Computer Science

Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed, and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.

Cardiovascular related diseases are the most significant health concern around the globe. The most crucial health indicator is blood pressure because it gives essential information about the health of a patient's heart. Cardiovascular diseases can be detected early and prevented if blood pressure is monitored continuously and regularly. Blood pressure cuffs, which are widely used to control blood flow in the arm or wrist when measuring blood pressure, are not practical for continuous blood pressure measurement. However, biosignals can be used for blood pressure estimation; but it is still critical and challenging. In this paper, we conducted a comprehensive analysis of feature extraction techniques for blood pressure estimation by using PPG signals. The feature extraction techniques were further divided into three subgroups to analyse the significance of each group. Group A includes timebased features; group B presents statistical feature extraction, and group C presents frequency domain-based features. The analysis employed several machine learning algorithms and compared their performance from many perspectives for the first time, to the best of our knowledge. The experimental results from two publicly available datasets demonstrated that the set of features belonging to group A were more reliable than other techniques for blood pressure estimation. We found that deep learning models achieved better performance than all traditional machine learning methods. We also found that the GRU model and Bi-LSTM achieved the best performance for time-domain features for blood pressure estimation. We believe the findings of this benchmark study will help researchers choose the most appropriate method for feature extraction and machine learning algorithms.

In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies' reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions. INDEX TERMS Fake review, fake review detection, feature engineering, machine learning, deep learning.

Online reviews have a significant influence on customers' purchasing decisions for any products or services. However, fake reviews can mislead both consumers and companies. Several models have been developed to detect fake reviews using machine learning approaches. Many of these models have some limitations resulting in low accuracy in distinguishing between fake and genuine reviews. These models focused only on linguistic features to detect fake reviews and failed to capture the semantic meaning of the reviews. To deal with this, this paper proposes a new ensemble model that employs transformer architecture to discover the hidden patterns in a sequence of fake reviews and detect them precisely. The proposed approach combines three transformer models to improve the robustness of fake and genuine behaviour profiling and modelling to detect fake reviews. The experimental results using semi-real benchmark datasets showed the superiority of the proposed model over state-of-the-art m...

Web services and Grid computing are technologies with a great impact these days. Both technologies are taking a syntactical based approach to describe services, and therefore, limited support is provided for service related tasks like: discovery, composition, etc. Semantic Web services try to overcome the limitations of syntactical based approaches by adding semantics to services descriptions. One of the most salient initiatives in Semantic Web services area is the Web Service Modeling Ontology(WSMO). WSMO provides a conceptual model and languages to express semantic descriptions of services. We believe that WSMO, the conceptual model, and WSML, the associated family of languages, with some extensions and/or restrictions could be the solution for semantically describe services in Grid environment. In order support this statement, we provide a modeling example of a Grid service using WSMO/WSML.

The challenge of finding services or resources in Web services and Grid environments has recently been the subject of a lot of attention. Different solutions to this problem were proposed, each with its specific model and realization. Although all solutions address the same problem, it is very difficult for a non-expert and even for an expert in the field to decide if one solution is better then another and why. In this paper we propose a systematic set of criteria, a framework, that can help in the evaluation of different discovery approaches. We exemplify the use of our framework on some of the most relevant discovery approaches in Web services and Grid areas. * The work is funded by the European Commission under the project Adaptive Services Grid. (ASG) 1

In this paper, we present the collaborative environment reference architecture (CERA) with the aim of supporting collaborative work environment (CWE) interoperability. The vision of CERA is to support users who are engaged in common collaborative spaces with similar work processes to work and collaborate seamlessly together, despite their use of proprietary CWE tools and systems. The underlying CERA concepts, design principles, and models are discussed, as well as the architectural decisions made as a result of the extended requirements analysis exercise. Furthermore, we present results from the Ecospace (http://www.ip-ecospace.org/) project as an example of a CERA instantiation which focuses on facilitating users collaborating across different CWE systems, namely BSCW, NetWeaver, and BC. We conclude with future research and implementation directions.

In underwater situations, clarity of images are degraded by light absorption and scattering. This causes one colour to dominate the image. In order to improve the perception of underwater images, we proposed an approach based on slide stretching. The objective of this approach is twofold. Firstly, the contrast stretching of RGB algorithm is applied to equalize the colour contrast in images. Secondly, the saturation and intensity stretching of HSI is used to increase the true colour and solve the problem of lighting. Interactive software has been developed for underwater image enhancement. Results of the software are presented in this paper.

Underwater images are affected by reduced contrast and non-uniform colour cast due to the absorption and scattering of light in the aquatic environment. This affects the quality and reliability of the image processing and therefore colour correction is a necessary pre-processing stage. In this paper, we propose an Unsupervised Colour Correction Method (UCM) for underwater image enhancement. UCM is based on colour balancing, contrast correction of RGB colour model and contrast correction of HSI colour model. Firstly, the colour cast is reduced by equalizing the colour values. Secondly, an enhancement to a contrast correction method is applied to increase the Red colour by stretching red histogram towards the maximum (i.e., right side), similarly the Blue colour is reduced by stretching the blue histogram towards the minimum (i.e., left side). Thirdly, the Saturation and Intensity components of the HSI colour model have been applied for contrast correction to increase the true colour using Saturation and to address the illumination problem through Intensity. We compare our results with three well known methods, namely Gray World, White Patch and Histogram Equalisation using Adobe Photoshop. The proposed method has produced better results than the existing methods.

Trust is an essential requirement for effective Human-Agent interaction as artificial agents are becoming part of human society in a social context. To blend into our society and maximize their acceptability and reliability, artificial agents need to adapt to the complexity of their surroundings, like humans. This adaptation should come through knowing whom to trust by evaluating the trustworthiness of its human mate. It is therefore required to build cognitive agents with trust models that may allow them to trust humans the same way a human trusts other humans keeping under consideration all factors influencing the human agent trust mechanism. Several antecedents within the cognitive system itself and the surroundings dynamically influence the trust mechanism. Personality, as a trusted antecedent has been found to have a substantial impact in predicting human interactor's trustworthiness that critically assists trust decision making. Current research, therefore, aims to infuse characteristics of respective humans as the antecedent of the human agent trust process. This is accomplished by incorporating into the trust model the agent's capability to perceive the personality traits of the human interactor. The current work is focused on introducing a trustworthiness assessment model (TAMFIS) based on fuzzy inference to assess human's trustworthiness towards artificial agents by exploring the human's personality traits that predict trustworthiness. The artificial agent could develop its character towards its human collaborators that will help it in effective interactions. The testing of the proposed architecture is carried out using Dempster Shafer Theory of belief and estimation. It is anticipated that the proposed trust model will effectively evaluate the trustworthiness of human collaborators and develop a more reliable human-agent trust relationship. INDEX TERMS Big 5 personality traits, multi-agent systems, trustworthiness, artificial agent, Dempster Shafer theory.

Increasing man-machine trust has burgeoned during the last few decades. The growing interest in trust-building has led to the study of the non-dichotomous nature of trust. Trust as social behavior is an integral part of effective team building. The major focus has been offered to study how humans build trust towards machines, whereas few attempts have been made to study the reverse. Studies have shown that trustworthiness perceptions initialize trust behavior whereas trust behavior influences subsequent trustworthiness perceptions. This paper presents the design and comparative analysis of evidential fuzzy multi-criteria decision-making (EFMCDM) based on multi-dimensional trust quantification schemes to quantify trust level with the human agent in a collaborative environment.

This paper presents an insight on the effects of mobile phone radiations on human health; specifically the health of elderly aged diabetic patients.The paper aims to expose the extremely negative effect of usingmobile phone& its radiation on two types of diabetic patients, one, who report high level of blood glucose, and others who report low level of blood glucose. The experimental study has been conducted on a group of diabetic patients owning age groups ranging from 51 to 85 years. Also, all patients are assumed as having established diabetes which means that they are progressive from the early diabetic phase. To guarantee a healthy practice, previous health record of all the patients was collected and ensured that previous history is taken into account during experiment and conclusions. Health conditions were monitored over 60 days maintaining similar dietary conditions. Blood samples of the patients were taken against different parameters i.e. random, fasting and normal blood sugar level etc.Blood glucose was calculated with usage of mobile phone for a specific amount of time. Conclusions were made on the basis of statistics gathered.

There are different schemes for streamed audio video (AV) IPTV content across mobile WiMAX to reduce packet loss and latency. The objective of this paper is to verify the effectiveness of forward error correction (FEC) techniques and to suggest the techniques for robustness problems and to analyze the issues either due to AV coding encoding or due to access network. The paper concentrates on handoff and handover delay, mobile WiMAX downlink burst mapping and the reliability of IPTV over LTE system.

Body area network (BAN) is an emerging technology in computer world, and plays very vigorous role in a society, mainly in health services. BAN helps in monitoring vital signs of a patient and can monitor patient's history in routine life activities to provide them accurate treatment. Doctors can check the complete details of patients from remote location and can recommend a suitable medication. The main purpose of this technology is to reduce the load at hospitals and provide efficient healthcare facility remotely using medical implant communication system (MICS) and Wireless medical telemetry system (WMTS). To monitor the patients in their natural environments is not practical when devices or sensors are connected through a wire that is why we use Wireless body area network (WBAN) to carrying out daily activities through unobtrusive and contented way. This technology can provide very cheaper, easier and quick respondent history of patient.

The Independent Evaluation Group is an independent unit within the World Bank Group; it reports directly to the Bank's Board of Executive Directors. IEG assesses what works, and what does not; how a borrower plans to run and maintain a project; and the lasting contribution of the Bank to a country's overall development. The goals of evaluation are to learn from experience, to provide an objective basis for assessing the results of the Bank's work, and to provide accountability in the achievement of its objectives. It also improves Bank work by identifying and disseminating the lessons learned from experience and by framing recommendations drawn from evaluation findings.

This paper illustrates effects of academic education on the crime ratio prevailing in the South Asian countries. The study introduces practical impacts amongst crime and education in South Asian countries and particularly in Pakistan, utilizing different information sources. The paper shows positive relationship between education and crime. The result demonstrates that in the Pakistan there are not enough opportunities available to accommodate the educated people. The empirical evidences indicate that the criminal activities raises the financial benefits more than those opportunities offered to the educated people. This study concludes that if private and public sector provide sufficient opportunities to adjust all the educated people can yield significant social advantages and can be a key approach device in the drive to diminish crime.

This paper illustrates a new electronic model for successful entrepreneurs. The authors have analyzed classical models namely 5ls Model, 10 C's of Marketing, 3-Race Planning, 4 C's marketing model & McKinsey's consumer decision journey, and have found certain inadequacy to fulfill the need of digital era in view of which the proposed model includes certain Tools, a Tranquil design, Contingency , Commitment level and feedback. This eventually helps in optimum utilization of electronic marketing tools. The proposed model is equally beneficial for Small scale and large scale businesses in order to gain visible growth and development which ultimately affect the economy of the state or country. This model helps entrepreneurs to flourish their business in a quick and enhanced way. The new design is comprehensive and brief; it fulfills the requirement of all types of trades which is comprehended in the paper.