API Information Research Articles

The HR Insight Sidebar Extension for LinkedIn Profile

This paper explores the "HR Insights Sidebar Extension," which enhances LinkedIn profile evaluation through a chatbot, aiding informed decision-making with real-time analysis. AI-driven algorithms extract key information like skills, experience, and endorsements, generating concise summaries to streamline evaluations. Leveraging the OpenAI Assistant API's Information Retrieval and Natural Language Processing, the system extracts data from various file types. Users query the chatbot via a natural language interface, receiving direct answers and saving time. Tools like ChatGPT and Proxy curl improve profile evaluation efficiency and effectiveness for students, researchers, and professionals. Keywords- large language models, GPT-3 summarization, proxy curl, implementation, and integration.

Mashup-Oriented Web API Recommendation Via Full-Text Semantic Mining of Developer Requirements

API recommendation differs from conventional recommendation systems in that it integrates the characteristics of development requirements, mashup, and API, where there are two primary challenges, i.e., how to effectively mine the personalized development requirements of developers and the sparseness of interaction data. This research proposes a novel model framework, called Bayesian Probabilistic Matrix Factorization Model with Text Similarity and Adversarial Training (SAMF), to address these two issues. Utilizing natural language processing technology to extract the full-text semantics of requirements documents, mashup descriptions, and API descriptions, fully mining personalized development requirements, and contextual information of mashups and APIs, and calculating text similarity, is our fundamental idea. Simultaneously, the collaborative filtering method is used to mine user needs, mashup, and API information from historical data, and the obtained text similarity is added to the training as auxiliary information. Furthermore, adversarial training is further incorporated to supplement data in order to minimize data sparsity and enhance model robustness and generalization. To assess the performance of SAMF, we run thorough experiments that illustrate the efficacy of each module of the model and explain the influence of hyperparameter settings. Specifically, compared to the baseline, the experimental findings demonstrate that SAMF can basically achieve better performance.

English for Scientific & Technical Writing

Abstract: ‘Communication is important not only in an organization but also in our daily life. When we use communication pertaining to technical, industrial or business matters belong to the category of technical or business communication’’. Scientific and technical writing, a form of technical communication, is a style of writing used in fields of diverse as computer hardware and software, engineering, chemistry, the aerospace, industry, robotics, finance, consumer electronics and biotechnology. Technical writing teams or departments are often referred to as Information development, User Assistance, Technical Documentation, or Technical Publications. Technical writers themselves may be called API writers, information developers, documentation specialists, documentation engineers, or technical content developers. Specific areas to be elaborated on the full paper. “ Nature of scientific and technical documentation-basics of scientific and technical documentation- organization in technical and scientific documentation – style in technical and scientific documentation –ABC of good technical and scientific documentation – history of technical and scientific documentation’. Broadly speaking, technical documentation can be categorized into three types, depending on the style of writing, the level of knowledge transferred and the target audience. End user assistance: These information products help a user understand how ton use a technical software or hardware product. Traditional technical documentation: here the objective of the writer is to communicate with a specific audience.

A Retrospective Analysis of Applications for Registration of Generic Medicines Processed by the Medicines Control Authority of Zimbabwe

BackgroundMany applications for registration of medicines are rejected because applicants fail to submit or resolve critical deficiencies in the quality, efficacy, and safety of the medicines. The study aimed to establish approval rates, processing timelines, and common deficiencies of generic medicines applications processed by the Medicines Authority of Zimbabwe (MCAZ).MethodA retrospective study of applications finalized by MCAZ between 2018 and 2020 was conducted. Data were collected from the assessment reports and verified with copies of letters sent to the applicants. Deficiencies were classified as administrative, quality, efficacy, and safety. Other characteristics collated included time to finalization, dosage form, region of origin, and therapeutic class.ResultsOf the 579 finalized applications, 74.1% were approved while 25.9% were refused. Approved applications had more review cycles (median = 3 cycles) compared to refused applications (median = 2 cycles). However, refused applications had longer review times (median = 25 months) compared to approved applications (median = 18 months). The majority of applications (83.0%) were from Asian manufacturers and intended for oral administration (66.1%). Medicines for the endocrine system (50.0%) and rheumatism/gout (53.3%) had lower approval rates compared to other therapeutical classes (p < 0.001). The most common reasons for refusal of applications included failure to respond to review queries (52.6%), deficiencies in the API information (54.7%), FPP specifications (42.7%), FPP stability data (36.0%), and pharmaceutical development (31.3%).ConclusionTo improve the quality of applications and evaluation outcomes, there may be a need for the regulatory authority to engage applicants through training and pre-submission meetings.

THE PRACTICAL RECOMMENDATIONS FOR THE IMPLEMENTATION OF ADVANCE PASSENGER INFORMATION SYSTEM (API / PNR) WITH THE AIM TO USE SUCH SYSTEM AS A COUNTERACTING CROSS-BORDER THREATS TOOL

The practical recommendations for the implementation of Advance Passenger Information System (API / PNR) were described in the article with the aim to use such system as a counteracting cross-border threats tool. The national approaches to the legal regulation of API / PNR data collection, storage and processing data on air passengers are analyzed. Since there is no complete system for processing Advance Passenger Information in Ukraine and currently it is possible only to check API information through national databases, tactical tasks for the system implementation in Ukraine have been identified based on a foreign experience review. It is argued that the implementation of such a systems will assist to ensure more effective and reliable protection of border security, in particular: helps to improve border control and increase the effectiveness of the fight against terrorism, organized crime, as well as facilitates to regulate migration; allows to reduce the workload of border control officers through the use of technology and automated means; improves people’s perception of security and facilitates more comfortable and smooth border crossing for law-abiding passengers; complements the procedures for checking documents and data (checking passports according to databases). The order of actions for system implementing is proposed. It is includes: development of needs and goals, determination of basic parameters of the national system, regulation of legal support for the functioning of the system (legal guarantees of personal data protection, legal base of data collection, exchange and processing), establishing of interagency cooperation mechanism.

PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.

Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation. In this article, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use all concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a distantly supervised training dataset for machine learning. Next, a cutting-edge deep learning model is trained to classify each candidate phrase (n-gram from input sentence) into a corresponding concept label. Finally, the dictionary and machine learning-based prediction results are combined for improved performance. Our method is validated with two HPO corpora, and the results show that PhenoTagger compares favorably to previous methods. In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ontologies. Experimental results on the NCBI disease corpus show that PhenoTagger without requiring manually annotated training data achieves competitive performance as compared with state-of-the-art supervised methods. The source code, API information and data for PhenoTagger are freely available at https://github.com/ncbi-nlp/PhenoTagger. Supplementary data are available at Bioinformatics online.

A Wizard of Oz Study Simulating API Usage Dialogues With a Virtual Assistant

Virtual Assistant technology is rapidly proliferating to improve productivity in a variety of tasks. While several virtual assistants for everyday tasks are well-known (e.g., Siri, Cortana, Alexa), assistants for specialty tasks such as software engineering are rarer. One key reason software engineering assistants are rare is that very few experimental datasets are available and suitable for training the AI that is the bedrock of current virtual assistants. In this paper, we present a set of Wizard of Oz experiments that we designed to build a dataset for creating a virtual assistant. Our target is a hypothetical virtual assistant for helping programmers use APIs. In our experiments, we recruited 30 professional programmers to complete programming tasks using two APIs. The programmers interacted with a simulated virtual assistant for help - the programmers were not aware that the assistant was actually operated by human experts. We then annotated the dialogue acts in the corpus along four dimensions: illocutionary intent, API information type(s), backward-facing function, and traceability to specific API components. We observed a diverse range of interactions that will facilitate the development of dialogue strategies for virtual assistants for API usage.

Information correspondence between types of documentation for APIs

Documentation for programming languages and their APIs takes many forms, such as reference documentation, blog posts or other textual and visual media. Prior research has suggested that developers switch between reference and tutorial-like documentation while learning a new API. Documentation creation and maintenance is also an effort-intensive process that requires its creators to carefully inspect and organize information, while ensuring consistency across different sources. This article reports on the relationship between information in tutorials and in API reference documentation of three libraries on the topics: regular expressions, unified resource location and Input/Output in the two programming languages, Java and Python. Our investigation reveals that about half of the sentences in the tutorials studied describe API Information, i.e. syntax, behaviour, usage and performance of the API, that could be found in the reference documentation. The remaining are tutorial specific use-cases and examples. We also elicited and analyzed six types of correspondences between sentences in tutorials and reference documentation, ranging from identical to implied. Based on our findings, we propose a general information reuse pattern as a structured abstraction to represent the systematic integration of information from the reference documentation into a tutorial. We report on the distribution of 38 instances of this pattern, and on the impact of applying the pattern automatically on the existing tutorials. This work lays a foundation for understanding the nature of information correspondence across different documentation types to inform and assist documentation generation and maintenance.

On the effectiveness of system API-related information for Android ransomware detection

Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore their data. Hence, the prompt detection of such attacks has a priority in comparison to other malicious threats. Previous works on Android malware detection mainly focused on Machine Learning-oriented approaches that were tailored to identifying malware families, without a clear focus on ransomware. More specifically, such approaches resorted to complex information types such as permissions, user-implemented API calls, and native calls. However, this led to significant drawbacks concerning complexity, resilience against obfuscation, and explainability. To overcome these issues, in this paper, we propose and discuss learning-based detection strategies that rely on System API information. These techniques leverage the fact that ransomware attacks heavily resort to System API to perform their actions, and allow distinguishing between generic malware, ransomware and goodware.We tested three different ways of employing System API information, i.e., through packages, classes, and methods, and we compared their performances to other, more complex state-of-the-art approaches. The attained results showed that systems based on System API could detect ransomware and generic malware with very good accuracy, comparable to systems that employed more complex information. Moreover, the proposed systems could accurately detect novel samples in the wild and showed resilience against static obfuscation attempts. Finally, to guarantee early on-device detection, we developed and released on the Android platform a complete ransomware and malware detector (R-PackDroid) that employed one of the methodologies proposed in this paper.

A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms

Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control flow graph of the application to obtain API information. Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calls, API frequency, and API sequence aspects are constructed. Ultimately, an ensemble model is constructed for conformity. We tested and compared the accuracy and stability of our detection models through a large number of experiments. The experiments were conducted on 10010 benign applications and 10683 malicious applications. The results show that our detection model achieves 98.98% detection precision and has high accuracy and stability. All of the results are consistent with the theoretical analysis in this paper.

보행자 경로안내 API정보를 활용한 대중교통 접근성 영향요인 분석 : 서울시 지하철역 보행 접근성을 중심으로

보행자 경로안내 API정보를 활용한 대중교통 접근성 영향요인 분석 : 서울시 지하철역 보행 접근성을 중심으로

Clustering Microsoft Windows Executables based on TF-IDF and API Information

The illegal software usage is 39% worldwide and malware is frequent in the illegal software. Toprotect attacks from malware, we use software filtering. The software filtering compares equivalenceof a testing software to an original one. This requires comparison between all the legal programs inthe market. So we have to reduce the number of comparisons by clustering programs in the market.Every market provides categories to programs such as image viewer, video player, audio player, andmessenger, etc. But it is not clear that these categories are best fit to filter malware. We suggest newcategories which are more suitable to classification experimentally. Our categories are automaticallymade from the K-means clustering algorithm based on TF-IDF and API information. Experimentalresults show that our clustering scheme is better than the existing categories to classify malware.

소프트웨어 품질향상을 위한 소스코드 기반의 테스트 케이스 자동 생성에 관한 연구

This paper proposes an automatic generation technology of test case based on API in source code for software`s quality improvement. The proposed technology is comprised of four processes which are analyzing source code by using the Doxygen open source tool, defining API specification by using analyzed results, creating test design, generating a test case by adapting Pairwise test technology. Analyzing source code by using the Doxygen open source tool is the phase in which API information in source code such as the API name, input parameter and return parameter are extracted. Defined API specification by using analyzed results is the phase where API informations, which is needed to generate test case, are defined as a form of database by SQLite database on the basis of extracted API information. Creating test design is the phase in which the scenario is designed in order to be composed as database by defining threshold of input and return parameters and setting limitations based on the defined API. Generating a test case by adapting Pairwise test technique is the phase where real test cases are created and changed into database by adapting Pairwise technique on the base of test design information. To evaluate the efficiency of proposed technology, the research was conducted by begin compared to specification based test case creation. The result shows wider test coverage which means the more cases were created in the similar duration of time. The reduction of manpower and time for developing products is expected by changing the process of quality improving in software developing from man-powered handwork system into automatic test case generation based on API of source code.

SSL VPN기반의 행위.순서패턴을 활용한 접근제어에 관한 연구

본 논문에서는 SSL VPN을 기반으로 사용자 인증과 사용자 단말의 무결성을 검증할 수 있는 네트워크 접근제어 기술을 제안한다. 사용자 단말이 VPN을 이용해 내부 네트워크에 접속할 때 사용자 인증과 사용자 단말의 보안패치, 바이러스 백신 등의 보안 서비스를 확인하는 안전성 검사를 수행한다. 그리고 변종의 악성코드를 탐지하기 위해 사용자 단말의 윈도우 API 정보를 통한 행위패턴을 바탕으로 악성코드를 탐지하고, 탐지의 신뢰도를 높이기 위해 순서패턴의 유사도를 비교하여 변종의 악성코드를 탐지하여 외부의 보안 위협으로부터 시스템을 보호한다. In this paper, we proposed SSL VPN-based network access control technology which can verify user authentication and integrity of user terminal. Using this technology, user can carry out a safety test to check security services such as security patch and virus vaccine for user authentication and user terminal, during the VPN-based access to an internal network. Moreover, this system protects a system from external security threats, by detecting malicious codes, based on behavioral patterns from user terminal's window API information, and comparing the similarity of sequential patterns to improve the reliability of detection.

Coordination of documentation research and information services by the American Petroleum Institute

AbstractCentralized processing of technical information has been achieved for the petroleum‐refining industry through the American Petroleum Institute's Central Abstracting Service. The API operates this Service under the guidance of committees made up of member‐company representatives. Both management and information people cooperate on decisions that affect the cost and quality of the centralized services. Behind these decisions lies the pooled documentation experience and research of the individual companies, augmented by occasional specific studies conducted by the professionals who man the Service. Industry experience and research are tapped by the service of committee members themselves (who frequently consult with subject experts in their own companies), by appointment of study teams of industry expert in the specific documentation phases involved, by sending questionnaires to petroleum companies, and by holding open‐meeting discussions with users of API information services. Specific examples are cited of studies on classification systems, patent abstracting, Soviet‐literature abstracting, and machine coding.