Deep Clustering and Deep Network Compression

The use of deep learning has grown increasingly in recent years, thereby becoming a much-discussed topic across a diverse range of fields, especially in computer vision, text mining, and speech recognition. Deep learning methods have proven to be robust in representation learning and attained extraordinary achievement. Their success is primarily due to the ability of deep learning to discover and automatically learn feature representations by mapping input data into abstract and composite representations in a latent space. Deep learning’s ability to deal with high-level representations from data has inspired us to make use of learned representations, aiming to enhance unsupervised clustering and evaluate the characteristic strength of internal representations to compress and accelerate deep neural networks.Traditional clustering algorithms attain a limited performance as the dimensionality in-creases. Therefore, the ability to extract high-level representations provides beneficial components that can support such clustering algorithms. In this work, we first present DeepCluster, a clustering approach embedded in a deep convolutional auto-encoder. We introduce two clustering methods, namely DCAE-Kmeans and DCAE-GMM. The DeepCluster allows for data points to be grouped into their identical cluster, in the latent space, in a joint-cost function by simultaneously optimizing the clustering objective and the DCAE objective, producing stable representations, which is appropriate for the clustering process. Both qualitative and quantitative evaluations of proposed methods are reported, showing the efficiency of deep clustering on several public datasets in comparison to the previous state-of-the-art methods.Following this, we propose a new version of the DeepCluster model to include varying degrees of discriminative power. This introduces a mechanism which enables the imposition of regularization techniques and the involvement of a supervision component. The key idea of our approach is to distinguish the discriminatory power of numerous structures when searching for a compact structure to form robust clusters. The effectiveness of injecting various levels of discriminatory powers into the learning process is investigated alongside the exploration and analytical study of the discriminatory power obtained through the use of two discriminative attributes: data-driven discriminative attributes with the support of regularization techniques, and supervision discriminative attributes with the support of the supervision component. An evaluation is provided on four different datasets.The use of neural networks in various applications is accompanied by a dramatic increase in computational costs and memory requirements. Making use of the characteristic strength of learned representations, we propose an iterative pruning method that simultaneously identifies the critical neurons and prunes the model during training without involving any pre-training or fine-tuning procedures. We introduce a majority voting technique to compare the activation values among neurons and assign a voting score to evaluate their importance quantitatively. This mechanism effectively reduces model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Empirically, we demonstrate that our pruning method is robust across various scenarios, including fully-connected networks (FCNs), sparsely-connected networks (SCNs), and Convolutional neural networks (CNNs), using two public datasets.Moreover, we also propose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike existing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts, with the aim of evaluating the importance of feature maps, inspired by novel neural network interpretability. A majority voting technique based on the degree of alignment between a se-mantic concept and individual hidden unit representations is utilized to evaluate feature maps’ importance quantitatively. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining crucial channels to accomplish effective CNN compression. Experimental results show the effectiveness of our filter selection criteria, which outperforms the state-of-the-art baselines.To conclude, we present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a founding contribution to the area of applying deep clustering to time-series data by presenting the first case study in the context of movement behavior clustering utilizing the DeepCluster method. The results are promising, showing that the latent space encodes sufficient patterns to facilitate accurate clustering of movement behaviors. Finally, we identify state-of-the-art and present an outlook on this important field of DTSC from five important perspectives

Leveraging service-oriented business applications to a rigorous rule-centric dynamic behavioural architecture.

Today’s market competitiveness and globalisation are putting pressure on organisations to join their efforts, to focus more on cooperation and interaction and to add value to their businesses. That is, most information systems supporting these cross-organisations are characterised as service-oriented business applications, where all the emphasis is put on inter-service interactions rather than intra-service computations. Unfortunately for the development of such inter-organisational service-oriented business systems, current service technology proposes only ad-hoc, manual and static standard web-service languages such as WSDL, BPEL and WS-CDL [3, 7]. The main objective of the work reported in this thesis is thus to leverage the development of service-oriented business applications towards more reliability and dynamic adaptability, placing emphasis on the use of business rules to govern activities, while composing services. The best available software-engineering techniques for adaptability, mainly aspect-oriented mechanisms, are also to be integrated with advanced formal techniques. More specifically, the proposed approach consists of the following incremental steps. First, it models any business activity behaviour governing any service-oriented business process as Event-Condition-Action (ECA) rules. Then such informal rules are made more interaction-centric, using adapted architectural connectors. Third, still at the conceptual-level, with the aim of adapting such ECA-driven connectors, this approach borrows aspect-oriented ideas and mechanisms, and proposes to intercept events, select the properties required for interacting entities, explicitly and separately execute such ECA-driven behavioural interactions and finally dynamically weave the results into the entities involved. To ensure compliance and to preserve the implementation of this architectural conceptualisation, the work adopts the Maude language as an executable operational formalisation. For that purpose, Maude is first endowed with the notions of components and interfaces. Further, the concept of ECA-driven behavioural interactions are specified and implemented as aspects. Finally, capitalising on Maude reflection, the thesis demonstrates how to weave such interaction executions into associated services

Rateless Space-Time Block Codes for 5G Wireless Communication Systems

This chapter presents a rateless space-time block code (RSTBC) for massive multiple-input multiple-output (MIMO) wireless communication systems. We discuss the principles of rateless coding compared to the fixed-rate channel codes. A literature review of rateless codes (RCs) is also addressed. Furthermore, the chapter illustrates the basis of RSTBC deployments in massive MIMO transmissions over lossy wireless channels. In such channels, data may be lost or are not decodable at the receiver end due to a variety of factors such as channel losses or pilot contamination. Massive MIMO is a breakthrough wireless transmission technique proposed for future wireless standards due to its spectrum and energy efficiencies. We show that RSTBC guarantees the reliability of the system in such highly lossy channels. Moreover, pilot contamination (PC) constitutes a particularly significant impairment in reciprocity-based multi-cell systems. PC results from the non-orthogonality of the pilot sequences in different cells. In this chapter, RSTBC is also employed in the downlink transmission of a multi-cell massive MIMO system to mitigate the effects of signal-to-interference-and-noise ratio (SINR) degradation resulting from PC. We conclude that RSTBC can effectively mitigate such interference. Hence, RSTBC is a strong candidate for the upcoming 5G wireless communication systems

The Relationship between Saudi Cadets’ Willingness to Communicate and Their English Language Proficiency

This study examines a group of Saudi military cadets studying English as a foreign language and their willingness to communicate (WTC) using English. The study investigated the relationship between the cadets’ WTC using English and their English vocabulary size, along with the cadets’ perceived English competence. In addition, the study looked at the relationship between the Saudi military cadets’ WTC using English and their L2 anxiety. A survey was designed for and administered to 113 Saudi military cadets with an average age of 20 years old. Correlation and regression analyses revealed an association between the Saudi cadets’ WTC in L2 and the size of their English vocabulary. Furthermore, the study found a strong linear relationship between the students’ perception of their competence in English and their WTC in L2. However, the analysis showed a negative relationship between the Saudi cadets’ WTC using English and their L2 anxiety

Flexural properties of sisal fibre/epoxy Composites

Nowadays, using natural fibres is becoming a very promising reinforcement for polymeric composites for different applications in mechanical and civil engineering. From research and industrial point of views, there are demands to understanding the potential of using such fibres and their impact on the mechanical properties of the composites. Moreover, exploring the potential of different types of natural fibres is an aim to overcome environmental pollution and degradation issues. Based on the literature, there is still demand on the need to understand natural fibre with polymer matrix for different loading conditions. This motivates the current study on flexural behaviour of sisal fibres reinforced epoxy composites. The composites were fabricated using hand layup techniques considering treated and untreated sisal fibres. 6% of NaOH chemical treatment was used owing to improve the interfacial adhesion of the fibres with the matrix. Three point bending technique was used in this study. SEM (Scanning Electron Microscopy) has been used to study the morphology of the failure surface of the composite after flexural tests. The result revealed that untreated fibres did not improve the composite’s flexural strength due to a poor interfacial adhesion of the fibre with the matrix, which is consistent with the morphology study, i.e. debonding, detachment, and fibre pull out were observed on the fractured surfaces. However, 6% NaOH treated sisal fibres significantly improved the composite’s flexural strength and modulus by about 76% and 162%, respectively compared to pure epoxy. Morphology study on flexural fracture surfaces of treated sisal fibre composite showed the bonding between epoxy matrix and treated sisal fibre has much improved with the aid of the 6% NaOH. Also, epoxy resin penetrated into core of the treated sisal fibres which in turn assisted to interlock the fibre in the bulk of the composites (high interfacial adhesion) resulting in the high performance of treated sisal fibre/epoxy composite’s flexural properties compared to the untreated fibres

Obstacles to Implementation of Online Booking in Saudi Travel Agencies

This paper examines current experience of online booking technology within travel agencies. A qualitative exploratory approach to research is adopted with a focus examining Saudi travel agent utilization and experience of booking systems and their attitudes and propensity to adopt online booking processes. Also it focuses on the travel agent perspective and identifies barriers to implementation of online booking technology. Fourteen semi-structured interviews were conducted with managers and staff at selected travel agencies in Saudi Arabia. A technology acceptance model (TAM) was applied and developed to address the online booking and purchase in travel services and to explain the factors influencing user acceptance of online booking and purchase in travel services. Findings showed that customer culture, lack of customer trust and security, e-payment process, lack of government support, internet services connections, Understanding of services and its benefits have efficiency considerable influences on the perceived usefulness and perceived ease of use of online booking/purchasing acceptance. Keywords: online booking technology; travel agencies in Saudi Arabia; attitudes; customer trust; security; customer cultur

0E2FA: Zero Effort Two-Factor Authentication

Smart devices (mobile devices, laptops, tablets, etc.) can receive signals from different radio frequency devices that are within range. As these devices move between networks (e.g., Wi-Fi hotspots, cellphone towers, etc.), they receive broadcast messages from access points, some of which can be used to collect useful information. This information can be utilized in a variety of ways, such as to establish a connection, to share information, to locate devices, and to identify users, which is central to this dissertation. The principal benefit of a broadcast message is that smart devices can read and process the embedded information without first being connected to the corresponding network. Moreover, broadcast messages can be received only within the range of the wireless access point that sends the broadcast, thus inherently limiting access to only those devices in close physical proximity, which may facilitate many applications that are dependent on proximity. In our research, we utilize data contained in these broadcast messages to implement a two-factor authentication (2FA) system that, unlike existing methods, does not require any extra effort on the part of the users of the system. By determining if two devices are in the same physical location and sufficiently close to each other, we can ensure that they belong to the same user. This system depends on something that a user knows, something that a user owns, and—a significant contribution of this work—something that is in the user’s environment

Characterization of bilayered matrix-type mucoadhesive buccal films containing tizanidine hydrochloride and piroxicam

Purpose: To formulate and characterize tizanidine hydrochloride (TZN) and piroxicam (PRX)-loaded bilayer mucoadhesive buccal films with an intention to improve the bioavailability and patient compliance in pain management.Methods: Bilayer buccal films were prepared by solvent evaporation technique using hydroxypropyl methylcellulose (HPMC) 15cps and polyvinylpyrrolidone (PVP K30 as immediate release (IR) layer forming polymers and HPMC K15 M, PVP K 90 along with various muco adhesive polymers (Carbopol P934, sodium alginate, etc), as sustained release (SR) layer forming polymers. The prepared films werecharacterized for thickness, weight variation, folding endurance, surface pH, swelling index,mucoadhesive strength, in vitro residence time, in vitro drug release, ex vivo permeation and drug release kinetics.Results: The prepared films were of largely uniform thickness, weight and drug content. Moisture loss (%) and folding endurance were satisfactory. Surface pH was compatible with salivary fluid. Disintegration time was 85 s for F1 and 115 s for F2 of IR films. In vitro dissolution studies showed 99.12 ± 1.2 % (F1) and 90.36 ± 1.8 % (F2) were released in 45 min. Based on the above results, F1 was chosen as the optimum formulation to be combined with SR layer of TZN. Amongst the SR layers of TZN in vitro drug release. The findings show that of F2 was 98.38 ± 0.82 % and correlated with ex vivo release. Drug release followed zero order release kinetics and mechanism of drug release was non-Fickian type diffusion. In vitro residence time was greater than 5 h.Conclusion: The findings show that the bilayer buccal films demonstrate the dual impact of deliveringPRX instantly from the IR layer, with good controlled release and permeation of TZN from the SR layer, thus providing enhanced therapeutic efficacy, drug bioavailability and patient compliance

Deep Time-Series Clustering: A Review

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives

ANTI-INVARIANT RIEMANNIAN SUBMERSIONS FROM LOCALLY CONFORMAL KAEHLER MANIFOLDS

Recently, Sahin [10] studied the anti-invariant Riemannian submersions from almost Hermitian manifolds onto Riemannian manifolds. In present work, these notions of anti-invariant and Lagrangian Riemannian submersions have been extended to locally conformal Kaehler manifolds. Certain decomposition results and the geometry of foliation have also been investigated