Reviewerly/exHarmony huggingface dataset:
Assessing the effects of accent-mismatched reference population databases on the performance of an automatic speaker recognition system

Automatic Speaker Recognition (ASR) systems are designed to provide the user with statistics relating to the similarity of two or more speech samples and to the typicality of those shared features in the wider population. When an ASR system is used as part of a forensic investigation, the user must decide what counts as the appropriate ‘wider population’ and select a reference database accordingly. While it has generally been held that the voices populating the reference database should be similar in accent to that of the samples under consideration, the degree to which the accents should correspond has until now not been investigated empirically. We report in this article on a study in which the composition of the reference database was systematically varied in terms of accent, using corpora of samples of Standard Southern British English and of three subvarieties spoken in North-East England (Newcastle, Sunderland, Middlesbrough).
Construction of Information Auditing Curriculum for Auditing Major in Big Data Era

In the era of “Internet+”, the audit work is integrated with the characteristics of information, and the audit work is gradually turning to information audit. Auditing must play its due role. Advanced auditing technology is inseparable. With the advent of the “Internet+” era, Audit Means should be Informationized. Auditors need to master information technology. The training of auditors is the main purpose of Auditing major in universities, the training of auditors can not meet the needs of the information age. Universities needs to conduct research on the training of information-based auditing personnel, change the thinking of talent training, improve the teaching concept and change the teacher structure, and make professional plans to meet the challenges of the “Internet+” era.
Developing hybrid learning models platform based on user experience

Abstract This study aims to develop a hybrid learning model on the brightspace platform to be more user friendly. The hybrid learning model is one of the sophistication of internet-based technology that is a trend today and will be sustainable in the future. Almost every activity requires digital skills. Hybrid learning activities that can be used to discuss using online forum features. However, there is still a lack of hybrid learning models that lack interaction between instructors and students who use social aspects. This expertise arises in the brightspace platform that is used in the hybrid learning activities, therefore, it requires the development of communication features that exist in the activities and resources in the brightspace SPADA application in the form of video conferencing features for instructors and participants. The method in this research is research and development (R&amp;D). Based on the application development offered, the video conferencing feature can answer the problems that have been discussed in the use of the brightspace platform from user experience.
Kalman Filter-Based Noise Reduction Framework for Posture Estimation Using Depth Sensor

Significant benefits can be achieved through the integration of cognitive computing technologies in healthcare delivery services, including physiotherapy. The traditional approach to physiotherapy requires attaching a marker-based tracking device with the patients and conducting analysis to diagnose by physiotherapists and chiropractors. Tracking efficiency of patient rehabilitation frequently depends on the physiotherapist's notes, which is tedious and prone to errors. In order to streamline the process of data collection and record-keeping, and to make more informed decisions on the effectiveness of prescribed therapy, depth sensors can be integrated with current physician practices. This paper is one of the very first attempts to assist physicians through proprietary Kinect sensor-based technologies. The goal is to make sure static posture estimation is highly accurate. Thus, this paper introduces the solution through a noise reduction framework where the Kalman filter and a recursive noise reduction algorithm are combined to improve the accuracy and the consistency of the human 3D skeleton motion data. The Kalman filter is used for the reduction of tremors by abnormal estimation of body joints in real-time using Kinect v2. The posture correction algorithm is incorporated in the proposed framework to reduce anthropometrically inconsistent estimation of limb lengths of the human body. The proposed posture correction algorithm was extensively validated on the proprietary data set.
Facial Landmark Localization Robust on the Eyes with Position Regression Network

Facial landmark localization is essential for robot-human interaction. In particular, the human eye is more important because it can grasp a person's interests. However, the traditional method does not consider eye changes from the dataset, so the limitation is clear, this paper presents a data augmentation method for acquiring various eye images and a method for creating a robust eye landmark model with 2-stage training. Experiments on augmented 300W-LP datasets show that our method outperforms performance than the previous method.
A Study on Image Hashing Techniques for Implementing Deduplication

Image deduplication is an approach to avoid duplicate images to be stored in cloud, which is currently required to increase the efficient utilization of cloud storage. Most of the times, the images uploaded by different users of cloud storage service need not to be unique. There is a possibility of storing same images by different users. This may lead to poor usage of cloud storage. To find duplicates, various image hashing techniques are widely used. The purpose of this paper is to review various image hashing methods which are used to generate hash value of the images. Using the hash value, we can conclude the uniqueness of the image. This can be achieved by using the “collision resistance” property of hash value. To measure the feasibility, the general necessity for image deduplication in cloud is discussed in this work. Additionally, the performance of few selected image hashing methods is compared with respect to robustness and discrimination.
Your Knock Is My Command: Binary Hand Gesture Recognition on Smartphone with Accelerometer

Motion-based hand gesture is an important scheme to allow users to invoke commands on their smartphones in an eyes-free manner. However, the existing scheme is facing some problems. On the one hand, the expression ability of one single gesture is limited. As a result, a gesture set consisting of multiple gestures is typically adopted to represent different commands. Users must memorize all gestures in order to make interaction successfully. On the other hand, the design of gestures needs to be complicated to express diverse intensions. However, complex gestures are difficult to learn and remember. In addition, complex gestures set a high recognition barrier to smart APPs. This leads to an imbalance problem. Different gestures have different recognition accuracy levels, which may result in instability of recognition precision in practical applications. To address these problems, this paper proposes a novel scheme using binary motion gestures. Only two simple gestures are required to express bit “0” and “1,” and rich information can be expressed through the permutation and combination of the two binary gestures. Firstly, four kinds of candidate binary gestures are evaluated for eyes-free interactions. Then, an online signal cutting and merging algorithm is designed to split accelerometer signals sequence into multiple separate gesture signal segments. Next, five algorithms, including Dynamic Time Warping (DTW), Naive Bayes, Decision Tree, Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory (BLSTM) Network, are adopted to recognize these segments of knock gestures. The BLSTM achieves the top performance in terms of both recognition accuracy and recognition imbalance. Finally, an Android application is developed to illustrate the usability of the proposed binary gestures. As binary gestures are much simpler than traditional hand gestures, they are more efficient and user-friendly. Our scheme eliminates the imbalance problem and achieves high recognition accuracy.
HeyLo: Visualizing User Interests from Twitter Using Emoji in Mixed Reality

We tackle the problem of analyzing a user's interests from social media content and subsequently visualizing these interests in an extended reality environment. We compare five models for extracting interests from Twitter users and how we can measure the effectiveness of these models. We also look at how these interest extraction models fit in the context of HeyLo, an extended reality computational creativity (XRCC) framework for visualizing potential conversational topics. The chosen interests for a particular person are visualized using emoji. We accomplish this by using an emoji2vector model to find the closest related emoji to a given interest. We perform a comparative analysis between the five interest extraction models on real-world users and their tweets, evaluating specificity, variance, and relevance.
Deep Neural Matching Models for Graph Retrieval

Graph retrieval from a large corpus of graphs has a wide variety of applications, e.g., sentence retrieval using words and dependency parse trees for question answering, image retrieval using scene graphs, and molecule discovery from a set of existing molecular graphs. In such graph search applications, nodes, edges and associated features bear distinctive physical significance. Therefore, a unified, trainable search model that efficiently returns corpus graphs that are highly relevant to a query graph has immense potential impact. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. We achieve this by constructing the product graph between the query and a candidate graph in the corpus, and then conduct a family of random walks on the product graph, which are then aggregated into the match score, using a network whose parameters can be trained. Experiments show the efficacy of our method, compared to competitive baseline approaches.
MINEO. Use of hyperspectral data for monitoring pollution from the lead-zinc mine, Mestersvig, in Northeast Greenland

Presently seven European countries are studying the opportunities for using hyperspectral data to monitor pollution from mines in different climatic and vegetational regimes. The project is called MINEO and is supported by the EU. The Greenland study site is the former lead/zinc mine in Mestersvig, Northeast Greenland, which was operated 1956–1963. Pollution is well documented and levels of a number of metals in soil and vegetation will be studied and compared with hyperspectral data obtained during an airborne hyperspectral survey in august 2000. Long term effects of pollution on the composition of arctic vegetation will also be studied at the arctic site. It is expected that use of hyperspectral data will give valuable knowledge of the extent of pollution and will be an important tool for planning of monitoring programs.
Pattern Storage and Recalling Analysis of Hopfield Network for Handwritten Odia Characters Using HOG

Auto-associative neural network is a well-known memory model for pattern storage and recall in the field of pattern recognition. In this paper, we present analysis of pattern storage capacity and recall efficiency for handwritten Odia characters with a recurrent auto-associative neural network named Hopfield network using histogram of oriented gradients (HOG) features. Correct and successful recalling of original stored patterns, noisy version of patterns and new patterns (not used in training) of each class are analysed in terms of recalling efficiency. Detail description of our experiment with NIT Rourkela Odia handwritten dataset is demonstrated in this paper, and the state-of-the-art result is remarkable and comparable with other proposed methods.
Calculational relation-algebraic proofs in the teaching tool

The proof checker CALCCHECK has been developed for teaching calculational proofs in the style of Gries and Schneider's textbook classic “A Logical Approach to Discrete Math”. While originally only AC-rewriting was supported, we now added also support for operators that are only associative, which is essential for convenience in reasoning about composition of (in particular) relations. We demonstrate how the CALCCHECK language including this and several other recent improvements supports readable and writeable machine-checked proofs of interesting relation-algebraic developments.
Performance of 2.4 GHz Xbee for Digital Image Transmission with Yagi Antenna

Abstract The use of xbee modules is generally used for sending sensor data. In wireless communication today not only sensor data is sent, but also images that have a higher level of complexity. This study is used to determine the performance of xbee based on the IEEE 802.15.4 protocol for sending image data. Yagi antennas are used to strengthen or support XBee performance when shipping. The stages of the simulation were carried out with the 2016 CST software. The VNA preameter test produced yagi antenna parameters with a working frequency of 2.4 Ghz return loss = -12.27 dB, VSWR = 1.545. The results of sending a vertical image resolution of 160 x 120 obtain an average delivery time of 4.05 seconds, a speed of 678.28 bytes / second and data loss of 29% with the furthest distance of 250 meters. The results of sending vertical images of resolution 320 x 240 obtained an average delivery time of 11.24 seconds, speed of 779.28 bytes / second and data loss of 20% with the furthest distance of 200 meters. The results of sending a horizontal image resolution of 160 x 120 obtain an average delivery time of 4.03 seconds, a speed of 727.15 bytes / second and data loss of 20% with the furthest distance of 450 meters. The result of sending a horizontal image resolution of 160 x 120 obtained an average sending time of 11.19 seconds, a speed of 735.52 bytes / second and data loss of 23.8% with the furthest distance of 400 meters.
Information literacy education in primary schools: A case study

This study focuses on teachers’ perspectives concerning information literacy teaching in two primary schools in Israel—one school that joined the national information and communications technology program and a second school that did not. The researchers used a qualitative research method during the 2015 academic year. Eighteen teachers were interviewed. The findings suggest that participation in the national information and communications technology program did not lead to the integration of information literacy in the school’s curriculum. A significant gap was discovered in both schools between the teachers’ perceptions—who understood the importance of teaching information literacy and its actual implementation.
A Discriminative Convolutional Neural Network with Context-aware Attention

Feature representation and feature extraction are two crucial procedures in text mining. Convolutional Neural Networks (CNN) have shown overwhelming success for text-mining tasks, since they are capable of efficiently extracting n -gram features from source data. However, vanilla CNN has its own weaknesses on feature representation and feature extraction. A certain amount of filters in CNN are inevitably duplicate and thus hinder to discriminatively represent a given text. In addition, most existing CNN models extract features in a fixed way (i.e., max pooling) that either limit the CNN to local optimum nor without considering the relation between all features, thereby unable to learn a contextual n -gram features adaptively. In this article, we propose a discriminative CNN with context-aware attention to solve the challenges of vanilla CNN. Specifically, our model mainly encourages discrimination across different filters via maximizing their earth mover distances and estimates the salience of feature candidates by considering the relation between context features. We validate carefully our findings against baselines on five benchmark datasets of classification and two datasets of summarization. The results of the experiments verify the competitive performance of our proposed model.
Hierarchical Auxiliary Learning

Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal (i.e. no hierarchy) and exclusive of one another (i.e. no overlap). CNN-based image classifiers built on this assumption, therefore, cannot take into account an innate hierarchy among target classes (e.g. cats and dogs in animal image classification) or additional information that can be easily derived from the data (e.g. numbers larger than five in the recognition of handwritten digits), thereby resulting in scalability issues when the number of target classes is large. Combining two related but slightly different ideas of hierarchical classification and logical learning by auxiliary inputs, we propose a new learning framework called hierarchical auxiliary learning, which not only address the scalability issues with a large number of classes but also could further reduce the classification/recognition errors with a reasonable number of classes. In the hierarchical auxiliary learning, target classes are semantically or non-semantically grouped into superclasses, which turns the original problem of mapping between an image and its target class into a new problem of mapping between a pair of an image and its superclass and the target class. To take the advantage of a superclass as a hint during the learning phase, we introduce an auxiliary block into a neural network, which generates auxiliary scores used as additional information for final classification/recognition; in this paper, we add the auxiliary block between the last residual block and the fully-connected output layer of the ResNet. Experimental results show that the proposed hierarchical auxiliary learning reduces classification errors up to 0.56, 1.6 and 3.56 percent with MNIST, SVHN and CIFAR-10 datasets, respectively.
Influence of Reading Habit on Student Academic Performance in a Senior Second School in Ibadan

Secondary school as a gateway for the development of other sectors of the educational system provides primary school leavers with the opportunity for qualitative education, the platform for skills and knowledge for vocational work, or higher institutions admission. In Nigeria, public discussions frequently focus on educational standards. This study aims to examine the influence of students' reading habits on students' academic performance. A descriptive survey design was adopted. The schools in Ibadan North LGA were randomly selected, making a total of five schools. Each of the participants was randomly selected, totaling 300 students. There was a significant positive relationship between student reading habits and student academic performance (r = 0.84). At F=1.31, there is no significant difference in the reading habit of male and female students. Reading habit enhances the academic performance of students. Therefore, students should be encouraged to inculcate in them the reading habit by providing enabling environment and infrastructures that will stimulate students to read.
Police FIR Registration and Tracking Using Consortium Blockchain

India is a developing country, and technology has played a major role in its development. We can see technological advancements in many areas, such as education, business, medical, banking, and agriculture. Unfortunately, the Indian Police Department remains devoid of technology in their systems up to certain extents. Also, with the rapid increase in population, the crime rates have increased. Therefore, it has become a gruelling task to manage these documents manually. The conventional system of visiting a police station for registering a first information report (FIR) or police complaint and getting updates needs to be replaced with a more convenient and transparent system. Hence, we propose a consortium blockchain architecture for the Indian Police Department, where all the police stations will be a part of this network. Consortium blockchain benefits from the privacy of private blockchain while leveraging the decentralized governance of public blockchain. In our proposed system, the client can register their FIR using a decentralized application. Our system ensures its acceptance and timely updates to be delivered to the victims. The scenario of FIR requires a highly secure and trustworthy workflow that has to be fast enough simultaneously.
Designing of Android Based Learning (Mobile Learning) for Multidiscipline Students

A breakthrough in the multimedia field is needed by creating varied learning strategies to attract students' interest in learning. The purpose of this study is to create learning media M-Learning to support the learning process in Vocational High School and to know the level of prevalence M-Learning as a medium of learning. The research method using Research and Development which has eight stages: 1) Identification of Research Needs, 2) Purpose Formulation, 3) Formulation of material items, 4) Formulation of success tools, 5) Media manuscript formulation, 6) Tests or product trials, 7) Revisions, 8) Manuscripts are ready to be produced. This media is developed using Android Studio. The result of this research is a dynamic Android-based application that is run online as a supporter of learning. The results of validator assessment and student response can be obtained from the media expert value at 65% in the sense of valid, acquiring a low score due to a lot of revision of the application. According to the material experts, the media obtains the value of 85% which means valid, and a validity score of 94.7% is obtained from students' responses that mean valid. The conclusion shows that validator assessments of learning media can be used as supporting self-study, especially for multidiscipline students.
Optimization of Extreme Learning Machine Using the Intelligence of Monarch Butterflies for Osteoporosis Diagnosis

The rapid loss of bones may lead to osteoporosis. The diagnosis of osteoporosis at earlier stage may save the life of an individual. This can be done only on accurate predictions. Many algorithms were used for osteoporosis diagnosis, and still, there remains a challenge in accurate prediction. Hence, to gain better accuracy, an efficient algorithm is proposed. The recently proposed meta-heuristic algorithm monarch butterfly optimization is used to optimize extreme learning machine for osteoporosis data classification. The experiments are validated using tenfold cross-validation method. These results are compared and analyzed with similar hybrids that are developed from scratch. It is found that proposed classifier was found to be outstanding in discriminating the osteoporosis dataset.