Reviewerly/exHarmony huggingface dataset:
The Effects of Tourism on Traditional Craftsmanship for The Sustainable Development of Historic Environments

Improving the quality of life and creating various economic benefits, conservation of cultural heritage can contribute to sustainable development as a concept having environmental, economic and socio-cultural aspects. Intangible cultural heritage as the chief reason of cultural diversity particularly guarantee sustainable development. In recent years, the increase in the concern about local ways of life, festivities, has motivated the conservation of intangible cultural heritage specifically, and contributed to the continuity of the implementation of traditional craftsmanship as a domain of intangible cultural heritage and guaranteed the sustainable development. The conservation of traditional craftsmanship necessitates providing the transmission of knowledge between master and apprentice and the continuity of practice. Through 20th century, rapidly changing life conditions, the demand for traditional craftsmanship has noticeably decreased causing the decrease in the number of practitioner craftsmen. It needs to regenerate the organic relationships of crafts with the changing life conditions for their conservation. Tourism can be a motivating force to regenerate interrelations with the increasing demand of tourists for traditional crafts. This paper aims to present the effects of tourism on crafts and discuss specific conservation approach focusing on the sustainable development of historic environments particularly focusing on 17 sustainable development goals highlighted within the 2030 Agenda for Sustainable Development published by United Nations General Assembly in 2015.Keywords: Traditional craftsmanship, tourism, conservation, historic environments, sustainable development
Cloud Computing Based Task Scheduling Management Using Task Grouping for Balancing

Task scheduling in cloud computing is a challenging and essential part to a landscape of technology for a variety of complex undertaking tasks on various resources in a proficient and scalable path. Generally, task scheduling focuses on the completion time from high priority to the low priority, that has led to load imbalance of the process. The aim of this research is to dynamically optimize the aforesaid issue of task scheduling at the both sides for the system-level and user-level. This constitutes give an advantages fairness algorithm based on weighted-fair queuing model, which is significantly more effectively than the existing, that focuses on simple single priority queuing. It intends to combine both time and cost priority task scheduling in a way that is beneficial for the user and dynamically optimized resource allocation for the service provider. The CloudSim toolkit was utilized to demonstrate the simulation of task size and queue weight for the task scheduling algorithm.
Domain is of the Essence: Data Deployment for City-Scale Multi-Camera Vehicle Re-Identification

In deep learning applications large annotated datasets are considered necessary for application development and improved model performance. This work aims to investigate the validity of this assumption when enlarging a given dataset, by secondary data, with a certain domain discrepancy. The paradigm for this evaluation is a vehicle reidentification system for city-scale multi-camera settings. In city-scale multi-camera settings, the field of view of the sensors are fixed, introducing a major domain discrepancy between different datasets. This work shows that the domain of training samples heavily influences the learned feature space embedding and thus leads to a domain-specific performance. We explore how different objective functions and transfer learning approaches cope with a domain discrepancy in the training data. Concluding, the general assumption “Data is of the essence” has to be refined. With respect to feature space embeddings, our findings propose, beyond data “Domain is of the essence”.
Do You Need Embeddings Trained on a Massive Specialized Corpus for Your Clinical Natural Language Processing Task?

We explore the impact of data source on word representations for different NLP tasks in the clinical domain in French (natural language understanding and text classification). We compared word embeddings (Fasttext) and language models (ELMo), learned either on the general domain (Wikipedia) or on specialized data (electronic health records, EHR). The best results were obtained with ELMo representations learned on EHR data for one of the two tasks(+7% and +8% of gain in F1-score).
Character-Based Deep Learning Approaches for Clinical Named Entity Recognition: A Comparative Study Using Chinese EHR Texts

Previous studies on clinical sequence labeling require large amounts of task specific knowledge in the form of handcrafted features. Using latest development in representation learning, this paper introduces BERT embedding as character based pretrained model and incorporates it with three competing deep learning models (CNN-LSTM, Bi-LSTM and Bi-LSTM-CRF) to extract clinical entities from electronic health records. A comparative evaluation based on CCKS-2017 task 2 benchmark dataset reveals that: (1) BERT embedding not only facilitates improving performance of clinical NER tasks but also acts as good candidate for building end-to-end NER model requiring no feature engineering from Chinese EHR. (2) Bi-LSTM-CRF has the highest performance, i.e., 93% F1 scores when it uses BERT embedding. This paper may enhance our understanding of how to use BERT embedding in clinical NER researches.
Automatic Detection of Obstacles in Railway Tracks Using Monocular Camera

This paper presents an algorithm for automatic detection of obstructions on railway tracks. Based on computer vision techniques, this algorithm extracts the railway tracks from the image feed and automatically detects obstacles that can endanger normal railway system operation, as well as the safety of its users. To segment the railway tracks, two techniques are explored. First, the Hough transform is used to detect straight lines, which proves to be inefficient when dealing with curves. To overcome this problem, an alternative solution is developed based on mathematical morphology techniques and BLOB (Binary Large OBject) analysis, leading to a more robust segmentation. The surrounding terrain is also subject to analysis. The algorithm’s performance is evaluated considering different scenarios with and without simulated anomalies, demonstrating the effectiveness of the proposed solution.
A Multi-tier Fusion Strategy for Event Classification in Unconstrained Videos

In this paper, we propose a novel fusion strategy of prediction vectors obtained from two different deep neural networks designed for the task of event recognition in unconstrained videos. Videos are suitably represented by a set of key-frames. Two types of features, namely spatial and temporal, are computed from a hybrid pre-trained CNN-RNN (Convolutional Neural Networks - Recurrent Neural Networks) framework for each video. These features are able to capture both the transient and long-term dependencies for understanding of the events. Frame-level and video-level prediction vectors are generated from two separate CNN-RNN (ResNet50-LSTM) frameworks exploiting spatial and temporal features respectively. The fusion is performed on these prediction vectors at different levels. The entire fusion framework relies on a concept of consolidation of probability distributions. This consolidation is implemented using conflation and a biasing technique. A multi-level fusion is achieved and at each level, a significant amount of classification accuracy is observed as improving. The experiment is performed on four benchmark datasets, namely, Columbia Consumer Video (CCV), Kodak’s Consumer Video, UCF-101 and Human Motion Database (HMDB). The increment in mAP values achieved by the proposed fusion strategy is much higher than the conventional fusion strategies in use. Also, the classification accuracies of all the four datasets are comparable to other state-of-the-art methods for event classification in unconstrained videos
Noise Induces Loss Discrepancy Across Groups for Linear Regression.

We study the effect of feature noise (measurement error) on the discrepancy between losses across two groups (e.g., men and women) in the context of linear regression. Our main finding is that adding even the same amount of noise on all individuals impacts groups differently. We characterize several forms of loss discrepancy in terms of the amount of noise and difference between moments of the two groups, for estimators that either do or do not use group membership information. We then study how long it takes for an estimator to adapt to a shift in the population that makes the groups have the same mean. We finally validate our results on three real-world datasets.
An Efficient Solver for Parametrized Difference Revision

We present GenC, an efficient and highly-parallel belief revision solver for paramatrized difference operators. GenC uses an AllSAT solver to enumerate the possible models of a formula, and then determines the output of revision through a series of bit comparisons. The result is a system that can calculate the result of revision for formulas with 100 variables and millions of clauses in just seconds; the running times obtained by GenC far surpass existing solvers for belief revision. The system also has many features that are useful for practical problems: it supports both interactive and offline data entry, it allows multiple formats for entering formulas, and it provides output in human-readable format. Most importantly, GenC is able to model revision by any parametrized difference operator, which allows a wide range of practical problems to be easily captured.
Improved Noise Covariance Estimation in Visual Servoing Using an Autocovariance Least-squares Approach

For pose estimation in visual servoing, by assuming the relative motion over one sample period to be constant, many existing works adopt a linear time-invariant (LTI) dynamic model. Since the standard feature point transformation is nonlinear, extended Kalman filtering (EKF) has become popular due to its simplicity. Thus, the problem at hand becomes filtering of an LTI system with a time-varying output matrix. To obtain satisfactory performance, accurate knowledge of the noise covariances is essential. Various methods have been proposed on how to adaptively update their values to improve performance. However, these techniques cannot guarantee the positive semidefiniteness (PSD) of the covariance estimates. In this paper, we propose to apply the autocovariance least-squares (ALS) approach to covariance identification in pose estimation. The ALS approach can provide reliable estimates of the covariance matrices while maintaining their PSD and imposing desired structural constraints. Our tests show that using the covariance estimates from the ALS method in EKF can reduce the average pose estimation error by more than 30% in simulation, and the average position estimation error by about 30% using experimental data, respectively, compared to a hand-tuned EKF.
AUDABLOK: Engaging Citizens in Open Data Refinement through Blockchain

This work describes how open data and human computation can be brought together through blockchain to foster the collaboration of citizens on the continuous enhancement of open data portals. For that, it contributes with a set of enhancements to the widely adopted data management tool Comprehensive Knowledge Archive Network (CKAN), to allow full audit and management of the change requests posed by citizens to datasets in open data portals. User contributions’ sustainability in time is tackled by providing rewards to users through AudaCoins, a currency that rewards citizens according to their refinement contributions, thus encouraging their continuous engagement with city co-creation activities.
Optimal Use of Verbal Instructions for Multi-robot Human Navigation Guidance

Efficiently guiding humans in indoor environments is a challenging open problem. Due to recent advances in mobile robotics and natural language processing, it has recently become possible to consider doing so with the help of mobile, verbally communicating robots. In the past, stationary verbal robots have been used for this purpose at Microsoft Research, and mobile non-verbal robots have been used at UT Austin in their multi-robot human guidance system. This paper extends that mobile multi-robot human guidance research by adding the element of natural language instructions, which are dynamically generated based on the robots’ path planner, and by implementing and testing the system on real robots. Generating natural language instructions from the robots’ plan opens up a variety of optimization opportunities such as deciding where to place the robots, where to lead humans, and where to verbally instruct them. We present experimental results of the full multi-robot human guidance system and show that it is more effective than two baseline systems: one which only provides humans with verbal instructions, and another which only uses a single robot to lead users to their destinations.
Alternative Vietnamese Speech Synthesis System with Phoneme Structure

This paper proposes an alternative approach for synthesizing Vietnamese speech. In order to improve the naturalness and intelligibility of the speech synthesizer, we propose the Vietnamese phoneme model and integrate into an end-to-end model. We employ the pipeline of Tacotron 2 model with attention scheme to generate high-quality speeches. As we replace the character embedding model with Vietnamese phoneme model, we obtain promising results. In addition, we also create a tool for converting Vietnamese text to phoneme sequence. The new phoneme embedding method can also be applied to various languages, while we can take the advantages of state-of-the-art models.
Particle Swarm Optimization to Obtain Weights in Neural Network

This research discusses the use of a class of heuristic optimization to obtain the weights in neural network model for time series prediction. In this case, Feed Forward Neural Network (FFNN) was chosen as the class of network architecture. The heuristic algorithm determined to obtain the weights in network was Particle Swarm Optimization (PSO). It is a non-gradient optimization technique. This method was used for optimizing the connection weights of network. The lags used as the input were selected based on the strong relationship with the current. The eight architectures were conducted to improve the accuracy of the neural network model. In each architecture, we repeated the running thirty times to get the statistics of mean and variance. The comparison of the performance of various architectures based on the minimum MSE and the stability of the results is presented in this paper. The optimal number of neurons in hidden layer was determined by these criteria. The proposed procedure was applied in air pollution data, i.e. Solid Particulate Matter (SPM). The results showed that the proposed procedure gave promising results in terms of prediction accuracy. A few neurons in hidden layer are strongly recommended in choosing the optimal architecture.
Keefektifan Teknik Modeling Berbasis Sinema Edukasi untuk Meningkatkan Efikasi Diri Akademik Siswa SMP

Phenomenon Students with low academic self-efficacy tend to avoid difficult tasks, because the task is considered a threat to him. Students have low aspirations and commitment in achieving the learning goals they set. When faced with difficult tasks, student is busy thinking about the deficiencies that exist in him, the disturbances he faces, and all the results that can be detrimental. When faced with difficult tasks, students with low self-efficacy will reduce their efforts and tend to give up quickly and slow to fix or regain their self-efficacy when experiencing failure. So we need a technique to reduce these rights, one of which is the application of modeling techniques based on cinema of educational. This study aims to determine the effectiveness modeling techniques based on cinema of educational to improve students' academic self-efficacy. This research method uses an experimental research approach with the type of one group pretest-posttest design. The conclusion of the study showed that the Paired Samples Test score was 0.026 &lt;0.05. These results can be concluded that modeling techniques based on cinema of educational are effective for increasing the academic self-efficacy of junior high school students.
Outlier Mining Methods Based on Graph Structure Analysis

Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap nonlinear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.
Towards Privacy and Security of Deep Learning Systems: A Survey.

Deep learning has gained tremendous success and great popularity in the pastfew years. However, recent research found that it is suffering several inherentweaknesses, which can threaten the security and privacy of the stackholders.Deep learning's wide use further magnifies the caused consequences. To thisend, lots of research has been conducted with the purpose of exhaustivelyidentifying intrinsic weaknesses and subsequently proposing feasiblemitigation. Yet few is clear about how these weaknesses are incurred and howeffective are these attack approaches in assaulting deep learning. In order tounveil the security weaknesses and aid in the development of a robust deeplearning system, we are devoted to undertaking a comprehensive investigation onattacks towards deep learning, and extensively evaluating these attacks inmultiple views. In particular, we focus on four types of attacks associatedwith security and privacy of deep learning: model extraction attack, modelinversion attack, poisoning attack and adversarial attack. For each type ofattack, we construct its essential workflow as well as adversary capabilitiesand attack goals. Many pivot metrics are devised for evaluating the attackapproaches, by which we perform a quantitative and qualitative analysis. Fromthe analysis, we have identified significant and indispensable factors in anattack vector, \eg, how to reduce queries to target models, what distance usedfor measuring perturbation. We spot light on 17 findings covering theseapproaches' merits and demerits, success probability, deployment complexity andprospects. Moreover, we discuss other potential security weaknesses andpossible mitigation which can inspire relevant researchers in this area.
Real-time DeepLabv3+ for pedestrian segmentation

In this paper, we propose a real-time pedestrian segmentation method that is built on the structure of the semantic segmentation method DeepLabv3+. We design a shallow network as the backbone of DeepLabv3+, and also a new convolution block is proposed to fuse multilevel and multitype features. We first train our DeepLabv3+ on the Cityscapes dataset to segment objects into 19 classes, and then we fine tune it with persons and riders in Cityscapes and COCO as the foreground and the other classes as the background to get our pedestrian segmentation model. The experimental results show that our DeepLabv3+ can achieve an 89.0% mean intersection-over-union pedestrian segmentation accuracy on the Cityscapes validation set. Our method also reaches a speed of 33 frames per second on images with a resolution of 720&#x00D7;1280 with a GTX 1080Ti graphics processing unit. Experimental results prove that our method can be applied to various scenes with fast speed.
Bagging deep autoencoders with dynamic threshold for semi-supervised anomaly detection

In the field of anomaly detection, anomalies are usually very rare compared with normal samples, which is not conducive to the construction of anomaly detection model. In this paper, we propose a semi-supervised anomaly detection algorithm based on deep autoencoder. With this algorithm, only normal samples are needed to train anomaly detection model. To improve the robustness of the algorithm, Bagging ensemble method is used to train and combine multiple deep autoencoders. In the process of Bagging, dynamic threshold for anomaly detection is applied to increase the diversity of individual autoencoder. Compared with other semi-supervised methods including one-class SVM, SOM and K-Means, our proposed method has obvious superiority in the behavior of anomaly detection.
Clustering of Tweets: A Novel Approach to Label the Unlabelled Tweets

Twitter is one of the fastest growing microblogging and online social networking site that enables users to send and receive messages in the form of tweets. Twitter is the trend of today for news analysis and discussions. That is why Twitter has become the main target of attackers and cybercriminals. These attackers not only hamper the security of Twitter but also destroy the whole trust people have on it. Hence, making Twitter platform impure by misusing it. Misuse can be in the form of hurtful gossips, cyberbullying, cyber harassment, spams, pornographic content, identity theft, common Web attacks like phishing and malware downloading, etc. Twitter world is growing fast and hence prone to spams. So, there is a need for spam detection on Twitter. Spam detection using supervised algorithms is wholly and solely based on the labelled dataset of Twitter. To label the datasets manually is costly, time-consuming and a challenging task. Also, these old labelled datasets are nowadays not available because of Twitter data publishing policies. So, there is a need to design an approach to label the tweets as spam and non-spam in order to overcome the effect of spam drift. In this paper, we downloaded the recent dataset of Twitter and prepared an unlabelled dataset of tweets from it. Later on, we applied the cluster-then-label approach to label the tweets as spam and non-spam. This labelled dataset can then be used for spam detection in Twitter and categorization of different types of spams.