This paper introduces significant improvements over the existing cryptanalysis approaches on Salsa20 and ChaCha stream ciphers. For the first time, we reduced the attack complexity on Salsa20/8 to the lowest possible margin. We introduced an attack on ChaCha7.25. It is the first attack of its type on ChaCha7.25/20. In our approach, we studied differential cryptanalysis of the Salsa20 and ChaCha stream ciphers based on a comprehensive analysis of probabilistic neutral bits (PNBs). The existing differential cryptanalysis approaches on Salsa20 and ChaCha stream ciphers first study the differential bias at specific input and output differential positions and then search for probabilistic neutral bits. However, the differential bias and the set of PNBs obtained in this method are not always the ideal combination to conduct the attack against the ciphers. The researchers have not focused on the comprehensive analysis of the probabilistic neutrality measure of all key bits concerning all possible output difference positions at all possible internal rounds of Salsa20 and ChaCha stream ciphers. Moreover, the relationship between the neutrality measure and the number of inverse quarter rounds has not been scrutinized yet. To address these study gaps, we study the differential cryptanalysis based on the comprehensive analysis of probabilistic neutral bits on the reduced-round Salsa20 and ChaCha. At first, we comprehensively analyze the neutrality measure of 256 key bits positions. Afterward, we select the output difference bit position with the best average neutrality measure and look for the corresponding input differential with the best differential bias. Considering all aspects, we present an attack on Salsa20/8 with a time complexity of 2241.62 and data complexity of 231.5, which is the best-known single bit differential attack on Salsa20/8 and then, we introduced an attack on ChaCha7.25 rounds with a time complexity of 2254.011 and data complexity of 251.81.

The recent decision by the National Institute of Standards and Technology (NIST) to standardize lattice-based cryptography has further increased the demand for security analysis. The Ring-Learning with Error (Ring-LWE) problem is a mathematical problem that constitutes such lattice cryptosystems. It has many algebraic properties because it is considered in the ring of integers, R, of a number field, K. These algebraic properties make the Ring-LWE based schemes efficient, although some of them are also used for attacks. When the modulus, q, is unramified in K, it is known that the Ring-LWE problem, to determine the secret information s ∈ R/qR, can be solved by determining s (mod q) ∈ Fqf for all prime ideals q lying over q. The χ2-attack determines s (mod q) ∈Fqf using chi-square tests over R/q ≅ Fqf. The χ2-attack is improved in the special case where the residue degree f is two, which is called the two-residue-degree χ2-attack. In this paper, we extend the two-residue-degree χ2-attack to the attack that works efficiently for any residue degree. As a result, the attack time against a vulnerable field using our proposed attack with parameter (q,f)=(67, 3) was 129 seconds on a standard PC. We also evaluate the vulnerability of the two-power cyclotomic fields.

In recent years, the market size for internet advertising has been increasing with the expansion of the Internet. Among the internet advertising technologies, affiliate services, which are a performance-based service, use cookies to track and measure the performance of affiliates. However, for the purpose of safeguarding personal information, cookies tend to be regulated, which leads to concerns over whether normal tracking by cookies works as intended. Therefore, in this study, the recent problems from the perspectives of affiliates, affiliate service providers, and advertisers are extracted, and a framework of cookie-independent measuring engagement method using access logs is proposed and open issues are discussed for future affiliate services.

With the growing global demand for seafood, sustainable aquaculture is attracting more attention than conventional natural fishing, which causes overfishing and damage to the marine environment. However, a major problem facing the aquaculture industry is the cost of feeding, which accounts for about 60% of a fishing expenditure. Excessive feeding increases costs, and the accumulation of residual feed on the seabed negatively impacts the quality of water environments (e.g., causing red tides). Therefore, the importance of raising fishes efficiently with less food by optimizing the timing and quantity of feeding becomes more evident. Thus, we developed a system to quantitate the amount of fish activity for the optimal feeding time and feed quantity based on the images taken. For quantitation, optical flow that is a method for tracking individual objects was used. However, it is difficult to track individual fish and quantitate their activity in the presence of many fishes. Therefore, all fish in the filmed screen were considered as a single school and the amount of change in an entire screen was used as the amount of the school activity. We divided specifically the entire image into fixed regions and quantitated by vectorizing the amount of change in each region using optical flow. A vector represents the moving distance and direction. We used the numerical data of a histogram as the indicator for the amount of fish activity by dividing them into classes and recording the number of occurrences in each class. We verified the effectiveness of the indicator by quantitating the eating and not eating movements during feeding. We evaluated the performance of the quantified indicators by the support vector classification, which is a form of machine learning. We confirmed that the two activities can be correctly classified.

With a rapidly escalating number of sophisticated cyber-attacks, protecting Internet of Things (IoT) networks against unauthorized activity is a major concern. The detection of malicious attack traffic is thus crucial for IoT security to prevent unwanted traffic. However, existing traditional malicious traffic detection systems which relied on supervised machine learning approach need a considerable number of benign and malware traffic samples to train the machine learning models. Moreover, in the cases of zero-day attacks, only a few labeled traffic samples are accessible for analysis. To deal with this, we propose a few-shot malicious IoT traffic detection system with a prototypical graph neural network. The proposed approach does not require prior knowledge of network payload binaries or network traffic signatures. The model is trained on labeled traffic data and tested to evaluate its ability to detect new types of attacks when only a few labeled traffic samples are available. The proposed detection system first categorizes the network traffic as a bidirectional flow and visualizes the binary traffic flow as a color image. A neural network is then applied to the visualized traffic to extract important features. After that, using the proposed few-shot graph neural network approach, the model is trained on different few-shot tasks to generalize it to new unseen attacks. The proposed model is evaluated on a network traffic dataset consisting of benign traffic and traffic corresponding to six types of attacks. The results revealed that our proposed model achieved an F1 score of 0.91 and 0.94 in 5-shot and 10-shot classification, respectively, and outperformed the baseline models.

Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.

The art gallery problem is to find a set of guards who together can observe every point of the interior of a polygon P. We study a chromatic variant of the problem, where each guard is assigned one of k distinct colors. A chromatic guarding is said to be conflict-free if at least one of the colors seen by every point in P is unique (i.e., each point in P is seen by some guard whose color appears exactly once among the guards visible to that point). In this paper, we consider vertex-to-point guarding, where the guards are placed on vertices of P, and they observe every point of the interior of P. The vertex-to-point conflict-free chromatic art gallery problem is to find a colored-guard set such that (i) guards are placed on P's vertices, and (ii) any point in P can see a guard of a unique color among all the visible guards. In this paper, it is shown that determining whether there exists a conflict-free chromatic vertex-guard set for a polygon with holes is NP-hard when the number of colors is k=2.

With the support of emerging technologies such as 5G, machine learning, edge computing and Industry 4.0, the Internet of Things (IoT) continues to evolve and promote the construction of future networks. Existing work on IoT mainly focuses on its practical applications, but there is little research on modeling the interactions among components in IoT systems and verifying the correctness of the network deployment. Therefore, the Calculus of the Internet of Things (CaIT) has previously been proposed to formally model and reason about IoT systems. In this paper, the CaIT calculus is extended by introducing broadcast communications. For modeling convenience, we provide explicit operations to model node mobility as well as the interactions between sensors (or actuators) with the environment. To support the use of UPPAAL to verify the temporal properties of IoT networks described by the CaIT calculus, we establish a relationship between timed automata and the CaIT calculus. Using UPPAAL, we verify six temporal properties of a simple “smart home” example, including Boiler On Manually, Boiler Off Automatically, Boiler On Automatically, Lights On, Lights Mutually, and Windows Simultaneously. The verification results show that the “smart home” can work properly.

Class imbalance is one of the challenges faced in the field of machine learning. It is difficult for traditional classifiers to predict the minority class data. If the imbalanced data is not processed, the effect of the classifier will be greatly reduced. Aiming at the problem that the traditional classifier tends to the majority class data and ignores the minority class data, imbalanced data over-sampling method based on iterative self-organizing data analysis technique algorithm(ISODATA) clustering is proposed. The minority class is divided into different sub-clusters by ISODATA, and each sub-cluster is over-sampled according to the sampling ratio, so that the sampled minority class data also conforms to the imbalance of the original minority class data. The new imbalanced data composed of new minority class data and majority class data is classified by SVM and Random Forest classifier. Experiments on 12 datasets from the KEEL datasets show that the method has better G-means and F-value, improving the classification accuracy.

In machine learning, data augmentation (DA) is a technique for improving the generalization performance of models. In this paper, we mainly consider gradient descent of linear regression under DA using noisy copies of datasets, in which noise is injected into inputs. We analyze the situation where noisy copies are newly generated and injected into inputs at each epoch, i.e., the case of using on-line noisy copies. Therefore, this article can also be viewed as an analysis on a method using noise injection into a training process by DA. We considered the training process under three training situations which are the full-batch training under the sum of squared errors, and full-batch and mini-batch training under the mean squared error. We showed that, in all cases, training for DA with on-line copies is approximately equivalent to the l2 regularization training for which variance of injected noise is important, whereas the number of copies is not. Moreover, we showed that DA with on-line copies apparently leads to an increase of learning rate in full-batch condition under the sum of squared errors and the mini-batch condition under the mean squared error. The apparent increase in learning rate and regularization effect can be attributed to the original input and additive noise in noisy copies, respectively. These results are confirmed in a numerical experiment in which we found that our result can be applied to usual off-line DA in an under-parameterization scenario and can not in an over-parametrization scenario. Moreover, we experimentally investigated the training process of neural networks under DA with off-line noisy copies and found that our analysis on linear regression can be qualitatively applied to neural networks.

In recent years, there has been a growing demand for urban digitization using cameras and light detection and ranging (LiDAR). Shadows are a condition that affects measurement the most. Therefore, shadow detection technology is essential. In this study, we propose shadow detection utilizing the LiDAR intensity that depends on the surface properties of objects but not on irradiation from other light sources. Unlike conventional LiDAR-intensity-aided shadow detection methods, our method embeds the un-correlation between luminance and LiDAR intensity in each position into the optimization. The energy, which is defined by the un-correlation between luminance and LiDAR intensity in each position, is minimized by graph-cut segmentation to detect shadows. In evaluations on KITTI and Waymo datasets, our shadow-detection method outperformed the previous methods in terms of multiple evaluation indices.

The thermal imaging pedestrian segmentation system has excellent performance in different illumination conditions, but it has some drawbacks(e.g., weak pedestrian texture information, blurred object boundaries). Meanwhile, high-performance large models have higher latency on edge devices with limited computing performance. To solve the above problems, in this paper, we propose a real-time thermal infrared pedestrian segmentation method. The feature extraction layers of our method consist of two paths. Firstly, we utilize the lossless spatial downsampling to obtain boundary texture details on the spatial path. On the context path, we use atrous convolutions to improve the receptive field and obtain more contextual semantic information. Then, the parameter-free attention mechanism is introduced at the end of the two paths for effective feature selection, respectively. The Feature Fusion Module (FFM) is added to fuse the semantic information of the two paths after selection. Finally, we accelerate method inference through multi-threading techniques on the edge computing device. Besides, we create a high-quality infrared pedestrian segmentation dataset to facilitate research. The comparative experiments on the self-built dataset and two public datasets with other methods show that our method also has certain effectiveness. Our code is available at https://github.com/mcjcs001/LEIPNet.

Visual saliency prediction has improved dramatically since the advent of convolutional neural networks (CNN). Although CNN achieves excellent performance, it still cannot learn global and long-range contextual information well and lacks interpretability due to the locality of convolution operations. We proposed a saliency prediction model based on multi-prior enhancement and cross-modal attention collaboration (ME-CAS). Concretely, we designed a transformer-based Siamese network architecture as the backbone for feature extraction. One of the transformer branches captures the context information of the image under the self-attention mechanism to obtain a global saliency map. At the same time, we build a prior learning module to learn the human visual center bias prior, contrast prior, and frequency prior. The multi-prior input to another Siamese branch to learn the detailed features of the underlying visual features and obtain the saliency map of local information. Finally, we use an attention calibration module to guide the cross-modal collaborative learning of global and local information and generate the final saliency map. Extensive experimental results demonstrate that our proposed ME-CAS achieves superior results on public benchmarks and competitors of saliency prediction models. Moreover, the multi-prior learning modules enhance images express salient details, and model interpretability.

Question answering (QA) systems are designed to answer questions based on given information or with the help of external information. Recent advances in QA systems are overwhelmingly contributed by deep learning techniques, which have been employed in a wide range of fields such as finance, sports and biomedicine. For generative QA in open-domain QA, although deep learning can leverage massive data to learn meaningful feature representations and generate free text as answers, there are still problems to limit the length and content of answers. To alleviate this problem, we focus on the variant YNQA of generative QA and propose a model CasATT (cascade prompt learning framework with the sentence-level attention mechanism). In the CasATT, we excavate text semantic information from document level to sentence level and mine evidence accurately from large-scale documents by retrieval and ranking, and answer questions with ranked candidates by discriminative question answering. Our experiments on several datasets demonstrate the superior performance of the CasATT over state-of-the-art baselines, whose accuracy score can achieve 93.1% on IR&QA Competition dataset and 90.5% on BoolQ dataset.

This paper addresses the novel task of detecting chorus sections in English and Japanese lyrics text. Although chorus-section detection using audio signals has been studied, whether chorus sections can be detected from text-only lyrics is an open issue. Another open issue is whether patterns of repeating lyric lines such as those appearing in chorus sections depend on language. To investigate these issues, we propose a neural-network-based model for sequence labeling. It can learn phrase repetition and linguistic features to detect chorus sections in lyrics text. It is, however, difficult to train this model since there was no dataset of lyrics with chorus-section annotations as there was no prior work on this task. We therefore generate a large amount of training data with such annotations by leveraging pairs of musical audio signals and their corresponding manually time-aligned lyrics; we first automatically detect chorus sections from the audio signals and then use their temporal positions to transfer them to the line-level chorus-section annotations for the lyrics. Experimental results show that the proposed model with the generated data contributes to detecting the chorus sections, that the model trained on Japanese lyrics can detect chorus sections surprisingly well in English lyrics, and that patterns of repeating lyric lines are language-independent.

Recently, many AI-aided layout design systems are developed to reduce tedious manual intervention based on deep learning. However, most methods focus on a specific generation task. This paper explores a challenging problem to obtain multiple layout design generation (LDG), which generates floor plan or urban plan from a boundary input under a unified framework. One of the main challenges of multiple LDG is to obtain reasonable topological structures of layout generation with irregular boundaries and layout elements for different types of design. This paper formulates the multiple LDG task as an image-to-image translation problem, and proposes a conditional generative adversarial network (GAN), called LDGAN, with adaptive modules. The framework of LDGAN is based on a generator-discriminator architecture, where the generator is integrated with conditional convolution constrained by the boundary input and the attention module with channel and spatial features. Qualitative and quantitative experiments were conducted on the SCUT-AutoALP and RPLAN datasets, and the comparison with the state-of-the-art methods illustrate the effectiveness and superiority of the proposed LDGAN.

In this paper, we propose a unified algebraic design of the generalized Moreau enhancement matrix (GME matrix) for the Linearly involved Generalized-Moreau-Enhanced (LiGME) model. The LiGME model has been established as a framework to construct linearly involved nonconvex regularizers for sparsity (or low-rank) aware estimation, where the design of GME matrix is a key to guarantee the overall convexity of the model. The proposed design is applicable to general linear operators involved in the regularizer of the LiGME model, and does not require any eigendecomposition or iterative computation. We also present an application of the LiGME model with the proposed GME matrix to a group sparsity aware least squares estimation problem. Numerical experiments demonstrate the effectiveness of the proposed GME matrix in the LiGME model.

Network traffic control and classification have become increasingly dependent on deep packet inspection (DPI) approaches, which are the most precise techniques for intrusion detection and prevention. However, the increasing traffic volumes and link speed exert considerable pressure on DPI techniques to process packets with high performance in restricted available memory. To overcome this problem, we proposed dual cuckoo filter (DCF) as a data structure based on cuckoo filter (CF). The CF can be extended to the parallel mode called parallel Cuckoo Filter (PCF). The proposed data structure employs an extra hash function to obtain two potential indices of entries. The DCF magnifies the superiority of the CF with no additional memory. Moreover, it can be extended to the parallel mode, resulting in a data structure referred to as parallel Dual Cuckoo filter (PDCF). The implementation results show that using the DCF and PDCF as identification tools in a DPI system results in time improvements of up to 2% and 30% over the CF and PCF, respectively.

Modern large scale distributed storage systems play a central role in data center and cloud storage, while node failure in data center is common. The lost data in failure node must be recovered efficiently. Locally repairable codes (LRCs) are designed to solve this problem. The locality of an LRC is the number of nodes that participate in recovering the lost data from node failure, which characterizes the repair efficiency. An LRC is called optimal if its minimum distance attains Singleton-type upper bound [1]. In this paper, using basic techniques of linear algebra over finite field, infinite optimal LRCs over extension fields are derived from a given optimal LRC over base field(or small field). Next, this paper investigates the relation between near-MDS codes with some constraints and LRCs, further, proposes an algorithm to determine locality of dual of a given linear code. Finally, based on near-MDS codes and the proposed algorithm, those obtained optimal LRCs are shown.

In this paper, we describe the Galois dual of rank metric codes in the ambient space FQn×m and FQmn, where Q=qe. We obtain connections between the duality of rank metric codes with respect to distinct Galois inner products. Furthermore, for 0 ≤ s < e, we introduce the concept of qsm-dual bases of FQm over FQ and obtain some conditions about the existence of qsm-self-dual basis.