Reviewerly/exHarmony huggingface dataset:
Research on Quadrotor UAV Path Planning Optimization Based on Multi-source Information Fusion Technology of Ant Colony Optimization Algorithm

In order to solve the problem of the lack of perceived environmental capability of the traditional quadrotor UAV(Unmanned Aerial Vehicle), a multi-sensor information fusion quadrotor UAV path planning technique based on Ant Colony Optimization Algorithm (ACO) algorithm is proposed. The path planning problem is transformed into a penalty function optimization, and global optimization is sought through the ACO algorithm. For the adaptive function including the obstacle limit and the length of the path information, the ACO algorithm finally obtains the shortest path. Through the integration of design ideas, sensor data processing data fusion and quadrotor UAV control and other key technologies, the quadrotor UAV responded in real time according to environmental changes, realizing the self-control of the quadrotor UAV. The experimental analysis shows that the multi-sensor information fusion control system used in this paper can sense the changes of the surrounding environment in real time, adjust the path planning of the quadrotor UAV in real time, and effectively improve the reliability and work efficiency of the quadrotor UAV.
A Model for Municipality Buildings Renting Auction on Algorand Blockchain

Blockchain is one of the vital supportive technologies of smart city services. Thanks to its characteristics, blockchain impacts almost all smart city services. Algorand network started working in 2019. Its speed of transactions and low cost makes it the platform of choice for a smart city blockchain implementation. This paper proposes a model for blockchain auction for renting real estate owned by the municipality. This service is part of the overall smart city services and relies heavily on other smart city governance services like identity information management, land register, and others. Implementing such a system simplifies the process of land and real estate management in the smart city making it more transparent. It lowers the costs and increases the income municipality earns from renting the asset.
Serverless Cloud Computing: State of the Art and Challenges

The serverless model represents a paradigm shift in the cloud: as opposed to traditional cloud computing service models, serverless customers do not reserve hardware resources. The execution of their code is event-driven (HTTP requests, cron jobs, etc.) and billing is based on actual resource usage. In return, the responsibility of resource allocation and task placement lies on the provider. While serverless in the wild is mainly advertised as a public cloud offering, solutions are actively developed and backed by solid actors in the industry to allow the development of private cloud serverless platforms. The first generation of serverless offers, “Function as a Service” (FaaS), has severe shortcomings that can offset the potential benefits for both customers and providers—in terms of spendings and reliability on the customer side, and in terms of resources multiplexing on the provider side. Circumventing these flaws would allow considerable savings in money and energy for both providers and tenants. This chapter aims at establishing a comprehensive tour of these limitations, and presenting state-of-the-art studies to mitigate weaknesses that are currently holding serverless back from becoming the de facto cloud computing model. The main challenges related to the deployment of such a cloud platform are discussed and some perspectives for future directions in research are given.
Rouge and Robot: The Disruptive Feminine

In future worlds, robots will participate and collaborate in human activities, and deeply interact with humans through personal, intimate and immediate actions around and even on the human body. Taking feminine rituals of grooming the body and highlighting body attributes as a focus, we investigate the intersection of biological and machine moving bodies, exploring vulnerable moments of touch. Introducing a robot into an entrenched cultural act that references the universal femaleUniversal female is a disruptive, critical tactic for rethinking dominant approaches to human–robot collaboration. Through a Deleuzian analysis of our video artwork code_red (2021), we propose an alternative theoretical interpretation of the interaction between an industrial roboticIndustrial robot arm and a female figure. We then turn to a broader consideration of how this particular analysis ties back into current thinking in collaborative, industrial and social roboticsSocial robotics, through a discussion of concepts of breaching the intimate zone, resonance, and micro-gestures in human–robot relationsHuman-robot relations.
Data, Site, Materials: Robotics and Digital Fabrication Within Installation Art

This chapter describes two practice-based research projects that render self-tracking data as installation artworks: NAVSTAR (2020) and Dendro/Volume (2020). For both works, personal data is collected and rendered as artworks within white cube exhibition spaces. These two artworks relate to practices of data physicalisation, where robotic fabricationRobot fabrication is used to translate data into physical artefacts. However, both NAVSTAR and Dendro/Volume depart from more conventional approaches to data visualisationVisualisation that focus on creating visual patterns and generating actionable insights into health dataHealth data. Instead, I use the term data installation to propose a situated approach to rendering health dataHealth data that draws attention to the relation between materialsMaterials, site, and data. As I see it, these factors are inherent, though largely underacknowledged, to data physicalisation. Expanding from this position, I suggest that how materialsMaterials and data relate to specific white cube and non-gallery sites warrants closer attention and offers expanded approaches to data physicalisation practice.
Application-Oriented Anonymization Framework for Social Network Datasets and IoT Environments

Everyday usage of online Internet services and the recent rise of the Internet of Things (IoT) cause the collection of a massive amount of data, including personal and sensitive information. Anonymization enables providers to share their datasets and preserve the privacy of individuals at the same time. It is a valuable tool for preserving individuals’ privacy in social network datasets and IoT environments. Researchers recently focused on developing a universal and robust anonymization method to keep privacy and preserve almost all data utility. Many various anonymization methods have been developed; however, none meet the requirements perfectly. The application-oriented anonymization has been recently discussed only for relational datasets. This paper introduces the framework for application-oriented anonymization for social network datasets and IoT environments. In our framework, it is not necessary to preserve all data utility but only the data utility specified by the data recipient. While requesting the anonymized social network data, the data receiver can specify the metrics that should be kept as close to the original graph as possible. While requesting anonymized data from the cloud in an IoT environment, the data receiver can prioritize attributes. It enables the data recipient to customize the anonymized data and the data provider to control the computing over their dataset. Moreover, we discuss the vulnerability of application-oriented anonymization to composition attacks.
Activity Detection from Encrypted Remote Desktop Protocol Traffic

An increasing amount of Internet traffic has its content encrypted. We address the question of whether it is possible to predict the activities taking place over an encrypted channel, in particular Microsoft’s Remote Desktop Protocol. We show that the presence of five typical activities can be detected with precision greater than 97% and recall greater than 94% in 30-s traces. We also show that the design of the protocol exposes fine-grained actions such as keystrokes and mouse movements which may be leveraged to reveal properties such as lengths of passwords.
Unbounded Revocable Decentralized Multi-Authority Attribute-Based Encryption Supporting Non-monotone Access Structures

Ciphertext-policy attribute-based encryption (CP-ABE) is a cryptographic technology that enforces an access control mechanism over encrypted data by specifying an access policy with encrypted data and introducing an attribute authority (AA) that manages user’s attributes. A CP-ABE with multiple attribute authorities and no central authority, a decentralized multi-authority CP-ABE (DMA-CP-ABE), can achieve more realistic attribute management than CP-ABE with a single authority. However, DMA-CP-ABE has an attribute revocation problem. As a different problem, the size of the public parameters of each AA is proportional to the size of the attribute universe managed by each AA. Moreover, since most existing DMA-CP-ABE schemes support only monotonic access structures, the size of the access policy specified in the ciphertext becomes large when an encryptor specifies a non-monotonic access policy in the ciphertext. Therefore, the DMA-CP-ABE that supports the attribute revocation, constant-size public and secret parameters (a.k.a unboundedness), and non-monotonic access structure is required. However, to the best of our knowledge, no one has proposed it yet. In this paper, we propose a new unbounded revocable DMA-CP-ABE (UR-DMA-CP-ABE) that supports a non-monotone access structure. We prove that our scheme achieves adaptively payload-hiding against chosen-plaintext attacks under the decisional linear (DLIN) assumption.
Efficient Distributed Keys Generation of Threshold Paillier Cryptosystem

Paillier cryptosystem is the building block of many cryptographic protocols. The secure keys generation without a trusted dealer is an essential scheme in a distributed system since the dealer may be under the threat of a single point of attack. We present a distributed keys generation scheme of the threshold Paillier’s encryption system using efficient multiparty computation. Our scheme consists of two offline and online phases where the offline phase can be implemented at any time well in advance of the computation phase. Both the public and the private keys are computed and verified in the presence of at least $$n\ge t+1$$ participants in the actual online phase. This gives an improvement on the previous studies where at least a number of $$2t+1$$ parties are required for the keys generation. Furthermore, the private communication complexity of our scheme is $$O(n^2)$$ field elements with no broadcast communication overhead which improves on the total communication complexity of [21]. Our protocol maintains the security against a static active adversary corrupting up to t participants with the small probability of error using message authentication codes. Also, the computed keys are t-private, i.e., any subset of equal or less than t parties cannot gain any information about the factorization of N.
Lightweight Permutation-Based Cryptography for the Ultra-Low-Power Internet of Things

The U.S. National Institute of Standards and Technology is currently undertaking a process to evaluate and eventually standardize one or more “lightweight” algorithms for authenticated encryption and hashing that are suitable for resource-restricted devices. In addition to security, this process takes into account the efficiency of the candidate algorithms in various hardware environments (e.g. FPGAs, ASICs) and software platforms (e.g. 8, 16, 32-bit microcontrollers). However, while there exist numerous detailed benchmarking results for 8-bit AVR and 32-bit ARM/RISC-V/ESP32 microcontrollers, relatively little is known about the candidates’ efficiency on 16-bit platforms. In order to fill this gap, we present a performance evaluation of the final-round candidates Ascon, Schwaemm, TinyJambu, and Xoodyak on the MSP430 series of ultra-low-power 16-bit microcontrollers from Texas Instruments. All four algorithms were explicitly designed to achieve high performance in software and have further in common that the underlying primitive is a permutation. We discuss how these permutations can be implemented efficiently in Assembly language and analyze how basic design decisions impact their execution time on the MSP430 architecture. Our results show that, overall, Schwaemm is the fastest algorithm across various lengths of data and associated data, respectively. Xoodyak has benefits when a large amount of associated data is to be authenticated, whereas TinyJambu is very efficient for the authentication of short messages.
Superpoly Recovery of Grain-128AEAD Using Division Property

The cube attack is a powerful cryptanalytic technique against stream ciphers. Cube attacks exploit the algebraic properties of symmetric ciphers by recovering a particular polynomial, the superpoly, and subsequently, the secret key. Nowadays, the division property-based approach has become very popular, allowing us to recover the exact superpoly cleverly. However, the computational cost to recover the superpoly becomes prohibitive as the number of rounds of the cipher increases. In this paper, we study NIST lightweight 3rd round candidate Grain-128AEAD in the light of division property-based cube attacks. We first introduce some good cubes of dimensions $$91,\,92,\,93,\,94$$ , and then we construct an algorithm to find conditional key bits for the cubes of Grain-128AEAD mentioned above. Next, we apply three-subset division property without unknown subset-based cube attacks to recover exact superpolies for $$192,\,193,\,194,\,195$$ -round Grain-128AEAD in the weak-key setting, which are the longest till now. Moreover, we are able to find good cubes that are used to build distinguishers of Grain-128AEAD in the weak-key setting. In particular, we show that Grain-128AEAD can be distinguished from a random source up to 193-rounds in the weak-key setting, which is the best zero-sum distinguisher of Grain-128AEAD till now using division property-based cube attacks.
Easy-ABE: An Easy Ciphertext-Policy Attribute-Based Encryption

Attribute-Based Encryption is widely recognized as a leap forward in the field of public key encryption. It allows to enforce an access control on encrypted data. Decryption time in ABE schemes can be long depending on the number of attributes and pairing operations. This drawback hinders their adoption on a broader scale. In this paper, we propose a non-monotone CP-ABE scheme that has no restrictions on the size of attribute sets and policies, allows fast decryption and is adaptively secure under the CBDH-3 assumption. To achieve this, we approached the problem from a new angle, namely using a set membership relation for access structure. We have implemented our scheme using the Java Pairing-Based Cryptography Library (JPBC) and the source code is available on GitHub.
Legal Issues In Distributed Ledger Technology (DLT) &amp; Blockchain In Brunei Darussalam

Blockchain and Distributed Ledger Technology (DLT) have changed the regulatory landscape globally. Regulators are working hard to create conducive environment for the deployment of DLT and the blockchain. However, robust growth of technology does not come with speedy regulatory changes. This includes reviewing and adapting regulatory requirements or procedures that may unintentionally inhibit innovation or render them non-viable due to lacuna in law. This article is an attempt to analyse the DLT and blockchain from legal perspectives in Brunei. The issues raised in the article warrant considerable merits of law makers’ attention. The article concludes with several suggestions and recommendations.The paper employs library research with main references to the policy papers, Act and legislations. Where necessary, the paper makes reference to other countries for comparative purposes. The paper includes several suggestions and recommendations for authority’s consideration. The findings suggest that despite of the existing enabling provisions in Brunei, there is a need to have acomprehensive regulations for blockchain and DLT due to excessive big data and other liabilities issues involves.
Precision-Mixed and Weight-Average Ensemble: Online Knowledge Distillation for Quantization Convolutional Neural Networks

Lightweight models with high accuracy is critical for edge intelligence. Although the Knowledge Distillation (KD) has been successfully applied to reduce the accuracy loss of quantized neural networks, especially for resource-constrained edge devices, the process of pre-training complex high-precision teacher networks in KD however, will bring huge training overhead. Recently proposed online distillation frameworks offer a good solution for teacher-free distillation, but the regularization effect and simple average aggregation of KD further weaken the representation capability of quantized models that have been reconstructed. In this work, we propose Precision-Mixed and Weight-Average Ensemble (PMWAE) consisting of multiple group members and a group leader. PMWAE provides additional knowledge by changing the bit-precision of the activation and generates aggregated weights for each member in group by attention-based mechanism. The ensemble knowledge is further passed to the group leader to obtain the final model. Extensive experiments on the CIFAR-10/100 and ImageNet-1K datasets show that our method outperforms the existing state-of-the-art methods, both on standard convolutions and depth-wise separable convolutions.
Text Semantic Communication Systems with Sentence-Level Semantic Fidelity

Semantic communication systems have been proposed for efficient text transmissions in recent years. However, most existing methods mainly focus on word-level recovery, which fails to reflect the semantic fidelity of the model. By leveraging the sentence-level semantic information, a semantic communication system architecture is proposed within the framework of deep learning based joint source-channel coding. Then, in order to evaluate the semantic fidelity of the system, a new metric of Semantic Similarity is proposed, which is more sensitive to semantic differences as compared to existing evaluation metrics, e.g., bilingual evaluation understudy. For guaranteeing the consistency between model training and performance evaluation, the proposed new metric is then incorporated into the objective function, based on which end-to-end performance optimization is performed. Extensive simulation results on European Parliament dataset demonstrate the effectiveness and necessity of the proposed method. Compared with the state-of-the-art, e.g., DeepSC, an improvement of up to 10% is achieved in terms of Semantic Similarity. Furthermore, significant performance gains are achieved by the proposed method in both regimes of low and medium signal-to-noise ratio, as compared to traditional separate source-channel coding methods.
GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users.
Discrepancy-Aware Federated Learning for Non-IID Data

Federated learning (FL) as an emerging edge intelligence paradigm allows clients to jointly train a model without exchanging raw data. Due to its excellent performance in privacy protection, FL has practical application in many areas. However, data heterogeneity across clients is a prevalent phenomenon and poses a significant challenge to FL. Although many FL algorithms have been proposed to address the issue of performance deterioration under non-independent and identically distributed (Non-IID), the improvement in performance is not significant. In this paper, we propose knowledge discrepancy-aware federated learning (KDAFL). It evaluates the local model with regard to each class and overall learning effect based on the discrepancy between local and global knowledge. In this way, each client assigns a new weight to each category of cross-entropy and decides if knowledge distillation is to be conducted. The awareness of discrepancy allows the client to adjust the local training according to the characteristics of the knowledge it learned, thus better solving the Non-IID issue. Extensive experiments have demonstrated the effectiveness of KDAFL, particularly in terms of the improvement of global model accuracy compared to other state-of-the-art algorithms.
Evaluasi Kinerja Auditor Dalam Mendeteksi Dan Mencegah Terjadinya Potensi Fraud (Kecurangan)

Researchers can formulate a research question, namely how the performance of the auditor and the internal control system in detecting and preventing potential fraud at the Kendari City Public Accounting Firm (KAP). The research objective is to determine the performance of auditors and internal control systems in detecting and preventing potential fraud at the Kendari City Public Accounting Firm (KAP). The data source used is primary data, namely through interviews of 2 (two) informants who work at KAP Kendari City and secondary data is collected through data collection, namely in the form of documents that are relevant to the auditor's performance in detecting and preventing potential fraud. The data obtained was processed and analyzed using a qualitative approach. The results of the study show that: the auditor's performance in detecting and preventing potential fraud at KAP Kendari City has been carried out adequately. It can be proven that it refers to the SOP that has been established and has implemented audit procedures with Computer Assisted Audit Techniques, namely using the Spreadsheet Application in the form of m.s Excel. The internal control system for the auditor's performance in detecting and preventing potential fraud has been implemented effectively and efficiently which can be seen from the existence of an understanding of the control environment, risk assessment, control activities, the existence of information and communication and the monitoring of internal controls that occur at the City Public Accountant Office Kendari. Auditors are responsible for detecting material misstatements in the financial statements and detecting material misstatements whether intentional or due to fraud.