Reviewerly/exHarmony huggingface dataset:
EatingTrak

In this paper, we present EatingTrak, an AI-powered sensing system using a wrist-mounted inertial measurement unit (IMU) to recognize eating moments in a near-free-living semi-wild setup. It significantly improves the SOTA in time resolution using similar hardware on identifying eating moments, from over five minutes to three seconds. Different from prior work which directly learns from raw IMU data, it proposes intelligent algorithms which can estimate the arm posture in 3D in the wild and then learns the detailed eating moments from the series of estimated arm postures. To evaluate the system, we collected eating activity data from 9 participants in semi-wild scenarios for over 113 hours. Results showed that it was able to recognize eating moments at three time-resolutions: 3 seconds and 15 minutes with F-1 scores of 73.7% and 83.8%, respectively. EatingTrak would introduce new opportunities in sensing detailed eating behavior information requiring high time resolution, such as eating frequency, snack-taking, on-site behavior intervention. We also discuss the opportunities and challenges in deploying EatingTrak on commodity devices at scale.
A Meta-Analysis: Emotional Intelligence and its Effect on Mathematics Achievement

Until now, many studies have been conducted on the correlation between emotional intelligence and mathematics achievement in Indonesia.However, there are different representations or conclusions regarding the results of the study.Therefore, this study aims to thoroughly investigate the effect of emotional intelligence and mathematics achievement on students in Indonesia and to detect the level of variation between studies using a meta-analysis approach.This study analyzed 36 primary studies with a sample of 2474 published in journals and campus repositories and filtered with certain eligibility criteria.To support the accuracy of the analysis results, JASP software is used.The results of the study found that the combined effect size value generated using the random-effect model estimation was (M = 0.65) with a standard error of (SEM = 0.07).This effect size belongs to the large effect category.Analysis of the study's level of variation was carried out by considering four moderator variables.The results of the analysis of moderator variables found that there were significant differences in the education level group (Qb = 62.94; p<0.05).Meanwhile, there was no difference in the publication type group (Qb = 0.64; p>0.05).and year of publication group (Qb = 4.16; p>0.05).These findings provide a strong theoretical foundation to improve students' mathematical achievement in the future.
Anomaly Detection Approach in Industrial Control Systems Based on Measurement Data

Anomaly detection problems in industrial control systems (ICSs) are always tackled by a network traffic monitoring scheme. However, traffic-based anomaly detection systems may be deceived by anomalous behaviors that mimic normal system activities and fail to achieve effective anomaly detection. In this work, we propose a novel solution to this problem based on measurement data. The proposed method combines a one-dimensional convolutional neural network (1DCNN) and a bidirectional long short-term memory network (BiLSTM) and uses particle swarm optimization (PSO), which is called PSO-1DCNN-BiLSTM. It enables the system to detect any abnormal activity in the system, even if the attacker tries to conceal it in the system’s control layer. A supervised deep learning model was generated to classify normal and abnormal activities in an ICS to evaluate the method’s performance. This model was trained and validated against the open-source simulated power system dataset from Mississippi State University. In the proposed approach, we applied several deep-learning models to the dataset, which showed remarkable performance in detecting the dataset’s anomalies, especially stealthy attacks. The results show that PSO-1DCNN-BiLSTM performed better than other classifier algorithms in detecting anomalies based on measured data.
ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA).However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed.In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique.We also propose to applying the novel method, schema graph expansion to recent language models.Then, we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore, we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.
Kurdish Handwritten character recognition using deep learning techniques

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems.
The Use and Challenges of Virtual Reality in Architecture

Technology has changed so much over the years and it has contributed a lot to the advancement of the many aspects of the industry as well as people's daily life.Architecture is not spared from this change and has benefited a lot from the development of this technology.With this, it is no secret that one aspect of technology that has changed the world is virtual reality.This study outlines the application of virtual reality in various sectors such as industrial, architecture design, science and technology, education as well as entertainment.There is a great potential of virtual reality in the future, especially in two different aspects namely technological and social.The importance of virtual reality in architecture is relying on the advancement of technologies, applications and processes.The use of virtual reality in architecture can be classified into several categories, namely landscape, building, interior, and exterior.Moreover, this study also elucidates the challenges of virtual reality in architecture and education.All in all, the virtual reality in architectural highlights the multiple advantages of multiple applications, and overcomes the defining challenges which may lead to the successful realization of virtual reality in architecture.
On component-wise dissimilarity measures and metric properties in pattern recognition

In many real-world applications concerning pattern recognition techniques, it is of utmost importance the automatic learning of the most appropriate dissimilarity measure to be used in object comparison. Real-world objects are often complex entities and need a specific representation grounded on a composition of different heterogeneous features, leading to a non-metric starting space where Machine Learning algorithms operate. However, in the so-called unconventional spaces a family of dissimilarity measures can be still exploited, that is, the set of component-wise dissimilarity measures, in which each component is treated with a specific sub-dissimilarity that depends on the nature of the data at hand. These dissimilarities are likely to be non-Euclidean, hence the underlying dissimilarity matrix is not isometrically embeddable in a standard Euclidean space because it may not be structurally rich enough. On the other hand, in many metric learning problems, a component-wise dissimilarity measure can be defined as a weighted linear convex combination and weights can be suitably learned. This article, after introducing some hints on the relation between distances and the metric learning paradigm, provides a discussion along with some experiments on how weights, intended as mathematical operators, interact with the Euclidean behavior of dissimilarity matrices.
ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning

Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-Shot Learning (FSL) problem across different domains has attracted rising attention. The core challenge of CD-FSL lies in the domain gap between the source and novel target datasets. Though many attempts have been made for CD-FSL without any target data during model training, the huge domain gap makes it still hard for existing CD-FSL methods to achieve very satisfactory results. Alternatively, learning CD-FSL models with few labeled target domain data which is more realistic and promising is advocated in previous work~\cite{fu2021meta}. Thus, in this paper, we stick to this setting and technically contribute a novel Multi-Expert Domain Decompositional Network (ME-D2N). Concretely, to solve the data imbalance problem between the source data with sufficient examples and the auxiliary target data with limited examples, we build our model under the umbrella of multi-expert learning. Two teacher models which can be considered to be experts in their corresponding domain are first trained on the source and the auxiliary target sets, respectively. Then, the knowledge distillation technique is introduced to transfer the knowledge from two teachers to a unified student model. Taking a step further, to help our student model learn knowledge from different domain teachers simultaneously, we further present a novel domain decomposition module that learns to decompose the student model into two domain-related sub parts. This is achieved by a novel domain-specific gate that learns to assign each filter to only one specific domain in a learnable way. Extensive experiments demonstrate the effectiveness of our method. Codes and models are available at https://github.com/lovelyqian/ME-D2N_for_CDFSL.
Factors influencing consumers’ willingness to accept service robots: Based on online reviews of Chinese hotels

The application of robots in service industry is increasing. Compared with related studies in other countries, the research on users' acceptance of mid-range and high-range hotel service robots in China is preliminary. Based on the interaction between Chinese consumers and hotel service robots, this study explored the factors that influence consumers' willingness to accept robots in human-robot interaction. According to the service robot integration willingness scale (performance efficacy, intrinsic motivation, anthropomorphism, social influence, facilitating conditions, and emotion), this study conducted content analysis and sentiment analysis on 4,107 online reviews from 68 mid-range and high-range hotels in Qunar. The results showed that users' overall evaluation of robot service in mid-range and high-range hotels is positive. The most frequently mentioned dimension by users is performance efficacy, followed by intrinsic motivation, anthropomorphism, and emotion, finally, the facilitating conditions, the five dimensions have positive impact on users' evaluation of service robots; the influence of social influence on human-robot interaction evaluation has not been found. This study supplements the research on service robot and provides a reference for hotel managers to make decisions.
Audio-domain position-independent backdoor attack via unnoticeable triggers

Deep learning models have become key enablers of voice user interfaces. With the growing trend of adopting outsourced training of these models, backdoor attacks, stealthy yet effective training-phase attacks, have gained increasing attention. They inject hidden trigger patterns through training set poisoning and overwrite the model's predictions in the inference phase. Research in backdoor attacks has been focusing on image classification tasks, while there have been few studies in the audio domain. In this work, we explore the severity of audio-domain backdoor attacks and demonstrate their feasibility under practical scenarios of voice user interfaces, where an adversary injects (plays) an unnoticeable audio trigger into live speech to launch the attack. To realize such attacks, we consider jointly optimizing the audio trigger and the target model in the training phase, deriving a position-independent, unnoticeable, and robust audio trigger. We design new data poisoning techniques and penalty-based algorithms that inject the trigger into randomly generated temporal positions in the audio input during training, rendering the trigger resilient to any temporal position variations. We further design an environmental sound mimicking technique to make the trigger resemble unnoticeable situational sounds and simulate played over-the-air distortions to improve the trigger's robustness during the joint optimization process. Extensive experiments on two important applications (i.e., speech command recognition and speaker recognition) demonstrate that our attack can achieve an average success rate of over 99% under both digital and physical attack settings.
A Feature Space-Restricted Attention Attack on Medical Deep Learning Systems

Deep neural network has shown a powerful performance in the medical image analysis of a variety of diseases. However, a number of studies over the past few years have demonstrated that these deep learning systems can be vulnerable to well-designed adversarial attacks, with minor disruptions added to the input. Since both the public and academia have focused on deep learning in the health information economy, these adversarial attacks would prove more important and raise security concerns. In this article, adversarial attacks on deep learning systems in medicine are analyzed from two different points of view: 1) white box and 2) black box. A fast adversarial sample generation method, Feature Space-Restricted Attention Attack is proposed to explore more confusing adversarial samples. It is based on a generative adversarial network with bound classification space to generate perturbations to achieve attacks. Meanwhile, it can employ an attention mechanism to focus this perturbation on the lesion region. This enables the perturbation closely associated with the classification information making the attack more efficient and invisible. The performance and specificity of the proposed attack method are demonstrated by conducting extensive experiments on three different types of medical images. Finally, it is expected that this work can assist practitioners become being of current weaknesses in the deployment of deep learning systems in clinical settings. And, it further investigates domain-specific features of medical deep learning systems to enhance model generalization and resistance to attacks.
Applications of universal parity quantum computation

We demonstrate the applicability of a universal gate set in the parity encoding, which is a dual to the standard gate model, by exploring several quantum gate algorithms such as the quantum Fourier transform and quantum addition. Embedding these algorithms in the parity encoding reduces the circuit depth compared to conventional gate-based implementations while keeping the multiqubit gate counts comparable. We further propose simple implementations of multiqubit gates in tailored encodings and an efficient strategy to prepare graph states.
Collective Operations Can Exponentially Enhance Quantum State Verification

Maximally entangled states are a key resource in many quantum communication and computation tasks, and their certification is a crucial element to guarantee the desired functionality. We introduce collective strategies for the efficient, local verification of ensembles of Bell pairs that make use of an initial information and noise transfer to few copies prior to their measurement. In this way the number of entangled pairs that need to be measured and hence destroyed is significantly reduced as compared to previous, even optimal, approaches that operate on individual copies. Moreover, the remaining states are directly certified. We show that our tools can be extended to other problems and larger classes of multipartite states.
Improvement of Operating Efficiency of Energy Cooperatives with the Use of “Crypto-Coin Mining”

Poland remains the most coal-dependent economy in the EU. To minimize that problem, which is particularly clear and dangerous in the period of a shortage of fossil fuels, the Polish authorities have decided to establish various institutions, organizational and regulatory solutions. Their role is to support the development of renewable energy sources and local energy communities. The latter are to focus on optimizing the production and consumption of energy in pursuit of energy self-sufficiency on a local scale. One such institution, set up in Poland over the last several years, is the energy cooperative, which is intended to increase the efficient use of the potential of renewable energy sources in rural and urban–rural areas. The authors of this article verify the assumptions, for instance, the number, composition or production and members’ consumption profiles, under which such a relatively new institution has the chance to develop. A novelty in this research paper is that the interests of the entities composing a given energy cooperative may additionally be secured by the use of surplus generation for crypto-coin mining, and thus the storage of energy in virtual currency. A dedicated mathematical model in mixed-integer programming technology was used, enriched with respect to previous research, making it possible for members of the cooperative to achieve energy independence while maximizing self-consumption and using their excess energy for processing cryptocurrency. This is in line with the global trend of “greening”; the processes of acquiring electronic money.
SegPC-2021: A challenge &amp; dataset on segmentation of Multiple Myeloma plasma cells from microscopic images

Multiple Myeloma (MM) is an emerging ailment of global concern. Its diagnosis at the early stages is critical for recovery. Therefore, efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Following the trend, a medical imaging challenge on “Segmentation of Multiple Myeloma Plasma Cells in Microscopic Images (SegPC-2021)” was organized at the IEEE International Symposium on Biomedical Imaging (ISBI), 2021, France. The challenge addressed the problem of cell segmentation in microscopic images captured from the slides prepared from the bone marrow aspirate of patients diagnosed with Multiple Myeloma. The challenge released a total of 775 images with 690 and 85 images of sizes 2040×1536 and 1920×2560 pixels, respectively, captured from two different (microscope and camera) setups. The participants had to segment the plasma cells with a separate label on each cell’s nucleus and cytoplasm. This problem comprises many challenges, including a reduced color contrast between the cytoplasm and the background, and the clustering of cells with a feeble boundary separation of individual cells. To our knowledge, the SegPC-2021 challenge dataset is the largest publicly available annotated data on plasma cell segmentation in MM so far. The challenge targets a semi-automated tool to ensure the supervision of medical experts. It was conducted for a span of five months, from November 2020 to April 2021. Initially, the data was shared with 696 people from 52 teams, of which 41 teams submitted the results of their models on the evaluation portal in the validation phase. Similarly, 20 teams qualified for the last round, of which 16 teams submitted the results in the final test phase. All the top-5 teams employed DL-based approaches, and the best mIoU obtained on the final test set of 277 microscopic images was 0.9389. All these five models have been analyzed and discussed in detail. This challenge task is a step towards the target of creating an automated MM diagnostic tool.
Platforms and Tools for SIA Research and Development

chapter Share on Platforms and Tools for SIA Research and Development Authors: Arno Hartholt View Profile , Sharon Mozgai View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, ApplicationOctober 2022 Pages 261–304https://doi.org/10.1145/3563659.3563668Published:03 November 2022Publication History 5citation5DownloadsMetricsTotal Citations5Total Downloads5Last 12 Months5Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multichannel Adjustable Single-Photon Router Based on Large Detuning

Quantum router as a key device in quantum information processing always attracts researchers' attention. This work suggests a single-photon (SP) router based on one bus-waveguide side coupled to numerous Jaynes-Cummings emitters (JCEs). Each JCE connects with a semi-infinite branch waveguide as an output channel. Different from the commonly used scheme that the JCEs are set in the tuning regime, we here let them work in the large-detuning regime. This change brings about the following amazing benefits for the SP router. Firstly, the number of channels can reach more than a thousand. Secondly, the router is adjustable, that is, the SP can be routed into any output channel on demand. Thirdly, the router has a remarkable monochromaticity. Fourthly, such a SP router can also depress the influence of cavity losses and interferential levels in atoms. Finally, the routing probability can reach approximately $100\mathrm{%}$ for each channel by adding a mirror and a phase shifter to the end of the bus waveguide. This work takes the Rydberg atoms ${}^{87}\mathrm{Rb}$ whose levels can be adjusted by an external electrostatic field as an example to demonstrate the feasibility of the router, which can be applied to quantum informatics.