Reviewerly/exHarmony huggingface dataset:
Guard@Lis: A new Augmented Reality Mobile Application for Birdwatching

The development of this work is aimed at the creation of a birdwatching application, with the purpose of helping to find and identify bird species and encourage the preservation of the habitat and of these species. In order to create a different system of bird observation, augmented reality techniques are used, allowing to present 3D representations referring to the models of some of these birds. The application will work by creating routes along the city of Leiria, having specific points along the route, defined for the observation of certain species. The whole development process of this application was thought as a didactic guide that allows users to be able to identify many different bird species, while allowing the user to show some of the main historical and tourist hotspots of Leiria.
SOM-VLAD Based Feature Aggregation for Face Recognition Using Keypoint Fusion

Over the years, quite a lot of research has been done on using different machine learning techniques for Face Recognition (FR) for identifying the faces of different people. Current FR techniques are still not accurate enough for real world scenarios and pose a lot of problems against varying illumination levels, pose variations, noise and occlusion in the image. Thus, a single keypoint extraction technique may not be suitable for all cases. Hence, in this paper a novel technique is proposed for Keypoint Fusion (KF) obtained by fusing SIFT, SURF and ORB keypoints which is more accurate and suitable for real time application. The paper is also focused on proposing a novel technique of using a Self-Organizing Map (SOM) and Vector of Locally Aggregated Descriptors (VLAD) for image clustering. VLAD is used to extend the SOM’s ability to cluster keypoint descriptors. Image classification is carried out using a SGD (Stochastic Gradient Descent) based SVM (Support Vector Machine) classifier. The performance of classification of the proposed framework on benchmark datasets (Grimace, Faces95 and Faces96) has been tabulated and compared with other standard techniques. It is seen that the proposed framework performs better than the BOW (Bag Of Words) model and the KF technique was accurate and quick enough to beat the traditional keypoint extraction techniques.
Improving pedestrian detection using convolutional neural network and saliency detection

Convolutional neural networks have achieved excellent results in pedestrian detection state of the art. They are able to learn features from raw images which makes them easy, practical and robust for multiple visual classification tasks. In this paper, we propose a further improvement of convolutional neural networks using saliency detection. First, we use contourlet transform for saliency detection to generate a region of interest (ROI). The generated saliency maps are then used to feed the convolutional network which will be used for both feature extraction and classification. The paper contribution is two fold : (1) We use saliency detection as a filter to remove the noisy information in the background, which allow the network to converge faster during the training process. (2) Saliency reduced complexity of the road scene which improve significantly the CNN classification performance. Experiments conducted on INRIA and Pascal VOC datasets achieves state-of-the-art performance.
Contextual Evaluation of Digital Media Through Experience-Focused Participatory Bodystorming in a Full-Scale Spatial Prototype

Distributed digital media and services are becoming more integrated to the spatial experience. This experience of the digital-hybrid needs a tangible method to asses its spatial legibility, digital interaction, and user experience. We propose a two-phased body-storming design and testing, to evaluate a novel concept of the Learning Branch for the financial industry, a flexible space that supports learning of digital financial services. The body storming exercises in both phases occurred within a full-scale, low-fidelity spatial prototype, where proposed interior environment was simulated with lightweight foam construction. The prototyped space was overlaid with digital experiences by introducing mobile apps and interactive media, from touch screens to visual projections. Final layer of service experience was added during the workshop sessions through enactment of service scenarios to test interactions between potential users and employees. The two-phase testing involved over 130 employees in the corporate bank, from various departments. Overall, the full-scale bodystorming provided an opportunity to have a ‘living-lab’ where new services could be evaluated through physical simulation, perform in-situ iteration and brainstorming of ideas, as well as uncover related issues and values related to HCI, such as privacy, in/exclusivity, complexity, acceptability, and implement-ability.
Identifying idealised vectors for emotion detection using CMA-ES

Detecting the emotional content of text is one of the most popular NLP tasks. In this paper, we propose a new methodology based on identifying "idealised" words that capture the essence of an emotion; we define these words as having the minimal distance (using some metric function) between a word and the text that contains the relevant emotion (e.g. a tweet, a sentence). We look for these words through searching the space of word embeddings using CMA-ES. Our method produces state of the art results, surpassing classic supervised learning methods.
Prototyping User Interfaces for Mobile Augmented Reality Applications

This paper presents a case study of the development and evaluation of user interfaces for mobile augmented reality applications. Two augmented reality applications were developed for Apple iPad using Unity and Apple’s ARKit software. The applications were developed to demonstrate augmented reality capabilities on mobile devices to students, industry, and potential sponsors. During development and evaluation of the demonstration applications, world-anchored floating 3D text for presentation of information was often off-screen and difficult to read. Pairing 2D screen-anchored text with 3D augmented reality objects improved readability but at the cost of reduced immersion.
A deep learning system that learns a discriminative model autonomously using difference images

In our proposed method, an object can be detected from time-series images taken by two or a few cameras. When one of the cameras detects that the object has moved, a system locates that object from the images taken by the other camera(s) by using the timestamps. Then, a position data of that object can be obtained autonomously without taking a lot of photographs of that object and teaching data to the system in advance. Moreover, the system passes the position data and a label of that object to YOLO, which is a learning model for discriminating objects. YOLO learns the data and become possible to indicate the label of the object. The results of an experiment showed that this system could discriminate the moving object only by two cameras without the previously prepared teaching data.1
Evolving music with emotional feedback

Computer based music generation (synthesis) has a rich history spanning several decades [1]. Current music evolution methods are interactive (periodic user evaluation to drive evolutionary selection), or otherwise feature-based where specific musical feature metrics are incorporated into the fitness function for synthesized music evaluation and selection [2]. In the former case, various musical styles and compositions have been evolved to suit user preferences, though evolved composition diversity and complexity are limited by user fatigue and the fitness function (for example, what musical features the user evaluates). In the latter case, evolved music diversity and complexity is similarly limited by fitness function metrics. Thus, metrics conforming to specific musical styles or genres will only result in the artificial evolution of musical compositions that resemble such styles or genres [1], [2].
Application of estimation of distribution algorithm for feature selection

Feature selection is a machine learning concept that entails selecting relevant features while eliminating irrelevant and redundant features. This process helps to speed up learning. In this paper, an Estimation of Distribution Algorithm (EDA) is applied to a feature selection problem originating from a legal business. The EDA was able to generate a realistic solution to the real-world problem.
Walking in the Head: Methods of Sonic Augmented Reality Navigation

This paper describes an experimental method for the navigation of immersive sonic augmented reality experiences. The focus is on tight integration of the audio experience with the real environment in conjunction with high quality immersion in both the real space as well as the augmented information presented throughout the experience. Drawing from lessons learned from two projects that present narrative content pertaining to the historic layers of two places in the form of location-specific immersive audio experiences embedded in the current, real environment, we discuss techniques of sonic immersion and auditory spatial navigation strategies. With custom-developed headsets the realization and performance of bone-conducted spatial sound is explored and design considerations for such experiences are formulated.
Research on the character recognition of Tibetan ancient document based on CNN

Tibetan ancient documents are treasures among the World Cultural Treasure, which is a valuable historical material for studying Tibetan politics, economy, culture and religion. At present, the research on Tibetan character recognition mainly focuses on printed and handwritten Tibetan, while the research on Tibetan ancient document character recognition lags behind. This paper presents a method of recognizing 562 common characters in Tibetan ancient document based on CNN, and the experimental results show that the recognition rate of this method is 99.85%, which proves the effectiveness of this method.
Finite Model Property for Modal Ideal Paraconsistent Four-Valued Logic

A modal extension M4CC of Arieli, Avron, and Zamansky's ideal paraconsistent four-valued logic 4CC is introduced as a Gentzen-type sequent calculus. The completeness theorem with respect to a Kripke semantics for M4CC is proved. The finite model property for M4CC is shown by modifying the completeness proof. The decidability of M4CC is obtained as a corollary.
Hybrid Framework for Web Services on Android Mobile Host

Growth in Mobile hardware manufacturing and Mobile data services, increases the use of mobile phones in day to day activities for personal work or business transactions.Increased use of mobile phones and mobile connectivity with web services is the new domain in information systems research.The smart phone is a true ubiquitous computing device provides a wide range of services to any time and any place.This paper introduces the novel framework for provisioning web services for smart phones with special focus on access web services through SMS messaging service as well as the HTTP protocol at our mobile Host.
Automatic Whitelist Generation for SQL Queries Using Web Application Tests

Stealing confidential information from a database has become a severe vulnerability issue for web applications. The attacks can be prevented by defining a whitelist of SQL queries issued by web applications and detecting queries not in list. For large-scale web applications, automated generation of the whitelist is conducted because manually defining numerous query patterns is impractical for developers. Conventional methods for automated generation are unable to detect attacks immediately because of the long time required for collecting legitimate queries. Moreover, they require application-specific implementations that reduce the versatility of the methods. As described herein, we propose a method to generate a whitelist automatically using queries issued during web application tests. Our proposed method uses the queries generated during application tests. It is independent of specific applications, which yields improved timeliness against attacks and versatility for multiple applications.
Jaya Algorithm Based Intelligent Color Reduction

The purpose of color quantization is to reduce colors in an image with the least parody. Clustering is a popularly used method for color quantization. Color image quantization is an essential action in several applications of computer graphics and image processing. Most of the quantization techniques are mainly based on data clustering algorithms. In this paper, a color reduction hybrid algorithm is proposed by applying Jaya algorithm for clustering. We examine the act of Jaya algorithm in the pre-clustering stage and K-means in the post-clustering phase, and the limitations of both the algorithms are overcome by their combination. The algorithms are compared by MSE and PSNR values of the four images. The MSE values are lower and PSNR values are higher for the proposed algorithm. The results explain that the proposed algorithm is surpassed both the K-means clustering and Jaya algorithm clustering for color reduction method.
Requirements Imprecision of Data Warehouse Design Fuzzy Ontology-Based Approach - Fuzzy Connector Case

Imprecision in decision systems can negatively affect the data warehouse (DW) quality during a bad interpretation case. In order to evaluate the imprecision expression in decisional requirements and differently to our previous paper, we present an ontological solution using our GLMR ontology model for fuzzy connector evaluation in a query-based requirement. For simplification reasons, we present in this paper only “and if possible” fuzzy connector case. We will propose a new solution combining two recent existent solutions. Although the fuzzy connector assessment was already treated but, according to our knowledge, never in the decision need context and proposing an ontological solution. The preliminary tests of our ontological solution are encouraging.
The examination of electronic signatures

Humans have used handwriting as a means of recording their thoughts for more than 4000 years. During that time, we have evolved from scratching cuneiform symbols on clay tablets to “texting” our friends. In more recent times, writing instruments have gone from bird quills, to steel-nibbed pens, to ballpoint and liquid ink pens, to a stylus on electronic signature tablets. Forensic handwriting identification techniques that evolved in the late 19th and early 20th centuries relied on characteristics of the nibbed pens of that era to add “pressure” information to the writing line. The resulting placement and quality of the “shading” in the writing line was a good indication of the speed and potential genuineness or nongenuineness of a writing.
Solving optimization problems with high conditioning by means of online whitening

Real-world optimization problems often have expensive objective functions in terms of cost and time. It is desirable to find near-optimal solutions with very few function evaluations. Surrogate-assisted optimizers tend to reduce the required number of function evaluations by replacing the real function with an efficient mathematical model built on few evaluated points. Problems with a high condition number are a challenge for many surrogate-assisted optimizers including SACOBRA. To address such problems we propose a new online whitening operating in the black-box optimization paradigm. We show on a set of high-conditioning functions that online whitening tackles SACOBRA's early stagnation issue and reduces the optimization error by a factor between 10 to 1012 as compared to the plain SACOBRA, though it imposes many extra function evaluations. Covariance matrix adaptation evolution strategy (CMA-ES) has for very high numbers of function evaluations even lower errors, whereas SACOBRA performs better in the expensive setting (≤ 103 function evaluations). If we count all parallelizable function evaluations (population evaluation in CMA-ES, online whitening in our approach) as one iteration, then both algorithms have comparable strength even on the long run. This holds for problems with dimension D ≤ 20.
Manual and Semi-manual Comparison in the Annotation of CAPT Speech Corpus

Abstract Annotation plays an important role in a robust computer aided pronunciation training (CAPT) system. Manual annotation is time and annotator consuming. Automatic annotation is more efficient, but the accuracy is lower. This paper proposes to provide phoneme level labelling candidates with ASR models, and compare this method with manual annotation. In the previous work, phoneme level mispronunciation patterns were modelled and detected to provide readable pronunciation erroneous tendencies (PETs) feedback. In this paper, it is proposed to use this model to provide phoneme level labelling candidates, then annotators could choose the appropriate labels and make final decision. Experimental results show that compared with manual annotation, the consistency rate of semi-manual annotation increased from 88.52% to 92.88%. In addition, the false positive rate (FPR) reduced by 3%, the posterior F1 score declined more than 3%. The reported results demonstrated the efficiency of the proposed method.
A Group Based Decision-making Mechanism for Speaker Verification

Speaker recognition has been a popular research topic in pattern recognition field. Speaker verification is an important procedure in speaker recognition. It is a process to accept or reject the identity claim of a speaker. How to make a decision is a critical problem in speaker verification system. This paper introduces a group-based mechanism which performs speaker verification system with Joint Factor Analysis (JFA). In this method, we first finds a similar speaker group for each test utterance, and then implements speaker verification. This paper also proposes a new efficient threshold setting method on the use of silhouettes. We carry out our comparison experiment on the TIMIT corpus and the experimental results show that compared with the traditional method, the overall recognition performance of the open-set speaker recognition system based on the new decision mechanism is improved.
Discovering New Sensitive Words Based on Sensitive Information Categorization

Sensitive word detection has popped out nowadays as the prosperity of internet technologies emerges. At the same time, some internet users diffuse sensitive contents which contains unhealthy information. But how to improve sensitive information classification accuracy and find new sensitive words has been an urgent demand in the network information security. On the one hand, the sensitive information classification result inaccurate, on the other hand, all the research methods can not find the new sensitive information, in other word, it does not automatically identify new sensitive information. We mainly improved the existing outstanding machine learning classification algorithm, experimental results show that this method can significantly improve the classification accuracy. Beside, by researching word similarity algorithm base on HowNet and CiLin, we can realize expanding the database of sensitive words continually (i.e., discovery the new sensitive word). Through the methodologies mentioned above, we have got a better accuracy and realized new sensitive word discovery technology which will be analyzed and presented in the paper.
Semantic fitness function in genetic programming based on semantics flow analysis

The search performance of conventional Genetic Programming (GP) methods is strongly guided by the performance of the fitness function. In each generation, the fitness function evaluates every program in the population and measures the distance between the final output of the programs and the desired output. Human programmers often rely on the feedback from the intermediate execution states, which are the semantics, to localize and resolve software bugs. However, the semantics of a program is seldom explicitly considered in the fitness function to assess the quality of a program in GP. In this paper, we invent methods to improve fitness evaluation leveraging semantics in GP. We propose semantics flow analysis for programs using information theoretic concepts. Next, we develop a novel semantic fitness evaluation technique to rank programs using semantics based on the semantics flow analysis. Our evaluation results show that adopting our method can improve the success rates in Grammar-Based GP.
Examining Elements of an Adaptive Instructional System (AIS) Conceptual Model

This paper examines the components, functions, and interactions of adaptive instructional systems (AISs) as a method to construct a conceptual model for use in the development of IEEE standards. AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each individual learner or team in the context of domain learning objectives. IEEE is exploring standards and best practices for AIS modeling, interoperability, and evaluation under its Project 2247 and affiliated working group. This paper was composed to document the interaction of learners with AISs in the context of a domain of instruction. The goal is to identify key interactions within AISs that drive instructional decisions, and to identify the data and methods required to support those machine-based instructions. In other words, we seek to identify methods to assess learner/team progress toward instructional objectives (e.g., knowledge, acquisition, skill development, performance, retention, and transfer of skills from instruction to operational/working environments. As part of the examination of AIS elements, we review a set of popular AIS architectures as a method of identifying what makes AISs unique from other instructional technologies. We conclude with recommendations for future AIS research and standards development.
Load Balancing in Cloud Through Multi Objective Optimization

The Scheduling and Load balancing in cloud is considered as NP complete problem where the tasks are assigned to the cloud are dynamic in nature so the heuristic approach can be followed to find the solution. Load balancing directly affects the reliability, response time, through put and energy efficiency of a server. The optimized solution for load balancing should consider various objectives like minimizing energy consumption and minimum execution time so that reduced cost. Balancing the load across cloud servers is possible through virtual machine (VM) migration from overloaded servers to under loaded servers conditionally. Even migration of VMs from under loaded servers may take place in cloud to release the under loaded servers and make them free so that the energy consumption can be improved.
The (1 + 1)-EA with mutation rate (1 + ϵ)/ <i>n</i> is efficient on monotone functions

An important benchmark for evolutionary algorithms are (strictly) monotone functions. For the (1 + 1)-EA with mutation rate c/n, it was known that it can optimize any monotone function on n bits in time O(n log n) if c < 1. However, it was also known that there are monotone functions on which the (1 + 1)-EA needs exponential time if c < 2.2. For c = 1 it was known that the runtime is always polynomial, but it was unclear whether it is quasilinear, and it was unclear whether c = 1 is the threshold at which the runtime jumps from polynomial to superpolynomial.