Reviewerly/exHarmony huggingface dataset:
Parallel implementation of the non-overlapping template matching test using CUDA

NIST (National Institute of Standards and Technology) statistical test recognized as the most authoritative is widely used in verifying the randomness of binary sequences. The Non-overlapping Template Matching Test as the 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> test of the NIST Test Suit is remarkably time consuming and the slow performance is one of the major hurdles in the testing process. In this paper, we present an efficient bit-parallel matching algorithm and segmented scan-based strategy for execution on Graphics Processing Unit (GPU) using NVIDIA Compute Unified Device Architecture (CUDA). Experimental results show the significant performance improvement of the parallelized Non-overlapping Template Matching Test, the running speed is 483 times faster than the original NIST implementation without attenuating the test result accuracy.
VibroScale

Smartphones, with their ubiquity and plethora of embedded sensors enable on-the-go measurement. Here, we describe one novel measurement potential, weight measurement, by turning an everyday smartphone into a weighing scale. We describe VibroScale, our vibration-based approach to measuring the weight of objects that are small in size. Being able to objectively measure the weight of objects in free-living settings, without the burden of carrying a scale, has several possible uses, particularly in weighing small food items. We designed a smartphone app and regression algorithm, which we termed VibroScale, that estimates the relative induced intensity of an object placed on the smartphone. We tested our proposed method using more than 50 fruits and other everyday objects of different sizes and weights. Our smartphone-based method can measure the weight of fruit without relying on an actual scale. Overall, we observed that VibroScale can measure one type of object with a mean absolute error of 12.4 grams and a mean absolute percentage error of 7.7%. We believe that in future this approach can be generalized to estimate calories and measure the weight of various types of objects.
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise

Operator-theoretic analysis of nonlinear dynamical systems has attracted much attention in a variety of engineering and scientific fields, endowed with practical estimation methods using data such as dynamic mode decomposition. In this paper, we address a lifted representation of nonlinear dynamical systems with random noise based on transfer operators, and develop a novel Krylov subspace method for estimating the operators using finite data, with consideration of the unboundedness of operators. For this purpose, we first consider Perron-Frobenius operators with kernel-mean embeddings for such systems. We then extend the Arnoldi method, which is the most classical type of Kryov subspace method, so that it can be applied to the current case. Meanwhile, the Arnoldi method requires the assumption that the operator is bounded, which is not necessarily satisfied for transfer operators on nonlinear systems. We accordingly develop the shift-invert Arnoldi method for Perron-Frobenius operators to avoid this problem. Also, we describe an approach of evaluating predictive accuracy by estimated operators on the basis of the maximum mean discrepancy, which is applicable, for example, to anomaly detection in complex systems. The empirical performance of our methods is investigated using synthetic and real-world healthcare data.
Distributed Machine Learning Using Data Parallelism on Mobile Platform

Machine learning has many challenges, and one of them is to deal with large datasets, because the size of them grows continuously year by year. One solution to this problem is data parallelism. This paper investigates the expansion of data parallelism to mobile, which became the most popular platform. Special client-server architecture was created for this purpose. The software implementation of this problem measures the mobile devices training capabilities and the efficiency of the whole system. The results show that doing distributed training on mobile cluster is possible and safe, but its performance depends on the algorithm’s implementation.
Forensic Technology for Source Camera Identification

Source camera identification is a major branch of forensic source identification. It’s purpose is to determine which camera was used to capture the image of unknown provenance only by using the image itself. We study the recent developments in the field of source camera identification and divide the techniques described in the literature into six categories: EXIF metadata, lens aberration, CFA and demosaicing, sensor imperfections, image statistical features and convolutional neural network. We describe in detail the general ideas of the approaches used in each category. We summarize the six techniques at the end of the article and point out the challenges for future forensic.
Universal Dependency Treebanks for Low-Resource Indian Languages: The Case of Bhojpuri

This paper presents the first dependency treebank for Bhojpuri, a resource-poor language that belongs to the Indo-Aryan language family. The objective behind the Bhojpuri Treebank (BHTB) project is to create a substantial, syntactically annotated treebank which not only acts as a valuable resource in building language technological tools, also helps in cross-lingual learning and typological research. Currently, the treebank consists of 4,881 annotated tokens in accordance with the annotation scheme of Universal Dependencies (UD). A Bhojpuri tagger and parser were created using machine learning approach. The accuracy of the model is 57.49% UAS, 45.50% LAS, 79.69% UPOS accuracy and 77.64% XPOS accuracy. The paper describes the details of the project including a discussion on linguistic analysis and annotation process of the Bhojpuri UD treebank.
An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge, emotional analysis has become a hot topic in psychology, health medicine and computer science. It has a high comprehensive and practical application value. Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research. The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period, so as to understand their normal state, abnormal state and the reason of state change from the information they wrote. In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences, and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model, an emotional analysis model using the emotional dictionary and multichannel convolutional neural network is proposed in this paper. Firstly, the input matrix of emotion dictionary is constructed according to the emotion information, and the different feature information of sentences is combined to form different network input channels, so that the model can learn the emotion information of input sentences from various feature representations in the training process. Then, the loss function is reconstructed to realize the semi supervised learning of the network. Finally, experiments are carried on COAE 2014 and self-built data sets. The proposed model can not only extract more semantic information in emotional text, but also learn the hidden emotional information in emotional text. The experimental results show that the proposed emotion analysis model can achieve a better classification performance. Compared with the best benchmark model gram-CNN, the F1 value can be increased by 0.026 in the self-built data set, and it can be increased by 0.032 in the COAE 2014 data set.
Don't Use Fingerprint, it's Raining!

This paper investigates how smartphone users perceive switching from their primary authentication mechanism to a fallback one, based on the context. This is useful in cases where the primary mechanism fails (e.g., wet fingers when using fingerprint). While prior work introduced the concept, we are the first to investigate its perception by users and their willingness to follow a system's suggestion for a switch. We present findings from a two-week field study (N=29) using an Android app, showing that users are willing to adopt alternative mechanisms when prompted. We discuss how context-awareness can improve the perception of authentication reliability and potentially improve usability and security.
A comparison of heuristics applied for studying the usability of websites

The study of the usability of interfaces of all types of applications, including websites, is still a very topical research area. The heuristic method is among the methods applied to evaluate the usability of interfaces. Many researchers use Nielsen’s heuristics developed in 1996, while others propose their own heuristic sets. This article aims to present the results of a study which consisted in a comparison of original heuristic sets with Nielsen’s heuristics. The original heuristic proposals selected for the analysis concern different interface types, but they have in common is the possibility of using them to study the usability of websites. On the basis of the literature research conducted, 9 sets of heuristics were distinguished, each of which was compared with Nielsen’s heuristics. The contribution of this article consists in a tabular comparison between 9 original proposals and 10 Nielsen’s heuristics.
C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation

Estimation of information theoretic quantities such as mutual information and its conditional variant has drawn interest in recent times owing to their multifaceted applications. Newly proposed neural estimators for these quantities have overcome severe drawbacks of classical kNN-based estimators in high dimensions. In this work, we focus on conditional mutual information (CMI) estimation by utilizing its formulation as a minmax optimization problem. Such a formulation leads to a joint training procedure similar to that of generative adversarial networks. We find that our proposed estimator provides better estimates than the existing approaches on a variety of simulated datasets comprising linear and non-linear relations between variables. As an application of CMI estimation, we deploy our estimator for conditional independence (CI) testing on real data and obtain better results than state-of-the-art CI testers
An Asynchronous and Low-Power True Random Number Generator using STT-MTJ

The emerging spin-transfer torque magnetic tunnel junction (STT-MTJ) technology exhibits interesting stochastic behavior combined with small area and low operation energy. It is, therefore, a promising technology for security applications, specifically the generation of random numbers. In this paper, STT-MTJ is used to construct an asynchronous true random number generator (TRNG) with low power and a high entropy rate. The asynchronous design enables the decoupling of the random number generation from the system clock, allowing it to be embedded in low-power devices. The proposed TRNG is evaluated by a numerical simulation, using the Landau-Lifshitz-Gilbert (LLG) equation as the model of the STT-MTJ devices. Design considerations, attack analysis, and process variation are discussed and evaluated. We show that our design is robust to process variation, thus achieving an entropy generating rate between 99.7 and 127.8 Mb/s with 6-7.7 pJ per bit for 90% of the instances.
Multi-audience tracking with RGB-D camera on digital signage

Digital signage is widely utilized in digital-out-of-home (DOOH) advertising for marketing and business. Recently, the combination of the digital camera and digital signage enables the advertiser to gather the audience demographic for audience measurement. Audience measurement is useful for the advertiser to understand the audience's behavior and improve their business strategies. When an audience is facing the digital display, the vision-based DOOH system will process the audience's face and broadcast a personalized advertisement. Most of the digital signage is available in an uncontrolled environment of public areas. Thus, it poses two main challenges for the vision-based DOOH system to track the audience's movement, which are multiple adjacent faces and occlusion by passer-by. In this paper, a new framework is proposed to combine the digital signage with a depth camera for tracking multi-face in the three-dimensional (3D) environment. The proposed framework extracts the audience's face centroid position (x, y) and depth information (z) and plots into the aerial map to simulate the audience's movement that is corresponding to the real-world environment. The advertiser can further measure the advertising effectiveness through the audience's behavior.
Yet Another E-Voting Scheme Implemented Using Hyperledger Fabric Blockchain

There are many papers whose authors propose various approaches to the construction of electronic voting (e-voting) systems that are resistant to various types of attacks. The two main properties of such systems are anonymity and verifiability. To provide anonymity, a blind signature or a ring signature is often used. To provide verifiability, distributed ledger technologies, in particular blockchain, have recently been used. One of these systems has been presented at ICCSA 2019. This system is implemented using Hyperledger Fabric blockchain platform and uses a blind signature to provide anonymity. One of the disadvantages of this system is that a compromised signer (an organizer generating a blind signature) could independently create valid ballots without detection. In this paper, we modify this system by replacing a blind signature with a linkable ring signature in order to eliminate this disadvantage. As a result, we combine linkable ring signature, Idemix and blockchain technologies to construct a decentralized anonymous e-voting system that does not rely on a trusted signer and can achieve the following properties: eligibility, unreusability, anonymity, and verifiability. In addition, the use of both a linkable ring signature and Idemix allows us to construct a two-stage anonymization, that increases the versatility of the proposed system. This allows us to use a blockchain platform (for example, Hyperledger Fabric) to implement the e-voting system, without making changes to platform standard signature scheme.
Fast Perturbative Algorithm Configurators

Recent work has shown that the ParamRLS and ParamILS algorithm configurators can tune some simple randomised search heuristics for standard benchmark functions in linear expected time in the size of the parameter space. In this paper we prove a linear lower bound on the expected time to optimise any parameter tuning problem for ParamRLS, ParamILS as well as for larger classes of algorithm configurators. We propose a harmonic mutation operator for perturbative algorithm configurators that provably tunes single-parameter algorithms in polylogarithmic time for unimodal and approximately unimodal (i.e., non-smooth, rugged with an underlying gradient towards the optimum) parameter spaces. It is suitable as a general-purpose operator since even on worst-case (e.g., deceptive) landscapes it is only by at most a logarithmic factor slower than the default ones used by ParamRLS and ParamILS. An experimental analysis confirms the superiority of the approach in practice for a number of configuration scenarios, including ones involving more than one parameter.
Designing human-god interfaces

In this paper, we report the results from the `Human-God Interfaces' project, which aimed to redesign traditional Catholic rituals, based on the principles of Tangible Interaction. Seven prototypes are presented. We discuss our learnings from the design process, highlighting how all prototypes follow the same recipe of incorporating a Catholic belief into a well-known everyday object and adding an `unknowable' element.
Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation

In light of recent works exploring automated pathological diagnosis, studies have also shown that medical text reports can be generated with varying levels of efficacy. Brain diffusion-weighted MRI (DWI) has been used for the diagnosis of ischaemia in which brain death can follow in immediate hours. It is therefore of the utmost importance to obtain ischaemic brain diagnosis as soon as possible in a clinical setting. Previous studies have shown that MRI acquisition can be accelerated using variable-density Cartesian undersampling methods. In this study, we propose an accelerated DWI acquisition pipeline for the purpose of generating text reports containing diagnostic information. We demonstrate that we can learn a semantic-preserving latent space for minor as well as extremely undersampled MR images capable of achieving promising results on a diagnostic report generation task.
A Fresh View of Linear Logic as a Logical Framework

One of the most fundamental properties of a proof system is analyticity, expressing the fact that a proof of a given formula F only uses subformulas of F. In sequent calculus, this property is usually proved by showing that the cut rule is admissible, i.e., the introduction of the auxiliary lemma A in the reasoning “if A follows from B and C follows from A, then C follows from B” can be eliminated. Mathematically, this means that we can inline the intermediate step A to have a direct proof of C from the hypothesis B. More importantly, the proof of cut-elimination shows that the proof of C follows directly from the axiomatic theory and B (and no external lemmas are needed). The proof of cut-elimination is usually a tedious process through several proof transformations, thus requiring the assistance of (semi-)automatic procedures to avoid mistakes. In a previous work by Miller and Pimentel, linear logic (LL) was used as a logical framework for establishing sufficient conditions for cut-elimination of object logics (OL). The OL's inference rules were encoded as an LL theory and an easy-to-verify criterion sufficed to establish the cut-elimination theorem for the OL at hand. Using such procedure, analyticity of logical systems such as LK (classical logic), LJ (intuitionistic logic) and substructural logics such as MALL (multiplicative additive LL) was proved within the framework. However, there are many logical systems that cannot be adequately encoded in LL, the most symptomatic cases being sequent systems for modal logics. In this paper we use a linear-nested sequent (LNS) presentation of SLL (a variant of linear logic with subexponentials) and show that it is possible to establish a cut-elimination criterion for a larger class of logical systems, including LNS proof systems for K, 4, KT, KD, S4 and the multi-conclusion LNS system for intuitionistic logic (mLJ). Impressively enough, the sufficient conditions for cut-elimination presented here remain as simple as the one proposed by Miller and Pimentel. The key ingredient in our developments is the use of the right formalism: we adopt LNS based OL systems, instead of sequent ones. This not only provides a neat encoding procedure of OLs into SLL, but it also allows for the use of the meta-theory of SLL to establish fundamental meta-properties of the encoded OLs. We thus contribute with procedures for checking cut-elimination of several logical systems that are widely used in philosophy, mathematics and computer science.
Recurrent Image Annotation with Explicit Inter-label Dependencies

Inspired by the success of the CNN-RNN framework in the image captioning task, several works have explored this in multi-label image annotation with the hope that the RNN followed by a CNN would encode inter-label dependencies better than using a CNN alone. To do so, for each training sample, the earlier methods converted the ground-truth label-set into a sequence of labels based on their frequencies (e.g., rare-to-frequent) for training the RNN. However, since the ground-truth is an unordered set of labels, imposing a fixed and predefined sequence on them does not naturally align with this task. To address this, some of the recent papers have proposed techniques that are capable to train the RNN without feeding the ground-truth labels in a particular sequence/order. However, most of these techniques leave it to the RNN to implicitly choose one sequence for the ground-truth labels corresponding to each sample at the time of training, thus making it inherently biased. In this paper, we address this limitation and propose a novel approach in which the RNN is explicitly forced to learn multiple relevant inter-label dependencies, without the need of feeding the ground-truth in any particular order. Using thorough empirical comparisons, we demonstrate that our approach outperforms several state-of-the-art techniques on two popular datasets (MS-COCO and NUS-WIDE). Additionally, it provides a new perspecitve of looking at an unordered set of labels as equivalent to a collection of different permutations (sequences) of those labels, thus naturally aligning with the image annotation task. Our code is available at: https://github.com/ayushidutta/multi-order-rnn.
Modelling and correction for polarization errors of a 600 mm aperture dynamic Fizeau interferometer

The polarization errors of large aperture dynamic interferometers based on the polarization phase shifting method are mainly coming from the effects of imperfect polarized elements and birefringence of large elements. Using the Lissajous ellipse fitting algorithm to correct the influence of the polarized device can effectively eliminate single and double frequency print through errors. We develop a wave plate model for analyzing the birefringence effect, and on this basis, we establish the relationship between the calculated phase and the ideal phase distribution. Experiments are carried out on a 600mm aperture Fizeau interferometer and then compared with the result acquired through the wavelength tuning method. The difference between PV is only 0.002λ.
Is the blockchain a relevant technology for the industry 4.0?

For use-cases in Industry 4.0, involving several stakeholders of a value chain around connected and autonomous smart robots, the blockchain Ethereum appears as a high potential technology. However, the use of digital certificates from a PKI also allows to meet the requirements with a more mature technology. This paper explores through the implementation of two proofs of concept the advantages of each of the technologies and shows that they could complement each other advantageously. The blockchain reinforces trust between independent stakeholders, involved around cyber-secure embedded smart robots distributed in a factory, enabling business development and traceability of the activities in a transparent way.