Reviewerly/exHarmony huggingface dataset:
An Efficient Specific Emitter Identification Method Based on Complex-Valued Neural Networks and Network Compression

Specific emitter identification (SEI) is a promising technology to discriminate the individual emitter and enhance the security of various wireless communication systems. SEI is generally based on radio frequency fingerprinting (RFF) originated from the imperfection of emitter's hardware, which is difficult to forge. SEI is generally modeled as a classification task and deep learning (DL), which exhibits powerful classification capability, has been introduced into SEI for better identification performance. In the recent years, a novel DL model, named as complex-valued neural network (CVNN), has been applied into SEI methods for directly processing complex baseband signal and improving identification performance, but it also brings high model complexity and large model size, which is not conducive to the deployment of SEI, especially in Internet-of-things (IoT) scenarios. Thus, we propose an efficient SEI method based on CVNN and network compression, and the former is for performance improvement, while the latter is to reduce model complexity and size with ensuring satisfactory identification performance. Simulation results demonstrated that our proposed CVNN-based SEI method is superior to the existing DL-based methods in both identification performance and convergence speed, and the identification accuracy of CVNN can reach up to nearly 100% at high signal-to-noise ratios (SNRs). In addition, SlimCVNN just has 10% ~ 30% model sizes of the basic CVNN, and its computing complexity has different degrees of decline at different SNRs; there is almost no performance gap between SlimCVNN and CVNN. These results demonstrated the feasibility and potential of CVNN and model compression.
Distilling Object Detectors via Decoupled Features

Knowledge distillation is a widely used paradigm for inheriting information from a complicated teacher network to a compact student network and maintaining the strong performance. Different from image classification, object detectors are much more sophisticated with multiple loss functions in which features that semantic information rely on are tangled. In this paper, we point out that the information of features derived from regions excluding objects are also essential for distilling the student detector, which is usually ignored in existing approaches. In addition, we elucidate that features from different regions should be assigned with different importance during distillation. To this end, we present a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector. Specifically, two levels of decoupled features will be processed for embedding useful information into the student, i.e., decoupled features from neck and decoupled proposals from classification head. Extensive experiments on various detectors with different backbones show that the proposed DeFeat is able to surpass the state-of-the-art distillation methods for object detection. For example, DeFeat improves ResNet50 based Faster R-CNN from 37.4% to 40.9% mAP, and improves ResNet50 based RetinaNet from 36.5% to 39.7% mAP on COCO benchmark. Code will be released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,2</sup> .
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction

In this paper, we propose HF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger flow reconstruction errors. More importantly, conditioned on the reconstructed flows, we then employ a Conditional Variational Autoencoder (CVAE), which captures the high correlation between video frame and optical flow, to predict the next frame given several previous frames. By CVAE, the quality of flow reconstruction essentially influences that of frame prediction. Therefore, poorly reconstructed optical flows of abnormal events further deteriorate the quality of the final predicted future frame, making the anomalies more detectable. Experimental results demonstrate the effectiveness of the proposed method. Code is available at https://github.com/LiUzHiAn/hf2vad.
Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey

Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.
NeRFactor

We address the problem of recovering the shape and spatially-varying reflectance of an object from multi-view images (and their camera poses) of an object illuminated by one unknown lighting condition. This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties. The key to our approach, which we call Neural Radiance Factorization (NeRFactor), is to distill the volumetric geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020] representation of the object into a surface representation and then jointly refine the geometry while solving for the spatially-varying reflectance and environment lighting. Specifically, NeRFactor recovers 3D neural fields of surface normals, light visibility, albedo, and Bidirectional Reflectance Distribution Functions (BRDFs) without any supervision, using only a re-rendering loss, simple smoothness priors, and a data-driven BRDF prior learned from real-world BRDF measurements. By explicitly modeling light visibility, NeRFactor is able to separate shadows from albedo and synthesize realistic soft or hard shadows under arbitrary lighting conditions. NeRFactor is able to recover convincing 3D models for free-viewpoint relighting in this challenging and underconstrained capture setup for both synthetic and real scenes. Qualitative and quantitative experiments show that NeRFactor outperforms classic and deep learning-based state of the art across various tasks. Our videos, code, and data are available at people.csail.mit.edu/xiuming/projects/nerfactor/.
Mixed Reality (MR) for Generation Z in Cultural Heritage Tourism Towards Metaverse

Abstract Generation Z is transforming tourism by demanding the cocreation of transformative experiences. Cultural heritage professionals must comprehend the needs and desires of the Gen Z to support the cocreation of transformative experiences. This study analysed the role of Mixed Reality (MR) from the perspective of Gen Z guests through 18 semi-structured interviews and inductive qualitative research. Participants believe that cultural heritage experiences can benefit from immersive technology. Technology supports cocreation of experiences between developers, service providers, DMOs, and consumers. Cultural heritage sites, as a key element of tourism destinations, should consider how to use MR to enhance consumer experiences. Participants express the opinion that cultural heritage sites and tourism destinations require considerable modernisation to create transformative experiences. Metaverse in tourism and cultural heritage sites will undoubtedly support Gen Z to cocreate transformational experiences.
VidTr: Video Transformer Without Convolutions

We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal information via stacked attentions and provide better performance with higher efficiency. We first introduce the vanilla video transformer and show that transformer module is able to perform spatio-temporal modeling from raw pixels, but with heavy memory usage. We then present VidTr which reduces the memory cost by 3.3× while keeping the same performance. To further optimize the model, we propose the standard deviation based topK pooling for attention (pooltopK_std), which reduces the computation by dropping non-informative features along temporal dimension. VidTr achieves state-of-the-art performance on five commonly used datasets with lower computational requirement, showing both the efficiency and effectiveness of our design. Finally, error analysis and visualization show that VidTr is especially good at predicting actions that require long-term temporal reasoning.
Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network

The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sentence representation module or a reasoning model. It proposes a joint optimization method based on multi-layer semantics called the Semantic Fusion Deep Matching Network (SCF-DMN) to explore the influence of sentence representation and reasoning models on reasoning performance. Experiments on text entailment recognition tasks show that the joint optimization representation reasoning method performs better than the existing methods. The sentence representation optimization module and the improved optimization reasoning model can promote reasoning performance when used individually. However, the optimization of the reasoning model has a more significant impact on the final reasoning results. Furthermore, after comparing each module's performance, there is a mutual constraint between the sentence representation module and the reasoning model. This condition restricts overall performance, resulting in no linear superposition of reasoning performance. Overall, by comparing the proposed methods with other existed methods that are tested using the same database, the proposed method solves the lack of in-depth interactive information and interpretability in the model design which would be inspirational for future improving and studying of natural language reasoning.
Efficient attribute-based encryption with attribute revocation for assured data deletion

Cloud storage allows customers to store their data on remote cloud servers. With the advantage of reducing the burden of data management and storage, an increasing number of users prefer to store their data on the cloud. While secure data deletion is a crucial, it is a challenging issue in cloud storage. Logically deleted data may be easily exposed to un-authorized users in the cloud storage scenario thanks to its salient features such as multi-tenancy, virtualization and elasticity. Moreover, cloud servers might not delete customers’ data as instructed for hidden business interest. Hence, assured deletion is highly sought after. It helps preserve cloud users’ data privacy and is a necessary component of data retention regulations in cloud storage. In this paper, we first investigate the goals of assured data deletion and formalize its security model.Then, we propose a key-policy attribute-based encryption scheme for assured deletion (AD-KP-ABE) of cloud data. Our construction makes use of the attribute revocation cryptographic primitive and Merkle Hash Tree to achieve fine-grained access control and verifiable data deletion. The proposed AD-KP-ABE enjoys desirable properties such as no secret key update, partial ciphertext update and assured data deletion. The detailed security proof and implementation results demonstrate the security and practicality of our proposal.
Postural stability predicts the likelihood of cybersickness in active HMD-based virtual reality

Cybersickness is common during virtual reality experiences with head-mounted displays (HMDs). Previously it has been shown that individual differences in postural activity can predict which people are more likely to experience visually-induced motion sickness. This study examined whether such predictions also generalise to the cybersickness experienced during active HMD-based virtual reality. Multisensory stimulation was generated by having participants continuously turn their heads from left to right while viewing the self-motion simulations. Real-time head tracking was then used to create ecological ('compensated') and non-ecological ('inversely compensated') head-and-display motion conditions. Ten (out of 20) participants reported feeling sick after being exposed to these self-motion simulations. Cybersickness did not differ significantly between the two compensation conditions. However, individual differences in spontaneous postural instability when standing quietly were found to predict the likelihood of subsequently experiencing cybersickness. These findings support recent proposals that postural measures can help diagnose who will benefit the most/least from HMD-based virtual reality.
Distance based parameter adaptation for Success-History based Differential Evolution

This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC2015 and CEC2017) benchmark sets in 10, 30, 50, and 100 dimensions for all SHADE, L-SHADE (SHADE with linear decrease of population size), and jSO algorithms. The proposed distance based parameter adaptation is designed to address the premature convergence of SHADE–based algorithms in higher dimensional search spaces to maintain a longer exploration phase. This design effectiveness is supported by presenting a population clustering analysis, along with a population diversity measure. Also, the new distance based algorithm versions (Db_SHADE, DbL_SHADE, and DISH) have obtained significantly better optimization results than their canonical counterparts (SHADE, L_SHADE, and jSO) in 30, 50, and 100 dimensional functions.
Privacy-Preserving Cloud-Based Road Condition Monitoring With Source Authentication in VANETs

The connected vehicular ad hoc network (VANET) and cloud computing technology allows entities in VANET to enjoy the advantageous storage and computing services offered by some cloud service provider. However, the advantages do not come free, since their combination brings many new security and privacy requirements for VANET applications. In this paper, we investigate the cloud-based road condition monitoring (RCoM) scenario, where the authority needs to monitor real-time road conditions with the help of a cloud server so that it could make sound responses to emergency cases timely. When some bad road condition is detected, e.g., some geologic hazard or accident happens, vehicles on site are able to report such information to a cloud server engaged by the authority. We focus on addressing three key issues in RCoM. First, the vehicles have to be authorized by some roadside unit before generating a road condition report in the domain and uploading it to the cloud server. Second, to guarantee the privacy against the cloud server, the road condition information should be reported in ciphertext format, which requires that the cloud server should be able to distinguish the reported data from different vehicles in ciphertext format for the same place without compromising their confidentiality. Third, the cloud server and authority should be able to validate the report source, i.e., to check whether the road conditions are reported by legitimate vehicles. To address these issues, we present an efficient RCoM scheme, analyze its efficiency theoretically, and demonstrate the practicality through experiments.
An Overview on Concept Drift Learning

Concept drift techniques aim at learning patterns from data streams that may change over time. Although such behavior is not usually expected in controlled environments, real-world scenarios can face changes in the data, such as new classes, clusters, and features. Traditional classifiers can be easily fooled in such situations, resulting in poor performances. Common concept drift domains include recommendation systems, energy consumption, artificial intelligence systems with dynamic environment interaction, and biomedical signal analysis (e.g., neurogenerative diseases). In this paper, we surveyed several works that deal with concept drift, as well as we presented a comprehensive study of public synthetic and real datasets that can be used to cope with such a problem. In addition, we considered a review of different types of drifts and approaches to handling such changes in the data. We considered different learners employed in classification tasks and the use of drift detection mechanisms, among other characteristics.
Decentralized Big Data Auditing for Smart City Environments Leveraging Blockchain Technology

The idea of big data has gained extensive attention from governments and academia all over the world. It is especially relevant for the establishment of a smart city environment combining complex heterogeneous data with data analytics and artificial intelligence (AI) technology. Big data is generated from many facilities and sensor networks in smart cities and often streamed and stored in the cloud storage platform. Ensuring the integrity and subsequent auditability of such big data is essential for the performance of AI-driven data analysis. Recent years has witnessed the emergence of many big data auditing schemes that are often characterized by third party auditors (TPAs). However, the TPA is a centralized entity, which is vulnerable to many security threats from both inside and outside the cloud. To avoid this centralized dependency, we propose a decentralized big data auditing scheme for smart city environments featuring blockchain capabilities supporting improved reliability and stability without the need for a centralized TPA in auditing schemes. To support this, we have designed an optimized blockchain instantiation and conducted a comprehensive comparison between the existing schemes and the proposed scheme through both theoretical analysis and experimental evaluation. The comparison shows that lower communication and computation costs are incurred with our scheme than with existing schemes.
Challenges of Doing Business in Africa: A Systematic Review

This paper provides a systematic review of challenges to doing business in Africa. It complements the extant literature by answering two critical questions: what are the linkages between the ease of doing business and economic development; and what are the challenges to doing business in Africa? In providing answers to these questions, the nexus between the ease of doing business and economic development is discussed in six main strands, namely: wealth creation and sharing; opportunities of employment; balanced regional and economic development; Gross Domestic Product (GDP) and GDP per capita; standards of living and exports. Moreover, challenges to doing business are articulated along the following lines: (i) issues related to the cost of starting a business and doing business; (ii) shortage of energy and electricity; (iii) lack of access to finance; and (v) high taxes and low cross-border trade.
Blockchain authentication of network applications: Taxonomy, classification, capabilities, open challenges, motivations, recommendations and future directions

As the first and last line of defence in many cases, authentication is a crucial part of a system. With authentication, any unauthorised access to the system can be prevented. This work maps the research landscape through two means. The first is a comprehensive taxonomy of blockchain technology in authentication over networking. The second is identification of different types of authentication systems under various platforms that use blockchain technology. This work also provides useful and classified information which can enhance the understanding of how various authentication systems can be combined with blockchain technology. In addition, problems associated with this blockchain technology and proposed solutions are surveyed to fulfil the requirements of the network applications. Moreover, this work highlights the importance, capabilities, motivations and challenges of blockchain technology with distinct applications in various fields. Finally, recommendations and future research directions are discussed.
Forensic-chain: Blockchain based digital forensics chain of custody with PoC in Hyperledger Composer

Advancements in Information Technology landscape over the past two decades have made the collection, preservation, and analysis of digital evidence an extremely important tool for solving cybercrimes and preparing court cases. Digital evidence plays an important role in cybercrime investigation, as it is used to link individuals with criminal activities. Thus it is of utmost importance to guarantee integrity, authenticity, and auditability of digital evidence as it moves along different levels of hierarchy in the chain of custody during cybercrime investigation. Modern day technology is more advanced in terms of portability and power. A huge amount of information is generated by billions of devices connected to the internet that needs to be stored and accessed, thus posing great challenges in maintaining the integrity and authenticity of digital evidence for its admissibility in the court of law. Handling digital evidences poses unique challenges because of the fact they are latent, volatile, fragile, can cross jurisdictional borders quickly and easily and in many cases can be time/machine dependent too. Thus guaranteeing the authenticity and legality of processes and procedures used to gather and transfer the evidence in a digital society is a real challenge. Blockchain technology's capability of enabling comprehensive view of transactions (events/actions) back to origination provides enormous promise for the forensic community. In this research we proposed Forensic-Chain: A Blockchain based Digital Forensics Chain of Custody, bringing integrity and tamper resistance to digital forensics chain of custody. We also provided Proof of Concept in Hyperledger Composer and evaluated its performance.