Reviewerly/exHarmony huggingface dataset:
Using Blockchain to Improve Collaborative Business Process Management: Systematic Literature Review

BlockChain Technology (BCT) has appeared with strength and promises an authentic revolution on business, management, and organizational strategies related to utilization of advanced software systems.In fact, BCT promotes a decentralized architecture to process management and the collaborative work between entities when these ones are working together in a business process.This paper aims to know what proposals exist to improve any stage of business process management using BCT because this technology could provide benefits in this management.For this purpose, this paper presents a systematic literature review in area of Collaborative Business Processes (CBP) in BCT domain to identify opportunities and gaps for further research.This paper concludes there is a rapid and growing interest of public bodies, scientific community and software industries to know opportunities that BCT offers to improve CBP management in a decentralized manner.However, although the topic is in early stages, there are very promising lines of research and relevant open issues, but there also is lack of scientific rigor in validation process into the different studies.
Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion

Haze removal from nighttime images is more difficult compared with daytime image dehazing due to the uneven illumination, low contrast and severe color distortion. In this paper, following the approaches based on Dark channel prior, we propose a simple yet effective approach using Retinex theory and Taylor series expansion for nighttime image dehazing, referred to as ‘RDT’. Existing nighttime image dehazing methods do not handle color shift and glow removal very well. In order to address these issues, we first propose to decompose the atmospheric light image from the input image based on the Retinex theory. Taylor series expansion is then introduced for the first time to accurately estimate the pointwise transmission map. Finally, during the following processes of image fusion and color transfer, the atmospheric light image and potential haze-free image are adopted to obtain the final haze-free image. The experimental results on benchmark nighttime haze images demonstrate the superior performance of our proposed RDT dehazing method over the state-of-the-art methods.
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition

Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs. This observation has motivated an increasing interest in few-shot video action recognition, which aims at learning new actions with only very few labeled samples. In this paper, we propose a depth guided Adaptive Meta-Fusion Network for few-shot video recognition which is termed as AMeFu-Net. Concretely, we tackle the few-shot recognition problem from three aspects: firstly, we alleviate this extremely data-scarce problem by introducing depth information as a carrier of the scene, which will bring extra visual information to our model; secondly, we fuse the representation of original RGB clips with multiple non-strictly corresponding depth clips sampled by our temporal asynchronization augmentation mechanism, which synthesizes new instances at feature-level; thirdly, a novel Depth Guided Adaptive Instance Normalization (DGAdaIN) fusion module is proposed to fuse the two-stream modalities efficiently. Additionally, to better mimic the few-shot recognition process, our model is trained in the meta-learning way. Extensive experiments on several action recognition benchmarks demonstrate the effectiveness of our model.
LOGAN

We introduce LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and chairs, while there is neither a pairing between shapes from the domains as supervision nor any point-wise correspondence between any shapes. Once trained, LOGAN takes a shape from one domain and transforms it into the other. Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes concatenate multi-scale shape features, resulting in an overcomplete representation. The translator is based on a generative adversarial network (GAN), operating in the latent space, where an adversarial loss enforces cross-domain translation while a feature preservation loss ensures that the right shape features are preserved for a natural shape transform. We conduct ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. We show that LOGAN is able to learn what shape features to preserve during shape translation, either local or non-local, whether content or style, depending solely on the input domains for training.
Assessing Phrasal Representation and Composition in Transformers

Deep transformer models have pushed performance on NLP tasks to new limits, suggesting sophisticated treatment of complex linguistic inputs, such as phrases.However, we have limited understanding of how these models handle representation of phrases, and whether this reflects sophisticated composition of phrase meaning like that done by humans.In this paper, we present systematic analysis of phrasal representations in state-of-the-art pre-trained transformers.We use tests leveraging human judgments of phrase similarity and meaning shift, and compare results before and after control of word overlap, to tease apart lexical effects versus composition effects.We find that phrase representation in these models relies heavily on word content, with little evidence of nuanced composition.We also identify variations in phrase representation quality across models, layers, and representation types, and make corresponding recommendations for usage of representations from these models.
We Can Detect Your Bias: Predicting the Political Ideology of News Articles

We explore the task of predicting the leading political ideology or bias of news articles.First, we collect and release a large dataset of 34,737 articles that were manually annotated for political ideology -left, center, or right-, which is well-balanced across both topics and media.We further use a challenging experimental setup where the test examples come from media that were not seen during training, which prevents the model from learning to detect the source of the target news article instead of predicting its political ideology.From a modeling perspective, we propose an adversarial media adaptation, as well as a specially adapted triplet loss.We further add background information about the source, and we show that it is quite helpful for improving article-level prediction.Our experimental results show very sizable improvements over using state-of-the-art pre-trained Transformers in this challenging setup.
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer

Video style transfer techniques inspire many exciting applications on mobile devices. However, their efficiency and stability are still far from satisfactory. To boost the transfer stability across frames, optical flow is widely adopted, despite its high computational complexity, e.g. occupying over 97% inference time. This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm. We adopt two teacher networks, one of which takes optical flow during inference while the other does not. The output difference between these two teacher networks highlights the improvements made by optical flow, which is then adopted to distill the target student network. Furthermore, a low-rank distillation loss is employed to stabilize the output of student network by mimicking the rank of input videos. Extensive experiments demonstrate that our student network without an optical flow module is still able to generate stable video and runs much faster than the teacher network.
Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation

This paper concerns user preference estimation in multi-round conversational recommender systems (CRS), which interacts with users by asking questions about attributes and recommending items multiple times in one conversation. Multi-round CRS such as EAR have been proposed in which the user's online feedback at both attribute level and item level can be utilized to estimate user preference and make recommendations. Though preliminary success has been shown, existing user preference models in CRS usually use the online feedback information as independent features or training instances, overlooking the relation between attribute-level and item-level feedback signals. The relation can be used to more precisely identify the reasons (e.g., some certain attributes) that trigger the rejection of an item, leading to more fine-grained utilization of the feedback information. To address aforementioned issue, this paper proposes a novel preference estimation model tailored for multi-round CRS, called Feedback-guided Preference Adaptation Network (FPAN). In FPAN, two gating modules are designed to respectively adapt the original user embedding and item-level feedback, both according to the online attribute-level feedback. The gating modules utilize the fine-grained attribute-level feedback to revise the user embedding and coarse-grained item-level feedback, achieving more accurate user preference estimation by considering the relation between feedback. Experimental results on two benchmarks showed that FPAN outperformed the state-of-the-art user preference models in CRS, and the multi-round CRS can also be enhanced by using FPAN as its recommender component.
Porous Lattice Transformer Encoder for Chinese NER

Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploit rich word boundary information.Previous work has extended RNNs to consume lattice inputs and achieved great success.However, due to the DAG structure and the inherently unidirectional sequential nature, this method precludes batched computation and sufficient semantic interaction.In this paper, we propose PLTE, an extension of transformer encoder that is tailored for Chinese NER, which models all the characters and matched lexical words in parallel with batch processing.PLTE augments self-attention with positional relation representations to incorporate lattice structure.It also introduces a porous mechanism to augment localness modeling and maintain the strength of capturing the rich long-term dependencies.Experimental results show that PLTE performs up to 11.4 times faster than state-of-the-art methods while realizing better performance.We also demonstrate that using BERT representations further substantially boosts the performance and brings out the best in PLTE.
Explainable Automated Fact-Checking: A Survey

A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked.However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline.In this survey, we focus on the explanation functionality -that is fact-checking systems providing reasons for their predictions.We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic.Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties.Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.
IR2V <scp>EC</scp>

We propose IR2V EC , a Concise and Scalable encoding infrastructure to represent programs as a distributed embedding in continuous space. This distributed embedding is obtained by combining representation learning methods with flow information to capture the syntax as well as the semantics of the input programs. As our infrastructure is based on the Intermediate Representation (IR) of the source code, obtained embeddings are both language and machine independent. The entities of the IR are modeled as relationships, and their representations are learned to form a seed embedding vocabulary . Using this infrastructure, we propose two incremental encodings: Symbolic and Flow-Aware . Symbolic encodings are obtained from the seed embedding vocabulary , and Flow-Aware encodings are obtained by augmenting the Symbolic encodings with the flow information. We show the effectiveness of our methodology on two optimization tasks (Heterogeneous device mapping and Thread coarsening). Our way of representing the programs enables us to use non-sequential models resulting in orders of magnitude of faster training time. Both the encodings generated by IR2V EC outperform the existing methods in both the tasks, even while using simple machine learning models. In particular, our results improve or match the state-of-the-art speedup in 11/14 benchmark-suites in the device mapping task across two platforms and 53/68 benchmarks in the thread coarsening task across four different platforms. When compared to the other methods, our embeddings are more scalable , is non-data-hungry , and has better Out-Of-Vocabulary (OOV) characteristics .
Blockchain Technology and Trust in Supply Chain Management: A Literature Review and Research Agenda

Applying blockchain technology for information sharing in supply chain is driven by many factors, but developing trust is one of the most proposed.However, trust is a multidimensional, intangible concept without an agreed-upon definition.Whereas some argue that trust is the main driver of blockchain technology, others have found a negative relationship.This study focuses on how applying blockchain in supply chain management can influence trust and proposes a corresponding research agenda.Trust and blockchain technology discussions are scattered throughout the literature.Thus, a systematic literature review was performed based on a conceptual trust framework.This study discovered a gap in linking trust theories to blockchain technology applications especially in supply chain management, and provided insights into trust's reciprocal nature.Current literature strongly expects trust as a consequence for blockchain adoption if considered in the technology.Simultaneously, trust in supply chain partners is strongly expected as an antecedent to blockchain because it requires openness in information sharing.Thus, propositions and agenda for future research are suggested.The research is limited to literature findings due to the immaturity and low scalability of blockchain technology adoption; however, the most reviewed articles in less than two years old, increase the results' accuracy.
A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization

Dealing with vast amounts of textual data requires the use of efficient systems.Automatic summarization systems are capable of addressing this issue.Therefore, it becomes highly essential to work on the design of existing automatic summarization systems and innovate them to make them capable of meeting the demands of continuously increasing data, based on user needs.This study tends to survey the scientific literature to obtain information and knowledge about the recent research in automatic text summarization specifically abstractive summarization based on neural networks.A review of various neural networks based abstractive summarization models have been presented.The proposed conceptual framework includes five key elements identified as encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset, and evaluation metric.A description of these elements is also included in this article.The purpose of this research is to provide an overall understanding and familiarity with the elements of recent neural networks based abstractive text summarization models with an up-to-date review as well as to render an awareness of the challenges and issues with these systems.Analysis has been performed qualitatively with the help of a concept matrix indicating common trends in the design of recent neural abstractive summarization systems.Models employing a transformer-based encoder-decoder architecture are found to be the new state-of-the-art.Based on the knowledge acquired from the survey, this article suggests the use of pre-trained language models in complement with neural network architecture for abstractive summarization task.
Graph Deep Learning: State of the Art and Challenges

The last half-decade has seen a surge in deep learning research on irregular domains and efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data. The graph has emerged as a particularly useful geometrical object in deep learning, able to represent a variety of irregular domains well. Graphs can represent various complex systems, from molecular structure, to computer and social and traffic networks. Consequent on the extension of CNNs to graphs, a great amount of research has been published that improves the inferential power and computational efficiency of graph-based convolutional neural networks (GCNNs). The research is incipient, however, and our understanding is relatively rudimentary. The majority of GCNNs are designed to operate with certain properties. In this survey we review of the state of graph representation learning from the perspective of deep learning. We consider challenges in graph deep learning that have been neglected in the majority of work, largely because of the numerous theoretical difficulties they present. We identify four major challenges in graph deep learning: dynamic and evolving graphs, learning with edge signals and information, graph estimation, and the generalization of graph models. For each problem we discuss the theoretical and practical issues, survey the relevant research, while highlighting the limitations of the state of the art. Advances on these challenges would permit GCNNs to be applied to wider range of domains, in situations where graph models have previously been limited owing to the obstructions to applying a model owing to the domains' natures.
Financial Inclusion, Income Inequality and Sustainable Economic Growth in Sub-Saharan African Countries

This study evaluates the relevance of inclusive financial access in moderating the effect of income inequality on economic growth in 48 countries in Sub-Saharan Africa (SSA) for the period 1995 to 2017. The findings using the Generalised Method of Moments (sys-GMM) technique show that inclusive financial access contributes to reducing inequality in the short run, contrary to the Kuznets curve. The result reveals a negative effect of financial access on the relationship between income inequality and economic growth. There is a positive net effect of inclusive financial access in moderating the impact of income inequality on economic growth. Given the need to achieve the Sustainable Development Targets in the sub-region, policymakers and other stakeholders of the economy must design policies and programmes that would enhance access to financial services as an essential mechanism to reduce income disparity and enhance sustainable economic growth.
Employee Commitment: The Relationship between Employee Commitment And Job Satisfaction

The aim of the current study is to examine the association between employee commitment and job satisfaction at selected private universities in Kurdistan region of Iraq. Organizational commitment predicts work variables, for example, turnover, hierarchical citizenship conduct, and employment execution. A percentage of the elements, for example, part stretch, strengthening, work shakiness and employability, and appropriation of authority have been appeared to be associated with a laborer's feeling of organizational commitment. This research was carried out at the three different private universities. A quantitative method was used to gather relevant information regarding the relationship between organizational commitment and job satisfaction. The results revealed that all three independent factors are correlated with job satisfaction. The result can be used as a guiding method for the university's management to increase their employees' commitment. Job satisfaction has a positive relation with employee commitment if employees are satisfied, they are organizationally committed we found that job satisfaction has a positive impact on employee commitment.
Hardware-Efficient, Fault-Tolerant Quantum Computation with Rydberg Atoms

Neutral-atom arrays have recently emerged as a promising platform for quantum information processing. One important remaining roadblock for the large-scale application of these systems is the ability to perform error-corrected quantum operations. To entangle the qubits in these systems, atoms are typically excited to Rydberg states, which could decay or give rise to various correlated errors that cannot be addressed directly through traditional methods of fault-tolerant quantum computation. In this work, we provide the first complete characterization of these sources of error in a neutral-atom quantum computer and propose hardware-efficient, fault-tolerant quantum computation schemes that mitigate them. Notably, we develop a novel and distinctly efficient method to address the most important errors associated with the decay of atomic qubits to states outside of the computational subspace. These advances allow us to significantly reduce the resource cost for fault-tolerant quantum computation compared to existing, general-purpose schemes. Our protocols can be implemented in the near term using state-of-the-art neutral-atom platforms with qubits encoded in both alkali and alkaline-earth atoms.9 MoreReceived 6 June 2021Revised 3 February 2022Accepted 12 April 2022DOI:https://doi.org/10.1103/PhysRevX.12.021049Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOptical pumpingQuantum algorithmsQuantum computationQuantum gatesQuantum information processingQuantum protocolsSpontaneous emissionQuantum InformationAtomic, Molecular & Optical
Concealed Data Poisoning Attacks on NLP Models

Adversarial attacks alter NLP model predictions by perturbing test-time inputs.However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data.In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input.For instance, we insert 50 poison examples into a sentiment model's training set that causes the model to frequently predict Positive whenever the input contains "James Bond".Crucially, we craft these poison examples using a gradient-based procedure so that they do not mention the trigger phrase.We also apply our poison attack to language modeling ("Apple iPhone" triggers negative generations) and machine translation ("iced coffee" mistranslated as "hot coffee").We conclude by proposing three defenses that can mitigate our attack at some cost in prediction accuracy or extra human annotation.