Reviewerly/exHarmony huggingface dataset:
Detection of Tennis Activities with Wearable Sensors

This paper aims to design and implement a system capable of distinguishing between different activities carried out during a tennis match. The goal is to achieve the correct classification of a set of tennis strokes. The system must exhibit robustness to the variability of the height, age or sex of any subject that performs the actions. A new database is developed to meet this objective. The system is based on two sensor nodes using Bluetooth Low Energy (BLE) wireless technology to communicate with a PC that acts as a central device to collect the information received by the sensors. The data provided by these sensors are processed to calculate their spectrograms. Through the application of innovative deep learning techniques with semi-supervised training, it is possible to carry out the extraction of characteristics and the classification of activities. Preliminary results obtained with a data set of eight players, four women and four men have shown that our approach is able to address the problem of the diversity of human constitutions, weight and sex of different players, providing accuracy greater than 96.5% to recognize the tennis strokes of a new player never seen before by the system.
Endemic Oblivious Transfer

Oblivious Transfer has played a crucial role in the design of secure multi party computation. Nevertheless, there are not many practical solutions that achieve simulation based security and at the same time instantiable based on different assumptions. In this work, we consider a simulation based security notion that we call endemic security. We show how to construct highly efficient oblivious transfer in the random oracle model that achieves endemic security under a wide range of assumptions, among them DDH, CDH, LWE and coding based assumptions. We construct a secure oblivious transfer based on DDH that takes only a single communication round which allows significant performance gains. We also instantiate our oblivious transfer with the Crystals.Kyber key agreement. Our implementation shows that both instantiations can be computed in under one millisecond. Further, we revisit, correct and improve existing oblivious transfer extension techniques. We provide an implementation of an oblivious transfer extension protocol in the ideal cipher model that is actively secure, processing up to 23 million OTs per second and up to 10 times faster than previous secure implementations. We also show that our framework can compute endemically secure OT extension and the base OTs in just two rounds.
Question Difficulty Prediction for Multiple Choice Problems in Medical Exams

In the ITS (Intelligent Tutoring System) services, personalized question recommendation is a critical function in which the key challenge is to predict the difficulty of each question. Given the difficulty of each question, ITS can allocate suitable questions for students with varied knowledge proficiency. Existing approaches mainly relied on expert labeling, which is both subjective and labor intensive. In this paper, we propose a Document enhanced Attention based neural Network(DAN) framework to predict the difficulty of multiple choice problems in medical exams. DAN consists of three major steps: (1) In addition to stem and options, DAN retrieves relevant medical documents to enrich the content of each question; (2) DAN breaks down the question's difficulty into two parts: the hardness for recalling the knowledge assessed by the question and the confusion degree to exclude distractors. For each part, DAN introduces corresponding attention layers to model it; (3) DAN combines two parts of difficulties together to predict the overall difficulty. We collect a real-world data set from one of the largest medical online education websites in China. And the experimental results demonstrate the effectiveness of the proposed framework.
The Closed-Form Expressions for the Bistatic Quantum Radar Cross Section of the Typical Simple Plates

Closed-form expressions for the bistatic quantum radar scattered by the circular and triangular plates with the single-photon incidence are derived in the field of the Fourier relationship between the surface atom distribution and the bistatic quantum radar cross section (BIQRCS). The proposed expressions are highly efficient useful tools to analytically estimate the BIQRCS of the typical simple plates in the condition of high frequency approximation. And the formulas have been preliminarily verified by the numerical solution for the typical incident angles. In addition, the analysis of the quantum effect of sidelobes of BIQRCS in scattering, which can be seen as a kind of quantum interference, is also presented. We expect that this achievement will apply to the modeling of bistatic quantum radar scattering.
Succinct Arguments for Bilinear Group Arithmetic

In their celebrated work, Groth and Sahai [EUROCRYPT'08, SICOMP' 12] constructed non-interactive zero-knowledge (NIZK) proofs for general bilinear group arithmetic relations, which spawned the entire subfield of structure-preserving cryptography. This branch of the theory of cryptography focuses on modular design of advanced cryptographic primitives. Although the proof systems of Groth and Sahai are a powerful toolkit, their efficiency hits a barrier when the size of the witness is large, as the proof size is linear in that of the witness. In this work, we revisit the problem of proving knowledge of general bilinear group arithmetic relations in zero-knowledge. Specifically, we construct a succinct zero-knowledge argument for such relations, where the communication complexity is logarithmic in the integer and source group components of the witness. Our argument has public-coin setup and verifier and can therefore be turned non-interactive using the Fiat-Shamir transformation in the random oracle model. For the special case of non-bilinear group arithmetic relations with only integer unknowns, our system can be instantiated in non-bilinear groups. In many applications, our argument system can serve as a drop-in replacement of Groth-Sahai proofs, turning existing advanced primitives in the vast literature of structure-preserving cryptography into practically efficient systems with short proofs.
HAES

Recommendation systems provide good guidance for users to find their favorite movies from an overwhelming amount of options. However, most systems excessively pursue the recommendation accuracy and give rise to over-specialization, which triggers the emergence of serendipity. Hence, serendipity recommendation has received more attention in recent years, facing three key challenges: subjectivity in the definition, the lack of data, and users' floating demands for serendipity. To address these challenges, we introduce a new model called HAES, a H ybrid A pproach for movie recommendation with E lastic S erendipity, to recommend serendipitous movies. Specifically, we (1) propose a more objective definition of serendipity, \em content difference and \em genre accuracy, according to the analysis on a real dataset, (2) propose a new algorithm named JohnsonMax to mitigate the data sparsity and build weak ties beneficial to finding serendipitous movies, and (3) define a novel concept of elasticity in the recommendation, to adjust the level of serendipity flexibly and reach a trade-off between accuracy and serendipity. Extensive experiments on real-world datasets show that HAES enhances the serendipity of recommendations while preserving recommendation quality, compared to several widely used methods.
Modeling Quiet Solar Luminosity Variability from TSI Satellite Measurements and Proxy Models during 1980–2018

A continuous record of direct total solar irradiance (TSI) observations began with a series of satellite experiments in 1978. This record requires comparisons of overlapping satellite observations with adequate relative precisions to provide useful long term TSI trend information. Herein we briefly review the active cavity radiometer irradiance monitor physikalisch-meteorologisches observatorium davos (ACRIM-PMOD) TSI composite controversy regarding how the total solar irradiance (TSI) has evolved since 1978 and about whether TSI significantly increased or slightly decreased from 1980 to 2000. The main question is whether TSI increased or decreased during the so-called ACRIM-gap period from 1989 to 1992. There is significant discrepancy between TSI proxy models and observations before and after the gap, which requires a careful revisit of the data analysis and modeling performed during the ACRIM-gap period. In this study, we use three recently proposed TSI proxy models that do not present any TSI increase during the ACRIM-gap, and show that they agree with the TSI data only from 1996 to 2016. However, these same models significantly diverge from the observations from 1981 and 1996. Thus, the scaling errors must be different between the two periods, which suggests errors in these models. By adjusting the TSI proxy models to agree with the data patterns before and after the ACRIM-gap, we found that these models miss a slowly varying TSI component. The adjusted models suggest that the quiet solar luminosity increased from the 1986 to the 1996 TSI minimum by about 0.45 W/m2 reaching a peak near 2000 and decreased by about 0.15 W/m2 from the 1996 to the 2008 TSI cycle minimum. This pattern is found to be compatible with the ACRIM TSI composite and confirms the ACRIM TSI increasing trend from 1980 to 2000, followed by a long-term decreasing trend since.
Eliciting Contact-Based and Contactless Gestures With Radar-Based Sensors

Radar sensing technologies now offer new opportunities for gesturally interacting with a smart environment by capturing microgestures via a chip that is embedded in a wearable device, such as a smartwatch, a finger or a ring. Such microgestures are issued at a very small distance from the device, regardless of whether they are contact-based, such as on the skin, or contactless. As this category of microgestures remains largely unexplored, this paper reports the results of a gesture elicitation study that was conducted with twenty-five participants who expressed their preferred user-defined gestures for interacting with a radar-based sensor on nineteen referents that represented frequent Internet-of-things tasks. This study clustered the $25 \times 19=475$ initially elicited gestures into four categories of microgestures, namely, micro, motion, combined, and hybrid, and thirty-one classes of distinct gesture types and produced a consensus set of the nineteen most preferred microgestures. In a confirmatory study, twenty new participants selected gestures from this classification for thirty referents that represented tasks of various orders; they reached a high rate of agreement and did not identify any new gestures. This classification of radar-based gestures provides researchers and practitioners with a larger basis for exploring gestural interactions with radar-based sensors, such as for hand gesture recognition.
Online transfer learning with multiple source domains for multi-class classification

The major objective of transfer learning is to handle the learning tasks on a target domain by utilizing the knowledge extracted from the source domain(s), when the labeled data in the target domain are not sufficient. Transfer learning can be classified into offline transfer learning (OffTL) and online transfer learning (OnTL), and OnTL has attracted much attention and research due to its more realistic scenario assumed in practice. There can be multiple source domains, therefore, OnTL with Multiple Source Domains has been studied in recent years and algorithms have been proposed. Nevertheless, it can be noted that existing research on OnTL with Multiple Source Domains only deals with binary classification tasks. In this paper, we make the first attempt to study OnTL with Multiple Source Domains for multi-class classification (MC), and propose an algorithm, referred to as Online Multi-source Transfer Learning for Multi-class classification (OMTL-MC) algorithm. OMTL-MC algorithm is built on two-stage ensemble strategy, in this way, the knowledge extracted from different source domains can be simultaneously online transferred to improve the performance of the classifier in the target domain. In order to deeper explore the underlying structure among multiple classes, an Extended Hinge Loss (EHL) function is adopted in OMTL-MC. We theoretically analyze the mistake bound of OMTL-MC algorithm. In addition, experiments on several popular datasets expound that the proposed OMTL-MC algorithm outperforms the other compared algorithms.
Efficient and Secure Non-Coherent OFDM-Based Overlapped Chaotic Chip Position Shift Keying System: Design and Performance Analysis

In this paper, we propose a novel non-coherent orthogonal frequency division multiplex (OFDM) based overlapped chaotic chip position shift keying (OCCPSK) system. Different from traditional non coherent chaotic systems, this system endeavors to achieve efficient and secure performances with no need to transmit reference chaotic sequences. At the transmitter, information bits are grouped into multiple subsets and respectively modulated by the position of chaotic chips. Then iterative chaotically shift-aided shuffling and overlapping operations are performed on the resultant multi-ary signals to enhance the security. Subsequently, OFDM modulations are performed and information-bearing signals are transmitted. At the legitimate receiver, with the known key parameters, reverse operations are carried out. Then the positions used for modulations can be formulated out to recover information bits via maximizing the Euclidean norm of received symbols. Since no reference chaotic signals are required to be transmitted, both efficiency and security performances are improved. Moreover, we derive the analytical symbol error rate (SER) and bit error rate (BER) expressions over fading channels, and theoretical spectrum efficiency, energy efficiency and security performances are analyzed. Simulation results verify the effectiveness of derivations, and demonstrate the high efficiency and security of this design.
Bayesian capsule networks for 3D human pose estimation from single 2D images

Deep Bayesian Networks are a hot topic in Deep Learning because this approach makes it possible to minimize both the epistemic and the homoscedastic uncertainty at the same time self balancing multiple and complementary losses for a given task, simply by employing standard operations such as dropout, mean squared error or cross-entropy. On the other hand, Capsule networks are a novel DNN architecture that offer a richer representation because each concept is represented by a number of different vectors. The bayesian formulation of the Capsule networks is still an open problem that we address in this paper. We present a bayesian formulation of Capsule networks and compare its performance against the state-of-the-art for the ill-posed regression problem of estimating the 3D human pose from a single 2D image. The results show that our network is very competitive with a much more straightforward solution. To enable fair comparisons the source code is openly available at https://github.com/rollervan/BCN_3DPose/
Adversarial Feedback Loop

Thanks to their remarkable generative capabilities, GANs have gained great popularity, and are used abundantly in state-of-the-art methods and applications. In a GAN based model, a discriminator is trained to learn the real data distribution. To date, it has been used only for training purposes, where it's utilized to train the generator to provide real-looking outputs. In this paper we propose a novel method that makes an explicit use of the discriminator in test-time, in a feedback manner in order to improve the generator results. To the best of our knowledge it is the first time a discriminator is involved in test-time. We claim that the discriminator holds significant information on the real data distribution, that could be useful for test-time as well, a potential that has not been explored before. The approach we propose does not alter the conventional training stage. At test-time, however, it transfers the output from the generator into the discriminator, and uses feedback modules (convolutional blocks) to translate the features of the discriminator layers into corrections to the features of the generator layers, which are used eventually to get a better generator result. Our method can contribute to both conditional and unconditional GANs. As demonstrated by our experiments, it can improve the results of state-of-the-art networks for super-resolution, and image generation.
Additive Explanations for Anomalies Detected from Multivariate Temporal Data

Detecting anomalies from high-dimensional multivariate temporal data is challenging, because of the non-linear, complex relationships between signals. Recently, deep learning methods based on autoencoders have been shown to capture these relationships and accurately discern between normal and abnormal patterns of behavior, even in fully unsupervised scenarios. However, validating the anomalies detected is difficult without additional explanations. In this paper, we extend SHAP -- a unified framework for providing additive explanations, previously applied for supervised models -- with influence weighting, in order to explain anomalies detected from multivariate time series with a GRU-based autoencoder. Namely, we extract the signals that contribute most to an anomaly and those that counteract it. We evaluate our approach on two use cases and show that we can generate insightful explanations for both single and multiple anomalies.
Value of Geologically Derived Features in Machine Learning Facies Classification

Abstract The aim of this work is to demonstrate how geologically interpretative features can improve machine learning facies classification with uncertainty assessment. Manual interpretation of lithofacies from wireline log data is traditionally performed by an expert, can be subject to biases, and is substantially laborious and time consuming for very large datasets. Characterizing the interpretational uncertainty in facies classification is quite difficult, but it can be very important for reservoir development decisions. Thus, automation of the facies classification process using machine learning is a potentially intuitive and efficient way to facilitate facies interpretation based on large-volume data. It can also enable more adequate quantification of the uncertainty in facies classification by ensuring that possible alternative lithological scenarios are not overlooked. An improvement of the performance of purely data-driven classifiers by integrating geological features and expert knowledge as additional inputs is proposed herein, with the aim of equipping the classifier with more geological insight and gaining interpretability by making it more explanatory. Support vector machine and random forest classifiers are compared to demonstrate the superiority of the latter. This study contrasts facies classification using only conventional wireline log inputs and using additional geological features. In the first experiment, geological rule-based constraints were implemented as an additional derived and constructed input. These account for key geological features that a petrophysics or geological expert would attribute to typical and identifiable wireline log responses. In the second experiment, geological interpretative features (i.e., grain size, pore size, and argillaceous content) were used as additional independent inputs to enhance the prediction accuracy and geological consistency of the classification outcomes. Input and output noise injection experiments demonstrated the robustness of the results towards systematic and random noise in the data. The aspiration of this study is to establish geological characteristics and knowledge to be considered as decisive data when used in machine learning facies classification.
Blockchain and edge computing–based architecture for participatory smart city applications

Summary Smart cities aim to provide smart governance with the emphasis on gaining high transparency and trust in public services and enabling citizen participation in decision making processes. This means on the one hand data generated from urban transactions need to be open and trustworthy. On the other hand, security and privacy of public data needs to be handled at different administrative and geographical levels. In this paper, we investigate the pivotal role of blockchain in providing privacy, self‐verification, authentication, and authorization of participatory transactions in open governance. We also investigate up to what extent edge computing can contribute toward management of permissioned sharing at specific administrative levels and enhance privacy and provide an economic approach for resource utilization in a distributed environment. We introduce a novel architecture that is based on distributed hybrid ledger and edge computing model. The architecture provides refined and secure management of data generated and processed in different geographical and administrative units of a city. We implemented a proof of concept of the architecture and applied it on a carefully designed use case, ie, citizen participation in administrative decisions through consensus. This use case highlights the need to keep and process citizen participation data at local level by deploying district chaincodes and only share consensus results through permissioned chaincodes. The results reveal that proposed architecture is scalable and provide secure and privacy protected environment for citizen participatory applications. Our performance test results are promising and show that under control conditions, the average registration time for a citizen transaction is about 42 ms, whilst the validation and result compilation of 100 concurrent citizens' transactions took about 2.4 seconds.
DocFigure: A Dataset for Scientific Document Figure Classification

Document figure classification (DFC) is an important stage of a document figure understanding system. The design of a DFC system required a well defined figure categories and dataset. To the best of the author's knowledge, the existing datasets related to classification of figures in the document images are limited with respect to their size and categories [1]-[3]. In this paper, we introduce a scientific figure classification dataset, named as DocFigure. The dataset consists of 33K annotated figures of 28 different categories present in the document images which correspond to scientific articles published in the CVPR, ECCV, ICCV, etc. conferences in last several years. Manual annotation of such a large number (33K) of figures is time consuming and cost ineffective. In this article, we design a web based annotation tool which can efficiently assign category labels to large number of figures with the minimum efforts of human annotators. To benchmark our generated dataset on classification task, we propose three baseline classification techniques using deep feature, deep texture feature and combination of both. In our analysis, we found that the combination of both deep feature and deep texture feature is more effective for document figure classification task than the individual features. The dataset and the code are publicly available at https://researchweb.iiit.ac.in/~jobin.kv/projects/.