Reviewerly/exHarmony huggingface dataset:
Research on Physical Education Teaching Model Based on Online and Offline Collaboration in the Colleges and Universities

As an important content of higher education in China, physical education plays an important role in enhancing the physical health of students in colleges and universities and cultivating their psychological quality and moral characters. Nowadays, the application value of Internet, cloud computing, big data and artificial intelligence has become increasingly prominent in the field of education. Against this backdrop, colleges and universities should actively use the information technology represented by the Internet to reform the traditional physical education teaching model and actively construct a physical education teaching model based on online to offline collaboration.
Supervised Online Hashing via Hadamard Codebook Learning

In recent years, binary code learning, a.k.a hashing, has received extensive attention in large-scale multimedia retrieval. It aims to encode high-dimensional data points to binary codes, hence the original high-dimensional metric space can be efficiently approximated via Hamming space. However, most existing hashing methods adopted offline batch learning, which is not suitable to handle incremental datasets with streaming data or new instances. In contrast, the robustness of the existing online hashing remains as an open problem, while the embedding of supervised/semantic information hardly boosts the performance of the online hashing, mainly due to the defect of unknown category numbers in supervised learning. In this paper, we proposed an online hashing scheme, termed Hadamard Codebook based Online Hashing (HCOH), which aims to solve the above problems towards robust and supervised online hashing. In particular, we first assign an appropriate high-dimensional binary codes to each class label, which is generated randomly by Hadamard codes to each class label, which is generated randomly by Hadamard codes. Subsequently, LSH is adopted to reduce the length of such Hadamard codes in accordance with the hash bits, which can adapt the predefined binary codes online, and theoretically guarantee the semantic similarity. Finally, we consider the setting of stochastic data acquisition, which facilitates our method to efficiently learn the corresponding hashing functions via stochastic gradient descend (SGD) online. Notably, the proposed HCOH can be embedded with supervised labels and it not limited to a predefined category number. Extensive experiments on three widely-used benchmarks demonstrate the merits of the proposed scheme over the state-of-the-art methods. The code is available at https://github.com/lmbxmu/mycode/tree/master/2018ACMMM_HCOH.
Zoom To Learn, Learn To Zoom

This paper shows that when applying machine learning to digital zoom for photography, it is beneficial to use real, RAW sensor data for training. Existing learning-based super-resolution methods do not use real sensor data, instead operating on RGB images. In practice, these approaches result in loss of detail and accuracy in their digitally zoomed output when zooming in on distant image regions. We also show that synthesizing sensor data by resampling high-resolution RGB images is an oversimplified approximation of real sensor data and noise, resulting in worse image quality. The key barrier to using real sensor data for training is that ground truth high-resolution imagery is missing. We show how to obtain the ground-truth data with optically zoomed images and contribute a dataset, SR-RAW, for real-world computational zoom. We use SR-RAW to train a deep network with a novel contextual bilateral loss (CoBi) that delivers critical robustness to mild misalignment in input-output image pairs. The trained network achieves state-of-the-art performance in 4X and 8X computational zoom.
Using the Computer Technology in Teaching Chemistry

The article possibility of creating electronic textbooks based on AutoPlay programm and Adobe Flash  programm and the deals with the use of electronic textbooks in teaching chemistry.
What an amazing day! #IndieWeb keeps growing. ...

What an amazing day! #IndieWeb keeps growing. So excited that we broke a record!! 11 Homebrew website Clubs across the globe. Including four new Clubs:
Measuring and analyzing the impact of the index of economic freedom on the growth of GDP in selected sample from the world countries for the period 2014-2015

led an increasing number of insurers governments economic freedom on the development and expansion of economic freedom and the development of indicators and the rise in many countries that contribute to the increase of economic freedom, has written many economists writers in the field Modern studies have shown that economic freedom a positive effect on economic growth and development , since the concept of economic freedom has intrinsic value is linked to the other freedoms that directly affect the economic growth, which was among the concerns of economic thinkers, as he became a target seeking him every country attainable and as a benchmark of the criteria adopted in the economies and the basis for assessing the economic performance. The study aimed to demonstrate the impact of economic freedom and economic growth in the surveyed sample countries were used a sample of (160) using a sample of selected countries, it has tried to study to know the impact of economic freedom index and internal branches, represented by (property rights, freedom from corruption, fiscal freedom, business freedom, labor freedom, monetary freedom, trade freedom, investment freedom, financial freedom) for the year 2014 and economic growth as the dependent variable for the year 2015, the failure of any time for a period of one year through the use of simple linear and non-linear regression analysis.    The results showed that the relationship between economic growth and the rate of change in the balance of economic freedom index is a direct correlation and moral any that the greater balance of economic freedom index of the country the greater the economic growth the rate, as demonstrated quantitative analysis that when increasing economic freedom index of the balance of one unit, the growth rate economic would be increased by (0.978(Proposed research study suggests those interested in economic management, taking into account the economic freedom and freedom of action and freedom of work and freedom, commercial and financial freedom and the fight against corruption and freedom of investment .... etc., when the study of economic growth factors, the analysis also suggests economists researchers interest factors and economic indicators is traditional when conducting research related to economic growth, inflation and unemployment, it may be the most important role or effect of the role of traditional economic factors or accepted.
Psychological perception of the user by analyzing tweets for negative emotions in local dialect

Explores real-time sentiment analysis (SA) of Twitter posts in Hindi, adopting a resource-based approach and classifying sentiment on a 3 method scale of negative, positive and neutral. Furthermore, the efficiency of different approaches like part-of-speech (POS) tagging and stop word removal are compared, and ways in which of improving the Hindi Sentiment. We focus on mining sentiments and analyzing them for Hindi language. Hindi is the 4th commonly spoken language in the world. With the increase in the amount of information being communicated via regional languages like Hindi, comes a promising opportunity of mining this information. Mining sentiments in Hindi comes with their share of issues and challenges. Hindi is morphological rich and is a free order language as compared to English, which adds complexity while handling the user-generated content. The scarcity of resources for the Hindi language brings challenges ranging from collection and generation of data-sets. We take up this challenge and work towards building resources- reviews, blogs annotated corpora and subjective lexicon for Hindi language.
Research on Project Teaching Design in Database course Teaching

This paper first analyzes the project reform in computer teaching, then expounds the teaching design of database course, and finally puts forward the project teaching strategy of database course, that is, the reasonable design of specific teaching content. Set the teaching goal clearly and have a reasonable grasp of the concrete teaching process.
Learning Image and Video Compression through Spatial-Temporal Energy Compaction

Compression has been an important research topic for many decades, to produce a significant impact on data transmission and storage. Recent advances have shown a great potential of learning image and video compression. Inspired from related works, in this paper, we present an image compression architecture using a convolutional autoencoder, and then generalize image compression to video compression, by adding an interpolation loop into both encoder and decoder sides. Our basic idea is to realize spatial-temporal energy compaction in learning image and video compression. Thereby, we propose to add a spatial energy compaction-based penalty into loss function, to achieve higher image compression performance. Furthermore, based on temporal energy distribution, we propose to select the number of frames in one interpolation loop, adapting to the motion characteristics of video contents. Experimental results demonstrate that our proposed image compression outperforms the latest image compression standard with MS-SSIM quality metric, and provides higher performance compared with state-of-the-art learning compression methods at high bit rates, which benefits from our spatial energy compaction approach. Meanwhile, our proposed video compression approach with temporal energy compaction can significantly outperform MPEG-4 and is competitive with commonly used H.264. Both our image and video compression can produce more visually pleasant results than traditional standards.
Teaching News Design and Data Visualization

News Design and Data Visualization is an upper level undergraduate course that combines the role that data has to deliver information to the public, informed with the visual practice of design and writing. The course is part of a news editing sequence in the department and reflects an emphasis on the importance of data analysis as students develop an understanding to use and build on basic data literacy skills with computational data sets paired with design and writing. As journalism careers are increasingly requiring students to develop and integrate these skills, this class seeks to build on these skills and help students devleop portfolio pieces for their professional careers.
Word Clustering in an Interactive Text Analysis Tool

A central operation of users of the text analysis tool Gavagai Explorer is to look through a list of words and arrange them in groups. This thesis explores the use of word clustering to automatical ...
Introduction to Python

Python is one of the main languages used for performing machine learning. It is a multi-purpose language that has been leveraged for device programming, object-oriented programming, machine learning, and so forth. In this chapter, you will learn
Virtual-to-Real Transfer via Dynamics Models (poster)

The virtual world is essential for deep reinforcement learning. Since the deep reinforcement learning agent learns the optimal policy by interacting with the environment in a trial and error manner, training the agent in the real world is not only cost expensive and time-consuming but also unsafe. Several researches are ongoing in the field of but not limited to drone, vehicle, and robot arm control as the deep reinforcement learning is proven to be an effective solution to sequential decision-making problems such as Atari games [2] and several board games including Go [4]. Due to the above issue, most of these researches are done in the virtual world that mimics the real world.
The artistic and pedagogical paradigm of mimesis in future specialists of the visual arts in the open air training

Purpose of the article is to substantiate the artistic and pedagogical paradigm of mimesis in the training of future specialists in the visual arts in the open air. The methodology is based on the terminological principle, the application of general scientific methods (analysis, synthesis, synthesizing). The scientific novelty lies in the fact that for the first time: the content of the category «artistic-pedagogical paradigm» is defined, the methodology is revealed and its universalism is proved in the training of future specialists in fine arts in the open air on the basis of ancient principles of mimesis, and advantages and disadvantages of this process are justified. Conclusions. The artistic and pedagogical paradigm of mimesis in the training of future specialists in the visual arts in the open air covers the appropriate objectives, selection and application of methods, techniques and means of depicting nature as an aesthetic semiotic successful implementation of color, tonal, plastic, rhythmic and compositional tasks of creating an objectively valuable artistic image of nature.
Submodular Batch Selection for Training Deep Neural Networks

Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today. We introduce a mini-batch selection strategy based on submodular function maximization. Our novel submodular formulation captures the informativeness of each sample and diversity of the whole subset. We design an efficient, greedy algorithm which can give high-quality solutions to this NP-hard combinatorial optimization problem. Our extensive experiments on standard datasets show that the deep models trained using the proposed batch selection strategy provide better generalization than Stochastic Gradient Descent as well as a popular baseline sampling strategy across different learning rates, batch sizes, and distance metrics.
Exploring the Digital Information Needs of Diaspora Communities: A User Study of the Lithuanian Diaspora

Collecting and representing the cultural heritage of diaspora communities have historically posed a challenge to cultural heritage institutions like libraries and archives. In the advent of Web 2.0 technologies, digital collections and platforms have often been offered as the latest panacea to the multitude of issues relating to these collections. Using results from a survey conducted on the topic of Lithuanian-American cultural heritage, this article explores the information needs and interests of the broader Lithuanian diaspora community. The results reveal a broad interest in digital resources related to this subject across a spectrum of both generational status and Lithuanian language skills. Furthermore, survey results also provide insight on which resources users are currently using to find information on this subject and potential areas of improvement.
Periodic Codes and Sound Localization

Inspired by the sound localization system of the barn owl, we define a new class of neural codes, called periodic codes, and study their basic properties. Periodic codes are binary codes with a special patterned form that reflects the periodicity of the stimulus. Because these codes can be used by the owl to localize sounds within a convex set of angles, we investigate whether they are examples of convex codes, which have previously been studied for hippocampal place cells. We find that periodic codes are typically not convex, but can be completed to convex codes in the presence of noise. We introduce the convex closure and Hamming distance completion as ways of adding codewords to make a code convex, and describe the convex closure of a periodic code. We also find that the probability of the convex closure arising stochastically is greater for sparser codes. Finally, we provide an algebraic method using the neural ideal to detect if a code is periodic. We find that properties of periodic codes help to explain several aspects of the behavior observed in the sound localization system of the barn owl, including common errors in localizing pure tones.
PerspectroScope: A Window to the World of Diverse Perspectives

This work presents PerspectroScope, a web-based system which lets users query a discussion-worthy natural language claim, and extract and visualize various perspectives in support or against the claim, along with evidence supporting each perspective. The system thus lets users explore various perspectives that could touch upon aspects of the issue at hand.The system is built as a combination of retrieval engines and learned textual-entailment-like classifiers built using a few recent developments in natural language understanding. To make the system more adaptive, expand its coverage, and improve its decisions over time, our platform employs various mechanisms to get corrections from the users. PerspectroScope is available at github.com/CogComp/perspectroscope.
GEAR: Geometry-Aware Rényi Information.

We advocate the use of a notion of entropy that reflects the relative abundances of the symbols in an alphabet, as well as the similarities between them. This concept was originally introduced in theoretical ecology to study the diversity of ecosystems. Based on this notion of entropy, we introduce geometry-aware counterparts for several concepts and theorems in information theory. Notably, our proposed divergence exhibits performance on par with state-of-the-art methods based on the Wasserstein distance, but enjoys a closed-form expression that can be computed efficiently. We demonstrate the versatility of our method via experiments on a broad range of domains: training generative models, computing image barycenters, approximating empirical measures and counting modes.
Exploring and Mapping Indoor Environment for Mobile Robots using Different Ways of Scanning

Exploring and mapping for any unknown indoor environment is one of the main topics that are currently been exploring all over the globe.However, A fully developed automated system for exploring and mapping indoors is not yet implemented commercially, although there are currently some systems that works automatic but partially or it misses some data from the surroundings which gives false statements.Also, in some scenarios some systems take plenty of time because of the huge processing data that is resulted from the sensors used.In this paper, we will demonstrate a complete process from exploring the indoor environment (Forward tracking) and mapping or path planning for the point that we desire to reach (Back tracking).A developed Kinematic model of a Robotino Wheeled Mobile Robot (WMR) is going to be used to implement our work including scanning techniques as well as path planning techniques.furthermore, two scanning methods are going to be used, one by other, to get the best result in time and accuracy for them.The two sensors are a matrix of Infrared sensors (IR) that are fixed around the WMR body and a Light detection and ranging sensor (LiDAR).A comparison is going to be discussed for the best results of these sensors.
Compatible Natural Gradient Policy Search

Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region optimization are equivalent if we use the natural parameterization of a standard exponential policy distribution in combination with compatible value function approximation. Moreover, we show that standard natural gradient updates may reduce the entropy of the policy according to a wrong schedule leading to premature convergence. To control entropy reduction we introduce a new policy search method called compatible policy search (COPOS) which bounds entropy loss. The experimental results show that COPOS yields state-of-the-art results in challenging continuous control tasks and in discrete partially observable tasks.
Shortest Paths in HSI Space for Color Texture Classification

Color texture representation is an important step in the task of texture classification. Shortest paths was used to extract color texture features from RGB and HSV color spaces. In this paper, we propose to use shortest paths in the HSI space to build a texture representation for classification. In particular, two undirected graphs are used to model the H channel and the S and I channels respectively in order to represent a color texture image. Moreover, the shortest paths is constructed by using four pairs of pixels according to different scales and directions of the texture image. Experimental results on colored Brodatz and USPTex databases reveal that our proposed method is effective, and the highest classification accuracy rate is 96.93% in the Brodatz database.
Telephonetic: Making Neural Language Models Robust to ASR and Semantic Noise

Speech processing systems rely on robust feature extraction to handle phonetic and semantic variations found in natural language. While techniques exist for desensitizing features to common noise patterns produced by Speech-to-Text (STT) and Text-to-Speech (TTS) systems, the question remains how to best leverage state-of-the-art language models (which capture rich semantic features, but are trained on only written text) on inputs with ASR errors. In this paper, we present Telephonetic, a data augmentation framework that helps robustify language model features to ASR corrupted inputs. To capture phonetic alterations, we employ a character-level language model trained using probabilistic masking. Phonetic augmentations are generated in two stages: a TTS encoder (Tacotron 2, WaveGlow) and a STT decoder (DeepSpeech). Similarly, semantic perturbations are produced by sampling from nearby words in an embedding space, which is computed using the BERT language model. Words are selected for augmentation according to a hierarchical grammar sampling strategy. Telephonetic is evaluated on the Penn Treebank (PTB) corpus, and demonstrates its effectiveness as a bootstrapping technique for transferring neural language models to the speech domain. Notably, our language model achieves a test perplexity of 37.49 on PTB, which to our knowledge is state-of-the-art among models trained only on PTB.