Reviewerly/exHarmony huggingface dataset:
Out-of-Distribution Detection for LiDAR-based 3D Object Detection

3D object detection is an essential part of automated driving, and deep neural networks (DNNs) have achieved state-of-the-art performance for this task. However, deep models are notorious for assigning high confidence scores to out-of-distribution (OOD) inputs, that is, inputs that are not drawn from the training distribution. Detecting OOD inputs is challenging and essential for the safe deployment of models. OOD detection has been studied extensively for the classification task, but it has not received enough attention for the object detection task, specifically LiDAR-based 3D object detection. In this paper, we focus on the detection of OOD inputs for LiDAR-based 3D object detection. We formulate what OOD inputs mean for object detection and propose to adapt several OOD detection methods for object detection. We accomplish this by our proposed feature extraction method. To evaluate OOD detection methods, we develop a simple but effective technique of generating OOD objects for a given object detection model. Our evaluation based on the KITTI dataset shows that different OOD detection methods have biases toward detecting specific OOD objects. It emphasizes the importance of combined OOD detection methods and more research in this direction.
Empirical Assessment on Interactive Detection of Code Smells

Code smell detection is traditionally supported by Non-Interactive Detection (NID) techniques, which enable devel-opers to reveal smells in later software versions. These techniques only reveal smells in the source code upon an explicit developer request and do not support progressive interaction with affect code. The later code smells are detected, the higher the effort to refactor the affected code. The notion of Interactive Detection (ID) has emerged to address NID's limitations. An ID technique reveals code smell instances without an explicit developer request, encouraging early detection of code smells. Even though ID seems promising, there is a lack of evidence concerning its impact on code smell detection. Our research focused on evaluating the effectiveness of the ID technique on code smell detection. For doing so, we conducted a controlled experiment where 16 subjects underwent experimental tasks. We concluded that using the ID technique led to an increase of 60% in recall and up to 13% in precision when detecting code smells. Consequently, developers could identify more refactoring opportunities using the ID technique than the NID.
Serious Games with SIAs

chapter Share on Serious Games with SIAs Authors: Patrick Gebhard View Profile , Dimitra Tsovaltzi View Profile , Tanja Schneeberger View Profile , Fabrizio Nunnari View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, ApplicationOctober 2022 Pages 527–546https://doi.org/10.1145/3563659.3563676Published:03 November 2022Publication History 5citation9DownloadsMetricsTotal Citations5Total Downloads9Last 12 Months9Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Subjective Quality Study for Video Frame Interpolation

Video frame interpolation (VFI) is one of the fundamental research areas in video processing and there has been extensive research on novel and enhanced interpolation algorithms. The same is not true for quality assessment of the interpolated content. In this paper, we describe a subjective quality study for VFI based on a newly developed video database, BVI-VFI. BVI-VFI contains 36 reference sequences at three different frame rates and 180 distorted videos generated using five conventional and learning based VFI algorithms. Subjective opinion scores have been collected from 60 human participants, and then employed to evaluate eight popular quality metrics, including PSNR, SSIM and LPIPS which are all commonly used for assessing VFI methods. The results indicate that none of these metrics provide acceptable correlation with the perceived quality on interpolated content, with the best-performing metric, LPIPS, offering a SROCC value below 0.6. Our findings show that there is an urgent need to develop a bespoke perceptual quality metric for VFI. The BVI-VFI dataset is publicly available and can be accessed at https://danielism97.github.io/BVI-VFI/.
DeepSym: Deep Symbol Generation and Rule Learning for Planning from Unsupervised Robot Interaction

Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended environments. Therefore symbol formation and rule extraction should be considered part of robot learning, which, when done properly, will offer scalability, flexibility, and robustness. Towards this goal, we propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning. Our robot interacts with objects using an initial action repertoire that is assumed to be acquired earlier and observes the effects it can create in the environment. To form action-grounded object, effect, and relational categories, we employ a binary bottleneck layer in a predictive, deep encoderdecoder network that takes the image of the scene and the action applied as input, and generates the resulting effects in the scene in pixel coordinates. After learning, the binary latent vector represents action-driven object categories based on the interaction experience of the robot. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, a decision tree is trained to reproduce its decoder function. Probabilistic rules are extracted from the decision paths of the tree and are represented in the Probabilistic Planning Domain Definition Language (PPDDL), allowing off-the-shelf planners to operate on the knowledge extracted from the sensorimotor experience of the robot. The deployment of the proposed approach for a simulated robotic manipulator enabled the discovery of discrete representations of object properties such as ‘rollable’ and ‘insertable’. In turn, the use of these representations as symbols allowed the generation of effective plans for achieving goals, such as building towers of the desired height, demonstrating the effectiveness of the approach for multi-step object manipulation. Finally, we demonstrate that the system is not only restricted to the robotics domain by assessing its applicability to the MNIST 8-puzzle domain in which learned symbols allow for the generation of plans that move the empty tile into any given position.
Saturation-induced phase error correction method in 3-D measurement based on inverted fringes

Saturation-induced phase error in three-dimensional (3-D) measurement is a challenge in fringe projection profilometry. This paper proposed a new, to the best of our knowledge, high dynamic range 3-D measurement method based on inverted fringes. For an efficient phase extraction method based on the 3fH+2fM+2fL algorithm, we just need to project a set of inverted fringes. Saturated pixels in the original fringes are replaced with unsaturated pixels in the inverted fringes. We analyzed 72 fringe replacement cases for the 3fH+2fM+2fL algorithm and gave the corresponding wrapped phase calculation. Experiments verify the validity of the proposed method. Since the proposed method does not require extensive exposure adjustments and complex projection intensity adjustment, the implementation is simple and convenient.
A non-iterative calibration method for the extrinsic parameters of binocular stereo vision considering the line constraints

The calibration accuracy of the binocular stereo vision system is important for precision measurement. The extrinsic parameters (structure parameters), which describe the relative position of the two cameras, need to be optimized before measurement. The optimization of extrinsic parameters is accomplished through iterations of minimizing an objective function. It is hard to decide the optimum objective function between various objective functions. The calibration accuracy is decreased when the objective function is incorrect, which can result in excessive optimization or fall into a local minimum (ambiguous solution), notably for planar targets. In this paper, we proposed a non-iterative calibration method for the extrinsic parameters of binocular stereo vision considering the line constraints of a planar target. Each line formed by every two calibration points can provide two independent constraints. Additional equations for solving the extrinsic parameters can be obtained using these constraints. We added these equations to the equations system established by the homography of the target plane and image plane. The extrinsic parameters of the binocular stereo vision system can be solved non-iteratively. Using the parameters calibrated by our method, greater improvements in the accuracy and stability of measurements occurred, compared to other existing methods. In the −45° to +45° measuring range, the maximum measurement error is no more than 0.037° and the average measurement error is no more than 0.022°. In the 0 to 28 mm measuring range, the maximum measurement error is no more than 0.039 mm and the average measurement error is no more than 0.012 mm. The maximum length measurement error is no more than 0.038 mm and the average length measurement error is no more than 0.011 mm while measuring a 240 × 180 mm2 target.
A Literature Review of Blockchain-Based Applications in Supply Chain

Blockchain technology is an emerging technology, and cryptocurrency is the most well-known and successful blockchain application. With the development of the concept of blockchain technology, scientists and practitioners have found the potential of blockchain technology in the supply chain, which has led to much research on blockchain applications. There have been many reviews on the subject, but most of them are specific to particular supply chains and lack specific research on published papers. This paper addresses this research gap by examining, through qualitative analysis, the study finds that blockchain applications are moving in the direction of efficient automation. Appropriate consensus algorithms in different supply chain contexts will improve efficiency while reducing costs, while smart contracts play a huge role in security. Therefore, this paper explored articles related to the topic and explored articles in recent years by keyword combination.
Artificial intelligence framework‐based ultra‐lightweight communication protocol for prediction of attacks in <scp>Internet of Things</scp> environment

Abstract The Internet of Things is an enhanced intelligent infrastructure that is created through the use of a number of different devices that are capable of self‐organization. Data privacy and protection are the primary problems brought up by this enormous network. In addition, the devices in the network with constrained resources are battery‐powered, thus they only have a limited capacity to store resources internally. Because of this, it is vital to discover solutions that are more resource‐optimized and connected to security in order to solve the issues that were caused by the network. The devices are being forced to maintain more complicated cryptographic algorithms for higher security. Therefore, this article proposes an integrated communication protocol that requires only symmetric key‐based cryptography with a deep learning convolutional neural network, which is employed for predicting the normal and attacked data from the input requested data. Here, the logical map is used to produce the symmetric keys due to its resistance against the attacks of key reset and device capture. This integrated model provides an ultra‐lightweight communication protocol with reduced attack detection time while enhancing the attack detection parameters such as precision, accuracy, F1‐score, and recall.
Metasurface for complete measurement of polarization Bell state

Abstract Bell state measurement is vital to quantum information technology. Conventional linear optical elements, however, cannot fully distinguish all polarization Bell states without assisting of additional degrees of freedom. Leveraging on a pair of binary-pixel metasurfaces, we demonstrate direct measurement of all four polarization Bell states. Each metasurface is designed to produce two output modes that linearly superpose three Bell states in the coincidence counting measurement. By rotating the polarizers, the coincidence counting measurement achieves a tunable anticorrelation between one and the other two Bell states, achieving Bell state detection efficiency of 75% in a single measurement. Complete and deterministic Bell state measurement is further realized by performing two measurements. Our work shows the advantage of utilization of metasurfaces in quantum detection schemes and is of great applicative interest for quantum dense coding, entanglement swapping, quantum teleportation protocols, and novel quantum information processing tasks.
CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM

Recent advances in convolutional neural networks (CNNs) for image feature extraction have achieved extraordinary performance, but back-propagation algorithms tend to fall into local minima. To alleviate this problem, this paper proposes a coordinate attention-support vector machine-convolutional neural network (CASVM). This proposed to enhance the model’s ability by introducing coordinate attention while obtaining enhanced image features. Training is carried out by back-propagating the loss function of support vector machines (SVMs) to improve the generalization capability, which can effectively avoid falling into local optima. The image datasets used in this study for benchmark experiments are Fashion-MNIST, Cifar10, Cifar100, and Animal10. Experimental results show that compared with softmax, CASVM can improve the image classification accuracy of the original model under different image resolution datasets. Under the same structure, CASVM shows better performance and robustness and has higher accuracy. Under the same network parameters, the loss function of CASVM enables the model to realize a lower loss value. Among the standard CNN models, the highest accuracy rate can reach 99%, and the optimal number of accuracy indicators is 5.5 times that of softmax, whose accuracy rate can be improved by up to 56%.
Improving across-dataset brain tissue segmentation for MRI imaging using transformer

Brain tissue segmentation has demonstrated great utility in quantifying MRI data by serving as a precursor to further post-processing analysis. However, manual segmentation is highly labor-intensive, and automated approaches, including convolutional neural networks (CNNs), have struggled to generalize well due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. This study introduces a novel CNN-Transformer hybrid architecture designed to improve brain tissue segmentation by taking advantage of the increased performance and generality conferred by Transformers for 3D medical image segmentation tasks. We first demonstrate the superior performance of our model on various T1w MRI datasets. Then, we rigorously validate our model's generality applied across four multi-site T1w MRI datasets, covering different vendors, field strengths, scan parameters, and neuropsychiatric conditions. Finally, we highlight the reliability of our model on test-retest scans taken in different time points. In all situations, our model achieved the greatest generality and reliability compared to the benchmarks. As such, our method is inherently robust and can serve as a valuable tool for brain related T1w MRI studies. The code for the TABS network is available at: https://github.com/raovish6/TABS .
Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression

As being one of the main representation formats of 3D real world and well-suited for virtual reality and augmented reality applications, point clouds have gained a lot of popularity. In order to reduce the huge amount of data, a considerable amount of research on point cloud compression has been done. However, given a target bit rate, how to properly choose the color and geometry quantization parameters for compressing point clouds is still an open issue. In this paper, we propose a rate-distortion model based quantization parameter selection scheme for bit rate constrained point cloud compression. Firstly, to overcome the measurement uncertainty in evaluating the distortion of the point clouds, we propose a unified model to combine the geometry distortion and color distortion. In this model, we take into account the correlation between geometry and color variables of point clouds and derive a dimensionless quantity to represent the overall quality degradation. Then, we derive the relationships of overall distortion and bit rate with the quantization parameters. Finally, we formulate the bit rate constrained point cloud compression as a constrained minimization problem using the derived polynomial models and deduce the solution via an iterative numerical method. Experimental results show that the proposed algorithm can achieve optimal decoded point cloud quality at various target bit rates, and substantially outperform the video-rate-distortion model based point cloud compression scheme.
A Mixed Review of Cash Flow Modeling: Potential of Blockchain for Modular Construction

Cash is considered the most critical resource in construction projects. However, many contractors fail to obtain adequate liquidity due to a lack of proper cash flow management. Therefore, numerous research studies have been conducted to address cash flow-related issues in the construction industry. However, the literature still lacks a comprehensive review of cash flow management, methods and topics, in the construction industry. This study contributes by providing a holistic, up-to-date, and thorough review of 172 journal articles on construction cash flow. To achieve this primary objective, the study applies a mixed review methodology using scientometric and systematic reviews. The scientometric analysis provides the most contributing scholars, the timeline of cash flow research attention, and keywords clustering. On the other hand, the systematic analysis categorizes the cash flow themes, identifies current literature gaps, and highlights future research areas in the cash flow domain. The results show that cash flow analysis gained more research attention in the last two decades, cash flow-based schedule is the most frequent topic in the literature, and optimization techniques are predominant in the literature. Consequently, the study highlights five potential research frontiers. Further, an automated payment framework for modular construction projects using Blockchain-based smart contracts is developed to address some of the literature limitations. This study provides a guideline for future research efforts and raises researchers’ awareness of the latest trends and methods of construction cash flow analysis.
Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction

In recent years, deep learning has been applied to the field of recommendation, which can learn complex user interaction features and make better recommendations. However, deep learning only focuses on the interaction of high-order features and neglects the low-order features. The DeepFM model combines the linear FM (Factorization Machines) model and the deep DNN (Deep Neural Network) model to realize the interactive learning of low-order and high-order features, but it does not take into account that user interests will change dynamically with time. When the data sparsity is high, it cannot be effectively recommended. Based on this, an improved DeepFM recommendation algorithm that combines depth feature extraction was proposed, named fDeepFM. Firstly, the word features are transformed into low-dimensional dense vectors through the Embedding layer. Then Doc2Vec is combined to mine item features with context, and the two are stitched together as the input to the FM model and DNN model. Subsequently, user features are input to the GRU (Gated Cyclic Unit) model according to different cycles to mine user features. Finally, the results of the FM model, DNN model, and GRU model are combined by linear stitching as the overall output of the fDeepFM model. Experiments were carried out on Movielens-20M and Amazon data sets. The experimental results showed that MAE, RMSE, F1-score, and AUC on the Movielens-20M data set were optimized by 1.69%, 2.4%, 1.67%, and 2.28%, respectively; On the Amazon dataset, MAE, RMSE, F1-score, and AUC are optimized by 3.2%, 3.86%, 1.63%, and 2.2% respectively compared with DeepFM.
Verbal and nonverbal communication in high-immersion virtual reality for language learners

Virtual Reality (VR) offers language learners a valuable environment for practicing language skills and other aspects essential for language development, interaction, and negotiation of meaning. For example, speaking practice in VR using avatars can reduce speaking anxiety and increase users’ sense of agency. Social spaces in VR present possibilities for verbal and nonverbal communication. This conceptual paper discusses the advantages and drawbacks of both types of communication in high-immersion VR and the implications for language learners. Language learners rely on multiple modes of communication, including nonverbal behavior such as gestures. However, VR often lacks behavioral anthropomorphism, i.e. when an avatar cannot speak, move, or act in a human-like way, which may negatively impact communication, especially at beginner level.
The Continuity of Locomotion

Natural interaction between multiple users within a shared virtual environment (VE) relies on each other's awareness of the current position of the interaction partners. This, however, cannot be warranted when users employ noncontinuous locomotion techniques, such as teleportation, which may cause confusion among bystanders. In this paper, we pursue two approaches to create a pleasant experience for both the moving user and the bystanders observing that movement. First, we will introduce a Smart Avatar system that delivers continuous full-body human representations for noncontinuous locomotion in shared virtual reality (VR) spaces. Smart Avatars imitate their assigned user's real-world movements when close-by and autonomously navigate to their user when the distance between them exceeds a certain threshold, i.e., after the user teleports. As part of the Smart Avatar system, we implemented four avatar transition techniques and compared them to conventional avatar locomotion in a user study, revealing significant positive effects on the observer's spatial awareness, as well as pragmatic and hedonic quality scores. Second, we introduce the concept of Stuttered Locomotion, which can be applied to any continuous locomotion method. By converting a continuous movement into short-interval teleport steps, we provide the merits of non-continuous locomotion for the moving user while observers can easily keep track of their path. Thus, while the experience for observers is similarly positive as with continuous motion, a user study confirmed that Stuttered Locomotion can significantly reduce the occurrence of cybersickness symptoms for the moving user, making it an attractive choice for shared VEs. We will discuss the potential of Smart Avatars and Stuttered Locomotion for shared VR experiences, both when applied individually and in combination.