Pattern String Research Articles

Time Complexity of Knuth Morris Algorithm and Rejang Algorithm in Rejang-Indonesian Translator

Among the pattern-matching algorithms is the Knuth-Morris algorithm. In order to minimize the number of comparisons required and, in the worst scenario, achieve an ideal O(n+m) running time, the Knuth-Morris search algorithm skips unneeded comparisons. Every character in the text and every character in the pattern must be checked at least once by the pattern-matching algorithm. The Knuth-Morris algorithm's primary goal is to preprocess the pattern string P in order to determine the failure function f, which displays P's precise shift, so that earlier comparisons can be reused. In order to extract the fundamental word of the attached sentence, words containing affixes are separated using the Rejang stemming method. The purpose of this research is to determine the time complexity of the Rejang method and the Knuth-Morris algorithm based on affix groups. The Rapid Application Development (RAD) approach, which entails planning, designing, building, and implementing, is used during the research stages. The research results have produced efficient and effective Knuth Morris algorithm and Rejang algorithm, where efficiency is indicated by the algorithm time complexity of O (log n), and effectiveness is indicated by the accuracy results of 99% against testing 6000 affixed words.

The Generalisations of n-Cutting Sites Splicing Languages via Yusof-Goode Splicing System using a Non-Palindromic Rule and Crossing Site

Yusof-Goode (Y-G) splicing system was introduced in the context of Formal Language Theory. Splicing system is a dry model that presents enzymatic activities between initial strings and restriction enzymes, while splicing language is the generated strings from the splicing system. Splicing language will yield either as new molecules or initial string itself, and can be either in adult or inert, limit or transient languages. In this paper, some mathematical results on generating and generalising the n-cutting sites splicing languages are established using a Y-G splicing system consisting of a single pattern of strings with non-palindromic rule and crossing site. Two lemmas are presented to discuss the Y-G splicing system when two and three cutting sites exist in a single pattern of string. Different characteristics concerning the features of left and right contexts are established. A theorem is then proposed based on the lemmas to generalise the n-cutting sites splicing languages resulting from a Y-G splicing system with a single pattern of string and a non-palindromic rule when n-cutting sites exist in a single pattern of string.

Multi-resolution remote sensing for flark area detection in boreal aapa mires

ABSTRACT Peatlands have suffered significant degradation globally due to human impacts, which has increased the need to monitor the condition and changes in peatland ecosystems. With remote sensing, point-based in-situ observations can be upscaled to larger areas but there is a need to develop scalable monitoring methods. We predicted wet flark area extent, a key ecological indicator for patterned flark fens, in five sites in central Finland. Our primary aim was to test how the spatial and spectral resolution of different optical satellite image products (Landsat 8–9, Sentinel-2, PlanetScope) affect flark area coverage prediction. We also tested if there were seasonal or site-specific differences in prediction accuracy. Lastly, we upscaled the flark area coverage to entire mire areas. We used unmanned aerial vehicle (UAV)-derived flark area classification as a ground reference data to compare satellite sensors’ prediction accuracies. We predicted flark area coverage using spectral bands and indices as predictor variables using random forest regression. All sensors provided accurate results with some differences between Landsat (pseudo-R2 32−84%, root-mean squared error (RMSE) 10 − 18%), Sentinel-2 (R2 61−92%, RMSE 6−14%), and PlanetScope (R2 46 − 92%, RMSE 6 − 17%). The shortwave infrared bands of Landsat and Sentinel-2 did not increase the prediction accuracy. There were notable site-specific variations in prediction accuracy despite all the sites having typical open aapa mire wet flark – dry string patterns. With single-site models, the prediction accuracies were similar for early and late summer data, but when transferring the models to the other sites, performance significantly decreased, especially with the models using the late-summer imagery. Finally, we successfully upscaled the flark area coverage across entire mire areas. Our results demonstrate that the combination of UAV and satellite imagery can be used successfully to monitor peatland conditions and changes in them.

Multi pattern matching algorithm for embedded computer network engineering intrusion detection system

In computer networks, security issues persist, and addressing hidden security risks is pivotal for ensuring network security. However, traditional single pattern matching algorithms like BM (Boyer-Moore) lack efficiency for network intrusion detection. This study employs multiple pattern matching algorithms to bolster the security of computer network engineering intrusion detection systems (IDS). A computer network intrusion detection system (NIDS) is designed using embedded technology to collect network logs and other pertinent data, subsequently comparing log data packets. The study delves into a multi pattern matching algorithm, AC (Aho-Corasick), which incorporates the SUNDAY algorithm to optimize unnecessary string matching jumps. Furthermore, the AC algorithm and BM algorithm are fused as control methods. Randomly generated 48M text data is utilized for testing purposes, comparing the AC algorithm, AC-BM algorithm, and AC-SUNDAY algorithm. For instance, when the pattern string length is 20 bytes, the memory consumption of the AC algorithm, AC-BM algorithm, and AC-SUNDAY algorithm is 12.2 MB, 9.8 MB, and 6.2 MB respectively. The findings indicate that applying the AC-SUNDAY algorithm in NIDS effectively reduces memory consumption and enhances the efficacy of network intrusion detection.

Homomorphic encrypted Yara rules evaluation

Malware signatures represent a powerful tool for malware detection and classification, widely used by security researchers and security solution providers. Yara rules describe malware based on string patterns that are evaluated on targeted files. Generally, the security provider sends signatures to the client endpoints and the rule evaluation is performed locally, such that the scanned files do not leave the client machines. However, if a zero-day vulnerability is discovered and the security provider exposes the corresponding signature, there is a considerable risk to also disclose the unpatched vulnerability. A solution is represented by homomorphic encrypted Yara rules, which can be evaluated on targeted files without being decrypted. In this article, we propose a homomorphic Yara rules evaluation method and do a comparative analysis with the HENFA method (Homomorphic Encryption for Finite Automata (Genise et al., 2019)). For our method, we propose a fully homomorphic exact string matching algorithm based on the TFHE scheme (Fully Homomorphic Encryption over the Torus (Chillotti et al., 2020)). In order to determine how suitable is the exact string matching approach in practice, we analyze a public Yara rules repository and generate various statistics about the patterns used in the Yara rules. For the two homomorphic Yara rules matching methods, both theoretical and experimental comparative evaluations are presented. Our proposed method implies ciphertexts with smaller sizes and has better efficiency in the average testing scenarios compared to the HENFA method.

Predicting the formation of NADES using a transformer-based model

The application of natural deep eutectic solvents (NADES) in the pharmaceutical, agricultural, and food industries represents one of the fastest growing fields of green chemistry, as these mixtures can potentially replace traditional organic solvents. These advances are, however, limited by the development of new NADES which is today, almost exclusively empirically driven and often derivative from known mixtures. To overcome this limitation, we propose the use of a transformer-based machine learning approach. Here, the transformer-based neural network model was first pre-trained to recognize chemical patterns from SMILES representations (unlabeled general chemical data) and then fine-tuned to recognize the patterns in strings that lead to the formation of either stable NADES or simple mixtures of compounds not leading to the formation of stable NADES (binary classification). Because this strategy was adapted from language learning, it allows the use of relatively small datasets and relatively low computational resources. The resulting algorithm is capable of predicting the formation of multiple new stable eutectic mixtures (n = 337) from a general database of natural compounds. More importantly, the system is also able to predict the components and molar ratios needed to render NADES with new molecules (not present in the training database), an aspect that was validated using previously reported NADES as well as by developing multiple novel solvents containing ibuprofen. We believe this strategy has the potential to transform the screening process for NADES as well as the pharmaceutical industry, streamlining the use of bioactive compounds as functional components of liquid formulations, rather than simple solutes.

RECTAL PROLAPSE IN LOCAL CATS

Rectal prolapse is a condition when one or more layers of the rectum pass through the anal orifice. Prolapse commonly occurs in young and old animals due to constipation, endoparasites, diarrhea, hereditary factors, loss of anal sphincter power and loosening of the rectal mucous membranes. A black cat with complaints of prolapse, good appetite and active cat. The purpose of the rectal prolapse surgery technique in cats is to improve the patient's condition and provide information about handling cases of rectal prolapse. Physical examination shows that the prolapsed rectum has a bright red color, the proximal part is pink while the distal part is red and shrinks. The blood test results show that the cat is fit for surgery. Pre-surgery, the bleki cat was first fasted to eat for 12 hours and drink for 6 hours. Administration of atropine sulfate premedication at a dose of 0.02 mg/kg BW (SC), sedation using xylazine at a dose of 2 mg/kg BW (IV), and anesthesia using ketamine at a dose of 15 mg/kg BW (IV). Cats were operated on using rectal repositioning technique. The rectum is inserted slowly manually, then suturing is done around the anus with a purse string pattern. Postoperative care was carried out by giving the analgesic tolfedine (4mg/kg BW, SC, q24h), non-steroidal anti-inflammatory pain relievers and the antibiotic cefotaxime (20 mg/kg BW, IV, q12h) to treat bacterial infections and giving betadine after the rectum was repositioned. The cat has rectal prolapse which is thought to be caused by eating dry food or giving hard food which causes the cat to experience tenesmus and often strains when making feces. To avoid recurrent prolapse, cats should be advised not to exercise excessively, cage cats, keep cages and cats clean and moist and replace dry food with soft and watery feed rich in protein.

Case Study on Ideological and Political Teaching of “Data Structure” from the Perspective of OBE—Taking “String Pattern Matching Algorithm”as an Example

Case Study on Ideological and Political Teaching of “Data Structure” from the Perspective of OBE—Taking “String Pattern Matching Algorithm”as an Example

Extracting forced vital capacity from the electronic health record through natural language processing in rheumatoid arthritis-associated interstitial lung disease.

To develop a natural language processing (NLP) tool to extract forced vital capacity (FVC) values from electronic health record (EHR) notes in patients with rheumatoid arthritis-interstitial lung disease (RA-ILD). We selected RA-ILD patients (n = 7485) in the Veterans Health Administration (VA) between 2000 and 2020 using validated ICD-9/10 codes. We identified numeric values in proximity to FVC string patterns from clinical notes in the EHR. Subsequently, we performed processing steps to account for variability in note structure, related pulmonary function test (PFT) output, and values copied across notes, then assigned dates from linked administrative procedure records. NLP-derived FVC values were compared to values recorded directly from PFT equipment available on a subset of patients. We identified 5911 FVC values (n = 1844 patients) from PFT equipment and 15 383 values (n = 4982 patients) by NLP. Among 2610 date-matched FVC values from NLP and PFT equipment, 95.8% of values were within 5% predicted. The mean (SD) difference was 0.09% (5.9), and values strongly correlated (r = 0.94, p < 0.001), with a precision of 0.87 (95% CI 0.86, 0.88). NLP captured more patients with longitudinal FVC values (n = 3069 vs. n = 1164). Mean (SD) change in FVC %-predicted per year was similar between sources (-1.5 [30.0] NLP vs. -0.9 [16.6] PFT equipment; standardized response mean = 0.05 for both). NLP of EHR notes increases the capture of accurate, longitudinal FVC values by three-fold over PFT equipment. Use of this NLP tool can facilitate pharmacoepidemiologic research in RA-ILD and other lung diseases by capturing this critical measure of disease severity.

Multimodal contrastive representation learning for drug-target binding affinity prediction

In the biomedical field, the efficacy of most drugs is demonstrated by their interactions with targets, meanwhile, accurate prediction of the strength of drug-target binding is extremely important for drug development efforts. Traditional bioassay-based drug-target binding affinity (DTA) prediction methods cannot meet the needs of drug R&D in the era of big data. Recent years we have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task. However, these models only considered a single modality of drug and target information, and some valuable information was not fully utilized. In fact, the information of different modalities of drug and target can complement each other, and more valuable information can be obtained by fusing the information of different modalities. In this paper, we introduce a multimodal information fusion model for DTA prediction that is called FMDTA, which fully considers drug/target information in both string and graph modalities and balances the feature representations of different modalities by a contrastive learning approach. In addition, we exploited the alignment information of drug atoms and target residues to capture the positional information of string patterns, which can extract more useful feature information in SMILES and target sequences. Experimental results on two benchmark datasets show that FMDTA outperforms the state-of-the-art model, demonstrating the feasibility and excellent feature capture capability of FMDTA. The code of FMDTA and the data are available at: https://github.com/bestdoubleLin/FMDTA.

Fine characterization of the capillary instability of free viscoelastic jets

The experimental approach presented in this article investigates low-velocity small-diameter cylindrical viscoelastic jets. It consists in producing and analyzing sets of jet portion images taken at different positions along the jet axis, i.e., from the nozzle producing the jet down to the spray region. A very significant part of the work was dedicated to develop analyzing tools: a proper orthogonal decomposition to identify the onset of the capillary instability, a procedure to determine the mean jet breakup length and a multi-scale tool to describe the jet local shape and its evolution along the jet axis. Used in a similar previous study, the multi-scale analysis has been extended in the present work. These tools are shown to be well suited to the experimental study conducted here, and in particular to meet the need for statistical analysis that free jets impose. They are applied to study the capillary instability of free jets of dilute polyethylene oxide solutions produced from a micrometer nozzle. The parameters of the study are the molar mass of the polymer and the velocity of the jet. Results on the detection of the capillary instability onset, the jet evolution dynamics towards the Bead On A String (BOAS) pattern, the local extensional relaxation time and the terminal elongational viscosity of the solutions, the bead coalescence mechanism and its influence on the size of the spray drops are obtained. Analysis of these results reveals that the polymers undergo two episodes of mechanical degradation during the process: the first one at injection, the second one during the elasto-capillary regime of the jet breakup. Moreover, the scenario in the literature suggesting a sufficiently relaxed axial tension of the flow for the capillary instability to occur is found to be not always valid. It is also shown that the average size of the drops correlates with the intensity of the bead coalescence mechanism that takes place on the BOAS. These results demonstrate the richness of the adopted approach and in particular of the multi-scale analysis to study the development of a fluid interfacial instability.

An Ensemble of Text Convolutional Neural Networks and Multi-Head Attention Layers for Classifying Threats in Network Packets

Using traditional methods based on detection rules written by human security experts presents significant challenges for the accurate detection of network threats, which are becoming increasingly sophisticated. In order to deal with the limitations of traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet payloads through natural language processing techniques to detect attack intent. However, due to the nature of packet payloads that contain binary and text data, a new approach is needed that goes beyond typical natural language processing techniques. In this paper, we study a token extraction method optimized for payloads using n-gram and byte-pair encoding techniques. Furthermore, we generate embedding vectors that can understand the context of the packet payload using algorithms such as Word2Vec and FastText. We also compute the embedding of various header data associated with packets such as IP addresses and ports. Given these features, we combine a text 1D CNN and a multi-head attention network in a novel fashion. We validated the effectiveness of our classification technique on the CICIDS2017 open dataset and over half a million data collected by The Education Cyber Security Center (ECSC), currently operating in South Korea. The proposed model showed remarkable performance compared to previous studies, achieving highly accurate classification with an F1-score of 0.998. Our model can also preprocess and classify 150,000 network threats per minute, helping security agents in the field maximize their time and analyze more complex attack patterns.

Graphs cannot be indexed in polynomial time for sub-quadratic time string matching, unless SETH fails

The string matching problem on a node-labeled graph G=(V,E) asks whether a given pattern string P equals the concatenation of node labels of some path in G. This is a basic primitive in various problems in bioinformatics, graph databases, or networks, and it was recently proven that solving it in time O(|E|1−ϵ|P|) or O(|E||P|1−ϵ) is hard under OVH, the Orthogonal Vectors Hypothesis, and thus under SETH, the Strong Exponential Time Hypothesis (Equi et al., 2019 [11]). We consider its indexed version, where the graph is indexed to support string queries. We show that, under OVH, no polynomial-time index of the graph performed in time O(|E|α) can support querying P in time O(|P|+|E|δ|P|β), with either δ<1 or β<1.We present our techniques as a general framework, introducing the notion of linear independent-components (lic) reduction, from which we derive our result. This allows us to also translate the quadratic conditional lower bound of Backurs and Indyk (2015) [48] for the problem of matching a query string inside a text, under edit distance, into an analogous tight quadratic lower bound for its indexed version. This improves the recent result of Cohen-Addad, Feuilloley and Starikovskaya (2019) [49], with a slightly different boundary condition. We also apply our technique to obtain the first quadratic indexing lower bounds for Fréchet distance and rooted unlabeled subtree-isomorphism queries.

An Intelligent Semi-Honest System for Secret Matching against Malicious Adversaries

With natural language processing as an important research direction in deep learning, the problems of text similarity calculation, natural language inference, question and answer systems, and information retrieval can be regarded as text matching applications for different data and scenarios. Secure matching computation of text string patterns can solve the privacy protection problem in the fields of biological sequence analysis, keyword search, and database query. In this paper, we propose an Intelligent Semi-Honest System (ISHS) for secret matching against malicious adversaries. Firstly, a secure computation protocol based on the semi-honest model is designed for the secret matching of text strings, which adopts a new digital encoding method and an ECC encryption algorithm and can provide a solution for honest participants. The text string matching protocol under the malicious model which uses the cut-and-choose method and zero-knowledge proof is designed for resisting malicious behaviors that may be committed by malicious participants in the semi-honest protocol. The correctness and security of the protocol are analyzed, which is more efficient and has practical value compared with the existing algorithms. The secure text matching has important engineering applications.

Abstract 211: Fibromuscular Dysplasia Causing Upper Extremity Acute Limb Ischemia

Fibromuscular dysplasia (FMD) is a non-atherosclerotic arterial disease that affects mainly the renal and cervical arteries. It can also affect peripheral arteries leading to acute or chronic ischemia of the limbs. We present a rare case of FMD causing acute limb ischemia. Case Presentation: A 45-year-old male presented with acute onset pain and coldness in his left upper extremity, associated with numbness and tingling sensation in his left hand. He denied any history of trauma or recent surgery. His medical history was significant for hypertension. He was a non-smoker and denied any use of illicit drugs. On exam, his left hand was pale, cold, with absent distal pulses. The capillary refill time was prolonged, and the skin was mottled. There was tenderness in the hand muscles with no signs of compartment syndrome. Rest of the exam was normal. A diagnosis of acute limb ischemia was made, and the patient was urgently taken to the cath lab. Angiogram showed string and beads pattern of the radial artery with distal occlusion suggestive of FMD. Balloon angioplasty of the radial artery failed to improve blood flow. He subsequently underwent radial artery bypass using autologous vein graft with restoration of blood flow. Renal and carotid arteries were unaffected on CTA. Discussion: Fibromuscular dysplasia is a non-atherosclerotic, non-inflammatory disease that affects medium to small sized arteries. It is characterized by hyperplasia and fibrosis within the arterial wall. The exact etiology of FMD is unknown, and it is believed to be multifactorial. Renal artery FMD may present with renovascular hypertension, cervical artery involvement may present with headache, tinnitus, and vertigo. Patients with peripheral artery involvement may present with intermittent claudication, acute limb ischemia, or limb loss. CTA, MRA, or conventional angiography are diagnostic. In patients with acute limb ischemia, emergent intervention with thrombectomy, embolectomy, or bypass surgery may be necessary. Anti platelets and anticoagulants are also used. Conclusion: FMD is a rare disease that can present with a variety of clinical manifestations, including acute limb ischemia. Prompt diagnosis and treatment are essential to prevent limb loss and other serious complications.

On the Multiple Pattern String Matching in DNA Databases

On the Multiple Pattern String Matching in DNA Databases

A Snort-based secure edge router for smart home

Cybercrimes are rising rapidly with the increasing use of the internet of things (IoT)-based gadgets at home. For instance, the Mirai-BotNet infected and compromised many IoT-based devices and routers, creating a zombie network of robots that can be controlled remotely. There is a need for a cost-effective, secure router for a smart home. This paper investigates and proposes a Snort-based secure edge router for smart home (SERfSH), which is resilient to many cyberattacks. SERfSH automatically generates Snort content rules by combining the extracted string, location information, header information, and sequential pattern. The experimental setup of SERfSH consists of a Raspberry Pi 4 model, an ESP32 microcontroller, six IoT devices, and a malicious actor machine. The proposed SERfSH is tested for 15 attacks, and the results show that 14 attacks were detected and 12 attacks were mitigated.

Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer.

Medical imaging is critical in clinical practice, and high value radiological reports can positively assist clinicians. However, there is a lack of methods for determining the value of reports. The purpose of this study was to establish an ensemble learning classification model using natural language processing (NLP) applied to the Chinese free text of radiological reports to determine their value for liver lesion detection in patients with colorectal cancer (CRC). Radiological reports of upper abdominal computed tomography (CT) and magnetic resonance imaging (MRI) were divided into five categories according to the results of liver lesion detection in patients with CRC. The NLP methods including word segmentation, stop word removal, and n-gram language model establishment were applied for each dataset. Then, a word-bag model was built, high-frequency words were selected as features, and an ensemble learning classification model was constructed. Several machine learning methods were applied, including logistic regression (LR), random forest (RF), and so on. We compared the accuracy between priori choosing pertinent word strings and our machine language methodologies. The dataset of 2790 patients included CT without contrast (10.2%), CT with/without contrast (73.3%), MRI without contrast (1.8%), and MRI with/without contrast (14.6%). The ensemble learning classification model determined the value of reports effectively, reaching 95.91% in the CT with/without contrast dataset using XGBoost. The logistic regression, random forest, and support vector machine also achieved good classification accuracy, reaching 95.89%, 95.04%, and 95.00% respectively. The results of XGBoost were visualized using a confusion matrix. The numbers of errors in categories I, II and V were very small. ELI5 was used to select important words for each category. Words such as "no abnormality", "suggest", "fatty liver", and "transfer" showed a relatively large degree of positive correlation with classification accuracy. The accuracy based on string pattern search method model was lower than that of machine learning. The learning classification model based on NLP was an effective tool for determining the value of radiological reports focused on liver lesions. The study made it possible to analyze the value of medical imaging examinations on a large scale.

A Chaotic Image Encryption Scheme Based on Genetic Central Dogma and KMP Method

In this paper, an image cryptosystem based on genetic central dogma (GCD), Knuth–Morria–Pratt (KMP) algorithm and a chaotic system is developed. The KMP algorithm is firstly used to bind DNA strings to obtain the next array, which participates in the design of the chaotic initial condition, and then the secure chaotic sequences are produced by employing the sliding idea in pattern string matching. In the present procedure, a DNA-level two-way pixel’s shuffle is achieved by a shared stack push operation and it is adopted in the permutation module for the purpose of accelerating the overall pixel’s shuffle. Subsequently, the pixel values are substituted by simulating the process of protein synthesis in GCD, in which the DNA replication and RNA replication form the basis of the DNA-triploid mutation and new RNA mutation rules, respectively. Experimental simulations and extensive cryptanalysis fully vindicate that superior security effects in addition to satisfactory low time complexity can be simultaneously obtained by the proposed confusion-substitution scheme.

A Study of Publicly Available Resources Addressing Legal Data-Sharing Barriers: Systematic Assessment.

BackgroundUnited States data protection laws vary depending on the data type and its context. Data projects involving social determinants of health often concern different data protection laws, making them difficult to navigate.ObjectiveWe systematically aggregated and assessed useful online resources to help navigate the data-sharing landscape.MethodsWe included publicly available resources that discussed legal data-sharing issues with some health relevance and published between 2010 and 2019. We conducted an iterative search with a common string pattern using a general-purpose search engine that targeted 24 different sectors identified by Data Across Sectors for Health. We scored each online resource for its depth of legal and data-sharing discussions and value for addressing legal barriers.ResultsOut of 3710 total search hits, 2721 unique URLs were reviewed for scope, 322 received full-text review, and 154 were selected for final coding. Legal agreements, consent, and agency guidance were the most widely covered legal topics, with HIPAA (The Health Insurance Portability and Accountability Act), Family Educational Rights and Privacy Act (FERPA), Title 42 of the Code of Federal Regulations Part 2 being the top 3 federal laws discussed. Clinical health care was the most prominent sector with a mention in 73 resources.ConclusionsThis is the first systematic study of publicly available resources on legal data-sharing issues. We found existing gaps where resources covering certain laws or applications may be needed. The volume of resources we found is an indicator that real and perceived legal issues are a substantial barrier to efforts in leveraging data from different sectors to promote health.