Automatic Text Summarization Using Fuzzy Inference

Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods

Directly Printable Frequency Signature Chipless RFID Tag for IoT Applications

This Paper proposes a low-cost, compact, flexible passive chipless RFID tag that has been designed and analyzed. The tag is a bowtie-shaped resonator based structure with 36 slots; where each patch is loaded with 18 slots. The tag is set in a way that each slot in a patch corresponds to a metal gap in the other patch. Hence there is no mutual interference, and high data capacity of 36 bits is achieved in such compact size. Each slot corresponds to a resonance frequency in the RCS curve, and each resonance corresponds to a bit. The tag has been realized for Taconic TLX-0, PET, and Kapton®HN (DuPontTM) substrates with copper, aluminum, and silver nanoparticle-based ink (Cabot CCI-300) as conducting materials. The tag exhibits flexibility and well optimized while remaining in a compact size. The proposed tag yields 36 bits in a tag dimension of 24.5 x 25.5 mm^2. These 36 bits can tag 2^36 number of objects/items. The ultimate high capacity, compact size, flexible passive chipless RFID tag can be arrayed in various industrial and IoT-based applications

Directly Printable Frequency Signatured Chipless RFID Tag for IoT Applications

This paper proposes a low-cost, compact, flexible passive chipless RFID tag that has been designed and analyzed. The tag is a bowtie-shaped resonator based structure with 36 slots; where each patch is loaded with 18 slots. The tag is set in a way that each slot in a patch corresponds to a metal gap in the other patch. Hence there is no mutual interference, and high data capacity of 36 bits is achieved in such compact size. Each slot corresponds to a resonance frequency in the RCS curve, and each resonance corresponds to a bit. The tag has been realized for Taconic TLX-0, PET, and Kapton (R) HN (DuPont (TM)) substrates with copper, aluminum, and silver nanoparticlebased ink (Cabot CCI-300) as conducting materials. The tag exhibits flexibility and well optimized while remaining in a compact size. The proposed tag yields 36 bits in a tag dimension of 24.5. 25.5 mm(2). These 36 bits can tag 2(36) number of objects/items. The ultimate high capacity, compact size, flexible passive chipless RFID tag can be arrayed in various industrial and IoT-based applications

Peningkatan quality of experience pada permainan online multiplayer berbasis Arduino dengan menggunakan MQTT server

Online multiplayer games require internet networks to play with opposing players more exciting because multiple players can fight each other. The game experiences lag, which is expressed as the quality of experience (QoE), is one of the most common problems for online multiplayer games, causing the games less exciting to play. This study examined the implementation of Message Queue Telemetry Transport (MQTT) as a communication protocol in multiplayer online games using Arduino and compared its performance against HTTP. QoE used data collected using the mean opinion score (MOS) method. The MQTT resulted in an average QoE score of 3.9 (Pingpong) and 4 (TicTacToe) MOS units, while on HTTP 3.8 (PingPong and TicTacToe). The use of the MQTT communication protocol can improve the QoE of multiplayer online game players compared to HTTP.Permainan online multiplayer memerlukan jaringan internet agar dapat bermain lebih menarik dengan pemain lawan karena beberapa pemain bisa saling melawan satu sama lain. Salah satu kondisi permainan ini yang paling umum adalah permainan mengalami lag, yang dinyatakan sebagai quality of experience (QoE), sehingga permainan kurang menarik untuk dimainkan. Penelitian ini melakukan kajian implementasi Message Queue Telemetry Transport (MQTT) sebagai protokol komunikasi penghubung pada permainan online multiplayer di papan berbasis Arduino dan membandingkan kinerja QoE-nya terhadap HTTP. Metode mean opinion score (MOS) digunakan untuk merekam data yang diperlukan untuk menganalisis QoE. MQTT memperoleh rata-rata skor QoE sebesar 3,9 (Pingpong) dan 4 (TicTacToe) satuan MOS, sedangkan HTTP memperoleh rata-rata skor sebesar 3,8 (PingPong dan TicTacToe). Penggunaan protokol komunikasi MQTT dapat meningkatkan QoE pemain dalam permainan online karena skor rata-rata QoE-nya lebih tinggi dibandingkan dengan HTTP

Asymmetric Information in Agriculture Supply Chain Management: A Literature Review

Food markets have been in a state of upheaval for some time now. Due to the current trend of numerous consumers favouring sustainable nutrition, the organic food market has proven to be an important market for both consumers and producers. This development enables consumers to continue to afford sustainable food in the future. Due to the complexity and non-transparency of value chains (especially in the organic food market) as well as the insufficient labelling of organic food, there is a lack of information in the organic food market. This often results in market failure. The aim of this research is to understand the problems caused by asymmetric information in the food supply chain and to present the principal-agent theory to detect and describe asymmetric information and as an economic model for understanding asymmetric information in the food supply chain. The principal-agent theory is most frequently used to explain and describe asymmetric information. The imperfection of principal-agent theory is due to the lack of and insufficient application of theories from related disciplines such as transaction theory and game theory. Furthermore, the theory assumes the existence of an informed agent and an uninformed principal. Finally, the analysis of information asymmetry is based on the existence of only principal and agent and neglects the information asymmetries in multi-level network-value chains. This paper presents a structured literature review that provides an overview of the current literature on the subject of asymmetric information in multi-level network-value chains. The identified studies are classified, and gaps are identified for future research

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

Producción CientíficaIn healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data

Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics

Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals

Leveraging Smartphone Sensor Data for Human Activity Recognition

Using smartphones for human activity recognition (HAR) has a wide range of applications including healthcare, daily fitness recording, and anomalous situations alerting. This study focuses on human activity recognition based on smartphone embedded sensors. The proposed human activity recognition system recognizes activities including walking, running, sitting, going upstairs, and going downstairs. Embedded sensors (a tri-axial accelerometer and a gyroscope sensor) are employed for motion data collection. Both time-domain and frequency-domain features are extracted and analyzed. Our experiment results show that time-domain features are good enough to recognize basic human activities. The system is implemented in an Android smartphone platform. While the focus has been on human activity recognition systems based on a supervised learning approach, an incremental clustering algorithm is investigated. The proposed unsupervised (clustering) activity detection scheme works in an incremental manner, which contains two stages. In the first stage, streamed sensor data will be processed. A single-pass clustering algorithm is used to generate pre-clustered results for the next stage. In the second stage, pre-clustered results will be refined to form the final clusters, which means the clusters are built incrementally by adding one cluster at a time. Experiments on smartphone sensor data of five basic human activities show that the proposed scheme can get comparable results with traditional clustering algorithms but working in a streaming and incremental manner. In order to develop more accurate activity recognition systems independent of smartphone models, effects of sensor differences across various smartphone models are investigated. We present the impairments of different smartphone embedded sensor models on HAR applications. Outlier removal, interpolation, and filtering in pre-processing stage are proposed as mitigating techniques. Based on datasets collected from four distinct smartphones, the proposed mitigating techniques show positive effects on 10-fold cross validation, device-to-device validation, and leave-one-out validation. Improved performance for smartphone based human activity recognition is observed. With the efforts of developing human activity recognition systems based on supervised learning approach, investigating a clustering based incremental activity recognition system with its potential applications, and applying techniques for alleviating sensor difference effects, a robust human activity recognition system can be trained in either supervised or unsupervised way and can be adapted to multiple devices with being less dependent on different sensor specifications