Reviewerly/exHarmony huggingface dataset:
OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering

The information in tables can be an important complement to text, making table-based question answering (QA) systems of great value.The intrinsic complexity of handling tables often adds an extra burden to both model design and data annotation.In this paper, we aim to develop a simple table-based QA model with minimal annotation effort.Motivated by the fact that table-based QA requires both alignment between questions and tables and the ability to perform complicated reasoning over multiple table elements, we propose an omnivorous pretraining approach that consumes both natural and synthetic data to endow models with these respective abilities.Specifically, given freely available tables, we leverage retrieval to pair them with relevant natural sentences for mask-based pretraining, and synthesize NL questions by converting SQL sampled from tables for pretraining with a QA loss.We perform extensive experiments in both few-shot and full settings, and the results clearly demonstrate the superiority of our model OmniTab, with the best multitasking approach achieving an absolute gain of 16.2% and 2.7% in 128-shot and full settings respectively, also establishing a new state-of-the-art on WikiTableQuestions.Detailed ablations and analyses reveal different characteristics of natural and synthetic data, shedding light on future directions in omnivorous pretraining.
The AI Doctor Is In: A Survey of Task-Oriented Dialogue Systems for Healthcare Applications

Task-oriented dialogue systems are increasingly prevalent in healthcare settings, and have been characterized by a diverse range of architectures and objectives.Although these systems have been surveyed in the medical community from a non-technical perspective, a systematic review from a rigorous computational perspective has to date remained noticeably absent.As a result, many important implementation details of healthcare-oriented dialogue systems remain limited or underspecified, slowing the pace of innovation in this area.To fill this gap, we investigated an initial pool of 4070 papers from well-known computer science, natural language processing, and artificial intelligence venues, identifying 70 papers discussing the system-level implementation of task-oriented dialogue systems for healthcare applications.We conducted a comprehensive technical review of these papers, and present our key findings including identified gaps and corresponding recommendations.
ASPECTNEWS: Aspect-Oriented Summarization of News Documents

Generic summaries try to cover an entire document and query-based summaries try to answer document-specific questions.But real users' needs often fall in between these extremes and correspond to aspects, high-level topics discussed among similar types of documents.In this paper, we collect a dataset of realistic aspect-oriented summaries, ASPECT-NEWS, which covers different subtopics about articles in news sub-domains.We annotate data across two domains of articles, earthquakes and fraud investigations, where each article is annotated with two distinct summaries focusing on different aspects for each domain.A system producing a single generic summary cannot concisely satisfy both aspects.Our focus in evaluation is how well existing techniques can generalize to these domains without seeing in-domain training data, so we turn to techniques to construct synthetic training data that have been used in query-focused summarization work.We compare several training schemes that differ in how strongly keywords are used and how oracle summaries are extracted.Our evaluation shows that our final approach yields (a) focused summaries, better than those from a generic summarization system or from keyword matching; (b) a system sensitive to the choice of keywords.
A Secure and Trusted Mechanism for Industrial IoT Network Using Blockchain

Industrial Internet-of-Things (IIoT) is a powerful IoT application, which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places, and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this article, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the MD to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation

Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow.In this work, we propose PLANET, a novel generation framework leveraging autoregressive self-attention mechanism to conduct content planning and surface realization dynamically.To guide the generation of output sentences, our framework enriches the Transformer decoder with latent representations to maintain sentence-level semantic plans grounded by bag-of-words.Moreover, we introduce a new coherence-based contrastive learning objective to further improve the coherence of output.Extensive experiments are conducted on two challenging longform text generation tasks including counterargument generation and opinion article generation.Both automatic and human evaluations show that our method significantly outperforms strong baselines and generates more coherent texts with richer contents.
The effect of management accounting and blockchain technology characteristics on supply chains efficiency

Blockchain is a new boom for the use of the Internet and will change the way in accounting and auditing as well as management accounting (MA); Blockchain as the biggest evidence of the growing use of this technology in the accounting field, its use leads to lower costs in the long run, avoiding human errors and controlling manipulation and fraud through immediate control of information and enhancing its integrity. However, this paper aims to investigate the impact of management accounting and Blockchain technology characteristics on supply chain efficiency in Jordanian Manufacturing Companies (JMC). Besides, the descriptive approach was conducted using the survey, 258 respondents were included in the survey from JMC. The results indicated that the characteristics of Blockchain technology and MA has a positive and significant impact on the efficiency of supply chains in JMC. However, this paper contributes to identifying the characteristics of Blockchain as well as management accounting methods and their role in increasing the efficiency of supply chains; to survive and continue in different business environments.
Achieving Reliable Human Assessment of Open-Domain Dialogue Systems

Evaluation of open-domain dialogue systems is highly challenging and development of better techniques is highlighted time and again as desperately needed.Despite substantial efforts to carry out reliable live evaluation of systems in recent competitions, annotations have been abandoned and reported as too unreliable to yield sensible results.This is a serious problem since automatic metrics are not known to provide a good indication of what may or may not be a high-quality conversation.Answering the distress call of competitions that have emphasized the urgent need for better evaluation techniques in dialogue, we present the successful development of human evaluation that is highly reliable while still remaining feasible and low cost.Self-replication experiments reveal almost perfectly repeatable results with a correlation of r = 0.969.Furthermore, due to the lack of appropriate methods of statistical significance testing, the likelihood of potential improvements to systems occurring due to chance is rarely taken into account in dialogue evaluation, and the evaluation we propose facilitates application of standard tests.Since we have developed a highly reliable evaluation method, new insights into system performance can be revealed.We therefore include a comparison of state-of-the-art models (i) with and without personas, to measure the contribution of personas to conversation quality, as well as (ii) prescribed versus freely chosen topics.Interestingly with respect to personas, results indicate that personas do not positively contribute to conversation quality as expected.
Uncertainty-Guided Pixel Contrastive Learning for Semi-Supervised Medical Image Segmentation

Recently, contrastive learning has shown great potential in medical image segmentation. Due to the lack of expert annotations, however, it is challenging to apply contrastive learning in semi-supervised scenes. To solve this problem, we propose a novel uncertainty-guided pixel contrastive learning method for semi-supervised medical image segmentation. Specifically, we construct an uncertainty map for each unlabeled image and then remove the uncertainty region in the uncertainty map to reduce the possibility of noise sampling. The uncertainty map is determined by a well-designed consistency learning mechanism, which generates comprehensive predictions for unlabeled data by encouraging consistent network outputs from two different decoders. In addition, we suggest that the effective global representations learned by an image encoder should be equivariant to different geometric transformations. To this end, we construct an equivariant contrastive loss to strengthen global representation learning ability of the encoder. Extensive experiments conducted on popular medical image benchmarks demonstrate that the proposed method achieves better segmentation performance than the state-of-the-art methods.
Recursive Kernel Density Estimation for Time Series

We consider the recursive estimation of the probability density function of continuous random variables from a strongly mixing random sample. We revisit here earlier research on this subject by considering a more general class of recursive estimators, including the usual ones. We derive the quadratic mean error of the considered class of estimators. Moreover, we establish a central limit theorem by using Lindeberg's method resulting in a simplification of the existing assumptions on the sequence of smooth parameters and the mixing coefficient. This is the main contribution of this paper. Finally, the feasibility of the proposed estimator is illustrated throughout an empirical study.
A Novel Virtual Optical Image Encryption Scheme Created by Combining Chaotic S-Box with Double Random Phase Encoding

The double random phase encoding (DRPE) system plays a significant role in encrypted systems. However, it is a linear system that leads to security holes in encrypted systems. To tackle this issue, this paper proposes a novel optical image encryption scheme that combines a chaotic S-box, DRPE, and an improved Arnold transformation (IAT). In particular, the encryption scheme designs a chaotic S-box to substitute an image. The chaotic S-box has the characteristics of high nonlinearity and low differential uniformity and is then introduced to enhance the security of the DRPE system. Chaotic S-boxes are resistant to algebraic attacks. An IAT is used to scramble an image encoded by the DRPE system. Meanwhile, three chaotic sequences are obtained by a nonlinear chaotic map in the proposed encryption scheme. One of them is used for XOR operation, and the other two chaotic sequences are explored to generate two random masks in the DRPE system. Simulation results and performance analysis show that the proposed encryption scheme is efficient and secure.
HICF: Hyperbolic Informative Collaborative Filtering

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic space has attracted considerable attention and achieved impressive performance in the recommender system recently. The advantage of hyperbolic recommendation lies in that its exponentially increasing capacity is well-suited to describe the power-law distributed user-item network whereas the Euclidean equivalent is deficient. Nonetheless, it remains unclear which kinds of items can be effectively recommended by the hyperbolic model and which cannot. To address the above concerns, we take the most basic recommendation technique, collaborative filtering, as a medium, to investigate the behaviors of hyperbolic and Euclidean recommendation models. The results reveal that (1) tail items get more emphasis in hyperbolic space than that in Euclidean space, but there is still ample room for improvement; (2) head items receive modest attention in hyperbolic space, which could be considerably improved; (3) and nonetheless, the hyperbolic models show more competitive performance than Euclidean models. Driven by the above observations, we design a novel learning method, named hyperbolic informative collaborative learning (HICF), aiming to compensate for the recommendation effectiveness of the head item while at the same time improving the performance of the tail item. The main idea is to adapt the hyperbolic margin ranking learning, making its pull and push procedure geometric-aware, and providing informative guidance for the learning of both head and tail items. Extensive experiments back up the analytic findings and also show the effectiveness of the proposed method. The work is valuable for personalized recommendations since it reveals that the hyperbolic space facilitates modeling the tail item, which often represents user-customized preferences or new products.
RBaaS: A Robust Blockchain as a Service Paradigm in Cloud-Edge Collaborative Environment

As a decentralized distributed ledger, blockchain is endowed with immutability, traceability, anonymity, and transparency, which has got rapid development in cryptocurrency and production as a new trend.In the meantime, the occurrence of Blockchain-as-a-Service (BaaS) helps in mitigation of the complexity and difficulty in deployment and management of blockchain systems and makes it easier to concentrate on business logic implementation for developers.However, most existing BaaS systems are hosted in the environment of cloud vendors, which has also incurred vendor lock-in risk and impairs the inherent trustless characteristic of blockchain.Though present BaaS systems own a level of availability by using cloud computing or edge computing as infrastructure, the availability is limited, considering the availability of network connections and the data center itself.In this article, a novel cloud-edge collaborative BaaS paradigm is proposed.With the assistance of redundant blockchain node candidates, leader election, and edge network self-healing components, the proposed BaaS system is equipped to extend BaaS to the on-premises edge or private cloud, and realize high availability of blockchain systems in edge-autonomy and cross-data-center scenarios.Through testing a simplified system in a simulated environment in cloud servers, this proposed BaaS system has an acceptable throughput under specific transaction sending rates without much performance degradation due to containerization and data synchronization compared with the original blockchain on-premises deployment method.
Comparative Analysis of Energy Costs of Asymmetric vs Symmetric Encryption-Based Security Applications

Public key algorithms are heavily used in many digital applications including key establishment schemes, secure messaging apps, and digital signature schemes in cryptocurrencies.Recent developments in the field of quantum computation have placed these algorithms at risk as they enable the implementation of more effective attacks to derive the secret key.Most notably Shor's algorithm exponentially speeds up solving the factoring, discrete logarithm (DLP), and elliptic-curve discrete logarithm (ECDLP) problems.To address this challenge, NIST has initiated a process to develop and standardize a new quantum-resistant public-key cryptographic algorithm.However, asymmetric encryption schemes are known to be computationally intensive, hence energy demanding.The proliferation of energy-constrained internet of things devices, combined with the need to adopt higher complexity quantum resilient cryptographic algorithms, makes it more challenging to continue to use public-key algorithms for all applications.One approach to address these challenges is to adopt symmetric key systems, which are known to be more energyefficient and more resilient to quantum computers-based attacks.This work performs a comprehensive comparison of energy costs between asymmetric and symmetric key schemes.This comparison is performed using two methods.The first approach uses the energy cost of data usage (ECDU) metric to evaluate the global energy costs associated with internet data usage.It was found that the annual energy consumed by applications associated with public-key cryptography globally is sufficient to provide electricity for 1000 UK households for a year.The second method uses an experimental technique based on constructing a smallscale network of wireless embedded devices.This is subsequently used to compare two key establishment schemes, symmetric and asymmetric, which allows for comparing the computation and communication costs of each solution in a controlled environment, and more importantly estimating the energy consumed by each device participating in the protocol.Our results show that a 58% saving in global energy costs of public keybased applications can be achieved by adopting symmetric key systems.It was also found that a 20% reduction of the energy consumed by a wireless device during a key agreement protocol, can be achieved if symmetric key encryption is used.
Knowledge-enhanced Black-box Attacks for Recommendations

Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items. Due to the security and privacy concerns, it is more practical to perform adversarial attacks under the black-box setting, where the architecture/parameters and training data of target systems cannot be easily accessed by attackers. However, generating high-quality fake user profiles under black-box setting is rather challenging with limited resources to target systems. To address this challenge, in this work, we introduce a novel strategy by leveraging items' attribute information (i.e., items' knowledge graph), which can be publicly accessible and provide rich auxiliary knowledge to enhance the generation of fake user profiles. More specifically, we propose a knowledge graph-enhanced black-box attacking framework (KGAttack) to effectively learn attacking policies through deep reinforcement learning techniques, in which knowledge graph is seamlessly integrated into hierarchical policy networks to generate fake user profiles for performing adversarial black-box attacks. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of the proposed attacking framework under the black-box setting.
Exploring interoperability of distributed Ledger and Decentralized Technology adoption in virtual enterprises

Abstract The breakthrough of Distributed Ledger Technologies (DLT) has enabled the emergence and implementation of a wide range of digital platforms in Virtual Enterprises (VE) which collaborate to provide digital services. DLT has the potential to revolutionize VE by offering transparent, decentralized, trustworthy, data provenance, reliable, and auditable features. Yet, the full deployment of DLT systems and digital platforms is still limited since some systems are operating in isolation. Hence, DLT interoperability is one of the challenges inhibiting widespread adoption of DLT platforms. DLT interoperability represents the ability for one distributed ledger platform to interact and share data with other legacy digital applications. It is inevitable to orchestrate these digital platforms fragments by introducing a cross-DLT platform integration to govern data usage within VE. Presently, already proposed approaches for DLT interoperability such as naive relay, sidechain, oracle solutions notary scheme, or relay chain are mostly not employed as they are either resource-intensive or too expensive to operate. Therefore, this paper presents a layered architecture that aids interoperability of DLT, and digital platforms based on IOTA Tangle. Design science method is adopted, and case demonstration is carried out to show how IOTA Tangle enable VE to provide an innovative virtual asset payment platform for seamless electric mobility as a service to clients. IOTA was employed as the DLT platform due to its data traceability, immutability, and tamper-proof features which allow for verification of integrity of data. IOTA offers flexibility and performance to support a reliable digital solution. Findings from this study presents a layered architecture that aids IOTA Tangle to make requests, inter-communicate, and share data via RESTful application programming interface as gateway with other external digital platforms deployed by VE to achieve an interoperable eco-system.
Evaluating Machine Learning Models and Their Diagnostic Value

Abstract This chapter describes model validation, a crucial part of machine learning whether it is to select the best model or to assess performance of a given model. We start by detailing the main performance metrics for different tasks (classification, regression), and how they may be interpreted, including in the face of class imbalance, varying prevalence, or asymmetric cost–benefit trade-offs. We then explain how to estimate these metrics in an unbiased manner using training, validation, and test sets. We describe cross-validation procedures—to use a larger part of the data for both training and testing—and the dangers of data leakage—optimism bias due to training data contaminating the test set. Finally, we discuss how to obtain confidence intervals of performance metrics, distinguishing two situations: internal validation or evaluation of learning algorithms and external validation or evaluation of resulting prediction models.
MultiSpanQA: A Dataset for Multi-Span Question Answering

Most existing reading comprehension datasets focus on single-span answers, which can be extracted as a single contiguous span from a given text passage.Multi-span questions, i.e., questions whose answer is a series of multiple discontiguous spans in the text, are common in real life but are less studied.In this paper, we present MultiSpanQA 1 , a new dataset that focuses on questions with multi-span answers.Raw questions and contexts are extracted from the Natural Questions (Kwiatkowski et al., 2019) dataset.After multi-span re-annotation, MultiSpanQA consists of over a total of 6,000 multi-span questions in the basic version, and over 19,000 examples with unanswerable questions, and questions with single-, and multispan answers in the expanded version.We introduce new metrics for the purposes of multispan question answering evaluation, and establish several baselines using advanced models.Finally, we propose a new model which beats all baselines and achieves the state-of-the-art on our dataset.