Reviewerly/exHarmony huggingface dataset:
Parsing Chinese with Combinatory Categorial Grammar: A Linguistic and Computational Study

Parsing Chinese language with CCG is very difficult because the architecture and assumptions of CCG do not fit well with facts from Chinese. Based on the concept of “realization” proposed by Zhu Dexi (1920–1992), this study sheds light on the discrepancy between CCG and Chinese syntax and puts forward a refined schema for Chinese compositionality. The discussion is supported by the data of Chinese CCGbank (CASS). Furthermore, by activating a function-based category setting and a noun/verb disambiguating tagging mechanism, we develop a rule-based mini-Chinese CCG parser without deep learning. The new NVN parser surpasses existing Chinese CCG parser C&amp;C in parsing effect (LF 85.9 vs. LF 74.6) on a partial PCTB 6.0 test set of 500 sentences.
An adaptive formal parallel technique with reputation integration for the enforcement of security policy in the cloud environment

Protecting sensitive data from unauthorized users is a major challenge while sharing data via cloud storage. This has been the objective of mechanisms to control access. This paper proposes an adaptive formal technique for security policy enforcement in Cloud environment. In this approach, the actions of the user performed in the cloud are modeled as a process algebra expression, with a new variant of Algebra Communication Process ACP, with reputation integration. Security policies are expressed by logical formula. Our system enables us to check whether the process meets the security policy and the reputation limit that is required. If it does not, automatic enforcement generates a new process that satisfies a security policy. To prove the efficacy of our security policy enforcement, a software prototype has been implemented and evaluate. The results show a decrease in computation costs and an improvement in cloud defense against insider attacks. It demonstrates also, that our solution outperforms SKMFA-SC (Prabha and Saraswathi, 2020), SEAPP (Hu et al., 2021), FDAC-TR (Yan et al., 2017), RMTAC (Lin et al., 2015), RRAC (Amoon et al., 2020) and DTRM (Lin et al., 2018) in terms of unauthorized access blocking rate, false positive and negative rates, average reputation evaluation accuracy rate, and average response system time.
Recognizing Motion Onset During Robot-assisted Body-weight Unloading is Challenging but Seems Feasible

Patients with neurological gait impairments fear to fall while performing locomotor tasks like sit to stand, stand to sit, and level-ground walking without assistance. This prevents them from participating in daily life. Multi-directional Body-Weight Support (BWS) systems aim to assist training in a safe environment to overcome this limitation. To ensure the safety and comfort during training, BWS systems should effectively and automatically assist patients with forces that support the desired locomotor task.Instead of manual switching of controllers through the therapist, we propose a machine learning-based motion onset recognition model that aims at automatic controller switching (4 gait related task classes in this paper). In addition to the data provided by the gait rehabilitation robot FLOAT, data of three Inertial Measurement Units (IMUs) attached to the sternum and middle of the outer thighs on 6 healthy participants were used to predict motion onset of these 4 gait-related tasks.Four train data sets have been built up from the synchronously obtained data from the IMUs and the BWS system by dividing them into observation windows with sizes of 100 ms and 200 ms and overlap factors of 50% and 90%. From each training dataset, 108 time-domain features have been extracted, ranked, and reduced using the Minimum Redundancy Maximum Relevance (MRMR) method from each train set before applying it to the classifier. The dominant features were applied for training and comparing four different classifiers. The performance of the classifiers has been evaluated based on leave one participant out cross-validation. The ensemble classifier obtained from the train data set with a window size of 100 ms and 90% overlap achieved the best performance with an F1_score of 83.7%.
Real-world Validation Study of Daily Activity Detection for the Elderly

The development of artificial intelligence has led to significant progress in activity detection; however, a proper evaluation of the activity detection performance is not guaranteed and is still lacking in real-life environments. In this study, to verify the stability and usefulness of activity detection in a real-world setting, we analyzed the activity detection performance for 40 elderly people using a human care robot.
An Autonomous Conversational Android that Acquires Human–Item Co-Occurrence in the Real World

The goal of this study is to develop an autonomous conversational robot that acquires knowledge about human society in the real world. In this study, we define experience data as image data obtained from a camera (visual information) and dialogue data obtained from a microphone (auditory information). From experience data, the robot acquires knowledge about the co-occurrence between humans and items, that is, whether it is usual or not for humans to have the items. Not only does the proposed system acquire knowledge, but also generates utterances based on acquired knowledge. We have implemented the proposed method on an android, a human-like robot. We conducted an experiment in which the android was placed in a real-world environment (shopping mall) and interacted with visitors. The percentage of positive responses to the robot's questions based on the acquired knowledge suggests that this system can acquire knowledge from experience data.
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach

Deep Learning (DL) based methods for object detection achieve remarkable performance at the cost of computationally expensive training and extensive data labeling. Robots embodiment can be exploited to mitigate this burden by acquiring automatically annotated training data via a natural interaction with a human showing the object of interest, hand-held. However, learning solely from this data may introduce biases (the so-called domain shift), and prevents adaptation to novel tasks. While Weakly-supervised Learning offers a well-established set of techniques to cope with these problems in general-purpose Computer Vision, its adoption in challenging robotic domains is still at a preliminary stage. In this work, we target the scenario of a robot trained in a teacher-learner setting to detect handheld objects. The aim is to improve detection performance in different settings by letting the robot explore the environment with a limited human labeling budget. We compare several techniques for WSL in detection pipelines to reduce model re-training costs without compromising accuracy, proposing solutions which target the considered robotic scenario. We show that the robot can improve adaptation to novel domains, either by interacting with a human teacher (Active Learning) or with an autonomous supervision (Semi-supervised Learning). We integrate our strategies into an on-line detection method, achieving efficient model update capabilities with few labels. We experimentally benchmark our method on challenging robotic object detection tasks under domain shift <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Usability of an Immersive Control System for a Humanoid Robot Surrogate

Social isolation is an issue that effects many people, especially those from ethic minorities, LGBTQIA+ communities, the elderly, those in long-term healthcare, and those living with life-limiting illnesses. It has become increasingly evident during the pandemic, when mental health issues have soared, and the importance of interacting with loved ones has been highlighted. While telecommunication software helped a great deal in these unprecedented circumstances, it does not allow for navigation in remote environments, and lacks high level interactions found in face-to-face communication. Therefore, this system has been developed to address these issues, and this study was being conducted to test the technical usability of the system when being used by healthy participants. It was found that 720p is the highest resolution that can be applied before the camera delay becomes unusable; though participants suggested that they would like the option to switch to 2k resolution should they be looking close up without moving. In addition, it became apparent that overall the participants were positive about the system, but would prefer a less bulky head-mounted display, and that the choice of which robot to use with the system (Nao or Pepper) was entirely down to individual preference based on the task being completed.
Spatio-Temporal Action Order Representation for Mobile Manipulation Planning

Social robots are used to perform mobile manipulation tasks, such as tidying up and carrying, based on instructions provided by humans. A mobile manipulation planner, which is used to exploit the robot’s functions, requires a better understanding of the feasible actions in real space based on the robot’s subsystem configuration and the object placement in the environment. This study aims to realize a mobile manipulation planner considering the world state, which consists of the robot state (subsystem configuration and their state) required to exploit the robot’s functions. In this paper, this study proposes a novel environmental representation called a world state-dependent action graph (WDAG). The WDAG represents the spatial and temporal order of feasible actions based on the world state by adopting the knowledge representation with scene graphs and a recursive multilayered graph structure. The study also proposes a mobile manipulation planning method using the WDAG. The planner enables the derivation of many effective action sequences to accomplish the given tasks based on an exhaustive understanding of the spatial and temporal connections of actions. The effectiveness of the proposed method is evaluated through practical machine experiments performed. The experimental result demonstrates that the proposed method facilitates the effective utilization of the robot’s functions.
Two-Stage Multi-Organ Automatic Segmentation with Low GPU Memory Occupancy

Abdominal multi organ segmentation is of great significance in medical diagnosis and research. As the abdominal CT usually has a high resolution and a high image size, automatic segmentation of the abdominal organs demands a high configuration of hardware. In this paper, we proposed a low GPU memory occupied two stage fully supervised automatic segmentation framework for abdomina113 organs: liver, spleen, pancreas, right kidney, left kidney, stomach, gallbladder, esophagus, aorta, inferior vena cava, right adrenal gland, left adrenal gland, and duodenum, and designed a lightweight 3D CNN refer to as Tiny-CED Net. The proposed Tiny-CED Net can accurately complete the automatic segmentation of the whole abdominal CT with the GPU memory occupation <2GB. The results show that the average DSC of our method reached 0.83. The average time consumption and max GPU memory occupied are less than 25s and 2GB.
Multi-Manipulator Motion Planning based on Gaussian Process with Probabilistic Inference

In this paper, we study the problem of using Gaussian process motion planning to plan the motion of a multi-manipulator. The DH (Denavit Hartenberg) method is used to establish the kinematic model of the multi-manipulator system. The GPMP2 algorithm is extended to the case of multi-manipulators, and constraints are added to the factor graph to avoid mutual collision between arms and limit the range, speed, and acceleration of joints. Because the GPMP2 algorithm does not include acceleration prior, we convert the limitation of acceleration into a speed limitation. Depending on the actual conditions of the different arms in the system, different constraints are imposed, which can better realize the cooperation between multi-manipulators. The optimized method is more reliable in solving the actual motion planning problem. Simulation results demonstrate the high effectiveness and feasibility of this method.
Model-based meta learning

This chapter discusses model-based meta learning methods. The inner learning loop in model-based meta learning can be viewed as a single model's feed-forward pass. The model embeds the current dataset D into activation state and makes predictions for test data based on this state. All inner-level learning is contained in the model's activation states and is purely feed-forward, while outer-level learning is carried out using recurrent networks, convolutional networks or hypernetworks [7].
Threats and side-effects of smart solutions in O&amp;G industry

The advancements in both hardware and software technologies have transformed the oil and gas a fully-automated and machine-dependent industry. Although this digital transformation improves the production efficiency, it can potentially create various forms of vulnerability and side-effects that can lead to disastrous events, such as toxic gas emission, fire hazards, and oil spills. In addition, the rapid change in the technologies creates doors to hack into the information technology (IT) platforms. Especially, the bridge between the IT and operational technology (OT) platform increases cyber-threats to the oil and gas operations. Hence, while developing smart technology for oil and gas, system engineers need to consider or beware of the downside of smart solutions. As such, the new and existing smart solutions should incorporate improved security techniques to enable the system's robustness. Moreover, the potential side-effects of smart solutions can hinder the production efficiency and become counter-productive. Accordingly, this chapter is dedicated to exploring the potential threats and side-effects of smart solutions and introducing possible solutions for these threats, so that we can have a robust and secure smart oil and gas industry.
Hermitian duality of left dihedral codes over finite fields

Let Fq2 be the finite field of q2 elements, where q is a power of a prime number, and let D2n=〈x,y|xn=1,y2=1,yxy=xn−1〉 be the dihedral group of 2n elements. Left ideals of the group algebra Fq2[D2n] are known as left dihedral codes over Fq2 of length 2n, and abbreviated as left D2n-codes. Let gcd(n,q)=1. In this paper, we give an explicit representation for the Hermitian dual code and the Hermitian hull of every left D2n-code over Fq2. On this basis, we determine all distinct Hermitian self-dual left D2n-codes, Hermitian linear complementary dual (LCD) left D2n-codes, and Hermitian self-orthogonal left D2n-codes over Fq2, respectively. Then we provide an explicit representation and a precise enumeration for these three subclasses of left D2n-codes. As an application, we provide several illustrative examples for obtaining Hermitian self-dual and Hermitian LCD left D2n-codes respectively.
A self-adaptive calibration tool for periscope of AR glass binocular disparity measurement

This paper first reviews the practical optical calibration constrains of the optical metrology equipment Periscope, which measures AR/VR glass’s binocular disparity by providing the numerical measurement of non-parallelism between two eyes’ optical axes. A self-adaptive calibration methodology with close loop feedback to track on the calibration tool’s accuracy is proposed to precisely calibrate the parallelism of the periscope’s two optical sensing channels and efficiently verify this parameter periodically with consistency over time or instruments. A few implementation schemes, including a passive target, an active selfreferenced target, and two different sensitivity enhanced targets are discussed in depth to compare the performance contributors: accuracy, repeatability, and system complexity, which leads to the recommendations for different application scenarios. Beyond the AR/VR disparity measurement a potential application based on the same methodology is introduced to evaluate the precision motion system’s accuracy and tolerance.
Quantitative performance evaluation in an augmented reality view enhancement driver assistant system

The temporally and spatially accurate display of information in augmented reality (AR) systems is essential for immersion and operational reliability when using the technology. We developed an assistant system using a head-mounted display (HMD) to hide visual restrictions on forklifts. We propose a method to evaluate the accuracy and latency of AR systems using HMD. For measuring accuracy, we compare the deviation between real and virtual markers. For latency measurement, we count the frame difference between real and virtual events. We present the influence of different system parameters and dynamics on latency and overlay accuracy.
Kernel embedding transformation learning for graph matching

• Developing a kernel embedding transformation learning model for graph matching. • Combining the kernelized unary alignment and local structure alignment into a joint framework. • Proposing an effective optimization algorithm to solve the joint framework. • Improving the matching performance significantly in the graph matching tasks with deformation and rotation variations. Graph matching , which aims to establish correspondences between two geometrical graphs, is a general and powerful tool for pattern recognition and computer vision . However, many factors degrade the matching accuracy. The graph structure suffering from deformation and rotation variations is a key issue in the process of matching. In this work, we propose a joint framework in the reproducing kernel Hilbert space (RKHS) for graph matching with deformation and rotation variations, which incorporates the kernelized unary alignment and local structure alignment into a joint framework. Specifically, the proposed method is able to enhance the node to node correspondence and the edge to edge correspondence and avoids the effect of deformation and rotation by maximizing the similarities between the source graph and the transformed target graph in the reproducing kernel Hilbert space. Meanwhile, an effective algorithm is presented to solve the joint framework. Comprehensive discussion, involving convergence analysis and parameter sensitive analysis , are as well proposed. Promising experimental results in the variety of graph matching tasks such as deformation and rotation are provided to evidence the superiority of the proposed method.
Development of Batik Modul Based on Local Culture of North Sumatra, Indonesia

This study aims to develop teaching materials in the form of local culture-based batik moduls that can be used as the development of Medan batik motives at Ardhina Batik Medan. This study uses the Research and Development (R&amp;D) research method and the research model uses the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The results of the study show that the results of the modul feasibility test according to 6 experts. The results of the data analysis carried out stated that the batik modul based on the local culture of North Sumatra at Ardhina Batik Medan was suitable for use with the "very good" criteria from material experts with an average score of 3 experts, namely 131 or 90.8%, and 3 media experts with a mean score of 141 or 88.5% in the very good category. Thus it can be concluded that the batik modul can be used in learning as well as a means to preserve and develop Medan's typical batik motives.With this modul, students can get to know batik and the process of batik as well as modify the typical motives of several regions into one combination motif (stylization).
THE EFFECT OF PREPAREDNESS MEASURES ON COMMUNITY PARTICIPATION IN FLOOD EMERGENCY RESPONSE IN RANGKASBITUNG DISTRICT

Every disaster brings victims, both human and property is a fact. But whatever the type of disaster, there is always an omen before it comes. This is where the urgency is to understand every sign that comes correctly and accurately. So, of course, knowledge, skills, and skills are needed on how the community, especially in disaster-prone areas, prepares anticipatory steps for the arrival of the disaster (Priyowidodo and Luik 2013). The readiness and skills of the community, especially families, are the primary keys to safety in dealing with disaster emergencies (Willem Rampangilei 2018). This study aims to determine the effect of preparedness measures on community participation in flood emergency response. This research uses research that uses correlational survey method. In this study, the independent variable is community participation in disaster emergency response, while the dependent variable is preparedness measures. This study aimed to determine the effect of preparedness measures on community participation in flood emergency response. The results of the survey: Preparedness measures (X) affect the community participation variable (Y) by 16%.
Entropic quantifier of spin–boson nonclassicality

Abstract Spin systems interacting with boson environments are ubiquitous in nature. To what extent quantum states in such systems depart from classicality is becoming an increasingly important issue. Here, we study nonclassicality in systems involving the interaction between a spin and a boson mode. We introduce a simple and effective method for quantifying spin–boson nonclassicality in terms of the entropy excess between the classical and the quantum Tsallis entropy. The method can be naturally extended to more general systems. Fundamental properties of the nonclassicality quantifier are revealed, which render it reasonable for spin–boson states. Basic features are illustrated by a variety of typical spin–boson states. As applications, we further investigate spin–boson nonclassicality in the Dicke model, which sheds light on the normal-superradiant phase transition and quantum metrology.
Lyapunov Robust Constrained-MDPs for Sim2Real Transfer Learning

Safety and robustness are two desired properties for any reinforcement learning algorithm. Constrained Markov Decision Processes (CMDPs) can handle additional safety constraints and Robust Markov Decision Processes (RMDPs) can perform well under model uncertainties. In this chapter, we propose to unify these two frameworks resulting in Robust Constrained MDPs (RCMDPs). The motivation is to develop a framework that can satisfy safety constraints while also simultaneously offer robustness to model uncertainties. We develop the RCMDP objective, derive gradient update formula to optimize this objective and then propose policy gradient based algorithms. We also independently propose Lyapunov-based reward shaping for RCMDPs, yielding better stability and convergence properties.
A Study on Efficient Reinforcement Learning Through Knowledge Transfer

Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. This survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.