Reviewerly/exHarmony huggingface dataset:
Election Verifiability in Receipt-Free Voting Protocols

Electronic voting is a prominent example of conflicting requirements in security protocols, as the triad of privacy, verifiability and usability is essential for their deployment in practice. Receipt-freeness is a particularly strong notion of privacy, stating that it should be preserved even if voters cooperate with the adversary. While there are impossibility results showing we cannot have receipt-freeness and verifiability at the same time, there are several protocols that aim to achieve both, based on carefully devised trust assumptions. To evaluate their security, we propose a general symbolic definition of election verifiability, extending the state of the art to capture the more complex structure of receipt-free protocols. We apply this definition to analyse, using ProVerif, recent protocols with promising practical features: BeleniosRF and several variants of Selene. Against BeleniosRF, we find several attacks showing that verifiability in Belenios does indeed suffer from the attempt to introduce receipt-freeness. On the other hand, Selene satisfies a weaker notion of receipt-freeness, but we show that it satisfies verifiability in stronger corruption scenarios. We introduce a general frame-work to compare the verifiability of these protocols in various corruption scenarios and conclude with an analysis of SeleneRF, an attempt to get the best of both that we formalise in this paper. In addition to extending the symbolic model, our results point to foundational gaps in current cryptographic models for election verifiability, as they fail to uncover attacks that we do.
Q-YOLOP: Quantization-Aware You Only Look Once for Panoptic Driving Perception

In this work, we present an efficient and quantization-aware panoptic driving perception model (Q-YOLOP) for object detection, drivable area segmentation, and lane line segmentation, in the context of autonomous driving. Our model employs the Efficient Layer Aggregation Network (ELAN) as its backbone and task-specific heads for each task. We employ a four-stage training process that includes pretraining on the BDD100K dataset, finetuning on both the BDD100K and iVS datasets, and quantization-aware training (QAT) on BDD100K. During the training process, we use powerful data augmentation techniques, such as random perspective and mosaic, and train the model on a combination of the BDD100K and iVS datasets. Both strategies enhance the model's generalization capabilities. The proposed model achieves state-of-the-art performance with an mAP@0.5 of 0.622 for object detection and an mIoU of 0.612 for segmentation, while maintaining low computational and memory requirements.
On the Incoercibility of Digital Signatures

We introduce incoercible digital signature schemes, a variant of a standard digital signature. Incoercible signatures enable signers, when coerced to produce a signature for a message chosen by an attacker, to generate fake signatures that are indistinguishable from real signatures, even if the signer is compelled to reveal their full history (including their secret signing keys and any randomness used to produce keys/signatures) to the attacker. Additionally, we introduce an authenticator that can detect fake signatures, which ensures that coercion is identified. We present a formal security model for incoercible signature schemes that comprises an established definition of unforgeability and captures new notions of weak receipt-freeness, strong receipt-freeness and coercion-resistance. We demonstrate that an incoercible signature scheme can be viewed as a transformation of any generic signature scheme. Indeed, we present two incoercible signature scheme constructions that are built from a standard signature scheme and a sender-deniable encryption scheme. We prove that our first construction satisfies coercion-resistance, and our second satisfies strong receipt-freeness. We conclude by presenting an extension to our security model: we show that our security model can be extended to the designated verifier signature scheme setting in an intuitive way as the designated verifier can assume the role of the authenticator and detect coercion during the verification process.
On the equivalence issue of a class of 2-dimensional linear maximum rank distance codes

In Neri et al. (2022) [20], the authors extended the family of 2-dimensional Fq2t-linear MRD codes recently found in Longobardi et al. (2023) [13]. Also, for t≥5 they determined equivalence classes of the elements in this new family and provided the exact number of inequivalent codes in it. In this article, we complete the study of the equivalence issue removing the restriction t≥5. Moreover, we prove that in the case when t=4, the linear sets of the projective line PG(1,q8) ensuing from codes in the relevant family, are not equivalent to any one known so far.
l<sub>1</sub> norm based sparse reconstruction approach to compressed ultrafast photography of velocity interferometer system for any reflector

The one-dimensional velocity interferometer system for any reflector (VISAR) is widely used in inertial confinement fusion (ICF). Compressed ultrafast photography (CUP) is recently combined with VISAR to enable two-dimensional imaging, in which, the time-varied two-dimensional fringe images reconstruction from one image is a challenging problem. In this Letter, a new l1 norm based sparse reconstruction approach is presented, to the best of our knowledge, in which: (1) fringe images are sparsely represented with a few coefficients over the discrete cosine transformation (DCT) basis; (2) the image reconstruction is formulated as an l1 norm based sparse coefficients optimization problem; and (3) the two-step iterative shrinkage/threshold algorithm combined with a soft-thresholding operator is proposed to efficiently solve such a problem. Finally, the results show that, compared with the usual method, reconstructed fringe images with clear boundaries and good continuity are obtained. Additionally, the maximum relative error of the velocity is reduced from 14% to 8%, which is a reduction of approximately half.
Detecting bots with temporal logic

Abstract Social bots are computer programs that act like human users on social media platforms. Social bot detection is a rapidly growing field dominated by machine learning approaches. In this paper, we propose a complementary method to machine learning by exploring bot detection as a model checking problem. We introduce Temporal Network Logic (TNL) which we use to specify social networks where agents can post and follow each other. Using this logic, we formalize different types of social bot behavior with formulas that are satisfied in a model of a network with bots. We also consider an extension of the logic where we explore the expressive power of including elements from hybrid logic in our framework. We give model checking algorithms for TNL and its hybrid extension, and show that the complexity of the former is in p and the latter in pspace .
Path-Following and Collision-Avoidance Controls of a Robot in a Large Building with One or More Elevators

For planning a robot’s path inside a large building with one or more elevators, we develop a topological map, called the building Voronoi graph. Using the building Voronoi graph, the robot finds the shortest path to the goal and follows the path. In the case where the robot detects an object with arbitrary shapes (e.g., human) while following the path, the robot avoids the object utilizing reactive control laws. The proposed reactive collision-avoidance control is unique in considering collision avoidance with map environments as well as (moving or static) objects having arbitrary shapes. As far as we know, our paper is novel in addressing how to make the robot follow the building Voronoi graph, while avoiding collision with map environments as well as objects with arbitrary shapes.
Double Deep Q-Network Next-Generation Cyber-Physical Systems: A Reinforcement Learning-Enabled Anomaly Detection Framework for Next-Generation Cyber-Physical Systems

In this work, we considered the problem of anomaly detection in next-generation cyber-physical systems (NG-CPS). For this, we used a double deep Q-network-enabled framework, where an agent was trained to detect anomalies in the traffic that does not match the behavior of the legitimate traffic at the end side. Furthermore, the proposed paradigm recognizes known and unknown anomalies by directly engaging with a simulation environment. Given that, it progressively develops its interpretation of anomalies to encompass new, previously unrecognized classes of anomalies by proactively exploring probable anomalies in data that have not been labeled. The method achieves this by concurrently optimizing the use of a limited amount of labeled abnormality data for better understanding (exploitation) and the identification of infrequent, unlabeled anomalies (exploration). During analysis, we observed that the proposed model achieves significant results in the context of average and greedy catching of anomalies in the presence of comparative models.
An Analysis of the Demonstration of Five-Year-Long Creative ICT Education Based on a Hyper-Blended Practical Model in the Era of Intelligent Information Technologies

In anticipation of the advanced information and communication technology (ICT) era’s profound impact on society and industry, the demand for strong technological comprehension and creativity is evident. Addressing this societal need, we have developed a hyper-blended educational model and program. It focuses on cultivating creative thinking and ICT understanding, tailored for elementary/middle school students, educators, administrators, and the public. Our program employs innovative pedagogical methods, encompassing subjects such as artificial intelligence (AI) ethics, blockchain, and cloud computing, providing forward-looking perspectives. With techniques such as Havruta dialogue and quantum learning, we promote engaging, convergent thinking. We blend artistic expression, physical activities, and technical education to encourage administrators’ creativity within educational settings. Over 2017–2021, involving 9596 participants, we observed consistently high satisfaction scores of around 4 on a 1–5 scale, with statistical significance. This underscores our program’s success in fostering creative thinking and ICT understanding across diverse domains. In conclusion, our study addresses the vital need for creativity and tech comprehension in an intricate technological landscape. Our hyper blended practical model (HPBM) serves diverse training groups, equipping them for future challenges. These findings guide future educational pursuits, emphasizing a seamless integration of creativity and tech understanding.
Convolutional Neural Network and Language Model-Based Sequential CT Image Captioning for Intracerebral Hemorrhage

Intracerebral hemorrhage is a severe problem where more than one-third of patients die within a month. In diagnosing intracranial hemorrhage, neuroimaging examinations are essential. As a result, the interpretation of neuroimaging becomes a crucial process in medical procedures. However, human-based image interpretation has inherent limitations, as it can only handle a restricted range of tasks. To address this, a study on medical image captioning has been conducted, but it primarily focused on single medical images. However, actual medical images often consist of continuous sequences, such as CT scans, making it challenging to directly apply existing studies. Therefore, this paper proposes a CT image captioning model that utilizes a 3D-CNN model and distilGPT-2. In this study, four combinations of 3D-CNN models and language models were compared and analyzed for their performance. Additionally, the impact of applying penalties to the loss function and adjusting penalty values during the training process was examined. The proposed CT image captioning model demonstrated a maximum BLEU score of 0.35 on the in-house dataset, and it was observed that the text generated by the model became more similar to human interpretations in medical image reports with the application of loss function penalties.
Dual Elite Groups-Guided Differential Evolution for Global Numerical Optimization

Differential evolution (DE) has shown remarkable performance in solving continuous optimization problems. However, its optimization performance still encounters limitations when confronted with complex optimization problems with lots of local regions. To address this issue, this paper proposes a dual elite groups-guided mutation strategy called “DE/current-to-duelite/1” for DE. As a result, a novel DE variant called DEGGDE is developed. Instead of only using the elites in the current population to direct the evolution of all individuals, DEGGDE additionally maintains an archive to store the obsolete parent individuals and then assembles the elites in both the current population and the archive to guide the mutation of all individuals. In this way, the diversity of the guiding exemplars in the mutation is expectedly promoted. With the guidance of these diverse elites, a good balance between exploration of the complex search space and exploitation of the found promising regions is hopefully maintained in DEGGDE. As a result, DEGGDE expectedly achieves good optimization performance in solving complex optimization problems. A large number of experiments are conducted on the CEC’2017 benchmark set with three different dimension sizes to demonstrate the effectiveness of DEGGDE. Experimental results have confirmed that DEGGDE performs competitively with or even significantly better than eleven state-of-the-art and representative DE variants.
Optimal Cyber Security Investment in a Mixed Risk Management Framework: Examining the Role of Cyber Insurance and Expenditure Analysis

Cyber security importance has escalated globally, driven by its pivotal role in shaping daily life, encompassing both personal and non-personal aspects. Cyber security breach probability functions play a crucial role in comprehending how cyber security investments affect vulnerability to cyber attacks. These functions employ mathematical models to guide decision making in cyber risk management. Thus, studying and improving them is useful in this context. In particular, using these models, this article explores the effectiveness of an integrated risk management strategy that merges insurance and security investments, aiming to minimize overall security expenses. Within this strategy, security investments contribute to reducing the insurance premium. This research investigates the optimal investment for this blended approach under total insurance coverage. When the integrated risk management strategy combining insurance and security investments is deemed the optimal choice, this paper reveals that the insurance premium tends to be the dominant component in the overall security expense in the majority of cases. This implies that the cost of insurance outweighs the cost of security investments.
Impact of financial development on domestic investment: Evidence from West African countries

Abstract This paper investigates the effect of financial development on domestic investment in West African countries. The study uses data from 1985 to 2019 and employs the pooled mean group technique. The main finding of the study is that financial development has a positive effect on domestic investment in the long run but an insignificant effect in the short run. Furthermore, remittances, real GDP per capita and trade openness increase investment rate. The results of causality tests support the view that investment is a channel through which financial development stimulates economic growth. Therefore, it is reasonable for the selected countries to formulate policies that promote domestic credit to the private sector in order to ease liquidity constraints and increase investment and economic growth.
Evaluating User Satisfaction Using Deep-Learning-Based Sentiment Analysis for Social Media Data in Saudi Arabia’s Telecommunication Sector

Social media has become common as a means to convey opinions and express the extent of satisfaction and dissatisfaction with a service or product. In the Kingdom of Saudi Arabia specifically, most social media users share positive and negative opinions about a service or product, especially regarding communication services, which is one of the most important services for citizens who use it to communicate with the world. This research aimed to analyse and measure user satisfaction with the services provided by the Saudi Telecom Company (STC), Mobily, and Zain. This type of sentiment analysis is an important measure and is used to make important business decisions to succeed in increasing customer loyalty and satisfaction. In this study, the authors developed advanced methods based on deep learning (DL) to analyse and reveal the percentage of customer satisfaction using the publicly available dataset AraCust. Several DL models have been utilised in this study, including long short-term memory (LSTM), gated recurrent unit (GRU), and BiLSTM, on the AraCust dataset. The LSTM model achieved the highest performance in text classification, demonstrating a 98.04% training accuracy and a 97.03% test score. The study addressed the biggest challenge that telecommunications companies face: that the company’s services influence customers’ decisions due to their dissatisfaction with the provided services.
Virtual Reality as a Tool for Sustainable Training and Education of Employees in Industrial Enterprises

The paper deals with the possibilities of using Virtual Reality in the training and safety of enterprises active in the raw materials industry. It examines the influence and impact on their employees. The main impetus for starting research in this area has been a need for more use of the full potential of Virtual Reality in the industrial sector. Virtual Reality (VR) has become a promising education and employee training tool. It provides an immersive and interactive learning environment, allowing users to engage with simulations, scenarios, and simulations in real time. VR can facilitate the acquisition of practical skills, help learners retain information better, and foster the development of soft skills, such as communication, teamwork, and leadership. The paper is divided into the following sections. The first two are devoted to the introduction to the issue and a review of the literature. The materials and methods section describes the possibilities of using photogrammetry to create virtual scenes and 3D models usable in Virtual Reality. This section also describes the research methods used to evaluate the approach for teaching and training employees. The last two sections evaluate and discuss the results achieved. Having regarded the research realized, it was found that our approach to researching the education of employees and the development of their skills brings excellent benefits and, compared to the traditional educational approach, is much more time-efficient so that employees can improve their work habits and behavior in a relatively short period. In employee training, VR can simulate real-life scenarios, providing workers with hands-on experience in a safe, controlled environment. This technology can also help companies save time and resources, eliminating the need for travel and reducing expenditure on expensive equipment. However, despite its many benefits, VR in education and training can be cost-demanding and requires specialized hardware and software, which may limit its widespread adoption.
A High-Precision Detection Model of Small Objects in Maritime UAV Perspective Based on Improved YOLOv5

Object detection by shipborne unmanned aerial vehicles (UAVs) equipped with electro-optical (EO) sensors plays an important role in maritime rescue and ocean monitoring. However, high-precision and low-latency maritime environment small-object-detection algorithms remain a major challenge. To address this problem, this paper proposes the YOLO-BEV (“you only look once”–“bird’s-eye view”) model. First, we constructed a bidirectional feature fusion module—that is, PAN+ (Path Aggregation Network+)—adding an extremely-small-object-prediction head to deal with the large-scale variance of targets at different heights. Second, we propose a C2fSESA (Squeeze-and-Excitation Spatial Attention Based on C2f) module based on the attention mechanism to obtain richer feature information by aggregating features of different depth layers. Finally, we describe a lightweight spatial pyramid pooling structure called RGSPP (Random and Group Convolution Spatial Pyramid Pooling), which uses group convolution and random channel rearrangement to reduce the model’s computational overhead and improve its generalization ability. The article compares the YOLO-BEV model with other object-detection algorithms on the publicly available MOBDrone dataset. The research results show that the mAP0.5 value of YOLO-BEV reached 97.1%, which is 4.3% higher than that of YOLOv5, and the average precision for small objects increased by 22.2%. Additionally, the YOLO-BEV model maintained a detection speed of 48 frames per second (FPS). Consequently, the proposed method effectively balances the accuracy and efficiency of object-detection in shipborne UAV scenarios, outperforming other related techniques in shipboard UAV maritime object detection.
Parallel Dense Video Caption Generation with Multi-Modal Features

The task of dense video captioning is to generate detailed natural-language descriptions for an original video, which requires deep analysis and mining of semantic captions to identify events in the video. Existing methods typically follow a localisation-then-captioning sequence within given frame sequences, resulting in caption generation that is highly dependent on which objects have been detected. This work proposes a parallel-based dense video captioning method that can simultaneously address the mutual constraint between event proposals and captions. Additionally, a deformable Transformer framework is introduced to reduce or free manual threshold of hyperparameters in such methods. An information transfer station is also added as a representation organisation, which receives the hidden features extracted from a frame and implicitly generates multiple event proposals. The proposed method also adopts LSTM (Long short-term memory) with deformable attention as the main layer for caption generation. Experimental results show that the proposed method outperforms other methods in this area to a certain degree on the ActivityNet Caption dataset, providing competitive results.