Reviewerly/exHarmony huggingface dataset:
Leveraging Blockchain Technology in Sustainable Supply Chain Management and Logistics

Traditional supply chain management (SCM) has some drawbacks concerning real-time and secure data about goods in transit. These require transparency to establish trust among various entities involved. Currently, it is being managed by third-party centralized controllers which make data more vulnerable. These disadvantages can be overcome by the adoption of blockchain in SCM. Blockchain is a decentralized ledger for storing, recording, managing, and transmitting data in a peer-to-peer (P2P) network. In such protocols, immutable cryptographic signatures named as hash are used to record transactions. It records information in such a manner that makes it completely impossible to alter, change, cheat or hack the system. Verification of transactions and consensus algorithms maintain data integrity and security. This paper works toward providing a brief survey on recent advances in SCM and comprehensively shows different ways of integrating blockchain technology in SCM according to different use cases. This paper comprehensively presents the evolution of blockchain technology and its current state. It provides a summary of different types of blockchain and the internal architecture of a standard blockchain protocol. The paper encapsulates some standard consensus methods such as proof of stake and proof of work. Further, the work summarizes various challenges associated with inculcating blockchain and highlights the major future research scopes in this context.
Protection measurements of computer network information security for big data

The great revolution in computer and communications networks expansion has a significant impact on the advancement of big data technology which causes various industries evolution. The use of this technology achieved impressive success and great results. Information security is closely related to the impact of big data application on a computer network. However, the security of information and big data on a computer network is affected by various external factors, which lead to information leakage and thus leads to large economic losses. To solve the above-mentioned problems, one of the most important things is to increase people's consciousness of protecting the security of computer network information from all angles and to develop preventive plans based on the real situation, mainly to assure the security and accuracy of the network information.
Doing Business and capital flight: role of financial development

Purpose The purpose of this paper is to investigate the effect of financial development on the Doing Business and capital flight contagion. And further, this study determines the threshold beyond which financial development reduces capital flight. Design/methodology/approach A two-step system generalized methods of moment empirical model with linear interaction between Doing Business and financial development was estimated. This study used data on 26 countries over 12 years (2004–2015). Findings The main results indicated that, although Doing Business had a significant positive effect on capital flight, the interactive term had a significant adverse effect on capital flight. This outcome suggests that to reduce capital flight, a well-reformed and efficient business environment should be embedded with an efficient, stable and well-developed financial sector. In addition, the authors found only South Africa has a robust financial framework beyond the threshold of 0.383, whereas Congo, Rep., Rwanda, Malawi, Sierra Leone and Congo, Dem. Rep. had the weakest financial system and sector in Sub-Saharan Africa. Research limitations/implications This study recommends that policymakers should initiate policies that would enhance financial development. Originality/value This study’s main contributions are that the authors estimated the threshold beyond which financial development helps the business environment reduce the rate of capital flight. Further, the authors have shown that financial development is a catalyst to propel the deterioration powers of the business environment against capital flight. Also, the authors have estimated the long-run effect of the variables of interest on capital flight.
Empowering the Human as the Fitness Function in Search-Based Model-Driven Engineering

In Search-Based Software Engineering, more than 100 works have involved the human in the search process to obtain better results. However, the case where the human completely replaces the fitness function remains neglected. There is a good reason for that; no matter how intelligent the human is, humans cannot assess millions of candidate solutions as heuristics do. In this work, we study the influence of using the Human as the Fitness Function (HaFF) on the quality of the results. To do that, we focus on Search-Based Model-Driven Engineering (SBMDE) because inspecting models should require less human effort than inspecting code thanks to the abstraction of models. Therefore, we analyze the impact of HaFF in a real-world industrial case study of feature location in models. Furthermore, we also consider a reformulation operation (replacement) in the evaluation because a recent work reported that this operation significantly reduces the number of iterations required in comparison to the widespread crossover and mutation operations. The combination of HaFF and the reformulation operation (HaFF_R) improves the results of the best baseline by 0.15% in recall and 14.26% in precision. Analyzing the results, we learned how to better leverage HaFF_R, which increased the improvement with regard to the best baseline to 1.15% in recall and 20.05% in precision. HaFF_R significantly improves precision because humans are immune to the main limitations of the baselines: vocabulary mismatch and tacit knowledge. A focus group confirmed the acceptance of HaFF. These results are relevant for SBMDE because feature location is one of the main activities performed during maintenance and evolution. Our results, and what we learned from them, can also motivate and help other researchers to explore the benefits of HaFF. In fact, we provide a guideline that further discusses how to apply HaFF to other software engineering problems.
Image Caption Generation and Comprehensive Comparison of Image Encoders

Image caption generation is a stimulating multimodal task. Substantial advancements have been made in thefield of deep learning notably in computer vision and natural language processing. Yet, human-generated captions are still considered better, which makes it a challenging application for interactive machine learning. In this paper, we aim to compare different transfer learning techniques and develop a novel architecture to improve image captioning accuracy. We compute image feature vectors using different state-of-the-art transferlearning models which are fed into an Encoder-Decoder network based on Stacked LSTMs with soft attention,along with embedded text to generate high accuracy captions. We have compared these models on severalbenchmark datasets based on different evaluation metrics like BLEU and METEOR.
Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC

This work presents a mixed-signal, reservoir-computing neural network (RC-NN) for at-home, real-time health monitoring using intelligent wearable device. The proposed technique is demonstrated on stress detection from electrocardiogram (ECG) signal, and heart diseases detection using a fusion artificial intelligence (AI) model that combines demographic and physiological information. The RC-NN uses a static, random reservoir layer with short-term memory to nonlinearly project input data to high-dimensional plane, and allow easy separation using linear AI model at the output layer. The RC-NN is designed in 65nm CMOS process, and detects stress and heart-diseases with mean accuracies of 92.8% and 86.8% respectively, while consuming 10.97nJ/inference and 2.57nJ/inference respectively.
Classification of Code-Mixed Text Using Capsule Networks

A major challenge in analysing social media data belonging to languages that use non-English script is its code-mixed nature.Recent research has presented state-of-the-art contextual embedding models (both monolingual s.a.BERT and multilingual s.a.XLM-R) as a promising approach.In this paper, we show that the performance of such embedding models depends on multiple factors, such as the level of code-mixing in the dataset, and the size of the training dataset.We empirically show that a newly introduced Capsule+biGRU classifier could outperform a classifier built on the English-BERT as well as XLM-R just with a training dataset of about 6500 samples for the Sinhala-English code-mixed data.
Automatic Design of Convolutional Neural Network Architectures Under Resource Constraints

With the rise of various smart electronics and mobile/edge devices, many existing high-accuracy convolutional neural network (CNN) models are difficult to be applied in practice due to the limited resources, such as memory capacity, power consumption, and spectral efficiency. In order to meet these constraints, researchers have carefully designed some lightweight networks. Meanwhile, to reduce the reliance on manual design on expert experience, some researchers also work to improve neural architecture search (NAS) algorithms to automatically design small networks, exploiting the multiobjective approaches that consider both accuracy and other important goals during optimization. However, simply searching for smaller network models is not consistent with the current research belief of “the deeper the better” and may affect the effectiveness of the model and thus waste the limited resources available. In this article, we propose an automatic method for designing CNNs architectures under constraint handling, which can search for optimal network models meeting the preset constraint. Specifically, an adaptive penalty algorithm is used for fitness evaluation, and a selective repair operation is developed for infeasible individuals to search for feasible CNN architectures. As a case study, we set the complexity (the number of parameters) as a resource constraint and perform multiple experiments on CIFAR-10 and CIFAR-100, to demonstrate the effectiveness of the proposed method. In addition, the proposed algorithm is compared with a state-of-the-art algorithm, NSGA-Net, and several manual-designed models. The experimental results show that the proposed algorithm can successfully solve the problem of the uncertain size of the optimal CNN model under the random search strategy, and the automatically designed CNN model can satisfy the predefined resource constraint while achieving better accuracy.
FaaS and Curious: Performance Implications of Serverless Functions on Edge Computing Platforms

Serverless is an emerging paradigm that greatly simplifies the usage of cloud resources providing unprecedented auto-scaling, simplicity, and cost-efficiency features. Thus, more and more individuals and organizations adopt it, to increase their productivity and focus exclusively on the functionality of their application. Additionally, the cloud is expanding towards the deep edge, forming a continuum in which the event-driven nature of the serverless paradigm seems to make a perfect match. The extreme heterogeneity introduced, in terms of diverse hardware resources and frameworks available, requires systematic approaches for evaluating serverless deployments. In this paper, we propose a methodology for evaluating serverless frameworks deployed on hybrid edge-cloud clusters. Our methodology focuses on key performance knobs of the serverless paradigm and applies a systematic way for evaluating these aspects in hybrid edge-cloud environments. We apply our methodology on three open-source serverless frameworks, OpenFaaS, Openwhisk, and Lean Openwhisk respectively, and we provide key insights regarding their performance implications over resource-constrained edge devices.
The Bored Ape Business Model: Decentralized Collaboration via Blockchain and NFTs

The Bored Ape Yacht Club (BAYC) NFTs stand out for their meteoric success. One underappreciated aspect of the business model adopted in the Bored Ape licenses is its use of decentralized collaboration or De-Collab. Buyers of Bored Ape NFTs are granted unlimited rights to commercialize the underlying Bored Ape associated with a particular sale of an NFT from the collection, which even includes the right to make derivative works from Bored Apes and keep the profits generated. The Bored Ape license’s adoption of a De-Collab approach presents tantalizing possibilities for the Creator Economy. Although it is too early to tell how successful this approach will be, recent deals of Bored Ape NFT owners with Universal Music and music producer Timbaland to create bands for the metaverse show the great potential De-Collab offers. As the Bored Ape ecosystem develops, further study should be made to evaluate the successes and failures of the De-Collab model as a source of innovation and creativity.
Blockchain for smart manufacturing systems: a survey

Purpose Smart manufacturing is the prime gripper for the transformation and upgrading of the manufacturing industry. Smart manufacturing systems (SMSs) largely determine how smart manufacturing evolves in technical and organizational dimensions and how it realizes values in products, production or services. SMSs are growing rapidly and receiving tons of attention from academic research and industrial practice. However, the development of SMSs is still in its fancy, and many issues wait to be identified and solved, such as single point failures, low transparency and ineffective resource sharing. Blockchain, an emerging technology deriving from Bitcoin, is competent to aid SMSs to conquer troubles due to its decentralization, traceability, trackability, disintermediation, auditability and etc. The purpose of this paper is to investigate the blockchain applications in SMSs, seek out the challenges faced by blockchain-enabled SMSs (BSMSs) and provide referable research directions and ideas. Design/methodology/approach A comprehensive literature review as a survey is conducted in this paper. The survey starts by introducing blockchain concepts, followed by the descriptions of a literature review method and the blockchain applications throughout the product life cycle in SMSs. Then, the key issues and challenges confronting BSMSs are discussed and some possible research directions are also proposed. It finally presents qualitative and quantitative descriptions of BSMSs, along with some conclusions and implications. Findings The findings of this paper present a deep understanding about the current status and challenges of blockchain adoption in SMSs. Furthermore, this paper provides a brand new thinking for future research. Originality/value This paper minutely analyzes the impacts that blockchain exerts on SMSs in view of the product life cycle, and proposes using the complexity science thinking to deal with BSMSs qualitatively and quantitatively, including tackling the current major problems BSMSs face. This research can serve as a foundation for future theoretical studies and enterprise practice.
Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Deep Gaussian processes (DGPs) have struggled for relevance in applications due to the challenges and cost associated with Bayesian inference. In this paper we propose a sparse variational approximation for DGPs for which the approximate posterior mean has the same mathematical structure as a Deep Neural Network (DNN). We make the forward pass through a DGP equivalent to a ReLU DNN by finding an interdomain transformation that represents the GP posterior mean as a sum of ReLU basis functions. This unification enables the initialisation and training of the DGP as a neural network, leveraging the well established practice in the deep learning community, and so greatly aiding the inference task. The experiments demonstrate improved accuracy and faster training compared to current DGP methods, while retaining favourable predictive uncertainties.
Demonstration of quantum-limited discrimination of multicopy pure versus mixed states

We demonstrate an optical receiver that achieves the quantum Chernoff bound for discriminating coherent states from thermal states in the multicopy scenario. In contrast, we find that repeated use of the receiver approaching the Helstrom bound for single-copy measurement is suboptimal in this multicopy case. Furthermore, for a large class of multicopy discrimination tasks between a pure and a mixed state, we prove that any Helstrom-bound achieving single-copy receiver is suboptimal by a factor of at least 2 in error-probability exponent compared to the multicopy quantum Chernoff bound. This behavior has a classical analog in the performance gap between soft-decision and hard-decision receivers for detecting a multicopy signal embedded in white Gaussian noise.
Stealing Machine Learning Models: Attacks and Countermeasures for Generative Adversarial Networks

Model extraction attacks aim to duplicate a machine learning model through query access to a target model. Early studies mainly focus on discriminative models. Despite the success, model extraction attacks against generative models are less well explored. In this paper, we systematically study the feasibility of model extraction attacks against generative adversarial networks (GANs). Specifically, we first define fidelity and accuracy on model extraction attacks against GANs. Then we study model extraction attacks against GANs from the perspective of fidelity extraction and accuracy extraction, according to the adversary’s goals and background knowledge. We further conduct a case study where the adversary can transfer knowledge of the extracted model which steals a state-of-the-art GAN trained with more than 3 million images to new domains to broaden the scope of applications of model extraction attacks. Finally, we propose effective defense techniques to safeguard GANs, considering a trade-off between the utility and security of GAN models.
Knowledge Guided Attention and Graph Convolutional Networks for Chemical-Disease Relation Extraction

The automatic extraction of the chemical-disease relation (CDR) from the text becomes critical because it takes a lot of time and effort to extract valuable CDR manually. Studies have shown that prior knowledge from the biomedical knowledge base is important for relation extraction. The method of combining deep learning models with prior knowledge is worthy of our study. In this paper, we propose a new model called Knowledge Guided Attention and Graph Convolutional Networks (KGAGN) for CDR extraction. First, to make full advantage of domain knowledge, we train entity embedding as a feature representation of input sequence, and relation embedding to capture weighted contextual information further through the attention mechanism. Then, to make full advantage of syntactic dependency information in cross-sentence CDR extraction, we construct document-level syntactic dependency graphs and encode them using a graph convolution network (GCN). Finally, the chemical-induced disease (CID) relation is extracted by using weighted context features and long-range dependency features both of which contain additional knowledge information We evaluated our model on the CDR dataset published by the BioCreative-V community and achieves an F1-score of 73.3%, surpassing other state-of-the-art methods. the code implemented by PyTorch 1.7.0 deep learning library can be downloaded from Github: https://github.com/sunyi123/cdr.
Primary user adversarial attacks on deep learning-based spectrum sensing and the defense method

The spectrum sensing model based on deep learning has achieved satisfying detection performence, but its robustness has not been verified. In this paper, we propose primary user adversarial attack (PUAA) to verify the robustness of the deep learning based spectrum sensing model. PUAA adds a carefully manufactured perturbation to the benign primary user signal, which greatly reduces the probability of detection of the spectrum sensing model. We design three PUAA methods in black box scenario. In order to defend against PUAA, we propose a defense method based on autoencoder named DeepFilter. We apply the long short-term memory network and the convolutional neural network together to DeepFilter, so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense. Extensive experiments are conducted to evaluate the attack effect of the designed PUAA method and the defense effect of DeepFilter. Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model. In addition, the experimental results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA without affecting the detection performance of the model.
Secure Blockchain-Internet of Vehicles (B-IoV) Mechanism using DPSO and M-ITA Algorithms

The Internet of Vehicles (IoV) is introduced to transfer/exchange the data among roadside units and vehicles through various smart devices in real time scenarios. However, the past works in IoV leads to various transparencies, security and privacy concerns while communicating and transferring the information among each other. This paper has proposed a blockchain-based trustworthy framework for vehicular networks by reducing the security issues like trust issue and quickly updating the status of vehicles via blockchain ledger. The proposed scheme provides the security at two different stages; 1) the first stage calculates the candidate's trust degree, data quality, and privacy via other vehicles through a Modified-Two-stage Auction Algorithm (M-ITA) scheme to select the trusted miners and maximize their utilities, and 2) in the second stage, a block selection method is introduced that uses the discrete particle swarm optimization (DPSO). In every time-slot, only selected miners validate the blocks and updated their position dynamically. The performance of proposed framework is analyzed through numerical analysis by improving the trustworthiness for data sharing in blockchain-based IoV using various security parameters.
Automatic Multi-Atlas Liver Segmentation and Couinaud Classification from CT Volumes

Primary Live Cancer (PLC) is the sixth most common cancer worldwide and its occurrence predominates in patients with chronic liver diseases and other risk factors like hepatitis B and C. Treatment of PLC and malignant liver tumors depend both in tumor characteristics and the functional status of the organ, thus must be individualized for each patient. Liver segmentation and classification according to Couinaud’s classification is essential for computer-aided diagnosis and treatment planning, however, manual segmentation of the liver volume slice by slice can be a time-consuming and challenging task and it is highly dependent on the experience of the user. We propose an alternative automatic segmentation method that allows accuracy and time consumption amelioration. The procedure pursues a multi-atlas based classification for Couinaud segmentation. Our algorithm was implemented on 20 subjects from the IRCAD 3D data base in order to segment and classify the liver volume in its Couinaud segments, obtaining an average DICE coefficient of 0.94.Clinical Relevance— The final purpose of this work is to provide an automatic multi-atlas liver segmentation and Couinaud classification by means of CT image analysis.