Reviewerly/exHarmony huggingface dataset:
Tangent Fisher Vector on Matrix Manifolds for Action Recognition

In this paper, we address the problem of representing and recognizing human actions from videos on matrix manifolds. For this purpose, we propose a new vector representation method, named tangent Fisher vector, to describe video sequences in the Fisher kernel framework. We first extract dense curved spatio-temporal cuboids from each video sequence. Compared with the traditional 'straight cuboids', the dense curved spatio-temporal cuboids contain much more local motion information. Each cuboid is then described using a linear dynamical system (LDS) to simultaneously capture the local appearance and dynamics. Furthermore, a simple yet efficient algorithm is proposed to learn the LDS parameters and approximate the observability matrix at the same time. Each video sequence is thus represented by a set of LDSs. Considering that each LDS can be viewed as a point in a Grassmann manifold, we propose to learn an intrinsic GMM on the manifold to cluster the LDS points. Finally a tangent Fisher vector is computed by first accumulating all the tangent vectors in each Gaussian component, and then concatenating the normalized results across all the Gaussian components. A kernel is defined to measure the similarity between tangent Fisher vectors for classification and recognition of a video sequence. This approach is evaluated on the state-of-the-art human action benchmark datasets. The recognition performance is competitive when compared with current state-of-the-art results.
Network Forensic Investigation Protocol to Identify True Origin of Cyber Crime

An increase in digitization is giving rise to cybercrimes. The existing network protocols are insufficient for collecting the required digital evidence of cybercrime, which eventually makes the process of forensic investigation difficult. In the current scenario of network forensics, the investigator with current capabilities can reach only up to the ISP. This is not primary evidence. Currently, available tools work only at the network layer. In this work, we propose a protocol that ensures tracking up to the true source by collecting beforehand forensically sound evidence. The proposed protocol can collect target data from the device in the form of a device fingerprint with the help of an agent process. The proposed methodology will help in proving non-repudiation, which is a well-known challenge in forensic cases. The fingerprint evidence generated by the proposed method has the capability of not getting obsolete even if the criminal tries to destroy evidence. The fingerprinting technique deployed uses a hash tree and generates evidence in such a way that this fingerprint can act as legal evidence. The security validation of the proposed system is done using the BAN logic. Formal verification is performed using the AVISPA tool. The system has been implemented as a prototype and hosted on AWS.
An approach to constructing genuinely entangled subspaces of maximal dimension

Genuinely entangled subspaces (GESs) are the class of completely entangled subspaces that contain only genuinely multiparty entangled states. They constitute a particularly useful notion in the theory of entanglement but also have found an application, for instance, in quantum error correction and cryptography. In a recent study (Demianowicz and Augusiak in Phys Rev A 98:012313, 2018), we have shown how GESs can be efficiently constructed in any multiparty scenario from the so-called unextendible product bases. The provided subspaces, however, are not of maximal allowable dimensions, and our aim here is to put forward an approach to building such. The method is illustrated with few examples in small systems. Connections with other mathematical problems, such as spaces of matrices of equal rank and the numerical range, are discussed.
Composing Task-Agnostic Policies with Deep Reinforcement Learning

The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines. To date, there has been plenty of work on learning task-specific policies or skills but almost no focus on composing necessary, task-agnostic skills to find a solution to new problems. In this paper, we propose a novel deep reinforcement learning-based skill transfer and composition method that takes the agent's primitive policies to solve unseen tasks. We evaluate our method in difficult cases where training policy through standard reinforcement learning (RL) or even hierarchical RL is either not feasible or exhibits high sample complexity. We show that our method not only transfers skills to new problem settings but also solves the challenging environments requiring both task planning and motion control with high data efficiency.
PulSec: Secure Element based framework for sensors anomaly detection in Industry 4.0

In Industry 4.0, the optimization decisions are obtained by applying extensive machine learning algorithms on huge data sets collected from wide array of sensors. This data is obtained aperiodically at the cloud end using a centralized aggregator present on the industrial site. This aperiodicity of data makes monitoring of the sensors difficult as it is hard to accurately estimate when the cloud end should expect data from a particular sensor. The reliable functioning of the sensors is important in industry 4.0 context as the efficiency of the applied machine learning algorithms directly depend on it. Above this, there are some processes which are critical and hence are monitored in real-time. Their state is important for many critical decisions (like in demand response) and immutable, tamper-proof status reporting is of prime concern. In this paper, we propose a secure element based framework called PulSec which emits a periodic secure pulse (like a human heartbeat) to solve these intricate problem arising out of a need to effectively monitor remote industrial sites. We show how anomalies, tampering can be quickly, effectively and accurately determined by just analyzing the secure pulse. We demonstrate its low overhead (bandwidth = 7.73 B/s, memory = 464 B, time = 500 ms), tamperproofness, security, portability using a realized prototype based on Multos M5-P19 Secure Element.
On the Construction of Binary Optimal LCD Codes with Short Length

A [Formula: see text]-ary linear code [Formula: see text] is called a linear complementary dual (LCD) code if it meets its dual trivially. Binary LCD codes play a significant role for their advantage of low complexity for implementations against side-channel attacks and fault injection attacks. In this paper, the problem of finding LCD codes over the binary field is discussed. Many new codes and new bounds are presented, as well as a table of optimal LCD codes (or upper and lower bounds on such codes) of length up to 30 bits.
Bayes-Adaptive Monte-Carlo Planning and Learning for Goal-Oriented Dialogues

We consider a strategic dialogue task, where the ability to infer the other agent's goal is critical to the success of the conversational agent. While this problem can be naturally formulated as Bayesian planning, it is known to be a very difficult problem due to its enormous search space consisting of all possible utterances. In this paper, we introduce an efficient Bayes-adaptive planning algorithm for goal-oriented dialogues, which combines RNN-based dialogue generation and MCTS-based Bayesian planning in a novel way, leading to robust decision-making under the uncertainty of the other agent's goal. We then introduce reinforcement learning for the dialogue agent that uses MCTS as a strong policy improvement operator, casting reinforcement learning as iterative alternation of planning and supervised-learning of self-generated dialogues. In the experiments, we demonstrate that our Bayes-adaptive dialogue planning agent significantly outperforms the state-of-the-art in a negotiation dialogue domain. We also show that reinforcement learning via MCTS further improves end-task performance without diverging from human language.
An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR Image

LGE CMR is an efficient technology for detecting infarcted myocardium. An efficient and objective ventricle segmentation method in LGE can benefit the location of the infarcted myocardium. In this paper, we proposed an automatic framework for LGE image segmentation. There are just 5 labeled LGE volumes with about 15 slices of each volume. We adopted histogram match, an invariant of rotation registration method, on the other labeled modalities to achieve effective augmentation of the training data. A CNN segmentation model was trained based on the augmented training data by leave-one-out strategy. The predicted result of the model followed a connected component analysis for each class to remain the largest connected component as the final segmentation result. Our model was evaluated by the 2019 Multi-sequence Cardiac MR Segmentation Challenge. The mean testing result of 40 testing volumes on Dice score, Jaccard score, Surface distance, and Hausdorff distance is 0.8087, 0.6976, 2.8727 mm, and 15.6387 mm, respectively. The experiment result shows a satisfying performance of the proposed framework. Code is available at https://github.com/Suiiyu/MS-CMR2019 .
Users' Preference Prediction of Real Estate Properties Based on Floor Plan Analysis

With the recent advances in e-commerce, it has become important to recommend not only mass-produced daily items, such as books, but also items that are not mass-produced. In this study, we present an algorithm for real estate recommendations. Automatic property recommendations are a highly difficult task because no identical properties exist in the world, occupied properties cannot be recommended, and users rent or buy properties only a few times in their lives. For the first step of property recommendation, we predict users' preferences for properties by combining content-based filtering and Multi-Layer Perceptron (MLP). In the MLP, we use not only attribute data of users and properties, but also deep features extracted from property floor plan images. As a result, we successfully predict users' preference with a Matthews Correlation Coefficient (MCC) of 0.166.
Towards supersensitive optical phase measurement using a deterministic source of entangled multiphoton states

Precision measurements of optical phases have many applications in science and technology. Entangled multi-photon states have been suggested for performing such measurements with precision that significantly surpasses the shot-noise limit. Until recently, such states have been generated mainly using spontaneous parametric down-conversion -- a process which is intrinsically probabilistic, counteracting the advantages that the entangled photon states might have. Here, we use a semiconductor quantum dot to generate entangled multi-photon states in a deterministic manner, using periodic timed excitation of a confined spin. This way we entangle photons one-by-one at a rate which exceeds 300 MHz. We use the resulting multi-photon state to demonstrate super-resolved optical phase measurement. Our results open up a scalable way for realizing genuine quantum enhanced super-sensitive measurements in the near future.
Two-Qubit Bloch Sphere

Three unit spheres were used to represent the two-qubit pure states. The three spheres are named the base sphere, entanglement sphere, and fiber sphere. The base sphere and entanglement sphere represent the reduced density matrix of the base qubit and the non-local entanglement measure, concurrence, while the fiber sphere represents the fiber qubit via a simple rotation under a local single-qubit unitary operation; however, in an entangled bipartite state, the fiber sphere has no information on the reduced density matrix of the fiber qubit. When the bipartite state becomes separable, the base and fiber spheres seamlessly become the single-qubit Bloch spheres of each qubit. Since either qubit can be chosen as the base qubit, two alternative sets of these three spheres are available, where each set fully represents the bipartite pure state, and each set has information of the reduced density matrix of its base qubit. Comparing this model to the two Bloch balls representing the reduced density matrices of the two qubits, each Bloch ball corresponds to two unit spheres in our model, namely, the base and entanglement spheres. The concurrence–coherence complementarity is explicitly shown on the entanglement sphere via a single angle.
Fast few-shot transfer learning for disease identification from chest x-ray images using autoencoder ensemble

We propose a fast few-shot learning framework that uses transfer learning to identify different lung and chest diseases and conditions from chest x-rays. Our model can be trained with as few as five training examples, making it potentially applicable for diagnosis of rare diseases. In this work, we divide different chest diseases into two disjoint categories: (i) base classes (with large training set) and (ii) novel classes (with a few training examples per class). Our method consists of two steps, namely feature extraction and classification. For the feature extraction, we employ a deep convolutional neural network, customized for chest x-rays. We train the feature extractor with data only from base classes. So the novel classes are unseen to the feature extractor during training. However, we use the feature extractor for extracting features from the data of novel classes resulting in transfer learning. Our classifier, on the other hand, uses the data only from the novel classes for training. We introduce the idea of autoencoder ensemble to design the classifier. Only a few feature vectors from each of the novel classes are used for training the classifier making it a few-shot learner. Incorporating new novel classes require training only the classifier which makes the entire process extremely fast. The performance of the classifier is evaluated on the test data from the novel classes. Experiments show _ 18% improvement in the F1 score compared to the baseline on identifying the novel diseases from publicly available chest x-ray dataset.
The Art of Natural Language Processing: Classical, Modern and Contemporary Approaches to Text Document Classification

In this tutorial we introduce three approaches to preprocess text data with Natural Language Processing (NLP) and perform text document classification using machine learning. The first approach is based on 'bag-of-' models, the second one on word embeddings, while the third one introduces the two most popular Recurrent Neural Networks (RNNs), i.e. the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. We apply all approaches on a case study where we classify movie reviews using Python and Tensorflow 2.0. The results of the case study show that extreme gradient boosting algorithms outperform adaptive boosting and random forests on bag-of-words and word embedding models, as well as LSTM and GRU RNNs, but at a steep computational cost. Finally, we provide the reader with comments on NLP applications for the insurance industry.
Particle swarm optimization algorithms with selective differential evolution for AUV path planning

Particle swarm optimization (PSO)-based algorithms are suitable for path planning of the Autonomous Underwater Vehicle (AUV) due to their high computational efficiency. However, such algorithms may produce sub-optimal paths or require higher computational load to produce an optimal path. This paper proposed a new approach that improves the ability of PSO-based algorithms to search for the optimal path while maintaining a low computational requirement. By hybridizing with differential evolution (DE), the proposed algorithms carry out the DE operator selectively to improve the search ability. The algorithms were applied in an offline AUV path planner to generate a near-optimal path that safely guides the AUV through an environment with a priori known obstacles and time-invariant non-uniform currents. The algorithm performances were benchmarked against other algorithms in an offline path planner because if the proposed algorithms can provide better computational efficiency to demonstrate the minimum capability of a path planner, then they will outperform the tested algorithms in a realistic scenario. Through Monte Carlo simulations and Kruskal-Wallis test, SDEAPSO (selective DE-hybridized PSO with adaptive factor) and SDEQPSO (selective DE-hybridized Quantum-behaved PSO) were found to be capable of generating feasible AUV path with higher efficiency than other algorithms tested, as indicated by their lower computational requirement and excellent path quality.
Local Fact Change Logic

We investigate a modal logic of model change, introducing a new operator which allows for changing the valuation at a particular state in a model. After investigating some properties of the logic, and aspects of its expressive power, we show it to be undecidable by way of a reduction using memory logic.
Security Under Message-Derived Keys: Signcryption in iMessage

At the core of Apple’s iMessage is a signcryption scheme that involves symmetric encryption of a message under a key that is derived from the message itself. This motivates us to formalize a primitive we call Encryption under Message-Derived Keys (EMDK). We prove security of the EMDK scheme underlying iMessage. We use this to prove security of the signcryption scheme itself, with respect to definitions of signcryption we give that enhance prior ones to cover issues peculiar to messaging protocols. Our provable-security results are quantitative, and we discuss the practical implications for iMessage.
Triplet-triplet decoherence in singlet fission

Singlet fission is commonly defined to involve a process by which an overall singlet state with local triplet structure spin decoheres into two triplet states, thereby completing the fission process. This process, often defined in loose terms involving the multiplicity of the overall state, is investigated here using a uniform Heisenberg spin chain subject to a dephasing environmental interaction. We introduce results from quantum information theory which enables the quantification of coherence and entanglement in a bi- and multipartite system. The calculated measures of these quantum effects can be linked to observables, such as magnetization and total spin, with simulations of the model and using theoretical methods. We demonstrate that these observables can act as a proxy for the coherence and entanglement measures. The decay of both of these between the two local triplets can be monitored, enabling a clear definition of the spin-decoherence process in singlet fission.
Simulating noisy quantum circuits with matrix product density operators

Simulating quantum circuits with classical computers requires resources growing exponentially in terms of system size. Real quantum computer with noise, however, may be simulated polynomially with various methods considering different noise models. In this work, we simulate random quantum circuits in one dimension with matrix product density operators (MPDOs), for different noise models such as dephasing, depolarizing, and amplitude damping. We show that the method based on matrix product states (MPSs) fails to approximate the noisy output quantum states for any of the noise models considered, while the MPDO method approximates them well. Compared with the method of matrix product operators (MPOs), the MPDO method reflects a clear physical picture of noise (with inner indices taking care of the noise simulation) and quantum entanglement (with bond indices taking care of two-qubit gate simulation). Consequently, in case of weak system noise, the resource cost of the MPDO will be significantly less than that of the MPO due to a relatively small inner dimension needed for the simulation. In case of strong system noise, a relatively small bond dimension may be sufficient to simulate the noisy circuits, indicating a regime that the noise is large enough for an “easy” classical simulation, which is further supported by a comparison with the experimental results on an IBM cloud device. Moreover, we propose a more effective tensor updates scheme with optimal truncations for both the inner and the bond dimensions, performed after each layer of the circuit, which enjoys a canonical form of the MPDO for improving simulation accuracy. With truncated inner dimension to a maximum value κ and bond dimension to a maximum value χ, the cost of our simulation scales as ∼NDκ3χ3, for an N-qubit circuit with depth D.6 MoreReceived 7 July 2020Accepted 1 March 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023005Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOpen quantum systemsQuantum information processingQuantum simulationTechniquesDensity matrix methodsMatrix product statesTensor network methodsQuantum InformationCondensed Matter, Materials & Applied Physics
A Study to Evaluate the Knowledge Regarding Computer Vision Syndrome among Medical Students

Introduction Electronic gadgets play a vital role in the society of this globalization era. Global awareness is not possible without these advances in technology. The trend of using notebooks, tablets, smartphones and digital devices had become the routine of our day today life. People use digita
Digital Assistant for the Visually Impaired

Technology advancement allows people to live their life easier and more efficient. However, not everyone felt the same, as some people with disabilities such as the visually impaired still struggle in life despite the presence of technology or smart devices to help them. Whether it's a problem within the user interaction of the system or the cost that is too expensive, people suffering from visual impairment still struggles on their day-to-day activities. In order to solve the problem, we introduced DAVID a digital assistant application aim to help the visually impaired in recognizing text on real-world objects and provide an audio feedback in real-time. It utilizes voice user interface technology such as speech recognizing and speech synthesis as the means of interaction through voice input.
Mask Dynamic Routing to Combined Model of Deep Capsule Network and U-Net

The capsule network is a novel architecture to encode feature attributes and spatial relationships of an image. By using the dynamic routing (DR) algorithm, a capsule network (CapsNet) model can be trained. However, the original three-layer CapsNet with the DR algorithm performs poorly on complex data sets, such as FashionMNIST, CIFAR-10, and CIFAR-100. This deficiency limits the wider application of capsule networks. In this article, we propose a deep capsule network model combined with a U-Net preprocessing module (DCN-UN). Local connection and weight-sharing strategies are adopted from convolutional neural networks to design a convolutional capsule layer in the DCN-UN model. This allows considerably reducing the number of parameters in the network model. Moreover, a greedy strategy is incorporated into the design of a mask DR (MDR) algorithm to improve the performance of network models. DCN-UN requires up to five times fewer parameters compared with the original CapsNet and other CapsNet-based models. The performance improvement achieved by the DCN-UN model with the MDR algorithm over the original CapsNet model with the DR algorithm is approximately 12% and 17% on the CIFAR-10 and CIFAR-100 data sets, respectively. The experimental results confirm that the proposed DCN-UN model allows preserving advantages of image reconstruction and equivariance mechanism in capsule networks. Moreover, an efficient initialization method is explored to enhance training stability and avoid gradient explosion.