Reviewerly/exHarmony huggingface dataset:
A General Web Page Extraction Method Aiming at Online Social Networks

Recently, online social networks such as Twitter and Facebook have become one of the most important information communication platforms, which contain large number of useful user data for various research fields. Extracting information from online social network pages is the fundamental key technique of online social network study. In this paper, we have proposed a general web page extraction method according to the structural feature of online social network web pages. Based on our method we can select useful information in web page with class tag frequency and after some simple string manipulation processes the exact content will be achieved. Furthermore, we have proposed an automatic particular regular expression generation algorithm which can make our content extraction method apply to many similar structure online social networks without re-writing the entire extraction process facing to each web site. The experimental results show that our approach has good adaptability and shows significant advantages in both precision and recall.
Rank Constrained Multi-view Clustering via Entropy

Multi-view data exist widely in real world and the expressed information from different views are different. However, different views are usually considered to have the same role in common multi-view clustering methods, which directly affect the quality of the clustering. In this paper, we proposed a multi-view subspace clustering algorithm based on information entropy, which introduces information entropy as the weighting term of views based on Laplace rank constrained and low rank tensor constrained. The proposed multi-view subspace clustering algorithm can capture the graphic structure of the data space more accurately and avoid the postprocessing of k-means. Experiments on some real datasets demonstrate the superiority of our proposed algorithm.
Perception of the AAC Graphic Symbols and SNS Emoticons for Emotional Words in Normal Adults

Objectives: Expressing emotional feelings is essential in one's effective communication functions and social participation.Although several emotional graphic symbols are available for AAC speakers, the usefulness of the emoticons provided by social network services needs to be identified for the potential of usage in the area of AAC.This study aims to identify the effect of two types of AAC graphic symbols (PCS and Ewha-AAC), and two types of SNS emoticons (face expression-oriented and face & body-part expression oriented emoticons used in KakaoTalk).Methods: Thirty-two normal adults (mean age= 25.91, SD= 4.68) accomplished the symbol-naming task for the eight emotional words ('worried' , 'fine' , 'surprised' , 'fearful' , 'angry' , 'happy' , 'sad' , and 'shy').After providing the visual stimuli on a computer screen, participants were asked to match the meaning from the given word choices.Participants' accuracy scores and response times were analyzed.Results: Higher task performance was found in the condition of KakaoTalk emoticon and Ewha-AAC symbols which represent the emotion by facial expression and body part motion.Likewise, a fast response was found in these two conditions.Conclusion: Considering the common characteristics of these two visual materials, the representation of both facial expression and body-part pose seems to facilitate participants' cognitive process of retrieving the meaning of the visual stimuli in an accurate and fast fashion.
The Disruptive Force of Smart Contracts

The fourth industrial revolution hints at technological advances that could disrupt entire industries. Already glimpses of the disruptive force can be seen with the advent of technologies such as cryptocurrencies, blockchain, adaptive and predictive algorithms and the use of big data. The smart contract has emerged from these technological advances as a sophisticated self-executing computer program with the capability of automatically adapting itself throughout the contract life cycle to achieve the contractual objectives of the parties. Smart contracts are fully autonomous, self-evolving and dynamic processes in the formation, interpretation and management of contracts. Smart contracts could also remove the need for trusted third party intermediaries in commercial transactions, and thereby fundamentally alter the traditional approach to contract theory, practices and dispute resolution processes. Smart contracts will likely be incorporated into commercial transactions in some way, shape or form but the extent of such integration remains uncertain. This new paradigm of contractual theory and practice certainty will bring about different challenges and opportunities as the legal and commercial world grapples with the ethical and philosophical issues of controlling a contracting system that, at its core, is intended to liberate transactions from central control.
Testing that a Local Optimum of the Likelihood is Globally Optimum Using Reparameterized Embeddings

Many mathematical imaging problems are posed as non-convex optimization problems. When numerically tractable global optimization procedures are not available, one is often interested in testing ex post facto whether or not a locally convergent algorithm has found the globally optimal solution. When the problem is formulated in terms of maximizing the likelihood function under a statistical model for the measurements, one can construct a statistical test that a local maximum is in fact the global maximum. A one-sided test is proposed for the case that the statistical model is a member of the generalized location family of probability distributions, a condition often satisfied in imaging and other inverse problems. We propose a general method for improving the accuracy of the test by reparameterizing the likelihood function to embed its domain into a higher-dimensional parameter space. We show that the proposed global maximum testing method results in improved accuracy and reduced computation for a physically motivated joint-inverse problem arising in camera-blur estimation.
Research on the “Three Docking” of Modern Apprenticeship Curriculum Standard and Professional Post of Automobile Specialty based on the Big Data Technology

Abstract Experience shows that employability is very important in occupational activities, especially for manual jobs, such as automobile jobs. However, at present, there is a disconnection between the courses of automobile majors in some schools and the demand for vocational positions, which does little to improve students’ employability. Therefore, based on the computer big data technology, this paper analyzes the existing curriculum standards of modern apprenticeship for automobile majors, and explores how to realize the three-way docking between the curriculum standards of modern apprenticeship for automobile majors and vocational posts under the condition of the Internet.
Three-branch Semantic Segmentation Network

Abstract Effective Semantic segmentation requires both spatial details and object-level semantic information. Meanwhile, context information is also important for complex scene understanding. However, it is hard to meet these demands simultaneously in the top-down CNN structure. In this paper, we tackle this problem with a Three-branch Semantic Segmentation Network (TSS Net). The proposed TSS Net consists of three parts, including a spatial network, a semantic network and a context network. The spatial network utilizes convolutional layers with small stride and a spatial pyramid pooling module to extract multi-scale spatial features. In the semantic network, multiple level features are combined to enhance semantic information. The context network integrates different scales contextual information to facilitate objects localization in complex scene. The proposed semantic segmentation framework has been evaluated on the CamVid and the Cityscapes datasets. Experimental results demonstrate that the TSS Net achieves state-of-the-art performance.
The effect of chemical commitments with exposure to gamma irradiation dosage and save time on test attraction and pullet limits of HDPE pack

Abstract This study aims to determine the effect of gamma irradiation doses and time of storage on the mechanical properties of HDPE packaging plastics, and to apply gamma irradiation techniques to improve the mechanical quality of materials, especially plastic-based products. Packaging plastic is the most popular packaging material used, many businesses choose plastic as packaging for their products Research Place for Polymer laboratories, Isotopes and Radiation Technology Application Centers, National Nuclear Energy Agency, Pasar Jumat, South Jakarta and Mechanical Test Laboratory of the faculty of Engineering, UHAMKA Pasar Rebo, East Jakarta. To determine the resistance of packaging plastic to gamma irradiation, the effect of gamma irradiation dosage and storage time on the mechanical properties of HDPE plastic packaging were carried out. The research will be conducted with Cobalt-60 gamma radiation sources at irradiation doses of 10, 15, 20, 25, and 30 kGy with a dose rate of 6 kGy / hour, then some irradiated plastic footage is stored at room temperature for 0 and 5 weeks. The mechanical properties tested were yield strength, tensile strength, and elongation at break using the strograph R1 tensile test tool. Gamma irradiation caused yield strength, tensile strength and elongation in the A brand HDPE packaging plastic increased from 0 to 5 kGy, then decreased by 5.31% at a dose of 10 kGy and then continued to increase to a dose of 25 kGy. The optimum increase occurred from doses 15 to 20 kGy, as much as 12.00%. For plastic B, the breakout extension (Eb) continues to increase as the irradiation dose increases to 30 kGy. The optimum Eb increase occurs from a dose of 0 to 5 kGy, at 9.80%. Increased mechanical properties are caused by free radical species formed during the irradiation process, so that a cross-bond is formed between HDPE plastic packaging molecules. While the decrease occurs due to the weakening of the cross bond between the HDPE packaging plastic molecules.
Anxiety detection using the AMAS-C test and feeling analysis on the Facebook social network

This research exposes the proposal of a web application based on Facebook, Meaning Cloud and AMAS-C test tools, with the purpose of predicting anxiety in university students. For this purpose, a prediction algorithm was developed that takes the processed information from the publications of the Facebook users and compares it with the information of the previous learning, giving as a result the prediction of total anxiety of the individual. Finally, this research exposes an average absolute error 6 of 84 possible points, corresponding to the AMAS-C test scale, where 5 correct predictions, 12 medium correct predictions and 1 erroneous predictions were obtained. The conclusion is that the tools selected together with the proposed algorithm could be used as an alternative to the AMAS-C test.
Reading Big Data

With your ability to understand manipulating data, it is now worth taking a moment to consider where data live. R works in memory, random access memory (RAM), not hard drives. A quick check of your system settings should reveal the amount of memory you have. We, the authors, use between 16 and 128 GB in our real-world systems, with the larger number being an expensive yet convenient habit. R cannot analyze data larger than available RAM. This becomes important when considering the ever larger file sizes one encounters in modern research. Too much data in RAM would stop R and your computer system from working. Thus
The Theoretical Framework Based on Network and the Research of “Three Parts Method” of Accounting Information Distortion in Digital Processing

Abstract The distortion of accounting information is an international problem. It is also a historical legacy. It is an important problem that all countries in the world need to study and solve at present. As an accountant, we should pay attention to this problem and try our best to solve it. According to the reason of accounting information distortion, this paper puts forward the method of accounting information distortion by three parts. It divides the distortion of accounting information into regular distortion, illegal distortion and behavioral distortion. This paper analyzes the different reasons of three ways of information distortion from the perspectives of accounting domain order, information asymmetry and human finiteness. This paper discusses the relationship among the three ways of distortion and puts forward the corresponding countermeasures.
Performance Optimization of Image Transmission based Turbo and Precoder codes

Abstract The amazing performance of the concatenated codes led to attract many researchers to study these characteristics and use theme in many applications in recent years. Because the concatenated code theory is not built from the previous theories, so the characteristics of these codes remain under study and clarification. Recently, the concatenated codes became one of the main options for forward error correcting codes (FEC) in third generation mobile standards such as Universal Mobile Telecommunications System UMTS. This paper investigates the effect joint source –and channel coding which is considered a proficient system for wireless transmission. A new class of FEC is used which is known as a precoder of rate 1 as inner code and non recursive turbo code as outer encoder for hybrid system and then compared with the performance of UMTS turbo code standard for DCT compressed image.
An Effective Pedagogy Toolkit for Learning in an Intelligent Virtual Environment

Throughout the progression of the pedagogy, educators are striving hard to bring up the systems centering on effective learning. This omnipresent trend has led to the ontogeny of innovative and culmination of many congregation technologies such as virtual reality (VR), artificial intelligence (AI), and natural language processing (NLP) ensuing as intelligent virtual learning environment (IVLE). Technology-enhanced encyclopedism can facilitate pupils with influential and high-quality learning experiences as compared to the traditional learning approach. This chapter portrays learning in intelligent virtual environment as an effective pedagogy approach. The pedagogic tool developed by the authors captures text written in English as an input and creates the envisioned virtual setting. The ability of natural language interface (NLI) for VR-based learning systems is the most significant attainment of the present work, which brings a novel perspective in the field pedagogy.
Leveraging the Crowd to Support the Conversation Design Process

Building a chatbot with human-like conversation capabilities is essential for users to feel more natural in task completion. Many designers try to collect human conversation data and apply them into a chatbot conversation, aiming that it could work like a human conversation. To support conversation design, we propose the idea of inviting the crowd into the design process, where crowd workers contribute to improving the designed conversation. To explore this idea, we developed ProtoChat, a prototype system that supports a conversation design process by (1) allowing the crowd to actively suggest new utterances based on designers' pre-written design and (2) visually representing crowdsourced conversation data so that designers can analyze and improve their conversation design. Results of an exploratory study indicated that the crowd is helpful in providing insights and ideas as designers explore the design space.
Disruption and creativity in live coding

Live coding is an artistic performance practice where computer music and graphics are created, displayed and manipulated in front of a live audience together with projections of the (predominantly text based) computer code. Although the pressures of such performance practices are considerable, until now only familiar programming aids (for example syntax highlighting and textual overlays) have been used to assist live coders. In this paper we integrate an intelligent agent into a live coding system and examine how that agent performed in two contrasting modes of operation-as a recommender, where on request the the agent would generate (but not execute) domain-appropriate code transformations in the performer's text editor, and as a disruptor, where the agent generated and immediately executed changes to the code and the streaming music being performed. A within-subjects study of six live coders suggested that participants of the study preferred the agent's disruptive mode as a mechanism for enhancing the creativity of their live coding performances.
Reflecting on the Results of the Initiative ETiE for Using Tablets in Primary Schools

Mobile devices, such as smartphones and tablets, have taken the world by storm. In education, they have exponentially increased the opportunities for mobile and ubiquitous learning, as their unique features give them a competitive edge over conventional teaching. In light of the above, the chapter summarizes and discusses the findings of a series of short research projects, conducted under the umbrella of the initiative Emerging Technologies in Education, involving the use of tablets for teaching science-related subjects, programming, and Greek mythology to kindergarten and primary school students. All in all, it was found that the learning outcomes, which can be considered as good compared to non-technologically enhanced teaching, are closely related to the teaching method, to certain tablets' features, and to the type of application being used. Then again, the impact on students' misconceptions was minimal. Finally, a number of suggestions to software developers as well as to education administrators and policymakers are being discussed.
Understanding Challenges Presented Using Emojis as a Form of Augmented Communication

Emojis are frequently used in social media and textual conversations. They are significant means of communication to visually help express emotions and describe objects. Previous studies have shown positive impacts of emojis used in human relations, memorization tasks and engagement with web content. Unicode version 6 includes 2923 emojis, which makes it difficult to use them effectively without a recommender system. We formulate recommending emojis as a complex prediction problem based on its diverse usage as a word and as a sentiment marker. People have individual usage and representations of emojis across different platforms as well as different interpretations based on the device. In order to increase the accuracy of the recommending process, we propose using a recommender system that applies personalization to suggest various emojis. This paper describes several baseline models we explored and the Long Short-Term Memory (LSTM) recurrent neural networks we trained to test the feasibility of extracting knowledge from emoji datasets to predict emoji usage.
On the Use of Feature-based Collaborative Explanations: An Empirical Comparison of Explanation Styles

Current attempts to explain recommendations mostly exploit a single type of data, i.e. usually either ratings provided by users for items in collaborative filtering systems, or item features in content-based systems. While this might be sufficient in straightforward recommendation scenarios, the complexity of other situations could require the use of multiple datasources, for instance, depending on the product domain. Even though hybrid systems have a long and successful history in recommender research, the connections between user ratings and item features have only rarely been used for offering more informative and transparent explanations. In previous work, we presented a prototype system based on a feature-weighting mechanism that constitutes an exception, allowing to recommend both items and features based on ratings while offering advanced explanations based on content data. In this paper, we empirically evaluate this prototype in terms of user-oriented aspects and user experience against to widely accepted baselines. Two user studies show that our novel approach outperforms conventional collaborative filtering, while a pure content-based system was perceived in a similarly positive light. Overall, the results draw a promising picture, which becomes particularly apparent from a user perspective when participants were specifically asked to use the explanations: they indicated in their qualitative feedback that they understood them and highly appreciated their availability.
Helping Developers Find and Share Debugging Hypotheses

Debugging remains one of the most challenging activities for developers. This has motivated the invention of numerous debugging tools, which help developers inspect program states through logging and breakpoints or attempt to localize defects through automated or interactive program analysis techniques.
The design of website-based sugarcane forecasting information system

Abstract Sugarcane ( Saccharum officianarum ) is widely used as raw material for sugar and MSG. Data on sugarcane production has not been used optimally, except for administrative purposes. The production data can be used to predict the yield of sugar cane of which can be utilized by cooperatives and farmers. This research was conducted to design an information system that can be used to forecast sugarcane yields in the working area of KUD Subur Malang, Indonesia. The information system design process is carried out by implementing Machine Learning. The results of sugarcane yield forecasting using machine learning implementation in KUD Subur showed the best results using the gradient boosting algorithm with 68% model accuracy. Web-based yield forecasting information system can be used as a production forecasting tool for KUD Subur to improve its business processes. Sugarcane forecasting information system can be well received by users.
Performance Evaluation for an E-Business Platform

E-business combines the Internet and network environment with the economic operations, reaching to the core of the business processes, strengthen the enterprises informatization and digitization level. Often e-business integrates different services which communicate with each other. Frameworks and collections of services can be developed in efficient manner using Service Oriented Architecture (SOA). Web services based on Soap protocol and Rest architectural style are key technologies, used to realize e-business applications frameworks. In this paper is presented an e-business platform example, based on SOAP and REST Web services, which can be accessed by mobile users through Android OS Smartphones, while the data traffic amount and cost are optimized. The digital content source is represented by the Bucharest Stock Exchange (BSE), but it also can be retrieved from on-line vendors, e-commerce, e-payment agents etc. A performance evaluation for the e-business SOAP and REST Web services, provided on 4G LTE and IEEE 802.11n WLAN channels, is presented in the paper, in a QoS approach, referring to the response time and throughput. The content and informational tools of the platform can be extended and configured to provide and comply with other types of e-business services as: e-banking, e-commerce, e-payment agents etc, according to different application patterns.