Reviewerly/exHarmony huggingface dataset:
dsDraw

Teaching computer science theory is often challenging due to the very nature of the field - its reliance on a solid mathematical foundation, logical reasoning, and the ability to use abstractions. As such, visualization tools have been developed to provide a deep and intuitive understanding of the concepts. However, most proprietary tools are bulky, unresponsive, require specific hardware and software, and often require license and training for their use. Most available free educational tools come with a predefined set of examples and offer very little flexibility and expressive power. In this paper, we describe our lightweight web application called dsDraw that allows instructors to record whiteboard-style animated lectures with accompanying instructional audio. Graphics are created within an editor using the mouse and keyboard or by invoking commands in an interactive console. The resulting videos can be exported to common file formats for use in other mediums.
Why Do Users Choose a Hotel over Others? Review Analysis Using Interpretation Method of Machine Learning Models

To date, existing research has attempted to extract user opinions relating to products and services by differentiating the products and services through their characteristics. However, it is difficult to obtain the characteristics of similar products and services in these studies. So-called competitive products and services are actually quite similar, though they might vary in their strengths and weaknesses across competitors. These advantages and disadvantages are essential for users when selecting products and services. On the other hand, in recent years, researchers have interpreted the output of machine learning models so that humans can understand the reason of the output. LIME and SP-LIME are typical approaches used in the literature. In this paper, we propose a new method using LIME and SP-LIME to compare the characteristics of the services of three competing hotels and investigate appropriate parameters for our method. We try to extract descriptive words for each competitor from review texts, taking a Japanese business hotel market and global game console market as examples.
What's Your Problem with the Dog Internet?

In this paper, we make an argument for using "the absurd" as a useful lens through which to critique modern developments in interactive technology. We argue that absurd positions are generative and engaging; they provide scope and direction for developing artefacts that people want to talk about and discuss. We argue for adopting absurd positions because; 1) as publicly funded academics, unbeholden to commercial interests, we can, 2) it's fun, and 3) doing so draws out, highlights, and plays with the often weird, fake, nonsense, bizarre, and surreal aspects of modern interactive technology artefacts - and the often weird situations that arise when interacting with those artefacts. In order to illustrate this argument, we present a number of case studies drawn from 10 years of our absurd research papers, many of which were published at previous iterations of this conference.
Models for Internal Clustering Validation Indexes Based on Hadoop-MapReduce

Cluster validation aims to both evaluate the results of clustering algorithms and predict the number of clusters. It is usually achieved using several indexes. Traditional internal clustering validation indexes (CVIs) are mainly based in computing pairwise distances which results in a quadratic complexity of the related algorithms. The existing CVIs cannot handle large data sets properly and need to be revisited to take account of the ever-increasing data set volume. Therefore, design of parallel and distributed solutions to implement these indexes is required. To cope with this issue, the authors propose two parallel and distributed models for internal CVIs namely for Silhouette and Dunn indexes using MapReduce framework under Hadoop. The proposed models termed as MR_Silhouette and MR_Dunn have been tested to solve both the issue of evaluating the clustering results and identifying the optimal number of clusters. The results of experimental study are very promising and show that the proposed parallel and distributed models achieve the expected tasks successfully.
Designing a Speculative Kit for Technology Imagination with Makers

Recent studies have articulated that DIY and the maker culture enrich practice-led HCI communities. Drawing on the specialties, intrinsical playfulness, and tangibility of the maker culture, we took a participatory and design fiction approach to technology imagination with a local maker community. We made a Speculative Kit composed of four series of catalogs and props of fictional products to embody the knowledge produced by an IoT research center. The tool kit will be used in situated making activities for technology practitioners to create alternatives for emerging technologies.
Outlier detection based on sparse coding and neighbor entropy in high-dimensional space

Outlier detection is an important branch in data mining and plays a vital role in broad range of applications including network-traffic anomaly detection, credit fraud prevention, etc. Based on the assumption that dataset can be approximately reconstructed by linear combinations of dictionary atoms, some detection algorithms initially project the data to a higher dimensional manifold such that data representation becomes sparse. Unlike previous sparse coding based approaches, our method SNOD (Sparse coding and Neighbor entropy based Outlier Detection) can detect local and global outliers and construct neighborhood in a self-manner. Finally, the outlier score of each sample using local reconstruction coefficients is computed. Experiments on several benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm.
Designing Multistage Search Systems to Support the Information Seeking Process

Due to the advances in information retrieval in the past decades, search engines have become extremely efficient at acquiring useful sources in response to a user’s query. However, for more prolonged and complex information seeking tasks, these search engines are not as well suited. During complex information seeking tasks, various stages may occur, which imply varying support needs for users. However, the implications of theoretical information seeking models for concrete search user interfaces (SUI) design are unclear, both at the level of the individual features and of the whole interface. Guidelines and design patterns for concrete SUIs, on the other hand, provide recommendations for feature design, but these are separated from their role in the information seeking process. This chapter addresses the question of how to design SUIs with enhanced support for the macro-level process, first by reviewing previous research. Subsequently, we outline a framework for complex task support, which explicitly connects the temporal development of complex tasks with different levels of support by SUI features. This is followed by a discussion of concrete system examples which include elements of the three dimensions of our framework in an exploratory search and sensemaking context. Moreover, we discuss the connection of navigation with the search-oriented framework. In our final discussion and conclusion, we provide recommendations for designing more holistic SUIs which potentially evolve along with a user’s information seeking process.
An Anonymous Transmission Algorithm Named Ripple Spreading for Blockchain

The emergence of blockchain technology profoundly reflects the importance of anonymity in personal data and personal identity. In this paper, we propose an anonymous transmission algorithm named ripple spreading specialized in anonymous payment in the blockchain network. The algorithm achieves the purpose of anonymous transmission by transmitting verification while jumping among multiple nodes, thereby, overcomes the defect of the lack of covert channel characteristics under the web environment. We firstly demonstrate the framework of our algorithm. Subsequently, we illustrate the converging and merging process of transmission in detail, and lastly, give a quantitative evaluation of this algorithm. The result of the experiment shows that once the number of paths as well as the average path length reaches a threshold, our algorithm can solve the main problem of anonymous transmission to a great extent and improve the anonymity function significantly.
An Osmosis-Based Intelligent Agent Scheduling Framework for Cloud Bursting in a Hybrid Cloud

An intelligent system to efficiently provision resources in a hybrid cloud environment is necessary due to the high level of complexity. The semi-permeable agent for hybrid cloud scheduling (SPAH) is a bio-inspired agent that adapts the biological process of osmosis into cloud bursting. The primary objective of the agent is to minimize the makespan. The framework and algorithm for the two phases of SPAH, to recognize the state and decide on action are presented. A QoS (Quality of Service) deadline factor metric is proposed to study the indirect impact of SPAH in deadline satisfaction. SPAH shows significant improvement in deadline satisfaction of up to 85% as compared to other cloud bursting techniques. This is the result of a reduced makespan and a reduced cumulative waiting time. The analysis of SPAH shows that it works in quadratic time complexity.
Using model’s temporal features and hierarchical structure for similar activity recognition

Using Sensor-based approach in activity recognition usually requires the deployment of many ambient sensors to objects and environments. Each sensor can be triggered by more than one activity, e.g., a touch sensor of a cooker can be triggered by cooking, doing dish and so on. An activity consists of some sensor events. When the number of same sensors are in the majority of two activities, the two activities are defined as similar activities which are difficult to distinguish. To address the challenge of recognizing similar activities, this paper conceives a new activity recognition approach incorporating high-dimensional features of duration and time block characteristics to improve the inference performance. In a further step, we take advantage of these similar activities to build a hierarchical structure model which can improve capacities of expandability and standardization. We design experiments of similar activity in our daily life to evaluate this solution. The results show that high-dimensional temporal features improved similar activity inference accuracy on an average of 1.88 times, and the use of hierarchical structure can generalize specific rules to standard ones which decreases similar activity recognition computation time on an average of 0.36 times.
Finding Maximal Non-redundant Association Rules in Tennis Data

The concept of association rules is well-known in data mining. But often redundancy and subsumption are not considered, and standard approaches produce thousands or even millions of resulting association rules. Without further information or post-mining approaches, this huge number of rules is typically useless for the domain specialist – which is an instance of the infamous pattern explosion problem. In this work, we present a new definition of redundancy and subsumption based on the confidence and the support of the rules and propose post-mining to prune a set of association rules. In a case study, we apply our method to association rules mined from spatio-temporal data. The data represent the trajectories of the ball in tennis matches – more precisely, the points/times the tennis ball hits the ground. The goal is to analyze the strategies of the players and to try to improve their performance by looking at the resulting association rules. Here, the domain specialist was able to select useful rules during post-mining. The proposed approach is general and could also be applied to other spatio-temporal data with a similar structure.
Hierarchical Quick Shift Guided Recurrent Clustering

We propose a novel density-based mode-seeking Hierarchical Quick Shift clustering algorithm with an optional Recurrent Neural Network (RNN) to jointly learn the cluster assignments for every sample and the underlying dynamics of the mode-seeking clustering process. As a mode-seeking clustering algorithm, Hierarchical Quick Shift constrains data samples to stay on similar trajectories. All data samples converging to the same local mode are assigned to a common cluster. The RNN enables us to learn quasi-temporal structures during the mode-seeking clustering process. It supports variable density clusters with arbitrary shapes without requiring the expected number of clusters a priori. We evaluate our method in extensive experiments to show the advantages over other density-based clustering algorithms.
A Multi-Threaded Cuckoo Search Algorithm for the Capacitated Vehicle Routing Problem

Cuckoo search is a bio-inspired algorithm based on the reproduction behavior of some cuckoo species. This metaheuristics seems promising to solve the capacitated vehicle routing problem. This paper analyzes the standard capacitated vehicle routing problem because the cuckoo search enables faster results with fewer parameters than other optimization algorithms. A new approach using a multi-threaded variant of the cuckoo search running on multiple CPU cores is being investigated, which allows the parallelization of optimization cycles. The approach uses a standard Java framework and takes into account multiple eggs per nest. A quantitative analysis investigates the new multi-threading variant compared to the standard one.
Face recognition based on fusion of SVD and Legendre moment

Abstract Face recognition system may be defined as identifying the human face from scenes of video or a static digital image using a computer application. Human face recognition has been gained huge attention as a result of the significant role in various applications such as security, medical applications, forensic evidence, etc. Human face recognition has been faced by many challenges such as illuminations, rotation, noise, blure, etc. Each challenge may need special handling, hence, the face identification and face verification considered as a difficult field. Several algorithms were applied, each one has weaknesses and strengths, these algorithms focus on the pre-processing stage or features extraction or features selections or classification or it may be focuses on all these steps. This paper suggested a new way based on the fusion of features resulted from applying a common known moment, Legendre with a vector of features produced by a applying singular value decomposition transform (SVD), The system has been tested on FEI Brazil database and achieved recognition rate from 95% to 100%, also the suggested algorithm applied on ORL Databases and achieved recognition rate exceed to 98%. Finally, the proposed method performed a higher recognition rate under uncontrolled environment and noisy and blur images.
Sociological Aspects of Big Data Privacy

Nowadays, information technology has entered the era of big data. It is regarded as a revolution that will transform how we live, work and think. In the network era, a large number of data will be generated every day. Most of these data have the participation of our individual users, and the left data footprints can be gathered together to discover a lot of personal privacy. Big data can bring better work and life experience to users, however, it also brings the risk of personal privacy disclosure. This paper will use some theories of sociology, including anomie theory, social control theory, cultural conflict theory, and social exchange theory, to understand the knowledge of big data privacy, and point out some technologies that could solve out the problem: anonymity technology, data encryption technology, different privacy.
KI/OSK: Practice Study of Load Sensitive Board for Farmers Market

In recent years, the retail industry has increasingly gained interest in the ICT system for enriching the customers' shopping experience and is now being deployed in some stores. As the most common implementation method is limited with its high cost and large consumption of space, it is a challenge for smaller or temporal stores to install such services. In this paper, we explore the usage of a load-sensitive board to improve the retail shopping experience specifically in smaller and temporal stores. As a case study, we develop and examine KI/OSK, an easy-to-install modular table-top retail application using SCALE a previously developed load sensing toolkit specifically for the Farmers Market. Our study uses iterative user research including surveys with Farmer's Market managers to assess design requirements, and testing and revising through a field study in a Farmers Market.
HaemoKBS: A knowledge-based system for real-time, continuous categorisation of adverse reactions in blood recipients

This work introduces HaemoKBS, a novel Haemovigilance decision support system for adverse reactions in blood recipients. Machine learning inference and rule-based reasoning were applied to build the underlying decision support models, namely to automatically extract evidence from different types of data included in hospital notifications and incorporate a priori expert knowledge. The ultimate aim is to dynamically learn and improve the reasoning abilities of the system and thus, be able to provide educated recommendations to hospital notifiers along with understandable explanations on the acquired knowledge. Experiments over the records of the Portuguese National Haemovigilance System from the last 10 years demonstrate the practical usefulness of HaemoKBS, which will contribute to a better depiction of the adverse reactions and to flag any incomplete notification enforcing data quality.
Research on Chinese Intent Recognition Based on BERT pre-trained model

As a sub-task in natural language understanding, intent recognition research plays an important role in it. The accuracy of intent recognition is directly related to the performance of semantic slot filling, the choice of data set, and the research that will affect subsequent dialogue systems. Considering the diversity in text representation, traditional machine learning has been unable to accurately understand the deep meaning of user texts. This paper uses a BERT pre-trained model in deep learning based on Chinese text knots, and then adds a linear classification to it. Using the downstream classification task to fine-tune the pre-trained model so that the entire model together maximizes the performance of the downstream task. This paper performs domain intent classification experiments on the Chinese text THUCNews dataset.Compared with recurrent neural network(RNN) and convolutional neural network(CNN) methods, this method can improve performance by 3 percentage points. Experimental results show that the BERT pre-trained model can provide better accuracy and recall of Chinese news text domain intent classification.
Extraction of ORB Features with an Improved Method

Improvement of the extraction means of the ORB (Oriented FAST and Rotated BRIEF) feature primarily includes optimization concerning excessive aggregation of ORB features and the improvement of the problem that the correct features could not be extracted when regional image illumination is too bright. First, the local self-adaptive threshold was calculated on the basis of the threshold and FAST features were extracted based on the local threshold as the candidate feature points. Then, image iteration was divided into disparate regions and the optimal feature points of the local region were selected as the extraction result. The experimental data showed that the aggregation level of the improved ORB feature lowered more obviously than that of the ORB feature, which adapted to local illumination and the threshold value with high stability; however, the time still met real-time demands.
Keywords Extraction Based on Word Relevance Degrees

Keywords extraction (KE) is an important part of many neural language processing (NLP) tasks which have attracted much attention in recent years. Graph-based KE methods have been widely studied because it is always unsupervised and can extract keywords with information among words. However, existing graph-based KE methods suffer from low time efficiency or large corpus dependency. In this work, we propose a new graph-based keywords extraction method which uses word relevance degrees to extract keywords and two word relevance degrees calculation algorithms. The proposed method doesn't rely on big corpus and experimental results show that the proposed method can extract keywords more efficient with higher performance on compared with TF-IDF, TextRank and KMST methods.
Place and music

This chapter reflects on the production of Concrete Antenna, a place-based sound installation in Newhaven, Edinburgh, between 2015–2016. It suggests that a relational and dynamic notion of place can inspire experimental composition techniques which blur sound and music. Place is imagined here as a composition itself: one that has elements which are seemingly permanent and patterned; and others which are fleeting, chaotic or imperceptible. Three themes of place-attentive approaches are developed to describe compositional and installation processes based on such an understanding of place: distributed listening; the place-specific-non-specific; and auditory topographies of chance. Together, these approaches outline a more-than-human compositional approach to responding to, and joining with, the fluxes of life of a place.
Fuzzy feed forward neural network (FFFNN) model for the Jakarta Islamic index (JII) forecasting

Abstract Feed Forward Neural Network (FFNN) model is the best model to forecast the time series data. In this research, The Fuzzy Feed Forward Neural Network (FFFNN) with backward propagation method is used to predict the Jakarta Islamic Index (JII) data time series in 2018. Fuzzy is used as input to the FFNN model because it is overcome the weaknesses of the inaccurate results of the FFNN when the data is unclear or incomplete. The purposes of this research are to explain the procedure to generate the FFFNN model. The steps of the FFFNN model prepare the input data to become a fuzzy number using Growth-S Curve fuzzification; the second is divide the data into two training and testing data, the third is determining the best neural network architecture with different neurons and hidden layers to get the best weights that used for the forecasting model. In this research, the best FFFNN model is built by 19 neurons and one hidden layer with 90% and 10% training and testing data, respectively. Therefore with the model obtained, forecasting produces the value of MSE 0.0018 in training and 0.0004 in testing. From the MSE values obtained, it can be concluded that the forecasting using FFFNN model is reasonable to predict.
Towards a Reliable Face Recognition System

Face Recognition (FR) is an important area in computer vision with many applications such as security and automated border controls. The recent advancements in this domain have pushed the performance of models to human-level accuracy. However, the varying conditions in the real-world expose more challenges for their adoption. In this paper, we investigate the performance of these models. We analyze the performance of a cross-section of face detection and recognition models. Experiments were carried out without any preprocessing on three state-of-the-art face detection methods namely HOG, YOLO and MTCNN, and three recognition models namely, VGGface2, FaceNet and Arcface. Our results indicated that there is a significant reliance by these methods on preprocessing for optimum performance.