Reviewerly/exHarmony huggingface dataset:
A Novel Mathematical Model to Design UAV Trajectory for Search and Rescue Operations in Disaster

In mass casualty incidents, whether natural or human-made, Search and Rescue Operations (SAR)Search and Rescue Operations (SAR) have a critical role in humanitarian problems. Time and information are crucial in victims’ survival. The high success rate in SAR operations depends on achieving reliable data about the number of victims, the severity of the failure, access status, and location status, etc. as soon as possible. Nowadays, the technology of Unmanned Aerial Vehicle (UAV) presents an opportunity to help the rescue teams with avoiding wasting time and accessing areas where searching by rescue teams are costly and impossible to go there. In this study, we design a mathematical modelMathematical model to get an optimal path planning to steer the UAVs based on the potential risk degree (PRD) of the candidate location in the affected area. The proposed model is inspired by the Travel Salesman Problem (TSP)Travel Salesman Problem (TSP) that selects the optimal tour giving priority to districts with high PRDs obtained using the concept of similarity measure in the spherical fuzzy environment, considering the power limitation of UAVs. The priority of candidate locations is evaluated by Jaccard, exponential, and square root cosine similarity measures. The applicability of the proposed model is demonstrated by applying it to an earthquake case. A sensitivity analysis is performed to approve the validity of the method.
Secret Sharing Schemes from Linear Codes over F2+vF2+v2F2

Secret sharing is an important concept in cryptography, however it is a difficult problem to determine the access structure of the secret sharing scheme based on a linear code. In this work, we construct two-weight linear codes over finite field by using linear codes over finite ring. We first study MacDonald codes over the finite ring F 2 + v F 2 + v 2 F 2 with v 3 = v . Then we give torsion codes of MacDonald codes of type α and β, which are two-weight linear codes. Finally we give the access structures of secret sharing schemes based on the dual codes of the two-weight codes.
An Algorithm Based on Attention Mask for Fine-grained Object Detection

The demands of the application of deep learning for the detection and marking of fine-grained object in the large field of view are increasingly prominent, which can be seen from automatic driving, traffic sign detection, satellite image analysis and so on. Most of the current studies focusing on the fine-grained object detection make an improvement based on the existing object detection algorithms to increase the detection accuracy of fine-grained object. This paper will propose a novel algorithm based on neural network feature constraints, which can realize the detection and marking of fine-grained object via network with the guidance of an attention map. In the procedures of neural network training, the Attention Mask is employed to constrain the loss function of the network and extract feature maps of key areas to alter the weights of key features through self-adaption. In this paper, combined with the needs of nematode detection project, taking nematode detection as an example, the ablation experiments with the employment of UNet network demonstrate that the accuracy rate of fine-grained object detection is increased from zero to around 85% with the additional loss function constraints.
MMPT'21: International Joint Workshop on Multi-Modal Pre-Training for Multimedia Understanding

Pre-training has been an emerging topic that provides a way to learn strong representation in many fields (e.g., natural language processing, computing vision). In the last few years, we have witnessed many research works on multi-modal pre-training which have achieved state-of-the-art performances on many multimedia tasks (e.g., image-text retrieval, video localization, speech recognition). In this workshop, we aim to gather peer researchers on related topics for more insightful discussion. We also intend to attract more researchers to explore and investigate more opportunities of designing and using innovative pre-training models for multimedia tasks.
Addressing Formulas for Elements of 3D Sparse Matrices in 1D Arrays

3D sparse matrices play significant role in many modern information retrieval methods. A huge number of computations with such matrices are performed by these methods, like clustering, latent semantic indexing. Since many of the elements in such matrices contain zeroes, thus a need to save space arises, either in memory or disk. Thus their storage in memory or disk should be very carefully designed. To save the space in memory or disk, efficient storage of elements and their retrieval is required. This paper explains two types of 3D sparse matrices known as lower triangular and upper triangular. The basic concept behind this paper is to calculate the formulas for accessing the address of elements of 3D sparse matrices in 1D array using row major and column major.
Requirements-driven reuse recommendation

This tutorial explores requirements-based reuse recommendation for product line assets in the context of clone-and-own product lines.
hadroNIO: Accelerating Java NIO via UCX

InfiniBand networks with bandwidths up to 400 Gbit/s and sub-microsecond latencies are more and more popular in HPC and cloud data centers. Many big-data frameworks, such as Apache Spark and Cassandra, are written in Java and use Java NIO socket channels, which are designed for Ethernet networks. Rewriting network code for such complex systems is typically not an option and thus, transparent solutions like IP over InfiniBand are used.In this paper, we present hadroNIO, a Java library, that transparently replaces the default NIO implementation, providing support for InfiniBand (as well as Ethernet and other transports) through the Unified Communication X (UCX) library. We compare our library against other transparent network acceleration solutions in an InfiniBand environment and also evaluate the overhead, that is introduced by using hadroNIO versus directly accessing UCX. We show that it is possible to achieve latencies as low as 3.1 μs, while also being able to leverage the full bandwidth of InfiniBand hardware with our fully transparent acceleration solution. In the future we aim at extending hadroNIO, and thus the NIO API, with RDMA directives.
Holographic Virtual Coach to Enable Measurement and Analysis of Physical Activities

Globally, there is a huge problem with lack of physical activity, obesity and many diseases due to malnutrition, lack of movement, sedentary lifestyle, etc. Exasperated by the current worldwide situation where almost all of the active population including children of all ages is sequestered at home with no opportunities to do physical exercises outdoors or in a gym, we propose a prototype of a Human Centric Cyber Physical System to measure and analyze physical activities by employing a holographic virtual coach. The system can identify the user from a multi-view camera setup at the begging of each session and a program-based algorithm will detect and analyze his or hers periodic activities. The algorithm can detect the beginning of a series of exercises of a given type, measures the duration of each of them, detects the end of the physical activity sequence, and measures the number of repetitions and deviation from a setup norm. The virtual coach application is managed by the user’s voice and gestures based on Microsoft Hololens.
A Theorem Proving Approach to Formal Verification of a Cognitive Agent

Theorem proving approaches have successfully been applied to verify various traditional software and hardware systems. However, it has not been explored how theorem proving can be applied to verify agent systems. We formalize a framework for verification of cognitive agent programs in a proof assistant. This enables access to powerful automation and provides assurance that our results are correct.
Multimodal Interactive Game Interface to Increase Intimacy with Characters

Although the number of games using voice recognition has increased in recent years, psychological effects such as intimacy with characters on the screen when using voice dialogue have not been clarified. In this paper, we realized a game interface that uses voice dialogue in combination with controller operation used in conventional games, and compared it with conventional games in terms of the effect on intimacy with characters and the overall enjoyment of the game.
Gestural Interaction of Gamelan Mobile Application: A Preliminary Study

Mobile applications have been developed as interfaces for musical purposes. The development of various musical mobile applications comes down to the interactions between users and the applications. Common touchscreen interaction of these applications is unnatural as it loses certain physical aspects such as movement, size, and effort. A preliminary study was conducted to find out how users would interact with gamelan mobile applications, as playing on the real instrument and on the applications are different. Results showed that even with the touchscreen interaction, users played on the applications using different combinations of hands and fingers. Factors on users’ familiarity with the playing techniques of the real instrument and the physical aspects of how the instruments are played influenced their interactions on the applications. Findings from this study gave us more information on underlying criteria to design gamelan mobile applications with more natural and expressive features.
Signal information in the Web of Science Core Collection

The author specifies signal information in bibliographies supporting science and research, which enables to use Web of Science Core Collection efficiently. Subscription to the alerts on new arrivals to the Web of Science Core Collection is based upon the preserved search and citation history. The subscribers are researchers and librarians who provide science and research information support. Intensive use of bibliographic databases was expected to replace selective dissemination of information, as the key technological processes of these alerttypes are identical. The author argues that these methods of differentiated bibliographic information services may be combined in providing alerts to researchers; she refers to regular requests as an example. Journal contents in the database may become one more option of individual information retrieval. Subscribers to rapid signal information may receive the alerts on articles published in domestic and foreign journals. These two methods would open up new opportunities within the system of science and research information support.
An Intelligent Evaluation Method of Japanese Teachers' Educational Ability Based on MOOC Environment

With the continuous integration of information technology into college teaching, the traditional single evaluation method has been unable to meet the needs of teachers' network teaching evaluation activities in the new era. Therefore, this paper discusses the limitations of the traditional use of MOOC resources for Japanese teaching, and proposes a teaching quality evaluation mechanism based on neural network. At the same time, genetic algorithm based on adaptive mutation probability is used to optimize the initial weights and thresholds of BP neural network to enhance the prediction accuracy and generalization ability of the model. The experimental analysis shows that the MOOC teaching evaluation model based on the improved BP neural network algorithm provides full play to the advantages of the artificial neural network, effectively weakens the artificial factors in determining the index weight, thus improving the teaching quality and providing a favorable mechanism for the intelligent evaluation of Japanese teaching quality.
Height Estimation from Single Aerial Imagery with a Deep Boundary-Guided Network

Extracting 3D information from single aerial image plays an important role in computer vision and remote sensing. However, due to the structural complexity of ground objects and noise introduced during the generation stage of ground truth labels, it is challenging to automatically recover the regularized height map from only one orthogonal photography. In this paper, we propose a novel deep network for estimating accurate and regularized height map from a single aerial image. The network mainly contains two sub-networks, namely the height map derivation sub-network and the boundary guidance sub-network. They are sequentially connected together, so that the corresponding boundary map can be directly calculated after the height map is obtained. We also propose a loss function suitable for semantic boundary guidance, which is similar to SSIM loss function at the edges of the ground targets. Apart from pursuing accuracy of height regression, boundary regularity constraints derived from semantic labels are also employed to form a joint metric criterion. We perform a qualitative and quantitative evaluations on ISPRS remote sensing dataset, and the result indicate that our framework improve both accuracy and regularity of estimated depth map.
Error Exponent Sensitivity of Sequential Probability Ratio Testing

We study mismatched sequential hypothesis testing. We analyze the type-I and and type-II error exponents when the actual distributions generating the observation are different from those used in the test. We derive the worst-case error exponents when the actual distributions generating the data are within a relative entropy ball of the test distributions and show the error exponent sensitivity of the test for small relative entropy balls.
Sketching and Sequence Alignment: A Rate-Distortion Perspective

Pairwise alignment of DNA sequencing data is a ubiquitous task in bioinformatics and typically represents a heavy computational burden. A standard approach to speed up this task is to compute “sketches” of the DNA reads (typically via hashing-based techniques) that allow the efficient computation of pairwise alignment scores. We propose a rate-distortion framework to study the problem of computing sketches that achieve the optimal tradeoff between sketch size and alignment estimation distortion. We consider the simple setting of i.i.d. error-free sources of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> and introduce a new sketching algorithm called “locational hashing.” While standard approaches in the literature based on min-hashes require <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B=(1/D)\cdot O(\log n)$</tex> bits to achieve a distortion <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> , our proposed approach only requires <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B=\log^{2}(1/D)\cdot O(1)$</tex> bits. This can lead to significant computational savings in pairwise alignment estimation.
On the Role of Eigendecomposition in Kernel Embedding

This paper proposes a special variant of Laplacian eigenmaps, whose solution is characterized by the underlying density and the eigenfunctions of the associated Hilbert-Schmidt operator of a similarity kernel function. In contrast to existing kernel-based spectral methods such as kernel principal component analysis and Laplacian eigenmaps, the new embedding algorithm only involves estimating density at each query point without any eigendecomposition of a matrix. A concrete example of dot-product kernels over hypersphere is provided to illustrate the applicability of the proposed framework.
Additive Noise Model Structure Learning Based on Rank Statistics

To examine the structural learning of the additive noise model in causal discovery, a new algorithm RCS (Rank-Correlation-Statistics) is proposed in combination with the rank correlation method. This algorithm can effectively process the multivariate linear Non-Gaussian data, and multivariate nonlinear non-Gaussian data. In this article, combined with hypothesis testing, a constraint method is proposed to select the potential neighbors of the target node, which greatly reduces the search space and obtains good time performance. Then the method is compared with the existing technology on 7 networks on the additive noise structure model. The results show that the RCS algorithm is superior to existing algorithms in terms of accuracy and time performance. Finally, it shows that the RCS algorithm has a good application on real data.
Infinite Von Mises-Fisher Mixture Model and Its Application to Gene Expression Data Clustering

In some applications of data mining, clustering analysis of directional data is often involved. In this case, conventional model-based clustering methods are not suitable for fitting the data of such type of structure. Therefore, a Dirichlet Process Mixture Model based on von Mises-Fisher distribution was proposed for the clustering analysis of directional data. The main motivation is that as a non-parametric Bayesian model, The Dirichlet process can automatically determine the complexity of the mixture model when the number of data categories is unknown. We use the accelerated Variational inference algorithm to quickly estimate the parameters involved in the model, which enables the method to be applied in applications with large data scale. The validity of the proposed model was verified by using different scale simulation data and clustering analysis of gene expression data.
HyperButton: In-video Question Answering via Interactive Buttons and Hyperlinks

Despite the advantages of video-based learning content, the video involves learners as passive viewers instead of active participants. We first conducted formative interviews with 12 participants to investigate natural user behaviors when learners have questions while watching online video lectures, and the result reveals that learners usually use web search to find answers for questions. Based on the formative interview results, we designed and developed HyperButton, an interactive video interface that supports collaborative question answering via in-video buttons and hyperlinks. In the user study, we find that HyperButton significantly improves the usability of the video interface. We further discuss design implications for an interactive video interface augmented with learner-generated material at scale.
Development and implementation of personalized health assessment management system based on J2EE

In view of the large number of users in the health examination center, the variety of business involved, and the high requirements for the robustness and scalability of the system architecture, this paper puts forward the design of personalized health assessment management system based on J2EE. On the basis of in-depth analysis of the C/S and B/S development modes, the n-tier distributed system based on J2EE architecture is established by comprehensively using J2EE development technology. The business logic of the system is divided into five independent layers: web layer, business appearance layer, business rule layer, data entity layer and data access layer. The evaluation rule management module realizes the function of data management of evaluation rules through the Internet. The evaluation classifier module implements the function that the users of the health examination center classify the evaluation data of service objects according to the evaluation rule data and the health examination data of service objects generated by the evaluation rule management module. The personal health management prescription generator module realizes the function of generating personal health management prescription according to the evaluation data generated by the evaluation classifier module.
Note on the Intuitionistic Logic of False Belief

In this paper we analyse logic of false belief in the intuitionistic setting. This logic, studied in its classical version by Steinsvold, Fan, Gilbert and Venturi, describes the following situation: a formula $\varphi$ is not satisfied in a given world, but we still believe in it (or we think that it should be accepted). Another interpretations are also possible: e.g. that we do not accept $\varphi$ but it is imposed on us by a kind of council or advisory board. From the mathematical point of view, the idea is expressed by an adequate form of modal operator $\mathsf{W}$ which is interpreted in relational frames with neighborhoods. We discuss monotonicity of forcing, soundness, completeness and several other issues. Finally, we mention the fact that it is possible to investigate intuitionistic logics of unknown truths.
Features of Monitoring the Quality of Interdisciplinary Knowledge of Students

The article discusses the problem of assessing interdisciplinary knowledge and skills of students, provides a comparative analysis of methods and forms of their assessment. The authors note the relevance of the problem in connection with the updating of the content and teaching methods based on a synergetic approach, the need for adequate comprehensive monitoring of interdisciplinary knowledge, taking into account their multidimensional nature. The article analyzes the methods and criteria for assessing knowledge, provides an overview of traditional and innovative forms of assessing knowledge.
Design. research. art.

opinion Design. research. art.: weaving voices to enrich HCI practice Author: Daria Loi Studio Loi LLC Studio Loi LLCView Profile Authors Info & Claims InteractionsVolume 28Issue 5September - October 2021 pp 19–21https://doi.org/10.1145/3477225Published:31 August 2021Publication History 0citation680DownloadsMetricsTotal Citations0Total Downloads680Last 12 Months147Last 6 weeks16 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
EEG Based Image Reconstruction Using Transformers

In this work the possibility of creating an end-to-end system being able to reconstruct natural images based on the subject’s EEG response has been evaluated. Herein, various Transformer-based model’s configurations have been implemented and tested in order to find the optimal solution for the given problem, taken into account the limitations of 16-channel EEG apparatus. Pretrained BigBiGAN neural network model has been used for the purpose of image featurization that is essential for the proper EEG feature mapping. The resulting image reconstruction performance was compared between the BigBiGAN and the model that is based on an original implementation of a simple DNN autoencoder. The presented implementation and obtained results show that on a limited amount of data it was very possible to build a system that is capable of successful reconstruction of images that are similar according to quantitative measures to the original graphical stimuli.