Reviewerly/exHarmony huggingface dataset:
Long-time Bell states of waveguide-mediated qubits via continuous measurement

In this paper we consider a scheme for achieving long-time sustainable Bell states of two distant qubits mediated by a one-dimensional waveguide whose outputs are subjected to time-continuous photon counting or homodyne detection. In both of the detection cases, it is shown that different Bell states can be obtained, for different initial states, in the long-time regime. In particular, we find that, in the case of photon counting, a cyclic jump among Bell states can be formed once the first photon is registered. While in the homodyne-detection case we further reveal that any steady Bell state can be achieved independent of detection efficiency, with a probability of 50% . The underlying physical reason for this is also analyzed. Our scheme is advantageous over previous studies in which only transient or intermittent Bell states can be generated. The long-time Bell states of distant qubits can be used for constructing quantum networks.
Detection of the quantum states containing at most k – 1 unentangled particles*

There are many different classifications of entanglement for multipartite quantum systems, one of which is based on the number of unentangled particles.In this paper, we mainly study the quantum states containing at most k -1 unentangled particles and provide several entanglement criteria based on different forms of inequalities which can both identify quantum states containing at most k -1 unentangled particles.We show that these criteria are more effective for some states by concrete examples.
Multi-modal Representation Learning for Video Advertisement Content Structuring

Video advertisement content structuring aims to segment a given video advertisement and label each segment on various dimensions, such as presentation form, scene, and style. Different from real-life videos, video advertisements contain sufficient and useful multi-modal content like caption and speech, which provides crucial video semantics and would enhance the structuring process. In this paper, we propose a multi-modal encoder to learn multi-modal representation from video advertisements by interacting between video-audio and text. Based on multi-modal representation, we then apply Boundary-Matching Network to generate temporal proposals. To make the proposals more accurate, we refine generated proposals by scene-guided alignment and re-ranking. Finally, we incorporate proposal located embeddings into the introduced multi-modal encoder to capture temporal relationships between local features of each proposal and global features of the whole video for classification. Experimental results show that our method achieves significantly improvement compared with several baselines and Rank 1 on the task of Multi-modal Ads Video Understanding in ACM Multimedia 2021 Grand Challenge. Ablation study further shows that leveraging multi-modal content like caption and speech in video advertisements significantly improve the performance.
Idler-free multi-channel discrimination via multipartite probe states

The characterisation of Quantum Channel Discrimination (QCD) offers critical insight for future quantum technologies in quantum metrology, sensing and communications. The task of multi-channel discrimination creates a scenario in which the discrimination of multiple quantum channels can be equated to the idea of pattern recognition, highly relevant to the tasks of quantum reading, illumination and more. Whilst the optimal quantum strategy for many scenarios is an entangled idler-assisted protocol, the extension to a multi-hypothesis setting invites the exploration of discrimination strategies based on unassisted, multipartite probe states. In this work, we expand the space of possible quantum enhanced protocols by formulating general classes of unassisted multi-channel discrimination protocols which are not assisted by idler modes. Developing a general framework for idler-free protocols, we perform an explicit investigation in the bosonic setting, studying prominent Gaussian channel discrimination problems for real world applications. Our findings uncover the existence of strongly quantum advantageous, idler-free protocols for the discrimination of bosonic loss and environmental noise. This circumvents the necessity for idler assistance to achieve quantum advantage in some of the most relevant discrimination settings, significantly loosening practical requirements for prominent quantum sensing applications.
Efficient Distribution of Quantum Circuits

Quantum computing hardware is improving in robustness, but individual computers still have small number of qubits (for storing quantum information). Computations needing a large number of qubits can only be performed by distributing them over a network of smaller quantum computers. In this paper, we consider the problem of distributing a quantum computation, represented as a quantum circuit, over a homogeneous network of quantum computers, minimizing the number of communication operations needed to complete every step of the computation. We propose a two-step solution: dividing the given circuit’s qubits among the computers in the network, and scheduling communication operations, called migrations, to share quantum information among the computers to ensure that every operation can be performed locally. While the first step is an intractable problem, we present a polynomial-time solution for the second step in a special setting, and a O(log n)-approximate solution in the general setting. We provide empirical results which show that our two-step solution outperforms existing heuristic for this problem by a significant margin (up to 90%, in some cases).
Offline Reinforcement Learning with Implicit Q-Learning

Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This trade-off is critical, because most current offline reinforcement learning methods need to query the value of unseen actions during training to improve the policy, and therefore need to either constrain these actions to be in-distribution, or else regularize their values. We propose an offline RL method that never needs to evaluate actions outside of the dataset, but still enables the learned policy to improve substantially over the best behavior in the data through generalization. The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state. This leverages the generalization capacity of the function approximator to estimate the value of the best available action at a given state without ever directly querying a Q-function with this unseen action. Our algorithm alternates between fitting this upper expectile value function and backing it up into a Q-function. Then, we extract the policy via advantage-weighted behavioral cloning. We dub our method implicit Q-learning (IQL). IQL demonstrates the state-of-the-art performance on D4RL, a standard benchmark for offline reinforcement learning. We also demonstrate that IQL achieves strong performance fine-tuning using online interaction after offline initialization.
Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios

Recently, autonomous driving has made substantial progress in addressing the most common traffic scenarios like intersection navigation and lane changing. However, most of these successes have been limited to scenarios with well-defined traffic rules and require minimal negotiation with other vehicles. In this paper, we introduce a previously unconsidered, yet everyday, high-conflict driving scenario requiring negotiations between agents of equal rights and priorities. There exists no centralized control structure and we do not allow communications. Therefore, it is unknown if other drivers are willing to cooperate, and if so to what extent. We train policies to robustly negotiate with opposing vehicles of an unobservable degree of cooperativeness using multi-agent reinforcement learning (MARL). We propose Discrete Asymmetric Soft Actor-Critic (DASAC), a maximum- entropy off-policy MARL algorithm allowing for centralized training with decentralized execution. We show that using DASAC we are able to successfully negotiate and traverse the scenario considered over 99% of the time. Our agents are robust to an unknown timing of opponent decisions, an unobservable degree of cooperativeness of the opposing vehicle, and previously unencountered policies. Furthermore, they learn to exhibit human-like behaviors such as defensive driving, anticipating solution options and interpreting the behavior of other agents.
An Illocutionary Acts Analysis on Barack Obama’s Speech

This research is entitled Illocutionary Acts Analysis of Barack Obama's Speeches.This research is conducted in order to analyze the utterances of Barack Obama's Speeches.Speeches became an important media for a president like Barack Obama to deliver message, persuade people, influence audience.The speech making process involved a long discussion between Obama and a particular team since they should consider some language aspects such as lexical choices and sentence structure in order to achieve certain goals.Austin (1975:3) stated that there are three types of speech acts, namely locutionary, illocutionary, and perlocutionary acts.But in this paper, the researchers focus to illocutionary acts.Illocutionary act has five types, they are representatives, directives, commissives, expressive, and declarative (Searle in Leech, 1983:205).So the writer uses the types of illocutionary acts to analyze the utterances of Barack Obama's Speeches.Problem discussed in this research is what types of illocutionary acts are found on Barack Obama's speech.This research utilized Speech Act theory of Austin (1962) in analyzing the data.The researched data consisted of two selected speeches delivered by Barack Obama after Osama bin Laden's death: May 1, 2011 and at 2012 Democratic National Convention September 6 2012.And in this research, types of illocutionary acts in Barack Obama's Speeches are found dominantly is representatives.
Efficient Multi-Modal Fusion with Diversity Analysis

Multi-modal machine learning has been a prominent multi-disciplinary research area since its success in complex real-world problems. Empirically, multi-branch fusion models tend to generate better results when there is a high diversity among each branch of the model. However, such experience alone does not guarantee the fusion model's best performance nor have sufficient theoretical support. We present the theoretical estimation of the fusion models' performance by measuring each branch model's performance and the distance between branches based on the analysis of several most popular fusion methods. The theorem is validated empirically by numerical experiments. We further present a branch model selection framework to identify the candidate branches for fusion models to achieve the optimal multi-modal performance by using the theorem. The framework's effectiveness is demonstrated on various datasets by showing how effectively selecting the combination of branch models to attain superior performance.
Technologies in Analysis and Computing Immediate Recalls

The research addresses the problem of developing technologies enabling automated assessment and contrasting immediate recalls with an original expository text. The Corpus compiled for the study comprises an expository text of 252 tokens and 134 immediate recalls performed by Russian natives (over 1500 tokens). The study highlights three core issues: (i) automated multi-factor assessment of text parameters; (ii) developing algorithms of proportional analysis to contrast texts; (iii) identifying main patterns employed in immediate recalls, i.e. association, clustering, and prediction. We also demonstrate and expand the implementation area of a web tool RuLingva computing over 33 text metrics. Overall, the research advances our understanding of immediate recalls models and offers foundations for more effective algorithms to assess and profile immediate text recalls. The research findings can be useful in language teaching and text production as taken together, the models identified have a potential to assist in solving practical (essay scoring), and theoretical (information extraction) problems.
An Indoor Navigation Support for the Student Halls of Residence using Augmented Reality: A Design Perspective

Augmented Reality (AR) technology has become increasingly popular due to its potential use in an indoor environment. AR technology enables virtual information, such as navigation instructions, to be merged into the actual environment via a mobile screen. Using an AR-based Indoor Navigation speeds uptime while also being interactive in searching for a particular building location. Every year when new semester students enrol in the university, some students will have difficulty finding a particular location on the campus. The most searched for building upon arrival at the university is the student halls of residence. While searching for it, students waste time asking others for information or looking for a nearby campus map. Therefore, this project investigates the requirements needed for an AR-based indoor navigation application to be applied within the student halls of residence and identifies technical issues through a small-scale prototype development within a small navigational area. Seventy-one students participated in the feasibility study by responding to a set of questionnaires related to the Student Residence AR indoor navigation application. At the same time, four users with and without previous experience with AR applications evaluated the prototype application. The results identified that the more the students have difficulty searching, the more they require additional time to reach their destination and seek help from others, an excellent reason to implement the Student Residence AR indoor navigation. In addition, the prototype evaluation results discussed issues related to arrow path confusion, distance accuracy, assistive guideline, and software development challenges in AR development that could be beneficial to future developers and researchers.
"That's in the eye of the beholder": Layers of Interpretation in Image Descriptions for Fictional Representations of People with Disabilities

Image accessibility is an established research area in Accessible Computing and a key area of digital accessibility for blind and low vision (BLV) people worldwide. Recent work has delved deeper into the question of how image descriptions should properly reflect the complexities of marginalized identity. However, when real subjects are not available to consult on their preferred identity terminology, as is the case with fictional representations of disability, the issue arises again of how to create accurate and sensitive image descriptions. We worked with 25 participants to assess and iteratively co-design image descriptions for nine fictional representations of people with disabilities. Through nine focus groups and nineteen interviews, we discovered five key themes which we present here along with an analysis of the layers of interpretation at work in the production and consumption of image descriptions for fictional representations.
Empirical Analysis of Deep Convolutional Generative Adversarial Network for Ultrasound Image Synthesis

Introduction: Recent research on Generative Adversarial Networks (GANs) in the biomedical field has proven the effectiveness in generating synthetic images of different modalities. Ultrasound imaging is one of the primary imaging modalities for diagnosis in the medical domain. In this paper, we present an empirical analysis of the state-of-the-art Deep Convolutional Generative Adversarial Network (DCGAN) for generating synthetic ultrasound images. Aims: This work aims to explore the utilization of deep convolutional generative adversarial networks for the synthesis of ultrasound images and to leverage its capabilities. Background: Ultrasound imaging plays a vital role in healthcare for timely diagnosis and treatment. Increasing interest in automated medical image analysis for precise diagnosis has expanded the demand for a large number of ultrasound images. Generative adversarial networks have been proven beneficial for increasing the size of data by generating synthetic images. Objective: Our main purpose in generating synthetic ultrasound images is to produce a sufficient amount of ultrasound images with varying representations of a disease. Methods: DCGAN has been used to generate synthetic ultrasound images. It is trained on two ultrasound image datasets, namely, the common carotid artery dataset and nerve dataset, which are publicly available on Signal Processing Lab and Kaggle, respectively. Results: Results show that good quality synthetic ultrasound images are generated within 100 epochs of training of DCGAN. The quality of synthetic ultrasound images is evaluated using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). We have also presented some visual representations of the slices of generated images for qualitative comparison. Conclusion: Our empirical analysis reveals that synthetic ultrasound image generation using DCGAN is an efficient approach. Other: In future work, we plan to compare the quality of images generated through other adversarial methods such as conditional GAN, progressive GAN.
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis

Even the most impressive achievement in frontal face synthesis is challenged by large poses and low-quality data given one single side-view face. We propose a synthesizer called SuperFront GAN (SF-GAN) to accept one or more low-resolution (LR) faces at the input to then output a high-resolution (HR) frontal face with various poses and such to preserve identity information. SF-GAN includes intra-class and inter-class constraints, which allow it to learn an identity-preserving representation from multiple LR faces in an improved, comprehensive manner. We adopt an orthogonal loss as the intra-class constraint that diversifies the learned feature-space per subject. Hence, each sample is made to complement the others to its max ability. Additionally, a triplet loss is used as the inter-class constraint: it improves the discriminative power of the new representation, which, hence, maintains the identity information. Furthermore, we integrate a super-resolution (SR) side-view module as part of the SF-GAN to help preserve the finer details of HR side-views. This helps the model reconstruct the high-frequency parts of the face (i.e. periocular region, nose, and mouth regions). Quantitative and qualitative results demonstrate the superiority of SF-GAN. SF-GAN holds promise as a pre-processing step to normalize and align faces before passing to CV system for processing.
Enhanced Whale Optimization Algorithm for the Eye Movement Recognition

In recent days, the Electrooculography (EOG) signals triggers the Human Computer Interface (HCI) control system.On the basis of the EOG signal pattern, the recognition of eye movements is performed to preside over the HCI system and perform a definite job on the category of eye movement.By means of several associated assessments, this work proposes a new technique for the recognition of eye movement on the basis of EOG signal by exploiting the Enhanced Whale Optimization Algorithm (E-WOA) with the aid of Neural Network (NN).To minimize the error function from the classifier, the E-WOA model is exploited.The developed model performance is examined by evaluating the adopted technique with the traditional techniques.From the outcomes, it is exhibited that higher performance of developed technique with the minimization of error analysis and evaluation regarding the recognition in terms of various performance metrics such as Accuracy, Precision, FPR, Specificity,Sensitivity, FNR, NPV, F1 Score and FDR.
SENTIMENT ANALYSIS FOR SOCIAL MEDIA: A SURVEY

With the rapid development of the Internet industry, an increasing number of social media platforms have been developed. These social media platforms have become the main channels for communication among most users. Opinions from social media platforms provide the most updated and inclusive information. Sentiments from opinions are a valuable data source for solving many issues. Therefore, sentiment analysis has developed into one of the most popular natural language processing fields. Hence, improving the performance of sentiment analysis methods or discovering new problems related to these methods is essential. In this context, we must be aware of the general information relevant to this area. This survey presents a summary of the necessary stages for building a complete model to be used in sentiment analysis. For each procedure, we list the popular techniques that have been widely used in recent years. In addition, discussions and comparisons related to these methods are provided. Additionally, we discuss the challenges and possible research directions for future research in this field.
The Secured Proficient Smart Electronic Voting System

In this contemporary democratic system, the election commission of India is facing difficulty in finding fake votes. Right now framework, the political decision commission of India is confronting trouble in discovering counterfeit votes. To beat this, we presented a novel crossover Electronic democratic machine utilizing a face acknowledgment framework. Right now and numerical models, for arranging face including Convolutional Neural Network. In this mechanized framework, in this paper recommended and advise an EVM machine which confirms the facial picture of a voter through a CNN based face recognizer to checks it with the pre-caught pictures gave in the database. The databases checked based on the outcome being sure, it is accept the voter is a valid one. This move towards to make the choice for an ideological group, if the outcome is negative, the voter can't make their choice. After this procedure, the pictures are put away in another database to check the democratic rate. After this process, the casted images are stored in another database to check the voting percentage.
Photobook Creation Using Face Recognition and Machine Learning

Nowadays, mobile photography plays a pivotal role in major parts of human lives. Every individual captures a lot of photographs in their personal device. However, management of these photographs by person becomes very crucial. Hence, photobook provides an attractive solution for personal storing and printing. In fact, today many commercial products are available to support the above point. But all of them require human annotation which is a time-consuming process and also leads to unreliable authentication. Hence, in this work, face recognition and deep learning techniques are utilized for photobook creation, wherein the user can create albums for a particular group of people or individuals and annotate them. Also, for every new photograph captured, the photobook will update itself automatically. Further, user interaction is introduced in the proposed work which makes it more reliable and results in good performance.
Symbol-Pair Distance of Repeated-Root Constacyclic Codes of Prime Power Lengths over \({{\mathbb{F}_{p^m}[u]}/{\langle u^3\rangle}}\)

Let p be a prime, s, m be positive integers, γ be a nonzero element of the finite field Fpm, and let R=Fpm[u]/⟨u3⟩ be the finite commutative chain ring. In this paper, the symbol-pair distances of all γ-constacyclic codes of length ps over R are completely determined.