Reviewerly/exHarmony huggingface dataset:
Development of a visual tool for the design of aggregate-oriented NoSQL databases

The aggregation model is the foundation of some of the main NoSQL databases. This model is characterized because the basic element of management is the concept of aggregate. Furthermore, another characteristic is that it is not necessary to create a definition schema in the database to store information as in the case of relational databases. It is for this reason that in general there are no sophisticated design tools for databases oriented towards aggregates since the schema is implicit in the stored information. However, a design tool allows better management of the information to be stored and especially the maintenance of the database. This article presents a visual tool that allows you to design a NoSQL database oriented towards aggregates, as well as manage different designs. On the other hand, the tool allows generating instances of the design containing data for a MongoDB-type document database.
Image Recognition Based on Denoising and Edge Detection

Face recognition refers to the process of detecting faces from images. In recent years, face recognition has attracted wide attention due to its non-contact method, fast recognition process, and stability. However, due to the noise in the image and the deficient performance of the face recognition algorithm, the existing face recognition algorithm has low robustness and low accuracy. This paper proposed a simulation model to tackle this issue, which can be utilized to analyze the correlation between model performance and image denoising. We use the control variable method to select the best combination of the image denoising and edge recognition methods. We note that the developed model obtained higher accuracy and consistency of edges.
Development of a Mathematical Model for the Software Defect Rework Process to Optimize Defect Rework—A Six-Sigma Case Study

The software development industry spends a staggering amount of resources on reworking the defects. The SMEs have limited resources; therefore, such enterprises look for cost-effective solutions. Past Six-Sigma studies in the software domain are limited to reducing defect generation opportunities. There is limited research that has dealt with the longer rework time and high cost during the rework phase. This work has attempted to fill into this niche by developing a mathematical model of defect rework-phase. This model is integrated with the DMAIC framework of Six-Sigma to formulate the defect rework process as a multi-objective problem between rework time and cost. Genetic algorithm was applied to obtain the non-dominated optimal solution. The proposed solution reduced the rework time by 31% at an optimum cost.
Advanced Authentication System

Advanced authentication system (AAS) is a new way of using password where user doesn’t need to remember complex text based passwords. In most of the important web-portals such as banking or data-management, the authentication process consists of textual passwords which is prone to many security attacks such as brute-force, keylogging, dictionary attack, shoulder surfing, etc. By using any of these methods, the hacker gets control of the user activity which will lead to misuse of data, or stealth of users’ money or information. So, in order to overcome these problems and provide users with better security, we have proposed our multi step system, where the user will enter a simple text password, then draw a pattern on the image of his/her choice which was recorded while registration along with additional security features such as users email validation using OTP.
Improved t-SNE in Anomaly Detection of Cloud Virtual Machine

Aiming at high computation time in the process of cloud virtual machine anomaly detection, a C-t-SNE algorithm suitable for cloud is proposed. The algorithm classifies the virtual machines according to the different tasks, constructs the projection space according to the relevant parameters of the cloud virtual machines. Before dimensionality reduction, comparison datasets are extracted from each cloud virtual machine according to the relationship between the cloud virtual machine and the task. In the process of dimensionality reduction, all cloud virtual machines are replaced by comparison datasets, so as to reduce the number of data comparison and the calculation time.
Robust image data hiding method based on multiple backups and pixel bit weight

Digital images are easily corrupted by attacks during transmission and most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, we propose a robust image data hiding method based on multiple backups and pixel bit weight (PBW). Especially multiple backups of every pixel bit are pre-embedded into a cover image according to a reference matrix. Since different pixel bits have different weights, the most significant bits (MSBs) occupy more weights on the secret image than those of the least significant bits (LSBs). Accordingly, some backups of LSBs are substituted by the MSBs to increase the backups of MSBs so that the quality of the extracted secret image can be improved. Experimental results show that the proposed algorithm is robust to cropping and noise attacks for secret image.
Unsupervised Recognition of the Logical Structure of Business Documents Based on Spatial Relationships

This paper presents the very first unsupervised and automatic system which can recognize the logical structure of business documents without any models or prior information about their logical structure. Our solution can process totally unknown new models of documents. We consider the problem of recognition of logical structures as a problem of detection, because we simultaneously have to localize and recognize the logical function of blocks of text. We assume that any document is composed of parts from several other models of documents. We have proposed a part-based spatial model suited for partial voting. Our proposed model presents the concept of Spatial Context (SC) as a spatial feature, which locally measure the distribution of spatial information around a point of reference. Our method is based on a Gaussian voting process providing a robust mechanism to detect elements of any logical structure. Our solution is suited for non-rigid structures and works well with a reduced number of images. This excellent property is not shared by the supervised approaches, especially methods based on neuronal networks.
Hierarchical Semantic Enhanced Directional Graph Network for Visual Commonsense Reasoning

Visual commonsense reasoning (VCR) task aims at boosting research of cognition-level correlations reasoning. It requires not only a thorough understanding of correlated details of the scene but also the ability to infer correlation with related commonsense knowledge. Existing approaches consider the region-word affinity to perform the semantic alignment between vision and linguistic domains, which neglect the implicit correspondence (e.g. word-scene, region-phrase, and phrase-scene) among visual concepts and linguistic words. Although efforts have been made to deliver promising results in previous work, these methods are still confronted with challenges when comes to make interpretable reasoning. Toward this end, we present a novel hierarchical semantic enhanced directional graph network. To be more specific, we design a Modality Interaction Unit (MIU) module, which captures high-order cross-modal alignment by aggregating the hierarchical vision-language relationships. Afterward, we propose a direction clue-aware graph reasoning (DCGR) module. In this module, valuable entities can be dynamically selected in each reasoning step, according to the importance of these entities. This leads to a more interpretable reasoning procedure. Ultimately, heterogeneous graph attention is introduced to filter the irrelevant parts of the final answers. Extensive experiments have been conducted on the VCR benchmark dataset, which demonstrates that our method can achieve competitive results and better interpretability compared with several state-of-the-art baselines.
Bayesian estimation of the inverted Beta-Liouville mixture models with extended variational inference

This paper addresses the problem of the Bayesian estimation of the inverted Beta-Liouville mixture model (IBLMM), which has a fairly flexible positive data modeling capability. This problem does not usually admit an analytically tractable solution. Sampling approaches (e.g., Markov chain Monte Carlo (MCMC)) can be used to address this problem. However, these approaches are usually computationally demanding, and as a result, they may be impractical for real-world applications. Therefore, we adopt the recently proposed extended variational inference (EVI) framework to address this problem in an elegant way. First, some lower bound approximations are introduced to the evidence lower bound (ELBO) (i.e., the original objective function) in the conventional variational inference (VI) framework, which yields a computationally tractable lower bound. Then, we can derive a form-closed analytical solution by taking this bound as the new objective function and optimizing it with respect to individual variational factors. We verify the effectiveness of this method by using it in two real applications, namely, text categorization and face detection.
Inverted Dirichlet State Space Model for Time Series Forecasting

In this work, we build a new time series state space model based on inverted Dirichlet distribution. We use the power steady modeling approach and we derive an analytical expression of the model latent variable using the maximum a posteriori technique. We also approximate the predictive density using local variational inference, and we validate our model on the electricity consumption time series dataset of Germany. A comparison with the generalized Dirichlet state space model is conducted, and the results demonstrate the merits of our approach in modeling continuous positive vectors.
Deep Learning Techniques for Automated Image Captioning

Srivastava, Siddharth Chaudhari, Yash Damania, Yash Jadhav, ParulAutomated Image Captioning involves understanding the semantic information of an image and expressing it in natural language. Among the many approaches proposed, deep learning-based techniques have achieved state-of-the-art results in solving this problem. In this paper, three primary, distinct deep learning-based approaches to solve this problem are introduced and compared: encoder-decoder frameworks, neuroevolution, and attention-based approaches. This paper covers their mechanisms and their performance, and highlights where they differ from each other. To conclude, the results of these approaches on benchmark dataset and metrics are presented.
A novel pattern-free phase unwrapping algorithm

Phase unwrapping plays a vital role in fringe projection profilometry which is an outstanding three-dimensional (3D) shape measurement method. This paper proposed a novel phase unwrapping algorithm that requires no extra patterns needed in conventional temporal phase unwrapping algorithms. The proposed method unwraps the wrapped phase map of the newest measurement with the help of the unwrapped one from the previous measurement. It applies temporal phase unwrapping algorithm first, and then detects the wrongly unwrapped points caused by the misalignment of phase edges, and finally corrects the phase of these points using spatial phase unwrapping algorithm. The method proposed can largely reduce the number of patterns needed for continuous dynamic measurement. Meanwhile, it can resist larger noise than multifrequency phase unwrapping algorithm because the noise of the reference phase map is not amplified. Besides, it can effectively solve the problem of abnormal phases caused by the misalignment between the phase edges of successive phase maps due to object motion. Moreover, the proposed algorithm is capable of retrieving the absolute phase map of multiple surfaces. The system developed with this algorithm can obtain the 3D shape of the object by just a single shot during the continuous dynamic measurement.
Cross-domain Person Re-Identification with Identity-preserving Style Transfer

Although great successes have been achieved recently in person re-identification (re-ID), there are still two major obstacles restricting its real-world performance: large variety of camera styles and a limited number of samples for each identity. In this paper, we propose an efficient and scalable framework for cross-domain re-ID tasks. Single-model style transfer and pairwise comparison are seamlessly integrated in our framework through adversarial training. Moreover, we propose a novel identity-preserving loss to replace the content loss in style transfer and mathematically show that its minimization guarantees that the generated images have identical conditional distributions (conditioned on identity) as the real ones, which is critical for cross-domain person re-ID. Our model achieved state-of-the-art results in challenging cross-domain re-ID tasks.
FPGA Realization of Stacked Auto-encoder with Three Fully Connected Layers

To accelerate the hardware design and reduce the resource requirements, this paper proposes a realization of a fully connected stacked auto-encoder (SAE) with fixed-point number representations in a field programmable gate array (FPGA). This SAE neural network structure can process the input feature space, including spectral-based features and high-order cumulants, to classify the modulation types intelligently. A series of synthesizable Verilog codes were created and simulated with Xilinx Vivado software. Matrix multiplication implemented by cyclic multiplication operation and activation function is realized by the piece-wise function method with Verilog codes. There is also an SAE model operating on GPU platform, and this paper compares the performance of SAE with fixed-point numbers on FPGA platform to that with floating-point numbers on GPU platform. From experiment results, the running speed of the SAE on FPGA is faster than that on GPU, but the precision of FPGA is lower than that on GPU within an acceptable range.
Towards an Efficient System for Differentially-private, Cross-device Federated Learning

This paper describes fresh ideas for a new system for federated averaging that aims to efficiently provide differential privacy even when a fraction of devices is malicious and there is no trusted core.
Crossing the Chasm between Ontology Engineering and Application Development: A Survey

The adoption of Knowledge Graphs (KGs) by public and private organizations to integrate and publish data has increased in recent years. Ontologies play a crucial role in providing the structure for KGs, but are usually disregarded when designing Application Programming Interfaces (APIs) to enable browsing KGs in a developer-friendly manner. In this paper we provide a systematic review of the state of the art on existing approaches to ease access to ontology-based KG data by application developers. We propose two comparison frameworks to understand specifications, technologies and tools responsible for providing APIs for KGs. Our results reveal several limitations on existing API-based specifications, technologies and tools for KG consumption, which outline exciting research challenges including automatic API generation, API resource path prediction, ontology-based API versioning, and API validation and testing.
Performance Monitoring for Processes and Threads Execution-Controlling

Strong parallelism can minimize computation time whilst increasing the cost of synchronization. It's vital to keep track of how processes and threads are working. It is understood that thread-based systems improve the productivity of complex operations. Threading makes it easier to the main thread to load, thus enhancing system performance. This paper focuses on the development of a system that has two main stages: monitoring and controlling of a program which have ability to run on a number of multicore system architectures, including those with (2, 4, and 8) CPUs. The algorithms associated with this work are built to provide the ability of: providing dependent computer system information, status checking for all existing processes with their relevant information, and run all possible processes/threads cases that compose the user program that might include one of these cases (Single-Processes/Single-Threads, Single-Process/Multi-Thread, Multi-Process/single-Thread, Multi-Process/Multi-Thread and Multi-Process/ Single-Multi-Thread). The monitoring phase provides complete information on User Program (UP) with all its processes and threads, such as (Name, ID, Elapsed Time, Total CPU Time, CPU usage, User Time, Kernel Time, Priority, RAM size, allocated core, read bytes and read count). The controlling phase controls the processes and threads by suspending/resuming/killing them, modifying their priority, and forcing them to a particular core.
Corridor-Based Motion Planning for Teleoperated Driving Tasks

Remote operation of road vehicles in complex urban scenarios causes a high workload for the human operator, which may lead to a stop-and-go driving behaviour. This contribution presents a supervisory control concept to raise the autonomy level, while the operator still remains the main decision-maker in all driving tasks. With the introduced approach, operators can define a corridor to the desired destination according to their perception to take into account in the motion planning process, for example, inadequate lane markings or obstacles still undetected by sensors. Within the specified corridor, the automated vehicle itself calculates the optimal motion in the dynamic environment. For this purpose, a new hybrid motion planning method has been developed that, in the first phase, determines an optimal path using a modified Constrained CHOMP algorithm. In the second phase, a collision-free trajectory is generated online along the optimal path by means of a linear time-varying model predictive control scheme under the consideration of time-varying system constraints. Real driving experiments confirm the effectiveness of the proposed approach and highlight both the achieved driving safety and the real-time capability.
Characteristics of kalisirah karst subterranean stream subdistrict of buayan, kebumen, central java with tracing test

Abstract The Karst Landscape Region (KRL) Gombong is an area that has developed subterranean stream and provides abundant water resources, one of which is the Kalisirah Springs in Sikayu Village, Buayan District, Kebumen. The Kalisirah spring supports thousands of surrounding communities, especially for domestic needs, fisheries and agriculture. The karst area is an area that is sensitive to change so it is necessary to know the character of a developing subterranean stream system, so that groundwater resources can be maintained in terms of quality and quantity. The method used is a tracing test with a qualitative and quantitative approach to obtain detailed information about the characteristics of the karst aquifer flow. Release of trace substances is carried out in Goa Pucung and the fluorometer is placed in the Kalisirah Springs. Release on September 23, 2018 at 9.57 p.m and September 24, 2018 at 3.25 p.m. Fluorometer records an increase in fluorescent concentration. Connection of Goa Pucung-Kalisirah through 2 karst windows (Kalijeblosan and Kaliwinong) that are visible in plain view. The concentration of released tracer substances can be recovered 26.18% in the Kalisirah springs, with discharge recovery reaching 4493%, which indicates a divergence and convergence of the Kalisirah subterranean stream aquifer system. Flow volume from the subterranean stream system of Goa Pucung-Kalisirah reached 23,403,064.49 liters with an average residence time of 24.11 hours. The cross-sectional area is 12.19 m2 and the diameter of the Flow-Channel reaches 3.94 m, and with hydraulic depth from the Flow-Channel 3.09 meters.
Behavior Analysis based on Local Object Tracking and its Bed-exit Application

Human behavior analysis is the process that consists of activity monitoring and behavior recognition and has become the core component of intelligent applications such as security surveillance and fall detection. Generally, the techniques involved in behavior recognition include sensor and vision-based processing. During the process, the activity information is typically required to ensure a good recognition performance. On the other hand, the privacy issue attracts much attention and requires a limited range of activity monitoring accordingly. We study behavior analysis for such privacy-oriented applications. A local object tracking (LOT) technique based on an infrared sensor array is developed in a limited monitoring range and is further realized to a practical bed-exit system in the clinical test environment. The experimental results show a correct recognition rate of 99% for 6 bedside activities. In addition, 89% of participants in a satisfaction survey agree on its effectiveness.
Comparison and analysis of secret image sharing principles

Secret image sharing (SIS) means to share a secret image into multiple shadow images, and the secret image can be recovered when a sufficient number of shadow images are obtained. SIS has the advantages of loss tolerance, simple encryption and decryption, scattered storage and so on. This paper describes three realization principles(i.e., the polynomial-based SIS, SIS based on the Chinese remainder theorem and visual secret sharing), verifies them with experiments, and calculates the PSNR, SSIM values and the time cost for each scheme to recover the image. Comparing the values of each index and the visual quality of the restored images can more intuitively reflect the advantages and disadvantages of each scheme. Finally, the advantages and disadvantages of each algorithm are analyzed by combining the theory and experimental results of the algorithm.
A Privacy-Preserving Anime Recommendation Method on Distributed Platform

The Japanese anime industry has directly promoted Japan’s economic development. There are many anime works, which types are also various. How to recommend users’ favorite anime from many anime has become a worthy problem. However, the anime recommendation methods face the following challenges. (1) Users tend to watch anime on multiple platforms and leave corresponding ratings. When using the similar users’ preferences for recommendation, the existence of distributed platform will lead to the disclosure of users’ privacy. (2) How to search similar users quickly and efficiently is also an urgent problem. Therefore, we use local-sensitive hashing (LSH) technology which can not only provide users with fast and efficient recommendation but also protect users’ privacy. LSH can index users by users’ rating data. Moreover, by projecting the index, similar users will be mapped to the same bucket. The time complexity of establishing offline indexes is O(1), and the target user can find similar users from the bucket directly. Therefore, we can achieve a privacy-aware anime recommendation. We use a real dataset provided by an anime website for experiment, and the experiment proves that our method is fast and effective.
Blockchain Implementation Using Python

This chapter covers an introductory overview of blockchain using Python code. This chapter will give a basic understanding of using Python codes in development of blockchain. The chapter throws light on beginner-level blockchain creation which will help in understanding developing an advance blockchain project using Python codes. This chapter covers basic building blocks which will help in creating various functions and methods to enhance the blockchain in terms of security. This will also help students in creating advanced level of Python program in creating better mining algorithms, better queue management, enhanced and secured transactions, consensus algorithm, wallets, and accounts.