Reviewerly/exHarmony huggingface dataset:
Virtual Big Heads: Analysis of Human Perception and Comfort of Head Scales in Social Virtual Reality

Virtual reality (VR) technologies provide a shared platform for collaboration among users in a spatial context. To enhance the quality of social signals during interaction between users, researchers and practitioners started augmenting users’ interpersonal space with different types of virtual embodied social cues. A prominent example is commonly referred to as the "Big Head" technique, in which the head scales of virtual interlocutors are slightly increased to leverage more of the display’s visual space to convey facial social cues. While beneficial in improving interpersonal social communication, the benefits and thresholds of human perception of facial cues and comfort in such Big Head environments are not well understood, limiting their usefulness and subjective experience.In this paper, we present a human-subject study that we conducted to understand the impact of an increased or decreased head scale in social VR on participants’ ability to perceive facial expressions as well as their sense of comfort and feeling of "uncanniness." We explored two head scaling methods and compared them with respect to perceptual thresholds and user preferences. We further show that the distance to interlocutors has an important effect on the results. We discuss implications and guidelines for practical applications that aim to leverage VR-enhanced social cues.
The human side of geoscientists: comparing geoscientists' and non-geoscientists' cognitive and affective responses to geology

Abstract. Geoscientists and non-geoscientists often struggle to communicate with each other. In this paper we aim to understand how geoscientists and non-geoscientists perceive geological concepts and activities, that is, how they think (cognitive responses) and feel (affective responses) about them. To this effect, using a mixed-methods approach, we compare mental models – people's representation of a phenomenon – of the subsurface, mining/quarrying, and drilling, between geoscientists (n=24) and non-geoscientists (n=38) recruited in Ireland. We identify four dominant themes which underlie their mental models: (1) degree of knowledge and familiarity, (2) presence of humans, (3) affective beliefs, and (4) beliefs about perceived impact of the activities. While the mental models of the non-geoscientists focused more on the perceived negative environmental and economic impacts of geoscience, as well as providing evidence of lay expertise, those of the geoscientists focused more on human interactions. We argue that mental models of geoscientists and non-geoscientists are the result of beliefs, including both cognitive and affective components, and that both components need to be acknowledged for effective dialogue between the two groups to take place.
A compact construction for non-monotonic key-policy attribute-based encryption

The attribute-based encryption (ABE) scheme with monotonic access structure cannot deal with an access structure that is associated with the negation of attributes, which is not convenient for real world applications. In this paper, we attempt to propose a more expressive non-monotonic ABE scheme through a new method. To achieve this goal, we firstly propose a linear two-mode identity-based broadcast encryption (TIBBE) scheme based on an identity-based broadcast encryption (IBBE) scheme. We introduce the concept of identity-based revocation (IBR) for this scheme without increasing the size of parameters in IBBE. The scheme is selective identity secure under the m-DBDHE assumption. Then, we convert the TIBBE scheme into a non-monotonic key-policy attribute-based encryption (KP-ABE) scheme with compact parameters. Our KP-ABE scheme could achieve constant-size ciphertexts, and the scale of the private keys grows linearly with the scale of the attribute set. Besides, the computational cost is the lowest compared with other existing non-monotonic KP-ABE schemes.
Secure Digital Voting System Based on Blockchain Technology

Electronic voting or e-voting has been used in varying forms since 1970s with fundamental benefits over paper-based systems such as increased efficiency and reduced errors. However, challenges remain to the achieving of wide spread adoption of such systems, especially with respect to improving their resilience against potential faults. Blockchain is a disruptive technology of the current era and promises to improve the overall resilience of e-voting systems. This article presents an effort to leverage benefits of blockchain such as cryptographic foundations and transparency to achieve an effective scheme for e-voting. The proposed scheme conforms to the fundamental requirements for e-voting schemes and achieves end-to-end verifiability. The article presents details of the proposed e-voting scheme along with its implementation using Multichain platform. The article also presents an in-depth evaluation of the scheme which successfully demonstrates its effectiveness to achieve an end-to-end verifiable e-voting scheme.
Snarky Ceremonies

Succinct non-interactive arguments of knowledge (SNARKs) have found numerous applications in the blockchain setting and elsewhere. The most efficient SNARKs require a distributed ceremony protocol to generate public parameters, also known as a structured reference string (SRS). Our contributions are two-fold: We give a security framework for non-interactive zero-knowledge arguments with a ceremony protocol. We revisit the ceremony protocol of Groth’s SNARK [Bowe et al., 2017]. We show that the original construction can be simplified and optimized, and then prove its security in our new framework. Importantly, our construction avoids the random beacon model used in the original work.
An intelligent clustering approach for improving search result of a website

These days, the internet has become part of our life, and thus web data usage has increased tremendously. We proposed a model that will improve the search result using clustering approach. Clustering is being used to group the data into the relevant folder so that accessing of information will be fast. The K-means clustering algorithm is very efficient in terms of speed and is suitable for large dataset. However, K-means algorithm has some drawbacks, such as the number of clusters need to be defined in starting itself, initialisation affects the output, and it often gets stuck to local optima. We proposed a hybrid model that determines the number of clusters itself and gives global optimal result. The number which has been obtained is passed as a parameter for the K-means. Thus, our novel hybrid model integrates the features of K-means and genetic algorithm. The model will have the best characteristics of K-means and genetic algorithm, and overcomes the drawbacks of K-means and genetic algorithm.
Quantum Information Technology

Quantum computing is an interdisciplinary field that seeks to understand the processing of information using quantum mechanics principles. Quantum computing is the exploitation of properties of quantum states such as superposition and entanglement to perform computation. It has shown its supremacy over conventional computing. Quantum computing can outperform much more than existing computers. As such, it represents a paradigm shift for computing technology in its development history. Currently, quantum information technology is state-led research and is considered as an emerging industry. Although a full-scale quantum computer has not yet been developed, it has great growth potential.
Chaotic time series prediction using DTIGNet based on improved temporal-inception and GRU

To improve the prediction accuracy of chaotic time series, deep extraction of the system evolutionary patterns is a key problem in modeling. In this paper, we propose a deep learning model of automatic multi-scale feature extraction for chaotic time series prediction. A hybrid deep neural network named deep temporal-inception module and gated recurrent unit network (DTIGNet) is designed. The improved temporal-inception module stacks dilated causal convolution of different depth to increase the network adaptability to different scales and improve the network nonlinear characterization ability, and an optional 1 × 1 convolutional kernel as shared residual connection. The model is applied to the Mackey-Glass system, Rossler system, Lorenz system and sunspots time series to verify the applicability and effectiveness in chaotic time series prediction. The results show that the DTIGNet proposed has higher accuracy and better performance compared with other methods according to the 6 prediction evaluation metrics adopted.
Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems

By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets.
Quantitative analysis of soil cadmium content based on the fusion of XRF and Vis-NIR data

Anthropogenic activities, such as sewage irrigation and the application of pesticides and fertilizers, are the main causes of Cadmium (Cd) pollution, which reduces soil quality and threatens the environment and human health. Although the traditional cadmium measurement methods are accurate, it involves complicated sample processing steps and complicated laboratory analysis, which is time-consuming and costly, and is often unfriendly to the environment. X-ray fluorescence (XRF) and visible near-infrared (vis–NIR) spectroscopy have been recognized as alternatives to measure soil heavy metal contamination in a cheap, fast, non-destructive, and environmentally conscious manner. In this study, 370 paddy soil samples from the Nanji area of Poyang Lake were taken as the research object, and the feasibility and effectiveness of XRF and vis-NIR spectroscopy for estimating soil chromium content were discussed, respectively, and the combination of the two was used to estimate soil Cd content. Combined with some spectral preprocessing methods, the quantitative analysis model of least squares support vector machine (LS-SVM) and leave-one-out cross validation with three different data fusions (Equal Weight Fusion, Coaddition Fusion and Outer Product Fusion) was established. The results showed the accuracy and stability of Equal Weight Fusion and Outer Product Fusion was better than the single spectrum quantitative analysis model. The model of Outer Product Fusion at LOOCV set had the best performance with the determination coefficient (R2) of 0.91, root mean square error of cross validation (RMSECV) of 0.12 ​mg/kg and the relative percent deviation (RPD) of 3.27, with R2 ​= ​0.90, RMSEP ​= ​0.13 ​mg/kg and RPD ​= ​3.11 in Prediction set, which can satisfy the detection requirements. This method was accurate, reliable, and can provide a reference for the research of soil heavy metal distribution investigation methods.
A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data

The critical task of recognizing human–object interactions (HOI) finds its application in the domains of surveillance, security, healthcare, assisted living, rehabilitation, sports, and online learning. This has led to the development of various HOI recognition systems in the recent past. Thus, the purpose of this study is to develop a novel graph-based solution for this purpose. In particular, the proposed system takes sequential data as input and recognizes the HOI interaction being performed in it. That is, first of all, the system pre-processes the input data by adjusting the contrast and smoothing the incoming image frames. Then, it locates the human and object through image segmentation. Based on this, 12 key body parts are identified from the extracted human silhouette through a graph-based image skeletonization technique called image foresting transform (IFT). Then, three types of features are extracted: full-body feature, point-based features, and scene features. The next step involves optimizing the different features using isometric mapping (ISOMAP). Lastly, the optimized feature vector is fed to a graph convolution network (GCN) which performs the HOI classification. The performance of the proposed system was validated using three benchmark datasets, namely, Olympic Sports, MSR Daily Activity 3D, and D3D-HOI. The results showed that this model outperforms the existing state-of-the-art models by achieving a mean accuracy of 94.1% with the Olympic Sports, 93.2% with the MSR Daily Activity 3D, and 89.6% with the D3D-HOI datasets.
Sentiment Analysis of Emirati Dialect

Recently, extensive studies and research in the Arabic Natural Language Processing (ANLP) field have been conducted for text classification and sentiment analysis. Moreover, the number of studies that target Arabic dialects has also increased. In this research paper, we constructed the first manually annotated dataset of the Emirati dialect for the Instagram platform. The constructed dataset consisted of more than 70,000 comments, mostly written in the Emirati dialect. We annotated the comments in the dataset based on text polarity, dividing them into positive, negative, and neutral categories, and the number of annotated comments was 70,000. Moreover, the dataset was also annotated for the dialect type, categorized into the Emirati dialect, Arabic dialects, and MSA. Preprocessing and TF-IDF features extraction approaches were applied to the constructed Emirati dataset to prepare the dataset for the sentiment analysis experiment and improve its classification performance. The sentiment analysis experiment was carried out on both balanced and unbalanced datasets using several machine learning classifiers. The evaluation metrics of the sentiment analysis experiments were accuracy, recall, precision, and f-measure. The results reported that the best accuracy result was 80.80%, and it was achieved when the ensemble model was applied for the sentiment classification of the unbalanced dataset.
Statistics in the Service of Science: Don’t Let the Tail Wag the Dog

Abstract Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog. These issues are illustrated by van Doorn et al. (this issue) in the context of using Bayes Factors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications (along with other problems identified here) can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination , which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization , which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios.
SPACX: Silicon Photonics-based Scalable Chiplet Accelerator for DNN Inference

In pursuit of higher inference accuracy, deep neural network (DNN) models have significantly increased in complexity and size. To overcome the consequent computational challenges, scalable chiplet-based accelerators have been proposed. However, data communication using metallic-based interconnects in these chiplet-based DNN accelerators is becoming a primary obstacle to performance, energy efficiency, and scalability. The photonic interconnects can provide adequate data communication support due to some superior properties like low latency, high bandwidth and energy efficiency, and ease of broadcast communication. In this paper, we propose SPACX: a Silicon Photonics-based Chiplet ACcelerator for DNN inference applications. Specifically, SPACX includes a photonic network design that enables seamless single-chiplet and cross-chiplet broadcast communications, and a tailored dataflow that promotes data broadcast and maximizes parallelism. Furthermore, we explore the broadcast granularities of the photonic network and implications on system performance and energy efficiency. A flexible bandwidth allocation scheme is also proposed to dynamically adjust communication bandwidths for different types of data. Simulation results using several DNN models show that SPACX can achieve 78% and 75% reduction in execution time and energy, respectively, as compared to other state-of-the-art chiplet-based DNN accelerators.
Explainable Artificial Intelligence in Data Science

Abstract A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already affects many sensitive realms (as in Politics, Social Sciences, or Law). The decision-making power handover to opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solutions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientific and humanistic areas. The first aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is beneficiated from part of the scientific and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the field of Data Science. The second objective is to develop some arguments justifying the authors’ bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justification and the explainer-explainee agreement.
Flexible and Generalized Real Photograph Denoising Exploiting Dual Meta Attention

Supervised deep learning techniques have been widely explored in real photograph denoising and achieved noticeable performances. However, being subject to specific training data, most current image denoising algorithms can easily be restricted to certain noisy types and exhibit poor generalizability across testing sets. To address this issue, we propose a novel flexible and well-generalized approach, coined as dual meta attention network (DMANet). The DMANet is mainly composed of a cascade of the self-meta attention blocks (SMABs) and collaborative-meta attention blocks (CMABs). These two blocks have two forms of advantages. First, they simultaneously take both spatial and channel attention into account, allowing our model to better exploit more informative feature interdependencies. Second, the attention blocks are embedded with the meta-subnetwork, which is based on metalearning and supports dynamic weight generation. Such a scheme can provide a beneficial means for self and collaborative updating of the attention maps on-the-fly. Instead of directly stacking the SMABs and CMABs to form a deep network architecture, we further devise a three-stage learning framework, where different blocks are utilized for each feature extraction stage according to the individual characteristics of SMAB and CMAB. On five real datasets, we demonstrate the superiority of our approach against the state of the art. Unlike most existing image denoising algorithms, our DMANet not only possesses a good generalization capability but can also be flexibly used to cope with the unknown and complex real noises, making it highly competitive for practical applications.