Reviewerly/exHarmony huggingface dataset:
Estimation of body direction based on gait for service robot applications

Recently, there have been several studies on the research and development of service robots, such as reception or waiter robots for facilities and companion robots for support of baggage transportation or guidance in public spaces. Several experimental results in real environments have been reported. To realize socially acceptable human–robot interaction for service robots, human recognition, including not only position but also body direction, around the robot is important. Using an RGB-D camera, it is possible to detect the posture of a person. However, because the viewing angle of the camera is narrow, it is difficult to recognize the environment around the robot with a single device. This study proposes the estimation of the body direction based on the gait, that is, not only the position and velocity, but also the state of the legs (stance or swing phase), using laser range sensors installed at shin height. We verify the effectiveness of the proposed method for several patterns of movement, which are seen when a person interacts with the service robot and evaluate measurement accuracy.
FIPA‐based reference architecture for efficient discovery and selection of appropriate cloud service using cloud ontology

Summary Cloud computing is considered the latest emerging computing paradigm and has brought revolutionary changes in computing technology. With the advancement in this field, the number of cloud users and service providers is increasing continuously with more diversified services. Consequently, the selection of appropriate cloud service has become a difficult task for a new cloud customer. In case of inappropriate selection of a cloud services, a cloud customer may face the vendor locked‐in issue and data portability and interoperability problems. These are the major obstacles in the adoption of cloud services. To avoid these complexities, a cloud customer needs to select an appropriate cloud service at the initial stage of the migration to the cloud. Many researches have been proposed to overcome the issues, but problems still exist in intercommunication standards among clouds and vendor locked‐in issues. This research proposed an IEEE multiagent Foundation for Intelligent Physical Agent (FIPA) compliance multiagent reference architecture for cloud discovery and selection using cloud ontology. The proposed approach will mitigate the prevailing vendor locked‐in issue and also alleviate the portability and interoperability problems in cloud computing. To evaluate the proposed reference architecture and compare it with the state‐of‐the‐art approaches, several experiments have been performed by utilizing the commonly used performance measures. Analysis indicates that the proposed approach enables significant improvements in cloud service discovery and selection in terms of search efficiency, execution, and response time.
On a hypergraph probabilistic graphical model

We propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs. The space of directed acyclic hypergraphs is much larger than the space of chain graphs. Hence Bayesian hypergraphs can model much finer factorizations than Bayesian networks or LWF chain graphs and provide simpler and more computationally efficient procedures for factorizations and interventions. Bayesian hypergraphs also allow a modeler to represent causal patterns of interaction such as Noisy-OR graphically (without additional annotations). We introduce global, local and pairwise Markov properties of Bayesian hypergraphs and prove under which conditions they are equivalent. We also extend the causal interpretation of LWF chain graphs to Bayesian hypergraphs and provide corresponding formulas and a graphical criterion for intervention.
Pancreas Segmentation-Derived Biomarkers: Volume and Shape Metrics in the UK Biobank Imaging Study

Quantitative imaging biomarkers derived from magnetic resonance imaging of the pancreas could reveal changes in pancreas organ volume and shape manifest in chronic disease. Recent developments in machine learning facilitate pancreas segmentation and volume extraction. Machine learning methods could also help in designing a data-driven approach to pancreas shape characterization. We present an automated pipeline for pancreas volume and shape characterization. We start off with deep learning-based segmentation; we show the impact of choice of loss function in pancreas segmentation by comparing a 3D U-Net model trained using soft Dice over cross-entropy loss. Then, a diffeomorphic algorithm for group-wise registration as well as manifold learning are used to extract prominent shape features from the segmentation masks. The technique shows potential in a subset (N = 3,909) of the UK Biobank imaging sub-study for (1) automated quality control, e.g. suboptimal pancreas coverage acquisitions; and (2) determining abnormal pancreas morphology, that might reflect different patterns of fat infiltration. To our knowledge, this work is the first to attempt learning pancreas shape features.
Bringing semantics into word image representation

The shift from one-hot to distributed representation, popularly referred to as word embedding has changed the landscape of natural language processing (nlp) and information retrieval (ir) communities. In the domain of document images, we have always appreciated the need for learning a holistic word image representation which is popularly used for the task of word spotting. The representations proposed for word spotting is different from word embedding in text since the later captures the semantic aspects of the word which is a crucial ingredient to numerous nlp and ir tasks. In this work, we attempt to encode the notion of semantics into word image representation by bringing the advancements from the textual domain. We propose two novel forms of representations where the first form is designed to be inflection invariant by focusing on the approximate linguistic root of the word, while the second form is built along the lines of recent textual word embedding techniques such as Word2Vec. We observe that such representations are useful for both traditional word spotting and also enrich the search results by accounting the semantic nature of the task. We conduct our experiments on the challenging document images taken from historical-modern collections, handwritten-printed domains, and Latin-Indic scripts. For the purpose of semantic evaluation, we have prepared a large synthetic word image dataset and report interesting results for the standard semantic evaluation metrics such as word analogy and word similarity.
SAFFO: A SIFT based approach for digital anastylosis for fresco recOnstruction

Anastylosis is an archaeological technique which focuses on the reconstruction of collapsed building and destroyed artworks, starting from the original pieces. Many digital approaches have been developed in the last decade, mainly based on 2D and 3D analysis of the structure of the fragments. These techniques aim at supporting the priceless work of the involved operators, mainly in the decision processes and in the resolution of positioning ambiguities. Techniques acting with this scope lie in the field of the digital anastylosis. In this paper we present SAFFO, a digital approach to 2D reconstruction of frescoes, based on the extraction of SIFT features from a painting. The approach appears to be very robust to false positives, resulting optimal in scenarios involving fragment sets containing spurious elements. The experiments have been performed on the DAFNE (Digital Anastylosis for Fresco challeNgE) dataset, which gathers more than 30 2D artworks and provides several tessellation for each. For its robustness against spurious fragments, SAFFO won the third place in the rank list of DAFNE Challenge 2019.
Efficient Qubit Routing for a Globally Connected Trapped Ion Quantum Computer

Abstract The cost of enabling connectivity in noisy intermediate‐scale quantum (NISQ) devices is an important factor in determining computational power. A qubit routing algorithm is created, which enables efficient global connectivity in a previously proposed trapped ion quantum computing architecture. The routing algorithm is characterized by comparison against both a strict lower bound, and a positional swap based routing algorithm. An error model is proposed, which can be used to estimate the achievable circuit depth and quantum volume of the device as a function of experimental parameters. A new metric based on quantum volume, but with native two‐qubit gates, is used to assess the cost of connectivity relative to the upper bound of free, all to all connectivity. The metric is also used to assess a square‐grid superconducting device. These two architectures are compared and it is found that for the shuttling parameters used, the trapped ion design has a substantially lower cost associated with connectivity.
Blockchain applications, challenges and evaluation: A survey

Blockchain is a promising technology, which may change the way of transactions and affect our lives in the future, and has attracted the attention of more and more scholars recently. This paper provides an overview of the important issues of blockchain and the aim is to lead researchers to comprehensive understand applications, challenges and evaluation from the technical perspective. The basic technology including authorization, incentive and consensus is presented, focusing on their latest methods. Then, a wide range of blockchain applications are described. Specially, some of the latest intersection and integration areas with blockchain are introduced. Moreover, research challenges of blockchain are summarized and analyzed. Finally, evaluation metrics of blockchain are presented and as far as we know, these metrics are first designed for evaluating blockchain performance.
Knowledge-to-Information Translation Training (KITT): An Adaptive Approach to Explainable Artificial Intelligence

Modern black-box artificial intelligence algorithms are computationally powerful yet fallible in unpredictable ways. While much research has gone into developing techniques to interpret these algorithms, less have also integrated the requirement to understand the algorithm as a function of their training data. In addition, few have examined the human requirements for explainability, so these interpretations provide the right quantity and quality of information to each user. We argue that Explainable Artificial Intelligence (XAI) frameworks need to account the expertise and goals of the user in order to gain widespread adoptance. We describe the Knowledge-to-Information Translation Training (KITT) framework, an approach to XAI that considers a number of possible explanatory models that can be used to facilitate users’ understanding of artificial intelligence. Following a review of algorithms, we provide a taxonomy of explanation types and outline how adaptive instructional systems can facilitate knowledge translation between developers and users. Finally, we describe limitations of our approach and paths for future research opportunities.
PVO-Based Reversible Data Hiding Exploiting Two-Layer Embedding for Enhancing Image Fidelity

Recently, Li et al. proposed a data hiding method based on pixel value ordering (PVO) and prediction error expansion (PEE). In their method, maximum and minimum values were predicted and the pixel values were modified to embed secret data. Thereafter, many scholars have proposed improvisations to the original PVO method. In this paper, a Reversible data hiding (RDH) method is proposed where the secret data is dispersed into two layers using different modes of operations. The second layer changes the dividing mode, and the first and the second layers do not take duplicate blocks. Under a fixed embedding capacity, threshold value and block size are controlled, complex blocks are filtered and the secret data is hidden in smooth blocks. This paper also compares the effectiveness of four well-known PVO series methods, the latest PVO methods, difference expansion (DE) method and reduced difference expansion (RDE) method. Experimental results show that the proposed method reduces distortion in the image, thereby enhancing the visual symmetry/quality compared to previous state-of-the-art methods and increasing its high application value.
A New Approach to Measure User Experience with Voice-Controlled Intelligent Assistants: A Pilot Study

Voice-controlled intelligent assistants use a conversational user interface (CUI), a system that relies on natural language processing and artificial intelligence to have verbal interactions with end-users. In this research, we propose a multi-method approach to assess user experience with a smart voice assistant through triangulation of psychometric and psychophysiological measures. The approach aims to develop a richer understanding of what the users experience during the interaction, which could provide new insights to researchers and developers in the field of voice assistant. We apply this new approach in a pilot study, and we show that each method captures a part of emotional variance during the interaction. Results suggest that emotional valence is better captured with psychometric measures, whereas arousal is better detected with psychophysiological measures.
The Dataset of the Experimental Evaluation of Software Components for Application Design Selection Directed by the Artificial Bee Colony Algorithm

The paper presents the swarm intelligence approach to the selection of a set of software components based on computational experiments simulating the desired operating conditions of the software system being developed. A mathematical model is constructed, aimed at the effective selection of components from the available alternative options using the artificial bee colony algorithm. The model and process of component selection are introduced and applied to the case of selecting Node.js components for the development of a digital platform. The aim of the development of the platform is to facilitate countrywide simultaneous online psychological surveys in schools in the conditions of unstable internet connection and the large variety of desktop and mobile client devices, running different operating systems and browsers. The module whose development is considered in the paper should provide functionality for the archiving and checksum verification of the survey forms and graphical data. With the swarm intelligence approach proposed in the paper, the effective set of components was identified through a directional search based on fuzzy assessment of the three experimental quality indicators. To simulate the desired operating conditions and to guarantee the reproducibility of the experiments, the virtual infrastructure was configured. The application of swarm intelligence led to reproducible results for component selection after 312 experiments instead of the 1080 experiments needed by the exhaustive search algorithm. The suggested approach can be widely used for the effective selection of software components for distributed systems operating in the given conditions at this stage of their development.
SalientGaze: Saliency-based gaze correction in virtual reality

Eye-tracking with gaze estimation is a key element in many applications, ranging from foveated rendering and user interaction to behavioural analysis and usage metrics. For virtual reality, eye-tracking typically relies on near-eye cameras that are mounted in the VR headset. Such methods usually involve an initial calibration to create a mapping from eye features to a gaze position. However, the accuracy based on the initial calibration degrades when the position of the headset relative to the users’ head changes; this is especially noticeable when users readjust the headset for comfort or even completely remove it for a short while. We show that a correction of such shifts can be achieved via 2D drift vectors in eye space. Our method estimates these drifts by extracting salient cues from the shown virtual environment to determine potential gaze directions. Our solution can compensate for HMD shifts, even those arising from taking off the headset, which enables us to eliminate reinitialization steps.
A Systematic Review of User-Centered Design Techniques

There are many systems that do not have an adequate level of usability, even though this is one of the most relevant quality attributes in software products. Therefore, it chooses to redesign these systems through techniques that provide favorable results in usability evaluations. Several authors have proposed the User-Centered Design framework as the right one to obtain optimal results with respect to metrics of this attribute, since it aims to design understandable software products, considering the needs and interests of end-users. Because of that, this article presents a systematic review to identify the techniques and tools that have been used to make redesigns of graphical user interfaces of software products following the User-Centered Design approach and that have been successful in usability measurements. It also shows the most reported usability evaluation methods in these cases, and the reasons why this process took place. A total of 146 studies were identified, of which 19 were selected as relevant to this review. According to the analysis, the most used technique to perform redesigns is prototyping and the usability evaluation method more employed is testing with users.
Handwritten Digit Recognition Using Ensemble Learning

In pattern recognition, the recognition of handwritten digits has always been a very challenging and tedious task. In this work, a simple novel approach is proposed to recognize the handwritten digits. The primary goal of this work is recognition of the handwritten digits by using ensemble learning. Ensemble learning improves convergence by decreasing the complexity of the model. The distribution of data in the random split and class-wise split for the base learners has been studied. Detailed analysis of how the load is distributed among the base learners and how it impacts the model accuracy and training time is also discussed. The overall trends of the ensemble model have also been analyzed.
RiderS: Towards a Privacy-Aware Decentralized Self-Driving Ride-Sharing Ecosystem

Computer vision, machine learning, and decentralized applications are paving the way for a future where autonomous systems will be able to truly interact without humans in the loop. Though in its infancy, self-driving technologies are a prime example of this phenomena. This paper proposes RiderS, a novel privacy-first decentralized self-driving ride-sharing ecosystem, which consists of everything needed to allow autonomous/self-driving vehicles to participate in a decentralized ride-sharing economy. At its core, RiderS relies on a self-sovereign biometric solution, which leverages computer vision to register and authenticate users via a privacy-first biometric authentication algorithm. We then propose a novel decentralized architecture to support a self-sustained ride-sharing ecosystem. We propose a novel proof-of-matching geo-aware consensus algorithm, which is used to validate, and reward self-driving vehicles. The key concept being that each vehicle leverages its built-in compute power to assist in verifying transactions in order to keep the ecosystem in check while being rewarded for the work. Experimental results show that we can perform about 10K Matches per second (KMs) using an NVIDIA Jetson AGX Xavier development kit. We analyzed the privacy aspects as well as the accuracy of our biometric solution with good matching rates. Furthermore, we evaluated the performance and scalability of our proposed consensus algorithm and compared it to similar proof-of-work based blockchains yielding promising results. We modeled our ride-sharing ecosystem using published statistics from Uber and show that we can support Uber-scale ride-sharing using our geo-location aware consensus algorithm through a what-if simulated exercise.
Two-Stream RGB-D Human Detection Algorithm Based on RFB Network

In order to effectively combine RGB image features with depth image features for human detection, this paper proposes a two-stream RGB-D human detection algorithm based on RFB network.The proposed algorithm mainly contains three parts: RGB-stream, Depth-stream and Channel Weight Fusion (CWF) strategy.(1) The RGB-stream extracts RGB image features using RFB-Net as the backbone network.(2) By analyzing the results of depth features visualization, we build the Depth-stream, which can effectively extract the depth image features.(3) The improved CWF strategy can enhance the effectiveness of important channels in RGB-D fusion features and improve the capability of the network expression.The experimental results show that the proposed algorithm has a significant improvement compared with other algorithms on two common datasets.
Topic-Guided Conversational Recommender in Multiple Domains

Conversational systems have recently attracted significant attention. Both the research community and industry believe that it will exert huge impact on human-computer interaction, and specifically, the IR/RecSys community has begun to explore Conversational Recommendation. In real-life scenarios, such systems are often urgently needed in helping users accomplishing different tasks under various situations. However, existing works still face several shortcomings: (1) Most efforts are largely confined in single task setting. They fall short of hands in handling tasks across domains. (2) Aside from soliciting user preference from dialogue history, a conversational recommender naturally has access to the back-end data structure which should be fully leveraged to yield good recommendations. In this paper, we thus present a Topic-guided Conversational Recommender ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TCR</i> ) which is specifically designed for the multi-domain setting. It augments the sequence-to-sequence (seq2seq) models with a neural latent topic component to better guide the response generation. To better leverage the dialogue history and the back-end data structure, we adopt a graph convolutional network (GCN) to model the relationships between different recommendation candidates while also capture the match between candidates and the dialogue history. We then seamlessly combine these two parts with the idea of pointer networks. We perform extensive evaluation on a large-scale task-oriented multi-domain dialogue dataset and the results show that our method achieves superior performance as compared to a wide range of baselines.
Negation detection for sentiment analysis: A case study in Spanish

Abstract Accurate negation identification is one of the most important tasks in the context of sentiment analysis. In order to correctly interpret the sentiment value of a particular expression, we need to identify whether it is in the scope of negation. While much of the work on negation detection has focused on English, we have seen recent developments that provide accurate identification of negation in other languages. In this paper, we provide an overview of negation detection systems and describe an implementation of a Spanish system for negation cue detection and scope identification. We apply this system to the sentiment analysis task, confirming also for Spanish that improvements can be gained from accurate negation detection. The paper contributes an implementation of negation detection for sentiment analysis in Spanish and a detailed error analysis. This is the first work in Spanish in which a machine learning negation processing system is applied to the sentiment analysis task. Existing methods have used negation rules that have not been assessed, perhaps because the first Spanish corpus annotated with negation for sentiment analysis has only recently become available.