Reviewerly/exHarmony huggingface dataset:
Real-Time Path Planning for Obstacle Avoidance in Intelligent Driving Sightseeing Cars Using Spatial Perception

The increasing prevalence of intelligent driving sightseeing vehicles in the tourism industry underscores the critical importance of real-time planning for effective local obstacle avoidance paths when these vehicles encounter obstacles during operation. To fulfill this requirement, it is imperative to establish real-time dynamic perception as the foundational element. Thus, this paper introduces a novel local path planning algorithm founded on the principles of spatial perception. In the diverse array of road environments characterized by varying spatial features, sightseeing vehicles can effectively achieve safe and comfortable obstacle avoidance maneuvers. The proposed approach employs a high-precision positioning module and a real-time dynamic perception module to acquire real-time spatial information pertaining to the sightseeing vehicle and the road environment. It comprehensively integrates spatiotemporal safety constraints and obstacle avoidance curvature constraints to derive control points for the obstacle avoidance path. Specific control points undergo optimization and adjustment, ultimately resulting in the generation of the obstacle avoidance spatiotemporal path through discrete interpolation using B-spline curves. These locally tailored paths are subsequently compared with local obstacle avoidance paths generated using Bezier curves. The empirical validation of the proposed local obstacle avoidance path algorithm is conducted through a combination of simulation analysis and real vehicle verification. The research outcomes affirm that the algorithm can indeed produce smoother local obstacle avoidance paths, resulting in reduced front-wheel steering angles and yaw angle variations. This enhancement substantially contributes to the overall stability of sightseeing vehicles during obstacle avoidance maneuvers.
AFTR: A Robustness Multi-Sensor Fusion Model for 3D Object Detection Based on Adaptive Fusion Transformer

Multi-modal sensors are the key to ensuring the robust and accurate operation of autonomous driving systems, where LiDAR and cameras are important on-board sensors. However, current fusion methods face challenges due to inconsistent multi-sensor data representations and the misalignment of dynamic scenes. Specifically, current fusion methods either explicitly correlate multi-sensor data features by calibrating parameters, ignoring the feature blurring problems caused by misalignment, or find correlated features between multi-sensor data through global attention, causing rapidly escalating computational costs. On this basis, we propose a transformer-based end-to-end multi-sensor fusion framework named the adaptive fusion transformer (AFTR). The proposed AFTR consists of the adaptive spatial cross-attention (ASCA) mechanism and the spatial temporal self-attention (STSA) mechanism. Specifically, ASCA adaptively associates and interacts with multi-sensor data features in 3D space through learnable local attention, alleviating the problem of the misalignment of geometric information and reducing computational costs, and STSA interacts with cross-temporal information using learnable offsets in deformable attention, mitigating displacements due to dynamic scenes. We show through numerous experiments that the AFTR obtains SOTA performance in the nuScenes 3D object detection task (74.9% NDS and 73.2% mAP) and demonstrates strong robustness to misalignment (only a 0.2% NDS drop with slight noise). At the same time, we demonstrate the effectiveness of the AFTR components through ablation studies. In summary, the proposed AFTR is an accurate, efficient, and robust multi-sensor data fusion framework.
Analysis of Sectoral Outcomes and Institutional Quality Nexus in Sub-Saharan Africa

Following the need for more recent rigorous empirical evidence on the role of institutions at sectoral level as well as the conflicting empirical evidences on the institutions-growth relationship in Africa, this study investigated the sectoral impacts of institutional quality in Sub-Saharan Africa (SSA). The study also revisited the role of institutions in the aggregate economy. The system GMM estimation procedure and a panel of 42 SSA countries were used over the period 2010 to 2018. The results indicate that contrary to the widely held view that institutions foster growth and development, the role of institutional quality in sectoral and aggregate economic performance in SSA generally remained muted. However, the results indicate that initial level of real GDP and labor are robust drivers of growth, particularly in the aggregate economy. The study therefore concludes that the sub-region requires institutional reform, enhanced human capital development and capital accumulation to drive sectoral and aggregate economic performance in SSA. JEL Classification: N20; F43; C23; N17
SIRUS.jl: Interpretable Machine Learning via Rule Extraction

1 is an implementation of the original Stable and Interpretable RUle Sets (SIRUS) algorithm in the Julia programming language (Bezanson et al., 2017).The SIRUS algorithm is a fully interpretable version of random forests, that is, it reduces thousands of trees in the forest to a much lower number of interpretable rules (e.g., 10 or 20).With our Julia implementation, we aimed to reproduce the original C++ and R implementation in a high-level language to verify the algorithm as well as making the code easier to read.We show that the model performs well on classification tasks while retaining interpretability and stability.Furthermore, we made the code available under the permissive MIT license.In turn, this allows others to research the algorithm further or easily port it to production systems.
When cool hospitality brand meets AI: exploring the matching effect of service agents and brand images on brand attitude

Purpose This paper aims to explore the matching effect of hospitality brand image (cool vs non-cool) and service agents (Artificial intelligence [AI] vs human staff) on brand attitude, with a focus on assessing the role of feeling right as a mediator and service failure as a moderator. Design/methodology/approach This paper tested the hypotheses through three experiments and a Supplementary Material experiment, which collectively involved 835 participants. Findings The results indicated that the adoption of AI by cool brands can foster the right feeling and enhance consumers’ positive brand attitudes. In contrast, employing human staff did not lead to improved brand attitudes toward non-cool brands. Furthermore, the study found that service failure moderated the matching effect between service agents and cool brand images on brand attitude. The matching effect was observed under successful service conditions, but it disappeared when service failure occurred. Practical implications The findings offer practical guidance for hospitality companies in choosing service agents based on brand image. Cool brands can swiftly transition to AI, reinforcing their modern, cutting-edge image. Traditional brands may delay AI adoption or integrate it strategically with human staff. Originality/value To the best of the authors’ knowledge, this paper represents one of the first studies to address the issue of selecting the optimal service agent based on hospitality brand image. More importantly, it introduces the concept of a cool hospitality brand image as a boundary condition in the framework of AI research, providing novel insights into consumers’ ambivalent responses to AI observed in previous studies.
Assessing Study Skill Needs for Information Technology and Computer Science Students in Technical and Vocational Universities

Not a great deal is known about what study skills are essential for success in the science, technology, engineering, and mathematics' disciplines, particularly in information technology (IT) and computer science (CS) programs in the technical and vocational education (TVE) in Taiwanese higher education. Since TVE accounts for more than half of the post-secondary enrolments in the country, and with the increasing demand for IT and CS talents, we studied perceptions of the importance and competency of such skills to identify the students' needs and demographic factors influencing them. A survey was administered to 1398 students in IT and CS programs in Taiwanese TVE universities. General skills were seen as the most important one among the study skills examined, and students felt competent using them. The needs to manage time, perform quantitative/mathematical tasks, and delegate were identified and these needs were affected by institutional quality, gender, and academic achievement. The results might be useful for further investigation in this area and guiding future plan to improve student performance in TVE.
An efficient context-aware approach for whole-slide image classification

Computational pathology for gigapixel whole-slide images (WSIs) at slide level is helpful in disease diagnosis and remains challenging. We propose a context-aware approach termed WSI inspection via transformer (WIT) for slide-level classification via holistically modeling dependencies among patches on WSI. WIT automatically learns feature representation of WSI by aggregating features of all image patches. We evaluate classification performance of WIT and state-of-the-art baseline method. WIT achieved an accuracy of 82.1% (95% CI, 80.7%-83.3%) in the detection of 32 cancer types on the TCGA dataset, 0.918 (0.910-0.925) in diagnosis of cancer on the CPTAC dataset, and 0.882 (0.87-0.890) in the diagnosis of prostate cancer from needle biopsy slide, outperforming the baseline by 31.6%, 5.4%, and 9.3%, respectively. WIT can pinpoint the WSI regions that are most influential for its decision. WIT represents a new paradigm for computational pathology, facilitating the development of digital pathology tools.
‘It was just a joke!’ Comedy and freedom of speech

Debates about controversial comedy are rife in public discourse. However, despite a great interest in wider issues surrounding freedom of expression, political philosophers have had curiously little to say about comedy. This is a costly omission because in mainstream public debates, many of the worries about the potential harms of comedy are often confused or conflated, and both the defences of comedians to use controversial material and calls for censorship of such material are usually under-theorised. This paper takes a step towards correcting this oversight by explaining the potential harms of comedy and identifying who should be held responsible for these harms. By transposing existing work on hate speech, three harms of comedy are diagnosed: that it can cause status harms, that it can silence speakers, and that it can motivate violence. Using linguistic theory and the philosophy of language, the paper argues that often, it is audience members and third parties who ought to be held morally responsible for these harms, and therefore, that comedians are not usually under moral duties to modify their comedic expression, even if it is harmful.
FedChain-Hunter: A reliable and privacy-preserving aggregation for federated threat hunting framework in SDN-based IIoT

In the development of the Industrial Internet of Things (IIoT), cyber threats and attacks have become major issues and concerns in Industry 4.0 due to the negative impacts on the infrastructures and services across organizations. Nevertheless, due to the issues in preserving privacy and transparency, there is a lack of threat intelligence sharing among parties, leading to the low performance in uncovering malicious actors. In fact, the method of gathering and exploiting such data has been getting more crucial in a trend of machine learning (ML) adoption in cybersecurity. In this scenario, Federated Learning (FL) can assume a significant role in constructing an ML-based threat hunting solution for IIoT networks. This can be achieved by harnessing data resources from diverse parties, utilizing a local training strategy that eliminates the need for centralized data collection. Hence, this paper proposes FedChain-Hunter, a blockchain and FL-based threat-hunting framework to mutually seek cyber threats while ensuring data privacy and the transparency in the contribution of data owners. Specifically, Software Defined Networking (SDN) with programmable and flexible security orchestration is used to easily monitor and gather appropriate security events in the IIoT network. In addition, the Fully Homomorphic Encryption (HE) and Differential Privacy (DP) are integrated into the FL scheme to provide strong security and privacy-preserving aggregation for each ML model update. Also, the blockchain adoption offers the transparency, auditability for collaboration and contribution management through a decentralized platform. The experimental results on 5 datasets indicate that FedChain-Hunter can achieve high performance for cyber threat detection with security, reliability, and privacy guarantee.
Computer Vision Algorithms for 3D Object Recognition and Orientation: A Bibliometric Study

This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few.
FPGA Based Accelerator for Image Steganography

In this study, a novel approach is proposed for enhancing the speed and efficiency of steganography system by designing a hardware model that adopts the image-in-image (grey-in-color) method and utilizes the Least Significant Bit (LSB) algorithm. The sizes of secret and cover images are chosen so that all secret bits can be embedded inside the LSB bits of the pixels for the three RBG matrices for the cover image and produce the color stego-image. On the receiving part, these embedded secret bits will be extracted from stego-image and reform the original secret image. Using XSG and Vivado design suite, a successful FPGA-based steganography system was developed as an accelerator tool with high-speed processing that reaches 250 times faster than the software-based system. The quality of the stego-image and the extracted secret image was calculated by three metrics, which are the Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Cross Correlation (CCR). The comparison metrics values affirm a high level of trust in the generated stego-image, and the extracted secret image being identical to the original. The FPGA chip utilized in the implemented system consumes only 1% of the hardware resources, and a remarkable speedup factor of 250 is achieved, indicating that expanding the system for larger image sizes becomes highly feasible. Moreover, this expansion can be accomplished economically by utilizing low-cost field-programmable gate array (FPGA) chips.
Investigating the effectiveness of one-class and binary classification for fraud detection

Abstract Research into machine learning methods for fraud detection is of paramount importance, largely due to the substantial financial implications associated with fraudulent activities. Our investigation is centered around the Credit Card Fraud Dataset and the Medicare Part D dataset, both of which are highly imbalanced. The Credit Card Fraud Detection Dataset is large data and contains actual transactional content, which makes it an ideal benchmark for credit card fraud detection. The Medicare Part D dataset is big data, providing researchers the opportunity to examine national trends and patterns related to prescription drug usage and expenditures. This paper presents a detailed comparison of One-Class Classification (OCC) and binary classification algorithms, utilizing eight distinct classifiers. OCC is a more appealing option, since collecting a second label for binary classification can be very expensive and not possible to obtain within a reasonable time frame. We evaluate our models based on two key metrics: the Area Under the Precision-Recall Curve (AUPRC)) and the Area Under the Receiver Operating Characteristic Curve (AUC). Our results show that binary classification consistently outperforms OCC in detecting fraud within both datasets. In addition, we found that CatBoost is the most performant among the classifiers tested. Moreover, we contribute novel results by being the first to publish a performance comparison of OCC and binary classification specifically for fraud detection in the Credit Card Fraud and Medicare Part D datasets.
USC-DCT: A Collection of Diverse Classification Tasks

Machine learning is a crucial tool for both academic and real-world applications. Classification problems are often used as the preferred showcase in this space, which has led to a wide variety of datasets being collected and utilized for a myriad of applications. Unfortunately, there is very little standardization in how these datasets are collected, processed, and disseminated. As new learning paradigms like lifelong or meta-learning become more popular, the demand for merging tasks for at-scale evaluation of algorithms has also increased. This paper provides a methodology for processing and cleaning datasets that can be applied to existing or new classification tasks as well as implements these practices in a collection of diverse classification tasks called USC-DCT. Constructed using 107 classification tasks collected from the internet, this collection provides a transparent and standardized pipeline that can be useful for many different applications and frameworks. While there are currently 107 tasks, USC-DCT is designed to enable future growth. Additional discussion provides explanations of applications in machine learning paradigms such as transfer, lifelong, or meta-learning, how revisions to the collection will be handled, and further tips for curating and using classification tasks at this scale.
Design and Implementation of Low-Power IoT RISC-V Processor with Hybrid Encryption Accelerator

The security and reliability of data transmission between IoT devices are considered to be major challenges in the development of IoT technology. This paper presents a low-power, low-cost RISC-V processor for IoT applications with an integrated hybrid encryption accelerator, which can achieve efficient and secure encryption and decryption of data transmitted between IoT devices. The hybrid encryption accelerator, which uses the SM3 and the SM4, respectively, as hash and symmetric encryption algorithms, achieves a balance between encryption security, high speed, and key-management convenience. Both the processor and encryption accelerator are designed using the Verilog HDL language and are subsequently implemented and evaluated on both FPGA and ASIC platforms. The performance of the proposed processor and that of the Hummingbird E203 and the XuanTie E902 are compared. It is shown that, on the FPGA platform, the total resource utilization rate is reduced by 39.1~66.2%. In a 90 nm CMOS process, it is shown that the power efficiency of the proposed processor is increased by 10~34.8% and the circuit area is reduced by 32.5~57.1%.
Quantum programming of the satisfiability problem with Rydberg atom graphs

Finding a quantum computing method to solve nondeterministic polynomial time (NP)-complete problems is currently of paramount importance in quantum information science. Here we propose and experimentally demonstrate a Rydberg atom approach to program the 3-SAT problem, the prototypical NP-complete problem which allows general programming of all NP problems. We use Rydberg atom graphs, each of which consists of Rydberg atom dimers and trimers coupled with quantum wires in the Rydberg blockade interaction regime, to formulate general Boolean expressions, and obtain their many-body ground states to determine the satisfiabilities of the given 3-SAT problem instances quantum mechanically.
Detecting continuous-variable entanglement in phase space with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi></mml:math> distribution

We prove a general class of continuous variable entanglement criteria based on the Husimi $Q$ distribution, which represents a quantum state in canonical phase space, by employing a theorem by Lieb and Solovej. We discuss their generality, which roots in the possibility to optimize over the set of concave functions, from the perspective of continuous majorization theory and show that with this approach families of entropic as well as second moment criteria follow as special cases. All derived criteria are compared with corresponding marginal based criteria, and the strength of the phase-space approach is demonstrated for a family of prototypical example states where only our criteria flag entanglement. Furthermore, we explore their optimization prospects in two experimentally relevant scenarios characterized by sparse data: Finite detector resolution and finite statistics. In both scenarios optimization leads to clear improvements enlarging the class of detected states and the signal-to-noise ratio of the detection, respectively.
A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

The symmetrical segmentation of brain tumor images is crucial for both clinical diagnosis and computer-aided prognosis. Traditional manual methods are not only asymmetrical in terms of efficiency but also prone to errors and lengthy processing. A significant barrier to the process is the complex interplay between the deep learning network for MRI brain tumor imaging and the harmonious compound of both local and global feature information, which can throw off the balance in segmentation accuracy. Addressing this asymmetry becomes essential for precise diagnosis. In answer to this challenge, we introduce a balanced, end-to-end solution for brain tumor segmentation, incorporating modifications that mirror the U-Net architecture, ensuring a harmonious flow of information. Beginning with symmetric enhancement of the visual quality of MRI brain images, we then apply a symmetrical residual structure. By replacing the convolutional modules in both the encoder and decoder sections with deep residual modules, we establish a balance that counters the vanishing gradient problem commonly faced when the network depth increases. Following this, a symmetrical threefold attention block is integrated. This addition ensures a balanced fusion of local and global image features, fine-tuning the network to symmetrically discern and learn essential image characteristics. This harmonious integration remarkably amplifies the network’s precision in segmenting MRI brain tumors. We further validate the equilibrium achieved by our proposed model using three brain tumor segmentation datasets and four metrics and by juxtaposing our model against 21 traditional and learning-based counterparts. The results confirm that our balanced approach significantly elevates performance in the segmentation of MRI brain tumor images without an asymmetrical increase in computational time.