Generating graphs using computational models is a critical activity with numerous applications, including social network research and biological network modeling. Despite sig-nificant breakthroughs in graph-generating technologies, modern machine learning for simulating complex real-world networks still requires effective improvement. Specifically, the ability to conditionally generate graphs while accounting for local and global structural elements has not been fully explored. This study presents a novel approach to graph generation that combines Transformer encoders' strong contextual understanding with Long Short Term Memory (LSTM) decoders' sequence modeling capabilities, all within a Conditional Variational Auto Encoder (CVAE) framework. Our methodology aims to fine-tune the structural features of generated graphs more accurately, representing an advancement in conditional graph generation. Our results, based on extensive tests versus standard generative models utilizing graph datasets, show that our model can more clearly tune global-level structural features' values better than conventional models.

In recent years, there has been a growing demand for techniques to artificially generate graphs. Various proposals have been made for graph generation models using machine learning. Among these models, GraphTune is a model that allows to specify the features of the generated graphs. GraphTune has not achieved sufficient accuracy when specified values are in ranges where there are few samples in the training dataset. Therefore, in this paper, we propose a method to improve the accuracy of GraphTune by retraining it using graphs generated by the model itself. Through experiments using real-world graphs, we demonstrate that the higher accuracy can be achieved compared to the conventional method.

Compression by Substring Enumeration (CSE), which is one of lossless data compression algorithms, and various versions of CSE have been proposed. In encoding of CSE, substrings of given fixed length and their frequencies within circular string for an input string are output as a codeword. The circular string is made by connecting the first symbol and the last symbol of an input string. In decoding of CSE, the circular string is reconstructed from its substrings and their frequencies. Furthermore, the minimum length of substrings for which the decoding does reconstruct the circular string has been proved, together with a reconstruction algorithm. However, the algorithm requires substrings to have no errors. Therefore, in this paper, we propose an error correcting algorithm which can detect one of substrings having one bit-flipping, one bit-insertion, or one bit-deletion error and correct the bit error. By applying the proposed algorithm, we can reconstruct a circular string from a set of substrings and their frequencies including only one substring which has at most one bit error.

In domains of cybersecurity, recent advancements in Machine Learning (ML) and Deep Learning (DL) have significantly enhanced Network Intrusion Detection Systems (NIDS), improving the effectiveness of cybersecurity operations. However, attackers have also leveraged ML/DL to develop sophisticated models that generate adversarial packets capable of evading NIDS detection. Consequently, defenders must study and analyze these models to prepare for the evasion attacks that exploit NIDS detection mechanisms. Unfortunately, conventional generation models often rely on unrealistic assumptions about attackers' knowledge of NIDS components, making them impractical for real-world scenarios. To address this issue, we present DiffuPac, a first-of-its-kind generation model designed to generate adversarial packets that evade detection without relying on specific NIDS components. DiffuPac integrates a pre-trained Bidirectional Encoder Representations from Transformers (BERT) with diffusion model, which, through its capability for conditional denoising and classifier-free guidance, effectively addresses the real-world constraint of limited attacker knowledge. By concatenating malicious packets with contextually relevant normal packets and applying targeted noising only to the malicious packets, DiffuPac seamlessly blends adversarial packets into genuine network traffic. Through evaluations on real-world datasets, we demonstrate that DiffuPac achieves strong evasion capabilities against sophisticated NIDS, outperforming conventional methods by an average of 6.69 percentage points, while preserving the functionality and practicality of the generated adversarial packets.

This paper proposes two variants of improved Compression by Substring Enumeration (CSE) with a finite alphabet. In previous studies on CSE, an encoder utilizes inequalities which evaluate the number of occurrences of a substring and a minimal forbidden word (MFW) to be encoded. Also, its codeword length is proportional to the difference between the upper and lower bounds deduced from the inequalities, but the lower bound is not tight. Therefore, in this paper, we derive a new tight lower bound and consequently propose a new CSE algorithm using the new inequality. We also propose a new encoding order of substrings and MFWs which are sorted by row and column marginal totals of the proper substrings and MFWs, instead of lexicographical order used in previous studies. We then propose a new CSE algorithm which is the first proposed CSE algorithm using the new encoding order. Experimental results show that compression ratios of all files of the Calgary corpus in the proposed algorithms are better than those of a previous study on CSE with a finite alphabet. Moreover, compression ratios under the second proposed CSE get better than or equal to that under a well-known compressor for 11 files amongst 14 files in the corpus.

Rivest and Shamir introduced a write-once memory (WOM), a model of storage devices whose storage elements have restrictions on state transitions, and they presented some coding methods to reuse a WOM. An interesting question about a WOM is how efficiently we can reuse it with the best coding method, and as an answer to the question, Fu and Han Vinck determined the capacity of Fiat and Shamir's generalized WOMs. In this paper, we extend their results, introducing write-constrained memories (WCMs) that consider state transition costs, and determining the capacity of WCMs under a certain type of cost constraints.

A balanced code is a set of words over {a, b} such that the number of a's and the number of b's in a word are equal, and many applications using balanced codes have been proposed so far. Recently, not only the original balanced code, but also balanced codes with some other constraints have been studied mainly for an application of data storage media. However, contrary to other typical sets of words satisfying some constraints, the capacities of such balanced codes have not been well studied up to this moment. In this paper, we focus on balanced codes satisfying various run-length constraints and analyze their capacities. More precisely, we exhibit lower bounds on the capacities, or present the explicit capacities for certain cases.

To formulate rewriting operations on flash memory, we extend Write-Once Memory (WOM) and introduce Erasable WOM (EWOM) which allows block erasures, and then we define codes to rewrite on them. To measure performances of EWOM codes, we introduce the rate tradeoff pair, which is derived from the sum rate. We give an outer bound of the region of the possible tradeoff pairs for a certain class of EWOM's. We also propose fixed-rate EWOM codes calledWOM2E codes that utilize existing WOM codes. It reveals that WOM2E scheme is optimal in terms of rate tradeoff pair when the block size of EWOM is “infinity.”

Rivest and Shamir introduced Write-Once Memory (WOM), a model of storage devices whose storage elements have restrictions on state transitions, and they presented some coding methods to reuse WOM. An interesting question about WOM is how efficiently we can reuse it with the best coding method, and as an answer to the question, Fu and Han Vinck determined the capacity of Fiat and Shamir's generalized WOM. In this paper, we extend their results, introducing Write-Constrained Memory (WCM) that considers state transition cost, and determining the capacity of WCM under a certain type of cost constraints.

Recent advances in the area of wireless body area networks (WBANs) open new horizons in areas ranging from mHealth to entertainment. Reliability of communications and power consumption are paramount to widespread adoption of this technology. In this paper, we use ambulatory electroen-cephalography (EEG) monitoring in the context of WBANs and describe some network topologies and coding performance using graph theoretic techniques.

The class of periodic-finite-type shifts (PFT's) is a class of sofic shifts that strictly includes the class of shifts of finite type (SFT's), and the zeta function of a PFT is a generating function for the number of periodic sequences in the shift. In this paper, we derive a useful formula for the zeta function of a PFT. This formula allows the zeta function of a PFT to be computed more efficiently than the specialization of a formula known for a generic sofic shift.

Periodic-finite-type shifts (PFTs) form a class of sofic shifts that strictly contains the class of shifts of finite type (SFTs). In this paper, we study PFTs from the viewpoint of certain “periods” that can be associated with them. We define three kinds of periods (descriptive, sequential, and graphical) for PFTs and investigate the relationships between them. The results of our investigation indicate that there are no specific relationships between these periods, except for the fact that the descriptive period of an irreducible PFT always divides its graphical period. Furthermore, we compute the number of periodic sequences in PFTs of a certain type, from which we obtain expressions for their zeta functions.

Periodic-finite-type shifts (PFTpsilas) form a class of sofic shifts that strictly contains the class of shifts of finite type (SFTpsilas). In this paper, we investigate how the notion of ldquoperiodrdquo inherent in the definition of a PFT causes it to differ from an SFT, and how the period influences the properties of a PFT.

A construction of Crocheniore, Mignosi and Restivo in the automata theory literature gives a presentation of a finite-type constrained system (FTCS) that is deterministic and has a relatively small number of states. This construction is thus a good starting point for determining the minimal deterministic presentation, known as the Shannon cover, of an FTCS. We analyze in detail the Crochemore-Mignosi-Restivo (CMR) construction in the case when the list of forbidden words defining the FTCS is of size at most two. We show that if the FTCS is irreducible, then an irreducible presentation for the system can be easily obtained from the CMR presentation. By studying the follower sets of the states in this irreducible presentation, we are able to explicitly determine the Shannon cover in some cases. In particular, our results show that the CMR construction directly yields the Shannon cover in the case of an irreducible FTCS with exactly one forbidden word, but this is not in general the case for FTCS's with two forbidden words