhibits low resolution in the surveillance videos, posing a challenge in achieving a balance between the response latency and the computational cost. To this end, a novel fall detection framework with age estimation based on cloud-fog computing architecture is proposed in this paper. Specifically, an optimized Soft Stage Regression-Shallow (SSR-S) network is presented to achieve excellent performance on age estimation for the low-resolution images collected by the edge layer. A fall pre-judgment mechanism and a Shallow-Convolutional Neural Networks (S-CNN) are proposed to make a better judgment on fall behaviors among different age groups at the fog layer and the cloud layer respectively. Besides, an age estimation-based priority algorithm is presented to prioritize the people aged 60 or older in fall detection at the cloud layer, with the aim to make a trade-off between the response latency and the computational overhead. Finally, extensive simulations have been conducted to evaluate the performance of our proposal. Experimental results have shown that the minimum Mean Absolute Error (MAE) of SSR-S reaches 7.59. The fall prejudgment mechanism can achieve 0% miss rate, and the accuracy of S-CNN reaches 90.5%. The detection speed of the overall framework is 17.0 frames per second (FPS).


Taking 5 minutes protects you for 5 months: Evaluating an anti-phishing awareness video

Abstract: Phishing is one of the biggest security threats to organizations. Anti-phishing awareness measures can improve phishing email detection rates. These measures need to be efficient, effective, and have an enduring impact over months, rather than days. Related research provides evidence of their effectiveness in the short term. However, questions remain as to how long this impact endures. We conducted a retention user study in two phases, with almost 200 participants in the first phase and almost 80 in the second phase, to determine whether a five-minute video retains its effectiveness five months after the intervention (similar to related work on more time-intensive measures). Our results suggest that short videos can indeed still exert a positive influence five months later. We also report on the video's influence on phishing detection strategies, as well as on viewers' confidence in this respect. Based on our results, we propose recommendations to inform the content of future awareness refreshment measures.


Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective

Abstract: Dimensional collapse in graph contrastive learning (GCL) confines node embeddings to their lower-dimensional subspace, diminishing their distinguishability. However, the causes and solutions of this curse remain relatively underexplored. In statistics, whitening presents a powerful tool to eliminate correlations among multiple variables. This motivates us to relieve the dimensional collapse of GCL from a whitening perspective. In this paper, we propose an intuitive analysis suggesting that high similarity scores of node embeddings may cause dimensional collapse, providing more evidence for its presence. Considering the success of whitening in statistics, we introduce a new plug-and-play module called the hitening raph ontrastive earning (WGCL) to address the dimensional collapse issue in existing GCL methods. WGCL plugin standardises the covariance matrices of dimensions, eliminating correlations among node embeddings' dimensions. Additionally, we enhance the conventional GCL training objective by introducing a mutual information maximisation loss between input features and node embeddings to maintain information capacity. Our experiments demonstrate that WGCL effectively addresses dimensional collapse, leading to an average improvement of 0.93% (up to 2.0%) in classification accuracy across three GCL backbones on nine widely-used datasets. The code to reproduce the experiments is available at https://github.com/acboyty/WGCL.


Multi-scale split matching three-dimensional reconstruction method of surface microcracks on the silicon nitride bearing roller

Abstract: Aiming at the characteristics of parametric discrete and non-significant depth of silicon nitride bearing roller surface microcracks. A multi-scale split matching method is proposed. To realize the full reconstruction of the characteristics of the three-dimensional morphology of the surface microcracks. Combined with the parametric discretization characteristics of the surface microcracks, a box filter is defined to do second-order Gaussian differentiation on the image. Construct a multiscale Hessian matrix in the scale space of the surface microcrack image. Determine the covariance-discrete feature points, and obtain the global covariance-discrete sparse point cloud of silicon nitride bearing roller surface microcracks by non-maximum value suppression. For the non-significant depth features of silicon nitride bearing roller surface microcracks. Dissect the surface microcrack distortion images. Divide each image into multiple regional pixel blocks and construct an aggregation function to determine the exact depth value of each pixel block at the cost of matching each pixel block with its surrounding pixel blocks. Dense point cloud reconstruction of non-significant depth features of silicon nitride bearing roller surface microcracks is realized. The average distribution of dense clusters of reprojection errors of the dense point cloud features is less than 0.4pix, the median residual of the field of view of the surface microcrack image is 0.57pix, and the total number of effectively extracted feature points is about 92.9 %. The detailed lossless recovery reconstruction of the parametric discrete, non-significant depth features of silicon nitride bearing roller surface microcracks is realized. A new idea is provided for the establishment of molecular dynamics geometric modeling of the surface microcrack extension process on the silicon nitride bearings roller.


Parametric Lie group structures on the probabilistic simplex and generalized Compositional Data

Abstract: In this paper we build a set of parametric quotient Lie group structures on the probabilistic simplex that can be extended to real vector space structures. In particular, we rediscover the main mathematical objects generally used when treating compositional data as elements associated to the quotient Lie group with respect to the equivalence relation induced by the scale invariance principle. This perspective facilitates the adaptation of the statistical methods used for classical compositional data to data that follows a more general equivalence relation.


Cost-sensitive thresholding over a two-dimensional decision region for fraud detection

Abstract: Credit fraud poses a challenging task in terms of detection. It can result in significant losses depending on the amount, so a cost-sensitive perspective needs to be taken. Classical approaches focus on estimating the probability of fraud and selecting a decision threshold, but they often fail to consider the transaction amount or account for the cumulative losses incurred within the sample. Consequently, these approaches can result in sub-optimal strategies. A new thresholding approach is proposed, based on the construction of a two-dimensional decision space with an estimated probability and the credit amount. This expansion allows more freedom for the optimal classification rule search, which is performed with a new algorithm. The proposed method generalizes previous approaches, so an improvement is consistently achieved. In addition, it allows a restricted search. This is shown in a study of two real data sets, comparing the results obtained by a wide range of classifiers.


Outlier detection in a multiset-valued information system based on rough set theory and granular computing

Abstract: Outlier detection on data with missing information values is especially tricky because the uncertainty caused by missing information values may contribute to an object being an outlier. A multiset-valued information system (MSVIS) is an information system (IS) in which information values are multisets. This kind of IS is a useful way of handling datasets with missing information values. In this paper, we study outlier detection in an MSVIS based on rough set theory and granular computing. First, some concepts of multisets and probability distribution sets are reviewed, and the fact that a weak one-to-one correspondence exists between multisets and rational probability distribution sets is illustrated. In this way, multisets may be treated as rational probability distribution sets. Then, an MSVIS can be induced by an incomplete information system (IIS) and viewed as the result of information fusion of multiple categorical ISs. Next, a tolerance relation in an MSVIS is constructed with the induced rational probability distribution sets. Then, the outlier factor in an MSVIS is formulated, and the corresponding outlier detection algorithm is proposed. Finally, the performance evaluation by AUC (area under the curve) and F1-score shows the superiority of the proposed algorithm over some existing algorithms.


Fast two-party signature for upgrading ECDSA to two-party scenario easily

Abstract: ECDSA is a standardized signature scheme and is widely used in many fields. However, most two-party ECDSA require a complicated multi-party computation technique to compute the multiplication of many shared secrets and necessitate expensive zero-knowledge proofs to deal with malicious adversaries. In this paper, we propose a simple yet fast two-party signing protocol based on ECDSA security, whose signing operation of each party is similar to the original ECDSA signing algorithm. Our protocol can enjoy the same efficiency as ECDSA and upgrades the existing ECDSA application to two-party scenario friendly. For this purpose, we first introduce a variant of ECDSA called combinatorial ECDSA, which is as secure as the standard ECDSA and can be easily divided into two parts. Then, we devise a two-party signing protocol without complicated multi-party computation from the combinatorial ECDSA and give the security proof based on ECDSA. Compared with the state-of-the-art two-party ECDSA, the signing result of each party in our protocol maintains the structure of ECDSA signature, which can be verified easily and avoids expensive zero-knowledge proofs. Last, we conduct an experimental evaluation, demonstrating that the performance of our combinatorial ECDSA and two-party signature is similar to ECDSA and the experimental results show that our proposed schemes are practical.


A game-theory-based scheme to facilitate consensus latency minimization in sharding blockchain

Abstract: Unbalanced consensus latency in reaching a consensus on transactions occurs due to differences in the ability of different committees to process transactions. As the consensus latency accumulates, the transaction throughput of the sharding blockchain significantly decreases. This article proposes a consensus latency minimization scheme to improve the transaction throughput. According to a time-varying directed graph and a non-cooperative game model solved by a distributed algorithm, we show that the generalized Nash equilibrium for all committees are to balance the differences in consensus latency among various committees and to minimize the consensus latency throughout the whole period. The security of the proposed scheme is analyzed by considering the peaceful equilibrium, and the committee actions asymptotically converge to a generalized Nash equilibrium. Through the comparisons with other baseline algorithms via simulation experiments, we demonstrate that our proposed scheme could result in the optimal consensus latency and throughput. Therefore, the proposed consensus latency minimization scheme effectively avoids the existing problem of the transaction throughput in sharding blockchain and provides a viable and security instrument for the sharding blockchain operator to improve the transaction throughput performance throughout the whole period.


Intelligent Deep-Learning Tutoring System to Assist Instructors in Programming Courses

Abstract: Artificial intelligence (AI) in programming teaching is something that still has to be explored, since in this area assessment tools that allow grading the students work are the most common ones, but there are not many tools aimed toward providing feedback to the students in the process of creating their program. In this work a small sized AI-based intelligent tutor that answers students programming questions and provides them with examples is presented. AI is becoming more and more popular as time passes, allowing to perform tasks automatically in a way that could not be done before. From predictions to customization, AI is being used in many areas, not being educational environments outside this situation. AI is being used in educational settings to customize contents or to provide personalized feedback to the students, among others. In this scenario, The tool has been tested by university students at the Universidad Rey Juan Carlos during the course Programming Fundamentals in the first course of their Biomedical Engineering degree to evaluate if it helps the students in the process of learning programming skills. One of the main goals was to provide guidance to the students without needing the instructor to be physically by their side. Even if the tool is still in its preliminary phase, it helped the students with their questions, providing accurate answers and examples. The students were able to use the intelligent tutor easily and they thought that it could be a useful tool to use in other courses.


Differentially private trajectory event streams publishing under data dependence constraints

Abstract: Differential Privacy (DP), as a de facto privacy-preserving paradigm, can be widely applied in numerous event streams publishing schemes to protect private events (i.e., trajectory points of users). However, most existing schemes assume these events are independent, which is a vulnerable assumption that can lead to unforeseeable privacy degradation. The privacy-preserving for event streams publishing under data dependence is still challenging because existing dependence quantify models cannot dynamically measure the degree of dependent relationships between events. In this paper, we investigate the privacy degradation issue of trajectory event streams publishing and propose (Θ,ϵ)-dependent privacy mechanism to mitigate this issue. Specifically, we introduce spatial dependence to model the dependent relationship between events in trajectory data and extend it to quantify the influence of multiple dependent events on the target event. We define the notion of max dissimilarity to formalize the privacy level of DP mechanisms in trajectory event streams publishing. The proposed notion of global dependent coefficient helps the private mechanism to provide the expected privacy level by calibrating the added noise. We further achieve ω-event privacy protection over trajectory event streams under data dependence constraints with (Θ,ϵ)-dependent privacy mechanism. Theoretical analysis proves our scheme provides the expected privacy guarantee under data dependence constraints. Extensive experiments validate our scheme is efficient and effective.


Indoor semantic segmentation based on Swin-Transformer

Abstract: In recent years, with the rapid development of Transformer in the field of natural language processing, many researchers have realized its potential and gradually applied it to the field of computer vision, with a proliferation of theoretical approaches represented by Vision Transformer (ViT) and Data-efficient image Transformer (DeiT). On the basis of ViT, the famous Swin-Transformer was proposed as one of the best computer vision neural network backbones, which can be widely used in tasks such as image classification, target detection and video recognition. However, in the field of image segmentation, the semantic segmentation of indoor scenes is still very challenging due to the wide variety of objects, large differences in object sizes, and a large number of overlapping objects with occlusion. Aiming at the problem that the existing semantic segmentation of RGB-D indoor scenes cannot effectively fuse multimodal features, in this paper, we propose a novel indoor semantic segmentation algorithm based on Swin-Transformer. It attempts to apply Swin-Transformer to the field of indoor RGBD semantic segmentation, and tests the performance of the model by conducting extensive experiments on the mainstream indoor semantic segmentation datasets NYU-Depth V2 and SUN RGB-D. The experimental results show that the Swin-L RGB+Depth setting achieves 52.44% MIoU on the NYU-Depth V2 data and 51.15% MIoU on the SUN RGB-D data set, which reflects an excellent performance in the field of indoor semantic segmentation. The improved performance of the Depth features on the indoor semantic segmentation model has also been demonstrated in the experiments by controlling the type of input features. Our source code is publicly available at https://github.com/YunpingZheng/ISSSW.


Residual attention fusion network for video action recognition

Abstract: Human action recognition in videos is a fundamental and important topic in computer vision, and modeling spatial–temporal dynamics in a video is crucial for action classification. In this paper, a novel attention module named Channel-wise Non-local Attention Module (CNAM) is proposed to highlight the important features both spatially and temporally. Besides, another new attention module named Channel-wise Attention Recalibration Module (CARM) is developed to focus on capturing discriminative features at channel level. Based on these two attention modules, a novel convolutional neural network named Residual Attention Fusion Network (RAFN) is proposed to model long-range temporal structure and capture more discriminative action features at the same time. More specifically, first, a sparse temporal sampling strategy is adopted to uniformly sample video data as input to RAFN along the temporal dimension. Secondly, the attention modules CNAM and CARM are plugged into residual network for highlighting important action regions around actors. Finally, the classification scores of four streams of RAFN are combined by late fusion. The experimental results on HMDB51 and UCF101 demonstrate the effectiveness and excellent recognition performance of our proposed method.


Bidirectional utilization of blockchain and privacy computing: Issues, progress, and challenges

Abstract: With the rapid development of information technology and the increasing popularity of personalized services, the flow of users' personal information in the Internet is inevitable between different platforms and applications, which greatly affects the trust relationship in the digital society. In recent years, an increasing number of researchers have been dedicated to exploring the applications of blockchain and privacy computing technologies in the process of data value release. Scholars have discovered that the combination of the two technologies can alleviate many pain points of each other. Specifically, privacy computing is a computational theory and method for protecting privacy of information throughout its lifecycle. Privacy computing enables the processing, analysis, and computation of data while preserving the confidentiality of the original information. Nevertheless, challenges related to data ownership and benefit distribution impede the establishment of a fair and effective collaborative environment among multiple parties, thereby restricting its further practical development. On the other hand, blockchain is a technology for establishing a trusted platform among mutually untrusted parties, characterized by decentralization, immutability, traceability, and incentive mechanisms. However, blockchain also faces security issues such as transparency of on-chain data and lack of protection in smart contract execution environments. Therefore, this paper aims to comprehensively analyze and explore the integration of blockchain and privacy computing from both perspectives, focusing on bidirectional utilization. We systematically categorize and summarize relevant literature in this field, employing problem-oriented approaches. Finally, we identify the existing challenges and propose potential improvement methods in this field.


Customer acceptance of frontline social robots—Human-robot interaction as boundary condition

Abstract: From an interactionist perspective, we argue that what happens and how service is delivered during the human-robot interaction may alter the extent to which customers accept service robots. Extending previous research on customer acceptance of service robots and human-robot interaction, we treat the elements during the human-robot interaction process as boundary conditions for the link between service robots' functional and social-emotional capabilities. Specifically, we examine (1) contact frequency between customers and service robots, (2) interdependence among service robots and human service employees, and (3) service complexity, moderate the relationship between service robots' capabilities and customer acceptance. With data collected from 997 customers who have past experience with service robots, we found that the effect of functional and social-emotional capabilities of service robots on customer acceptance are more salient when contact frequency is low rather than high, interdependence among service robots and service employees is high rather than low, and service complexity is low rather than high. Our findings provide new insights into customer acceptance of social robots in the service settings.


Mineral exploration using trace element composition of pyrite grains from till: A case study from the Petäjäselkä Au occurrence, northern Finland

Abstract: Geochemical anomalies and indicator mineral studies in glacial sediments are commonly used to define mineral exploration targets in glaciated areas. Trace element composition of sediment-hosted heavy minerals, such as pyrite and chalcopyrite, can be potentially used to fingerprint mineral deposits. This study presents results from trace element compositions of pyrite in the heavy mineral concentrates of till and mineralized bedrock samples from the orogenic gold ore occurrences in the Petäjäselkä area, Central Lapland belt, northern Finland. The trace element contents of pyrite grains were evaluated by using a robust compositional data analysis workflow, including symmetric pivot coordinates and robust principal component analysis (PCA). Four different pyrite textures (A-D) were identified from the gold ore occurrences. Compositional data analysis showed that the heavy mineral pyrite correlated with Pyrite D in the ore occurrence and confirmed the discrimination power of pyrite in the heavy mineral grains as a tool for source characterization of transported sedimentary material. The study also discusses the difficulties of finding suitable grains for the LA-ICP-MS, highlighting the importance of proper heavy mineral grain recovery procedures and the factors of oxidization and weathering processes influencing the geochemical compositions of the pyrite grains.


Parallel Acyclic Joins: Optimal Algorithms and Cyclicity Separation

Abstract: We study equi-join computation in the massively parallel computation (MPC) model. Currently, a main open question under this topic is whether it is possible to design an algorithm that can process any join with load O(N polylog N/p 1/ρ* ) — measured in the number of words communicated per machine — where N is the total number of tuples in the input relations, ρ * is the join’s fractional edge covering number, and p is the number of machines. We settle the question in the negative for the class of tuple-based algorithms (all the known MPC join algorithms fall in this class) by proving the existence of a join query with ρ * = 2 that requires a load of Ω ( N/p 1/3 ) to evaluate. Our lower bound provides solid evidence that the “AGM bound” alone is not sufficient for characterizing the hardness of join evaluation in MPC (a phenomenon that does not exist in RAM). The hard join instance identified in our argument is cyclic, which leaves the question of whether O(N polylog N/p 1/ρ* ) is still possible for acyclic joins. We answer this question in the affirmative by showing that any acyclic join can be evaluated with load O(N / p 1/ρ* ), which is asymptotically optimal (there are no polylogarithmic factors in our bound). The separation between cyclic and acyclic joins is yet another phenomenon that is absent in RAM. Our algorithm owes to the discovery of a new mathematical structure — we call “canonical edge cover” — of acyclic hypergraphs, which has numerous non-trivial properties and makes an elegant addition to database theory.


Re-construction of original face images from plastic surgery images using harmony search algorithm and IRBFN model

Abstract: Face Reconstruction is the only biometric that allows you to perform passive identification in one to many environments. By changing the geometry and texture of the facial region, there can be an increased intraclass variability between images of an individual before and after surgery. Even so, the nonlinear changes caused by plastic surgery have a hard time being modelled by facial reconstruction systems. An algorithm for plastic surgery face reconstruction is presented here, that uses Harmony Search to confront the challenges involved. A non-disjoint algorithm generates the face granules, each representing different information with varying resolutions and sizes. A Scale-Invariant Feature Transform (SIFT) and Co-occurrence of Adjacent Local Binary Patterns were applied to extract discriminating information from granules of face images. As the concluding step, we used Harmony Search Method (HSM) to combine the various responses to attain the optimal solution. Finally Improved Radial Basis Function Network (IRBFN) algorithm is used for feature matching. The feature matching predict, where the face is recognized or not. The modified method, proposed in this work, has the better performance in classifying more images with higher accuracy. An obtained sensitivity of 100 % and specificity of 97.5 % was achieved by the proposed classifier. The results proved that it is possible to use the developed algorithms understand different pre and post surgical image conditions, evaluate their strength and future development trends, and provide all-facial features basis for detection of pre and post surgical images.


Reweighted robust and discriminative latent subspace projection for face recognition

Abstract: Subspace projection has been widely studied to implement the feature extraction for face recognition. However, it scarcely facilitates exploring the label information in the subspace projection procedure. The feature extraction and classification are usually divided into two separate procedures. This fails to explore the discriminative information from the training data, and thus degrades the discrimination of the extracted features and lowers the face recognition accuracy. To address this issue, we present a novel reweighted robust and discriminative latent subspace projection (ReRDLSP) method which organically integrates the reweighted latent low-rank representation model and discriminative ridge regression into a union framework. We employ a reweighted nuclear norm minimization to realize the low-rank regularization, which provides adaptive weighted values assignment, regularizes the representation matrix adaptively, and thus boosts the capability and flexibility of the method. Moreover, a discriminative ridge regression method is presented by employing a nonnegative relaxation, which enlarges the margins of the face samples from different classes as much as possible and boosts the freedom of fitting the label information. The computational complexity and convergence analysis are also discussed in detail. Extensive experiments are demonstrated and very promising results are achieved compared with some state-of-the-art face recognition methods.


CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents

Abstract: In this paper, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models (LLMs). To tackle this problem, we first present an uncertainty estimation method for LLMs to classify whether the command is certain (i.e., clear) or not (i.e., ambiguous or infeasible). Once the command is classified as uncertain, we further distinguish it between ambiguous or infeasible commands leveraging LLMs with situational aware context prompts. For ambiguous commands, we disambiguate the command by interacting with users via question generation with LLMs. We believe that proper recognition of the given commands could lead to a decrease in malfunction and undesired actions of the robot, enhancing the reliability of interactive robot agents. We present a dataset for robotic situational awareness consisting of pairs of high-level commands, scene descriptions, and labels of command type (i.e., clear, ambiguous, or infeasible). We validate the proposed method on the collected dataset and pick-and-place tabletop simulation environment. Finally, we demonstrate the proposed approach in real-world human-robot interaction experiments.


PASTS: Progress-aware spatio-temporal transformer speaker for vision-and-language navigation

Abstract: Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of an independent speaker model to provide pseudo instructions for data augmentation. However, current speaker models based on Long-Short Term Memory (LSTM) lack the ability to attend to features relevant at different locations and time steps. To address this, we propose a novel progress-aware spatio-temporal transformer speaker (PASTS) model that uses the transformer as the core of the network. PASTS uses a spatio-temporal encoder to fuse panoramic representations and encode intermediate connections through steps. Besides, to avoid the misalignment problem that could result in incorrect supervision, a speaker progress monitor (SPM) is proposed to enable the model to estimate the progress of instruction generation and facilitate more fine-grained caption results. Additionally, a multifeature dropout (MFD) strategy is introduced to alleviate overfitting. The proposed PASTS is flexible to be combined with existing VLN models. The experimental results demonstrate that PASTS outperforms previous speaker models and successfully improves the performance of previous VLN models, achieving state-of-the-art performance on the standard Room-to-Room (R2R) dataset.


Reactive mobile manipulation based on dynamic dual-trajectory tracking

Abstract: The letter presents an online obstacle avoidance strategy for a nonholonomic mobile manipulator, including dual-trajectory generation for the mobile base(MB) and end effector(EE), as well as whole-body reactive obstacle avoidance. The feasible region of the manipulator's workspace is generated by traversing the path of the MB, and the spatio-temporal trajectory of the EE is generated by quadratically constrained quadratic programming(QCQP) planning, satisfying the physical constraints and obstacle avoidance constraints. The reactive obstacle avoidance strategy is based on quadratic programming(QP), using singularity avoidance and EE trajectory tracking tasks as soft constraints with different priorities. Considering the nonholonomic constraints and motion differences of MB, we employ a dynamic tracking approach to avoid obstacles of MB. Additionally, an adaptive weight method is utilized to adjust the priority of MB path tracking task, effectively preventing joint velocity jitter during the insertion process. Our strategy has been proven to generate obstacle avoidance trajectories for EE in real-time and ensure safe joint velocities for MB and the manipulator through simulation and real-world scenarios.


A state-of-the-art on federated learning for vehicular communications

Abstract: With the increasing number of connected vehicles on the road, vehicular communications have become an important research area. Federated learning (FL), a distributed machine learning technique that enables learning from distributed data sources while preserving data privacy and efficient learning, has the advantages of privacy preservation, low latency, and energy efficiency, making it a suitable technique for vehicular communications. In vehicular communications, where dynamic data privacy, real-time decision-making, and energy conservation are paramount, FL's inherent benefits find unique relevance. The preservation of data privacy is of utmost concern in vehicular networks, where sensitive information is exchanged in real time. FL addresses this concern by allowing vehicles to collaboratively train machine learning models without sharing raw data, thus ensuring the privacy and security of vehicular users. Furthermore, FL's decentralized nature enables low-latency model updates, crucial for Intelligent Transportation Systems (ITS) real-time applications like collision avoidance and traffic management, where split-second decisions can be life-saving. Additionally, FL can contribute to energy efficiency by reducing the need for centralized data aggregation, which is particularly beneficial in resource-constrained vehicular environments. The paper provides a comprehensive survey of the state-of-the-art in FL for vehicular communications, including existing frameworks and architectures, challenges, and applications in various aspects of vehicular communications. The survey analyzes various FL techniques, their strengths and weaknesses, and the performance metrics of FL in vehicular networks. The paper concludes by presenting the open research issues and future directions for FL in vehicular communications. Our survey provides a timely and comprehensive overview of the recent advances in this emerging research field and serves as a valuable reference for researchers and practitioners in both FL and vehicular communications.


Few-shot Incremental Event Detection

Abstract: Event detection tasks can enable the quick detection of events from texts and provide powerful support for downstream natural language processing tasks. Most such methods can only detect a fixed set of predefined event classes. To extend them to detect a new class without losing the ability to detect old classes requires costly retraining of the model from scratch. Incremental learning can effectively solve this problem, but it requires abundant data of new classes. In practice, however, the lack of high-quality labeled data of new event classes makes it difficult to obtain enough data for model training. To address the above mentioned issues, we define a new task, few-shot incremental event detection, which focuses on learning to detect a new event class with limited data, while retaining the ability to detect old classes to the extent possible. We created a benchmark dataset IFSED for the few-shot incremental event detection task based on FewEvent and propose two benchmarks, IFSED-K and IFSED-KP. Experimental results show that our approach has a higher F1-score than baseline methods and is more stable.


Analyzing transparency and malicious insiders prevention for cloud computing environment

Abstract: The emerging cloud technology and its integration with cloud services from an enterprise standpoint have provided a gateway for Cloud Service Providers (CSPs) to garner consumers and their data. Due to the growing increase of Cloud Service Users (CSUs), it is pertinent to provide an adequate amount of cloud security to prevent detrimental impacts to businesses. Two large challenges CSPs face for cloud security include transparency and malicious insiders. The primary objective of this work is to provide a framework that analyzes CSPs based on evaluation metrics such as data breaches, data loss, account hijacking, insecure APIs, DoS, malicious insiders, abuse of cloud services, insufficient due diligence, and shared technology concerns. The security index of a CSP is then computed based on the listed evaluation metrics and given a score that translates to a linguistic rating of the CSP. Factors within transparency and malicious insiders are further broken down within the framework to emphasize various components. Transparency factors include published audit certifications, consumer base, membership of cloud organizations, and published incidents. Malicious insiders are sub-sectioned into personnel-related prevention, policy-related prevention, third-party CSP prevention, technical-related prevention, and hypervisor-related prevention. With this scoring contribution, analysis is able to be done on a CSP's potential threats within the network, which can then be visualized into recommended security controls or preventative measures. Finally, we conducted case studies to demonstrate the application of the scoring analysis using our proposed framework and to evaluate the security levels of two extensively employed CSPs.


Data Mining for Cyber Biosecurity Risk Management – a comprehensive review

Abstract: The world is currently facing the era of Cyberbiosecurity, also known as Biocybersecurity, or Digital Biosecurity, which poses a few unique security vulnerabilities. A significant percentage of the scientific, agricultural, and health communities are still unaware of the unique security complexities that have resulted from fusion of the supply chain, infrastructure, cyber, and life and medical sciences. Measurement, analysis, and mitigation of cyberattacks on biological systems are the goals of Cyberbiosecurity. Data mining is a promising avenue for further investigation as a means of mitigating cyber-attacks for research purposes. Data mining is the process of extracting useful patterns, information, and expertise from massive datasets. In the domain of Cyberbiosecurity, data mining techniques have received little attention. The purpose of this survey on data mining in cybersecurity is to determine the state of the art in cybersecurity issues, including different types of assaults and data mining methods that could be used to address these issues. This review's findings shed insight on the characteristics of security issues as well as gaps in Cyberbiosecurity. Furthermore, the review's conclusions show that most responses to Cyberbiosecurity attacks advocate for national or international government/private organizations to raise public awareness of these attacks and equip their clients/dealers with secure methods to transmit data online. These attempts to raise awareness can be divided into four distinct levels, each with its own set of techniques and objectives, resulting in a holistic strategy to addressing the difficulties of Cyberbiosecurity. A notable gap found in this review is the absence of appropriate countermeasures against cyberattacks employing data mining in the biological systems domain such as healthcare, agriculture, biomedical research, and other domains. The goal of this review is to increase knowledge among Cyberbiosecurity experts and to develop innovative solutions by utilizing cutting-edge data mining techniques in this sector.


Improved mixture differential attacks on 6-round AES-like ciphers towards time and data complexities

Abstract: AES is the most widely used symmetric cipher. Many cryptographic primitives are designed following the structure of AES, called AES-like ciphers. Mixture differential cryptanalysis is a structural cryptanalysis technique for AES, which gave the best key recovery attack on 5-round AES in the chosen-plaintext setting and attacks with practical data and memory complexities on round-reduced AES. In this paper, we study the mixture differential attacks on 6 rounds of AES-like ciphers, and propose two improved attacks towards optimizing time and data complexities respectively. As an application to AES, we give the improved mixture differential attack on 6 rounds of AES-128 with time complexity 262.74, reducing by a factor of 210.26 compared with the previous mixture differential attack. For low data complexity, we show the improved 6-round mixture differential attack on AES-128 with data complexity 224.05, reducing by a factor of about 4. We also apply two attacks to 6 super-rounds of Saturnin, one of the second-round candidates in NIST lightweight cryptography standardization process, which is the first security evaluation of Saturnin against mixture differential cryptanalysis.


Practical implications to becoming agile organizations: NASA case study

Abstract: Many organizations have adopted agile values and principles in their products and services to provide increasing value to their customers. Although agile started with software development, it has been applicable and used in different industries and disciplines, including the space industry. This article presents the practical implications of teams transitioning to be agile teams using a general inductive analysis of 33 individuals from different NASA agile teams. The findings and recommendations are part of a larger effort performed as part of the development of a model for agile leadership. Some organizations may see agile as very risky and costly, and this article provides some guidance derived from the data to help support planning for agile transformation efforts.


ِA new occurrence of rift-related damtjernite (ultramafic) lamprophyre, Gebel Anweiyib area, Arabian Nubian shield: Insights from bulk rock geochemistry and remote sensing data analysis

Abstract: The occurrence of lamprophyre dikes is being reported first time in the Gebel Anweiyib area, south Eastern Desert, Egypt. The investigation of the ultramafic lamprophyre (damtjernite) dikes intruded into the crustal younger granites and metasediments based on their spectral characteristics, mineralogical and geochemical compositions. Three multispectral sensors of Landsat-8, ASTER and S2 have been used to detect and characterize the lithologic units and the lamprophyre dikes. The reflectance and absorption signatures of the granitic rocks and lamprophyre dikes were the basis for detecting them from the remotely sensed data using the application of advanced image processing algorithms including FCC, BR, PCA, MNF, and DS. Geochemical studies inferred that the damtjernite dikes are under-saturated with SiO2 (<38.89 wt%), alkaline with total alkalis (avg. 4 wt%), metaluminous with A/CNK (0.22–0.51), and ultramafic with MgO (13.88–19.23 wt%). They exhibit unique geochemical characteristics of mantle (with high MgO, Ni, Cr, and Mg#) coupled with crustal (high Sr, Ba, Zr, Ga and REEs) sources, similar to global ultramafic lamprophyres. High TiO2 and highly fractionated REEs with low HREEs (avg. 23 ppm) relative to LREEs (avg. 354 ppm) contents, yielding residual garnet in melts. Low partial melting of primitive garnet lherzolite can account the obtained REEs in the ultramafic lamprophyres. These features signify their derivation from a deeper enriched asthenospheric (plume-type) magma like OIB, which is attested by the high Nb/La (avg. 1.32), Nb/U (avg. 102), La/Yb (avg. 43), and low Y/Nb < 0.52, Rb/Sr < 1, Rb/Ba (<0.14), and Sr/Ba (<0.9), suggesting their derivation from a mantle-derived source rather than crustal rocks. High concentrations of TiO2 and Fe2O3 in the ultramafic lamprophyres, reflecting their formation from a fertile peridotite by mantle plume-derived melts with K-residual phlogopite rather than amphibole as obtained by low Ba/Rb and high Rb/Sr ratios. The Anweiyib lamprophyres are anorogenic and confined to being derived from rifting regions, and their magma is not contaminated with crustal sources during emplacement.


A two-stage model extraction attack on GANs with a small collected dataset

Abstract: Due to their capacity for image generation, GAN models may be considered as a solution for the use of private data, which enhances their commercial value. However, unlike discriminative models such as CNNs, extraction attacks on GANs have not received significant attention, with only a few relevant works available. This paper proposes a novel two-stage extraction attack on GANs that does not require access to the victim GAN model. In the first stage, data augmentation is performed using GAN inversion techniques and vector arithmetic, tailored to scenarios with small query budgets. The second stage comprises two types of training: the innocent transfer training on unprocessed collected samples and the additional training on the augmented set. We provide three options for additional training: training of the discriminator alone (D), training of the generator alone (G), and training of both (D+G). We then conduct a quantity and quality evaluation of our extraction attacks compared to the baseline attack. Experiment results demonstrate that our proposed approach can successfully extract a GAN model, where the distribution of the extracted GAN closely resembles the distribution of the target GAN, even with a hundred generated images of the target GAN. Specifically, we achieve stable performance with better extraction, a more disentangled latent space for the extracted GAN, and significant semantic attribute editing performance using the two-stage extraction with additional training of the discriminator (D). By demonstrating that even a hundred leaked images may result in severe extraction, this study raises serious concerns regarding GAN model leakage.


Demodulation of interferometry parameters with multiple-harmonic superposition based on the DTFT and semi-blind spectral analysis

Abstract: The core issue of multi-surface wavelength-shifting interferometry at free cavity lengths is mainly centered on adaptively matching the tuning step. To avoid harmonic aliasing and undersampling, the discrete-time Fourier transform method with global spectrum densification is used to comprehensively analyze the spectral characteristics under different sampling cases and cavity lengths, then a composite multi-surface wavelength-shifting interferometry algorithm is proposed accordingly. The proposed method includes a preprocessing algorithm and three spectral criteria to identify and avoid undesirable sampling problems for in-situ measurements. The algorithm performance is quantitatively evaluated from several aspects of the simulation analysis. A flat plate with an average thickness of 20 mm was repeatedly measured using a Fizeau wavelength-shifting interferometer in the experiments, and the PVQ and RMS errors at different measurement distances were less than 5 mm and 1 mm, respectively.


An statistical model for the short-term albedo estimation applied to PV bifacial modules

Abstract: Albedo estimation is an essential input parameter for bifacial PV modules. However, there was no clear agreement on which albedo values should be used and how they should be measured, either with satellite measurements or using onsite measurements which can be obtained from nearby meteorological stations. Since long-term measurements are not always available for new PV system locations, short-term albedo measurements are also used as input parameters in PV system performance estimation models. Short-term albedo presents high variability due to factors such as weather, seasonality or changes in the surrounding surface among others. In addition, apparently random albedo variations of 60% can be observed, even during consecutive days or within the same day. Therefore, this study presents a two-parameter exponential model that modelates the albedo data statistical distribution with an error of less than 5% in all cases and from which it is possible to determine the reliability of the obtained data. This model has been evaluated at a set of locations, with different surface types and climates, and assessed using onsite and satellite data. The impact of the methodology used to estimate the albedo data uncertainty and its reliability has also been studied as a function of several parameters such as the global horizontal irradiance and measurement time, allowing the process optimisation and enhancing its reliability.


Multiple attributes based physical layer authentication through propagation scenario identification in the internet of vehicles

Abstract: The Internet of Vehicles (IoV) systems improve road safety through coordination among vehicles, but they are vulnerable to impersonation attack due to their broadcast communication nature. Existing cryptographic authentication approaches are complex for resource-limited IoV devices, which motivated the development of less complex Physical Layer Authentication (PLA) approaches. However, the existing PLA approaches use all channel attributes for all communication scenarios and do not update reference attributes used to authenticate newly observed attributes during authentication, which leads to poor performance when the attributes become insensitive to communication scenarios. To address these limitations, we propose a multiple attributes-based PLA scheme that flexibly selects effective attributes according to current communication scenarios for improved authentication. First, the historical channel attributes including the ricean K-Factor (KF), the Delay Spread (DS), and the Azimuth Spread of Arrival (ASA) together with the communication scenarios (i.e., Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)) information of transmitters are used to establish a relationship and train a Long Short-Term Memory (LSTM) neural network to identify current communication scenarios. The softmax function is then used to evaluate the scenario identification performance of each attribute and the attributes with maximum softmax values are dynamically selected for authentication. Second, to track and update the reference attributes, we use the historical attributes together with the past location and communication scenarios information of transmitters and train our model to predict future attributes for authentication. Finally, we use the Euclidean distance to measure the similarity between the selected and the predicted attributes to authenticate the transmitters. We validate the effectiveness of our approach and compare its performance with the existing methods through extensive simulations using realistic radio channel characteristics generated from the Quasi Deterministic Radio channel Generator (QuaDRiGa) platform. The results of the evaluation show that our system improves authentication performance and its false alarm and miss detection are respectively 19.19% and 52% lower than the existing approaches.


Let model keep evolving: Incremental learning for encrypted traffic classification

Abstract: Encrypted Traffic Classification (ETC) is valuable for many network management and security solutions as it provides insights into applications active on the network. However, the network environment constantly evolves, and new applications emerge in an endless stream daily, which gradually makes well-trained ETC models ineffective. The conventional approach to adapting new applications is to re-train the models on a re-formed dataset with both pre-existing and new application samples. The major limitation is that requiring redundant computing resources and sufficient storage spaces. In this work, we propose an Incremental Learning (IL) framework based on multi-view sequences fusion, MISS, to keep ETC models evolving with new applications. The key novelty of MISS is three-fold: extract cross-view information from multi-view sequences to capture sufficient knowledge; propose an exemplar selection algorithm from communication patterns to reduce redundant consumption; design a pair of branches from the learnability of parameters to mitigate accuracy loss during evolution. MISS outperforms the existing IL methods of ETC, and the state-of-the-art ETC models using the classic IL framework, on the real-world network traffic datasets, which achieves satisfactory improvements of 11.37%↑ and 1.58%↑. Furthermore, we comprehensively perform incremental experiments to evaluate the evolution ability of MISS, which is able to select representative exemplars of old applications, counteract the adverse effects of homogeneous applications, and keep evolving with unknown applications.


Third-party software library migration at the method-level using multi-objective evolutionary search

Abstract: Software developers commonly use third-party software libraries to reduce implementation efforts and mitigate errors in their source code while building high-quality and reliable software. To support software evolution, newer libraries are released periodically with added features, improvements in performance as well as critical updates such as bug fixes. Hence, older, existing (source) libraries need to be replaced with their newer, updated (target) counterparts, in a process known as library migration. Typically, library migration is a time-consuming, manual, and error-prone process that requires developers to analyze the source and target library code and its documentation. Specifically, developers examine Application Programming Interface (API) implementations and documentation to replace each source API with a target API without modifying the underlying software functionality. While recent works have used various techniques to recommend suitable target library replacements, these approaches do not generalize well, e.g., when a source library method needs to be replaced by one or more target library APIs or methods. To address this limitation, we propose the use of multi-objective evolutionary algorithms to identify suitable method replacements during library migration. In particular, we formulate library migration at the method-level as a multi-objective combinatorial optimization problem and examine the performance of 7 multi-objective evolutionary algorithms: UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD. We use method signature and documentation similarity, and co-occurrence probability to accurately recommend one or more target library methods. We evaluate our approach by conducting an empirical study on 9 popular Java library migrations mined from 57,447 open-source projects on GitHub. Our results demonstrate that UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD achieve 90%, 89%, 94%, 90%, 91%, 94%, and 71% precision on average, and 83%, 23%, 58%, 63%, 58%, 60% and 17% average recall respectively. In the interest of reproducibility, we make all code and results publicly available at: http://bit.ly/MOO-api-migration.


LiDAR-based Online Control Barrier Function Synthesis for Safe Navigation in Unknown Environments

Abstract: This paper presents a novel extension of the Control Barrier Function (CBF) as the low-level safety controller for autonomous mobile robots navigating in unknown environments. The main challenges of implementing CBF in real-world situations arise from the absence of a model or the lack of an exact one for the environment. Additionally, online learning is needed for the robot to maneuver in an unknown environment which leads to dealing with the sampled data set size, memory, and computational complexity. We address these challenges by designing an online non-parametric Lidar-based safety function using the Gaussian process (GP). It is both efficient in data size and eliminates the requirement to store previous data. Then, a CBF is synthesized using the proposed safety function to rectify the safe control input. The effectiveness of the Lidar-based CBF synthesis for navigation in unknown environments was validated by conducting experiments on unicycle-type robots.


Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation in Robotic Tasks

Abstract: Robots acting in human environments will often face new situations and can benefit from transferring prior experience. Priors could enable robots to handle new tasks zero-shot and help prevent failures, which can be particularly costly in real robot applications. Due to their interpretable nature, causal Bayesian Networks (CBN) are popular for modeling cause-effect relations between semantically meaningful environment features and their effects on action success. While the CBN structure is often intuitively transferable to a new context, its probability distribution might change, requiring data-intensive relearning. In this work, we propose three strategies that utilize semantic similarity and relatedness between the variables of two CBNs to generate and transfer informed CBN distribution priors. We evaluate the parameter prior accuracy in five different transfer scenarios, including sim-2-real, transferring parameters to more complex tasks with a larger number of parameters and even between two different tasks, which is particularly challenging. We show that the priors lead to better distribution estimates, particularly under a limited amount of new experiments, and improve the robot's ability to predict and prevent action failures by up to 50%.


Event-Triggered Image Moments Predictive Control for Tracking Evolving Features Using UAVs

Abstract: This paper presents a novel approach for tracking deformable contour targets using Unmanned Aerial Vehicles (UAVs). The proposed scheme combines image moments descriptor and Event-Triggered (ET) Nonlinear Model Predictive Control (NMPC) for efficient and accurate tracking. The deformable contour model allows adaptation to the evolving target's shape, while the proposed event-triggered scheme achieves improved computational efficiency and extended flight duration while generating new control sequences for the UAV. Real-world experimental validation as well as a comparative simulation performance analysis validate the scheme, showcasing its robustness in handling complex scenarios. This approach holds promise for various applications, such as surveillance and autonomous navigation.


Multidimensional Force Sensor Dynamic Compensation Based on Multistrategy Improved Sparrow Search Algorithm

Abstract: Multi-dimensional force sensors are extensively utilized in industrial milling machining for measuring cutting force and in intelligent robot applications for measuring joint force. These sensors offer several advantages, including improved static characteristics, well-established static calibration techniques, and temperature compensation technology. However, with the increasing demand for measuring dynamic forces in various applications, force sensors need to possess enhanced dynamic characteristics. Unfortunately, strain force sensors typically exhibit low intrinsic frequency and damping ratio, resulting in slower dynamic response of the sensor.To address this issue and enhance the dynamic performance of multidimensional force sensors, this study proposes a dynamic compensation method based on an improved sparrow search algorithm.Chebyshev chaotic mapping was implemented to increase randomness and ergodicity in the initial population. An adaptive weight factor was incorporated to improve the position update formula of finders and the ratio of vigilantes. These changes enhanced the algorithm’s ability to conduct early global searches and late local depth mining. Subsequently, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -distribution changes and Chebyshev chaotic perturbations were introduced to expand the local search capability.The enhanced sparrow search algorithm improves the optimization capabilities of the original algorithm. By utilizing dynamic calibration experimental data from three-dimensional force sensors, the algorithm’s effectiveness was verified. The results indicate that the method successfully reduces the overshooting amount in each channel of the sensors and shortens the regulation time. Consequently, the dynamic performance of the three-dimensional force sensors is improved, and the algorithm proves to be effective, practical, and robust in compensating for the sensors’ dynamic behavior.


Salient Feature Pyramid Network for Ship Detection in SAR Images

Abstract: Recently, convolutional neural networks (CNNs) have achieved remarkable performance in SAR ship detection. However, intense background interference in SAR images and the inherent attributes of the ship targets severely hinder further improvements in detection accuracy. To address these problems, we propose a novel anchor-free detector with a salient feature pyramid network (SFPN) and an intersection-over-union (IoU) branch. First, SFPN aggregates multi-level semantic features and generates salient features for ship targets through a saliency extraction (SE) module. Then, a saliency fusion (SF) module merges the salient features and FPN features. In particular, we introduce a heatmap branch in SFPN as an extra supervision signal to direct the network to focus on salient regions containing ship targets rather than interference. Second, considering the shape characteristic that the length of a ship target is much larger than its width, we apply the IoU branch to the detection head to predict localization confidence for each detected bounding box. Extensive experiments conducted on High-Resolution SAR Images Dataset (HRSID) and SAR-Ship-Dataset demonstrate that the proposed method effectively improves detection accuracy and achieves state-of-the-art performance.


Toward identifying malicious encrypted traffic with a causality detection system

Abstract: The main methods for protecting user privacy and addressing cybersecurity problems caused by encrypted traffic are non-decryption detection approaches. However, these methods face problems such as the small number of trainable features and imbalanced training datasets, which seriously affect the robustness of existing malicious encrypted traffic detection systems (TDSs). To address these issues, we identify malicious encrypted traffic with a causality explainable detection system (METCXDS). Our detection tasks increase the number of noncryptographic features and eliminate noise features to improve interpretability. First, we extract and recombine temporal and statistical features to increase the number of trainable features. Then, the influence of causal features on the results is enhanced by weighting causal reasoning features, and noise features with low weights are removed to improve the causal interpretability of the detection results. Furthermore, samples are generated through a Wasserstein generative adversarial network (WGAN) that learns the causal feature distribution to balance the training samples. Finally, the METCXDS performance is evaluated through the detection balance index and causal feature index. On the CICIDS2017 and DoHBrw2020 datasets, compared with previous TDSs, our system improves the overall F1 score by up to >10 %. And the causality of the detection results is verified by the causal effects, which identify the causes of anomalies in encrypted traffic and clarify the decision-making process of the detection system.


An accurate and robust strain field smoothing method based on polynomial fitting and anisotropic diffusion in digital image correlation

Abstract: Digital Image Correlation (DIC) is a popular optical measurement method. However, the accuracy of strain values obtained by DIC may be adversely affected by random noise. This paper proposes an Anisotropic Nonlinear Diffusion Filtering (ADF) method, combining Pointwise Least Square (PLS) with the Perona-Malik (PM) model, extracts accurate and smooth strain fields from noisy displacement fields. The strain field is extracted point by point using PLS and then smoothed by the PM model. The ADF method was tested on simulated and real speckle deformation experiments. The results indicate that the ADF method significantly reduces the negative impact of inappropriate calculation parameters, such as the strain window size, compared to the PLS method. Furthermore, the PM model was found to be better suited for smoothing strain fields compared to other image denoising models. The ADF method demonstrates excellent noise suppression performance and accurate strain identification in both simulated and real experiments.


A region-based hierarchical image compression method with simulated visual perception

Abstract: Most learned image compression methods focus on reducing the average code length of the image, which is unreasonable for the human visual system. Thus, appropriate bit allocation gains significance. This paper presents a novel end-to-end method for perceptual image compression, emphasizing content prioritization and a region-based hierarchical strategy. Firstly, our modified class activation mapping (CAM) can serve as the visual perception network for simulating human visual perception. On this basis, we develop an efficient compression system that leverages a discretized hybrid entropy model to allocate bits automatically according to the perceptual prioritization of image content. In our compression system, an image is compressed jointly and hierarchically with different standards, guided by the importance map extracted by the visual perception network. We introduce content-weighted perceptual metrics to evaluate visual quality more reasonably and objectively. Experiments on publicly available datasets demonstrate that our method outperforms traditional codecs and recent content-oriented learned methods in overall performance. Compared with other methods, the proposed method reconstructs images that are more friendly to the human visual system.


An efficient adaptive Masi entropy multilevel thresholding algorithm based on dynamic programming

Abstract: Masi entropy multilevel thresholding can utilize additive and non-extensive information in images to effectively segment a complex image. However, the entropy index of Masi entropy cannot be selected automatically, and the time complexity of the multilevel algorithm by exhaustive searching grows exponentially with the increase of the threshold numbers. To address these two problems, an adaptive entropy index selection strategy based on image histogram information is proposed first. To improve the computation efficiency, an efficient solution for the adaptive Masi entropy multilevel thresholding algorithm based on dynamic programming (DP + AMasi) is also proposed. The DP + AMasi algorithm is compared with the Masi entropy multilevel thresholding algorithm by exhaustive search and state-of-the-art metaheuristic algorithms on three benchmark datasets. The effectiveness of the DP + AMasi is verified by fitness function values, Uniformity Measure, Davies Bouldin index, and CPU run time. In addition, the Wilcoxon test is used to analyze the differences between algorithms.


Probing teleported quantum correlations in a two-qubit system inside a coherent field

Abstract: This paper investigates the impact of dipole interactions and the initial quantum state configurations within a quantum system on teleported quantum correlations involving a pair of qubits embedded in a single-mode field. The analysis encompasses an exploration of non-separability, steerability, non-locality, and steered coherence of the teleported state, considering different initial Bell states for the two-qubit channel. The initial atomic setting state primarily influences the teleported quantum correlations. The study highlights that dipole interaction exert a substantial effect on both the maximum and minimum values of quantum correlations. Furthermore, increasing this interaction demonstrates the potential to enhance control over the decay of correlations induced by the detuning parameter.


A time series anomaly detection method based on series-parallel transformers with spatial and temporal association discrepancies

Abstract: Multivariate time series anomaly detection methods can discover malfunctions in a complex system by detecting anomalies in the monitoring data. Multivariate time series have complex temporal and spatial correlations. Existing methods that explicitly model both correlations only extract semantic information sequentially in a single temporal-spatial or spatial-temporal order. However, different orders have a significant influence on the method's performance. In addition, a static graph structure trained on normal data has difficulties capturing the change of correlations between features when anomalies occur, limiting the method's performance. This paper proposes the Spatial-Temporal Anomaly Transformer (STAT), which extracts semantic information comprehensively by combining both spatial-temporal and temporal-spatial orders. STAT uses the Temporal Anomaly Transformer and the Spatial Anomaly Transformer (SAT) to explicitly compute temporal and spatial association discrepancies to conduct anomaly detection. Since the topological structure is usually unknown, SAT adopts a trainable hypersphere to learn the prior-association between features on normal data and computes the distance between the attention weights and the center of the hypersphere as the spatial association discrepancy. AUC and two recently proposed evaluation metrics are used to compare STAT with various typical methods on public data sets, proving that our method can achieve state-of-the-art performance.


Anthropomorphic chatbots' for future healthcare services: Effects of personality, gender, and roles on source credibility, user satisfaction, and intention to use

Abstract: In healthcare, chatbots are used to automate interactions between healthcare professionals and patients. To make these conversations realistic, designers anthropomorphized chatbots (e.g., attributed human characteristics to chatbot) by equipping them with personality (warmth/competence), attributable gender (male/female), and role (prevention/diagnosis/therapy). Notwithstanding, scholarly inquiry has predominantly disregarded these factors when ascertaining the proclivity of individuals to utilize chatbots in healthcare. Based on human-machine interactions and counter-stereotypes theories, the aim of this research is to evaluate the conditions under which chatbot anthropomorphism can increase users' satisfaction and intention of using related services. Analyses conducted on a sample of 1147 users show that chatbots' personality does not directly influence intention of being used in healthcare. Instead, this relationship is mediated by chatbots' perceived credibility and user satisfaction, and moderated by chatbots' anthropomorphism, gender, and role. Specifically, when there is a mismatch between chatbot's gender and its stereotypical descriptive property (i.e., competence for female chatbots; warmth for male chatbots), the chatbot is more credible, satisfying, and inviting to use. Moreover, chatbots with female anthropomorphic characteristics or with a low level of anthropomorphism are better suited for prevention roles such as counseling, while chatbots with male characteristics are more appropriate for therapeutic roles.


FeSAD ransomware detection framework with machine learning using adaption to concept drift

Abstract: This paper proposes FeSAD, a framework that will allow a machine learning classifier to detect evolutionary ransomware. Ransomware is a critical player in the malware space that causes hundreds of millions of dollars of damage globally and evolves quickly. The evolution of ransomware in machine learning classifiers is often calculated as concept drift. Concept drift is dangerous as changes in the behavior of ransomware can easily lead to misclassifications, and misclassification can harm individuals and businesses. Our proposed framework consists of a feature selection layer, drift calibration layer and drift decision layer that allows a machine learning classifier to detect and classify concept drift samples reliably. We evaluate the FeSAD framework in various concept drift scenarios and observe its ability to detect drifting samples effectively. The FeSAD framework is also evaluated on its ability to extend the lifespan of a classifier. The results obtained by this research show that FeSAD can successfully and reliably classify ransomware and benign samples while under concept drift and can extend the time between retraining.


Quantitative analysis of facial soft tissue using weighted cascade regression model applicable for facial plastic surgery

Abstract: Localization of facial landmarks plays an important role in the measurement of facial metrics applicable for beauty analysis and facial plastic surgery. The first step in detecting facial landmarks is to estimate the face bounding box. Clinical images of patients' faces usually show intensity non-uniformity. These conditions cause common face detection algorithms do not perform well in face detection under varying illumination. To solve this problem, a modified fuzzy c-means (MFCM) algorithm is used under varying illumination modeling. The cascade regression method (CRM) has an appropriate performance in face alignment. This algorithm has two main drawbacks. (1) In the training phase, increasing the real data without considering normal data can lead to over-fitting. To solve this problem, a weighted CRM (WCRM) is presented. (2) In the test phase, using a mean shape causes the initial shape to be either near to or far from the face shape. To overcome this problem, a Procrustes-based analysis is presented. One of the most important steps in facial landmark localization is feature extraction. In this study, to increase detection accuracy of the cephalometric landmarks, local phase quantization (LPQ) is used for feature extraction in all three channels of RGB color space. Finally, the proposed algorithm is used to measure facial anthropometric metrics. Experimental results show that the proposed algorithm has a better performance in facial landmark localization than other compared algorithms.


Personal CAPTCHA-based authentication protocol

Abstract: In recent years, the number of personal accounts assigned to a single user has considerably increased. According recent study, the average employee had above 190 individual login credentials. The most important problems users face with such a large number of passwords are the strength of passwords and the user's ability to remember them. Researchers have shown that, in general, users are aware of what a secure password should look like, but may forgot these security measures in favour of more convenient passwords, depending largely on the type of account (le Bras, 2015). Repeating the same password across multiple platforms or creating it using dictionary words has also proven to be a common practice among many people. In this article, a new password reminder scheme will be presented. The goal was for the user to create a CAPTCHA-like image with a hidden meaning that only the user would be able to reveal. The image must have some connection to the person's experience and memory or unique knowledge. With such an image, presented each time the user logs in, he is asked to associate a password consisting of two or more words and a number. If the image is appropriately selected and associated with a strong association with the person's visual memory, the chances of recalling the long password he created in this way should not be a problem.


Joint Analysis and Segmentation of Time-Varying Data with Outliers

Abstract: Principal-Component Analysis (PCA) is a fundamental tool in data science and machine learning, used for compressing, analyzing, visualizing, and processing large datasets. At the same time, temporal segmentation is important for coherent component analysis of big data collections generated by time-varying distributions. However, both segmentation and PCA can be critically affected and misled by corrupted points that often exist in big data collections. To address these issues, we propose a novel and robust method for joint segmentation and principal-component analysis of time-varying data, based on L1-norm formulations. Our proposed method estimates robust L1-norm principal components (L1-PCs) over different temporal horizons and combines them to perform outlier detection, data segmentation, and subspace estimation. Numerical studies on real-world data, including videos and smartphone-sensed human body motion measurements, corroborate the merits of the proposed method in terms of segmentation, PCA, and outlier detection/removal.


A multi-objective evolutionary algorithm based on dimension exploration and discrepancy evolution for UAV path planning problem

Abstract: Path planning is a crucial process for unmanned aerial vehicles (UAVs) and involves finding a path that is both short and safe. However, with the ever-increasing complexity of the environment, solving the UAV path-planning problem is challenging. Traditional path-planning methods cannot handle conflicting goals effectively, and existing objective methods lack targeted exploration mechanisms, resulting in unsatisfactory outcomes. By modeling the UAV path-planning problem via multi-objective optimization, this study designed a reasonable objective function composition for the model and considered obstacle avoidance as a hard constraint to satisfy the actual situation. A multi-objective evolutionary algorithm based on dimensional exploration and discrepancy evolution (MOEA-2DE) is presented. In particular, MOEA-2DE utilizes dimensional perturbation to identify key dimensions to facilitate prior exploration and enhance the targeted search. An adaptive evolution strategy based on population discrepancy was employed to assess the evolution process, and various methods were adopted to balance convergence and diversity. The effectiveness of the MOEA-2DE was demonstrated through the design of two intricate terrain sets and comparisons with various classic and state-of-the-art multi-objective evolutionary algorithms (MOEAs), including those designed for UAV path planning across multiple metrics. The results verify the superiority of MOEA-2DE in terms of both convergence speed and final effect.


A comparative study of Augmented Reality rendering techniques for industrial assembly inspection

Abstract: In the manufacturing industry, Augmented Reality (AR) has shown significant potential in enhancing operators’ capabilities while performing inspection and assembly activities. However, the augmented visualization of virtual models on physical components can present challenges and potential misunderstandings, as the visualization mode greatly influences the perception of components and the amount of information received. This study investigates the impact of rendering techniques on user performance during industrial assembly inspection tasks. In particular, a set of rendering techniques, suitable for industrial AR applications, have been selected and implemented through a dedicated AR tool. The selected AR rendering techniques have been compared, in terms of qualitative and quantitative metrics, in order to test their efficiency and effectiveness for industrial assembly inspection activities. 17 domain experts and 33 representative users have been involved in the experimental study which outcomes reveal that the rendering techniques play a significant role in assisting operators for identifying design discrepancies in industrial products. Furthermore, a correlation has been observed between the two AR rendering techniques best suited to support industrial inspection activities and the types of assembly errors detected.


Counting roots of fully triangular polynomials over finite fields

Abstract: Let Fq be a finite field with q elements, f∈Fq[x1,…,xn] a polynomial in n variables and let us denote by N(f) the number of roots of f in Fqn. In this paper we consider the family of fully triangular polynomials, i.e., polynomials of the formf(x1,…,xn)=a1x1d1,1+a2x1d1,2x2d2,2+…+anx1d1,n⋯xndn,n−b, where di,j>0 for all 1≤i≤j≤n. For these polynomials, we obtain explicit formulas for N(f) when the augmented degree matrix of f is row-equivalent to the augmented degree matrix of a linear polynomial or a quadratic diagonal polynomial.


Development of a novel multi-line laser triangulation scanning system based on the rotary diffraction grating

Abstract: The laser triangulation scanning system (LTSS) is widely employed in three-dimensional (3D) measurement fields owing to its high measurement accuracy. However, due to the limitations of the exploited motion, the traditional LTSS fails to be satisfactory for certain precise and efficient industrial applications. In this paper, a new multi-line laser triangulation scanning system (MLTSS) via integrating a rotary diffraction grating was proposed to address this problem. The image sequence of the divergent laser lines with variable angles on the object can be captured as the grating rotates. This enables to obtain the multiple profiles simultaneously during one frame. A novel method to distinguish laser lines in a single frame which enhances the measurement range was described. The measurement principle and workflow of the proposed system are explained in detail. The calibration algorithm of light planes of the proposed system utilizing the equivalent grating equation is simplified compared to the traditional algorithm. The developed setup and the measured object remain stationary throughout the whole scanning, which relieves the mechanical error caused by the motion. Moreover, the utilized grating facilitates to miniaturize the device. The experiments demonstrate the validity of the proposed system.


<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si142.svg" display="inline" id="d1e860"><mml:mi mathvariant="sans-serif">ThreadAbs</mml:mi></mml:math>: A template to build verified thread-local interfaces with software scheduler abstractions

Abstract: This paper presents ThreadAbs, an extension of the layer-based software formal verification toolkit CCAL (Gu et al., 2018). ThreadAbs is specifically designed to provide better expressiveness and proof management for thread abstraction in multithreaded libraries. Thread abstraction isolates the behavior of each thread from others when providing a top-level formal specification for software. Compared to the original CCAL, ThreadAbs offers significant improvements in this regard. CCAL is a verification framework that enables a layered approach to building certified software, as demonstrated by multiple examples (Gu et al. 2016; Li et al. 2021; Shin et al. 2019). Obviously, its main targets usually include multithread libraries, which support significant improvement in the utilization and isolation of system resources. However, it poses new challenges for formal verification. Firstly, it requires a sudden change in the granularity of concurrency during the implementation and verification of the target software. Typically, systems are associated with software schedulers that are built on top of several underlying components in the system (e.g., thread spawn, yield, sleep, and wake-up). Due to the software scheduler, these systems can be divided into low-level components consisting of modules that the software scheduler depends on (e.g., allocators for shared resources and scheduling queues) and high-level components that use software schedulers (e.g., condition variables, semaphores, and IPCs). Therefore, software formal verification on those systems has to provide proper method to deal with those distinct features, which is usually abstracting other threads' behavior as much as possible to provide an independent thread model and its formal specification. Secondly, it requires handling side effects from other threads, such as dynamic resource allocation from parents with proper isolation of all threads from each other. CCAL has limited support for two crucial aspects of formal verification in multithreaded systems. Firstly, its previous thread abstraction method does not handle the side effects caused by a parent thread during dynamic initial state allocation properly. Secondly, the proofs produced by CCAL are tied to CertiKOS, which makes it challenging to use them for similar proofs that use CCAL as their verification toolkit. To address these issues, we introduce ThreadAbs, a new mechanized methodology that provides proper thread abstraction to reason about multithreaded programs in conjunction with CCAL. We also extend the previous CertiKOS proof with ThreadAbs to demonstrate its usability and expressiveness.


Design optimization of a hexapod vibration isolation system for electro-optical payload

Abstract: The electro-optical payloads on mobile platforms generally suffer undesirable vibrations generated by maneuvers and turbulence. These vibrations are in six degrees of freedom and cause line-of-sight jitters, resulting in image blurring and loss of tracking accuracy. In this paper, a Hexapod Vibration Isolation System (HVIS) is proposed and optimized to solve this problem. The optimization aims to centralize and minimize the natural frequencies of HVIS, for expanding the vibration isolation bandwidth and improving the vibration isolation in the higher frequency band. Considering that the design space for HVIS is limited and interfered with the frames of the mobile platform, a non-collision algorithm is proposed and applied in the optimization to obtain the feasible optimal design. The optimization result shows that the natural frequency bandwidth has been reduced by 42.9%, and the maximum natural frequency is reduced by 30.2%. The prototypes of initial and optimal designs are manufactured and tested. Both simulated and experimental results demonstrate the validity of the optimization, and the optimal design provides a maximum of 15 dB more isolation in rotation direction than the initial design.


Deep generative network for image inpainting with gradient semantics and spatial-smooth attention

Abstract: As a powerful forgery operation in the image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, especially in boundary and high semantic areas. To address this issue, a novel end-to-end network with gradient semantics and spatial-smooth attention (GS-SSA) is proposed, which combines a gradient learning network and an image inpainting network. The gradient learning network is meant to properly anticipate the gradient semantics in the hole region and get a complete gradient semantic map. The image inpainting network utilizes the complete gradient semantic map to better repair the missing pixels and obtain the final inpainting results. Moreover, spatial-smooth attention is introduced into the image inpainting network, considering both the spatial structural relations and the surrounding information in the hole region. Experimental results on public datasets show the superiority of the proposed method, especially in large-hole region tasks.


STSIC: Swin-transformer-based scalable image coding for human and machine

Abstract: Recently, as the visual data captured by smart terminal devices is mainly served for machine analysis, e.g., image classification, object detection, and sometimes also for human viewing in special scenarios, a new compression framework should be proposed to meet this demand. In this work, an image coding framework for machine analysis and human viewing simultaneously is proposed, termed STSIC. In order to leverage the relationship between the two tasks, STSIC is built upon a scalable structure, comprising a base layer for machine analysis and image preview at low quality, as well as an enhancement layer for high-quality image reconstruction. More specifically, features are extracted through the backbone and transmitted to the cloud side for tasks on the base layer. Different from the vast majority of existing works, we try to employ the Swin-Transformer as the backbone instead of a convolutional neural network (CNN). Additionally, due to the hierarchical structure of the Swin-Transformer, a feature fusion strategy is applied to generate a common feature that is more friendly to both human and machine tasks. The enhancement layer transfers the residuals between the original images and the reconstructed images from the base layer, thereby improving the quality of image reconstruction for human viewing. The experimental results show that STSIC outperforms relevant benchmarks on object detection, especially under low bitrates. As for image reconstruction, the advantage is weakened, but it still surpasses HEVC on the rate-distortion performance.


Depth as attention to learn image representations for visual localization, using monocular images

Abstract: Image retrieval algorithms are widely used in visual localization tasks. In visual localization, we can benefit from retrieving the images depicting same landmark taken from a pose similar to the query. However, state-of-the-art image retrieval algorithms are optimized mainly for landmark retrieval, and do not take camera pose into account. To address this limitation, we propose novel Depth Attention Network (DeAttNet). DeAttNet leverages both visual and depth information in learning a global image representation. Depth varies for similar features captured from different camera poses. Based on this insight, we employ depth within an attention mechanism to discern and emphasize the salient regions. In our method, we utilize monocular depth estimation algorithms to render depth maps. Compared to RGB only image descriptors, significant improvements are obtained with the proposed method on Mapillary Street Level Sequences, Pittsburgh and Cambridge Landmark datasets.


Octagonal lattice-based triangulated shape descriptor engaging second-order derivatives supplementing image retrieval

Abstract: Erstwhile shape description schemes lack primarily in establishing trade-offs with accuracy and computational load. Accordingly, a lightweight shape descriptor offering precise definition and compaction of high-frequency features is contributed in this paper using a simple geometrical shape for localization and shape characterization. Initially, the input image is octagonally tessellated and triangularly decomposed into sub-regions whose side-wise differences are evaluated and subjected to second-order differentiation to produce three high-frequency values representing triangle corners. The resultant is processed by the law of sines to yield localized shape features exhibiting congruence and is reiterated on the residual regions, followed by a novel octal encoding scheme encompassing maximal variations in the localized regions. The resulting features are globally fabricated into shape histograms in a non-overlapping manner representing the shape vector. This scheme validated on widely popular benchmark shape datasets demonstrates superior retrieval and recognition accuracies greater than 93% which is lacking in its competitors.


Atmospheric dependence of the direct, diffuse, and global clear-sky conversion ratios between solar photosynthetic active irradiance and photon flux

Abstract: The ratio of the global solar photosynthetic active radiation (PAR) photon flux (in μmol/m2s) to global solar PAR irradiance (in W/m2) is of interest to convert one into another. This ratio is usually considered as a constant value close to its extraterrestrial value, 4.55μmol/J. However, this ratio depends on the spectral composition of solar radiation at ground level and it is different for the diffuse and beam components of solar irradiance. Under clear-sky conditions, the three PAR ratios (global, beam and diffuse) are determined by the local atmospheric composition and the relative air mass. In this work, the SMARTS spectral irradiance model with MERRA-2 atmospheric inputs is used to evaluate the dependence of these ratios under clear-sky conditions with air mass, aerosol optical depth (AOD), precipitable water vapor and ozone column. The accuracy of the SMARTS beam spectral irradiance is previously assessed using local spectroradiometer measurements. The clear-sky ratios for the diffuse and direct components increase with increasing air mass, while the global ratio shows only a weak air mass dependence. The clear-sky ratios can be modeled with simple bi-variate linear models in air mass and AOD. These results can be used in similar climatic regions to convert PAR flux to PAR irradiance and vice versa with increased accuracy for the global, direct, and diffuse radiation under cloud-free conditions.


Modeling and Assessing Hip-Knee Joint Coordination Based on Cyclograms Using a Portable Motion Capture System

Abstract: Cyclograms are useful for describing interjoint coordination in clinical gait analyses. In this paper, we establish a model for generating hip-knee cyclograms at different walking speeds by using B-Spline curves, which could provide reference standards for evaluating hip-knee joint coordination. We use the shape similarity between individual and reference cyclograms to develop a hip-knee joint coordination assessment metric: the dynamic time warping-shape context (DTW-SC). The DTW algorithm is used to match corresponding points between the hip-knee cyclograms, and the matched results are visualized to show abnormal changes in gait patterns. The distance between corresponding points is determined through shape context descriptors and applied to quantify the hip-knee joint coordination of the subjects. To verify the effectiveness of the proposed approach, a stereo vision-based portable lower limb motion capture system is developed to collect hip and knee joint angle data from five healthy and five hemiplegic subjects walking at comfortable speeds. Experimental results show that the range of the modeling error is [0.84°, 2.76°]. DTW-SC metric can visualize and quantify the abnormal patterns of the subject’s hip-knee joint coordination. The work is meaningful in evaluating the hip-knee joint coordination of hemiplegic patients.


Sampling-Based Reactive Synthesis for Nondeterministic Hybrid Systems

Abstract: This letter introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybrid system as a two-player game, where the nondeterminism is an adversarial player whose objective is to prevent achieving temporal and reachability goals. The aim is to synthesize a winning strategy – a reactive (robust) strategy that guarantees the satisfaction of the goals under all possible moves of the adversarial player. Our proposed approach involves growing a (search) game-tree in the hybrid space by combining sampling-based motion planning with a novel bandit-based technique to select and improve on partial strategies. We show that the algorithm is probabilistically complete, i.e., the algorithm will asymptotically almost surely find a winning strategy, if one exists. The case studies and benchmark results show that our algorithm is general and effective, and consistently outperforms state of the art algorithms.