Reviewerly/exHarmony huggingface dataset:
An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, a variety of location privacy-preserving schemes have been recently proposed. Among these schemes, the dummy-based location privacy-preserving (DLP) scheme is a widely used approach to achieve location privacy for mobile users. However, the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices. Moreover, the DLP scheme is inadequate to resist against an adversary with side information. Thus, how to effectively select a dummy location is still a challenge. In this paper, we propose a novel lightweight dummy-based location privacy-preserving scheme, named the enhanced dummy-based location privacy-preserving(Enhanced-DLP) to address this challenge by considering both computational costs and side information. Specifically, the Enhanced-DLP adopts an improved greedy scheme to efficiently select dummy locations to form a k-anonymous set. A thorough security analysis demonstrated that our proposed Enhanced-DLP can protect user privacy against attacks. We performed a series of experiments to verify the effectiveness of our Enhanced-DLP. Compared with the existing scheme, the Enhanced-DLP can obtain lower computational costs for the selection of a dummy location and it can resist side information attacks. The experimental results illustrate that the Enhanced-DLP scheme can effectively be applied to protect the user’s location privacy in IoT applications and services.
Shared-Memory Exact Minimum Cuts

The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weighted sum of the cut edges. In this paper, we engineer the fastest known exact algorithm for the problem. State-of-the-art algorithms like the algorithm of Padberg and Rinaldi or the algorithm of Nagamochi, Ono and Ibaraki identify edges that can be contracted to reduce the graph size such that at least one minimum cut is maintained in the contracted graph. Our algorithm achieves improvements in running time over these algorithms by a multitude of techniques. First, we use a recently developed fast and parallel inexact minimum cut algorithm to obtain a better bound for the problem. Afterwards, we use reductions that depend on this bound to reduce the size of the graph much faster than previously possible. We use improved data structures to further lower the running time of our algorithm. Additionally, we parallelize the contraction routines of Nagamochi et al. . Overall, we arrive at a system that significantly outperforms the fastest state-of-the-art solvers for the exact minimum cut problem.
Towards automating smart homes

The existing smart-home ecosystem has the capability to perceiving the ambient environment by using cutting-edge sensing technologies but is limited to reacting autonomously and timely. Successfully predicting the subsequent human activity can effectively infer human intention and instruct the smart homes to react in a timely, customized and accurate way. However, predicting the next activity and its precise occurrence period are challenging due to the complexity of modelling human behaviour. In this paper, the Long Short-Term Memory (LSTM) network equipped with temporal information is investigated to understand whether integrated temporal information on the model has better prediction performance or not. Our results highlight that, accurately integrating the temporal information into the models bring better prediction accuracy. In terms of modelling and further predicting human activity, comprehending the contextual-temporal dynamics is highly significant.
Self-Improving Autonomic Systems for Antifragile Cyber Defence: Challenges and Opportunities

Antifragile systems enhance their capabilities and become stronger when exposed to adverse conditions, stresses or attacks, making antifragility a desirable property for cyber defence systems that operate in contested military environments. Self-improvement in autonomic systems refers to the improvement of their self-* capabilities, so that they are able to (a) better handle previously known (anticipated) situations, and (b) deal with previously unknown (unanticipated) situations. In this position paper, we present a vision of using self-improvement through learning to achieve antifragility in autonomic cyber defence systems. We first enumerate some of the major challenges associated with realizing distributed self-improvement. We then propose a reference model for middleware frameworks for self-improving autonomic systems and a set of desirable features of such frameworks.
Who is Mona L.? Identifying Mentions of Artworks in Historical Archives

Named entity recognition (NER) plays an important role in many information retrieval tasks, including automatic knowledge graph construction. Most NER systems are typically limited to a few common named entity types, such as person, location, and organization. However, for cultural heritage resources, such as art historical archives, the recognition of titles of artworks as named entities is of high importance. In this work, we focus on identifying mentions of artworks, e.g. paintings and sculptures, from historical archives. Current state of the art NER tools are unable to adequately identify artwork titles due to the particular difficulties presented by this domain. The scarcity of training data for NER for cultural heritage poses further hindrances. To mitigate this, we propose a semi-supervised approach to create high-quality training data by leveraging existing cultural heritage resources. Our experimental evaluation shows significant improvement in NER performance for artwork titles as compared to baseline approach.
Puncturable Proxy Re-Encryption Supporting to Group Messaging Service

This work envisions a new encryption primitive for many-to-many paradigms such as group messaging systems. Previously, puncturable encryption (PE) was introduced to provide forward security for asynchronous messaging services. However, existing PE schemes were proposed only for one-to-one communication, and causes a significant overhead for a group messaging system. In fact, the group communication over PE can only be achieved by encrypting a message multiple times for each receiver by the sender's device, which is usually suitable to restricted resources such as mobile phones or sensor devices. Our new suggested scheme enables to re-encrypt ciphertexts of puncturable encryption by a message server (i.e., a proxy) so that computationally heavy operations are delegated to the server who has more powerful processors and a constant power source. We then proposed a new Puncturable Proxy Re-Encryption (PPRE) scheme. The scheme is inspired by unidirectional proxy re-encryption (UPRE), which achieves forward secrecy through fine-grained revocation of decryption capability by integrating the PE scheme. This paper first presents a forward secure PPRE in the group messaging service. Our scheme is IND-CCA secure under 3-weak Decision Bilinear Diffie-Hellman Inversion assumption.
Visual-based Tracking and Control Algorithm Design for Quadcopter UAV

The problems of tracking moving target of quad-rotor UAV involve target tracking in image sequence and flight control of UAV. Because Camshift algorithm is easily disturbed by background with similar color and has poor robustness to occlusion interference when tracking objects in image sequence. A new tracking algorithm based on Kalman filter and Camshift algorithm with multi-feature fusion is proposed. On this basis, the horizontal displacement between the target and the UAV is calculated as the control input, and a position-attitude outer-inner loop controller is designed to ensure the target near the center of view of the camera in order to track the target efficiently. Experimental and simulation results verify the performance of the algorithm.
Using R libraries to facilitate sensitivity analysis and to calibrate system dynamics models

System Dynamics ReviewVolume 35, Issue 3 p. 255-282 Notes and Insights Using R libraries to facilitate sensitivity analysis and to calibrate system dynamics models Jim Duggan, Corresponding Author Jim Duggan james.duggan@nuigalway.ie orcid.org/0000-0002-7507-8617 Correspondence to: Jim Duggan, School of Computer Science, National University of Ireland Galway, Galway, Ireland H91 TK33. E-mail: james.duggan@nuigalway.ieSearch for more papers by this author Jim Duggan, Corresponding Author Jim Duggan james.duggan@nuigalway.ie orcid.org/0000-0002-7507-8617 Correspondence to: Jim Duggan, School of Computer Science, National University of Ireland Galway, Galway, Ireland H91 TK33. E-mail: james.duggan@nuigalway.ieSearch for more papers by this author First published: 11 September 2019 https://doi.org/10.1002/sdr.1638Citations: 5 Accepted by Yaman Barlas Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume35, Issue3July/September 2019Pages 255-282 RelatedInformation
<i>Tabula</i>Nearly<i>Rasa:</i>Probing the Linguistic Knowledge of Character-level Neural Language Models Trained on Unsegmented Text

Recurrent neural networks (RNNs) have reached striking performance in many natural language processing tasks. This has renewed interest in whether these generic sequence processing devices are inducing genuine linguistic knowledge. Nearly all current analytical studies, however, initialize the RNNs with a vocabulary of known words, and feed them tokenized input during training. We present a multi-lingual study of the linguistic knowledge encoded in RNNs trained as character-level language models, on input data with word boundaries removed. These networks face a tougher and more cognitively realistic task, having to discover any useful linguistic unit from scratch based on input statistics. The results show that our “near tabula rasa” RNNs are mostly able to solve morphological, syntactic and semantic tasks that intuitively presuppose word-level knowledge, and indeed they learned, to some extent, to track word boundaries. Our study opens the door to speculations about the necessity of an explicit, rigid word lexicon in language learning and usage.
An Improved RRT Based 3-D Path Planning Algorithm for UAV

The ability to avoid collision with dynamic threats is critical to the safe operation of unmanned aerial vehicle (UAV). This paper presents an improved Rapidly-exploring Random Tree (RRT) based 3-D path planning algorithm for UAV to find a safe and feasible path, which the dynamic threats in a complex environment are also considered. The Artificial Potential Field (APF) algorithm is combined with the proposed algorithm to accelerate the convergence speed and optimize the found path with fewer search nodes to a shorter distance. A curve smoothing method is used for fitting the nodes to smooth the found path. Effectiveness of our proposed improved-RRT algorithm is demonstrated by numerical simulations and promising results are obtained.
Perceptual Vibration Hashing by Sub-Band Coding: An Edge Computing Method for Condition Monitoring

High data throughput during real-time vibration monitoring can easily lead to network congestion, insufficient data storage space, heavy computing burden, and high communication costs. As a new computing paradigm, edge computing is deemed to be a good solution to these problems. In this paper, perceptual hashing is proposed as an edge computing form, aiming not only to reduce the data dimensionality but also to extract and represent the machine condition information. A sub-band coding method based on wavelet packet transform, two-dimensional discrete cosine transform, and symbolic aggregate approximation is developed for perceptual vibration hashing. When the sub-band coding method is implemented on a monitoring terminal, the acquired kilobyte-long vibration signal can be transformed into a machine condition hash occupying only a few bytes. Therefore, the efficiency of condition monitoring can benefit from the compactness of the machine condition hash, while comparable diagnostic and prognostic results can still be achieved. The effectiveness of the developed method is verified with two benchmark bearing datasets. Considerations on practical condition monitoring applications are also presented.
A Comparative Study of Machine Learning Techniques for Emotion Recognition

Humans share emotions which they exhibit through facial expressions. Automatic human emotion recognition algorithm in images and videos aims at detection, extraction, and evaluation of these facial expressions. This paper provides a comparison between various multi-class prediction algorithms employed on the Cohn-Kanade dataset (Lucey in The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression, pp. 94–101, 2010 [1]). The different machine learning algorithms can be used to provide emotion recognition task. We have compared the performance of K-nearest neighbors, Support Vector Machine, and neural network.
Echo State Property of Neuronal Cell Cultures

Physical reservoir computing (PRC) utilizes the nonlinear dynamics of physical systems, which is called a reservoir, as a computational resource. The prerequisite for physical dynamics to be a successful reservoir is to have the echo state property (ESP), asymptotic properties of transient trajectory to driving signals, with some memory held in the system. In this study, the prerequisites in dissociate cultures of cortical neuronal cells are estimated. With a state-of-the-art measuring system of high-dense CMOS array, our experiments demonstrated that each neuron exhibited reproducible spike trains in response to identical driving stimulus. Additionally, the memory function was estimated, which found that input information in the dynamics of neuronal activities in the culture up to at least 20 ms was retrieved. These results supported the notion that the cultures had ESP and could thereby serve as PRC.
SAT Encodings of the At-Most-k Constraint

At universities, some fields of study offer multiple branches to graduate in. These branches are defined by mandatory and optional courses. Configuring a branch manually can be a difficult task, especially if some courses have already been attended. Hence, a tool providing guidance on choosing courses is desired. Feature models enable modelling such behaviour, as they are designed to define valid configurations from a set of features. Unfortunately, the branches contain constraints instructing to choose at least k out of n courses in essence. Encoding some of these constraints naïvely in propositional calculus is practically infeasible. We develop a new encoding by combining existing approaches. Furthermore, we report on our experience of encoding the constraints of the computer science master at TU Braunschweig and discuss the impact for research on configurability.
Attribute-based evaluation for recommender systems

Research in Recommender Systems evaluation remains critical to study the efficiency of developed algorithms. Even if different aspects have been addressed and some of its shortcomings - such as biases, robustness, or cold start - have been analyzed and solutions or guidelines have been proposed, there are still some gaps that need to be further investigated. At the same time, the increasing amount of data collected by most recommender systems allows to gather valuable information from users and items which is being neglected by classical offline evaluation metrics. In this work, we integrate such information into the evaluation process in two complementary ways: on the one hand, we aggregate any evaluation metric according to the groups defined by the user attributes, and, on the other hand, we exploit item attributes to consider some recommended items as surrogates of those interacted by the user, with a proper penalization. Our results evidence that this novel evaluation methodology allows to capture different nuances of the algorithms performance, inherent biases in the data, and even fairness of the recommendations.
Governing the entrepreneurial discovery of blockchain applications

Purpose The purpose of this paper is to examine the institutional context of the entrepreneurial discovery of blockchain applications. Design/methodology/approach This paper draws on institutional and entrepreneurial theory to introduce the economic problem entrepreneurship in the early stages of new technologies, examines the diversity of self-governed hybrid solutions to coordinating entrepreneurial information and draws policy implications. Findings To perceive a valuable and actionable market opportunity, entrepreneurs must coordinate distributed non-price information under uncertainty with others. One potential class of transaction cost economising solution to this problem is private self-governance of information coordination within hybrids. This paper explores a diverse range of entrepreneurial hybrids coalescing around blockchain technology, with implications for innovation policy. Originality/value This paper points to the problem of how the defining of the innovation problem as either choice-theoretic or contract-theoretic changes the remit of innovation policy. Innovation policy and blockchain policy should extend beyond correcting sub-optimal investments or removing barriers to action, to incorporate how polices impact entrepreneurial choices over governance structures to coordinate information.
A geometric model of surface motion measurement by objective speckle imaging

Abstract When a specimen with a diffuse surface is illuminated using coherent laser light, an objective speckle pattern is formed within the adjacent space by the scattered light rays. The proposed geometrical model describes how the objective speckle pattern moves as a conceptual rigid-body within the volume of space adjacent to the illuminated area in response to motions of that area. For small motions, the distance between the illuminated area and the camera sensor is approximately constant. Thus, the locus of all possible parts of the adjacent space that may be sampled by the camera forms the surface of a hemisphere. The speckles within that hemisphere move as a rigid-body, so that a measurement of the speckle motion at one part is representative of the motion of the whole hemisphere. This geometrical approach can be interpreted intuitively, so facilitating understanding of how a specific experimental arrangement and type of surface motion affect observed speckle motion. Despite the relative simplicity of the proposed Speckle Hemisphere Model, the speckle motion sensitivity characteristics that it predicts are identical with the findings from the prior more abstract mathematical approaches. Experimental measurements are made to illustrate example practical applications. In addition, guidance is provided on how to tailor measurement sensitivities for desired types of object motion.
Cloud Computing: Vulnerability and Threat Indications

An innovative approach of providing and delivering resources is the new emerging technology "cloud computing." The time demands a reduction in expenditure as an end result of financial restrictions, where cloud computing has established productive position and is seeing immense large-scale investment. Still, despite the significance, users and clients are nervous at the prospects and security issues of cloud technology. Threat and vulnerability factors are majorly, one of the scrutinizing issues in cloud, if it is not properly secured due to which, a direct control loss over the system is creating nevertheless accountable threat. This paper explains, based on present scenarios, how the vulnerabilities analysis will evaluate current emerging threats of cloud technology. The paper focuses to provide indication of the recent threats and vulnerabilities in cloud. The study will help the providers and users to make knowledgeable decisions about threat mitigation and threat countermeasures within a cloud strategy.
Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm

A neural network's topology greatly influences its generalization ability. Many approaches to topology optimization employ heuristics, for example genetic algorithms, oftentimes consuming immense computational resources. In this contribution, we present a genetic algorithm for network topology optimization which can be deployed effectively in low-resource settings. To this end, we utilize the TensorFlow framework for network training and operate with several techniques reducing the computational load. The genetic algorithm is subsequently applied to the MNIST image classification task in two different scenarios.
Lhasa-Tibetan Speech Synthesis Using End-to-End Model

With the development of deep learning technology, speech synthesis based on deep neural networks has gradually become the mainstream method in the field of speech synthesis. In this paper, we explored the Tacotron2 model for Lhasa-Tibetan dialect speech synthesis by constructing a feature prediction network with a seq2seq structure which maps the character vector to Mel spectrum, and combining with the WaveNet model trained in a semi-supervised way to synthesize the Mel spectrum into a time domain waveform. The model avoids processing front-end text analysis that requires extensive prior knowledge in Lhasa-Tibetan dialect and reduces the need of a large amount of transcribed speech data. Experimental results show that the proposed method is effective and has higher clarity and naturalness than other related synthesis models for Lhasa-Tibetan dialect.
Query Rewriting for DL Ontologies Under the ICAR Semantics

In the current paper we propose a general framework for answering queries over inconsistent DL knowledge bases. The proposed framework considers the ICAR semantics and is based on a rewriting algorithm that can be applied over arbitrary DLs. Since the problem of ICAR-answering is known to be intractable for DLs other than DL-Lite, our algorithm may not terminate. However, we were able to describe sufficient termination conditions and to show that they are always satisfied for instance queries and TBoxes expressed in the semi-acyclic- $$\mathcal {EL}_{\bot }$$ as well as in DL-Lite $$_{bool}$$ . Interestingly, recent results on UCQ-rewritability and existing techniques can be used within the proposed framework, to check if the conditions are satisfied for a given query and ontology expressed in a DL for which the problem is in general intractable.