Reviewerly/exHarmony huggingface dataset:
A Survey on the Usage of Blockchain Technology for Cyber-Threats in the Context of Industry 4.0

A systematic review of the literature is presented related to the usage of blockchain technology (BCT) for cyber-threats in the context of Industry 4.0. BCT plays a crucial role in creating smart factories and it is recognized as a core technology that triggers a disruptive revolution in Industry 4.0. Beyond security, authentication, asset tracking and the exchange of smart contracts, BCTs allow terminals to exchange information according to mutually agreed rules within a secured manner. Consequently, BCT can play a crucial role in industrial sustainability by preserving the assets and the environment and by enhancing the quality of life of citizens. In this work, a classification of the most important cyber-attacks that occurred in the last decade in Industry 4.0 is proposed based on four classes. The latter classes cover scanning, local to remote, power of root and denial of service (DoS). BCT is also defined and various types belong to BCT are introduced and highlighted. Likewise, BCT protocols and implementations are discussed as well. BCT implementation includes linear structure and directed acyclic graph (DAG) technology. Then, a comparative study of the most relevant works based on BCT in Industry 4.0 is conducted in terms of confidentiality, integrity, availability, privacy and multifactor authentication features. Our review shows that the integration of BCT in industry can ensure data confidentiality and integrity and should be enforced to preserve data availability and privacy. Future research directions towards enforcing BCT in the industrial field by considering machine learning, 5G/6G mobile systems and new emergent technologies are presented.
Digital image steganography and steganalysis: A journey of the past three decades

Abstract Steganography is the science and art of covert communication. Conversely, steganalysis is the study of uncovering the steganographic process. The evolution of steganography has been paralleled by the development of steganalysis. In this game of hide and seek, the two player’s steganography and steganalysis always want to break the other down. Over the past three decades, research has produced a plethora of remarkable image steganography techniques (ISTs). The major challenge for most of these ISTs is to achieve a fair balance between the metrics such as high hiding capacity (HC), better imperceptibility, and improved security. This study aims to present an exhaustive scrutiny of various ISTs from the classical to recent developments in the spatial domain, with respect to various image steganographic metrics. Further, the current status, recent developments, open challenges, and promising directions in this field are also highlighted.
Mediating Role of Knowledge Management in the Relationship between Organizational Learning and Sustainable Organizational Performance

Organizations operating in the intensive knowledge-based sector seek efficient management approaches and sustainable development practices to perform efficiently in the dynamic business environment. Knowledge management practice and organizational learning are significant factors in order to achieve sustainable organizational performance in a rapidly changing business environment. Based on the scientific literature analysis, there is still a lack of evidence related to the mediating role of the whole knowledge management cycle, including the five knowledge management processes (knowledge acquisition, creation, storage, sharing, and application) in the relationship between organizational learning and sustainable organizational performance for organizations operating in intensive knowledge-based sectors. This study aimed to examine the impact of the whole knowledge management cycle on the relationship between organizational learning and sustainable organizational performance in intensive knowledge-based sectors, specifically the audit and consulting companies in the Middle East region. Systematic scientific literature analysis, expert evaluation (structured questionnaire), and structural equation modeling (SEM) technique were used to develop and verify the research model. Data was collected through a structured questionnaire distributed among auditing experts working in a knowledge-based sector—audit and consulting companies in the Middle East region. The research results supported the hypotheses stating that organizational learning positively affects knowledge acquisition, storage, sharing, application processes, and sustainable organizational performance. However, the results verified that organizational learning has an insignificant impact on the Middle Eastern audit and consulting companies’ knowledge creation process.
Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching

Knowledge distillation extracts general knowledge from a pretrained teacher network and provides guidance to a target student network. Most studies manually tie intermediate features of the teacher and student, and transfer knowledge through predefined links. However, manual selection often constructs ineffective links that limit the improvement from the distillation. There has been an attempt to address the problem, but it is still challenging to identify effective links under practical scenarios. In this paper, we introduce an effective and efficient feature distillation method utilizing all the feature levels of the teacher without manually selecting the links. Specifically, our method utilizes an attention based meta network that learns relative similarities between features, and applies identified similarities to control distillation intensities of all possible pairs. As a result, our method determines competent links more efficiently than the previous approach and provides better performance on model compression and transfer learning tasks. Further qualitative analyses and ablative studies describe how our method contributes to better distillation.
Innovative blockchain-based farming marketplace and smart contract performance evaluation

E-Agriculture, or Smart Farming, refers to the design, development, and application of innovative methods to use modern information and communication technologies (ICTs), such as the Internet of Things (IoT) and machine learning, to move towards more sustainable agricultural and farming practices. The integration of blockchain technology in farming is gaining attention for its potential to migrate from the centralized and monopolistic model that shapes today’s food value chain. This paper highlights the fact that most of today’s blockchain-based farming frameworks focus on food tracking and traceability. Only rarely does research focus on the design of digital marketplaces to support the trading of agricultural goods between farmers and potentially interested third party stakeholders; equally rarely are performance evaluations performed for the proposed frameworks. The latter is where this paper contributes the most by, not only proposing a novel blockchain-based farming marketplace platform (called “FarMarketplace”), but also a comprehensive methodology to help software solution integrators to better understand and measure how a given configuration setting of such a platform can influence the overall quality of service performance in the long run.
An intelligent hybrid wavelet-adversarial deep model for accurate prediction of solar power generation

A novel intelligent hybrid model is proposed in this paper for accurate prediction and modeling of solar power plants using the wavelet transform package (WTP) and generative adversarial networks (GANs). In the proposed model, the wavelet transform is deployed for decomposing the solar energy signal into the sub-harmonics followed by the statistical feature selection analyses. The GAN model as a deep learning approach is proposed for learning each sub-frequency and predicting the future of the solar energy in the short-time window. Due to the high complexity of the solar irradiance data when training, an evolutionary algorithm based on dragonfly algorithm (DA) is suggested to train the generative and discriminator networks in the GAN. Moreover, a three-phase adaptive modification is suggested to enhance the search capabilities of the DA optimization. The efficiency and appropriate performance of the proposed model is examined and compared with the most successful models such as artificial neural networks (ANNs), support vector machine (SVM), time series, auto-regressive moving average (ARMA), and original GAN on several benchmarks for varied forecast time horizons. The simulation results on the datasets of two regions show the mean absolute percentage error (MAPE) of 0.0282 and 0.0262, when 1-pace forecast horizon, for the two regions which increase up to 0.0531 and 0.0631 for 6-pace forecast horizon. Moreover, the root mean absolute error (RMSE) is 0.0473 and 0.0479 for the two regions at 1-pace forecast horizon which increased up to 0.0895 and 0.0946 for 6-pace forecast horizon. These results show the more precise performance of the proposed forecast deep learning as well as the more optimal performance of the modified DA over the other algorithms shown in the results.
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval

Multimodal pre-training has propelled great advancement in vision-and-language research.These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computation cost mainly from cross-modal attention in Transformer architecture.When applied to reallife applications, such latency and computation demand severely deter the practical use of pre-trained models.In this paper, we study Image-text retrieval (ITR), the most mature scenario of V+L application, which has been widely studied even prior to the emergence of recent pre-trained models.We propose a simple yet highly effective approach, LightningDOT that accelerates the inference time of ITR by thousands of times, without sacrificing accuracy.LightningDOT removes the time-consuming cross-modal attention by pre-training on three novel learning objectives, extracting feature indexes offline, and employing instant dot-product matching with further re-ranking, which significantly speeds up retrieval process.In fact, Light-ningDOT achieves new state of the art across multiple ITR benchmarks such as Flickr30k, COCO and Multi30K, outperforming existing pre-trained models that consume 1000× magnitude of computational hours. 1 * Equal Contribution. 1 Code and pre-training checkpoints
Efficient Attentions for Long Document Summarization

The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization.In this paper, we propose HEPOS, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source.We further conduct a systematic study of existing efficient self-attentions.Combined with HEPOS, we are able to process ten times more tokens than existing models that use full attentions.For evaluation, we present a new dataset, GOVREPORT, with significantly longer documents and summaries.Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed.Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.
Automated Concatenation of Embeddings for Structured Prediction

Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.Recent work found that better word representations can be obtained by concatenating different types of embeddings.However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the everincreasing number of embedding types makes it a more difficult problem.In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search.Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward.We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model, which is fed with the sampled concatenation as input and trained on a task dataset.Empirical results on 6 tasks and 21 datasets show that our approach outperforms strong baselines and achieves state-of-the-art performance with fine-tuned embeddings in all the evaluations.
Gender Bias in Machine Translation

Abstract Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, studies of gender bias in MT still lack cohesion. This advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.
Block Storage Optimization and Parallel Data Processing and Analysis of Product Big Data Based on the Hadoop Platform

The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood products. We reviewed various storage and retrieval technologies for the big data resources. A block storage layout optimization method based on the Hadoop platform and a parallel data processing and analysis method based on the MapReduce model are proposed. A multireplica consistent hashing algorithm based on data correlation and spatial and temporal properties is used in the parallel data processing and analysis method. The data distribution strategy and block size adjustment are studied based on the Hadoop platform. A multidata source parallel join query algorithm and a multi-channel data fusion feature extraction algorithm based on data-optimized storage are designed for the big data resources of seafood products according to the MapReduce parallel frame work. Practical verification shows that the storage optimization and data-retrieval methods provide supports for constructing a big data resource-management platform for seafood products and realize efficient organization and management of the big data resources of seafood products. The execution time of multidata source parallel retrieval is only 32% of the time of the standard Hadoop scheme, and the execution time of the multichannel data fusion feature extraction algorithm is only 35% of the time of the standard Hadoop scheme.
Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems

Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions are vulnerable to poisoning attacks from malicious clients and servers. In this paper, we aim to mitigate the impact of the central server and malicious clients by designing a Privacy-preserving Byzantine-robust Federated Learning (PBFL) scheme based on blockchain. Specifically, we use cosine similarity to judge the malicious gradients uploaded by malicious clients. Then, we adopt fully homomorphic encryption to provide secure aggregation. Finally, we use blockchain system to facilitate transparent processes and implementation of regulations. Our formal analysis proves that our scheme achieves convergence and provides privacy protection. Our extensive experiments on different datasets demonstrate that our scheme is robust and efficient. Even if the root dataset is small, our scheme can achieve the same efficiency as FedSGD.
The More, the Merrier: Combining Heuristic Estimators for Satisficing Planning

We empirically examine several ways of exploiting the information of multiple heuristics in a satisficing best-first search algorithm, comparing their performance in terms of coverage, plan quality, speed, and search guidance. Our results indicate that using multiple heuristics for satisficing search is indeed useful. Among the combination methods we consider, the best results are obtained by the alternation method of the "Fast Diagonally Downward" planner.
Selecting Quality Twitter Content for Events

Social media sites such as Twitter contain large amounts of user contributed messages for a wide variety of real-world events. While some of these "event messages" might contain interesting and useful information (e.g., event time, location, participants, opinions), others might provide little value (e.g., using heavy slang, incomprehensible language) to people interested in learning about an event. Techniques for effective selection of quality event content may therefore help improve applications such as event browsing and search.In this paper, we explore approaches for finding representative messages among a set of Twitter messages that correspond to the same event, with the goal of identifying high quality, relevant messages that provide useful event information. We evaluate our approaches using a large-scale dataset of Twitter messages, and show that we can automatically select event messages that are both relevant and useful.
Ropossum: An Authoring Tool for Designing, Optimizing and Solving Cut the Rope Levels

We present a demonstration of Ropossum, an authoring tool for the generation and testing of levels of the physics-based game, Cut the Rope. Ropossum integrates many features: (1) automatic design of complete solvable content, (2) incorporation of designer’s input through the creation of complete or partial designs, (3) automatic check for playability and (4) optimization of a given design based on playability. The system includes a physics engine to simulate the game and an evolutionary framework to evolve content as well as an AI reasoning agent to check for playability. The system is optimised to allow on-line feedback and realtime interaction.
ICBS: The Improved Conflict-Based Search Algorithm for Multi-Agent Pathfinding

Conflict-Based Search (CBS) and its generalization, Meta-Agent CBS are amongst the strongest newly introduced algorithms for Multi-Agent Path Finding. This paper introduces ICBS, an improved version of CBS. ICBS incorporates three orthogonal improvements to CBS which are systematically described and studied. Experimental results show that each of these improvements reduces the runtime over basic CBS by up to 20x in many cases. When all three improvements are combined, an even larger improvement is achieved, producing state-ofthe art results for a number of domains.
Optimizing quantum optimization algorithms via faster quantum gradient computation

We consider a generic framework of optimization algorithms based on gradient descent. We develop a quantum algorithm that computes the gradient of a multi-variate real-valued function $f:\mathbb{R}^d\rightarrow \mathbb{R}$ by evaluating it at only a logarithmic number of points in superposition. Our algorithm is an improved version of Stephen Jordan's gradient computation algorithm, providing an approximation of the gradient $\nabla f$ with quadratically better dependence on the evaluation accuracy of $f$, for an important class of smooth functions. Furthermore, we show that most objective functions arising from quantum optimization procedures satisfy the necessary smoothness conditions, hence our algorithm provides a quadratic improvement in the complexity of computing their gradient. We also show that in a continuous phase-query model, our gradient computation algorithm has optimal query complexity up to poly-logarithmic factors, for a particular class of smooth functions. Moreover, we show that for low-degree multivariate polynomials our algorithm can provide exponential speedups compared to Jordan's algorithm in terms of the dimension $d$. One of the technical challenges in applying our gradient computation procedure for quantum optimization problems is the need to convert between a probability oracle (which is common in quantum optimization procedures) and a phase oracle (which is common in quantum algorithms) of the objective function $f$. We provide efficient subroutines to perform this delicate interconversion between the two types of oracles incurring only a logarithmic overhead, which might be of independent interest. Finally, using these tools we improve the runtime of prior approaches for training quantum auto-encoders, variational quantum eigensolvers (VQE), and quantum approximate optimization algorithms (QAOA).
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift

In this paper we revisit the method of off-policy corrections for reinforcement learning (COP-TD) pioneered by Hallak et al. (2017). Under this method, online updates to the value function are reweighted to avoid divergence issues typical of off-policy learning. While Hallak et al.’s solution is appealing, it cannot easily be transferred to nonlinear function approximation. First, it requires a projection step onto the probability simplex; second, even though the operator describing the expected behavior of the off-policy learning algorithm is convergent, it is not known to be a contraction mapping, and hence, may be more unstable in practice. We address these two issues by introducing a discount factor into COP-TD. We analyze the behavior of discounted COP-TD and find it better behaved from a theoretical perspective. We also propose an alternative soft normalization penalty that can be minimized online and obviates the need for an explicit projection step. We complement our analysis with an empirical evaluation of the two techniques in an off-policy setting on the game Pong from the Atari domain where we find discounted COP-TD to be better behaved in practice than the soft normalization penalty. Finally, we perform a more extensive evaluation of discounted COP-TD in 5 games of the Atari domain, where we find performance gains for our approach.
Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network

In this work, we propose a novel method named Weighted Channel Dropout (WCD) for the regularization of deep Convolutional Neural Network (CNN). Different from Dropout which randomly selects the neurons to set to zero in the fully-connected layers, WCD operates on the channels in the stack of convolutional layers. Specifically, WCD consists of two steps, i.e., Rating Channels and Selecting Channels, and three modules, i.e., Global Average Pooling, Weighted Random Selection and Random Number Generator. It filters the channels according to their activation status and can be plugged into any two consecutive layers, which unifies the original Dropout and Channel-Wise Dropout. WCD is totally parameter-free and deployed only in training phase with very slight computation cost. The network in test phase remains unchanged and thus the inference cost is not added at all. Besides, when combining with the existing networks, it requires no re-pretraining on ImageNet and thus is well-suited for the application on small datasets. Finally, WCD with VGGNet-16, ResNet-101, Inception-V3 are experimentally evaluated on multiple datasets. The extensive results demonstrate that WCD can bring consistent improvements over the baselines.