Reviewerly/exHarmony huggingface dataset:
Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation

As robots gain capabilities to enter our humancentric world, they require formalism and algorithms that enable smart and efficient interactions. This is challenging, especially for robotic manipulators with complex tasks that may require collaboration with humans. Prior works approach this problem through reactive synthesis and generate strategies for the robot that guarantee task completion by assuming an adversarial human. While this assumption gives a sound solution, it leads to an “unfriendly” robot that is agnostic to the human intentions. We relax this assumption by formulating the problem using the notion of regret. We identify an appropriate definition for regret and develop regret-minimizing synthesis framework that enables the robot to seek cooperation when possible while preserving task completion guarantees. We illus-trate the efficacy of our framework via various case studies.
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI

We introduce a visually-guided task-and-motion planning benchmark, which we call the ThreeDWorld Trans-port Challenge. In this challenge, an embodied agent is spawned randomly in a simulated physical home environment and required to transport a small set of objects scattered around the house with containers. We build this benchmark challenge using the ThreeDWorld simulation: a virtual 3D environment where all objects respond to physics, and a robot agent can be controlled using a fully physics-driven navigation and interaction API. We evaluate several existing agents on this benchmark. Experimental results suggest that: 1) a pure RL model struggles on this challenge; 2) state-of-the-art hierarchical planning-based agents can transport some objects but are still far from solving this task. We anticipate that this benchmark will empower researchers to develop more intelligent physics-aware robot learning algorithms.
Very Low-Resolution Moving Vehicle Detection in Satellite Videos

This paper proposes a practical end-to-end neural network framework to detect tiny moving vehicles in satellite videos with low imaging quality. Some instability factors such as illumination changes, motion blurs, and low contrast to the cluttered background make it difficult to distinguish true objects from noise and other point-shaped distractors. Moving vehicle detection in satellite videos can be carried out based on background subtraction or frame differencing. However, these methods are prone to produce lots of false alarms and miss many positive targets. Appearance-based detection can be an alternative but is not well-suited since classifier models are of weak discriminative power for the vehicles in top view at such low resolution. This article addresses these issues by integrating motion information from adjacent frames to facilitate the extraction of semantic features and incorporating the Transformer to refine the features for key points estimation and scale prediction. Our proposed model can well identify the actual moving targets and suppress interference from stationary targets or background. The experiments and evaluations using satellite videos show that the proposed approach can accurately locate the targets under weak feature attributes and improve the detection performance in complex scenarios.
Cross-Database Micro-Expression Recognition Based on a Dual-Stream Convolutional Neural Network

Cross-database micro-expression recognition (CDMER) under semi supervised conditions is a difficult task, where the target (testing) and source (training) samples come from different microexpression (ME) databases, resulting in the inconsistency of the feature distributions between each other, and hence affecting the performance of many existing MER methods.To address this problem, we propose a dual-stream convolutional neural network (DSCNN) for dealing with CDMER tasks.In the DSCNN, two stream branches are designed to study temporal and facial region cues in ME samples with the goal of recognizing MEs.In addition, in the training process, the domain discrepancy loss is used to enforce the target and source samples to have similar feature distributions in some layers of the DSCNN.Extensive CDMER experiments are conducted to evaluate the DSCNN.The results show that our proposed DSCNN model achieves a higher recognition accuracy when compared with some representative CDMER methods.
A Deep Explainable Model for Fault Prediction Using IoT Sensors

IoT sensors and deep learning models can widely be applied for fault prediction. Although deep learning models are considerably more potent than many conventional machine learning models, they are not transparent. This paper first examines different deep learning techniques to carry out univariate time series analysis based on vibration sensors installed on four industrial bearings to predict a fault occurring in a predefined time window. Several recurrent neural networks are used to develop fault prediction models. An empirical evaluation of these models shows that all models perform well; however, hybrid models outperform other models when the time window increases. Then, instance-wise feature selection has been considered to highlight the most contributing features for its outputs regarding any input. In this problem, the main challenge is to propose a trainable feature selection model with the minimum number of selected features whilst its performance is close to the baseline model. This paper develops a novel explainable method called the Gumbel-Sigmoid eXplanator (GSX) to tackle these problems. In a nutshell: (i) we have developed a differentiable and trainable selector, and (ii) we utilize regularization to control the number of features for each instance flexibly. The proposed method is model agnostic, and empirical evaluations on two datasets show that GSX can not only solve the problems identified with two other state-of-the-art methods but also outperform them in terms of accuracy and run-time.
Systematic Literature Mapping of User Story Research

User stories are a widely used artifact in Agile software development.Currently, only a limited number of secondary studies have reviewed the research on the user story technique.These research reviews focused on specific research topics related to ambiguity of requirements, effort estimation, and the application of Natural Language Processing.To our knowledge, a systematic mapping of all user story research has not been performed.To this end, we study the academic literature to investigate what user stories research has been performed, what types of problems have been identified, what sort of solutions or other types of research outcomes have been achieved, how mature the research is, and what research gaps exist.We followed Systematic Mapping Study guidelines to synthesize the currently available academic research on user stories.In total, we found 186 unique peer-reviewed studies, published in the period 2001-2021.We observed that research on the user story technique and its use had grown exponentially over the last seven years.Further, using a five-dimensional classification framework -requirements engineering activity, problem class, outcome class, type of research, type of publication -we observed several patterns in the classification of these studies across the different framework dimensions, which provided insights into the state-of-the-art and maturity of the research.We also identified four research gaps: the paucity of focused literature reviews; a lack of research on the role that user stories play in human cognition and interaction; a lack of comprehensive and mature solutions for resolving ambiguity issues with user stories early in the project; and a lack of validation and evaluation of proposed solutions.Several research opportunities are suggested, making our paper a useful reference for future research on user stories allowing researchers to clearly position their contributions.
Motion Feature Aggregation for Video-Based Person Re-Identification

Most video-based person re-identification (re-id) methods only focus on appearance features but neglect motion features. In fact, motion features can help to distinguish the target persons that are hard to be identified only by appearance features. However, most existing temporal information modeling methods cannot extract motion features effectively or efficiently for v ideo-based re-id. In this paper, we propose a more efficient Motion Feature Aggregation (MFA) method to model and aggregate motion information in the feature map level for video-based re-id. The proposed MFA consists of (i) a coarse-grained motion learning module, which extracts coarse-grained motion features based on the position changes of body parts over time, and (ii) a fine-grained motion learning module, which extracts fine-grained motion features based on the appearance changes of body parts over time. These two modules can model motion information from different granularities and are complementary to each other. It is easy to combine the proposed method with existing network architectures for end-to-end training. Extensive experiments on four widely used datasets demonstrate that the motion features extracted by MFA are crucial complements to appearance features for video-based re-id, especially for the scenario with large appearance changes. Besides, the results on LS-VID, the current largest publicly available video-based re-id dataset, surpass the state-of-the-art methods by a large margin. The code is available at: https://github.com/guxinqian/Simple-ReID.
Robust Architecture-Agnostic and Noise Resilient Training of Photonic Deep Learning Models

Neuromorphic photonic accelerators for Deep Learning (DL) have increasingly gained attention over the recent years due to their ability for ultra fast matrix-based calculations and low power consumption providing a great potential for DL implementations to deal with a wide range of different applications. At the same time, physical properties of the optical components hinder their application since they introduce a number of limitations, such as easily saturated activation functions as well as the existence of various noise sources. As a result, photonic DL models are especially challenging to be trained and deployed, compared with regular DL models, since traditionally used methods do not take into account the aforementioned constraints. To overcome these limitations and motivated by the fact that the information lost in one layer cannot be easily recovered when gradient-descent based algorithms are employed, we propose a novel training method for photonic neuromorphic architectures that is capable of taking into account a wide range of limitations of the actual hardware, including noise sources and easily saturated activation mechanisms. Compared to existing works, the proposed method takes a more holistic view of the training process, focusing both on the initialization process, as well as on the actual weight updates. The effectiveness of the proposed method is demonstrated on a variety of different problems and photonic neural network (PNN) architectures, including a noisy photonic recurrent neural network evaluated on high-frequency time series forecasting and a deep photonic feed-forward setup consisting of a transmitter, noisy channel, and receiver, which is used as an intensity modulation/direct detection system (IM/DD).
Unified Low-Rank Tensor Learning and Spectral Embedding for Multi-View Subspace Clustering

Multi-view subspace clustering aims to utilize the comprehensive information of multi-source features to aggregate data into multiple subspaces. Recently, low-rank tensor learning has been applied to multi-view subspace clustering, which explores high-order correlations of multi-view data and has achieved remarkable results. However, these existing methods have certain limitations: 1) The learning processes of low-rank tensor and label indicator matrix are independent. 2) Variable contributions of different views to the consistent clustering results are not discriminated. To handle these issues, we propose a unified framework that integrates low-rank tensor learning and spectral embedding (ULTLSE) for multi-view subspace clustering. Specifically, the proposed model adopts the tensor singular value decomposition (t-SVD) based tensor nuclear norm to encode the low-rank property of the self-representation tensor, and a label indicator matrix via spectral embedding is simultaneously exploited. To distinguish the importance of various views, we learn a quantifiable weighting coefficient for each view. An effective recursion optimization algorithm is also developed to address the proposed model. Finally, we conduct comprehensive experiments on eight real-world datasets with three categories. The experimental results indicate that the proposed ULTLSE is advanced over existing state-of-the-art clustering methods.
Precision Clinical Medicine Through Machine Learning: Using High and Low Quantile Ranges of Vital Signs for Risk Stratification of ICU Patients

Remote monitoring of patients in the intensive care unit (ICU) is a crucial observation and assessment task that is necessary for precision medicine. In this work, we follow the state-of-the-art in risk stratification through machine learning-based prediction, but with minimal features that rely on vital signs, the most commonly used physiological variables obtained inside and outside hospitals. We provide a formal representation of a feature engineering algorithm and report the development and validation of three reproducible machine learning prediction models: ICU patient readmission, abnormality, and next-day vital sign measurements. For the readmission model, we proposed two solutions for data with imbalanced classes and applied five binary classification algorithms to each solution. For the abnormality model, we applied the same five algorithms to predict whether a patient will show abnormal health conditions. Our findings indicate that we can still achieve a reasonable performance using these machine learning models by focusing on low and high quantile ranges of vital signs. The best accuracy achieved in the readmission model was around 67.53%, with an area under the receiver operating characteristic (AUROC) of 0.7376. The highest accuracy achieved in the abnormality model was around 67.40%, with an AUROC of 0.7379. For the next-day vital sign measurements model, we provide three approaches for selecting model predictors and apply the eXtreme Gradient Boosting (XGB) and Random Forest Regression (RFR) algorithms to each solution. We found that, in general, the use of the most recent vital sign measurements achieves the least prediction error. Considering the large investment from the medical industry in patient monitoring devices, the developed models will be incorporated into an Intelligent ICU Patient Monitoring (IICUPM) module that can potentially facilitate the delivery of high quality care by implementing cost-efficient policies for handling the patients who utilize ICU resources the most.
A Comprehensive Comparison of Open-Source Libraries for Handwritten Text Recognition in Norwegian

In this paper, we introduce an open database of historical handwritten documents fully annotated in Norwegian, the first of its kind, allowing the development of handwritten text recognition models (HTR) in Norwegian. In order to evaluate the performance of state-of-the-art HTR models on this new base, we conducted a systematic survey of open-source HTR libraries published between 2019 and 2021, identified ten libraries and selected four of them to train HTR models. We trained twelve models in different configurations and compared their performance on both random and scripter-based data splitting. The best recognition results were obtained by the PyLaia and Kaldi libraries which have different and complementary characteristics, suggesting that they should be combined to further improve the results.
Probing for the Usage of Grammatical Number

A central quest of probing is to uncover how pre-trained models encode a linguistic property within their representations. An encoding, however, might be spurious-i.e., the model might not rely on it when making predictions. In this paper, we try to find encodings that the model actually uses, introducing a usage-based probing setup. We first choose a behavioral task which cannot be solved without using the linguistic property. Then, we attempt to remove the property by intervening on the model's representations. We contend that, if an encoding is used by the model, its removal should harm the performance on the chosen behavioral task. As a case study, we focus on how BERT encodes grammatical number, and on how it uses this encoding to solve the number agreement task. Experimentally, we find that BERT relies on a linear encoding of grammatical number to produce the correct behavioral output. We also find that BERT uses a separate encoding of grammatical number for nouns and verbs. Finally, we identify in which layers information about grammatical number is transferred from a noun to its head verb.
Secure and Temporary Access Delegation With Equality Test for Cloud-Assisted IoV

With the continuous development of the Internet of Vehicles (IoV), the cloud-assisted IoV is becoming an emerging and attractive paradigm, in which a growing number of IoV users use the cloud server to store the data collected from their smart devices, thereby relieving local costs to a great extent. In this case, considering that the third-party cloud as a commercial mechanism cannot guarantee data security, users choose to encrypt their own data before uploading it to the cloud server. Although the privacy and confidentiality of the uploaded data can be protected, the encryption method hinders data search due to its inherent “decrypt all-or-nothing” feature. To achieve encryption and data search on privacy-preserving data, many existing works of literature design various public key encryption with keyword search schemes that, however, suffers the disadvantage that they can work only between ciphertext encrypted under the same public key, which means that it is not suitable for some computations on cloud. Furthermore, most schemes also have failed to find out how to delegate the searched data securely and efficiently when the data owner is not convenient to address the data. Therefore, this study tends to test the equivalence between messages encrypted under different public keys for basic secure computation and delegating the right of decrypting searched data to the specified user. Then, a lightweight proxy re-encryption scheme with the equality test and temporary delegation (PRE-ET-TD) was put forward for the cloud-assisted IoV. Besides, the proposed scheme is collusion-resistant and proven secure against chosen ciphertext attack (CCA) in the random oracle model. Meanwhile, the experimental simulation indicates that the presented PRE-ET-TD is feasible and efficient.
Improved YOLOX’s Anchor-Free SAR Image Ship Target Detection

For the characteristics of synthetic aperture radar (SAR) images, such as the dense arrangement of ship targets on shore, which are easily affected by land, the sparse distribution of small ships in the deep sea, which are easily missing detect, and also the existence of a lot of negative sample background areas. We propose a new method based on improved YOLOX as Anchor Free target detection method, which greatly improves the training efficiency compared with the preset anchor box. In view of the problem that densely packed ship targets are easy to miss, and the ship target in the deep sea is weak and the distribution is sparse. We propose improved corner efficient intersection over union (ICEIOU) to further comprehensively consider the regression parameters, and the loss function is optimized for the network training process. We use Adaptive-NMS to adaptively adjusts the non-maximum suppression (NMS) threshold value for the dense arrangement and sparse distribution of ships, and atrous convolution is used to improve the problem of some detailed information loss. And combined with coordinate attention mechanism, high-speed and high-precision ship target detection is realized. The experimental results show that compared with the original YOLOX method, the detection precision of the method in this paper on HRSID dataset is improved from the original 88.81% to 91.48%, and the mAP index is improved from the original 87.72% to 91.76%, which is obviously better than the comparison method.
FCE: Feedback Based Counterfactual Explanations for Explainable AI

Artificial Intelligence provides accurate predictions for critical applications (e.g., healthcare, finance), but lacks the ability to explain its internal mechanism in most applications which require high interaction with humans. Even if many studies analyze machine learning models and their learning behaviour and eventually provide an interpretation of the inner mechanics of these models, these studies often entail a simpler surrogate model generate explanations by producing a piece of interpretable information such as feature scores. The crucial caveat against these studies is the lack of human involvement in the design and evaluation of explanations, consequently giving rise to trust issues and lack of acceptance and understanding. To this end, we address this limitation by involving humans in the counterfactual explanation generation process which is enriched with user feedback and thus enhancing the automated explanations which are better aligned with user expectations. In this paper, we propose a user feedback based counterfactual explanation approach for explainable Artificial Intelligence. In our work, we utilize feedback in two ways: first, feedback to customize the explanations locally that helps in providing the neighbourhood to discern the feasible explanations; and second, to evaluate the generated explanations.
Toward Successful DevOps: A Decision-Making Framework

DevOps (development and operations) is a set of collaborative practices that automate delivery of new software updates with the aim to reduce the development life cycle and produce quality software products. Software organizations face several barriers while adopting DevOps practices as the integration of development and operation teams requires merger of different processes, tools, and skill sets. This study aims to develop a prioritization-based framework of the DevOps best practices based on evidence collected from industry experts. To attain the study aims, firstly, a systematic literature review was conducted to identify DevOps best practices reported in the literature. Next, a questionnaire survey study was conducted to receive insight from industry practitioners for the identified best practice. Finally, the fuzzy-AHP technique was applied to prioritize the best practices concerning to the significance for DevOps process. We believe that the identified best practices, their categorization and fuzzy-AHP based framework will help industry experts to revise and improve their strategies to make the DevOps process sustainable.