Reviewerly/exHarmony huggingface dataset:
Fully Dynamic Password Protected Secret Sharing: Simplifying PPSS Operation and Maintenance

A scheme which is based on splitting a secret among a set of servers (e.g. in a cloud) where a threshold, t, of them is needed for reconstruction, is advantageous in many ways for the mechanism to work regardless of the exact subset (of size at least t) which participates in the reconstruction. The advantages of such threshold-based cryptographic schemes include: availability, possibility of or compatibility with load balancing among servers, security (as less than the threshold server compromise does not give the secret away), fast and more efficient reconstruction/application of the secret, as well as providing other flexibilities in system design. Building on recent significant performance advantage made in the area of Password Protected Secret Sharing (PPSS), we construct an efficient scheme where the user works in reconstruction is independent of the set of servers participating (prior scheme assumed the subset needs to be known in advance, which typically involves a round of communication/ synchronization). We call such schemes “Dynamic Subset PPSS.” We further extend the scheme not only to allow dynamic subset but to also dynamically change the set of servers involved, where enrolling and dis-enrolling servers is done autonomously by the servers themselves without the presence of the user; we call such scheme “Fully Dynamic PPSS.” For practical large scale deployment of PPSS schemes, clientless server enrollment and dis-enrollment seems fundamental, however, the previous schemes do not generally deal (or cannot be extended to deal) with this issue (which allows better system flexibility and does not depend on servers synchronizing with the set of users). To maintain the set of servers we, first, reduce it from proactive secret sharing in dynamic groups, and for minimizing communication within the set of servers we also develop a Paillier encryption method with linear communication.
Efficient Data Presentation Method for Building User Preference Model Using Interactive Evolutionary Computation

Hara, Akira Kushida, Jun-ichi Yasuda, Ryohei Takahama, TetsuyukiIn this paper, we propose data presentation methods for building user preference models efficiently in recommendation systems by interactive genetic algorithm. The user preference model of the recommender agent is represented by a three-layer neural network (NN). In order to generate training data for this NN, it is necessary to present some sample items to the user and obtain the user’s evaluation values. Based on the idea that the evaluation distribution of the presented data should not be biased, we propose two types of data presentation methods. One is the selection of three types of data such as like, dislike and neutral. The other is the Inverse Proportional Selection of sampling points based on the frequencies of already presented data. As the results of experiments using four types of pseudo-users, we found that the Inverse Proportional Selection was the most efficient method in learning the preferences of users.
A Ceramic Image Restoration Scheme Based on Cloud Platform

Aiming at the poor performance of traditional clustering algorithm in image inpainting, an improved algorithm based on Hadoop distributed platform is proposed for ceramic image inpainting. For the defect that the threshold parameters can not be determined automatically in the process of salient edge structure extraction, we propose a threshold descent extraction algorithm based on Canny edge detection. Then, the improved algorithm is combined with fuzzy clustering and parallelized on Hadoop platform, which is applied to crack extraction, fitting and repair of ceramic image. The experimental results show that such scheme improves the final repair effect of the damaged edge, reduces the running time of the algorithm, and greatly improves the overall efficiency of the restored image.
The NLP Program Development of Intrapersonal Intelligence Enhancement for the COVID-19 Crisis Management and the Analysis of Its Effect

Purpose: Following the COVID-19 crisis, the solidarity among people in our society has discontinued and activity restrictions have increased, and as a result, mental stress has risen significantly. Such a global pandemic crisis has prolonged despite numerous efforts have been made at the national level to address it. Therefore, along with the national crisis management, the need for a professional education program for enhancing the mental health of individuals has been called for. Accordingly, in this paper, as a method of the consciousness training for overcoming the pandemic, an education program was designed and executed to increase the intrapersonal intelligence, thereby demonstrating the effect of the education. Method: As a program to enhance the intrapersonal intelligence, pre-class was operated to minimize face-to-face classes for the short term in consideration of the pandemic situation. Towards this end, a program was designed with the teaching and learning structure of Flipped Learning to maximize the effect and efficiency of education. The program’s curriculum was developed into 10 sessions for adult learners, and a social media platform was utilized to operate the pre-class. Furthermore, the NLP coaching tool was used as a main teaching method for the program. In addition, to examine and validate the effect of the program, the corresponding sample t-test was conducted, while the educational effect was measured via the pre- and post-comparative validation between the experimental group and the control group. Results: As a result of executing the intrapersonal intelligence improvement program and conducting the preand post-tests, there was a significant difference in the scores for the entire intrapersonal intelligence test. The positive result of this program is analyzed to be a result of the systematic teaching design. Along with which, it may be interpreted as a result of enhancing the understanding of the self-reflection, immersion for learning, and the motivation for learning by undertaking the Flipped Learning based class strategies satisfying the needs of the 21st century learners. Conclusion: Moving forward, disasters and crises following the changes in the climate environment and industrial advancements are expected to continue in our society. Accordingly, a professional emotional management program which can help learners cope with the crisis situation should be in place. Moreover, the teaching and learning method and the operating system in a contactless situation should evolve as well. In this point of view, unlike the existing studies, this study has achieved distinction by reflecting educational elements satisfying the needs of the times and performing innovative teaching methods.
Blockchain-Based Government Project Fund Tracking and Management System

Varada, M. V. Abdulla, Wissam Salih Udayakumar, Aparna Raghu, Haritha Thomas, Manoj V.Across the globe, governments are testing blockchain in various administrative processes including public relations. Blockchain technology provides a secure platform and ensures data integrity. And its applications are resistant to breakdown and are secured using cryptography; hence, it can be used for government applications. Hyperledger fabric is one of the prominent open-source framework of blockchain which is used to develop a “permissioned” network system. When various project funds are issued by the government, often a large amount is unutilized for the actual project, due to corruption. It is necessary to leverage the hyperledger fabric for the formation of a network which allows efficient fund distribution systems resulting in a better society. In this paper, we explored adding permissioned support of hyperledger fabric to government fund distribution system.
Development of an Automatic Document to Digital Record Association Feature for a Cloud-Based Accounting Information System

Documents such as contracts, receipts, and sales invoices are proofs of transactions generated by various functions of business organizations. Though some organizations have initiatives to digitize paper-based proof of transactions, their business processes do not remove paper trails entirely. Organizations normally scan business document transactions, manually classify digitized documents, and associate digitized documents to digital records in a database management system. Hence, the digitization process introduced more work rather than efficiency. This study seeks to eliminate the additional work brought about by document digitization process. It specifically looks at the application of image enhancing techniques and open-source Optical Character Recognition (OCR) technology to automatically classify and associate business documents to digital records in a database management system. The study presents how an alternative document digitizer and image enhancing feature is integrated into an accounting information system to facilitate automatic classification and association of digitized documents to specific database records. The application of image cropping and grayscale color processing image enhancing techniques contributed to achieving an average of 90% level of confidence in extracting field labels while 91.5% level of confidence in extracting field values in business documents.
Analyzing and Designing Conversational Banking Service Architecture for Banking Company

In the current technological era, companies compete to find new innovations to support the services they offer. Banking companies are constantly developing new innovations for the applications they need to make access and use easier and more practical. Since the chatbot is already widespread in order to be able to answer the information requested by users quickly and easily, this chatbot must be trained to respond more precisely to users. Next up is the well-known voice command that allows users to control applications using voice. This feature is very helpful for application users who are active in a very tight time. The two groundbreaking innovations mentioned above are expected to make loaders competitive in this technological age.
On the Development of Low-Cost Autonomous UAVs for Generation of Topographic Maps

Topographic maps are used by a variety of disciplines that require detailed information about the features of the Earth’s surface. These maps are traditionally created by surveyors in the field by measuring sets of points throughout a coverage area. These measurements are then converted into a topographic map representing the terrain by the use of contour lines. The focus of this research is to reduce the cost and time efforts for surveying land to generate topographic maps. We introduce an approach for developing a low-cost autonomous quadcopter UAV that uses LiDAR and CMOS image (camera) data to perform coverage area survey to generate topographic maps. The terrain data acquired by our approach is post-processed on a dedicated server that displays the layered topographic map to the user in a web browser.
Low Cost Classification Method for Differentiated White Blood Cells using Digital Image Processing and Machine Learning Algorithms

There are numerous commercially available technologies for counting blood components, however, these require expensive clinical equipment that is not affordable in most developing countries. In this manuscript, a low cost method for classification algorithm to calculate differentiated white blood cells through digital image processing, along with machine learning techniques is introduced. 98 images from ten (10) volunteers were used for this study. These samples were taken with the Venoject system; with a vein blood extraction, smear, and Wright staining to visualize differentiated white cells. The images were preprocessed to highlight their main characteristics (nucleus morphology, plasma membrane definition, cell color, etc.). These characteristics were labeled using the Bag of Visual Words (BoVW) method and classified using the Ensemble Subspace K Nearest Neighbor (ESkNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and K Nearest Neighbor (KNN) models. Finally, data training and evaluation was performed. As a result, a database of peripheral blood smear pictures and an automatic counting system with 96.4% accuracy detection was attained.
Research Report: Parsing PEGs with Length Fields in Software and Hardware

Since parsers are the line of defense between binaries and untrusted data, they are some of the most common sources of vulnerabilities in software. Language-Theoretic Security provides an approach to implement hardened parsers. We specify the binary format as a formal grammar and implement a recognizer for this formal grammar. However, most binary formats use constructs such as the length field, repeat field, and an offset instruction. Most grammar formats do not support these features. Building on PEGs and calc-regular languages, we propose Calc-Parsing Expression Grammars (Calc-PEGs), a formalization of parsing expression grammars that supports the length field. We design an algorithm to parse Calc-PEGs in $O(n^{2})$ time and a parallel algorithm to parse Calc-PEGs in O(n) time. We also present Pegmatite, a tool to generate these parsers in C, with an option to generate VHDL code.
Evolution of the Conventional Business School

This chapter picks up where the previous chapter ended, mainly to discuss how emerging competition impacts the evolution of the modern business school. Several key challenged are identified as potentially slowing down evolution, among which conservatism among key faculty members and the culture in a business school that these create might be paramount. The chapter then discusses how emerging pedagogical innovations seem to further accentuate the need for change. The emerging computer-based technology combined with practical “learning on the job” seem particularly key here. The chapter concludes by discussing what might be some of the key shortcomings that business schools now might face to cope with the challenge to evolve. A fragmented, silo-orientated, organizational structure often seems to represent a particular challenge here.
Variable Mesh Optimization for Continuous Optimization and Multimodal Problems

Optimization problems with continuous variables are very often in real-life scenarios. This is why several studies have focused on the use of metaheuristics to solve these problems, known as continuous optimization problems. This chapter focuses on the robust population-based metaheuristic Variable Mesh Optimization (VMO). We review the fundamental aspects of VMO for continuous optimization. Moreover, we analyze the variants of VMO that have been proposed to solve multimodal problems, i.e. those with multiple optima at several search subspaces. The chapter uses a homogeneous algorithmic framework to describe VMO and its variants with niching strategies for multimodal problems.
Cognition of Graphical Notation for Processing Data in ERDAS IMAGINE

This article presents an evaluation of the ERDAS IMAGINE Spatial Model Editor from the perspective of effective cognition. Workflow models designed in Spatial Model Editor are used for the automatic processing of remote sensing data. The process steps are designed as a chain of operations in the workflow model. The functionalities of the Spatial Model Editor and the visual vocabulary are both important for users. The cognitive quality of the visual vocabulary increases the comprehension of workflows during creation and utilization. The visual vocabulary influences the user’s exploitation of workflow models. The complex Physics of Notations theory was applied to the visual vocabulary on ERDAS IMAGINE Spatial Model Editor. The results were supplemented and verified using the eye-tracking method. The evaluation of user gaze and the movement of the eyes above workflow models brought real insight into the user’s cognition of the model. The main findings are that ERDAS Spatial Model Editor mostly fulfils the requirements for effective cognition of visual vocabulary. Namely, the semantic transparency and dual coding of symbols are very high, according to the Physics of Notations theory. The semantic transparency and perceptual discriminability of the symbols are verified through eye-tracking. The eye-tracking results show that the curved connector lines adversely affect the velocity of reading and produce errors. The application of the Physics of Notations theory and the eye-tracking method provides a useful evaluation of graphical notation as well as recommendations for the user design of workflow models in their practice.
Prediction of heavy metal Cd content in basin soil with time series input

Abstract The content of heavy metal Cd in basin soil is important to human health and environmental management. Traditional pollution prediction models have problems such as a single input dimension and the inability to retain historical legacy information of time series data. This paper uses high-dimensional time series data as model inputs. A Backward Propagation Network (BP) and two variants of Recurrent Neural Network (RNN) named Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks were employed to develop Cd content prediction models of basin soil heavy metal. According to the experimental results, we explored the performance of different models about the prediction of the heavy metal Cd content. Experimental results show that the BP neural network converges faster but has a greater error. Compared with the BP neural network, the LSTM neural network error is decreased by 0.0895, 0.3124, 0.0159, 0.1533 and the GRU neural network error is decreased by 0.0743, 0.2985, 0.0259, 0.1441 in term of MAE, MAPE, MSE, RMSE. The experimental results showed the GRU is more efficient in time and space compared to the LSTM neural network. It is more suitable for high-precision content prediction of the heavy metal Cd.
Reversible Jump MCMC for Deghosting in MSPSR Systems

This paper deals with bistatic track association and deghosting in the classical frequency modulation (FM)-based multi-static primary surveillance radar (MSPSR). The main contribution of this paper is a novel algorithm for bistatic track association and deghosting. The proposed algorithm is based on a hierarchical model which uses the Indian buffet process (IBP) as the prior probability distribution for the association matrix. The inference of the association matrix is then performed using the classical reversible jump Markov chain Monte Carlo (RJMCMC) algorithm with the usage of a custom set of the moves proposed by the sampler. A detailed description of the moves together with the underlying theory and the whole model is provided. Using the simulated data, the algorithm is compared with the two alternative ones and the results show the significantly better performance of the proposed algorithm in such a simulated setup. The simulated data are also used for the analysis of the properties of Markov chains produced by the sampler, such as the convergence or the posterior distribution. At the end of the paper, further research on the proposed method is outlined.
Mechanized Type Safety for Gradual Information Flow

We model a security-typed language with gradual information flow labels in a proof assistant, demonstrate its potential application to parsing and securing sensitive user input data, present the semantics as a definitional interpreter, and prove type safety. We compare the language features and properties of various existing gradual security-typed languages, shedding light on future designs.
Ferromagnetic Heisenberg spin chain in a resonator

We investigate the properties of a generalized Rabi model by replacing the two-level atom in Rabi model with a ferromagnetic Heisenberg spin chain. We find that the dynamical behavior of the system can be divided into four categories. The energy spectrum of the ground state and some low excited states are obtained. When the magnons and the photon are in resonance, the model is exactly solvable and the rigorous solution is obtained. Near the resonance point where the detuning is small, the system is studied with the help of perturbation theory. This model has a spontaneously breaking of parity symmetry, suggesting the existence of a quantum phase transition. The critical exponent from the normal phase is computed.
A Classification Method Based on Ensemble Learning of Deep Learning and Multidimensional Scaling

In this paper, a classification method based on an ensemble learning of deep learning and multidimensional scaling is proposed for a problem of discrimination of large and complex data. The advantage of the proposed method is improving the accuracy of results of the discrimination by removing the latent structure of data which have low explanatory power as noise, and this is done by transforming original data into a space spanned by dimensions which explain the latent structure of the data. Using numerical examples, we demonstrate the effectiveness of the proposed method.
Estimating Human Pose with both Physical and Physiological Constraints

With the constant improvement and perfection in theories of rehabilitation exercises, the industrial chain of rehabilitation medical big data, which combines computer vision, sensors, human-computer interaction, and other advanced technologies, is developing rapidly. As an important way to understand and analyze human behavior, human pose estimation plays an important role in helping to identify patients' rehabilitation state, and then design targeted individual rehabilitation plan, and improve the objectivity of rehabilitation plan design. Therefore, human pose recognition technology has become a research hotspot in the field of rehabilitation medicine. However, there are not many studies on human pose estimation methods under scene constraints. To solve this problem, a body pose estimation method based on physical constraint and physiological constraint modeling was proposed in this paper. Three main constraints were established to make full use of 3D scene information and medical information to improve the body pose estimation results. Finally, through experiments, it is proved that the method proposed in this paper can greatly improve the results of 3D human pose estimation and can better cope with joint abnormalities in the process of fitting and accelerate the speed of fitting.
Semi-Supervised Learning Of Monocular 3D Hand Pose Estimation From Multi-View Images

Most modern hand pose estimation methods rely on Convolutional Neural Networks (CNNs), which typically require a large training dataset to perform well. Exploiting unlabeled data provides a way to reduce the required amount of annotated data. We propose to take advantage of a geometry-aware representation of the human hand, which we learn from multiview images without annotations. The objective for learning this representation is simply based on learning to predict a different view. Our results show that using this objective yields clearly superior pose estimation results compared to directly mapping an input image to the 3Djoint locations of the hand if the amount of 3D annotations is limited. We further show the effect of the objective for either case, using the objective for pre-learning as well as to simultaneously learn to predict novel views and to estimate the 3D pose of the hand.