Reviewerly/exHarmony huggingface dataset:
Toward the Development of an Empirical Model of Air Pollution Impact on Solar PV Output for Industry Use

The absence of practical models for estimating the impact of air pollution on solar output presents a challenge for forecasting of solar electricity production and creates more uncertainty for financing and insuring solar plants. While the physics of irradiance attenuation due to aerosols are well understood, complex atmospheric conditions and lack of detailed atmospheric measurement make them impractical for industry or small-scale solar users to calculate its impact on PV power generation. Simple, empirical models to quantify the overall effect from real-world observations are scarce. In this study, we make use of both the experimental approach as well as large-scale real-world observational data from more than 15 sites to empirically evaluate the impact of air pollution on PV production using only common weather parameters. We show that the impact of PM2.5 on irradiance and, hence, PV output is approximately linear at low and moderate levels of PM2.5, with a 100 μg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> increase corresponding to a reduction of around 15 ± 5% relative under clear sky conditions, which is broadly consistent with previous preliminary results reported from literature. This effect saturates at higher PM2.5 concentrations beyond 300 μg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and is reduced at high cloud coverage. The methodology developed in this study represents a viable path towards establishing a common ground for standards. The results obtained are the most comprehensive so far in terms of breadth and depth among similar empirical studies using ground-based observations. They provide a basis for establishing a functional model for industry practitioners in the PV community.
Experience Prototyping: Smart Assistant for Autonomous Mobility Concept

The paper describes the subsequent development of two bachelor’s degree projects as part of a commissioned research project for the Czech car manufacturer Škoda Auto. The first bachelor’s degree project by R. Hnilica from the field of industrial design focused on developing an interaction design concept of a smart personal assistant, suitable for both children and adult passengers in autonomous cars. The follow-up bachelor’s degree project of a small robotic assistant by D. Plachý delivered a working prototype demonstrating partial functionality of the design concept. In the paper, we focus more on the design setup and the description of the process than on the technical details of our solution.
On Autonomous Mobile Robot Exploration Projects in Robotics Course

Autonomous mobile robot exploration can be considered a representative task where multiple problems need to be addressed and solutions integrated into a software framework that exhibits desired autonomous behavior of the robot. The problem includes online decision-making in selecting new navigational waypoints towards which the robot is autonomously navigated to explore not yet covered part of the environment. A mobile robot’s navigation consists of localization, mapping, planning, and execution of the plan by following the path toward the waypoint. For these very reasons, we decided to include mobile robot exploration as one of the tasks in our Artificial Intelligence in Robotics course opened at the Faculty of Electrical Engineering, Czech Technical University in Prague. In this paper, we present our experience running the course, where the students start with relatively small isolated tasks that are then integrated into a full exploration framework. We share the students’ feedback on our initial approach for the task that becomes a mandatory part of the course evaluation and grading.
Planning Poker Simulation with the Humanoid Robot NAO in Project Management Courses

Learning how to predict effort is important for any project team. In agile teams, Planning Poker is one of the most popular estimation game-based techniques used to come to a consensus about how much work should be done in a given iteration. Usually, the product owner or scrum master facilitates planning poker with their team. This paper presents the application of the humanoid robot NAO as a robotic coach and facilitator of planning poker with university students in two project management courses. The paper describes the design of the planning poker simulation, the programming of the robot, as well as the implementation and evaluation of the application “Planning poker with NAO” in two on-campus pilot studies with 29 university students. The evaluation aimed to investigate students’ perceptions of the design of the simulation and its effects on students’ understanding of agile estimation. The results show that the design of planning poker facilitated by the NAO robot was helpful for students to understand the concept of relative estimation in agile teams.
A Short Curriculum on Robotics with Hands-On Experiments in Classroom Using Low-Cost Drones

This paper presents some feedback on a curriculum on robotics developed for Master’s level students at ESIEA, a graduate school of engineering in France. The main particularity of this curriculum is that it is composed of only three short modules (control and estimation, computer vision, ROS), with a small number of hours (18hrs each), but proposes hands-on experiments with drones to students. Experiments are done in the classrooms with low-cost drones as exercises integrated into the practical work sessions. The detail of the curriculum, the pedagogic approach, and examples of experiments proposed to the students are presented in this paper.
Enabling Lightweight Privilege Separation in Applications with MicroGuards

Application compartmentalization and privilege separation are our primary weapons against ever-increasing security threats and privacy concerns on connected devices. Despite significant progress, it is still challenging to privilege separate inside an application address space and in multithreaded environments, particularly on resource-constrained and mobile devices. We propose MicroGuards, a lightweight kernel modification and set of security primitives and APIs aimed at flexible and fine-grained in-process memory protection and privilege separation in multithreaded applications. MicroGuards take advantage of hardware support in modern CPUs and are high-level enough to be adaptable to various architectures. This paper focuses on enabling MicroGuards on embedded and mobile devices running Linux kernel and utilizes tagged memory support to achieve good performance. Our evaluation show that MicroGuards add small runtime overhead (less than 3.5%), minimal memory footprint, and are practical to get integrated with existing applications to enable fine-grained privilege separation.
Multi-armed SPHINCS$$^{+}$$

Hash-based signatures are a type of Digital Signature Algorithms that are positioned as one of the most solid quantum-resistant constructions. As an example SPHINCS $$^{+}$$ , has been selected as a standard during the NIST Post-Quantum Cryptography competition. However, hash-based signatures suffer from two main drawbacks: signature size and slow signing process. In this work, we give a solution to the latter when it is used in a mobile device. We take advantage of the fact that hash-based signatures are highly parallelizable. More precisely, we provide an implementation of SPHINCS $$^{+}$$ on the Snapdragon™ 865 Mobile Platform taking advantage of its eight CPUs and their vector extensions. Our implementation shows that it is possible to have a speed-up of 15 times when compared to a purely sequential and non-vectorized implementation. Furthermore, we evaluate the performance impact of side-channel protection using vector extensions in the SPHINCS $$^{+}$$ version based on SHAKE.
Comprehensive Preimage Security Evaluations on Rijndael-Based Hashing

The Meet-in-the-Middle (MITM) attack is one of the most powerful cryptanalysis techniques, as seen by its use in preimage attacks on MD4, MD5, Tiger, HAVAL, and Haraka-512 v2 hash functions and key recovery for full-round KTANTAN. An efficient approach to constructing MITM attacks is automation, which refers to modeling MITM characteristics and objectives into constraints and using optimizers to search for the best attack configuration. This work focuses on the simplification and renovation of the most advanced superposition framework based on Mixed-Integer Linear Programming (MILP) proposed at CRYPTO 2022. With the refined automation model, this work provides the first comprehensive analysis of the preimage security of hash functions based on all versions of the Rijndael block cipher, the origin of the Advanced Encryption Standard (AES), and improves the best-known results. Specifically, this work has extended the attack rounds of Rijndael 256-192 and 256-256, reduced the attack complexity of Rijndael 256-128 and 128-192 (AES192), and filled the gap of preimage security evaluation on Rijndael versions with a block size of 192 bits.
Smart Contract-Based E-Voting System Using Homomorphic Encryption and Zero-Knowledge Proof

As an indispensable part of establishing modern representative democratic organizations, election is based on a voting process on site or remotely. With the rapid development of information technology, the application of electronic voting systems in practice is significantly increasing in recent years. Consequently, whether an electronic voting system is secure and reliable enough is the most critical factor of the systems. Whereas, most of the existing proposals neglect to confirm the trustworthiness of the administrator, which may impact the security and availability of the system. For this purpose, we propose an up-to-date electronic voting system based on smart contract using additively homomorphic encryption and non-interactive zero-knowledge proof. In our work, we utilize a concise zero-knowledge proof algorithm and an inbound oracle in combination to allow voters to verify the fidelity of the administrator. We prove the feasibility, efficiency, and scalability of our system can satisfy a majority of application scenarios including large-scale voting. In particular, we evaluate the time performance and cost performance and demonstrate its merits including the low cost in many functions and linear performance when generating zero-knowledge proof.
NFT Trades in Bitcoin with Off-Chain Receipts

Non-fungible tokens (NFTs) are digital representations of assets stored on a blockchain. It allows content creators to certify authenticity of their digital assets and transfer ownership in a transparent and decentralized way. Popular choices of NFT marketplaces infrastructure include blockchains with smart contract functionality or layer-2 solutions. Surprisingly, researchers have largely avoided building NFT schemes over Bitcoin-like blockchains, most likely due to high transaction fees in the BTC network and the belief that Bitcoin lacks enough programmability to implement fair exchanges. In this work we fill this gap. We propose an NFT scheme where trades are settled in a single Bitcoin transaction as opposed to executing complex smart contracts. We use zero-knowledge proofs (concretely, recursive SNARKs) to prove that two Bitcoin transactions, the issuance transaction $$\textsf{tx}_0$$ and the current trade transaction $$\textsf{tx}_n$$ , are linked through a unique chain of transactions. Indeed, these proofs function as “off-chain receipts” of ownership that can be transferred from the current owner to the new owner using an insecure channel. The size of the proof receipt is short, independent of the total current number of trades n, and can be updated incrementally by anyone at anytime. Marketplaces typically require some degree of token ownership delegation, e.g., escrow accounts, to execute the trade between sellers and buyers that are not online concurrently, and to alleviate transaction fees they resort to off-chain trades. This raises concerns on the transparency and purportedly honest behaviour of marketplaces. We achieve fair and non-custodial trades by leveraging our off-chain receipts and letting the involved parties carefully sign the trade transaction with appropriate combinations of $$\texttt {sighash}$$ flags.
A Deep-Learning Approach for Predicting Round Obfuscation in White-Box Block Ciphers

It has been proven that side-channel analysis such as differential computation/fault analysis can break white-box implementations without reverse engineering efforts. In 2020, Sun et al. proposed noisy rounds as a countermeasure to mitigate the side-channel attacks on white-box block ciphers. The principle is to desynchronize the computation traces of cryptographic implementations by introducing several redundant round functions. In this paper, we propose a multi-label classification method and three deep-learning models (CNN, RNN, and CRNN) to predict the locations of the obfuscated rounds. The experimental results show that the obfuscation of noisy rounds also could not be identified by the deep-learning model. However, the RNN is more effective than the CNN and CRNN with fewer time costs. Subsequently, we investigate the influence of specific components such as the key, affine masking, and transformation matrix on round obfuscation recognition. The extended experiments demonstrate that without the transformation matrix, the deep learning models can successfully distinguish the noisy rounds.
POSTER: Using Verifiable Credentials for Authentication of UAVs in Logistics

Verifiable Credentials (VCs) have been widely adopted as a format for asserting information through use of digital signature technique, and offer versatile usages. The World Wide Web Consortium (W3C) has standardized the data model for VCs [1], and the motivation for this study is to verify the applicability of its data model through a concrete, realistic scenario. For verification, we have chosen a scenario where Unmanned Aerial Vehicles (UAVs) collaborate to deliver packages. As a contribution of this study, we demonstrate how we can selectively disclose attributes of UAVs yet ensure its mission under an authorized contract. Furthermore, we propose a method for issuing receipts through use of VCs and Verifiable Presentations (VPs) that guarantees completion of delivery while UAVs remain anonymous. For this function, we developed a new format for VP, namely ‘VP with Message.’ These features have been implemented through a demo application.
Secret Key Recovery Attack on Masked and Shuffled Implementations of CRYSTALS-Kyber and Saber

Shuffling is a well-known countermeasure against side-channel attacks. It typically uses the Fisher-Yates (FY) algorithm to generate a random permutation which is then utilized as the loop iterator to index the processing of the variables inside the loop. The processing order is scrambled as a result, making side-channel attacks more difficult. Recently, a side-channel attack on a masked and shuffled implementation of Saber requiring 61,680 power traces to extract the long-term secret key was reported. In this paper, we present an attack that can recover the long-term secret key of Saber from 4,608 traces. The key idea behind the 13-fold improvement is to recover FY indexes directly, rather than by extracting the message Hamming weight and bit flipping, as in the previous attack. We capture a power trace during the execution of the decryption algorithm for a given ciphertext, recover FY indexes 0 and 255, and extract the corresponding two message bits. Then, we modify the ciphertext to cyclically rotate the message, capture a power trace, and extract the next two message bits with FY indexes 0 and 255. In this way, all message bits can be extracted. By recovering messages contained in $$k*l$$ chosen ciphertexts constructed using a new method based on error-correcting codes of length l, where k is the module rank, we recover the long-term secret key. To demonstrate the generality of the presented approach, we also recover the secret key from a masked and shuffled implementation of CRYSTALS-Kyber, which NIST recently selected as a new public-key encryption and key-establishment algorithm to be standardized.
POSTER: Integration of End-to-End Security and Lightweight-SSL for Enhancing Security and Efficiency of MQTT

Message Queue Telemetry Transport (MQTT) is one of the most popular Internet of Things (IoT) communication protocols. The MQTT standards suggest the adoption of TLS/SSL in the underlying layer to facilitate the authentication and protect the transmissions. A MQTT system consists brokers, publishers, and subscribers. However, a TLS/SSL-enabled MQTT system still cannot protect the privacy against a curious broker. The End-to-End (E2E) MQTT security is one such mechanism to ensure the security between a publisher and a subscriber, and to protect the privacy against a curious broker. Here, we notice one weakness of such E2E-MQTT solutions. The layering of the TLS/SSL channels and the E2E channel incurs the extra overhead that a message is encrypted and decrypted triple times, and it increases the broker’s unnecessary loading. In this study, we leverage the benefits of adopting both TLS/SSL and the E2E channel, while enhancing the efficiency. We keep the authentication mechanism and the integrity function of TLS/SSL, but eliminate its encryption. Some preliminary designs and evaluations verify the merits of the integrated approach.
POSTER: A Fine-Grained Metric for Evaluating the Performance of Adversarial Attacks and Defenses

Over the past decade, the development of both adversarial attack methods and defense strategies has accelerated rapidly. Classification accuracy has been predominantly used as the sole metric for assessing model performance. However, when the reported accuracy rates of two models are identical or very similar, it becomes challenging to determine which model is superior. To address this issue and offer more insights into model performance, this study introduces a novel classification performance metric: the confidence gap. This metric is defined as the difference in confidence level between the true label and either the top 1 prediction or the second-best prediction, depending on the accuracy of the image classification. The confidence level, as indicated by its sign, reflects the correctness of the classification and provides more detailed information on the robustness of the classification result. Recognizing that evaluation results may be inconsistent when employing different criteria, we recommend that future research in this field should report the confidence gap alongside accuracy rates.
Automatic Search Model for Related-Tweakey Impossible Differential Cryptanalysis

The design and analysis of dedicated tweakable block ciphers constitute a dynamic and relatively recent research field in symmetric cryptanalysis. The assessment of security in the related-tweakey model is of utmost importance owing to the existence of a public tweak. This paper proposes an automatic search model for identifying related-tweakey impossible differentials based on the propagation of states under specific constraints, which is inspired by the research of Hu et al. in ASIACRYPT 2020. Our model is universally applicable to block ciphers, but its search efficiency may be limited in some cases. To address this issue, we introduce the Locality Constraint Analysis (LCA) technique to impossible differential cryptanalysis and propose a generalized automatic search model. Technically, we transform our models into Satisfiability Modulo Theories (SMT) problems and solve them using the STP solver. We have applied our tools to several tweakable block ciphers, such as Joltik-BC, SKINNY, QARMA, and CRAFT, to evaluate their effectiveness and practicality. Specifically, we have discovered 7-round related-tweakey impossible differentials for Joltik-BC-192, and 12-round related-tweak impossible differentials, as well as 15-round related-tweakey impossible differentials for CRAFT for the first time. Based on the search results, we demonstrate that the LCA technique can be effectively performed when searching and determining the contradictory positions for the distinguisher with long trails or ciphers with large sizes in impossible differential cryptanalysis.
Round-Efficient Security Authentication Protocol for 5G Network

The security access problem of Fifth Generation Mobile communication (5G) system has become a research hotspot. The 3GPP defines 5G authentication and key agreement (5G AKA) protocol, which is used to protect the access security of mobile devices in 5G network. But it is found that there are still some security loopholes and some unrealistic system assumptions which play a decisive role in security, such as user tracking, pre-shared key K disclosure, security between service network and home network, which makes 5G vulnerable to various attacks. Therefore, we propose a security authentication protocol for 5G network based on Schnorr signature and bilinear algorithm to solve the above problems. Then the security of the protocol is proved by using Scyther. Finally, the theoretical analysis verifies the security of the protocol, and the security comparison and efficiency analysis further verify that the protocol has good resistance performance.
Using Model Optimization as Countermeasure against Model Recovery Attacks

Machine learning (ML) and Deep learning (DL) have been widely studied and adopted for different applications across various fields. There is a growing demand for ML implementations as well as ML accelerators for small devices for Internet-of-Things (IoT) applications. Often, these accelerators allow efficient edge-based inference based on pre-trained deep neural network models for IoT setting. First, the model will be trained separately on a more powerful machine and then deployed on the edge device for inference. However, there are several attacks reported that could recover and steal the pre-trained model. For example, recently an attack was reported on edge-based machine learning accelerator demonstrated recovery of target neural network models (architecture and weights) using cold-boot attack. Using this information, the adversary can reconstruct the model, albeit with certain errors due to the corruption of the data during the recovery process. Hence, this indicate potential vulnerability of implementation of ML/DL model on edge devices for IoT applications. In this work, we investigate generic countermeasures for model recovery attacks, based on neural network (NN) model optimization technique, such as quantization, binarization, pruning, etc. We first study and investigate the performance improvement offered and how these transformations could help in mitigating the model recovery process. Our experimental results show that model optimization methods, in addition to achieving better performance, can result in accuracy degradation which help to mitigate model recovery attacks.