Reviewerly/exHarmony huggingface dataset:
Research on the Application of virtual reality technology in smart city construction

short-paper Research on the Application of virtual reality technology in smart city construction Share on Author: Zhiping Zhang Xinyu Unversity, China Xinyu Unversity, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 108Pages 1–3https://doi.org/10.1145/3469213.3470311Online:18 August 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
On the Design of Social Robots Using Sheaf Theory and Smart Contracts

The incorporation of robots in the social fabric of our society has taken giant leaps, enabled by advances in artificial intelligence and big data. As these robots become increasingly adept at parsing through enormous datasets and making decisions where humans fall short, a significant challenge lies in the analysis of robot behavior. Capturing interactions between robots, humans and IoT devices in traditional structures such as graphs poses challenges in the storage and analysis of large data sets in dense graphs generated by frequent activities. This paper proposes a framework that uses the blockchain for the storage of robotic interactions, and the use of sheaf theory for analysis of these interactions. Applications of our framework for social robots and swarm robots incorporating imperfect information and irrationality on the blockchain sheaf are proposed. This work shows the application of such a framework for various blockchain applications on the spectrum of human-robot interaction, and identifies key challenges that arise as a result of using the blockchain for robotic applications.
Effectiveness of the COVID-19 Contact-Confirming Application Incorporating Secondary Indirect Contact Notification Function on Decreasing the Number of Infectors

To decrease the number of COVID-19 infectors, Ministry of Health, Labour and Welfare developed and released the COVID-19 contact-confirming application (COCOA) on 19th June 2020. Users of the application can know whether they contacted infectors who are/were positive of COVID-19 or not. If the contacted persons stay indoors, the number of new infectors can be decreasing. However, as of October 2020, the number of infectors of COVID-19 is increasing in Japan. Therefore, the additional function to decrease the number of infectors is necessary. In this paper, the function of the secondary indirect contact notification is proposed. In other words, the application informs whether they contacted infectors or not to not only the primary direct contact persons but also the secondary indirect contact persons. To verify the effectiveness of the additional function on decreasing infectors, we develop a simulator incorporating it. Herein, we report the simulator and effectiveness of the secondary indirect contact notification on decreasing infectors.
Web-Based Classroom Loan Information System (Case Study Student Organizations at Muhammadiyah University of Sidoarjo)

This research was aimed to create a website based information system that can be used for summarize the flow and facilitate borrowing space in the University of Muhammadiyah Sidoarjo by student organizations. The research method used by researchers is the waterfall method, but the data collection technique uses several ways, namely observation, interviews, and also literature study. Researchers made observations in student organizations to obtain data, in addition to met with the Directorate of Asset and Environmental Management to obtain data. The result of this research is that student organizations can borrow space online, and make it easier to recap data and also minimize duplicate data in the system. With more efficient procedures, the borrowing process becomes easier and more practical
Asymptotically Scale-Invariant Multi-Resolution Quantization

A multi-resolution quantizer is a sequence of quantizers where the output of a coarser quantizer can be deduced from the output of a finer quantizer. In this paper, we propose an asymptotically scale-invariant multi-resolution quantizer, which performs uniformly across any choice of average quantization step, when the length of the range of input numbers is large. Scale invariance is especially useful in worst case or adversarial settings, ensuring that the performance of the quantizer would not be affected greatly by small changes of storage or error requirements. We also show that the proposed quantizer achieves a tradeoff between rate and error that is arbitrarily close to the optimum.
The Relation between the Combination Levels in the Structure of Concepts Entities and the Amount of Semantic Relations in Improving Information Retrieval Performance in UN, FAO’s Agricultural Ontology

The Relation between the Combination Levels in the Structure of Concepts Entities and the Amount of Semantic Relations in Improving Information Retrieval Performance in UN, FAO’s Agricultural Ontology
A Gray Box Conceptual Model for Accountability and Ethics in Business Contracts

Current computational models are inadequate for the purposes of modeling interactions between autonomous parties in a way that highlights and supports their accountability. We propose a new conceptual model for business contracts based on norms motivated by a review of real-life business contracts. Our conception is of a gray box, reflecting the idea that a contract makes the participants accountable to one another and to outside entities, and therefore calls for the exposure of sufficient implementation details. The model consists of a recursively applicable taxonomy of clause types. In a preliminary study, we found that computer scientists are able to effectively identify the concepts introduced in this model, thereby indicating its potential for building Internet applications that support accountability.
Utilization of extracted data from digital camera images with defected tensile specimens

Image Processing is a multidimensional procedure for finding out the displacement which is flourishing expeditiously in the last few years. The rudimentary conceptualization in deformation measurement of explained characteristic (subset) and then images are examined. This technique is adequate for connecting and correlating the digital images of a targeting object synchronized deformation. This research mainly focused on data extraction from a high-resolution camera of defected and non-defected tensile specimens with an image processing system. This the main aim of this research. In this paper’s research purpose, we are using a tensile test specimen, micro measurement apparatus, an extensometer, and a high-resolution digital camera. By the digital camera images are taken with the same time interval of every second. Here camera specifications such as shutter speed 1/30, focal 2.8, ISO 250etc all are remaining the same. The pixel of these images is 5472 * 3648 and this research mainly perceives pixels’ centroids to measure all required data. For this procedure, MATLAB was used for the image processing system to recognize the pixels’ centroids by self-procedure. Here pixels’ centroids were mainly used so that this value is more precise and accurate for using image processing and utilization for extraction. By this research, mainly discovered the defected and non-defected specimen’s behavior from images.
Research on the Optimal Project of Unmanned Vehicles with Complex Constraints

With the continuous and rapid development of China's social economy, the number of motor vehicles continues to show a rapid growth trend, and the number of road traffic accidents continues to rise. The inducible factors of road traffic accidents include weak road infrastructure, imperfect legal system, vehicle failure and improper driver operation, etc. Among them, improper operation of drivers is the primary cause of major and malignant road traffic accidents, so improving the quality of driving behavior is the key to curb the situation of frequent traffic accidents [6]. And this also puts forward higher requests for autonomous driving technology. This paper mainly studies the optimal project of complex constraints for autonomous vehicles. By studying the optimal control problem model established by vehicle motion planning, the collision discrimination method based on graph area was found, and the constraints of collision avoidance between rectangular vehicles were established. Among the rectangular, double circle and single circle vehicle models, the rectangular model has a better constraint effect in the scene.
Towards Secure Evaluation of Online Functionalities

To date, ideal functionalities securely realized with secure multi-party computation (SMPC) mainly considers functions of the private input of a fixed number of a priori known parties. In this paper, we generalize these definitions such that protocols implementing online algorithms in a distributed fashion can be proven to be privacy-preserving. Online algorithms compute online functionalities that allow parties to join and leave over time, to provide multiple inputs and to obtain multiple outputs. In particular, the set of parties participating changes over time, i. e., at different points in time different sets of parties evaluate a function over their private inputs. To this end, we propose the notion of an online trusted third party that allows to prove the security of SMPC protocols implementing online functionalities or online algorithms, respectively. We show that any online functionality can be implemented perfectly secure in the presence of a semi-honest adversary, if strictly less than 1/2 of the parties participating are corrupted. We show that the same result holds in the presence of a malicious adversary if it corrupts strictly less than 1/3 of the parties and always allows the corrupted parties to arrive.
A top-N Recommendation Approach Based on Reliable Users

In order to improve the recommendation accuracy and robustness of recommendation algorithm, a top-N recommendation algorithm based on reliable users is proposed in this paper. We first present a method to choose reliable users for target user using the degree of trust between users. Then based on the rating information of reliable users, we can choose candidate recommendation items using some approaches. Finally, we can provide top-N recommendation results for target user using the strategy of average aggregation. Using MovieLens dataset, we carried out a comparative experiment. The results show that the proposed algorithm improves the recommendation recall. More importantly, it has better robustness.
Scalable Query Processing and Query Engines over Cloud Databases: Models, Paradigms, Techniques, Future Challenges

Scalable query processing and scalable query engines over Cloud databases is a vibrant area of research, which has recently emerged within both the academic and industrial research community. This area has been further stirred-up by the current explosion of big data management and analytics models and techniques that, usually executed within the internal layer of public as well as private Clouds, pose severe (and new!) challenges to the annoying distributed query processing optimization problem in (distributed) database systems. Among other, taming the complexity of query execution plays a leading role, especially considering the typical Cloud environment that includes tens and tens of different-in-granularity data processing tasks (also at a different scale) over large-scale clusters. Inspired by these considerations, this paper focuses on models, paradigms, techniques and future challenges of scalable query processing and query engines over Cloud databases, by reporting on state-of-the-art results as well as emerging trends, with also criticisms on future work that we should expect from the community.
High-Order Joint Information Input For Graph Convolutional Network Based Action Recognition

Graph Convolution Network (GCN)-based networks for human action recognition, accepting 3D skeleton sequence as input, have gained much attention and good performances recently. In this paper, joint information enhanced with rich higher-order features/attributes is proposed to lift up their recognition performances. All joints in a spatio-temporal skeleton are described in terms of a set of 3-component vectors by referring up to 3 joint neighbors in the spatio-temporal domain. The referred joints are physically connected in spatial or corresponded in temporal domain. Our rich high-order joint information is fed as inputs to two kinds of GCN-based networks in two ways: early fusion and late fusion. Early fusion is to concatenate these 3-components vectors as different channels at input nodes and late fusion is to feed each 3-component vector to a multi-stream GCN network separately and then fuse the output from each stream for action recognition decision. We also propose to cascade a view-adaptive (VA) sub-network to further promote the performance. Experiments show that our approach is capable of boosting the accuracy of original GCN networks in both early or late fusion styles by up to 1.57% and 2.55%, respectively (in cross-subject (CS) protocol) when using NTU RGB-D 60 dataset for evaluations.
The Study of Cross-Cultural English Communication Using Computer Technology

Abstract English cross-cultural communication learning is an important part of English teaching. Improving the teaching quality of English cross-cultural communication is conducive to improving students’ interest in Learning English. This paper firstly explains the deficiencies of English cross-cultural communication learning and the characteristics of network education and multimedia education, and at the same time discusses the study of English cross-cultural communication carried out by computer technology for readers’ reference.
Biased autoencoder for collaborative filtering with temporal signals

Recommendation systems are used in various types of online platforms and in e-commerce. Collaborative filtering (CF) is one of the most popular approaches for recommendation systems and has been widely studied in academia. In recent years, several models based on neural networks that can discover nonlinear relationships have been proposed and compared to traditional CF models. The results showed that they performed better in terms of their prediction accuracy. However, these models do not consider user bias and item bias together, and they do not include temporal signals. This paper proposes a biased autoencoder model (Biased AutoRec) for CF, which is built on the well-known AutoRec CF approach. Several approaches are also proposed to integrate temporal signals into the Biased AutoRec model to merge the power of nonlinearity and temporal signals. Experiments on several public datasets showed that the new models outperformed the AutoRec model, which outperformed the prediction accuracy of previous state-of-the-art CF models (i.e., biased matrix factorization, RBM-CF, LLORMA).
Deblurring Face Images Using Deep Networks

Image deblurringDeblurring entails the recovery of an unknown true image from a blurry image. Image deblurring is an ill-posed problem, and therefore, it is crucial to leverage additional properties of the data to successfully recover the lost facial details in the deblurred image. Priors such as sparsity [2, 13, 16], low-rank [14], manifold [10], and patch similarity [22] have been proposed in the literature to obtain a regularized solution.
Natural image target detection based on improved SSD algorithm

At present, the SSD algorithm is widely used in target detection and has achieved good experimental results. SSD is a convolutional neural network-based algorithm target detection algorithm. This paper starts from the convolutional neural network, explicitly describes the model structure of the SSD algorithm, the generation of a prior box, the realization of the predictor.In the experiment, the SSD algorithm improved through parameter optimization, mainly including the Default box and the learning rate. Meanwhile, the Faster R-CNN algorithm was selected for the comparative experiment, and the experimental results showed that the all-around performance of the SSD algorithm was superior.
Research on adaptive histogram modification algorithm for color image

In order to overcome image detail lossing, distortion and over-enhancement caused by histogram equalization, a novel algorithm for color image enhancment based on adaptive histogram modification is proposed. According to the distribution of gray-scale, the algorithm will construct the target gray sequence, choose the right mapping mode, and adaptively modify the image after histogram equalization base on gray mapping. An adaptive judgment method and flow of gray mapping is also designed. The experiments show that the algorithm can not only effectively overcome the over-enhancement and distortion, restoration the image partial detail but also improve the images' color to be more vivid and lively. The effect of color image enhancement is remarkable.
Improving the Design of a User Interface Through Automated Usability Checks

Software engineers usually do not have enough knowledge in the area of usability to develop user interfaces with a high degree of usability on their own. An initial study with 20 software engineers showed that, although usability is an important aspect for software engineers, they only spend an average of 18.14 h per project on usability-related tasks. An integrated software support solution that incorporates relevant usability knowledge would be extremely useful. Based on an analysis, a plug-in for Microsoft Visual Studio has been developed. The plug-in automatically checks usability aspects of a user interface in the background. Usability-related knowledge is stored in an ontology and accessed by the plug-in during runtime. The plug-in has been evaluated with 19 software engineers. The results are mostly positive. Users find that the plug-in is useful and supports their daily engineering work by helping them to quickly enhance the usability of their software solution.
Satins, lattices, and extended Euclid's algorithm

Abstract Motivated by the design of satins with draft of period m and step a , we draw our attention to the lattices $$L(m,a)=\langle (1,a),(0,m)\rangle$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>L</mml:mi> <mml:mo>(</mml:mo> <mml:mi>m</mml:mi> <mml:mo>,</mml:mo> <mml:mi>a</mml:mi> <mml:mo>)</mml:mo> <mml:mo>=</mml:mo> <mml:mo>⟨</mml:mo> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>a</mml:mi> <mml:mo>)</mml:mo> <mml:mo>,</mml:mo> <mml:mo>(</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mi>m</mml:mi> <mml:mo>)</mml:mo> <mml:mo>⟩</mml:mo> </mml:mrow> </mml:math> where $$1\le a&lt;m$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>≤</mml:mo> <mml:mi>a</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mi>m</mml:mi> </mml:mrow> </mml:math> are integers with $$\gcd (m,a)=1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>gcd</mml:mo> <mml:mo>(</mml:mo> <mml:mi>m</mml:mi> <mml:mo>,</mml:mo> <mml:mi>a</mml:mi> <mml:mo>)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . We show that the extended Euclid's algorithm applied to m and a produces a shortest no null vector of L ( m , a ) and that the algorithm can be used to find an optimal basis of L ( m , a ). We also analyze square and symmetric satins. For square satins, the extended Euclid's algorithm produces directly the two vectors of an optimal basis. It is known that symmetric satins have either a rectangular or a rombal basis; rectangular basis are optimal, but rombal basis are not always optimal. In both cases, we give the optimal basis directly in terms of m and a .
A security enhancement for the two way clustering algorithm for image mining

Abstract Image mining is one of the mining technique which is used for the mining of the image from the huge databases. The Image mining is used in the various sectors for the prospective use of the mining techniques. The increase in the data theft become one of the biggest challenge for the image repositories. This paper proposes the security architecture for the image repositories which is used for the image mining. The salient featured algorithm called as Imago-Sec Algorithm is proposed and used for the efficient providing of the security to the image repositories which is used for the image mining. The relative mining technique is used for the mining of the data from the image database. The Proposed algorithm is a homomorphic token algorithm which uses a single key contribution for both the encryption and decryption of the data in the image databases. The experimental results thus follow which provides that efficiency and throughput of the security that is being implemented.
Learning to Slim Deep Networks with Bandit Channel Pruning

Recent years, deep neural network has achieved great success in machine vision, natural language processing, and reinforcement learning. While deploying these models on embedded devices and large clusters faces challenge in high energy consumption and low efficiency. In this paper, we propose an effective approach named Bandit Channel Pruning (BCP) to accelerate neural network by channel-level pruning.Inspired by autoML, we use Multi-Armed Bandit (MAB) method to explore and exploit the impact of each channel on model performance. Specifically, we use the loss value of model’s output as penalty term to find the set of redundant channels. In addition, we prove that the change of this loss value can be used as criterion of channel redundant. We analyze the complexity of BCP and give the upper bound of search times.Our approach is validated with several deep neural networks, including VGGNet, ResNet56, ResNet110, on different image classification datasets. Extensive experiments on these models and datasets demonstrate the performance of this method is better than state-of-the-art channel pruning methods.
A Quantum Variational Approach to Debugging Combinational Logic Circuits

The application of a quantum approach to combinational logic circuit debugging for future use in Very Large-Scale Integration system design is presented. Although previous work in this area has sped up debugging solutions through the use of satisfiability solvers, the solutions remain classically intractable for large systems. Additionally, whilst many quantum algorithms have shown promise in solving classically intractable problems, there have been few domain-specific implementations, particularly where hard and soft constraints exist and multiple solutions are required. A Quantum Alternating Operator Ansatz is applied to a reduction of the satisfiability problem into a maximum independent set problem. While the Quantum Alternating Operator Ansatz approach constrains the optimization to feasible solutions, the resulting ansatz is both too large for current Noisy Intermediate-Scale Quantum devices and to simulate using a Hamiltonian formulation with gradient-based optimisers. Therefore, a circuit driven simulation is preferred. A small example of a debugging problem is simulated using the aforementioned approach.