Human-centric Computing and Information Sciences

With rapid growth of social networking service on Internet, huge amount of information are continuously generated in real time. As a result, sentiment analysis of online reviews and messages has become a popular research issue [1]. In this paper a novel modified Chi Square-based feature clustering and weighting scheme is proposed for the sentiment analysis of twitter message. Along with the part of speech tagging, the discriminability and dependency of the words in the tagged training dataset are taken into account in the clustering and weighting process. The multinomial Naïve Bayes model is also employed to handle redundant features, and the influence of emotional words is raised for maximizing the accuracy. Computer simulation with Sentiment 140 workload shows that the proposed scheme significantly outperforms four existing representative sentiment analysis schemes in terms of the accuracy regardless of the size of training and test data.

Navigation systems help users access unfamiliar environments. Current technological advancements enable users to encapsulate these systems in handheld devices, which effectively increases the popularity of navigation systems and the number of users. In indoor environments, lack of Global Positioning System (GPS) signals and line of sight with orbiting satellites makes navigation more challenging compared to outdoor environments. Radio frequency (RF) signals, computer vision, and sensor-based solutions are more suitable for tracking the users in indoor environments. This article provides a comprehensive summary of evolution in indoor navigation and indoor positioning technologies. In particular, the paper reviews different computer vision-based indoor navigation and positioning systems along with indoor scene recognition methods that can aid the indoor navigation. Navigation and positioning systems that utilize pedestrian dead reckoning (PDR) methods and various communication technologies, such as Wi-Fi, Radio Frequency Identification (RFID) visible light, Bluetooth and ultra-wide band (UWB), are detailed as well. Moreover, this article investigates and contrasts the different navigation systems in each category. Various evaluation criteria for indoor navigation systems are proposed in this work. The article concludes with a brief insight into future directions in indoor positioning and navigation systems.

The common cause of the mass mortality of fishes in a fish farm is a power failure. There can be many reasons for the power failures but most of them are the result of natural disasters, systematic faults, ill-willed sabotages, or mistakes of employees. Such incidents usually have a bad influence upon small or medium-scale farmers. To deal with unexpected power failures, this research proposes a status monitoring system utilizing both PLC and ICT. Consistent supply of electric power in Fish Farms is essential for the operation and management of farm facilities regardless of them being indoors or afloat. The statistics show that the power failure and the red tides are the major causes of ruining farmer’s earnings. The proposed system is autonomous and simple enough for the employees to operate such that prompt actions can be taken in times of crisis. There have been some concerns about applying the PLC technology as it often involves loss of transmission signals. To minimize signal losses, use of RUDP in the transmission layer was proposed as well. At the same time, a GUI-based operational mode has been embedded in the Android application to enhance user convenience for observing and managing farm’s activities such as controlling of seawater and farm accesses and checking locations, photoperiods, withdrawal periods, and system’s batteries. The proposed design can be quite suitable for the Vertical Fish Farm.

In a traditional classroom environment, instructor enthusiasm has been shown to enhance student’s emotion, affective perceptions, intrinsic motivation, and cognitive outcome. Additionally, emotional response theory argues that enthusiastic verbal and nonverbal cues of an instructor will induce positive emotional states in learners, which in turn, enact learners’ approach behaviours in the learning process. Therefore, should a pedagogical agent convey enthusiastic behaviours in a multimedia learning environment? Literature and theoretical reviews offer two competing views. The first view, based on emotional response theory, predicts that enthusiastic verbal and nonverbal cues of a pedagogical agent can induce higher positive emotions in learners, which in turn, enhance affective perceptions, intrinsic motivation, and cognitive outcome. However, the second view, based on cognitive load theory, suggests that pedagogical agent enthusiasm may increase extraneous cognitive load (additional processing in the mind), which negatively impact emotion, affective perceptions, intrinsic motivation, and cognitive outcome. To investigate the effects of agent enthusiasm, seventy-two university freshmen interacted with either an enthusiastic agent or a neutral agent (operationalized through vocal tones, facial expression, gestures, and remarks) that simulates the instructional role of a virtual tutor that delivers narrative demonstrations on how to predict the outputs of C-Programming algorithms. The results of our study showed that pedagogical agent enthusiasm significantly enhanced emotion, intrinsic motivation, affective perceptions, and cognitive outcome. Moreover, mediation analyses revealed that the facilitating effects of agent enthusiasm on intrinsic motivation, affective perceptions of the learning environment, and affective perceptions of the pedagogical agent were fully mediated by a learner’s positive emotion, thus demonstrating that the framework of emotional response theory can be applied to learner-agent interaction in a multimedia learning environment. Implications and suggestions for future research related to pedagogical agent enthusiasm are discussed in this paper.

In order to estimate the location of the user, the previous studies introduced many sensor deployment methods applying the sensor network. The important issues to consider when placing the sensors are a configuration cost and detection area of a sensor network. In other words, the sensors consisting the network should be optimally deployed by taking into account the coverage and connectivity of them. In this paper, a sensor signal is modeled as the Gaussian distribution based signal points group, and signal points in overlapping region between two different sensors are classified by e-SVM (support vector machine) method as each sensor group. In addition, a trilateration technique is used for estimating the position of a moving object. At this time, we efficiently deploy the sensors with f-Apriori method to maintain the connectivity between the sensors as well as to apply the trilateration. The proposed method can be utilized for optimal placement of sensors if we know a detection range of one sensor. In this paper, we introduce more effective and adaptive deployment method to consist the sensor network as taking into account the cost and the situation.

Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

The basic idea behind the vehicular ad-hoc network (VANET) is the exchange of traffic information between vehicles and the surrounding environment to offer a better driving experience. Privacy and security are the main concerns for meeting the safety aims of the VANET system. In this paper, we analyse recent VANET schemes that utilise a group authentication technique and found important vulnerabilities in terms of driving safety. These systems also suffer from vulnerabilities in terms of management efficiency and computational complexity. To defeat these problems, we propose a lightweight scheme, SD2PA, based on a general hash function for VANET. The proposed scheme overcomes the non-safe driving problem that resulted from the critical driving area. Moreover, the vehicle authentication is only done once by the VANET system administrator during the vehicle’s moving, so the authentication redundancy for the entire system is reduced and system management efficiency is enhanced. The SD2PA scheme also provides anonymity to protect the vehicle’s privacy, unless an important action needs to be taken against a malicious vehicle. A deep computational cost and communicational overhead analysis indicates that SD2PA is better than related schemes, as well as efficiently meeting VANET’s security and privacy needs.

The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.

This paper presents the development of a Human Activity Recognition (HAR) system that uses a network of nine inertial measurement units situated in different body parts. Every unit provides 3D (3-dimension) acceleration, 3D angular velocity, 3D magnetic field orientation, and 4D quaternions. This system identifies 33 different physical activities (walking, running, cycling, lateral elevation of arms, etc.). The system is composed of two main modules: a feature extractor for obtaining the most relevant characteristics from the inertial signals every second, and a machine learning algorithm for classifying between the different activities. This paper focuses on the feature extractor module, evaluating several types of features and proposing different normalization approaches. This paper also analyses the performance of every sensor included in the inertial measurement units. The main experiments have been done using a public available dataset named REALDISP Activity Recognition dataset. This dataset includes recordings from 17 subjects performing 33 different activities in three different scenarios. Final results demonstrate that the proposed HAR system significantly improves the classification accuracy compared to previous works on this dataset. For the best configuration, the system accuracy is 99.1%. This system has been also evaluated with the OPPORTUNITY dataset obtaining competitive results.