Reviewerly/exHarmony huggingface dataset:
A time series anomaly detection method based on series-parallel transformers with spatial and temporal association discrepancies

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.