Reviewerly/exHarmony huggingface dataset:
A noise-based stabilizer for convolutional neural networks

Overfitting occurs when one tries to train a large model on small amount of data. Regularizing a neural network using prior knowledge remains a topic of research as it is not concluded how much prior information can be given to the neural network. In this paper, a novel algorithm is introduced which uses regularization to train a neural network without increasing the dataset. A trivial prior information of a class label is supplied to the model while training. Laplace noise is introduced to the intermediate layer for more generalization. The results show significant improvement in accuracy on the standard datasets for a simple Convolutional Neural Network (CNN). While the proposed method outperforms previous regularization techniques like dropout and batch normalization, it can also be applied with them for further improvement in the performance. On the variants of MNIST, proposed algorithm achieved an average 48% increment in the test accuracy.
Bayesian nonparametric estimation of ROC surface under verification bias

The receiver operating characteristic (ROC) surface, as a generalization of the ROC curve, has been widely used to assess the accuracy of a diagnostic test for three categories. A common problem is verification bias, referring to the situation where not all subjects have their true classes verified. In this paper, we consider the problem of estimating the ROC surface under verification bias. We adopt a Bayesian nonparametric approach by directly modeling the underlying distributions of the three categories by Dirichlet process mixture priors. We propose a robust computing algorithm by only imposing a missing at random assumption for the verification process but no assumption on the distributions. The method can also accommodate covariates information in estimating the ROC surface, which can lead to a more comprehensive understanding of the diagnostic accuracy. It can be adapted and hugely simplified in the case where there is no verification bias, and very fast computation is possible through the Bayesian bootstrap process. The proposed method is compared with other commonly used methods by extensive simulations. We find that the proposed method generally outperforms other approaches. Applying the method to two real datasets, the key findings are as follows: (1) human epididymis protein 4 has a slightly better diagnosis ability compared to CA125 in discriminating healthy, early stage, and late stage patients of epithelial ovarian cancer. (2) Serum albumin has a prognostic ability in distinguishing different stages of hepatocellular carcinoma.
Consideration on the variation of financial data of institutions for canonical correlation analysis

In these days, progress of e-government and spread of electrical data lead to the prevail of public open databases, and also lead to the large amount of data analysis applications applying to these official open data. With regard to data analysis method, Canonical Correlation Analysis, which is one of the basic data analysis method and also data visualization method, is becoming the requisite skill for data scientists in this Big Data era. This paper examines the open data of financial data of education institutions. Especially, we focus on the higher education institutions and their financial data. In addition, we examine the variation of data and its problem to the data analysis. We aim to apply this analysis method and the result of consideration for supporting the improvement of quality assurance of higher education institutions.
An Asymmetric Optical Cryptosystem of Double Image Encryption Based on Optical Vortex Phase Mask Using Gyrator Transform Domain

Background: Optical Vortex (OV) has attracted attention amongst many researchers. Paper proposes a nonlinear scheme of image encryption based on Optical Vortex (OV) and Double Random Phase Encoding (DRPE) in the Gyrator Transform (GT) domain under phase truncation operations. Objective: The amplitude and phase truncation operation in the image encryption generates two decryption keys and convert the method to nonlinear. It has also been proposed opto-electronically. Original image can only be decrypted with correct values of OV, GT rotation angles and Decryption Keys (DKs). Methods: A novel asymmetric image encryption scheme, using optical vortex mask has been proposed in view of amplitude and phase truncation operation. The scheme becomes more strengthened by the parameters used in the Optical Vortex (OV) and by taking the (n)th power operation in the encryption path and (n)th root operation in the decryption path. Results: It shows that for each of the encryption parameters, binary image has greater sensitivity as compared to the grayscale image. This scheme inflates the security by making use of OV-based Structured Phase Mask (SPM) as it expands the key space. The scheme has also been investigated for its robustness and its sensitivity against various attacks such as noise and occlusion attacks under number of iterations. Conclusion: This scheme provides solution to the problem of key space with the use of GT rotational angles and OV phase mask thus enhances the security. The scheme has been verified based on various security parameters such as occlusion, noise attacks, CC, entropy etc.
User experience evaluation focused on viewpoints and embodiment

The sense of embodiment of users in interactive graphics applications controlled by virtual reality devices involves several senses. This paper presents a user experience evaluation focused on user viewpoints (first and third-person perspectives) and their influence on the sense of embodiment of users, in a mobile game wearing a Head Mounted Display device. More precisely, we show the significant influence of the use of first-person perspective, the synchronism between movements of the 3D game character and those from the player in real-world and participant's gender on the sense of embodiment. With respect to the genre, it was shown that the women participants perceived the SoE in both points of view, regardless of the game character being always in motion. As for the men participants, the sense of agency was influenced by the level of synchronism between player's and character's movements, resulting in the SoE being perceived only in the first-person perspective. We also concluded that the different points of view did not affect the user experience when playing the game, except for items that involved comfort and nausea.
Rapid Prototyping of Augmented Reality &amp; Virtual Reality Interfaces

This course introduces participants to rapid prototyping techniques for augmented reality and virtual reality interfaces. Participants will learn about both physical prototyping with paper and Play-Doh as well as digital prototyping via new visual authoring tools for AR/VR. The course is structured into four sessions. After an introduction to AR/VR prototyping principles and materials, the next two sessions are hands-on, allowing participants to practice new physical and digital prototyping techniques. These techniques use a combination of new paper-based AR/VR design templates and smartphone-based capture and replay tools, adapting Wizard of Oz for AR/VR design. The fourth and final session will allow participants to test and critique each other's prototypes while checking against emerging design principles and guidelines. The instructor has previously taught the techniques to broad student audiences with a wide variety of non-technical backgrounds, including design, architecture, business, medicine, education, and psychology, who shared a common interest in user experience and interaction design. The course is targeted at non-technical audiences including HCI practitioners, user experience researchers, and interaction design professionals and students. A useful byproduct of the course will be a small portfolio piece of a first AR/VR interface designed iteratively and collaboratively in teams.
Effect of Lattice Topologies and Distance Measurements in Self-Organizing Map for Better Classification

Self-Organizing Map (SOM) is a widely used algorithm in artificial neural network for classification. Despite the general success of this algorithm, there are several limitations which some of them are poor classification accuracy and slow rates of convergence when the standard lattice topology and distance measurement are implemented. This paper investigates the performance of SOM using different topologies and different distance measurements. The results obtained showed that SOM with hexagonal topology and Euclidean distance measurement outperforms other topologies and distance measurement using at any scale datasets.
Clustering Stability via Concept-based Nonnegative Matrix Factorization

One of the most important contributions of topic modeling is to accurately and the ectively discover and classify documents in a collection of texts by a number of clusters/topics. However, finding an appropriate number of topics is a particularly challenging model selection question. In this context, we introduce a new unsupervised conceptual stability framework to access the validity of a clustering solution. We integrate the proposed framework into nonnegative matrix factorization (NMF) to guide the selection of desired number of topics. Our model provides a exible way to enhance the interpretation of NMF for the effective clustering solutions. The work presented in this paper crosses the bridge between stability-based validation of clustering solutions and NMF in the context of unsupervised learning. We perform a thorough evaluation of our approach over a wide range of real-world datasets and compare it to current state-of-the-art which are two NMF-based approaches and four Latent Dirichlet Allocation (LDA) based models. the quantitative experimental results show that integrating such conceptual stability analysis into NMF can lead to significant improvements in the document clustering and information retrieval the ectiveness.
A New Mammography Lesion Classification Method Based on Convolutional Neural Network

Breast cancer is a malignant tumor disease that is extremely high incidence among women around the world. And the cause of the disease is still unclear, the key to prevention and treatment of breast cancer is detection, diagnosis and treatment early. Mammography is the first choice for early diagnosis, however, limited experts have difficulty dealing with a large number of mammography images. Therefore, this paper is mainly devoted to the study of intelligent classification of mammography images, and applies the latest image processing method---Convolutional Neural Network (CNN) to study the classification of mammography images. In addition, we propose a new classification method of mammography images, which is to classify the lesions of normal (N), benign (B) and malignant (M) under different gland types.
Comparative Study of Feature Selection Methods for Medical Full Text Classification

There is a lot of work in text categorization using only the title and abstract of the papers. However, in a full paper there is a much larger amount of information that could be used to improve the text classification performance. The potential benefits of using full texts come with an additional problem: the increased size of the data sets. To overcome the increased the size of full text data sets we performed an assessment study on the use of feature selection methods for full text classification. We have compared two existing feature selection methods (Information Gain and Correlation) and a novel method called k-Best-Discriminative-Terms. The assessment was conducted using the Ohsumed corpora. We have made two sets of experiments: using title and abstract only; and full text. The results achieved by the novel method show that the novel method does not perform well in small amounts of text like title and abstract but performs much better for the full text data sets and requires a much smaller number of attributes.
Teaching AI Algorithms with Games Including Mahjong and FightTheLandlord on the Botzone Online Platform

This paper presents a course design, named Algorithms in Game AI, as an undergraduate elective course. The course mainly focuses on common and state-of-the-art algorithms in the game AI area, including game tree based algorithms and reinforcement learning. Powered by Botzone, our game AI platform, we designed different types of assignments for this course to bring a rich and fun learning experience. We chose several games including two popular Chinese classic ones, namely Mahjong and FightTheLandlord, which are both collaborative, stochastic and partially observable. To our best knowledge, it is the first time to adopt these games in AI courses, thus providing a new benchmark for game AI education. To encourage participation and reduce frustration, milestone-based competitions and bonus tasks were adopted. In this paper, we present the structure, teaching tools, techniques, performance and findings of this course. By reviewing students' performance and feedback, we found that students enjoyed the games we provided. We also found that reinforcement learning algorithms did not perform as well as other algorithms in limited time and resources.
SIG

Augmented reality and spatial information manipulation is being increasingly used as part of environ- ment integrated form factors and wearable device such as head-mounted displays. The integration of this exciting technology in many aspects of peoples' lives is transforming the way we understand computing, pushing the boundaries of Spatial Interfaces into virtual but embedded environments. We think that the time is ripe for a renewed discussion about the role of Augmented Reality within Spatial Interfaces. With this SIG we want to expand the discussion related to Spatial Interfaces and the way they impact interaction with the world in two areas. First, we aim to critically discuss the definition of Spatial Interfaces and outline the common components that build such interfaces in today's world. Second, we would like the community to reflect on the path ahead and focus on the potential of what kind of experiences can Spatial Interfaces achieve today
Scene Retrieval from Multiple Resolution Generated Images Based on Text-to-Image GAN

Text-to-image Generative Adversarial Network (GAN) is a deep learning model that generates an image from an input sentence. It is expressly attracting attentions because of its applicability of the generated images. However, many existing studies have still focused on generation of high-quality images, and there are few studies focusing on application of the generated images since text-to-image GANs still cannot produce visually pleasing images in the complicated tasks. In this paper, we apply a text-to-image GAN as a generator of query images for a scene retrieval task to show availability of the visually non-pleasant images. The proposed method utilizes a low-resolution generated image that focuses on a sentence and a high-resolution generated image that focuses on each word of the sentence to retrieve a desired scene. With this mechanism, the proposed method realizes a high-accuracy scene retrieval from a sentence input. Experimental results show the effectiveness of our method.
Revamp Perception of Bitcoin Using Cognizant Merkle

The bitcoin network adopts the regulation of peer-to-peer network for storing and retrieving data. Data synchronization and consistent verification is important in peer-to-peer networks for proving truth of data. Proof of existence of data is important for many applications and it can be verified by means of searching process. The goal of this paper is to minimize the time of verifying the transaction residence in the shared ledger of bitcoin network. In global ledger management like blockchain, the bitcoin transaction verification and validation is fundamental which is mainly used by miners for providing the proof-of-work for the transactions to achieve block reward and providing trust among the peers. In this paper we have provided a new way of verifying the transaction existence in blockchain by means of altering the data structure of Bitcoin Merkle into a new form of Cognizant Merkle which modifies the structure of existing bitcoin system into a new form which uses less memory and achieve more speed compared to Bitcoin.
MirrorMe

Public transport can be a place where commuters feel rushed or stressed. Missing your train, a delayed bus or crowdedness at the station does not induce happiness among most people. As a consequence, prosocial behaviour like offering someone a seat is displayed less often. We discuss the design and design process of MirrorMe, a simple communal game to induce positive mood of commuters. MirrorMe aims to increase prosocial behaviour through mimicry. Commuters are challenged to "make a face" and thereby connect to other commuters. MirrorMe will be installed in a public display close to a train station enabling access to all commuters and passers-by. This work addresses the need for games and play in public setting to stimulate prosocial behaviour. It exemplifies how multidisciplinary HCI approaches in a gamejam setting can contribute to real life challenges. We conclude with open questions for impact evaluation in future work.
Diagnosis of Common Diseases Using Type-2 Fuzzy System

High level of expertise is required for human disease diagnosis which is a complicated and difficult process. Each disease is characterised with the set of observable sign and symptoms. Based on these symptoms to understand patient health problems and to make a diagnosis of these diseases with their clear definition is difficult. The diagnosis of the disease is based on knowledge of doctor physicians. Fuzzy logic is one of the best approaches to design knowledge-based system for diagnosis of the diseases. In this paper, the design of a type-2 fuzzy system is performed for diagnosis of the common diseases using proper values of the inputs. The input symptoms and output diseases are defined for construction of the fuzzy rule base. The relationships are presented using type-2 IF-Then rules. Based on the fuzzy rules the design of type-2 fuzzy inference system is performed. The designed system will help the physician to diagnose common diseases such as common cold and flu.
Online test and management system using blockchain network

The current online test service only focused on convenience, but did not offer a solution to securing reliability. Also current online test service, which are centralized server storage and management methods, will not be able to maintain such systems in the event of SoF(Single-point of Failure) or physical problems. In this paper, we discuss online test and management system using private blockchain and CP-ABE(Ciphertext-Policy Attribute-Based Encryption, create an `Access tree' based on the user attribute to encrypt the information) algorithm. In the system, the private blockchain network is constructed with the nodes of the system manager, examiners, and examinees, and each node has different authority. Since the data encrypted by the CP-ABE algorithm cannot be decrypted without the corresponding attribute, it is impossible to read or modify the data.
Image Compression-Encryption Scheme Based on 2D DWT, SPIHT and Qi Hyper-chaos

With an ever increase usage of multi-media content, such audio, image and video across networks, image compression and security of content has become almost a need to users who would want to transmit over such networks. Transmitting over such networks, especially with images, may result in some information in an image being lost. For that reason good and efficient encryption methods are needed to store and transmit images while preserving the best imaginable quality features enhanced with security. This paper suggests or develops an efficient image compression-encryption scheme based on Discrete wavelet decomposition, SPIHT and Qi hyperchaos. Pseudo-random numbers generated from Qi hyper chaos and which are in line with the Federal Information Processing Standards (FIPS) and National Institute of Standards and Technology (NIST) were used in this study. The random numbers were used to change the image characteristics and in essence to hide the visual information related to image. To achieve the selective Compression-encryption, the 2-D wavelet transform was used to decompose the image into details and approximation components, Set Partitioning in Hierarchical Tree compression method was used to compress the decomposed LL2 image before being encrypted with the bits stream key generated from the Qi hyper-chaos using exclusive or operation. The experimental results demonstrate that the proposed scheme is good for data compression and encryption.
GPU Parallel Visibility Algorithm for a Set of Segments Using Merge Path

In this paper, we present an efficient parallel algorithm for computing the visibility region for a point in a plane among a non-intersecting set of segments. The algorithm is based on the cascading divide-and-conquer technique and uses merge path to evenly distribute the workload between processors. We implemented the algorithm on NVIDIA's CUDA platform where it performed with a speedup up to 76x with respect to the serial CPU version.
An Effective Metaheuristic for Bi-objective Feature Selection in Two-Class Classification Problem

Feature selection is known as a very useful technique in machine learning practice as it may result in the development of more straightforward models with better accuracy. Traditionally, feature selection is considered as a single-objective problem, however, it can be easily formulated in terms of two objectives. The solving of such problems requires the application of appropriate multi-objective optimization methods that do not always offer equally good solutions even under the same conditions. This paper focuses on the development of a metaheuristic optimization approach for bi-objective feature selection problem in two-class classification. We consider the solving of this problem in terms of minimization of both misclassification error and feature subset size. For solving the considered problem, an adaptation of the Multi-Objective Adaptive Memory Programming (MOAMP) metaheuristic based on the tabu search strategy is proposed. Our MOAMP adaption has been utilized to obtain the sets of most relevant features for two real classification problems with two classes. Finally, using popular Pareto front quality indicators, the obtained results have been compared with the sets of non-dominated solutions derived by the well-known NSGA2 algorithm. The conducted research allows concluding about the ability of the MOAMP adaptation to get a better efficient frontier for the same number of objective function calls.