Reviewerly/exHarmony huggingface dataset:
Continuous Travel In Virtual Reality Using a 3D Portal

In virtual reality (VR), continuous movement often leads to users experiencing vection, or cybersickness. To circumvent vection, users are typically given the choice to use teleportation. However, teleportation leads to less spatial awareness compared to continuous movement. One approach to combating cybersickness in continuous movement is to restrict the user's field of view (FOV). This is typically done by occluding the user's peripheral vision with a vignette. We developed a FOV restricting continuous locomotion system that does not occlude the user's FOV and instead uses a 3D portal to display the continuous movement in a limited area in the user's FOV. We found that this system reduces nausea and disorientation compared to continuous locomotion. However, our system did not significantly increase spatial awareness compared to teleportation.
Enabling automated integration of architectural languages: An experience report from the automotive domain

Modern automotive software systems consist of hundreds of heterogeneous software applications, belonging to separated function domains and often developed within distributed automotive ecosystems consisting of original equipment manufactures, tier-1 and tier-2 companies. Hence, the development of modern automotive software systems is a formidable challenge. A well-known instrument for coping with the tremendous heterogeneity and complexity of modern automotive software systems is the use of architectural languages as a way of enabling different and specific views over these systems. However, the use of different architectural languages might come with the cost of reduced interoperability and automation as different languages might have weak to no integration. In this article, we tackle the challenge of integrating two architectural languages heavily used in the automotive domain for the design and timing analysis of automotive software systems: AMALTHEA and Rubus Component Model. The main contributions of this paper are (i) a mapping scheme for the translation of an AMALTHEA architecture into a Rubus Component Model architecture where high-precision timing analysis can be run, and the back annotation of the analysis results on the starting AMALTHEA architecture; (ii) the implementation of the proposed scheme, which uses the concept of model transformations for enabling a full-fledged automated integration; (iii) the application of such automation on three industrial automotive systems being the brake-by-wire, the full blown engine management system and the engine management system. We discuss and evaluate the proposed contributions using an online, experts survey and the above-mentioned use cases. Based on the evaluation results, we conclude that the proposed automation mechanism is correct and applicable in industrial contexts. Besides, we observe that the performance of the automation mechanism does not degrade when translating large models with several thousands of elements. Eventually, we conclude that experts in this field find the proposed contribution industrially relevant.
Deep curriculum learning in task space for multi-class based mammography diagnosis

Mammography is used as a standard screening procedure for the potential patients of breast cancer. Over the past decade, it has been shown that deep learning techniques have succeeded in reaching near-human performance in a number of tasks, and its application in mammography is one of the topics that medical researchers most concentrate on. In this work, we propose an end-to-end Curriculum Learning (CL) strategy in task space for classifying the three categories of Full-Field Digital Mammography (FFDM), namely Malignant, Negative, and False recall. Specifically, our method treats this three-class classification as a “harder” task in terms of CL, and creates an “easier” sub-task of classifying False recall against the combined group of Negative and Malignant. We introduce a loss scheduler to dynamically weight the contribution of the losses from the two tasks throughout the entire training process. We conduct experiments on an FFDM dataset of 1,709 images using 5-fold cross validation. The results show that our curriculum learning strategy can boost the performance for classifying the three categories of FFDM compared to the baseline strategies for model training.
Deep Semantic Versioning for Evolution and Variability

The development cycles in the software industry are shrinking due to the increasing demands for shorter time to market and the incremental development style of agile methodologies. Pragmatic software engineering approaches rely on careful product management, a strong versioning discipline, and a feature development strategy to avoid that newly merged code disrupts existing systems. Versioning is critical when managing software product lines and ensuring that all their variants are kept in operation amidst all the performed changes. Such methodologies enforce functional correctness through strong efforts in regression testing, with the associated exponential growth in complexity.
Few-Shot Multi-Agent Perception

We study few-shot learning (FSL) under multi-agent scenarios, in which participating agents only have local scarce labeled data and need to collaborate to predict query data labels. Though each of the agents, such as drones and robots, has minimal communication and computation capability, we aim at designing coordination schemes such that they can collectively perceive the environment accurately and efficiently. We propose a novel metric-based multi-agent FSL framework which has three main components: an efficient communication mechanism that propagates compact and fine-grained query feature maps from query agents to support agents; an asymmetric attention mechanism that computes region-level attention weights between query and support feature maps; and a metric-learning module which calculates the image-level relevance between query and support data fast and accurately. Through analysis and extensive numerical studies, we demonstrate that our approach can save communication and computation costs and significantly improve performance in both visual and acoustic perception tasks such as face identification, semantic segmentation, and sound genre recognition.
Data Governance in the Dairy Industry

Data governance is a growing concern in the dairy farm industry because of the lack of legal regulation. In this commentary paper, we discuss the status quo of the available legislation and codes, as well as some possible solutions. To our knowledge, there are currently four codes of practice that address agriculture data worldwide, and their objectives are similar: (1) raise awareness of diverse data challenges such as data sharing and data privacy, (2) provide data security, and (3) illustrate the importance of the transparency of terms and conditions of data sharing contracts. However, all these codes are voluntary, which limits their adoption. We propose a Farmers Bill of Rights for the dairy data ecosystem to address some key components around data ownership and transparency in data sharing. Our hope is to start the discussion to create a balanced environment to promote equity within the data economy, encourage proper data stewardship, and to foster trust and harmony between the industry companies and the farmers when it comes to sharing data.
Recognizing Social Signals with Weakly Supervised Multitask Learning for Multimodal Dialogue Systems

Social signal processing is a methodology that is used to infer human inner states, including attitudes, sentiments and impressions, from verbal and nonverbal multimodal information. The difficulty in training a social signal recognition model is that the ground-truth (target) labels given by multiple coders often disagree because the annotation of social signals such as sentiments is a subjective and ambiguous task. We introduce weakly supervised learning (WSL) algorithms to such an inaccurate supervision setting in which the target label is not necessarily accurate. The novel challenge in this paper is to explore an effective WSL strategy for recognizing social signals. The strategy is verified through two multimodal datasets including audio, visual, and linguistic data collected in a human-agent dialogue setting. First, we clarify that the proposed WSL strategy for deep neural networks (DNNs), called tri-teaching works well in almost all classification tasks. Second, we demonstrate the effectiveness of integrating WSL and multitask learning (MTL), which exploits several label types in the datasets. Third, we show that our proposed approach achieves less accuracy degradation than an existing training algorithm for a DNN (curriculum learning) in a cross-corpus setting, with a maximum improvement of 7.2%.
A Unified Information Extraction System Based on Role Recognition and Combination

In this paper, we propose a unified information extraction system, which handles event extraction (EE) and relation extraction (RE) tasks. Given context and schema, event extraction aims to extract the events and the specific roles in the events, and relation extraction extracts all SPO triples. We formulate event extraction and relation extraction as one extraction schema, that is, role recognition and role combination. We use Multi-Label Pointer Network (MLPN) to recognize composite roles that contain both event/relation and role information and simultaneously train a Co-occurrence Matrix (CM) to determine the co-occurrence relationship of composite roles, i.e., whether two roles describe the same event/relation. Using such a Unified model based on Role Recognition and Combination (URRC) and corresponding combination strategy, we implement three tasks: sentence-level event extraction, document-level event extraction, and relation extraction. In LIC 2021, our model achieved 6th in the Multi-format Information Extraction racing track with an average \(F_1\) score of 77.44% in the final test dataset of three subtasks.
Research on Civil Aviation Passenger Question Intention Recognition Based on Text Classification Method of Self-attention and Deep Neural Network

To solve the information loss and raise the accuracy of intention recognition of civil aviation travel question answering (QA) system, a hybrid model that is based on Self-attention Mechanism, convolutional neural network (CNN) and long-short term memory network (LSTM), namely SACL is proposed based on considering the characteristics of passenger travel questions. Firstly, Web Crawler is used to obtain the common questions of passenger travel in civil aviation airport to obtain a data set, and get the Word Vector Matrix after data preprocessing. Then the Word Vector Matrix of question is input into the serial structure of CNN and attention mechanism to obtain the Feature Vector that contains the local information of the question, and input the Word Vector Matrix of question into LSTM to get the Feature Vector containing the Global Structure Information of the text. The two Feature Vectors are spliced for getting the feature representation of the text, and then input it into the fully connected neural network and softmax layer to get the text classification of the question. The gradient descent method (GDM) is used as the algorithm of training, and the classification data of the model are compared with four models. The proposed SACL model can accurately identify the intention of passengers’ questions, which is of great significance to raise the degree of accuracy and speed of extraction of the answers in passenger QA system of civil aviation airport.
Canonical Saliency Maps: Decoding Deep Face Models

As Deep Neural Network models for face processing tasks approach human-like performance, their deployment in critical applications such as law enforcement and access control has seen an upswing, where any failure may have far-reaching consequences. We need methods to build trust in deployed systems by making their working as transparent as possible. Existing visualization algorithms are designed for object recognition and do not give insightful results when applied’ to the face domain. In this work, we present ‘Canonical Saliency Maps’, a new method which highlights relevant facial areas by projecting saliency maps onto a canonical face model. We present two kinds of Canonical Saliency Maps: image-level maps and model-level maps. Image-level maps highlight facial features responsible for the decision made by a deep face model on a given image, thus helping to understand how a DNN made a prediction on the image. Model-level maps provide an understanding of what the entire DNN model focuses on in each task, and thus can be used to detect biases in the model. Our qualitative and quantitative results show the usefulness of the proposed canonical saliency maps, which can be used on any deep face model regardless of the architecture.
Is Facebook Easier to Use than WeChat?

Given the dominant role that Facebook and WeChat play in their respective contexts, researchers have explored users’ behavior and uses of these two globally leading social networking sites (SNSs). However, there is scant research comparing the interface features of these two SNSs and their contextual interactions within and across cultures. Utilizing Sun's “culturally localized user experience” (CLUE) framework [27, 28], this study employs critical data analysis to investigate the defining interface features of Facebook and WeChat in four sections of moments/status, official accounts, digital currency, and group chat. Our study expands the understanding of cultural considerations in interface design and encourages factoring contextualized use of technology and the sociological approach to interface design. The findings reveal cultural and local differences are the driving force for the unique interface features of the two SNSs and highlight “glocalization” (mixing forces into local elements) in SNS design [27].
Classification of Channel Coding Structure Based on 1-D Inception Architecture

The channel coding structure classification based on the traditional method usually requires the coding type and prior knowledge to be known, which is completely invalid for the unknown signals that are usually intercepted in the current communication environment. In order to solve this problem, this paper proposes a method of blind classification of channel coding structure based on one-dimensional Inception. First, we use MATLAB to generate signal datasets with different channel coding structures under different signal-to-noise ratios (SNR), and then construct a Convolutional Neural Network (CNN) model based on the one-dimensional Inception, and use the datasets to train the network model. The final classification accuracy rate is 96%. This experimental result demonstrates the effectiveness of the proposed method.
An Insight into Fuzzy Logic Computation Technology and Its Applications in Agriculture and Meteorology

Speaking of recent advances, many computing technologies have been applied to several domains and have proved to provide more approximate and acceptable results. Fuzzy logic being one of them has been very useful in solving many real-world problems that are inherent for their uncertainty, complexity, impreciseness and a high degree of randomness. Soft computing aims to mimic human thinking and thus solve problems as a human does. The systems embedded with one or more soft computing technologies tend to make decisions quicker (reducing the processing timeframe) and more accurate in the light of uncertain and indefinite data. This paper aims at an extensive review of fuzzy logic also unraveling some of the applications of the same in the field of agricultural science and meteorology.
Features of The Development of Intelligent Scientific and Educational Internet Resources

The purpose of this work is to develop methods, technologies and tools for creating and maintaining intelligent scientific and educational Internet resources (ISEIR) based on a service-oriented approach and Semantic Web technologies. The main purpose of ISEIR is to provide meaningful access to scientific and educational information resources of a given field of knowledge and integrated information processing services. According to the preliminary concept, an intelligent scientific and educational Internet resource will be an information system accessible via the Internet, which provides ontology-based systematization and integration of scientific knowledge, data and information resources into a single information space, meaningful effective access to them, as well as supporting their use in solving various scientific and educational tasks. ISEIR is equipped with an ergonomic web-based user interface and special editors designed to manage the knowledge integrated into it. The proposed approach to the construction of intelligent scientific and educational Internet resources is the basis of the developed technology for creating and maintaining information environments for distributed learning.
Machine Translation System Based on Deep Learning

The translation fusion of multi-machine translation is a strategy that has been proposed very early to improve the quality of machine translation. It has been fully discussed under the framework of statistical machine translation, and further breakthroughs in performance have entered a bottleneck period. Since the fusion methods of existing methods are mostly limited to surface features and lack effective deep fusion strategies, this paper encodes source language sentences and multiple system translations separately to achieve fusion at the coding level. In the specific decoding, we limit the decoding space, so that the translation model can obtain a higher performance improvement on less data. When increasing the values of the two hyper-parameters α and β, the translation effect can be gradually improved. The performance of the model reaches the maximum when a and b are respectively set to 0.4, and the translation performance will decrease if it continues to increase.
An Extensible Software Architecture for Intelligent Assistant

This article introduces an intelligent assistant architecture that allows replacing existing modules and plugging new modules without requiring any code changes thanks to its modular and extensible structure. Our proposal supports multiple natural languages and at the same time allows the integration of any external artificial intelligence or IoT component easily. The software that we developed understands the commands in English and Turkish languages and transmits the answers to the user in these languages. Through our domain-specific language, different artificial intelligence components can be integrated into the system. Thanks to this domain specific language, only specific conversations are detected and the artificial intelligence module can be called. However, human speech contains many synonyms and we cannot always achieve the exact word by word. For this reason, we offer a system that understands the meanings of sentences and learns what the user wants to do when necessary.
Adversarial Machine Learning-Driven Fake Task Anticipation in Mobile Crowdsensing Systems

Mobile Crowdsensing (MCS) enables access to distributed sensing sources of personalized devices so to support various smart services. Services and applications that can benefit from MCS are various such as transportation, health care, public safety, smart mobility and many others. Distributed nature and lack of a pre-established trust mechanism in MCS systems make them vulnerable in the presence of various threats that can be initiated by either sensing data providers or service requesters. While it is relatively easier to detect and eliminate false sensing data submissions via outlier detection, tasks that are submitted to keep the MCS servers and participating devices occupied and clogged are challenging since these attacks can be planned intelligently. In this paper, we investigate the potential of adversarial machine learning to anticipate fake / illegitimate task submissions to MCS systems. To this end, we empower a threat anticipation mechanism that leverages a Generative Adversarial Network (GAN) to inject adversarial samples of fake / illegitimate tasks to an MCS system. We evaluate the impact of GAN-driven attacks in terms of Adversarial Attack Success Rate (AASR) and Attack Severity (AS). Our numerical results show that the potential risk and severity of the offensive use of Machine Learning is significantly higher than an adversarial baseline that injects fake tasks solely based on random noise samples.
A Path-Based Personalized Recommendation Using Q Learning

Recently, knowledge graph-based personalized recommendation systems have been proposed to exploit rich structured information. In particular, knowledge graph reasoning via knowledge embedding is increasingly introduced to preserve semantic information in the knowledge graph. However, due to the large dimensional representations of the millions of entities and relations, knowledge graph reasoning requires excessive computational resources. Moreover, selecting meaningful latent features in the knowledge graph between users and items is a challenging issue. In this paper, to impose efficient and accurate personalized recommendations, we propose a path-based recommendation using Q learning. Our model also offers a good explanation for recommendations. Experimental results show that the proposed method provides higher accuracy, compared to a uniform random walk based algorithm.
Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions

Drug representations have played an important role in cheminformatics. However, in the healthcare domain, drug representations have been underused relative to the rest of Electronic Health Record (EHR) data, due to the complexity of high dimensional drug representations and the lack of proper pipeline that will allow to convert clinical drugs to their representations. Time-varying vital signs, laboratory measurements, and related time-series signals are commonly used to predict clinical outcomes. In this work, we demonstrated that using clinical drug representations in addition to other clinical features has significant potential to increase the performance of mortality and length of stay (LOS) models. We evaluate the two different drug representation methods (Extended -Connectivity Fingerprint- ECFP and SMILES-Transformer embedding) on clinical outcome predictions. The results have shown that the proposed multimodal approach achieves substantial enhancement on clinical tasks over baseline models. U sing clinical drug representations as additional features improve the LOS prediction for Area Under the Receiver Operating Characteristics (AUROC) around %6 and for Area Under Precision-Recall Curve (AUPRC) by around % 5. Furthermore, for the mortality prediction task, there is an improvement of around % 2 over the time series baseline in terms of AUROC and %3.5 in terms of AUPRC. The code for the proposed method is available at https://github.com/tanlab/MIMIC-III-Clinical-Drug-Representations.