Reviewerly/exHarmony huggingface dataset:
PBCert: Privacy-Preserving Blockchain-Based Certificate Status Validation Toward Mass Storage Management

In the recent years, the vulnerabilities of conventional public key infrastructure are exposed by the real-world attacks, such as the certificate authority’s single-point-of-failure or clients’ private information leakage. Aimed at the first issue, one type of approach is that multiple entities are introduced to assist the certificate operations, including registration, update, and revocation. However, it is inefficient in computation. Another type is to make the certificate information publicly visible by bringing in the log servers. Nevertheless, the data synchronization among log servers may lead to network latency. Based on the second approach, the blockchain-based public key infrastructure schemes are proposed. Through these type of schemes, all the certificate operations are stored in the blockchain for public audit. However, the issue of revoked certificates’ status storage is worth paying attention, especially in the setting with massive certificates. In addition, the target web server that a client wants to access is exposed in the process of certificate status validation. In this paper, we propose a privacy-preserving blockchain-based certificate status validation scheme called PBCert to solve these two issues. First, we separate the revoked certificates control and storage plane. Only the minimal control information (namely, certificate hashes and related operation block height) is stored in the blockchain and it uses external data stores for the detailed information about all revoked certificates. Second, we design an obscure response to the clients’ certificate status query for the purpose of privacy preserving. Through the security analysis and experiment evaluation, our scheme is significant in practice.
Probabilistic recursive reasoning for multi-agent reinforcement learning

Humans are capable of attributing latent mental contents such as beliefs, or intentions to others. The social skill is critical in everyday life to reason about the potential consequences of their behaviors so as to plan ahead. It is known that humans use this reasoning ability recursively, i.e. considering what others believe about their own beliefs. In this paper, we start from level-1 recursion and introduce a probabilistic recursive reasoning (PR2) framework for multi-agent reinforcement learning. Our hypothesis is that it is beneficial for each agent to account for how the opponents would react to its future behaviors. Under the PR2 framework, we adopt variational Bayes methods to approximate the opponents' conditional policy, to which each agent finds the best response and then improve their own policy. We develop decentralized-training-decentralized-execution algorithms, PR2-Q and PR2-Actor-Critic, that are proved to converge in the self-play scenario when there is one Nash equilibrium. Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge. Our experiments show that it is critical to reason about how the opponents believe about what the agent believes. We expect our work to contribute a new idea of modeling the opponents to the multi-agent reinforcement learning community.
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples

We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack. These statistics can be easily computed and calibrated by randomly corrupting inputs. They exploit certain anomalies that adversarial attacks introduce, in particular if they follow the paradigm of choosing perturbations optimally under p-norm constraints. Access to the log-odds is the only requirement to defend models. We justify our approach empirically, but also provide conditions under which detectability via the suggested test statistics is guaranteed to be effective. In our experiments, we show that it is even possible to correct test time predictions for adversarial attacks with high accuracy.
Finger-Counting-Based Gesture Recognition within Cars Using Impulse Radar with Convolutional Neural Network

The diversion of a driver’s attention from driving can be catastrophic. Given that conventional button- and touch-based interfaces may distract the driver, developing novel distraction-free interfaces for the various devices present in cars has becomes necessary. Hand gesture recognition may provide an alternative interface inside cars. Given that cars are the targeted application area, we determined the optimal location for the radar sensor, so that the signal reflected from the driver’s hand during gesturing is unaffected by interference from the motion of the driver’s body or other motions within the car. We implemented a Convolutional Neural Network-based technique to recognize the finger-counting-based hand gestures using an Impulse Radio (IR) radar sensor. The accuracy of the proposed method was sufficiently high for real-world applications.
Emergence of classical objectivity of quantum Darwinism in a photonic quantum simulator

Quantum-to-classical transition is a fundamental open question in physics frontier. Quantum decoherence theory points out that the inevitable interaction with environment is a sink carrying away quantum coherence, which is responsible for the suppression of quantum superposition in open quantum system. Recently, quantum Darwinism theory further extends the role of environment, serving as communication channel, to explain the classical objectivity emerging in quantum measurement process. Here, we used a six-photon quantum simulator to investigate classical and quantum information proliferation in quantum Darwinism process. In the simulation, many environmental photons are scattered from an observed quantum system and they are collected and used to infer the system’s state. We observed redundancy of system’s classical information and suppression of quantum correlation in the fragments of environmental photons. Our results experimentally show that the classical objectivity of quantum system can be established through quantum Darwinism mechanism.
Saliency-guided level set model for automatic object segmentation

The level set model is a popular method for object segmentation. However, most existing level set models perform poorly in color images since they only use grayscale intensity information to defined their energy functions. To address this shortcoming, in this paper, we propose a new saliency-guided level set model (SLSM), which can automatically segment objects in color images guided by visual saliency. Specifically, we first define a global saliency-guided energy term to extract the color objects approximately. Then, by integrating information from different color channels, we define a novel local multichannel based energy term to extract the color objects in detail. In addition, unlike using a length regularization term in the conventional level set models, we achieve segmentation smoothness by incorporating our SLSM into a graph cuts formulation. More importantly, the proposed SLSM is automatically initialized by saliency detection. Finally, the evaluation on public benchmark databases and our collected database demonstrates that the new SLSM consistently outperforms many state-of-the-art level set models and saliency detecting methods in accuracy and robustness.
Design and Analysis of Two FTRNN Models With Application to Time-Varying Sylvester Equation

In this paper, to accelerate the convergence speed of Zhang neural network (ZNN), two finite-time recurrent neural networks (FTRNNs) are presented via devising two novel design formulas. For verifying the advantages of the proposed FTRNN models, a solution application to time-varying Sylvester equation (TVSE) is given. Compared with the conventional ZNN model, the presented new FTRNN models in this paper are theoretically proved to have better convergence performance, and they are more effective for online solving TVSE within finite time. At last, the superiority and effectiveness of the new FTRNN models for solving TVSE are verified by numerical simulations.
An Energy-Efficient Reconfigurable DTLS Cryptographic Engine for Securing Internet-of-Things Applications

This paper presents the first hardware implementation of the Datagram Transport Layer Security (DTLS) protocol to enable end-to-end security for the Internet of Things (IoT). A key component of this design is a reconfigurable prime field elliptic curve cryptography (ECC) accelerator, which is 238x and 9x more energy-efficient compared to software and state-of-the-art hardware respectively. Our full hardware implementation of the DTLS 1.3 protocol provides 438x improvement in energy-efficiency over software, along with code size and data memory usage as low as 8 KB and 3 KB respectively. The cryptographic accelerators are coupled with an on-chip low-power RISC-V processor to benchmark applications beyond DTLS with up to two orders of magnitude energy savings. The test chip, fabricated in 65 nm CMOS, demonstrates hardware-accelerated DTLS sessions while consuming 44.08 uJ per handshake, and 0.89 nJ per byte of encrypted data at 16 MHz and 0.8 V.
Ambiguity as a Resource to Inform Proto-Practices

Skin conductance is an interesting measure of arousal level, largely unfamiliar to most end-users. We designed a mobile application mirroring end-users’ skin conductance in evocative visualizations, purposefully made ambiguous to invite rich interpretations. Twenty-three participants used the system for a month. Through the lens of a practice-based analysis of weekly interviews and the logged data, several quite different—sometimes even mutually exclusive—interpretations or proto-practices arose: as stress management; sports performance; emotion tracking; general life logging; personality representation; or behavior change practices. This suggests the value of a purposefully open initial design to allow for the emergence of broader proto-practices to be followed by a second step of tailored design for each identified goal to facilitate the transition from proto-practice to practice. We contribute to the HCI discourse on ambiguity in design, arguing for balancing openness and ambiguity with scaffolding to better support the emergence of practices around biodata.
Fuzzy neighborhood-based differential evolution with orientation for nonlinear equation systems

Solving nonlinear equation systems (NESs) plays a vital role in science and engineering. Systems of nonlinear equations typically have more than root. Most of the classical methods cannot locate multiple roots in a single run. Finding these multiple roots in a single run is a difficult task in numerical computation. To effectively and reliably find the multiple roots of NES simultaneously, we propose a fuzzy neighborhood-based differential evolution with orientation (FNODE). FNODE is novel for two reasons: (1) it hasan improved fuzzy neighborhood, where the sub-populations are generated according to the fuzzy rule and the distribution of individuals; and (2) an orientation-based mutation is used, where the orientation information of the neighborhood individual’s migration is integrated into the mutation to produce promising offspring. To evaluate the performance of FNODE, we used 30 NESs with diverse features as the test suite. The experimental results demonstrate that FNODE is capable of successfully solving most of the problems in the test suite and provide better results than the other methods.
A Minimax Game for Instance based Selective Transfer Learning

Deep neural network based transfer learning has been widely used to leverage information from the domain with rich data to help domain with insufficient data. When the source data distribution is different from the target data, transferring knowledge between these domains may lead to negative transfer. To mitigate this problem, a typical way is to select useful source domain data for transferring. However, limited studies focus on selecting high-quality source data to help neural network based transfer learning. To bridge this gap, we propose a general Minimax Game based model for selective Transfer Learning (MGTL). More specifically, we build a selector, a discriminator and a TL module in the proposed method. The discriminator aims to maximize the differences between selected source data and target data, while the selector acts as an attacker to selected source data that are close to the target to minimize the differences. The TL module trains on the selected data and provides rewards to guide the selector. Those three modules play a minimax game to help select useful source data for transferring. Our method is also shown to speed up the training process of the learning task in the target domain than traditional TL methods. To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning. To examine the generality of our method, we evaluate it on two different tasks: item recommendation and text retrieval. Extensive experiments over both public and real-world datasets demonstrate that our model outperforms the competing methods by a large margin. Meanwhile, the quantitative evaluation shows our model can select data which are close to target data. Our model is also deployed in a real-world system and significant improvement over the baselines is observed.
Competitive residual neural network for image classification

We propose a novel residual network called competitive residual network (CoRN) for image classification. The proposed network is composed of residual units having two identical blocks each containing convolutional filters, batch normalization, and a maxout unit. The maxout unit enables the competition among the convolutional filters and reduces the dimensionality of the convolutional layer. The proposed network outperforms the original residual network by a significant margin and test errors on benchmark datasets (CIFAR-10/100 and SVHN) are comparable to the state-of-the-art. Using the ensemble network, we achieve a test error of 3.85% on CIFAR-10, 18.17% on CIFAR-100 and 1.59% on SVHN.
Formal Verification of Quantum Algorithms Using Quantum Hoare Logic

We formalize the theory of quantum Hoare logic (QHL) [TOPLAS 33(6),19], an extension of Hoare logic for reasoning about quantum programs. In particular, we formalize the syntax and semantics of quantum programs in Isabelle/HOL, write down the rules of quantum Hoare logic, and verify the soundness and completeness of the deduction system for partial correctness of quantum programs. As preliminary work, we formalize some necessary mathematical background in linear algebra, and define tensor products of vectors and matrices on quantum variables. As an application, we verify the correctness of Grover's search algorithm. To our best knowledge, this is the first time a Hoare logic for quantum programs is formalized in an interactive theorem prover, and used to verify the correctness of a nontrivial quantum algorithm.
Clustering-Driven Deep Embedding With Pairwise Constraints

Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have been presented. In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for nonparametric clustering using a neural network. We present a clustering-driven embedding based on a Siamese network that encourages pairs of data points to output similar representations in the latent space. Our pair-based model allows augmenting the information with labeled pairs to constitute a semi-supervised framework. Our approach is based on analyzing the losses associated with each pair to refine the set of constraints. We show that clustering performance increases when using this scheme, even with a limited amount of user queries. We demonstrate how our architecture is adapted for various types of data and present the first deep framework to cluster three-dimensional (3-D) shapes.
QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships

Many natural la guage questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but the semantic parsing task of mapping questions into those models has formidable challenges. We present QUAREL, a dataset of diverse story questions involving qualitative relationships that characterize these challenges, and techniques that begin to address them. The dataset has 2771 questions relating 19 different types of quantities. For example, “Jenny observes that the robot vacuum cleaner moves slower on the living room carpet than on the bedroom carpet. Which carpet has more friction?” We contribute (1) a simple and flexible conceptual framework for representing these kinds of questions; (2) the QUAREL dataset, including logical forms, exemplifying the parsing challenges; and (3) two novel models for this task, built as extensions of type-constrained semantic parsing. The first of these models (called QUASP+) significantly outperforms off-the-shelf tools on QUAREL. The second (QUASP+ZERO) demonstrates zero-shot capability, i.e., the ability to handle new qualitative relationships without requiring additional training data, something not possible with previous models. This work thus makes inroads into answering complex, qualitative questions that require reasoning, and scaling to new relationships at low cost. The dataset and models are available at http://data.allenai.org/quarel.
Path Planning Aware of Robot’s Center of Mass for Steep Slope Vineyards

Summary Steep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A * algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.
Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

This paper provides interested beginners with an updated and detailed introduction to the field of Intelligent Tutoring Systems (ITS).ITSs are computer programs that use artificial intelligence techniques to enhance and personalize automation in teaching.This paper is a literature review that provides the following: First, a review of the history of ITS along with a discussion on the interface between human learning and computer tutors and how effective ITSs are in contemporary education.Second, the traditional architectural components of an ITS and their functions are discussed along with approaches taken by various ITSs.Finally, recent innovative ideas in ITS systems are presented.This paper concludes with some of the author's views regarding future work in the field of intelligent tutoring systems.
Resolution-invariant Person Re-Identification

Exploiting resolution invariant representation is critical for person Re-Identification (ReID) in real applications, where the resolutions of captured person images may vary dramatically. This paper learns person representations robust to resolution variance through jointly training a Foreground-Focus Super-Resolution (FFSR) module and a Resolution-Invariant Feature Extractor (RIFE) by end-to-end CNN learning. FFSR upscales the person foreground using a fully convolutional auto-encoder with skip connections learned with a foreground focus training loss. RIFE adopts two feature extraction streams weighted by a dual-attention block to learn features for low and high resolution images, respectively. These two complementary modules are jointly trained, leading to a strong resolution invariant representation. We evaluate our methods on five datasets containing person images at a large range of resolutions, where our methods show substantial superiority to existing solutions. For instance, we achieve Rank-1 accuracy of 36.4% and 73.3% on CAVIAR and MLR-CUHK03, outperforming the state-of-the art by 2.9% and 2.6%, respectively.
DEFT: A corpus for definition extraction in free- and semi-structured text

Definition extraction has been a popular topic in NLP research for well more than a decade, but has been historically limited to welldefined, structured, and narrow conditions.In reality, natural language is messy, and messy data requires both complex solutions and data that reflects that reality.In this paper, we present a robust English corpus and annotation schema that allows us to explore the less straightforward examples of term-definition structures in free and semi-structured text.
A novel local region-based active contour model for image segmentation using Bayes theorem

Local region-based active contour methods have been widely used to segment images with intensity inhomogeneity. However, this process can hardly segment images well when influenced by different noise. To simultaneously strengthen the anti-noise ability and preserve the distinction in segmenting images with intensity inhomogeneity, we propose characterizing image regions using local prior region descriptors under the Bayesian criterion for image segmentation. Based on the framework of Bayes theorem, a spatial regularization of connectivity maps based on a Markov random field (MRF) is introduced as the prior probability in our model. The connectivity maps enhance noise robustness by building a relationship between a pixel and its adjacent pixels. Additionally, the conditional probability of the image intensity in each local region is assumed to satisfy a Gaussian distribution with different means and deviations. Furthermore, to decrease time costs, we use the sparse field method (SFM) and compute the means and variances of the intensities in each local region before the evolution of the contour. Extensive experiments have demonstrated that the proposed method is superior to state-of-the-art active contour methods in terms of time efficiency and noise robustness.