Reviewerly/exHarmony huggingface dataset:
Digital Geometry for Virtual Museum Based on Field Studies

Abstract The virtual museum is one direction of museum development. This digital museum allows freedom of access, number of accesses health-safety for the visitor, and security of collections. The design of the virtual museum space was developed simultaneously, to assist the presence of intellectual and emotional experiences, approaching the capabilities of conventional physical museums. Efforts to digitally reconstruct for museum space are strived continuously, by multiplying the room design variants and sharpening the attribute database. The attributes referred to here, are the geometry of the showroom platform, in the form of volume, percentage of pavilion cover, circulation, and arrangement of collections. More number of collections on digital space geometry designs and their attributes will enrich and sharpen the immersive performance of the virtual museum design in the future. This geometry data is also useful for further refining research, such as acoustic analysis and lighting design. The research method started with observation in 16 museums and exhibition galleries in East Java. The results of the observations were continued by collecting 24 variants of the general geometry design of the pavilion configuration space. The second stage is done with selection and tabulation based on similarities, to minimized repetition. At this stage, the analysis which is recorded from field observations regarding visibility, circulation comfort, and noise, was applied, so that the seven core geometric designs were obtained. Within the third stage, digital geometric drawing was created using Trimble SketchUp. Validation was done by testing the results, in the sound propagation ray-tracing software. The results of the study were seven digital geometries of the pavilion design variant. These geometries are capable of using as the basis, for developing more digital gallery or museum space design, with good quality space-visitor interaction.
Branchboozle

We present Branchboozle, a side-channel that can be configured on top of a hidden Pattern History Table (PHT) now found in modern Branch Prediction Units (BPUs). In a similar fashion to known BranchScope attacks, Branchboozle works by closely monitoring the directional predictions issued by the BPU, i.e., whether a branch is predicted Taken or Non-Taken. However, our attack exclusively focuses on analyzing the predictions issued by a secondary, mostly-undocumented 3-bit PHT, whereas BranchScope attacks manipulate the predictions issued by a textbook-like 2-bit PHT.
Cervical Cell Detection Benchmark with Effective Feature Representation

As deep convolutional neural networks have shown promising performance in medical image analysis, a number of deep learning based cervical cytology diagnosis methods were developed in recent years. Most studies have achieved available performance in cell classification or segmentation, however, there still exists some challenges for effective screening. Cervical cell detection is a more significant task in cytology diagnosis for cancers. In this paper, we propose a detection framework with effective feature representation for automatic cervical cytology analysis. We employ elastic transformation and a channel and spacial attention module to obtain a more powerful feature extractor. The experimental results demonstrate the efficiency and accuracy improved by our effective feature representation.
A Meta-analysis on the variables related with Youth Career Decision Level

The purpose of this study is to derive variables related to the level of career decision-making through meta-analysis and to aggregate accumulated individual studies on the variables. To this end, the study examined the individual, family, school, and career-related variables that affect the level of career decision-making, and confirmed the moderating effects of each variable. The major findings were as follows: First, the effect size of the variable group that influenced the career decision level was home, individual, and school career. Second, the sub-factors of personal variables affecting career decision-making level showed significant effects of psychological independence, self-identity and self-efficacy. Family cohesiveness and family adaptation showed significant effects in the family variable group, and sub-factors with large effect size were not found in school variables, but the effect size of teaching support and practical satisfaction was confirmed. Career attitude maturity was found to be significant in the career-related group.
On Stopping Sets of AG Codes Over Certain Curves With Separated Variables

In this paper we study stopping sets of AG codes over a family of curves, denoted by <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f,g</sub> , that includes several important curves with applications in coding theory. We present results concerning stopping sets of one-point and m-point codes over <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f,g</sub> , generalizing some results for Hermitian codes presented by Anderson and Matthews.
Comparative study for Image Forgery Analysis between JPEG and Double JPEG Compression

Image forgery detection is the process of creating fake images has been extremely simple with the introduction of powerful computer graphics editing softwares such as Adobe photoshop, GIMP and many more, basically it deals with digital images. Data compression functions that encode the pristine image with a few bits in data compression. The main objective is to shorten the image redundancy and to effectively store or send data and reduce the storage capacity of pictures, videos and to improve the efficiency of collection and distribution but fixate on running that without lose efficiency and image data in excess. In this paper, We address two techniques of image compression. The first technique is based entirely on Joint Photographic Experts Group (JPEG) and the second technique is based entirely on Double Joint Photographic Experts Group (DJPEG). The simulation results were included and various grades criteria on various images are linked.
Academic Field and Future Influence Prediction for Scholar Profile Construction

Collecting scholar information from massive academic resources to construct scholar profiles can provide a reference for various academic activities. For a scholar profile, except basic attributes such as age, gender, job title, some potential attributes need to be predicted for describing the scholar. The scholars academic field and future influence are two important attributes for constructing profile. How to predict the potential attributes precisely is a challenge. Towards that, we propose two models to predict academic field and influence respectively for constructing scholar profile in this paper. In detail, we propose a double-layer attention model of paper information and journal information representation model for predicting scholars academic field based on multiple data sources. We also propose a two-level fusion model based on feature combination for predicting scholars’ future academic influence. Extensive experiments are conducted on AMiner dataset, and the results prove that our models are superior to the state-of-the-art methods.
Hennessy-Milner properties via topological compactness

We give Hennessy-Milner classes for intuitionistic, dual-intuitionistic and bi-intuitionistic logic interpreted in intuitionistic Kripke models, and generalise these results to modal (dual- and bi-)intuitionistic logics. Our main technical tools are image-compact and pre-image-compact relations that provide a semantical description of modal saturation properties.
Design and Implementation of Indian Coin Identification System using ANN Approach

Identification, validation, sorting and counting of the coin, being legal tender is always a challenging task. The condition becomes critical when the coins are degraded due to its long use or in case if the coins to be identified exists in large number. In this paper, we proposed effective technique for identification of valid Indian coins using Artificial Neural Network approach. To bring in to reality, the said approach is implemented using natural evolution characteristic of the genetic algorithm. For the validation, different filters and edge detection algorithms are used and finally, the results are disclosed for better performance.
Complicated human action understanding by massive-scale graph discovering technique

In order to solve the problem that the first frame of human motion prediction is discontinuous, we notice that the prediction time is short due to the influence of uncertain factors such as motion speed and motion amplitude. In this work, an end-to-end model based on bidirectional gating loop unit (GRU) and attention mechanism, biagru-seq2seq, is proposed. The encoder of our deep model adopts bidirectional Gru structure to input data from both positive and negative directions simultaneously. Meanwhile, the decoder part adopts unidirectional GRU structure and adds attention mechanism to encode the output of the encoder into a vector sequence containing multiple subsets. Also, a large-scale graph discovery framework is used to identify the various human action components. Subsequently, the input and output data of the decoder are fed into the residual at the same time. In the designed tensorflow framework, we use human3.6m, the largest open data set of motion capture data at present, to fulfill human motion prediction applications. Comprehensive experimental results have shown that the proposed model can not only substantially reduce the short-term motion prediction error, but also accurately predict multi frame human action recognition.
3D_DEN: Open-Ended 3-D Object Recognition Using Dynamically Expandable Networks

Service robots, in general, have to work independently and adapt to the dynamic changes happening in the environment in real-time. One important aspect in such scenarios is to continually learn to recognize newer object categories when they become available. This combines two main research problems namely continual learning and 3D object recognition. Most of the existing research approaches include the use of deep Convolutional Neural Networks (CNNs) focusing on image datasets. A modified approach might be needed for continually learning 3D object categories. A major concern in using CNNs is the problem of catastrophic forgetting when a model tries to learn a new task. Despite various proposed solutions to mitigate this problem, there still exist some downsides of such solutions, e.g., computational complexity, especially when learning substantial number of tasks. These downsides can pose major problems in robotic scenarios where real-time response plays an essential role. Towards addressing this challenge, we propose a new deep transfer learning approach based on a dynamic architectural method to make robots capable of open-ended learning about new 3D object categories. Furthermore, we make sure that the mentioned downsides are minimized to a great extent. Experimental results showed that the proposed model outperformed state-of-the-art approaches with regards to accuracy and also substantially minimizes computational overhead. Code is available online at: https://github.com/sudhakaranjain/3DDEN https://github.com/sudhakaranjain/3D_DEN.
Fast I2SDBSCAN Based on Integral Volume of 3D Histogram: Application to Color Layer Separation in Document Images

The optical reading of administrative documents using automatic analysis and recognition is a very demanding area in terms of the document quality in order to guarantee effective recognition of their content. Nowadays, when using color in administrative documents, digital experts have seen the importance of color support in facilitating access to the content of scanned documents, especially in the presence of quality degradation, stamps, handwritten notes, and marks on the text. In order to meet this current need, we propose a new method of color layer segmentation intended for document images. This makes it possible to simplify the separation and access to certain information which is very complex or impossible to extract from the image without color processing. It is a new variant of the original DBSCAN approach (called I2SDBSCAN, for integral double space DBSCAN), adapted to pixel clustering of document images guided by color densities. The use of integral volume in 3D color histogram and the coupling between Cartesian and colorimetric spaces have made it possible to considerably reduce the computation times. Experiments prove the effectiveness of the proposed method.
High capacity Reversible Data Hiding scheme using prediction tuning model

With the growing importance of data security and information hiding, techniques of data hiding in images have become widespread. Existing reversible data hiding techniques face the problem of errors due to inaccurate results of prediction models as well as the scope of improvement in terms of embedding capacity, which decreases the efficiency of security of sensitive data. In this paper, we propose a hyperparameter beta ( β) for combining two regression models trained with different data. The first regression model is trained over the entire image and the second regression model with a limited block of surrounding pixels in neighbouring regions of pixels in the image. For optimal results, parameters are tuned using Particle Swarm Optimization (PSO), and we have validated our results on standard test images. This novel technique outperforms the past techniques by improving data hiding in terms of parameters like embedding capacity (embedding rate), eliminating the errors from wrong predictions while keeping the visual quality of images preserved.
A golden age for computing frontiers, a dark age for computing education?

There is no doubt that the body of knowledge spanned by the computing disciplines has gone through an unprecedented expansion, both in depth and breadth, over the last century. In this position paper, we argue that this expansion has led to a crisis in computing education: quite literally the vast majority of the topics of interest of this conference are not taught at the undergraduate level and most graduate courses will only scratch the surface of a few selected topics. But alas, industry is increasingly expecting students to be familiar with emerging topics, such as neuromorphic, probabilistic, and quantum computing, AI, and deep learning. We provide evidence for the rapid growth of emerging topics, highlight the decline of traditional areas, muse about the failure of higher education to adapt quickly, and delineate possible ways to avert the crisis by looking at how the field of physics dealt with significant expansions over the last centuries.
Research on Performance Evaluation of Autonomous Underwater Vehicle’ Path Planning in Complex Scenes

Aiming at the problems of lack of operation for path guidance in complex scene, difficulty in quantifying evaluation index of path planning, and over idealization of evaluation results, this paper analyzes the common working conditions faced by autonomous underwater vehicle in complex scene, establishes a complete quantitative evaluation index model, uses analytic hierarchy process (AHP) to weight the index, and proposes an improved cloud-gravity-center evaluation method, the expert evaluation method transforms the fixed comment set of cloud model into the quantitative score of path planning' effectiveness in complex scenarios, and improves the A* algorithm based on the energy cost of yawing in the evaluation index model, based on the complex scene, the effectiveness evaluation of the path before and after the improvement of A* algorithm is carried out. The simulation results show that the method is practical in unstructured scene, and the analysis of evaluation results can effectively guide path planning for task of autonomous underwater vehicle, and has high application value.
Low Cost Heterogeneous ARIA S-Box Implementation for CPA-Resistance

Implementing countermeasure against power analysis-based attack is a critical issue in cryptographic hardware implementation. Protection schemes such as masking or Threshold Implementation (TI) have been proposed for hardware protection, but they have shortages like insufficient protection ability, or excessive hardware overhead. In this paper, we present low cost hetero S-box hardware implementation, where S-box groups for ARIA algorithm can be implemented using the coefficients with different hardware cost. Additional area reduction scheme using isomorphism sharing between S-boxes are also proposed. The proposed heterogeneous ARIS S-box has been implemented using 28nm CMOS process, and it showed 39% area saving with 30% of power saving. The proposed hardware also passed the security test, showing that it is verified as secure against power analysis-based attacks.
Interactive Application Deployment Planning for Heterogeneous Computing Continuums

Distributed applications in industry are modular compositions involving containers, functions and other executable units. To make them deployable and executable in production, they need to be assigned to heterogeneous resources in computing continuum’s, consisting of multiple clouds, devices and other runtime platforms. Existing assignment processes are neither transparent nor interactive. To overcome this limitation, we introduce the Continuum Deployer, a tool capable of reading application descriptions (e.g. Helm charts for Kubernetes), interactively performing and comparing assignment algorithms (e.g. to multiple K8s and K3s cloud/edge/fog/device resources), and exporting deployment files (e.g. Kubernetes manifests). We evaluate the tool with empirical package analysis, exemplary deployments and a synthetic experiment to appropriately address scalability concerns.
Web Development Computer Networks

Web development purpose of website design research is to find out how the users interact with websites, what are their needs and what can be done to solve their problems. Make improvements to your website in small incremental ways to make sure they make the right impact. developing a simple single static page of plain text to complex Web-based Internet applications, electronic businesses, and social network services.
Emotion Role Identification in Social Network

Emotion is a status that combines people’s feelings, thoughts, and behaviors and plays a crucial role in the communication between people. Considerable study suggests that human emotions can also be conveyed through online interactions. For a systematical literature review, we find that few studies focus on the influence of some special users on the process of emotional transmission in online social networks. To fill this gap, we first introduce the definition of emotion role, they are special users who play an important role in the process of emotion contagion of online social networks. We then propose an Emotion Role Mining approach (ERM) to detect users’ emotion roles in social networks. A set of features and measures is proposed and calculated to identify and represent these users based on the analysis of their emotion influence and long-term emotional preferences. Experiments and evaluations are conducted to demonstrate the practicability and usefulness of the proposed approach using Micro-blog data. Comparison experiments indicate that the proposed approach outperforms several baseline methods.
Moving Image Frame Interpolation: Neural Networks and Classical Toolsets Compared

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Frame interpolation is the process of synthesizing a new frame in between the existing frames in an image sequence. It has emerged as a key algorithmic module in motion picture effects since its large scale use in the making of the movie “The Matrix.” This paper presents a review and a new unified view of the classical algorithms used to create in-between frames, representing most of the past 20 years of their evolution. This is used to benchmark the recent deep learning algorithms against two of the best industrial retimers available. A significantly expanded data set of 140,000 frames is used for testing. In the context of highresolution material, we find that techniques relying principally on deep neural networks (DNNs) do not clearly outperform the classical ideas. It is only with the emergence of hybrid approaches in 2019 that we see DNNs adding significantly to the performance in this space. Despite the hype surrounding DNNs, we find that there is still more work to be done</i> .
Security of IoT with Blockchain: Basic Study

Internet of things has emerged to be a ubiquitous technology which has been proved to be a boon for the field of computer science. Many of the domains of computer science can be enhanced by the novel technology of IoT. With the advent of IoT, the need for unique identity to each IoT device came to the scene. Since IoT is comparatively a new domain in computer science and also involves the technology of embedded systems, there comes the necessity to focus on a secure architecture for IoT. This paper will focus on the enhancement of security in IoT by merging the technology of blockchain with it. IoT currently has a centralized architecture and has the security issues but blockchain is a decentralized and distributed platform and implementing blockchain in IoT thus resolves many security threats and vulnerabilities. Furthermore, the issues of data distortion can also be resolved since blockchain includes cryptographic algorithms at the backend. IoT with integrated blockchain can be competent of decentralization, autonomy and trustworthiness. The coordination between millions of IoT devices which in future is also going to increase rapidly can be tracked with high security of blockchain. This paper explores efficiency of blockchain in IoT application. Along with this paper discusses some case studies which is based on blockchain based IoT. There are some steps are described which will give a theoretical idea about how to build a blockchain in an IoT system.
Numerical Gaussian Process Kalman Filtering for Spatiotemporal Systems

We present a novel Kalman filter (KF) for spatiotemporal systems called the numerical Gaussian process Kalman filter (NGPKF). Numerical Gaussian processes have recently been introduced as a physics-informed machine-learning method for simulating time-dependent partial differential equations without the need for spatial discretization while also providing uncertainty quantification of the simulation resulting from noisy initial data. We formulate numerical Gaussian processes as linear Gaussian state space models. This allows us to derive the recursive KF algorithm under the numerical Gaussian process state space model. Using two case studies, we show that the NGPKF is more accurate and robust, given enough measurements, than a spatial discretization-based KF.
Overview of Monocular Depth Estimation Based on Deep Learning

Monocular depth estimation aims to estimate depth information from a single image. It plays an important role in various applications including SLAM, robotics and autonomous driving and so on. Monocular depth estimation is often described as an ill-posed problem. With the rise of deep neural networks, monocular depth estimation based on deep learning have also developed greatly. In this paper, we review some representative monocular depth estimation methods based on deep learning according to different training manners: supervised, self-supervised and weakly supervised. We then compare these three types of methods and illustrated their application scenarios. Finally, we separately analyze the potential improvements of the three types of methods.