Facial expression recognition using lightweight deep learning modeling

Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and the softmax classifier is used to classify the expressions. Our method achieved an accuracy of 92.50% on the JAFFE dataset and 99.30% on the CK+ dataset. SSAE-FER performs well compared to the other comparative methods in the same domain

Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm

Recommender systems are intelligent data mining applications that deal with the issue of information overload significantly. The available literature discusses several methodologies to generate recommendations and proposes different techniques in accordance with users’ needs. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. The biggest challenge for a recommender system is to produce meaningful recommendations by using contextual user-item rating information. A context is a vast term that may consider various aspects; for example, a user’s social circle, time, mood, location, weather, company, day type, an item’s genre, location, and language. Typically, the rating behavior of users varies under different contexts. From this line of research, we have proposed a new algorithm, namely Kernel Context Recommender System, which is a flexible, fast, and accurate kernel mapping framework that recognizes the importance of context and incorporates the contextual information using kernel trick while making predictions. We have benchmarked our proposed algorithm with pre- and post-filtering approaches as they have been the favorite approaches in the literature to solve the context-aware recommendation problem. Our experiments reveal that considering the contextual information can increase the performance of a system and provide better, relevant, and meaningful results on various evaluation metrics

NATURE INSPIRED ARCHITECTURE

The thesis explores the various ways in which Biomimicry can be used to develop sustainable solutions that are derived from the natural cycles of organisms. Some of these include the use of solar and wind energy, as well as waste management. Aside from these, the thesis also explores the various ways in which Biomimicry can be used to develop more sustainable solutions for interior and exterior designs. The thesis explores different ways to help in sustainable architecture for the future as we are heading into the future. By using computational programming, we are able to design innovative solutions to help offset the carbon footprint of the planet. The design goal is to create a façade system inspired from the principles of biomimicry which helps in offsetting the carbon footprint of building over its lifespan

An Overview of Medium Access Control and Radio Duty Cycling Protocols for Internet of Things

The Internet of Things (IoT) applications such as smart grids, smart agriculture, smart cities, and e-healthcare are popular nowadays. Generally, IoT end devices are extremely sensitive to the utilization of energy. The medium access control (MAC) layer is responsible for coordination and access of the IoT devices. It is essential to design an efficient MAC protocol for achieving high throughput in IoT. Duty cycling is a fundamental process in wireless networks and also an energy-saving necessity if nodes are required to operate for more than a few days. Numerous MAC protocols along with different objectives have been proposed for the IoT. However, to the best of our knowledge, only limited work has been performed dedicated to covering MAC and radio duty cycling (RDC). Therefore, in this study, we propose a systematic cataloging system and use if to organize the most important MAC and RDC proposals. In this catalog, each protocol has been categorized into main ideas, advantages, applications, limitations, innovative features, and potential future improvements. Our critical analysis is different from previous research studies, as we have fully covered all recent studies in this domain. We discuss challenges and future research directions

A Novel Cluster Matching-Based Improved Kernel Fisher Criterion for Image Classification in Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) is a popular approach to reducing distributional discrepancies between labeled source and the unlabeled target domain (TD) in machine learning. However, current UDA approaches often align feature distributions between two domains explicitly without considering the target distribution and intra-domain category information, potentially leading to reduced classifier efficiency when the distribution between training and test sets differs. To address this limitation, we propose a novel approach called Cluster Matching-based Improved Kernel Fisher criterion (CM-IKFC) for object classification in image analysis using machine learning techniques. CM-IKFC generates accurate pseudo-labels for each target sample by considering both domain distributions. Our approach employs K-means clustering to cluster samples in the latent subspace in both domains and then conducts cluster matching in the TD. During the model component training stage, the Improved Kernel Fisher Criterion (IKFC) is presented to extend cluster matching and preserve the semantic structure and class transitions. To further enhance the performance of the Kernel Fisher criterion, we use a normalized parameter, due to the difficulty in solving the characteristic equation that draws inspiration from symmetry theory. The proposed CM-IKFC method minimizes intra-class variability while boosting inter-class variants in all domains. We evaluated our approach on benchmark datasets for UDA tasks and our experimental findings show that CM-IKFC is superior to current state-of-the-art methods

A Step toward Next-Generation Advancements in the Internet of Things Technologies

The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world

Aggression Detection in Social Media from Textual Data Using Deep Learning Models

It is an undeniable fact that people excessively rely on social media for effective communication. However, there is no appropriate barrier as to who becomes a part of the communication. Therefore, unknown people ruin the fundamental purpose of effective communication with irrelevant—and sometimes aggressive—messages. As its popularity increases, its impact on society also increases, from primarily being positive to negative. Cyber aggression is a negative impact; it is defined as the willful use of information technology to harm, threaten, slander, defame, or harass another person. With increasing volumes of cyber-aggressive messages, tweets, and retweets, there is a rising demand for automated filters to identify and remove these unwanted messages. However, most existing methods only consider NLP-based feature extractors, e.g., TF-IDF, Word2Vec, with a lack of consideration for emotional features, which makes these less effective for cyber aggression detection. In this work, we extracted eight novel emotional features and used a newly designed deep neural network with only three numbers of layers to identify aggressive statements. The proposed DNN model was tested on the Cyber-Troll dataset. The combination of word embedding and eight different emotional features were fed into the DNN for significant improvement in recognition while keeping the DNN design simple and computationally less demanding. When compared with the state-of-the-art models, our proposed model achieves an F1 score of 97%, surpassing the competitors by a significant margin

Accountable and Transparent TLS Certificate Management: An Alternate Public-Key Infrastructure with Verifiable Trusted Parties

Current Transport Layer Security (TLS) Public-Key Infrastructure (PKI) is a vast and complex system; it consists of processes, policies, and entities that are responsible for a secure certificate management process. Among them, Certificate Authority (CA) is the central and most trusted entity. However, recent compromises of CA result in the desire for some other secure and transparent alternative approaches. To distribute the trust and mitigate the threats and security issues of current PKI, publicly verifiable log-based approaches have been proposed. However, still, these schemes have vulnerabilities and inefficiency problems due to lack of specifying proper monitoring, data structure, and extra latency. We propose Accountable and Transparent TLS Certificate Management: an alternate Public-Key Infrastructure (PKI) with verifiable trusted parties (ATCM) that makes certificate management phases; certificate issuance, registration, revocation, and validation publicly verifiable. It also guarantees strong security by preventing man-in-middle-attack (MitM) when at least one entity is trusted out of all entities taking part in the protocol signing and verification. Accountable and Transparent TLS Certificate Management: an alternate Public-Key Infrastructure (PKI) with verifiable trusted parties (ATCM) can handle CA hierarchy and introduces an improved revocation system and revocation policy. We have compared our performance results with state-of-the-art log-based protocols. The performance results and evaluations show that it is feasible for practical use. Moreover, we have performed formal verification of our proposed protocol to verify its core security properties using Tamarin Prover