Reviewerly/exHarmony huggingface dataset:
Data Librarianship in 21st Century: Issues and Challenges

The nature of scientific research in the twenty-first century is more collaborative and data intensive (NSFP 2010). ICTs have made it possible to assess, understand, and arrive at novel conclusions by incorporating rigorous computation, simulation, and modelling. Massive amounts of born-digital and digitised data are being created at a rapid rate in a wide range of ways. Digital data are not only the results of study, but also the sources of new theories that enable fresh scientific understanding and spur innovation (NSFP 2008). Due to this, a lot of data has been generated that has to be reused and shared with others for further analysis and interpretation. UNESCO recognized that valuable progress could be stimulated in both developed and developing countries by assuring that compilations of reliable data would be readily available to all scientists, researchers, academicians, engineers and policymakers. A good number of initiatives have been taken worldwide by many institutions (KIIT-Germany, Purdue-USA, and University of Cambridge etc.), funding agencies (NSF, NIH, NEH, and UNESCO etc.), publishers (Nature, PLOS ONE, Lancet, Springer, and Elsevier etc.), governments (India, Canada, Germany, UK, USA etc.), learned bodies and other stakeholders to develop a sustainable ecosystem for research data sharing and re-use for the better science. Industries, academic and research institutions in India and abroad have started to hiring “data librarian” (sometimes data curator, data manager, databrarian, research data librarian, data service librarian, repository manager, etc.) to manage, archive and share of reproducible large quantitative research data. Generally, scientists are generating data by pursuing research but less interested about data preservation and share. So the information professionals can play the role efficiently and satisfy the social need. A few LIS schools in Europe have been conducting short term training courses on Research Data Management (RDM) and Data Science to fill the employers need, unfortunately, none of the Indian LIS schools yet not have adopted any data management component in their curriculum except DRTC. In this context there is a need to identify the core competencies of information professionals required in order to manage research data for long-term preservation, dissemination, and advocacy in the digital environment. It also investigates, what are the issues faced by the data librarians and stack holders to provide research data services (RDS) to the scientific community. By understanding what employers seek in data librarians, LIS curriculum developers can better implement courses and pedagogy that will help meet employer needs as well as developing qualified candidates for these new job titles in academic and research libraries.
Hybrid Low-Cost Quantum-Safe Key Distribution

A hybrid approach to quantum-safe cyber security that leverages the strengths of Quantum Key Distribution (QKD) and post-quantum computation while mitigating the weaknesses of both can enable quantum-safe cyber-infrastructures for improved security of defense, finance/banking, and utility systems.
Implementing Spatio-Temporal Graph Convolutional Networks on Graphcore IPUs

Artificial neural networks have been used for a multitude of regression tasks, and their descendants have expanded the domain to many applications such as image and speech recognition, filtering of social networks, and machine translation. While conventional and recurrent neural networks work well on data represented in Euclidean space, they struggle with data in non-Euclidean space. Graph Neural Networks (GNN) expand recurrent neural networks to directly process sparse representations of graphs, but they are computationally expensive, which invites the use of powerful hardware accelerators. In this paper, we investigate the viability of the Graphcore Intelligence Processing Unit (IPU) for efficient implementation of Spatio-Temporal Graph Convolutional Networks. The results show that IPUs are well suited for this task.
Towards Adapting Metamodelling Technique for an Online Social Networks Forensic Investigation (OSNFI) Domain

With the ease of use of smart devices, most data is now kept and exchanged in digital forms such as images, diaries, calendars, movies, and so on.Digital forensic investigation is a new technology that emerged from criminals' who extensively use computers and digital storage devices to commit different types of crimes.To address this issue, a new domain called Online Social Networks Forensic (OSNF) was created to investigate these dynamic crimes perpetrated on social media platforms.OSNFI seeks to obtain, organise, investigate, and visualise user information as direct, objective, and fair evidence.Considering the millions of individuals using social media to share and communicate, they are becoming increasingly relevant for criminal investigations.In forensics investigation of online social network, there are currently major problems such as: lack of structured procedures, the lack of unified automated methods, and the lack of a theoretical context.The use of non-uniform and ad hoc forensic techniques and procedures not only reduces the effectiveness of the process, but also affects the reliability and creditability of the proof in criminal proceedings.As a result, this paper will provide a method derived from the software engineering domain known as metamodelling, which will integrate OSNFI knowledge into an artifact known as a metamodel.
Panoramic amplitude-fluctuation electronic speckle pattern interferometry system for inner vibration measurement

We propose a panoramic vibration measurement system for the inner cylindrical surface of an object based on amplitude fluctuation electronic speckle pattern interferometry and developed according to the Michelson interference configuration. A conical mirror, aligned to the axis of the inner cylindrical surface, is illuminated to transform a single camera view into a panoramic view. After transposing the observation coordinate system, a complete inner 360-deg vibration pattern can be reconstructed. The principle of transformation from a Cartesian-coordinate system to a cylindrical-coordinate system is introduced in detail. The panoramic vibration of the inner circular tube is determined, and the first six modes are analyzed. The comparison and error analysis of the proposed system with the ANSYS simulation results prove the accuracy and feasibility of the system.
Context-aware video encoding as a network-based media processing (NBMP) workflow

Leveraging processing capabilities and resources in a network is a trending approach in accomplishing complex media processing tasks. At the same time, efficiently utilizing available resources while ensuring the potential for scalability and distribution is key. However, deploying, operating and maintaining such complex media service workflows on different cloud services, at the edge or on-premise can be a very complex and time-consuming task. In this paper, we will present an approach that addresses these challenges by utilizing state-of-the-art technologies and standards for advanced multimedia services such as the MPEG Network-based Media Processing (NBMP) standard. We will apply the presented approach for implementing bandwidth reduction and optimization strategies by using context aware video encoding. Implemented as an automated NBMP workflow, the context aware encoding method with the support of machine learning models avoids computationally heavy test encodes. The models are trained on complex datasets composed of 40+ video attributes and generate an optimal encoding ladder as an output (bitrate/resolution pairs). In comparison to the conventional per-title encoding method, we observed significant savings in terms of storage and delivery costs, while maintaining the same visual quality.
DRMM: A novel data mining-based emotion transfer detecting method for emotion prediction of social media

With the progress of the Internet and information technology, emotion analysis has been applied to analyse the emotional orientation and evolution trend of online public opinion of online tweets. At present, most of the existing methods use econometric model and machine learning algorithm to predict the trend of online public opinion. Although these methods have achieved good prediction results, they do not take into account the influence of internal factors on network public opinion prediction, such as mutual migration among emotion classes. The emotion may change dynamically because different events trigger it in the evolution process. In this view, this article proposes a novel method, called Deviation Rule Markov Model (DRMM), to predict the emotional change trend of Internet users in online public opinion by analysing the correlation between Internet users’ emotional categories. Structurally, the proposed DRMM involves various processes such as pre-processing, emotion classification, data mining and transfer prediction. For the processing of network comment data, the proposed model initially undergoes pre-processing to delete unnecessary data. Then, the extended fuzzy emotion ontology is used to annotate the emotion class of the comment data. Besides, an extended association rule mining algorithm is used in the emotion association analysis process to obtain the transfer probability between emotion classes. Moreover, Markov chain is used to construct an emotional state transition matrix to predict the transition probability of positive or negative emotions. According to the predicted single emotion transfer probability results, the analytic hierarchy process is used to assign values to different emotion classes, and finally, the transfer probability of the overall emotion in a certain period is obtained. Compared with the actual case, the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model are 2.7119 and 3.7254, respectively, which has good prediction performance.
Avoiding excess computation in asynchronous evolutionary algorithms

Abstract Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast‐evaluating solutions at the expense of higher‐quality, slow‐evaluating ones. We have previously introduced a steady‐state parent selection strategy, SWEET (“Selection whilE EvaluaTing”), that sometimes selects individuals that are still being evaluated and allows them to reproduce early. We perform a takeover‐time analysis that confirms that this strategy gives slow‐evaluating individuals that have higher fitnesses an increased ability to multiply in the population. We also find that SWEET appears effective at improving optimization performance on problems in which solution quality is positively correlated with evaluation time. We evaluate our approach on six simulated real‐valued optimization problems and three real‐world applications: an autonomous vehicle controller problem that involves tuning a spiking neural network and two adversarial EA problems. We further evaluate SWEET versus a basic asynchronous process in a simulated setting. We present evidence that SWEET outperforms basic asynchronous processes in a use‐case in which performance is positively correlated with evaluation time, and performs comparably (and often better) than basic asynchronous processes in several use‐cases where performance is negatively correlated with evaluation time. That said, in the cases where performance and evaluation time are negatively correlated the variance of outcomes for SWEET is notably high.
Scalable Differentially Private Clustering via Hierarchically Separated Trees

We study the private k-median and k-means clustering problem in d dimensional Euclidean space. By leveraging tree embeddings, we give an efficient and easy to implement algorithm, that is empirically competitive with state of the art non private methods. We prove that our method computes a solution with cost at most O(d3/2 log n)⁆ OPT + O(kd2 log2 n/ε2), where ε is the privacy guarantee. (The dimension term, d, can be replaced with O(log k) using standard dimension reduction techniques.) Although the worst-case guarantee is worse than that of state of the art private clustering methods, the algorithm we propose is practical, runs in near-linear, Õ (nkd), time and scales to tens of millions of points. We also show that our method is amenable to parallelization in large-scale distributed computing environments. In particular we show that our private algorithms can be implemented in logarithmic number of MPC rounds in the sublinear memory regime. Finally, we complement our theoretical analysis with an empirical evaluation demonstrating the algorithm's efficiency and accuracy in comparison to other privacy clustering baselines.
E-Commerce Personalized Recommendation Model Based on Semantic Sentiment

The real economy has moved to online electronic market transactions as a result of the rapid development of Internet technology. Online shopping makes up a growing portion of transactions in China’s e-commerce market, and the number of users who are aware of online payment transactions on mobile phones is rising. Online shopping platforms like Taobao and JD.com, which are all exemplary online shopping platforms, are constantly emerging. However, because there is so much product information available when shopping online, it can be challenging for users to locate the information they need. Recently developed personalized recommender systems have successfully addressed this issue. The system can predict the user’s preferences through extensive data analysis, and it then pushes the predicted information to the user interface, greatly increasing the user’s purchasing efficiency and the advantages of e-commerce. As a result, in the modern era, research on the personalized recommendation model in e-commerce has become increasingly popular. In this study, semantic sentiment analysis—which is improved on the traditional semantic sentiment analysis algorithm—is introduced in the research of a personalized recommendation system, and 1000 users are chosen for an experimental study. On the user’s personalized product recommendation, the improved semantic sentiment analysis and other widely used personalized recommendation algorithms are compared. According to the survey results, the average transaction success rate is 71.3 percent, and the maximum search time is 1.74 milliseconds when collaborative filtering recommendation algorithm is used. Semantic sentiment analysis has reduced search times to a maximum of 1.42 milliseconds and increased transaction success rates to 87.9 percent. After the addition of semantic sentiment analysis, it is clear that the personalized recommendation system model has a higher accuracy in recommending the products that users have expressed an interest in, which can have a greater positive impact on e-commerce transactions.
Quantum measurement detection algorithms

Abstract In this paper, we introduce and study the quantum measurement detection algorithms (QMDA), whose objective is to detect whether unwanted measurements are being taken in a quantum circuit or not by applying the Zeno effect. A QMDA is a quantum circuit that includes three unitary matrices, one of them being applied numerous times consecutively, and whose initial state is fixed when no foreign measurements occur. One example is the Elitzur–Vaidman bomb tester, which is generalized by the QMDA definition, allowing the detection of measurements that are taken in an unknown basis and in circuits with an arbitrary number of qubits. We prove some key properties and limitations of these algorithms, as well as studying the performance of the Elitzur–Vaidman bomb tester and its possible improvements. Some extensions of the definition would lead to algorithms such as the counterfactual communication one.
Big Data, Small Personas: How Algorithms Shape the Demographic Representation of Data-Driven User Segments

Derived from the notion of algorithmic bias, it is possible that creating user segments such as personas from data results in over- or under-representing certain segments (FAIRNESS), does not properly represent the diversity of the user populations (DIVERSITY), or produces inconsistent results when hyperparameters are changed (CONSISTENCY). Collecting user data on 363M video views from a global news and media organization, we compare personas created from this data using different algorithms. Results indicate that the algorithms fall into two groups: those that generate personas with low diversity-high fairness and those that generate personas with high diversity-low fairness. The algorithms that rank high on diversity tend to rank low on fairness (Spearman's correlation: -0.83). The algorithm that best balances diversity, fairness, and consistency is Spectral Embedding. The results imply that the choice of algorithm is a crucial step in data-driven user segmentation, because the algorithm fundamentally impacts the demographic attributes of the generated personas and thus influences how decision makers view the user population. The results have implications for algorithmic bias in user segmentation and creating user segments that not only consider commercial segmentation criteria but also consider criteria derived from ethical discussions in the computing community.
Desain DESAIN USER INTERFACE PADA TOKO ONLINE HELM INDONESIA BERBASIS WEB

The growth rate of internet users in recent years has increased very rapidly and is growing rapidly. This triggers the creation of a new business model in the form of a marketplace that is used to buy and sell goods. This study aims to load an online store that is simple and lightweight but can meet the needs of sellers who use this website.&#x0D; Application development uses methods while the prototype in modeling the application uses use case diagrams, class diagrams, activity diagrams, and sequence diagrams. The research is expected to produce a web-based marketplace application that facilitates the transaction process of consignment providers and consumers at the Indonesian Helmet Shop.&#x0D; Furthermore, the design of this messaging service application prototype still needs to be developed in terms of user interface and user experience so that consumers can be more comfortable in transacting.
A Combination Method of Resampling and Random Forest for Imbalanced Data Classification

In the research of imbalanced data classification, the resampling effect of the existing resampling and random forest combination technology is greatly affected by the characteristic dimension of the training set, resulting in the unsatisfactory classification effect of the model. To solve this problem, a combination method of resampling and Random Forest for imbalanced data classification is proposed. The core idea is to resample the original data set multiple times by using the feature subset of each subtree in the random forest. By reducing the number of features used in a single resampling, the influence of feature dimension on resampling effect is reduced; Different feature subsets are used for resampling in different subtrees to improve the diversity of base classifiers. Based on SMOTE, B-SMOTE and ADASYN resampling techniques, three different optimization algorithms PSRF, PBSRF and PADRF are realized respectively. Taking the geometric mean and recall rate as evaluation indexes, the experimental results on 10 groups of KEEL data show that compared with the algorithm before optimization, the geometric mean values of the three algorithms are increased by 0.85%, 2.93% and 0.79% respectively, and the recall rates are increased by 1.44%, 3.26% and 1.07% respectively.
A Relevant, Hard and Diverse Triplet Sampling Method for Multi-Label Remote Sensing Image Retrieval

Deep metric learning based methods that characterize the semantic similarity of images on an embedding (i.e., metric) space have attracted great attention for content-based image retrieval (CBIR) in remote sensing (RS). One of the common strategies to learn a metric space is based on the selection of triplets of similar (positive) and dissimilar (negative) images for a given anchor image. The accurate selection of image triplets is a difficult task. This is more evident when each training image is annotated by multiple class labels (i.e., multi-labels). To address this problem, in this paper, we propose a novel deep learning based triplet sampling method to select a small set of the most representative and informative triplets for multi-label RS CBIR problems. The proposed method consists of two main steps. In the first step, the proposed method selects a small set of anchors (which are diverse to each other in the embedding space) from each training mini-batch by an iterative algorithm. In the second step, the sets of positive and negative images are selected for each anchor based on the relevancy, hardness and diversity of the images among each other by a novel strategy. Experimental results show the effectiveness of the proposed method on CBIR problems for multi-label RS images.
Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning

Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.
Extracting More Quantum Randomness With Non-Uniform Quantization

Random numbers generated via quantum process is indeterministic, which are of essential to the cryptographic communication and the large-scale computer modeling. However, in realistic scenarios, classical noises can inevitably contaminate the raw sequences of a quantum randomness number generator (QRNG), and then compromise the security of the QRNG. Min-entropy is a useful approach that can quantify the randomness independent of side-information. To enhance the extractable randomness of the raw sequences arising from the QRNG, we propose a new method which exploits non-uniform quantization methods instead of uniform sampling methods and effectively enhances the extractable randomness from the QRNG at a high quantum-to-classical-noise ratio (QCNR). Given a QCNR as 50 dB and a 16-bit analog-to-digital converter (ADC), the worst-case conditional min-entropy of the non-uniform quantization scheme is improved by nearly 11% compared with that of the uniform sampling scheme.