Reviewerly/exHarmony huggingface dataset:
Enhancing VAEs for collaborative filtering

Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model's inability to learn deeper representations with more than one hidden layer for each network.
Blockchain technology for creative industries: Current state and research opportunities

Blockchain based NFTs (non-fungible tokens) and smart contracts provide creative industries with exciting opportunities. While they have created a great deal of frenzy in markets, in the frenzy there emerges real value for the industries. Traditionally, creators in the creative industries often need to rely on powerful intermediaries to distribute and profit from their creations. NFT and smart contracts provide creators much closer access to content consumers/buyers. Along this line of thought, this article provides marketing researchers with an overview of the unfolding adoption of NFTs and smart contracts in creative industries. We start by pointing out the market frictions and consequent “transaction costs” that creators face traditionally when distributing their creative content to consumers/buyers. Then, we present the basic ideas of smart contracts and NFTs, discussing how they can transform the market by reducing these transaction costs. Meanwhile, we point out limitations and challenges that creators, buyers, and marketplaces might face in the adoption of NFTs and smart contracts. Finally, we raise an abundance of unexplored research questions interesting to both marketing researchers and practitioners.
Two-level parameter cooperation-based population regeneration framework for differential evolution

Differential evolution (DE) has shown excellent performance in dealing with optimization problems in continuous space, but it still faces the problems of population stagnation and premature convergence. To address these difficulties and enhance the performance of existing DE algorithms, this paper proposes a two-level parameter cooperation-based population regeneration (TPPR) framework that can easily be integrated with various DE algorithms without increasing the time consumption. In the TPPR framework, whether an individual executes the regeneration operation depends on a specific cooperation strategy for a macroparameter and a microparameter, and the regeneration operation updates the positions of the selected individuals using a dynamic coordination strategy for the Cauchy and Gaussian distributions, that are constructed using those two parameters. Comparative experiments of ten representative DE algorithms with and without the TPPR framework on thirty functions with different dimensions from the IEEE CEC 2014 test platform and four real-life problems are conducted to verify the effectiveness of the TPPR framework. The comparative results based on the improvement rate and deterioration rate indicate that using the TPPR framework, the performance of the adopted DE algorithms can be significantly improved with a very high probability, and the possibility of deterioration is very low.
Modeling and analysis of port supply chain system based on Fabric blockchain

With the development of international trade, the core position of port transportation progressively emerges. However, most current port supply chain systems adopt a centralized data management mode. Each operator in the supply chain has its platform and data set, which is prone to form information islands and can create trust issues, resulting in the inefficient overall operation of the supply chain. To improve the efficiency of the supply chain, a port supply chain system based on Fabric blockchain (Fabric-PSChain) is designed and implemented. In the proposed system, four smart contracts are developed by taking advantage of the blockchain’s decentralized, tamper-free, distributed consensus, combined with the Role-Based Access Control Policy (RBACP), realizing the functions of access control policy formulation, enterprise list query, cargo order uploading, and order query. At the same time, the regulator node is added to the Fabric-PSchain system to enhance the credibility and security of data and meet regulatory compliance requirements. Experimental results show that the Fabric-PSChain system has vital confidentiality and data sharing that can speed up the operation of the port supply chain business and has significant application value for the shipping logistics industry. • A port supply chain system based on Fabric Blockchain is designed and implemented. • Four smart contracts are designed taking advantage of Blockchain in such a system. • Access control policy, corporation list query, and cargo order query are realized. • A regulator node is designed to tackle the credibility and security of data and requirements.
Platform Utilising Blockchain Technology for eLearning and Online Education for Open Sharing of Academic Proficiency and Progress Records

This is a preliminary study of the present status of blockchain adoption in the educational sector. The aim of this study is to increase public awareness of blockchain technology whilst emphasising the advantages, risks, difficulties, and hazards associated with its use in educational contexts. This article further analyses the fundamental technological principles and application characteristics of blockchain technology and proposes a blockchain-based solution to the issues associated with online education. Through the use of blockchain technology, a student's accomplishments may be monitored more precisely whilst remaining anonymous. Additionally, this ensures that companies have trustworthy digital certificates that safeguard their intellectual property whilst allowing for learning via the use of intelligent contracts. According to the findings, blockchain technology has tremendous potential for improving education in general, and online education in particular. The study's findings shall inspire the creation of a decentralised online education system.
SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.
Aphid–Ant Mutualism: A novel nature-inspired​ metaheuristic algorithm for solving optimization problems

Swarm intelligence algorithms, which are developed for solving complex optimization problems designed by focusing on simulating the social behavior of one species of simple animals. However, simple animals utilize cooperation to work together that result in more complex and smarter behaviors. This paper proposes a novel population-based optimization paradigm for solving NP-hard problems called “Aphid–Ant Mutualism (AAM)” which is inspired by a unique relationship between aphids and ants’ species. This relationship is called ‘mutualism’. Despite the previous studies that the social behaviors of aphids and ants were simulated, AAM models mutual interaction among aphids and ants in nature. Thus, AAM has new features by incorporating heterogeneous individuals consisting of aphids and ants that live in various colonies together and have different decentralized learning behaviors and objectives. Inspired by nature, colony-based information exchange and using different search strategies including focusing on the individual’s personal knowledge, learning from other colony’s members and information sharing with adjacent colonies are used. This mutualism leads to converging to the global optimum and avoids premature convergence. Performance of AAM is assessed using statistical evaluation, convergence analysis, and a non-parametric Wilcoxon rank-sum test with a 5% significance degree on forty-one benchmarks selected from well-known functions of recent studies and more challenging benchmark functions called CEC 2014, CEC 2017 and also CEC-C06 2019 test suite. Statistical results and comparisons with other meta-heuristic algorithms demonstrate that the AAM algorithm provides promising and competitive outcomes. Furthermore, it can produce more accurate solutions with a faster convergence rate to the global optima.
Using U-Net network for efficient brain tumor segmentation in MRI images

Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net. Apart from providing real-time segmentation of MRI scans, the proposed architecture does not need large amount of data to train the proposed lightweight U-Net. Moreover, no additional data augmentation step is required. The lightweight U-Net shows very promising results on BITE dataset and it achieves a mean intersection-over-union (IoU) of 89% while outperforming the standard benchmark algorithms. Additionally, this work demonstrates an effective use of the three perspective planes, instead of the original three-dimensional volumetric images, for simplified brain tumor segmentation.
Robust Botnet DGA Detection: Blending XAI and OSINT for Cyber Threat Intelligence Sharing

We investigated 12 years DNS query logs of our campus network and identified phenomena of malicious botnet domain generation algorithm (DGA) traffic. DGA-based botnets are difficult to detect using cyber threat intelligence (CTI) systems based on blocklists. Artificial intelligence (AI)/machine learning (ML)-based CTI systems are required. This study (1) proposed a model to detect DGA-based traffic based on statistical features with datasets comprising 55 DGA families, (2) discussed how CTI can be expanded with computable CTI paradigm, and (3) described how to improve the explainability of the model outputs by blending explainable AI (XAI) and open-source intelligence (OSINT) for trust problems, an antidote for skepticism to the shared models and preventing automation bias. We define the XAI-OSINT blending as aggregations of OSINT for AI/ML model outcome validation. Experimental results show the effectiveness of our models (96.3&#x0025; accuracy). Our random forest model provides better robustness against three state-of-the-art DGA adversarial attacks (CharBot, DeepDGA, MaskDGA) compared with character-based deep learning models (Endgame, CMU, NYU, MIT). We demonstrate the sharing mechanism and confirm that the XAI-OSINT blending improves trust for CTI sharing as evidence to validate our proposed computable CTI paradigm to assist security analysts in security operations centers using an automated, explainable OSINT approach (for second opinion). Therefore, the computable CTI reduces manual intervention in critical cybersecurity decision-making.
Interpretable fuzzy clustering using unsupervised fuzzy decision trees

In clustering process, fuzzy partition performs better than hard partition when the boundaries between clusters are vague. Whereas, traditional fuzzy clustering algorithms produce less interpretable results, limiting their application in security, privacy, and ethics fields. To that end, this paper proposes an interpretable fuzzy clustering algorithm—fuzzy decision tree-based clustering which combines the flexibility of fuzzy partition with the interpretability of the decision tree. We constructed an unsupervised multi-way fuzzy decision tree to achieve the interpretability of clustering, in which each cluster is determined by one or several paths from the root to leaf nodes. The proposed algorithm comprises three main modules: feature and cutting point-selection, node fuzzy splitting, and cluster merging. The first two modules are repeated to generate an initial unsupervised decision tree, and the final module is designed to combine similar leaf nodes to form the final compact clustering model. Our algorithm optimizes an internal clustering validation metric to automatically determine the number of clusters without their initial positions. The synthetic and benchmark datasets were used to test the performance of the proposed algorithm. Furthermore, we provided two examples demonstrating its interest in solving practical problems.
The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users

There is an exponential growth of the use of AI applications in organisations. Due to the machine learning capability of artificial intelligence (AI) applications, it is critical that such systems are used continuously in order to generate rich use data that allow them to learn, evolve and mature into a better fit for their user and organisational context. This research focuses on the actual use of conversational AI, in particular AI chatbot, as one type of workplace AI application to answer the research question: how do employees experience the use of an AI chatbot in their day-to-day work? Through a qualitative case study of a large international organisation and by performing an inductive analysis, the research uncovers the different ways in which users appropriate the AI chatbot and identifies two key dimensions that determine their type of use: the dominant mode of interaction and the understanding of the AI chatbot technology. Based on these dimensions, a taxonomy of users is presented, which classifies users of AI chatbots into four types: early quitters, pragmatics, progressives, and persistents. The findings contribute to the understanding of how conversational AI, particularly AI chatbots, is used in organisations and pave the way for further research in this regard. The implications for practice are also discussed.
Blockchain smart contracts for supply chain finance: Mapping the innovation potential in Australia-China beef supply chains

This paper explores and demonstrates the innovation potential of blockchain and smart contracts for supply chain finance (SCF) based on cross-border beef supply chains from Australia to China. Our study adopts mechanism design and design-driven activities, and specifically employs the Agents Events Data (AED) process mapping method, which is a hybrid approach that combines Business Process Redesign (BPR) and the Resources Events Assets (REA) accounting model. The AED method consists of three sequential stages: (1) map supply chain's present state or condition; (2) introduce blockchain and smart contracts to improve supply chain processes and “traditional” SCF models; and (3) evaluate the technology and innovation impact and create new models for SCF innovation. This study identified examples of how financial risks can be mitigated or reduced with blockchain and smart contracts; and, where the credit financing itself could be fundamentally transformed. Based on this analysis, the paper's contribution is twofold: First, it proposes the group buying business model as a basis of whole-of-supply-chain finance promising new areas for SCF models that can reduce financial cost and improve cash flow performance due to greater buyer-led financing certainty and the direct involvement of buy-side demand for supply-side improvements. Second, the study demonstrates the utility of the AED process mapping as a holistic framework with fine granular levels for future empirical and fieldwork and opens up new avenues for research.
Align and Tell: Boosting Text-Video Retrieval With Local Alignment and Fine-Grained Supervision

Text-video retrieval is one of the basic tasks for multimodal research and has been widely harnessed in many real-world systems. Most existing approaches directly compare the global representation between videos and text descriptions and utilize the global contrastive loss to train the model. These designs overlook the local alignment and the word-level supervision signal. In this paper, we propose a new framework, called Align and Tell, for text-video retrieval. Compared to the previous work, our framework contains additional modules, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , two transformer decoders for local alignment and one captioning head to enhance the representation learning. First, we introduce a set of learnable queries to interact with both textual representations and video representations and project them to a fixed number of local features. After that, local contrastive learning is performed to complement the global comparison. Moreover, we design a video captioning head to provide additional supervision signals during training. This word-level supervision can enhance the visual presentation and alleviate the cross-modal gap. The captioning head can be removed during inference and does not introduce extra computational costs. Extensive empirical results demonstrate that our Align and Tell model can achieve state-of-the-art performance on four text-video retrieval datasets, including MSR-VTT, MSVD, LSMDC, and ActivityNet-Captions.
A new application of measurement of alternatives and ranking according to compromise solution (MARCOS) in solar site location for electricity and hydrogen production: A case study in the southern climate of Iran

In recent years, declining fossil fuel reserves and increasing environmental concerns led to higher utilization of renewable energy source (RES). One of the RES is Solar energy which is abundantly found in different areas of the globe, particularly in Iran. The aim of this research is to select a suitable site for constructing a solar power plant to generate electricity-hydrogen in southern Iran, Kerman province. For this purpose, a new hybrid Multi criteria decision making method is used. The Stepwise Weight Assessment Ratio Analysis (SWARA)method is used to weigh the criteria and the Measurement of alternatives and ranking according to Compromise solution (MARCOS)method is used to rank cities. The results show that Sirjan is the most proper site for developing solar projects. Also, the technical and economic evaluation of a one MW power plant in this city is examined. The results show the amount of electricity generation is equal to 2064.2 MWh/yr and Levelized Cost of Energy equals to 6.31cents/kWh. In addition, in four scenarios, the size of the electrolyzer is optimized based on the minimum cost and maximum amount of hydrogen production. Also, the hydrogen production rate and Levelized Cost of Hydrogen equals to 30.77 ton/yr and 2.54$/kg, respectively. • The hybrid SWARA-MARCOS method in the field of energy was presented. • Pearson coefficient in MARCOS method compared to other MCDM methods was over 80%. • Sirjan was the best Kerman's city to develop solar projects from sustainable aspects. • The size and number of electrolyzers for producing hydrogen was optimized. • For the proposed system, the LCOE and LCOH were 6.31cents/kWh and 2.54$/kg.
LAE-Net: A locally-adaptive embedding network for low-light image enhancement

In the low-light enhancement task, one of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation and color fidelity. In natural scenes, the multi-distribution of frequency and illumination characteristics in the spatial domain makes the balance more difficult. To solve this problem, we propose a Locally-Adaptive Embedding Network, namely LAE-Net, to realize high-quality low-light image enhancement with locally-adaptive kernel selection and feature adaptation for multi-distribution issues. Specifically, for the frequency multi-distribution, we rethink the spatial-frequency characteristic of human eyes, experimentally explore the relationship among the receptive field size, the image spatial frequency and the light enhancement properties, and propose an Entropy-Inspired Kernel-Selection Convolution, where each neuron can adaptively adjust the receptive field size according to its spatial frequency characterized by information entropy. For the illumination multi-distribution, we propose an Illumination Attentive Transfer subnet, where the neurons can simultaneously sense global consistency and local details, and accordingly hint where to focus the efforts on, thereby adjusting the refined features. Extensive experiments with ablation analysis show the effectiveness of our method and the proposed method outperforms many related state-of-the-art techniques on four benchmark datasets: MEF, LIME, NPE and DICM.
Single image super-resolution based on directional variance attention network

Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet.