Reviewerly/exHarmony huggingface dataset:
Towards Quantum One-Time Memories from Stateless Hardware

A central tenet of theoretical cryptography is the study of the minimal assumptions required to implement a given cryptographic primitive. One such primitive is the one-time memory (OTM), introduced by Goldwasser, Kalai, and Rothblum [CRYPTO 2008], which is a classical functionality modeled after a non-interactive 1-out-of-2 oblivious transfer, and which is complete for one-time classical and quantum programs. It is known that secure OTMs do not exist in the standard model in both the classical and quantum settings. Here, we propose a scheme for using quantum information, together with the assumption of stateless (i.e., reusable) hardware tokens, to build statistically secure OTMs. Via the semidefinite programming-based quantum games framework of Gutoski and Watrous [STOC 2007], we prove security for a malicious receiver making at most 0.114n adaptive queries to the token (for n the key size), in the quantum universal composability framework, but leave open the question of security against a polynomial amount of queries. Compared to alternative schemes derived from the literature on quantum money, our scheme is technologically simple since it is of the "prepare-and-measure" type. We also give two impossibility results showing certain assumptions in our scheme cannot be relaxed.
The Effort Savings from Using NLP to Classify Equivalent Requirements

When Considering What and how to reuse, one must understand the differences and similarities of the systems being developed; this activity is part of the domain analysis. Among the several ways to perform domain analysis, identifying equivalent requirements (that is, the common elements in the domain) is scalable and noninvasive, and it supports the consolidation of requirements.
Hypertracing: Tracing Through Virtualization Layers

Cloud computing enables on-demand access to remote computing resources. It provides dynamic scalability and elasticity with a low upfront cost. As the adoption of this computing model is rapidly growing, this increases the system complexity, since virtual machines (VMs) running on multiple virtualization layers become very difficult to monitor without interfering with their performance. In this paper, we present hypertracing, a novel method for tracing VMs by using various paravirtualization techniques, enabling efficient monitoring across virtualization boundaries. Hypertracing is a monitoring infrastructure that facilitates seamless trace sharing among host and guests. Our toolchain can detect latencies and their root causes within VMs, even for boot-up and shutdown sequences, whereas existing tools fail to handle these cases. We propose a new hypervisor optimization, for handling efficient nested paravirtualization, which allows hypertracing to be enabled in any nested environment without triggering VM exit multiplication. This is a significant improvement over current monitoring tools, with their large I/O overhead associated with activating monitoring within each virtualization layer.
Bootstrapping of Semantic Relation Extraction for a Morphologically Rich Language

This article focuses on the use of a bootstrapping approach for the extraction of semantic relations that exist between two different concepts in a Tamil text. The proposed system, bootstrapping approach to semantic UNL relation extraction (BASURE) extracts generic relations that exist between different components of a sentence by exploiting the morphological richness of Tamil. Tamil is essentially a partially free word order language which means that semantic relations that exist between the concepts can occur anywhere in the sentence not necessarily in a fixed order. Here, the authors use Universal Networking Language (UNL), an Interlingua framework, to represent the word-based features and aim to define UNL semantic relations that exist between any two constituents in a sentence. The morphological suffix, lexical category and UNL semantic constraints associated with a word are defined as tuples of the pattern used for bootstrapping. Most systems define the initial set of seed patterns manually. However, this article uses a rule-based approach to obtain word-based features that form tuples of the patterns. A bootstrapping approach is then applied to extract all possible instances from the corpus and to generate new patterns. Here, the authors also introduce the use of UNL ontology to discover the semantic similarity between semantic tuples of the pattern, hence, to learn new patterns from the text corpus in an iterative manner. The use of UNL Ontology makes this approach general and domain independent. The results obtained are evaluated and compared with existing approaches and it has been shown that this approach is generic, can extract all sentence based semantic UNL relations and significantly increases the performance of the generic semantic relation extraction system.
OnGIS

Geospatial data sources are heterogeneous and backed by different data management technologies. This brings problems in data integration as well as their subsequent interpretation. This article proposes a technique for choosing the relevant data source out of many such sources, given a complex spatial query. Each source is described with a set of prototypical queries that are algorithmically arranged into a lattice. Upon query execution, the lattice is searched for an element matching best the input query. The matching algorithm makes use of the GeoSPARQL query containment enhanced with OWL 2 QL semantics. The technique is implemented in a prototypical system called OnGIS.
Human Action Recognition Based on Inertial Sensors and Complexity Classification

In this article, a human action recognition technique based on complexity classification is proposed. Considering the features of human actions such as continuity, individuality, variety randomness, the demands for recognition of different types of actions are different, the problem of action recognition can be classified into simple action recognition and complex action recognition -- the classification criterions are given respectively. Meanwhile, the hardware design of data acquisition device is introduced and the angle variation is chosen to represent the user's body state changes. For simple actions, a real-time recognition algorithm based on template matching performed well on cost control, and a method based on BLSTM-RNN is used for complex motion recognition to improve the accuracy of identification.
Locally Low-Rank Regularized Video Stabilization With Motion Diversity Constraints

This paper presents a novel motion aware regularization model with diversity constraints for motion smoothing in video stabilization. Differing from the global path optimization methods, the proposed model puts an emphasis on the relations of inter-frame motions and incorporates sliding windowed low-rank and smoothness constraints. The rationale behind it is cinematography rules which assume that camera motions can be divided into diverse patterns: zero velocity, constant velocity, and acceleration motion. Firstly, a locally motion aware fidelity term is adopted in light of the local motion stationarity. Secondly, to improve the robustness of the model for different motion patterns, a locally low-rank constrained regularization term is further introduced by considering the motion correlation in a local temporal window. Moreover, to cope with the over-smoothing problem in rapid motion situations with extreme acceleration, a motion steering kernel and varying window length are employed to enhance the flexibility of the proposed model. The experimental results demonstrate the superiority of the proposed optimization model and the efficiency to suppress over-smoothing when rapid motions occur. Meanwhile, we compare the results with some state-of-the-art methods quantitatively and qualitatively, and our method can achieve a comparable or even better stabilization effect.
A multi-projection deep computation model for smart data in Internet of Things

The double-projection deep computation model (DPDCM) proved to be effective for big data feature learning. However, DPDCM cannot capture the underlying correlations over the different modalities enough since it projects the input data into only two subspaces. To tackle this problem, this paper presents a multi-projection deep computation model (MPDCM) to generalize DPDCM for smart data in Internet of Things. Specially, MPDCM maps the input data into multiple nonlinear subspaces to learn the interacted features of IoT big data by substituting each hidden layer with a multi-projection layer. Furthermore, a learning algorithm based on back-propagation and gradient descent is designed to train the parameters of the presented model. Finally, extensive experiments are conducted on two representative datasets, i.e, Animal-20 and NUS-WIDE-14, to verify the presented model by comparing with DPDCM. Results show that the presented model achieves higher classification accuracy than DPDCM, proving the potential of the presented model to drill smart data for Internet of Things.
Empirical feature analysis for dialogue breakdown detection

Chat-oriented dialogue systems sometimes generate inappropriate response utterances to user utterances that cause dialogue breakdown. Detecting such inappropriate utterances and suppressing them will support the continuation of the dialogue. Although a previous state-of-the-art dialogue breakdown detector leveraged dialogue-act transitions and word-based similarities between utterance pairs, these features are insufficient to evaluate the appropriateness of question-answering or relatedness between utterances that share few topic words. In this paper, we propose novel features to assess these problems, and examine their effectiveness for improving the performance of dialogue breakdown detection.
Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine

This article presents the linear Proximal Support Vector Machine (PSVM) to the image steganalysis, and further generates a very efficient method called PSVM-LSMR through implementing PSVM by the state-of-the-art optimization method Least Square Minimum-Residual (LSMR). Also, motivated by extreme learning machine (ELM), a nonlinear algorithm PSVM-ELM is proposed for the image steganalysis. It is shown by the experiments with the wide stego schemes and rich steganalysis feature sets in both the spatial and JPEG domains that the PSVM can achieve comparable performance with Fisher Linear Discriminant (FLD) and ridge regression, and its computational time is far more less than that of them on large feature sets. The PSVM-LSMR is comparable to Ridge Regression implemented by LSMR (RR-LSMR), and both of them require the least computational time among all the competitions when dealing with medium or large feature sets. The nonlinear PSVM-ELM performs comparably or even better than FLD and ridge regression for the spatial domain steganographic schemes, and its computational time is apparently less than that of them on large feature sets.
Comparing Methods for Assessment of Facial Dynamics in Patients with Major Neurocognitive Disorders

Assessing facial dynamics in patients with major neurocognitive disorders and specifically with Alzheimer’s disease (AD) has shown to be highly challenging. Classically such assessment is performed by clinical staff, evaluating verbal and non-verbal language of AD-patients, since they have lost a substantial amount of their cognitive capacity, and hence communication ability. In addition, patients need to communicate important messages, such as discomfort or pain. Automated methods would support the current healthcare system by allowing for telemedicine, i.e., lesser costly and logistically inconvenient examination. In this work we compare methods for assessing facial dynamics such as talking, singing, neutral and smiling in AD-patients, captured during music mnemotherapy sessions. Specifically, we compare 3D ConvNets, Very Deep Neural Network based Two-Stream ConvNets, as well as Improved Dense Trajectories. We have adapted these methods from prominent action recognition methods and our promising results suggest that the methods generalize well to the context of facial dynamics. The Two-Stream ConvNets in combination with ResNet-152 obtains the best performance on our dataset, capturing well even minor facial dynamics and has thus sparked high interest in the medical community.
Brand &gt; Logo: Visual Analysis of Fashion Brands

While lots of people may think branding begins and ends with a logo, fashion brands communicate their uniqueness through a wide range of visual cues such as color, patterns and shapes. In this work, we analyze learned visual representations by deep networks that are trained to recognize fashion brands. In particular, the activation strength and extent of neurons are studied to provide interesting insights about visual brand expressions. The proposed method identifies where a brand stands in the spectrum of branding strategy, i.e., from trademark-emblazoned goods with bold logos to implicit no logo marketing. By quantifying attention maps, we are able to interpret the visual characteristics of a brand present in a single image and model the general design direction of a brand as a whole. We further investigate versatility of neurons and discover “specialists” that are highly brand-specific and “generalists” that detect diverse visual features. A human experiment based on three main visual scenarios of fashion brands is conducted to verify the alignment of our quantitative measures with the human perception of brands. This paper demonstrate how deep networks go beyond logos in order to recognize clothing brands in an image.
Estimated Spectra of Higher Order Spectral Null Codes

We present an estimate of the power density function (spectrum) of binary Kth-order spectral null codes. We work out the auto-correlation model in detail for secondand third-order spectral null codes. We compare the autocorrelation functions and spectra predicted by the model with those generated by full-set Kth-order spectral null block codes.
Radical-Locator Polynomials and Row-Echelon Partial Syndrome Matrices With Applications to Decoding Cyclic Codes

Partial syndrome matrices and weak-locator polynomials have received considerable attention in recent years due to their applications in decoding cyclic codes. The technical contribution of this paper is threefold: (1) cyclic codes can be decoded by the newly proposed partial syndrome matrices, which generalize the previously known results on the determination of error positions; (2) a new type of polynomial associated with error locations, called radical-locator polynomial, is defined, which includes the weak-locator polynomial as a special case; and (3) a novel class of matrices with many zero entries, called row-echelon partial syndrome matrices, is presented, based on the Newton identities, for efficiently decoding cyclic codes. It is also shown that the radical-locator polynomials can be obtained from the determinants of the above-mentioned (row-echelon) partial syndrome matrices.
Integrating AMS data with structural studies from granitoid rocks of the Eastern Dharwar Craton, south India: Implications on successive fabric development and regional tectonics

Five successive deformation phases have been identified from structural analysis of granitoid and gneissic rocks of the study area. The magnetic foliation data of the studied granitoids show two distinct NW and NE trending sets. The NW trending magnetic foliation is found to be essentially parallel to the mesoscale planar structures of regional D1-D2 deformations and is thus correlated with the D1-D2 phases. On the contrary, the NE trending magnetic foliation appears to be kinematically compatible with the NW-SE compression direction deciphered from conjugate ductile shear zone patterns of the D3 event. Combining AMS, field and microstructural data, the granitoid outcrop have been separated into older D1-D2 and younger D3 domains. The published geochronological data from the area when compared with the established D1-D2 and D3 domains, shows a good correlation pointing tentatively the timing of the D1-D2 deformation phase to be ∼2550 Ma while the D3 phase to be ∼2524 Ma. Hence the study highlights the importance of combining AMS data with field and microstructural studies to constraint the timing of regional successive deformation imprints. It also helps to identify the chronologically separated emplacement phases of the granitoid massif in question, which would not have been possible otherwise.
Comprehensive Side-Channel Power Analysis of XTS-AES

XTS-advanced encryption standard (AES) is an advanced mode of AES for data protection of sector-based devices. It features two secret keys instead of one, and an additional tweak for each data block. These characteristics make the mode not only resistant against cryptoanalysis attacks, but also more challenging for side-channel attack. In this paper, we comprehensively analyze the side-channel power leakage of various XTS-AES implementations and invent effective attacks. We first run a simple power analysis of a software implementation. For a hardware implementation on field-programmable gate array (FPGA), we analyze side-channel leakage of the particular modular multiplication in XTS-AES mode. In addition, we utilize the relationship between two consecutive block tweaks and propose a method to work around the masking of ciphertext by the tweak. These attacks are verified on an FPGA implementation of XTS-AES. The results show that XTS-AES is susceptible to side-channel power analysis attacks, and therefore dedicated protections are required for security of XTS-AES in storage devices.
Adaptive appearance separation for interactive image segmentation based on Dense CRF

Interactive segmentation has recently become a hot topic for its wide application. The authors propose an efficacious appearance separation model for interactive binary segmentation, which incorporates the difference of foreground and background colour models and the difference of corresponding geodesic models into the popular densely connected conditional random field (Dense CRF) framework. The proposed method can adaptively set relevant parameter values in this framework according to the characteristics of target images in a per-image manner, therefore, it gets rid of the dependence on specific datasets. After accomplishing a mean-field inference, the authors are able to get satisfactory results without the time-consuming parameter learning process and multiple iterative optimisations. Overall, the proposed approach is highly efficient and mitigates the contradiction between accuracy and segmentation efficiency. In addition, the proposed approach reduces the efforts of scribble-style interaction from users. The experimental results on three famous datasets show that the proposed method is superior to the other five new algorithms released in recent years regarding accuracy, and is faster than or close to them in runtime.
Exploiting Reliable and Scalable Multicast Services in IaaS Datacenters

A large number of servers are interconnected using a specific datacenter network to deliver the infrastructure as a service (IaaS). Multicast can jointly utilize the network resources and further reduce the consumption of network bandwidth more than individual unicast. The source of a multicast service, however, does not need to be in a specific location as long as certain constraints are satisfied. This means the multicast can have uncertain sources, which could reduce the network resource consumption more than a traditional multicast service and further improve the quality of service. In this paper, we propose a novel reliable multicast service with uncertain sources named ReMUS. The goal is to minimize the sum of the transfer cost and the recovery cost, although finding such a ReMUS is very challenging. Thus, we design a source-based multicast method to solve this problem by exploiting the flexibility of sources when no recovery nodes exist in the network. Furthermore, we design a general multicast method to jointly exploit the benefits of uncertain sources and recovery nodes to minimize the total cost of ReMUS. We conduct extensive evaluations under Internet2 and datacenter networks. The results indicate that our methods can efficiently realize the reliable and scalable multicast with uncertain sources, irrespective of the settings of networks and multicasts. To the best of our knowledge, we are the first to study the reliable multicast service under uncertain sources.
A shortcut to marking 3D target curves on curved surface via a galvanometric laser scanner

Marking arbitrary three-dimensional (3D) target curves on given objects with curved surface is required in many industrial fields, such as fabric prepreg placement in composite material part fabrication, product assembly, surface painting for decoration, etc. A shortcut to the solution of this intractable problem is proposed by utilizing a galvanometric laser scanner (GLS) with the aid of a camera. Without using the existing tedious GLS calibration procedures, the proposed method directly establishes a mapping between the 3D coordinates of the laser spots on the object surface and the control voltages of the scanner. A single-hidden layer feedforward neural network (SLFN) is employed to model the mapping. By projecting a dense grid of laser spots on the object to be marked and simultaneously taking only one image, the SLFN model is trained in minutes via a linear solving mechanism. Experiments demonstrate that the trained SLFN model has a good generalization performance for marking 3D target curves. The 3D laser marking errors on experimental objects are less than 0.5 mm. The proposed method is especially suitable for on-site use and can be conveniently extended to multiple GLSs for marking large complex objects.
Channel Code Using Constrained-Random-Number Generator Revisited

A construction of a channel code by using a source code with decoder side information is introduced. The encoder and decoder pair of any source code can be used for the construction. Constrained-random-number generators, which generate random numbers satisfying a condition specified by a function and its value, are used to construct stochastic encoders and decoders. The result suggests that we can divide the channel coding problem into the problems of channel encoding and source decoding with side information.
Approximate Bayes multi‐target tracking smoother

An approximate Bayes multi-target tracking (MTT) smoother that is directly applicable to MTT is proposed in this study. By using labelled random finite set, the proposed smoother inherently produced trajectories of the targets. The smoother is based on Bayes forward–backward smoothing scheme, which involves forward filtering followed by backward smoothing. It is shown that if the multi-target transition kernel involved in the backward smoothing stage considers no birth and death of targets, then the smoothing density is of the same form as the filtering density. To avoid the exponential complexity of computation, an efficient approximation that utilises the m-best S-D assignment algorithm to truncate the smoothing density is presented. A Gaussian mixture implementation of the smoother for a linear system is also given. Simulation results in MTT scenarios demonstrate that the smoother outperforms generalised labelled multi-Bernoulli filter in terms of optimal sub-pattern assignment metric.