Reviewerly/exHarmony huggingface dataset:
Approximating Optimisation Solutions for the Travelling Officer Problem with Neural Networks

Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques. Deep neural networks (DNNs) are a potentially promising and under-explored solution to solve these problems due to their powerful function approximation abilities, and their fast feed-forward computation. In this paper, we outline a method for converting an orienteering problem into a classification problem, and design a multi-layer deep learning network to approximate traditional optimisation solutions to this problem. We test the performance of the network on a real-world parking violation dataset, and conduct a generic study that empirically shows the critical architectural components that affect network performance for this problem.
Secure Electronic Transactions via Cryptography Techniques

In recent years, many proposals for electronic payments via open network have been emerged. One of them is Secured Electronic Transaction (SET) protocol advertised through VISA and MASTERCARD that is presently spread all-inclusive. Although SET has numerous advantages over other proposals for straightforwardness and sociability, there seems to be an agreement on relative inefficiency of the protocol. In this paper, we simply give a brief overview of the secure electronic transaction with the help of the cryptography. This paper divide into different two sections in the first section give the brief introduction of the secure electronic transaction, the second section give the overview of the cryptography, third section for legal risk in electronic transaction and finally an overview of the secure transaction with the help of cryptography.
The Appraisal Theory of Emotion in Human–Computer Interaction

This chapter reviews the appraisal theory of emotion and how it has been employed in human–computer interaction (HCI) research. This theory views emotion as a process that evaluates the subjective significance of an event. We demonstrate the usefulness of the perspective for HCI, as emotion is defined in terms of the events of the task environment and the goals and knowledge of the subject. Importantly, the appraisal theory ties these factors together in a cognitive appraisal process order to explain the variety of subjective emotional experiences. This is important for two reasons. First, a strong theoretical commitment allows researchers and designers to derive testable hypotheses from the theory. Second, only a theory that ties together goals, knowledge and emotion can explain the behaviour and experiences of users, who often have multiple—and at times conflicting—goals and motivations that may dynamically change in response to events in the environment.
Heterogeneous Information Network Embedding with Convolutional Graph Attention Networks

Heterogeneous Information Networks (HINs) are prevalent in our daily life, such as social networks and bibliography networks, which contain multiple types of nodes and links. Heterogeneous information network embedding is an effective HIN analysis method, it aims at projecting network elements into a lower-dimensional vector space for further machine learning related evaluations, such as node classification, node clustering, and so on. However, existing HIN embedding methods mainly focus on extracting the semantic-related information or close neighboring relations, while the high-level proximity of the network is also important but not preserved. To address the problem, in this paper we propose CGAT, a semi-supervised heterogeneous information network embedding method. We optimize the graph attention network by adding additional convolution layers, thereby we can extract multiple types of semantics and preserve high-level information in HIN embedding at the same time. Also, we utilize label information in HINs for semi-supervised training to better obtain the model parameters and HIN embeddings. Experimental results on real-world datasets demonstrate the effectiveness and efficiency of the proposed model.
RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search

Early neural network architectures were designed by so-called "grad student descent". Since then, the field of Neural Architecture Search (NAS) has developed with the goal of algorithmically designing architectures tailored for a dataset of interest. Recently, gradient-based NAS approaches have been created to rapidly perform the search. Gradient-based approaches impose more structure on the search, compared to alternative NAS methods, enabling faster search phase optimization. In the real-world, neural architecture performance is measured by more than just high accuracy. There is increasing need for efficient neural architectures, where resources such as model size or latency must also be considered. Gradient-based NAS is also suitable for such multi-objective optimization. In this work, we extend a popular gradient-based NAS method to support one or more resource costs. We then perform in-depth analysis on the discovery of architectures satisfying single-resource constraints for classification of CIFAR-10.
A Comparative Study to Assess Human Machine Interaction Mechanisms for Large Scale Displays

This work presents a new comparative study for manipulating animated 3D content on a large scale display using two user interaction mechanisms. The first interaction mechanism relies on a traditional cursor-based user interface (UI) with onscreen interaction elements. The second interaction mechanism adopts a gesture-based UI without any visible interaction elements. The user's distance from the display is significantly farther compared to a traditional desktop to correctly gauge the impact of a cursor-based UI compared to a gesture-based UI on a large scale display. Both interaction mechanisms are extensively evaluated via a user study in terms of usability, user experience (UX), and ergonomics. Through both quantitative and the qualitative evaluation, the results show that a gesture-based interface is a preferred method to manipulate animated 3D content compared to a cursor-based UI on a large scale display in terms of usability and UX. The results also demonstrate that using a gesture-based UI does not rank higher in terms of physical and cognitive ergonomics compared to a cursor-based UI.
Flexible engineering educational concept: Insight into students’ competences growth in creativity, activity, cooperation

The recent studies on employee’s flexibility are showing that the amount of career-ready employees with the required competences, who are also motivated, is rising. In many operational work positions in modern manufacturing companies, employees must manage a wider range of issues and responsibilities than before. Engineering education has been and will continue to be vital in everyday life of people around the real world, but students must have competences in development of creativity, activity, and cooperation to keep up with the speed of innovations. The article responds to the outputs of the National Project "Key Competences Formation and Effective Support of Students Mobility at Technology Faculties" in the context of the work and use of technology knowledge and didactics. It is also in the intention of the Faculty of Electrical Engineering and Information Technology, University of Zilina, Slovakia, through the knowledge obtained from the project research to offer each interested educational institutions an opportunity to adopt flexible education environment concept to support students’ creativity, activity, cooperation concept (CAC) in order to focus better on improving student´s learning procedure and increasing the quality of instruction procedure. The paper brings the results of the current national educational agency research project focused on effectiveness of flexible didactics with new virtual technologies, organization forms and methods in student-focused concept through the processing of experimental data of the electronics study program in the period of 2017- 2019. The implementation of a new CAC has proven an effective flexible learning environment to work well as a method for elevating key career-ready competences. The main benefit of the study is the new insight into students´ competences growth on the engineering career-ready knowledge scaffold with significant findings and recommendations. The innovative concept is based on standardized automated hardware, virtual instrumentation software and simulation-driven support with a new assessment to identify and reflect on the students´ competences learning experiences outcomes during didactic cycle to examine the flexible environment helpfulness of virtual tools and simulation driven learning and to explore how the concept enhances graduates career-ready creativity, activity, cooperation competences growth piloted with a multinational company that is a producer of automated equipment and virtual instrumentation software.
Improved nonlinear observable degree analysis using data fusion

For nonlinear Kalman filtering, process noise and observation noise affect the accuracy of system filtering, and filtering accuracy is related to observable degree. That is, the calculation of observable degree will be affected by process noise and observation noise. Traditional solutions for observable degree of nonlinear systems do not take noise into account. In this paper, the observable degree theory is solved by using the Cramer-Rao Lower Bound and the Lie derivative in differential geometry theory. The process noise and the observation noise are taken into account in the calculation matrix of the nonlinear observable degree. The paper proposes a new method based on condition number fusion. The iterative algorithm is used to make the condition number of the fused matrix reach the minimum value. In the result, the error of observable degree calculation can be reduced. The validity of the proposed method is verified by simulation, and the calculation theory of nonlinear observable degree is improved.
Lecture Video Summarization Using Subtitles

Video summarization is a process of reducing the video content in order to generate a summary in the video format. The summary created should contain the most important parts of the original video. With the video content increasing at a rapid rate day by day, the automatic video summarization will be beneficial for anyone who wants to save time and learn more in less time. This paper gives an insight of summarizing lecture videos using subtitles. From the evaluations done on NPTEL (National Programme on Technology Enhanced Learning) videos against human-made summaries, it is found that the proposed approached is effective in doing the same. It shows that the punctuations in the subtitles play a major role in summarizing lecture videos. By using the punctuations along with text in subtitles, it gives an average ROGUE precision of 0.822, an average recall of 0.802 and an average F-measure of 0.805.
Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging

Image scale carries crucial information in medical imaging, e.g. the size and spatial frequency of local structures, lesions, tumors and cell nuclei. With feature transfer being a common practice, scale-invariant features implicitly learned from pretraining on ImageNet tend to be preferred over scale-covariant features. The pruning strategy in this paper proposes a way to maintain scale covariance in the transferred features. Deep learning interpretability is used to analyze the layer-wise encoding of scale information for popular architectures such as InceptionV3 and ResNet50. Interestingly, the covariance of scale peaks at central layers and decreases close to softmax. Motivated by these results, our pruning strategy removes the layers where invariance to scale is learned. The pruning operation leads to marked improvements in the regression of both nuclei areas and magnification levels of histopathology images. These are relevant applications to enlarge the existing medical datasets with open-access images as those of PubMed Central. All experiments are performed on publicly available data and the code is shared on GitHub.
Predictive Analysis of Machine Learning Algorithms for Breast Cancer Diagnosis

Cancer is one of the fastest growing disease around the world and subpart of it Breast Cancer that is growing rapidly and mostly affecting women. Early treatment of this disease is helpful and can act as an early prevention to the upcoming major cure. However this can only be possible only if women are able to know that they are suffering with such disease and this can be only diagnosed if they come up with it and openly sharing with family and Doctors about the disease. This can lead to be a bit challenging task as to detect this disease among women using mammography as patient communication can affect mammography performance. This disease has had many ideas and myths as to how we can diagnosed it but Machine Learning the subset of Artificial Intelligence that can help Doctors and Surgeons to learn from past experiments. To treat upcoming patients with similar anomalies has had the major help of saving many patients with its set of algorithms and set of applications it provides. This paper will be focusing on five of the popular supervised Machine Learning algorithms for Diagnosing Breast Cancer this will be K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB) and Decision Tree (DT) and the algorithm Random Forest gave the best results and the K Nearest Neighbor was the second best performing algorithm that produce desired results and the algorithm Naïve Bayes was the least performing algorithm.
An Approach to Mine Frequent Item Sets Considering Negative Item Values

High utility item set mining identifies the mining item set whose utility satisfies a given limit. Some studies rated the item's values as positive. However, an item set may have negative item values in some applications. Thus, the revelation of high utility item sets with negative item values is required for proper decision-making. This paper proposes a quantitative approach considering negative item values for mining high utility items. An example set out to explain the approach.
An Analysis of Different Notions of Effectiveness in k-Anonymity

k-anonymity is an approach for enabling privacy-preserving data publishing of personal, sensitive data. As a result of this anonymisation process, the utility of the sanitised data is generally lower than on the original data. Quantifying this utility loss is therefore important to estimate the usefulness of the resulting datasets. In this paper, we analyse several of these utility aspects.
Open-vocabulary models for source code

Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new software development tools, such as tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale.
Penalty-based Sequence Generative Adversarial Networks with Enhanced Transformer for Text Generation

In this paper, we propose a new model to solve the problem of text generation, which is based on the concept of seqGAN, combines self-attention with modeling localness, and introduces the penalty-based objective function. This model has much better performance than the original model. In the original model, the generator's ability of the text feature extraction is insufficient. We introduce self-attention with modeling localness, which greatly enhances its ability to capture long distance and shortrange dependencies. In addition, we use the penalty-based objective function instead of the loss function of the original model to solve the problem of mode collapse. Experimental results demonstrate that our model consistently outperforms several state-of-the-art text generation methods in the quality of generated texts.
Study of Copy-Move Forgery Detection Techniques in Images

With the evolution of tools used for image editing in today's world, the images can be manipulated with ease. Resizing, cloning, cropping, etc., becomes easy and fast while on the other end, a great challenge in front of the world is determining whether an image has been tempered or not. A very popular manipulation method for tempering images is Copy-move forgery in which a part of an image is duplicated and pasted in some other location of the same image. Major research has been going on in detection of copy-move forgery. A detailed review and analytical discussion has been presented in this paper along with pros and cons of each of the detection techniques used for copy-move forgery detection.
A Separate 3D-SegNet Based on Priority Queue for Brain Tumor Segmentation

To exploit three-dimensional (3D) context information and improve 3D medical image semantic segmentation, we propose a separate 3D-SegNet (S3D-SegNet) that combines 2D-SegNet with 1D-SegNet for brain tumor segmentation challenge. First, 2D-SegNet which has 4 encoding layers and 4 decoding layers is utilized to gain the 2D-features in a slice. Second, one-dimensional (1D) features are reassembled from the 2D features in the z-axis. Third, to use context along the z-axis, the 1D-features are inputted into 1DSegNet with 3 encoding and 3 decoding layers and then are classified feature-wise. Besides, we proposed a priority queue based on the weight values of samples for training to focus on the difficult and rare sample. Experimental results show that the separate 3D-SegNet can obtain higher accurate than 2D-SegNet and the priority queue can increase the speed of training convergence.
CHASE

Internet of Things (IoT) can be seen as a variety of interconnected things (e.g., RFID tags, sensors, actuators, mobile phones) to provide a certain service to final users. The interaction between these things and the user in these environments is challenging due to interoperability, consistency of interactions, easy installation and so on. Then, there is a need to provide tools for the User Experience (UX) evaluation in IoT scenarios and, in this work, we propose CHASE, a checklist to facilitate UX evaluation in IoT scenarios. First, CHASE was constructed based on the results of a literature review that identified methods, instruments, and characteristics of human-thing interaction and evaluation in these scenarios. Then, fourteen IoT experts reviewed a preliminary version of the checklist, followed by three Human-Computer Interaction (HCI) experts. Finally, three researchers used chase to evaluate the user experience in a real IoT environment to collect authentic feedback. This paper also presents a series of important points for the evaluation of UX in IoT environments based on the literature review, the opinion of the experts, and the IoT application evaluations.
DeepMEL: Compiling Visual Multi-Experience Localization into a Deep Neural Network

Vision-based path following allows robots to autonomously repeat manually taught paths. Stereo Visual Teach and Repeat (VT&R) [1] accomplishes accurate and robust long-range path following in unstructured outdoor environments across changing lighting, weather, and seasons by relying on colour-constant imaging [2] and multi-experience localization [3]. We leverage multi-experience VT&R together with two datasets of outdoor driving on two separate paths spanning different times of day, weather, and seasons to teach a deep neural network to predict relative pose for visual odometry (VO) and for localization with respect to a path. In this paper we run experiments exclusively on datasets to study how the network generalizes across environmental conditions. Based on the results we believe that our system achieves relative pose estimates sufficiently accurate for in-the-loop path following and that it is able to localize radically different conditions against each other directly (i.e. winter to spring and day to night), a capability that our hand-engineered system does not have.
Texture Gradient and Deep Features Fusion-Based Image Scene Geometry Recognition System Using Extreme Learning Machine

Image scene geometry recognition is an important task for reconstruct the 3D information of a single image which is beneficial for computer vision applications, such as 3D TV, video categorization. In this paper, a novel architecture for the image scene geometry recognition based on the feature-level fusion of convolutional neural networks (CNN) features and low-level texture gradient features is presented. The main advantages of using low-level features are; simple to extract and contain rich information of image scene geometry. Next, it is evaluated on a novel scene dataset that is constructed by following the twelve different image scene geometries (1000 samples for each category) and experimental results exhibit that proposed system achieves higher accuracy than applying the CNN alone. Additionally, by utilizing the extreme learning machine (ELM) as a classifier, the proposed system achieves 86.29% recognition accuracy that is superior the existing baseline methods.
Expergefactor

We present a prototype for an alarm clock app that is based on sonic interaction design, called the Expergefactor. It consists of two parts that solely rely on rotating the phone and receiving auditory feedback. In an awakening challenge, the mobile phone has to be rotated to find a random position following sonic clues. For setting the snoozing timer, the phone is turned and gives swelling sonic feedback for each minute more.
A Classification Surrogate Model based Evolutionary Algorithm for Neural Network Structure Learning

Designing neural networks often requires a large number of artificial intelligence experts. However, such manual processes are time-consuming and require numerous resources. In this paper, we try to search neural network structures automatically for the image classification task. Moreover, considering the huge computational cost of neural architecture search (NAS), we attempt to apply a classification surrogate model based multi-objective evolutionary algorithm to search neural network architectures (CSMEA-Net). The algorithm combines two objectives, i.e., minimizing the validation error and the computational complexity measured by the number of floating-point operations (FLOPs) to achieve Pareto Optimality. In addition, we improve the components of the cell-based search space. The performance of network architectures discovered by our method is evaluated on CIFAR-10 and CIFAR-100 datasets. The experimental results show that the proposed approach can find a higher-performance neural network architecture compared with both hand-crafted as well as automatically-designed networks.
An Algorithm for the Contension Inconsistency Measure Using Reductions to Answer Set Programming

We present an algorithm for determining inconsistency degrees wrt. the contension inconsistency measure [] which utilizes three-valued logic to determine the minimal number of atoms that are assigned truth value B (paradoxical/both true and false). Our algorithm is based on an answer set programming encoding for checking for upper bounds and a binary search algorithm on top of that. We experimentally show that the new algorithm significantly outperforms the state of the art .