Reviewerly/exHarmony huggingface dataset:
Overbounding the effect of uncertain Gauss‐Markov noise in Kalman filtering

<h3>Abstract</h3> Prior work established a model for uncertain Gauss-Markov (GM) noise that is guaranteed to produce a Kalman filter (KF) covariance matrix that overbounds the estimate error distribution. The derivation was conducted for the continuous-time KF when the GM time constants are only known to reside within specified intervals. This paper first provides a more accessible derivation of the continuous-time result and determines the minimum initial variance of the model. This leads to a new, non-stationary model for uncertain GM noise that we prove yields an overbounding estimate error covariance matrix for both sampled-data and discrete-time systems. The new model is evaluated using covariance analysis for a one-dimensional estimation problem and for an example application in Advanced Receiver Autonomous Integrity Monitoring (ARAIM).
“Turning the Invisible Visible”: Transdisciplinary Bioart Explorations in Human-DNA Interaction

Hybrid interactive systems that combine living and digital components can engage, educate, and inform users, and are of growing interest in the HCI community. Advances in synthetic biology are transforming what is possible to do with these living media interfaces (LMIs). Bioart is a practice in which artists, often using synthetic biology methods, work with living organisms to creatively explore the human relationship with nonhuman organisms. We present results from an interview study with expert bioartists as well as our hands-on experience in a bioart project where we created poetry-infused wine by encoding and inserting a Persian Sufi poem into the DNA sequence of living yeast cells. We find that engaging in bioart practice generates transdisciplinary fluency with implications for access and activism and our understanding of the qualities of living media. We further explore the qualitative aspects of interacting directly with DNA and implications for sustainable futures.
Towards Efficient Detection and Crowd Management for Law Enforcing Agencies

Each year more than two million Muslims from around the world come to perform Hajj in Makah. It is considered the world's largest recorded human gathering during any worshiping event. Safety makes one of the main concerns with regards to managing such large crowds for ensuring that stampedes and other similar overcrowding accidents are avoided. For this purpose, 5000 cameras are installed around the holy sites for monitoring purposes. Due to the continuous nature of surveillance systems in generating video data, it is almost impossible to efficiently and accurately monitor an event of this size in real-time. Analyzing such huge data has required a lot of human resources. Therefore, there is a great need for advanced intelligent techniques to automatically count and manage such large crowds. In order to create an advanced intelligent system that contributes to crowds counting and managing through the surveillance system. In this paper, we propose an accurate computer vision-based approach to crowd management using Convolutional Neural Network (CNN). Our proposed framework is three folds. In the first fold, our own dataset for pilgrim detection is created, covering both sparse and dense crowds. In the second fold, a Faster-RCNN object detection model is trained to detect and count the number of pilgrims. In the third fold, utilizing the resources efficiently the surveillance system has used frame differencing technique to differentiate between motion and static video frames. Only in the case of some sort of motion, we will pass these frames to the pilgrims counting model to tell us about the number of pilgrims in the video. When the number of pilgrims counting is exceeded from the pre-defined threshold the system will automatically trigger the alarm pointing the camera to the location to inform the concerned authorities to take action appropriate measures. Along with that, only the dense crowd will be monitored by law enforcement and for better management. Our experiments show that Faster Region CNN (Faster RCNN) is suitable for accurate detection when compared with other state-of-art crowd management techniques so far reported.
Human-Nature Relations in Urban Gardens: Explorations with Camera Traps

As cities grow, their people become increasingly distanced from nature except within private and public green spaces. Sensing technologies provide a means to harness curiosity about the animals living in these spaces, and possibly also connect interest to care. Yet little is known as to how people may use these technologies, or the implications for human-nature relations. To learn more, we gave commercial camera traps to ten adult participants to understand how they explored their gardens, what they wanted to learn, and what they did with this knowledge. We discovered trade-offs between control and care; the usefulness of different media and mystery; the temporalities of engaging in natural sensing practice; and a prevalence of sharing media within households. We discuss design for convivial cohabitation with the creatures in our garden. This research contributes to better human-nature relations through citizen sensing, as well as HCI for urban biodiversity conservation.
AISpeech-SJTU ASR System for the Accented English Speech Recognition Challenge

This paper describes the AISpeech-SJTU ASR system for the Interspeech-2020 Accented English Speech Recognition Challenge (AESRC). This task is challenging due to the diversity of pronunciation accuracy, intonation speed and pronunciation of some syllables. All participants were restricted to develop their systems based on the speech and text corpora provided by the organizer. To work around the data-scarcity problem, data augmentation was first explored including noise simulation, SpecAugment, speed perturbation and TTS simulation. Moreover, SOTA CNN-transformer-based joint CTC-attention system was built and accent adaptation was proposed to train an accent robust system. Finally, the first-pass recognition hypotheses generated from CTC head were rescored by forward, backward LSTM-LM and the attention head. Our system with the best configuration achieves second place in the challenge, resulting in a word error rate (WER) of 4.00% on dev set and 4.47% WER on test set, while WER on test set of the top-performing, second runner-up and official baseline systems are 4.06%, 4.52%, 8.29%, respectively.
Detection of Sarcasm on Amazon Product Reviews using Machine Learning Algorithms under Sentiment Analysis

Most of the people express their ideas, views and opinions over social media. These feedbacks or comments carry an emotion in them. This data can be either straight forward or sarcastic. This text has to be analyzed and make the reviewers understand the exact intent of the writer. Sentiment analysis is used to analyze the perspective of text. Sarcasm can also be present in the text which is a bitter way of conveying the information. Selection of the dataset is the initial task. Dataset is retrieved from Amazon datasets. The next task is preprocessing of data which includes tokenization of data, polarity identification, stemming and lemmatization. Later, feature extraction is done, which includes term frequency, Inverse document frequency and n-gram. The classification algorithms are used such as Support Vector Machine (SVM), K Nearest Neighbors and Random forest are implemented. Further the calculation of results is evaluated by the parameter accuracy.
Assessing the Students' Understanding and their Mistakes in Code Review Checklists: An Experience Report of 1,791 Code Review Checklist Questions from 394 Students

Code review is a widely-used practice in software development companies to identify defects. Hence, code review has been included in many software engineering curricula at universities worldwide. However, teaching code review is still a challenging task because the code review effectiveness depends on the code reading and analytical skills of a reviewer. While several studies have investigated the code reading techniques that students should use to find defects during code review, little has focused on a learning activity that involves analytical skills. Indeed, developing a code review checklist should stimulate students to develop their analytical skills to anticipate potential issues (i.e., software defects). Yet, it is unclear whether students can anticipate potential issues given their limited experience in software development (programming, testing, etc.). We perform a qualitative analysis to investigate whether students are capable of creating code review checklists, and if the checklists can be used to guide reviewers to find defects. In addition, we identify common mistakes that students make when developing a code review checklist. Our results show that while there are some misconceptions among students about the purpose of code review, students are able to anticipate potential defects and create a relatively good code review checklist. Hence, our results lead us to conclude that developing a code review checklist can be a part of the learning activities for code review in order to scaffold students' skills.
Design and Evaluation of a Distance-Driven User Interface for Asynchronous Collaborative Exhibit Browsing in an Augmented Reality Museum

Augmented reality museums allow visitors to jointly view and interact with exhibits. However, real-time exhibit browsing does not accommodate latecomers to museums and offers limited support to temporally separated visitors. To stimulate asynchronous exhibit browsing, we developed a distance-driven user interface that divided the augmented reality exhibit-egocentric space into four distance ranges, each having a set of social networking features and different privileges for exhibit viewing and interaction. The user interface enables asynchronous exhibit browsing for visitors participating at different times. We conducted empirical studies to evaluate how the user interface affected visitors' collaborative exhibit browsing in terms of perceived usability and learning gains. The results show that the perceived usability of the user interface is consistently high across all distance ranges. The user interface stimulates collaborative exhibit browsing with significant improvements in learning efficiency and learning attention durations, although learning satisfaction showed no difference across the participants. Implications for how the distance-driven user interface can be generalized for other interactive applications are discussed.
Speed Control for Leader-Follower Robot Formation Using Fuzzy System and Supervised Machine Learning

Mobile robots are endeavoring toward full autonomy. To that end, wheeled mobile robots have to function under non-holonomic constraints and uncertainty derived by feedback sensors and/or internal dynamics. Speed control is one of the main and challenging objectives in the endeavor for efficient autonomous collision-free navigation. This paper proposes an intelligent technique for speed control of a wheeled mobile robot using a combination of fuzzy logic and supervised machine learning (SML). The technique is appropriate for flexible leader-follower formation control on straight paths where a follower robot maintains a safely varying distance from a leader robot. A fuzzy controller specifies the ultimate distance of the follower to the leader using the measurements obtained from two ultrasonic sensors. An SML algorithm estimates a proper speed for the follower based on the ultimate distance. Simulations demonstrated that the proposed technique appropriately adjusts the follower robot’s speed to maintain a flexible formation with the leader robot.
Learning Hierarchical Review Graph Representations for Recommendation

The user review data have been demonstrated to be effective in solving different recommendation problems. Previous review-based recommendation methods usually employ sophisticated compositional models, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to learn semantic representations from the review data for recommendation. However, these methods mainly capture the local dependency between neighboring words in a word window, and they treat each review equally. Therefore, they may not be effective in capturing the global dependency between words and tend to be easily biased by noise review information. In this paper, we propose a novel review-based recommendation model, named Review Graph Neural Network (RGNN). Specifically, RGNN builds a specific review graph for each individual user/item, which provides a global view about the user/item properties to help weaken the biases caused by noise review information. A type-aware graph attention mechanism is developed to learn semantic embeddings of words. Moreover, a personalized graph pooling operator is proposed to learn hierarchical representations of the review graph to form the semantic representation for each user/item. We compared RGNN with state-of-the-art review-based recommendation approaches on two real-world datasets. The experimental results indicate that RGNN consistently outperforms baseline methods, in terms of Mean Square Error (MSE).
Three decades of software reference architectures: A systematic mapping study

Software reference architectures have played an essential role in software systems development due to the possibility of knowledge reuse. Although increasingly adopted by industry, these architectures are not yet completely understood. This work presents a panorama on existing software reference architectures, characterizing them according to their context, goals, perspectives, application domains, design approaches, and maturity, as well as the industry involvement for their construction. For this, we planned and conducted a systematic mapping study. During last decade, the number of reference architectures in very diverse application domains has increased, resulting from efforts of industry, academia, and through their collaborations. Academic reference architectures are oriented to facilitate the reuse of architectural and domain knowledge. The industry has focused on architectures for standardization with certain maturity level. However, the great amount of architectures studied in this work have been designed without following a systematic process, and they lack the maturity to be used in real software projects. Further investigations can be oriented to gathering empirical evidences, from different sources than academic data libraries, that allow to understand how references architectures have been constructed, utilized, and maintained during the whole software life-cycle. • Academia has led to the creation of classical-preliminary reference architectures. • The industry has driven standardization and maturity of reference architectures. • Collaboration between industry-academia has increased during the last decade. • The use of architectural approaches has decreased in the previous decade. • Approximately 80% of reference architectures have low maturity levels.
Automatic Face Mask Recognition System With FCM AND BPNN

In the year 2020, a pandemic situation occurs throughout the world with COVID-19.It is a virus which can be transmitted from person to another with cough or sneeze. It has become mandatory to use face mask in order to get away from the infection of virus. A person is allowed into the office or any other organization only if the person is having a mask. Entry into the organization is restricted without the face mask. Here an automated face mask detection system is proposed for with Median filtering and Back propagation neural network (BPNN).91% accuracy is achieved with the proposed methodology. This computerized face mask detection technique is found to be very apt in the present pandemic situation in these days.
Subdomain Adaptation With Manifolds Discrepancy Alignment

Reducing domain divergence is a key step in transfer learning. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this article, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use the low-dimensional manifold to represent the subdomain, and align the local data distribution discrepancy in each manifold across domains. A manifold maximum mean discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called transfer with manifolds discrepancy alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Experimental studies show that TMDA is a promising method for various transfer learning tasks.
An Overview of Group Navigation in Multi-User Virtual Reality

Group navigation techniques can allow both collocated and distributed collaborators to explore a shared virtual environment together. In this paper, we review the different facets, the resulting challenges, and previous implementations of group navigation in the literature and derive four broad and non-exclusive topic areas for future research on the subject. Our overarching goal is to underline the importance of optimizing navigation processes for groups and to increase the awareness of group navigation techniques as a relevant solution approach in this regard.
Quantum Computing Platforms: Assessing the Impact on Quality Attributes and SDLC Activities

Practical quantum computing is rapidly becoming a reality. To harness quantum computers’ real potential in software applications, one needs to have an in-depth understanding of all such characteristics of quantum computing platforms (QCPs), relevant from the Software Engineering (SE) perspective. Restrictions on copying, deletion, the transmission of qubit states, a hard dependency on quantum algorithms are few, out of many, examples of QCP characteristics that have significant implications for building quantum software.Thus, developing quantum software requires a paradigm shift in thinking by software engineers. This paper presents the key findings from the SE perspective, resulting from an in-depth examination of state-of-the-art QCPs available today. The main contributions that we present include i) Proposing a general architecture of the QCPs, ii) Proposing a programming model for developing quantum software, iii) Determining architecturally significant characteristics of QCPs, and iv) Determining the impact of these characteristics on various Quality Attributes (QAs) and Software Development Life Cycle (SDLC) activities.We show that the nature of QCPs makes them useful mainly in specialized application areas such as scientific computing. Except for performance and scalability, most of the other QAs (e.g., maintainability, testability, and reliability) are adversely affected by different characteristics of a QCP.
Robot Speed Adaption in Multiple Trajectory Planning and Integration in a Simulation Tool for Human-Robot Interaction

Abstract Speed and separation monitoring (SSM) is one of the four permissible collaborative operations in human-robot interaction (HRI). At all times, it must be ensured that the speed-dependent separation distance is maintained. To guarantee this, the robot speed or the robot path can be adapted. In this paper, the robot speed adaption for multiple trajectories is implemented in an HRI simulation tool and tested in an application example. Thereby, numerous complex process situations, such as a temporary robot stop or obstacles in the collaborative workspace, can be simulated. The simulation tool enables a comprehensive simulation, analysis and optimisation of human and robot motions within the HRI, already in the planning phase.
Evaluation in Human-Computer Interaction – Beyond Lab Studies

Many research contributions in human-computer interaction are based on user studies in the lab. However, lab studies are not always possible, and they may come with significant challenges and limitations. In this course, we take a broader look at different approaches to doing research. We present a set of evaluation methods and research contributions that do not rely on user studies in labs. The discussion focuses on research approaches, data collection methods, and tools that can be conducted without direct interaction between the researchers and the participants.
Action Word Prediction for Neural Source Code Summarization

Source code summarization is the task of creating short, natural language descriptions of source code. Code summarization is the backbone of much software documentation such as JavaDocs, in which very brief comments such as "adds the customer object" help programmers quickly understand a snippet of code. In recent years, automatic code summarization has become a high value target of research, with approaches based on neural networks making rapid progress. However, as we will show in this paper, the production of good summaries relies on the production of the action word in those summaries: the meaning of the example above would be completely changed if "removes" were substituted for "adds." In this paper, we advocate for a special emphasis on action word prediction as an important stepping stone problem towards better code summarization - current techniques try to predict the action word along with the whole summary, and yet action word prediction on its own is quite difficult. We show the value of the problem for code summaries, explore the performance of current baselines, and provide recommendations for future research.
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Text Data

This paper presents a method to pre-train transformer-based encoder-decoder automatic speech recognition (ASR) models using sufficient target-domain text. During pre-training, we train the transformer decoder as a conditional language model with empty or artifical states, rather than the real encoder states. By this pre-training strategy, the decoder can learn how to generate grammatical text sequence before learning how to generate correct transcriptions. Contrast to other methods which utilize text only data to improve the ASR performance, our method does not change the network architecture of the ASR model or introduce extra component like text-to-speech (TTS) or text-to-encoder (TTE). Experimental results on LibriSpeech corpus show that the proposed method can relatively reduce the word error rate over 10%, using 960 hours transcriptions.
Class-Conditional Defense GAN Against End-To-End Speech Attacks

In this paper we propose a novel defense approach against end-to-end adversarial attacks developed to fool advanced speech-to-text systems such as DeepSpeech and Lingvo. Unlike conventional defense approaches, the proposed approach does not directly employ low-level transformations such as autoencoding a given input signal aiming at removing potential adversarial perturbation. Instead of that, we find an optimal input vector for a class conditional generative adversarial network through minimizing the relative chordal distance adjustment between a given test input and the generator network. Then, we reconstruct the 1D signal from the synthesized spectrogram and the original phase information derived from the given input signal. Hence, this reconstruction does not add any extra noise to the signal and according to our experimental results, our defense-GAN considerably outperforms conventional defense algorithms both in terms of word error rate and sentence level recognition accuracy.