Predicting the Future Popularity of Academic Publications Using Deep Learning by Considering It as Temporal Citation Networks

The growth in Information Technology (IT) jobs is predicted to reach 15 percent between 2021 and 2031. The growth of IT jobs has resulted in a remarkable change in all infrastructure, such as information, skills, and domains covered in IT job profiles. Unfortunately, job roles and skills in this field remain undefined. The gap between the supply and demand needs in the IT workforce must be filled immediately with an appropriate strategy. To fulfill industry needs, an in-depth analysis of IT job profiles is important. Therefore, it is important for educational programs to identify the competencies needed by the industry to update their output. This study aims to identify the job profiles required for IT job specialists by analyzing real-world job posts published online to identify hidden meanings from a textual database. A systematic semantic methodology was proposed using an average-linkage hierarchical clustering analysis. It resembles a tree structure technique to discover relevant phrases, relationships, and hidden meanings through semantic analysis. Occurrences of the most frequent words and phrases were extracted to reveal the domain knowledge of each IT job cluster. The result is a systematic semantic analysis of the IT job profile comprising the programming language, specialized type, duty, database, tools, and frameworks. The justification for each job profile was validated by 10 IT professionals from various private and government companies in Indonesia through Focus Group Discussions (FGD).INDEX TERMS Information technology job profile, skills, Average-Linkage Hierarchical Clustering analysis, most frequent word, most frequent phrase.

Section: Visualization and Job Profile Analysismentioning

confidence: 99%

Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis

Siswipraptini,

Warnars,

Ramadhan

et al. 2023

“…The taxonomy of traffic measurements and time-series traffic analyses, which are particularly relevant to this study, is presented in Table I. --Prediction of complex time-series variations by advanced analysis, and difference analysis from actual measurements ARIMA [19] [20], LSTM [21], CNN [22] with model prediction In the analysis of networks, methods have been proposed for multimedia analysis at the application layer [7], as represented by web access, and for analyzing the relationship between citations of publications based on the graph theory [8]. The traffic addressed in this paper is more about lower-layer flows defined by port numbers in the transport layer and IP addresses in the IP layer.…”

Section: Related Workmentioning

confidence: 99%

Network Anomaly Detection Through IP Traffic Analysis With Variable Granularity

Kamamura,

Takei,

Nishiguchi

et al. 2023

A network anomaly detection method is proposed for large-scale, wide-range Internet Protocol (IP) networks. Because network behavior is projected onto communication traffic, anomaly detection can be achieved by properly analyzing the communication traffic flows. However, in wide-area IP networks, communication traffic flows are encapsulated by headers assigned by communication carriers and thus are observed as more macroscopic information. Therefore, accurately detecting the occurrence of anomalies in individual communication flows is difficult because the flow observation results obtained by flow measurement protocols such as IP Flow Information Export (IPFIX) are the result of superimposing various communication flows with different characteristics. In this study, we propose an anomaly-detection method based on time-series traffic flows. First, we decompose superimposed traffic flows into individual flows using our implemented system called the Fast xFlow Proxy, which can decompose traffic flows to a fine granularity. Our method detects anomalies in the decomposed flows based on a simple correlation analysis and dynamic threshold configuration. Our extensive simulation shows that, if we observe individual flows using the Fast xFlow Proxy, our method can detect anomalies caused by service failures with almost 100% accuracy. Our method can achieve an accuracy of approximately 80%-90% even in more difficult detection cases, such as small traffic fluctuations or noisy situations.

“…Technology is constantly trying to mimic human intelligence, which is why AI is currently receiving so much attention [1]. AI has been extensively used across numerous fields, including healthcare systems [2], social media trend prediction [3], citation prediction [4], and multi-agent systems [5].…”

Section: Introductionmentioning

confidence: 99%

Rational Coordination in Cognitive Agents: A Decision-Theoretic Approach Using ERMM

Mazhar,

Kausar

2023

Over the years, research in multi-agent systems has become increasingly popular. Agents evolve by interacting with their environment and must communicate with other agents in order to do various cooperative tasks. The research aims to provide efficient coordination among cooperative cognitive agents in unpredictable multi-agent situations. Xiang's rational agent model addresses scenarios when no social conventions or predefined communication protocols exist for the agents' interaction and then makes decisions by recursive modeling. We address the deficiencies of the loosely coupled framework and the problem of mispredictions in Xiang's architecture. It is based on Lawniczak's Architecture for generic cognitive agents and an enhanced model of Xiang's Recursive Modeling Method for coordinated decision-making in multi-agent situations. We instruct the cognitive agent to learn about other agents from past mispredictions and then consider its best choice. The feedback module is incorporated so agents can learn to maximize their joint expected reward. It filters the mispredictions and evaluates the error rate. We compare the enhanced method with the Recursive Modeling Method. The results show that mispredictions are corrected from 33% to 10.9% and errors in perception get reduced 22% to 0.097%, as the system progresses. Overall, the approach demonstrates superior performance. It significantly lowers the rate of mispredictions about other agents' actions and takes 30% to 42% less time and 55.4 % fewer moves than RMM.