OpenRaiser/Intern-Atlas huggingface dataset:
Close-reading of Linked Data: a case study in regards to the quality of online authority files
Venue: arXiv cs.DB 2019
Year: 2019

More and more cultural institutions use Linked Data principles to share and connect their collection metadata. In the archival field, initiatives emerge to exploit data contained in archival descriptions and adapt encoding standards to the semantic web. In this context, online authority files can be used to enrich metadata. However, relying on a decentralized network of knowledge bases such as Wikidata, DBpedia or even Viaf has its own difficulties. This paper aims to offer a critical view of these linked authority files by adopting a close-reading approach. Through a practical case study, we intend to identify and illustrate the possibilities and limits of RDF triples compared to institutions' less structured metadata.
Blockchain Storage Load Balancing Among DHT Clustered Nodes
Venue: arXiv cs.DC 2019
Year: 2019

In Bitcoin, to independently verify whether new transactions are correct or not, a type of a node called "Full Node" has to hold the whole of historical transactions. The transactions are stored in ledger called "Blockchain. " Blockchain is an append-only data structure. Thus, to operate Full Nodes, the required storage capacity would grow too large for resource-constrained devices. Due to the limitation, the existing lightweight node scheme is that a node relies on other Full Nodes. In this thesis, to reduce storage capacity with keeping the independence of each node, we propose a storage load balancing scheme "KARAKASA" using Distributed Hash Table (DHT). In KARAKASA, nodes distributedly keep the whole blockchain among DHT networked nodes. We evaluated KARAKASA from the view of storage capacity and independence. As a result, a node in a cluster does not need to trust other nodes. We concluded that nodes in a DHT cluster can behave like Full Nodes without holding the whole blockchain.
A Linear-complexity Multi-biometric Forensic Document Analysis System, by Fusing the Stylome and Signature Modalities
Venue: arXiv cs.CL 2019
Year: 2019

Forensic Document Analysis (FDA) addresses the problem of finding the authorship of a given document. Identification of the document writer via a number of its modalities (e.g. handwriting, signature, linguistic writing style (i.e. stylome), etc.) has been studied in the FDA state-of-the-art. But, no research is conducted on the fusion of stylome and signature modalities. In this paper, we propose such a bimodal FDA system (which has vast applications in judicial, police-related, and historical documents analysis) with a focus on time-complexity. The proposed bimodal system can be trained and tested with linear time complexity. For this purpose, we first revisit Multinomial Naïve Bayes (MNB), as the best state-of-the-art linear-complexity authorship attribution system and, then, prove its superior accuracy to the well-known linear-complexity classifiers in the state-of-the-art. Then, we propose a fuzzy version of MNB for being fused with a state-of-the-art well-known linear-complexity fuzzy signature recognition system. For the evaluation purposes, we construct a chimeric dataset, composed of signatures and textual contents of different letters. Despite its linear-complexity, the proposed multi-biometric system is proven to meaningfully improve its state-of-the-art unimodal counterparts, regarding the accuracy, F-Score, Detection Error Trade-off (DET), Cumulative Match Characteristics (CMC), and Match Score Histograms (MSH) evaluation metrics.
Robust One-Class Kernel Spectral Regression
Venue: arXiv cs.LG 2019
Year: 2019

The kernel null-space technique and its regression-based formulation (called one-class kernel spectral regression, a.k.a. OC-KSR) is known to be an effective and computationally attractive one-class classification framework. Despite its outstanding performance, the applicability of kernel null-space method is limited due to its susceptibility to possible training data corruptions and inability to rank training observations according to their conformity with the model. This work addresses these shortcomings by studying the effect of regularising the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation (OC-KSR). In this respect, first, the effect of a Tikhonov regularisation in the Hilbert space is analysed where the one-class learning problem in presence of contaminations in the training set is posed as a sensitivity analysis problem. Next, driven by the success of the sparse representation methodology, the effect of a sparsity regularisation on the solution is studied. For both alternative regularisation schemes, iterative algorithms are proposed which recursively update label confidences and rank training observations based on their fit with the model. Through extensive experiments conducted on different data sets, the proposed methodology is found to enhance robustness against contamination in the training set as compared with the baseline kernel null-space technique as well as other existing approaches in a one-class classification paradigm while providing the functionality to rank training samples effectively.
Extending a model for ontology-based Arabic-English machine translation
Venue: arXiv cs.CL 2019
Year: 2019

The acceleration in telecommunication needs leads to many groups of research, especially in communication facilitating and Machine Translation fields. While people contact with others having different languages and cultures, they need to have instant translations. However, the available instant translators are still providing somewhat bad Arabic-English Translations, for instance when translating books or articles, the meaning is not totally accurate. Therefore, using the semantic web techniques to deal with the homographs and homonyms semantically, the aim of this research is to extend a model for the ontology-based Arabic-English Machine Translation, named NAN, which simulate the human way in translation. The experimental results show that NAN translation is approximately more similar to the Human Translation than the other instant translators. The resulted translation will help getting the translated texts in the target language somewhat correctly and semantically more similar to human translations for the Non-Arabic Natives and the Non-English natives.
Exploration of Performance and Energy Trade-offs for Heterogeneous Multicore Architectures
Venue: arXiv cs.DC 2019
Year: 2019

Energy-efficiency has become a major challenge in modern computer systems. To address this challenge, candidate systems increasingly integrate heterogeneous cores in order to satisfy diverse computation requirements by selecting cores with suitable features. In particular, single-ISA heterogeneous multicore processors such as ARM big.LITTLE have become very attractive since they offer good opportunities in terms of performance and power consumption trade-off. While existing works already showed that this feature can improve system energy-efficiency, further gains are possible by generalizing the principle to higher levels of heterogeneity. The present paper aims to explore these gains by considering single-ISA heterogeneous multicore architectures including three different types of cores. For this purpose, we use the Samsung Exynos Octa 5422 chip as baseline architecture. Then, we model and evaluate Cortex A7, A9, and A15 cores using the gem5 simulation framework coupled to McPAT for power estimation. We demonstrate that varying the level of heterogeneity as well as the different core ratio can lead to up to 2.3x gains in energy efficiency and up to 1.5x in performance. This study further provides insights on the impact of workload nature on performance/energy trade-off and draws recommendations concerning suitable architecture configurations. This contributes in fine to guide future research towards dynamically reconfigurable HSAs in which some cores/clusters can be disabled momentarily so as to optimize certain metrics such as energy efficiency. This is of particular interest when dealing with quality-tunable algorithms in which accuracy can be then traded for compute effort, thereby enabling to use only those cores that provide the best energy-efficiency for the chosen algorithm.
Adversarial Domain Adaptation for Stance Detection
Venue: arXiv cs.LG 2019
Year: 2019

This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim. Stance detection is a major component of automated fact checking. As annotating stances in different domains is a tedious and costly task, automatic methods based on machine learning are viable alternatives. In this paper, we focus on adversarial domain adaptation for stance detection where we assume there exists sufficient labeled data in the source domain and limited labeled data in the target domain. Extensive experiments on publicly available datasets show the effectiveness of our domain adaption model in transferring knowledge for accurate stance detection across domains.
Asymmetric Construction of Low-Latency and Length-Flexible Polar Codes
Venue: arXiv cs.IT 2019
Year: 2019

Polar codes are a class of capacity-achieving error correcting codes that have been selected for use in enhanced mobile broadband in the 3GPP 5th generation (5G) wireless standard. Most polar code research examines the original Arikan polar coding scheme, which is limited in block length to powers of two. This constraint presents a considerable obstacle since practical applications call for all code lengths to be readily available. Puncturing and shortening techniques allow for flexible polar codes, while multi-kernel polar codes produce native code lengths that are powers of two and/or three. In this work, we propose a new low complexity coding scheme called asymmetric polar coding that allows for any arbitrary block length. We present details on the generator matrix, frozen set design, and decoding schedule. Our scheme offers flexible polar code lengths with decoding complexity lower than equivalent state-of-the-art length-compatible approaches under successive cancellation decoding. Further, asymmetric decoding complexity is directly dependent on the codeword length rather than the nearest valid polar code length. We compare our scheme with other length matching techniques, and simulations are presented. Results show that asymmetric polar codes present similar error correction performance to the competing schemes, while dividing the number of SC decoding operations by up to a factor of 2 using the same codeword length
On the Variance of Unbiased Online Recurrent Optimization
Venue: arXiv cs.LG 2019
Year: 2019

The recently proposed Unbiased Online Recurrent Optimization algorithm (UORO, arXiv:1702.05043) uses an unbiased approximation of RTRL to achieve fully online gradient-based learning in RNNs. In this work we analyze the variance of the gradient estimate computed by UORO, and propose several possible changes to the method which reduce this variance both in theory and practice. We also contribute significantly to the theoretical and intuitive understanding of UORO (and its existing variance reduction technique), and demonstrate a fundamental connection between its gradient estimate and the one that would be computed by REINFORCE if small amounts of noise were added to the RNN's hidden units.
A Comparison of Information Retrieval Techniques for Detecting Source Code Plagiarism
Venue: arXiv cs.IR 2019
Year: 2019

Plagiarism is a commonly encountered problem in the academia. While there are several tools and techniques to efficiently determine plagiarism in text, the same cannot be said about source code plagiarism. To make the existing systems more efficient, we use several information retrieval techniques to find the similarity between source code files written in Java. We later use JPlag, which is a string-based plagiarism detection tool used in academia to match the plagiarized source codes. In this paper, we aim to generalize on the efficiency and effectiveness of detecting plagiarism using different information retrieval models rather than using just string manipulation algorithms.
Storm: a fast transactional dataplane for remote data structures
Venue: arXiv cs.DC 2019
Year: 2019

RDMA is an exciting technology that enables a host to access the memory of a remote host without involving the remote CPU. Prior work shows how to use RDMA to improve the performance of distributed in-memory storage systems. However, RDMA is widely believed to have scalability issues, due to the amount of active protocol state that needs to be cached in the limited NIC cache. These concerns led to several software-based proposals to enhance scalability by trading off performance. In this work, we revisit these trade-offs in light of newer RDMA hardware and propose new guidelines for scaling RDMA. We show that using one-sided remote memory primitives leads to higher performance compared to send/receive and kernel-based systems in rack-scale environments. Based on these insights, we design and implement Storm, a transactional dataplane using one-sided read and write-based RPC primitives. We show that Storm outperforms eRPC, FaRM, and LITE by 3.3x, 3.6x, and 17.1x, respectively, on an Infinband EDR cluster with Mellanox ConnectX-4 NICs.
Shoulder Surfing: From An Experimental Study to a Comparative Framework
Venue: arXiv cs.CR 2019
Year: 2019

Shoulder surfing is an attack vector widely recognized as a real threat - enough to warrant researchers dedicating a considerable effort toward designing novel authentication methods to be shoulder surfing resistant. Despite a multitude of proposed solutions over the years, few have employed empirical evaluations and comparisons between different methods, and our understanding of the shoulder surfing phenomenon remains limited. Barring the challenges in experimental design, the reason for that can be primarily attributed to the lack of objective and comparable vulnerability measures. In this paper, we develop an ensemble of vulnerability metrics, a first endeavour toward a comprehensive assessment of a given method's susceptibility to observational attacks. In the largest on-site shoulder surfing experiment (n = 274) to date, we verify the model on four conceptually different authentication methods in two observation scenarios. On the example of a novel hybrid authentication method based on associations, we explore the effect of input type on the adversary's effectiveness. We provide first empirical evidence that graphical passwords are easier to observe; however, that does not necessarily mean that the observed information will allow the attacker to guess the victim's password easier. An in-depth analysis of individual metrics within the clusters offers insight into many additional aspects of the shoulder surfing attack not explored before. Our comparative framework makes an advancement in evaluation of shoulder surfing and furthers our understanding of observational attacks. The results have important implications for future shoulder surfing studies and the field of Password Security as a whole.
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Venue: arXiv cs.LG 2019
Year: 2019

The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall. We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. In addition, its memory compression allows it to scale to thousands of unknown labels. Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification. In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.
Impact of Adaptive Consistency on Distributed SDN Applications: An Empirical Study
Venue: arXiv cs.NI 2019
Year: 2019

Scalability of the control plane in a software-defined network (SDN) is enabled by means of decentralization of the decision-making logic, i.e., by replication of controller functions to physically or virtually dislocated controller replicas. Replication of a centralized controller state also enables the protection against controller failures by means of primary and backup replicas responsible for managing the underlying SDN data plane devices. In this paper, we investigate the effect of the deployed consistency model on scalability and correctness metrics of the SDN control plane. In particular, we compare the strong and eventual consistency, and make a case for a novel adaptive consistency approach. The existing controller platforms rely on either strong or eventual consistency mechanisms in their state distribution. We show how an adaptive consistency model offers the scalability benefits in terms of the total request-handling throughput and response time, in contrast to the strong consistency model. We also outline how the adaptive consistency approach can provide for correctness semantics that are unachievable with the eventual consistency paradigm in practice. The adaptability of our approach provides a balanced and tunable tradeoff of scalability and correctness for the SDN application implemented on top of the adaptive framework. To validate our assumptions, we evaluate and compare the different approaches in an emulated testbed with an example of a load balancer controller application. The experimental setup comprises up to five extended OpenDaylight controller instances and two network topologies from the area of service provider and data center networks.
Online Clustering by Penalized Weighted GMM
Venue: arXiv cs.LG 2019
Year: 2019

With the dawn of the Big Data era, data sets are growing rapidly. Data is streaming from everywhere - from cameras, mobile phones, cars, and other electronic devices. Clustering streaming data is a very challenging problem. Unlike the traditional clustering algorithms where the dataset can be stored and scanned multiple times, clustering streaming data has to satisfy constraints such as limit memory size, real-time response, unknown data statistics and an unknown number of clusters. In this paper, we present a novel online clustering algorithm which can be used to cluster streaming data without knowing the number of clusters a priori. Results on both synthetic and real datasets show that the proposed algorithm produces partitions which are close to what you could get if you clustered the whole data at one time.
The few-get-richer: a surprising consequence of popularity-based rankings
Venue: arXiv cs.IR 2019
Year: 2019

Ranking algorithms play a crucial role in online platforms ranging from search engines to recommender systems. In this paper, we identify a surprising consequence of popularity-based rankings: the fewer the items reporting a given signal, the higher the share of the overall traffic they collectively attract. This few-get-richer effect emerges in settings where there are few distinct classes of items (e.g., left-leaning news sources versus right-leaning news sources), and items are ranked based on their popularity. We demonstrate analytically that the few-get-richer effect emerges when people tend to click on top-ranked items and have heterogeneous preferences for the classes of items. Using simulations, we analyze how the strength of the effect changes with assumptions about the setting and human behavior. We also test our predictions experimentally in an online experiment with human participants. Our findings have important implications to understand the spread of misinformation.
Distributed Ledger Privacy: Ring Signatures, Möbius and CryptoNote
Venue: arXiv cs.CR 2019
Year: 2019

Distributed ledger and blockchain systems are expected to make financial systems easier to audit, reduce counter-party risk and transfer assets seamlessly. The key concept is a token controlled by a cryptographic private key for spending, and represented by a public key for receiving and audit purposes. Ownership transfers are authorized with digital signatures and recorded on a ledger visible to numerous participants. Several ways to enhance the privacy of such ledgers have been proposed. In this paper we study two major techniques to enhance privacy of token transfers with the help of improved cryptography: Möbius and CryptoNote. The comparison is illuminating: both techniques use "ring signatures" and some form of "stealth addressing" or key derivation techniques, yet each does it in a completely different way. Möbius is more recent and operates in a more co-operative way (with permission) and is not yet specified at a sufficiently detailed level. Our primary goal is to explore the suitability of these two techniques for improving the privacy of payments on cryptographic ledgers. We explain various conflicting requirements and strategic choices which arise when trying to conceal the identity of participants and the exact details of transactions in our context while simultaneously enabling fast final settlement of tokens with a reasonable level of liquidity. We show that in these systems, third-party observers see obfuscated settlement. We finish with a summary of explicit warnings and advice for implementors of such systems.
A Random Access G-Network: Stability, Stable Throughput, and Queueing Analysis
Venue: arXiv cs.IT 2019
Year: 2019

The effect of signals on stability, throughput region, and delay in a two-user slotted ALOHA based random-access system with collisions is considered. This work gives rise to the development of random access G-networks, which can model virus attacks or other malfunctions and introduce load balancing in highly interacting networks. The users are equipped with infinite capacity buffers accepting external bursty arrivals. We consider both negative and triggering signals. Negative signals delete a packet from a user queue, while triggering signals cause the instantaneous transfer of packets among user queues. We obtain the exact stability region, and show that the stable throughput region is a subset of it. Moreover, we perform a compact mathematical analysis to obtain exact expressions for the queueing delay by solving a Riemann boundary value problem. A computationally efficient way to obtain explicit bounds for the queueing delay is also presented. The theoretical findings are numerically evaluated and insights regarding the system performance are derived.