Reviewerly/exHarmony huggingface dataset:
The Investors’ Behavior towards the Relationship between Bitcoin, Litcoin, Dash Coins, and Gold: A Portfolio Modeling Approach

This study considers a market-based economy that is composed of two asset classes: one is a digital, cryptocurrency, and the other is real, gold.We demonstrated that coins like (BTC, LTC, and DASH) can substitute a traditional safe haven "gold" in an intertemporal investment portfolio to become a new form of safe haven.The cryptocurrency follows a Jump-diffusion process.However, gold prices follow an Ornstein-Uhlenbek process to characterize the stochastic nature of the market.The stochastic optimal control approach, combined with the strategic asset allocation and the intertemporal utility theory, are used through the derivation of a Hamilton-Jacobi-Bellman (HJB) equation to determine an explicit solution of the optimal allocation problem for investors with CRRA utility function.We considered the Gamma Lévy process to solve the optimization problem.By using the secant method, we determined numerically the optimal percentage invested in the two asset classes at each time over the holding period.Our results showed that an investor can substitute gold by coins (BTC, LTC, DASH) from an investment portfolio perspective.Although Gold is supposed to be the traditional safe-haven asset, the digital currency seems to emerge as a new form of safe-haven value in a risky environment.
Research Status of Data Application Based on Optical Motion Capture Technology

research-article Research Status of Data Application Based on Optical Motion Capture Technology Share on Authors: Jia Zeng Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Xiangzhen He Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Yerong Hu Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Yihao Zhang Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Huaiyuan Yang Northwest Minzu University, China Northwest Minzu University, ChinaView Profile , Shuaihang Zhou Northwest Minzu University, China Northwest Minzu University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 48Pages 1–8https://doi.org/10.1145/3469213.3470248Online:18 August 2021Publication History 0citation18DownloadsMetricsTotal Citations0Total Downloads18Last 12 Months18Last 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
Taxonomy of Edge Blockchain Network Designs

Blockchains have been increasingly employed in use cases at the network’s edge, such as autonomous vehicles and edge computing. These use cases usually establish new blockchain networks due to operation costs, performance constraints, and the lack of reliable connectivity to public blockchains. The design of these edge blockchain networks heavily influences the quality attributes of blockchain-oriented software deployed upon them. This paper presents a taxonomy of edge blockchain network designs successfully utilized by the existing literature and analyzes their availability when facing failures at nodes and networks. This taxonomy benefits practitioners and researchers by offering a design guide for establishing blockchain networks for edge use cases.
The Effect of Augmented Reality (AR) Flashcard on Early Literacy of Early Childhood

Early literacy for early childhood is important because it is related to reading provisions. This literation ability, should be owned by children at the age of 4-5 years. But the reality shows not all children have this ability. Some children still have difficulty recognizing the alphabet. These conditions need to get attention. This study aims to determine the effect of augmented reality (AR) flashcard media on early childhood literacy. The research design used quasi-experimental quantitative research with non-equivalent design. The research subjects were 24 children aged 4-5 years, which were divided into 2 groups, namely the experimental group and the control group. The experimental group used AR flashcard media that displays 3D images for early literacy learning, while the control group used 2D animation videos. Hypothesis testing used independent sample t-test with SPSS 22 for windows. The results showed that AR flashcard media had a greater influence on children's early literacy than 2D animated video media. This is evidenced by the more developed children's ability of early literacy in the experimental group than in the control group. The conclusion is AR flashcard media is a good media because of its superiority of insert an audio on 3D image that could explain a lot information and increase student's attractiveness. Children could see its shape, imitate it, and listen its name from displayed object. AR flashcard media was also a media with the use of ICT that could be delivered by guessing alphabet so that it became an interactive media and it could make that learning became fun for children.
MUMC: Minimizing uncertainty of mixture of cues

Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations. Images can have multiple visual and language cues such as places, captions, and tags. In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions. We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident. We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions. This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study. Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr). Here, we provide project link for Deep Bayesian VQG: https://delta-lab-iitk.github.io/BVQG/.
Securing data in Decentralized Cloud Storage for Authorized Encrypted Search with Privacy-Preserving on Healthcare Databases

Nowadays, in the medical field number of applications will do develop for overcoming the complexity of previous work. By using information technology and computer science to provide various new techniques and medical equipment has improved digitalization in the healthcare sector. In the existing system, much more advancement is providing to overcome the time and money of patients and perform exact treatments and store patient’s confidential records securely but the most important issues are security. To address the existing security issues to design and develop the proposed research work on security, i.e. for patient’s confidential health data records in database servers. Existing work during data transmission can only protect the patient’s data records, but they can’t stop the insider attacks. In the proposed research work, first implement front-end security with the help of keylogging technique, second to store patient’s confidential data in multiple data servers or chunks and to prevent the insider attacks and third and most important is access policy of search for encrypted data of multi-authority. The addition of this analysis work is to specify patient data reports in several chunks securely and applying the cryptosystems for the security goals of a patient’s confidential records. Especially, proposed work advantages of SHA hashing technique to perform every user for access of particular data records. This research work explores secure data storage and sharing using the proposed AES 256 encryption algorithm and Role Base Access Control (RBAC) for secure data access schemes for the end-user. This work also carried out a backup server approach it works like a proxy storage server for ad hoc network data recovery for all distributed data servers.
An Innovative Method for the Management of the Building Process

Through always more innovative IT applications, the construction industry is moving towards the digitalization of the building process, from digital design to automated manufacturing and smart buildings. Big data are now able to take us into an augmented reality—the virtual sibling of the real one—where buildings become cognitive and predictive, capable to actively interact with people. Towards this direction, the capability to store and organize building information at any level of its lifecycle is essential, especially when the multi-criteria evaluation of buildings’ behavior is required. Within this framework, the chapter introduces the review of the standards and best practices, mainly focusing on BIM as the driven method for the management of the entire building process. Hence, the methodology to develop a new assessment tool is presented, according to the investigation of the current platforms for the evaluation of buildings’ performances, and the pursued methods to define new additional shared parameters, specifically customized for the application to retrofit design solutions with timber-based construction systems.
Quantum Memory Multiple Access

Quantum is an emerging technology with constant research efforts in its various fields. One of the distinct advantage with the quantum technology is the use of qubits which gives inherent parallelism and security. However, at the same time qubits poses unique challenges due to their quantum properties. Quantum computation can further be enhanced with the use of quantum memory. In this paper we propose a technique to access stored quantum state multiple times or by multiple users at same instance.
A security policy hardening framework for Socio-Cyber-Physical Systems

Modern systems are heterogeneous inter-operating subsystems of different aspects that can be physical, technical, cybernetics, and even social like agent operators in smart grids or smart transportations. With the social dimension, we name these systems by Socio-Cyber-Physical Systems (SCPS). However, ensuring safety, correctness, and security against attacks that can be either technical or socio-technical based threats is challenging in the presence of components of different aspects. The main difficulty resides on how well security policies are expressed, integrated, and reinforced within a SCPS; in addition to how SCPS are designed and precisely specified. For a better precision, we rely on formal methods to develop a sound approach that models SCPS entities, especially their demeanour and interactions. Further, we formally specify security requirements and policies in SCPS. For security analysis, we develop an algorithm that automatically reinforces the specified security policies and also checks the validity of the requirements for a SCPS model in the presence or absence of attacks. Finally, we validate the approach on a real case scenario of SCPS in the presence of social and technical threats.
Monocular 3D Human Pose Estimation by Multiple Hypothesis Prediction and Joint Angle Supervision

Human pose estimation in 3D from monocular images is a challenging inverse problem due to ambiguity in lifting 2D projection to 3D space. In this article we have made three contributions in order to solve 3D pose estimation. First, a new DNN architecture is proposed to generate multiple feasible 3D pose hypotheses from a given image. Second, we generate weights for the proposed hypotheses using ordinal supervision. These weights are used to predict the final 3D pose from the generated hypotheses. Finally, we report a new regularizer to enforce that the predicted skeleton is consistent with the restriction of anthropomorphic constraints. We compare the results of our algorithm with other state-of-the art approaches on the Human 3. 6m benchmark dataset. Our algorithm reports competitive results.
Comparative Performance of Collaborative Filtering Recommendations Methods for Explaining Recommendations

In this paper a comparative performance of some collaborative filtering methods for recommender systems is presented. The literature review focuses on identifying some models for explaining of recommendations. Methods discussed here are those based on probabilistic models and based on biclustering techniques. In addition, we analyze a memory-based method and three recommendation probabilistic models, in order to know theirs performance and like these work. Experiments carried out using Netflix dataset presented good results for model-based methods compared to memory-based method, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also in the recommendation and prediction accuracy.
Aplicación de prácticas ágiles en el proceso de enseñanza-aprendizaje: la perspectiva del docente y el alumno [Not available in English]

No English translation of this document was provided by the author or conference organizers.
Introducing Asynchronous Remote Usability Testing in Practice: An Action Research Project

Asynchronous remote usability testing is a usability evaluation method where users and evaluators are separated both in time and space, and users are directly involved in producing the evaluation results. This paper reports from an action research project on introduction of asynchronous remote usability testing in the IT organization of a bank. The IT organization had extensive experience with traditional usability evaluation but was interested in introducing asynchronous remote usability testing into their repertoire of evaluation methods. The project was initiated with an intensive two-month collaboration followed by a six-month maturation phase. The process of introducing the method and the motivations of the IT organization for introducing the method are described in detail. The findings indicate that the conceptual understanding of usability evaluation and the engagement of the users are crucial for the outcome of the evaluation, and the tool and materials used for the evaluation was an obstacle for high-quality results.
Real-Time 3D Hand-Object Pose Estimation for Mobile Devices

Interest in 3D hand pose estimation is rapidly growing, offering the potential for real-time hand gesture recognition in a range of interactive VR/AR applications, and beyond. Most current 3D hand pose estimation models rely on dedicated depth-sensing cameras and/or specialised hardware support to handle both the high computation and memory requirements. However, such requirements hinder the practical application of such models on mobile devices or in other embedded computing contexts. To address this, we propose a lightweight model for hand and object pose estimation specifically targeting mobile applications. Using RGB images only, we show how our approach achieves real-time performance, comparable accuracy, and an 81% model size reduction compared with state-of-the-art methods, thereby supporting the feasibility of the model for deployment on mobile platforms.
Study on measurement error analysis and correction method of ground flash data in Hunan

According to the principle of positioning method, the lightning location detected by the lightning location system is determined. However, due to the different installation distances or functions of the instruments, different monitoring systems may have different lightning monitoring data in the same area, and the same system may also have errors in different areas. In order to reduce the error of lightning detection system caused by the positioning method and improve the reliability and generality of lightning data. According to the principle of lightning location, the possible errors of different positioning methods, measuring station distances and lightning distances, etc. are analyzed, and the screening method of monitoring data is given. The results show that the positioning accuracy of time difference is higher than that of magnetic orientation. In accordance with their merits, the four ranked positioning methods are three-station hybrid, four-station algorithm, two-station hybrid and two-station magnetic positioning. The lightning distribution in the three-station mode and the lightning distribution in the four-station mode are relatively uniform, which has small correlation with the layout of the stations, and the four station algorithm is the second. Lightning distribution of two-station mode is not uniform, which has a big correlation with the layout of the stations, and weak and small thunder and lightning mainly concentrates near the stations. In the lightning location system operated by Changsha Meteorological Bureau, the station layout is too dense, and the adjacent distance is only 15km-40km, so the amount of detected thunder and lightning is large, and the accumulation phenomenon is serious. There are a few station layouts of Hunan Provincial Meteorological Bureau, and the interval is large, mostly in 150km-250km, and the total amount of monitoring data is relatively small. Through screening, the reliability and versatility of two sets of lightning data in Hunan and Changsha have been significantly improved.
Geometric Regularization of Local Activations for Knowledge Transfer in Convolutional Neural Networks

In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving a “student” model to adapt such that its local features exhibit similar geometrical characteristics to those of an “instructor” model, at corresponding layers. The investigated functions, inspired by manifold-to-manifold distance measures, are designed to compare the neighboring information inside the feature space of the involved activations without any restrictions in the features’ dimensionality, thus enabling knowledge transfer between different architectures. Experimental evidence demonstrates that the proposed technique is effective in different settings, including knowledge-transfer to smaller models, transfer between different deep architectures and harnessing knowledge from external data, producing models with increased accuracy compared to a typical training. Furthermore, results indicate that the presented method can work synergistically with methods such as knowledge distillation, further increasing the accuracy of the trained models. Finally, experiments on training with limited data show that a combined regularization scheme can achieve the same generalization as a non-regularized training with 50% of the data in the CIFAR-10 classification task.
Adaptive Frequency Hopping Policy for Fast Pose Estimation

Existing methods for human pose estimation using motion imitation usually suffer from a mismatch of the frequency between policy and demonstrations: the policy runs at a much higher frequency to enable the agent to get track with the demonstrations, which usually leads to unbearablely long traing time. In this work, we propose an adaptive frequency hopping policy which adaptively ajusts the frequency of policy to accelerate the training process. At the meantime, we design a control policy with muti early termination conditions to bias desired distributions for convergence. Experimental results demonstrate that our method achieves equivalent quantitative quality with a reduction of 50% of training time in comparison to the baselines.
YOLO as a Region Proposal Network for Diagnosing Breast Cancer

Cytological images of various types are increasingly being classified with the use of neural networks. But deep learning-based image classification systems are heavily reliant on manually sampled RoI (Region of Interest) patches. A lot of time and effort are required to extract RoI patches from whole slide images or larger images that are too complex to be processed by neural networks. A region proposal network (RPN) is an efficient way to automate the extraction of RoIs. In this study, we have proposed the use of the YOLOv3 network as an RPN to suggest RoIs in images from fine needle aspiration cytology of breast tissue. Patches from the suggested RoIs are fed into a Convolutional Neural Network (CNN) for the classification of benign and malignant lesions and ultimately, the diagnosis of Ductal Carcinoma in breast. The YOLO+CNN model yields a highly satisfactory classification accuracy of 95.73%, 100% specificity, 92.4% sensitivity and a precision score of 1.
Sparse-shot Learning with Exclusive Cross-Entropy for Extremely Many Localisations

Object localisation, in the context of regular images, often depicts objects like people or cars. In these images, there is typically a relatively small number of objects per class, which usually is manageable to annotate. However, outside the setting of regular images, we are often confronted with a different situation. In computational pathology, digitised tissue sections are extremely large images, whose dimensions quickly exceed 250’000 × 250’000 pixels, where relevant objects, such as tumour cells or lymphocytes can quickly number in the millions. Annotating them all is practically impossible and annotating sparsely a few, out of many more, is the only possibility. Unfortunately, learning from sparse annotations, or sparse-shot learning, clashes with standard supervised learning because what is not annotated is treated as a negative. However, assigning negative labels to what are true positives leads to confusion in the gradients and biased learning. To this end, we present exclusive cross-entropy, which slows down the biased learning by examining the second-order loss derivatives in order to drop the loss terms corresponding to likely biased terms. Experiments on nine datasets and two different localisation tasks, detection with YOLLO and segmentation with Unet, show that we obtain considerable improvements compared to cross-entropy or focal loss, while often reaching the best possible performance for the model with only 10-40% of annotations.
Implementing Multilevel Graphical Password Authentication Scheme in Combination with One Time Password

At present, everyone makes instantaneous digital transaction by using applications like PhonePe, Google pay etc. Password is necessary to prove the user authentication. At the same time, user authentication is also verified by validating one time password. A shoulder surfer may have the possibility to crack the password during entry time. In this paper, we present a novel, clear, recall-based graphical password scheme where the user is required to figure out the triangle for some exact permutations of the password on the active display. The token holder is likely to pick the correct password permutations in the expected order cyclically per every login endeavor. For example, the user must select the first permutation of the password and that permutation must form a triangle on the visual display. After the correct triangle is identified in the visual display, a one time password is sent to the user. The user is required to enter the one time password in the text box given in the visual display. If the one time password is correct then the login attempt is successful; otherwise, the login attempt is failed. Likewise, the user is instructed to identify triangles with other permutations of the password for the remaining login attempts.