Reviewerly/exHarmony huggingface dataset:
Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?

Chatbots are becoming increasingly popular as a human-computer interface. The traditional best practices normally applied to User Experience (UX) design cannot easily be applied to chatbots, nor can conventional usability testing techniques guarantee accuracy. WeightMentor is a bespoke self-help motivational tool for weight loss maintenance. This study addresses the following four research questions: How usable is the WeightMentor chatbot, according to conventional usability methods?; To what extend will different conventional usability questionnaires correlate when evaluating chatbot usability?; And how do they correlate to a tailored chatbot usability survey score?; What is the optimum number of users required to identify chatbot usability issues?; How many task repetitions are required for a first-time chatbot users to reach optimum task performance (i.e. efficiency based on task completion times)? This paper describes the procedure for testing the WeightMentor chatbot, assesses correlation between typical usability testing metrics, and suggests that conventional wisdom on participant numbers for identifying usability issues may not apply to chatbots. The study design was a usability study. WeightMentor was tested using a pre-determined usability testing protocol, evaluating ease of task completion, unique usability errors and participant opinions on the chatbot (collected using usability questionnaires). WeightMentor usability scores were generally high, and correlation between questionnaires was strong. The optimum number of users for identifying chatbot usability errors was 26, which challenges previous research. Chatbot users reached optimum proficiency in tasks after just one repetition. Usability test outcomes confirm what is already known about chatbots - that they are highly usable (due to their simple interface and conversation-driven functionality) but conventional methods for assessing usability and user experience may not be as accurate when applied to chatbots.
Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.
Forensic Similarity for Digital Images

In this paper, we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g., training samples, of a forensic trace is not required to make a forensic similarity decision on it in the future. To do this, we propose a two-part deep-learning system composed of a convolutional neural network-based feature extractor and a three-layer neural network, called the similarity network. This system maps the pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated the system accuracy of determining whether two image patches were captured by the same or different camera model and manipulated by the same or a different editing operation and the same or a different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces and importantly show efficacy on “unknown” forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.
Calibration of Pre-trained Transformers

Pre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrated.Specifically, do these models' posterior probabilities provide an accurate empirical measure of how likely the model is to be correct on a given example?We focus on BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) in this work, and analyze their calibration across three tasks: natural language inference, paraphrase detection, and commonsense reasoning.For each task, we consider in-domain as well as challenging outof-domain settings, where models face more examples they should be uncertain about.We show that: (1) when used out-of-the-box, pretrained models are calibrated in-domain, and compared to baselines, their calibration error out-of-domain can be as much as 3.5× lower;(2) temperature scaling is effective at further reducing calibration error in-domain, and using label smoothing to deliberately increase empirical uncertainty helps calibrate posteriors out-of-domain. 1
Experimental quantum speed-up in reinforcement learning agents

As the field of artificial intelligence advances, the demand for algorithms that can learn quickly and efficiently increases. An important paradigm within artificial intelligence is reinforcement learning1, where decision-making entities called agents interact with environments and learn by updating their behaviour on the basis of the obtained feedback. The crucial question for practical applications is how fast agents learn2. Although various studies have made use of quantum mechanics to speed up the agent's decision-making process3,4, a reduction in learning time has not yet been demonstrated. Here we present a reinforcement learning experiment in which the learning process of an agent is sped up by using a quantum communication channel with the environment. We further show that combining this scenario with classical communication enables the evaluation of this improvement and allows optimal control of the learning progress. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor. The device interfaces with telecommunication-wavelength photons and features a fast active-feedback mechanism, demonstrating the agent's systematic quantum advantage in a setup that could readily be integrated within future large-scale quantum communication networks.
T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval

Text-video retrieval is a challenging task that aims to search relevant video contents based on natural language descriptions. The key to this problem is to measure text-video similarities in a joint embedding space. However, most existing methods only consider the global cross-modal similarity and overlook the local details. Some works incorporate the local comparisons through cross-modal local matching and reasoning. These complex operations introduce tremendous computation. In this paper, we design an efficient global-local alignment method. The multi-modal video sequences and text features are adaptively aggregated with a set of shared semantic centers. The local cross-modal similarities are computed between the video feature and text feature within the same center. This design enables the meticulous local comparison and reduces the computational cost of the interaction between each text-video pair. Moreover, a global alignment method is proposed to provide a global cross-modal measurement that is complementary to the local perspective. The global aggregated visual features also provide additional supervision, which is indispensable to the optimization of the learnable semantic centers. We achieve consistent improvements on three standard text-video retrieval benchmarks and outperform the state-of-the-art by a clear margin.
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a mesh-based view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID dat-sets. Source code and models are available under https://github.com/chenhao2345/GCL.
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

Tiancheng Zhao, Kaige Xie, Maxine Eskenazi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Node Embedding over Temporal Graphs

In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors

We present a method to infer 3D pose and shape of vehicles from a single image. To tackle this ill-posed problem, we optimize two-scale projection consistency between the generated 3D hypotheses and their 2D pseudo-measurements. Specifically, we use a morphable wireframe model to generate a fine-scaled representation of vehicle shape and pose. To reduce its sensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarse representation which improves robustness. We also integrate three task priors, including unsupervised monocular depth, a ground plane constraint as well as vehicle shape priors, with forward projection errors into an overall energy function.
SFNet: Learning Object-Aware Semantic Correspondence

We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic geometric deformations to train a convolutional neural network (CNN) for this task. Using these masks as part of the supervisory signal offers a good compromise between semantic flow methods, where the amount of training data is limited by the cost of manually selecting point correspondences, and semantic alignment ones, where the regression of a single global geometric transformation between images may be sensitive to image-specific details such as background clutter. We propose a new CNN architecture, dubbed SFNet, which implements this idea. It leverages a new and differentiable version of the argmax function for end-to-end training, with a loss that combines mask and flow consistency with smoothness terms. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms the state of the art on standard benchmarks.
Findings of the WMT 2019 Shared Tasks on Quality Estimation

We report the results of the WMT19 shared task on Quality Estimation, i.e. the task of predicting the quality of the output of machine translation systems given just the source text and the hypothesis translations.The task includes estimation at three granularity levels: word, sentence and document.A novel addition is evaluating sentence-level QE against human judgments: in other words, designing MT metrics that do not need a reference translation.This year we include three language pairs, produced solely by neural machine translation systems.Participating teams from eleven institutions submitted a variety of systems to different task variants and language pairs.
Medical image registration using deep neural networks: A comprehensive review

Image-guided interventions are saving the lives of a large number of patients where the image registration should indeed be considered as the most complex and complicated issue to be tackled. On the other hand, a huge progress in the field of machine learning has recently made by the possibility of implementing deep neural networks on the contemporary many-core GPUs. It has opened up a promising window to challenge with many medical applications in more efficient and effective ways, where the registration is not an exception. In this paper, a comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented. The review is systematic and encompasses all the related works previously published in the field. Key concepts, statistical analysis from different points of view, confining challenges, novelties and main contributions, key-enabling techniques, future directions, and prospective trends all are discussed and surveyed in details in this comprehensive review. This review allows a deep understanding and insight for the readers active in the field who are investigating the state-of-the-art and seeking to contribute the future literature.
Phishing Attacks Survey: Types, Vectors, and Technical Approaches

Phishing attacks, which have existed for several decades and continue to be a major problem today, constitute a severe threat in the cyber world. Attackers are adopting multiple new and creative methods through which to conduct phishing attacks, which are growing rapidly. Therefore, there is a need to conduct a comprehensive review of past and current phishing approaches. In this paper, a review of the approaches used during phishing attacks is presented. This paper comprises a literature review, followed by a comprehensive examination of the characteristics of the existing classic, modern, and cutting-edge phishing attack techniques. The aims of this paper are to build awareness of phishing techniques, educate individuals about these attacks, and encourage the use of phishing prevention techniques, in addition to encouraging discourse among the professional community about this topic.
The Metaverse: A New Challenge for the Healthcare System: A Scoping Review

(1) Background: The metaverse is now a reality, and it interests the scientific community, the educational setting, and medical care. Considering the number of people in front of screens, especially children and adolescents, the metaverse could and should become a place of health promotion. Consequently, the objective of the present study was to review the current literature to detect articles that connected the metaverse with prevention and treatment, education and training, and research setting. (2) Methods: Articles were searched on Pubmed, Web of Science, and Scopus, including English-written papers published until 12 August 2022. They were screened against the eligibility criteria and discussed narratively. (3) Results: The literature published is poor; only 21 articles were included, and 11 of them were added in a second moment. These articles were mainly reviews of the literature or editorials. The aspects related to this virtual world in terms of health prevention and the treatment of clinical conditions, education and training, and research have been narratively discussed. (4) Conclusions: The metaverse could be considered a useful instrument to arrive easily and quickly to the population. Given its importance, today, different studies and investments are required to develop proper health promotion programs that are feasible and valid in the metaverse.
Quantum fidelity measures for mixed states

Applications of quantum technology often require fidelities to quantify performance. These provide a fundamental yardstick for the comparison of two quantum states. While this is straightforward in the case of pure states, it is much more subtle for the more general case of mixed quantum states often found in practice. A large number of different proposals exist. In this review, we summarize the required properties of a quantum fidelity measure, and compare them, to determine which properties each of the different measures has. We show that there are large classes of measures that satisfy all the required properties of a fidelity measure, just as there are many norms of Hilbert space operators, and many measures of entropy. We compare these fidelities, with detailed proofs of their properties. We also summarize briefly the applications of these measures in teleportation, quantum memories and quantum computers, quantum communications, and quantum phase-space simulations.
Training Neural Machine Translation to Apply Terminology Constraints

This paper proposes a novel method to inject custom terminology into neural machine translation at run time.Previous works have mainly proposed modifications to the decoding algorithm in order to constrain the output to include run-time-provided target terms.While being effective, these constrained decoding methods add, however, significant computational overhead to the inference step, and, as we show in this paper, can be brittle when tested in realistic conditions.In this paper we approach the problem by training a neural MT system to learn how to use custom terminology when provided with the input.Comparative experiments show that our method is not only more effective than a state-of-the-art implementation of constrained decoding, but is also as fast as constraint-free decoding.
Efficient partition of integer optimization problems with one-hot encoding

Quantum annealing is a heuristic algorithm for solving combinatorial optimization problems, and hardware for implementing this algorithm has been developed by D-Wave Systems Inc. The current version of the D-Wave quantum annealer can solve unconstrained binary optimization problems with a limited number of binary variables. However, the cost functions of several practical problems are defined by a large number of integer variables. To solve these problems using the quantum annealer, integer variables are generally binarized with one-hot encoding, and the binarized problem is partitioned into small subproblems. However, the entire search space of the binarized problem is considerably larger than that of the original integer problem and is dominated by infeasible solutions. Therefore, to efficiently solve large optimization problems with one-hot encoding, partitioning methods that extract subproblems with as many feasible solutions as possible are required. In this study, we propose two partitioning methods and demonstrate that they result in improved solutions.
RSVQA: Visual Question Answering for Remote Sensing Data

This paper introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method introduced in this article, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task. We report the results obtained by applying a model based on Convolutional Neural Networks (CNNs) for the visual part and on a Recurrent Neural Network (RNN) for the natural language part to this task. The model is trained on the two datasets, yielding promising results in both cases.
Intelligent metaphotonics empowered by machine learning

In the recent years, a dramatic boost of the research is observed at the junction of photonics, <em>machine learning</em> and <em>artificial intelligence</em>. A new methodology can be applied to the description of a variety of photonic systems including optical waveguides, nanoantennas, and metasurfaces. These novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices. Artificial intelligence and machine learning penetrate rapidly into the fundamental physics of light, and they provide effective tools for the study of the field of <em>metaphotonics</em> driven by optically induced electric and magnetic resonances. &nbsp;Here we overview the evaluation of metaphotonics induced by artificial intelligence and present a summary of the concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.