Reviewerly/exHarmony huggingface dataset:
Mobile Head Tracking for eCommerce and Beyond

Shopping is difficult for people with motor impairments. This includes online shopping. Proprietary software can emulate mouse and keyboard via head tracking. However, such a solution is not common for smartphones. Unlike desktop and laptop computers, they are also much easier to carry indoors and outdoors. To address this, we implement and open source button that is sensitive to head movements tracked from the front camera of iPhone X. This allows developers to integrate in eCommerce applications easily without requiring specialized knowledge. Other applications include gaming and use in hands-free situations such as during cooking, auto-repair. We built a sample online shopping application that allows users to easily browse between items from various categories and take relevant action just by head movements. We present results of user studies on this sample application and also include sensitivity studies based on two independent tests performed at 3 different distances to the screen.
A Light-weight ESalsa20 Ciphering based on 1D Logistic and Chebyshev Chaotic Maps

Information security has become a significant component in Information and Communication Technology (ICT). Different ciphering methods have been introduced as a solution to supply confidentiality and act a greater role in systems of information security. The majority of these methods devour a lot of computing resources, for example, time of CPU, memory, and force. Salsa20 is a modern and efficient stream encryption algorithm and its versions have the fastest stream codes available today. Instead of the broken Salsa20/7 and the not safe method Salsa20/12, the characterizes of Salsa20 are depicted by the execution of all rounds of ciphering to obtain a high diffusion to avoid the known attacks. In this paper, a method for enhancing the Salsa20 algorithm (ESalsa20) is proposed that uses a chaotic layer to achieve the diffusion faster than the traditional Salsa20. The results of histogram analysis, comparison between proposal Esalsa20 and original Salsa20 based on the results of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Average Difference (AD), Universal Image Quality (UQI), Normalized Cross-Correlation (NCC), Normalized Absolute Error (NAE), and execution time showed that the proposal improved ESalsa20 has less complexity and more secure than the original Salsa20. According to the obtained results, it is observed the proposed model is faster than the original algorithm, and it is achieved the best value for all experiments, the best values of NAD =0.3689770561, NCC=0.3890752653, and UQI=0.3777468183.Keywords: Cryptography, stream cipher, Salsa, Chaos Theory,1D logistic function, Chebyshev function
AI-based anomaly detection for cyberattacks on Windows systems - Creation of a prototype for automated monitoring of the process environment

Cyber security has become an increasingly important topic in recent years. The increasing popularity of systems and devices such as computers, servers, smartphones, tablets and smart home devices is causing a rapidly increasing attack surface. In addition, there are a variety of security vulnerabilities in software and hardware that make the security situation more complex and unclear. Many of these systems and devices also process personal or secret data and control critical processes in the industry. The need for security is tremendously high. The owners and administrators of modern computer systems are often overwhelmed with the task of securing their systems as the systems become more complex and the attack methods increasingly intelligent. In these days a there are a lot of encryption and hiding techniques available. They are used to make the detection of malicious software with signature based scanning methods very difficult. Therefore, novel methods for the detection of such threats are necessary. This paper examines whether cyber threats can be detected using modern artificial intelligence methods. We develop, describe and test a prototype for windows systems based on neural networks. In particular, an anomaly detection based on autoencoders is used. As this approach has shown, it is possible to detect a wide range of threats using artificial intelligence. Based on the approach in this work, this research topic should be continued to be investigated. Especially cloud-based solutions based on this principle seem to be very promising to protect against modern threats in the world of cyber security.
Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.
CATATAN KECIL MENGENAI DESAIN RISET DESKRIPTIF KUALITATIF

Qualitative description (QD) is a label used in qualitative research for studies that are descriptive in nature and widely used to describe of social-related phenomena. The philosophy of QD is pragmatic approach, overtones of other qualitative approach and probably the least theoretical of the qualitative approaches. This variability may indicate that there are no clear boundaries between QD and other strategies. QD has been identified as important and appropriate for research questions focused on discovering the who, what, and where of event or experiences and on gaining insight from a poorly understood phenomenon. QD should be the method of choice only when a description of a phenomenon is desired. Purposeful and maximum variation sampling may be used in QD studies. Data collection techniques usually include minimally-to-moderately structured open-ended interviews with individuals or focus groups. Qualitative content analysis using modifiable coding systems is analysis strategy of choice in QD studies. QD studies result in a rich, straight description of experience, perception, or event using language from the collected data through lon-inference interpretation during data analysis.
SICS: Secure and Dynamic Middlebox Outsourcing

There is an increasing trend that enterprises outsource their middlebox processing to a cloud for lower cost and easier management. However, outsourcing middleboxes brings threats to the enterprise's private information, including the traffic and rules of middleboxes, all of which are visible within the cloud. Existing solutions for secure middlebox outsourcing either incur significant performance overhead or do not support incremental updates. In this article, we present a secure and dynamic middlebox outsourcing framework, SICS, short for Secure In-Cloud Service. SICS encrypts each packet header and uses a label for in-cloud rule matching, which enables the cloud to perform its functionalities correctly with minimum header information leakage. Evaluation results show that SICS achieves higher throughput, faster construction and update speed, and lower resource overhead at the enterprise and in the cloud when compared with existing solutions.
Integration of barometric and GPS altimeters via adaptive data fusion algorithm

Summary An adaptive integrated navigation system, consisting of barometric altimeter (Baro) and GPS, is presented. The integration is achieved by using an adaptive data fusion algorithm with the filter gain correction. Proposed adaptive data fusion algorithm utilizes the measurement noise scale factor in order to reduce the effects of the incorrect barometric altimeter measurements on the estimation procedure. By using the algorithm, incorrect baro measurements are corrected by the system without any impact on the good estimation behavior. Performance of the proposed Baro/GPS integrated system is examined by simulations for altitude estimation of an aircraft.
Heterogeneous Distance Learning Based on Kernel Analysis-Synthesis Dictionary for Semi-Supervised Image to Video Person Re-Identification

Image to video person re-identification (IVPR), i.e., matching between pedestrian video and image, is an important task in practice. Although several methods have been presented for IVPR, most of these methods investigate the IVPR problem under the supervised setting, and require a large number of labeled image-video pairs for training. In this article, we study the IVPR problem under the semi-supervised setting, and propose a Kernel Analysis-synthesis Dictionary based heterogeneous Distance Learning (KADDL) approach. Specifically, KADDL first learns two pairs of kernel analysis-synthesis dictionaries from the labeled and unlabeled training image-video data in the kernel space. With the learned dictionary pairs, the heterogeneous image and video features can be transformed into coding coefficients of the same representation space, such that the gap between image and video can be bridged. Then, KADDL learns a discriminative distance metric over the transformed coding coefficients, to make the coding coefficients of positive image-video pair become similar, while those of negative image-video pair dissimilar. To make better use of the unlabeled data, we further designed a reliability-based semi-supervised strategy for KADDL. Experiments on several publicly available pedestrian sequence datasets demonstrate the effectiveness of the proposed approach.
A Distortion-Aware Multi-Task Learning Framework for Fractional Interpolation in Video Coding

Motion-compensated prediction adopts fractional-pixel interpolation to obtain the best motion vector. Traditional fixed interpolation filters cannot handle various content and structures well, and existing convolutional neural network based methods cannot fully exploit the distortion characteristics for fractional interpolation. Therefore, this paper proposes a distortion-aware multi-task learning framework (DA-MLF) to perform fractional interpolation. First, a multi-task training framework is proposed to provide the distortion characteristics as complementary information for improving the performance of subsequent interpolation. Then, a uniform interpolation sub-network is proposed to accomplish fractional interpolation, which utilizes the feature fusion module to fuse abundant local features, and the distortion awareness module to capture the multi-scale information of compression artifacts. Furthermore, DA-MLF is integrated into High Efficiency Video Coding (HEVC) test model, and multiple experiments are performed to evaluate the effectiveness of our method. On HEVC testing sequences, DA-MLF achieves 5.0%, 4.0% and 1.7% BD-rate reduction on average compared to the HEVC baseline, under low-delay P, low-delay B and random-access configurations, respectively. The experimental results validate that our framework not only achieves the best interpolation performance but also has the lowest computational complexity compared with state-of-the-art methods.
Multi-Stage Transfer Learning with an Application to Selection Process

In multi-stage processes, decisions happen in an ordered sequence of stages. Many of them have the structure of dual funnel problem: as the sample size decreases from one stage to the other, the information increases. A related example is a selection process, where applicants apply for a position, prize, or grant. In each stage, more applicants are evaluated and filtered out, and from the remaining ones, more information is collected. In the last stage, decision-makers use all available information to make their final decision. To train a classifier for each stage becomes impracticable as they can underfit due to the low dimensionality in early stages or overfit due to the small sample size in the latter stages. In this work, we proposed a \textit{Multi-StaGe Transfer Learning} (MSGTL) approach that uses knowledge from simple classifiers trained in early stages to improve the performance of classifiers in the latter stages. By transferring weights from simpler neural networks trained in larger datasets, we able to fine-tune more complex neural networks in the latter stages without overfitting due to the small sample size. We show that it is possible to control the trade-off between conserving knowledge and fine-tuning using a simple probabilistic map. Experiments using real-world data demonstrate the efficacy of our approach as it outperforms other state-of-the-art methods for transfer learning and regularization.
Visualisation and knowledge discovery from interpretable models

Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem. These models are also capable of visualisation of the classifier and decision boundaries: they are the angle based variants of Learning Vector Quantization. We have demonstrated the algorithms on a synthetic dataset and a real-world one (heart disease dataset from the UCI repository). The newly developed classifiers helped in investigating the complexities of the UCI dataset as a multiclass problem. The performance of the developed classifiers were comparable to those reported in literature for this dataset, with additional value of interpretability, when the dataset was treated as a binary class problem.
Eye and Voice Control for an Augmented Reality Cooking Experience

The fast rhythm of our lives makes it hard for us to find time to cook. When we do, we often have to choose between the commodity of cooking the same familiar food several times and the uncertainty of trying something new. This paper introduces AREasyCooking, an application which uses new technologies to help a user make healthier decisions about their culinary habits. Thus, using augmented reality and bar code reading, AREasyCooking helps users bring in the application the ingredients they have available. The application uses two major sources of recipes (well-known cooks and traditional recipes) to offer users possible variants of dishes with available ingredients. Finally, AREasyCooking guides users step by step through videos in preparing the recipe. Controlling these movies is done with the help of the eyes and the voice since hands are busy cooking.
EMPLOYEES’ PERCEPTIONS TOWARDS THE RELATIONSHIP BETWEEN SAFETY MANAGEMENT PRACTICES AND SAFETY PERFORMANCE OF SMES INDUSTRY IN KELANTAN

Safety management practices have been aggressively put into action to encourage employees' involvement and commitment towards safety-related matters but yet there are still many incidents and accidents reported involving small and medium enterprises industry. However, the study involving employees' perceptions of safety management practices and small and medium-sized safety performance companies, in particular, are also missing. This research, therefore, tests the attitudes of the workers about the partnership between safety management activities, namely adherence to safety management, employee engagement o safety, safety management program, safety culture, and safety training for better safety efficiency. Security reduction strategies enhance working conditions; favourably influence the actions and activities of workers about health, thus reducing occupational injuries. This research makes use of questionnaires as a method. The data were analysed using Structural Equation Modelling (PLS-SEM) for the Partial Least Squares. The identified Small Medium Enterprise in Kelantan received a total of 350 questionnaires, with a response rate of 300 questionnaires (85.71 percent). The analysis showed that safety monitoring activities had a good association with protection efficiency across all dimensions. These findings provide valuable guidance to researchers in identifying the mechanisms by which they may improve safety at work.
Comparison of Vehicle Categorisation by Convolutional Neural Networks using MATLAB

The number of cars driving on the roads is increasing every year. Due to the large number and especially the weight of passing vehicles, roads are in critical condition and need more and more investments for their repair. This incurs significant costs for road operators. Article goals are the visual classification of passing vehicles over the tested platform using neural networks. The differences, performance and their structures comparisons of these two neural networks, AlexNet and GoogLeNet, will be described.
Time sensitivity and self-organisation in Multi-recurrent Neural Networks

Model optimisation is a key step in model development and traditionally this was limited to parameter tuning. However, recent developments and enhanced understanding of internal dynamics of model architectures have led to various exploration to optimise and enhance performance through model extension and development. In this paper, we extend the architecture of the Multi-recurrent Neural Network (MRN) to incorporate self-learning recurrent link ratios and periodically attentive hidden units. We contrast and show the superiority of these extensions to the standard MRN for a complex financial prediction task. The superiority is attributed to i) the ability of the self-learning recurrent link ratios to dynamically utilise data to identify optimal parameters of its memory mechanism and ii) the periodically attentive units enabling the hidden layer capture temporal features that are sensitive to different periods of time. Finally, we evaluate our extended MRNs (Self-Learning MRN (SL-MRN) and Periodically Attentive MRN (PA-MRN)), against two current state-of the-art models (Long-Short Term Memory and Support Vector Machines) for an eye state detection task. Our preliminary results demonstrate that the PA-MRN and SL-MRN outperform both state-of-the-art models. These results demonstrate that the MRN extensions are suitable models for machine learning applications and these findings would be further explored.
Adversarial Robustness of Model Sets

Machine learning models are vulnerable to very small adversarial input perturbations. Here, we study the question of whether the list of predictions made by a list of models can also be changed arbitrarily by a single small perturbation. Clearly, this is a harder problem since one has to simultaneously mislead several models using the same perturbation, where the target classes assigned to the models might differ. This attack has several applications over models designed by different manufacturers for a similar purpose. One might want a single perturbation that acts differently on each model; like only mis-leading a subset, or making each model predict a different label. Also, one might want a perturbation that misleads each model the same way and thereby create a transferable perturbation. Current approaches are not applicable for this general problem directly. Here, we propose an algorithm that is able to find a perturbation that satisfies several kinds of attack patterns. For example, all the models could have the same target class, or different random target classes, or target classes designed to be maximally contradicting. We evaluated our algorithm using three model sets consisting of publicly available pre-trained ImageNet models of varying capacity and architecture. We demonstrate that, in all the scenarios, our method is able to find visually insignificant perturbations that achieve our target adversarial patterns <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Benchmarking data mining approaches for traveler segmentation

The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers.
BDB-Net: Boundary-Enhanced Dual Branch Network for Whole Brain Segmentation

Whole brain segmentation is of large value for scientific research and clinical practice. Because of the long processing time of multi-atlas based segmentation methods, deep learning based methods have been extensively studied in recent years. Under the framework of the state-of-the-art method spatially localized atlas network tiles (SLANT), we proposed a novel Boundary-enhanced Dual Branch Network (BDB-Net) for whole brain segmentation. It contains a semantic segmentation branch and a boundary detection branch. Through delicate coupling of these branches, both the texture and shape information could be better utilized. Besides, we also designed a dual-heads architecture for the segmentation branch to incorporate more context information and reduce the information truncation. Furthermore, in view of the symmetricity of the brain, we proposed a new splitting pattern of the volumes and reduce the number of networks. The utility of these improvements was validated through experiments conducted on two dataset. The experimental results shown the proposed method could obtain better results using less training time (97% less) and testing time (50% less) compared with the SLANT.
SR<sup>2</sup>: Super-Resolution With Structure-Aware Reconstruction

Image reconstruction is particularly difficult when the type of image degradations are unknown. This may be the case if the acquisition device is unknown or the images stem from an uncontrolled environment like the internet. Yet, it may be important to reconstruct a specific piece of information from the image, such as digits from signs or vehicle license plates. Existing works incorporate such prior information with a sequential super-resolution and classification pipeline. However, this approach is prone to error propagation.In this work, we propose a new approach of connecting classification and super-resolution in parallel within a multi-task network. We show that this architecture is able to preserve structures and to remove noisy pixels although the network itself has never been trained on noisy data. We also show that this design allows to transparently trade classification and super-resolution quality. On upsampling by factor 4, we outperform sequential approaches in terms of SSIM by 10% and improve classification by 69%.