Reviewerly/exHarmony huggingface dataset:
Subject indexing in humanities: a comparison between a local university repository and an international bibliographic service

Purpose As the humanities develop in the realm of increasingly more pronounced digital scholarship, it is important to provide quality subject access to a vast range of heterogeneous information objects in digital services. The study aims to paint a representative picture of the current state of affairs of the use of subject index terms in humanities journal articles with particular reference to the well-established subject access needs of humanities researchers, with the purpose of identifying which improvements are needed in this context. Design/methodology/approach The comparison of subject metadata on a sample of 649 peer-reviewed journal articles from across the humanities is conducted in a university repository, against Scopus, the former reflecting local and national policies and the latter being the most comprehensive international abstract and citation database of research output. Findings The study shows that established bibliographic objectives to ensure subject access for humanities journal articles are not supported in either the world's largest commercial abstract and citation database Scopus or the local repository of a public university in Sweden. The indexing policies in the two services do not seem to address the needs of humanities scholars for highly granular subject index terms with appropriate facets; no controlled vocabularies for any humanities discipline are used whatsoever. Originality/value In all, not much has changed since 1990s when indexing for the humanities was shown to lag behind the sciences. The community of researchers and information professionals, today working together on digital humanities projects, as well as interdisciplinary research teams, should demand that their subject access needs be fulfilled, especially in commercial services like Scopus and discovery services.
Bidirectional quantum-controlled teleportation using six-qubit cluster state without remote joint operation

Utilizing a six-qubit cluster state as quantum channel, a bidirectional controlled quantum teleportation (BCQT) scheme was put forward by Yan in [Int. J. Theor. Phys. 52, 3870 (2013)]. However, in order to accomplish the task, two agents must cooperate to perform a quantum controlled phase gate operation on their two qubits. In view of the present technique, such kind of remote joint operation is difficult to be realized. In this paper, based on the same cluster state, we propose a different BCQT scheme to avoid the remote joint operation. Besides, an unnecessary waste of classical messages in Yan’s scheme is point out.
Privacy and data protection in mobile cloud computing: A systematic mapping study

As a result of a shift in the world of technology, the combination of ubiquitous mobile networks and cloud computing produced the mobile cloud computing (MCC) domain. As a consequence of a major concern of cloud users, privacy and data protection are getting substantial attention in the field. Currently, a considerable number of papers have been published on MCC with a growing interest in privacy and data protection. Along with this advance in MCC, however, no specific investigation highlights the results of the existing studies in privacy and data protection. In addition, there are no particular exploration highlights trends and open issues in the domain. Accordingly, the objective of this paper is to highlight the results of existing primary studies published in privacy and data protection in MCC to identify current trends and open issues. In this investigation, a systematic mapping study was conducted with a set of six research questions. A total of 1711 studies published from 2009 to 2019 were obtained. Following a filtering process, a collection of 74 primary studies were selected. As a result, the present data privacy threats, attacks, and solutions were identified. Also, the ongoing trends of data privacy exercise were observed. Moreover, the most utilized measures, research type, and contribution type facets were emphasized. Additionally, the current open research issues in privacy and data protection in MCC were highlighted. Furthermore, the results demonstrate the current state-of-the-art of privacy and data protection in MCC, and the conclusion will help to identify research trends and open issues in MCC for researchers and offer useful information in MCC for practitioners.
Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding

Spatio-temporal video grounding aims to retrieve the spatio-temporal tube of a queried object according to the given sentence. Currently, most existing grounding methods are restricted to well-aligned segment-sentence pairs. In this paper, we explore spatio-temporal video grounding on unaligned data and multi-form sentences. This challenging task requires to capture critical object relations to identify the queried target. However, existing approaches cannot distinguish notable objects and remain in ineffective relation modeling between unnecessary objects. Thus, we propose a novel object-aware multi-branch relation network for object-aware relation discovery. Concretely, we first devise multiple branches to develop object-aware region modeling, where each branch focuses on a crucial object mentioned in the sentence. We then propose multi-branch relation reasoning to capture critical object relationships between the main branch and auxiliary branches. Moreover, we apply a diversity loss to make each branch only pay attention to its corresponding object and boost multi-branch learning. The extensive experiments show the effectiveness of our proposed method.
Spatial case revision in case-based reasoning for risk assessment of geological disasters

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Uncovering hidden semantics of set information in knowledge bases

Knowledge Bases (KBs) contain a wealth of structured information about entities and predicates. This paper focuses on set-valued predicates, i.e., the relationship between an entity and a set of entities. In KBs, this information is often represented in two formats: (i) via counting predicates such as numberOfChildren and staffSize, that store aggregated integers, and (ii) via enumerating predicates such as parentOf and worksFor, that store individual set memberships. Both formats are typically complementary: unlike enumerating predicates, counting predicates do not give away individuals, but are more likely informative towards the true set size, thus this coexistence could enable interesting applications in question answering and KB curation. In this paper we aim at uncovering this hidden knowledge. We proceed in two steps. (i) We identify set-valued predicates from a given KB predicates via statistical and embedding-based features. (ii) We link counting predicates and enumerating predicates by a combination of co-occurrence, correlation and textual relatedness metrics. We analyse the prevalence of count information in four prominent knowledge bases, and show that our linking method achieves up to 0.55 F1 score in set predicate identification versus 0.40 F1 score of a random selection, and normalized discounted gains of up to 0.84 at position 1 and 0.75 at position 3 in relevant predicate alignments. Our predicate alignments are showcased in a demonstration system available at https://counqer.mpi-inf.mpg.de/spo.
Predicting the Focus of Negation: Model and Error Analysis

The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances.In this paper, we experiment with neural networks to predict the focus of negation.Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network.Experimental results show that doing so obtains the best results to date.Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.
Geometry-aware domain adaptation for unsupervised alignment of word embeddings

We propose a novel manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and the target languages.Our approach formulates the alignment learning problem as a domain adaptation problem over the manifold of doubly stochastic matrices.This viewpoint arises from the aim to align the second order information of the two language spaces.The rich geometry of the doubly stochastic manifold allows to employ efficient Riemannian conjugate gradient algorithm for the proposed formulation.Empirically, the proposed approach outperforms state-of-the-art optimal transport based approach on the bilingual lexicon induction task across several language pairs.The performance improvement is more significant for distant language pairs.
Hardware-Centric AutoML for Mixed-Precision Quantization

Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support flexible bitwidth (1–8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off accuracy, latency, energy, and model size, which is both time-consuming and usually sub-optimal. There are plenty of specialized hardware accelerators for neural networks, but little research has been done to design specialized neural networks optimized for a particular hardware accelerator. The latter is demanding given the much longer design cycle of silicon than neural nets. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which automatically determine the quantization policy, and we take the hardware accelerator’s feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate the direct feedback signals to the RL agent. Compared with other conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4–1.95 $$\times $$ and the energy consumption by 1.9 $$\times $$ with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design.
How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds

Text-based games are long puzzles or quests, characterized by a sequence of sparse and potentially deceptive rewards. They provide an ideal platform to develop agents that perceive and act upon the world using a combinatorially sized natural language state-action space. Standard Reinforcement Learning agents are poorly equipped to effectively explore such spaces and often struggle to overcome bottlenecks---states that agents are unable to pass through simply because they do not see the right action sequence enough times to be sufficiently reinforced. We introduce Q*BERT, an agent that learns to build a knowledge graph of the world by answering questions, which leads to greater sample efficiency. To overcome bottlenecks, we further introduce MC!Q*BERT an agent that uses an knowledge-graph-based intrinsic motivation to detect bottlenecks and a novel exploration strategy to efficiently learn a chain of policy modules to overcome them. We present an ablation study and results demonstrating how our method outperforms the current state-of-the-art on nine text games, including the popular game, Zork, where, for the first time, a learning agent gets past the bottleneck where the player is eaten by a Grue.
Clinical Concept Linking with Contextualized Neural Representations

In traditional approaches to entity linking, linking decisions are based on three sources of information -the similarity of the mention string to an entity's name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB).In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity.We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology.We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al., 2018), which create a token representation that integrates the surrounding context of the mention and concept name.We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al., 2013).Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.
High-fidelity simultaneous detection of a trapped-ion qubit register

Qubit state detection is an important part of a quantum computation. As the number of qubits in a quantum register increases, it is necessary to maintain high-fidelity detection to accurately measure the multiqubit state. Here we present experimental demonstration of high-fidelity detection of a multiqubit trapped-ion register with an average single-qubit detection error of 4.2(1.5) ppm and a four-qubit state detection error of 17(2) ppm, limited by the decay lifetime of the qubit, using a single-photon-sensitive camera with fast data collection, excellent temporal and spatial resolution, and low instrumental crosstalk.
Malicious Domain Detection Based on K-means and SMOTE

The Domain Name System (DNS) as the foundation of Internet, has been widely used by cybercriminals. A lot of malicious domain detection methods have received significant success in the past decades. However, existing detection methods usually use classification-based and association-based representations, which are not capable of dealing with the imbalanced problem between malicious and benign domains. To solve the problem, we propose a novel domain detection system named KSDom. KSDom designs a data collector to collect a large number of DNS traffic data and rich external DNS-related data, then employs K-means and SMOTE method to handle the imbalanced data. Finally, KSDom uses Categorical Boosting (CatBoost) algorithm to identify malicious domains. Comprehensive experimental results clearly show the effectiveness of our KSDom system and prove its good robustness in imbalanced datasets with different ratios. KSDom still has high accuracy even in extremely imbalanced DNS traffic.
Handwritten Urdu character recognition via images using different machine learning and deep learning techniques

Objectives: This research presents a model for Urdu Handwritten Character Recognition via images using various Machine Learning and Deep Learning Techniques.The main objective of this research is to provide comparative study on Urdu Handwritten Characters from images dataset.Methods/Statistical analysis: In this research paper, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithm, Multi-Layer Perceptron (MLP), Concurrent Neural Network (CNN), Recurrent Neural Network (RNN) and Random Forest Algorithm (RF) have been implemented in order to evaluate most suitable technique for Urdu Handwritten Characters Recognition via images.Findings: Ample amount of research work has been carried out on English Language but it is clearly shown through the conducted literature review that very lesser amount of work has been done on Urdu Handwritten Characters Recognition using images.Furthermore, It has been analyzed from this research that CNN models are most efficient compared to RF, SVM and MLP as to produce reliable results in terms of optimal accuracy.Therefore, using the CNN model is a viable choice to recognize Urdu handwritten characters from the images.And proposed study provides significant contribution in automatic learning of Urdu handwritten Characters.
Development of Smartphone App as Media to Learn Impulse-Momentum Topics for High School Students

One popular advanced technology nowadays is smartphone.Smartphones allow various functions beyond the traditional function of mobile phones as communication devices.The technology in smartphones has potential applications in many areas including education.In this study, we report our research which aims to: (1) produce a smartphone app as media for learning Impulse-Momentum; (2) determine the appropriateness and quality of the app; and (3) describe the effectiveness of the smartphone app to help students in learning the concept of Impulse-Momentum.We use developmental research method within 4D (define, design, develop, disseminate) model.The smartphone app is reviewed by physics education expert and teacher.The field testing involves a group which consists of 26 high school students.The students are given pre-and post-tests before and after they use the developed learning media, respectively.It aims to investigate the impact of the media to students' conceptual understanding.The normalized gain (N-gain) score of their pre-and post-tests is 0.74 which can be categorized as high improvement.
Sudden death of entanglement induced by a minimal thermal environment

We study the dynamics of two interacting two-level systems (qubits) having one of them isolated and the other coupled to a single mode electromagnetic field in a thermal state. The field plays the role of a small environment, in contrast to the usual approach of modelling an environment via a thermal reservoir with many degrees of freedom. We find the analytical solution of the proposed model, which allows us to investigate the consequences of the coupling to the small environment on characteristic quantum features of the two-qubit system. We study the time evolution of quantum entanglement and coherence, verifying the dependence on the relevant coupling constants as well as the influence of the effective temperature of the environment. Interestingly, we find that both sudden death and sudden birth of entanglement may occur in such a simple system. We also discuss a different partition, in which the isolated qubit is considered to be coupled to a composite environment, constituted by the other qubit plus the field mode.
Quantum Secure Multi-party Private Set Intersection Cardinality

Abstract As we know that data sharing, a critical element in social networks, has the benefits of exploring important information, while also has the disadvantage of information leakage. Therefore, without the reliable third party arbitration agency, it is impossible to share information privately by distrustful multi-party. In this paper, we proposed a protocol called Quantum Secure Multi-party Private Set Intersection Cardinality (QSMS-IC), which has the capability of resisting quantum attacks. QSMS-IC, the extension of two-parity private set intersection cardinality which was proposed in Information Sciences(2016,147-158), utilizes quantum transformation, quantum measurements and quantum parallelism to solve multi-party private set intersection cardinality problems. Compared with two-party PSI-CA protocols, our proposed protocol can solve the data sharing among multi-party without the reliable third party arbitration agency. It also can be used in numerous applications and more suitable to the actual cases. For instance, large-scale social networks and privacy-preserving data ming.
Counterfactual Explanation of Machine Learning Survival Models

A method for counterfactual explanation of machine learning survival models is proposed.One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine learning survival model in the form of survival functions.A condition that establishes the difference between survival functions of the original example and the counterfactual is introduced.This condition is based on using a distance between mean times to event.It is shown that the counterfactual explanation problem can be reduced to a standard convex optimization problem with linear constraints when the explained black-box model is the Cox model.For other black-box models, it is proposed to apply the wellknown Particle Swarm Optimization algorithm.Numerical experiments with real and synthetic data demonstrate the proposed method.
Relationships Between Body Postures and Collaborative Learning States in an Augmented Reality Study

In this paper we explore how Kinect body posture sensors can be used to detect group collaboration and learning, in the context of dyad pairs using augmented reality system. We leverage data collected during a study (N = 60 dyads) where participant pairs learned about electromagnetism. Using unsupervised machine learning methods on Kinect body posture sensor data, we contribute a set of dyad states associated with collaboration quality, attitudes toward physics and learning gains.