String Values Research Articles

A semi-supervised framework fusing multiple information for knowledge graph entity alignment

Entity alignment (EA) is a fundamental task for cross linguistic knowledge graphs (KGs) understanding and interaction, which is committed to matching entities from different graphs based on their inherent semantics. Methods based on graph neural networks (GNNs) dominate the EA task, however, the majority of them ignore the higher-order information among entities in the KGs. Meanwhile, as important auxiliary information, the relational semantics, string information of entity names and attribute information of entities are insufficiently exploited during the inference phase. In addition, labeled alignment data is universally insufficient across various datasets, which limits the performance of the model. In this paper, we propose a Semi-supervised EA framework that Comprehensively considers both Structural and Attribute information within KGs (SCSA) to address these problems above. Specifically, our approach first leverages hypergraph neural networks (HGNN) to aggregate relational semantic information and graph convolutional networks (GCNs) with a highway filtering strategy to acquire the embedding representation of entities precisely. Then, we propose a bidirectional filtering technique with a combination of entity, attribute and string values to create pseudo-labeled data and lead the model for iteratively training. We implement our proposed framework on several publicly recognized cross-lingual datasets. The experimental results indicate that our framework outperforms almost all state-of-the-art (SOTA) methods.

Enhanced performance prediction of ATL model transformations

Model transformation languages are domain-specific languages used to define transformations of models. These transformations consist of the translation from one modeling formalism into another or just the updating of a given model. Such transformations are often described declaratively and are often implemented based on very small models that cover the language of the input model. As a result, transformation developers are often unable to assess the time required to transform a larger model.Hence, we propose a prediction approach based on machine learning which uses a set of model characteristics as input and provides a prediction of the execution time of a transformation defined in the Atlas Transformation Language (ATL). In our previous work (Groner et al., 2023), we already showed that support vector regression in combination with a model characterization based on the number of model elements, the number of references, and the number of attributes is the best choice in terms of usability and prediction accuracy for the transformations considered in our experiments.A major weakness of our previous approach is that it fails to predict the performance of transformations that also transform attribute values of arbitrary length, such as string values. Therefore, we investigate in this work whether an extension of our feature sets that describes the average size of string attributes can help to overcome this weakness.Our results show that the random forest approach in combination with model characterizations based on the number of model elements, the number of references, the number of attributes, and the average size of string attributes filtered by the 85th percentile of their variance is the best choice in terms of the simple way to describe a model and the quality of the obtained prediction. With this combination, we obtained a mean absolute percentage error (MAPE) of 5.07% over all modules and a MAPE of 4.82% over all modules excluding the transformation for which our previous approach failed. Whereas, we obtained previously a MAPE of 38.48% over all modules and a MAPE of 4.45% over all modules excluding the transformation for which our previous approach failed.

مرحل حماية المسافة وتأثير MOV على مناطق المرحل بإستخدام الماتلاب

Transmission lines are an important part of the power system. Transmission lines are used to transmit power and are prone to many breakdowns. Therefore, protection relays are used to protect transmission lines. The purpose of the protection system is to isolate the faulty part from the healthy part, because large currents generated by faults may cause damage to electrical equipment. One of the protection relays is the distance relay and is mainly used in the transmission line. Sometimes these relays are used for backup protection. Distance relays to determine impedance need voltage and current. Transmission lines are usually protected by a distance protection relay. Distance relays are of the high-speed class and can provide transmission lines. Protection distance relays that use impedance measurements to determine the presence and location of faults, are "fooled" by a series capacitor mounted on the line, when the presence or absence of a capacitor in the fault circuit is not known in advance. This is because the capacitor cancels or compensates for some of the line inductance, so the relay may sense a fault in its first region when the fault is actually in the second or third region of the protection. Similarly, Zone one fault can be considered reverse faults. In fact, this can cause some costly operating errors. Series compensated lines are protected from overvoltage by metal-oxide-varistors (MOVs) connected in parallel with the capacitor bank. The nonlinear characteristics of MOV devices add complexity to fault analysis and distance protection operation. During faults, the impedance of the line is modified by an equivalent impedance of the parallel MOV/capacitor circuit. Worth noting is a method that determines the L and C string values of the font and the MOV effect at the time of the error. This is done by analyzing the synchronous and sub-synchronous content of the voltage and current signals separately which provides enough information to calculate the L and C series of the line. In this paper, the transmission line impedance Z, Z(f) as a function of frequency has been calculated and plotted by the sequence characteristics and the studied MOV effect on line protection and fault location. Proper operation of the distance protection relay is also shown, and its effect on relay regions. Keywords: Distance protection relay, transmission line, zones of relay, MOV.

Tarsis: An effective automata‐based abstract domain for string analysis

AbstractIn this paper, we introduce Tarsis, a new abstract domain based on the abstract interpretation theory that approximates string values through finite state automata. The main novelty of Tarsis is that it works over an alphabet of strings instead of single characters. On the one hand, such an approach requires a more complex and refined definition of the lattice operators and of the abstract semantics of string operators. On the other hand, it is in position to obtain strictly more precise results than state‐of‐the‐art approaches. We compare Tarsis both with simpler domains and with the standard automata model, targeting case studies containing standard yet challenging string manipulations. The performance gain w.r.t. the standard automata model is also assessed, measuring the speed‐up gained by Tarsis. Experiments confirm that Tarsis can obtain precise results without incurring in excessive computational costs.

Intelligent System for Providing Migration Through Dynamic Data Deserialization

The purpose of research. Timely provision of data transfer between information systems allows you to quickly exchange resources. However, applications may have different data formats and structures. Therefore, the aim of the research was to develop models, methods and algorithms for a system of asynchronous deserialization of a data string, providing an increase in the efficiency of determining data models by generating strongly typed objects.Methods. The way to deserialize models from data involves line-by-line decomposition of a JSON-file line with the definition of key-value types and their correlation with the data model: character, string, number, boolean value. After that, the web controller conducts asynchronous generation of the class and its objects. To classify string values, serialized string value classifiers are used. For asynchronous generation of objects, a system of “contracts” of models and algorithms for executing and converting these models are used.Results. The deserializer consists of a system of four model analysis controllers and a value generation algorithm. A simple single model deserialization model allows the model to be mapped to relational database table headers to enable model migration between systems. The generated objects are represented by static data types, which ensures that they can be written to any DBMS system using built-in tools. A complex model represents a block of values as a system of different models. Software has been developed for connecting source and target databases, which allows you to migrate data from the created models. Generated values are represented as full-fledged objects and can be used to create a web interface for applications, edit data models, and manage the model system.Conclusion. Experimental studies on deserialization of models from a JSON string containing complex model classes showed an average accuracy of determining the data type of models in 92% of cases, in particular when determining the types of values "character" and "string". The created models are presented in the form of a data table and can be used to ensure the migration of these models.

PhyloString: A web server designed to identify, visualize, and evaluate functional relationships between orthologous protein groups across different phylogenetic lineages.

Proteins are biological units whose essence is defined by their functional relationships with other proteins or biomolecules such as RNA, DNA, lipids, or carbohydrates. These functions encompass enzymatic, structural, regulatory, or physical interaction roles. The STRING database (Nucleic Acids Research, 8 Jan 2021;49(D1): D605-12) provides an index that defines the functional interaction networks between proteins in model organisms. To facilitate the identification, visualization, and evaluation of potential functional networks across organisms from different phylogenetic lineages, we have developed PhyloString (https://biocomputo.ibt.unam.mx/phylostring/), a web server that utilizes the indices of the STRING database. PhyloString decomposes these functional networks into modules, representing cohesive units of proteins grouped based on their similarity of STRING values and the phylogenetic origins of their respective organisms. This study presents and thoroughly discusses examples of such functional networks and their modules identified using PhyloString.

Demystifying Performance Regressions in String Solvers

Over the past few years, SMT string solvers have found their applications in an increasing number of domains, such as program analyses in mobile and Web applications, which require the ability to reason about string values. A series of research has been carried out to find quality issues of string solvers in terms of its correctness and performance. Yet, none of them has considered the performance regressions happening across multiple versions of a string solver. To fill this gap, in this paper, we focus on solver performance regressions (SPRs), i.e., unintended slowdowns introduced during the evolution of string solvers. To this end, we develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SPRFinder</i> to not only generate test cases demonstrating SPRs, but also localize the probable causes of them, in terms of commits. We evaluated the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SPRFinder</i> on three state-of-the-art string solvers, i.e., Z3Seq, Z3Str3, and CVC4. The results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SPRFinder</i> is effective in generating SPR-inducing test cases and also able to accurately locate the responsible commits. Specifically, the average running time on the target versions is 13.2× slower than that of the reference versions. Besides, we also conducted the first empirical study to peek into the characteristics of SPRs, including the impact of random seed configuration for SPR detection, understanding the root causes of SPRs, and characterizing the regression test cases through case studies. Finally, we highlight that 149 unique SPR-inducing commits were discovered in total by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SPRFinder</i> , and 27of them have been confirmed by the corresponding developers.

Improvement of Key Financial Performance Indicators in the Insurance Industry Using Machine Learning – A Quantitative Analysis.

AI and Machine learning are playing a vital role in the financial domain in predicting future growth and risk and identifying key performance areas. We look at how machine learning and artificial intelligence (AI) directly or indirectly alter financial management in the banking and insurance industries. First, a non-technical review of the prior machine learning and AI methodologies beneficial to KPI management is provided. This paper will analyze and improve key financial performance indicators in insurance using machine learning (ML) algorithms. Before applying an ML algorithm, we must determine the attributes directly impacting the business and target attributes. The details must be manually mapped from string values to fit the model and its required datatypes for applying these specific features to an ML model. We propose hashing to convert string values to numeric values for data analysis within our model. After the string values are hashed, we can introduce our model. In our case, we have chosen to use a decision tree model. Decision Trees are beneficial for this use case as this algorithm generates rulesets that govern the target value output. These rulesets can then be applied to the financial dataset and infer the “best fit” value that might be wrong/missing. Finally, because of the model, we can use this most accurate data version to detect general ledger transactional data patterns.

Analyzing Dynamic Code

Dynamic languages, such as JavaScript, employ string-to-code primitives to turn dynamically generated text into executable code at run-time. These features make standard static analysis extremely hard if not impossible, because its essential data structures, i.e., the control-flow graph and the system of recursive equations associated with the program to analyze, are themselves dynamically mutating objects. Nevertheless, assembling code at run-time by manipulating strings, such as by eval in JavaScript, has been always strongly discouraged, since it is often recognized that “ eval is evil ,” leading static analyzers to not consider such statements or ignoring their effects. Unfortunately, the lack of formal approaches to analyze string-to-code statements pose a perfect habitat for malicious code, that is surely evil and do not respect good practice rules, allowing them to hide malicious intents as strings to be converted to code and making static analyses blind to the real malicious aim of the code. Hence, the need to handle string-to-code statements approximating what they can execute, and therefore allowing the analysis to continue (even in the presence of dynamically generated program statements) with an acceptable degree of precision, should be clear. To reach this goal, we propose a static analysis allowing us to collect string values and to soundly over-approximate and analyze the code potentially executed by a string-to-code statement.

Sequence Data Matching and Beyond: New Privacy-Preserving Primitives Based on Bloom Filters

Bloom filter encoding has widely been used as an efficient masking technique for privacy-preserving matching functions. The existing matching techniques, however, are limited to relatively simple types such as string, categorical and signal numerical values. In this paper, we propose a new scheme that significantly extends the class of matching primitives that are based on privacy-preserving Bloom filter mechanism. These primitives include sequence data matching and popular distance-based machine learning algorithms such as KNN and SVM. Our scheme hash-maps a sequence data vector into the Bloom filter space while checking the similarity of the data points efficiently with negligible utility loss by adding a timestamp (bit) for each element in the data represented with its neighboring values. Furthermore, it includes a Laplace-like perturbation method on the constructed Bloom filters to address the weakness of deterministic probability led by encoding techniques. As a result, the proposed work guarantee the private data records are difficult to be discriminated due to collisions and differential privacy. The experimental results on three real-scenario based datasets illustrate that our method can achieve a significantly better trade-off between utility and privacy than the state-of-the-art differential privacy-based method by adding Laplace noise to the data directly.

Improving the Quality of Web-Based Data Imputation With Crowd Intervention

Data incompleteness is a common data quality problem in databases. Recent work proposes to retrieve missing string values from the World Wide Web for higher imputation recall, but on the other hand, takes the risk of introducing web noises into the imputation results. So far there lacks an effective way to control the quality of web-based data imputation, given the complexity of the quality model and lacking of enough ground truth data. In this article, an EM-based quality model is first built for web-based data imputation which investigates three key factors jointly, i.e., precision of web sources, correlation among web sources, and precision and recall of the employed extractors. However, the accuracy of the EM-based quality model could be harmed when the EM (Expectation Maximization) assumption that “the majority agree on the truth” does not hold in some cases. To solve this problem, we introduce crowd intervention to help improve the quality model. While a straightforward but expensive way is to let the crowd to identify all these undesirable cases and provide the right imputation values for these blanks, a most crowd-economic way is to select a small set of blanks for crowd-based imputation, whose results could help to adjust the EM-based quality model towards a better one. To achieve this, an adaptive blank selection strategy is proposed to select a sequence of blanks for crowd-based imputation. Also, we work on finding a proper time to stop further crowd intervention for the balance of crowd efficiency and quality improvement. Our experiments performed on three real world and one simulated data collections prove that the proposed quality model can effectively help improve the quality of the web-based imputation results by more than 15 percent, while our crowd cost saving strategy saves more than 75 percent crowd cost.

An end-to-end learning-based cost estimator

Cost and cardinality estimation is vital to query optimizer, which can guide the query plan selection. However traditional empirical cost and cardinality estimation techniques cannot provide high-quality estimation, because they may not effectively capture the correlation between multiple tables. Recently the database community shows that the learning-based cardinality estimation is better than the empirical methods. However, existing learning-based methods have several limitations. Firstly, they focus on estimating the cardinality, but cannot estimate the cost. Secondly, they are either too heavy or hard to represent complicated structures, e.g., complex predicates. To address these challenges, we propose an effective end-to-end learning-based cost estimation framework based on a tree-structured model, which can estimate both cost and cardinality simultaneously. We propose effective feature extraction and encoding techniques, which consider both queries and physical operations in feature extraction. We embed these features into our tree-structured model. We propose an effective method to encode string values, which can improve the generalization ability for predicate matching. As it is prohibitively expensive to enumerate all string values, we design a patten-based method, which selects patterns to cover string values and utilizes the patterns to embed string values. We conducted experiments on real-world datasets and experimental results showed that our method outperformed baselines.

Attack Synthesis for Strings using Meta-Heuristics

Information leaks are a significant problem in modern computer systems and string manipulation is prevalent in modern software. We present techniques for automated synthesis of side-channel attacks that recover secret string values based on timing observations on string manipulating code. Our attack synthesis techniques iteratively generate inputs which, when fed to code that accesses the secret, reveal partial information about the secret based on the timing observations, leading to recovery of the secret at the end of the attack sequence. We use symbolic execution to extract path constraints, automata-based model counting to estimate the probability of execution paths, and meta-heuristic methods to maximize information gain based on entropy for synthesizing adaptive attack steps.

Privacy preserving record linkage meets record linkage using unencrypted data

IntroductionPrivacy preserving record linkage (PPRL) resolves privacy concerns because of its capabilities to link encrypted identifiers. It encrypts identifiers using bloom filters and performs record matching based on encrypted data using dice coefficient similarity. Matching data based on hashed identifiers impacts the performance of linkage due to loss of information.&#x0D; Objectives and ApproachWe propose a technique to optimize the bloom filter parameters and examine if the optimal parameters increase the performance of the linkage in terms of precision, recall, and f-measure. Let us consider a set of string values and calculate the similarity between any two of them using the Jaro-Winkler method. Now let us encrypt the string values using bloom filters and calculate the similarity between any two of them using the dice coefficient technique. Optimal parameters of bloom filters are those that minimize the difference between the calculated similarities using Jaro-Winkler vs. the calculated similarities using the dice coefficient technique.&#x0D; ResultsUsing publically available data, several first name and last name datasets each comprising 1000 unique values were generated. The following values for bloom filter parameters were considered: q in q-grams (q=1,2,3), bit array length (l=50,100,200,500,1000), number of hash functions (k=5,10,20,50). The following five setups of bloom filters were able to minimize the difference between the calculated similarities on encrypted data using the dice coefficient technique, and the calculated similarities on unencrypted data using the Jaro-Winkler method: q=1,l=1000,k=50/q=1,l=500,k=20/ q=2,l=1000,k=50/ q=3,l=500,k=50. These setups were considered to perform data linkage over 10 synthetically-generated datasets. Results show that PPRL was able to achieve similar performance compared to data linkage over unencrypted data.&#x0D; Conclusion/ImplicationsThis study showed that optimal parameters of bloom filters minimized loss of information resulting from data encryption. Experimental findings indicated that PPRL using optimal parameters of bloom filters achieves almost the same performance as data linkage on unencrypted data in terms of precision, recall, and f-measure.

FEDERAL: A Framework for Distance-Aware Privacy-Preserving Record Linkage

In privacy-preserving record linkage, a number of data custodians encode their records and submit them to a trusted third-party who is responsible for identifying those records that refer to the same real-world entity. In this paper, we propose FEDERAL, a novel record linkage framework that implements methods for anonymizing both string and numerical data values, which are typically present in data records. These methods rely on a strong theoretical foundation for rigorously specifying the dimensionality of the anonymization space, into which the original values are embedded, to provide accuracy and privacy guarantees under various models of privacy attacks. A key component of the applied embedding process is the threshold that is required by the distance computations, which we prove can be formally specified to guarantee accurate results. We evaluate our framework using three real-world data sets with varying characteristics. Our experimental findings show that FEDERAL offers a complete and effective solution for accurately identifying matching anonymized record pairs (with recall rates constantly above 93 percent) in large-scale privacy-preserving record linkage tasks.

Crowd-Guided Entity Matching with Consolidated Textual Data

Entity matching (EM) identifies records referring to the same entity within or across databases. Existing methods using structured attribute values (such as digital, date or short string values) may fail when the structured information is not enough to reflect the matching relationships between records. Nowadays more and more databases may have some unstructured textual attribute containing extra consolidated textual information (CText) of the record, but seldom work has been done on using the CText for EM. Conventional string similarity metrics such as edit distance or bag-of-words are unsuitable for measuring the similarities between CText since there are hundreds or thousands of words with each piece of CText, while existing topic models either cannot work well since there are no obvious gaps between topics in CText. In this paper, we propose a novel cooccurrence-based topic model to identify various sub-topics from each piece of CText, and then measure the similarity between CText on the multiple sub-topic dimensions. To avoid ignoring some hidden important sub-topics, we let the crowd help us decide weights of different sub-topics in doing EM. Our empirical study on two real-world datasets based on Amzon Mechanical Turk Crowdsourcing Platform shows that our method outperforms the state-of-the-art EM methods and Text Understanding models.

Privacy-preserving matching of similar patients

The identification of similar entities represented by records in different databases has drawn considerable attention in many application areas, including in the health domain. One important type of entity matching application that is vital for quality healthcare analytics is the identification of similar patients, known as similar patient matching. A key component of identifying similar records is the calculation of similarity of the values in attributes (fields) between these records. Due to increasing privacy and confidentiality concerns, using the actual attribute values of patient records to identify similar records across different organizations is becoming non-trivial because the attributes in such records often contain highly sensitive information such as personal and medical details of patients. Therefore, the matching needs to be based on masked (encoded) values while being effective and efficient to allow matching of large databases.Bloom filter encoding has widely been used as an efficient masking technique for privacy-preserving matching of string and categorical values. However, no work on Bloom filter-based masking of numerical data, such as integer (e.g. age), floating point (e.g. body mass index), and modulus (numbers wrap around upon reaching a certain value, e.g. date and time), which are commonly required in the health domain, has been presented in the literature. We propose a framework with novel methods for masking numerical data using Bloom filters, thereby facilitating the calculation of similarities between records. We conduct an empirical study on publicly available real-world datasets which shows that our framework provides efficient masking and achieves similar matching accuracy compared to the matching of actual unencoded patient records.

An Effective Method for Matching Patient Records from Multiple Databases using Neural Network

matching is the method of identifying records that denote the similar real world entity or item .The record matching method is helpful for matching health care data . Many problems occur while linking medical records from various databases. Comparing these medical data to other data is challenging because even small mistakes, for example data entry errors and lacking data. The earlier research proposed that estimate field matching represent a technique to solve the issue by finding similar string values in several representations. In our proposed system, we are proposing the Neural network based matching patient records in multiple databases. We can enhance the performance of the record matching method by introducing the Neural network approach. This technique is can improve the overall performance of the system. Among many Neural network techniques, we are using the Elman Back propagation network technique.

Generation of String Test Input from Web using Regular Expression

To generate realistic test data is a challenging for software testers. Realistic test data generation for certain input type is hard to automate and therefore laborious. So in this paper, string inputs will be used from the internet by performing web queries based on the key identifiers appearing in the source code of the program under test. The resultant URLs will be generated, tokenised and the collected regular expressions are matched with the text and get valid string values. General Terms Software testing, Web query, Web pages

Real-time deep virtual machine introspection and its applications

Virtual Machine Introspection (VMI) provides the ability to monitor virtual machines (VM) in an agentless fashion by gathering VM execution states from the hypervisor and analyzing those states to extract information about a running operating system (OS) without installing an agent inside the VM. VMI's main challenge lies in the difficulty in converting low-level byte string values into high-level semantic states of the monitored VM's OS. In this work, we tackle this challenge by developing a real-time kernel data structure monitoring (RTKDSM) system that leverages the rich OS analysis capabilities of Volatility, an open source computer forensics framework, to significantly simplify and automate analysis of VM execution states. The RTKDSM system is designed as an extensible software framework that is meant to be extended to perform application-specific VM state analysis. In addition, the RTKDSM system is able to perform real-time monitoring of any changes made to the extracted OS states of guest VMs. This real-time monitoring capability is especially important for VMI-based security applications. To minimize the performance overhead associated with real-time kernel data structure monitoring, the RTKDSM system has incorporated several optimizations whose effectiveness is reported in this paper.