Reviewerly/exHarmony huggingface dataset:
On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks

The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multi-graph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In particular, a large fraction of tail graphs usually have small sizes. Though recent graph neural networks (GNNs) can learn powerful graph-level representations, they treat the graphs uniformly and marginalize the tail graphs which suffer from the lack of distinguishable structures, resulting in inferior performance on tail graphs. To alleviate this concern, in this paper we propose a novel graph neural network named SOLT-GNN, to close the representational gap between the head and tail graphs from the perspective of knowledge transfer. In particular, SOLT-GNN capitalizes on the co-occurrence substructures exploitation to extract the transferable patterns from head graphs. Furthermore, a novel relevance prediction function is proposed to memorize the pattern relevance derived from head graphs, in order to predict the complements for tail graphs to attain more comprehensive structures for enrichment. We conduct extensive experiments on five benchmark datasets, and demonstrate that our proposed model can outperform the state-of-the-art baselines.
What Should You Know? A Human-In-the-Loop Approach to Unknown Unknowns Characterization in Image Recognition

Unknown unknowns represent a major challenge in reliable image recognition. Existing methods mainly focus on unknown unknowns identification, leveraging human intelligence to gather images that are potentially difficult for the machine. To drive a deeper understanding of unknown unknowns and more effective identification and treatment, this paper focuses on unknown unknowns characterization. We introduce a human-in-the-loop, semantic analysis framework for characterizing unknown unknowns at scale. We engage humans in two tasks that specify what a machine should know and describe what it really knows, respectively, both at the conceptual level, supported by information extraction and machine learning interpretability methods. Data partitioning and sampling techniques are employed to scale out human contributions in handling large data. Through extensive experimentation on scene recognition tasks, we show that our approach provides a rich, descriptive characterization of unknown unknowns and allows for more effective and cost-efficient detection than the state of the art.
Scientific inquiry learning with a simulation: providing within-task guidance tailored to learners’ understanding and inquiry skill

In scientific inquiry learning, within-task guidance tailored to the learner’s domain knowledge and inquiry skill may be essential to promote intended learning outcomes. However, due to dynamic complexity across the timeline of inquiry learning, principles for designing tailored guidance are elusive. In this study, experienced tutors provided just-in-time guidance to 11 learners. We analysed tutor-learner interactions to investigate how tutors adapted guidance. We found tutors provided five types of guidance: prompts, support for domain knowledge, assessments, hints, and feedback. Guidance was provided when learners made errors, expressed difficulties, or asked questions; or when the tutor judged a learner successfully demonstrated a skill and was ready to progress to a follow-on skill. Based on these results, we propose a model for tailored, just-in-time guidance in simulation-assisted inquiry learning environments.
Breast Cancer: Breast Tumor Detection Using Deep Transfer Learning Techniques in Mammogram Images

Breast cancer has become the second leading cause of death among women. Mammogram images are the best and most often used method for detecting breast cancer. This study aims to build an accurate breast tumor detection model that classifies mammogram images into normal and abnormal (tumor). Transfer learning and data augmentation approaches were employed to prevent imagery overfitting problems using three pre-trained convolutional neural networks: AlexNet, VGG16, and VGG19 without trainable layers. The dataset was gathered from two different databases to ensure diversity in the images. We gathered 4000 images from each Digital Database for Screening Mammography and the Chinese Mammography Database. The obtained results were very satisfying according to the previous studies. 99.5% and 100% accuracy were obtained from AlexNet and VGG16 (with trainable layers), better than the existing breast tumour detection models. The applied filters in the pre-processing phase were effective based on the obtained results. Also, we determined that adopting a pre-trained model with trainable layers provides better results than using the model as it is without training the layers.
Multi–feature fusion tracking algorithm based on peak–context learning

Object tracking is considered a critical process in most applications of computer vision. Recently, tracking algorithms that include correlation filter in their frameworks have gained massive popularity due to their high efficiency. The previous algorithms aim to learn the correlation filter by leveraging over all features of the target and its neighbors. However, in this paper, a new tracking algorithm that merges an elastic net constraint and a contextual information into the training scheme is proposed to estimate the target location successfully. The novel optimization problem can significantly strengthen the peak value of the target and effectively eliminate the distractive features. Moreover, most of the correlation filter trackers only use one single feature, which has poor ability under a sophisticated environment. For this reason, a multi–feature fusion strategy is proposed in the framework that embeds multiple features to enhance the tracking performance. Consequently, a multi–scale adaptive model is implemented to improve the tracking stability through scale variations. Besides, an updating mechanism is applied within the proposed framework to reduce the tracking drift. Extensive quantitative and qualitative experiments on challenging benchmarks show that this unified tracker model achieves impressive performance compared to correlation filter and deep trackers.
The Power of Affection: Exploring the Key Drivers of Customer Loyalty in Virtual Reality-Enabled Services

The accelerating growth of virtual reality (VR) technology and evolving customer needs make multifarious challenges and opportunities for service industries. Based on the Technology Acceptance Model (TAM) and Theory of Affection Responses, we explored the key drivers of customer loyalty in virtual reality-enabled services through a large-scaled survey data collected from VR users in four major cities of Pakistan. The study employs the partial least squares structural equation modeling (PLS-SEM). We verified that the authenticity of the VR experience and TAM dimensions (ease of use, usefulness of VR) are the key drivers of customer loyalty béhavioral in VR-enabled services. Furthermore, results revealed that Affective responses (i.e., enjoyment, emotional involvement, and flow state) significantly mediated the relationships between the drivers and customer loyalty (continued use, recommendation, and willingness to pay premium). Implications for researchers and VR practitioners were also provided.
An Integrated Development Model of Cultural and Creative Industry and Rural Tourism Industry Based on Data Mining

In the later stage of industrialization or postindustrialization era, the driving force of human social and economic development mainly comes from technological innovation, cultural creativity, and the development of creative industries, high-tech industries, and service industries. The cultural and creative industries meet the needs of the postindustrial era and the era of knowledge economy. This shows the importance of culture, knowledge, and technology in the rapid economic development. This paper introduces data mining technology and obtains a data mining method system suitable for this study. It mines and analyzes data related to cultural and creative industries and rural tourism industries. It finally obtained that the degree of integration of the two industries in the past six years was 0.498, 0.496, 0.487, 0.498, 0.499, and 0.5, respectively.
Security analysis of smart contract based rating and review systems: the perilous state of blockchain-based recommendation practices

Nowadays, Blockchain-based rating/review systems are gaining popularity as a backbone for recommender systems due to the inherent cryptographically secured decentralised architecture, immutability, user anonymity, and inclusion of smart contracts. However, the existing Blockchain-based rating/review systems address resistance to the standard attacks, i.e. collusion attack, user threatening, and unfair rating. Still, they do not present security analyses of smart contracts that may result in substantial threats to the users of the systems. This manuscript presents an in-depth study of twelve publicly available security analysis tools and standard vulnerabilities in smart contracts and reviews. The experimental setup uses a two-step approach for selecting the security analysis tool. The first step identifies the seven tools their proposers or independent researchers have compared, and the second step proposes a new method for selecting tools based on continuous improvement. Our experimental results show security issues in 51.72% of the analysed smart contracts of four Blockchain-based rating/review systems. 6.67% of vulnerable smart contracts exhibit high-level severity threats that raise an alarming condition for the current state of system developments.
Lightweight Modifications in the Advanced Encryption Standard (AES) for IoT Applications: A Comparative Survey

The Internet of Things is a network of billions of interconnected devices that communicate over the internet. These massive devices exchange massive amounts of data every second, and that data may be sensitive and must be protected from attackers. A cryptographic solution could be used to address these issues. Conventional cryptography is unsuitable for IoT devices with limited resources; lightweight cryptography is an alternative solution. Many researchers have presented new designs and modified the existing algorithms to become lightweight versions. This paper presents a recent comparison among numerous modifications of the very popular block cipher AES-128 algorithm that have been published between 2016 and 2021 in the light-weight symmetric block cipher cryptographic field based on security, throughput, and encryption time with various message lengths. The results of the comparison indicate that the modifications, which included modifying the ShiftRow operation, modifying the MixColumn operation and combining it with the AddRoundKey operation to perform in a single cycle, and reducing the number of rounds to 6, were faster in implementation and had greater security than other studies cited.
Untangling the critical success factors for blockchain adoption in supply chain: a social network analysis

With the advent of Industry 4.0, Blockchain is attracting Supply Chain (SC) practitioners and researchers thanks to its decentralized and trustworthy data governance features. As Blockchain adoption in SC is nascent, this article provides a Systematic Literature Review of the critical success factors for this phenomenon to help organizations meet its challenges. 56 selected articles were first thematically analyzed with NVivo to identify and conceptually categorize the factors. Then, we performed a social network analysis under VOSviewer to understand the research trends in our topic and explain the implicit ties between the identified factors. Finally, we inferred further theoretical implications of the analyzed literature in light of the ‘Technology, Organization, Environment’ framework. Hence, this study contributes to academic and practical knowledge by explaining the natures of the interdependent factors for Blockchain adoption in SC and of their potential links. We also propose opportunities for future research to extend our findings.
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems

The development of personalized recommendation has significantly improved the accuracy of information matching and the revenue of e-commerce platforms. Recently, it has two trends: 1) recommender systems must be trained timely to cope with ever-growing new products and ever-changing user interests from online marketing and social network; 2) state-of-the-art recommendation models introduce deep neural network (DNN) modules to improve prediction accuracy. Traditional CPU-based recommender systems cannot meet these two trends, and GPU-centric training has become a trending approach. However, we observe that GPU devices in training recommender systems are underutilized, and they cannot attain an expected throughput improvement as what it has achieved in Computer Vision (CV) and Neural Language Processing (NLP) areas. This issue can be explained by two characteristics of these recommendation models: First, they contain up to a thousand of input feature fields, introducing fragmentary and memory-intensive operations; Second, the multiple constituent feature interaction submodules introduce substantial small-sized compute kernels. To remove this roadblock to the development of recommender systems, we propose a novel framework named PICASSO to accelerate the training of recommendation models on commodity hardware. Specifically, we conduct a systematic analysis to reveal the bottlenecks encountered in training recommendation models. We leverage the model structure and data distribution to unleash the potential of hardware through our packing, interleaving, and caching optimization. Experiments show that PICASSO increases the hardware utilization by an order of magnitude on the basis of state-of-the-art baselines and brings up to 6× throughput improvement for a variety of industrial recommendation models. Using the same hardware budget in production, PICASSO on average shortens the walltime of daily training tasks by 7 hours, significantly reducing the delay of continuous delivery.
Gated Multimodal Fusion with Contrastive Learning for Turn-Taking Prediction in Human-Robot Dialogue

Turn-taking, aiming to decide when the next speaker can start talking, is an essential component in building human-robot spoken dialogue systems. Previous studies indicate that multi-modal cues can facilitate this challenging task. However, due to the paucity of public multimodal datasets, current methods are mostly limited to either utilizing unimodal features or simplistic multimodal ensemble models. Besides, the inherent class imbalance in real scenario, e.g. sentence ending with short pause will be mostly regarded as the end of turn, also poses great challenge to the turn-taking decision. In this paper, we first collect a large-scale annotated corpus for turn-taking with over 5,000 real human-robot dialogues in speech and text modalities. Then, a novel gated multimodal fusion mechanism is devised to utilize various information seamlessly for turn-taking prediction. More importantly, to tackle the data imbalance issue, we design a simple yet effective data augmentation method to construct negative instances without supervision and apply contrastive learning to obtain better feature representations. Extensive experiments are conducted and the results demonstrate the superiority and competitiveness of our model over several state-of-the-art baselines.
Enhancing cybersecurity by generating user-specific security policy through the formal modeling of user behavior

Organizations today are faced with the difficult challenge of balancing the embrace of new and emerging technology, and securing their systems and data that support critical business functions. Although there have been significant advances in security enforcement technology, attackers are still able to compromise organizations and access. The impacts of computer intrusions have become so untenable that many organizations are looking at a drastic rethinking of their approach to the security of internal networks. This approach is called Zero Trust and it seeks to remove all notion of a trusted internal network boundary. The benefits of Zero Trust include significantly increasing the work that attackers would need to perform to achieve their objectives. But Zero Trust will also increase the management complexity for internal security teams. These teams will need a way to collect data and enforce policy decisions based upon analysis. This process will need to be done for all organizational systems, and data, and it will need to be done in all access contexts. Our approach uses formal methods to model and examine end-users security-related behaviors. Researchers have found that the users’ security decisions correlate with factors including demographics, personality traits, decision-making styles, and risk-taking preferences. We describe these behaviors by using Finite-State Automata (FSA). This allows for the automated formulation of linear-time security properties based on Timed Computation Tree Logic (TCTL). The logic is then used to check the satisfaction of collected and observed security behaviors against policy. This formal behavioral analysis could be combined with other security and network data during the context analysis process that needs to occur for each Zero Trust access request. Other network or host security data could include address identifiers, tokens, event data, packet inspection, running process data, cyber threat intelligence, and much more. Our method allows organizations that embrace a Zero Trust philosophy to generate context specific security policies that can be automatically verified for correctness and completion.
Privacy Sensitive Speech Analysis Using Federated Learning to Assess Depression

Recent studies have used speech signals to assess depression. However, speech features can lead to serious privacy concerns. To address these concerns, prior work has used privacy-preserving speech features. However, using a subset of features can lead to information loss and, consequently, non-optimal model performance. Furthermore, prior work relies on a centralized approach to support continuous model updates, posing privacy risks. This paper proposes to use Federated Learning (FL) to enable decentralized, privacy-preserving speech analysis to assess depression. Using an existing dataset (DAIC-WOZ), we show that FL models enable a robust assessment of depression with only 4--6% accuracy loss compared to a centralized approach. These models also outperform prior work using the same dataset. Furthermore, the FL models have short inference latency and small memory footprints while being energy-efficient. These models, thus, can be deployed on mobile devices for real-time, continuous, and privacy-preserving depression assessment at scale.
A new approach to estimate and delineate the geothermal gradient of Iran

In spite of numerous deep wells drilled for oil, gas, deep water and geothermal purposes, and several local geothermal studies carried out using geochemistry analysis in Iran, lack of a comprehensive geothermal map is palpable. In this paper, nominated reliable surface and subsurface thermal data are gathered, examined and corrected to be involved in mapping process. In order to do so, 107 drilled wells accompanying with geothermal gradient data extracted from geothermometry analysis (available in hot springs) were nominated based on data quality and a proper dispersion. All selected points were evaluated and eventually, a real temperature gradient together with a temperature log were calculated in every location. Therefore, convergent interpolation technique was applied to estimate and distribute the temperature data points to the whole surface. According to the final proposed map, geothermal gradient is increased around geologically active areas such as quaternary volcanoes and active faults such that it is evidently possible to segregate high gradient areas into two major parts including volcano-related and active-fault related areas. Approximate average geothermal gradient in non-volcanic basins like Zagros, Kopet-dagh, central Persian Gulf, Qom, Moghan and Zabol regions are determined as 21 °C/km, 30 °C/km, 31.5 °C/km, 26.7 °C/km, 30 °C/km and 25 °C/km respectively and it reaches to about 149 °C/km around volcanic areas like Sabalan stratovolcano. Undertaken comprehensive survey will be of great assistance in future exploration of geothermal resources in Iran land.
ADT: Anti-Deepfake Transformer

Recently almost all the mainstream deepfake detection methods use Convolutional Neural Networks (CNN) as their backbone. However, due to the overreliance on local texture information which is usually determined by forgery methods of training data, these CNN-based methods cannot generalize well to unseen data. To get out of the predicament of prior methods, in this paper, we propose a novel transformer-based framework to model both global and local information and analyze anomalies of face images. In particular, we design attention leading module, multi-forensics module and variant residual connections for deepfake detection, and leverage token-level contrast loss for more detailed supervision. Experiments on almost all popular public deepfake datasets demonstrate that our method achieves state-of-the-art performance in cross-dataset evaluation and comparable performance in intra-dataset evaluation.
Raw Source and Filter Modelling for Dysarthric Speech Recognition

Acoustic modelling for automatic dysarthric speech recognition (ADSR) is a challenging task. Data deficiency is a major problem and substantial differences between the typical and dysarthric speech complicates transfer learning. In this paper, we build acoustic models using the raw magnitude spectra of the source and filter components. The proposed multi-stream model consists of convolutional and recurrent layers. It allows for fusing the vocal tract and excitation components at different levels of abstraction and after per-stream pre-processing. We show that such a multi-stream processing leverages these two information streams and helps s model towards normalising the speaker attributes and speaking style. This potentially leads to better handling of the dysarthric speech with a large inter-speaker and intra-speaker variability. We compare the proposed system with various features, study the training dynamics, explore usefulness of the data augmentation and provide interpretation for the learned convolutional filters. On the widely used TORGO dysarthric speech corpus, the proposed approach results in up to 1.7% absolute WER reduction for dysarthric speech compared with the MFCC base-line. Our best model reaches up to 40.6% and 11.8% WER for dysarthric and typical speech, respectively.
Fostering The Robustness Of White-Box Deep Neural Network Watermarks By Neuron Alignment

The wide application of deep learning techniques is boosting the regulation of deep learning models, especially deep neural networks (DNN), as commercial products. A necessary prerequisite for such regulations is identifying the owner of deep neural networks, which is usually done through the watermark. Current DNN watermarking schemes, particularly white-box ones, are uniformly fragile against a family of functionality equivalence attacks, especially the neuron permutation. This operation can effortlessly invalidate the ownership proof and escape copyright regulations. To enhance the robustness of white-box DNN watermarking schemes, this paper presents a procedure that aligns neurons into the same order as when the watermark is embedded, so the watermark can be correctly recognized. This neuron alignment process significantly facilitates the functionality of established deep neural network watermarking schemes.