Reviewerly/exHarmony huggingface dataset:
Human baristas and robot baristas: How does brand experience affect brand satisfaction, brand attitude, brand attachment, and brand loyalty?

This study investigates the antecedents and consequences of brand satisfaction with the moderating role of type of barista. For this, data were collected from customers who used a coffee shop operated by robot baristas and customers who used a coffee shop operated by human baristas. The data analysis results showed that the four types of brand experience, such as sensory, affective, behavioral, and intellectual brand experiences, help to enhance brand satisfaction, which positively affects brand attitude, brand attachment, and brand loyalty. Finally, the type of barista plays a moderating role in the relationship between (1) sensory brand experience and brand satisfaction and (2) intellectual brand experience and brand satisfaction.
Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

Abstract The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.
ElasticFace: Elastic Margin Loss for Deep Face Recognition

Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equally learned using a fixed penalty margin. However, such a learning objective is not realistic for real data with inconsistent inter-and intra-class variation, which might limit the discriminative and generalizability of the face recognition model. In this paper, we relax the fixed penalty margin constrain by proposing elastic penalty margin loss (ElasticFace) that allows flexibility in the push for class separability. The main idea is to utilize random margin values drawn from a normal distribution in each training iteration. This aims at giving the decision boundary chances to extract and retract to allow space for flexible class separability learning. We demonstrate the superiority of our ElasticFace loss over ArcFace and CosFace losses, using the same geometric transformation, on a large set of mainstream benchmarks. From a wider perspective, our ElasticFace has advanced the state-of-the-art face recognition performance on seven out of nine mainstream benchmarks. All training codes, pre-trained models, training logs will be publicly released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
VisDrone-DET2021: The Vision Meets Drone Object detection Challenge Results

Object detection on the drone faces a great diversity of challenges such as small object inference, background clutter and wide viewpoint. In contrast to traditional detection problem in computer vision, object detection in bird-like angle can not be transplanted directly from common-in-use methods due to special object texture in sky‘s view. However, due to the lack of a comprehensive data set, the number of algorithms that focus on object detection using data captured by drones is limited. So the VisDrone team gathered a massive data set and organized Vision Meets Drones: A Challenge (VisDrone2021) in conjunction with the IEEE International Conference on Computer Vision (ICCV 2021) to advance the field. The collected dataset is the same as the previous dataset object detection challenge. Specifically, the team needed to predict the bounding boxes of the objects of ten predefined classes. We received results from a number of teams using different approaches, and this article describes the 8 team’s approach. We conducted a detailed analysis of the assessment results and summarized the challenges. More information can be found at: http://www.aiskyeye.com/.
An incremental learning mechanism for object classification based on progressive fuzzy three-way concept

Fuzzy concept has been an important methodology for data analysis, especially in the classification research. Particularly, fuzzy concept could directly process the continuous data through contrasting the numerical data into the membership degree of object to attribute. However, the classical fuzzy concept only focuses on the positive information, that is, the information about membership degree, while ignoring non-membership degree. Meanwhile, since the limitations of individual cognition and cognitive environment, the concept learning is progressive. Inspired by these thoughts, we design an incremental learning mechanism based on progressive fuzzy three-way concept for object classification in dynamic environment. In this paper, the object and attribute learning operators are first defined to obtain fuzzy three-way concept. Then, a progressive fuzzy three-way concept and its corresponding concept space are learned considering the progressive process of concept learning. Moreover, the object classify mechanism and dynamic update mechanism based on the progressive concept space are proposed, and their effectiveness is verified by numerical experiments. Finally, an incremental learning mechanism is further designed for dynamic increased data and compared with other fuzzy classify methods. All the experimental results carried on ten datasets from UCI and KEEL illustrate the proposed learning mechanism is an excellent object classify algorithm.
From Show to Tell: A Survey on Deep Learning-Based Image Captioning

Connecting Vision and Language plays an essential role in Generative Intelligence. For this reason, large research efforts have been devoted to image captioning, i.e. describing images with syntactically and semantically meaningful sentences. Starting from 2015 the task has generally been addressed with pipelines composed of a visual encoder and a language model for text generation. During these years, both components have evolved considerably through the exploitation of object regions, attributes, the introduction of multi-modal connections, fully-attentive approaches, and BERT-like early-fusion strategies. However, regardless of the impressive results, research in image captioning has not reached a conclusive answer yet. This work aims at providing a comprehensive overview of image captioning approaches, from visual encoding and text generation to training strategies, datasets, and evaluation metrics. In this respect, we quantitatively compare many relevant state-of-the-art approaches to identify the most impactful technical innovations in architectures and training strategies. Moreover, many variants of the problem and its open challenges are discussed. The final goal of this work is to serve as a tool for understanding the existing literature and highlighting the future directions for a research area where Computer Vision and Natural Language Processing can find an optimal synergy.
A Numerical Splitting and Adaptive Privacy Budget-Allocation-Based LDP Mechanism for Privacy Preservation in Blockchain-Powered IoT

Blockchain has gradually attracted widespread attention from the research community of the IoT, due to its decentralization, consistency, and other attributes. It builds a secure and robust system by generating a backup locally for each participant node to collectively maintain the network. However, this feature brings some privacy concerns since all nodes can access the chain data, users’ sensitive information under risk of leakage. The local differential privacy (LDP) mechanism can be a promising way to address this issue as it implements data perturbation before uploading to the chain. While traditional LDP mechanisms cannot fit well with the blockchain since the requirements of a fixed input range, large data volume, and using the same privacy budget, which are practically difficult in a decentralized environment. To overcome these problems, we propose a novel LDP mechanism to split input numerical data and implement perturbation by digital bits, which does not require a fixed input range and large data volume. In addition, we use an iteration approach to adaptively allocate the privacy budget for different perturbation procedures that minimize the total deviation of perturbed data and increase the data utility. We employ mean estimation as the statistical utility metric under the same and randomized privacy budgets to evaluate the performance of our novel LDP mechanism. The experiment results indicate that the proposed LDP mechanism performs better in different scenarios, and our adaptive privacy budget allocation model can significantly reduce the deviation of the perturbation function to provide high data utility while maintaining privacy.
Immersive virtual reality technology in a three-dimensional virtual simulated store: Investigating telepresence and usability

This study explores whether using immersive virtual reality technology (iVR) in a virtual simulated store (VSS) enhances perceived telepresence and usability compared to conventional desktop technology. Two experiments were carried out in a between-groups methodological design that involved a total of 111 participants who completed a simulated shopping trip. In the first experiment a conventional desktop computer setup was used to create the VSS using mouse and keyboard for navigation. The second experiment used iVR1 technology including a head-mounted display, body-tracking sensors, and hand-held motion-tracked controllers, which enabled navigation via body movement in a walk-around iVR environment. Participants purchased grocery items within each VSS2 environment, and post-hoc measures of perceived telepresence and usability ratings were compared. Results showed that participants in the iVR group experienced stronger feelings of immersion and perceived naturalness of interactions with the store environment compared to the desktop group. Both factors potentially lead to enhanced perceived telepresence. However, the visual realism of virtual products and the grasping interface for iVR control of product inspection could be further enhanced by advances in iVR technology. This study has implications for food marketing research as the use of immersive iVR can potentially lead to more naturalistic shopping behaviour in VSSs.
Privacy-preserving raw data collection without a trusted authority for IoT

With the rapid developments of IoT technologies, a large amount of real-time data is collected and shared, having important impacts on many applications, such as business advertisement and decision-making assistance. However, most users are unwilling to share their personal data directly to any third party for either academic research or commercial analysis because personal data contains private or sensitive information, such as economic status or living habits. Balancing the utility of big data and users’ privacy is a vital issue in academia and industry. In this paper, a privacy-preserving raw data collection scheme for IoT is proposed, in which the participant's data is collected and obfuscated with the other participants’ data within a group in order to mask the individual's privacy. Specifically, individual data is kept in its raw format to enhance its value for the data consumer, while no other users outside of the user herself know the source of the collected data. In addition, no trusted authority (TA) is needed in our proposed scheme, which is more suitable for real-world applications. Moreover, efficiency analysis is performed by simulation, and the result shows that our proposed scheme is practical for IoT systems.
Integration of deep feature extraction and ensemble learning for outlier detection

It is obvious to see that most of the datasets do not have exactly equal number of samples for each class. However, there are some tasks like detection of fraudulent transactions, for which class imbalance is overwhelming and one of the classes has very low (even less than 10% of the entire data) amount of samples. These tasks often fall under outlier detection. Moreover, there are some scenarios where there may be multiple subsets of the outlier class. In such cases, it should be treated as a multiple outlier type detection scenario. In this article, we have proposed a system that can efficiently handle all the aforementioned problems. We have used stacked autoencoders to extract features and then used an ensemble of probabilistic neural networks to do a majority voting and detect the outliers. Such a system is seen to have a better and reliable performance as compared to the other outlier detection systems in most of the datasets tested upon. It is seen that use of autoencoders clearly enhanced the outlier detection performance.
Participatory Research Principles in Human-Centered Design: Engaging Teens in the Co-Design of a Social Robot

Social robots are emerging as an important intervention for a variety of vulnerable populations. However, engaging participants in the design of social robots in a way that is ethical, meaningful, and rigorous can be challenging. Many current methods in human–robotic interaction rely on laboratory practices, often experimental, and many times involving deception which could erode trust in vulnerable populations. Therefore, in this paper, we share our human-centered design methodology informed by a participatory approach, drawing on three years of data from a project aimed to design and develop a social robot to improve the mental health of teens. We present three method cases from the project that describe creative and age appropriate methods to gather contextually valid data from a teen population. Specific techniques include design research, scenario and script writing, prototyping, and teens as operators and collaborative actors. In each case, we describe the method and its implementation and discuss the potential strengths and limitations. We conclude by situating these methods by presenting a set of recommended participatory research principles that may be appropriate for designing new technologies with vulnerable populations.
Theoretical and technical potential evaluation of solar power generation in Iran

Nowadays, utilizing solar energy for power production at high efficiency and in a cost-effective status is a challenging issue for power plant engineers. This challenge would be answered by considering several affecting parameters such as technical, economic, and environmental criteria. In this investigation, in order to provide an assessment for implementing solar power plants in the southeast of Iran, Sistan and Baluchistan province, a multi-criteria decision making (MCDM) approach is linked to a geographic information system (GIS). The MCDM approach is used to appraise the effective criteria for implementing solar power plants. The environment, orography, economic and climate are selected as the important criteria. Each criterion is assessed for the defined location of the investigation (Sistan and Baluchistan province) and in addition, GIS is employed to provide a geographical-graphical valuation to determine the most appropriate place for installing a large-scale solar power production plant. The solar systems considered in this study are photovoltaic (PV) collectors and concentrated solar power (CSP) generation plants (e.g. solar trough collectors). Technical and theoretical valuations are made to specify the amount of solar power which can be harnessed in Sistan and Baluchistan. In overall, it is demonstrated that this specific location in the southeast of Iran has the technical potential to provide 7,419 TWh/y and 8,758 TWh/y of solar electricity by installing CSP and PV technologies, respectively.
Combining Blockchain Technology and the Physical Internet to Achieve Triple Bottom Line Sustainability: A Comprehensive Research Agenda for Modern Logistics and Supply Chain Management

Integrating triple bottom line (TBL) goals into supply chains (SCs) is a challenging task which necessitates the careful coordination of numerous stakeholders’ individual interests. Recent technological advancements can impact TBL sustainability by changing the design, structure, and management of modern SCs. Blockchain technology enables immutable data records and facilitates a shared data view along the supply chain. The Physical Internet (PI) is an overarching framework that can be applied to create a layered and comprehensive view of the SC. In this conceptual paper, I define and combine these technologies and derive several high-level research areas and research questions (RQ) to investigate adoption and management as well as structural SC issues. I suggest a theory-based research agenda for the years to come that exploits the strengths of rigorous academic research, while remaining relevant for industry. Furthermore, I suggest various well-established theories to tackle the respective research questions and provide specific directions for future research.
Live Face De-Identification in Video

We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having the perception (pose, illumination and expression) fixed. We achieve this by a novel feed-forward encoder-decoder network architecture that is conditioned on the high-level representation of a person's facial image. The network is global, in the sense that it does not need to be retrained for a given video or for a given identity, and it creates natural looking image sequences with little distortion in time.
Causality extraction based on self-attentive BiLSTM-CRF with transferred embeddings

Causality extraction from natural language texts is a challenging open problem in artificial intelligence. Existing methods utilize patterns, constraints, and machine learning techniques to extract causality, heavily depending on domain knowledge and requiring considerable human effort and time for feature engineering. In this paper, we formulate causality extraction as a sequence labeling problem based on a novel causality tagging scheme. On this basis, we propose a neural causality extractor with the BiLSTM-CRF model as the backbone, named SCITE (Self-attentive BiLSTM-CRF wIth Transferred Embeddings), which can directly extract cause and effect without extracting candidate causal pairs and identifying their relations separately. To address the problem of data insufficiency, we transfer contextual string embeddings, also known as Flair embeddings, which are trained on a large corpus in our task. In addition, to improve the performance of causality extraction, we introduce a multihead self-attention mechanism into SCITE to learn the dependencies between causal words. We evaluate our method on a public dataset, and experimental results demonstrate that our method achieves significant and consistent improvement compared to baselines.
Learning to Generate Synthetic Data via Compositing

We present a task-specific approach to synthetic data generation. Our framework employs a trainable synthesizer network that is optimized to produce meaningful training samples by assessing the strengths and weaknesses of a `target' classifier. The synthesizer and target networks are trained in an adversarial manner wherein each network is updated with a goal to outdo the other. Additionally, we ensure the synthesizer generates realistic data by pairing it with a discriminator trained on real-world images. Further, to make the target classifier invariant to blending artefacts, we introduce these artefacts to background regions of the training images so the target does not over-fit to them. We demonstrate the efficacy of our approach by applying it to different target networks including a classification network on AffNIST [46], and two object detection networks (SSD, Faster-RCNN) on different datasets. On the AffNIST benchmark, our approach is able to surpass the baseline results with just half the training examples. On the VOC person detection benchmark, we show improvements of up to 2.7% as a result of our data augmentation. Similarly on the GMU detection benchmark, we report a performance boost of 3.5% in mAP over the baseline method, outperforming the previous state of the art approaches by as much as 7.5% in individual categories.
A Novel EMR Integrity Management Based on a Medical Blockchain Platform in Hospital

Recent advancements in information and communication technology is enabling a significant revolution in e-Health research and industry. In the case of personal medical data sharing, data security and convenience are crucial requirements to the interaction and collaboration of electronic medical record (EMR) systems. However, it’s hard for current systems to meet these requirements as they have inconsistent structures in terms of security policies and access control models. A new solution direction is essential to enhance data-accessing while regulating it with government mandates in privacy and security to ensure the accountability of the medical usage data. Blockchain seems to pave the way for revolution in the conventional healthcare industry benefiting by its unique features such as data privacy and transparency. In this paper, a blockchain-based medical platform using a smart contract is proposed to secure the EMR management. This approach provides patients a comprehensive, immutable log and easy access to their medical information across different departments within the hospital. A case study for hospital is built on a permissioned network, and a series of experimental tests are performed to demonstrate the usability and efficiency of the designed platform. Lastly, a benchmark study by leveraging various performance metrics is made and the outcomes indicate that the designed platform surpasses the ability of existing works in various aspects. The results of this work reveal that the proposed solution has the great potential to accelerate the development of a decentralized digital healthcare ecosystem.