Reviewerly/exHarmony huggingface dataset:
Using electrooculography to track closed-eye movements.

There are several areas in the study of visual cognition—including memory, imagery, and human-machine interaction—where researchers are interested in how the eyes move behind closed eyelids. However, reliably and affordably measuring closed-eye movements has proven elusive. Electrooculography (EOG) offers a low-cost solution to monitoring closed-eye gaze position, but it is not without its challenges. To determine the direction and amplitude of eye movements, the electrical potentials measured by EOG somehow must be calibrated with the angular displacement of the eye. EOG is also susceptible to noise arising from various sources, such as electromyographic activity and electrode impedance. Here we describe a method for estimating a corrected EOG signal by calibrating it with an industry-standard, pupil-corneal reflection (PCR) eye tracker. First, data were collected while simultaneously using both eye-tracking techniques as participants performed a simple horizontal saccade task with their eyes open under conditions of normal illumination and complete darkness. The EOG signal, when using only a standard calibration procedure, was less precise than that of PCR and tended to overestimate saccadic amplitude. We applied robust regression methods to the EOG and PCR data recorded in normal illumination to estimate a calibration factor to adjust the EOG signal acquired in darkness. This adjustment yielded an EOG-based measure of saccade end-points that was more comparable—in both accuracy and precision—to that obtained from the PCR data. Having validated this calibration procedure, we applied it to compute an adjusted EOG measure of saccadic amplitude in another condition where participants’ eyes were closed. This adjustment likely improved our measurement of how accurately participants were able to execute closed-eye movements to remembered target locations. We propose that the refinement and application of this methodology can advance research under conditions where researchers would like to measure the kinematics of closed-eye movements.
Get! Mimetypes! Right! (Crazy New Idea)

This paper identifies three technical requirements - availability of data, sustainable hosting and resolvable URIs for hosted data - as minimal pre-conditions for Linguistic Linked Open Data technology to develop towards a mature technological ecosystem that third party applications can build upon. While a critical amount of data is available (and it continues to grow), there does not seem to exist a hosting solution that combines the prospects of long-term availability with an unrestricted capability to support resolvable URIs. In particular, data hosting services do currently not allow data to be declared as RDF content by means of their media type (mime type), so that the capability of clients to recognize formats and to resolve URIs on that basis is severely limited.
Multimodal Generative Neural Network for Anomaly Events Detection and Localization in Videos

Anomaly detection has gained more and more attention with the popularity of automatic surveillance services. However, the variety and uncertainty of abnormal objects and events make the detection and localization of them difficult. In this paper, we propose a Multimodal Generative Neural Network (MGNN), unsupervised anomaly events detection and anomaly objects localization method based on Generative Adversarial Network. Our architecture contains two components, an appearance generation network and a motion generation network. In training, only normal frames are fed into networks. At testing time, since the trained model has learned reconstruction of normal frames, frames with abnormal object will be reconstructed poorly. We use this poor reconstruction to detect abnormal frames. Our experiments with UCSD pedestrian 2 dataset show that our approach achieves 96.5% Area Under Curve (AUC) in frame-level detection and 94.1% AUC in pixel-level detection.
SECCEG: A Secure and Efficient Cryptographic Co-processor Based on Embedded GPU System

With the rise of IoT, e-commerce, and 5G, the demands of secure communications and identity authentications dramatically increase, which largely rely on high-volume cryptographic computing. Meanwhile, driven by deep learning, the embedded GPU system is rapidly evolving. In this paper, we discuss the feasibility of turning the lightweight and energy efficient system into a cryptographic co-processor, where security and performance are two daunting challenges. From the aspect of security, we leverage the available resources in the embedded GPU system to achieve on-chip uninterrupted cryptographic computing, secure key storage, and trusted system bootstrapping. From the aspect of performance, targeting the prevailing digital signature algorithm Ed25519, we develop an entire framework to make full use of the system’s cryptographic computing power, including the Ed25519 implementation with embedded GPU acceleration and a high-performance network processing architecture. In Jetson Xavier and Jetson Xavier NX, we implement a prototype called SECCEG and conduct comprehensive experiments to evaluate its performance. SECCEG can serve as a network cryptographic accelerator via TCP/IP stack with \(3.6 \times 10^6\) ops signature generation and \(1.0 \times 10^6\) ops for signature verification. At the performance-power ratio, SECCEG achieves 122 kops/W and 35.6 kops/W for signature generation and verification, respectively, which is 1 to 2 multitude higher than ARM CPU, FPGA and discrete GPU implementations.
Quantum Polarization of Qudit Channels

We provide a generalization of quantum polar codes to quantum channels with qudit-input, achieving the symmetric coherent information of the channel. Our scheme relies on a channel combining and splitting construction, where a two-qudit unitary randomly chosen from a unitary 2-design is used to combine two instances of a qudit-input channel. The inputs to the synthesized bad channels are frozen by sharing EPR pairs between the sender and the receiver, so our scheme is entanglement assisted. Using the fact that the generalized two-qudit Clifford group forms a unitary 2-design, we conclude that the channel combining operation can be chosen from this set. Moreover, we show that polarization also happens for a much smaller subset of two-qudit Cliffords, which is not a unitary 2-design. Finally, we show how to decode the proposed quantum polar codes on Pauli qudit channels.
Evaluation of Clustering Algorithms on HPC Platforms

Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson–Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform.
Autonomous Removal of Perspective Distortion of Elevator Button Images based on Corner Detection

Elevator button recognition is a critical function to realize the autonomous operation of elevators. However, challenging image conditions and various image distortions make it difficult to recognize buttons accurately. To fill this gap, we propose a novel deep learning-based approach, which aims to autonomously correct perspective distortions of elevator button images based on button corner detection results. First, we leverage a novel image segmentation model and the Hough Transform method to obtain button segmentation and button corner detection results. Then, pixel coordinates of standard button corners are used as reference features to estimate camera motions for correcting perspective distortions. Fifteen elevator but-ton images are captured from different angles of view as the dataset. The experimental results demonstrate that our proposed approach is capable of estimating camera motions and removing perspective distortions of elevator button images with high accuracy.
Authenticated quantum dialogue protocol based on four-particle entangled states

Many protocols of quantum dialogue are based on the assumption that the user is legitimate. In fact, the assumption is ideal. Therefore, it is necessary to identify whether the user is legitimate or not. In this paper, by adding identity authentication, a quantum dialogue protocol is proposed based on four-particle entangled states. For preparing the entangled state, we first give the description of the preparation process and draw the diagram of the prepared quantum circuit. Next, the nonlocal correlation properties of the entangled state are investigated. By associating identity authentication with channel security detection, the consumption of quantum resources is reduced to a certain extent. Furthermore, a random number r and a one-way hash function are added to increase the security of the protocol. In the end, compared with other protocols, our protocol has an improvement in efficiency.
LT-LM: A Novel Non-Autoregressive Language Model for Single-Shot Lattice Rescoring

Neural network-based language models are commonly used in rescoring approaches to improve the quality of modern automatic speech recognition (ASR) systems. Most of the existing methods are computationally expensive since they use autoregressive language models. We propose a novel rescoring approach, which processes the entire lattice in a single call to the model. The key feature of our rescoring policy is a novel non-autoregressive Lattice Transformer Language Model (LT-LM). This model takes the whole lattice as an input and predicts a new language score for each arc. Additionally, we propose the artificial lattices generation approach to incorporate a large amount of text data in the LT-LM training process. Our single-shot rescoring performs orders of magnitude faster than other rescoring methods in our experiments. It is more than 300 times faster than pruned RNNLM lattice rescoring and N-best rescoring while slightly inferior in terms of WER.
Optimal Rates for Nonparametric F-Score Binary Classification via Post-Processing

This work studies the problem of binary classification with the F-score as the performance measure. We propose a post-processing algorithm for this problem which fits a threshold for any score base classifier to yield high F-score. The post-processing step involves only unlabeled data and can be performed in logarithmic time. We derive a general finite sample post-processing bound for the proposed procedure and show that the procedure is minimax rate optimal, when the underlying distribution satisfies classical nonparametric assumptions. This result improves upon previously known rates for the F-score classification and bridges the gap between standard classification risk and the F-score. Finally, we discuss the generalization of this approach to the set-valued classification.
Beating standard quantum limit via two-axis magnetic susceptibility measurement

We report a metrology scheme which measures magnetic susceptibility of an atomic spin ensemble along the $x$ and $z$ direction and produces parameter estimation with precision beating the standard quantum limit. The atomic ensemble is initialized via one-axis spin squeezing with optimized squeezing time and parameter $\phi$ to be estimated is assumed as uniformly distributed between 0 and $2\pi$. One estimation of $\phi$ can be produced with every two magnetic susceptibility data measured along the two axis respectively, which has imprecision scaling $(1.43\pm{}0.02)/N^{0.687\pm0.003}$ with respect to the number N of atomic spins. The measurement scheme is easy to implement and thus one step towards practical application of quantum metrology.
QEnclave - A practical solution for secure quantum cloud computing

We introduce a secure hardware device named a QEnclave that can secure the remote execution of quantum operations while only using classical controls. This device extends to quantum computing the classical concept of a secure enclave which isolates a computation from its environment to provide privacy and tamper-resistance. Remarkably, our QEnclave only performs single-qubit rotations, but can nevertheless be used to secure an arbitrary quantum computation even if the qubit source is controlled by an adversary. More precisely, attaching a QEnclave to a quantum computer, a remote client controlling the QEnclave can securely delegate its computation to the server solely using classical communication. We investigate the security of our QEnclave by modeling it as an ideal functionality named Remote State Rotation. We show that this resource, similar to previously introduced functionality of remote state preparation, allows blind delegated quantum computing with perfect security. Our proof relies on standard tools from delegated quantum computing. Working in the Abstract Cryptography framework, we show a construction of remote state preparation from remote state rotation preserving the security. An immediate consequence is the weakening of the requirements for blind delegated computation. While previous delegated protocols were relying on a client that can either generate or measure quantum states, we show that this same functionality can be achieved with a client that only transforms quantum states without generating or measuring them.
PD-Net: Point Dropping Network for Flexible Adversarial Example Generation with $L_{0}$ Regularization

It is a challenging task to generate adversarial point clouds, considering the irregular structure of a point cloud, the large search space, and the requirement of imperception to humans. In this paper, a flexible adversarial point cloud generation method, named Point Dropping Network (PD-Net), is proposed, which can be trained to craft adversarial examples in a single forward pass. The network is designed to launch untargeted black-box attacks to deep 3D models through point dropping regularized by the $L_{0}$ norm, in contrast to the widely adopted point perturbation methods. To enable incorporation into a deep neural network, the probability of a point to be dropped, which can be described by a Bernoulli distribution, is approximated by a hard concrete distribution. The network of PD-Net consists of an encoder and a decoder, where the former encodes geometric information of each point and the latter learns to drop points from their local features in an unsupervised way. Experiments on two popular deep 3D models (including PointNet and PointNet++) show that the proposed PD-Net degrades the recognition accuracy to a large extent and achieves a high flexibility at the same time.
Combination of Two Visual Servoing Techniques in Contour Following Task

The research presents the planar contour following task for a Six-DOF manipulator by using the combination of two visual servoing techniques. Firstly, to control the camera to a position that parallels to the contour plane, Shortest Path Visual Servoing (SPVS) technique is applied. Using SPVS technique in the first stages has the advantage of ensuring the system can converge at any initial position and the camera trajectory will be a straight line. Then, Image-Based Visual Servoing (IBVS) technique is applied to conduct the contour following task. The interaction matrix used in IBVS is formed based on four virtual feature points that are generated from the desired feature command point extracted from the contour. IBVS is applied to conduct the contour following task because IBVS is least affected by noise, so the results are more accurate. The effectiveness of the proposed approach is verified based on the simulation result by using a camera attached at the end effector of the Six-DOF PUMA 560 manipulator.
A Convenient and High-Accuracy Multicamera Calibration Method Based on Imperfect Spherical Objects

Multi-camera systems have important applications in industrial online measurement, attracting wide interest due to their encouraging performance. However, how to develop a convenient and high-accuracy multi-camera calibration model is a big challenge. Traditional calibration methods based on 2D object require the preparation of high-precision and large-scale calibration object, and have poor adaptability to the spatial distribution of cameras. To address these issues, a convenient and accurate multi-camera calibration method is proposed based on imperfect spherical object. Specifically, a special calibration object, i.e., an imperfect sphere with many coded targets is designed for calibrating the multi-camera system, where the optical axes of the cameras are different in orientations and converge into the measurement field. Then, a Euclidean reconstruction method is employed to calculate the camera poses and the spatial coordinates of feature points in the local coordinate system after adaptive grouping of cameras are completed. Moreover, a graph theory based optimal path transformation (OPT) algorithm is developed to obtain the camera poses and spatial point coordinates in the global coordinate system (GCS), and a spherical projection optimization (SPO) algorithm is designed to spheroidize the spatial coordinates of feature points. At the end, the camera poses and the spatial coordinates of feature points in the GCS are optimized by bundle adjustment algorithm. We build a multi-camera system and conduct extensive experiments to demonstrate the superiority of the proposed method.
Digital Integrity

Abstract Researchers working with digital evidence must be aware of its vulnerabilities if they hope to maximize its value for justice and accountability. In this paper, we highlight two important vulnerabilities: the impermanent nature of digital evidence, and its susceptibility to attack from online dis/misinformation campaigns. We argue that to overcome the challenge that the impermanent nature of digital evidence presents, researchers can implement rapid response archiving techniques that, while not as rigorous as protocols set out for the collection of evidence in legal cases, could leave them with digital breadcrumbs to aid with the recovery of deleted digital evidence. We further propose that researchers consider implementing a stance of radical transparency in the collection, synthesis, and presentation of digital evidence with the goal of fortifying it against dis/misinformation campaigns.
Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches

Width-based planning methods deal with conjunctive goals by decomposing problems into subproblems of low width. Algorithms like SIW thus fail when the goal is not easily serializable in this way or when some of the subproblems have a high width. In this work, we address these limitations by using a simple but powerful language for expressing finer problem decompositions introduced recently by Bonet and Geffner, called policy sketches. A policy sketch R over a set of Boolean and numerical features is a set of sketch rules that express how the values of these features are supposed to change. Like general policies, policy sketches are domain general, but unlike policies, the changes captured by sketch rules do not need to be achieved in a single step. We show that many planning domains that cannot be solved by SIW are provably solvable in low polynomial time with the SIW_R algorithm, the version of SIW that employs user-provided policy sketches. Policy sketches are thus shown to be a powerful language for expressing domain-specific knowledge in a simple and compact way and a convenient alternative to languages such as HTNs or temporal logics. Furthermore, they make it easy to express general problem decompositions and prove key properties of them like their width and complexity.
Teaching mathematics to reflect on the COVID-19 pandemic

A didactic proposal based on e-learning to reflect on the pandemic from a mathematical point of view is described and evaluated. The severe effect of the COVID-19 pandemic in Spain obliged the schools to adapt teaching to the distance learning format. The mathematical content associated with the information about the pandemic appearing in the media is accessible to high school students (grade 11th), so it can be adapted to a purely e-learning format. The adaptation which is proposed here involves a set of tasks, simulations, and documents to enable understanding the meaning of the curves of infections and to develop a simplified mathematical model to discuss time evolution of the pandemic and our role as citizens to stop its spread. A proper organization of different resources in the Edmodo learning management system was used to design a didactic sequence that is intended to drive students along the modelling process and lead them to the reflection on the information that determine a mathematical model, the importance of mathematics to understand real situations and the influence of the policies and actions that can be taken to contribute to stop the pandemic. The details of the didactical sequence as well as the results of a strengths, weaknesses, opportunities, and threats analysis of the e-learning approach to put it into practice are also discussed.
Recommendations at Videoland

Share on Recommendations at Videoland Authors: Mateo Gutierrez Granada RTL Nederland B.V., Netherlands RTL Nederland B.V., NetherlandsView Profile , Daan Odijk RTL Nederland B.V., Netherlands RTL Nederland B.V., NetherlandsView Profile Authors Info & Claims RecSys '21: Fifteenth ACM Conference on Recommender SystemsSeptember 2021 Pages 580–582https://doi.org/10.1145/3460231.3474617Online:13 September 2021Publication History 0citation278DownloadsMetricsTotal Citations0Total Downloads278Last 12 Months278Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on Chinese Text Classification Based on WAE and SVM

For Internet information explosion, the user is in urgent need to quickly screening in huge amounts of information is concerned about the problems on effective information, focus on using machine learning and deep learning algorithms for Chinese text classification research, this paper proposes a comprehensive method of WAE and SVM on the Chinese text classification. In this paper, the text classification process, whether to introduce the WAE theme model to participate in the text classification of two scenarios. Experiments show that adding WAE topic model before SVM classification algorithm can improve classification accuracy and reduce classification time.
Games of Incomplete Information: A Framework Based on Belief Functions

This paper proposes a model for incomplete games where the knowledge of the players is represented by a Dempster-Shafer belief function. Beyond an extension of the classical definitions, it shows such a game can be transformed into an equivalent hypergraphical complete game (without uncertainty), thus generalizing Howson and Rosenthal’s theorem to the framework of belief functions and to any number of players. The complexity of this transformation is finally studied and shown to be polynomial in the degree of k-additivity of the mass function.