Reviewerly/exHarmony huggingface dataset:
Synchronous bidirectional inference for neural sequence generation

In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result token by token. The neural approaches, such as LSTM and self-attention networks, are now able to make full use of all the predicted history hypotheses from left side during inference, but cannot meanwhile access any future (right side) information and usually generate unbalanced outputs (e.g. left parts are much more accurate than right ones in Chinese-English translation). In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively. First, we introduce a novel beam search algorithm that facilitates synchronous bidirectional decoding. Then, we present the core approach which enables left-to-right and right-to-left decoding to interact with each other, so as to utilize both the history and future predictions simultaneously during inference. We apply the proposed model to both LSTM and self-attention networks. Furthermore, we propose a novel fine-tuning based parameter optimization algorithm in addition to the simple two-pass strategy. The extensive experiments on machine translation and abstractive summarization demonstrate that our synchronous bidirectional inference model can achieve remarkable improvements over the strong baselines.
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition

Skeleton-based human action recognition is becoming popular due to its computational efficiency and robustness.Since not all skeleton joints are informative for action recognition, attention mechanisms are adopted to extract informative joints and suppress the influence of irrelevant ones.However, existing attention frameworks usually ignore helpful scenario context information.In this paper, we propose a cross-attention module that consists of a self-attention branch and a cross-attention branch for skeleton-based action recognition.It helps to extract joints that are not only more informative but also highly correlated to the corresponding scenario context information.Moreover, the cross-attention module maintains input variables' size and can be flexibly incorporated into many existing frameworks without breaking their behaviors.To facilitate end-to-end training, we further develop a scenario context information extraction branch to extract context information from raw RGB video directly.We conduct comprehensive experiments on the NTU RGB+D and the Kinetics databases, and experimental results demonstrate the correctness and effectiveness of the proposed model.
Unsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic Hashing

Unsupervised hashing methods have attracted considerable attention in large-scale remote sensing (RS) image retrieval, due to their capability for massive data processing with significantly reduced storage and computation. Although existing unsupervised hashing methods are suitable for operational applications, they exhibit limitations when accurately modeling the complex semantic content present in RS images using binary codes (in an unsupervised manner). To address this problem, in this letter, we introduce a novel unsupervised hashing method that takes advantage of the generative nature of probabilistic topic models to encapsulate the hidden semantic patterns of the data into the final binary representation. Specifically, we introduce a new probabilistic latent semantic hashing (pLSH) model to effectively learn the hash codes using three main steps: 1) data grouping, where the input RS archive is clustered into several groups; 2) topic computation, where the pLSH model is used to uncover highly descriptive hidden patterns from each group; and 3) hash code generation, where the data probability distributions are thresholded to generate the final binary codes. Our experimental results, obtained on two benchmark archives, reveal that the proposed method significantly outperforms state-of-the-art unsupervised hashing methods.
Frontline Employees' Acceptance of and Resistance to Service Robots in Stationary Retail - An Exploratory Interview Study

Due to rising online competition, increasing cost pressure and cross-channel customer journeys, stationary retail has tried to develop innovative value propositions and co-create value with customers through new technologies, which are expected to profoundly change the stationary retail’s service systems. Among other technologies, service robots are said to have the potential to revitalise interactive value creation in stationary retail. However, the integration of such technologies poses new challenges. Prior research has looked at customers’ acceptance of service robots in stationary retail settings, but few studies have explored their counterparts – the frontline employees’ (FLEs) perspective. Yet, FLEs’ acceptance of service robots is crucial to implement service robots for retail innovation. To explore FLEs’ acceptance of and resistance to service robots, a qualitative exploratory interview study is conducted. It identifies decisive aspects, amongst others loss of status or role incongruency. The findings extend prior studies on technology acceptance and resistance and reveal i.a. that FLEs perceive service robots as both a threat and potential support. Moreover, they feel hardly involved in the co-creation of use cases for a service robot, although they are willing to contribute.
K-Nearest Neighbor (K-NN) based Missing Data Imputation

The performance of the classification algorithm depends on the quality of the training data. Data quality is an important factor that affects the data mining classification results. However, one problems that often found is missing data. Effect many missing data is a less optimal classification model. Because it is can deletes important information that affect the performance of the algorithm. One method used to recover missing data is to fill it, as known as imputation. This study uses the K-NN method as an imputation carried out in several cases that have different mechanisms and missing data model. On these imputed dataset then apply classification with Naive Bayes algorithm. In this study, analyzes the performance of imputation method using Naive Bayes algorithm on the basis of accuracy for handling missing data. The results, handling missing data with K-NN-based imputation can reach the accuracy of complete data in each case with a low accuracy difference.
Finite-Component Multicriticality at the Superradiant Quantum Phase Transition

We demonstrate the existence of finite-component multicriticality in a qubit-boson model where biased qubits collectively coupled to a single-mode bosonic field. The interplay between biases and boson-qubit coupling produces a rich phase diagram which shows multiple superradiant phases and phase boundaries of different orders. In particular, multiple phases become indistinguishable in appropriate bias configurations, which is the signature of multicriticality. A series of universality classes characterizing these multicritical points are identified. Moreover, we present a trapped-ion realization with the potential to explore multicritical phenomena experimentally using a small number of ions. The results open a novel way to probe multicritical universality classes in experiments.
La competencia digital en el docente universitario

Digital skills in the work of higher education is a very fruitful field that is constantly developing due to the adequacy of teachers in the face of the demand for information and communication technologies and their impact on the educational field. This article deals with the current panorama and its dynamics against the requirements that the teacher must develop and the perspectives of the university and students. On the other hand, the perspective of new trends in relation to the panorama of virtual learning environments and their link in university education, is addressed. In addition, a final discussion is presented on the possibilities of the development of competencies in the higher academic task.
Re-Synchronization Using the Hand Preceding Model for Multi-Modal Fusion in Automatic Continuous Cued Speech Recognition

Cued Speech (CS) is an augmented lip reading system complemented by hand coding, and it is very helpful to the deaf people. Automatic CS recognition can help communications between the deaf people and others. Due to the asynchronous nature of lips and hand movements, fusion of them in automatic CS recognition is a challenging problem. In this work, we propose a novel re-synchronization procedure for multi-modal fusion, which aligns the hand features with lips feature. It is realized by delaying hand position and hand shape with their optimal hand preceding time which is derived by investigating the temporal organizations of hand position and hand shape movements in CS. This re-synchronization procedure is incorporated into a practical continuous CS recognition system that combines convolutional neural network (CNN) with multi-stream hidden markov model (MSHMM). A significant improvement of about 4.6% has been achieved retaining 76.6% CS phoneme recognition correctness compared with the state-of-the-art architecture (72.04%), which did not take into account the asynchrony issue of multi-modal fusion in CS. To our knowledge, this is the first work to tackle the asynchronous multi-modal fusion in the automatic continuous CS recognition.
A Systematic Literature Review on Privacy by Design in the Healthcare Sector

In this digital age, we are observing an exponential proliferation of sophisticated hardware- and software-based solutions that are able to interact with the users at almost every sensitive aspect of our lives, collecting and analysing a range of data about us. These data, or the derived information out of it, are often too personal to fall into unwanted hands, and thus users are almost always wary of the privacy of such private data that are being continuously collected through these digital mediums. To further complicate the issue, the infringement cases of such databanks are on a sharp rise. Several frameworks have been devised in various parts of the globe to safeguard the issue of data privacy; in parallel, constant research is also being conducted on closing the loopholes within these frameworks. This study aimed to analyse the contemporary privacy by design frameworks to identify the key limitations. Seven contemporary privacy by design frameworks were examined in-depth in this research that was based on a systematic literature review. The result, targeted at the healthcare sector, is expected to produce a high degree of fortification against data breaches in the personal information domain.
Qualitative case-based reasoning and learning

The development of autonomous agents that perform tasks with the same dexterity as performed by humans is one of the challenges of artificial intelligence and robotics. This motivates the research on intelligent agents, since the agent must choose the best action in a dynamic environment in order to maximise the final score. In this context, the present paper introduces a novel algorithm for Qualitative Case-Based Reasoning and Learning (QCBRL), which is a case-based reasoning system that uses qualitative spatial representations to retrieve and reuse cases by means of relations between objects in the environment. Combined with reinforcement learning, QCBRL allows the agent to learn new qualitative cases at runtime, without assuming a pre-processing step. In order to avoid cases that do not lead to the maximum performance, QCBRL executes case-base maintenance, excluding these cases and obtaining new (more suitable) ones. Experimental evaluation of QCBRL was conducted in a simulated robot-soccer environment, in a real humanoid-robot environment and on simple tasks in two distinct gridworld domains. Results show that QCBRL outperforms traditional RL methods. As a result of running QCBRL in autonomous soccer matches, the robots performed a higher average number of goals than those obtained when using pure numerical models. In the gridworlds considered, the agent was able to learn optimal and safety policies.
Nabiha: An Arabic Dialect Chatbot

Nowadays, we are living in the era of technology and innovation that impact various fields, including sciences.In computing and technology, many outstanding and attractive programs and applications have emerged, including programs that try to mimic the human behavior.A chatbot is an example of the artificial intelligence-based computer programs that try to simulate the human behavior by conducting a conversation and an interaction with the users using natural language.Over the years, various chatbots have been developed for many languages (such as English, Spanish, and French) to serve many fields (such as entertainment, medicine, education, and commerce).Unfortunately, Arabic chatbots are rare.To our knowledge, there is no previous work on developing a chatbot for the Saudi Arabic dialect.In this study, we have developed "Nabiha," a chatbot that can support conversation with Information Technology (IT) students at King Saud University using the Saudi Arabic dialect.Therefore, Nabiha will be the first Saudi chatbot that uses the Saudi dialect.To facilitate access to Nabiha, we have made it available on different platforms: Android, Twitter, and Web.When a student wants to talk with Nabiha, she can download an application, talk with her on Twitter, or visit her website.Nabiha was tested by the students of the IT department, and the results were somewhat satisfactory, considering the difficulty of the Arabic language in general and the Saudi dialect in particular.
Fast camera spatial characterization of photonic polarization entanglement

Abstract Scalable technologies to characterize the performance of quantum devices are crucial to creating large quantum networks and quantum processing units. Chief among the resources of quantum information processing is entanglement. Here we describe the full temporal and spatial characterization of polarization-entangled photons produced by Spontaneous Parametric Down Conversions using an intensified high-speed optical camera, Tpx3Cam. This novel technique allows for precise determination of Bell inequality parameters with minimal technical overhead, and for new characterization methods for the spatial distribution of entangled quantum information. The fast-optical camera could lead to multiple applications in Quantum Information Science, opening new perspectives for the scalability of quantum experiments.
Fault-tolerant weighted union-find decoding on the toric code

Quantum error correction requires decoders that are both accurate and efficient. To this end, union-find decoding has emerged as a promising candidate for error correction on the surface code. In this work, we benchmark a weighted variant of the union-find decoder on the toric code under circuit-level depolarizing noise. This variant preserves the almost-linear time complexity of the original while significantly increasing the performance in the fault-tolerance setting. In this noise model, weighting the union-find decoder increases the threshold from $0.38%$ to $0.62%$, compared to an increase from $0.65%$ to $0.72%$ when weighting a matching decoder. Further assuming quantum nondemolition measurements, weighted union-find decoding achieves a threshold of $0.76%$ compared to the $0.90%$ threshold when matching. We additionally provide comparisons of timing as well as low error rate behavior.
Device-Free Human Gesture Recognition With Generative Adversarial Networks

Recent advances in device-free wireless sensing have created the emerging technique of device-free human gesture recognition (DFHGR), which could recognize human gestures by analyzing their shadowing effect on surrounding wireless signals. DFHGR has many potential applications in the fields of human-machine interaction, smart home, intelligent space, etc. State-of-the-art work has achieved satisfactory recognition accuracy when there are a sufficient number of training samples. However, it is time consuming and labor intensive to collect samples, thus how to realize DFHGR under a small training sample set becomes an urgent problem to solve. Motivated by the excellent ability of the generative adversarial network in synthesizing samples, in this article, we explore and exploit the idea of leveraging it to realize virtual samples augmentation. Specifically, we first design a single scenario network with new architecture and better-designed loss function to generate virtual samples using a few number of real samples. Then, we further develop a scenario transferring network to generate virtual samples by utilizing the real samples not only from the current scenario but also from another available scenario as well, which could improve the quality of synthesized samples with the extra knowledge learned from another scenario. We design an mmWave-based DFHGR testbed to test the proposed networks, extensive experimental results demonstrate that the augmented virtual samples are of high quality and facilitate DFHGR systems to achieve better accuracy.
Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network

Human bodies exhibit various shapes for different identities or poses, but the body shape has certain similarities in structure and thus can be embedded in a low-dimensional space. This paper presents an autoencoder-like network architecture to learn disentangled shape and pose embedding specifically for the 3D human body. This is inspired by recent progress of deformation-based latent representation learning. To improve the reconstruction accuracy, we propose a hierarchical reconstruction pipeline for the disentangling process and construct a large dataset of human body models with consistent connectivity for the learning of the neural network. Our learned embedding can not only achieve superior reconstruction accuracy but also provide great flexibility in 3D human body generation via interpolation, bilinear interpolation, and latent space sampling. The results from extensive experiments demonstrate the powerfulness of our learned 3D human body embedding in various applications.
Project-Based Learning Approach for Teaching Mobile Application Development Using Visualization Technology

This article is devoted to the experience of applying the Blended Project-Based Learning Approach Using Visualization Technology in teaching mobile application development for IT-students. The Blended Project-Based Learning Approach Using Visualization Technology is based on a project-based method, pair programming, teamwork and using of digital educational resources as a visual learning content. The authors describe the impact of the used teaching methods and digital educational resources on the student's cognitive skills during the mobile application development. During workshop the students gained hands-on experience on mobile application development through «active doing» method, interactive tasks and building different types of mobile application projects. The workshop's curriculum is developed in terms of the scaffolding. So, the students improve the skills develop projects starting from simple «Hi, Kazakhstan!» to a complete software with functions of search and update from the Internet. The well-designed project topics on development mobile application arise the interest and provide the high motivation of the learners. &#x0D; The impact of Blended Project-Based Learning Approach is evaluated through questionnaire, testing and evaluation of mobile application projects developed by students. The effectiveness of the blended approach for teaching mobile application development is confirmed by the empirical data
Data Validation and Verification Using Blockchain in a Clinical Trial for Breast Cancer: Regulatory Sandbox

Background The integrity of data in a clinical trial is essential, but the current data management process is too complex and highly labor-intensive. As a result, clinical trials are prone to consuming a lot of budget and time, and there is a risk for human-induced error and data falsification. Blockchain technology has the potential to address some of these challenges. Objective The aim of the study was to validate a system that enables the security of medical data in a clinical trial using blockchain technology. Methods We have developed a blockchain-based data management system for clinical trials and tested the system through a clinical trial for breast cancer. The project was conducted to demonstrate clinical data management using blockchain technology under the regulatory sandbox enabled by the Japanese Cabinet Office. Results We verified and validated the data in the clinical trial using the validation protocol and tested its resilience to data tampering. The robustness of the system was also proven by survival with zero downtime for clinical data registration during a Amazon Web Services disruption event in the Tokyo region on August 23, 2019. Conclusions We show that our system can improve clinical trial data management, enhance trust in the clinical research process, and ease regulator burden. The system will contribute to the sustainability of health care services through the optimization of cost for clinical trials.
Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data

Multi-view data can represent objects from different perspectives and thus provide complementary information for data analysis. A topic of great importance in multi-view learning is to locate a low-dimensional latent subspace, where common semantic features are shared by multiple data sets. However, most existing methods ignore uncorrelated items (i.e., view-specific features) and may cause semantic bias during the process of common feature learning. In this article, we propose a non-negative correlated and uncorrelated feature co-learning (CoUFC) method to address this concern. More specifically, view-specific (uncorrelated) features are identified for each view when learning the common (correlated) feature across views in the latent semantic subspace. By eliminating the effects of uncorrelated information, useful inter-view feature correlations can be captured. We design a new objective function in CoUFC and derive an optimization approach to solve the objective with the analysis on its convergence. Experiments on real-world sensor, image, and text data sets demonstrate that the proposed method outperforms the state-of-the-art multiview learning methods.
A Data-Driven Approach for Twitter Hashtag Recommendation

This paper addresses the hashtag recommendation problem using high average-utility pattern mining. We introduce a novel framework called PM-HRec (Pattern Mining for Hashtag Recommendation). It consists of two main stages. First, offline processing transforms the corpus of tweets into a transactional database considering the temporal information of the tagged tweets (tweets with hashtags). The method discovers the temporal top k high average utility patterns. Irrelevant tagged tweets and the ontology of tagged tweets are also constructed offline. Second, an online processing inputs the utility patterns, the ontology, and the irrelevant tagged tweets to extract the most relevant hashtags for a given orpheline tweet (tweet without hashtags). Extensive experiments were carried out on large tweets collections. The proposed PM-HRec outperforms the existing state of the art hashtag recommendation approaches in terms of quality of recommended hashtags and runtime processing.