Reviewerly/exHarmony huggingface dataset:
A Reflection on the Development of an Erasmus Mundus Digital Library Learning (DILL) Programme

The purpose of this article is to reflect on the development of an Erasmus Mundus Digital Library Learning (DILL) program to understand the impact of the changes in technology on the role, skills and competencies of the digital librarian in contemporary society. This reflective article is based on the writer’s experience as a participant observer of the DILL program on its 10 years of development. A literature review was also employed to gather information from scholarly articles that were analyzed to help answer the research questions. Analysis of data followed a thematic approach where key ideas were identified and grouped together into themes that have been used to structure the discussions in the article. The discussions in this article have implications for further developments of education for information, particularly relating to the development of digital library learning programs in different contexts. Any effective digital library program should focus on content within a specific context, with required technology and a unique digital library education curriculum that is separate from LIS syllabi. The program may also be more effective if its curriculum content is designed to evenly assess the professional knowledge, generic skills and specific personal competencies of today’s digital librarians.
On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging

There is much interest in developing algorithms based on 3D convolutional neural networks (CNNs) for performing regression and classification with brain imaging data and more generally, with biomedical imaging data. A standard assumption in learning is that the training samples are independently drawn from the underlying distribution. In computer vision, where we have millions of training examples, this assumption is violated but the empirical performance may remain satisfactory. But in many biomedical studies with just a few hundred training examples, one often has multiple samples per participant and/or data may be curated by pooling datasets from a few different institutions. Here, the violation of the independent samples assumption turns out to be more significant, especially in small-to-medium sized datasets. Motivated by this need, we show how 3D CNNs can be modified to deal with dependent samples. We show that even with standard 3D CNNs, there is value in augmenting the network to exploit information regarding dependent samples. We present empirical results for predicting cognitive trajectories (slope and intercept) from morphometric change images derived from multiple time points. With terms which encode dependency between samples in the model, we get consistent improvements over a strong baseline which ignores such knowledge.
Compound truncated Poisson normal distribution: Mathematical properties and Moment estimation

The proposal of efficient distributions is a crucial step for decision making in practice. Mixture models are adjustment tools which are often used to describe complex phenomena. However, as one disadvantage, such models impose hard inference procedures, submitted to a large number of parameters. To solve this issue, this paper proposes a new model which is able to describe multimodal, symmetric and asymmetric behaviors with only three parameters, called compound truncated Poisson normal (CTPN) distribution. Some properties of the CTPN law are derived and discussed: characteristic and cumulant functions and ordinary moments. A moment estimation procedure for CTPN parameters is also provided. This procedure consists of solving one nonlinear equation in terms of a single parameter. An application with images of synthetic aperture radar (SAR) is made. The results present evidence that the CTPN can outperform the \begin{document}$ \mathcal{G}^0 $\end{document} , \begin{document}$ \mathcal{K} $\end{document} and BGN (laws commonly used in SAR literature), as well as GBGL models.
A Statistical Learning Ontology for Managing Analytics Knowledge

This paper focuses on the use of knowledge management techniques to help organisations tap into the power of statistical learning when conducting analytics. Its main contribution is in the use of an ontology development process to derive the essential concepts required for an ontology to represent variables of interest and their interrelationships with each other and with statistical datasets. This ontology is developed with the help of two case studies in the area of digital marketing and commodity pricing. A number of competency questions have been designed to map to user requirements in both case studies. A prototype system has been developed using a semantic modelling tool and a semantic data repository to demonstrate that the proposed ontology can support the competency questions via semantic queries.
Privacy-preserving Recommendation for Location-based Services

Location-based recommendation services, such as Foursquare, enhance the convenience in the life of consumers. Users are usually sensitive to disclose their personal information. Unavoidable security concerns arise because malicious third parties could misuse confidential information, such as the users’ preferences. The mainstream research to this problem is employing the privacy-preserving k-NN search algorithm. However, two major bottlenecks exist. One is that it only provides the nearest points of interest (POI) to the users without any recommendations based on the users’ behavior history. This limited service eventually results in a situation in which no user would prefer to continue using it. The other is that only a single user holds the private key; thus, the service providers cannot obtain any user’s information to analyze to make a profit. To solve the first problem, our proposed protocol provides recommendation services by adopting collaborative filtering techniques with an encrypted database based on fully homomorphic encryption aside from encrypting both the user’s location and preferences. For the second problem, a privacy service provider (PSP) is designed to generate and hold the private key. Thus, service providers can homomorphically compute aggregate information concerning user behavior patterns and send the encrypted results to PSP to ensure decryption while maintaining the privacy of individual users. Compared with the previous studies, the novelty of the proposed protocol is the design of a commercially valuable privacy recommendation mechanism that could benefit both consumers and service providers on LBS.
Research Directions on Big IoT Data Processing using Distributed Ledger Technology: A Position Paper

The significant growth and adoption of Internet of Things (IoT) solutions has led to tremendous increase in the generation of data. The need for high speed data processing has become very important to meet with the ever increasing volume and velocity of IoT data, due to the large scale and distributed nature of IoT infrastructure and networks. Present cloud based technologies are struggling to meet up with these needs for real time data processing in the midst of enormous amounts of data. The success of bitcoin has inspired more research in the application of Distributed ledger technologies in various domains. The decentralized nature of these platforms have enabled security and privacy of data in previous research and their architecture has a potential for enabling large scale decentralized data processing. In this paper, we identify some open areas of research in the use of distributed ledger technology and propose a framework for storing, analyzing and ensuring the security of large volumes of IoT data.
Points of Interest Recommendations Based on Check-In Motivations

During a trip, tourists are mostly dependent on their mobile phone to select their next points of interest (POI). A mobile application that recommends POIs such as tourist attractions or restaurants is based on the user's location data such as check-in history. This article recommends a novel approach to leverage the check-in data captured by location-based social networks (LBSNs) with an aim to improve POI recommendations through personalized explanations. The proposed algorithm generates a user's motivation profile, and its applicability is presented by analyzing a dataset extracted from a popular LBSN. A between-subject experiment ( N = 182) is conducted that shows explanations generated using a user's motivation profile increase transparency, which leads to intent to use the LBSN. Perceived usefulness of the LBSN also increases intent to use. The study indicates that when suggesting a POI, recommender system developers include explanations based on user's motivation behavior profile.
Blockchain Anchoring of Public Registries

Governments across the world are testing different uses of the blockchain for the delivery of their public services. Blockchain hashing-or the insertion of data in the blockchain (anchoring)-is one of the potential applications of the blockchain in this space. With this method, users can apply special scripts to add their data to blockchain transactions, ensuring both immutability and publicity. Blockchain hashing also secures the integrity of the original data stored on central governmental databases. The objective of this paper is to analyse the use of data hashing (anchoring) on the blockchain for public state-owned registries. This paper starts by analysing possible scenarios of hashing on the blockchain and assesses in which cases it may work and in which it is less likely to add value to a public administration. Second, the paper also compares this method with traditional digital signatures using PKI (Public Key Infrastructure) and discusses standardisation in each domain. Third, it also addresses issues related with concepts such as "distributed ledger technology" and "permissioned blockchains." Finally, it raises the question of whether blockchain hashing is an effective solution for electronic governance, and concludes that its value is controversial, even if it is improved by PKI and other security measures. In this regard, we claim that governments need to identify pain points in governance in the first place, and then consider the trade-offs of the blockchain as a potential solution versus other alternatives.
Style Transfer with Adversarial Learning for Cross-Dataset Person Re-identification

Person re-identification (ReID) has witnessed great progress in recent years. Existing approaches are able to achieve significant performance on the single dataset but fail to generalize well on another datasets. The emerging problem mainly comes from style difference between two datasets. To address this problem, we propose a novel style transfer framework based on Generative Adversarial Networks (GAN) to generate target-style images. Specifically, we get the style representation by calculating the Garm matrix of the three-channel original image, and then minimize the Euclidean distance of the style representation on different images transferred by the same generator while image generation. Finally, the labeled source dataset combined with the style-transferred images are all used to enhance the generalization ability of the ReID model. Experimental results suggest that the proposed strategy is very effective on the Market-1501 and DukeMTMC-reID.
Joint statistics matching for camera model identification of recompressed images

Source camera identification has been well studied in laboratory environment where the training and test samples are all original images without recompression. However, image compression is quite common in the real world, when the training and test images are double JPEG compressed with different quantization tables, the identification accuracy of existing methods decreases dramati- cally. To address this challenge, we propose a novel iterative algorithm namely joint first and second order statistics matching (JSM) to learn a feature projection that projects the training and test fea- tures into a low dimensional subspace to reduce the shift caused by image recompression. Inspired by transfer learning, JSM aims to learn a new feature representation from original feature space by simultaneously matching the first and second order statistics between training and test features in a principled dimensionality reduction procedure. After the feature projection, the divergence between training and test features caused by recompression is reduced while the discriminative properties are preserved. Extensive experiments on public Dresden Image Database verify that JSM significantly outperforms several state-of-the-art methods on camera model identification of recompressed images.
Using DODAS as deployment manager for smart caching of CMS data management system

Abstract DODAS stands for Dynamic On Demand Analysis Service and is a Platform as a Service toolkit built around several EOSC-hub services designed to instantiate and configure on-demand container-based clusters over public or private Cloud resources. It automates the whole workflow from service provisioning to the configuration and setup of software applications. Therefore, such a solution allows using “any cloud provider”, with almost zero effort. In this paper, we demonstrate how DODAS can be adopted as a deployment manager to set up and manage the compute resources and services required to develop an AI solution for smart data caching. The smart caching layer may reduce the operational cost and increase flexibility with respect to regular centrally managed storage of the current CMS computing model. The cache space should be dynamically populated with the most requested data. In addition, clustering such caching systems will allow to operate them as a Content Delivery System between data providers and end-users. Moreover, a geographically distributed caching layer will be functional also to a data-lake based model, where many satellite computing centers might appear and disappear dynamically. In this context, our strategy is to develop a flexible and automated AI environment for smart management of the content of such clustered cache system. In this contribution, we will describe the identified computational phases required for the AI environment implementation, as well as the related DODAS integration. Therefore we will start with the overview of the architecture for the pre-processing step, based on Spark, which has the role to prepare data for a Machine Learning technique. A focus will be given on the automation implemented through DODAS. Then, we will show how to train an AI-based smart cache and how we implemented a training facility managed through DODAS. Finally, we provide an overview of the inference system, based on the CMS-TensorFlow as a Service and also deployed as a DODAS service.
Implementing a large application(LSTM) on the multi-FPGA system: Flow-in-Cloud

In order to cope with computation cost and energy required for the recent deep learning technology, domain specific systems have been used in the cloud computing so as to be shared by many application developers efficiently. Although GPU(Graphics Processing Unit)s and more specialized systems like TPU (Tensor Processing Unit)s have been popularly utilized, FPGAs have been receiving an attention especially for their power efficiency and flexibility. Since energy efficiency is one of the most important issues in recent cloud computing, a lot of researches to use FPGAs in the cloud have been asserted[3], and commercial systems including Amazon's F1 instance are available. However, the performance improvement in the FPGA is limited by the upper limit of the FPGA resource, even an FPGA in the cloud. Thus, in order to implement a large deep layer learning application, we must use an expensive high-end FPGA or adopt a lightweight algorithm sacrificing throughput and accuracy. To deal with this problem, in the project “Power-saving AI engine and platform with heterogeneous engine integrated cloud” supported by NEDO started to develop a large-scale AI system called Flow-in-Cloud (FiC)[4]. FiC is consisting of a number of middle-scale economical FPGAs interconnected with high communication bandwidth network. From an HLS (High Level Synthesis) programmer, a lot of FPGAs can be handled as if they were a single large FPGA. The programmer can implement a large-scale deep learning model without caring resources on a single FPGA. FiC is managed by the Flow-OS, and shared efficiently by many users. Although FiC is designed to build a heterogeneous computing system, the current prototype is consisting of multiple FPGA boards called “FiC-SW” each of which provides both the switching and computing capabilities. Here, as a case study of such an energy efficient multi-FPGA board computing, we implemented the inference part of Long Short Term Memory (LSTM) [1].
Comparison of robust methods for extracting descriptors and facial matching

In recent years, facial recognition has been a major problem in the field of computer vision, which has attracted a lot of interest in previous years because of its use in different applications by different domains, it is a very important step in facial recognition that is based on the extraction of facial descriptors. In this article, we compared robust methods to extract relevant information from the face with different variations, to validate our experience, we have the text on a large database of face images. The simulation results show that the SURF detector exceeds other detectors in terms of Recognition rate and speed of determination of the similar point.
Steady-state entanglement and heat current of two coupled qubits in two baths without rotating wave approximation*

We study the steady-state entanglement and heat current of two coupled qubits, in which two qubits are connected with two independent heat baths (IHBs) or two common heat baths (CHBs). We construct the master equation in the eigenstate representation of two coupled qubits to describe the dynamics of the total system and derive the solutions in the steady-state with stronger coupling regime between two qubits than qubit–baths. We do not make the rotating wave approximation (RWA) for the qubit–qubit interaction, and so we are able to investigate the behaviors of the system in both the strong coupling regime and the weak coupling regime, respectively. In an equilibrium bath, we find that the entanglement decreases with the bath temperature and energy detuning increasing under the strong coupling regime. In the weak coupling regime, the entanglement increases with coupling strength increasing and decreases with the bath temperature and energy detuning increasing. In a nonequilibrium bath, the entanglement without RWA is useful for entanglement at lower temperatures. We also study the heat currents of the two coupled qubits and their variations with the energy detuning, coupling strength and low temperature. In the strong (weak) coupling regime, the heat current increases (decreases) with coupling strength increasing when the temperature of one bath is lower (higher) than the other, and the energy detuning leads to a positive (negative) effect when the temperature is low (high). In the weak coupling regime, the variation trend of heat current is opposite to that of coupling strength for the IHB case and the CHB case.
The Application and Development of Big Data in Transport Logistics Industry in China

This paper comprehensively summarizes the status in quo, requirements, system structure, contents and theories of the development and application of big data decision analysis system in transport logistics industry in China. First of all, from the perspective of the transport industry, the main participants in logistics industry can be divided into four types, and, analyzing their requirements respectively according to the roles and responsibilities of different types of participants. And then, this paper explains the relationship among the different information systems involved in decision analysis of big data in transport logistics and describes the data resource distribution and contents of these systems. After that, the paper summarizes six common methods of big data analysis. The development and application of transport logistics big data decision analysis system is in a stage of rapid development. Under the guidance of the competent authorities of transport industry, the logistics big data resource sharing system is basically established, which integrates multi-sector, multi-transport mode and multi-regional system platforms based on the LOGINK (The National Transportation Logistics Public Information Platform) and provides a variety of data services for government, business and research.
K-Means Clustering on Multiple Correspondence Analysis Coordinates

On April 18, 2017, the International Federation of Classification Societies (IFCS) issued a challenge to its members and the classification community to analyze a data set of 928 low back pain patients. In this paper, we present our contribution in terms of a cluster analysis of this data set. We will discuss our data cleaning process, which we view as a two-pronged approach: inferring values that are missing not at random and imputing values that are missing at random. We will also discuss the challenges in clustering mixed data types and the required data transformation prior to applying a clustering algorithm. We call our proposed data transformation process split-then-join. Finally, we offer our interpretation of the clustering results with respect to validation variables and we present some thoughts on selecting important variables to classify new observations.
A Distributed Ensemble of Deep Convolutional Neural Networks with Random Forest for Big Data Sentiment Analysis

Big data has become an important issue for a large number of research areas. With the advent of social networks, users can express their feelings about the products they bought or the services they used every day. Also, they can share their ideas and interests, discuss current issues. Therefore, Big Data sentiment analysis has become important in decision-making processes. In this paper, we propose a novel distributed ensemble of deep convolutional neural networks with random forest for sentiment analysis, which is tailored to handle large-scale data and improve classification accuracy. Experimental results on two real-world data sets confirm the claim.
Object Re-Identification Based on Deep Learning

With the explosive growth of video data and the rapid development of computer vision technology, more and more relevant technologies are applied in our real life, one of which is object re-identification (Re-ID) technology. Object Re-ID is currently concentrated in the field of person Re-ID and vehicle Re-ID, which is mainly used to realize the cross-vision tracking of person/vehicle and trajectory prediction. This chapter combines theory and practice to explain why the deep network can re-identify the object. To introduce the main technical route of object Re-ID, the examples of person/vehicle Re-ID are given, and the improvement points of existing object Re-ID research are described separately.
A Prism Module for Semantic Disentanglement in Name Entity Recognition

Natural Language Processing has been perplexed for many years by the problem that multiple semantics are mixed inside a word, even with the help of context.To solve this problem, we propose a prism module to disentangle the semantic aspects of words and reduce noise at the input layer of a model.In the prism module, some words are selectively replaced with task-related semantic aspects, then these denoised word representations can be fed into downstream tasks to make them easier.Besides, we also introduce a structure to train this module jointly with the downstream model without additional data.This module can be easily integrated into the downstream model and significantly improve the performance of baselines on named entity recognition (NER) task.The ablation analysis demonstrates the rationality of the method.As a side effect, the proposed method also provides a way to visualize the contribution of each word.