Reviewerly/exHarmony huggingface dataset:
Exploring the Employment and Entrepreneurship Model of the Deaf Under the Background of “Internet+”

Currently, the cause of the deaf in China is under active construction. The rise of “Internet+” has brought new opportunities for the employment and entrepreneurship of the deaf, and also created the possibility for the transformation and upgrading of the employment and entrepreneurship of the deaf. However, the Internet has not yet fully played its role in assisting the disabled in employment and entrepreneurship. It faces many obstacles from the society and the deaf themselves, and it is urgent to break the deadlock. In the context of the Internet, how to promote the employment and entrepreneurship model of the deaf.
Research on Chinese Translation of Multivariate English Passive Structure Based on Big Data

The passive phenomenon is a common phenomenon in English language structure and Chinese language structure. Although both of them are called passive phenomena, there are some differences in expression between them. This article analyzes the content of the "English-Chinese Parallel Corpus" of a school from the multivariate perspective of big data, and deeply studies the characteristics and forms of passive structure. The author discovered the linguistic features in the passive structure of the original English language and some influencing factors that may be ignored by the translator during the translation process. This article hopes to provide some help to future English translators.
DatEthics: Ethical Data-Centric Design of Intelligent Behaviour

The Internet of Things makes human activity data – what people do, how they move, how they socialise – an abundant resource. However, this rich and intimate perspective on people, which uniquely shape and characterise their behaviours, can have tremendous ethical implication if data is handled irresponsibly. Being personal, contextual and accessible, mobile devices are key facilitators of (ir)responsible collection and use of data. In this workshop, we will use the Future Workshop approach to develop a research agenda towards ethical data-centric design of intelligent behaviours. As part of this approach, we will (1) criticise the current mechanisms and infrastructure to frame ethical challenges, (2) fantasise on futures which support user and designer values, and (3) implement a research agenda for the MobileHCI community to emphasise the barriers to tackle. The outcomes of this workshop will foster ethical research and inspire the MobileHCI community.
REDATS: A RESTful Data Trading System with Blockchain

With the proliferation of Internet of Things (IoT) devices and the obtained large amount of data, vast machine learning applications have become popular. Due to the uneven distribution of data, data trading as an efficient mechanism to solve the information island problem has emerged. Existing data sharing mechanism fail to incorporate data privacy feature and the RESTful service with user authentication. In this work, driven by real-world data trading requirements, a novel lightweight authentication and blockchain based data trading system is proposed to facilitate on-line data trading. We authenticate the users with ellipse curve cryptography. Then the data are treated as goods. With blockchain, we add each transaction on the mainchain so that the third-party authentication is not a must by distributed authentication. A Restful interface is implemented so that IoT devices and applications can directly interact with users. A prototype system has been implemented with unspent transaction output (UTXO) structure to demonstrate the main features of proposed architecture. The proposed system is flexible and can be easily adapted to various application scenarios.
Enterprise development forecast analysis under environmental policy based on knowledge graph

Abstract In order to fully explore the relationship between environmental policy and enterprise development, and further improve the forecasting effect of environmental enterprise development, this paper proposes a method of forecasting enterprise development status under environmental policy based on knowledge graph. This method extracts environmental policies and corporate information from the public website of the environmental department to build an environmental corporate knowledge graph, combines cosine similarity and regression algorithms to establish a prediction model for environmental policies and corporate development, and uses the model to analyse environmental policies and predict corporate future development. Experiments show that this method makes full use of the advantages of knowledge graphs in relation analysis and graphical display, has good accuracy and recall, greatly reduces the time cost of enterprise forecasting, and has strong application value.
Text Enhancement for Paragraph Processing in End-to-End Code-switching TTS

Current end-to-end code-switching Text-to-Speech (TTS) can already generate high quality two languages speech in the same utterance with single speaker bilingual corpora. When the speakers of the bilingual corpora are different, the naturalness and consistency of the code-switching TTS will be poor. The cross-lingual embedding layers structure we proposed makes similar syllables in different languages relevant, thus improving the naturalness and consistency of generated speech. In the end-to-end code-switching TTS, there exists problem of prosody instability when synthesizing paragraph text. The text enhancement method we proposed makes the input contain prosodic information and sentence- level context information, thus improving the prosody stability of paragraph text. Experimental results demonstrate the effectiveness of the proposed methods in the naturalness, consistency, and prosody stability. In addition to Mandarin and English, we also apply these methods to Shanghaiese and Cantonese corpora, proving that the methods we proposed can be extended to other languages to build end-to-end code- switching TTS system.
The Benefits of Improvisational Theatre

The principles of purpose, journey and connectivity as unfolded in the three-pillar model (3-P-Model) have a strong resemblance to the basics of improvisational theatre (improv). That makes this form of art suitable to explore these principles in a literally playful manner. In this article the author discusses parallels between business transformation processes and the skills of that very agile, creative and highly connected art of acting.The article shows that organisations can be regarded as a kind of improvisational theatre, which means that it is helpful to learn to act in a proficient way, or at least transfer key insights to shift the interaction and collaboration in the organisation. So, improv theatre can be seen as a highly effective coping mechanism for dealing with challenging organisational and leadership situations, which is also an emerging topic in neuroscientific research.
Research on a Pedestrian Detection Algorithm Based on Improved SSD Network

Abstract When the SSD network uses an independent feature layer to detect objects, there is no connection between the layers, which leads to the problem of insufficient expression of contextual features. This paper proposes a pedestrian detection algorithm based on improved SSD network. The algorithm uses cross-layer feature adaptive fusion, and combines residual channel attention modules with different convolution rates of holes. While increasing the receptive field, this algorithm enhances important features and weakens unimportant features. Make the extracted features more directional, thereby improving the accuracy of pedestrian detection. Experiment on the improved network and algorithm on the INRIA pedestrian detection dataset, and the mixed pedestrian dataset extracted from the COCO dataset and the Crowd human dataset. The experimental results show that the average precision of pedestrian detection in the two datasets is improved by 1.7% and 4.0% respectively compared with the original network.
Determining the pseudo magnitude of the o Oph A star using Iris 5.59 software observed from the Astronomy Laboratory, Department of Physics, Faculty of Mathematics and Natural Science, State University of Malang

o Oph A star is a star in the Ophiuchus star cluster. o Oph A stands for omicron Ophiuchi A. Ophiuchus is a zodiac between Scorpio and Sagittarius in the current era, the 9th zodiac is calculated sequentially from Aries and finally, the 13th is Pisces. A magnitude scale is a unit for measuring the brightness of a star. The magnitude of stars that can be observed from the earth is called pseudo magnitude. Observation and data collection of o Oph A star was held on Monday, September 30th, 2019, at 18:47 West Indonesian Time at the Astronomy Laboratory, Physics Department, FMIPA, State University of Malang with position (7°57'38.7"S 112° 37'09.4"E) which coincided around the planet Jupiter. The method used is a descriptive method with the star photometry technique, which is assisted by the Celestron 2000 telescope, sky sensor 2000, and a digital camera 8 MP to observe and take images of the conditions around the o Oph A star. Then identify the results of these images using the Stellarium Mobile v1.25 application. After obtaining information about the pseudo magnitude of the e Oph star as a standard star in the Stellarium Mobile v1.25 application that is equal to 3.25, then analyzed with Iris 5.59 software with a constant magnitude of 16.548. A pseudo magnitude of the o Oph A star is obtained (5.085±0.00) with a measurement error of 0.29%.
On Factorization of Sixth-Degree Polynomial of Type-(3,3)

A special type of monic polynomial equations of degree 6 with real coefficients has been chosen, and it is shown how to obtain the complete factorization provided the given equation is factorisable in to two cubic equations satisfying some special conditions. Important examples and several results have been provided in order to establish the ideas and methodologies.
Vision-Based Fall Detection Using Body Geometry

Falling is a major health problem that causes thousands of deaths every year, according to the World Health Organization. Fall detection and fall prediction are both important tasks that should be performed efficiently to enable accurate medical assistance to vulnerable population whenever required. This allows local authorities to predict daily health care resources and reduce fall damages accordingly. We present in this paper a fall detection approach that explores human body geometry available at different frames of the video sequence. Especially, the angular information and the distance between the vector formed by the head -centroid of the identified facial image- and the center hip of the body, and the vector aligned with the horizontal axis of the center hip, are then used to construct distinctive image features. A two-class SVM classifier is trained on the newly constructed feature images, while a Long Short-Term Memory (LSTM) network is trained on the calculated angle and distance sequences to classify falls and non-falls activities. We perform experiments on the Le2i fall detection dataset and the UR FD dataset. The results demonstrate the effectiveness and efficiency of the developed approach.
Highlighting of CMS conceptions

The abstract is containing the highlight of main content system management (CMS) conceptions. Wrote about similarities and differences of the logical principles of CMS working, highlighting of these classifications, selecting signs for optimal select the CMS for anything sites.
A Relative Position-based Bacterial Foraging Optimization for Numerical Optimization

Bacterial foraging optimization (BFO) algorithm has been widely applied to various optimization problems. However, BFO often suffers from premature convergence and lacking of population information exchanging. To overcome these shortcomings, a relative position-based bacterial foraging optimization (RPBFO) is proposed. The three-layer circulation structure is replaced by a single-layer circulation structure in this algorithm. The relative position-based updating method is used to replace the absolute position-based updating method in the chemotactic operation. The reproduction step of BFO is eliminated. And the escape strategy is employed in the elimination-dispersal operation. Then the optimization results of the RPBFO algorithm are tested on 11 benchmark functions. The results show that the optimization ability of the RPBFO algorithm is significantly better than the original BFO and GA algorithms. On most benchmark functions, it also shows a better performance in convergence speed and accuracy than the PSO algorithm.
Crisis in Technical Education in India: Evolving Contours of the Computer and Information Sciences Discipline

Since about the turn of the millennium there have been massive transformative changes in the computer and information science (CIS) discipline requiring substantive overhauling of existing systems of technical education—curricula, courses offered, teaching skills, supportive infrastructure and, importantly, placement possibilities. Even as the Indian CIS education system (in terms of number of institutions offering such courses, enrolment, placement, etc.) has been on a growth path at least since the mid-1980s or so in recent decade a series of disruptive technologies have necessitated a complete revamping of the existing curricula and methods of imparting education under the CIS discipline. This has led to different educational institutions responding differently—some seeing this as beyond their capabilities while others hoping to catch up with the new dispensation. Addressing the major churning taking place in the sphere of professional education in CIS in India, this paper has made an attempt at empirically tracking the contours and extent of changes as may be surmised from the All India Council of Technical Education (AICTE) data, mainly. The study takes into account a wide variety of CIS courses those are/were offered in technical institutions in India over about the last couple of decades and analyses the implications of such performance of the CIS education in India. State-level disaggregated data are analysed and are supplemented with policy measures at national and subnational levels in addressing the challenges of disruptive technologies in education.
Research the Method of Joint Segmentation and POS Tagging for Tibetan using BiGRU-CRF

Tibetan word segmentation and part-of-speech tagging are the most basic parts of Tibetan natural language processing, and its accuracy and performance have a crucial impact on many subsequent tasks. Considering the insufficiency of the pipeline model of word segmentation and part-of-speech tagging, this paper uses an integrated model of BiGRU-CRF word segmentation and part-of-speech tagging based on deep learning to simultaneously process two tasks of Tibetan word segmentation and part-of-speech tagging in one step. After conducting experiments on the Tibetan corpus collected in "Humanistic Tibet", the joint F1 value of Tibetan word segmentation and part-of-speech tagging was 92.48%.
Application of Ackermann’s Recurrence Relation and Moore Machine on Cryptography

Digital communication has emerged as an important aspect in today’s world. Due to which we need to secure the data to a great extent otherwise there will be data loss. Various methods have been developing day by day to ensure data protection high level. This paper is based on the application of recurrence relation namely, Ackermann’s function, and using concept of Moore machine in Cryptography. Encryption and Decryption can be done by the aforesaid methods in order to pass the message from sender to receiver securely.
Construction of Evaluation System Platform of University Work in Data Mining Technology

As an important aspect of the overall evaluation of education, teaching quality evaluation in Colleges and universities is not only the main body and foundation of education evaluation, but also an indispensable part in teaching activities. The development of information technology on the Internet has accumulated a lot of data for teaching and management, and created a prerequisite for the construction. Still in the primary stage of data backup, query and simple statistics, and the mining and analysis of evaluation data is still in the stage of manual processing, which makes these data unable to give full play its due value. Nowadays, “survival by quality” has become the inevitable choice of every university. Whether the teaching quality can be guaranteed or not depends on the teaching quality and teaching level of teachers. Therefore, we must have a scientific evaluation system to monitor the teaching quality of teachers. In this paper, how to make more scientific and reasonable use rationally analyze and guide teachers’ teaching is deeply studied.
Counter Based Authentication Verification to Secure Patient Data in Cloud

Hospital patient record that is stored in public cloud demands high level of security and access control. To guarantee the proper user access, an authentication scheme is required that can follow up data access. In this paper, counter based authentication verification is introduced. This method utilizes token generation and counter strategy. Elliptic curve based digital signature is employed in token generation. Along with the generated token, the data is encrypted and a counter value is appended to it. Whenever an authorized user views or modifies the stored data, the counter value is updated. Thus, this method significantly identifies an unauthorized data access.
Machine-Driven Content Marketing

Content marketing is a crucial aspect of digital marketing in modern firms. By generating content that is interesting and engaging, companies have the two-fold advantage of promoting their products in a relatable way, while increasing familiarity and engagement with the brand. As data scientists at Credit Suisse, we value our content teams because their voice is the bank's voice. We strive to provide them with the best tools to increase their articles' success. With the help of machine learning, we have created digital products that allow them to improve articles before publication, recommend them to the most interested readers, and track their performance. The chapter begins with a brief introduction to content marketing, followed by an overview of our data, a review of the business challenges we have encountered, and the machine learning solutions we have developed in order to provide the best data insights to our internal and external stakeholders. We close the chapter with a brief summary of our work.
Integrate Candidate Answer Extraction with Re-Ranking for Chinese Machine Reading Comprehension

Machine Reading Comprehension (MRC) research concerns how to endow machines with the ability to understand given passages and answer questions, which is a challenging problem in the field of natural language processing. To solve the Chinese MRC task efficiently, this paper proposes an Improved Extraction-based Reading Comprehension method with Answer Re-ranking (IERC-AR), consisting of a candidate answer extraction module and a re-ranking module. The candidate answer extraction module uses an improved pre-training language model, RoBERTa-WWM, to generate precise word representations, which can solve the problem of polysemy and is good for capturing Chinese word-level features. The re-ranking module re-evaluates candidate answers based on a self-attention mechanism, which can improve the accuracy of predicting answers. Traditional machine-reading methods generally integrate different modules into a pipeline system, which leads to re-encoding problems and inconsistent data distribution between the training and testing phases; therefore, this paper proposes an end-to-end model architecture for IERC-AR to reasonably integrate the candidate answer extraction and re-ranking modules. The experimental results on the Les MMRC dataset show that IERC-AR outperforms state-of-the-art MRC approaches.
Social Unrest Events Prediction by Contextual Gated Graph Convolutional Networks

In a wide range of social unrest events prediction, the dynamic graph convolutional network (DGCN) has been successfully leveraged to achieve reliable performance. The innovation of DGCN mainly focuses on capturing the temporal features of unrest events. Inspired by the DGCN, we propose a new graph convolutional network model called Contextual Gated Graph Convolutional Network (CGGCN), which is adopted to predict and analyze social unrest events. The CGGCN uses the contextual gated layer, which improves the layer-wise propagation rules of graph convolutional networks. The contextual gated layer can re-learn the keyword representation to capture the contextual semantic features of unrest events by using squeeze and excitation module. The principle of the squeeze and excitation module is to increase the weight of meaningful words for event prediction and to suppress weaker ones. In our work, we obtain historical texts including published news and short tweets related to social unrest events. Based on these historical texts data, the CGGCN can predict the occurrence of social unrest events efficiently. In addition, we propose a method for establishing the evolution graph of unrest events. In this way, we can use several core words to summarize the evolution of the event. Finally, we conduct extensive experiments on the unrest events data sets. The experimental results show that the CGGCN leads by about 5%–7% in the performance of prediction compared with other mainstream methods.
Solution to Big Data Security Issues

Organizations now have knowledge of big data significance, but new challenges stand up with new inventions. These challenges are not only limited to the three Vs of big data, but also to privacy and security. Attacks on big data system ranges from DDoS to information theft, ransomware to end user level security. So implementing security to big data system is a multiple phase-based ongoing process in which security is imposed from perimeter level to distributed file system security, cloud security to data security, storage to data mining security, and so on. In this chapter, the authors have identified some key vulnerable point related to big data and also proposed a security model.
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning

Option discovery and skill acquisition frameworks are integral to the functioning of a hierarchically organized Reinforcement learning agent. However, such techniques often yield a large number of options or skills, which can be represented succinctly by filtering out any redundant information. Such a reduction can decrease the required computation while also improving the performance on a target task. To compress an array of option policies, we attempt to find a policy basis that accurately captures the set of all options. In this work, we propose Option Encoder, an auto-encoder based framework with intelligently constrained weights, that helps discover a collection of basis policies. The policy basis can be used as a proxy for the original set of skills in a suitable hierarchically organized framework. We demonstrate the efficacy of our method on a collection of grid-worlds evaluating the obtained policy basis on downstream tasks and demonstrate qualitative results on the Deepmind-lab task.