Reviewerly/exHarmony huggingface dataset:
Trends, key actors and use cases in QKD technologies: an analysis of the research and innovation frontier using web-based methods

This work is part of the activities of the PON project “Development of quantum systems and technologies for IT security in communication networks” (QUANCOM) which aims to the realization of a metropolitan quantum communication network through the collaboration between universities, research centers and companies operating in the communication market area.The paper presents an analysis of the research and innovation frontier related to Quantum Technologies and conducted through web-based methods and techniques, which allow the acquisition of high granularity information also from unconventional sources. The work aims to trace the strategic positioning of the QUANCOM project with reference to the evolution of the state of the art in order to: i) enucleate the most promising lines of research; ii) outline emerging market trends; iii) provide evidence on the innovativeness of existing technologies; iv) identify use cases and opportunities for industrial exploitation of the project results.Structured and unstructured information sources are used. The former is represented by national and international databases concerning: i) European research projects, ii) scientific publications and research reports, iii) patents, iii) market and investment trends. The second ones refer to websites of research centers, companies, innovation facilitators.The methodology involved performing 3 steps of analysis that led to specific results:1.through the analysis of the research frontier, 69 H2020 projects on Quantum Technologies were identified.2.the reconnaissance of the technology frontier led to the identification of a highly specific panel of 731 patents divided into 4 sub-areas focused on: i) QKD equipments; ii) terrestrial and satellite QKD networks; iii) layer 3 cryptography; iv) security technologies in Distributed Ledger applications.3.the reconstruction of the innovation ecosystem conducted through the web content mining approach, led, from a panel of more than 18,000 actors, to the identification of 142 public and private stakeholder that carry out research and innovation activities on the reference domain or commercially enhance solutions based on quantum technologies.
Low Power and Area Efficient Approximate 2D-DCT Architecture for Wireless Capsule Endoscopy

Wireless Capsule Endoscopy (WCE) is an efficient method to diagnose the Gastrointestinal Tract for determining various digestive diseases accurately. WCE module requires low power Discrete Cosine Transform (DCT) architecture for image compression because the transmission of the image data consumes about 90% of the total battery power. The image data should be compressed initially to reduce the power of the image data to be transmitted and the communication bandwidth without affecting the image quality significantly as the physician may apply the zooming effect to perform a detailed diagnosis. Without proper clarity in the images, it will be very difficult for the physician to diagnose the disease. Therefore, an optimized image compression algorithm is required to maintain the sufficient video quality with low power consumption. This paper introduces an approximate DCT architecture with approximate coefficients for low power consumption. The proposed design is multiplier-free as it uses shift and add architecture for the implementation of a multiplier. An inexact adder is also introduced in the two-dimensional DCT (2D-DCT) architecture to achieve better performance in terms of power, area and delay. It achieves the performance improvement due to approximation of DCT coefficients and inexact adders. The proposed design is implemented using Verilog HDL and synthesized in Synopsys Design Compiler using Faraday’s 180nm technology library. It achieves about the reduction of 9.4%, 47.17% and 8.36% in area, power and delay respectively compared to the most recent existing design.
Hardware Accelerator for Capsule Network based Reinforcement Learning

Convolutional neural networks are widely used in reinforcement learning. Capsule networks are gaining popularity over the traditional convolutional neural networks in many classification tasks. A capsule is a multidimensional activity vector consisting of neurons that represent the features of a specific type of entity such as an object or part of an object. In this paper, we explore the capability of a Capsule Network for deep reinforcement learning-based applications. Our proposed capsule network architecture with the same number of parameters as that of a convolutional neural network for reinforcement learning takes on average nine times lesser number of network update iterations than that of a convolutional neural network. We also propose a hardware accelerator for deep Q-learning that uses the capsule network as a deep Q-network instead of a convolutional neural network. We have implemented a capsule network-based deep Q-learning architecture for inference on the Xilinx Kintex UltraScale field-programmable gate array. We have tested the network on pygame based environments. Our hardware implementation achieves an overall speedup of 77.45x as compared to the software implementation of the capsule network for deep reinforcement learning on Intel Xeon CPU E5-1607, 4 core, @3.1GHz and 10.86x as compared to implementation on Nvidia Ge-Force GTX1080 GPU.
A Survey of the First Five Years of eRisk: Findings and Conclusions

Early risk detection is a challenging task in which multiple objectives are involved. For this task, getting timely predictions with a reasonable confidence level becomes critical. Over the last five years, eRisk shared-task campaigns have focused on stimulating research in extracting evidences of different types of risk from Internet data (e.g., diagnosis of a mental disorder), fostering the development of early risk detection systems (ERDS). In this chapter, we present an overview of the key aspects and stages involved in the design and development of ERDS. Despite the heterogeneity observed, common elements can be identified from the wealth of systems submitted since eRisk’s outset. In particular, we review and summarise what has been learnt from each team contribution, specially focusing on how they built their systems to tackle different early detection tasks. This chapter concludes with several findings and lessons that have been discovered and learnt along the way.
Ecosystem Simulator

Genetic algorithms (GA) are a subclass of machine learning methods that allow an automated determination of optimal points. Learning games seem to be very well suited for teaching GA principles, although learning games are still not state of the art. Accordingly, this article presents the development and evaluation of the learning game Ecosystem Simulator for teaching GA principles. The development cycle is described here, which includes the selection of a game theme, the identification of the learning content, the definition of the game mechanics to two subsequent iterations consisting each of development and evaluation. The evaluation of the second development iteration’s prototype reveals attaining high scores both for learning motivation and intrinsic motivation along with a significant increase in knowledge. Thus, a learning game has been developed, which, in view of its rather young development timeline, seems to offer an appealing gaming experience combined with decent learning outcomes. All in all–as a motivating learning game–the Ecosystem Simulator enriches GA teaching.
Support Personalized Weighted Local Differential Privacy Skyline Query

The potential privacy risks in certain situations are of concern because of the frequent sharing of data during skyline queries, leading to leakage of users’ private information. The most common privacy-preserving technique is to anonymize data by removing or changing certain information, for which an attack with specific background knowledge would render the privacy protection ineffective. To overcome these difficulties, this study proposes a personalized weighted local differential privacy method (PWLDP) to protect data privacy during skyline querying. Compared with existing studies of skyline queries under privacy protection, the degree of privacy protection can be quantitatively analyzed, and the processing of data privacy lies with the user, who quantitatively perturbs the processing according to the sensitivity of the weights of different attributes to avoid substantial information loss. The performance of the proposed PWLDP is verified by comparing PWLDP and LDP on different datasets, the average privacy leakage reduction of 62.22% and 51.67% is obtained for experiments conducted on different datasets relative to the iDP-SC algorithm, and the experimental results demonstrate the efficiency and advantages of the proposed method.
The Use of Mixed-Reality Sport Platforms in Social Media Sentiment Analysis during COVID-19

Sport and physical activity are very closely related to people's health. The COVID-19 pandemic has made everyone aware of the importance of maintaining regular physical activity. The lockdowns and mandatory social distancing experienced during the epidemic underlined the importance of new sports platforms that bring traditional sports, such as cycling, to the virtual world. This work focuses on the ZWIFT cycling application as an exemplary mixed-reality sport platform. Sentiment analysis (or opinion mining) aims to explore the emotions behind the opinions expressed in texts on different topics. We used sentiment analysis of social media platforms (Twitter and Reddit) to provide valuable information on the culture surrounding mixed reality sports platforms.
Hydration Knowledge, Fluid Replacement Habit and Hydration Status among Development Athletics Athletes in Malaysian Sports School Pahang

Objective: This study aims to identify the hydration knowledge, fluid replacement habit and hydration status among development athletics athletes in Malaysia Sports School Pahang.Methodology: Cross-sectional study was conducted among 63 athletes aged 15.6 ±1.7 (male= 51%, female= 49%) that take part in 3 different events (sprinters n=22, field events n=21, long-distance n=20).The hydration knowledge and fluid replacement habit were identified using a validated questionnaire.Meanwhile, the hydration status was assessed before the athletes started their morning training using urine refractometer.Result: The mean score for knowledge and fluid replacement habit were 6.98±1.20 and 6.81±1.30respectively.The specific gravity (USG) mean score was 1.034±0.045,which indicated for severely dehydrated.More than half of the athletes (57%) were categorized as significantly dehydrated.Next, there was a strong and positive correlation between hydration knowledge and fluid replacement habit score (r=0.638,p<0.0001).In addition, this study also found there was an inverse correlation between hydration status with hydration knowledge (r=-0.108,p=0.402) and hydration status with fluid replacement habit (r=-0.088,p=0.492), but the correlation was small and not significant.Conclusion: The finding of this study identifies the need of education strategy for athletes to improve their knowledge and behavior toward the hydration status.In addition, facilities and infrastructure such as the availability of water dispensers might reduce the risk of dehydration among athletes.
Lightweight MapReduce Application Service Integrity Auditing on the Cloud

Due to the advantages of MapReduce in both cost-saving and effectiveness for large-scale computation-intensive applications, a growing number of tenants are considering migrating their applications to the MapReduce cloud. For financial interests, an untrusted cloud service provider (CSP) may violate the service level agreement (SLA) when providing service to tenants. Therefore, essential security mechanisms are needed to protect the integrity of MapReduce services. To address this issue, a novel service accountability scheme is proposed to audit compliance for MapReduce on the cloud in this paper. The proposed scheme inserts auditing points into tenant’s applications and constructs a tamper-proof list at run time. By verifying the auditing list, the MapReduce cloud can be responsible and accountable, which can detect whether CSP should be held responsible for its service. Finally, the proposed scheme is deployed in experimental business processes, and its effectiveness is evaluated.
Classifying the Knowledge and Information Processing competency of High School Students and Exploring Affecting Factors

지식정보사회가 고도화됨에 따라 핵심역량 중에서도 다양한 정보의 홍수 속에서 필요한 지식과 정보를 수집, 분석 및 활용할 수 있는 지식정보처리 역량의 함양이 더욱 강조되고 있다. 본 연구는 고등학생의 지식정보처리 역량 유형을 분류하기 위해서 잠재프로파일 분석을 수행하였으며, 머신러닝 기법 중 랜덤 포레스트를 적용하여 지식정보처리 역량이 높은 잠재프로파일로의 분류에 미치는 영향요인을 탐색하는 것을 목적으로 한다. 이를 위해 한국교육종단연구 2013 6차년도 자료를 사용하였다. 주요 결론은 다음과 같다. 첫째, 고등학생의 지식정보처리 역량 유형은 ‘역량 우수 집단’, ‘역량 양호 집단’, ‘역량 부진 집단’의 3가지 유형으로 분류되었다. 둘째, 지식정보처리 역량 우수 집단과 나머지 집단 간 분류에 학생 및 학교 요인이 주요한 영향을 미치는 것으로 나타났다. 구체적으로는 학생 요인 중 진로성숙도의 영향이 큰 것으로 나타났다. 학교 요인 중에서는 교과 내용의 의미, 발견 및 탐구학습 수업, 과정 중심 평가, 학생 참여형 수업, 역량 기반 수업 등 수업 및 평가 영역이 주요한 영향을 미치는 것으로 나타났다. 마지막으로, 연구 결과를 바탕으로 지식정보처리 역량 함양을 위한 시사점이 논의되었다.As the knowledge and information society advances, among core competencies, the knowledge and information processing competency which involves collecting, analyzing, and utilizing necessary knowledge and information in the midst of a flood of information is being emphasized. The purpose of this study was to classify high school students’ knowledge and information processing competency through latent profile analysis and to explore factors affecting the classification into latent profiles with high knowledge and information processing competency by applying the random forest machine learning technique. For these purposes, data from the sixth year of the Korean Educational Longitudinal Study 2013 was used. The main conclusions are as follows. First, the types of high school students’ knowledge and information processing competency were classified into three types: advanced group, intermediate group, and beginner group. Second, it was found that student and school factors had a major influence on the classification between the advanced and other groups. Specifically, among the student factors, career maturity was found to have had a large effect. Among the school factors, it was found that classes and evaluation areas such as the meaning of subject content, inquiry and discovery-based classes, process-based evaluation, participatory classes, and competency-based classes had a major impact. Based on these results, implications for enhancing knowledge and information processing competency are discussed.
Semi-supervised dual-graph regularization non-negative matrix factorization with local coordinate and orthogonal constraints for image clustering

Due to significant performance in the representation of data points, non-negative matrix factorization (NMF) has been widely applied in machine-learning fields, such as dimension reduction, image representation, feature extraction, data mining, and so on. However, classical NMF suffers from a common issue, low efficiency in representing the internal geometric structure of data and sparsity limitation. To circumvent this problem, we innovatively propose a semi-supervised NMF algorithm called semi-supervised dual-graph regularization non-negative matrix factorization (LOSDNMF), into which dual-graph and bi-orthogonal constraints are embedded to reduce the inconsistency between the original matrix and the basic vectors while maintaining the manifold structures of the data and feature spaces. This strategy can fully explore the potential geometry information of the data, which is extremely beneficial to enhance the learning ability of the model. In addition, the local coordinate constraints are introduced to ensure good sparsity of the coefficient matrix and simplify the calculation. Furthermore, an iterative updating scheme for the optimization problem of LOSDNMF and its convergence proofs are also provided in detail. The effectiveness of the proposed method is verified on eight benchmark datasets. Experimental results show that our method can effectively improve clustering performance.
End-to-end deep multispectral image compression based on interspectral prediction network

Multispectral images have numerous features and a wide range of applications. However, traditional image compression methods, such as JPEG2000 and 3D-SPIHT, do not make effective use of spectral information. We propose a deep compression framework based on interspectral prediction to take full advantage of spectral correlation when using temporal correlation for interframe prediction in video compression. First, two-dimensional and three-dimensional convolutions were used to obtain spatial and spectral information for predicting the original image. Then, we applied a residual neural network to compress the residual information of the image. Subsequently, a decoder was employed to reconstruct the multispectral image based on the compressed image and residual information. All components were jointly trained by a single loss function that considered the tradeoff between the compression bit rate and decoded image quality. The experimental results showed that our proposed method outperformed other traditional compression algorithms, including JPEG2000, 3D-SPIHT, and PCA+JPEG2000, in terms of peak signal-to-noise ratio and spectral angle and is equivalent to or even better than some image compression algorithms based on deep neural networks.
CBi-GNN: Cross-Scale Bilateral Graph Neural Network for 3D Object Detection

3D object detection from LiDAR point clouds is a challenging task, since the point clouds are irregular and sparse. Existing one-stage methods mainly predict the 3D bounding box of 3D objects by extracting deep down-scaled features of point clouds from low-level (high-resolution, HR) feature maps to high-level (low-resolution, LR). Nonetheless, most of these methods ignore geometric context information of the down-scaled feature maps across scales, especially only using the LR feature will result in incomplete structure and less location accuracy of 3D objects. In this paper, we propose a novel cross-scale graph network-based one-stage 3D object detector to fully exploit the geometric contexts of the voxels between the down-scaled feature maps. Specifically, we first employ a 3D sparse convolution neural network to form different resolutions of feature maps of voxels. We then dynamically construct a cross-scale bilateral graph to search the neighbor non-empty voxels in the HR feature map with a fixed radius for each non-empty voxel in the LR feature map. In the constructed graph, we present a bilateral attention mechanism (i.e., self-attention and spatial attention) in the HR feature map and encode each non-empty voxel in the LR feature map by aggregating the HR features to obtain the attention features. In addition, we design a non-local part pooling operation to improve the score of the detected bounding box of 3D objects. Finally, we formulate a multi-task loss to train our network for regression of the 3D bounding box of the 3D objects. Experiments on the challenging KITTI’s 3D/BEV benchmark show that our proposed detector outperforms all one-stage 3D object detectors and is comparable to two-stage 3D object detectors. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/csjxchen/CBi-GNN</uri> .
Cooperative Job Scheduling and Data Allocation in Data-Intensive Parallel Computing Clusters

In data-intensive parallel computing clusters, it is important to provide deadline-guaranteed service to jobs while minimizing resource usage (e.g., network bandwidth and energy). Under the current computing framework (that first allocates data and then schedules jobs), in a busy cluster with many jobs, it is difficult to achieve high data locality (hence low bandwidth consumption), deadline guarantee, and high energy savings simultaneously. We model the problem to simultaneously achieve these three objectives using integer programming. Due to the NP-hardness of the problem, we propose a heuristic Cooperative job Scheduling and data Allocation method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSA</i> ). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSA</i> novelly reverses the order of data allocation and job scheduling in the current computing framework. Job-scheduling-first enables <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSA</i> to proactively consolidate tasks with more common requested data to the same server when conducting deadline-aware scheduling, and also consolidate the tasks to as few servers as possible to maximize energy savings. This facilitates the subsequent data allocation step to allocate a data block to the server that hosts most of this data's requester tasks, thus maximally enhancing data locality. To achieve the tradeoff between data locality and energy savings with specified weights, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSA</i> has a cooperative recursive refinement process that recursively adjusts the job schedule and data allocation schedule. We further propose two enhancement algorithms (i.e., minimum k-cut data reallocation algorithm and bipartite based task reassignment algorithm) to further improve the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSA</i> through additional data reallocation and task reassignment, respectively. Trace-driven experiments in the simulation and the real cluster show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSA</i> outperforms other schedulers in supplying deadline-guarantee and resource-efficient services and the effectiveness of each enhancement. Also, the enhancement algorithms are effective in improving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSA</i> .