Reviewerly/exHarmony huggingface dataset:
Egocentric Early Action Prediction via Adversarial Knowledge Distillation

Egocentric early action prediction aims to recognize actions from the first-person view by only observing a partial video segment, which is challenging due to the limited context information of the partial video. In this article, to tackle the egocentric early action prediction problem, we propose a novel multi-modal adversarial knowledge distillation framework. In particular, our approach involves a teacher network to learn the enhanced representation of the partial video by considering the future unobserved video segment, and a student network to mimic the teacher network to produce the powerful representation of the partial video and based on that predicting the action label. To promote the knowledge distillation between the teacher and the student network, we seamlessly integrate adversarial learning with latent and discriminative knowledge regularizations encouraging the learned representations of the partial video to be more informative and discriminative toward the action prediction. Finally, we devise a multi-modal fusion module toward comprehensively predicting the action label. Extensive experiments on two public egocentric datasets validate the superiority of our method over the state-of-the-art methods. We have released the codes and involved parameters to benefit other researchers. 1
Using Data Mining Techniques for Detecting Dependencies in the Outcoming Data of a Web-Based System

The increasing amount of data from web systems data is becoming one of the most valuable resources for information retrieval and knowledge discovery. The huge content of information makes it an important area for data mining research. To analyze the dependencies of the outcoming data, expressed as query scenarios, we present a new approach for evaluating the behavior of interactive web systems by applying different data mining techniques to solve the problem. We propose tools that take outcoming logs as input, analyze them, and provide information about web client actions. Qualitative and quantitative automatic evaluation of the data can explain the connections between the most significant parameters of the system in particular scenarios. In this paper, we propose a new method, which can be used to efficiently verify the type of client behavior of a web system or design of the system. The analysis of results demonstrates the possibility of efficient pattern search.
SSBAS-FA: A secure sealed-bid e-auction scheme with fair arbitration based on time-released blockchain

Electronic auctions are becoming more and more popular in business activities, which provide the professionals with competitive prices for their products, pitching the suppliers directly against each other to allow suppliers to bid online against a published specification. In sealed-bid e-auctions, bidders send sealed-bid envelopes to auctioneer and auctioneer only can open them after a certain period of time to announce the highest bidder. In order to solve the issue of the opacity of whole sealed-bid auction process, researchers try to solve this problem by incorporating the blockchain technique into the e-auction systems with the properties of unforgeable transaction and public ledger of the blockchain. However, since the auctioneer might open the bid envelope before the deadline of the auction, it can tamper the bid price by colluding with malicious bidders for personal profit. In this paper, we propose a time-released blockchain-based sealed-bid e-auction scheme. We use time-lock encryption to ensure that the bidders’ bid data will not be available to anyone until a specified time, thus guaranteeing the security of the bidders’ data. At the same time, we use smart contracts for auction verification, which further improves the security of the scheme and avoids tampering by malicious auctioneers. In addition, we propose a secure sealed-bid e-auction scheme with fair arbitration (namely, SSBAS-FA), that is based on time-released blockchain to provide a fair arbitration mechanism in punishing malicious bidders. We give the deployment of a time-released blockchain that eliminates the demand of the trusted third party to perform the validation of the smart contract. We provide the fair arbitration for the bidder and auctioneer. The security analysis and performance evaluation indicate that our scheme is secure, fair and efficient in sealed-bid e-auction system.
Bipolar fuzzy based least squares twin bounded support vector machine

Data classification is a key domain of research in real-world applications. One of the big challenges of real-world data classification is to tackle the presence of noise and outliers. In this paper, we handle the computational cost of Bipolar evaluation pairs score value based support vector machine model by formulating two efficient variants, referred to as bipolar fuzzy least squares support vector machines and bipolar fuzzy least squares twin bounded support vector machines, in which the score value is obtained as a bipolar evaluation pair with membership and non-membership functions. The solution of primal problems is attained by solving a system of linear equations that leads to less training time complexity unlike other fuzzy based twin models obtain the solution by handling two quadratic programming problems. Furthermore, our proposed models minimize the impact of noise in the data and facilitate the separation of support vectors from noise. The proposed works have been computationally analyzed on many publicly available real-world benchmark datasets as well as simulated artificial datasets in the non-linear case for different significant levels of noise, namely 0% (noise-free) and 5% (noise-corrupted). In comparison to other similar models, our suggested model is showing phenomenal generalization performance by controlling the negative impact of noise and needs substantially less training time. The results of the proposed models are also validated using quality metrics including AUC, F 1 -score, G-mean, and Precision Predictive Value.
Exploring the geothermal potential of Peut Sagoe volcano using Landsat 8 OLI/TIRS images

Thermal infrared remote sensing is an emerging technique to determine thermal anomalies in geothermal and volcanic areas. This study investigates the potential of thermal infrared imaging to observe geothermal prospecting and thermal activity in the Peut Sagoe volcano. The volcano has not been completely studied because it is located in a remote frontier area surrounded by dense rainforest and high terrain conditions. We used operational land imager (OLI)/thermal infrared sensor (TIRS) images of Landsat 8 acquired from 2014 to 2020 to get the actual hydrothermal mineral alteration, vegetation changes (Normalized Difference Vegetation Index; NDVI), and land surface temperature (LST) of the volcanic areas, which correlate with geothermal characteristics, such as fumarole and warm ground that exist on the surface of Peut Sagoe volcano. The combination of band ratios such as 7/5, 5/4, and 6/7 effectively estimates the hydrothermal mineral alteration in the volcanic area, which is dominated by several minerals, such as clay, iron oxide, and OH/H2O-, SO4-, and CO-bearing minerals. Result analyses of low NDVI and high LST values are related to the vegetation changes and thermal status of the crater area, the top of the volcano, and the warm ground on the western side of the mountain. These identities demonstrate the geothermal potential of the Peut Sagoe volcano. The LST data of OLI/TIRS images indicate increased thermal activity of the volcano from 22–26°C in 2014 to 30–35°C in 2017. However, the temperature of the volcano relatively decreased to 25°C in 2015 and 2020. In order to reduce the solar effects, the LST data from Landsat were also validated with ASTER data using a combination of night and daytime images. The results show a pattern of high thermal anomalies from 2014 – 2020 in several areas suspected of having geothermal manifestations, such as craters and hot springs, the distribution of these anomalies corresponds to LST in Landsat 8 data. Moreover, NDVI values at the potential geothermal location also reduced in 2014 and 2017 due to an increasing underground volcanic activity. Results revealed that remote sense imagery is an effective approach to identifying the geothermal potential and monitoring the thermal status of volcanos in frontier areas.
Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images

Recently, deep learning techniques are widely used in various computer vision applications such as pattern recognition, data classification, object detection, image enhancement, etc. Deep learning frameworks outperform classical algorithms due to their flexibility and interoperability. The main aim of this work is to remove Gaussian-Impulse noise with blind (unknown) noise densities in digital images. The proposed work has two phases; in phase 1, the noisy image is preprocessed with a novel Pseudo–Convolutional Neural Network (P-CNN) having no tunable parameters. The Pseudo-CNN is a customized three-layered network inspired by conventional convolutional neural networks. The first-level feature detector has k number of (2d+1)×(2d+1) Weight Initialized Adaptive Window (WIAW) filters. The weights or coefficients of WIAW filters are initialized using the noise probabilistic distribution. The loss function is not estimated in P-CNN because there is only one forward pass and no backpropagation. The PCNN has excellent Impulse noise rejection capability. In phase 2, a modified Convolutional Neural Network (CNN) is applied to the preprocessed image to obtain a latent clean image. To improve the network performance, we have utilized residual learning and batch normalization. The high degree of localized pixel correlation established by P-CNN helps the modified CNN to learn compact representations and salient features of the input image. The proposed method not only denoises images corrupted with blind noise levels of Gaussian-Impulse noise but is also effective for removing Impulse noise in digital images. Our experimental results imply that the proposed method gives good qualitative and quantitative results compared to various state-of-the-art techniques. Convolutional neural networks are pertinent for parallel computation using powerful GPUs, which help to improve the denoising performance.
An evaluation of potential attack surfaces based on attack tree modelling and risk matrix applied to self-sovereign identity

Self-Sovereign Identity (SSI) empowers users to govern their digital identity and personal data. This approach has changed the identity paradigm where users become the central governor of their identity; hence the rapid growth of the SSI model. Utilizing the security and privacy properties of blockchain, together with other security technologies, SSI purports to provide a robust security and privacy service. However, this governing power for users comes with a greater accountability and security risk, as not all users are capable or trained in its use and therefore in its efficient application. This trade-off requires a systematic evaluation of potential attacks on the SSI system and their security risks. Hitherto, there have been no noteworthy research studies performed to evaluate potential attacks on the SSI system and their security risks. This paper proposes an easy, efficient and economical approach to perform an evaluation of potential attacks on the SSI system and their security risks. This approach utilises a combination of an attack tree model and risk matrix model to perform this evaluation of potential attacks and their security risks, in addition to outlining a systematic approach including describing the system architecture and determining its assets in order to perform this evaluation of potential attacks and their security risks. This evaluation work has identified three potential attacks on the SSI system: faking identity, identity theft and distributed denial of service attacks, and performed their security risk evaluation utilising the proposed approach. Finally, this paper has proposed several mitigation strategies for the three evaluated attacks on the SSI system. This proposed evaluation approach is a systematic and generalised approach for evaluating attacks and their security risks, and can be applied to any other IT system.
Perspectives of Librarians on Awareness and Readiness of Academic Libraries to Integrate Artificial Intelligence for Library Operations and Services in Nigeria

This study examined the perspectives of librarians on the awareness and readiness of academic libraries to integrate artificial intelligence (AI) for library operations and services in Nigeria. Six libraries were drawn from the six geopolitical zones in Nigeria. An open-ended questionnaire posted to the respondents through the mail was used for data collection. Collected data was analyzed using thematic analysis. The findings reveal that the Nigerian academic libraries are aware of the integration of AI systems in libraries and how it is now being adopted and used in many academic libraries around the globe. However, this is not so in the Nigerian context. The findings also reveal that the librarians have mixed feelings about the readiness of academic libraries to integrate AI systems into library operations and services. Based on the librarian perception, the result implies that integration of AI in library operation and services will take the library to the next level by reducing human errors due to repetitiveness in library tasks, and if care is not taken, AI system might take their job role. The challenges reported that academic libraries in Nigeria may likely face the problem of funding, inadequate experts, limited power supply, limited budget to procure the technology and training personnel that will be in charge of the system maintenance. The study recommends the need for adequate funding for libraries to be able to procure, AI and other information and communication technology infrastructure; and recruitment of librarians with relevant requisite skills to work with the technology.
Vector space modelling-based intelligent binary image encryption for secure communication

AbstractAbstractIn this research work, a secured routing technique is developed based on the LEACH algorithm. Many secure routing algorithms were proposed in the past by modifying the LEACH algorithm with trust management techniques, key generation, and distribution technique and intrusion detection algorithms. Most present solutions are unable to address the issue of modifying and intercepting assaults. To tackle this problem, a new secured routing algorithm is developed in this work that divides the image and messages into sub-components, represent them in a matrix format and add them to get a steganographic image which is encrypted using a suitable encryption algorithm selected by the rule system developed in this research work. Moreover, intelligent agents are incorporated in all the sensor nodes in the network to communicate the encrypted images to the neighbor nodes more reliably.Subject Classification: Primary 93A30Secondary 49K15Keywords: EncryptionCryptographySensorNetworkLEACH algorithmSteganographic image
VSiM: Improving QoE Fairness for Video Streaming in Mobile Environments

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to optimize multiple users’ experiences jointly, but users often suffer short connection durations and frequent handoffs because of their high mobility. This paper proposes an end-to-end scheme, VSiM, to support mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement on average of the averaged QoE.
Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

Social development organizations increasingly employ artificial intelligence (AI)-enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team management tasks and activities. Based on the unified theory of acceptance and use of technology (UTAUT), this study explores various factors influencing employees' use of AI-enabled tools. The study extends the model in two ways: a) by evaluating the impact of these tools on the employees' collaboration and b) by exploring the moderating role of AI aversion. Data were collected through an online survey of employees working with AI-enabled tools. The analysis of the research model was conducted using partial least squares (PLS), with a two-step model - measurement and structural models of assessment. The results revealed that the antecedent variables, such as effort expectancy, performance expectancy, social influence, and facilitating conditions, are positively associated with using AI-enabled tools, which have a positive relationship with collaboration. It also concluded a significant effect of AI aversion in the relationship between performance expectancy and use of technology. These findings imply that organizations should focus on building an environment to adopt AI-enabled tools while also addressing employees' concerns about AI.
FAIR assessment tools: evaluating use and performance

Publishing research data using a findable, accessible, interoperable, and reusable (FAIR) approach is paramount to further innovation in many areas of research. In particular in developing innovative approaches to predict (eco)toxicological risks in (nano or advanced) material design where efficient use of existing data is essential. The use of tools assessing the FAIRness of data helps the future improvement of data FAIRness and therefore their re-use. This paper reviews ten FAIR assessment tools that have been evaluated and characterized using two datasets from the nanomaterials and microplastics risk assessment domain. The tools were grouped into four categories: online and offline self-assessment survey based, online (semi-) automated and other tools. We found that the online self-assessment tools can be used for a quick scan of a user's dataset due to their ease of use, little need for experience and short time investment. When a user is looking to assess full databases, and not just datasets, for their FAIRness, (semi-)automated tools are more practical. The offline assessment tools were found to be limited and unreliable due to a lack of guidance and an under-developed state. To further characterize the usability, two datasets were run through all tools to check the similarity in the tools' results. As most of the tools differ in their implementation of the FAIR principles, a large variety in outcomes was obtained. Furthermore, it was observed that only one tool gives recommendations to the user on how to improve the FAIRness of the evaluated dataset. This paper gives clear recommendations for both the user and the developer of FAIR assessment tools.
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification

Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of designing complex discriminative network architectures, we reveal that the inherent feature distribution of histopathological image data can serve as a very effective guide for instance classification. We propose a cluster-conditioned feature distribution modeling method and a pseudo label-based iterative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.
Relation constraint self-attention for image captioning

Self-attention based Transformer has been successfully introduced in the encoder-decoder framework of image captioning, which is superior in modeling the inner relations of inputs, i.e., image regions or semantic words. However, relations in self-attention are usually too dense to be fully optimized, which may result in noisy relations and attentions. Meanwhile, the prior relations, e.g., visual relation and semantic relation between objects, which are essential for understanding and describing an image, are ignored by current self-attention. Thus, the relation learning of self-attention in image captioning is biased, which leads to a dilution of the concentration of attentions. In this paper, we propose a Relation Constraint Self-Attention (RCSA) model to enhance the relation learning of self-attention in image captioning by constraining self-attention with prior relations. RCSA exploits the prior visual and semantic relation information from scene graph as constraint factors. And then it builds constraints for self-attention through two sub-modules: an RCSA-E encoder module and an RCSA-D decoder module. RCSA-E introduces the visual relation information to self-attention in encoder, which helps generate a sparse attention map by omitting the attention weights of irrelevant regions to highlight relevant visual features. RCSA-D extends the keys and values of self-attention in decoder with the semantic relation information to constrain the learning of semantic relation, and improve the accuracy of generated semantic words. Intuitively, RCSA-E endows model with an ability to figure out which region to omit and which region to focus by visual relation information; RCSA-D then strengthens the relation learning of the focused regions and improves the sentence generation with semantic relation information. Experiments on the MSCOCO dataset demonstrate the effectiveness of our proposed RCSA.
Augmented Score-CAM: High resolution visual interpretations for deep neural networks

There is an increasing demand to understand how neural networks make decisions when classifying images. Recent deep learning models have a black box architecture that limits the ability to explore their functionality. This paper presents an explainable method called Augmented Score-CAM, built on top of the existing Score-CAM and the existing image augmentation techniques. Unlike previous methods that relied on one input image to generate class activation maps, we adopt the image augmentation technique used to train convolutional neural networks. We use the input image to create a set of augmented images and generate a class activation map for each. The final activation map is obtained by combining augmented activation maps. We evaluate the model by performing qualitative and quantitative experiments. Augmented Score-CAM outperformed Score-CAM in terms of human trust, faithfulness, and object localization. Our model passed the sanity check and was found to be sensitive to network and dataset randomization. Moreover, we proposed the use of feature space augmentations by embedding neural style transfer in the model.