Reviewerly/exHarmony huggingface dataset:
Research on the construction of urban wisdom logistics platform based on blockchain

Authors:
raw_author_name: Xin Zhao
raw_affiliation_string: Shandong Jiaotong University (China)
raw_author_name: Huaqiong Liu
raw_affiliation_string: Shandong Jiaotong University (China)

Abstract: As a modern industry, smart logistics is of great significance for promoting the transformation and development of the green economy, optimising the government's macro-control, and creating an upgraded version of China's economy. In order to improve the efficiency of logistics, improve the low information transparency and poor security of urban logistics industry, according to the current situation of the logistics industry, based on the technical characteristics of the blockchain, the model of the smart logistics platform is constructed, and then the smart logistics platform is designed from the technical architecture of the smart logistics platform, the security layer, the business application layer, the data layer, and the data layer, and finally, this paper clarifies the aspects of reducing the cost of logistics operation, improving the transparency and traceability of data information, strengthening the information security, and optimising the supply chain management. Finally, this paper clarifies that blockchain has a significant impact on the development of smart logistics from the aspects of reducing logistics operation cost, improving data information transparency and traceability, strengthening information security, and optimising supply chain management.
Roadside LiDAR background filtering method based on differences in the distance of points in the spherical coordinates

Authors:
raw_author_name: Fujin Hou
raw_affiliation_string: Shandong Hi-Speed Construction Management Group Co., Ltd. (China)
raw_author_name: Yan Li
raw_affiliation_string: Shandong Hi-Speed Construction Management Group Co., Ltd. (China)
raw_author_name: Jianqing Wu
raw_affiliation_string: Shandong University (China); Suzhou Research Institute of Shandong University (China)

Abstract: To obtain object micro traffic information, LiDAR plays an important role in intelligent perception. Many background filtering methods based on LiDAR can detect the target, but the data process is not simple enough and data information will be lost easily. In this work, from the principle of original point cloud data capture, an advanced background filtering algorithm based on PCAP (Packet Capture) file is proposed. Firstly, a data extraction algorithm for PCAP files is designed. Then, one non-target frame is chosen as the background frame. The distance difference method is used to filter the background under the spherical coordinate system with the relative horizontal angle and vertical angle as discrete coordinates. The algorithm is improved twice by reconstructing the background frame based on the hierarchical clustering method and changing the distance difference threshold. Finally, the effectiveness of the three algorithms is verified.
Modeling analysis of flight support vehicle scheduling based on fuzzy colored petri nets

Authors:
raw_author_name: Caidong Zhan
raw_affiliation_string: Radar NCO School of Air Force Early Warming Academy (China)
raw_author_name: Peng Hang
raw_affiliation_string: Radar NCO School of Air Force Early Warming Academy (China)

Abstract: In order to better describe the flight support vehicle scheduling system, a fuzzy colored Petri net (FCPN) model is proposed, and its formal definition and specific inference algorithm are listed. A dispatching model with active strategies is developed using FCPN. An application example yields that the FCPN model significantly reduces the model complexity and is able to more accurately describe the vehicle scheduling system.
Two step vehicle trajectory reconstruction strategy based on an improved outlier detection and data smoothing algorithm

Authors:
raw_author_name: Rui Cao
raw_affiliation_string: Chang'an University (China)

Abstract: Vehicle trajectory data contains abundant traffic information and plays a key role in traffic flow research. Due to unsuitable sensor position or perspective, there are some local errors in existing vehicle trajectory data sets. To solve this problem, a two-step vehicle trajectory data reconstruction strategy is proposed in this paper. In the first step, an outlier detection algorithm combines the cluster-based local outlier factor (CBLOF) and the k-means clustering algorithm (KCBLOF) is proposed to identify outlier in object dataset. In the second step, Savitzky-Golay (SG) filter is selected to smooth the data flow containing noise. In this paper, the prototype data of I-80 highway in the open source trajectory data set NGSIM is used for case study. Taking vehicle 1 as an example, the speed and acceleration data of vehicle 1 are reconstructed. Time Windows with length of 11 and 21 are selected respectively to implement SG filter. The results show that when the time window value is 21, the trajectory data shows a better smoothed result, and the data noise can be obviously eliminated, which verifies that the trajectory data smoothing strategy proposed in this paper has a certain reliability.
Human Curriculum Effects Emerge with In-Context Learning in Neural Networks.

Authors:
raw_author_name: Russin, Jacob
raw_affiliation_string: Department of Computer Science, Department of Cognitive, Linguistic, and Psychological Sciences, Brown University.
raw_author_name: Pavlick, Ellie
raw_affiliation_string: Department of Computer Science, Carney Institute for Brain Science, Brown University.
raw_author_name: Frank, Michael J.
raw_affiliation_string: Department of Cognitive, Linguistic, and Psychological Sciences, Carney Institute for Brain Science, Brown University.

Abstract: Human learning is sensitive to rule-like structure and the curriculum of examples used for training. In tasks governed by succinct rules, learning is more robust when related examples are blocked across trials, but in the absence of such rules, interleaving is more effective. To date, no neural model has simultaneously captured these seemingly contradictory effects. Here we show that this same tradeoff spontaneously emerges with "in-context learning" (ICL) both in neural networks trained with metalearning and in large language models (LLMs). ICL is the ability to learn new tasks "in context" - without weight changes - via an inner-loop algorithm implemented in activation dynamics. Experiments with pretrained LLMs and metalearning transformers show that ICL exhibits the blocking advantage demonstrated in humans on a task involving rule-like structure, and conversely, that concurrent in-weight learning reproduces the interleaving advantage observed in humans on tasks lacking such structure.
An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution

Authors:
raw_author_name: Ji-Jian Chin
raw_affiliation_string: University of Plymouth, UK

Abstract: Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption Standard (AES) in Cypher Block Chaining (CBC) mode. A primary goal of the research is to conduct a thorough evaluation of the security and performance of the system. In order to assess search performance, a variety of database settings were extensively tested, and the system's security was tested by simulating intricate threat scenarios such as count attacks and leakage abuse. The efficiency of operation and cryptographic robustness of the SSE system are critically examined by these reviews.
Calibration-Free Mobile Eye-Tracking Using Corneal Imaging

Authors:
raw_author_name: Moayad Mokatren
raw_affiliation_string: The Department of Information Systems, University of Haifa, Haifa 3498838, Israel
raw_author_name: Tsvi Kuflik
raw_affiliation_string: The Department of Information Systems, University of Haifa, Haifa 3498838, Israel
raw_author_name: Ilan Shimshoni
raw_affiliation_string: The Department of Information Systems, University of Haifa, Haifa 3498838, Israel

Abstract: In this paper, we present and evaluate a calibration-free mobile eye-traking system. The system’s mobile device consists of three cameras: an IR eye camera, an RGB eye camera, and a front-scene RGB camera. The three cameras build a reliable corneal imaging system that is used to estimate the user’s point of gaze continuously and reliably. The system auto-calibrates the device unobtrusively. Since the user is not required to follow any special instructions to calibrate the system, they can simply put on the eye tracker and start moving around using it. Deep learning algorithms together with 3D geometric computations were used to auto-calibrate the system per user. Once the model is built, a point-to-point transformation from the eye camera to the front camera is computed automatically by matching corneal and scene images, which allows the gaze point in the scene image to be estimated. The system was evaluated by users in real-life scenarios, indoors and outdoors. The average gaze error was 1.6∘ indoors and 1.69∘ outdoors, which is considered very good compared to state-of-the-art approaches.
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data

Authors:
raw_author_name: Matej Grcić
raw_affiliation_string: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
raw_author_name: Petra Bevandić
raw_affiliation_string: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
raw_author_name: Zoran Kalafatić
raw_affiliation_string: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
raw_author_name: Siniša Šegvić
raw_affiliation_string: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia

Abstract: Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous. Previous work has addressed dense out-of-distribution detection by discriminative training with respect to off-the-shelf negative datasets. However, real negative data may lead to over-optimistic evaluation due to possible overlap with test anomalies. To this end, we extend this approach by generating synthetic negative patches along the border of the inlier manifold. We leverage a jointly trained normalizing flow due to a coverage-oriented learning objective and the capability to generate samples at different resolutions. We detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on benchmarks for out-of-distribution detection in road-driving scenes and remote sensing imagery despite minimal computational overhead.
Service robots and their implications for service delivery

Authors:
raw_author_name: Jochen Wirtz
raw_author_name: Werner H. Kunz
raw_author_name: Stefanie Paluch
raw_author_name: Valentina Pitardi

Abstract: The digital service revolution will significantly change the way we do business. Service robots in various forms and shapes are a big part of this revolution. In this chapter, we illustrate the implication of service robots for the service sector. We compare service robots with traditional self-service technologies (SSTs) and human service personnel and identify opportunities and challenges. In the Service Robot Deployment Model (SRD Model), we highlight promising areas for human-robot collaboration and discuss the challenges of service robot deployment. Finally, we derive managerial implications for the service frontline in this new reality with service robots.
A Generative Approach to Person Reidentification

Authors:
raw_author_name: Andrea Asperti
raw_affiliation_string: Department of Informatics—Science and Engineering (DISI), University of Bologna, 40126 Bologna, Italy

Abstract: Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation.
Keyboard Data Protection Technique Using GAN in Password-Based User Authentication: Based on C/D Bit Vulnerability

Authors:
raw_author_name: Jaehyuk Lee
raw_affiliation_string: Process Development Team, Fescaro, Suwon 16512, Republic of Korea
raw_author_name: Kyungroul Lee
raw_affiliation_string: Department of Information Security Engineering, Mokpo National University, Muan 58554, Republic of Korea

Abstract: In computer systems, user authentication technology is required to identify users who use computers. In modern times, various user authentication technologies, including strong security features based on ownership, such as certificates and security cards, have been introduced. Nevertheless, password-based authentication technology is currently mainly used due to its convenience of use and ease of implementation. However, according to Verizon’s “2022 Data Breach Investigations Report”, among all security incidents, security incidents caused by password exposures accounted for 82%. Hence, the security of password authentication technology is important. Consequently, this article analyzes prior research on keyboard data attacks and defense techniques to draw the fundamental reasons for keyboard data attacks and derive countermeasures. The first prior research is about stealing keyboard data, an attack that uses machine learning to steal keyboard data to overcome the limitations of a C/D bit attack. The second prior research is an attack technique that steals keyboard data more efficiently by expanding the features of machine learning used in the first prior research. In this article, based on previous research findings, we proposed a keyboard data protection technique using GAN, a Generative Adversarial Network, and verified its feasibility. To summarize the results of performance evaluation with previous research, the machine learning-based keyboard data attack based on the prior research exhibited a 96.7% attack success rate, while the study’s proposed method significantly decreased the attack success rate by approximately 13%. Notably, in all experiments, the average decrease in the keyboard data classification performance ranged from a minimum of −29% to a maximum of 52%. When evaluating performance based on maximum performance, all performance indicators were found to decrease by more than 50%.
Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure–Activity Relationships

Authors:
raw_author_name: Tomoyuki Miyao
raw_affiliation_string: Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan; Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan

Abstract: In the pursuit of optimal quantitative structure–activity relationship (QSAR) models, two key factors are paramount: the robustness of predictive ability and the interpretability of the model. Symbolic regression (SR) searches for the mathematical expressions that explain a training data set. Thus, the models provided by SR are globally interpretable. We previously proposed an SR method that can generate interpretable expressions by humans. This study introduces an enhanced symbolic regression method, termed filter-induced genetic programming 2 (FIGP2), as an extension of our previously proposed SR method. FIGP2 is designed to improve the generalizability of SR models and to be applicable to data sets in which cost-intensive descriptors are employed. The FIGP2 method incorporates two major improvements: a modified domain filter to eradicate diverging expressions based on optimal calculation and the introduction of a stability metric to penalize expressions that would lead to overfitting. Our retrospective comparative analysis using 12 structure–activity relationship data sets revealed that FIGP2 surpassed the previously proposed SR method and conventional modeling methods, such as support vector regression and multivariate linear regression in terms of predictive performance. Generated mathematical expressions by FIGP2 were relatively simple and not divergent in the domain of function. Taken together, FIGP2 can be used for making interpretable regression models with predictive ability.
Searching near and far: The attentional template incorporates viewing distance.

Authors:
raw_author_name: Surya Gayet
raw_affiliation_string: Experimental Psychology, Helmholtz Institute, Utrecht University.
raw_author_name: Sushrut Thorat
raw_affiliation_string: Donders Institute for Brain, Cognition and Behaviour, Radboud University.
raw_author_name: Marius V. Peelen
raw_affiliation_string: Donders Institute for Brain, Cognition and Behaviour, Radboud University.

Abstract: According to theories of visual search, observers generate a visual representation of the search target (the "attentional template") that guides spatial attention toward target-like visual input. In real-world vision, however, objects produce vastly different visual input depending on their location: your car produces a retinal image that is 10 times smaller when it is parked 50 compared to 5 m away. Across four experiments, we investigated whether the attentional template incorporates viewing distance when observers search for familiar object categories. On each trial, participants were precued to search for a car or person in the near or far plane of an outdoor scene. In "search trials," the scene reappeared and participants had to indicate whether the search target was present or absent. In intermixed "catch-trials," two silhouettes were briefly presented on either side of fixation (matching the shape and/or predicted size of the search target), one of which was followed by a probe-stimulus. We found that participants were more accurate at reporting the location (Experiments 1 and 2) and orientation (Experiment 3) of probe stimuli when they were presented at the location of size-matching silhouettes. Thus, attentional templates incorporate the predicted size of an object based on the current viewing distance. This was only the case, however, when silhouettes also matched the shape of the search target (Experiment 2). We conclude that attentional templates for finding objects in scenes are shaped by a combination of category-specific attributes (shape) and context-dependent expectations about the likely appearance (size) of these objects at the current viewing location. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Optimizing freeform lenses for extended sourceswith algorithmic differentiable ray tracing andtruncated hierarchical B-splines

Authors:
raw_author_name: Bart de Koning
raw_author_name: Matthias Möller

Abstract: We propose a method for optimizing the geometry of a freeform lens to redirect the light emitted from an extended source into a desired irradiance distribution. We utilize a gradient-based optimization approach with MITSUBA 3, an algorithmic differentiable non-sequential ray tracer that allows us to obtain the gradients of the freeform surface parameters with respect to the produced irradiance distribution. To prevent the optimizer from getting trapped in local minima, we gradually increase the number of degrees of freedom of the surface by using Truncated Hierarchical B-splines (THB-splines) during optimization. The refinement locations are determined by analyzing the gradients of the surface vertices. We first design a freeform using a collimated beam (zero-etendue source) for a complex target distribution to demonstrate the method's effectiveness. Then, we demonstrate the ability of this approach to create a freeform that can project the light of an extended Lambertian source into a prescribed target distribution.
A machine learning-based hybrid recommender framework for smart medical systems

Authors:
raw_author_name: Jianhua Wei
raw_affiliation_string: The Bidding Procurement Office, The First Affiliated Hospital of Xi’an Medical University, Xian, China
raw_author_name: Lili Zhao
raw_affiliation_string: Department of Scientific Research, The First Affiliated Hospital of Xi’an Medical University, Xi’an, China
raw_author_name: Lin Han
raw_affiliation_string: Hospital Evaluation and Accreditation Office, The First Affiliated Hospital of Xi’an Medical University, Xian, China
raw_author_name: Peng Yan
raw_affiliation_string: The Bidding Procurement Office, The First Affiliated Hospital of Xi’an Medical University, Xian, China
raw_author_name: Shengyu Wang
raw_affiliation_string: Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xi’an Medical University, Xi’an, China

Abstract: This article presents a hybrid recommender framework for smart medical systems by introducing two methods to improve service level evaluations and doctor recommendations for patients. The first method uses big data techniques and deep learning algorithms to develop a registration review system in medical institutions. This system outperforms conventional evaluation methods, thus achieving higher accuracy. The second method implements the term frequency and inverse document frequency (TF-IDF) algorithm to construct a model based on the patient’s symptom vector space, incorporating score weighting, modified cosine similarity, and K-means clustering. Then, the alternating least squares (ALS) matrix decomposition and user collaborative filtering algorithm are applied to calculate patients’ predicted scores for doctors and recommend top-performing doctors. Experimental results show significant improvements in metrics called precision and recall rates compared to conventional methods, making the proposed approach a practical solution for department triage and doctor recommendation in medical appointment platforms.