Reviewerly/exHarmony huggingface dataset:
Distributed Relational Algebra at Scale

Relational algebra forms a basis of primitive operations suitable for applications in graphs and networks, program analysis, deductive databases, and constraint logic programming. Despite its expressive power, relational algebra has not received the same attention in high-performance-computing research as more common primitives like stencil computations, floating-point operations, numerical integration, and sparse linear algebra. Furthermore, specific challenges in addressing representation and communication among distributed portions of a relation, especially for inherently imbalanced relations, have previously thwarted successful scaling of relational algebra applications to supercomputers. In this paper, we present a set of efficient algorithms to effectively parallelize and scale key relational algebra primitives. We introduce a hybrid hash-tree approach to representing distributed imbalanced relations and permitting efficient communication. Finally, we demonstrate the scalability of our implementation with a fixed-point algorithm computing the transitive closure of a large graph (generating over 276 billion edges) on 32,768 processes.
Image Encryption Method Using Differential Expansion Technique, AES and RSA Algorithm

In recent era, providing security is more difficult using traditional encryption techniques. To avoid this vulnerability, it is advisable to combine two traditional encryption algorithms. Next, use a Differential Expansion technique to instill an enciphered confidential key into the concealed image. One possible case can be the malicious person is ignorant about the cipher text and the enciphered key. The probability of attack is negligible. On the other hand, if the hacker knows the cipher key then with the help of private key he can get the plaintext image. The proposed algorithm is robust and provides a better security when compared with other existing algorithms. It will be implemented in MATLAB.
Impacts of Wind Speed and Humidity on the Performance of Photovoltaic Module

Photovoltaic modules present the most considered renewable energy source for non-solated and rural areas, thanks to its several benefits. Thus, in order to ensure sufficient production of energy by solar cells, they should be in operation. However, they face some factors, during their production periods, which affect their efficiency. This study has focused on the impacts of Wind speed and Humidity on electric parameters of Photovoltaic Modules like serial and shunt resistances, Current, Voltage and Power as well as their performance. It has been explained that the efficiency of solar cells can be varied not only by the temperature and the irradiance, but also by the Humidity and the Wind speed. Their influences have been demonstrated theoretically. The aim of this study is to take into consideration the effects of these two factors mutually when the Photovoltaic modules are in operation.
Sentiment Classification of Tourism Based on Rules and LDA Topic Model

Mastering the sentimental state of tourists can provide decision-making reference for scenic spot management and business operation. The core of tourist sentiment analysis is the construction of tourism sentiment classification model. At present, there is less research on emotions in the field of tourism, and the classification accuracy of the existing tourism sentiment model needs to be improved. Therefore, a method of combining sentiment lexicon and machine learning to construct tourism emotion model is presented. The tourist texts were collected from micro-blog and travel website reviews to construct tourism emotional lexicon firstly. By comparing the score of the single sentence text sentiment with threshold, the sentences with the clear emotion polarity were extracted, and we constructed a classifier preliminary with NB algorithm. Then the paper used the LDA topic model to correct the sentiment classification model, and the new model was used to classify of the other sentences. Finally, the two results were integrated as the end classification result. Through experiments on the Lijiang travel text, it is found that the accuracy of the hybrid method increased 13.85% than the sentiment lexicon method, and increased 8.51% than in machine learning.
Developing a Virtual Reality Experience with Game Elements for Tourism: Kayak Simulator

Several advances have been made in Virtual Reality (VR) technologies allowing users to enjoy different applications and experiences through this immersive technology. In this work, we use the design and creation methodology to develop a VR Kayak simulator with the aim to represent a local tourist attraction in a virtual world. This experience is enhanced with information related to activities to highlight the local area, inviting the user to visit as a tourist. To test the VR experience, we performed usability tests with 70 random volunteers in order to evaluate the usability and user experience. The results indicate that the VR experience is effective and efficient as well as contributes to give more information relating to tourism activities for the local tourist.
Shallow2Deep: Indoor scene modeling by single image understanding

Dense indoor scene modeling from 2D images has been bottlenecked due to the absence of depth information and cluttered occlusions. We present an automatic indoor scene modeling approach using deep features from neural networks. Given a single RGB image, our method simultaneously recovers semantic contents, 3D geometry and object relationship by reasoning indoor environment context. Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling. It involves multi-level convolutional networks to parse indoor semantics/geometry into non-relational and relational knowledge. Non-relational knowledge extracted from shallow-end networks (e.g. room layout, object geometry) is fed forward into deeper levels to parse relational semantics (e.g. support relationship). A Relation Network is proposed to infer the support relationship between objects. All the structured semantics and geometry above are assembled to guide a global optimization for 3D scene modeling. Qualitative and quantitative analysis demonstrates the feasibility of our method in understanding and modeling semantics-enriched indoor scenes by evaluating the performance of reconstruction accuracy, computation performance and scene complexity.
Design of Face Recognition System Based on Convolutional Neural Network

This paper designs a service robot-oriented face recognition system. For mobile robots, face recognition is a very important function among them. The system includes three parts: face acquisition and preprocessing, model establishment, and model training. Among them, the face collection link uses the face detection function in opencv, and optimizes the interference factors to establish its own data set. A convolutional neural network was designed and constructed, and the model was trained on its own data set. The accuracy rate obtained on the test set was 97.63%. Finally, the trained model was applied to the actual system. The system model is simple, occupies a small amount of memory, and can be applied to the actual application scenario of the robot moving forward, which can quickly and accurately detect and recognize human faces.
GAN-Based Face Attribute Editing

Recently, a variety of methods using the Generative Adversarial Network (GAN) for face editing have been proposed.However, the existing methods cannot control the editing content of the face elements according to the user-specified attributes or need to train a conditional GAN for editing tasks, which means it is difficult to add new attributes in the future.In this paper, a method to edit face attributes by editing the latent variable with the help of a pre-trained unconditional GAN and a linear classification model is proposed.In particular, face attribute editing is divided into two separate stages: Firstly, based on the optimization function, the generative model does a latent variable search to generate a high-quality face image that is similar to the input image.Secondly, by editing the latent variable of the GAN, the attribute of the generated face image can be modified indirectly, so it is almost unaffected by the training process and network structure of GAN, which means it is a flexible method for nearly all GAN network.Images of the FFHQ dataset are edited by attribute labels defined in Celeba dataset for experiments.These experiments prove that our method can edit a variety of face images that vary with race, gender, age, and camera shooting angle.The overall quality of the edited image is not inferior to the other face attribute editing method, and attribute classification for edited image shows a 92.6% attribute editing success rate of the proposed method.
Multi-Target Tracking with Trajectory Prediction and Re-Identification

Due to the complexity and clutter of real-world scenes, occlusion becomes a long-lasting difficulty in object tracking. Most existing tracking methods cannot effectively handle occlusion. In this paper, we propose a novel tracking framework that combines trajectory prediction and multi-cue appearance modeling to deal with the occlusion difficulty. When a target is completely occluded by background or other targets, it is unable to observe the target position. Therefore, we propose a Long Short-Term Memory (LSTM) model that merges attention mechanism and interaction module to predict the locations of all targets in the next frame. Considering that partial occlusion and inaccuracy of object bounding boxes often take place, we propose a multi-branch deep network architecture combining global and local features to realize accurate tracking and person re-identification (ReID). According to the experimental results on multiple benchmark datasets, our method achieves state-of the-art performance and outperforms many existing approaches.
QoS-Aware Data Placement for MapReduce Applications in Geo-Distributed Data Centers

With growing data volumes and the scaling of data center clusters, communication resources often become a bottleneck in service provisioning for many MapReduce applications (e.g., training machine learning models). Therefore, data placements that bring data blocks closer to data consumers (e.g., MapReduce applications) are seen as a promising solution. In this article, we propose an efficient data-placement technique that considers network traffic reduction as well as QoS guarantees for the data blocks to optimize the communication resources. We first formulate the joint optimization of the data-placement problem, propose a generic model for minimizing communication costs, and show that the joint data-placement problem is NP-hard. To solve this problem, we propose a heuristic algorithm considering traffic flows in the network topology of data centers by first seeking optimal QoS-aware data placement based on golden division on a Zipflike replica distribution, then transforming the joint data-placement problem into a block-dependence tree (BDT) construction problem, and finally reducing the BDT construction to a graph-partitioning problem. The experimental results demonstrate that our data-placement approach could effectively improve the performance of MapReduce jobs with lower communication costs and less job execution time for big-data processing.
Playing first-person shooter games with machine learning techniques and methods using the VizDoom Game-AI research platform

Artificial Intelligence in the form of machine learning is employed in games to control non-human computer-players, agents or bots. However, most of these games such as Atari took place in 2D environments that were not fully observable to the agents. Currently, it is of extreme significance to employ such machine learning techniques and methods in 3D environments such as Doom. Therefore, In this paper, we train agents on the health gathering scenario of the classical first-person shooter game Doom by first presenting the Direct Future Prediction to train an agent that uses a simple architecture with no additional supervisory signals, then differentiate and compare the performance of the agents trained by using several different machine learning techniques, and the AI reinforcement learning platform ‘VizDoom’, a 3D partially observable environment, with interesting enhanced properties that makes agents to stand out from inbuilt AI agents and human players. We have continued to use computer games as a benchmark for the performance of AI as having been so successful in the past. We also compared the results of our findings to conclude the performance of the agents trained with different machine learning techniques. The agents performed well against both human players and inbuilt game agents.
Compliance of “Principles of transparency and best practice in scholarly publishing” in Korean academic society-published journals listed in Journal Citation Reports

Purpose: The “Principles of transparency and best practice in scholarly publishing” are of increasing importance in an open science environment as a way to increase the transparency and quality of academic society journals. However, little previous research has investigated the application of this new guideline in practice. The aim of this study was to investigate the degree to which this guideline is being applied by Korean academic society– published journals listed in Journal Citation Reports.Methods: The researchers investigated the homepages of 59 Korean academic society– published journals to evaluate whether they had adopted the 33 items listed in the guideline. Based on the information available on the journals’ homepages, each item was classified as ‘yes’ or ‘no’ within the four categories of basic journal information, publication ethics, copyright and archiving information, and profit model.Results: The basic journal information category was generally well-practiced, with the exceptions of the peer review process, readership, and author fees. The copyright and licensing information category was also well-practiced, with the exception of policies on posting accepted articles with third parties and archiving items. However, most items in the publication ethics category were not well practiced, with the exception of authorship and intellectual property. All items in the profit model category were infrequently implemented.Conclusion: These findings serve as a good indicator for Korean journal editors of areas for improvement. It may be helpful to review journals’ publication policies and homepages to comply with international publishing standards.
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning

Aiming at a comprehensive and concise tutorial survey, recap of variational inference and reinforcement learning with Probabilistic Graphical Models are given with detailed derivations. Reviews and comparisons on recent advances in deep reinforcement learning are made from various aspects. We offer detailed derivations to a taxonomy of Probabilistic Graphical Model and Variational Inference methods in deep reinforcement learning, which serves as a complementary material on top of the original contributions.
Joint discriminative attributes and similarity embeddings modeling for zero-shot recognition

Zero-shot recognition (ZSR) is an appealing technique when addressing novel categories without any training data. However, existing methods face two critical limitations: 1) most existing works directly exploit user-defined attributes to transfer knowledge across classes, yet the importance to learn discriminative attributes is neglected; 2) previous studies typically consider the semantic similarity embedding, while there are limited research efforts on exploring the visual similarity embedding. In this paper, we propose a novel approach for ZSR to address the above issues. Specifically, we develop a simple yet effective strategy to learn discriminative attributes via regressing from labels, which yields superior performance in the semantic similarity space. Meanwhile, to our best knowledge, we are among the first to consider the visual similarity embedding, which fully explores the discriminative information of visual features to benefit the final performance. Moreover, we combine the visual and semantic similarity embeddings, which enriches the information in the joint similarity space and greatly reduces the gap between visual features and semantic attributes. Extensive experiments on five benchmarks show that our method achieves state-of-the-art results on both conventional and generalized zero-shot recognition tasks.
Experimental Evaluation of Bounded-Depth LSM Merge Policies

Modern NoSQL databases use log-structured merge (LSM) storage architectures to support high write throughput. LSM architectures aggregate writes in a mutable MemTabte (stored in memory), which is regularly flushed to disk, creating a new immutable file called an SSTable. Periodically, some of the SSTables are chosen to be merged - replaced with a single SSTable containing their union. A merge policy (a.k.a. compaction policy) specifies when to do merges and which SSTables to combine. A bounded depth merge policy is one that guarantees that the number of SSTables never exceeds a given parameter k, typically in the range 3-10. Bounded-depth policies are useful in applications where low read latency is crucial, but they and their underlying combinatorics are not yet well understood. This paper compares several bounded-depth policies, including representative policies from industrial NoSQL databases and two new ones based on recent theoretical modeling. The results validate the proposed theoretical model and show that, compared to the existing policies, the newly proposed policies can have substantially lower write amplification.
Privacy Bargaining with Fairness: Privacy-Price Negotiation System for Applying Differential Privacy in Data Market Environments

Digital data are an essential resource for intelligent decision making. As the value of digital data increases, digital markets, where data owner and consumers can deal with data, have also been attracting attention as a means to obtain data. However, the collection of digital data can lead to privacy breaches, which are a substantial impediment that hinders an individual's willingness to provide data. Differential privacy, which is a de facto standard for privacy protection in statistical databases, can be applied to solve the privacy violation problem. To apply differential privacy to the data market, the amount of noise and corresponding data price must be determined; however, this matter has not yet been studied. In this work, we propose a fair negotiation method that can set the appropriate price and noise parameter in the differentially private data market environment. We suggest a data market framework with a market manager that acts as a broker between the data provider and consumer. We also propose a negotiation technique to determine the data price and noise parameter ε using Rubinstein bargaining considering social welfare to prevent unfair transactions. We validate that the proposed negotiation technique can determine an appropriate level of ε and unit price without unfair trade to either the data provider and the consumer.
Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition

Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies-nested dichotomies constructed from domain knowledge-or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy's topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems.
Pyramid: A General Framework for Distributed Similarity Search on Large-scale Datasets

Similarity search is a core component in various applications such as image matching and product recommendation. However, single-machine solutions are usually insufficient due to the large cardinality of modern datasets. We present Pyramid, a general and efficient framework for distributed similarity search. Pyramid supports search with popular similarity functions including Euclidean distance, angular distance and inner product. Different from existing distributed solutions that are based on KD-tree or locality sensitive hashing (LSH), Pyramid is based on the Hierarchical Navigable Small World graph (HNSW), which is the state-of-the-art similarity search algorithm. To achieve high query processing throughput, Pyramid partitions a dataset into sub-datasets containing similar items for index building and assigns a query to only some of the sub-datasets for query processing. Experiments on large-scale datasets show that Pyramid produces quality results for similarity search, achieves high query processing throughput and low latency, and is robust under node failure and straggler.
Performance Characterization and Modeling of Serverless and HPC Streaming Applications

Industrial and scientific streaming applications require support for different types of processing and the management of heterogeneous infrastructure over a dynamic range of scales: from the edge to the cloud and HPC, and intermediate resources. Serverless is an emerging service that combines high-level middleware services, such as distributed execution engines for managing tasks, with low-level infrastructure. It offers the potential of usability and scalability but adds to the complexity of managing heterogeneous and dynamic resources. In response, we extend Pilot-Streaming to support serverless platforms. Pilot-Streaming provides a unified abstraction for resource management for HPC, cloud, and serverless, and allocates resource containers independent of the application workload removing the need to write resource-specific code. Understanding the performance and scaling characteristics of streaming applications and infrastructure presents another challenge. StreamInsight provides insight into the performance of streaming applications and infrastructure, their selection, configuration, and scaling behavior. Underlying StreamInsight is the universal scalability law, which permits the accurate quantification of scalability properties of streaming applications. Using experiments on HPC and AWS Lambda, we demonstrate that StreamInsight provides an accurate model for a variety of application characteristics, e. g., machine learning model sizes and resource configurations.