Reviewerly/exHarmony huggingface dataset:
Yolov5 Vehicle Detection Model in Fog Based on Channel Attention Enhancement

Abstract Vehicle detection in foggy weather plays an indispensable role in the field of intelligent transportation. This article proposes an improved YOLOv5 vehicle detection model based on the problems of insufficient detection accuracy and high fault tolerance of most algorithms in foggy weather. First, the AOD-Net network is used for defogging preprocessing of the original image. Then, the SE attention mechanism is fused in the C3 module of the Backbone feature extraction backbone network to adaptively allocate weight information, enhance the attention to important features, and reduce the impact of noise and irrelevant information. Finally, BiFPN is used in the Neck feature fusion network to replace the original PANet and enhance the model’s feature fusion ability. Experiments are conducted on the Cityscapes and RTTS datasets, and the results show that the improved YOLOv5 algorithm in this article has significant improvements in precision, recall rate, and average precision mean compared to the original model, with increases of 8.4%, 9.5%, and 9.2%, respectively. It can better adapt to vehicle detection tasks in foggy weather.
Distributed Photovoltaic Optimization Configuration Based on Marine Predators Algorithm in Low Voltage Transformer Area

Abstract This article proposes a method for determining the scale and location of Distributed Photovoltaic (DPV)under the operational constraints of low-voltage transformer areas. Firstly, a low-voltage transformer areas operation model and a DPV output model were established. Determine the access nodes and rated capacity of the DPV using the indicators of minimizing voltage fluctuations and minimizing line loss rate in the optimized configuration model; Then, the maximum capacity of DPV that can be installed in the transformer area is determined with the goal of maximizing the photovoltaic penetration rate in DPV accessible nodes; Use Marine Predators Algorithm(MPA) to solve nonlinear problems in the model. Finally, simulation analysis was conducted using an actual low-voltage transformer area as an example to demonstrate the effectiveness of the proposed model and method, ensuring the safe and stable operation of the low-voltage transformer area when connected to a high proportion of photovoltaic energy.
Applications of Deep Learning-Based Methods on Surveillance Video Stream by Tracking Various Suspicious Activities

Security and safety are facilitated by the use of modern video surveillance systems in public settings including markets, shops, hospitals, banks, highways, universities, city administration offices, and smart cities. Using security software, an anomaly in the video can be detected swiftly and accurately. Video of riots, traffic violations, stampedes, and items like weapons left at sensitive locations, as well as abandoned luggage, illustrates anomalous activities and creatures that must be detected automatically and in the present. Because of the ambiguous nature of anomalous occurrences in videos, fluctuating ambient conditions, the complexity of human actions, and the lack of relevant datasets, video anomalies are difficult to detect. The data collected by surveillance cameras is massive regularly. Watching and detecting any abnormal occurrences or unwanted incidents in long-hour live video broadcasts are both difficult and time-consuming. Detecting erroneous video patterns automatically can allow for a significant reduction in manual monitoring efforts. The detection of video anomalies is therefore a hot topic in computer vision research in recent years. Computer vision algorithms and video processing techniques are used to automatically detect abnormal events in videos. Anomalies, abnormalities, strange, and outliers, among others, refer to irregular shapes found in films. With recent advances in deep learning (DL), algorithms that use this power of representation have benefited. Deep neural networks (DNNs) are known for their ability to learn complex representations and extract intricate and abstract information from inputs. Several types of sequential data can be processed by recurrent neural networks, including audio signals, temporal series, as well as text. With CNN (convolutional neural networks), spatial patterns can be extracted for tasks such as object detection and image classification. During the past few years, difficulty-level techniques have become the standard method of completing a wide range of tasks. Video feeds from a surveillance system are used as an example of deep learning in this chapter.
Cloud computing resource load prediction based on improved VMD and attention mechanism

Abstract Dynamic adjustment of resource supply according to users’ resource load is one of the important technologies to achieve efficient management of cloud computing resources. In order to accurately obtain users’ demand for resource load in the future, based on quantum particle swarm optimization (QPSO), a prediction model QVMD_AM_LSTM was proposed to optimize variational mode decomposition (VMD) and to add attention mechanism to AM_LSTM. A comparative experiment was conducted on the open-source dataset cluster-trace-v2018 from Alibaba Cloud. The outcomes show that compared with LSTM, AM-LSTM, GRU-LSTM, Refined-LSTM, Stacked-LSTM and other existing prediction models, the mean square error of the QVMD_AM_LSTM model proposed in this article decreases by 8-14, and the correlation coefficient rises by 6%-11%. QVMD_AM_LSTM model has higher prediction accuracy.
Enhanced Security Contraband Detection through Integration of Attention Mechanism in R3Det

Abstract Detecting dangerous goods in security images is a challenging task. To overcome the challenges of localization difficulty and directional feature loss of contraband in X-ray images, our proposed solution, R3Det, employs the Convolutional Block Attention Module (CBAM). By integrating ResNeSt into the original detector, our detector includes a soft attention mechanism to redistribute weights among feature channels. This enhances the network’s ability to extract important features and facilitates extraction of target objects features under complex backgrounds. Subsequently, we introduced the spatial and channel attention mechanism during the connection between the backbone and the Feature Pyramid Network (FPN), enabling the model to focus on significant features while ignoring complex background information, then the following Feature Refinement Module to achieve feature alignment in a pixel-by-pixel manner. Our approach successfully achieved rotating target detection in the background of complex X-ray images. Through end-to-end training, our proposed method achieves a 2.6% improvement over the original detector, with a mean Average Precision (mAP) of 86.7%. Notably, our approach showed remarkable results in detecting sensors, pressure, and firetrackers. Now, we have deployed our proposed method on actual security machines for hazardous material detection tasks.
Similar Graph Face Clustering Based on Graph Convolutional Neural Network

Abstract Face clustering is primarily a method for grouping a large number of face images and has important applications in the fields of label-free face image annotation and image management. Traditional machine learning clustering algorithms do not work well on face image data and are unable to learn effectively on complex face image features. Recent research has turned to the use of graph convolutional neural networks (GCNs) to learn contextual information from neighbourhood features between face images for inference, which can significantly improve performance. Unlike the conventional link prediction of face data points and confidence prediction between vertices by using GCNs, for this paper a similarity graph based face clustering algorithm is proposed. Construction of subgraphs by K-nearest neighbour algorithm, Learn the similarity between the k-nearest neighbor subgraphs of each face instance by constructing subgraphs with the k-nearest neighbor algorithm. Using a bottom-up clustering strategy to merge subgraphs to complete the clustering, this algorithm improves the clustering results on complex face features by 20% and 7% compared to traditional machine learning methods such as K-means and DBSCAN respectively, improving the clustering accuracy and reducing the computational complexity.
Constacyclic and Skew Constacyclic Codes Over a Finite Commutative Non-chain Ring

For anPrakash, Om Islam, Habibul Verma, Ram Krishna odd prime p, this article studies the $$\lambda $$ -constacyclic and skew $$\lambda $$ -constacyclic codes of arbitrary length over the finite commutative non-chain ring $$R=\mathbb {F}_{p^m}[u,v,w]/\langle u^{2}-1,v^{2}-1,w^{2}-1,uv-vu,vw-wv,wu-uw\rangle $$ , where $$\lambda $$ is a unit in R. By using the decomposition method, we determine the structure of $$\lambda $$ -constacyclic and skew $$\lambda $$ -constacyclic codes. Also, the necessary and sufficient conditions of these codes to be self-dual are obtained. Further, it is shown that the Gray images of $$\lambda $$ -constacyclic and skew $$\lambda $$ -constacyclic codes of length n over R are quasi-twisted and skew quasi-twisted codes, respectively of length 8n and index 8 over $$\mathbb {F}_{p^m}$$ . Finally, two non-trivial examples are given to validate the obtained results.
An Application of Twisted Group Rings in Secure Group Communications

In this paper we introduce a group key management protocol for secure group communications in a non-commutative setting. To do so, we consider a group ring over the dihedral group with a twisted multiplication using a cocycle. The protocol is appropriate for the so-called post-quantum era andGómez Olvera, Maria Dolores López-Ramos, Juan Antonio Torrecillas, Blas it is shown that the security of the initial key agreement is equivalent to the protocol given for just two communication parties, i.e., there is no information leakage as the number of users grows. Moreover we show that further rekeying messages provide forward and backward security, that means that no former or future user in a communication group can get information on previous or new future keys.
THE IMPLEMENTATION OF PROBLEM BASED LEARNING (PBL) IN STUDENTS WRITING SKILLS ON ANALYTICAL EXPOSITION TEXT

Problem based learning is an approach that makes the students think critically to solve problems. Because in writing a text, students have to make a statement that should be critical. Especially when writing an analytical exposition text in which they should persuade the readers to agree with their statements. Therefore, this study aimed to know whether implementing problem-based learning in analytical exposition text affects the students' writing skills. This quantitative study used pre-experimental method where the writers carried out a pretest and posttest for 36 students as participation. This study was conducted in one of the ULP classes in SMK N 2 Semarang in the academic year 2022/2023. The pretest and posttest data were analyzed using a writing assessment rubric. This study found out if the students' score increased where the mean score pretest was 69.33 to 89.41 for the posttest. So, it was shown that PBL significantly affects students’ writing skills in making an analytical exposition test
Quantum Coherence from a Few Incoherent Bosons

Abstract An engineered hybrid interaction of a few incoherent bosons with a cooled discrete‐level system may give rise to substantial quantum coherence, an essential resource with diverse applications for quantum technology. This is in contrast to the expected requirement of intense and coherent external drives in current technologies. Substituting such external drives with internal hybrid processes stimulated by mere thermal energy may then contribute to reduced energy consumption in observing and using quantum phenomena. This low‐energy emergence of quantum coherence is then suitable for proof‐of‐principle exploration with recent experiments in platforms such as trapped ions and superconducting circuits.
Postdigital Educational Futures

This entry departs from promissory imaginaries about educational technology to show how postdigital research adopts a critical stance on futures. Futures are observed as both a research object and a methodological frame to understand an increasingly complex and technology-dense reality. Temporalities are identified as follows: futures in the present, near futures, far futures, and alternative futures. These situated time-spaces cater for different methodological approaches. Anticipatory methods are elaborated in relation to a near future, while far futures can be grasped through speculatively methods. Alternative futures are usually formulated in the literary vein of science fiction. The tendency in recent futures studies is to make explicit dimensions of ethics, care, and values in the crafting of educational futures.
Post-quantum Hybrid KEMTLS Performance in Simulated and Real Network Environments

Integrating Post-Quantum Cryptography (PQC) in network protocols presents a significant challenge. The efficiency of the Transport Layer Security (TLS) protocol can be significantly impacted by adopting larger PQC public keys and signatures. In this context, the KEMTLS approach has emerged as a promising solution, leveraging PQC Key Encapsulation Mechanisms (KEMs) with smaller sizes to replace handshakes and signatures. Besides efficiency, security and reliability are also at risk when adopting new cryptographic primitives. Hybrid PQC methodologies minimize these risks by adding classical cryptographic schemes into the post-quantum protocols. This work introduces the incorporation of hybrid PQC into the KEMTLS and KEMTLS-PDK protocols, integrating elliptic curve-based KEMs. We perform a comprehensive benchmarking analysis across diverse network conditions, KEM types, and security parameters to evaluate the effectiveness of hybrid models. Moreover, we conduct a comparative assessment between our hybrid KEMTLS and a hybrid PQTLS variant, which uses handshake signatures. Our results demonstrate that the performance penalty incurred by hybrid KEMTLS, compared to PQC-only KEMTLS, remains negligible within specific security thresholds. Overall, our findings highlight the advantages of adopting hybrid protocols, outweighing the marginal performance trade-offs observed at higher security parameters. These results underscore the practical feasibility of employing hybrid PQC protocols, enhancing network security in an interconnected environment.
Folding Schemes with Selective Verification

In settings such as delegation of computation where a prover is doing computation as a service for many verifiers, it is important to amortize the prover's costs without increasing those of the verifier. We introduce folding schemes with selective verification. Such a scheme allows a prover to aggregate m NP statements $$x_i\in \mathcal {L}$$ in a single statement $$x\in \mathcal {L}$$ . Knowledge of a witness for x implies knowledge of witnesses for all m statements. Furthermore, each statement can be individually verified by asserting the validity of the aggregated statement and an individual proof $$\pi _i$$ with size sublinear in the number of aggregated statements. In particular, verification of statement $$x_i$$ does not require reading (or even knowing) all the statements aggregated. We demonstrate natural folding schemes for various languages: inner product relations, vector and polynomial commitment openings and relaxed R1CS of NOVA. All these constructions incur a minimal overhead for the prover, comparable to simply reading the statements.
Physical Zero-Knowledge Proofs for Five Cells

Five Cells is a logic puzzle consisting of a rectangular grid, with some cells containing a number. The player has to partition the grid into pentominoes such that the number in each cell must be equal to the number of edges of that cell that are borders of pentominoes. In this paper, we propose two physical zero-knowledge proof protocols for Five Cells using a deck of playing cards, which allow a prover to physically show that he/she knows a solution of the puzzle without revealing it. In the optimization of our first protocol, we also develop a technique to reduce the number of required cards from quadratic to linear in the number of cells, which can be used in other zero-knowledge proof protocols related to graph coloring as well.
Quotable Signatures for Authenticating Shared Quotes

Quotable signature schemes are digital signature schemes with the additional property that from the signature for a message, any party can extract signatures for (allowable) quotes from the message, without knowing the secret key or interacting with the signer of the original message. Crucially, the extracted signatures are still signed with the original secret key. We define a notion of security for quotable signature schemes and construct a concrete example of a quotable signature scheme, using Merkle trees and classical digital signature schemes. The scheme is shown to be secure, with respect to the aforementioned notion of security. Additionally, we prove bounds on the complexity of the constructed scheme. Finally, concrete use cases of quotable signatures are considered, using them to combat misinformation by bolstering authentic content on social media. We consider both how quotable signatures can be used, and why using them could help mitigate the effects of fake news.
ACE-HoT: Accelerating an Extreme Amount of Symmetric Cipher Evaluations for (High-order) Avalanche Tests

In this work, we tackle the problem of estimating the security of iterated symmetric ciphers in an efficient manner, with tests that do not require a deep analysis of the internal structure of the cipher. This is particularly useful during the design phase of these ciphers, especially for quickly testing several combinations of possible parameters defining several cipher design variants. We consider a popular statistical test that allows us to determine the probability of flipping each cipher output bit, given a small variation in the input of the cipher. From these probabilities, one can compute three measurable metrics related to the well-known full diffusion, avalanche and strict avalanche criteria. This highly parallelizable testing process scales linearly with the number of samples, i.e., cipher inputs, to be evaluated and the number of design variants to be tested. But, the number of design variants might grow exponentially with respect to some parameters. The high cost of Central Processing Unit (CPU)s makes them a bad candidate for this kind of parallelization. As a main contribution, we propose a framework, ACE-HoT, to parallelize the testing process using multi-Graphics Processing Units (GPUs). Our implementation does not perform any intermediate CPU-GPU data transfers. The diffusion and avalanche criteria can be seen as an application of discrete first-order derivatives. As a secondary contribution, we generalize these criteria to their high-order version. Our generalization requires an exponentially larger number of samples, in order to compute sufficiently accurate probabilities. As a case study, we apply ACE-HoT on most of the finalists of the National Institute of Standards and Technologies (NIST) lightweight standardization process, with a special focus on the winner ASCON.
Privacy-Preserving Edit Distance Computation Using Secret-Sharing Two-Party Computation

The edit distance is a metric widely used in genomics to measure the similarity of two DNA chains. Motivated by privacy concerns, we propose a 2PC protocol to compute the edit distance while preserving the privacy of the inputs. Since the edit distance algorithm can be expressed as a mixed-circuit computation, our approach uses protocols based on secret-sharing schemes like Tinier and SPD $${\mathbb {Z}}_{2^k}$$ ; and also daBits to perform domain conversion and edaBits to perform arithmetic comparisons. We modify the Wagner-Fischer edit distance algorithm, aiming at reducing the number of rounds of the protocol, and achieve a flexible protocol with a trade-off between rounds and multiplications. We implement our proposal in the MP-SPDZ framework, and our experiments show that it reduces the execution time respectively by 81% and 54% for passive and active security with respect to a baseline implementation in a LAN. The experiments also show that our protocol reduces traffic by two orders of magnitude compared to a BMR-MASCOT implementation.
The Pattern Design of A Camouflage Suit Based on the Cycle Generative Adversarial Network Algorithm

Camouflage refers to an essential means of protection for military reconnaissance. However, the traditional method of camouflage image generation does not allow for end-to-end generation. The algorithm of Cycle Generative Adversarial Network adopted in this article can not only keep the features of original pictures but also realize the end-to-end generation, which can better solve seasonal problems better. The generation model and the discrimination model are trained using the concept of the cyclic confrontation game of Cycle GAN. In the training process, the loss function served to stimulate the background image and camouflage images mapping to each other. The generated image is captured into the recognition model for recognition, so as to provide feedback on the findings. Finally, the camouflage image with background image characteristics is output to realize the generation of an end-to-end camouflage image. The camouflage evaluation index is used to detect the quality of color, texture, and edge of the experimental output image. The generated image shows a good camouflage effect in the color, texture, and comparison of edges, thus verifying the effectiveness of the practical scheme.
Promoting pre- and in-service teachers’ digital competence by using reverse mentoring

Abstract The study investigated the effectiveness of reverse mentoring as an innovative instructional method to promote pre- and in-service teachers’ digital competence. We conducted a quasi-experimental intervention study with 90 pre-service and 57 in-service teachers who took part in two-day online-workshops. The effectiveness of the reverse mentoring-intervention, where pre- and in-service teachers worked together in pairs to jointly develop ideas for the use of digital technology in the classroom, was compared to collaborative learning formats among peers in university seminars for pre-service teachers (control group 1) and professional development workshops for in-service teachers (control group 2). Technological-pedagogical knowledge (TPK), self-efficacy and positive beliefs about teaching with digital technology were investigated as outcomes. Pre- and in-service teachers in all groups gained in self-efficacy beliefs, whereby reverse mentoring was not more effective than the control conditions. TPK and positive beliefs about teaching with digital technology did not change over the course of the interventions. The results show that pre-service teachers can effectively act as mentors for in-service teachers to support their self-efficacy beliefs regarding the use of digital technology. However, we did not find evidence that reverse mentoring is more effective than peer learning among pre-service and in-service teachers to promote digital competence.