Reviewerly/exHarmony huggingface dataset:
A new distinguishing attack on reduced round ChaCha permutation

This work concentrates on differential-linear distinguishing attacks on the prominent ARX-based permutation ChaCha. Here, we significantly improve the 7-round differential-linear distinguisher for ChaCha permutation by introducing a new path of linear approximation. We first introduce a new single-bit differential distinguisher for the 3.5th round of the permutation that assists us in inventing a new path for the differential-linear distinguisher. We show that one can distinguish a 7-round ChaCha permutation with time complexity of [Formula: see text]. This improves the recent work of Coutinho et al. (in: Advances in Cryptology-ASIACRYPT 2022-28nd International Conference on the Theory and Application of Cryptology and Information Security, Taipei, Taiwan, December 5-9, 2012, Springer, 2022), which achieved time complexity [Formula: see text]. We also propose a distinguisher for the 7.25-round of ChaCha permutation and this is the first distinguishing attack for more than 7-round of ChaCha permutation. We provide theoretical proofs and the corresponding experimental results for the linear approximations that we use for differential-linear distinguisher. We point out that the existing multibit distinguishing attacks on the cipher ChaCha are invalid. These attacks are worked only for the ChaCha permutation.
Deep feature fusion‐based stacked denoising autoencoder for tag recommendation systems

Abstract With the rapid development of artificial intelligence technology, commercial robots have gradually entered our daily lives. In order to promote product dissemination, shopping guide robots are a new service options of commerce platforms that use tag recommendation systems to identify users' intentions. A large number of applications combine user historical tagging information with the multi‐round dialogue ability of shopping guide robots to help users efficiently search for and retrieve products of interest. Recently, tensor decomposition methods have become a common approach for modelling entity interaction relationships in tag recommendation systems. However, due to the sparsity of data, these methods only consider low‐order information of entities, making it difficult to capture the higher‐order collaborative signals among entities. Recommendation methods by autoencoders can effectively extract abstract feature representations while they only focus on the two‐dimensional relationship between users and items, ignoring the interaction relationship among users, items and tags in real complex recommendation scenarios. The authors focus on modelling the similarity relationship among entities and propose a method called deep feature fusion tag (DFFT) based on the deep feature fusion of stacked denoising autoencoders. This method can extract high‐order information with different embedding dimensions and fuse them in a unified framework. To extract robust feature representations, the authors inject random noise (mask‐out/drop‐out noise) into the tag information corresponding to users and items to generate corrupted input data, and then utilise autoencoders to encode the interaction relationship among entities. To further obtain the interaction relationship with different dimensions, different encoding layers are stacked and combined to produce a better expanded model which can reinforce each other. Finally, a decoding component is used to reconstruct the original input data. According to the experimental results on two common datasets, the proposed DFFT method outperforms other baselines in terms of the F1@N, NDCG@N and Recall@N evaluation metrics.
Disruptive AI: the response depends on the buyer’s political ideology

Purpose The recent pandemic disrupted the way in which businesses transact with each other. In response to maintaining cleanliness in business-to-business (B2B) settings, artificial intelligence (AI)-enabled robots have been adopted as substitutes for cleaning personnel, yet their implications remain largely underexamined. This study aims to examine B2B buyer responses to cleaning information notices (human vs AI-enabled agent) placed at either the entry to the premises or the sales counter, thereby adding to the nascent literature in this line of inquiry. Design/methodology/approach Three field experiments were conducted across diverse B2B businesses (wholesalers in Studies 1–2 and a commercial business in Study 3). To achieve greater empirical rigor and generalizability, this research used diverse stimuli across different B2B settings. In addition, the results ruled out alternate explanations and shed light upon political ideology as a boundary condition. Finally, a single-paper meta-analysis confirmed H1 , consolidating the established effect. Findings Featuring over 1,000 B2B buyers, the results show that politically liberal B2B buyers express greater preference for human over AI-performed cleaning while labor-orientated buyers are indifferent. Importantly, this effect is driven by greater relaxation associated with humans, which in turn, increases their future patronage and referral intent. Originality/value The results enrich the collective knowledge of the adoption of AI-enabled robots, reinforcing for marketing practitioners and businesses that the reliance on human-based outcomes remains a preferred touchpoint in B2B settings, particularly for liberals.
An optimized training approach for meteor detection with an attention mechanism to improve robustness on limited data

Researchers have extensively used convolutional neural networks to detect meteor falls on Earth. However, when dealing with limited available data, these networks may need more robustness to classify new real-world images correctly. This study proposes an optimized training approach of a pre-trained model with an attention mechanism to achieve better generalization results in such a scenario. We compare two architectures, an optimized base model and another version with an attention mechanism. Furthermore, we present a new and publicly available optical meteor dataset that merges several public data sources. We used the merged dataset to train classification models combined with a stratified five-fold cross-validation strategy to determine the reliability of the prediction. The experimental results from both architectures showed good and similar performance. To further determine the best architecture, we performed an additional analysis with visual explanations in new observations. The architecture with an attention mechanism was the best model achieving a false alarm ratio of 2.6% and an accuracy of 97%.
Making Informed Decisions in the Volatile Crypto Market: An Analysis of Portfolio Risk and Return

Despite widespread skepticism linked to cryptocurrencies, they are constantly gaining the interest of scholars, investors, media, and regulators. Recognizing the importance that portfolio risk maintains for crypto participants, this study attempts to shed light on this issue. We investigate the risk-return tradeoffs of the most tradable cryptocurrencies based on portfolio diversification techniques. Three different crypto portfolios containing a diverse number of cryptocurrencies were created to analyze the diversification risk from a historical perspective. Data concerning daily prices and their trade volume was collected from the Coin Market Cap database and covered the period from 1 January 2016 to 31 December 2022. The results regarding the risk-reward tradeoff stand in line with the portfolio theory, where higher expected returns offset higher risk. On average, the portfolio composed of 10 cryptocurrencies offers better optimization than the one with five, as it generates the same returns with lower risk. The year 2018 reflects the maximum diversification benefits in the three portfolios, corresponding to the period when cryptocurrencies gained massive popularity. From the managerial perspective, results inform crypto and institutional investors of the possible diversification benefits of the 15 most traded cryptocurrencies.
Use of Eye-Tracking Methodology for Learning in College Students: Systematic Review of Underlying Cognitive Processes

Learning processes and outcomes have been one of the main focuses of interest in educational research. The main objective of this systematic review is to provide a comprehensive and integrative view of the application of eye tracking to identify cognitive processes involved in college students’ learning process. The bibliographic sources for this review were databases Web of Science, PsycINFO and Scopus. After carrying out the screening process by two independent reviewers, 17 articles were included according to prespecified inclusion criteria. In general, it was observed a deeper learning and comprehension of the concepts when visual or picture stimulus were presented compared to text cues. This systematic review is a rapid updated of the processes involved in learning in college students, providing a comprehensive view of the current situation to design innovative educational programs.
Reducing the Security Margin Against a Differential Attack in the TinyJambu Cryptosystem

TinyJambu is one the 10 finalists in the NIST Lightweight Cryptography (LWC) Standardization Project. This Authenticated Encryption with Associated Data algorithm is very fast and extremely small in terms of the hardware needed for its implementation. In this work, we study a differential cryptanalytic attack against TinyJambu. It is an analysis that goes deeper than previous works found in the literature as well as it also obtains better differential probabilities than those of other studies. More precisely, we develop a differential forgery attack against nonce and associated data with probability 2−66.7712, what is much better than that one obtained by the own designers of value 2−80 and better than the best probability 2−70.12 obtained by other authors. In brief, we have reduced the margin of security against a forgery attack for this proposal of lightweight cryptosystem.
A CNN-Based Approach for Face Recognition Under Different Orientations

Face recognition is a difficult task in the realm of computer vision and image analysis. A face recognition model can recognize a face in an image automatically. When the face is orientated in different angles, most of the face recognition systems fail to identify the face, resulting in the degradation of the performance of the facial recognition system. To handle this issue, dataset containing face images should be created in such a manner that it should contain the facial images at varying angles. Face recognition systems based on convolutional neural networks (CNNs) have gained popularity because of its effectiveness in delivering authenticity by automatically identifying faces. The proposed system consists of a robust CNN model for detecting and recognizing the face at varying angles. The proposed CNN model is tested on three datasets, namely ‘MepcoECE’ dataset which was created with 24 subjects in real-time which consists of facial images at varying angles, and two benchmark datasets, namely ORL dataset and GTF dataset, and achieved a superior performance in benchmark datasets.
Overcoming traditional <scp>ETL</scp> systems architectural problems using a <scp>service‐oriented</scp> approach

Abstract Developing analytical systems imposes several challenges related not only to the amount and heterogeneity of the involved data but also to the constant need to readapt and evolve to overcome new business challenges. Data are a determinant factor in the success of analytical and decision‐making applications, being its nature, availability, and quality, crucial aspects for planning and structuring populating analytical systems. Today's users are more demanding, requiring adaptable and flexible analytical applications, which impose serious challenges on extract‐transform‐load (ETL) systems design and development for ensuring flexible and robust data populating services, operating 24/7, and managing and processing large volumes of data. Thus, we should design and implement ETL processes using innovative and up‐to‐date approaches, having real application evidence. In this paper, we present a service‐oriented implementation for ETL design and development. We mapped and implemented some of the most conventional ETL processes in a service‐oriented architecture, to demonstrate the application and benefits that this kind of approach will provide to ETL systems project development.
Quantitative modelling and analysis of BDI agents

Abstract Belief–desire–intention (BDI) agents are a popular agent architecture. We extend conceptual agent notation ( Can )—a BDI programming language with advanced features such as failure recovery and declarative goals—to include probabilistic action outcomes, e.g. to reflect failed actuators, and probabilistic policies, e.g. for probabilistic plan and intention selection. The extension is encoded in Milner’s bigraphs. Through application of our BigraphER tool and the PRISM model checker, the probability of success (intention completion) under different probabilistic outcomes and plan/event/intention selection strategies can be investigated and compared. We present a smart manufacturing use case. A significant result is that plan selection has limited effect compared with intention selection. We also see that the impact of action failures can be marginal—even when failure probabilities are large—due to the agent making smarter choices.
Adversarial Cluster-Level and Global-Level Graph Contrastive Learning for node representation

Graph contrastive learning aims to learn informative and discriminative node representations for downstream tasks by maximizing the mutual information between representations of different augmentation views of the same node. However, according to the multi-view information bottleneck principle, redundant information in learned representations can negatively impact the performance of downstream tasks. To avoid this issue, we propose Adversarial Cluster-Level and Global-Level Graph Contrastive Learning (ACG-GCL) for learning minimal sufficient node representations. ACG-GCL is optimized alternately under an adversarial learning framework with a min–max objective. At the min step, ACG-GCL eliminates redundant information in both the graph structure and feature content of nodes while producing a new graph that provides multi-view information. At the max step, ACG-GCL seeks to preserve shared task-relevant information in the learned representation by maximizing the mutual information of node-cluster level and node-global level representations. We demonstrate the effectiveness of the proposed method on the tasks of node classification and node clustering tasks. Code is available at https://github.com/tangq123/ACG-GCL.
Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture

Formal languages are commonly used to model the semantics of instruction set architectures (e.g. ARM). The majority of prior work on these formal languages focuses on concrete instruction execution and validation tasks. We present a novel Haskell-based modelling approach which allows the creation of flexible and versatile architecture models based on free monads and a custom expression language. Contrary to existing work, our approach does not make any assumptions regarding the representation of memory and register values. This way, we can implement non-concrete software analysis techniques (e.g. symbolic execution where values are SMT expressions) on top of our model as interpreters for this model. In contrast to prior work, our modelling approach is therefore explicitly focused on the creation of custom ISA interpreters. We employ our outlined approach to create an abstract model and a concrete interpreter for the RISC-V base instruction set. Based on this model, we demonstrate that custom interpreters can be implemented with minimal effort using dynamic information flow tracking as a case study.
Dark Patterns and Online Consumer Vulnerability

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Single-shot pruning and quantization for hardware-friendly neural network acceleration

Applying CNN on embedded systems is challenging due to model size limitations. Pruning and quantization can help, but are time-consuming to apply separately. Our Single-Shot Pruning and Quantization strategy addresses these issues by quantizing and pruning in a single process. We evaluated our method on CIFAR-10 and CIFAR-100 datasets for image classification. Our model is 69.4% smaller with little accuracy loss, and runs 6–8 times faster on NVIDIA Xavier NX hardware.
P4CTM: Compressed Traffic Pattern Matching Based on Programmable Data Plane

Pattern matching is an important technology applied to many security applications. Most network service providers choose to compress network traffic for better transmission, which brings the challenges of compressed traffic matching. However, existing works focus on improving the performance of uncompressed traffic matching or only realize the compressed traffic matching on end-host that can not keep pace with the dramatic increase in traffic. In this paper, we present P4CTM, a proof-of-concept method to conduct efficient compressed traffic matching on the programmable data plane. P4CTM uses the two-stage scan scheme to skip some bytes of compressed traffic, the 2-stride DFA combines with the compression algorithm to condense the state space, and the wildcard match to downsize the match action tables in the programmable data plane. The experiment indicates that P4CTM skips 83.10% bytes of compressed traffic, condenses the state space by order of magnitude, and reduces most of the table entries.
Fog‐Sec: Secure end‐to‐end communication in fog‐enabled IoT network using permissioned blockchain system

Abstract The technological integration of the Internet of Things (IoT)‐Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT‐Fog‐Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog‐enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog‐enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog‐enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog‐based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device‐to‐device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state‐of‐the‐art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss.
Practical Multi-Key Homomorphic Encryption for More Flexible and Efficient Secure Federated Average Aggregation

In this work, we introduce a lightweight communication-efficient multi-key approach suitable for the Federated Averaging rule. By combining secret-key RLWE-based HE, additive secret sharing and PRFs, we reduce approximately by a half the communication cost per party when compared to the usual public-key instantiations, while keeping practical homomorphic aggregation performances. Additionally, for LWE-based instantiations, our approach reduces the communication cost per party from quadratic to linear in terms of the lattice dimension.
An Extractive Text Summarization Model Based on Rhetorical Structure Theory

In response to the issues of excessive redundant information and unclear hierarchical structure in abstracts, this paper proposes an extractive text summarization method based on rhetorical structure theory. First, use Bert to tokenize the text and divide it into smaller text units; Then, construct a text parsing tree based on the rhetorical structure theory, labeling the relationships between tree nodes and whether they are central information. Furthermore, set the extraction depth and the key EDU sequences are sequentially output as the final summary. Experimental analysis shows that this method effectively reduces redundant information in summary sentences, outperforms the current best benchmark model in terms of the ROUGE index, and improves the accuracy and hierarchical structure of the abstract.
Blockchain Based E-Analysis of Social Media Forums for Crypto Currency Phase Shifts

Cryptocurrencies have experienced rapid growth and gained popularity worldwide. As a result, social media platforms have become an important source of information for investors and traders seeking to make decisions regarding cryptocurrencies. However, due to the sheer volume of data and the lack of effective analysis techniques, it can be challenging to identify key patterns and trends within social media forums. In this research paper, a blockchain based methodology is proposed for analysis of social media forums for cryptocurrency phase shifts. Specifically, we use sentiment analysis and topic modeling techniques to identify key themes and trends within cryptocurrency social media forums, and then use blockchain technology to verify and authenticate the data. Our results demonstrate that our approach is effective in identifying phase shifts in cryptocurrency markets. In recent years, social media forums have become a hub for discussions on cryptocurrency. The vast amount of data generated in these forums can be analyzed to predict the price shifts of different cryptocurrencies. However, traditional analysis methods may not be efficient enough to extract relevant information from this vast data. Therefore, this research study proposes a blockchain-based analysis approach to social media forums for cryptocurrency phase shifts. The proposed approach uses blockchain to ensure data immutability, and smart contracts to execute the analysis. The results obtained from the analysis can be used for prediction of price shifts of different crypto-currencies.