Reviewerly/exHarmony huggingface dataset:
Internet of Things and Deep Learning

In this talk, we first survey the latest deep learning technology, presenting both theoretical and practical perspectives that are most relevant to our topic. Next, we review general problems and tasks in text/language processing, and underline the distinct properties that differentiate language processing from other tasks such as speech and image object recognition. More importantly, we highlight the general issues of language processing, and elaborate on how new deep learning technologies are proposed and fundamentally address these issues. We then place particular emphasis on several important applications: 1) web search, 2) online recommendation and 3) machine translation. For each of the tasks we discuss what particular architectures of deep learning models are suitable given the nature of the task, and how learning can be performed efficiently and effectively using end-to-end optimization strategies. Beyond providing a systematic review of the general theory, we also present hands-on experience in building state-of-the-art systems. In the talk, we will share our practice with concrete examples drawn from our first-hand experience in major research benchmarks and some industrial scale applications which we have been working on extensively in recent years.
Millennials as Second Language Users. The Analysis of Communication Patterns Employed for L1 and L2 Interactions

The character of global communication is changing due to multifarious reasons of social, technological or even political nature. The taxonomy created by Howe and Strauss (2000) and by Chester (2002) differentiates between various age generations—baby boomers, generation X, Millennials (generation Y), and Z generation, and each of them seems to constitute a unique group thinking, working, living, and communicating in its specific and age-appropriate way. Millennials have been labelled as those who adapt quickly to a world undergoing rapid technological changes. They are also the ones who have been called “Generation Me”, as they follow self-centered life approach and are characterized by the rise of individualism in society. Although this generation has been greatly shaped by the technological advances, they are deficient in soft skills (Gibson and Sodeman, 2014) comprising the core of effective communication. The intention of this chapter is to describe a changing nature of communication of advanced young adults, students of English as a second language. The prime aim is to reveal most commonly used communication channels as well as to present various factors of sociopragmatic nature (e.g., their attitude to fluency and correctness in L1 and L2) that the respondents pay attention to. The article shall demonstrate the outcomes of a research study conducted among a cross-cultural millennial sample in order to verify typical attributes of generation Y concerning their approach to communication in general and L2 specifically.
A novel bottom-up keypoints based hexagon bolts detection method

Hexagon bolts are common standard parts in manufacturing industry and generally accompanied with anti-loosing components like lockwire and locking tab, to strengthen fastening effect. However, incorrect assembly will cause failure, even accelerate bolt loosening. Consequently, misassembly inspection is necessary. For an automatic inspection system based on computer vision, the localization information of hexagon bolts centers and corners is needed. Object detection and keypoints detection technologies develop rapidly, and some researches have applied these technologies to hexagon bolts and their keypoints detection. These existing methods use the top-down strategy, which need two separate models for each stage. This strategy is common and mature, but still has some shortcomings. First, detection models suffer from the imbalance between positive and negative samples. Second, the quality of bounding box detection will affect keypoints detection. Third, two separate models cause computation redundancy. To overcome these problems, we propose a novel bottom-up keypoints based hexagon bolts detection method. With the small dataset, relatively high-resolution images and limited computation resources, our method achieves a good balance between speed and accuracy. We build two hexagon bolts datasets, one contains locking tab with large field of view (LFOV), another contains lockwire with small field of view (SFOV). Images with large field of view have more complex background. We test our model on both two datasets, and the results show that our method is effective. For the SFOV, our model reaches 0.950 AP and 0.974 recall. For the LFOV, our model reaches 0.883 AP and 0.934 recall.
An Approach of Short Advertising Video Generation Using Mobile Phone Assisted by Robotic Arm

Recently, Short Advertising Video has become an increasingly dominant form of advertisement on social media. However, making Short Advertising Video is a challenging task for micro and small businesses, since it requires professional skills and years of experience. In this paper, we present a novel approach of Short Advertising Video generation assisted by robotic arms. We analyzed the professional composition and imaging of advertising videos, and transformed them into an automatic shooting process during the production of Short Advertising Video, assisted by a robotic arm. Practically, we applied our approach in two kinds of robotic arms and the results showed that robotic arm assist solution can highly enhance the efficiency and effect of making Short Advertising Video. In addition, our video generation approach can save time and money for novice users from micro and small business who has very limit resources and budget. And, we believe that our approach might overturn the existing production model of the Short Advertising Video propagated in the online business and social media.
Research on Classification of Vehicle Driving

The current research direction of active suspension is primarily focused on the design of feedback control based on pavement information, whilst research effort in actively detecting driving pavement information is rarely put in. Good pavement information recognition system is especially important for the development of suspension technology. This text adopts the binocular vision system to obtain the road two-dimensional image. Firstly, the Semi-Global Block Matching(SGBM)is used for stereo matching and three-dimensional reconstruction. Secondly, the "cleansing" algorithm is then used to remove the erroneous data points in the road vertical contour model. Finally, Multi-class support vector machines (SVM) are trained by using road feature data sets. Vector machine. The research results show that the road two-bit image acquired by the binocular vision system can provide the road surface feature basis for the control of the active suspension system and classification model of road feature data set training can also extract useful information from road images and classify them correctly.
Leveraging Software Bots to Enhance Developers' Collaboration in Online Programming Communities

Software bots are applications that are integrated into human communication channels, serving as an interface between users and other tools. Due to their focus on task automation, bots have become particularly relevant for Open Source Software (OSS) projects hosted on GitHub. While bots are adopted to save developers' costs, time, and effort, the interaction of these bots can be disruptive to the community. My research goal is two-fold: (i) identify problems caused by bots that interact in pull requests, and (ii) help bot designers to enhance existing bots, thereby improving the partnership with contributors and maintainers. Toward this end, we are interviewing developers to understand what are the problems on the human-bot interaction and how they affect human collaboration. Afterwards, we will employ Design Fiction to capture the developers' vision of bots' capabilities, in order to define guidelines for the design of bots on social coding platforms, and derive requirements for a meta-bot to deal with the problems. This work contributes more broadly to the design and use of software bots to enhance developers' collaboration and interaction.
Multi-Domain Global Correlation Degree Branching Entropy Method for Microblog Text Word Segmentation

Word segmentation is a basic topic in the field of natural language processing, and improving the accuracy of word segmentation is a key problem. With the popularity of microblog, accurate word segmentation for microblog text has become a hot spot. However, microblog texts often contain information about multiple related domains, ambiguous words in multi-domain will lead to the decline of word segmentation accuracy. Based on the model theory of word vector and branching entropy, this paper proposes a multi-domain global correlation degree branching entropy method for microblog text word segmentation. This model is applied to microblog text about house price topic in Beijing. The precision, recall and F-measure of this method are compared with branching entropy model proposed by Zhang[6], and the experimental results show that our method outperforms it.
RhinoSmart: a smartphone based system for rhino-cell segmentation

Nasal cytology studies microscopic analysis of the cells contained in the nasal mucosa and represents a useful diagnostic methodology able to recognize and differentiate some nasal pathologies. The standard clinical protocol provides for the observation of at least fifty microscopic fields and cell counting to obtain cell population distribution. In this paper we show as well-known image processing algorithms can be implemented on a common smartphone to support cell counting, taking a photo of the observation fields directly from the microscope. The system designed, called RhinoSmart, processes the images acquired on the smartphone itself and extracts the cellular elements. These cropped images can be observed and counted directly by the specialists or, better can be sent to a server system that classifies cells and send back the report in a short time.
Low-Cost Online Handwritten Symbol Recognition System in Virtual Reality Environment of Head-Mounted Display

Virtual reality is an important technology in the digital media industry, providing a whole new experience for most people. However, its manipulation method is more difficult than the traditional keyboard and mouse. In this research, we proposed a new low-cost online handwriting symbol recognition system to accurately identify symbols by user actions. The purpose was low cost processing without requiring a server. Experimental results showed that the average success rate of recognition was 99.8%. The execution time averaged a significantly low 0.03395 s. The proposed system is, respectively, highly reliable and at a low cost. This implies that the proposed system is suitable for applications in real-time environments.
Very Short-Term Solar Irradiance Forecasting at a Sub-Minute Scale Based on WT-Cnns

Abstract with the increasing of penetration of photovoltaic power, the solar irradiance forecasting has become a concern for power management. Due to the non-stationary and stochastic feature of the solar irradiance in a very time-term scale, the solar irradiance is difficult to accurately forecast. In this paper, the second-level solar irradiance is predicted using a deep-learning-based convolutional neural network (CNN) with the help of wavelet transformation (WT) and bootstrap sampling methods in time-frequency domain. The proposed method extracts the useful information from the reconstructed solar irradiance ‘image’ matrix in the frequency domain to form the next second level solar irradiance. The results show that the proposed model can learn the past features comprehensively, indicating significant potential for further study.
Research on a Text Topic Mining Algorithm Based on Big Data

Abstract Topic modeling has been widely used to mine topics from documents. However, a key disadvantage of topic modeling is that it requires large amounts of data (e.g. thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have as many documents. Given a small number of documents, the topic generated by the classic topic model LDA is very poor. Even in the case of a large amount of data, unsupervised learning of the topic model will still produce unsatisfactory results. In recent years, knowledge-based topic models have been proposed which require human users to provide some prior domain knowledge to guide the model to generate better topics. Our research takes a completely different approach. We recommend learn like humans, i.e. retain the results learned in the past and use them to help future learning. When faced with a new task, we first mine some reliable (transcendental) knowledge from past learning/modeling results, and then use it to guide model inference to produce more coherent topics. This approach is possible because there is readily available big data on the Web. The algorithm mines two forms of knowledge: the chain must be (Meaning two words should be in the same topic) and cannot be linked (meaning two words should not be in the same topic). Two issues of automatic knowledge mining are discussed, namely the problem of erroneous knowledge and knowledge transferability. Use from the experimental results of the review documents of 100 product areas show that the method proposed in this paper has a significant improvement over the state-of-the-art baseline.
Bures and trace-distance correlations of quantum wells in open microcavities linked by an optical waveguide

We explore the two quantum-wells correlations in two microcavities linked by a waveguide and leaking its photons to the external environment. Each microcavity contains quantum well (QW) and filled by a linear optical medium. We examine the effects of the physical parameters on the generation and robustness of the Bures-norm entanglement and the trace-norm measurement-induced non-locality correlation. The generations and the robustness of the Bures and trace-distance correlations depend crucially on the cavity-QW and fiber cavity couplings as well as on the optical linear medium density. The optical susceptibility, the spontaneous emission and the microcavity dissipation rates, in addition to the couplings of cavity-QW and the fiber-cavity control the regularity, amplitudes, and frequencies of the Bures and trace-distance correlations.
The Process of Metadata Management for Radar Target Classification Algorithm Development

This paper presents an approach to manage metadata (target class labels) for the recorded primary Doppler radar data. This information is necessary for further research and development of target classification algorithms. For this purpose, a labelling methodology and an application radar data analysis and target labelling was developed. The application includes radar records file processing, Doppler filtering, tag creation, data visualizationand tag database. For the better context of analysed data, an interface to Geographic Information Software (GIS) program is included as well. GIS program allows overlaying radar Plan Position Indicator (PPI) data with map data, airplane transponder tracks, drone flight path log and other support data. Finally, the application notes and observations are presented.
How to Pick Your Friends A Game Theoretic Approach to P2P Overlay Construction

A major limitation of many blockchain systems is the lack of strong identities in the underlying P2P network. This allows any nodes to attack the system by creating multiple false personas, thereby disrupting the network's connectivity and sabotaging its operation. In this paper, we focus on P2P networks, and explore practical ways to defend them from such attacks. To do so, we employ a game theoretic approach to the management of each peer's list of known nodes and to the overlay construction mechanisms that utilize this list. We consider the interaction between the defender and attacker as a game. We show that the cost of attacks can be driven up substantially if the defender utilizes available information about peers it chooses to connect to, such as their IP address. In addition to theoretical analysis of the underlying game, we apply our approach to the Bitcoin P2P network and derive effective and practical strategies that guarantee a high safety level against attacks.
TUBE!

The installation investigates the interaction of body within confined textile structure (knitted tube 1, hanging + knitted tube 2 on floor) and document body and movement explorations changing the aesthetic expression. The ephemeral embodied explorations are documented by using machine learning model Posenet that can estimate human movement. This will reflect on the outcome of existing methods and studying how the EDI (embodied design ideation) framework can be used to develop new methods for fashion and textile design processes. The EDI (embodied design ideation) method has been actively referred to by the researchers both in the field of human-computer interaction (HCI) and embodied interaction. These methods help to view body and material within the thought context and draw relationship that is used to inform a design. Posenet is used as a sketching tool to document spatial levels and structural characteristics of body while moving. This further identifies how and where movement occurs. The results question the limitation of technology and highlight through experiment when and where Posenet stops identifying body movement.
Synthetic Error Dataset Generation Mimicking Bengali Writing Pattern

While writing Bengali using English keyboard, users often make spelling mistakes. The accuracy of any Bengali spell checker or paragraph correction module largely depends on the kind of error dataset it is based on. Manual generation of such error dataset is a cumbersome process. In this research, We present an algorithm for automatic misspelled Bengali word generation from correct word through analyzing Bengali writing pattern using QWERTY layout English keyboard. As part of our analysis, we have formed a list of most commonly used Bengali words, phonetically similar replaceable clusters, frequently mispressed replaceable clusters, frequently mispressed insertion prone clusters and some rules for Juktakkhar (constant letter clusters) handling while generating errors.
On Maxitive Image Processing

Most digital processing nowadays is in digital format. Many transformations, such as contrast enhancement, restoration, color correction, etc. can be achieved through very simple and versatile algorithms However, a major branch of image processing algorithms makes extensive use of spatial transformations such as rotation, translation, zoom, anamorphosis, homography, distortion, derivation, etc. that are only defined in the analog domain. A digital image processing algorithm that mimics a spatial transformation is usually designed by using the so-called kernel based approach. This involves two kernels to ensure the continuous to discrete interplay: a sampling kernel and a reconstruction kernel, whose choice is highly arbitrary. The maxitive kernel based approach can be seen as an extension of the conventional kernel based approach and it reduces the impact of this arbitrary choice. It consists in replacing at least one of the kernels by a normalized fuzzy subset of the image plane. This replacement in the digital image spatial transformation framework leads to computing the convex set of all the images that would be obtained by using a (continuous convex) set of conventional kernels. Use of this set generates a kind of robustness that can reduce the risk of false interpretation. Medical imaging, for example, would be an application that could benefit from such an approach.
Use of Interpretable Evolved Search Query Classifiers for Sinhala Documents

Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.
A Surrogate Model Based Genetic Algorithm for Complex Problem Solving

Abstract It is well known that when the fitness function is relatively complex, the optimization time cost of the genetic algorithm will be extremely huge. To address this issue, the surrogate model was employed to predict the fitness value of the optimization problem, to reduce the number of actual calculated fitness values. In this paper, BP neural network, the least square method and support vector machine were fused in the genetic algorithm to evaluate partial individuals’ fitness. Sufficient benchmark numerical experiments were conducted, and the results proved that the strategy could reduce the calculating counts of fitness function on similar accuracy basis compared with simple genetic algorithm.
Mathematical Formulation of Prediction based Task Scheduling Method in Cloud Computing

Now a days, Cloud Computing has become generalized and very popular term. Cloud computing is continuing to grow at an unprecedented scale. Cloud Computing relies on concept of shared computing resources instead of having personal resources. It delivers computing resources on demand where resources are dynamically assigned and reassigned among multiple users and scale up and down in response to users needs. The power, scalability, flexibility and pay-as-you-go economics of cloud has made it the underpinning foundation of digital transformation. Task Scheduling is always a key issue in any computing model. Traditional task scheduling methods are improper for cloud computing. Cloud computing conveys new encounters for task scheduling due to on-demand service model, pay-as-you-go economics model, heterogeneity in hardware capabilities and guarantee to meet Quality of Service (QoS). In this research paper we have presented mathematical formulation of prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing requirements and also to benefit cloud providers for effective resource management.
Model Renderer Design with Style Image

A model renderer with style image generation is designed combine with illumination technology and style transfer technology for the illumination problems, that may be encountered in the process of computer image graphics design and production.In order to achieve a better rendering effect of the model, the rendering results are combined with style transfer innovatively.Different styles of model images can be applied in scenes such as games and movies to facilitate future development.