Reviewerly/exHarmony huggingface dataset:
EX3: Explainable Attribute-aware Item-set Recommendations

Existing recommender systems in the e-commerce domain primarily focus on generating a set of relevant items as recommendations; however, few existing systems utilize underlying item attributes as a key organizing principle in presenting recommendations to users. Mining important attributes of items from customer perspectives and presenting them along with item sets as recommendations can provide users more explainability and help them make better purchase decision. In this work, we generalize the attribute-aware item-set recommendation problem, and develop a new approach to generate sets of items (recommendations) with corresponding important attributes (explanations) that can best justify why the items are recommended to users. In particular, we propose a system that learns important attributes from historical user behavior to derive item set recommendations, so that an organized view of recommendations and their attribute-driven explanations can help users more easily understand how the recommendations relate to their preferences. Our approach is geared towards real world scenarios: we expect a solution to be scalable to billions of items, and be able to learn item and attribute relevance automatically from user behavior without human annotations. To this end, we propose a multi-step learning-based framework called Extract-Expect-Explain (EX3), which is able to adaptively select recommended items and important attributes for users. We experiment on a large-scale real-world benchmark and the results show that our model outperforms state-of-the-art baselines by an 11.35% increase on NDCG with adaptive explainability for item set recommendation.
The Effect of Perceptions on Service Robot Usage Intention: A Survey Study in the Service Sector

The current age of artificial intelligence, along with the advent of robots, portends increased use of innovative technologies in the tourism industry, with higher levels of service innovation than in many other industries. In addition, factors such as an approaching worldwide pandemic have limited the amount of physical contact that people can have. So as a result, the use of service robots in service areas, such as tourism, has recently become controversial. In this study, accommodation customers’ perceptions of advantages and disadvantages about robots and the effect of the perceived value of their intention to use them were investigated. Within the scope of the research, data were collected from 1408 people living in various cities in Turkey through an online survey. The data were analyzed by structural equation modeling. As a result of the analyses, it was found that the perception of advantage and the perceived value affect the intention to use service robots positively and significantly. It has been determined that the perception of disadvantage affects the intention to use service robots negatively and significantly. The research results show that the accommodation companies should be innovative and rapidly transition to robotization, as in the manufacturing industry. Advanced technological innovation applications, such as service robots, will play an essential role in the revival of the tourism industry, especially during the global epidemic.
AI-Envisioned Blockchain-Enabled Signature-Based Key Management Scheme for Industrial Cyber–Physical Systems

This article proposes a new blockchain-envisioned key management protocol for artificial intelligence (AI)-enabled industrial cyber–physical systems (ICPSs). The designed key management protocol enables key establishment among the Internet of Things (IoT)-enabled smart devices and their respective gateway nodes. The blocks partially constructed with secure data from smart devices by fog servers are provided to cloud servers that are responsible for completing blocks, and then mining those blocks for verification and addition in the blockchain. The most important application of the private blockchain construction is to apply AI algorithms for accurate predictions in Big data analytics. A detailed security analysis along with formal security verification show that the proposed scheme resists various potential attacks in an ICPS environment. Moreover, practical testbed experiments have been conducted using the multiprecision integer and rational arithmetic cryptographic library (MIRACL). Furthermore, a detailed comparative analysis shows superiority of the proposed scheme over recent relevant schemes. In addition, the practical implementation using the blockchain for the proposed scheme demonstrates the total computational costs when the number of transactions per block and also the number of blocks mined in the blockchain are varied.
Deep learning-based election results prediction using Twitter activity

Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded with detailed experimental results along with discussion on the outcomes of sentiment analysis for real-world forecasting and approval of general elections in Pakistan.
FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models

Federated learning (FL) has recently emerged as a promising distributed machine learning (ML) paradigm. Practical needs of the "right to be forgotten" and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training data from the trained FL model. Existing unlearning techniques in the context of ML, however, are no longer in effect for FL, mainly due to the inherent distinction in the way how FL and ML learn from data. Therefore, how to enable efficient data removal from FL models remains largely under-explored. In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning method-ology that can eliminate the influence of a federated client’s data on the global FL model while significantly reducing the time used for constructing the unlearned FL model. The basic idea of FedEraser is to trade the central server’s storage for unlearned model’s construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL. A novel calibration method is further developed to calibrate the retained updates, which are further used to promptly construct the unlearned model, yielding a significant speed-up to the reconstruction of the unlearned model while maintaining the model efficacy. Experiments on four realistic datasets demonstrate the effectiveness of FedEraser, with an expected speed-up of 4× compared with retraining from the scratch. We envision our work as an early step in FL towards compliance with legal and ethical criteria in a fair and transparent manner.
Tensor pooling-driven instance segmentation framework for baggage threat recognition

Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper, we addressed this challenge using a novel multi-scale contour instance segmentation framework that effectively identifies the cluttered contraband data within the baggage X-ray scans. Unlike standard models that employ region-based or keypoint-based techniques to generate multiple boxes around objects, we propose to derive proposals according to the hierarchy of the regions defined by the contours. The proposed framework is rigorously validated on three public datasets, dubbed GDXray, SIXray, and OPIXray, where it outperforms the state-of-the-art methods by achieving the mean average precision score of 0.9779, 0.9614, and 0.8396, respectively. Furthermore, to the best of our knowledge, this is the first contour instance segmentation framework that leverages multi-scale information to recognize cluttered and concealed contraband data from the colored and grayscale security X-ray imagery.
MSAR-Net: Multi-scale attention based light-weight image super-resolution

Recently, single image super-resolution (SISR), aiming to preserve the lost structural and textural information from the input low resolution image, has witnessed huge demand from the videos and graphics industries. The exceptional success of convolution neural networks (CNNs), has absolutely revolutionized the field of SISR. However, for most of the CNN-based SISR methods, excessive memory consumption in terms of parameters and flops, hinders their application in low-computing power devices. Moreover, different state-of-the-art SR methods collect different features, by treating all the pixels contributing equally to the performance of the network. In this paper, we take into consideration both the performance and the reconstruction efficiency, and propose a Light-weight multi-scale attention residual network (MSAR-Net) for SISR. The proposed MSAR-Net consists of stack of multi-scale attention residual (MSAR) blocks for feature refinement, and an up and down-sampling projection (UDP) block for edge refinement of the extracted multi-scale features. These blocks are capable of effectively exploiting the multi-scale edge information, without increasing the number of parameters. Specially, we design our network in progressive fashion, for substituting the large scale factors (× 4) combinations, with small scale factor (×2) combinations, and thus gradually exploit the hierarchical information. In parallel, for modulation of multi-scale features in global and local manners, channel and spatial attention in MSAR block is being used. Visual results and quantitative metrics of PSNR and SSIM exhibit the accuracy of the proposed approach on synthetic benchmark super-resolution datasets. The experimental analysis shows that the proposed approach outperforms the other existing methods for SISR in terms of memory footprint, inference time and visual quality.
Lateral Transfer Learning for Multiagent Reinforcement Learning

Some researchers have introduced transfer learning mechanisms to multiagent reinforcement learning (MARL). However, the existing works devoted to cross-task transfer for multiagent systems were designed just for homogeneous agents or similar domains. This work proposes an all-purpose cross-transfer method, called multiagent lateral transfer (MALT), assisting MARL with alleviating the training burden. We discuss several challenges in developing an all-purpose multiagent cross-task transfer learning method and provide a feasible way of reusing knowledge for MARL. In the developed method, we take features as the transfer object rather than policies or experiences, inspired by the progressive network. To achieve more efficient transfer, we assign pretrained policy networks for agents based on clustering, while an attention module is introduced to enhance the transfer framework. The proposed method has no strict requirements for the source task and target task. Compared with the existing works, our method can transfer knowledge among heterogeneous agents and also avoid negative transfer in the case of fully different tasks. As far as we know, this article is the first work denoted to all-purpose cross-task transfer for MARL. Several experiments in various scenarios have been conducted to compare the performance of the proposed method with baselines. The results demonstrate that the method is sufficiently flexible for most settings, including cooperative, competitive, homogeneous, and heterogeneous configurations.
Security and Privacy Aspects of Cloud Computing: A Smart Campus Case Study

The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, cost-effective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365. While there are many advantages in such an environment, security issues such as data privacy, data security, access control, cyber-attacks, and data availability, along with performance and reliability issues, exist. Efficient security and privacy measures should be implemented by cloud service providers to ensure the privacy, confidentiality, integrity, and availability of data services. However, cloud service providers have not been providing enough secure and reliable services to end users. Blockchain is a technology that is improving cloud computing. This revolutionary technology offers persuasive data integrity properties and is used to tackle security problems. This research presents a detailed analysis of privacy and security challenges in the cloud. We demonstrate the importance of security challenges in a case study in the context of smart campus security, which will encourage researchers to examine security issues in cloud computing in the future.
Changes in University Teacher’s Pedagogical Activity in the Context of Digitalization of Education

The purpose of this article is to consider the changes in university teacher’s pedagogical activity in the conditions of a rapid transformation of higher education and propose a theoretical and empirical substantiation of the importance of teacher’s understanding of the value-semantic attitude to pedagogical activity, which is not limited only to mastering digital competencies, but consists in mastering pedagogical knowledge that allows you to critically analyze the productivity of modern educational strategies and methods. The conducted research was based on the methodology of post-non-classical science, which implies the widespread use of qualitative research methods that reflect the convergence of explanatory and interpretative research approaches, the use of representations, opinions, attitudes, and values of the research participants as full data. The article describes the results of studying the university teachers’ attitude to changes, the value orientations of the ongoing changes, as well as the main difficulties faced by university teachers in the context of digitalization of education – the design of educational and training programs, modern educational and methodological complex, the organization of productive self-directed work and communication with students in the educational process. The main directions of overcoming the identified difficulties are given – the construction of flexible curricula within the framework of the Federal State Educational Standard of Higher Education, the development of the digital educational environment of the university and updating the content of the professional development programs for teaching staff. The conclusion is made about the increasing importance of pedagogical knowledge in teacher’s professional activity in the conditions of a modern university.
Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms

In today’s rapidly changing and highly competitive industrial environment, a new and emerging business model—fast fashion—has started a revolution in the apparel industry. Due to the lack of historical data, constantly changing fashion trends, and product demand uncertainty, accurate demand forecasting is an important and challenging task in the fashion industry. This study integrates k-means clustering (KM), extreme learning machines (ELMs), and support vector regression (SVR) to construct cluster-based KM-ELM and KM-SVR models for demand forecasting in the fashion industry using empirical demand data of physical and virtual channels of a case company to examine the applicability of proposed forecasting models. The research results showed that both the KM-ELM and KM-SVR models are superior to the simple ELM and SVR models. They have higher prediction accuracy, indicating that the integration of clustering analysis can help improve predictions. In addition, the KM-ELM model produces satisfactory results when performing demand forecasting on retailers both with and without physical stores. Compared with other prediction models, it can be the most suitable demand forecasting method for the fashion industry.
FlexPushdownDB

Modern cloud databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck: caching and computation pushdown. While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited. In this paper we present FlexPushdownDB (FPDB) , an OLAP cloud DBMS prototype that supports fine-grained hybrid query execution to combine the benefits of caching and computation pushdown in a storage-disaggregation architecture. We build a hybrid query executor based on a new concept called separable operators to combine the data from the cache and results from the pushdown processing. We also propose a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Our experimental evaluation on the Star Schema Benchmark shows that the hybrid execution outperforms both the conventional caching-only architecture and pushdown-only architecture by 2.2X. In the hybrid architecture, our experiments show that Weighted-LFU can outperform the baseline LFU by 37%.
The UK: The Post-Brexit, Ghost Election

In the context of the UK’s impending withdrawal from the European Union, we characterise the 2019 EU election in the UK as a ‘ghost campaign’, which did not have the attention of the major parties, let alone the voter. Such a backdrop may have invited parties to innovate or experiment with Facebook campaigning strategies, but the campaign was largely what one might expect in terms of the exploitation of the affordances of Facebook: cautious. Similarly, it followed the patterns of previous UK European Parliamentary elections by focusing largely on national and domestic politics with the parties offering persuasive communication intended to deliver them votes on election day. Perhaps unsurprisingly, Brexit dominated the topical focus of party Facebook posts.
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems

Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this work, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks with an integrated attention mechanism to learn the dependency between messages traversing the in-vehicle network. Post deployment in a vehicle, TENET employs a robust quantitative metric and classifier, together with the learned dependencies, to detect anomalous patterns. TENET is able to achieve an improvement of 32.70% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, and 48.14% lower inference time compared to the best performing prior works on automotive anomaly detection.
ScrapNet: An Efficient Approach to Trash Classification

As people have become more aware of their actions and how they affect their surroundings, they have realized the dire state of the environment.As a result, the recycling movement has gained momentum as a measure to save it.Contemporarily, the recycling industry has not seen a major shift and the problems that existed decades earlier persist.Trash classification is at the core of these problems, because if you can't classify it, obviously you can't recycle it.Manual classification often leads to misclassification as humans classify or judge things based on their experiences, knowledge, and not just absolute facts.Additionally, if the waste to be sorted is toxic, being in direct contact may be physically harmful to the people involved.Until a solution is found for this problem, the recycling industry won't be on par with the rise in recycling culture.Thus, this is the problem we have set out to solve; this paper proposes a Deep Learning model based on EfficientNet Architecture that can classify different kinds of trash with an accuracy high enough to make it a viable solution for the industry while using a comparatively lower number of parameters than existing methods.We achieved an accuracy of 98% on the TrashNet dataset, the standard data for Trash classification, and outperformed all existing models.Additionally, as no large dataset with a varied set of trash images was present, we created a new dataset of 8135 images by combining various datasets and standardized them, achieving a classification accuracy of 92.87% with EfficientNet B3.
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

Surface normal estimation from a single image is an important task in 3D scene understanding. In this paper, we address two limitations shared by the existing methods: the inability to estimate the aleatoric uncertainty and lack of detail in the prediction. The proposed network estimates the per-pixel surface normal probability distribution. We introduce a new parameterization for the distribution, such that its negative log-likelihood is the angular loss with learned attenuation. The expected value of the angular error is then used as a measure of the aleatoric uncertainty. We also present a novel decoder framework where pixel-wise multi-layer perceptrons are trained on a subset of pixels sampled based on the estimated uncertainty. The proposed uncertainty-guided sampling prevents the bias in training towards large planar surfaces and improves the quality of prediction, especially near object boundaries and on small structures. Experimental results show that the proposed method outperforms the state-of-the-art in ScanNet [4] and NYUv2 [33], and that the estimated uncertainty correlates well with the prediction error. Code is available at https://github.com/baegwangbin/surface_normal_uncertainty.