Reviewerly/exHarmony huggingface dataset:
Effective face recognition based on anomaly image detection and sequential analysis of neural descriptors

In this paper, we explore the possibility to improve efficiency of face recognition using information about anomaly input images. Indeed, modern publicly-available datasets typically contain images of mostly middle-aged and Caucasian people, which cause most algorithms to fail on photos of older people or children, rarer ethnicities, poor-quality images, etc. Detection of such anomaly data and its subsequent rejection helps to improve the classification accuracy. We propose a novel algorithm, at the first stage of which a convolutional neural network is used to detect anomalies in input images. This network is trained on a specially created set of rare data. The second stage is the sequential analysis of neural descriptors extracted from input faces to improve the computational efficiency of classification. An experimental study on the VGGFace2 and MS-Celeb-1M datasets using neural network descriptors, including contemporary InsightFace models, demonstrated the effectiveness of the proposed algorithm.
ECHO: An Efficient Heuristic Viewpoint Determination Method on Frontier-Based Autonomous Exploration for Quadrotors

As a popular drone application, autonomous exploration suffers from low efficiency. To address the issue of repeated and unnecessary exploration, especially in a large-scale and cluttered environment, this letter proposes an efficient heuristic viewpoint determination method on frontier-based autonomous exploration, which includes viewpoint generation, evaluation, and refinement. A Gaussian sampler is employed to randomly generate higher-quality initial viewpoints; meanwhile, a fresh heuristic evaluation function is designed to select the next viewpoint; besides, a refinement strategy is presented to improve the viewpoint. Extensive simulations and real-world tests indicate that the proposed method outperforms the state-of-the-art frontier-based method by 15%-25% in almost all scenarios.
AI and Blockchain Empowered Metaverse for Web 3.0: Vision, Architecture, and Future Directions

As one of the most prominent parts of the Internet, World Wide Web (WWW) has achieved great success and penetrated every area of our lives. However, the cu rrent WWW still suffers from inefficiency, growing concerns about user privacy and data ownership, and poor Quality of Experience. Therefore, we propose a promising architecture for the next-generation WWW, Web 3.0, which is underpinned by Artificial Intelligence (Al) and Blockchain-empowered Metaverse (AlB-Metaverse). Fueled by native Al, Web 3.0 based on AlB-Metaverse can provide users with a personalized experience and enable smart decision-making with high efficiency. Backed by Blockchain, the AIB-Metaverse-based Web 3.0 is decentralized, which can help users regain full control of data and protect the ownership of generated data while preserving privacy. Furthermore, this new architecture can provide a ubiquitous immersive experience to users during real-time interaction with digital avatars in the Metaverse. To verify the effectiveness of Al in Web 3.0, we propose an Al-based approach for Metaverse video delivery, which can significantly enhance the quality of immersive experience perceived by users in Web 3.0. In addition, we pinpoint the challenges faced by the proposed AlB-Metaverse-based Web 3.0 and highlight pertinent research directions in the future.
A Blockchain-Assisted Intelligent Edge Cooperation System for IoT Environments With Multi-Infrastructure Providers

While edge computing has the potential to offer low-latency services and overcome the limitations of traditional cloud computing, it presents new challenges in terms of trust, security, and privacy (TSP) in Internet of Things environments. Cooperative edge computing (CEC) has emerged as a solution to address these challenges through resource sharing among edge nodes. However, for multi-infrastructure providers, incentive and trust mechanisms among edge nodes are crucial technical issues that must be addressed alongside system latency and reliability to meet performance requirements. In this article, we propose a blockchain-assisted intelligent edge cooperation system (BIECS) to systematically solve these issues. By leveraging blockchain technology, we construct trust among edge nodes and employ an incentive mechanism for resource sharing among multi-infrastructure providers. We formulate the system performance optimization as a multiobjective joint optimization problem and solve it efficiently through a two-stage strategy for selecting edge nodes. We first design an improved long short term memory (LSTM) model for resource prediction and then select edge nodes for executing offloaded tasks and handling the corresponding blockchain process related to each task execution. To evaluate the performance of BIECS, we implement the system based on Hyperledger Fabric and design extensive experiments. Our proposed system achieves better performance in terms of system delay, throughput, and resource utilization compared to state-of-the-art schemes for edge cooperation.
Patient Assistance System Based on Hand Gesture Recognition

We propose a two-stage hand gesture recognition architecture to support a patient assistance system. Some medical conditions limit mobility, and the patient must rely on medical staff to meet their needs. In such cases, a phone or intercom is not convenient to call for help. A vision-based system operated by changing the orientation of fingers can be used to send specific messages without making arm movements. However, vision-based hand gesture recognition is hindered by occlusion, background clutter, and variations in illumination. Therefore, we developed a two-stage architecture: The first stage produces a saliency map to simplify recognition and the second stage performs classification. A novel combined loss function optimizes the saliency detection model and makes the saliency map more precise. An adaptive kernel-based channel attention layer is used to emphasize salient features. The proposed architecture achieved precise saliency detection on four benchmark datasets and high-accuracy recognition on two. We designed an interface for patients to send specific messages to the medical staff using hand gestures. The interface help patients request assistance and connect with medical staff without leaving the bed or involving a third party.
Complex Optical Remote-Sensing Aircraft Detection Dataset and Benchmark

Aircraft detection in remote sensing images is significant in both military and civilian fields, such as air traffic control and battlefield dynamic monitoring. Deep learning methods can achieve promising detection performance with sufficient and labeled samples. However, current aircraft datasets are mainly from a single data source and lack diverse scenes and targets, making it difficult to train a robust and generalized detector. Therefore, we manually label and construct a complex optical remote sensing aircraft target detection dataset (CORS-ADD) from Google Earth and multiple satellites such as WorldView-2, WorldView-3, Pleiades, Jilin-1, and IKONOS. It contains 7,337 images covering typical airports and various rare scenes, including the aircraft carrier, ocean and land with flying aircraft. The dataset consists of 32,285 civil and military aircraft instances, including bombers, fighters, and early warning aircraft. These targets range from 4×4 pixels to 240×240 pixels and are all labeled with both horizontal bounding box (HBB) and oriented bounding box (OBB) annotations. The various scenes and sufficient instances can fully support the training and evaluation of data-driven algorithms. Meanwhile, based on the constructed dataset, we train and evaluate several detectors to provide a benchmark and help promote the development of aircraft detection techniques.
Distributed Learning for Metaverse over Wireless Networks

Metaverse is envisioned to be a human-centric framework that creates an interface for users to immerse themselves in education, professional training, and entertainment by accessing a virtual world. The quality of immersive experiences (QoIE) naturally comes out as a metric to measure the multi-sensory multimedia (MSMM) communication provided by Metaverse networks, we first propose a human-centric MSMM communication framework and highlight the asymmetric uplink-downlink transmission mechanism by identifying their different responsibilities. This MSMM framework raises the need for advanced communication technologies and more computational resources to support the deployment of AI-enabled Metaverse services. Task-oriented communication (TOC), can enhance conventional data-oriented communication by shifting from data rate maximization to task completion communication, especially deep learning-based TOC (DL-TOC) can build up a joint communication and task completion architecture. The idea of investigating distributed computational resources of end users to perform local learning, and only share model parameters with the central server, known as distributed learning framework, becomes popular, which saves communication resources and provides privacy protection. Then, it is introduced as a beneficial scheme to enable training ML models for both Al-enabled services and the DL-TOC scheme in a distributed manner. Specifically, we propose three distributed learning variants to address the heterogeneity of Metaverse networks from different aspects. Next, a case study is proposed to demonstrate how the proposed distributed learning frameworks can assist attention-aware communication for Metaverse. Finally, we identify the challenges and some promising research directions.
Deep Learning - Powered Radio Frequency Fingerprint Identification: Methodology and Case Study

Radio frequency fingerprint identification (RFFI) is an authentication technique that identifies wireless devices by analyzing the characteristics of the received physical layer signals. In recent years, RFFI has been significantly enhanced by deep learning. A neural network (NN) is often leveraged to predict device identity. As a data-driven approach, deep learning requires the collection of large amounts of data for NN training. In addition, the RFFI system should be evaluated on datasets collected under various conditions to assess the system's robustness. However, only a few RFFI datasets are publicly available, and there are no clear guidelines for building an RFFI testbed for data collection. This article presents a tutorial to build both closed-set and openset RFFI systems. A LoRa-RFFI testbed is created as a case study and the implementation details are described in depth. The LoRa-RFFI testbed involves 60 commercial-off-the-shelf (COTS) LoRa development boards as devices to be identified, and a USRP N210 software-defined radio (SDR) platform for physical layer signal reception. Experiments are carried out using the implemented LoRa-RFFI testbed, and the collected datasets are made publicly available online. It is anticipated that this work can aid the research community in constructing RFFI testbeds and facilitate the development of RFFI research.
Online Semi-Supervised Classification on Multilabel Evolving High-Dimensional Text Streams

The multilabel learning task aims to predict the associated multiple classes of a given example simultaneously. Such task becomes more challenging when data arrives in stream since it requires concept drift adaptative, robust, and fast algorithm. In this article, we present an online semi-supervised classification algorithm (OSMTS) for multilabel text streams. By leveraging a few labeled instances, OSMTS dynamically maintains the subspace of terms for each label with a set of evolving micro-clusters. For multilabel classification, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest micro-clusters are employed for prediction by using a nonparametric Dirichlet model. To handle the gradual concept drift in term space, the triangular time function is adopted to calculate the difference between term arriving time and cluster life span. Whereas, abrupt concept drift is dealt by considering two procedures: 1) deleting outdated micro-cluster by exploiting the exponential decay function and 2) creating new micro-clusters by adopting the Chinese restaurant process based on the Dirichlet process. The conducted experimental study provides a comparison with 12 state-of-the-art algorithms on nine datasets in terms of classification performance, runtime, and memory consumption.
Monte Carlo Planning in Hybrid Belief POMDPs

Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online partially observable Markov decision processes (POMDPs) solvers do not support hybrid beliefs directly. In particular, these solvers do not address the added computational burden due to an increasing number of hypotheses with the planning horizon, which can grow exponentially. As part of this work, we present a novel algorithm, Hybrid Belief Monte Carlo Planning (HB-MCP) that utilizes the Monte Carlo Tree Search (MCTS) algorithm to solve a POMDP while maintaining a hybrid belief. We illustrate how the upper confidence bound (UCB) exploration bonus can be leveraged to guide the growth of hypotheses trees alongside the belief trees. We then evaluate our approach in highly aliased simulated environments where unresolved data association leads to multi-modal belief hypotheses.
Fog-Assisted Blockchain Radio Access Network for Web3

Recently, Web3 has received extensive attention as the next generation Internet and is in rapid development, which can highly integrate the physical world with the digital world. The blockchain that can build trust and fog computing with distributed computing mode can fit well with the decentralization idea of Web3. The combination of the two can provide an effective solution for the implementation of Web3. This article proposes a new network architecture, fog-assisted blockchain radio access network (FogBC-RAN), which supports stable and mutual trust connections on a global scale, and can smoothly coordinate multi-dimensional resources to support new services such as holographic communication, digital twin, and sensory interconnection. With the interconnection of ten thousand chains in the future Web3, the cross-chain information transfer process based on Polkadot for charging sharing is presented, which can realize flexible cross-chain transactions. Meanwhile, for the mainstream computing-intensive applications of Web3, considering the latency, energy consumption and transaction cost, a computing offloading strategy based on matching game is proposed to minimize the system cost. Finally, the simulation results demonstrate the effectiveness of the offloading mechanism proposed in FogBC-RAN.
MEC-DA: Memory-Efficient Collaborative Domain Adaptation for Mobile Edge Devices

It is prevalent for a mobile edge device to conduct local inference using a compact machine learning model, which achieves lower latency and less compromise of data privacy as compared to cloud-based inference. To work in a new environment, the compact model needs to be adapted to the target data from the environment so as to maintain a high inference accuracy. However, directly applying domain adaptation to the compact model leads to a low inference accuracy. Hence, a scheme called memory-efficient collaborative domain adaptation (MEC-DA) is developed in this paper to boost the compact model's inference accuracy on the target data while preserving data privacy. It first deploys a large model to the mobile edge devices where domain adaptation is conducted to adapt the large model to the target data. This process requires training of the large model, which causes high memory consumption. A new method called lite residual hypothesis transfer (LRHT) is thus designed to achieve memory-efficient domain adaptation. The knowledge of the large model is then transferred to the compact model via knowledge distillation. To prevent the compact model from forgetting the knowledge of the source data, a collaborative knowledge distillation (Co-KD) method is developed to unify the source data on the server and the target data on an edge device to update the compact model. MEC-DA can protect data privacy and handle user mobility properly via secure aggregation and user selection, respectively. Extensive experiments on several tasks of object recognition show that MEC-DA improves the inference accuracy by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$12.5\%$</tex-math></inline-formula> , as compared to the state-of-the-art schemes.
TeeBench: Seamless Benchmarking in Trusted Execution Environments

Trusted Execution Environments (TEEs) have enabled building secure systems that operate on untrusted machines. However, TEEs' architecture questions previous performance findings. The existing relational algorithms have been designed for traditional CPUs. Prior work has shown that these algorithms underperform in TEEs and, in most cases, can not be easily reused. Moreover, they frequently used benchmarks pertinent to CPUs and ignored TEE-specific metrics essential to understand the performance differences. Therefore, there is a need for a fair benchmarking approach for TEE algorithms.
Sampling over Union of Joins

Data scientists often draw on multiple relational data sources for analysis. A standard assumption in learning and approximate query answering is that the data is a uniform and independent sample of the underlying distribution. To avoid the cost of join and union, given a set of joins, we study the problem of obtaining a random sample from the union of joins without performing the full join and union. We present a general framework for random sampling over the set union of chain, acyclic, and cyclic joins, with sample uniformity and independence guarantees. We study the novel problem of union of joins size evaluation and propose two approximation methods based on histograms of columns and random walks on data. We propose an online union sampling framework that initializes with cheap-to-calculate parameter approximations and refines them on the fly during sampling. We evaluate our framework on workloads from the TPC-H benchmark and explore the trade-off of the accuracy of union approximation and sampling efficiency.
PyNKDV: An Efficient Network Kernel Density Visualization Library for Geospatial Analytic Systems

Network kernel density visualization (NKDV) is an important tool for many application domains, including criminology and transportation science. However, all existing software tools, e.g., SANET (a plug-in for QGIS and ArcGIS) and spNetwork (an R package), adopt the naïve implementation of NKDV, which does not scale to large-scale location datasets and high-resolution sizes. To overcome this issue, we develop the first python library, called PyNKDV, which adopts our complexity-reduced solution and its parallel implementation to significantly improve the efficiency for generating NKDV. Moreover, PyNKDV is also user friendly (with four lines of python code) and can support commonly used geospatial analytic systems (e.g., QGIS and ArcGIS). In this demonstration, we will use three large-scale location datasets (up to 7.71 million data points), provide different python scripts (in the Jupyter Notebook), and install existing software tools (i.e., SANET and spNetwork) for participants to (1) explore different functionalities of our PyNKDV library and (2) compare its practical efficiency with existing software tools.
Digital technology usage and entrepreneurship in Africa

In relation to actual digital usage, empirical research is limited on whether digital technology promotes entrepreneurial development in African economies. This study, therefore, investigates the impact of digital technology usage on entrepreneurship in selected African countries. This study contributes to the literature by using 20 selected African countries over the period of 2012–2016. Unlike existing studies, this paper differentiates between the impact of business, government and individual usage of digital technology by employing the 6th, 7th and 8th pillars of the Network Readiness Index. The study employs panel least square and instrumental variable (IV) estimating techniques and panel corrected standard errors (PCSEs) to analyze the data. The results show that digital technology usage has a positive and significant impact on entrepreneurship in Africa. Further analysis shows that individual usage and business usage have a positive and significant impact on business creation. Social media network usage has a positive and significant impact on entrepreneurial development. The study suggests that the growth of entrepreneurship can be enhanced through an ICT usage enabling environment. Individual usage and business usage including social media usage of digitalization are the main drivers of entrepreneurial development in the region.
Proof Systems for Super- Strict Implication

Abstract This paper studies proof systems for the logics of super-strict implication $$\textsf{ST2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ST</mml:mi> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> – $$\textsf{ST5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>ST</mml:mi> <mml:mn>5</mml:mn> </mml:mrow> </mml:math> , which correspond to C.I. Lewis’ systems $$\textsf{S2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> – $$\textsf{S5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mn>5</mml:mn> </mml:mrow> </mml:math> freed of paradoxes of strict implication. First, Hilbert-style axiomatic systems are introduced and shown to be sound and complete by simulating $$\textsf{STn}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>STn</mml:mi> </mml:math> in $$\textsf{Sn}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Sn</mml:mi> </mml:math> and backsimulating $$\textsf{Sn}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Sn</mml:mi> </mml:math> in $$\textsf{STn}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>STn</mml:mi> </mml:math> , respectively (for $${\textsf{n}} =2, \ldots , 5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mn>5</mml:mn> </mml:mrow> </mml:math> ). Next, $$\textsf{G3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> -style labelled sequent calculi are investigated. It is shown that these calculi have the good structural properties that are distinctive of $$\textsf{G3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> -style calculi, that they are sound and complete, and it is shown that the proof search for $$\mathsf {G3.ST2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mn>3</mml:mn> <mml:mo>.</mml:mo> <mml:mi>ST</mml:mi> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> is terminating and therefore the logic is decidable.