Reviewerly/exHarmony huggingface dataset:
Solution method for ill-conditioned problems based on a new improved fruit fly optimization algorithm

Abstract Based on deeply analysis for optimization process of basic fruit fly optimization algorithm (FOA), a new improved FOA (IFOA) method is proposed, which modifies random search direction, increases the adjustment coefficient of search radius, and establishes a multi-sub-population solution mechanism. The proposed method can process the nonlinear objective function that has non-zero and non-negative extreme points. In the paper, IFOA method is applied to ill-conditioned problem solution in the field of surveying data processing. Application of the proposed method on two practical examples show that solution accuracy of IFOA is superior to that of three well-known intelligent optimization algorithms and two existing improved FOA methods, and it is also better than truncated singular value decomposition method and ridge estimation method. In addition, compared with intelligent search method represented by particle swarm optimization algorithm, The IFOA method has the advantages of less parameter settings, simple optimization process and easy program implementation. So, IFOA method is feasible, effective and practical in solving ill-conditioned problems.
Design and implementation for a fast reaction testing and training system to fight sports based on real data acqusition

In order to master the training state and the characteristics of fighting athletes, it need to obtain a lot of real-time data. By analyzing the characteristics of the test and training process of fighting sports, a defensive and offensive model of sport response test and training is constructed, wh ich is easy to monitor and collect the human fighting sports data, aiming at the special needs for the fast response and data accquisition of sport training. Based on this model, the basic requirements of fast response training and testing system are summed up, and a four-layer architecture design scheme is put forward by using software engineering technology, which is realized by computer hardware and software technology combined with real-time data acquisition and interface design. Finally, the practical verification is carried out by the application of the system, and the visual expression is given by the graph, and the design and implement can meet the requirement of the task.
Automatic perceptual judgment using neural networks

The aim of the present study is to investigate the performance of automatic perceptual judgment models built with neural networks. In previous studies, Franco et al. (1997) used HMM-derived scores based on posterior probabilities of phone segments, demonstrating a high correlation with human raters. Deville et al. (1999) also used an HMM/ANN recognition approach, and showed how the results of automatic speech recognition can be used for perceptual judgments. However, since most previous studies made use of automatic speech recognition in their analysis, the present study provides a different approach: using features and raw data. Native speakers of English will listen to English sentences produced by native and non-native speakers of English, transcribe what they heard, and respond to one of three perceptual judgements: foreign-accentedness, fluency, and comprehensibility. The data will be fed into prediction models in three different ways; one with annotated features (pauses, durations, etc), another with Mel Frequency Cepstral Coefficients (MFCC), and the other with Mel-spectrograms. The performance of the models will be measured by analyzing the correlation between the judgments by models and by human raters. The preliminary results of this study will be used to build more accurate automatic proficiency judgment models.
What if Compulsory Schooling was a 21st Century Invention?

The digital technological revolution offers new ways for classrooms to operate and challenges the concept of whether brick and mortar schools should exist at all. At the same time, the changes to society as we move from a knowledge-based economy to an intelligent and innovation-based economy challenges us to reassess the purpose of education. This chapter investigates an overarching counterfactual question, “What if compulsory schooling was invented in the twenty-first century”? We used a foresight methodology, based on “anticipation,” to conceptualize possible models for a future system of compulsory schooling arising from an analysis of contemporary catalysts for remodeling. While anticipation does not predict the future, the concept is that when a current system and a model of a system interplay, they impact each other to change both the present as well as possible futures. The design principles of cities, such as Freiburg (Germany), Poundbury (England), and Christie Walk (Australia), which have been developed around the idea of ecologically sustainable and decentralized cities, are focused on approaches to living that can provide a springboard for exploring the impact of changing employment, economic, technological, and social change on future schooling models. Magnetic Resonance Imaging (MRI) has opened up a new field of study to investigate neuroscience, which can inform teaching practice. Postmodern and indigenous ways of thinking provide different insights about how schooling might be reconceptualized. Alternative models of future schooling are conceptualized about (i) the role of the learner and teacher, (ii) design of a school, and (iii) the purpose of compulsory schooling. For each area of remodeling, deviations to current practices as well as paradigm shifts are framed as part of scenario building. Related questions include: how schooling might be different if it had been created today for the first time? How might it better meet the needs of contemporary society? What aspects of schooling now might be lost if it was only invented in the twenty-first century? What are possible side effects from any change ideas as part of research practice? A vital aspect of this chapter is to explore the concept of learning as a general concept versus the more specific concept of schooling. We are at the precipice of a new vision of schooling based on a counterfactual way of thinking about the future of schooling as we have known it in the West.
A Novel Algorithm of Radar Emitter Identification Based Convolutional Neural Network and Random Vector Functional-Link

Conventional radar emitter identification methods are faced with intensive noise and complex electromagnetic environment, which degrades the performance. Aiming at the feature extraction and identification at low signal-to-noise ratio (SNR), an improved algorithm is developed for the analysis and identification. First, the approach forms the time-frequency distribution and preprocesses the 2-dimensional image with whitening and normalization operations. Then, convolutional neural network (CNN) is introduced to generate high-level and abstract representations. Next, random vector functional link (RVFL) is trained to promote the fast feature learning. Finally, identification is implemented by picking out the maximum of RVFL output. 6 types of simulated emitters are used to validate the proposed algorithm. Experimental results show that the selected types of radar waveforms with additive Gaussian white noise can reach 85% accuracy when SNR up to −2 dB. Compared with the state-of-the-art techniques in feature extraction and identification, the proposed algorithm outperforms in the robustness and comprehensive performance.
Assessments of cancer-free lymph nodes for the prediction of disease progression

Abstract Background Histological changes in the cancer-free lymph nodes (LNs) can add to the risk prediction of developing distant metastasis for LN-positive breast cancer patients. As Whole Slide Images (WSI) demonstrated suitability for detection of LN metastasis with high accuracy based on Convolutional Neural Networks (CNN), we extended the assessments to cancer-free LNs to determine whether early signs of disease progression could be identified. Methods Clinico-pathological data, including level of LNs in the axilla, were available for 143 LN-positive breast cancer patients. All resected LNs were digitised (×40 magnification), totalling to 1,054 WSI. CNN models were based on standard ResNet architecture, and trained using 224x224 sized WSI patches, to classify germinal centres (GC), follicles, sinus histiocytosis, and adipose tissue as defined by 2 independent pathologists. To increase the robustness of the CNN, training data was augmented using image rotation and colour perturbation. A weighted cross entropy was used as a training loss function to account for the imbalance in the four types of patches. Confusion matrix and one-vs-all AUC scores were computed on WSIs patches extracted from held-out test patients to assess the discriminative power of the trained CNN models. Multivariate and cross-validated L2-regularised proportional hazard regression analyses were used for outcome prediction. Results An increased number and smaller sized GCs in cancer-free LNs, the prevalence of metastatic LNs in the low axilla, the location of GCs in metastatic LNs, and lymphocytic infiltration at the tumour invasive front were all indicative of developing distant metastasis in LN-positive breast patients. Metastatic LNs were found exclusively in the low axilla in 77% of patients. Amongst the 913 H&E WSI of cancer-free LNs, 21% presented with GC. Patch level AUC scores of detecting each type of tissue region ranged between 0.82 to 0.94 on held-out patients quantifying the effectiveness of CNN to be used as topological feature detector. Conclusions Addition planned WSI analyses of large breast cancer cohorts will expand on these data, capturing histological features in LNs, with the aim of identifying high-risk and low-risk disease, and aiding in pathological prognostication. Legal entity responsible for the study The authors. Funding Breast Cancer Research Trust, Breast Cancer Now, CRUK. Disclosure All authors have declared no conflicts of interest.
Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition.

Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, including (a) inability to use uncertainty sampling with black-box models, (b) lack of robustness to labeling noise, and (c) lack of transparency. In response, we propose a transparent batch active sampling framework by estimating the error decay curves of multiple feature-defined subsets of the data. Experiments on four named entity recognition (NER) tasks demonstrate that the proposed methods significantly outperform diversification-based methods for black-box NER taggers, and can make the sampling process more robust to labeling noise when combined with uncertainty-based methods. Furthermore, the analysis of experimental results sheds light on the weaknesses of different active sampling strategies, and when traditional uncertainty-based or diversification-based methods can be expected to work well.
CG Animation Creator: Auto-rendering of Motion Stick Figure Based on Conditional Adversarial Learning

As an important part of animation production, the existing method for drawing and rendering the CG animated characters according to motion information mostly relays on expensive manual processing. By adopting the conditional adversarial learning, an automatic animation rendering system for geometry structure attribute is proposed, using a deep convolution generative adversarial network called “pix2pixHD”. A training database containing a variety of motion stick figure is established for different virtual characters to achieve an end-to-end training system to verify this idea. The trained generator is the desired CG animation creator which shows great performance on visual quality and time efficiency proved by the experimental results.
Optimizing Convolutional Neural Network Architecture Using a Self-adaptive Harmony Search Algorithm

In recent years, the advance of GPUs led to the development in neural networks and deep learning. However, it is difficult to find a good CNN architecture people desire. In the past, people had to manually find the CNN architecture, and this is quite time-consuming and labor-intensive. In this paper, we use a self-adaptive harmony search algorithm to find the optimized convolutional neural network architecture for image recognition. The system architecture is divided into two phases. In the first phase, we search for the most suitable layer length of a CNN. In the second phase, we fine-tune the architecture found in the first phase or a pre-trained architecture. In the experiments, three popular and well-known datasets are used to evaluate the proposed methods and the state-of-the-art CNN search methods. The experimental results show that our methods achieve competitive performances compared with the other methods on CIFAR-10, and have the best performance among all the methods on MNIST and Caltech-101.
Cycle Generative Adversarial Network for Unpaired Sketch-to-Character Translation

This research investigated the capabilities of Cycle-Consistent adversarial network (Cycle GAN) in the application of stick figure sketches to cartoon character translation. Few studies focused on the task of generating a variety of poses and facial expression of cartoon characters from simple sketches of stick figures, based on unpaired dataset samples. Furthermore, existing studies showed low performance in detecting rare pose features. In this research, two datasets have been created which consists of paired and unpaired images of manually drawn sketches and cartoon characters. The performance of Cycle GAN has been compared against a paired based model, Pix2Pix, by using qualitative and quantitative evaluation measurements. Results show that Pix2Pix outperforms Cycle GAN in accurately mapping cartoon characters to stick figures. Despite that, the Cycle GAN still managed to produce competing results.
Fully in Tensor Computation Manner: A One-Shot Dense 3D Acquisition Method using Structured Light

Tensor computation evolves fast towards a prosperous platform in recent years e.g. PyTorch. An immediate advantage of using tensor computation is that one does not need to implement low-level parallelism to attain efficient computation, which is of simplicity for both research and application development. We discovered that a simple maneuver “tensor shift” could perform neighborhood manipulation in very efficient parallel manner. Based on “tensor shift”, we derive the tensor-version of a renowned correspondence search algorithm: Semi-Global Matching (SGM), which we prefix the name as tensor-SGM (tSGM). To evaluate our idea, we build-up a novel and practical one-shot structured light 3D acquisition system, which yields accurate and dense (per-pixel) reconstruction results using off-the-shelf hardware. This is the first fully tensorised 3D reconstruction system published to our best knowledge and it first tries to migrate a time-certified computer vision algorithm into tensor-computation. We consider that we are rendering real efforts to the community by advancing 3D reconstruction towards tensor computation era.
CP-ABE Based Mobile Cloud Computing Applicatıon for Secure Data Sharing

Mobile cloud computing becomes more popular in recent years because of the availability of the internet and smartphones. Maintaining security in mobile clouds is the most challenging issue because mobile devices are having limited computational resources and most of the operations are done through the internet. Therefore, it is necessary to safeguard data access from unauthorized access. In this paper Ciphertext based Attribute-Based Encryption is implemented. It offers better data security and privacy using efficient access control and privilege management. We have measured the performance of Key Policy Based-ABE and Cipher Text Policy Based-ABE, results show that Cipher Text Policy-Based ABE takes less time to perform encryption, key generation, decryption and offers better security compared to KP-ABE.
Radically Relational Tools: A Design Framework to Explore Materials through Embodied Processes

In a context where fashion design and retail activities are increasingly transferring to the digital environment, mediating touch has become a matter of concern for the field. However, as literature shows, articulating our sensory experiences with materials is a challenging task. This position paper explores the experiential knowledge observed through embodied design processes. This is done by analysing our diverse approaches to introduce tools that help designers to understand people’s perceptual experience with textile materials. We build on our previous research that identified relevant embodied processes to textile selection, and reflect on how we have explored how sensing technology can augment and empower each of these processes, thereby supporting design. We conclude by discussing the learning outcomes of analysing such tools, in order to reflect on the future of our research applied to the digital realm.
Fearless steps, NASA’s first heroes:<b> </b>Conversational speech analysis of the Apollo-11 mission control personnel

Between 1963 and 1972, a massive team of dedicated scientists, engineers, and specialists at the NASA Mission Control Center (MCC) worked seamlessly together in a cohesive manner to successfully carry out multiple manned missions to the moon. All communications between personnel were carried out over multiple inter-connected audio channels and recorded on two 30-track analog tapes. Digitization of the entire Apollo-11 mission tapes made possible through the UTDallas-CRSS Fearless Steps (FS) initiative contains the recordings of all the crew members including the three astronauts. With over 600 speakers in constant communication ensuring the astronauts’ safety throughout the mission, the success of the Apollo missions can be attributed largely to the MCC crew members. The focus of this effort is thus, to analyze their high-level group dynamics and intricate communication characteristics and gain insight into the success parameters involved in such large-scale time-critical operations. The 100 hour FS Challenge corpus highlights multiple salient moments like Lift-Off and Lunar-Landing and poses its’ own challenges for core speech tasks. This effort aims at analyzing spontaneous multi-speaker conversations in recordings often degraded by various noise types. Analysis of speech characteristics under varying stress, overlap, and noise conditions are observed to develop novel speech-activity and speaker models. These domain-specific models are leveraged to develop state-of-the-art SLT systems.
From Unstructured Text to TextCube

The real-world big data are largely unstructured, interconnected, and dynamic, in the form of natural language text. It is highly desirable to transform such massive unstructured data into structured knowledge. Many researchers rely on labor-intensive labeling and curation to extract knowledge from such data, which may not be scalable, especially considering that a lot of text corpora are highly dynamic and domain specific. We believe that massive text data itself may disclose a large body of hidden patterns, structures, and knowledge. With domain-independent and domain-dependent knowledge bases, we propose to explore the power of massive data itself for turning unstructured data into structured knowledge. By organizing massive text documents into multidimensional text cubes, we show structured knowledge can be extracted and used effectively. In this talk, we introduce a set of methods developed recently in our group for such an exploration, including mining quality phrases, entity recognition and typing, multi-faceted taxonomy construction, and construction and exploration of multi-dimensional text cubes. We show that data-driven approach could be a promising direction at transforming massive text data into structured knowledge.
Design considerations for rotation scanning projection moiré topography

Projection moire topography is an optical technique for three-dimensional surface reconstruction based on the geometric interference between two optical grids. To expand the measurement area and to maintain a high measurement resolution, a rotation scanning projection moire profilometry technique is developed here. We present the mathematical description of an arbitrarily arranged projection moire model, and derive the expression for phase and coordinates. On this basis, we establish the relation between the system parameters and rotation parameters, making it possible to calculate the system parameters and perform surface reconstruction for any rotation angle, using the same projection moire model.
Capturing Outlines of Planar Generic Images by Simultaneous Curve Fitting and Sub-division

In this paper, a new technique has been designed to capture the outline of 2D shapes using cubic B´ezier curves. The proposed technique avoids the traditional method of optimizing the global squared fitting error and emphasizes the local control of data points. A maximum error has been determined to preserve the absolute fitting error less than a criterion and it administers the process of curve subdivision. Depending on the specified maximum error, the proposed technique itself subdivides complex segments, and curve fitting is done simultaneously. A comparative study of experimental results embosses various advantages of the proposed technique such as accurate representation, low approximation errors and efficient computational complexity.
An Efficient Algorithm to Mine High Average-Utility Sequential Patterns

Mining high-utility sequential patterns (HUSP) has attracted increasing attention in recent years. Current studies of HUSP mining, however, don’t consider the length of sequential patterns, which result in the problem of a huge amount of longer sequential patterns in the mining results. In order to obtain more decision-making information, this paper introduces an average utility measure and proposes HAUSPM method to efficiently mine high average-utility sequential patterns (HAUSP) by considering the length of sequential patterns. Extensive experiments show that the total number of HAUSP mined by HAUSPM is fewer than HUSP under the same minimum utility.
Sosialisasi Gerakan Literasi Dini Kepada Guru TK dan PAUD Makanatut Thalibin di Kecamatan Aluh-aluh

ABSTRACT: Community service is aimed to socialize the early literacy movement for teachers and parents of PAUD I students in Pulantan Village, Aluh-Aluh District. This dedication was held on Thursday, March 22, 2018 in PAUD Makanatut Thalibin. The target of this activity is PAUD teachers and parents of students. The reason for implementing this service is a school that is far from the city center, a low level of education from parents of students so that the planting of early literacy to their children is often unthinkable to them, even the planting of early literacy they submit to school. The method in this socialization uses the lecture method, discussion and question and answer, to provide knowledge on the importance of early literacy in children. After this implementation it can be concluded that the awareness of students' parents the importance of instilling a culture of early literacy for children, while for PAUD teachers Makanatut Thalibin they understand that many methods of teaching early literacy are fun for students.