Syntax Patterns Research Articles

John Robots, Thurgood Martian, and the Syntax Monster: A New Argument Against AI Judges

Abstract This paper argues that an AI judge is conceptually undesirable and not just something that lies beyond the state of the art in computer science. In a nutshell, even if an AI system could accurately predict how a good human judge would decide a particular case, its prediction would be the product of correlations between such factors as patterns of syntax in bodies of legal texts. This approach of AI systems is insufficient for basing their output on the sort of rationales that are expected of valid judicial decisions in any desirable legal system. Thus, by their very nature, AI systems are incapable of providing valid legal decisions in any such system.

Experiences with using ears for stakeholder requirements deve-lopment of the safe integration of unmanned aircrafts in the airspace project

Requirements permeate the entire lifecycle of a system or product, from identifying the stakeholders’ needs to disposal, including verification and validation activities. Therefore, the process for the requirements development must be carefully observed and established in order to ensure that the requirements are complete, consistent, and correct. The use of an appropriate predefined structure can improve the requirements’ elicitation, enabling the reduction of ambiguities and the detection of duplications or omissions. Thus, this article proposes the use of syntax patterns during the development of the stakeholders’ requirements in the context of an airspace project. The result of applying the proposal of this article in a real case was a list of initial requirements in standardized language with their traceability guaranteed.

Correlating Language and Music for the Activation of Human Mind

As Heinrich Heine puts it, “Where words leave off, music begins”. Music and language belong to a common origin i.e. sound, a form of energy. In other words, language and music are two sons of the same mother. Language and music both engage the auditory system, utilizing similar neural pathways to process sound and rhythm. Just as language relies on patterns of sounds and syntax to convey meaning, music utilizes melody, harmony, and structure to evoke emotions and communicate messages. A language always talks about the speech sounds that are produced by the vocal cords while music is the outcome of non-oral sounds or sometimes may be the oral sounds. Both language and music have the ability to elicit powerful emotional responses in individuals, stirring feelings of joy, sadness, or nostalgia. The rhythmic and melodic elements in music can enhance language processing by providing a rhythmic framework that aids in memorization and comprehension. Though language and music are not always complementary to each other but one seems incomplete in absence of the other. Language and music share the capacity to convey complex ideas and narratives, allowing us to express our thoughts and experiences in nuanced and creative ways. Studies have shown that exposure to music can improve language skills, including vocabulary acquisition, syntax comprehension, and verbal fluency. The use of prosody, intonation, and rhythm in language mirrors the melodic and rhythmic components of music, highlighting their interconnectedness. Musical training has been found to enhance linguistic abilities, as musicians often demonstrate better pitch discrimination, phonological awareness, and language learning skills. Language and music can both serve as cultural markers, reflecting and preserving the traditions, values, and identities of different communities. The shared neural processes involved in language and music suggest a deep-rooted correlation, as they tap into fundamental mechanisms of auditory perception and cognitive processing. The present research article is a descriptive study on the relationship between language and music and their effects on the mind and body of the human beings. In the first section of this paper, the linguistic aspects that are akin to music have been attempted to be pointed out. Similarly, in the following section, the core of the music has been discussed in context to language. In the third section, a comparative study of language and music has been done on the basis of the points discussed in the preceding sections. Finally, the paper also discusses as how the amalgamation of the two i.e. language and music can be helpful in activating the human mind and the body.

A graph database for life cycle inventory using Neo4j

Life cycle inventory (LCI) databases constitute the basis for the life cycle assessment (LCA) of a product system. LCI data involve complex relationships between the production activities and the environment. The currently used relational database employs a rigid schema structure of two-dimensional tables and lacks direct support for the complex relationships between LCI data. In this paper, a graph database was designed and constructed using Neo4j graph database management system. First, an LCI knowledge graph (LCIKG) model for the graph database is proposed, which employs the labeled property graph structure and describes the LCI data and the semantic relationships among LCI data concepts. Second, Ecoinvent datasets were successfully used to construct the LCI graph database by automatically extracting Cypher syntax patterns; then, Neo4j was used to store, visualize, and retrieve LCI data. Finally, a set of queries have been executed to evaluate the performance of the graph database; a case study has been provided to demonstrate the effectiveness of the proposed graph database. The graph database can effectively reduce the time and effort to query and process LCI data of a product system. Moreover, the dynamic schema of the LCIKG model promotes the scalability and interoperability of LCI data. The completed work provides a feasible solution for the issues and challenges in current LCI research and promotes the wide application of LCA.

Male and Female Compliment Patterns and Topics Used by Bts Fans’ Comments on Hybe Labels

Compliment is a polite expression of praise and admiration, an action or situation that implies admiration or respect. One of the important features that evaluation produces is compliments: compliments reflect cultural values, beliefs and speaker. Non-linguistic factor that can affect the use of language, such as compliments, is gender. Male and female use different words, so the manner they provide compliments are also different. This study was conducted to discover the male and female compliment patterns and topics used in BTS fans’ comments on the Hybe Labels’ YouTube channel. The researcher used qualitative descriptive method in this study. It analyzes the form of compliment given by male and female fans to BTS through YouTube comments. This final project explained, analyzed and interpreted the fan compliments to BTS. The results of this study showed that male and female BTS fans’ compliments follow a simple syntax pattern, using adjectival compliments rather than verbs of liking. These compliment topics are appearance, hard work, performance, possession, achievement, skill, and talent. The most used theme in this survey is compliment for skill, primarily from male fans. In summary, there is not much difference between male and female fans when it comes to complementing BTS's Dynamite music video on Hybe Labels' YouTube channel.

Utilizing source code syntax patterns to detect bug inducing commits using machine learning models

Detecting Bug Inducing Commit (BIC) or Just in Time (JIT) defect prediction using Machine Learning (ML) based models requires tabulated feature values extracted from the source code or historical maintenance data of a software system. Existing studies have utilized meta-data from source code repositories (we named them GitHub Statistics or GS), n-gram-based source code text processing, and developer’s information (e.g., the experience of a developer) as the feature values in ML-based bug detection models. However, these feature values do not represent the source code syntax styles or patterns that a developer might prefer over available valid alternatives provided by programming languages. This investigation proposed a method to extract features from its source code syntax patterns to represent software commits and investigate whether they are helpful in detecting bug proneness in software systems. We utilize six manually and two automatically labeled datasets from eight open-source software projects written in Java, C++, and Python programming languages. Our datasets contain 642 manually labeled and 4014 automatically labeled buggy and non-buggy commits from six and two subject systems, respectively. The subject systems contain a diverse number of revisions, and they are from various application domains. Our investigation shows the inclusion of the proposed features increases the performance of detecting buggy and non-buggy software commits using five different machine learning classification models. Our proposed features also perform better in detecting buggy commits using the Deep Belief Network generated features and classification model. This investigation also implemented a state-of-the-art tool to compare the explainability of predicted buggy commits using our proposed and traditional features and found that our proposed features provide better reasoning about buggy commit detection compared to the traditional features. The continuation of this study can lead us to enhance software effectiveness by identifying, minimizing, and fixing software bugs during its maintenance and evolution.

Approaches to Teaching the Works of Cormac McCarthy

Approaches to Teaching the Works of Cormac McCarthy

A Real-Time Detection Method of Software Configuration Errors Based on Fine-Grained Configuration Item Types

With the continuous expansion of software scale and the continuous complexity of software functions, abnormal parameter configuration often brings adverse effects to the software system and even leads to system failure. Therefore, a method is needed to detect whether the parameter configuration is correct. Most of the current configuration errors detection methods focus on the passive diagnosis after the system failure, which cannot solve the potential delay problem of configuration error. This paper proposes an automatic real-time detection method of software configuration errors, in which the configuration items are classified based on the fine-grained configuration item types and related syntax patterns, and the configuration constraint rule base is generated. Then, the real-time exception detection of configuration update operation is realized by using the file operation monitoring function. The experimental results show that this method can accurately classify the types of configuration items and verify the effectiveness of detecting software configuration update exceptions in real time through the constraint rule base. The classification accuracy reaches up to 90.4% on MySQL and 87.4% on Apache.

Stylistic Analysis of Ode to the West Wind by PB Shelley

Stylistics is concerned with the analysis of variation and characteristics of language. Stylistics has gathered imminent role in ascertaining language use, especially poetic language. In this research paper,I have explored the intentional deviation from the conventional rules in the poem Ode to West Wind by P.B Shelly. The poem is rich from a stylistic point of view because the application of deliberate misspellings, creativity in style, the irregularity in the syntax patterns, and the phonological and graphological deviations make the poem a fascinating read. I have noticed that Shelly has freed himself from the structural and traditional norms of language and has presented a new dimension in poetry.This article is the analysis done against the backdrop of deviations at syntactical, morphological, and grammatical levels.

Functional-realistic CT image super-resolution for early-stage pulmonary nodule detection

Early-stage pulmonary nodule detection is challenging for Computer-aided Diagnosis systems (CADs) in clinical practice. It always relies on large-scale annotated pathological images. Unfortunately, the limited voxels of earlier-stage nodules can aggravate the risk of escaping diagnosis. Due to high-dose CT and bronchoscope potential threats, CT image super-resolution has become a suboptimal way to tackle the problem. Therefore, we proposed a deep generative adversarial network (GAN) architecture based on a deep grammar model, called FRGAN (Functional-Realistic GAN). By using region proposal network (RPN), the bottom semantic features are recommended and classified as the basic units of functional structure. Local pathological images can be hierarchically aggregated with corresponding to different semantic patterns as parse trees. Refer to their EMRs, and we use TreeGAN to generate the correct syntax patterns for each early-stage pulmonary nodule candidates. We report the generated results of the super-resolution images and feed them into a convolutional network to assess the functional loss of the generated results along to different parse trees. Within the contextual and generative losses, we rebuild a novel objective function paralleling with TreeGAN. The aim is to boost the sensibility of pulmonary nodule detection with more functional-realistic data augmentation. Experimental results show that our generator can faster generate more realistic SR images with pathological features. Moreover, it could be a data augmentation tool for some deep architecture to overcome sample imbalance.

Lenses for Web Data

Putting data on the web typically involves implementing two transformations: one to convert the data into HTML, and another to parse modifications out of interactions with clients. Unfortunately, in current systems, these transformations are usually implemented using two separate functions—an approach that replicates functionality across multiple pieces of code, and makes programs difficult to write, reason about, and maintain. This paper presents a different approach: an abstraction based on formlets that makes it easy to bridge the gap between data stored on a server and values embedded in HTML forms. We introduce formlenses , which combine the advantages of formlets with those of lenses to provide compositional, bidirectional form-based views of Web data. We show that formlenses can be viewed as monoidal functors over lenses, analogously to formlets, which are applicative functors. Finally, we investigate the connection between linearity and bidirectional transformations and describe a translation from a linear pattern syntax into formlens combinators.

An Efficient Hybrid Computing Environment to Develop a Confidential and Authenticated IoT Service Model

The applications and scope of the Internet of Things (IoT) goes on increasing when cloud computing combines with IoT. Cloud enriches the capacity of IoT in various sectors such as Home Automation, Healthcare, Industrial IoT, etc. One of the major challenges in construction of today’s smart environment is to ensure the confidentiality and authentication in data transmission of the environment. Many existing systems attempted to provide a secure IoT environment but failed to ensure the efficiency in its security. Due to this problem, the capability of IoT and Cloud comes down and suffers to produce an optimal environment. In this regard, an optimal hybrid computing model containing cloud and edge computing has been proposed to develop a confidence assessment system to ensure the security in a IoT environment. The proposed model is an amalgamation of confidence assessment system and utility pattern of dynamic load balancing in cloud and edge computing. The effective design of the edge network and edge policy minimizes the resource utilization and improves the capability of the confidence assessment system. In the proposed system, the utility factor pattern is embedded in cloud and utility syntax pattern is embedded in the edge policy to enhance the applications of IoT and Cloud services. The edge network helps the edge policy in embedding the utility syntax pattern based on confidence assessment system. The proposed model has been compared with various existing models and results in producing an optimal secure IoT environment.

Heterogeneous tree structure classification to label Java programmers according to their expertise level

Open-source code repositories are a valuable asset to creating different kinds of tools and services, utilizing machine learning and probabilistic reasoning. Syntactic models process Abstract Syntax Trees (AST) of source code to build systems capable of predicting different software properties. The main difficulty of building such models comes from the heterogeneous and compound structures of ASTs, and that traditional machine learning algorithms require instances to be represented as n-dimensional vectors rather than trees. In this article, we propose a new approach to classify ASTs using traditional supervised-learning algorithms, where a feature learning process selects the most representative syntax patterns for the child subtrees of different syntax constructs. Those syntax patterns are used to enrich the context information of each AST, allowing the classification of compound heterogeneous tree structures. The proposed approach is applied to the problem of labeling the expertise level of Java programmers. The system is able to label expert and novice programs with an average accuracy of 99.6%. Moreover, other code fragments such as types, fields, methods, statements and expressions could also be classified, with average accuracies of 99.5%, 91.4%, 95.2%, 88.3% and 78.1%, respectively.

SIAS-miner: mining subjectively interesting attributed subgraphs

Data clustering, local pattern mining, and community detection in graphs are three mature areas of data mining and machine learning. In recent years, attributed subgraph mining has emerged as a new powerful data mining task in the intersection of these areas. Given a graph and a set of attributes for each vertex, attributed subgraph mining aims to find cohesive subgraphs for which (some of) the attribute values have exceptional values. The principled integration of graph and attribute data poses two challenges: (1) the definition of a pattern syntax (the abstract form of patterns) that is intuitive and lends itself to efficient search, and (2) the formalization of the interestingness of such patterns. We propose an integrated solution to both of these challenges. The proposed pattern syntax improves upon prior work in being both highly flexible and intuitive. Plus, we define an effective and principled algorithm to enumerate patterns of this syntax. The proposed approach for quantifying interestingness of these patterns is rooted in information theory, and is able to account for background knowledge on the data. While prior work quantified the interestingness for the cohesion of the subgraph and for the exceptionality of its attributes separately, then combining these in a parameterized trade-off, we instead handle this trade-off implicitly in a principled, parameter-free manner. Empirical results confirm we can efficiently find highly interesting subgraphs.

Concrete Syntax with Black Box Parsers

Context: Meta programming consists for a large part of matching, analyzing, and transforming syntax trees. Many meta programming systems process abstract syntax trees, but this requires intimate knowledge of the structure of the data type describing the abstract syntax. As a result, meta programming is error-prone, and meta programs are not resilient to evolution of the structure of such ASTs, requiring invasive, fault-prone change to these programs. Inquiry: Concrete syntax patterns alleviate this problem by allowing the meta programmer to match and create syntax trees using the actual syntax of the object language. Systems supporting concrete syntax patterns, however, require a concrete grammar of the object language in their own formalism. Creating such grammars is a costly and error-prone process, especially for realistic languages such as Java and C++. Approach: In this paper we present Concretely, a technique to extend meta programming systems with pluggable concrete syntax patterns, based on external, black box parsers. We illustrate Concretely in the context of Rascal, an open-source meta programming system and language workbench, and show how to reuse existing parsers for Java, JavaScript, and C++. Furthermore, we propose Tympanic, a DSL to declaratively map external AST structures to Rascal's internal data structures. Tympanic allows implementors of Concretely to solve the impedance mismatch between object-oriented class hierarchies in Java and Rascal's algebraic data types. Both the algebraic data type and AST marshalling code is automatically generated. Knowledge: The conceptual architecture of Concretely and Tympanic supports the reuse of pre-existing, external parsers, and their AST representation in meta programming systems that feature concrete syntax patterns for matching and constructing syntax trees. As such this opens up concrete syntax pattern matching for a host of realistic languages for which writing a grammar from scratch is time consuming and error-prone, but for which industry-strength parsers exist in the wild. Grounding: We evaluate Concretely in terms of source lines of code (SLOC), relative to the size of the AST data type and marshalling code. We show that for real programming languages such as C++ and Java, adding support for concrete syntax patterns takes an effort only in the order of dozens of SLOC. Similarly, we evaluate Tympanic in terms of SLOC, showing an order of magnitude of reduction in SLOC compared to manual implementation of the AST data types and marshalling code. Importance: Meta programming has applications in reverse engineering, reengineering, source code analysis, static analysis, software renovation, domain-specific language engineering, and many others. Processing of syntax trees is central to all of these tasks. Concrete syntax patterns improve the practice of constructing meta programs. The combination of Concretely and Tympanic has the potential to make concrete syntax patterns available with very little effort, thereby improving and promoting the application of meta programming in the general software engineering context.

Of Gods and Kings: The Case for Reading “Milcom” in Zephaniah 1:5bβ

Abstract Manuscript evidence disagrees between reading the final word in Zeph 1:5 as meaning “their king,” “Molech,” or “Milcom.” The affinity of these three readings with the MT consonantal text, combined with the lack of agreement among the manuscript evidence, allows for plausible arguments supporting each of these readings. A majority of commentators conclude that this entity must refer to a deity, but disagree on which manuscript tradition to follow. The present essay surveys each of these readings, arguing that “Milcom” best fits the literary context and syntax patterns in Zeph 1:5bβ. This essay examines comparable text-critical difficulties in 2 Sam 12:30; 1 Chr 20:2; 1 Kgs 11:7; Jer 49:1, 3; and Amos 5:26, concluding that similar examples of the MT reinterpreting the name “Milcom” as “their king” occur in Jer 49:1, 3.

Correlation between SYNTAX Score and Pattern of Risk Factors in Patients Referred for Coronary Angiography in Cardiology Department, Menoufia University

Objectives: The role of SYNTAX (SX) score in assessing the complexity of coronary artery disease (CAD) is well established. In this study we investigate the relationship between conventional risk factors of CAD with its complexity using SX score. Methods: The study consisted of 52 patients with CAD who were admitted to Cardiology Department—Menoufia University Hospitals for elective coronary angiography. The overall SX score was calculated prospectively using the SX score algorithm. Then comparison was done between populations with and without each risk factor. Results: There was a statistically significant correlation between age, hypertension, diabetes mellitus, dyslipidemia and smoking with SX score results (p 0.05). In a multivariate regression analysis, including conventional risk factors of CAD as independent covariates, it revealed that aging, having diabetes mellitus and smoking were identified as significant independent risk factors for CAD complexity. Conclusion: Advanced age, having diabetes mellitus and cigarette smoking are considered to be independent risk factors for the complexity of CAD. Therefore, when these factors present, we expect that the SX score of the patient to be high indicating a complex CAD.

Introducing the Special Issue: Clausal and Nominal Complements in Monolingual and Bilingual Grammars

To introduce this Special Issue entitled Clausal and Nominal Complements in Monolingual and Bilingual Grammars, we begin by explaining what originally motivated this Special Issue [...]

Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge

ObjectiveMental health is becoming an increasingly important topic in healthcare. Psychiatric symptoms, which consist of subjective descriptions of the patient’s experience, as well as the nature and severity of mental disorders, are critical to support the phenotypic classification for personalized prevention, diagnosis, and intervention of mental disorders. However, few automated approaches have been proposed to extract psychiatric symptoms from clinical text, mainly due to (a) the lack of annotated corpora, which are time-consuming and costly to build, and (b) the inherent linguistic difficulties that symptoms present as they are not well-defined clinical concepts like diseases. The goal of this study is to investigate techniques for recognizing psychiatric symptoms in clinical text without labeled data. Instead, external knowledge in the form of publicly available “seed” lists of symptoms is leveraged using unsupervised distributional representations. Materials and MethodsFirst, psychiatric symptoms are collected from three online repositories of healthcare knowledge for consumers—MedlinePlus, Mayo Clinic, and the American Psychiatric Association—for use as seed terms. Candidate symptoms in psychiatric notes are automatically extracted using phrasal syntax patterns. In particular, the 2016 CEGS N-GRID challenge data serves as the psychiatric note corpus. Second, three corpora—psychiatric notes, psychiatric forum data, and MIMIC II—are adopted to generate distributional representations with paragraph2vec. Finally, semantic similarity between the distributional representations of the seed symptoms and candidate symptoms is calculated to assess the relevance of a phrase. Experiments were performed on a set of psychiatric notes from the CEGS N-GRID 2016 Challenge. Results & conclusionOur method demonstrates good performance at extracting symptoms from an unseen corpus, including symptoms with no word overlap with the provided seed terms. Semantic similarity based on the distributional representation outperformed baseline methods. Our experiment yielded two interesting results. First, distributional representations built from social media data outperformed those built from clinical data. And second, the distributional representation model built from sentences resulted in better representations of phrases than the model built from phrase alone.

Pattrens of NSM Syntax of Semantic Primitives Prototypes in the Fields of Custom and Religion in Bali


 
 
 Efforts in documenting variations and clusterrs of Balinese lexicons, particularly in the fields of custom and religion, have been gradually made. The patterns of NSM syntax of prototypes of semantic primitives were thoroughly examined. Thus, an example of the use of the lexicon that represents each prototype of semantic primitives was made to complete the list of the lexicon previously studied.Therefore, NSM dictionary on prototypes of semantic primitives can be easily come into being.Data were obtained by the methods of participative observation, during which the techniques of Focus Group Discussion, recording, and note-taking were employed (Bungin, 2001:57; Moleong, 2000).In-depth interviews to several informants in the two regencies were carried out. The results of the interview were then cross-referenced to such key informants as tradition and community leaders. Data were comprehensively studied by the methods of padan of which relevant theories were used to study data and agih of which supporting equipment was sourced from the language itself (Sudaryanto, 2003). The results of data analysis showed that the patterns of NSM syntax were novelties by combination of several prototypes of semantic primitives accordingly. The patterns comprise of simple patterns, complex patterns, and the related specific patterns.