Reviewerly/exHarmony huggingface dataset:
Reverse Multi-Delimiter Compression Codes

An enhanced version of a recently introduced family of variable length binary codes with multiple pattern delimiters is presented and discussed. These codes are complete, universal, synchronizable, they have monotonic indexing and allow a standard search in compressed files. Comparing the compression rate on natural language texts demonstrates that introduced codes appear to be much superior to other known codes with similar properties. A fast byte-aligned decoding algorithm is constructed, which operates much faster than the one for Fibonacci codes.
A Deep Learning Based Channel Estimation for High Mobility Vehicular Communications

In this paper, a decision-directed (DD)-channel estimation (CE) algorithm by employing deep learning (DL) is proposed for high mobility vehicular environments. The proposed algorithm relies on DL for channel prediction without prior knowledge about channel statistics, such as the channel covariance matrix and its time variation. Therefore, it does not require any prior Doppler rate estimation, which is highly complicated in high mobility environments. Based on the performed simulations, comparing to the Kalman filter-based DD-CE algorithm, our DL-based algorithm results in more reliable communications. It has been shown that comparing to the minimum mean square error (MMSE)-based DD-CE algorithm, our DL-based algorithm imposes much lower complexity to the system and the performance degradation is small compared to the MMSE-based DD-CE algorithm that requires the exact value of the Doppler rate.
Ontology Opportunities and Challenges: Discussions from Semantic Data Integration Perspectives

With the tremendous growth of digital information, big data management has become an emerging research area in recent years. In big data, handling a variety of data is significantly similar to dealing with the volume and velocity-rich information. Big data often deals with the storage of redundant information in heterogeneous formats from multiple data sources. To effectively manipulate the information of big data, the organizations necessitate data integration among the variety of data sources. Most of the real-time applications heavily rely on the evolution of available heterogeneous information sources. Consequently, data integration deals with the heterogeneity in the syntactic, schematic, or semantic structure of big data sources. In essence, data integration meets the interoperability problems in the big data environment while mapping the data or schema. Ontology-based data integration has been emerged as an active research area to overcome these constraints. Semantic web technology plays a crucial role in managing the disparate big data sources. With the assistance of semantic knowledge, the previous research works integrate the disparate data sources. Thus, this work provides a brief survey of semantic data integration in a big data environment. Semantic data integration is the process of inter-linking the data based on the conceptual representation and the relationships of the data. This work describes the significance of ontology in heterogeneous data sources integration with the advantages of big data. Furthermore, it presents the use-case of the 360-degree view of the customer for semantic integration in a real-world scenario. Finally, it concludes that the ontology-based integration models offer potential benefits for big data manipulation in different application fields.
Regolith mapping and geochemistry of hydrothermally altered, weathered and clay minerals, Western Jahajpur belt, Bhilwara, India

Mapping of regolith is significant to identify and discriminate geomorphic expression, regolith evolution, weathering histories and assistance in clarification of relationships between parent and surface materials. In this research work, the hydrothermally altered regolith of Western Jahajpur Belt, Bhilwara, India is studied using ASTER and AVIRIS imageries by PCA, decorrelation stretching supervised classification image processing methods. Results of decorrelation stretching of ASTER spectral bands 6, 7 and 9 showed the regolith in blue, green and red colour with the associated geological formations. Analyses of VNIR-SWIR spectral bands of ASTER by PCA showed the bed rocks associated to the regolith green, yellow, white and red colours, respectively. The minerals of the regolith and parent geological formations are identified using AVIRIS data by supervised classification and endmember spectral analyses. The study also identified six regolith-landforms which include the regolith (vegetation cover), crop land (vegetation cover), water, crop land with vegetation, alluvium and regolith (ferruginous and calcareous mineral zone). The discrimination of regoliths and geological formations are verified in the field and confirmed by XRF and ICP-MS geochemical analysis of samples.
Towards Blockchain-Enabled Searchable Encryption

Distributed Leger Technologies (DLTs), most notably Blockchain technologies, bring decentralised platforms which eliminate a single trusted third party and avoid the notorious single point of failure vulnerability. Since Nakamoto’s Bitcoin cryptocurrency system, an enormous number of decentralised applications have been proposed on top of these technologies, aiming at more transparency and trustworthiness than their traditional counterparts. Unfortunately, Blockchain introduces very subtle implications for other desirable properties such as privacy. In this work, we demonstrate these subtle implications for Blockchain-based searchable encryption solutions, which are one specific use case of cloud computing services. These solutions build on top of Blockchain and attempt to achieve both the standard privacy property and the new fairness property, which requires that search operations are carried out faithfully and are rewarded accordingly. We show that directly replacing the server in an existing searchable encryption solution with a Blockchain will cause undesirable operational cost, privacy loss, and security vulnerabilities. The analysis results indicate that a dedicated server is still needed to achieve the desired privacy guarantee. To this end, we propose two frameworks which can be instantiated based on most existing searchable encryption schemes. Through analysing these two frameworks, we affirmatively show that a carefully engineered Blockchain-enabled solution can achieve the desired fairness property while preserving the privacy guarantee of the original searchable encryption scheme simultaneously.
Multi-hop Route Planning Based on Environment Information for Path-Following UAVs

Flight path planning is one of the key research issues in the field of unmanned aerial vehicles (UAVs). There have been various approaches proposed to find or plan an optimal or appropriate path for numerous UAV applications. The obtained paths will be followed as a navigation once the UAV flies. Related works have utilized different methods to find the paths for different situations or considerations in the flight environments. Several bio-inspired algorithms such as PSO, GA, ABC, and ACO as well as the graph-based A* algorithm were usually utilized in the solutions of path planning. In this paper, the concept of Floyd-Warshall algorithm and a grid-based map presenting environmental information are utilized to find an optimal path with minimum hop count. A simulator is also developed for this work. Several simulation results with different grid sizes are illustrated. This study presents a preliminary trial work of grid-based multi-hop route planning for UAVs. Both the grid model and concerned environmental information can be extended for further complex researches.
Non-malleability Against Polynomial Tampering

We present the first explicit construction of a non-malleable code that can handle tampering functions that are bounded-degree polynomials. Prior to our work, this was only known for degree-1 polynomials (affine tampering functions), due to Chattopadhyay and Li (STOC 2017). As a direct corollary, we obtain an explicit non-malleable code that is secure against tampering by bounded-size arithmetic circuits. We show applications of our non-malleable code in constructing non-malleable secret sharing schemes that are robust against bounded-degree polynomial tampering. In fact our result is stronger: we can handle adversaries that can adaptively choose the polynomial tampering function based on initial leakage of a bounded number of shares. Our results are derived from explicit constructions of seedless non-malleable extractors that can handle bounded-degree polynomial tampering functions. Prior to our work, no such result was known even for degree-2 (quadratic) polynomials.
Generative Adversarial Networks for Stochastic Video Prediction With Action Control

The ability of predicting future frames in video sequences, known as video prediction, is an appealing yet challenging task in computer vision.This task requires an in-depth representation of video sequences and a deep understanding of real-word causal rules.Existing approaches for tackling the video prediction problem can be classified into two categories: deterministic and stochastic methods.Deterministic methods lack the ability of generating possible future frames and often yield blurry predictions.On the other hand, although current stochastic approaches can predict possible future frames, their models lack the ability of action control in the sense that they cannot generate the desired future frames conditioned on a specific action.In this paper, we propose new generative adversarial networks (GANs) for stochastic video prediction.Our framework, called VPGAN, employs an adversarial inference model and a cycle-consistency loss function to empower the framework to obtain more accurate predictions.In addition, we incorporate a conformal mapping network structure into VPGAN to enable action control for generating desirable future frames.In this way, VPGAN is able to produce fake videos of an object moving along a specific direction.Experimental results show that the combination of VPGAN with a pre-trained image segmentation model outperforms existing stochastic video prediction methods.
Benchmarking Predictive Models in Electronic Health Records: Sepsis Length of Stay Prediction

Forecasting Sepsis length of stay is a challenge for hospitals worldwide. Although there are many attempts to improve sepsis length of stay prediction; however, there is still lack of baselining prediction metrics that can give better results for sepsis length of stay prediction in management hospital systems. This paper introduces a research architecture to predict and benchmark the Length of Stay (LOS) for Sepsis diagnoses from electronic medical records using the machine learning models. The architecture considered the time factor to identify the outperforming algorithms for Sepsis LOS prediction. This work contributes to the field of predictive modelling and information visualization for hospital management systems. Our results showed that the ensemble methods in particular the random forest (RF) outdo other classification models to predict the LOS for Sepsis from electronic medical records for Intensive Care Unit "ICU"-based hospitalizations.
Fusion of Multiple Simple Convolutional Neural Networks for Gender Classification

Gender classification using face images is one of the most important and challenging tasks in automated face analysis, especially in unrestricted scenarios. Gender classification has become related to a growing number of applications. Nevertheless, the performance of existing methods on real-world images is still lacking. In this paper, we show that the performance of a simple convolutional neural network can be improved by learning multiple representations. We employ a simple feature fusion method using two simple convolutional neural network architectures. Our proposed method aims to replace the complex convolutional neural networks with two simple Principal Component Analysis network (PCANet) trained on different patch sizes. In addition, the high dimensional feature vector generated from each PCANet is reduced using whitening PCA. We evaluate our method on Gallagher's database which identified as among the hardest databases for gender classification. Our approach shows a comparative performance in comparison with state-of-the-art approaches.
SmartMonit: Real-Time Big Data Monitoring System

Modern big data processing systems are becoming very complex in terms of large-scale, high-concurrency and multiple talents. Thus, many failures and performance reductions only happen at run-time and are very difficult to capture. Moreover, some issues may only be triggered when some components are executed. To analyze the root cause of these types of issues, we have to capture the dependencies of each component in real-time. In this paper, we propose SmartMonit, a real-time big data monitoring system, which collects infrastructure information such as the process status of each task. At the same time, we develop a real-time stream processing framework to analyze the coordination among the tasks and the infrastructures. This coordination information is essential for troubleshooting the reasons for failures and performance reduction, especially the ones propagated from other causes.
A<sup>2</sup>Net: Adjacent Aggregation Networks for Image Raindrop Removal

Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens. In this paper, we propose a new Adjacent Aggregation Network (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net) with lightweight architectures to remove raindrops from single images. Instead of directly cascading convolutional layers, we design an adjacent aggregation architecture to better fuse features for rich representations generation, which can lead to high quality images reconstruction. To further simplify the learning process, we utilize a problem-specific knowledge to force the network focus on the luminance channel in the YUV color space instead of all RGB channels. By combining adjacent aggregating operation with color space transformation, the proposed A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net can achieve state-of-the-art performances on raindrop removal with significant parameters reduction.
Learning self-play agents for combinatorial optimization problems

Abstract Recent progress in reinforcement learning (RL) using self-play has shown remarkable performance with several board games (e.g., Chess and Go) and video games (e.g., Atari games and Dota2). It is plausible to hypothesize that RL, starting from zero knowledge, might be able to gradually approach a winning strategy after a certain amount of training. In this paper, we explore neural Monte Carlo Tree Search (neural MCTS), an RL algorithm that has been applied successfully by DeepMind to play Go and Chess at a superhuman level. We try to leverage the computational power of neural MCTS to solve a class of combinatorial optimization problems. Following the idea of Hintikka’s Game-Theoretical Semantics, we propose the Zermelo Gamification to transform specific combinatorial optimization problems into Zermelo games whose winning strategies correspond to the solutions of the original optimization problems. A specially designed neural MCTS algorithm is then introduced to train Zermelo game agents. We use a prototype problem for which the ground-truth policy is efficiently computable to demonstrate that neural MCTS is promising.
Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy

A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based detection algorithm for C4d immunostaining in renal allografts. The objective of this study is to assess the diagnostic performance of the algorithm by comparing pathologists' diagnoses and analyzing the associations of the algorithm with clinical data. C4d immunostaining slides of renal allografts were obtained from two different institutions (100 slides from the Asan Medical Center and 86 slides from the Seoul National University Hospital) and scanned using two different slide scanners. Three pathologists and the algorithm independently evaluated each slide according to the Banff 2017 criteria. Subsequently, they jointly reviewed the results for consensus scoring. The result of the algorithm was compared with that of each pathologist and the consensus diagnosis. Clinicopathological associations of the results of the algorithm with allograft survival, histologic evidence of microvascular inflammation, and serologic results for donor-specific antibodies were also analyzed. As a result, the reproducibility between the pathologists was fair to moderate (kappa 0.36–0.54), which is comparable to that between the algorithm and each pathologist (kappa 0.34–0.51). The C4d scores predicted by the algorithm achieved substantial concordance with the consensus diagnosis (kappa = 0.61), and they were significantly associated with remarkable microvascular inflammation (P = 0.001), higher detection rate of donor-specific antibody (P = 0.003), and shorter graft survival (P < 0.001). In conclusion, the deep learning-based C4d detection algorithm showed a diagnostic performance similar to that of the pathologists.
Proof-of-Concept of Uncompressed 4K Video Transmission from Drone through mmWave

Drones are attracting increasing attention in varieties of research fields because of their flexibility and are expected to be applied to a wide range of potential applications, among which the super-high-resolution video surveillance system using drones especially gains the authors' research attention. Surveillance systems using cameras with fixed locations always suffer the blind spots due to the blockage or inappropriate deployments. Instead, by using the drones equipped with cameras, the surveillance performance can be drastically improved due to their high mobilities. The video quality is also a key factor of the surveillance performance. In face recognition, one of the most important surveillance applications, the uncompressed video can greatly improve the detection accuracy, but it is difficult to transmit uncompressed video in real time due to the huge data sizes. To address the issue, we propose to use the ultra-high speed mmWave communication for the video transmission from drones. Moreover, due to the limited battery energy and computing power in drones, we introduce the edge computing and propose to offload all the computation from the drones to the ground station. In addition, a proof-of-concept prototype hardware of the proposed uncompressed 4K video transmission system from drones through mmWave is developed, and the experiments results are consistent with the system design expectations.
Research on Railway Intelligent Operation and Maintenance and Its System Architecture

With the continuous development of railway informatization, railway operation and maintenance system also needs to be more intelligent. Starting from the basic concepts of operation and maintenance, intelligent operation and maintenance and railway intelligent operation and maintenance, according to the current operation and maintenance situation of some systems in the railway industry, this paper studies the overall architecture and workflow of the railway intelligent operation and maintenance system. Intelligent operation and maintenance technology can provide technical means for ensuring railway transportation safety, improving maintenance efficiency and reducing operation and maintenance cost. The implementation of railway intelligent operation and maintenance system will make railway operation and maintenance move forward from traditional decentralized mode to centralized and unified mode, so as to make railway operation and maintenance work more scientific and efficient and improve the overall operation and maintenance level of railway.
Resolving Data De-Duplication issues on Cloud

Data de-duplication refers to provide the cloud providers a way to manage the uncontrollable data and the challenges of cloud storage. The success of IOT and social media led us to face big data challenges. Big data is interesting but also becomes a critical challenge for cloud service providers. Data storage and management become also the topic of discussion where big data generate opportunity for business as well as come with big issues for the cloud providers. In this paper, we discussed the issues of redundant data and techniques to prevent the data redundancy on the cloud. Version control system for data de-duplication is also discussed for the solution of this problem.
Fuzzy Cognitive Map Clustering to Assess Local Knowledge of Ecosystem Conservation in Ecuador

Smallholder farmers in developing countries such as Ecuador have contributed widely to ecosystem conservation. Although they have been affected by the consequences of climate change, the synergy of Pachamama care and technical knowledge has become key to sustaining dynamics in their local territories. This paper presents a combined methodology approach based on fuzzy cognitive mapping (FCM) and hierarchical cluster analysis (HCA) to capture perceptions of ecosystem conservation. The purpose was to assess the impact of the “Biocorridors for Living Well” program on the application of local knowledge by smallholder farmers. The findings showed that program managers had a limited narrow focus based on the general program ideology, whereas peasants’ perceptions revealed a more complex map, highlighting the importance of building capacities beyond sustainable agricultural techniques. Also, both groups were aware of the contribution of ecosystem conservation to rural development. However, it is essential to stress that the contribution of smallholder farmers to conservation was critical, and most of the time, their efforts were not valued. Therefore, to scale up program outcomes, national and international institutions require an understanding of local peasants’ perceptions to guarantee action sustainability and to avoid repetitive actions in their local territories.
The Biological and Motivational Effects of Aerobic Exercise With Virtual Reality

Purpose: There is no significant evidence of both biological and motivational effects between virtual reality exercises to traditional/conventional exercise. The aim of this study is to assess the biological and motivational effects of aerobic exercises with virtual reality and traditional exercise methods. Methods: 20 students participated as control and 60 students were divided randomly and equally into two groups, virtual reality exercise group (VRE) and traditional exercise group (TE). Two groups completed the exercise training. Tests were applied to the three groups at the beginning and in the end of the study. An activity monitor and pulse oximeter were used to measure energy expenditure during exercises. Results: After eight weeks, the data from pulse oximeter showed no statistical significance between the exercise groups. The active energy expenditure, PAL and step counts were statistically significant in TE group. There was a statistically significant increase in the sleep quality of the TE group (p = .000). There was no statistical significance among the anxiety scores of all groups. In the spring semester, there was a statistically significant increase in both VRE and TE groups compared to the fall semester (p < .05). Conclusion: The virtual reality exercises produce greater motivational effects, whereas traditional exercises bring greater physical improvements.