Reviewerly/exHarmony huggingface dataset:
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack

Representation learning is widely used in NLP for a vast range of tasks.However, representations derived from text corpora often reflect social biases.This phenomenon is pervasive and consistent across different neural models, causing serious concern.Previous methods mostly rely on a pre-specified, user-provided direction or suffer from unstable training.In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task.We aim to denoise bias information while training on the downstream task, rather than completely remove social bias and pursue static unbiased representations.Experiments show the effectiveness of our method, both on the effect of debiasing and the main task performance.
Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures

We study the problem of Event Causality Identification (ECI) to detect causal relation between event mention pairs in text.Although deep learning models have recently shown state-of-the-art performance for ECI, they are limited to the intra-sentence setting where event mention pairs are presented in the same sentences.This work addresses this issue by developing a novel deep learning model for document-level ECI (DECI) to accept intersentence event mention pairs.As such, we propose a graph-based model that constructs interaction graphs to capture relevant connections between important objects for DECI in input documents.Such interaction graphs are then consumed by graph convolutional networks to learn document context-augmented representations for causality prediction between events.Various information sources are introduced to enrich the interaction graphs for DECI, featuring discourse, syntax, and semantic information.Our extensive experiments show that the proposed model achieves state-of-the-art performance on two benchmark datasets.
DIGITAL HUMAN SKILLS FORM THE CORPORATE ECONOMY AND BUSINESS DEVELOPMENT

Digitalization, intensified by the COVID-19 pandemic, presents great opportunities for economic growth and development of businesses. At the same time, it brings challenges such as new requirements for human skills. To facilitate digital transition in the corporate sector and obtain its benefits, there is high demand for people skilled in the existing digital technologies and able to adapt to changes quickly. Digital transformation demands lifelong learning and reinforces the link between education, training, and work. Educators and other professionals in the field need to adjust to being “twenty-first century educators” and to consider a range of new digital skills that will allow students to develop a variety of professional and personal abilities. To examine human skills required by digitalization, this study set out to investigate elements of an existing digital learning environment in a university based on feedback from its 2020 spring term students. The research is grounded in a social cognitive perspective of self-regulation and in recent contributions from the multidisciplinary humancomputer interaction (HCI) field. The data came from the total of 2,347 university students, was collected through an online survey, evaluated using the SPSS software, and qualitatively analyzed. The study revealed that students pinpointed three groups of problems in online learning: the process of concentration and self-regulation; the process of getting feedback from tutors and peers; and technical difficulties with staying connected. The researchers attempted to shed light on self-regulation as an essential human skill that is required for successful business and corporate development in the digitalized environment.
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation

Self-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data.The common practice is to construct synthetic data based on a randomly sampled subset of large-scale monolingual data, which we empirically show is sub-optimal.In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data.To this end, we compute the uncertainty of monolingual sentences using the bilingual dictionary extracted from the parallel data.Intuitively, monolingual sentences with lower uncertainty generally correspond to easy-to-translate patterns which may not provide additional gains.Accordingly, we design an uncertainty-based sampling strategy to efficiently exploit the monolingual data for self-training, in which monolingual sentences with higher uncertainty would be sampled with higher probability.Experimental results on large-scale WMT English⇒German and English⇒Chinese datasets demonstrate the effectiveness of the proposed approach.Extensive analyses suggest that emphasizing the learning on uncertain monolingual sentences by our approach does improve the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side. 1
Bridge-Based Active Domain Adaptation for Aspect Term Extraction

As a fine-grained task, the annotation cost of aspect term extraction is extremely high.Recent attempts alleviate this issue using domain adaptation that transfers common knowledge across domains.Since most aspect terms are domain-specific, they cannot be transferred directly.Existing methods solve this problem by associating aspect terms with pivot words (we call this passive domain adaptation because the transfer of aspect terms relies on the links to pivots).However, all these methods need either manually labeled pivot words or expensive computing resources to build associations.In this paper, we propose a novel active domain adaptation method.Our goal is to transfer aspect terms by actively supplementing transferable knowledge.To this end, we construct syntactic bridges by recognizing syntactic roles as pivots instead of as links to pivots.We also build semantic bridges by retrieving transferable semantic prototypes.Extensive experiments show that our method significantly outperforms previous approaches.
Exploratory Machine Learning with Unknown Unknowns

In conventional supervised learning, a training dataset is given with ground-truth labels from a known label set, and the learned model will classify unseen instances to known labels. In real situations, when the learned models do not work well, users generally attribute the failure to the inadequate selection of learning algorithms or the lack of enough labeled training samples. In this paper, we point out that there is an important category of failure, which owes to the fact that there are unknown classes in the training data misperceived as other labels, and thus their existence was unknown from the given supervision. Such problems of unknown unknown classes can hardly be addressed by common re-selection of algorithms or accumulation of training samples. For this purpose, we propose the exploratory machine learning, where in this paradigm once user encounters unsatisfactory learning performance, she can examine the possibility and, if unknown unknowns really exist, deploy the optimal strategy of feature space augmentation to make the unknown classes observable and be enabled for learning. Theoretical analysis and empirical study on both synthetic and real datasets validate the efficacy of our proposal.
Check It Again:Progressive Visual Question Answering via Visual Entailment

While sophisticated Visual Question Answering models have achieved remarkable success, they tend to answer questions only according to superficial correlations between question and answer.Several recent approaches have been developed to address this language priors problem.However, most of them predict the correct answer according to one best output without checking the authenticity of answers.Besides, they only explore the interaction between image and question, ignoring the semantics of candidate answers.In this paper, we propose a select-and-rerank (SAR) progressive framework based on Visual Entailment.Specifically, we first select the candidate answers relevant to the question or the image, then we rerank the candidate answers by a visual entailment task, which verifies whether the image semantically entails the synthetic statement of the question and each candidate answer.Experimental results show the effectiveness of our proposed framework, which establishes a new state-of-the-art accuracy on VQA-CP v2 with a 7.55% improvement.
Cost-Effective Blockchain-based IoT Data Marketplaces with a Credit Invariant

Billions of Internet of Things (IoT) devices deployed today collect massive amounts of potentially valuable data. To efficiently utilize this data, markets must be developed where data can be traded in real time. Blockchain technology offers a potential platform for these types of markets. However, previous proposals using blockchain technology either require trusted third parties such as data brokers, or necessitate a large number of on-chain transactions to operate, incurring excessive overhead costs. This paper proposes a trustless data trading system that minimizes both the risk of fraud and the number of transactions performed on chain. In this system, data producers and consumers come to binding agreements while trading data off chain and they only settle on chain when a deposit or withdrawal of funds is required. A credit mechanism is also developed to further reduce the incurred fees. Additionally, the proposed marketplace is benchmarked on a private Ethereum network running on a lab-scale testbed and the proposed credit system is simulated so to analyze its risks and benefits.
Multimodal Fusion via Teacher-Student Network for Indoor Action Recognition

Indoor action recognition plays an important role in modern society, such as intelligent healthcare in large mobile cabin hospitals. With the wide usage of depth sensors like Kinect, multimodal information including skeleton and RGB modalities brings a promising way to improve the performance. However, existing methods are either focusing on a single data modality or failed to take the advantage of multiple data modalities. In this paper, we propose a Teacher-Student Multimodal Fusion (TSMF) model that fuses the skeleton and RGB modalities at the model level for indoor action recognition. In our TSMF, we utilize a teacher network to transfer the structural knowledge of the skeleton modality to a student network for the RGB modality. With extensive experiments on two benchmarking datasets: NTU RGB+D and PKU-MMD, results show that the proposed TSMF consistently performs better than state-of-the-art single modal and multimodal methods. It also indicates that our TSMF could not only improve the accuracy of the student network but also significantly improve the ensemble accuracy.
Blockchain in agricultural supply chain management

The problem of minimizing the number of intermediaries in the supply chain is long overdue in the logistics industry. How to carry out logistics operations without the participation of a large number of intermediaries, whose main task is to guarantee the transaction and document flow? Is this possible with Blockchain technology? While this technology is still evolving, there are still many challenges that remain to be addressed. One of the main challenges in implementing this technology in logistics is to reach agreement on its use among all stakeholders. When interaction between different stakeholders in the supply chain with different interests is achieved, then the full potential of this technology to improve the efficiency of logistics processes will be revealed. This will facilitate the emergence of new business models and processes in global trade logistics and increase transparency in the supply chain. Smart contracts with embedded business rules promise not only to reduce transaction costs but to create more agile value chains that enable closer cooperation.
Adversarial Robustness through Disentangled Representations

Despite the remarkable empirical performance of deep learning models, their vulnerability to adversarial examples has been revealed in many studies. They are prone to make a susceptible prediction to the input with imperceptible adversarial perturbation. Although recent works have remarkably improved the model's robustness under the adversarial training strategy, an evident gap between the natural accuracy and adversarial robustness inevitably exists. In order to mitigate this problem, in this paper, we assume that the robust and non-robust representations are two basic ingredients entangled in the integral representation. For achieving adversarial robustness, the robust representations of natural and adversarial examples should be disentangled from the non-robust part and the alignment of the robust representations can bridge the gap between accuracy and robustness. Inspired by this motivation, we propose a novel defense method called Deep Robust Representation Disentanglement Network (DRRDN). Specifically, DRRDN employs a disentangler to extract and align the robust representations from both adversarial and natural examples. Theoretical analysis guarantees the mitigation of the trade-off between robustness and accuracy with good disentanglement and alignment performance. Experimental results on benchmark datasets finally demonstrate the empirical superiority of our method.
ERNIE-Doc: A Retrospective Long-Document Modeling Transformer

Transformers are not suited for processing long documents, due to their quadratically increasing memory and time consumption.Simply truncating a long document or applying the sparse attention mechanism will incur the context fragmentation problem or lead to an inferior modeling capability against comparable model sizes.In this paper, we propose ERNIE-DOC, a document-level language pretraining model based on Recurrence Transformers (Dai et al., 2019).Two welldesigned techniques, namely the retrospective feed mechanism and the enhanced recurrence mechanism, enable ERNIE-DOC 1 , which has a much longer effective context length, to capture the contextual information of a complete document.We pretrain ERNIE-DOC to explicitly learn the relationships among segments with an additional document-aware segment-reordering objective.Various experiments were conducted on both English and Chinese document-level tasks.ERNIE-DOC improved the state-of-the-art language modeling result of perplexity to 16.8 on WikiText-103.Moreover, it outperformed competitive pretraining models by a large margin on most language understanding tasks, such as text classification and question answering.
Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments

Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square—for the planning phase—and a simple priority-based speed control—as the method for conflict resolution—is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs.
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering

Relation linking is a crucial component of Knowledge Base Question Answering systems.Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text.However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of.We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence.Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets.These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.
UAV Task Allocation Based on Clone Selection Algorithm

With the continuous development of computer and network technology, the large-scale and clustered operations of drones have gradually become a reality. How to realize the reasonable allocation of UAV cluster combat tasks and realize the intelligent optimization control of UAV cluster is one of the most challenging difficulties in UAV cluster combat. Solving the task allocation problem and finding the optimal solution have been proven to be an NP-hard problem. This paper proposes a CSA-based approach to simultaneously optimize four objectives in multi-UAV task allocation, i.e., maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results show that, compared with the genetic algorithm, the proposed method has better performance on solving the UAV task allocation problem with multiple objectives.
Quantum cryptography technique: A way to improve security challenges in mobile cloud computing (MCC)

Quantum cryptography concentrates on the solution of cryptography that is imperishable due to the reason of fortification of secrecy which is applied to the public key distribution of quantum. It is a very prominent technology in which 2 beings can securely communicate along with the sights belongings to quantum physics. However, on basis of classical level cryptography, the used encodes were bits for data. As quantum utilizes the photons or particles polarize ones for encoding the quantized property. This is presented in qubits as a unit. Transmissions depend directly on the inalienable mechanic's law of quantum for security. This paper includes detailed insight into the three most used and appreciated quantum cryptography applications that are providing its domain-wide service in the field of mobile cloud computing. These services are (it) DARPA Network, (ii) IPSEC implementation, and (iii) the twisted light HD implementation along with quantum elements, key distribution, and protocols.
The Centerline Extraction Algorithm of Weld Line Structured Light Stripe Based on Pyramid Scene Parsing Network

Based on the good feature learning ability of the pyramid scene parsing network, a method for extracting the centerline of structured light stripes of weld lines based on the pyramid scene parsing network and Steger algorithm is proposed. This method avoids the traditional complex weld image preprocessing technology, and simplifies the operation steps of extracting the centerline of the structured light stripe of the weld image line. In this paper, the pyramid scene parsing network is used to predict the pixels containing weld feature information. Through the pyramid pooling module, the local and global context feature information is fused to supplement the feature information of the weld edge, and then the Steger algorithm is used to extract the weld feature centerline. The results show that the method in this paper can accurately extract the centerline position of the structured light stripe of the weld line under the interference of reflection, and the average value reaches 86.8% on the accuracy evaluation index mean intersection over union, the 18.93 pixels on the weld extraction accuracy index root mean square error, and the average time of extracting the center line of structured light stripe of weld line is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.188s$ </tex-math></inline-formula> .
hQChain

Blockchain facilitates a broad spectrum of applications such as transaction of cryptocurrency, catering to financial services, designing and constructing smart cities and so on. It has astounding benefits including accountability, consistency and decentralization. Smart healthcare can be exemplified as utilizing propitious electronic technology safeguarded with blockchain for superior diagnosis of the disorders, improvised and cost-effective treatment of the patients, and enhanced quality of lives. Since, blockchain in smart healthcare architecture hosts substantial amount of patient data queueing models play a pivotal role to efficiently process the data. In this paper, it highlights the concepts of blockchain, then delve into the smart healthcare architecture and then deal with the several queueing models that already exist. It proposes the model i.e. hQChain which is inculcating M1,b/Mb/1 queueing model into blockchain based smart healthcare architecture. It offers a queuing mathematical and analytical model to analyze and study the performance measurement of hQChain model.
Time Series-Analysis Based Engineering of High-Dimensional Wide-Area Stability Indices for Machine Learning

Information representative of actual power system dynamics is usually buried in masses of phasor measurement unit (PMU) data. To take full advantage of these data in early anticipation of stability loss, we propose to implement the high dimensional stability index (HDSI). This method allows the extraction of more than 500-labeled attributes describing generator response signals, such as speed and rate of change of transient energy function (RoCoTE). A combined 31 functions are computed from spectrum analysis based on the Periodogram and Welch methods, Lyapunov exponents, and wavelet transform approaches. The test databases are built by simulating faults on each line in the IEEE 39- and 68-bus networks. Applying comparative time-series analysis to such signal responses to disturbances then highlights the texture matrix of the stability attributes. A 10-fold support vector machine (SVM) is used to implement a HDSI-based stability prediction model, with its performance then compared to the artificial neural network (ANN), decision trees (DT), random forest (RF), and adaptive boosting (AdaBoost) models available in the statistical package R. While most methods performed similarly, with ~100% accuracy on test cases using the same set of HDSI-based attributes, the RF classifier with its associated Gini feature importance allows for explicit feature ranking and interpretation, which results in prioritization of frequency-domain over time-domain features.