Reviewerly/exHarmony huggingface dataset:
UnlockMe: Social Interactions when Co-located in Online Activities

All the activities that we do online, by either preference or obligation, deprive us of social interactions especially the impromptu ones. We present UnlockMe, a concept that aims at preserving the social link, the disposition to come across other people serendipitously when engaged in online activities such as purchasing goods, working from home or when viewing media and entertainment. Our concept relies on virtual co-location detection (both synchronous and asynchronous), to allow users to engage with other people with whom they would have been likely to interact when doing the same activities offline in the physical world. We developed and illustrated UnlockMe with six scenarios and low-fidelity prototyping to test it with 10 participants who were isolated due to the coronavirus disease (COVID-19) pandemic. Our findings reveal multimedia recommendations from close social connections to be the best scenario for UnlockMe followed by online shopping and connecting with the local community.
A pipeline for facial animations on low budget VR productions

This work presents a new approach to developing character facial animations for Virtual Reality projects focusing on low budget productions. Facial animations are mostly time-consuming and complex and often involve expensive equipment and software inaccessible to the vast majority of professionals. This work aims to present an alternative facial animation pipeline using a hybrid approach and combining traditional software, algorithms, and processes to offer alternatives for producing high-quality facial animations for characters focusing on Virtual Reality applications.
High Throughput Hardware for Hoeffding Tree Algorithm with Adaptive Naive Bayes Predictor

Hoeffding tree algorithm is a popular online decision tree algorithm capable of learning from huge data streams. The algorithm involves complex time consuming computations in the leaves of the tree for each data instance. These computations involve a lot of parallelisms which can be exploited and implemented in a field programmable gate array to achieve speedup. This paper presents a hardware accelerator for Hoeffding tree algorithm with adaptive naive bayes predictor in the leaves. The proposed system is capable of accelerating data streams with both nominal and numeric attributes using minimum hardware resources for huge datasets. It is implemented on a Xilinx VC707 board based on Virtex-7 XC7VX485T field programmable gate array. The implemented system is about 9x faster than StreamDm(C++), a well known reference software implementation for the standard forest cover type dataset.
The Dynamics of Productive and Non-Productive Spaces in Coastal Tourism Areas in Denpasar

Abstract Tourism in Bali has redefined the use of space. The formation of new activities carried out by new users, has changed the way people use space. Spaces exist because they form and some use, create governance and bring up spatial practices that may be hampered by the current digital age. This research will examine the process of redefinition of space, the formation of productive and non-productive spaces in accordance with the representation expected by the community. In this study, Sanur Beach, a traditional area that has become a tourism space, will be used as a case study. The method Qualitative, by mapping activities based on time segments for space use, interviews to determine user expectations and experiences, identifying patterns to find out how governments regulate space and building data representations through simulations of the movement of space users. The most significant result is the presence of a new identity in the meaning of space, which has been transformed due to spatial practices that are influenced by space users and the development of digital technology. Thus, the use of space in the era of global tourism in which different cultural backgrounds of space users affect the definition of space. This is where the space planner needs to consider how the representation of space by the user has implications for the regulations of the government and customary regulation.
Fast Subspace Clustering Based on the Kronecker Product

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> / <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> ). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.
Optimal Strategies For Comparing Covariates To Solve Matching Problems

Many machine learning tasks can be posed as matching problems in which we are given a “probe” entry that we expect matches some of the entries in our “gallery”. The general solution to these problems is to retrieve matching entries based on statistical dependencies between the probe and the gallery data that are learned using complex models. Often, however, there are other common covariates to the probe and gallery data which might be easily inferred and may explain some of the statistical dependencies between the two. In this paper we present a probabilistic framework to derive optimal matching strategies based only on covariate features for three broad tasks, namely N-way classification, pairwise verification and ranking. We use canonical metrics to determine the maximum performance that can be expected if only covariate features are used and determine the marginal gain of using complex models. We find that covariate matching achieves an EER within 10% of a CNN in the verification task, and an MAP within 22% of the a DNN based model in the ranking task.
Countermeasures of Improving the Professionalism of English Teaching in Higher Vocational Colleges Based on New Media Technology

With China's higher vocational education entering the stage of connotation improvement, the existing teaching objectives, curriculum, teaching form and teaching evaluation of Higher Vocational English can not meet the reform requirements of curriculum development based on working process. Through the questionnaire survey, this paper analyzes the current situation and confusion of English Teaching in Higher Vocational Colleges in China. Around the vocational English education in higher vocational colleges, this paper puts forward the ideas of English curriculum reform. The results show that: (1) Setting up vocational English reflects the professionalism of Higher Vocational English teaching content; (2) Creating open teaching situation reflects the openness of teaching form; (3) Constructing students' English language practice files reflects the practicality of teaching process; (4) Adopting multiple evaluation system improves the multiple effectiveness of teaching evaluation.
Chapter 2: On the Nature of Thinking and Its Composition

A psychological study of thinking, of course, depends on the overall psychological theory that is applied. The basic postulate of the psychological theory from which we proceed may be formulated as...
Smart Online University Party Building System Based on Association Rules Data Mining

Smart online university party building system based on association rules data mining is designed and shown in the paper. According to the need of data patching, the association rules related to the target attribute will be searched by reasonably reducing the value of support, that is, find the corresponding conditional functional dependencies. For enhancing the data robustness, the neural model is applied into the training stage of the model. Furthermore, the system is implemented through the platform design. The user interface and the experience are jointly improved through the test.
Application of Particle Vector Model in Smart Teaching Management System

In the current education model, in order to improve the teaching quality of teachers, the established teaching system can push the teaching data and conclusions to the teachers themselves or the school, which helps to understand the teaching situation in many ways, so as to improve the teaching methods of teachers in the classroom. Develop targeted solutions to the problems that arise. This paper is based on the particle swarm algorithm, combined with particle cluster calculation and feature intensive fusion, to analyze the teacher's behavior in the classroom scene, aiming to improve the accuracy of teacher behavior recognition, make full use of video resources to assist in the analysis of the teacher's overall behavior, thereby automatically generating teaching Ways to evaluate.
Solving the Workflow Satisfiability Problem Using General Purpose Solvers

The workflow satisfiability problem (WSP) is a well-studied problem in access control seeking allocation of authorised users to every step of the workflow, subject to workflow specification constraints. It was noticed that the number <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> of steps is typically small compared to the number of users in the real-world instances of WSP; therefore <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is considered as the parameter in WSP parametrised complexity research. While WSP in general was shown to be W[1]-hard, WSP restricted to a special case of user-independent (UI) constraints is fixed-parameter tractable (FPT). However, restriction to the UI constraints might be impractical. To efficiently handle non-UI constraints, we introduce the notion of branching factor of a constraint. As long as the branching factors of the constraints are relatively small and the number of non-UI constraints is reasonable, WSP can be solved in FPT time. Extending the results from Karapetyan et al. (2019), we demonstrate that general-purpose solvers are capable of achieving FPT-like performance on WSP with arbitrary constraints when used with appropriate formulations. This enables one to tackle most of practical WSP instances. While important on its own, we hope that this result will also motivate researchers to look for FPT-aware formulations of other FPT problems.
Sketch-SNet: Deeper Subdivision of Temporal Cues for Sketch Recognition

Sketch recognition is essential in sketch-related researches. Different from the natural image, the sparse pixel distribution of sketch discards the visual texture which encourages researchers to explore the temporal information of sketch. Using of million-scale datasets, we explore the invariable structure and specific order of strokes in sketch. Prior works based on Recurrent Neural Network (RNN) output different features with changed stroke orders. In particular, we adopt a novel method by employing a Graph Convolutional Network (GCN) to extract invariable structural feature under any orders of strokes. Compared with traditional comprehension of sketch, we further split the temporal information of sketch into two types of feature, invariable structural feature (ISF) and drawing habits feature (DHF) with the aim of finer feature extraction in temporal information. We propose a two-branch GCN-RNN network, Sketch-SNet, to extract two types of feature respectively. The GCN branch is used to extract the ISF through receiving various shuffled strokes of an input sketch. The RNN branch takes the original order to extract DHF by learning the pattern of strokes' order. Extensive experiments on the Quick-Draw dataset demonstrate that our further subdivision of temporal information improves the performance of sketch recognition which surpasses state-of-the-art by a large margin.
L2ers’ predictions of syntactic structure and reaction times during sentence processing

This paper investigates how Korean L2 learners of English predict upcoming syntactic structure based on a newly received word during sentence processing. Studies like Linzen and Jaeger (2016) suggest that readers use their probabilistic inference developed by their experience of the language to which they have been exposed to predict the most appropriate syntactic structure. This study replicates the experiment for L2ers following Linzen and Jaeger (ibid.), which investigates the way of predicting syntactic structure by using the subcategorization frame of a verb to understand L1 language processing. We employ the information-complexity metrics such as surprisal, entropy, and entropy reduction to quantify the uncertainty/unexpectedness of a given word that reflects the processing difficulty during sentence processing. The results show that L2ers’ tendency to read different regions of a sentence varies. Reading times are longer in the verb and the ambiguous regions of the structurally ambiguous than of the structurally unambiguous sentences. Likewise, reading times are longer in the disambiguating region of the unambiguous than of the ambiguous sentences. Reading times are also longer when the surprisal increases in the disambiguating region. Overall, the findings reveal that such information-complexity metrics as entropy reduction and surprisal play an instrumental role in accounting for the aspects of sentence processing by Korean L2 learners of English. (Shinhan University · Dongguk University)
Analysis of the rainfall event in 2018-2019 using the air stability index method at the Meteorological Station of Sultan Iskandar Muda Banda Aceh

Abstract The purpose of the present research is to determine the forecast of rain events with good results and can provide information on rain events to be right on target. In this study, an analysis of rainfall events in 2018 and 2019 was carried out using rainfall and radiosonde data at the Meteorological Station of Sultan Iskandar Muda Banda Aceh. The analysis used with the atmospheric stability index method, which was taken by Radiosonde data, obtained information on atmospheric parameters such as temperature, dew point, pressure, and wind. This parameter can be used to predict rain events using the air stability index, where the indices used are Showalter Index (SI), Lifted Index (LI), CAPE, Total-Total Index (TT), K-Index, Severe Weather Threat Index (SWEAT). From the results of the stability indices verified by rainfall data observed by the Meteorological Station of Sultan Iskandar Muda. The results showed that the LI, SI, TT, and KI indices were good enough to predict rainfall, while the CAPE and SWEAT indices were still not good at predicting rainfall events. This information can then be used as a basic reference which is quite good to use through the upper air stability index approach in develop the rainfall forecast value.
Detection of Online Prostitution in Twitter Platform Using Machine Learning Approach

Twitter is one of the social media used for online prostitution. Machine learning is a technological approach that can detect the existence of accounts on Twitter. This research used a CRISP-DM Method. The algorithms used are SVM, Random Forest, and Naive Bayes. Crawling using hashtags containing online prostitution is a solution to get data about online prostitution accounts. From the results of data labeling, there are two data set models. The first set of data models is the data set of accounts for prostitution and accounts for non-prostitution without hashtags prostitution. And the second is the data set of accounts for prostitution with non-prostitution accounts with prostitution hashtags. The study results show that for the data set 1 model, features that can distinguish between prostitution accounts and non-prostitution accounts are the number of followers, tweets, age of accounts, and content (words and hashtags). For data set 2, distinguishing between prostitution accounts and non-prostitution accounts with prostitution hashtags is the number of tweets and content (hashtags and words). Afterward, it is known that SVM has the highest accuracy rate, namely 98,83% for data set model 1, while Random Forest has the highest accuracy for dataset model 2, namely, 82,93% for data set model 2. Furthermore, to know the best model between model dataset1 and model dataset2, we also test the model with the same new data as 150 random data. The result is that dataset model 2 is better than dataset model l because it makes fewer errors in predictions, which is only 29 errors compared to dataset 1 with 37 errors.
Product Fraud Judgment Based on Random Forest Model

With the rise of IoT technology, more and more companies are using this technology for daily work production. The technology will generate a lot of data in the process of application. If the data can be used reasonably, it will help the company make better decisions. This paper is based on the random forest model to realize the judgment of fraudulent product behavior. Due to the large amount of data given by the material, we first perform feature engineering on the data to obtain processed data that can be used for modeling, and then use the random forest algorithm for data classification and regression. Experiments show that the accuracy of the random forest model is 98. 48. Compared with the logistic regression model and the naive Bayes model, it has better data classification and regression capabilities.
In-house development, implementation and evaluation of machine learning software for automated clinical scan processing

Objectives Scanning of myocardial perfusion patients on a dedicated cardiac gamma camera (GE Discovery NM 530c) requires careful positioning between stress and rest acquisitions. The offset between scans is routinely measured through image registration and analysis of the transformation matrix. Accurate registration requires a 3D mask to be drawn manually over the left ventricle, excluding any significant extracardiac tracer uptake. This work sought to automate mask drawing as part of a new, more efficient system for checking relative patient position. Objectives were to Methods Algorithm development utilised 9604 manually drawn segmentation masks (10% for validation, 10% for testing). The NiftyNet platform was used to train, optimise and test a convolutional neural network. The algorithm was packaged as a clinical tool and utilised prospectively alongside the manual technique. The software was evaluated for 343 patients to ensure adequate functioning and to assess performance. Results The difference in patient offset measurements between manual and automated methods was small (mean of −0.01 mm (±0.4 mm) in the test dataset, mean difference of −0.05 mm (±0.5 mm) during clinical evaluation). The position-check software was found to be reliable during prospective evaluation, producing segmentations that adequately enclosed the left ventricle in all cases. Conclusion This work demonstrates that established machine learning technology and modest hardware can be used to create automated segmentation tools that perform well in the clinic.
Highly Efficient and Optimized Methodology to Store Video Data by Using the Hybrid Approach

In this digital world, Video and Audio (unstructured) data have increased exponentially over the past decade. CCTV surveillance used as a method of maintaining the records for audit to provide Safety and Security as well as Legal, Insurance, Financial, and Health. Storage of video data made safe and secure to the society and organization. However, it is a challenge, in storing the different formats of video data and utilization of space is a constraint, time limit on data storage as well, and not easy to retrieve the data based on the event. Currently, the CCTV transmitted video data is stored in the database server without any changes in the conventional approach. In our hypothesis efficient data stored for an extended period for, further processing. Proposed Hybrid solution with three stages to store the video data in the Specific system.1. Removing the redundant frames by using an efficient background subtraction algorithm 2. Threshold/Entropy approach on Keyframe Extraction and 3. FFmpeg module to compress the file. Executed our solution on a real-time CCTV system in a retail store consists of three cameras to calculate the reduction percentage and improvement of the storage time limit in the specific system. Effectively retrieving Videos from the database without any loss at any point in time. Storage space reduced by 40%, so that increases the period from 60 days to 90 days and it is easy to retrieval the data based on the date and time.
Detection of Human Behavioral Events Combining Perceptual Causality

In this paper, we propose a computational model of human behavior events analysis that integrates perceptual causality. Design and establish a causal rule representation method based on default logic, which provides a concise syntax and semantic formal tool for potential causality. Through the analysis of the internal structure of the event, the technical framework of causality with the smallest action unit as the entity is established, which makes the traditional different entities, objects, events, states and actions naturally interrelated. By establishing the perceptual causal relationship between human goals and objects, events, reasoning the intention of human goals, and predicting their behavior, it can effectively solve the problem of human action occlusion and action error detection. The effectiveness of this method is evaluated on a new video dataset.
Summarizing Documents on SNS using Successive KeyGraph

Social networking services (SNS) are important sources of news for the world. By summarizing text on SNS, we will be able to grasp what topics in the news are going on. However, because texts keep growing in SNS, we need to process huge amounts of data very efficiently. Therefore, it is necessary to apply a fast and low-memory algorithm. In this investigation, we propose Successive KeyGraph as a method of keywords extraction. The extracted keywords work effectively for news summarization. In Successive KeyGraph, we update keygraph over time. KeyGraph extracts the author's claims and outputs the results immediately. Also, we hold co-occurrence count which we need to make a keygraph current by using Lossy Counting. We apply several preprocessing to manage unknown words and to improve morphological analysis.