Reviewerly/exHarmony huggingface dataset:
Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods from AlphaGo to Muzero have enjoyed huge success in discrete domains, such as chess and Go. Unfortunately, in real-world applications like robot control and inverted pendulum, whose action space is normally continuous, those tree-based planning techniques will be struggling. To address those limitations, in this paper, we present a novel model-based reinforcement learning frameworks called Critic PI2, which combines the benefits from trajectory optimization, deep actor-critic learning, and model-based reinforcement learning. Our method is evaluated for inverted pendulum models with applicability to many continuous control systems. Extensive experiments demonstrate that Critic PI2 achieved a new state of the art in a range of challenging continuous domains. Furthermore, we show that planning with a critic significantly increases the sample efficiency and real-time performance. Our work opens a new direction toward learning the components of a model-based planning system and how to use them.
Identification of nonlinear vibration response signals for high speed automata

Because the current high-speed automata's nonlinear vibration response signal identification method has the problem of low vibration response signal identification accuracy, a high-speed automata's nonlinear vibration response signal identification method is proposed to monitor the surrounding environment safely according to the characteristics of the vibration signal, By decomposing the time-domain dimension of the vibration signal, extracting the characteristic information of different signals, and using the denoising method combining median filtering and singular value decomposition denoising to denoise the nonlinear vibration signal of high-speed automata, so as to achieve High-speed automata nonlinear vibration response signal recognition. The experimental results show that the identification method of nonlinear vibration response signals of high-speed automata improves the identification accuracy of nonlinear vibration response signals.
A Progressive Image Inpainting Algorithm with a Mask Auto-update Branch

Recently, learning-based image inpainting methods have made inspiring progress with squared or irregular holes. The generative adversarial networks (GANs) have been able to produce visually realistic and semantically correct results. However, most existing methods generate the results by one stage. They may have a slight advantage in computation time, but more information is lost during the inpainting process. Due to the lack of sufficient context information, these inpainting approaches cannot inpaint large holes in natural images very well. This paper proposes a progressive image inpainting algorithm for solving the above problem. This algorithm synthesizes different image components in a parallel manner within one stage. Moreover, this paper design a branch, which transmits the image features to the generative model iteratively. In each iteration, we adopt a mask auto-updating mechanism to shrink the boundary of a hole. Finally, the generative component can shrink the large corrupted regions in natural images and yield promising inpainting results.
Design and Implementation of Mobile Nursing Information System for Liver, Gallbladder, Spleen and Stomach Patients Based on PDA

According to the research project of medical PDA, the characteristics of domestic and foreign medical PDA products in the current market are analyzed in detail. For the needs of patients with liver, gallbladder, spleen and stomach disease, the design of mobile data collection system based on PDA is introduced with related technologies. The system is mainly supported by such system, and adopts strict three-tier architecture, namely data access layer, business logic layer and user interface layer, to realize the standardization and integration of information resources. Finally, the mobile data collection terminal based on PDA is designed in detail, including mobile data terminal structure, terminal software function design, development platform selection design, etc. In the test part, we write test cases, after strict testing, and make the system achieve the expected object.
Combating False Negatives in Adversarial Imitation Learning

In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the desired behavior. However, as the trained policy learns to be more successful, the negative examples (the ones produced by the agent) become increasingly similar to expert ones. Despite the fact that the task is successfully accomplished in some of the agent's trajectories, the discriminator is trained to output low values for them. We hypothesize that this inconsistent training signal for the discriminator can impede its learning, and consequently leads to worse overall performance of the agent. We show experimental evidence for this hypothesis and that the ‘False Negatives’ (i.e. successful agent episodes) significantly hinder adversarial imitation learning, which is the first contribution of this paper. Then, we propose a method to alleviate the impact of false negatives and test it on the BabyAI environment. This method consistently improves sample efficiency over the baselines by at least an order of magnitude.
SemApp: A Semantic Approach to Enhance Information Retrieval

The present work proposed a semantic retrieval approach to treat the issues of semantic ambiguity of indexed terms, the uncertainty, and imprecision that is inherent in the information retrieval process. The proposed approach constitutes of three different phases. The query meaning was discovered in the first phase by formulating a set of candidate queries from possible contexts. A score for each alternative was calculated based on its semantic tree and inherent dispersion between its concepts. This score assesses the overall meaning of the alternative query. This phase was finished by selecting the candidate query that attains the highest score to be the best representative to the original query. A semantic index was built in the second phase exploiting the classic and semantic characteristics of the document concepts to finally assign a weight for each concept to estimate its relative importance. The third phase proposed a ranking model that utilizes the semantic similarities and relations between concepts to calculate the query-document relevance. This ranking model is based on a query likelihood language model and a conceptual weighting model. The validity of the proposed approach was evaluated through performance comparisons with the related benchmarks measured in terms of the standard IR performance metrics. The proposed approach outperformed the compared baselines and improved the measured metrics. A statistical significance test was conducted to guarantee that the obtained improvements are true enhancements and are not a cause of random variation of the compared systems. The statistical test supported the hypothesis that the obtained improvements were significant.
Cases of Writing Education for Self-Expression Using an Online Platform and the Operation Plans Thereof －Focusing on SNS and OVP Writing

This study started from recognizing the fact that writing education for self-expression using an online platform is necessary for those university students who have been engaging in routinized, non-face activities due to COVID-19, and who have thus become very familiar with communication via an online platform. As things are, writing education as a university liberal arts subject has limitations for students who are not only familiar with writing in a digital world, so to speak, but who are expected to respond to our rapidly changing society using only traditional writing educational methods. To overcome these limitations, this study first emphasized the necessity of writing education for self-expression using an online platform that would serve as a window for communication. Secondly, this study proposed cases of writing education for self-expression using an online platform operated as a non-curricular program at University A. Participating students were highly satisfied with the topic of the non-curricular program, the content of the program operation, and the educational method of the program itself. In addition, the students were also highly satisfied with the possibility of using the program after they completed it, stating that their future participation would instill in them a sense of empowerment. These results show that writing education for self-expression using an online platform can contribute to the writing confidence of learners in various communication areas. Also, this study emphasized that non-curricular programs for self-expression using an online platform can be expanded to incorporate traditional university writing subjects, and further presented curriculum and operation plans to operate within current curricular writing subjects.
Scalabeling: Linear Slider Supported Labeling for the Classification of Streaming Data

Supervised Machine Learning algorithms need labeled datasets in order to train models for the classification of new unlabeled data. For most purposes, especially in the field of computer vision, suitable tools to support the labeling process have already been developed. However, for some use cases convenient methods are still not available. In order to bridge this gap, we propose a new lightweight labeling tool for highly sensitive streaming data like human body data from a sensor wristband. The proposed system Scalabeling uses a linear slider as the main interface for configurable user created labels. The system records user inputs from this slider, converts them to labels and combines them with data from an asynchronously received data stream. Finally the resulting labeled dataset is saved and can be used in various machine learning systems. We implemented this tool and performed a preliminary evaluation by applying it to a real-world labeling problem from an ongoing research project. Our findings show a good user acceptance as well as a significant reduction of labour costs for labeling and preprocessing.
Implementation of quantum gates using decision diagrams

This article proposes a set of algorithms to compress the representation of quantum states and emulate their evolution when modeling gates representing quantum information processes. The employed methodology is based on detailed descriptions of the data structures that represent the states. Procedural languages have been applied to reduce the computational costs of the memory access processes. A modification of the binary trees is used as the framework for the entire development. The required memory and processing time are considerably reduced when this approach is applied when implementing quantum gates and algorithms.
Intelligent Information Retrieval Module Based on Learning to Rank

With the rapid expansion of information on the internet nowadays, the query results produced by Relational Database (RDB) can no longer satisfy the complex search requests made by users. Traditional information retrieval modules generally have problems such as low retrieval efficiency, poor scalability, and low intelligence. In this paper, this paper proposed an intelligent information retrieval module based on the Learning to Rank (L TR) to optimizes the relevance score in the information retrieval process. Experimental results conducted in production environment show that our proposed method improved accuracy of relevance score by 15.84% and accelerate the retrieval speed by 4.15 times. In addition, the interpretability of the retrieval results and the selection of key features are also analyzed and realized.
Long-Range Comprehensive Modeling for Fine-Grained Visual Classification

Recognition of objects from subcategories with very subtle differences is more challenging than traditional classification tasks due to the large intra-class and small inter-class variation. Recent studies treat the discriminative representation learning of local detailed parts or global object context as the key solution of the fine-grained visual classification. However, most approaches do not deal with the negative impact of the complex surroundings on semantic extraction of the object or model the relationships between different local parts. In this paper, we propose a simple yet effective long-range comprehensive modeling (LRCM) framework to solve the challenge. We capture robust and discriminative features based on multi-stage multi-granularity classification (MSMGC) mechanism and novel features distribution alignment modules (FDAM) at first and then exploit the relationships of local features between different stages over the convolution neural networks by a comprehensive transformer (CT). We evaluate our method on a challenging benchmark dataset and achieve competitive performance with the current state-of-the-art methods. We empirically analyze the contributions of our practice along with explanatory visualizations. Code can be found at https://github.com/luyu-fan/LRCM.
Intelligent Augmented Video Streaming Services Using Lightweight QR Code Scanner

Video streaming is a multimedia service that continuously transmits the data over the Internet and presents the content on user screens without predownloading the entire video. Augmented video streaming is an advanced version of video streaming, where the video is enriched with additional embedded information in video frames. These additional data aim to provide a better user experience. Using QR code is one of the efficient approaches to incorporate information into video streams in this context. However, receiving the data in the embedded QR code is considered a challenging task owing to video quality and view angles. This paper proposes a lightweight two-stage QR code decoder for augmented video streaming using deep learning technologies. In the first stage, the position of the embedded QR code is detected using an online object detection algorithm. In the second stage, the detected region of the QR code is fed into a QR code reader to extract the embedded data. The experimental results show that the proposed decoder achieves high performances in terms of response time and decoding accuracy while being very lightweight, which is promising to be implemented in smartphones.
읽기 교육내용의 비판과 실천적 재구성

This study aims to examine how current educational content should be transformed to enable readers to demonstrate their capabilities as practical knowledge. To this end, we examined our reading education in contrast to the Common Core State Standards (CCSS) in the United States. The results showed that reading curriculum is biased toward the reading function, and it is necessary to revise the educational content that was seemingly selected without evidence and focus on key functions. Furthermore, when selecting functions, it is necessary to enhance the system and affiliation between categories, as well as to increase the specificity, practicality and consistency of functional statements, by presenting them as principles of iterative deepening when arranging them. Accordingly, we proposed a content system table centered on “function” and “genre, text, attitude, and context” as categories of different layers. And decoding, reading fluency, changes of high school ‘reading’ subjects are required to enhance practicality of reading education, This is expected to increase the practicality of reading education as practical knowledge.
OpenCLTM Implementation of Rapid Image Restoration Kernels Based on Blind/Non-blind Deconvolution Techniques for Heterogeneous Parallel Systems

Over the years, image enhancement is one of the research areas in the field of image processing and computer vision. In the real world, images or videos captured and transmitted by different sensors are not fit for further processing. Image restoration is the sub-branch of image enhancement used to reconstruct the original image from blur images using restoration models. It plays an immense role in many real-time applications such as traffic monitoring, vigilance system, human identification, and recognition, while it also requires faster implementation. In this paper, we have discussed different image degradation models and the most well-known restoration techniques such as Inverse, Pseudo Inverse filter, Wiener filter, Constraint Least Square Error filter, and Lucy Richardson filter are used to reestablish an original image from a distorted one. Apart from that, we have developed OpenCL kernels for those above filters for faster image reconstruction, which are also easily deployed on multiple heterogeneous parallel systems. At last, we have done a performance analysis of both OpenCL CPU and GPU implementation of the above restoration techniques in terms of accuracy and time consumption. Different performance metrics like Root Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, and Structural Similarity Index have used to evaluate the accuracy of the above techniques.
Assumption-Based Argumentation Is Logic Programming with Projection

We revisit the semantic relations between Assumption-Based Argumentation (ABA) and Logic Programming (LP) based on the recent development of model-based semantics for ABA frameworks. This effort is motivated by the close resemblance between the computation of complete ABA models and the computation of Przymuzinski’s partial stable models for logic programs. As we show these concepts coincide ipsis litteris, multiple results about the different ABA semantics (preferred, grounded, stable, semi-stable, ideal, eager) and corresponding LP semantics (regular, well-founded, stable, L-stable, ideal, eager) follow. Our approach also introduces a new translation from ABA frameworks to logic programs that has better properties than the one available in the literatue, including lower computational complexity. The combination of our new translation and model-based ABA semantics is the key to all of our results. It is also known that the more traditional assumption extension and labelling-based semantics for ABA can be obtained from ABA models using an operation called tuple projection, so it follows from our results that ABA is LP with projection.
Notice of Retraction: Cascade Network with Boundary-Adaptive Filter for Surgical Gesture Recognition

Retracted.
A Fast Training Method using Bounded Continual Learning in Image Classification

These days, Deep neural networks (DNNs) are showing good performance in image classification. They bring sufficient performance not only in specific fields such as medical images and meteorological observation images, but also in fields necessary for daily life. However, for them to be more useful practically, they should be able to add new tasks to suit a changing environment. This is the same as saying that they should be able to learn by adding new data. Unfortunately, since catastrophic forgetting is an inevitable feature of connectionist model, it is very difficult to capture both accuracy and computational efficiency in continuous task learning. In this paper, we propose Bounded Continual Learning (BCL) based on inductive transfer learning. BCL extracts feature values from sub-models created by separating base datasets and train a new classifier. BCL showed very good performance in training time efficiency to learn tasks in a sequential. We demonstrate our approach is flexible and efficient by various classification tasks based on the CIFAR dataset.
Testing UX Performance and Reception by Combining Emulated Android GUI with Virtual Reality Prototyping

This paper explores an application of immersive Virtual Reality (iVR) as a medium to combine Android Graphical User Interface (GUI) development and CAD-based virtual prototypes to improve user experience testing. We also present tentative experimental findings, that a virtualized GUI need not be realistically placed or sized to be evaluated in an iVR environment .
A Comprehensive Effectual Load-Balancing Method in Cloud Computing

In the era of continuous economic growth, application development strategies demand strong computing infrastructure that may inculcate a high cost in return to the firms. The solution of this cost problem is Cloud Computing which assures the on-demand delivery of computing resources. But every solution has hidden challenges, and this paper focuses on identifying the significant challenges in a cloud computing environment. One among them is distributing workloads across multiple computing resources. This paper addresses the fundamental difficulties of load-balancing in the cloud environment. Meanwhile, the paper further discusses the probable improvised techniques used to overcome cloud system problems. The gist of paper lies in solving the aforementioned problems by including a chain of logical analysis and generating the algorithm that leads to the appropriate solution. The prime focus is on job queue making strategy that appropriately allocates various jobs to CPUs on the basis of assigned priority or without priority. It also deals with some of the major problems of load-balancing in cloud environment like a timeout. To conclude, this paper exhibits how this approach partially fits into the notable AWS and GAE cloud architecture. This paper presents the various load-balancing problems in cloud computing environment and holds a clear intention to provide readers an overview of issues faced and also simulating further interest to pursue more advanced research in it.
An Interpretable Recommendation Model for Gerontological Care

Recommender systems have been successfully applied to diverse areas, but their use in the healthcare domain is still rare. One challenge of applying recommender systems to this domain is related to legal concerns about the consequences of provided recommendations. In this work, we advance an expert-in-the-loop, explanation-first approach to tackle this challenge in a specific healthcare niche: gerontological care. A key aspect of the proposed approach is that both recommendations and explanations reflect the structured questionnaire employed by the practitioner to identify patient needs. Another key aspect is that a clinical dataset of patient assessments and respective assigned interventions is used to estimate effects of alternative interventions during the recommendation process. To evaluate the feasibility of this modelling approach, an explanation style was designed with help of practitioners, and a recommendation model was devised and evaluated against a clinical dataset, which was collected by a partner research group working on gerontological primary care. When compared to other traditional recommendation models, the attained precision was competitive across several evaluation conditions. The results suggest that the proposed approach is feasible and may point new ways of adapting recommender systems to play an assistive role in health care.