Reviewerly/exHarmony huggingface dataset:
Quantifying entanglement preservability of experimental processes

Preserving entanglement is a crucial dynamical process for entanglement-based quantum computation and quantum-information processes, such as one-way quantum computing and quantum key distribution. However, the problem of quantifying the ability of an experimental process to preserve two-qubit entanglement in experimentally feasible ways is not well understood. Accordingly, herein, we consider the use of two measures, namely composition and robustness, for quantitatively characterizing the ability of a process to preserve entanglement, referred to henceforth as entanglement preservability. A fidelity benchmark is additionally derived to identify the ability of a process to preserve entanglement. We show that the measures and introduced benchmark are experimentally feasible and require only local measurements on single qubits and preparations of separable states. Moreover, they are applicable to all physical processes that can be described using the general theory of quantum operations, e.g., qubit dynamics in photonic and superconducting systems. Our method extends the existing tools for analyzing channels, e.g., channel resource theory, to quantify entanglement preservability for non-trace-preserving quantum processes. The results are of significant interest for applications in quantum-information processing in which entanglement preservation is required.
Machine Reading of Historical Events

Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities.Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it.Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of taskspecific and general-purpose feature embeddings for the classification.Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms.
Design and Implementation of Database Management for Presidency of Diyala University

In our life, computer has entered in all fields and its impact appeared in solving many of problems. One of those problems is the ability to store and deal with a huge amount of data. Also, the consequent method of indexing, retrieving and preserving that data requires cost and effort we need for a long time. In this paper, the authors designed and implemented a new method of database for the Office of Security Permits at University of Diyala to resolve the routine process and change the traditional work into an advanced digital work. This database has designed based on a software application with interface programed in C# and using Microsoft Access. The results show that this database management system has reflected remarkable achievement in administrational work at Office of Security Permits because it makes everything easy to deal with such as storing, adding, deleting data and printing the final reports
Development of mobile application for Malay translated hadith search engine

This paper presents the development of mobile application for Malay Translated Hadith search engine. Limitations of current Hadith web application are the design is to optimize its usage on desktop computer but not on mobile devices, which requires simple and user friendly interface. Besides that, web application also needs internet connection to use. Due to increase usage of mobile application among mobile phone users, many existing web applications have moved to mobile based applications to cater for increasing numbers of mobile users. In this study, a mobile application for Android and iOS mobile application has been developed using Flutter framework, a hybrid mobile application framework. A Malay Translated hadith search engine mobile application can easily assist those who are seeking knowledge to learn more about certain topics in hadith, a second source of Islamic knowledge. This mobile application has search and directory features for users to browse the 2028 Sahih Bukhari hadith collection. Users can enter their query using search features to find selected hadith in Malay language. Queries will be processed for searching relevant hadith and display the results to the user. Evaluation using Recall and Precision shows that on the average Recall is 73% and Precision is 33%. Functionality testing is also conducted to test against the functional requirements or specifications. Results shows all requirements are successfully tested.
A Choreography-Driven Approach to APIs: The OpenDXL Case Study

We propose a model-driven approach based on formal data-driven choreographies to model message-passing applications. We apply our approach to the threat intelligence exchange (TIE) services provided by McAfee through the OpenDXL industrial platform. We advocate a chain of model transformations that (i) devises a visual presentation of communication protocols, (ii) formalises a global specification from the visual presentation that captures the data flow among services, (iii) enables the automatic derivation of specifications for the single components, and (iv) enables the analysis of software implementations.
Momentum-Based Policy Gradient Methods

In the paper, we propose a class of efficient momentum-based policy gradient methods for the model-free reinforcement learning, which use adaptive learning rates and do not require any large batches. Specifically, we propose a fast important-sampling momentum-based policy gradient (IS-MBPG) method based on a new momentum-based variance reduced technique and the importance sampling technique. We also propose a fast Hessian-aided momentum-based policy gradient (HA-MBPG) method based on the momentum-based variance reduced technique and the Hessian-aided technique. Moreover, we prove that both the IS-MBPG and HA-MBPG methods reach the best known sample complexity of $O(\epsilon^{-3})$ for finding an $\epsilon$-stationary point of the non-concave performance function, which only require one trajectory at each iteration. In particular, we present a non-adaptive version of IS-MBPG method, i.e., IS-MBPG*, which also reaches the best known sample complexity of $O(\epsilon^{-3})$ without any large batches. In the experiments, we apply four benchmark tasks to demonstrate the effectiveness of our algorithms.
TLPG-Tracker: Joint Learning of Target Localization and Proposal Generation for Visual Tracking

Target localization and proposal generation are two essential subtasks in generic visual tracking, and it is a challenge to address both the two efficiently. In this paper, we propose an efficient two-stage architecture which makes full use of the complementarity of two subtasks to achieve robust localization and high-quality proposals generation of the target jointly. Specifically, our model performs a novel deformable central correlation operation by an online learning model in both two stages to locate new target centers while generating target proposals in the vicinity of these centers. The proposals are refined in the refinement stage to further improve accuracy and robustness. Moreover, the model benefits from multi-level features aggregation in a neck module and a feature enhancement module. We conduct extensive ablation studies to demonstrate the effectiveness of our proposed methods. Our tracker runs at over 30 FPS and sets a new state-of-the-art on five tracking benchmarks, including LaSOT, VOT2018, TrackingNet, GOT10k, OTB2015.
User Generated Social Tags Versus Librarian Generated Subject Headings, A Comparative Study in the Domain of History

Social tagging allows users to assign any free-form keywords as tags to any digital resources through a decentralised way. Many information scientists find that there are similarities through their studies between usergenerated social tags and the librarian-generated subject headings for the libraries. The present study was conducted to identify the similarity and dissimilarity between user-generated social tags and librarian-generated subject terms of 1000 books in the domain of History. The study also conducted to identify whether social tags can replace controlled vocabularies. The study finds that only a small portion of terms overlaps with each other (3.54 % of social tags &amp; 56.07 % of SLSH terms) and Spearman’s rank correlation proves that there is a good association between overlapping terms. Jaccard similarity coefficient highlights that users and the librarian use different terminologies (as J = 0.13, 0.12 &amp; 0.11). Individual title wise comparison also defines that 90 per cent (88.4 %) of all book titles where users and the librarian use at least one common term. Users use the least subject &amp; non-subject terms but use some personal tags for personal benefit whereas the librarian use only subject &amp; non-subject terms. Matching with each book title clarifies that for describing resources users mostly use title based keywords (696) whereas the librarian use very little title based keywords (113). The study clearly defines that social tags can enhance the experience of library users. If it can be exploited properly it can complement to controlled vocabularies but can not replace the controlled vocabularies used for libraries a long time. Overall the study explicitly identifies the viability regarding the adoption of social tags into the library databases where the resources in the field of history will be accessed.
Design and implementation a network mobile application for plants shopping center using QR code

During the revolution of developing mobile phone applications, they can be used in different fields like business, health, transportation, communications and tourism, and other uses. This paper presents QR based information management system for plants shopping centers. This system includes two main sides: mobile and server. The proposed application is used as a substitute for the human guide that each visitor needs in the plants shopping center. The complete information can be provided on any seedling displayed in the shop depending on the QR code technology. Each branch of the same enterprise includes sub-server that is linked to the main server using a private computer network. The server side contains information of all plants and all branches for such enterprise in the form of text and image in different languages. The proposed application facilitates the movement of the customer inside the place, the ease in preparing bills electronically that helps the visitor to save the time of queue to preparing the bill and paying. The proposed system is tested over different case studies to prove the efficiency in terms of information and selling management.
Large Scale Network Embedding: A Separable Approach

Many successful methods have been proposed for learning low-dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience, especially on large-scale or dynamic networks, where these methods become almost impossible to implement. In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information. We compare our SMF framework with approximate SVD algorithms and demonstrate SMF can capture more information when factorizing a given matrix. We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes. By implementing separability, our algorithm reduces the redundant efforts to embed irrelevant nodes, yielding scalability to large networks. To further incorporate complex information into SepNE, we discuss several methods that can be used to leverage high-order proximities in large networks. We demonstrate the effectiveness of SepNE on several real-world networks with different scales and subjects. With comparable accuracy, our approach significantly outperforms state-of-the-art baselines in running times on large networks.
Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts

Concept graphs are created as universal taxonomies for text understanding in the open domain knowledge.The nodes in concept graphs include both entities and concepts.The edges are from entities to concepts, showing that an entity is an instance of a concept.In this paper, we propose the task of learning interpretable relationships from open domain facts to enrich and refine concept graphs.The Bayesian network structures are learned from open domain facts as the interpretable relationships between relations of facts and concepts of entities.We conduct extensive experiments on public English and Chinese datasets.Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both English and Chinese datasets.Concept Graph Facts • • • f 1 : (s 1 , r 1 , o 1 ) f n : (s n
Towards a Hybrid Verification Methodology for Communication Protocols (Short Paper)

We present our preliminary work towards a comprehensive solution for the hybrid (static + dynamic) verification of open distributed systems, using session types. We automate a solution for binary sessions where one endpoint is statically checked, and the other endpoint is dynamically checked by a monitor acting as an intermediary between typed and untyped components. We outline our theory, and illustrate a tool that automatically synthesises type-checked session monitors, based on the Scala language and its session programming library (lchannels).
Facilitating Physical Activity through On-Site Quantified-Self Data Sharing

With the development of sensing technology and the popularization of quantified-self devices, there are increasing types of health-related data that can be sensed, visualized and presented to the user. However, most existing quantified-self applications are designed to support self-management and self-reflection; only a few studies so far have investigated the social aspect of quantified-self data. In this study, we investigated the social role of quantified-self data by introducing the design and evaluation of SocialBike—a digitally augmented bicycle that aims to increase the user’s intrinsic motivation in physical activity through on-site quantified-self data sharing. We conducted a controlled experiment on a cycling simulation system. Two forms of SocialBike’s on-bike display were evaluated with 36 participants. We used the Intrinsic Motivation Inventory to collect quantitative data about users’ intrinsic motivation in physical activity; the cycling simulation system recorded quantitative data about user behavior. Qualitative data was collected through semi-structured interviews. We conducted paired sample t-test to analyze both types of quantitative data; qualitative data were analyzed by the method of thematic analysis. The results show that SocialBike’s front display significantly increased users’ intrinsic motivation in physical activity. A total of nine themes were identified from the qualitative analysis, providing supplementary explanations for the quantitative results and additional insights into the overall design.
Gated POS-Level Language Model for Authorship Verification

Authorship verification is an important problem that has many applications. The state-of-the-art deep authorship verification methods typically leverage character-level language models to encode author-specific writing styles. However, they often fail to capture syntactic level patterns, leading to sub-optimal accuracy in cross-topic scenarios. Also, due to imperfect cross-author parameter sharing, it's difficult for them to distinguish author-specific writing style from common patterns, leading to data-inefficient learning. This paper introduces a novel POS-level (Part of Speech) gated RNN based language model to effectively learn the author-specific syntactic styles. The author-agnostic syntactic information obtained from the POS tagger pre-trained on large external datasets greatly reduces the number of effective parameters of our model, enabling the model to learn accurate author-specific syntactic styles with limited training data. We also utilize a gated architecture to learn the common syntactic writing styles with a small set of shared parameters and let the author-specific parameters focus on each author's special syntactic styles. Extensive experimental results show that our method achieves significantly better accuracy than state-of-the-art competing methods, especially in cross-topic scenarios (over 5\% in terms of AUC-ROC).
A Dynamic Thermal-Allocation Solution to the Complex Economic Benefit for a Data Center

Data centers, which provide computing services and gain profits, are indispensable to every city in the information era. They offer computation and storage while consuming energy and generate thermal discharges. To maximize the economic benefit, the existing research studies on the data center workload management mostly leverage the dynamical power model, i.e., the power-aware workload allocation. Nevertheless, we argue that for the complex relationship between the economic benefit and so many attributes, such as computation, energy consumption, thermal distribution, cooling, and equipment life, the thermal distribution dominates the others. Thus, thermal-aware workload allocation is more efficient. From the perspective of economic benefits, we propose a mathematical model for thermal distribution of a data center and study which workload distribution could determinately change the thermal distribution in the dynamic data center runtime, so as to reduce the cost and improve the economic benefits under the guarantee of service provisioning. By solving the thermal environment evaluation indexes, RHI (Return Heat Index) and RTI (Return Temperature Index), as well as heat dissipation models, we define quantitative models for the economic analysis such as energy consumption model for the busy servers and cooling, energy price model, and the profit model of data centers. Numerical simulation results validate our propositions and show that the average temperature of the data center reaches the best values, and the local hot spots are avoided effectively in various situations. As a conclusion, our studies contribute to the thermal management of the dynamic data center runtime for better economic benefits.
Teacher Educators in Neoliberal Times: A Phenomenological Self-Study

In Sweden, and most Western countries, pervasive neoliberal policies have dramatically transformed the entire education sector in a matter of decades. As teacher educators, we have experienced how neoliberal currents have pushed Swedish teacher education towards a teacher training paradigm which may risk undermining the foundations for professional judgement. Moreover, the Bologna Process and the introduction of New Public Management have had significant consequences for what it means to be a teacher educator. In this study, we present our everyday experiences of being teacher educators, immersed in a teacher education culture in Sweden which has evolved under the pressures of neoliberalism. To address these complex lived experiences we engaged in a phenomenological first-person account. Three main themes emerged from an analysis of lived experience descriptions: (a) Alignment Slaves; (b) Audit Puppets; (c) Techno Phobes. These themes reflect different lived dimensions of being teacher educators confronted with neoliberal agendas. The paper concludes with a call for resistance to bring about change within teacher education.
The “Russian Hirsch”: Predictors of Citation Usage of Scholarly Works in the RSCI

The article investigates the predictors of citation rate in the Russian Science Citation Index (RSCI) for Russian publications in psychology. Four groups of indicators are analyzed: formal attributes of a publication (12 indicators), parameters of publication visibility on eLibrary (3 indicators) and PsyJournals (2 indicators) that define accessibility of publication to potential readers, and author-based citation parameters (3). Special attention is paid to citation parameters as qualitative characteristics of the author’s method of elaborating the scientific text and construing dialogue (in the form of citations) with other researchers. Relationship between the indicators analyzed and the RSCI citation rate is proven statistically using the multivariate statistical methods of factor analysis and cluster analysis. For each of the four groups, the strongest predictors of citation rate are identified by multiple regression analysis, which are then compared by their predictive power. It is shown that visibility (accessibility) indicators are the best predictors of citation rate, followed by formal publication attributes and, finally, citation type parameters as having the lowest predictive power. The method of logistic regression allows to identify the ultimate predictors of citation rate and measure their accuracy in predicting whether a publication is low- or highly cited, which is 77.3% and 72.9% for the indicators of visibility on PsyJournals and eLibrary (respectively), 69.9% for formal attributes, and 60.9% for citation parameters. A publication that has few in-text citations is very likely to have a low RSCI citation rate, yet a high number of in-text citations does not guarantee a high citation impact. Recommendations are provided for authors to increase their citation rates. The sample is represented by 662 publications in six Russian psychology journals, each indexed in the RSCI, Web of Science, and Scopus.
Privacy aware crowd-counting using thermal cameras

Visual analytics has been in the limelight as a powerful tool to support large scale management of places, people, and activities. Harnessing the power of machine learning is possible to quickly identify critical issues across thousands of cameras. Several stakeholders have voiced concerns about privacy. Visual analytics techniques can be used in facial recognition thus enabling fine-grain user tracking. This paper addresses such privacy concerns for some specific scenarios. It explores the feasibility of visual analytics in using low-cost/low-resolution thermal cameras thus delivering context-awareness information yet protecting user’s privacy. This paper proposes a model able to classify and count humans, in indoor hallway settings, using low-resolution thermal pictures. The model is designed to work with YOLOv3 and leverages the power of deep-learning. Results show that it is possible to classify and count humans with over 90% accuracy based on the images from a low-cost 80x60 pixel thermal camera. The results were evaluated against the ground truth checked by a human agent and recorded through a regular camera. The study exposed possibilities and limits offered by low-cost thermal cameras and identifies the potential application scenarios. The dataset including both real and thermal images used for the training and the testing will be made available to the scientific community.