Reviewerly/exHarmony huggingface dataset:
A new method for pornographic video detection with the integration of visual and textual information

With the development of computer and mobile network, multimedia information is increasing exponentially. Moreover, pornographic video information has been widely spread. This paper proposed a new method for pornographic video detection through textual and visual information fusion to improve the effect of pornographic video detection. Visual features are combined with related textual features in a new way to detect porn videos. Experiments prove that the proposed method is an effective way to monitor porn web videos than other methods.
How to Design Dialogue Scenarios and Estimate Main Dialogue Parameters for a Voice-Controlled Man-Machine Interface

In this paper we classify and analyze dialogue scenarios in automated human-machine systems with speech recognition. We take into account how to identify and correct recognition errors. Also, we evaluate and compare the scenarios according to how long the dialogue lasts, provided that the dialogue completes successfully with at least some given probability. Based on the results of our work, we formulated recommendations for developers of speech dialogue systems to choose a dialogue scenario; the actual scenario depends on how reliable the used speech blocks are.
Polynomial functions over dual numbers of several variables

Let [Formula: see text] be a positive integer. For a commutative ring [Formula: see text], the ring of dual numbers of [Formula: see text] variables over [Formula: see text] is the quotient ring [Formula: see text], where [Formula: see text] is the ideal generated by the set [Formula: see text]. This ring can be viewed as [Formula: see text] with [Formula: see text], where [Formula: see text] for [Formula: see text]. We investigate the polynomial functions of [Formula: see text] whenever [Formula: see text] is a finite commutative ring. We derive counting formulas for the number of polynomial functions and polynomial permutations on [Formula: see text] depending on the order of the pointwise stabilizer of the subring of constants [Formula: see text] in the group of polynomial permutations of [Formula: see text]. Further, we show that the stabilizer group of [Formula: see text] is independent of the number of variables [Formula: see text]. Moreover, we prove that a function [Formula: see text] on [Formula: see text] is a polynomial function if and only if a system of linear equations on [Formula: see text] that depends on [Formula: see text] has a solution.
Duplicate Question Management and Answer Verification System

Management of large data sets of question papers can be cumbersome, especially when dealing with potential duplicate or erroneous questions. The addition of a natural language system that automatically handles these issues would greatly speed up the verification of such data sets. This is a tool for identifying semantic similarity between sentences in plain-text English. Handpicked features were selected which included simple structural features and word embedding features using word2vec with multiple distance metrics between the resulting sentence vectors. The model is trained on weak hardware allowing for sufficiently high accuracy on low end machines. Results demonstrate the effectiveness of boosting for improving the performance of simple learning models, allowing for complex learning in the absence of high end hardware.
The Validity of Computer Technologies in Promoting College Students’ Entrepreneurship Education Based on the E-commerce

In recent years, with the rapid development of the network technology, the related research on college students’ e-commerce entrepreneurship is continuously fermenting. However, while more and more attention has been paid, the relevant research has not made great breakthroughs. There is no certain induction, sorting out and research on the relevant policies issued by the state of e-commerce entrepreneurship for college students. There is no internal relationship between the e-commerce entrepreneurship for college students and the innovation entrepreneurship for college students. The mode and the thinking of the e-commerce entrepreneurship are mostly at the basic stage, and more or less imprinted with foreign experience and brand. The research lacks the profound theoretical basis, and in the current researches, there are few links between college students’ e-commerce entrepreneurship model and the entire college students’ education system and the Internet development system. There is a lack of internal linkages among various models.
The Justice Syndicate: using iPads to increase the intensity of participation, conduct agency and encourage flow in live interactive performance

The Justice Syndicate is an interactive performance, featuring an audience who take on the role of jurors considering a difficult case. Participants receive evidence, witness testimonies and prompts to vote and discuss the case on iPads. With this practice-as-research project we explored what are the most effective means of inviting people to participate; how to widen their ‘horizon of participation’; how to heighten the intensity of interaction in order to increase the level of ‘agentive behaviour’ of the participants; and how to create a sense of Flow in participants. We found that an effective solution to the fear of experiencing or causing embarrassment is for the invitation to participate to come from a machine and for there to be no distinction between ‘audience’ and ‘participants’. This also proved an effective way of stimulating a high intensity of ‘agentive behaviour’ among audience members, although it did not automatically lead to a greater feeling of agency. Applying an adapted version of Lindinger and colleagues’ [(2013). “The (St)Age of Participation: Audience Involvement in Interactive Performances.” Digital Creativity 24 (2): 119–129. doi:10.1080/14626268.2013.808966] codification of how to stimulate a state of Flow in audience members also proved effective in creating a highly immersive experience.
Multi-scale Generative Model for Image Completion

Inpainting a large hole of an image is always a challenging task, especially for high-resolution images. Recently, deep learning based approach has brought excellent ability to deal with this issue. However, these methods always generate blurred edge and distorted details, which makes the inpainting images unreal. Besides, assessment for generated images is also challenging. Traditional assessment way uses various formulations, which only shows the partial image condition or distorted degree compared to original images, so this method cannot correctly reflect people's perception. To deal with these problems, we propose a multi-scale generative model which can gradually generate novel text to avoid distorted details, and the multi-scale losses can also eliminate blurred edges between inpainting results and the original region. To evaluate images, we also propose a deep convolutional neural network to do image quality assessment, which is closer to human perception. Experiments on multiple datasets show that our approach can generate more plausible inpainting results both on traditional evaluation criteria and our image quality assessment network.
Application Design and Implementation of Intelligent Bracelet in Wisdom Campus Construction

This paper proposes the application of wearable Band in the construction of wisdom campus based on the existing technology of Internet of Things in the university. From the design and implementation of the applicable functions of the intelligent bracelet in colleges to the coverage of application scenes, the analysis, design and implementation have been carried out, and good expectations have been achieved in the functional implementation of personnel information positioning, attendance, health and movement step counting on the wisdom campus, which is worthy of further promotion and application.
Measures of Random Forest Pruning: Comparative Study and Experiment on Diabetic Monitoring

In this paper, we propose a comparative study between 3 random forest pruning measures using simultaneously or separately performance and diversity. The measures will be used with a Sequential Forward Selection (SFS) path to reduce the number of initial trees. The methods are applied on a dataset from the UCI Repository and a diabetic monitoring application. The results allow obtaining ensembles of smaller sizes with similar or even exceeding, in some cases, performance of the initial forest with considerable improvements in the case of use of performance and diversity.
Conditional Training Based GM and GM-OPELM Data Fusion Schemes in Wireless Sensor Networks

As a key infrastructure of Internet of Things (loT), wireless sensor networks (WSN) can be utilized in a wide range of applications. The prediction based data fusion methods provide effective tools to reduce the amount of data transmissions while maintaining prediction accuracy. Recently a grey prediction model (GM) combining optimally-pruned extreme learning machine (OPELM) data fusion method has been proposed and shown to have good performance. However, the existing GM- OPELM method performs model training and broadcasting before each prediction, resulting in high complexity and energy consumption. In this paper the conditional training based GM (CT-GM) and GM-OPELM (CT-GM-OPELM) are proposed. By introducing an error threshold, the algorithms only perform model training when the prediction error is beyond the threshold. Compared with existing GM and GM-OPELM methods, the CT- GM and CT-GM-OPELM methods not only can achieve the higher rate of acceptable prediction and better time efficiency but also has significant reduction in the energy consumption on model training and transmissions.
Auto Image Comment via Deep Attention

In this paper, we propose an automatic image comment algorithm the state-of-the-art algorithm of image description. We employ an encoder-decoder model to extract the image feature in a deep learning style. In addition, an attention mechanism is added into the deep model for more efficient feature extraction. The model is trained and parameterized with the image comment dataset proposed in this paper. After training, the obtained deep model can generate comments automatically for a given image. In the experiment part, we evaluate the proposed method using both subjective way and objective way. We found that the proposed method can generate reasonable comments from the perspective of human beings. Meanwhile, the model was adjusted to achieve the best effect based on evaluation indexes such as BLEU and CIDEr.
A computational complexity analysis of tunable type inference for Generic Universe Types

We address questions related to the computational complexity of inferring types for a particular type system, Generic Universe Types (GUT) [1], [2], for Java programs. GUT is useful for many applications, such as program verification [3], thread synchronization, and memory management. However, requiring the programmer to explicitly provide type information is onerous, which motivates the problem of automatically inferring types. In contrast to classical type systems, ownership type systems such as GUT may have multiple typings that satisfy the type system's rules. It is therefore appropriate for the inference to be tunable — that is, the programmer can indicate preferences for certain typings via breakable constraints and/or by partial annotations. A question then is whether efficient algorithms exist for the type inference problem. In this work we establish the following results for the type inference problem for GUT [2]. (1) The tunable type inference problem that allows breakable constraints is NP-hard, (2) an encoding of the problem as boolean satisfiability (SAT), as in prior work, is indeed a polynomial-time reduction, (3) P ≠ NP implies that the problem is not approximable in polynomial time within an approximation ratio of n1−ϵ for any ϵ>0, and (4) while some restricted versions of the problem of practical interest, such as when breakable constraints are forbidden, are in P, others remain NP-hard. Our results justify the prior approach to the problem that is based on reduction to SAT. Apart from these results, given the observation in prior work that instances of the problem that arise in practice appear to be easy, we address the natural question as to what hard instances may look like, and whether they may arise in practice. We identify a class of hard instances of the problem by devising a method to generate such instances starting at instances of the Vertex Cover problem, which is known to be NP-hard. We then analyze the structural properties of such instances as compared to easy instances of similar size. We find that for the classes of instances we consider, certain SAT structural parameters may be predictive of empirical hardness.
SSIM-based adaptation for DASH with SVC in mobile networks

Dynamic Adaptive Streaming over HTTP (DASH) depends on adjustment of the quality of a video stream to the available network conditions. In order to increase Quality of Experience, average video quality should be maximized, while keeping the quality switching frequency at low levels. However, achieving high average quality with low switching frequency in highly fluctuating mobile network conditions is a tricky optimization problem. In order to overcome this problem, dynamic structure of Scalable Video Coding (SVC) is utilized in this paper. Another challenge in the quality adaptation algorithms is to proper assessment of the video quality. Most of the adaptation algorithms takes either bitrate or representation level as the input that is used to evaluate the quality of the video. However, bitrate is not strongly correlated with the quality, as it depends on the content of the video. Likewise, representation quality relationship entirely bound to encoding. In this paper, in order to have a more reliable adaptation input, SSIM is used while representing the quality of the video stream. The proposed adaptation is compared with a successful SVC DASH adaptation algorithm using both subjective and objective tests. As a result, considerably higher scores are achieved in terms of both switching frequency and average quality.
Finding Useful Features for Facial Expression Recognition and Intensity Estimation by Neural Network

Facial expression intensity has been proposed to digitize the degree of facial expressions in order to retrieve impressive scenes from lifelog videos. The intensity is calculated based on the correlation of facial features compared to each facial expression. However, the correlation is not determined objectively. It should be determined statistically based on the contribution score of the facial features necessary for expression recognition. Therefore, the proposed method recognizes facial expressions by using a neural network and calculates the contribution score of input toward the output. First, the authors improve some facial features. After that, they verify the score correctly by comparing the accuracy transitions depending on reducing useful and useless features and process the score statistically. As a result, they extract useful facial features from the neural network.
Investigating Methods of Determining Number of Hidden Units in Deep Learning for Taxi Recommender System

With modern ubiquitous computing environments, recommender systems have become a major part of modern intelligent services. Taxi recommender system allows both passengers and drivers to minimize waiting times and obtain the current location of each other. In this paper, we present a taxi recommender system based on deep learning for catching taxis. In deep learning technique, random selections of hyperparameters lead to overfitting or underfitting problems in prediction or classification. In particular, determining number of hidden units is one of the critical issues facing research. Therefore, we investigated how hidden units affect the performance of deep learning for taxi recommender system and compared the results of these existing methods for determining number of hidden units. Deep Learning algorithms, such as Deep Neural Network (DNN), which have been successfully used, were employed for classifying road segments. Finally, our proposed system can spot regions, where a passenger can catch a taxi within walkable distance.
CRFPOST

Classifying and tagging words into different lexical classes, as a fundamental process in language processing, is necessary to be addressed given the constant evolution of language, and in this case is the Filipino language. As a part of this effort, the researchers introduce in this paper a Linear-chain Conditional Random Fields (CRF) Part-of-Speech Tagger for Filipino texts with CRF providing an edge in sequence labelling as compared to generative models and other classifiers. The tool developed utilized a tag set containing 218 POS tags (69 basic and 161 compound) and Filipino text corpus with 15,166 sentences randomly picked from Wikipedia and translated to Filipino by students under linguist supervision. After experimentation, the researchers show that there is a 90.59% accuracy rate for tagging Filipino texts using CRF for POS tagging. Despite CRFPOST's utilization of word and tag sequence features produces a high performance in tagging, there are still improvements for future work. Recommendations are the inclusion of linguistic tools such as morphological analyzer and named entity recognition for better performance.
Woodie. An Urban Robot for Embodied Hybrid Placemaking

This paper presents the design of Woodie, a free-moving urban robot which draws on the ground with conventional chalk sticks, using the public space as a large art canvas. We outline the motivation and design considerations, which led the design process of the lightweight robotic device aiming to stage creative placemaking activities. Along this path, we relate to the various roles of 'embodiment' apparent in this placemaking investigation, such as the robot's physical appearance and coupling with the urban environment; the engagement of visitors into natural tangible interactions; and their involvement of physical activities around the precinct. We discuss our observation on how those embodied interactions emerged, and further elaborate on them as perspectives to inform future work.
Adaptive Interface Model with Eye-Tracking Evaluation

Adaptive interface has become one of the possible ways to consider a variety of users characteristic in managing the design process. By focusing on the skill of each user, this research showed the beginner-advanced user model of adaptive design can be able to recover the incapability of universal design. The study is objected to look up a new paradigm of universal design that should meet an adaptive design based on users skill characteristics, supported by eye-tracking evaluation. We develop a model of an interface design by using a case study of Student Centered E-Learning Environment (SCELE) Faculy of Computer Science, Universitas Indonesia with rapid contextual design (RCD) methodology. By using Eye Tribe tool, the proposed model proved the significance of beginner and advanced users' model through the differences behavior while doing the task. Based on the result, we conclude that adaptive design can be developed based on users skill characteristics over a universal design.
A Lab Experiment Using a Natural Language Interface to Extract Information from Data: The NLIDB Game

This paper makes a case for the challenge of an inductive approach to research in economics and management science focused on the use of a natural language interface for action-based applications tailored to business-specific functions. Natural language is a highly dynamical and dialectical process drawing on human cognition and, reflexively, on economic behaviour. The use of natural language is ubiquitous to human interaction and, among others, permeates every facet of companies’ decision-making. Therefore, we take up this challenge by designing and conducting a lab experiment – conceived and named by us as NLIDB game – based on an inductive method using a novel natural language user interface to database (NLIDB) query application system. This interface has been designed and developed by us in order both (i) to enable managers or practitioners to make complex queries as well as ease their decision-making process in certain business areas, and thus (ii) to be used by experimental economists exploring the role of managers and business professionals. The long-term goal is to look for patterns in the experimental data, working to develop a possible research hypothesis that might explain them. Our preliminary findings suggest that experimental subjects are able to use this novel interface more effectively with respect to the more commons graphical interfaces company-wide. Most importantly, subjects make use of cognitive heuristics during the treatments, achieving pragmatic and satisficing rather than theoretically oriented optimal solutions, especially with incomplete or imperfect information or limited computation capabilities. Furthermore, the implementation of our NLIDB roughly translates into savings of transaction costs, because managers can make queries without recurring to technical support, thus reducing both the time needed to have effective results from business decisions and operating practices, and the costs associated with each outcome.
A Conceptual Model of an Intelligent Platform for Security Risk Assessment in SMEs

SMEs are increasingly targeted by cyberattacks and usually less in control of their Information Security Management System than larger organizations due to a lack of resources. Risk assessment can help them to determine which changes are needed bearing in mind their constraints. However, common frameworks for risk assessments are more suitable for large organizations. Some of them have been designed specifically for SMEs but still target an audience of information security experts and are considered as time-consuming by SMEs. This article aims at tackling those issues by introducing a conceptual model of an Intelligent Platform for supporting SMEs in security risk assessment process. The design research method was used to develop a model taking into account the inputs from relevant stakeholders collected via interviews. The model was validated and improved with case studies where quick security risk assessments in three different SMEs have been performed following the activities that the proposed model is supposed to perform.
Improving Disease Named Entity Recognition for Clinical Trial Matching

Disease named entity recognition (NER) is an important enabling technology to develop various downstream biomedical natural language processing applications. This is a challenging task, which requires addressing potential ambiguities due to variable contextual usage of the disease name mentions in clinical texts. In particular, clinical trial texts have unique complexities compared to patient-focused clinical reports or information-rich biomedical research articles, as they typically define drug testing eligibility requirements for patient cohorts via compound contextual and logical relationships. In this paper, we propose a novel disease NER model for clinical trial texts by using deep contextual embeddings with relevant domain-specific features, word embeddings, and character embeddings in a bidirectional long short-term memory network-conditional random field (BiLSTM-CRF) framework. Experiments and analyses on a clinical trial dataset and the benchmark NCBI scientific article dataset show the effectiveness of the proposed model.