Reviewerly/exHarmony huggingface dataset:
Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition

Attention-based sequence-to-sequence automatic speech recognition (ASR) requires a significant delay to recognize long utterances because the output is generated after receiving entire input sequences. Although several studies recently proposed sequence mechanisms for incremental speech recognition (ISR), using different frameworks and learning algorithms is more complicated than the standard ASR model. One main reason is because the model needs to decide the incremental steps and learn the transcription that aligns with the current short speech segment. In this work, we investigate whether it is possible to employ the original architecture of attention-based ASR for ISR tasks by treating a full-utterance ASR as the teacher model and the ISR as the student model. We design an alternative student network that, instead of using a thinner or a shallower model, keeps the original architecture of the teacher model but with shorter sequences (few encoder and decoder states). Using attention transfer, the student network learns to mimic the same alignment between the current input short speech segments and the transcription. Our experiments show that by delaying the starting time of recognition process with about 1.7 sec, we can achieve comparable performance to one that needs to wait until the end.
Biometric Blockchain: A Secure Solution for Intelligent Vehicle Data Sharing

The intelligent vehicle (IV) has become a promising technology that could revolutionize our life in smart cities sooner or later. However, it yet suffers from many security vulnerabilities. Traditional security methods are incapable to secure the IV data sharing against malicious attacks. Blockchain, as expected by both research and industry communities, has emerged as a good solution to address these issues. The major issues in IV data sharing are trust, data accuracy, and reliability of data sharing in the communication channel. Blockchain technology, previously working for the cryptocurrency, has been recently applied to build trust and reliability in peer-to-peer networks with similar topologies of IV data sharing. In this chapter, we present a new framework, namely biometric blockchain (BBC), for secure IV data sharing. In our new scheme, biometric information is exploited as a cue to record who is responsible in the data sharing activities, while the proposed BBC technology serves as the backbone of the IV data sharing architecture. Hence, the proposed BBC technology provides a more reliable trust environment between the vehicles while personal identities are traceable in the proposed new scheme.
Robust classification of eating sound collected in natural meal environment

Increasing the number of chewing can help reduce obesity. Nevertheless, it is difficult for a person to keep track of his mastication rate without the help of an automatic mastication counting device. Such devices do exist, but they are big and non-portable and are not suitable for daily use. In our previous work, we proposed an optimization model for classification of chewing, swallowing, and speaking activities using sound data collected by a bone conduction microphone in a natural eating environment. In this paper, we aim to implement a system that could automatically recognize a person's eating gestures (e.g., mastication, swallowing, and utterance) in real time. In realizing this, it is necessary to add the other sounds such as noises in the model so that it is more robust to natural meal environment. Therefore, in this study, we proposed an optimization for classification method adding the other sounds to three eating activities.
Operation Status Tracking for Legacy Manufacturing Systems via Vibration Analysis

Tracking the status of manufacturing systems is important for analyzing the performance of a manufacturing process. Unfortunately, legacy manufacturing systems are technologies from the yesteryears which have no Internet connectivity and very often are not programmable. Gathering operational information of such systems is often done manually with poor temporal resolution. This work proposes an Internet-of-things (IoT) approach that uses vibration sensors to track the operation status of legacy manufacturing systems. One of the challenges of using vibration data is to identify the meaning of the vibration without prior knowledge of the vibration profile and without the privilege to interrupt the manufacturing process. Although there are many existing works that capture and analyze vibration data, these existing works very often only focus on fault diagnosis and prognosis. Our work focuses on using the vibration data to monitor the operation status of a manufacturing machine. Experimental results show that the proposed vibration analysis method is able to track the operation status of a machine with more than 90% accuracy, in the worst case with 90.2% and standard uncertainty of 3.6%.
Variable Selection and Parameter Tuning for BART Modeling in the Fragile Families Challenge

Our goal for the Fragile Families Challenge was to develop a hands-off approach that could be applied in many settings to identify relationships that theory-based models might miss. Data processing was our first and most time-consuming task, particularly handling missing values. Our second task was to reduce the number of variables for modeling, and we compared several techniques for variable selection: least absolute selection and shrinkage operator, regression with a horseshoe prior, Bayesian generalized linear models, and Bayesian additive regression trees (BART). We found minimal differences in final performance based on the choice of variable selection method. We proceeded with BART for modeling because it requires minimal assumptions and permits great flexibility in fitting surfaces and based on previous success using BART in black-box modeling competitions. In addition, BART allows for probabilistic statements about the predictions and other inferences, which is an advantage over most machine learning algorithms. A drawback to BART, however, is that it is often difficult to identify or characterize individual predictors that have strong influences on the outcome variable.
Estimation of student's engagement based on the posture

It is important for teachers to grasp students' engagement in order to improve the quality of lectures. However, in the e-learning environment, there is no teacher to grasp the students' engagement and it may cause ineffective learning. The purpose of this study is to grasp the students' engagement by using a pressure mat and web camera. We recorded students' postural data, that is upper body pressure distribution and upper body pose, during e-learning lectures. Then we extracted 38 features from upper body pressure distribution and 33 features from upper body pose for every minute, selected proper features and trained classifiers to estimate whether he or she was engaged in the lecture. As a result, the average accuracy was 79.3% for student-dependent estimation. This result shows it is possible to predict the student's engagement automatically.
PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection

Object detection methods can be divided into two categories that are the two-stage methods with higher accuracy but lower speed and the one-stage methods with lower accuracy but higher speed. In order to inherit the advantages of both approaches, a novel dense object detector, called Path Augmented RetinaNet (PA-RetinaNet), is proposed in this paper. It not only achieves a better accuracy than the two-stage methods, but also maintains the efficiency of the one-stage methods. Specifically, we introduce a bottom-up path augmentation module to enhance the feature exaction hierarchy, which shortens the information path between lower feature layers and topmost layers. Furthermore, we address the class imbalance problem by introducing a Class-Imbalance loss, where the loss of each training sample is weighted by a function of its predicted probability, so that the trained model focuses more on hard examples. To evaluate the effectiveness of our PA-RetinaNet, we conducted a number of experiments on the MS COCO dataset. The results show that our method is 4.3% higher than the existing two-stage method, while the speed is similar to the state-of-the-art one-stage methods.
Cross-cultural contextualisation for recommender systems

Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender Systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural difference is often considered a barrier to technology transfer or adoption. However, few studies focus on how the cultural factor influences recommendation despite cultural difference largely affects user preferences in the RSs. Furthermore, existing researches have mainly analysed evaluation results of their recommendation to reveal cultural differences, rather than utilising the cross-cultural factors into RSs. In this paper, we propose a novel concept of cross-cultural contextualisation and a model to compute the cross-cultural factor affecting users (countries or cultures) preferences by using matrix factorisation and clustering techniques. In addition, we discuss how to apply the model to RSs in CH domain through cross-domain recommendation techniques. Note that the two computational techniques were used to analyse cross-cultural factors which impact to user preferences, rather than to recommend items. In other words, the proposed model and computing results capable of utilisation into the other RSs as well as various research fields. Results of experiments with a real-world dataset showed effectiveness of the proposed model and supported that there is cultural difference influencing users’ rating behaviours. Furthermore, a systematic analysis of dataset and the experimental results presented that individual users could be considered as country-wise groups for the model to analyse the cross-cultural factors.
Learning a Semantic Space for Modeling Images, Tags and Feelings in Cross-Media Search

This paper contributes a new, real-world web image dataset for cross-media retrieval called FB5K. The proposed FB5K dataset contains the following attributes: (1) 5130 images crawled from Facebook; (2) images that are categorized according to users' feelings; (3) images independent of text and language rather than using feelings for search. Furthermore, we propose a novel approach through the use of Optical Character Recognition (OCR) and explicit incorporation of high-level semantic information. We comprehensively compute the performance of four different subspace-learning methods and three modified versions of the Correspondence Auto Encoder (Corr-AE), alongside numerous text features and similarity measurements comparing Wikipedia, Flickr30k and FB5K. To check the characteristics of FB5K, we propose a semantic-based cross-media retrieval method. To accomplish cross-media retrieval, we introduced a new similarity measurement in the embedded space, which significantly improved system performance compared with the conventional Euclidean distance. Our experimental results demonstrated the efficiency of the proposed retrieval method on three different public datasets.
Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification

Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis. In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification. Specifically, we first propose a lexicon-enhanced attention mechanism by combining the sentiment lexicon with an attention mechanism to incorporate sentiment linguistic knowledge into deep learning methods. Second, we introduce a multi-head attention mechanism in the deep neural network to interactively capture the contextual information from different representation subspaces at different positions. Furthermore, we stack a LAN model to build a hierarchical sentiment classification model for large-scale text. Extensive experiments are conducted to evaluate the effectiveness of the proposed models on four popular real-world sentiment classification datasets at both the sentence level and the document level. The experimental results demonstrate that our proposed models can achieve comparable or better performance than the state-of-the-art methods.
An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

The problem of duplicate track determination called “Track-to-Track association” occurs when a target is reported by different sensors, and it is regarded as one of the most important challenges in distributed multi-sensor tracking systems. The present study aimed to propose a density-based fuzzy clustering method for solving the track-to-track association problem in distributed multi-sensor tracking systems. Unlike the previously published solutions, the proposed method does not need any information about the number of targets, due to the use of the density-based clustering approach. Proposed method has low computational overhead and can be used in real-time tracking systems. In addition, the proposed method uses the maximum entropy approach to determine the membership degree of single target related tracks and combines them. This paper presents three scenarios including sensors with complete and incomplete overlapping by considering the bias and a different number of sensors and targets for evaluating the proposed method based on the Monte Carlo simulation. The results indicate the improvement of the efficiency in comparison with the FTF approach. The efficiency of proposed method's results is close to the results of Bayesian minimum mean square error criterion that gives best possible results.
RecTour 2019

The Workshop on Recommenders in Tourism (RecTour) 2019, which is held in conjunction with the 13th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topics of the submissions that we received. The topics of this year's workshop include context-aware recommendations, group recommender systems, hotel recommendations, destination characterization, next-POI recommendation, user interaction and experience, preference elicitation, user modeling and application of machine learning algorithms in the context of tourism recommender systems.
Generic Multi-keyword Ranked Search on Encrypted Cloud Data

Although searchable encryption schemes allow secure search over the encrypted data, they mostly support conventional Boolean keyword search, without capturing any relevance of the search results. This leads to a large amount of post-processing overhead to find the most matching documents and causes an unnecessary communication cost between the servers and end-users. Such problems can be addressed efficiently using a ranked search system that retrieves the most relevant documents. However, existing state-of-the-art solutions in the context of Searchable Symmetric Encryption (SSE) suffer from either (a) security and privacy breaches due to the use of Order Preserving Encryption (OPE) or (b) non-practical solutions like using the two non-colluding servers. In this paper, we present a generic solution for multi-keyword ranked search over the encrypted cloud data. The proposed solution can be applied over different symmetric searchable encryption schemes. To demonstrate the practicality of our technique, in this paper we leverage the Oblivious Cross Tags (OXT) protocol of Cash et al. (2013) due to its scalability and remarkable flexibility to support different settings. Our proposed scheme supports the multi-keyword search on Boolean, ranked and limited range queries while keeping all of the OXT’s properties intact. The key contribution of this paper is that our scheme is resilience against all common attacks that take advantage of OPE leakage while only a single cloud server is used. Moreover, the results indicate that using the proposed solution the communication overhead decreases drastically when the number of matching results is large.
Comparative evaluation of augmented reality-based assistance for procedural tasks: a simulated control room study

This research explores the design, implementation, and evaluation of a prototype augmented reality application that assists operators in performing procedural tasks in control room settings. Our prototype uses a tablet display to supplement an operator's natural view of existing control panel elements with sequences of interactive visual and attention guiding cues. An experiment, conducted using a nuclear power plant simulator, examined university students completing both standard and emergency operating procedures. The augmented reality condition was compared against two other conditions – a paper-based procedure condition using paper manuals and a computer-based procedure condition using digital procedures presented on a desktop display. The results demonstrated that the augmented reality -based procedure system had benefits in terms of reduced mental workload in comparison to the other two conditions. Regarding task completion time, accuracy, and situation awareness, the augmented reality condition had no significant difference when compared against the computer-based procedure condition but performed better than the paper-based procedure condition. It was also found that the augmented reality condition resulted in fewer intra-team inquiry communication exchanges in comparison to both paper-based and computer-based conditions. The augmented reality condition, however, yielded poorer memory retention score when assessed against the other two conditions.
Learning to Generate Comments for API-Based Code Snippets

Comments play an important role in software developments. They can not only improve the readability and maintainability of source code, but also provide significant resource for software reuse. However, it is common that lots of code in software projects lacks of comments. Automatic comment generation is proposed to address this issue. In this paper, we present an end-to-end approach to generate comments for API-based code snippets automatically. It takes API sequences as the core semantic representations of method-level API-based code snippets and generates comments from API sequences with sequence-to-sequence neural models. In our evaluation, we extract 217K pairs of code snippets and comments from Java projects to construct the dataset. Finally, our approach gains 36.48% BLEU-4 score and 9.90% accuracy on the test set. We also do case studies on generated comments, which presents that our approach generates reasonable and effective comments for API-based code snippets.
Text Mining in Cybersecurity: Exploring Threats and Opportunities

The number of cyberattacks on organizations is growing. To increase cyber resilience, organizations need to obtain foresight to anticipate cybersecurity vulnerabilities, developments, and potential threats. This paper describes a tool that combines state of the art text mining and information retrieval techniques to explore the opportunities of using these techniques in the cybersecurity domain. Our tool, the Horizon Scanner, can scrape and store data from websites, blogs and PDF articles, and search a database based on a user query, show textual entities in a graph, and provide and visualize potential trends. The aim of the Horizon Scanner is to help experts explore relevant data sources for potential threats and trends and to speed up the process of foresight. In a requirements session and user evaluation of the tool with cyber experts from the Dutch Defense Cyber Command, we explored whether the Horizon Scanner tool has the potential to fulfill its aim in the cybersecurity domain. Although the overall evaluation of the tool was not as good as expected, some aspects of the tool were found to have added value, providing us with valuable insights into how to design decision support for forecasting analysts.
Detection and Classification of Breast Cancer in Mammographic Images Using Efficient Image Segmentation Technique

Breast cancer has become one of the major types of cancer-caused deaths among women of different countries throughout the world. One of the major problems of this type of cancer disease are quick detection or identifying of disease in early stages. In the cases of technologically lagging countries mortality rates are very high due to lack of early diagnosis technology of disease. According to the opinion of different clinical experts, today mammography is one of the most effective diagnosis technologies in medical science domain. So there is a requirement for more accurate methods which can easily diagnose any type of abnormalities in women breast without any kind of human intervention with higher accuracy rates. Segmentation is an approach that is very much required to identify the unambiguous region from the mammogram image. Intensity, texture, and shapes are extracted from the segmented mammogram image. The role of image processing is to detect cancer in human body when input data is in the form of images. For mammogram image classification, the feature extraction of an image with statistical parameter measurement is very important approach. Different types of feature extraction methods are generally used for better classification of abnormality present in mammogram. This technique will provide higher accuracy rates at a comparative higher speed. The statistical parameter includes entropy, mean, regression, correlation, skew, standard deviation. The experimental results achieved 89% accuracy, 74% specificity, and 89% sensitivity, illustrating the usefulness of the technique for identifying and classifying the cancer in mammogram images with maintaining more accuracy.
CNN for human activity recognition on small datasets of acceleration and gyro sensors using transfer learning

This paper describes an activity recognition method for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge by Team TDU-DSML. The CNN model reported in our 2018 SHL Challenge was adopted. 5-second FFT spectrogram images from all axes of acceleration and gyro sensor data were treated as input data. We confirmed that a multiple-sensor input model combining acceleration and gyro sensors improves the recognition rate. However, there was insufficient training data in the SHL dataset for the target sensor labeled as Hand. To overcome this difficulty, a transfer learning method was applied to the pre-training model from other sensors labeled as Hips and Torso. After evaluation from all combination of sensors, the transfer learning model from Acc_norm and Gyr_x for Hips and Torso had the best recognition rate of 82.1% at Hand in the submission phase.