Reviewerly/exHarmony huggingface dataset:
Deep Generative Models for Synthetic Data

Growing interest in synthetic data has stimulated development and advancement of a large variety of deep generative models for a wide range of applications. However, as this research has progressed, its streams have become more specialized and disconnected from each other. For example, models for synthesizing text data for natural language processing cannot readily be compared to models for synthesizing health records. To mitigate this isolation, we propose a data-driven evaluation framework for generative models for synthetic data based on five high-level criteria: representativeness, novelty, realism, diversity and coherence of a synthetic data sample relative to the original data-set regardless of the models' internal structures. The criteria reflect requirements different domains impose on synthetic data and allow model users to assess the quality of synthetic data across models. In a critical review of generative models for sequential data, we examine and compare the importance of each performance criterion in numerous domains. For example, we find that realism and coherence are more important for synthetic data for natural language, speech and audio processing, while novelty and representativeness are more important for healthcare and mobility data. We also find that measurement of representativeness is often accomplished using statistical metrics, realism by using human judgement, and novelty using privacy tests.
An Active Appearance Model based Face Recognition from Surveillance Video

For the past few years, the Face Recognition (FR) is becoming active research area in the field of Face Identification and verification. The Deep convolutional neural networks (CNNs) are extensively becoming popular in the field of FR in recent years but works with labelled datasets containing very large number of training samples. It is also difficult task to collect large number of Face images for training the model. Decision tree (DT) and-Nearest Neighbour (K-NN) performs better when the training set is small and computationally expensive when the training samples increases. In order to overcome the problem of low recognition and high computation complexity of Face Recognition (FR) space, this research paper proposes a Support Vector Machine (SVM) based FR to recognize the faces from video frames and still images. During the SVM training, the   parameters  are optimized with particle swarm optimization (PSO) technique , which enhances the FR rate.  In this method initially, the noise is eliminated from the probe image using Adaptive median Filter (AMF) and then the feature vector is generated using the combination of Active Appearance Model(AAM) and the shape model.  The recognition performance is analyzed on UPC Video Database, YouTube Face Database(YTF),ORL database and Yale B face sets . The main application of this research is to identify Faces from Poor quality surveillance video as well as still images. Keywords:Face Recognition, Support Vector Machine, Adaptive Median Filter, Particle Swam Optimization
Analysis on the Path of Talent Cultivation in Higher Vocational Education under the Guidance of “Double High”

In today’s society, higher vocational colleges are an important place for cultivating high-quality talents. In the process of higher vocational education, talent training is an important task. Whether we can cultivate the outstanding talents needed by our society plays an important role in measuring the development and running level of higher vocational colleges. In the actual training of innovative and high-quality talents, it is not completed in a short time. For a long time, it is difficult to meet the actual development requirements of society when training outstanding talents. When cultivating outstanding talents, higher vocational colleges should innovate the actual training channels for outstanding talents, achieve a good combination of professional education and theoretical education, set goals for training outstanding talents, and fully demonstrate the quality and effects of higher vocational education. Cultivate outstanding talents for a modern society. Based on this, this article mainly explores and analyzes the talent training path of higher vocational education under the guidance of “double high”, so as to cultivate outstanding talents for our society and provide theoretical references for the smooth development of relevant research in the future.
Scientific and Anatomical Character Reading from the Face Using Deep Learning

This paper presents brief overview of the Character Reading from the Face using Deep Learning techniques. From the earliest ages to the present day character reading from the face, has had a fascination for mankind. As everyone is aware that Face is the Index of the Mind. Character-reading from the face ranks as a science, if based upon anatomical principles and a practical knowledge of the values and properties of brain, bone, nerve, muscle, blood tissue, and hair. In future many businesses use these techniques in promoting their sales and services to their customers/consumers. This technique is also used for education psychologist, family counselors, psychiatrist and doctors to study the psychological behavior based on face reading. It stands apart from palmistry, thought-reading or astrology, and does not presume to predict the future or dive into the past. This paper shows the forest of the Title, in later papers in depth analysis will be discussed. This paper briefly explains different tasks such as Image Captioning, Image Question Answering, Document Summarization and Machine Translation by integrating with science (physiognomy) and computers using deep neural network working architectures built for computer vision like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN).
peningkatan Peningkatan Hasil Belajar Ilmu Pengetahuan Sosial Melalui Metode Diskusi Kelompok Kecil STUDENT TEAMS ACHIEVEMENT DIVISIONS

Improving learning outcomes depends on the teacher teaching students. teachers must have strategies so that students can learn effectively and efficiently, and can recognize the expected goals of learning that takes place in the classroom. The focus of the research is on how to improve social studies learning outcomes through the Student Teams Achievement Divisions small group discussion method for Class III students at SD Negeri Gambiran 03 for the academic year 2020-2021? The purpose of this study is to improve the quality of learning on cooperative materials by using the small group discussion method Student Teams Achievement This study uses a qualitative approach to the type of CAR with the research subject of teachers and third grade students. data collection using interviews, observation and documentation. The results of this study show the improvement of social studies learning outcomes through the small group discussion method Student Teams Achievement Divisions. In the third grade students of SD Negeri Gambiran 03 there was an increase, as evidenced by the increase in student learning outcomes from pre-cycle to cycle 1 which increased, from cycle 1 to cycle II also increased.
Hybrid Particle Swarm Optimization with Ant-Lion Optimization: Experimental in Benchmarks and Applications

A major pitfall in the standard version of Particle Swarm Optimization (PSO) is that it might get stuck in the local optima. To escape this issue, a novel hybrid model based on the combination of PSO and AntLion Optimization (ALO) is proposed in this study. The proposed method, called H-PSO-ALO, uses a local search strategy by employing the Ant-Lion algorithm to select the less correlated and salient feature subset. The objective is to improve the prediction accuracy and adaptability of the model in various datasets by balancing the exploration and exploitation processes. The performance of our method has been evaluated on 30 benchmark classification problems, CEC 2017 benchmark problems, and some well-known datasets. To verify the performance, four algorithms, including FDR-PSO, CLPSO, HFPSO, MPSO, are elected to be compared with the efficiency of H-PSO-ALO. Considering the experimental results, the proposed method outperforms the others in many cases, so it seems it is a desirable candidate for optimization problems on real-world datasets.
VALUE EDUCATION : A SOCIOLOGICAL PERSPECTIVE

Education without values is like a flower without fragrance. Character building is equally relevant as career building. There is a saying that values are, “ better caught than taught”, but in reality values are both caught and taught. The bombardment of new technological devices and information explosion has made it very necessary to teach the values to this present generation. As a result value based education must be imparted in today’s educational institutions. Education is a powerful instrument of social change and human progress, it is also a powerful tool to cultivate values in an individual. Therefore allthe educational institutes have greater responsibility to impart learning and cultivation of values through education.This paper attempts to discuss the role of value based education from the sociological perspective and to review different opinions given by different educational commissions. The research methodology include relevant data collected from thebooks and other sources.
Boxhead: A Dataset for Learning Hierarchical Representations

Disentanglement is hypothesized to be beneficial towards a number of downstream tasks. However, a common assumption in learning disentangled representations is that the data generative factors are statistically independent. As current methods are almost solely evaluated on toy datasets where this ideal assumption holds, we investigate their performance in hierarchical settings, a relevant feature of real-world data. In this work, we introduce Boxhead, a dataset with hierarchically structured ground-truth generative factors. We use this novel dataset to evaluate the performance of state-of-the-art autoencoder-based disentanglement models and observe that hierarchical models generally outperform single-layer VAEs in terms of disentanglement of hierarchically arranged factors.
Comparison of Machine Learning Classifiers for Reducing Fitness Evaluations of Structural Optimization

Metaheuristic algorithms have been widely used to solve structural optimization problems. Despite their powerful search capabilities, these algorithms often require a large number of fitness evaluations. Constructing a machine learning classifier to identify which individuals should be evaluated using the original fitness evaluation is a great solution to reduce the computational cost. However, there is still a lack of a thorough comparison between machine learning classifiers when integrating into the optimization process. This paper aims to evaluate the efficiencies of different classifiers in eliminating unnecessary fitness evaluations. For this purpose, the weight optimization of a double-layer grid structure comprising 200 members is used as a numerical experiment. Six machine learning classifiers selected for assessment in this study include Artificial Neural Network, Support Vector Machine, k-Nearest Neighbor, Decision Tree, Random Forest, and Adaptive Boosting. The comparison is made in terms of the optimal weight of the structure, the rejection rate as well as the computing time. Overall, it is found that the AdaBoost classifier achieves the best performance.
Signal Interpretation in Two-Phase Fluid Dynamics Through Machine Learning and Evolutionary Computing

The paper shows how techniques of machine learning and evolutionary computing can assist in making human expertise in sensor data interpretation explicit and suitable for computer execution. The study refers to a specific task in two-phase fluid dynamics, i. e. the interpretation of probe signals detected in gas-liquid flow. Given a raw probe signal, the corresponding twostate signal needs to be constructed which denotes the presence of the two phases. Due to the lack of knowledge about the processes on a micro scale, no exact procedure exists for accomplishing this task. However, operators are capable of interpreting visually presented probe signals through experience and intuition. To imitate their performance, a prototype signal interpretation procedure was designed manually, and its parameters tuned with genetic algorithms. in an alternative approach, skill acquisition was performed automatically, using inductive machine learning. The induced signal interpretation procedures were tested successfully on air-water pipe flow under laboratory conditions.
What is the Semantic Web?

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
New trends and open problems in fuzzy logic and approximate reasoning

This short paper about fuzzy set-based approximate reasoning first emphasizes the three main semantics for fuzzy sets: similarity, preference and uncertainty. The difference between truth-functional many-valued logics of vague or gradual propositions and non fully compositional calculi such as possibilistic logic (which handles uncertainty) or similarity logics is stressed. Then, potentials of fuzzy set-based reasoning methods are briefly outlined for various kinds of approximate reasoning: deductive reasoning about flexible constraints, reasoning under uncertainty and inconsistency, hypothetical reasoning, exception-tolerant plausible reasoning using generic knowledge, interpolative reasoning, and abductive reasoning (under uncertainty). Open problems are listed in the conclusion.
Parsing of Myanmar Sentences With Function Tagging

This paper describes the use of Naive Bayes to address the task of assigning function tags and context free grammar (CFG) to parse Myanmar sentences. Part of the challenge of statistical function tagging for Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex morphological system. Function tagging is a pre-processing step for parsing. In the task of function tagging, we use the functional annotated corpus and tag Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information. We propose Myanmar grammar rules and apply context free grammar (CFG) to find out the parse tree of function tagged Myanmar sentences. Experiments show that our analysis achieves a good result with parsing of simple sentences and three types of complex sentences.
DyRep: Learning Representations over Dynamic Graphs

Representation Learning over graph structured data has received significant attention recently due to its ubiquitous applicability. However, most advancements have been made in static graph settings while efforts for jointly learning dynamic of the graph and dynamic on the graph are still in an infant stage. Two fundamental questions arise in learning over dynamic graphs: (i) How to elegantly model dynamical processes over graphs? (ii) How to leverage such a model to effectively encode evolving graph information into low-dimensional representations? We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes). Concretely, we propose a two-time scale deep temporal point process model that captures the interleaved dynamics of the observed processes. This model is further parameterized by a temporal-attentive representation network that encodes temporally evolving structural information into node representations which in turn drives the nonlinear evolution of the observed graph dynamics. Our unified framework is trained using an efficient unsupervised procedure and has capability to generalize over unseen nodes. We demonstrate that DyRep outperforms state-of-the-art baselines for dynamic link prediction and time prediction tasks and present extensive qualitative insights into our framework.
Bandit Algorithms in Information Retrieval

This monograph provides an overview of bandit algorithms inspired by various aspects of Information Retrieval (IR), such as click models, online ranker evaluation, personalization or the cold-start problem. Using a survey style, each chapter focuses on a specific IR problem and explains how it was addressed with various bandit approaches. Within each section, all the algorithms are presented in chronological order. The monograph shows how specific concepts related to bandit algorithms. This comprehensive, chronological approach enables the author to explain the impact of IR on the development of new bandit algorithms as well as the impact of bandit algorithms on the development of new methods in IR. The survey is primarily intended for two groups of readers: researchers in Information Retrieval or Machine Learning and practicing data scientists. It is accessible to anyone who has completed introductory to intermediate level courses in machine learning and/or statistics.
Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep Networks

Depth cues have been proved very useful in various computer vision and robotic tasks. This paper addresses the problem of monocular depth estimation from a single still image. Inspired by the effectiveness of recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods using concatenation or weighted average schemes, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through an extensive experimental evaluation, we demonstrate the effectiveness of the proposed approach and establish new state of the art results for the monocular depth estimation task on three publicly available datasets, i.e., NYUD-V2, Make3D and KITTI.
Virtual reality through the customer journey: Framework and propositions

While virtual reality's importance is increasingly recognized in marketing, its role in the customer journey remains nebulous. We define virtual reality through the customer journey (VRCJ) as firms' use of computer-mediated interactive environments capable of offering sensory feedback to engage consumers, strengthen consumer/brand relationships, and drive desired consumer behaviors at any stage of their journey. To better understand VRCJ, we classify VR archetypes, formats, and content features, followed by the development of a conceptual framework and an associated set of propositions of VRCJ. We conclude by discussing important theoretical and practical implications that arise from our analyses.
A novel chaotic image encryption algorithm based on extended Zigzag confusion and RNA operation

In this paper, a novel chaotic image encryption algorithm based on extended Zigzag confusion and RNA operation is proposed. Firstly, a novel chaotic system is designed, which can be regarded as an improvement of traditional Logistic map and Sine map. Then it can be used to generate chaotic sequence combined with SHA 512 hash function. Secondly, extended Zigzag confusion scheme is proposed. It not can only process non square matrix, but also can choose from four directions to determine the position where encryption starts. This scheme can solve the problem of some elements keep in the same position in each encryption round. Thirdly, RNA operation is performed. In RNA computing, the choice of operators is determined according to the category of amino acids, and the coding rules of RNA matrix are completely controlled by chaotic sequence, which makes the results of operation more unpredictable. Finally, this encryption method can extend to color image. The data from experimental simulation and performance analysis show that this encryption algorithm has higher safety performance.
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations. However, these high-dimensional observation spaces present a number of challenges in practice, since the policy must now solve two problems: representation learning and task learning. In this work, we tackle these two problems separately, by explicitly learning latent representations that can accelerate reinforcement learning from images. We propose the stochastic latent actor-critic (SLAC) algorithm: a sample-efficient and high-performing RL algorithm for learning policies for complex continuous control tasks directly from high-dimensional image inputs. SLAC provides a novel and principled approach for unifying stochastic sequential models and RL into a single method, by learning a compact latent representation and then performing RL in the model's learned latent space. Our experimental evaluation demonstrates that our method outperforms both model-free and model-based alternatives in terms of final performance and sample efficiency, on a range of difficult image-based control tasks. Our code and videos of our results are available at our website.
A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification

Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology examination can identify cells of early-stage colon tumors in small tissue image slices. But, such examination is time-consuming and exhausting on high resolution images. In this paper, we present a new framework for colonoscopy pathology whole slide image (WSI) analysis, including lesion segmentation and tissue diagnosis. Our framework contains an improved U-shape network with a VGG net as backbone, and two schemes for training and inference, respectively (the training scheme and inference scheme). Based on the characteristics of colonoscopy pathology WSI, we introduce a specific sampling strategy for sample selection and a transfer learning strategy for model training in our training scheme. Besides, we propose a specific loss function, class-wise DSC loss, to train the segmentation network. In our inference scheme, we apply a sliding-window based sampling strategy for patch generation and diploid ensemble (data ensemble and model ensemble) for the final prediction. We use the predicted segmentation mask to generate the classification probability for the likelihood of WSI being malignant. To our best knowledge, DigestPath 2019 is the first challenge and the first public dataset available on colonoscopy tissue screening and segmentation, and our proposed framework yields good performance on this dataset. Our new framework achieved a DSC of 0.7789 and AUC of 1 on the online test dataset, and we won the [Formula: see text] place in the DigestPath 2019 Challenge (task 2). Our code is available at https://github.com/bhfs9999/colonoscopy_tissue_screen_and_segmentation.