The importance of benchmarks for assessing the values of language models has been pronounced due to the growing need of more authentic, human-aligned responses.However, existing benchmarks rely on human or machine annotations that are vulnerable to value-related biases.Furthermore, the tested scenarios often diverge from real-world contexts in which models are commonly used to generate text and express values.To address these issues, we propose the Value Portrait benchmark, a reliable framework for evaluating LLMs' value orientations with two key characteristics.First, the benchmark consists of items that capture real-life user-LLM interactions, enhancing the relevance of assessment results to real-world LLM usage.Second, each item is rated by human subjects based on its similarity to their own thoughts, and correlations between these ratings and the subjects' actual value scores are derived.This psychometrically validated approach ensures that items strongly correlated with specific values serve as reliable items for assessing those values.Through evaluating 44 LLMs with our benchmark, we find that these models prioritize Benevolence, Security, and Self-Direction values while placing less emphasis on Tradition, Power, and Achievement values.Also, our analysis reveals biases in how LLMs perceive various demographic groups, deviating from real human data. 1

Visual persuasion, which uses visual elements to influence cognition and behaviors, is crucial in fields such as advertising and politicalcommunication. With recent advancements in artificial intelligence, there is growing potential to develop persuasive systems that automatically generate persuasive images tailored to individuals. However, a significant bottleneck in this area is the lack of comprehensivedatasets that connect the persuasiveness of images with the personal information about those who evaluated the images. To address this gap and facilitate technological advancements in personalized visual persuasion, we release the Personalized Visual Persuasion (PVP) dataset, comprising 28,454 persuasive images across 596 messages and 9 persuasion strategies. Importantly, the PVP dataset provides persuasiveness scores of images evaluated by 2,521 human annotators, along with their demographic and psychological characteristics (personality traits and values). We demonstrate the utility of our dataset by developing a persuasive image generator and an automated evaluator, and establish benchmark baselines. Our experiments reveal that incorporating psychological characteristics enhances the generation and evaluation of persuasive images, providing valuable insights for personalized visual persuasion.

The performance of learning-based object detection algorithms, which attempt to both classify and locate objects within images, is determined largely by the quality of the annotated dataset used for training. Two types of labelling noises are prevalent: objects that are incorrectly classified (categorization noise) and inaccurate bounding boxes (localization noise); both noises typically occur together in large-scale datasets. In this paper we propose a distillation-based method to train object detectors that takes into account both categorization and localization noise. The key insight underpinning our method is that the early-learning phenomenon - in which models trained on noisy data with mixed clean and false labels tend to first fit to the clean data, and memorize the false labels later -- manifests earlier for localization noise than for categorization noise. We propose a method that uses models from the early-learning phase (before overfitting to noisy data occurs) as a teacher network. A plug-in module implementation compatible with general object detection architectures is developed, and its performance is validated against the state-of-the-art using PASCAL VOC, MS COCO and VinDr-CXR medical detection datasets.

Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging. While a few previous studies have attempted to leverage semi-automatic methods for cost-effective bounding box annotation, such methods have limitations in efficiently handling numerous multi-class objects. To effectively accelerate 3D annotation pipelines, we propose iDet3D, an efficient interactive 3D object detector. Supporting a user-friendly 2D interface, which can ease the cognitive burden of exploring 3D space to provide click interactions, iDet3D enables users to annotate the entire objects in each scene with minimal interactions. Taking the sparse nature of 3D point clouds into account, we design a negative click simulation (NCS) to improve accuracy by reducing false-positive predictions. In addition, iDet3D incorporates two click propagation techniques to take full advantage of user interactions: (1) dense click guidance (DCG) for keeping user-provided information throughout the network and (2) spatial click propagation (SCP) for detecting other instances of the same class based on the user-specified objects. Through our extensive experiments, we present that our method can construct precise annotations in a few clicks, which shows the practicality as an efficient annotation tool for 3D object detection.

With the increasing popularity of containers for deploying microservices, ensuring the security of container networks has become a vital concern. However, current security solutions rely on a host's operating system (OS) to enforce network policies for container traffic. This design incurs severe overhead and cannot guarantee container network security when attackers gain access to the host's OS. Therefore, we propose HardWhale, a hardware-isolated network security enforcement system for containers that delivers high-performance and robust network security without depending on the host's OS. HardWhale leverages a smartNIC, physically isolating the entire container traffic inspection stack from the host and accelerating inspection tasks. Inspection policies securely reside within the smartNIC and are updated in runtime without involving the host, due to our isolated policy management mechanism. This design ensures robust network security for containers, even if the host is exposed to attackers. Evaluations show that HardWhale protects containers against various network attacks in compromised environments and improves HTTP throughput threefold and HTTP latency 2.3-fold compared to state-of-the-art solutions.

Interactive segmentation methods have been investigated to address the potential need for additional refinement in automatic segmentation via human-in-the-loop techniques. For accurate segmentation of 3D images, we propose Slice-and-Conquer, a novel planar-to-3D pipeline formulating volumetric mask construction into two stages: 1) 2D interactive segmentation and 2) guided 3D segmentation. Specifically, the first stage enables users to focus on a single 2D slice and provides the corresponding 2D prediction results as strong shape priors. Taking the planar guidance, an accurate 3D mask can be constructed with minimal interactions. To support a flexible iterative refinement, our system recommends a next slice to annotate at the end of the second stage. Since volumetric segmentation can be completed by consecutively annotating a few recommended 2D slices, our method significantly reduces the cognitive burden of exploring volumetric space for users. Through extensive experiments on various datasets of 3D biomedical images, we demonstrate the effectiveness of the proposed pipeline.

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

Summary Secure mechanisms have been adapted to satisfy the needs of mobile subscribers; however, the mobile environment is quite different from a desktop PC or laptop‐based environment. The existing attack patterns in mobile environments are also quite different, and the countermeasures applied should be enhanced. In regards to usability, the mobile environment is based on mobility, and thus, mobile devices are designed and developed to enhance the owner's efficiency. To avoid forgetting passwords, people are willing to adopt simple alphanumeric‐character combinations, which are easy to remember and convenient to enter. As a result, the passwords have a high probability of being cracked or exposed. In this paper, we study the potential security problems caused by simple and weak passwords, discuss drawbacks of some conventional works, and propose 3 creative schemes to increase the complexity and strength of passwords by applying the envisioned features. Note that our proposals are based on the assumption that the textual passwords are not difficult for users to remember or enter and do not cause inconvenience to users. In other words, the proposed methods can increase the complexity of simple passwords without the awareness of users.

Including mobile environment, conventional security mechanisms have been adapted to satisfy the needs of users. However, the device environment-IoT-based number of connected devices is quite different to the previous traditional desktop PC- or mobile-based environment. Based on the IoT, different kinds of smart and mobile devices are fully connected automatically via device controller, such as smartphone. Therefore, controller must be secure compared to conventional security mechanism. According to the existing security threats, these are quite different from the previous ones. Thus, the countermeasures applied should be changed. However, the smart device-based authentication techniques that have been proposed to date are not adequate in terms of usability and security. From the viewpoint of usability, the environment is based on mobility, and thus devices are designed and developed to enhance their owners’ efficiency. Thus, in all applications, there is a need to consider usability, even when the application is a security mechanism. Typically, mobility is emphasized over security. However, considering that the major characteristic of a device controller is deeply related to its owner’s private information, a security technique that is robust to all kinds of attacks is mandatory. In this paper, we focus on security. First, in terms of security achievement, we investigate and categorize conventional attacks and emerging issues and then analyze conventional and existing countermeasures, respectively. Finally, as countermeasure concepts, we propose several representative methods.

Recently, Vijayakumaret al.proposed a dual authentication and key management scheme for secure data transmission in vehicular ad-hoc networks. As described by Vijayakumaret al., a dual authentication scheme and the corresponding group key management mechanism are illustrated successively. The authors claimed that the proposed scheme is resistant to replay attack and masquerade attack. However, we find that the proposed scheme given by Vijayakumaret al.is still vulnerable to replay attack, which could be conducted by reusing previously acquired messages. Moreover, this scheme cannot resist masquerade attack toward the system. For the above consideration, in this paper, modifications toward the existing protocol are presented, so as to provide adequate security assurance toward the mentioned attacks.

As a crucial component of Internet‐of‐Thing (IoT), vehicular ad hoc networks (VANETs) have attracted increasing attentions from both academia and industry fields in recent years. With the extensive VANETs deployment in transportation systems of more and more countries, drivers’ driving experience can be drastically improved. In this case, the real‐time road information needs to be disseminated to the correlated vehicles. However, due to inherent wireless communicating characteristics of VANETs, authentication and group key management strategies are indispensable for security assurance. Furthermore, effective road message dissemination mechanism is of significance. In this paper, we address the above problems by developing a certificateless authentication and road message dissemination protocol. In our design, certificateless signature and the relevant feedback mechanism are adopted for authentication and group key distribution. Subsequently, message evaluating and ranking strategy is introduced. Security analysis shows that our protocol achieves desirable security properties. Additionally, performance analysis demonstrates that the proposed protocol is efficient compared with the state of the art.

Smart devices have already become a dally necessity of our life since they play a pivot role to record massive amount of Information of our personal life. Current Internet of TWngs era has been seeing more and more people relying on their devices. However, most people are losing patience to set complex passwords to ensure the security for their devices. To avoid forgetting the passwords, people are more willing to choose simple alphanumeric character combinations that are easy for them to remember and convenient to enter. Therefore, their passwords are of high probability to be cracked or exposed. In tWs paper, we study the potential security problems caused by simple and weak passwords, discuss the drawbacks of some conventional works, and propose three schemes to Increase the complexity of simple passwords. Note that our proposals are based on the prediction that the textual passwords are not difficult for users to remember or enter and the proposed schemes can effldently prevent passwords from being cracked or exposed.

Reducing memory access conflicts is a crucial part of the design of Transactional Memory (TM) systems since the number of running threads increases and long latency transactions gradually appear: without an efficient contention management, there will be repeated aborts and wasteful rollback operations. In this paper, we present a dynamic backoff control algorithm developed for complexity-effective and distributed contention management in Hardware Transactional Memory (HTM) systems. Our approach aims at controlling the restarting intervals of aborted transactions, and can be easily applied to the various TM systems. To this end, we have profiled the applications of the STAMP benchmark suite and have identified those “problem” transactions which repeatedly cause aborts in the applications with the attendant high contention rate. The proposed algorithm alleviates the impact of these repeated aborts by dynamically adjusting the initial exponent value of the traditional backoff approach. In addition, the proposed scheme decreases the number of wasted cycles down to 82% on average compared to the baseline TM system. Our design has been integrated in LogTM-SE where we observed an average performance improvement of 18%.

Data redundancy is occurs for two reasons: one is sensor coverage duplication and the other one is sensor event. We propose a novel clustering method that considers node sensor coverage and event. Also our method introduces a repeater that helps to hop by hop transmission in order to cope with energy-holes and link-failure problems. This method prevents data loss caused by node link disconnections, thus it collects the data reliably. According to the performance analysis results, our method decreases energy consumption and increases transmission efficiency.

The curvatures of an object play an important role in the shape reconstruction of that object. In this research, we suggest a method of estimating unknown curvatures using a coarse-resolution force torque sensor. First, a method of estimating unknown curvatures using large motions in terms of rolling and sliding is proposed. Next, a tracking algorithm for an unknown surface based on an inner position loop control algorithm is presented. An application of this method to the finger of a robot hand is also discussed. Finally, the algorithm is verified by simulations and experiments for several cylinders with different radii.

Clustering protocol that is used in wireless sensor network is an efficient method that extends the lifetime of the entire network. However, when this method is applied to an environment in which collected data of the sensor node easily overlap, sensor nodes unnecessarily consumes so much energy. In the case of clustering technique that use a threshold, the lifetime of the network is extended but the degree of accuracy of collected data is low. Therefore it is hard to trust the data and improvement is needed. In addition, it is hard for the clustering protocol that uses multi-hop transmission to normally collect data because the selection of a cluster head node occurs at random and therefore the link of nodes is often disconnected. Considering the whole networks, nodes nearer the sink have to take heavier traffic load. Therefore, nodes around the sink would deplete their energy faster, leading to what is called an energy hole around the sink. Accordingly this paper suggested a cluster-formation algorithm that reduces unnecessary energy consumption and that works with an alleviated link disconnection. According to the result of performance analysis that used environment data, the suggested method lets the nodes consume less energy than the existing clustering method and the transmission efficiency is increased and the entire lifetime is prolonged by about 30%.

Recently, vehicular sensor networks (VSNs) have emerged as a new wireless sensor network paradigm that is envisioned to revolutionize driving experiences and traffic control systems. Many existing routing protocols for VSNs have good performance on data routing in a city environment. In this paper, we propose a traffic control aware routing for VSNs, which is called PUT. It considers two modules of (i) the traffic control aware selection of vertices through which a packet is passed toward its destination and (ii) the greedy forwarding strategy by which a packet is forwarded between two adjacent vertices. The simulation results show that the proposed PUT outperforms conventional protocols in terms of packet delivery ratio, end-to-end delay and routing overhead.

In sensor networks, clustering protocol such as LEACH is an efficient method to increase whole networks lifetime. However, this protocol result in high energy consumption at the cluster head node. Hence, this protocol must changes the cluster formation and cluster head node in each round to prolong the network lifetime. But this method also causes a high amount of energy consumption during the set-up process of cluster formation. In order to improve energy efficiency, in this paper, we propose a new cluster formation algorithm named EEVAR (an Energy Efficient Variable Area Routing protocol). This scheme decreases unnecessary repetitious set-up process and prolongs steady-state process. As a result of analysis and comparison, our scheme reduces energy consumption of nodes, and improve the efficiency of communications in sensor networks compared with current clustering methods.