Improving the Performance of Low-resourced Speaker Identification with Data Preprocessing

Automatic speaker identification is done to tackle daily security problems. Speech data collection is an essential but very challenging task for under-resourced languages like Burmese. The speech quality is crucial to accurately recognize the speaker’s identity. This work attempted to find the optimal speech quality appropriate for Burmese tone to enhance identification compared with other more richy resourced languages on Mel-frequency cepstral coefficients (MFCCs). A Burmese speech dataset was created as part of our work because no appropriate dataset available for use. In order to achieve better performance, we preprocessed the foremost recording quality proper for not only Burmese tone but also for nine other Asian languages to achieve multilingual speaker identification. The performance of the preprocessed data was evaluated by comparing with the original data, using a time delay neural network (TDNN) together with a subsampling technique that can reduce time complexity in model training. The experiments were investigated and analyzed on speech datasets of ten Asian languages to reveal the effectiveness of the data preprocessing. The dataset outperformed the original dataset with improvements in terms of  equal error rate (EER). The evaluation pointed out that the performance of the system with the preprocessed dataset improved that of the original dataset

Scene Segmentation for Interframe Forgery Identification

A common type of video forgery is inter-frame forgery, which occurs in the temporal domain, such as frame duplication, frame insertion, and frame deletion. Some existing methods are not effective to detect forgeries in static scenes. This work proposes static and dynamic scene segmentation and performs forgery detection for each scene. Scene segmentation is performed for outlier detection based on changes of optical flow. Various similarity checks are performed to find the correlation for each frame. The experimental results showed that the proposed method is effective in identifying forgeries in various scenes, especially static scenes, compared with existing methods

Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach

Sociable Robot ‘Lometh’: Exploring Interactive Regions of a Product-Promoting Robot in a Supermarket

The robot ‘Lometh’ is an information-presenting robot that naturally interacts with people in a supermarket environment. In recent years, considerable effort has been devoted to the implementation of robotic interfaces to identify effective behaviors of communication robots focusing only on the social and physical factors of the addresser and the hearer. As attention focus and attention target shifting of people differs based on the human visual focus and the spatiality, this study considered four interactive regions, considering the visual focus of attention as well as the interpersonal space between robot and human. The collected primary data revealed that 56% attention shifts occurred in near peripheral field of view regions and 44% attention shifts in far peripheral field of view regions. Using correspondence analysis, we identified that the bodily behaviors of the robot showed the highest success rate in the left near peripheral field of view region. The verbal behaviors of the robot captured human attention best in the right near peripheral field of view region. In this experiment of finding a socially acceptable way to accomplish the attention attracting goals of a communication robot, we observed that the robots’ affective behaviors were successful in shifting human attention towards itself in both left and right far- peripheral field of view regions, so we concluded that for far field of view regions, designing similar interaction interventions can be expected to be successful

CNN Based Covid-19 Detection from Image Processing

Covid-19 is a respirational condition that looks much like pneumonia. It is highly contagious and has many variants with different symptoms. Covid-19 poses the challenge of discovering new testing and detection methods in biomedical science. X-ray images and CT scans provide high-quality and information-rich images. These images can be processed with a convolutional neural network (CNN) to detect diseases such as Covid-19 in the pulmonary system with high accuracy. Deep learning applied to X-ray images can help to develop methods to identify Covid-19 infection. Based on the research problem, this study defined the outcome as reducing the energy costs and expenses of detecting Covid-19 in X-ray images. Analysis of the results was done by comparing a CNN model with a DenseNet model, where the first achieved more accurate performance than the second

Emergency Data Transmission Mechanism in VANETs using Improved Restricted Greedy Forwarding (IRGF) Scheme

One of the most critical tasks in Vehicular Ad-hoc Networks (VANETs) is broadcasting Emergency Messages (EMs) at considerable data delivery rates (DDRs). The enhanced spider-web-like Transmission Mechanism for Emergency Data (TMED) is based on request spiders and authenticated spiders to create the shortest route path between the source vehicle and target vehicles. However, the adjacent allocation is based on the DDR only and it is not clear whether each adjacent vehicle is honest or not. Hence, in this article, the Improved Restricted Greedy Forwarding (IRGF) scheme is proposed for adjacent allocation with the help of trust computation in TMED. The trust and reputation score value of each adjacent vehicle is estimated based on successfully broadcast emergency data. The vehicles’ position, velocity, direction, density, and the reputation score, are fed to a fuzzy logic (FL) scheme, which selects the most trusted adjacent node as the forwarding node for broadcasting the EM to the destination vehicles. Finally, the simulation results illustrate the TMED-IRGF model’s efficiency compared to state-of-the-art models in terms of different network metrics

The Potential of a Low-Cost Thermal Camera for Early Detection of Temperature Changes in Virus-Infected Chili Plants

One effect of viral infection on plant physiology is increased stomata closure so that the transpiration rate is low, which in turn causes an increase in leaf temperature. Changes in plant leaf temperature can be measured by thermography using high-resolution thermal cameras. The results can be used as an indicator of virus infection, even before the appearance of visible symptoms. However, the higher the sensor resolution of the thermal camera, the more expensive it is, which is an obstacle in developing the method more widely. This article describes the potential of thermography in detecting Tobacco mosaic virus infection in chili-pepper plants using a low-cost camera. A FLIR C2 camera was used to record images of plants in two treatment groups, non-inoculated (V0) and virus-inoculated plants (V1). Significantly, V1 had a lower temperature at 8 and 12 days after inoculation (dai) than those of V0, but their temperature was higher than V0 before symptoms were visible, i.e., at 17 dai. Thermography using low-cost thermal cameras has potency to detect early viral infection at 8 dai with accuracy levels (AUC) of 80.0% and 86.5% based on k-Nearest Neighbors and Naïve Bayes classifiers, respectively

The Evaluation of DyHATR Performance for Dynamic Heterogeneous Graphs

Dynamic heterogeneous graphs can represent real-world networks. Predicting links in these graphs is more complicated than in static graphs. Until now, research interest of link prediction has focused on static heterogeneous graphs or dynamically homogeneous graphs. A link prediction technique combining temporal RNN and hierarchical attention has recently emerged, called DyHATR. This method is claimed to be able to work on dynamic heterogeneous graphs by testing them on four publicly available data sets (Twitter, Math-Overflow, Ecomm, and Alibaba). However, after further analysis, it turned out that the four data sets did not meet the criteria of dynamic heterogeneous graphs. In the present work, we evaluated the performance of DyHATR on dynamic heterogeneous graphs. We conducted experiments with DyHATR based on the Yelp data set represented as a dynamic heterogeneous graph consisting of homogeneous subgraphs. The results show that DyHATR can be applied to identify link prediction on dynamic heterogeneous graphs by simultaneously capturing heterogeneous information and evolutionary patterns, and then considering them to carry out link predicition. Compared to the baseline method, the accuracy achieved by DyHATR is competitive, although the results can still be improved

Analytical Approach to Parameter Determination in Kaiser Function for Power-weighted Antenna Array Design

Window methods that are frequently used in the design of finite impulse response filters are also applicable to antenna array designs. This paper explores the application of a Kaiser function in a power-weighted antenna array design, focusing on the determination of the Kaiser function’s β parameter. The determination, which includes the calculation, optimization, and validation of the β parameter, was carried out based on a specific configuration of a linear antenna array design. The observation of this exploration emphasized the suppression of the sidelobe level (SLL) and the width of main lobe (WML) performance. By changing the β parameter, the Kaiser function is capable of approximating different window methods, since it plays an important role in defining the set of weighting coefficients for a specifically targeted SLL. Kaiser function application in power-weighted antenna array designs with a linear arrangement indicates the need of β parameter optimization because of the disagreement between the obtained SLL and the targeted SLL. The optimized β parameter produced a smaller SLL error for even and odd numbers of elements. From the validation, the average SLL error percentage for a targeted SLL of 25 dB, 35 dB, and 45 dB was 6%, 4.31%, 6.10%, respectively

Smart Card-based Access Control System using Isolated Many-to-Many Authentication Scheme for Electric Vehicle Charging Stations

In recent years, the Internet of Things (IoT) trend has been adopted very quickly. The rapid growth of IoT has increased the need for physical access control systems (ACS) for IoT devices, especially for IoT devices containing confidential data or other potential security risks. This research focused on many-to-many ACS, a type of ACS in which many resource-owners and resource-users are involved in the same system. This type of system is advantageous in that the user can conveniently access resources from different resource-owners using the same system. However, such a system may create a situation where parties involved in the system have their data leaked because of the large number of parties involved in the system. Therefore, ‘isolation’ of the parties involved is needed. This research simulated the use of smart cards to access electric vehicle (EV) charging stations that implement an isolated many-to-many authentication scheme. Two ESP8266 MCUs, one RC522 RFID reader, and an LED represented an EV charging station. Each institute used a Raspberry Pi Zero W as the web and database server. This research also used VPN and HTTPS protocols to isolate each institute’s assets. Every component of the system was successfully implemented and tested functionally