Reviewerly/exHarmony huggingface dataset:
Designing blockchain systems to prevent counterfeiting in wine supply chains: a multiple-case study

Purpose The purpose of this paper is to contribute to the debate on blockchain (BC) adoption for preventing counterfeiting by investigating BC systems where different options for BC feeding and reading complement the use of BC technology. By grounding on the situational crime prevention, this study analyses how BC systems can be designed to effectively prevent counterfeiting. Design/methodology/approach This is a multiple-case study of five Italian wine companies using BC to prevent counterfeiting. Findings This study finds that the desired level of upstream/downstream counterfeiting protection that a brand owner intends to guarantee to customers through BC is the key driver to consider in the design of BC systems. The study identifies which variables are relevant to the design of feeding and reading processes and explains how such variables can be modulated in accordance with the desired level of counterfeiting protection. Research limitations/implications The cases investigated are Italian companies within the wine sector, and the BC projects analysed are in the pilot phase. Practical implications The study provides practical suggestions to address the design of BC systems by identifying a set of key variables and explaining how to properly modulate them to face upstream/downstream counterfeiting. Originality/value This research applies a new perspective based on the situational crime prevention approach in studying how companies can design BC systems to effectively prevent counterfeiting. It explains how feeding and reading process options can be configured in BC systems to assure different degrees of counterfeiting protection.
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation

Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind.In this work, we aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions.Our intuition is based on the hypothesis that a universal cross-language representation leads to better multilingual translation performance.To this end, we propose mRASP2, a training method to obtain a single unified multilingual translation model.mRASP2 is empowered by two techniques: a) a contrastive learning scheme to close the gap among representations of different languages, and b) data augmentation on both multiple parallel and monolingual data to further align token representations.For English-centric directions, mRASP2 outperforms existing best unified model and achieves competitive or even better performance than the pre-trained and fine-tuned model mBART on tens of WMT's translation directions.For non-English directions, mRASP2 achieves an improvement of average 10+ BLEU compared with the multilingual Transformer baseline.Code, data and trained models are available at https://github.com/PANXiao1994/mRASP2.
EfficientNet

To start this chapter, we will look at some different reinforcement learning techniques that have been applied to convolutional neural networks. We’re going to look at different variants of the same basic idea of having the computer explore different neural network architectures for us. We will look at some of the research which builds up to our next neural network, EfficientNet, which was partially built using these techniques.
Energy System 4.0: Digitalization of the Energy Sector with Inclination towards Sustainability

The United Nations' sustainable development goals have emphasized implementing sustainability to ensure environmental security for the future. Affordable energy, clean energy, and innovation in infrastructure are the relevant sustainable development goals that are applied to the energy sector. At present, digital technologies have a significant capability to realize the target of sustainability in energy. With this motivation, the study aims to discuss the significance of different digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), edge computing, blockchain, and big data and their implementation in the different stages of energy such as generation, distribution, transmission, smart grid, and energy trading. The study also discusses the different architecture that has been implemented by previous studies for smart grid computing. Additionally, we addressed IoT-based microgrids, IoT services in electrical equipment, and blockchain-based energy trading. Finally, the article discusses the challenges and recommendations for the effective implementation of digital technologies in the energy sector for meeting sustainability. Big data for energy analytics, digital twins in smart grid modeling, virtual power plants with Metaverse, and green IoT are the major vital recommendations that are discussed in this study for future enhancement.
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks

We consider adversarial examples for image classification in the black-box decision-based setting. Here, an attacker cannot access confidence scores, but only the final label. Most attacks for this scenario are either unreliable or inefficient. Focusing on the latter, we show that a specific class of attacks, Boundary Attacks, can be reinterpreted as a biased sampling framework that gains efficiency from domain knowledge. We identify three such biases, image frequency, regional masks and surrogate gradients, and evaluate their performance against an ImageNet classifier. We show that the combination of these biases outperforms the state of the art by a wide margin. We also showcase an efficient way to attack the Google Cloud Vision API, where we craft convincing perturbations with just a few hundred queries. Finally, the methods we propose have also been found to work very well against strong defenses: Our targeted attack won second place in the NeurIPS 2018 Adversarial Vision Challenge.
African Rural Youth Engagement in Agribusiness: Achievements, Limitations, and Lessons

Engaging rural youth in agribusiness has become an important strategy to create employment opportunities in Africa. To this end, governments and development partners have implemented various interventions that facilitate youth engagement in agribusiness for several years. However, there is a dearth of evidence on what worked and what did not work well, making it difficult to inform evidence-based policy making. In an effort to fill this knowledge gap, a comprehensive literature review of the outcomes of interventions related to youth engagement in agribusiness was conducted using a deductive coding approach. Results showed that the interventions implemented by governments and development partners across Africa have succeeded in producing favorable outcomes despite some limitations. Interventions that integrate capacity development, financial support for startups, and continuous mentorship on the technical and financial aspects of youth-run agribusiness projects proved successful in enhancing youth engagement in agribusiness. This suggests that the design and implementation of future interventions should be based on an integrated approach that considers diversity of youths’ aspirations and shared capabilities, interests, expectations, as well as challenges associated with access to resources and participation in collective action. The design of future interventions should also be built on strong partnerships among rural communities, academia, research, and private sector for increased impact on livelihood improvements.
A U-net based approach to epidermal tissue segmentation in whole slide histopathological images

Malignant melanoma is a severe and aggressive type of skin cancer, with a rapid decrease in survival rate if not diagnosed and treated at an early stage. Histopathological examination of hematoxylin and eosin stained tissue biopsies under a light microscope is currently the gold standard for diagnosis. However, this manual examination is a difficult and time-consuming task, and diagnosis is often subject to intra- and inter-observer variability. With more pathology departments starting to convert conventional glass slides into digital resources, a Computer Aided Diagnostic (CAD) system that can automate part of the diagnostic process will help address these challenges. It is expected to reduce examination time, increase diagnostic accuracy, and reduce diagnostic variations. An important initial step in developing such a system is an automated epidermis segmentation algorithm, since several important diagnostic factors are within or seen relatively to the epidermis’ location. In this paper, we propose a new epidermis segmentation technique built on Convolutional Neural Networks. We trained an U-net based architecture end-to-end, with $$\sim 380\hbox {k}$$ overlapping high resolution image patches at 512 $$\times$$ 512 pixels, extracted and augmented from 36 digitized histopathological images from two different clinical sites, to discriminate pixels as either epidermal or non-epidermal. The proposed technique was evaluated on 33 test images, where we achieved a mean Positive Predictive Value at $$0.89\pm 0.16$$ , Sensitivity at $$0.92\pm 0.1$$ , Dice Similarity Coefficient at $$0.89\pm 0.13$$ and a Matthews Correlation Coefficient at $$0.89\pm 0.11$$ , showing a superior performance when compared to existing techniques. Our algorithm also proves to be robust to variations in staining, tissue thickness and laboratory pre-processing.
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects

Visual question answering (VQA) models have been shown to over-rely on linguistic biases in VQA datasets, answering questions “blindly” without considering visual context. Adversarial regularization (AdvReg) aims to address this issue via an adversary sub-network that encourages the main model to learn a bias-free representation of the question. In this work, we investigate the strengths and shortcomings of AdvReg with the goal of better understanding how it affects inference in VQA models. Despite achieving a new state-of-the-art on VQA-CP, we find that AdvReg yields several undesirable side-effects, including unstable gradients and sharply reduced performance on in-domain examples. We demonstrate that gradual introduction of regularization during training helps to alleviate, but not completely solve, these issues. Through error analyses, we observe that AdvReg improves generalization to binary questions, but impairs performance on questions with heterogeneous answer distributions. Qualitatively, we also find that regularized models tend to over-rely on visual features, while ignoring important linguistic cues in the question. Our results suggest that AdvReg requires further refinement before it can be considered a viable bias mitigation technique for VQA.
Can 3D Pose Be Learned from 2D Projections Alone?

3D pose estimation from a single image is a challenging task in computer vision. We present a weakly supervised approach to estimate 3D pose points, given only 2D pose landmarks. Our method does not require correspondences between 2D and 3D points to build explicit 3D priors. We utilize an adversarial framework to impose a prior on the 3D structure, learned solely from their random 2D projections. Given a set of 2D pose landmarks, the generator network hypothesizes their depths to obtain a 3D skeleton. We propose a novel Random Projection layer, which randomly projects the generated 3D skeleton and sends the resulting 2D pose to the discriminator. The discriminator improves by discriminating between the generated poses and pose samples from a real distribution of 2D poses. Training does not require correspondence between the 2D inputs to either the generator or the discriminator. We apply our approach to the task of 3D human pose estimation. Results on Human3.6M dataset demonstrates that our approach outperforms many previous supervised and weakly supervised approaches.
Pushing the Limits of Low-Resource Morphological Inflection

Recent years have seen exceptional strides in the task of automatic morphological inflection generation.However, for a long tail of languages the necessary resources are hard to come by, and state-of-the-art neural methods that work well under higher resource settings perform poorly in the face of a paucity of data.In response, we propose a battery of improvements that greatly improve performance under such low-resource conditions.First, we present a novel two-step attention architecture for the inflection decoder.In addition, we investigate the effects of cross-lingual transfer from single and multiple languages, as well as monolingual data hallucination.The macroaveraged accuracy of our models outperforms the state-of-the-art by 15 percentage points. 1Also, we identify the crucial factors for success with cross-lingual transfer for morphological inflection: typological similarity and a common representation across languages.
Evaluating Recurrent Neural Network Explanations

Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network’s decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular prediction. In previous works, some of these methods were not yet compared to one another, or were evaluated only qualitatively. We close this gap by systematically and quantitatively comparing these methods in different settings, namely (1) a toy arithmetic task which we use as a sanity check, (2) a five-class sentiment prediction of movie reviews, and besides (3) we explore the usefulness of word relevances to build sentence-level representations. Lastly, using the method that performed best in our experiments, we show how specific linguistic phenomena such as the negation in sentiment analysis reflect in terms of relevance patterns, and how the relevance visualization can help to understand the misclassification of individual samples.
Artificial intelligence in seismology: Advent, performance and future trends

Realistically predicting earthquake is critical for seismic risk assessment, prevention and safe design of major structures. Due to the complex nature of seismic events, it is challengeable to efficiently identify the earthquake response and extract indicative features from the continuously detected seismic data. These challenges severely impact the performance of traditional seismic prediction models and obstacle the development of seismology in general. Taking their advantages in data analysis, artificial intelligence (AI) techniques have been utilized as powerful statistical tools to tackle these issues. This typically involves processing massive detected data with severe noise to enhance the seismic performance of structures. From extracting meaningful sensing data to unveiling seismic events that are below the detection level, AI assists in identifying unknown features to more accurately predicting the earthquake activities. In this focus paper, we provide an overview of the recent AI studies in seismology and evaluate the performance of the major AI techniques including machine learning and deep learning in seismic data analysis. Furthermore, we envision the future direction of the AI methods in earthquake engineering which will involve deep learning-enhanced seismology in an internet-of-things (IoT) platform.
Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework

Online underground forums have been widely used by cybercriminals to exchange knowledge and trade in illicit products or services, which have played a central role in the cybercriminal ecosystem. In order to combat the evolving cybercrimes, in this paper, we propose and develop an intelligent system named iDetective to automate the analysis of underground forums for the identification of key players (i.e., users who play the vital role in the value chain). In iDetective, we first introduce an attributed heterogeneous information network (AHIN) for user representation and use a meta-path based approach to incorporate higher-level semantics to build up relatedness over users in underground forums; then we propose Player2Vec to efficiently learn node (i.e., user) representations in AHIN for key player identification. In Player2Vec, we first map the constructed AHIN to a multi-view network which consists of multiple single-view attributed graphs encoding the relatedness over users depicted by different designed meta-paths; then we employ graph convolutional network (GCN) to learn embeddings of each single-view attributed graph; later, an attention mechanism is designed to fuse different embeddings learned based on different single-view attributed graphs for final representations. Comprehensive experiments on the data collections from different underground forums (i.e., Hack Forums, Nulled) are conducted to validate the effectiveness of iDetective in key player identification by comparisons with alternative approaches.
Biomedical Named Entity Recognition with Multilingual BERT

We present the approach of the Turku NLP group to the PharmaCoNER task on Spanish biomedical named entity recognition.We apply a CRF-based baseline approach and multilingual BERT to the task, achieving an Fscore of 88% on the development data and 87% on the test set with BERT.Our approach reflects a straightforward application of a state-of-the-art multilingual model that is not specifically tailored to either the language nor the application domain.
Image Encryption Algorithm Based on Logistic and Two-Dimensional Lorenz

In recent years, experts and scholars in the field of information security have attached great importance to the security of image information.They have proposed many image encryption algorithms with higher security.In order to further improve the security level of image encryption algorithm, this paper proposes a new image encryption algorithm based on two-dimensional Lorenz and Logistic.The encryption test of several classic images proves that the algorithm has high security and strong robustness.This paper also analyzes the security of encryption algorithms, such as analysis of the histogram, entropy process of information, examination of correlation, differential attack, key sensitivity test, secret key space analysis, noise attacks, contrast analysis.By comparing the image encryption algorithm proposed in this paper with some existing image encryption algorithms, the encryption algorithm has the characteristics of large secret key space, sensitivity to the key, small correlation coefficient and high contrast.In addition, the encryption algorithm is used.It can also resist noise attacks.
Interpreting Deep Learning-Based Networking Systems

While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable for network operators. The lack of interpretability makes DL-based networking systems prohibitive to deploy in practice. In this paper, we propose Metis, a framework that provides interpretability for two general categories of networking problems spanning local and global control. Accordingly, Metis introduces two different interpretation methods based on decision tree and hypergraph, where it converts DNN policies to interpretable rule-based controllers and highlight critical components based on analysis over hypergraph. We evaluate Metis over two categories of state-of-the-art DL-based networking systems and show that Metis provides human-readable interpretations while preserving nearly no degradation in performance. We further present four concrete use cases of Metis, showcasing how Metis helps network operators to design, debug, deploy, and ad-hoc adjust DL-based networking systems.
Product Knowledge Graph Embedding for E-commerce

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The ¶oincare embedding is also employed to handle complex entity structures. We use a real-world dataset from \textslgrocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks.
A Coherent and Privacy-Protecting Biometric Authentication Strategy in Cloud Computing

Abstract The Biometric authentication has become progressively more desired in current years. With this expansion of cloud computing, database holders be influenced to expand this extensive volume of biometric information &amp; detection operations to CLOUD for eradicate of this high-priced storage and result overheads, is still conveys possible dangers to users’ seclusion. In this document, we recommend an well-organized, well planned and confidentiality-protecting biometric classification strategy. Particularly, biometric information was encrypted &amp; farmed out for Cloud database. For complete a biometric confirmation, server holder encrypts the inquiry information and proposes that to cloud. The Cloud implements recognition tasks on the encrypted server and sends this conclusion to the server holder. The systematic protection assessment specifies the recommended system is protected still if attackers can fake detection appeals and conspire through the cloud. Evaluated with previous protocols, investigational and new outcomes prove the recommended strategy accomplishes enhanced performance in both preparation and discovery measures.
Parameter concentrations in quantum approximate optimization

The quantum approximate optimization algorithm (QAOA) has become a cornerstone of contemporary quantum applications development. In QAOA, a quantum circuit is trained---by repeatedly adjusting circuit parameters---to solve a problem instance. Several recent findings have reported parameter concentration effects in QAOA and their presence has become one of folklore: while empirically observed, the concentrations have not been defined and analytical approaches remain scarce, focusing on limiting system sizes while neglecting parameter scaling as system size increases. We found that optimal QAOA circuit parameters concentrate as an inverse polynomial in the problem size, providing an optimistic result for improving circuit training. Our results are analytically demonstrated for QAOA at $p=1,2$ (corresponding to 2 and 4 tunable parameters, respectively). The technique is also applicable for higher depths wherein the concentration effect is cross verified numerically. Parameter concentrations allow for training on a fraction $w&lt;n$ of qubits to assert that these parameters are nearly optimal on $n$ qubits, thereby reducing training time.