Reviewerly/exHarmony huggingface dataset:
An Analysis of NIST SP 800-90A

We investigate the security properties of the three deterministic random bit generator (DRBG) mechanisms in NIST SP 800-90A [2]. The standard received considerable negative attention due to the controversy surrounding the now retracted $$\mathsf{{DualEC\text {-}DRBG}}$$ , which appeared in earlier versions. Perhaps because of the attention paid to the DualEC, the other algorithms in the standard have received surprisingly patchy analysis to date, despite widespread deployment. This paper addresses a number of these gaps in analysis, with a particular focus on $$\mathsf{{HASH\text {-}DRBG}}$$ and $$\mathsf{{HMAC\text {-}DRBG}}$$ . We uncover a mix of positive and less positive results. On the positive side, we prove (with a caveat) the robustness [13] of $$\mathsf{{HASH\text {-}DRBG}}$$ and $$\mathsf{{HMAC\text {-}DRBG}}$$ in the random oracle model (ROM). Regarding the caveat, we show that if an optional input is omitted, then – contrary to claims in the standard— $$\mathsf{{HMAC\text {-}DRBG}}$$ does not even achieve the (weaker) property of forward security. We then conduct a more informal and practice-oriented exploration of flexibility in the standard. Specifically, we argue that these DRBGs have the property that partial state leakage may lead security to break down in unexpected ways. We highlight implementation choices allowed by the overly flexible standard that exacerbate both the likelihood, and impact, of such attacks. While our attacks are theoretical, an analysis of two open source implementations of $$\mathsf{{CTR\text {-}DRBG}}$$ shows that these potentially problematic implementation choices are made in the real world.
Discovering varying patterns of Normal and interleaved ADLs in smart homes

People may do the same activity in many different ways hence, modeling and recognizing that activity based on data gathered through simple sensors like motion sensor is a complex task. In this paper, we propose an approach for activity mining and activity tracking which identifies frequent normal and interleaved activities that individuals perform. With this capability, we can track the occurrence of regular activities to monitor users and detect changes in an individual’s behavioral pattern and lifestyle. We have tested the proposed method using the datasets of Washington State University CASAS and the Massachusetts Institute of Technology (MIT) smart home projects. The obtained results show considerable improvements compared with existing methods.
Reverse Densely Connected Feature Pyramid Network for Object Detection

The wide and extreme diversity of object size is an ever-lasting challenging issue in object detection research. To address this problem, we propose Reverse Densely Connected Feature Pyramid Network (Rev-Dense FPN), a novel multi-scale feature transformation and fusion method for object detection. Through reverse dense connection, we directly fuse all the feature maps of higher levels than the current one. This avoids useful contextual information on the higher level to vanish when passed down to lower levels, which is a key disadvantage of widely used feature fusion paradigms such as recursive top-down connection. Therefore, a more powerful hierarchical representation structure can be obtained by effectively aggregating multi-level contexts. We apply Rev-Dense FPN on SSD framework, which reaches 81.1% mAP (mean average precision) on the PASCAL VOC 2007 dataset and 31.2 AP on the MS COCO dataset. The results show that Rev-Dense FPN is more effective in dealing with diversified object sizes.
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables

The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossless compression schemes for general latent variable models, however, is still an open problem. Bits-Back with Asymmetric Numeral Systems (BB-ANS), recently proposed by Townsend et al. (2019), makes bits-back coding practically feasible for latent variable models with one latent layer, but it is inefficient for hierarchical latent variable models. In this paper we propose Bit-Swap, a new compression scheme that generalizes BB-ANS and achieves strictly better compression rates for hierarchical latent variable models with Markov chain structure. Through experiments we verify that Bit-Swap results in lossless compression rates that are empirically superior to existing techniques. Our implementation is available at https://github.com/fhkingma/bitswap.
THE EXPANSION OF THE CLASSROOM THROUGH MOBILE IMMERSIVE LEARNING

In this article we describe how mobile devices enable immersive learning experiences.Expensive Head Mounted Displays (HMDs) are a thing of the past, as even inexpensive alternatives in combination with a smartphone make it possible to immerse oneself in virtual worlds.Learners then take these worlds as realistic, which can lead to cognitively and physically behaving as in real life.Immersive learning then may contribute to the dissolution of space and time and to more authentic learning experiences.The limitations of spatial classrooms can thus be broken down and expanded more and more, in the K-12 classroom as well as in higher education.
Extrinsic calibration of stereo camera and three-dimensional laser scanner

The paper describes an approach that allows solving the problem of extrinsic calibration of a multi-beam lidar and a stereo camera. The approach does not impose any restrictions on the place in which calibration should be performed. Calibration is performed using a calibration board, which is a flat rectangle with special markers. Three-dimensional correspondences are used for calibration. First, a search for the three-dimensional coordinates of the corner points of the calibration board in the coordinate systems of the stereo pair cameras as well as in the coordinate system of the lidar is made. Next, using the optimization methods, calibration parameters are calculated. The results of a series of virtual and real experiments show that the algorithm allows the calibration to be performed with an accuracy comparable to that of sensors. The proposed approach allows one to improve the calibration accuracy due to the simultaneous use of information from two cameras of the stereo pair and is suitable for lidars with both the low and high point density.
Neural Replicator Dynamics

Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as nonstationarity and instability. In this paper, we first demonstrate that standard softmax-based policy gradient can be prone to poor performance in the presence of even the most benign nonstationarity. By contrast, it is known that the replicator dynamics, a well-studied model from evolutionary game theory, eliminates dominated strategies and exhibits convergence of the time-averaged trajectories to interior Nash equilibria in zero-sum games. Thus, using the replicator dynamics as a foundation, we derive an elegant one-line change to policy gradient methods that simply bypasses the gradient step through the softmax, yielding a new algorithm titled Neural Replicator Dynamics (NeuRD). NeuRD reduces to the exponential weights/Hedge algorithm in the single-state all-actions case. Additionally, NeuRD has formal equivalence to softmax counterfactual regret minimization, which guarantees convergence in the sequential tabular case. Importantly, our algorithm provides a straightforward way of extending the replicator dynamics to the function approximation setting. Empirical results show that NeuRD quickly adapts to nonstationarities, outperforming policy gradient significantly in both tabular and function approximation settings, when evaluated on the standard imperfect information benchmarks of Kuhn Poker, Leduc Poker, and Goofspiel.
One‐Step Implementation of <i>N</i>‐Qubit Nonadiabatic Holonomic Quantum Gates with Superconducting Qubits via Inverse Hamiltonian Engineering

Abstract A protocol is proposed to realize one‐step implementation of the N ‐qubit nonadiabatic holonomic quantum gates with superconducting qubits. The inverse Hamiltonian engineering is applied in designing microwave pulses to drive superconducting qubits. By combining curve fitting, the wave shapes of the designed pulses can be described by simple functions, which are not hard to realize in experiments. To demonstrate the effectiveness of the protocol, a three‐qubit holonomic controlled π‐phase gate is taken as an example in numerical simulations. The results show that the protocol holds robustness against noise and decoherence. Therefore, the protocol may provide an alternative approach for implementing N ‐qubit nonadiabatic holonomic quantum gates.
A Hybrid Model for Information Security Risk Assessment

Many industry standards and methodologies were introduced which has brought forth the management of threats assessment and risk management of information assets in a systematic manner.This paper will review and analyze the main processes followed in IT risk management frameworks from the perspective of the threat analysis process using a threat modeling methodology.In this study, the authors propose a new assessment model which shows that systematic threat analysis is an essential element to be considered as an integrated process within IT risk management frameworks.The new proposed model complements and fulfills the gap in the practice of assessing information security risks.
3D Virtual World BPM Training Systems: Process Gateway Experimental Results

It is important for companies that their operational employees have profound knowledge of the processes in which their work is embedded. 3D virtual world (VW) environments are promising for learning, especially for complex processes that have deviations from the standard flow. We design a 3D VW process training environment to improve process learning, particularly for complex processes with alternative flows, represented with gateways in process models. We adopt the method of loci, which suggests the mental traversal of routines for improving learning. Our experiment with 145 participants compares the level of knowledge acquired for a sample process with our 3D VW environment and a 2D depiction. We found that the 3D VW environment significantly increases the level of process knowledge acquired across the typical gateways in processes. Our results contribute to our understanding of how individuals learn knowledge of processes via 3D environments. With a low initial investment, practitioners are encouraged to invest in 3D training systems for processes, since these can be set up once and reused multiple times for various employees.
Gender-preserving Debiasing for Pre-trained Word Embeddings

Word embeddings learnt from massive text collections have demonstrated significant levels of discriminative biases such as gender, racial or ethnic biases, which in turn bias the down-stream NLP applications that use those word embeddings. Taking gender-bias as a working example, we propose a debiasing method that preserves non-discriminative gender-related information, while removing stereotypical discriminative gender biases from pre-trained word embeddings. Specifically, we consider four types of information: \emph{feminine}, \emph{masculine}, \emph{gender-neutral} and \emph{stereotypical}, which represent the relationship between gender vs. bias, and propose a debiasing method that (a) preserves the gender-related information in feminine and masculine words, (b) preserves the neutrality in gender-neutral words, and (c) removes the biases from stereotypical words. Experimental results on several previously proposed benchmark datasets show that our proposed method can debias pre-trained word embeddings better than existing SoTA methods proposed for debiasing word embeddings while preserving gender-related but non-discriminative information.
<i>SLFAT</i>: Client-Side Evil Twin Detection Approach Based on Arrival Time of Special Length Frames

In general, the IEEE 802.11 network identifiers used by wireless access points (APs) can be easily spoofed. Accordingly, a malicious adversary is able to clone the identity information of a legitimate AP (LAP) to launch evil twin attacks (ETAs). The evil twin is a class of rogue access point (RAP) that masquerades as a LAP and allures Wi-Fi victims’ traffic. It enables an attacker with little effort and expenditure to eavesdrop or manipulate wireless communications. Due to the characteristics of strong concealment, high confusion, great harmfulness, and easy implementation, the ETA has become one of the most severe security threats in Wireless Local Area Networks (WLANs). Here, we propose a novel client-side approach, Speical Length Frames Arrival Time ( SLFAT ), to detect the ETA, which utilizes the same gateway as the LAP. By monitoring the traffic emitted by target APs at a detection node, SLFAT extracts the arrival time of the special frames with the same length to determine the evil twin’s forwarding behavior. SLFAT is passive, lightweight, efficient, hard to be escaped. It allows users to independently detect ETA on ordinary wireless devices. Through implementation and evaluation in our study, SLFAT achieves a very high detection rate in distinguishing evil twins from LAPs.
Attention-Based Recurrent Neural Networks (RNNs) for Short Text Classification: An Application in Public Health Monitoring

In this paper, we propose an attention-based approach to short text classification, which we have created for the practical application of Twitter mining for public health monitoring. Our goal is to automatically filter Tweets which are relevant to the syndrome of asthma/difficulty breathing. We describe a bi-directional Recurrent Neural Network architecture with an attention layer (termed ABRNN) which allows the network to weigh words in a Tweet differently based on their perceived importance. We further distinguish between two variants of the ABRNN based on the Long Short Term Memory and Gated Recurrent Unit architectures respectively, termed the ABLSTM and ABGRU. We apply the ABLSTM and ABGRU, along with popular deep learning text classification models, to a Tweet relevance classification problem and compare their performances. We find that the ABLSTM outperforms the other models, achieving an accuracy of 0.906 and an F1-score of 0.710. The attention vectors computed as a by-product of our models were also found to be meaningful representations of the input Tweets. As such, the described models have the added utility of computing document embeddings which could be used for other tasks besides classification. To further validate the approach, we demonstrate the ABLSTM’s performance in the real world application of public health surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE). A strong positive correlation was observed between the ABLSTM surveillance signal and the real-world asthma/difficulty breathing syndromic surveillance data. The ABLSTM is a useful tool for the task of public health surveillance.
Algorithm for solving unconstrained unitary quantum brachistochrone problems

We introduce an iterative method to search for time-optimal Hamiltonians that drive a quantum system between two arbitrary, and in general mixed, quantum states. The method is based on the idea of progressively improving the efficiency of an initial, randomly chosen, Hamiltonian, by reducing its components that do not actively contribute to driving the system. We show that our method converges rapidly even for large dimensional systems, and that its solutions saturate any attainable bound for the minimal time of evolution. We provide a rigorous geometric interpretation of the iterative method by exploiting an isomorphism between geometric phases acquired by the system along a path and the Hamiltonian that generates it, and discuss resulting similarities with Grover's quantum search algorithm. Our method is directly applicable as a powerful tool for state preparation and gate design problems.
Adaptive Gradient-Based Meta-Learning Methods

We build a theoretical framework for designing and understanding practical meta-learning methods that integrates sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms. Our approach enables the task-similarity to be learned adaptively, provides sharper transfer-risk bounds in the setting of statistical learning-to-learn, and leads to straightforward derivations of average-case regret bounds for efficient algorithms in settings where the task-environment changes dynamically or the tasks share a certain geometric structure. We use our theory to modify several popular meta-learning algorithms and improve their meta-test-time performance on standard problems in few-shot learning and federated learning.
Using learned optimizers to make models robust to input noise

State-of-the art vision models can achieve superhuman performance on image classification tasks when testing and training data come from the same distribution. However, when models are tested on corrupted images (e.g. due to scale changes, translations, or shifts in brightness or contrast), performance degrades significantly. Here, we explore the possibility of meta-training a learned optimizer that can train image classification models such that they are robust to common image corruptions. Specifically, we are interested training models that are more robust to noise distributions not present in the training data. We find that a learned optimizer meta-trained to produce models which are robust to Gaussian noise trains models that are more robust to Gaussian noise at other scales compared to traditional optimizers like Adam. The effect of meta-training is more complicated when targeting a more general set of noise distributions, but led to improved performance on half of held-out corruption tasks. Our results suggest that meta-learning provides a novel approach for studying and improving the robustness of deep learning models.
AUTOMATIC CALIBRATION AND CO-REGISTRATION FOR A STEREO CAMERA SYSTEM AND A THERMAL IMAGING SENSOR USING A CHESSBOARD

Abstract. This paper presents a system calibration method for a trifocal sensor, which is sensitive to different spectral bands. The trifocal camera system consists of a stereo camera, operating in the visual (VIS) spectrum and a thermal imaging camera, operating in the Long-Wave- Infrared (LWIR) spectrum. Intrinsic parameters and spatial alignment are determined simultaneously. As calibration target a passive aluminium chessboard is used. Corner detection and subsequent bundle adjustment is done on all synchronized image triplets. The remaining reprojection errors are in the sub-pixel range and enable the system to generate metric point clouds, colored with thermal intensities in real-time.
Using Trusted Execution Environments for Secure Stream Processing of Medical Data

Processing sensitive data, such as those produced by body sensors, on third-party untrusted clouds is particularly challenging without compromising the privacy of the users generating it. Typically, these sensors generate large quantities of continuous data in a streaming fashion. Such vast amount of data must be processed efficiently and securely, even under strong adversarial models. The recent introduction in the mass-market of consumer-grade processors with Trusted Execution Environments (TEEs), such as Intel SGX, paves the way to implement solutions that overcome less flexible approaches, such as those atop homomorphic encryption. We present a secure streaming processing system built on top of Intel SGX to showcase the viability of this approach with a system specifically fitted for medical data. We design and fully implement a prototype system that we evaluate with several realistic datasets. Our experimental results show that the proposed system achieves modest overhead compared to vanilla Spark while offering additional protection guarantees under powerful attackers and threat models.
Estimating Feature-Label Dependence Using Gini Distance Statistics

Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RKHS). Two Gini distance based dependence measures are explored: Gini distance covariance and Gini distance correlation. Unlike Pearson covariance and correlation, which do not characterize independence, the above Gini distance based measures define dependence as well as independence of random variables. The test statistics are simple to calculate and do not require probability density estimation. Uniform convergence bounds and asymptotic bounds are derived for the test statistics. Comparisons with distance covariance statistics are provided. It is shown that Gini distance statistics converge faster than distance covariance statistics in the uniform convergence bounds, hence tighter upper bounds on both Type I and Type II errors. Moreover, the probability of Gini distance covariance statistic under-performing the distance covariance statistic in Type II error decreases to 0 exponentially with the increase of the sample size. Extensive experimental results are presented to demonstrate the performance of the proposed method.
Youth and Artificial Intelligence: Where We Stand

This spotlight seeks to share Youth and Media’s initial learnings and key questions around the intersection between AI and youth (ages 12-18), in the context of domains such as education, health and well-being, and the future of work. It aims to encourage various stakeholders — including policymakers, educators, and parents and caregivers — to consider how we can empower young people to meaningfully interact with AIbased technologies to promote and bolster learning, creative expression, and wellbeing, while also addressing key challenges and concerns.
Mediative Sugeno’s-TSK Fuzzy Logic Based Screening Analysis to Diagnosis of Heart Disease

Fuzzy logic is an approach which deals with the incomplete information to handle the imperfect knowledge.In the present research paper we have proposed a new approach that can handle the imperfect knowledge, in a broader way that we will consider the unfavourable case also as the intuitionistic fuzzy logic does.The mediative fuzzy logic is an extensive approach of intuitionistic fuzzy logic, which provides a solution, when there is a contradiction in the expert knowledge for favourable as well as unfavourable cases.The purpose of the present paper is to design a mediative fuzzy inference system based Sugeno-TSK model for the diagnosis of heart disease.Our proposed method is the extension of Sugeno-TSK fuzzy logic controller in the form of Sugeno-TSK mediative fuzzy logic controller.
The Use of Unlabeled Data Versus Labeled Data for Stopping Active Learning for Text Classification

Annotation of training data is the major bottleneck in the creation of text classification systems.Active learning is a commonly used technique to reduce the amount of training data one needs to label.A crucial aspect of active learning is determining when to stop labeling data.Three potential sources for informing when to stop active learning are an additional labeled set of data, an unlabeled set of data, and the training data that is labeled during the process of active learning.To date, no one has compared and contrasted the advantages and disadvantages of stopping methods based on these three information sources.We find that stopping methods that use unlabeled data are more effective than methods that use labeled data.