Reviewerly/exHarmony huggingface dataset:
Remote Eye-Tracking for Cognitive Telerehabilitation and Interactive School Tasks in Times of COVID-19

In the attempt to mitigate the effects of COVID-19 lockdown, most countries have recently authorized and promoted the adoption of e-learning and remote teaching technologies, often with the support of teleconferencing platforms. Unfortunately, not all students can benefit from the adoption of such a surrogate of their usual school. We were asked to devise a way to allow a community of children affected by the Rett genetic syndrome, and thus unable to communicate verbally, in writing or by gestures, to actively participate in remote rehabilitation and special education sessions by exploiting eye-gaze tracking. As not all subjects can access commercial eye-tracking devices, we investigated new ways to facilitate the access to eye gaze-based interaction for this specific case. The adopted communication platform is a videoconferencing software, so all we had at our disposal was a live video stream of the child. As a solution to the problem, we developed a software (named SWYG) that only runs at the “operator” side of the communication, at the side of the videoconferencing software, and does not require to install other software in the child’s computer. The preliminary results obtained are very promising and the software is ready to be deployed on a larger base. While this paper is being written, several children are finally able to communicate with their caregivers from home, without relying on expensive and cumbersome devices.
PathPair2Vec: An AST path pair-based code representation method for defect prediction

Software project defect prediction (SDP) can predict the bug probability of software by their features and allocate their testing efforts. The existing software defect prediction methods can be divided into two categories: methods based on traditional handcrafted features and methods based on automatically made abstract features, especially those made by deep learning. The current research indicates that deep learning-based automatic features can achieve better performance than handcrafted features. Code2vec (Alon et al. 2019) is one of the best source code representation models, which leverages deep learning to learn automatic representations from code. In this paper, inspired by code2vec, we propose a new AST path pair-based source code representation method (PathPair2Vec) and apply it to software project defect prediction. We first propose the concept of the short path to describe each terminal node and its control logic. Then, we design a new sequence encoding method to code the different parts of the terminal node and its control logic. Finally, by pairs of short paths, we describe the semantic information of code and fuse them by an attention mechanism. Experiments on the PROMISE dataset show that our method improves the F1 score by 17.88% over the state-of-the-art SDP method, and the AST path pair-based source code representation can better identify the defect features of the source code.
AeroVR: An immersive visualisation system for aerospace design and digital twinning in virtual reality

ABSTRACT One of today’s most propitious immersive technologies is virtual reality (VR). This term is colloquially associated with headsets that transport users to a bespoke, built-for-purpose immersive 3D virtual environment. It has given rise to the field of immersive analytics—a new field of research that aims to use immersive technologies for enhancing and empowering data analytics. However, in developing such a new set of tools, one has to ask whether the move from standard hardware setup to a fully immersive 3D environment is justified—both in terms of efficiency and development costs. To this end, in this paper, we present AeroVR —an immersive aerospace design environment with the objective of aiding the component aerodynamic design process by interactively visualizing performance and geometry. We decompose the design of such an environment into function structures, identify the primary and secondary tasks, present an implementation of the system, and verify the interface in terms of usability and expressiveness. We deploy AeroVR on a prototypical design study of a compressor blade for an engine.
Gaussian Mixture Particle Jump-Markov-CPHD Fusion for Multitarget Tracking Using Sensors With Limited Views

In this article, we propose a multisensor cardinalized probability density hypothesis (CPHD) filter for tracking an unknown number of targets that may maneuver over time by using a sensor network with partially overlapping fields of views (PO-FoVs). We develop a novel, Gaussian mixture particle (GMP) implementation of the jump-Markov CPHD filter to deal with highly non-linear/non-Gaussian models and target maneuvers. The concepts of zero-forcing and zero-avoiding originally used in density approximation are introduced to elucidate a key difference between geometric and arithmetic averaging approaches, which are extended for joint target-state and mode fusion with regard to each PO-FoV for which distributed flooding is used for internode communication. The resulting GMP-JMCPHD fusion algorithm comprises three FoV-oriented steps: splitting, fusion, and merging. Simulations are provided to demonstrate the effectiveness of the proposed approaches.
Data-Driven Anomaly Detection Approach for Time-Series Streaming Data

Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.
A Truth-Inducing Sybil Resistant Decentralized Blockchain Oracle

Many blockchain applications use decentralized oracles to trustlessly retrieve external information as those platforms are agnostic to real-world information. Some existing decentralized oracle protocols make use of majority-voting schemes to determine the outcomes and/or rewards to participants. In these cases, the awards (or penalties) grow linearly to the participant stakes, therefore voters are indifferent between voting through a single or multiple identities. Furthermore, the voters receive a reward only when they agree with the majority outcome, a tactic that may lead to herd behavior. This paper proposes an oracle protocol based on peer prediction mechanisms with non-linear staking rules. In the proposed approach, instead of being rewarded when agreeing with a majority outcome, a voter receives awards when their report achieves a relatively high score based on a peer prediction scoring scheme. The scoring scheme is designed to be incentive compatible so that the maximized expected score is achieved only with honest reporting. A non-linear stake scaling rule is proposed to discourage Sybil attacks. This paper also provides a theoretical analysis and guidelines for implementation as reference.
Semi-Supervised ASR by End-to-End Self-Training

While deep learning based end-to-end automatic speech recognition (ASR) systems have greatly simplified modeling pipelines, they suffer from the data sparsity issue. In this work, we propose a self-training method with an end-to-end system for semi-supervised ASR. Starting from a Connectionist Temporal Classification (CTC) system trained on the supervised data, we iteratively generate pseudo-labels on a mini-batch of unsupervised utterances with the current model, and use the pseudo-labels to augment the supervised data for immediate model update. Our method retains the simplicity of end-to-end ASR systems, and can be seen as performing alternating optimization over a well-defined learning objective. We also perform empirical investigations of our method, regarding the effect of data augmentation, decoding beamsize for pseudo-label generation, and freshness of pseudo-labels. On a commonly used semi-supervised ASR setting with the WSJ corpus, our method gives 14.4% relative WER improvement over a carefully-trained base system with data augmentation, reducing the performance gap between the base system and the oracle system by 50%.
Requirements Change Management Challenges of Global Software Development: An Empirical Investigation

The software organizations rapidly adopting global software development (GSD) to gain the economic and strategic benefits. Besides, GSD faces many challenges that mainly concerned with the requirements change management (RCM). This study aims to identify and empirically validate the factors that can negatively influence the RCM process in GSD. To this end, literature review and questionnaire survey were conducted for the investigation and validation of RCM challenges. A total of 31 RCM challenges were identified. We have further classified the identified challenges in organization types, organization size and based on experts' opinions with the aim to provide a clear understanding of the RCM process and its challenges to the practitioners. Based on these identified challenges, we believe that this study can provide a framework for tackling problems associated with RCM activities in GSD environment, which is significant to success and progression of GSD organizations.
LEED: Label-Free Expression Editing via Disentanglement

Recent studies on facial expression editing have obtained very promising progress. On the other hand, existing methods face the constraint of requiring a large amount of expression labels which are often expensive and time-consuming to collect. This paper presents an innovative label-free expression editing via disentanglement (LEED) framework that is capable of editing the expression of both frontal and profile facial images without requiring any expression label. The idea is to disentangle the identity and expression of a facial image in the expression manifold, where the neutral face captures the identity attribute and the displacement between the neutral image and the expressive image captures the expression attribute. Two novel losses are designed for optimal expression disentanglement and consistent synthesis, including a mutual expression information loss that aims to extract pure expression-related features and a siamese loss that aims to enhance the expression similarity between the synthesized image and the reference image. Extensive experiments over two public facial expression datasets show that LEED achieves superior facial expression editing qualitatively and quantitatively.
A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT

We present a novel supervised word alignment method based on cross-language span prediction.We first formalize a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence.Since this step is equivalent to a SQuAD v2.0 style question answering task, we solve it using the multilingual BERT, which is fine-tuned on manually created gold word alignment data.It is nontrivial to obtain accurate alignment from a set of independently predicted spans.We greatly improved the word alignment accuracy by adding to the question the source token's context and symmetrizing two directional predictions.In experiments using five word alignment datasets from among Chinese, Japanese, German, Romanian, French, and English, we show that our proposed method significantly outperformed previous supervised and unsupervised word alignment methods without any bitexts for pretraining.For example, we achieved 86.7 F1 score for the Chinese-English data, which is 13.3 points higher than the previous state-of-the-art supervised method. 1
Memories for third-person experiences in immersive virtual reality

We typically experience the world from a first-person perspective (1PP) but can sometimes experience events from a third-person perspective (3PP) much as an observer might see us. Little is known about how visual perspective influences the formation of memories for events. We developed an immersive virtual reality paradigm to examine how visual perspective during encoding influences memories. Across two studies, participants explored immersive virtual environments from first-person and third-person avatar perspectives while wearing an Oculus Rift headset. Memory was tested immediately (Study One and Study Two) and following a one-week delay (Study Two). We assessed the accuracy of visual memory using cued recall questions and spatial memory by asking participants to draw maps of the layout of each environment (Study One and Study Two). Additional phenomenological ratings were included to assess visual perspective during remembering (Study Two). There were no differences in the accuracy of visual information across the two studies, but 3PP experiences were found to increase spatial memory accuracy due to their wider camera field of view when compared to 1PP experiences. Our results also demonstrate that 3PP experiences create 3PP memories, as reflected by an increase in subjective ratings of observer-like perspectives during remembering. In sum, visual perspective during memory formation influences the accuracy of spatial but not visual information, and the vantage point of memories during remembering.
Automatic Exam Correction Framework (AECF) for the MCQs, Essays, and Equations Matching

Automatic grading requires the adaption of the latest technologies. It has become essential especially when most of the courses became online courses (MOOCs). The objectives of the current work are (1) Reviewing the literature on the text semantic similarity and automatic exam correction systems, (2) Proposing an automatic exam correction framework (HMB-AECF) for MCQs, essays, and equations that is abstracted into five layers, (3) Suggesting equations similarity checker algorithm named “HMB-MMS-EMA”, (4) Presenting an expression matching dataset named “HMB-EMD-v1”, (5) Comparing the different approaches to convert textual data into numerical data (Word2Vec, FastText, Glove, and Universal Sentence Encoder (USE)) using three well-known Python packages (Gensim, SpaCy, and NLTK), and (6) Comparing the proposed equations similarity checker algorithm (HMB-MMS-EMA) with a Python package (SymPy) on the proposed dataset (HMB-EMD-v1). Eight experiments were performed on the Quora Questions Pairs and the UNT Computer Science Short Answer datasets. The best-achieved highest accuracy in the first four experiments was 77.95% without fine-tuning the pre-trained models by the USE. The best-achieved lowest root mean square error (RMSE) in the second four experiments was 1.09 without fine-tuning the used pre-trained models by the USE. The proposed equations similarity checker algorithm (HMB-MMS-EMA) reported 100% accuracy over the SymPy Python package which reported 71.33% only on “HMB-EMD-v1”.
Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people's consumption behaviors and changed the development model of the financial industry.The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also.Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT.However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data.In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods.In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network.The approach is distributedly performed on the clusters of Apache Spark GraphX and Hadoop to process the large dataset in parallel.The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.
ABOships—An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations

Availability of domain-specific datasets is an essential problem in object detection. Datasets of inshore and offshore maritime vessels are no exception, with a limited number of studies addressing maritime vessel detection on such datasets. For that reason, we collected a dataset consisting of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including nine types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and we subsequently used the CSRT tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN, R-FCN, SSD and EfficientDet). The algorithms were previously trained on the Microsoft COCO dataset. We compared their accuracy based on feature extractor and object size. Our experiments showed that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter.
Spatial-temporal phase unwrapping algorithm for fringe projection profilometry

In this paper, a generalized spatial-temporal phase unwrapping algorithm (STPUA) is proposed for extracting the absolute phase of the isolated objects with intricate surfaces. This proposed algorithm can eliminate thoroughly the order jumps of various temporal phase unwrapping algorithms (TPUAs), while inheriting the high measuring accuracy of quality-guided phase unwrapping algorithms (QGPUAs). Differing from the traditional phase unwrapping algorithms, wrapped phase is first divided into several regional wrapped phases, which can be extracted successively according to its areas and unwrapped individually by QGPUAs. Meanwhile, a series of reliable points from the fringe order map obtained from the code deformed patterns are selected to map the corresponding regional unwrapped phases into an absolute phase. The radii of selecting reliable points can provide the high measuring robustness compared with the classical point-to-point TPUAs for the complex surfaces and the motion blur, while keeping the same number of patterns. Therefore, the proposed STPUA combining SPUAs and TPUAs also can be employed in real-time three-dimensional (3D) reconstruction. Theoretical analysis and experimental results are performed to verify the effectiveness and capability of the proposed algorithm.
Elementary Teachers' Readiness toward the Online Learning Policy in the New Normal Era during Covid-19

Online-based learning system is a recent policy established by the Indonesian government in all schools as a result of Covid-19 pandemic.Shifting face to face schools with online learning-based system creates its own problems, mainly for elementary school teachers.This study aims to investigate the readiness of elementary school teachers towards the adoption of online-based learning policies during Covid-19.There are six indicators to measure teacher readiness, they are (1) availability of learning content, (2) availability of technological devices, (3) proficiency to apply the technology, (4) ability to purchase internet data, (5) availability of internet signals, and (6) student conditions.This research was conducted using descriptive quantitative method.Data was collected through a survey of 250 elementary school teachers in Indonesia.The results indicated that 50% of teachers were less available with the learning content; 24% of teachers had inadequate technology tools; 67.6% were less adapted at implementing technology; 20.4% were less able to buy internet data packages; 40.4% of teachers stated their problem finding internet signals and 53.2% of the students were less prepared for online learning.The research concluded that the teacher is not ready for the implementation of online learning policies.
Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models

Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts.To elucidate the mechanisms by which the models accomplish this behavior, this study applies causal mediation analysis to pre-trained neural language models.We investigate the magnitude of models' preferences for grammatical inflections, as well as whether neurons process subject-verb agreement similarly across sentences with different syntactic structures.We uncover similarities and differences across architectures and model sizesnotably, that larger models do not necessarily learn stronger preferences.We also observe two distinct mechanisms for producing subject-verb agreement depending on the syntactic structure of the input sentence.Finally, we find that language models rely on similar sets of neurons when given sentences with similar syntactic structure.