Reviewerly/exHarmony huggingface dataset:
Managing Human Capital with Employee Clustering Through the Interplay of the Persona Concept

Nowadays, employees are seen as active Human Capital Management (HCM) consumers. Megatrends such as individualization and flexible work impact the workforce, especially generations Y and Z. Finding and retaining talent is a challenge that companies try to address, from employer branding to covering the whole lifespan of the company-employee relationship. Management is likely to know every employee in small and medium-sized enterprises (SMEs); however, larger companies need to categorize their human capital into certain entities. This paper explores the usability of the Persona concept for HCM. Persona is a service design approach widely applied in marketing, and a few research papers have argued that personas may have a role in improved people-oriented HCM. In short, personas aim to create a limited set of archetypes that differ from each other regarding demo- and psychographics. Generally, they are a mix of real-life people and some imaginary attributes. Different personas will likely request different services to feel fully motivated and supported. The core target of this article was to identify the common awareness of personas among HCM experts across industries and countries. Also, the ideation of future use of Personas was studied with marketing experts actively using personas in their professions. The qualitative study based on twelve semi-structured individual interviews was conducted in November-December 2022. The results indicate that the HCM community is well aware of the persona concept, which is widely used. However, the current usage of personas is mainly focused on employer branding and recruitment, and only in a few cases the usage covered the design of internal HCM processes. The study also revealed some limitations for HCM that are not present in the marketing field. Demands for transparency, equality, data privacy, and personal integrity set challenges in clustering the workforce. The results indicate the further need for research on best practices of creating and using Personas in a way that ethically serves the interests of both the company and the employee.
Media Pengenalan Peti Kemas Logistik Menggunakan Augmented Reality Berbasis Android

Containers are the most important part in the world of logistics, where the container is a medium for sending cargo or goods from the sender to the recipient. A container is a square-shaped box that is specially carved with a certain size made of iron or aluminum with a door on one side, the box has an important role in the export and import of goods. The study developed the Augmented Reality app using the Markerless and Markerbased methods to visualize Android-based 3D Logistics Box objects. The method used in this application development is the Software Development Life Cycle model Waterfall. Logistic container identification materials that will be used as 3D objects on applications are: dry container, isotank container, open top container, open side container, reefer container and flatrack container. The result of the development of this app is a container recognition media that runs on the Android smartphone using Augmented Reality technology. Based on the results of functionality testing / blackbox testing, it can be concluded that the application runs according to its function and the input/output process successfully produces a valid output. The marker accuracy test obtained a total percentage level of precision of 66.6%, and the markerless accurate test obtains a total level of accuration of 100%. Usability testing by conducting a questionnaire on 30 respondents could conclude all aspects of usability, functionality, efficiency and portability obtain a percentage score of Likert’s scale of 91%.
Cross-modal Semantic Communications in 6G

In 6G communication, semantic communication is considered one of the most promising directions to fulfill users’ demands for immersive multi-modal experiences, low latency, and high reliability. We proposes a cross-modal semantic communication approach based on deep learning, where both semantic coding and decoding are carefully crafted to provide optimum performance. Firstly, cross-modal semantic fusion is designed to enable end-to-end data transmission, driven by various task requirements of multi-modal business users. In addition, the proposed approach for evaluation on the semantic similarity is highly effective. It consists of a siamese network and a pseudo-siamese network, which can accurately obtain the matching loss between modal contents. Finally, the simulation results show that the proposed cross-modal semantic communication approach outperforms traditional communication systems, especially in low SNR scenarios. The similarity of cross-modal semantic communication improves by more than 53% compared to the traditional approaches, demonstrating its superiority and feasibility. Overall, our solution can meet the increasing demands of modern communication and facilitate seamless and intuitive experiences for users.
Blockchain-based Multi-Factor Group Authentication in Metaverse

Under the metaverse scenario, users communicate with metaverse platform server through public wireless channels, and the transmitted sensitive information is vulnerable to security threats from potential malicious adversary. In addition, when there is a large number of users perform group authentication simultaneously, it is easy to cause network congestion and overload of the main server. The article presents a blockchain-based multi-factor group authentication scheme suitable for metaverse scenario. In particular, in order to avoid the defects of single-factor authentication, our scheme uses multi-factor authentication data including biological information to participate in the generation of anchor key. In our scheme, the user’s multi-factor authentication data is uniquely bound to the smart device and formed into a key-value pair and stored in the blockchain. And the tamper-resistance of the blockchain is used to trace malicious adversary. In addition, we design the group authentication mechanism to optimize the signaling process to avoid network congestion. Finally, security analysis demonstrates that our scheme can provide extra security functions such as malicious adversary traceability. Meanwhile, the performance analysis demonstrates that our scheme can improve the efficiency of authentication.
Blockchian Empowered Reliable Computation Offloading and Resource Allocation for Mobile Edge Computing Networks

The new paradigm that combines directed acyclic graph (DAG) blockchain with mobile edge computing (MEC) can enable secure edge task execution and maintain the auditability and integrity of trust management. A critical issue is that, blockchain cannot guarantee the trustworthiness of data sources off the chain even though the data have been recorded on the chain. To address this problem, we develop a trusted MEC (T-MEC) framework in this paper. To be specific, we first design an off-chain trust management mechanism, wherein the trust of each edge node is evaluated according to the quality of task execution results, and the trust can be further verified off the chain by any edge node with the shared and predefined trust management rule. Then, we design an on-chain trust based DAG consensus mechanism, wherein the on-chain transaction transaction security is positively correlated with off-chain trust value. The transactions issued by the edge node with higher off-chain trust can achieve higher on-chain security. In addition, we jointly optimize the computation offloading and resource allocation to minimize the system latency. Numerical results compare system latency and security performance between the optimized T-MEC and the benchmark schemes. In particular, the optimized T-MEC can achieve a 20.42% gain of system latency.
Referring Image Segmentation via Text Guided Multi-Level Interaction

We focus on the problem of segmenting entities that can match given natural language referring expressions. Existing works tackle this problem by fusing visual and linguistic modalities implicitly, failing to well align features from informative words in expressions and object regions in images. This makes it difficult to accurately infer referred entities, especially for scenes with complex object dependencies or interactions. In this paper, we propose a Text Guided Multi-level Interaction (TGMI) method to effectively address this challenging task. Specifically, the proposed TGMI referring segmentation method imitates the way humans process complex referring dependencies, from local target localization to global relation lookup. The proposed TGMI method has the following advantages: a) It highlights image areas described by language through gradually fusing the features of each word and the given image. b) It enables accurate relation matching between entire sentences and images. c) It ensures correct information fusion at both local and global levels. Experimental results on three standard datasets show significant improvements over all compared baseline models, demonstrating the effectiveness of our method.
Secure and Privacy-preserving Network Slicing in 3GPP 5G System Architecture

Network slicing in 3GPP 5G system architecture has introduced significant improvements in the flexibility and efficiency of mobile communication. However, this new functionality poses challenges in maintaining the privacy of mobile users, especially in multi-hop environments. In this paper, we propose a secure and privacy-preserving network slicing protocol (SPNS) that combines 5G network slicing and onion routing to address these challenges and provide secure and efficient communication. Our approach enables mobile users to select network slices while incorporating measures to prevent curious RAN nodes or external attackers from accessing full slice information. Additionally, we ensure that the 5G core network can authenticate all RANs, while avoiding reliance on a single RAN for service provision. Besides, SPNS implements end-to-end encryption for data transmission within the network slices, providing an extra layer of privacy and security. Finally, we conducted extensive experiments to evaluate the time cost of establishing network slice links under varying conditions. SPNS provides a promising solution for enhancing the privacy and security of communication in 5G networks.
Maximum Key-based Joint Topology Construction and Routing over Quantum Satellite Networks

Quantum key resources in quantum satellite networks are fundamental to confidential data forwarding, which highly depends on the network topology. In this paper, we propose a network topology construction scheme via formulating the dynamic link establishment among network nodes as an integer linear programming to maximize the number of available quantum keys in the topology. Then we solve this topology optimization problem through subgradient method based on the Lagrange duality theory. Under the optimized topology, we further propose a key-based routing and resource allocation (KRRA) algorithm to forward secret keys for ground stations. The simulation results show that our scheme performs better than the benchmark method with the minimum route distance in terms of the number of quantum keys and the success probability of real-time secret key forwarding service.
3D Hand Pose Estimation and Gesture Recognition Based on Hand-Object Interaction Information

Gesture recognition and hand pose estimation are two important tasks in the field of computer vision; however, in hand-object interaction scenarios, it is still challenging to implement gesture recognition and hand pose estimation using monocular RGB images since the hands are often obscured by objects. Most current studies treat these two problems as separate tasks. This paper proposes a multi-task network that combines gesture recognition and hand pose estimation tasks in a single network to solve this issue. The network uses a novel hand-object feature interaction module to improve the accuracy of gesture recognition and pose estimation by reasoning about the context between hand and object features through Transformer that encodes hand features and object features in different manners. In addition, to improve the performance of the multi-task network, we use a collaborative learning method that combines the output features of different tasks to promote each other, thus further improving the overall performance. Our model achieves high accuracy and performance for both gesture recognition and gesture pose estimation tasks on both the FPHA and HO-3D datasets, demonstrating the effectiveness and feasibility of the method.
Two-Server Private Information Retrieval with High Throughput and Logarithmic Bandwidth

In this paper, we propose a high-throughput, logarithmic-bandwidth private information retrieval (PIR) scheme, which enables a client to securely retrieve any record from a database replicated on two remote servers without revealing the record index. Specifically, based on the puncturable pseudorandom function (PPRF), a client first leverages randomized mapping to locate a logarithmic-size punctured PRF key capable of replacing a randomized set of arbitrary size while concealing a chosen element within the set. With the key, the client then generates corresponding PIR queries for two servers, achieving logarithmic communication complexity. Upon receiving the queries, the servers independently perform level-I PPRF evaluations on partitioned databases, i.e., buckets, to obtain intermediate results, and cooperatively perform level-II PPRF evaluations on these results to provide fast PIR responses. The two-level recursive approach can significantly reduce the servers’ computational complexity from linear to sublinear, ensuring high throughput. Finally, we conduct a comprehensive security analysis and develop a proof-of-concept prototype to demonstrate the efficiency and security of the proposed PIR scheme.
Distributed Computation Offloading Based on Deep Reinforcement Learning and Blockchain in Internet of Vehicles

With the rapid development of Internet of Vehicles (IoV), the traditional centralized cloud computing cannot efficiently satisfy the computation requirements of vehicles. The deployment of mobile edge server (MEC) on road side unit (RSU) can shorten the distance between vehicles and computing servers so as to reduce the service delay. In order to provide wide coverage and more computation resources, we introduce high altitude platform system (HAPS) as a central node for computation controlling in the IoV system. To improve data security and user privacy, blockchain technology is applied to verify and determine the offloading strategy between RSUs. Aiming at minimizing the sum of the transmission and computation time, an optimization problem is formulated under the power and offloading constraints. The deep reinforcement learning (DRL) with the soft actor-critic (SAC) algorithm is utilized to solve the problem. Simulation results demonstrate the time reduction by the application of HAPS and significant superiority of the proposed scheme compared to the existing approaches.
Multi-cell MIMO Semantic Communication based Distributed Training

Driven by deep learning, natural language processing(NLP) has achieved great success in analyzing and understanding large volumes of text. As a result, a large number of communication transmission methods based on NLP have emerged, which can identify the semantic information behind data and compress the amount of transmitted data to achieve better transmission performance. However, applying natural language processing to practical scenarios still faces numerous challenges, such as user interference among multiple cells and limited computing power at base stations. To address these issues, we propose a multi-cell multiple input multiple output(MIMO) semantic communication transmission method. We also consider the limited computing power at base stations and introduce a distributed training method. Each base station trains independently and uploads its network weights to a central server for fitting. The fitted weights are then used as the initialization parameters for the next round of training. Simulation results show that our proposed transmission method is robust under different signal-to-noise ratios (SNR). Additionally, the distributed training at each base station can better understand the semantic information from other knowledge bases.
An Efficient Multimodal Fusion Pipeline for Nighttime 3D Object Detection with PointrendPainting

Accurate detection of objects in the three-dimensional world is a core problem for autonomous vehicles and ensures the safety of the vehicles. Nevertheless, the complex real world has different degrees of occlusion and night driving conditions, and a lack of detail in images of road scenes in low-light conditions may increase the risk of collisions in autonomous vehicles. And many previous unimodal methods do not solve such problems well. In this paper, we propose PointrendPainting, which fuses image and point cloud data in the early data processing stage. The method exploits the efficient object boundary segmentation effect of Pointrend and fuses LiDAR point clouds to effectively improve the detection of occluded objects. In addition, to solve the problem of night driving safety, we leverage Cycle-Consistent Generative Adversarial Networks (CycleGAN) to generate nighttime road images. Then train the model on the new nighttime KITTI. Our method generally improves 2%-3% over previous work and can run well under nighttime conditions. And simple car class detection accuracy up to 91.77%.
Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality

In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server. Our objective is to minimize the pose estimation uncertainty of the MAR device by periodically selecting a proper set of camera frames for uploading to update the 3D map. To address the challenges of the dynamic uplink data rate and the time-varying pose of the MAR device, we propose a digital twin (DT)-based approach to 3D map management. First, a DT is created for the MAR device, which emulates 3D map management based on predicting subsequent camera frames. Second, a model-based reinforcement learning (MBRL) algorithm is developed, utilizing the data collected from both the actual and the emulated data to manage the 3D map. With extensive emulated data provided by the DT, the MBRL algorithm can quickly provide an adaptive map management policy in a highly dynamic environment. Simulation results demonstrate that the proposed DT-based 3D map management outperforms benchmark schemes by achieving lower pose estimation uncertainty and higher data efficiency in dynamic environments.
Privacy-Preserving Model Aggregation for Asynchronous Federated Learning

We present a novel privacy-preserving model aggregation for asynchronous federated learning, named PPA-AFL that removes the restriction of synchronous aggregation of local model updates in federated learning, while enabling the protection of the local model updates against the server. In PPA-AFL, clients can proactive decide when to engage in the training process, and sends local model updates to the server when the updates are available. Thus, it is not necessary to keep synchronicity with other clients. To safeguard client updates and facilitate local model aggregation, we employ Paillier encryption for local update encryption and support homomorphic aggregation. Furthermore, secret sharing is utilized to enable the sharing of decryption keys and facilitate privacy-preserving asynchronous aggregation. As a result, the server remains unable to gain any information about the local updates while asynchronously aggregating to produce the global model. We demonstrate the efficacy of our proposed PPA-AFL framework through comprehensive complexity analysis and extensive experiments on a prototype implementation, high-lighting its potential for practical adoption in privacy-sensitive asynchronous federated learning scenarios. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Image Structural Similarity-Based Channel Similarity Analysis across Multiple Frequency Bands in the Office Campus Environment

The coexistence of multiple frequency systems operating in well-separated frequency bands show great promise for achieving both breadth and depth of coverage. It is necessary to investigate frequency-dependent channel characteristics to facilitate multi-band collaborative communications. By treating the channel power spectrums as two-dimensional images and comparing the structural similarity (SSIM) between the images, a channel similarity index named weighted SSIM (WSSIM) is proposed for a evaluation of the similarity degree numerically. A power-dependent weight is introduced to reduce noise interference to similarity judgment. A large amount of simulation experiments have been conducted across multiple frequency bands in the outdoor office campus environment to obtain channel data. The statistical distributions of the WSSIM, which reflect the similarities between channels of different frequencies, are also presented.
QoE-based Deep Reinforcement Learning for Resource Allocation in Real Time XR Video Transmission

With the rapid development of mobile internet technology, extended reality (XR) applications have significantly increased. However, the ultra low latency and ultra high speed requirements of XR pose a huge challenge to wireless resource allocation. Therefore, this paper studies the dynamic resource block (RB) allocation for real-time cloud XR video transmission. Unlike existing works focusing on static RB allocation, we consider a dynamic assignment strategy appropriated for a user-moving scenario and employ deep reinforcement learning (DRL) methods. Specifically, we propose and compare two DRL approaches based on the soft actor-critic (SAC) model with powerful exploration capabilities: a centralized single-SAC (S-SAC) and a distributed multi-SAC (M-SAC) method. The former treats the base station as a single agent, while the latter treats all RBs as different independent agents. Multi-agent model reduces the neural network’s exponential output dimension to a linear order of magnitude. Moreover, we develop a novel reward function combining immediate and delayed rewards to solve the reward sparsity problem in DRL. Extensive simulations demonstrate that the proposed methods obtain more than 50% performance gain over various benchmarks for both fixed and random video frame size scenarios.
A New JSCC Scheme for Transmission of Gaussian Sources Over AWGN Channels

This paper is concerned with joint source-channel coding (JSCC) for the transmission of Gaussian sources over additive white Gaussian noise (AWGN) channels. A Gaussian source is first discretized by a ternary embedded quantizer with adjustable quantization levels and then protected by rate-matching block Markov superposition transmission of repetition (BMST-R) codes, a very special class of convolutional low-density generator matrix (LDGM) codes. In contrast to the Lloyd-Max algorithm, which minimizes the distortion, the proposed quantizer minimizes the discrete entropy under the distortion constraint, which can be solved by a simulated annealing algorithm. Also different from the existing schemes, the rate-flexible BMST-R codes allow us to encode ternary symbols in different quantization planes with different rates matching with the (conditional) entropy. Numerical results show that the code rate of the proposed scheme can approach the theoretical bound while the distortion is dominated by the quantizer.