Reviewerly/exHarmony huggingface dataset:
Unravelling the Complexity of Blockchain and EU Competition Law

Blockchain1 is a technology that, similar to the World Wide Web (i.e., the Internet), was born by a desire to transact more freely, with less or no reliance on the State or third parties acting as intermediaries.2 Initially used in relation to Bitcoin,3 blockchain’s practical application has expanded significantly, and appears to be moving into the mainstream.4 Blockchain is believed to eventually become ubiquitous,5 particularly in light of the evolution of Artificial Intelligence, the Internet of Things, the ever growing importance of data, the need for secure storage, management and processing of large datasets, and the meteoric rise in the development and use of all things digital. It is against this background that the European Commission (the ‘Commission’) has launched a programme for the financing of blockchain-related projects.6 But what is blockchain, really? A blockchain is a type of distributed ledger technology (‘DLT’) that employs cryptography, mathematics, and algorithms to record and synchronise data in ‘chains of blocks’.7 Put simply, a blockchain is a database (or ledger), in which data are stored, shared, and synchronised across a distributed network of multiple nodes or computers,8 enabling parties that do not otherwise trust each other to transact on a peer-to-peer basis.
ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis

Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been successfully applied to Natural Language Processing (NLP), especially in sentiment analysis. NLP can execute numerous functions to achieve significant results through RNN and CNN. Likewise, previous research shows that RNN achieved meaningful results than CNN due to extracting long-term dependencies. Meanwhile, CNN has its advantage; it can extract high-level features using its local fixed-size context at the input level. However, integrating these advantages into one network is challenging because of overfitting in training. Another problem with such models is the consideration of all the features equally. To this end, we propose an attention-based sentiment analysis using CNN and two independent bidirectional RNN networks to address the problems mentioned above and improve sentiment knowledge. Firstly, we apply a preprocessor to enhance the data quality by correcting spelling mistakes and removing noisy content. Secondly, our model utilizes CNN with max-pooling to extract contextual features and reduce feature dimensionality. Thirdly, two independent bidirectional RNN, i.e ., Long Short-Term Memory and Gated Recurrent Unit are used to capture long-term dependencies. We also applied the attention mechanism to the RNN layer output to emphasize each word’s attention level. Furthermore, Gaussian Noise and Dropout as regularization are applied to avoid the overfitting problem. Finally, we verify the model’s robustness on four standard datasets. Compared with existing improvements on the most recent neural network models, the experiment results show that our model significantly outperformed the state-of-the-art models.
Collaborative Decision-Making: Concepts and Supporting Information and Communication Technology Tools and Systems

Collaboration means in substance that several entities such as humans, computers, robots, enterprises and so on jointly perform a certain task instead of working individually so that a better result could be obtained. Decision-making is a specific form of activity, commonly carried out by human agents, which is meant to eventually select a certain course of action which is expected to result in attaining a desired result. The chapter is meant to present a concise and balanced view of the basic concepts and main classes of supporting information and communication tools and systems regarding decision-making processes carried out by several collaborating human agents called participants. The reasons for collaboration are briefly explained followed by an exposure of collaboration application in the multi-participant decision-making settings. Having presented the classification of decision problems and decision-making units, the main phases of a specific multi-participant form of Herbert Simon’s decision process model are described followed by the presentation of two main forms of close and soft collaboration, namely consensus building and crowdsourcing, respectively. The need for technology support offered to collaborating participants is justified and two main classes of decision supporting systems, namely Decision support systems and the ever more largely used platforms, are addressed. A practical example of an open ended and evolving platform is presented. Open questions about the further role the information and communication tools in multi-participant decision-making processes are eventually formulated from two perspectives, digital humanism and dataism, respectively.
STA-TSN: Spatial-Temporal Attention Temporal Segment Network for action recognition in video

Most deep learning-based action recognition models focus only on short-term motions, so the model often causes misjudgments of actions that are combined by multiple processes, such as long jump, high jump, etc. The proposal of Temporal Segment Networks (TSN) enables the network to capture long-term information in the video, but ignores that some unrelated frames or areas in the video can also cause great interference to action recognition. To solve this problem, a soft attention mechanism is introduced in TSN and a Spatial-Temporal Attention Temporal Segment Networks (STA-TSN), which retains the ability to capture long-term information and enables the network to adaptively focus on key features in space and time, is proposed. First, a multi-scale spatial focus feature enhancement strategy is proposed to fuse original convolution features with multi-scale spatial focus features obtained through a soft attention mechanism with spatial pyramid pooling. Second, a deep learning-based key frames exploration module, which utilizes a soft attention mechanism based on Long-Short Term Memory (LSTM) to adaptively learn temporal attention weights, is designed. Third, a temporal-attention regularization is developed to guide our STA-TSN to better realize the exploration of key frames. Finally, the experimental results show that our proposed STA-TSN outperforms TSN in the four public datasets UCF101, HMDB51, JHMDB and THUMOS14, as well as achieves state-of-the-art results.
GREENPEG – exploration for pegmatite minerals to feed the energy transition: first steps towards the Green Stone Age

Abstract The GREENPEG project, which is funded by the European Commission Horizon 2020 ‘Climate action, environment, resource efficiency and raw materials’ programme, aims to develop multimethod exploration toolsets for the identification of European, buried, small-scale (0.01–5 million m 3 ) pegmatite ore deposits of the Nb–Y–F (NYF) and Li–Cs–Ta (LCT) chemical types. The project is being coordinated by the Natural History Museum of the University of Oslo and involves four exploration services/mining operators, one geological survey, one non-profit helix association of administration, industry and academia, two consulting companies and five academic institutions from eight European countries. The target raw materials are Li, high-purity quartz for silica and metallic Si, ceramic feldspar, rare earth elements, Ta, Be and Cs, which are naturally concentrated in granitic pegmatites. Silicon and Li are two of the most sought-after green technology metals as they are essential for photovoltaics and Li-ion batteries for electric cars, respectively. GREENPEG will change the focus of exploration strategies from large-volume towards small-volume, high-quality ores and overcome the lack of exploration technologies for pegmatite ore deposits by developing toolsets tailored to these ore types. This contribution focuses on the methods applied in the GREENPEG project and as such provides a potential pathway towards the ‘Green Stone Age’ from the perspective of pegmatite-sourced minerals.
An exploration into the impact of augmented reality on EFL learners’ Reading comprehension

This study investigated how Augmented Reality (AR) impacted EFL learners’ reading comprehension and attitudes toward utilizing AR. A mixed-method was used with a sample composed of 64 upper-intermediate EFL learners in a state university in Iran. Independent and paired sample t-test were used to investigate the experimental group’s reading comprehension and possible differences between groups’ reading comprehension after utilizing AR. The quantitative findings indicated that the experimental group showed a significantly higher reading comprehension level than the control group. The thematic analysis of the semi-structured interviews revealed that students enjoyed using augmented reality and preferred it to traditional reading comprehension methods. Furthermore, AR increased students’ interest in reading comprehension tasks and enhanced their willingness to use AR-based approaches in EFL classes.
Evaluation of underwater image enhancement algorithms based on Retinex and its implementation on embedded systems

The improvement of underwater imaging has advanced significantly due to its contribution to marine engineering and underwater exploration. This fact has been reflected in recent years with the proposal of numerous algorithms that improve the quality of underwater images. A benchmarking of three algorithms based on the Retinex models implemented on five high-performance embedded systems is presented herein. These algorithms are the Single Scale Retinex Model (SSR), Multi-Scale Retinex Model (MSR), and the Multi-Scale Retinex Model with Color Restoration (MSRCR). These algorithms perform the histogram equalization to distribute pixels, reduce the predominant color, perform color and contrast correction, and achieve an automatic white balance to improve illumination. This paper employs five edge devices such as Beagle Board, Odroid-XU4, Raspberry Pi 4, Jetson Nano, and Jetson TX2 to enhance underwater images and benchmark their performance. Four quality metrics without a reference image such as UIQM, UCIQUE, BRISQUE and Entropy are used to evaluate the quality of the enhanced underwater images. The MSRCR algorithm achieves the best quality results when it is implemented on Jetson TX2 embedded system. It has a difference of 0.46 s in the processing time of 147 × 196 pixels images concerning a high-performance personal computer (PC). Implementing these algorithms on embedded systems offers an excellent cost-benefit ratio versus a traditional PC, considering image quality metrics, precision, accuracy, energy consumption, price, lightweight, size, portability, and reliability. These findings hold great promise for unmanned and self-propelled underwater vehicles with artificial vision for exploration.
Vision-Language Transformer for Interpretable Pathology Visual Question Answering

Pathology visual question answering (PathVQA) attempts to answer a medical question posed by pathology images. Despite its great potential in healthcare, it is not widely adopted because it requires interactions on both the image (vision) and question (language) to generate an answer. Existing methods focused on treating vision and language features independently, which were unable to capture the high and low-level interactions that are required for VQA. Further, these methods failed to offer capabilities to interpret the retrieved answers, which are obscure to humans where the models’ interpretability to justify the retrieved answers has remained largely unexplored. Motivated by these limitations, we introduce a vision-language transformer that embeds vision (images) and language (questions) features for an interpretable PathVQA. We present an interpretable <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tra</u> nsformer-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> ath- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VQA</u> (TraP-VQA), where we embed transformers’ encoder layers with vision and language features extracted using pre-trained CNN and domain-specific language model (LM), respectively. A decoder layer is then embedded to upsample the encoded features for the final prediction for PathVQA. Our experiments showed that our TraP-VQA outperformed the state-of-the-art comparative methods with public PathVQA dataset. Our experiments validated the robustness of our model on another medical VQA dataset, and the ablation study demonstrated the capability of our integrated transformer-based vision-language model for PathVQA. Finally, we present the visualization results of both text and images, which explain the reason for a retrieved answer in PathVQA.
Secure Dynamic Groups Data Sharing with Modified Revocable Attribute-Based Encryption in Cloud

Cloud computing provides a flexible and convenient way for data sharing, which brings various benefits for both the society and individuals. But there exists a natural resistance for users to directly outsource the shared data to the cloud server since the data often contain valuable information. Although it causes many security issues, cloud service providers are not at the same level of trust as users. To preserve the privacy of data against non-trusted Cloud Service Provider (CSP) files, current solutions implement Cryptographic methods (for example, encryption methods) and deliver decryption keys only to authorized users. However, data sharing in the cloud among authorized users remains a difficult problem, especially when it comes to dynamic user groups. Most of the research on dynamic group data exchange has been done in the cloud with many algorithms, such as Attribute-Based Encryption (ABE), Ciphertext Attribute-Based Encryption (CP-ABE) to provide better security in dynamic cloud users with multiple authorities, but they still face challenges, either lack of performance or rely on a trusted server, and are not suitable for distribution with the problem of eliminating attributes. Thus, the Revocation user cannot get shared data before and after. To solve this in particular, we first suggest an effective Modified Revocable Attribute-Based Encryption (MR-ABE) system with the quality of ciphertext allocation by applying and integrating both Identity-Based Encryption (IBE) and CP-ABE techniques. It can provide confidential forward / backward of encrypted data by delivering user revocation attributes and updating encrypted text simultaneously. Next, we perform Fine-grained access control and data exchange for on-demand services with dynamic user groups on the cloud. Experimental data show that our proposed system is more efficient and scalable than the latest generation solutions.
Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics.
Reversible Data Hiding With Brightness Preserving Contrast Enhancement by Two-Dimensional Histogram Modification

Recently, contrast enhancement with reversible data hiding (CE-RDH) has been proposed for digital images to hide useful data into contrast-enhanced images. In existing schemes, one-dimensional (1D) or two-dimensional (2D) histogram is equalized during the process of CE-RDH so that an original image can be exactly recovered from its contrast-enhanced version. However, noticeable brightness change and color distortion may be introduced by applying these schemes, especially in the case of over enhancement. To preserve image quality, this paper presents a new 2D histogram based CE-RDH scheme by taking brightness preservation (BP) into account. In particular, the row or column of histogram bins with the maximum total height are chosen to be expanded at each time of histogram modification, while the row or column of bins to be expanded next are adaptively chosen according to the change of image brightness. Experimental results on three color image sets demonstrate efficacy and reversibility of the proposed scheme. Compared with the schemes using 1D histogram, image brightness can be preserved more finely by modifying the generated 2D histogram. Moreover, our proposed scheme preserves image color and brightness while achieving better image quality than the existing schemes.
FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been developed mainly on Convolutional Neural Networks (CNNs), and suffer severe degradation when applied to fully quantized vision transformers. In this work, we demonstrate that many of these difficulties arise because of serious inter-channel variation in LayerNorm inputs, and present, Power-of-Two Factor (PTF), a systematic method to reduce the performance degradation and inference complexity of fully quantized vision transformers. In addition, observing an extreme non-uniform distribution in attention maps, we propose Log-Int-Softmax (LIS) to sustain that and simplify inference by using 4-bit quantization and the BitShift operator. Comprehensive experiments on various transformer-based architectures and benchmarks show that our Fully Quantized Vision Transformer (FQ-ViT) outperforms previous works while even using lower bit-width on attention maps. For instance, we reach 84.89% top-1 accuracy with ViT-L on ImageNet and 50.8 mAP with Cascade Mask R-CNN (Swin-S) on COCO. To our knowledge, we are the first to achieve lossless accuracy degradation (~1%) on fully quantized vision transformers. The code is available at https://github.com/megvii-research/FQ-ViT.
Graph Attention Networks

The attention mechanism has been successfully used in many sequence-based tasks such as machine translation [Bahdanau et al., 2015, Gehring et al., 2017, Vaswani et al., 2017], machine reading [Cheng et al., 2016], and so on. Compared with GCN which treats all neighbors of a node equally, the attention mechanism could assign different attention score to each neighbor, thus identifying more important neighbors. It is intuitive to incorporate the attention mechanism into the propagation steps of Graph Neural Networks. In this chapter, we will talk about two variants: GAT and GAAN.
Fast Multiview Clustering With Spectral Embedding

Spectral clustering has been a hot topic in unsupervised learning owing to its remarkable clustering effectiveness and well-defined framework. Despite this, due to its high computation complexity, it is unable of handling large-scale or high-dimensional data, particularly multi-view large-scale data. To address this issue, in this paper, we propose a fast multi-view clustering algorithm with spectral embedding (FMCSE), which speeds up both the spectral embedding and spectral analysis stages of multi-view spectral clustering. Furthermore, unlike conventional spectral clustering, FMCSE can acquire all sample categories directly after optimization without extra k-means, which can significantly enhance efficiency. Moreover, we also provide a fast optimization strategy for solving the FMCSE model, which divides the optimization problem into three decoupled small-scale sub-problems that can be solved in a few iteration steps. Finally, extensive experiments on a variety of real-world datasets (including large-scale and high-dimensional datasets) show that, when compared to other state-of-the-art fast multi-view clustering baselines, FMCSE can maintain comparable or even better clustering effectiveness while significantly improving clustering efficiency.
A survey on artificial intelligence in histopathology image analysis

Abstract The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning‐based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field. This article is categorized under: Application Areas &gt; Health Care