Reviewerly/exHarmony huggingface dataset:
Memory Augmented Neural Network Adaptive Controllers: Performance and Stability

In this article, we propose a novel control architecture, inspired from neuroscience, for adaptive control of continuous-time systems. The proposed architecture, in the setting of standard neural network (NN)-based adaptive control, augments an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">external working memory</i> to the NN. The controller, through a write operation, writes the hidden layer feature vector of the NN to the external working memory and can also update this information with the observed error in the output. Through a read operation, the controller retrieves information from the working memory to modify the final control signal. First, we consider a simpler estimation problem to theoretically study the effect of an external memory and prove that the estimation accuracy can be improved by incorporating memory. We, then, consider a model reference NN adaptive controller for linear systems with matched uncertainty to implement and illustrate our ideas. We prove that the resulting controller leads to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniformly bounded</i> stable closed-loop system. Through extensive simulations and specific metrics, such as peak deviation and settling time, we show that memory augmentation improves learning significantly. Importantly, we also provide evidence for and insights on the mechanism by which this specific memory augmentation improves learning.
Analyzing Usability of Educational Websites Using Automated Tools

As websites are developed by organization to share information with their users/visitors, so it is very important for organization that their websites must have accessibility and usability. There are some parameters against which an organization can check its websites accessibility and usability. It can help organizations to develop an efficient and up to date website as a result they can achieve their goals. To find out the accessibility and usability of websites, in this paper we are using three automated tools Qualidator, Website Grader and Website Analyzer. This paper evaluates ten public universities of KPK, Pakistan and grade the sites according to their scores
Evaluation of the Naturalness of Chatbot Applications

Chatbots are intelligent machine-to-human conversation systems. The main benefit of developing and deploying a chatbot in any business domain is that it can behave as a personal assistant. A chatbot is expected to be close in efficiency of interaction to a human personnel in responding to customers questions. The aim of this study is to evaluate the performance and naturalness of interaction of chatbot applications. The performance of a personal chatbot has been compared to a human personal in order to evaluate the naturalness. The evaluation was made by conducting an experiment on two expert users using cognitive walkthrough method. Findings of the experiment suggests that the human-machine chatbot is able to be close to the human performance in terms of interaction, although in some minor cases, the human-human chat applications perform better.
TiK‐means: Transformation‐infused <i>K</i>‐means clustering for skewed groups

Abstract The K ‐means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK‐means and contributes a K ‐means‐type algorithm that assigns observations to groups while estimating their skewness‐transformation parameters. The resulting groups and transformation reveal general‐structured clusters that can be explained by inverting the estimated transformation. Further, a modification of the jump statistic chooses the number of groups. Our algorithm is evaluated on simulated and real‐life data sets and then applied to a long‐standing astronomical dispute regarding the distinct kinds of gamma ray bursts.
Human Computer Interaction Based on Gestural Cues Recognition/Sign Language to Text Conversion

Human computer interaction is very wide-ranging and diverse field regarding research and design activity. This interaction between humans and computer systems can be done through various methods. Gesture recognition offers a natural and intuitive way for interaction. It is a natural and effective mean of communication and interaction for hearing-impaired people. Gestural cue is a category of non-verbal communication in which noticeable body actions transfer specific messages. This paper presents a gesture recognition system for the development of a Human Computer Interaction (HCI) using Leap Motion Sensor (LMS). LMS is a device proficient with tracking hand motions or gestures. The objective of this research is development of an HCI system that will convert sign language to text for hearing impaired people. Through hand or body gestures, the disabled can easily convey their message to the caregiver or robot. Sign language has been known for providing natural and intuitive way to interact with computers or machines and robots. We are employing three recognition techniques of Sign Language to Text Conversion (SLTC) to determine the performance of the model. Artificial Neural Network (ANN), Geometric Template Matching and Cross Correlation techniques were employed for static gesture recognition and the best results were acquired from geometric template matching.
Energy-Aware Self-Adaptation for Application Execution on Heterogeneous Parallel Architectures

Hardware in High Performance Computing environments in recent years have increasingly become more heterogeneous in order to improve computational performance. An additional aspect of such systems is the management of power and energy consumption. The increase in heterogeneity requires middleware and programming model abstractions to eliminate additional complexities that it brings, while also offering opportunities such as improved power management. In this paper, we explore application level self-adaptation including aspects such as automated configuration and deployment of applications to different heterogeneous infrastructure and for their redeployment. This therefore not only mitigates complexities associated with heterogeneous devices but aims to take advantage of the heterogeneity. The overall result of this paper is a self-adaptive framework that manages application Quality of Service (QoS) at runtime, which includes the automatic migration of applications between different accelerated infrastructures. Discussion covers when this migration is appropriate and quantifies the likely benefits.
Dynamically detecting USB attacks in hardware

Malicious USB devices can disguise themselves as benign devices (e.g., keyboard, mouse, etc.) to insert malicious commands on end devices. Advanced software-based detection schemes (deeper operating system level) are used to identify the malicious nature of such mimic devices. However, a powerful adversary (e.g., as rootkits or advanced persistent threats) can subvert those software-based detection schemes. To address these concerns, we present our ongoing work to dynamically detect these threats in hardware. Specifically, we utilize a novel hardware-assistance mechanism to collect unaltered USB data at the physical layer which is fed into a machine learning-based classifier to determine the true nature of the USB device.
Lossy Image Compression with Filter Bank Based Convolutional Networks

Filter bank based convolutional networks (FBCNs) enable efficient separable multiscale and multidirectional decomposition with a convolutional cascade of 1-D radial and directional filter banks. In this paper, we propose a two-stage subband coding framework for FBCN analysis coefficients using a SPIHT-like algorithm and subsequent primitive-based adaptive arithmetic coding (AAC). The SPIHT-like algorithm extends spatial orientation tree to exploit inter-subband dependency between subbands of different scales and directions. Mutual information is estimated for information-theoretical measurement to formulate such dependencies. Various primitives are designed adaptively encode the generated bitstream by fitting its varying lists and passes. Neural networks are leveraged to improve probability estimation for AAC, where nonlinear prediction is made based on contexts regarding scales, directions, locations and significance of analysis coefficients. Experimental results show that the proposed framework improves the lossy coding performance for FBCN analysis coefficients in comparison to the state-of-the-arts subband coding schemes SPIHT.
Deep learning based face recognition system with smart glasses

Individuals with prosopagnosia have difficulty in identifying different people by their faces. Our goal is to design and develop a face recognition system with wearable glasses to recognize faces and provide identity information to users. Unlike other existing systems that run locally on glasses or cellphones, we introduce a client-server architecture system for facial identification. We designed and implemented applications both on a pair of smart glasses and a cellphone to capture images and communicate with the server. Deep Convolutional Neural Networks (CNN) were chosen to build our face recognition on the back-end system and we achieved 98.18% accuracy for face recognition. The system is designed to handle new identities and new faces without having to rebuild the model.
Internet Usage Patterns Mining from Firewall Event Logs

Understanding users' behavior of internet usage is essential for the quality of service (QoS) analysis on the internet. If the internet providers can better understand their users, they may be able to provide better service, and also enhance the quality of the service. In general, the information about users' behavior is stored as the internet access log files, called event logs, on the server. To have the patterns of users' behavior from the event logs, this work aims to extract an interesting pattern of inappropriate user behaviors through the method of internet usage patterns mining. The primary mechanism of the proposed method is the Generalized Sequential Pattern (GSP) algorithm, which is an algorithm of sequential pattern mining. This study uses real event logs from an organization in Thailand. The results have identified exciting findings that have made possible to propose some improvements and increasing the QoS of the internet service.
Dealing with crosscutting and dynamic features in component software using aspect‐orientation: requirements and experiences

Coping with crosscutting and dynamic features of software systems has significant impacts on software modularity, reusability, adaptability, and maintainability. However, dealing with such features is a tremendous challenge for different software paradigms, particularly component-based software development (CBSD) where components can be used as black box software units. Fortunately, dealing with crosscutting features is the main focus of aspect-orientation (AO). To compromise, a plethora of different AO and CBSD combinations are available. There is, however, little consensus on how AO and CBSD can be combined and what AO qualities need to be integrated for getting maximum benefits from the combination. In the aim to reach a consensus and increase the understanding of requirements for proper combinations that deal with crosscutting and dynamic features, the authors conduct a domain analysis study of existing combination endeavours; they first identify a set of requirements ought to be fulfilled for proper modelling of crosscutting and dynamic features in component software using AO; second, they group the identified requirements into an evaluation model; third, they check the capability of existing combinations to validate the evaluation model. The study highlighted mandatory requirements for proper combinations and revealed that none of the existing endeavours validates the evaluation model.
A Bio-Inspired Algorithm for Virtual Machines Allocation in Public Clouds

The rapid spread of cloud computing during the past few years has increased the need to improve cloud services. The infrastructure-as-a-service (IaaS) model is among the highly challenging cloud computing models that has been subject to many optimization proposals. In particular, the scheduling algorithms followed by IaaS resource management systems to allocate virtual machine requests to physical cloud severs play an important role in the performance of the entire cloud. This paper proposes a distributed scheduling algorithm for IaaS cloud computing services by imitating the locust behavior in nature. The simulation results show that the proposed Locust algorithm reduces the average turnaround time of requests without compromising the server utilization.
External prior learning and internal mean sparse coding for image denoising

Image prior and sparse coding learning methods have important uses in image denoising. Many denoising methods learn priors either from the noisy image itself or an external clean image dataset. But using only these as priors does not always reconstruct the image effectively. In addition, when the image is corrupted by noise, the local sparse coding coefficient obtained from a noisy image patch is inaccurate, restricting denoising performance. We present a noise removal framework based on external prior learning and an internal mean sparse coding method, making use of the innate sparsity and nonlocal self-similarity (NSS) of natural images. Specifically, we first obtain external priors from a clean natural image dataset by Gaussian mixture model. The external priors are applied to guide the subspace clustering of internal noisy image patches, and a compact dictionary is generated for each internal noisy patch cluster. Then an internal mean sparse coding strategy based on NSS is introduced into the sparse representation model, whose regularization parameters then are deduced through a Bayesian framework. An iterative shrinkage method is employed to solve the l1-optimization problem in the sparse representation model. Application of the noise removal model to 16 test images demonstrates denoising performance exceeding other competing methods.
Scene Categorization by Deeply Learning Gaze Behavior in a Semisupervised Context

Accurately recognizing different categories of sceneries with sophisticated spatial configurations is a useful technique in computer vision and intelligent systems, e.g., scene understanding and autonomous driving. Competitive accuracies have been observed by the deep recognition models recently. Nevertheless, these deep architectures cannot explicitly characterize human visual perception, that is, the sequence of gaze allocation and the subsequent cognitive processes when viewing each scenery. In this paper, a novel spatially aware aggregation network is proposed for scene categorization, where the human gaze behavior is discovered in a semisupervised setting. In particular, as semantically labeling a large quantity of scene images is labor-intensive, a semisupervised and structure-preserved non-negative matrix factorization (NMF) is proposed to detect a set of visually/semantically salient regions from each scenery. Afterward, the gaze shifting path (GSP) is engineered to characterize the process of humans perceiving each scene picture. To deeply describe each GSP, a novel spatially aware CNN termed SA-Net is developed. It accepts input regions with various shapes and statistically aggregates all the salient regions along each GSP. Finally, the learned deep GSP features from the entire scene images are fused into an image kernel, which is subsequently integrated into a kernel SVM to categorize different sceneries. Comparative experiments on six scene image sets have shown the advantage of our method.
Generation of Movement Explanations for Testing Gesture Based Co-Operative Learning Applications

The paper proposes an explanation framework for machine learning based gesture recognition systems to increase trust, and also provide users an interface to ask questions about the recognition result. Gestures have three components: a) handshape, b) location, and c) movement. Several techniques exist for handshape and location recognition explanation, but very limited analysis exists on explaining movement. The challenge is that modeling the movement between handshapes in a gesture require dynamic modeling of arm kinematics using differential equations. The arm models can be of various complexity, but many of them may not be explainable. Our approach in this paper, is to mine hybrid system models of gestures using a coalition of hand-shape recognition technology and explainable kinematic models. The hybrid dynamical systems are mined using video data collected from users. Change in dynamics of a test user is expressed using the parameters of the kinematic equations. The parameters are converted into human understandable explanations by experts in movement analysis. The novel outcome is the combination of fault detection in hybrid dynamical systems and machine learning to provide explanation for recognition of continuous events. We have applied our technique on 60 users for 20 ASL gestures. Results show that the mined parameters of the kinematic equations can represent each gesture with precision of 83 %, recall of 80 % and accuracy of 82 %.
Augmented Reality: A Systematic Literature Review and Prospects for Future Research in Marketing and Advertising Communication

The combination of digital technology and the reality has further strengthened the aspect of modern society life to cater for new developments in the industrial revolution 4.0. Augmented reality is a technology that superimposes computer-generated images on a user’s view of the real world. However, the knowledge of augmented reality in the context of marketing and advertising communication is still scarce that needs to be discovered to identify the necessities in the study. Thus, by conducting a systematic literature review, this article seeks to answer four main research questions: (1) Which areas of augmented reality research have been conducted in? (2) What methodologies are being utilized to research augmented reality in advertising and consumer behaviours? (3) What theories or construct are being utilized to research augmented reality in advertising and consumer behaviours? (4) What are the research gaps in augmented reality in advertising and consumer behaviours? The study was a review of the 10 years period of publication from 2009 until 2018.
Towards Persuasive Recommender Systems

The primary objective of recommender systems, in a general sense, is to recommend items to users rather than to persuade users to get those items. Hence, a recommender system is not a persuasive technology by itself. In recent years, however, there has been increasing attention in the literature towards augmenting persuasiveness features into recommender systems. Several researchers have discussed the feasibility of enriching recommendations with persuasive messages. Nonetheless, there is a lack of works that discuss how to incorporate personalized and dynamic persuasive capabilities to recommender systems. To mitigate this issue, we propose the Personalized Persuasive RS (PerPer) framework. The PerPer adopts learning automata concepts to dynamically choose a suitable persuasive strategy for users in a personalized manner. PerPer is general enough to be plugged into different recommenders and to consider several persuasive strategies. PerPer aims to provide a simple and straightforward way to incorporate persuasive features to recommenders. By this, it would have the potential of increasing users' perceived acceptance of recommendations.
Phase chaotic encryption and efficiency evaluation for an image multiplexing method

We propose a new form of chaotic encryption of information contained in a multiplexed package, in which it has been possible to compress an amount of 1521 gray level images of human faces in a complex function, by using theta modulation and dynamic range optimization techniques. The phase is encrypted by using the confusion technique, through a chaotic mask (CM) that modify the phase values of the package, and then performing a linear transformation (LT) to store the encrypted phase in a commercial image format. The encryption key is composed of the generated seed values of the CM besides the scale factor of the LT. The cryptanalysis performed consists of a brute-force attack with a million iterations, besides evaluating the sensitivity of the system to slight variations of the encryption key, evidencing the strength of the method against this type of attacks. This work also shows advances in the efficiency achieved by the multiplexing method, regarding N number of multiplexed images; percentage of memory savings achieved, and the quality factors obtained when comparing the recovered information with the original one. All these parameters allow to compare our results with recent publications in this area.
DHOOM

Runtime verification employs dedicated hardware or software monitors to check whether program properties hold at runtime. However, these monitors often incur high area and performance overheads depending on whether they are implemented in hardware or software. In this work, we propose DHOOM, an architectural framework for runtime monitoring of program assertions, which exploits the combination of a reconfigurable fabric present alongside a processor core with the vestigial on-chip Design-for-Debug hardware. This combination of hardware features allows DHOOM to minimize the overall performance overhead of runtime verification, even when subject to a given area constraint. We present an algorithm for dynamically selecting an effective subset of assertion monitors that can be accommodated in the available programmable fabric, while instrumenting the remaining assertions in software. We show that our proposed strategy, while respecting area constraints, reduces the performance overhead of runtime verification by up to 32% when compared with a baseline of software-only monitors.
Semantic web technologies as enablers for truly connected mobility within smart cities

Most car manufacturers predict that in the first half of the next decade there will be fully autonomous vehicles on our roads. Such vehicles would have to communicate in order to mitigate problems caused by single-viewpoint approach. So there are a lot of researches and developments when it comes to communication layer of V2X (Vehicle-to-Everything), but there is still a lot to be done when it comes to data layer of this communication. This is why we propose using Semantic Web Technologies (SWT) to fill in gaps within data layer of V2X communication. By using SWT (Semantic Web Technologies) and Linked data, we plan to interconnect various data sources, in order to provide homogeneous way for connected autonomous vehicles (CAV) to access relevant information. Such information is currently contained in three distinctive type of sources. These are: Geo-stationary Static data sources (Maps, City models), Geostationary Dynamic data sources (IoT devices) and Non-geostationary Dynamic sources (Vehicles). Using SWT, our goal is to develop ontology(s), in such a way that in-vehicle algorithms can extract and process information about environment they are in, while taking into account available network bandwidth.